Alexeev and Pyle 2003

© The European Bank for Reconstruction and Development, 2003.Published by Blackwell Publishing, 9600 Garsington Road, Oxford OX4 2DQ, UK and 350 Main Street, Malden, MA 02148, USA.

Economics of TransitionVolume 11 (1) 2003, 153–175

Blackwell Publishing LtdOxford, UKECOTThe Economics of Transition0967-0750© The European Bank for Reconstruction and Development20031111000Original ArticleMeasuring the Unofficial Economy in the Former Soviet RepublicsAlexeev and Pyle

A note on measuring the unofficial economy in the former Soviet Republics

1

Michael Alexeev* and William Pyle**

*

Department of Economics, Indiana University, Bloomington, IN 47405, USA. E-mail: [email protected]

**

Department of Economics, Middlebury College, Middlebury, VT 05753, USA. E-mail: [email protected]

Abstract

This note argues that the most commonly used estimates of the size of the unoffi-cial economies in the former Soviet republics are flawed. Most important, they arebased on calculations that disregard the variation in unofficial economic activityacross space in the pre-transition Soviet Union. In addition, these estimates appearto understate the size of the unofficial economies in these countries. We proposealternative estimates and find that they are more strongly related to the institu-tional factors commonly used to explain the size of the unofficial sector. Our estim-ates also show that the size of a country’s pre-transition unofficial economy is animportant predictor of its size during the transition. This suggests that the size ofthe unofficial economy is to a large extent a historical phenomenon only partlydetermined by contemporary institutional factors.

JEL classification: O17, P2, P3.Keywords: Hidden economy, transition economies.

1

We are grateful to two anonymous referees and the seminar participants at the Harriman Institute (ColumbiaUniversity) and Middlebury College for comments and discussion.

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1. Introduction

Conventional wisdom holds that the unofficial economies in post-socialist coun-tries are quite large. With state institutions still developing the capacity to regulateand measure market-based activities, it is widely believed that a significant shareof the goods and services produced in these economies does not enter into officialestimates of GDP. But how much? The answer is not trivial in its consequences;a better sense of the size and nature of the unofficial economy may lead to a re-assessment of output dynamics during the transition and influence policies frompublic finance to social protection. However, arriving at a precise answer to thisquestion is impossible, and even a reasonable approximation is not easy to come by.

Kaufmann and Kaliberda (1996) were the first to apply a consistent method-ology to measuring the unofficial economies in former socialist countries. Drawingon the same approach, Johnson

et al

. (1997) updated Kaufmann and Kaliberda’swork, producing estimates for the size of the unofficial economy in eleven formerSoviet republics and six Central and East European countries in 1995. Subsequentstudies by Johnson

et al

. (1998), Friedman

et al

. (2000), Schneider and Enste (2000),Rosser

et al

. (2000) and Johnson and Kaufmann (2001) used these estimates toevaluate the causes and consequences of cross-country variation in unofficial eco-nomic activity. Although this work has done much to advance understanding ofunofficial economies during the transition, we feel that the size estimates ofKaufmann and Kaliberda (1996) and Johnson

et al

. (1997) were significantly flawedby an assumption that the unofficial economies of the pre-transition former Sovietrepublics accounted for the same 12 percent share of their total GDP.

Below we address this important element of the discussion of the post-socialistunofficial economy. Using data from the Berkeley-Duke survey project, we showboth that the average size of the unofficial economies in the Soviet republics wassignificantly larger than assumed by Kaufmann and Kaliberda (1996) and thatthere was a good deal of variation in their size across space.

2

On the basis of theBerkeley–Duke data, we re-estimate the size of the unofficial economies at the endof the socialist period. Using these new estimates, we apply the same methodologyused by both Kaufmann and Kaliberda (1996) and Johnson

et al

. (1997) to trace thedynamics of the unofficial economy during the transition. This leads us to a revisedset of estimates for the size of unofficial economies in 1995.

We conclude with a series of side-by-side comparisons. These demonstrate thatour revisions are more closely related than the previous estimates to a set of govern-ance variables that have been shown to be associated with unofficial economicactivity. Perhaps even more important, we show with our estimates that the sizeof a country’s unofficial economy in 1989 had a statistically significant impact on

2

Our use of Berkeley-Duke data is justified in part because it was the only family budget survey specificallyaimed at studying the unofficial or second economy in the USSR and because Kaufmann and Kaliberdaclaim to have derived their estimates, in part, from Berkeley-Duke data.

Measuring the Unofficial Economy in the Former Soviet Republics

155

the size of its unofficial economy in 1995. This finding differs from Johnson

et al

.(1997) and suggests that the size of the unofficial economy is to a large extent ahistorical phenomenon only partly determined by contemporary governance indic-ators, which themselves may be historically determined and not easily modified bypolicy initiatives.

This finding can be viewed in a more general context. The transition of theformer Soviet republics represents a fascinating natural experiment with respect tothe impact of policies on economic growth and the development of new institu-tions. These countries began their transitions with a similar legacy of Soviet insti-tutions but then diverged substantially during the 1990s. However, in order tointerpret their transition experiences appropriately, one needs to remember thatnot all of the initial conditions of the former Soviet republics were the same. Weargue that one important difference had to do with the extent of informal economicactivities, and this distinguishing factor needs to be taken into account when ana-lyzing the institutional developments in these economies.

3

2. The electricity consumption methodology

The initial study by Kaufmann and Kaliberda started from the observation that inthe short run electricity consumption and total economic activity move in more orless parallel fashion, with near unit-elasticity.

4

Thus the difference between thegrowth rates of measured GDP and electricity consumption can yield an estimateof the change in the size of the unofficial economy, which Kaufmann and Kaliberdadefine as ‘the unrecorded value added by any deliberate misreporting or evasionby a firm or individual (p. 83).’

Kaufmann and Kaliberda sub-divided the post-socialist countries into threegroups on the basis of differences in the timing and scope of policy reform.

5

Sinceenergy prices were liberalized earlier and more extensively in Central and EasternEurope, that region’s countries were assumed to be more efficient consumers ofenergy than others in the post-socialist world. Electricity consumption, that is, waspresumed to rise more slowly or fall more quickly relative to rises or falls, respectively,

3

We are grateful to an anonymous referee for bringing this point to our attention.

4

Dobozi and Pohl (1995) first addressed the degree of correlation between measured output and electricityconsumption during the transition. Kafumann and Kaliberda note several biases in using electricity con-sumption as a proxy for overall GDP. Applying a near unit-elasticity assumption could lead to incorrectlyhigh estimates of total output if the economy slumps and electricity is primarily a fixed cost, or if electricitycosts rise because of a lack of basic maintenance, or if energy users substitute from other sources of electricity.Downward biases could result from improved efficiency in electricity use, price-induced substitution awayfrom electricity, or a shift in output away from electricity-intensive industries. On balance, they feel that theupward and downward biases offset each other.

5

Kaufmann and Kaliberda refer to this as the ‘conservative elasticity scenario.’ It is the one adopted in thecalculations presented in Johnson, Kaufmann and Shleifer (1997). We, as well, rely upon it exclusively.

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in total output. Energy prices were liberalized later in the Baltic economies and stilllater and not as comprehensively in the rest of the former Soviet Union.

6

Followingthis tri-partite division, Kaufmann and Kaliberda applied the output elasticity ofelectricity-consumption measures that are noted in Table 1.

By themselves, these elasticities are useful only for estimating the change in thesize of the unofficial economy for given dynamics in measured output and electricityconsumption. In order to use them for computing the relative size of a country’sunofficial economy at a given point in time, one needs to work from a baselineestimate, i.e., an estimate of the size of the unofficial economy at some initial date.For example, we would conclude that a country’s unofficial economy accounts for11.7 percent of its total GDP if over a prior interval of time official (i.e., measured)GDP and electricity consumption grew by 3.0 percent and 5.0 percent, respectively,

and

if the estimated size of the unofficial economy at the beginning of that intervalwas 10.0 percent.

7

A different estimate for the baseline would necessarily yield adifferent estimate for the unofficial economy at the later date.

6

Real electricity prices per kilowatt hour were generally lower in the CIS region than in Central and EasternEurope and the Baltic countries during 1992–95. Even within the CIS in the mid-1990s prices differedsignificantly across countries from about $0.01 per kWh in Azerbaijan to $0.03–0.04 in Kazakhstan andMoldova. (Prices converted to US dollars for most CIS countries are available for selected years from EBRD

Transition Reports

and from EBRD website; URL: http://www.ebrd.com/pubs/tr/01/russian/lefttab.pdf.Also, US dollar prices for the Czech Republic, Hungary, and Poland can be found in IEA,

Energy Prices &Taxes

, Quarterly Statistics publications.) To some extent, the assumption of different elasticities of electricityconsumption with respect to output changes (see Table 1) takes account of different price dynamics duringtransition. More important, changes of electricity prices were unlikely to change the patterns of electricityconsumption over a relatively short period of time. Even for the developed countries, most estimates oflong-term price elasticity of demand for electricity have been in the

−

0.05 to

−

0.5 range (see Atkinson andManning, 1995). Over the medium term, these elasticities would be even smaller. Moreover, meaningfulreductions of electricity consumption per unit of output would presumably require significant investments.Given the paucity of investment resources in most economies in transition, electricity consumption per unitof output has probably been relatively stable in the first half of 1990s. In any case, more or less reliableadjustment of electricity demand for price changes is difficult and outside the scope of this paper.

7

This figure is calculated with the assumption of unitary output elasticity of electricity consumption andaccording to the formula (105

−

92.7)/105

=

11.7 percent, where 92.7

=

90 · 1.03.

Table 1. Output elasticity of electricity-consumption

Elasticity Central and East Europe Baltic Countries Former Soviet Union

Output Rises 0.9 1.0 1.15Output Falls 1.11 1.0 0.87

Note: Output elasticity of electricity-consumption measures the percentage change in measuredelectricity consumption divided by the percentage change in total GDP.


157

Thus, to estimate the size of the unofficial economy in the former Soviet repub-lics during the transition, Kaufmann and Kaliberda needed to work from estimatesof their sizes in the pre-transition period. Since there were no widely recognizedestimates of the sizes of the unofficial economies in the Soviet republics, Kaufmannand Kaliberda assumed that the size of the unofficial economy was uniform acrossthe USSR. Without being specific, they wrote:

On the basis of the Berkeley-Duke research project on the USSR Second Economy … as wellas the work of J. Braithwaite, estimates ranging roughly between 10 and 15 percent of totaleconomic activity were arrived at. Thus … we use the 1989 midpoint estimate share ofunofficial activities of 12 percent (pp. 93–94).

They relied upon other country-level studies to establish the size of unofficial eco-nomies in 1989 in Central and Eastern Europe.

8

All the estimates appear in the firstcolumn of Table 2.

By comparing the dynamics of measured output and electricity consumptionand applying the noted estimates of elasticities, Kaufmann and Kaliberda arrivedat size estimates for 1994. Using a year’s worth of additional data, Johnson

et al

.(1997) followed the same approach and produced estimates for 1995.

9

As shown inTable 2, they found that the unofficial economy represented a significant share oftotal output both prior to and during the transition. But, whereas in Central andEastern Europe there was generally little change in its size relative to measuredoutput, there was a significant increase in its relative magnitude in the formerSoviet republics. This trend appears to have been most pronounced in the Caucasianstates of Azerbaijan and Georgia where unofficial activity is reported to have jumpedfrom twelve percent to well over half of total GDP.

The estimates of Kaufmann and Kaliberda (1996) and Johnson

et al

. (1997) havebeen recognized as the most comprehensive, internally consistent set of estimatesof the unofficial economy in post-socialist countries. But their approach has beencriticized. Generally, unofficial economy activities can vary greatly with respect tothe amount of electricity that they require; likewise, the differences in elasticitiesacross space and time can be considerable and quite difficult to calibrate (Schneiderand Enste, 2000). Lacko (2000), although an advocate of using electricity consump-tion, also voices skepticism at some of the estimates produced by Kaufmann andKaliberda’s methodology. In particular, she suggests that their finding of signific-ant cross-country variation in unofficial economy dynamics does not pass the believ-ability test. She proposes an alternative methodology which involves econometricallyestimating per capita household electricity consumption. Her methodology is

8

See footnote 25 in Kaufmann and Kaliberda (1996).

9

Kaufmann and Kaliberda produce estimates for 1994 and present them in a bar graph that indicates therelative sizes of unofficial economies across republics. Exact percentages are not provided. Johnson

et al

.(1997) use the same methodology and baseline estimates to update the estimates for 1995. They provide theexact percentages for both 1994 and 1995.

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Alexeev and Pyle

considerably more complicated than Kaufmann and Kaliberda’s and it has its ownshortcomings, some of which are noted by Schneider and Enste (2000). Mostimportantly, Lacko’s assumption that all informal economic activities take place inthe household sector may be problematic, particularly for the economies in trans-ition. Also, her approach requires the type of data that are not readily availablefor some of these economies. When the appropriate data are lacking, her estimatesdo not pass the believability test either. For example, she estimates that the sharesof the hidden economy in Latvia and Lithuania far exceed those in Russia,Uzbekistan, and Kazakhstan. This is hard to reconcile with anecdotal evidence andcommon perception. Given these considerations and the fact that Kaufmann andKaliberda’s estimates have been more commonly cited, we concentrate on theirapproach. We will, however, compare our estimates with those by Lacko as well.

Table 2. Unofficial economy’s share of total output: Kaufmann and Kaliberda’s methodology

Central and Eastern Europe 1989 1994 1995

Bulgaria 22.8 29.1 36.2Czech Republic 6.0 17.6 11.3Hungary 27.0 27.7 29.0Poland 15.7 15.2 12.6Romania 22.3 17.4 19.1Slovakia 6.0 14.6 5.8

Former Soviet Union

Azerbaijan 12.0 58.0 60.6Belarus 12.0 18.9 19.3Estonia 12.0 25.1 11.8Georgia 12.0 63.5 62.6Kazakhstan 12.0 34.1 34.3Latvia 12.0 34.2 35.3Lithuania 12.0 28.7 21.6Moldova 12.0 39.7 35.7Russia 12.0 40.3 41.6Ukraine 12.0 45.7 48.9Uzbekistan 12.0 9.5 6.5

Note: Neither Kaufmann and Kaliberda (1996) nor Johnson et al. (1997) presented estimates for Armenia,Kyrgyzstan, Tadzhikistan or Turkmenistan. See p. 176 in Johnson et al. (1997).Source: Johnson et al. (1997).


159

We agree with Kaufman and Kaliberda that changes in electricity consump-tion can serve as a consistent, albeit very rough, metric for assessing unofficialeconomy dynamics across countries. We further note that they have tried toaccount for the likely variation in output elasticities of electricity consumptionacross the transition countries. But we feel that their estimates of the pre-transitionbaseline were flawed. We find there to be compelling evidence that the size ofthe unofficial economy in 1989 was not uniform across all Soviet republics. Wealso feel that there is good reason to believe that it amounted to more than 12 percentof total Soviet GDP at that time. In the next section, we develop these two pointsby using the publications of the same Berkeley-Duke project that was referredto by Kaufmann and Kaliberda. This leads us to dramatically different estimatesof the Soviet republics’ unofficial economies circa 1989 and, hence, for 1995 aswell.

3. The alternative estimates of the size of the unofficial economy in the FSU

Soviet and Western researchers have proposed a number of estimates of the size ofthe unofficial economy in the Soviet Union.

10

Usually, these estimates were notpresented as a share of GDP because it is often unclear to what extent unofficialeconomic activities add to the official GDP. For example, if a home owner rents outpart of his home in a transaction that is not declared to the tax authorities, this ispresumably part of the unofficial economy. But the official GDP includes not onlythe value of paid rent, but also the imputed rent on owner-occupied housing.Therefore, the unofficial rental income would be included in the GDP although itmight be misclassified as imputed rent instead of paid rent. Also, GDP calculationsare often based on sources such as consumer surveys that may take into accountmany unofficial transactions.

11

We will disregard these methodological issues andfollow Kaufmann and Kaliberda in assuming that the share of the unofficial eco-nomy in GDP is roughly equal to some measure of the share of unofficial economicactivity in all economic activity. For example, one such measure may be the shareof labour inputs used in the unofficial economy.

As we mentioned in the previous section, Kaufmann and Kaliberda did notfully develop the justification for their 1989 estimates. They simply made a rathergeneral reference to various issues of the

Berkeley-Duke Occasional Papers on the

10

Rutgaizer (1992) presents a survey of estimates made by Soviet researchers who, as he stresses, providedvirtually no substantiation for their numbers. The estimates from other surveys of emigrants out of theUSSR are reported in Ofer and Vinokur (1992) and Millar (1987). Note, however, that unlike the Berkeley-Duke project, neither of these surveys focused on the study of the underground economy.

11

See Smith (1997) for arguments along these lines.

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Alexeev and Pyle

Second Economy in the USSR

(BDOP) and a report by Braithwaite (1995).

12

We havebeen able to find only two more or less general estimates of the size of the Sovietunofficial economy based on the Berkeley-Duke survey data in the BDOP.

13

According to Treml’s (1992) estimates, 11.8 percent of all working time in theSoviet urban economy in 1979 was spent on private economic activities. Assumingthat the productivity in the unofficial economy was the same as in the officialeconomy, this number suggests that 12 percent of GDP would be a reasonableestimate for the size of the unofficial economy in 1979. Is the assumption of equallabour productivity in the two economies reasonable? There are arguments bothfor and against. For example, the unofficial economy’s productivity may be higher,because it offered much more powerful incentives. On the other hand, the capital-labour ratio in the unofficial economy was probably lower than in the official one,suggesting that productivity in the former was also lower, other things beingequal.

14

Ideally, we would like to have estimates for the value of output rather thaninputs in both economies.

Based on the same Berkeley-Duke survey data, Grossman (1991) estimates thatSoviet urban households derived on average over 30 percent of their total incomefrom unofficial or second economy sources. In evaluating these estimates, oneshould keep in mind that household income from unofficial sources probablyincluded at least part of the capital income from the second economy, while officialcapital income, to the extent it was not distributed in transfers and wage subsidies,accrued to the state. Nonetheless, a comparison of Treml’s and Grossman’s figuressuggests that productivity was probably higher in the unofficial economy than inits official counterpart.

Whether or not one accepts either the 12 percent or the 30 percent figure as asuitable estimate of the share of the unofficial economy in total GDP, it is almostcertain that this share increased between 1979, the modal reference year for theBerkeley-Duke survey, and 1989. The reasons for this growth, particularly in thesecond half of the 1980s, included increased imbalances between household incomeand opportunities to spend it in the official economy, new opportunities for unoffi-cial economic activities engendered by greater autonomy of state enterprises, and

12

See footnote 23 in Kaufmann and Kaliberda. Braithwaite’s estimate of approximately 13 percent of thelabour force being engaged in the unofficial economy relates to the post-1992 period. More important, herestimate is derived under the apparent assumption that none of the full-time officially employed individualsparticipated in the unofficial economy. We think that this assumption is at great odds with casual observa-tion and undermines the validity of Braithwaite’s estimates.

13

A note on terminology is in order. Both Grossman and Treml defined the second economy in the SovietUnion as all economic activity that either is directly for private gain or knowingly contravenes the law, orboth. Kaufman and Kaliberda define the unofficial economy as constituting activity that is not recorded inthe official GDP. Clearly, Grossman’s definition is broader because it includes private legal economic activ-ities that were included in the country’s official statistics. But for urban USSR circa 1979, the year to whichboth Grossman’s and Treml’s second economy estimates refer, this distinction is likely to be unimportantsince the official private sector was negligible.

14

Note, however, that a significant share of unofficial income was obtained by using state-owned capital atstate-owned enterprises.


161

Gorbachev’s anti-alcohol campaign that resulted in an explosion of bootleggingand exacerbated inflationary pressures.

15

Later, we will extrapolate the estimatesbased on Berkeley-Duke data from 1979 to 1989 and show that Kaufmann andKaliberda significantly underestimated the size of the Soviet unofficial economyprior to the transition. More important, the Berkeley-Duke study makes clear thatthere were substantial differences in the volume of unofficial economic activityacross the former Soviet republics. Grossman (1991), in breaking down urbanhousehold income data from official and unofficial sources for several groups offormer Soviet republics, presents the most comprehensive data that can be used tomake quantitative estimates in this regard. Table 3 summarizes the relevant numbers.

Grossman does not present incomes for some of the republics. Therefore, forthose republics we had to make estimates using aggregated regional data. Forexample, to calculate incomes for the Baltic republics, we took out the data onRussia from the Russia-Baltic subzone, using the following formula:

I

B

=

(

I

RB

·

N

RB

–

I

R

·

N

R

)/(

N

RB

–

N

R

),

where

I

stands for per capita income,

N

denotes the number of observations, andsubscripts B, RB, and R refer to the Baltic republics, the Russia-Baltic subzone, and

15

The estimates of the dynamics of the unofficial economy made by the Soviet researchers are summarizedin Rutgaizer (1992). Alexeev and Treml (1994) provide some econometric evidence of its growth. See alsoAlexeev (1997) for a discussion of the reasons for the growth of the underground economy in the USSR inthe late 1980s.

Table 3. Per capita incomes from official and unofficial sources: Berkeley-Duke survey

Region Number of observations

Socialist income roubles/

year

Total personal income roubles/

year

Unofficial income roubles/

year

North zone 1,944 1,280 1,890 610Russia-Baltic subzone 1,384 1,270 1,780 510

Russia 1,057 1,330 1,830 500Belorussia-Moldavia-Ukraine 560 1,300 2,170 870

Ukraine 361 1,350 2,190 840South zone excluding Armenia 488 896 1,780 884

Notes: 1. North zone includes Russia, Ukraine, Belorussia, the Baltic republics, and Moldavia. Southzone includes all other republics. We exclude Armenia because, like Kaufmann and Kaliberda we donot have the electricity consumption data that would be necessary for our subsequent estimates. 2. Werefer to the former Soviet republics by their Soviet names.Source: Grossman (1991), pp. 13–16.

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Alexeev and Pyle

Russia, respectively. We then assumed that the size of the unofficial economy wasthe same across republics within each region for which we could not obtain separ-ate estimates. For example, Belorussia and Moldavia were presumed to have thesame shares. And we assumed the same for the republics in the South zone aswell as for those in the Baltic region. The results of these calculations are shown inTable 4 below. Notice that the shares of unofficial incomes vary in the range from0.27 in Russia to 0.50 in the South zone.

As we suggested earlier, the fractions in the last column of Table 4 probablyoverstate the share of the unofficial economy in Soviet GDP because they do notaccount for capital income accruing to the Soviet state. Thus, on the basis ofTreml’s (1992) estimates of labour inputs, we adopt the 12 percent figure as aconservative estimate of the share of unofficial economic activity in total GDP.However, unlike Kaufmann and Kaliberda, we take this share to refer to 1979 sincethat was the year for which Treml made his estimate.

In order to determine the share of the unofficial economy in the Soviet Unionin 1989, we use the same technique that Kaufmann and Kaliberda used to projecttheir estimates from 1989 to 1995. According to the CIA estimates, between 1980and 1989, Soviet official GDP grew by approximately 21 percent. Over the sameperiod, electric power output increased by about 36 percent.

16

Assuming that in

16

The CIA estimates of Soviet GNP growth for 1980 are reported in Levine (1983). Noren and Kurtzweg(1993) present CIA GNP growth estimates for 1981–89 and electricity output growth rates for the sameperiod. The growth rate of electricity output for 1980 was calculated based on

Narodnoe

(1981). The CIAmight have overestimated Soviet economic growth in the 1980s. If so, then our estimates of the size ofinformal economy at the end of the Soviet period would be too conservative. However, even if the CIAgrowth numbers were significantly inflated (for example by 50 percent) this would have only a small effecton our regressions in Section 4.

Table 4. Per capita incomes from official and unofficial sources for 11 Soviet Republics: Berkeley-Duke Survey

Region Number of observations

Socialist income roubles/

year

Total personal income roubles/

year

Unofficial income roubles/

year

Share of unofficial

income roubles/

year

Russia 1,057 1,330 1,830 500 0.27Baltic republics 327 1,076.1 1,618.4 542.3 0.34Belorussia-Moldavia 199 1,209.3 2,133.7 924.4 0.43Ukraine 361 1,350 2,190 840 0.38South zone excluding Armenia 488 896 1,780 884 0.50

Source: Authors’ calculations based on Grossman (1991), pp. 13–16.


163

1979 the unofficial economy contributed 12 percent of total GDP and using unitaryelasticity of total GDP with respect to electricity output, we can infer that by 1989the unofficial economy share grew to about 22 percent.

17

This is the base estimatethat we will use in our further calculations.

Next, we determine the unofficial economy shares in eleven Soviet republics.(Following Kaufmann and Kaliberda, we exclude four republics from our calcula-tions because we do not have adequate electricity consumption data to estimateunofficial economy shares in 1995.) While Grossman’s estimates of second eco-nomy household incomes in different republics may not measure the share of theunofficial economy in the GDP, we assume that his estimates adequately reflect thesize of unofficial economies in these republics in relative terms. We assume, forexample, that because South zone households obtained 50 percent of their incomesfrom the second economy versus only 27 percent in Russia, the share of the unofficialeconomy in GDP was almost twice as high in the republics of the South zone as itwas in Russia. Given the relative shares shown in Table 4 and using republicanGNP data we can calculate the size of the unofficial economy in each republic,making sure that the share of the unofficial economy for these republics as a groupequals 22 percent. Table 5 presents the results. In order to obtain the 22 percentshare, we had to scale the shares implied by Grossman by approximately two-thirds.For example, for Azerbaijan, 0.5 · 0.66

=

0.33.Finally, we project our 1989 unofficial economy estimates to 1995, again using

Kaufmann and Kaliberda’s technique. Using our estimates for the unofficial eco-nomy in 1989 and the GDP and electricity output indices from Johnson

et al

. (1997),we calculate the 1995 shares according to the following formula:

18

UE

95

=

1

−

(1

−

UE

89

) ·

GDP

OF

/GDPEL

where UE denotes the share of the unofficial economy in total GDP and GDPOF

and GDPEL denote, respectively, the indices of official GDP and total output asproxied for by electricity consumption. As indicated in Table 1, the latter index wascalculated on the basis of the assumed cross-regional variation in the elasticity ofelectricity consumption noted in Table 1.19

The results of these calculations are presented in the next to last column ofTable 6. For comparison, we also show the estimates that Johnson et al. (1997)

17 This number is obtained as (1.36 − 0.88 · 1.21)/1.36 ≈ 0.22. We use unitary elasticity of output with respectto electricity consumption for the 1979–89 period essentially by default, as we do not have strong priors forthe value of this elasticity in the former USSR.18 To derive this formula, normalize the total 1989 GDP to 1. Then the official 1995 GDP can be obtained asGDPOF · (1 − UE89), implying that the unofficial economy in 1995 is GDPEL − GDPOF · (1 − UE89). The formulafor the share of this unofficial economy is then obtained by dividing the latter expression by GDPEL.19 The effect of price changes on electricity consumption is, no doubt, difficult to control for. Kaufmann andKaliberda relied upon discussions with World Bank economists to arrive at the three-tiered classificationscheme presented in Table 1 and we have no reason to believe that we can improve upon their estimates.For more discussion of this issue see our footnote 6.

164 Alexeev and Pyle

obtained using Kaufmann and Kaliberda’s methodology. In the next section, wewill compare both their and our estimates for 1995 in a series of side-by-sideregressions.

4. Side-by-side comparisons

One might expect that the more intrusive and the less helpful government is vis-à-vis business, the more likely firms are to exit into the unofficial economy. Moreburdensome taxation and regulation, more corruption and poorer provision ofpublic goods all are likely to encourage avoidance of government’s reach. Butevaluating this proposition econometrically presents problems. First, variables thatmeasure state policies and capabilities tend to be highly correlated and so multi-collinearity becomes a problem when more than one are used as regressors. Second,governance may be endogenous to the size of the unofficial economy. For example,when firms enter the unofficial economy, they pay less tax and thereby diminish

Table 5. Estimated shares of unofficial economy in 11 Republics of the FSU, 1989

Official GNP

(mill. rub.)

Unofficial economy

share, 1979

Extrapolated shares of unofficial

economy, 1989

Unofficial GNP

(mill. rub.)

Total GNP

(mill. rub.)

Azerbaijan 14,697 0.50 0.33 7,166 21,863Belarus 40,100 0.43 0.29 16,058 56,158Estonia 7,977 0.34 0.22 2,265 10,242Georgia 14,900 0.50 0.33 7,265 22,165Kazakhstan 46,363 0.50 0.33 22,606 68,969Latvia 12,488 0.34 0.22 3,546 16,034Lithuania 12,897 0.34 0.22 3,662 16,559Moldova 12,681 0.43 0.29 5,078 17,759Russia 626,300 0.27 0.18 137,786 764,086Ukraine 164,761 0.38 0.25 55,847 220,608Uzbekistan 32,430 0.50 0.33 15,813 48,243

Total 985,594 0.34 0.22 277,094 1,262,688

Sources: Official GNP is from World Bank (1993), Table 2b, pp. 10–11. We used the GNP data for 1990,because this was the earliest year for which such data were available to us. Given that we used thesedata only as relative weights, the difference between 1990 and 1989 must have been negligible.Estimates of unofficial economy shares for 1979 are based on Grossman (1991).

Measuring the Unofficial Economy in the Former Soviet Republics 165

government’s ability to provide public goods. This simultaneity bias can only bealleviated if appropriate instrumental variables can be found.

Several recent studies have uncovered a strong empirical relationship betweenthe size of unofficial economies and governance. Johnson et al. (1997) found unof-ficial economy size in transition countries to be inversely related to fairer taxation,fewer regulations and more effective provision of public goods. Johnson et al.(1998) and Friedman et al. (2000) further confirmed strong associations betweenunofficial activity and measures of governance using expanded databases thatincluded OECD and developing countries.

In addition to governance, we argue that it is also reasonable to expect that acountry’s traditions and norms would play an important role in determining theextent of unofficial economic activity. Of course, the nature of the unofficial eco-nomy during transition is different from that under the ancien regime. While underthe Soviet system resale for profit and most other private economic activities wereconducted informally, the main reason for informality during transition has beenevasion of taxes and regulation. However, even though many economic activitiesthat used to belong to the informal sector became perfectly legal at the start of thetransition, there are reasons to believe that there is a link between the pre- andpost-reform magnitudes of the informal economy. Our argument for continuity inthis respect is based on two considerations.

Table 6. Estimates of unofficial economies in 1995

Unofficial economy

share, 1989 (%)

Index of official

GDP, 1995 (1989 ==== 100)

Index of electricity

output, 1995 (1989 ==== 100)

Unofficial economy

share, 1995 (%)

Unofficial economy share, 1995 Johnson

et al. (1997) (%)

Azerbaijan 32.8 31.4 70.1 69.9 60.6Belarus 28.6 56.1 61.2 34.5 19.3Estonia 22.1 69.1 68.9 21.9 11.8Georgia 32.8 16.0 37.6 71.4 62.6Kazakhstan 32.8 46.5 62.3 49.8 34.3Latvia 22.1 47.3 62.3 40.9 35.3Lithuania 22.1 45.1 50.6 30.6 21.6Moldova 28.6 43.0 58.8 47.8 35.7Russia 18.0 49.1 74.0 45.6 41.6Ukraine 25.3 39.0 67.0 56.5 48.9Uzbekistan 32.8 84.0 79.0 28.5 6.5

Sources: Columns 1 and 4 are based on authors’ calculations. Columns 2, 3 and 5 are from Table 1 in Johnsonet al. (1997).


First, despite the changing character of informal activities, deeper and morestable economic, political, sociological, and psychological factors have continued toplay a role in determining the size of the informal economy. These factors includethe popularly perceived commitment of government to certain economic policies,public attitudes towards informality and trust in the official institutions, the endur-ance of an informal economic infrastructure such as connections and trust amongeconomic agents and the degree of property rights protection and contract enforce-ment in the informal sector. We view the pre-reform size of the informal economyas a proxy for such factors and, therefore, as a potential determinant of the extentof informal activities in a country relative to other economies in transition.

Second, the quantitative link between the size of the informal sector before andafter reforms is an empirical issue. The electricity consumption methodology wouldnot be directly affected by the reasons for informal economic activities unless con-sumption of electricity per unit of output has changed either in the formal or in theinformal sector. As we discussed, the possibility of such changes is, of course, themajor criticism of the methodology. We note, however, that there are argumentsboth for and against improved energy efficiency due to transition processes. Theelectricity consumption approach remains the only consistent methodology thatcan be used with the available data. In any case, our use of pre-reform estimates asa starting point for projecting the size of the informal economy into the transitionperiod is identical to the technique employed by Kaufmann and Kaliberda.

We now turn to a side-by-side comparison of the performance of our estimatesof second economy size with those of Johnson et al. (1997). While our point estim-ates of the relevant coefficients are broadly similar to theirs, we find two importantdifferences that support our estimates. First, our regressions exhibit a better fit asmeasured by both the R2 values and the statistical significance of the coefficients,which, of course, are equivalent for the bivariate regressions. Second, the coeffi-cient on the pre-transition size of the unofficial economy is highly significant inseveral of our regressions, while it is statistically insignificant in all of the cor-responding Johnson et al. (1997) regressions. We discuss both differences in turnand in some detail.

The value of R2 can serve as a test of the accuracy of the dependent variableonly under rather limited circumstances. For example, let the true relationshipbetween a dependent variable Y and an independent variable X be Y = α + βX + ε,where α and β are constant coefficients and ε is a random error that satisfies theOLS assumptions. Suppose now that we have two measures of Y that have errorsdenoted, respectively, by J and K. In other words, under the first measure weobserve values Y + J, while under the second we observe Y + K. Suppose also thatwhen we regress Y + J and Y + K on X, we obtain the respective R2 values of rJ andrK. It is easy to show that if the values of J and K are uncorrelated with either Y orε, then rK > rJ implies that the variance of K is lower than the variance of J. Thiswould indeed suggest that Y + K is a better estimate of Y than Y + J if both K andJ have the same mean. However, if K and J have different means, the value of R2


says nothing about which error has a smaller mean. This is because the differencein the means of the measurement errors of the dependent variables would bereflected only in the point estimates of the constant term. Therefore, if the aboveassumptions on the measurement errors are satisfied, the better fit of our regres-sions favours our estimates of the unofficial economies across the former Sovietrepublics. As for the higher average size of the unofficial economy according to ourestimates, we can rely only on the arguments presented in the previous section.

With the above discussion in mind, consider Table 7. It reports the outcomesfrom three pairs of bivariate regressions, each of which uses the same governancevariables as regressors as Johnson et al. (1997).20 The third column in Table 7 repro-duces results presented in Johnson et al. (1997) for 15 transitioning countries – thesix East European countries and nine of the former Soviet republics listed in thebottom half of Table 2. The results recorded in column four are similarly derivedwith the exception that they are based on our estimates for the relative size of theunofficial economy.21 In addition, we ran the same regressions for an expanded setof 17 countries, including Belarus and Uzbekistan, both of which were omitted byJohnson et al. (1997).22 The expanded regression results for Johnson et al.’s (1997)and our estimates are presented side-by-side in the first two columns of Table 7.Columns five and six show the results of the same regressions run on just theeleven former Soviet republics for which we developed new estimates.

All the variables in these regressions have the expected negative signs. Bettergovernance, that is, is associated with smaller unofficial economies. But the seriesusing our re-estimates offer stronger support for this proposition. Comparing theR2 values across each pair of regressions using the same governance variable andthe same sample of countries, the ones using our re-estimates are almost uniformlyhigher. With the exception of ‘regulation’ (for which we do not have a completeset of data for all seventeen countries), governance variables explain more of the

20 Tables 2 and 3 in Johnson et al. (1997) also present regressions with several other governance variableswhich we have chosen not to present here. The data for ‘internal liberalization’ were not available publicly.‘External liberalization’ and ‘large-scale privatization’ do not relate as directly to governance and firm-leveldecisions to enter the underground economy as variables measuring the legal, regulatory and tax environ-ments. ‘Rule of law’ was constructed in 1997, two years subsequent to the unofficial economy data; theorigins of the ‘legal extensiveness’ data were not clear.21 In later studies using cross-section data that included OECD and developing countries, Johnson et al.(1998) and Friedman et al. (2000) continued to use the same set of unofficial economy size estimates fortransition countries. These had been constructed with output and electricity consumption data through1995. These later studies, however, regressed these estimates on a number of governance indicators that hadbeen created after 1995.22 They argue that the governments in these two countries were so repressive that ‘entrepreneurs [could not]switch into the unofficial sector.’ We disagree with this reasoning. Generally, all governments try to prevententry into the unofficial sector. And, no doubt, there is a good deal of variation in their capabilities. Butthese are the very sort of differences in governance that our regressors are designed to capture. In Johnsonet al. (1998) and Friedman et al. (2000), the argument about the uniqueness of these countries is not madeand Belarus and Uzbekistan are included in the regressions.


Tab

le 7

. Reg

ress

ions

of

unof

fici

al e

cono

my

(as

% o

f G

DP)

on

gove

rnan

ce i

ndic

ator

s

17 t

rans

itio

n co

untr

ies

15 t

rans

itio

n co

untr

ies

(exc

lude

s B

elar

us a

nd

Uzb

ekis

tan)

11 F

SU c

ount

ries

Gov

erna

nce

Indi

cato

rJo

hnso

n et

al.

Aut

hors

John

son

et a

l.A

utho

rsJo

hnso

n et

al.

Aut

hors

Gov

R2

Gov

R2

Gov

R2

Gov

R2

Gov

R2

Gov

R2

Leg

al s

afeg

uard

sO

LS

−0.0

41

(−2.

57)*

0.30

6−0

.060

(−

4.37

)**

0.56

0−0

.060

(−−−−

4.83

)**

0.64

2−0

.074

(−

5.91

)**

0.72

9−0

.044

(−

1.45

)0.

190

−0.0

55

(−2.

40)*

0.39

1

IV−0

.036

(−

1.84

)0.

300

−0.0

65

(−3.

86)*

*0.

557

−0.0

59

(−3.

96)*

*0.

642

−0.0

83

(−5.

35)*

*0.

719

Cri

me

and

co

rrup

tion

OL

S−0

.039

(−

2.66

)*0.

321

−0.0

56

(−4.

30)*

*0.

552

−−−− 0.0

55

(−−−−4.

84)*

*0.

643

−0.0

67

(−5.

54)*

*0.

703

−0.0

41

(−1.

57)

0.21

6−0

.048

(−

2.36

)*0.

382

IV−0

.035

(−

1.90

)0.

318

−0.0

64

(−3.

84)*

*0.

540

−0.0

59

(−3.

97)*

*0.

641

−0.0

82

(−4.

98)*

*0.

669

Tax

bur

den

OL

S−0

.055

(−

2.07

) 0.

223

−0.0

86

(−3.

56)*

*0.

458

−0.1

16

(−−−−6.

49)*

*0.

764

−0.1

38

(−7.

23)*

*0.

801

−0.0

27

(−0.

61)

0.03

9−0

.043

(−

1.15

)0.

127

IV−0

.066

(−

1.82

)0.

214

−0.1

17

(−3.

41)*

*0.

394

−0.1

16

(−−−−4.

92)*

*0.

764

−0.1

63

(−6.

09)*

*0.

777

Reg

ulat

ion†

OL

S−0

.132

(−

3.48

)**

0.48

2−0

.139

(−

3.06

)**

0.41

9−0

.130

(−−−−

3.35

)**

0.48

4−0

.139

(−

2.94

)*0.

419

−0.1

81

(−4.

77)*

*0.

765

−0.1

77

(−5.

55)*

*0.

815

IV−0

.192

(−

3.59

)**

0.37

8−0

.204

(−

3.23

)**

0.32

7−0

.187

(−

3.43

)**

0.39

3−0

.202

(−

3.09

)**

0.33

3


Leg

al e

ffec

tive

ness

OL

S−0

.086

(−

2.31

)*0.

263

−0.1

25

(−3.

60)*

*0.

464

−0.1

30

(−−−−4.

21)*

*0.

578

−0.1

56

(−4.

57)*

*0.

616

−0.0

75

(−1.

21)

0.13

9−0

.088

(−

1.74

)0.

251

IV−0

.088

(−

1.80

)0.

263

−0.1

60

(−3.

41)*

*0.

427

−0.1

44

(−3.

59)*

*0.

570

−0.2

01

(−4.

27)*

*0.

567

Not

es: N

umbe

rs i

n pa

rent

hese

s ar

e t-

stat

isti

cs; n

umbe

rs u

nder

‘Gov

’ are

coe

ffic

ient

s on

gov

erna

nce

vari

able

s.*

Sign

ific

ant

at 5

% l

evel

; **

Sign

ific

ant

at 1

% l

evel

.R

esul

ts i

n bo

ld a

re t

hose

rep

orte

d i

n Jo

hnso

n et

al.

(199

7).

† Fo

r ‘r

egul

atio

n,’ n

o ob

serv

atio

ns f

or K

azak

hsta

n an

d U

zbek

ista

n.So

urce

s: T

he l

egal

saf

egua

rds,

cri

me

and

cor

rupt

ion

and

tax

bur

den

var

iabl

es w

ere

take

n fr

om t

he D

ecem

ber

1995

–Jan

uary

199

6 is

sue

of t

heC

entr

al E

urop

ean

Eco

nom

ic R

evie

w (

Ree

d, 1

995)

. A p

anel

of

eigh

t ex

pert

s w

as a

sked

to

rank

26

tran

siti

on c

ount

ries

on

the

basi

s of

a n

umbe

rof

fac

tors

aff

ecti

ng b

usin

ess.

Gra

des

wer

e gi

ven

on a

sca

le o

f 0

to 1

0, w

ith

10 r

epre

sent

ing

the

best

env

iron

men

t fo

r bu

sine

ss.

The

pan

elm

embe

rs i

nclu

ded

: D

irk

Dam

rau,

Sal

omon

Bro

ther

s In

tern

atio

n; S

usan

ne G

ahle

r, J

P M

orga

n; D

onal

d G

reen

, Pl

anE

con;

And

reas

Gum

mic

h,D

euts

che

Ban

k R

esea

rch;

Pet

er H

avlik

, Vie

nna

Inst

itut

e fo

r C

ompa

rati

ve E

cono

mic

Stu

die

s; J

onat

han

Hof

fman

, CS

Firs

t B

osto

n; J

ames

Lis

ter-

Che

ese,

Ind

epen

den

t St

rate

gy; a

nd W

erne

r V

arga

, Cre

dit

anst

alt

Ban

kver

ein.

In

thei

r ar

ticl

e, J

ohns

on e

t al

. (19

97)

refe

r to

the

‘tax

fai

rnes

s’ a

nd‘c

orru

ptio

n’ v

aria

bles

fro

m t

he s

urve

y in

the

Cen

tral

Eur

opea

n Ec

onom

ic R

evie

w; t

he s

urve

y ac

tual

ly a

sked

abo

ut ‘

tax

burd

en’

and

‘cr

ime

and

corr

upti

on.’

The

reg

ulat

ion

vari

able

was

cre

ated

by

expe

rts

at t

he H

erit

age

Foun

dat

ion

(see

ww

w.h

erit

age.

org/

ind

ex).

The

ori

gina

l sca

le w

as f

rom

0 t

o4,

wit

h 0

mea

ning

tha

t th

ere

is l

ittl

e or

no

corr

upti

on, e

xist

ing

regu

lati

ons

are

stra

ight

forw

ard

, not

bur

den

som

e an

d a

pplie

d u

nifo

rmly

to

all

busi

ness

es. W

e fo

llow

Joh

nson

et

al. (

1997

) by

rev

ersi

ng t

he s

cale

so

that

4 m

eans

the

leas

t bu

rden

som

e re

gula

tion

, allo

win

g us

to

cons

iste

ntly

have

the

pro

-bus

ines

s go

vern

ance

sco

red

wit

h th

e hi

gher

num

bers

.A

s re

port

ed i

n Jo

hnso

n et

al.

(199

7), w

e us

e th

e E

urop

ean

Ban

k fo

r R

econ

stru

ctio

n an

d D

evel

opm

ent’s

‘leg

al e

ffec

tive

ness

’ ind

ex f

or 1

995.

17 t

rans

itio

n co

untr

ies

15 t

rans

itio

n co

untr

ies

(exc

lude

s B

elar

us a

nd

Uzb

ekis

tan)

11 F

SU c

ount

ries

Gov

erna

nce

Indi

cato

rJo

hnso

n et

al.

Aut

hors

John

son

et a

l.A

utho

rsJo

hnso

n et

al.

Aut

hors

Gov

R2

Gov

R2

Gov

R2

Gov

R2

Gov

R2

Gov

R2

Tab

le 7

(co

nt).

Reg

ress

ions

of

unof

fici

al e

cono

my

(as

% o

f G

DP)

on

gove

rnan

ce i

ndic

ator

s


variation in the size of the second economy as we have estimated it. Furthermore,plugging our re-estimates into the larger seventeen country dataset, we find thatall five of the variables are significant at the 1 percent level; only one, however, issignificant at this level using the Johnson et al. (1997) estimates.

Because of the potential simultaneity between governance and the unofficialeconomy, Table 7 also presents the results using two variables to instrument forthe governance regressor: the distance of the country’s capital city from Brusselsand a dummy variable that indicates whether the country used to be part of theSoviet Union.23 The comparative results are similar to those in the OLS regressions.

Given that Johnson et al. (1997) use identical 1989 unofficial economy shares forall FSU republics, it is not surprising that their regressions do not show a sig-nificant relationship between the pre- and post-transition shares of the unofficialeconomy when controlling for governance factors (see Table 8). In substantiveterms, however, as we argued earlier, one would expect the historical size ofunofficial economy in the country to affect its size at a later date. Notice also, thatin the absence of any cross-country variation in the rate of structural change duringthe 1989–95 period, the correlation between the two sets of shares would havebeen perfect. Did the transition produce such a radical change of regime withrespect to the unofficial economy share of GDP that the post-transition governanceindicators became the only significant explanatory factor? In contrast to Johnsonet al. (1997), our estimates show that this was not the case. The pre-transitionshares of the unofficial economy are positive and highly significant in all our OLSregressions for 15 economies and are significant at least at the 10 percent level inour 17-economy regressions (see Table 8).24 While this correlation is natural becausewe work with levels (shares) rather than growth rates, we argued earlier that thecorrelation is enhanced because the pre-transition shares of unofficial economycould be viewed as proxies for the cultural environment and historical factors ineach country.25 To the extent that this is the case, the positive relationship betweenthe 1989 and 1995 shares suggests that the shares of the unofficial economy arepath-dependent and only partly determined by the post-transition governanceenvironment.

We present additional results in the Appendix tables. All offer further confir-mation of the conclusions drawn from Table 7. Table A1 reproduces regressionsthat control for being a former Soviet republic. Table A2 presents the same set of

23 Our use of the distance of the country’s capital from Brussels as an instrument is based on the idea thatit roughly captures cultural and historical legacies that have an impact on governance.24 We should note that there might well be additional factors that we do not consider that explain thevariation in the size of the unofficial economy. For instance, Georgia, Azerbaijan and Moldova all experi-enced periods of civil war. We expect, however, that such an experience would be at least partly capturedby the governance variables.25 Using growth rate of the shares during 1989–95 period would have presented its own problems, becausehigher initial shares would tend to grow slowly simply because they would have less room for growth. Inthe limit, if a country’s share of unofficial economy is 100 percent, it could only decline.


Tab

le 8

. Reg

ress

ions

of

unof

fici

al e

cono

my

(as

% o

f G

DP)

on

gove

rnan

ce i

ndic

ator

s an

d es

tim

ated

sha

re o

f un

offi

cial

eco

nom

y in

198

9

17 t

rans

itio

n co

untr

ies

15 t

rans

itio

n co

untr

ies

(exc

ludi

ng B

elar

us a

nd U

zbek

ista

n)

John

son

et a

l.A

utho

rsJo

hnso

n et

al.

Aut

hors

Gov

Init

R2

Gov

Init

R2

Gov

Init

R2

Gov

Init

R2

Leg

al s

afeg

uard

sO

LS

−0.0

45

(−2.

73)*

0.72

8 (1

.04)

0.35

5−0

.037

(−

2.02

)#0.

920

(1.7

7)#

0.64

0−0

.063

(−−−−

5.15

)**

0.66

5 (1

.36)

0.69

0−0

.048

(−

3.58

)**

1.09

0 (2

.93)

*0.

842

IV−0

.041

(−

2.06

)#0.

697

(0.9

8)0.

353

−0.0

31

(−1.

04)

1.05

1 (1

.52)

0.63

7−0

.064

(−

2.06

)*0.

672

(0.9

8)0.

690

−0.0

46

(−2.

26)*

1.12

6 (2

.41)

*0.

841

Cri

me

and

cor

rupt

ion

OL

S−0

.043

(−

2.84

)*0.

744

(1.0

7)0.

373

−0.0

35

(−2.

22)*

0.97

6 (2

.06)

#0.

656

−0.0

58

(−−−−5.

17)*

*0.

676

(1.3

8)0.

692

−0.0

43

(−3.

90)*

*1.

188

(3.5

6)**

0.85

6

IV−0

.041

(−

2.14

)*0.

731

(1.0

4)0.

372

−0.0

31

(−1.

17)

1.05

4 (1

.66)

0.65

5−0

.064

(−

4.31

)**

0.72

1 (1

.44)

0.68

5−0

.045

(−

2.50

)*1.

151

(2.7

1)*

0.85

5

Tax

bur

den

OL

S−0

.057

(−

2.12

)#0.

545

(0.7

3)0.

251

−0.0

42

(−1.

39)

1.15

1 (2

.15)

*0.

592

−0.1

18

(−−−−6.

61)*

*0.

442

(1.0

1)0.

786

−0.0

97

(−5.

11)*

*0.

989

(3.3

4)**

0.89

7

IV−0

.076

(−

2.05

)#0.

614

(0.8

0)0.

226

−0.0

85

(−1.

24)

0.60

9 (0

.64)

0.52

9−0

.124

(−

5.29

)**

0.45

4 (1

.12)

0.78

4−0

.109

(−

3.05

)**

0.86

4 (2

.01)

#0.

893

Reg

ulat

ion†

OL

S−0

.144

(−

3.51

)**

−0.5

53

(−0.

84)

0.51

1−0

.051

(−

1.11

)1.

586

(2.9

9)*

0.66

7−0

.143

(−−−−

3.41

)**

−0.5

85

(−−−−0.

87)

0.51

7−0

.039

(−−−−

0.80

)1.

772

(3.1

3)**

0.69

2

IV−0

.215

(−

3.68

)**

−0.9

59

(−1.

26)

0.38

8−0

.088

(−

1.22

)1.

318

(1.9

6)#

0.65

0−0

.210

(−

3.54

)**

−0.9

67

(−1.

24)

0.40

4−0

.066

(−−−−

0.83

)1.

562

(2.0

8)#

0.68

4

Leg

al e

ffec

tive

ness

OL

S−0

.088

(−

2.32

)*0.

481

(0.6

6)0.

285

−0.0

62

(−1.

43)

1.14

(2

.13)

#0.

595

−0.1

31

(−−−−4.

13)*

*0.

323

(0.5

8)0.

589

−0.0

87

(−2.

50)*

1.29

7 (3

.06)

**0.

785

IV−0

.100

(−

2.03

)#0.

501

(0.6

8)0.

280

−0.0

86

(−1.

02)

0.92

5 (1

.12)

0.

585

−0.1

52

(−3.

69)*

*0.

341

(0.6

0)0.

574

−0.1

14

(−1.

83)#

1.08

6 (1

.84)

#0.

774

Not

es: N

umbe

rs in

par

enth

eses

are

t-s

tati

stic

s; n

umbe

rs u

nder

‘Gov

’ are

coe

ffic

ient

s on

gov

erna

nce

vari

able

s; ‘I

nit’

is t

he s

hare

of

the

unof

fici

alec

onom

y in

198

9 as

est

imat

ed b

y Jo

hnso

n et

al.

and

the

aut

hors

, res

pect

ivel

y.# S

igni

fica

nt a

t 10

% l

evel

; * S

igni

fica

nt a

t 5%

lev

el; *

* Si

gnif

ican

t at

1%

lev

el.

Res

ults

in

bold

are

tho

se r

epor

ted

in

John

son

et a

l. (1

997)

.†

For

‘reg

ulat

ion’

, no

obse

rvat

ions

for

Kaz

akhs

tan

and

Uzb

ekis

tan.

Sour

ces:

See

not

e in

Tab

le 7

. All

gove

rnan

ce d

ata

are

for

1995

.


bivariate regressions run in Table 7 but makes the comparison between our andLacko’s estimates. Table A3 runs the same regression as in Table 7 but for a newerset of governance variables recently released by the World Bank. The R2 values forour estimates and the significance of the coefficients are consistently higher in thepairwise comparisons.

5. Conclusion

In this paper, we argue that the most commonly used estimates of the shares of theunofficial economy in the former Soviet republics are flawed in two importantrespects. First, the estimates of Kaufmann and Kaliberda and Johnson et al. (1997)disregard the variation in the development of unofficial economies across space inthe former Soviet Union. Second, their estimates for 1989 and, as a consequence,for 1995 significantly understate the average size of unofficial economies in thisentire group of countries. We propose our own estimates and compare the two setsby evaluating how strongly they are related to the institutional factors commonlyused to explain the size of the unofficial sector. We find that our estimates, com-bined with Kaufmann and Kaliberda’s estimates for a few other transition eco-nomies, are more strongly associated with the explanatory variables as measuredby the R2 values and the statistical significance of the coefficients in almost allregressions. We also demonstrate the better fit of our estimates relative to thosegenerated by Lacko (2000).

In addition, we find that our 1989 estimates remain a significant explanatoryfactor in our regressions even in the presence of governance variables, suggestingperhaps that the pre-transition starting point is important above and beyond theinstitutional environment that was achieved in the process of transition.

References

Alexeev, M. (1997). ‘The Russian underground economy in transition’, in Lippert, O. andM. Walker (eds.), The Underground Economy: Global Evidence of Its Size and Impact,Vancouver, Canada: The Fraser Institute, pp. 255–73.

Alexeev, M. and V. Treml (1994). ‘The growth of the second economy in the Soviet Unionand its impact on the system’, in Campbell, R. (ed.), The Postcommunist Economic Trans-formation, Boulder: Westview Press, pp. 221–47.

Atkinson, J. and N. Manning (1995). ‘A survey of international energy elasticities’, inBarker, T., Ekins, P. and Johnstone, N. (eds.), Global Warming and Energy Demand, Lon-don: Routledge, pp. 47–105.

Braithwaite, J. (1995). ‘From second economy to informal sector: The Russian labor marketin transition’, ESP Discussion Paper Series, No. 58, Washington, DC: World Bank.

Dobozi, I. and G. Pohl (1995). ‘Real output decline in transition economies – forget GDP, trypower consumption data!’ Transition Newsletter, 6:1–2, pp. 17–18.

Friedman, E., S. Johnson, D. Kaufmann and P. Zoido-Lobaton (2000). ‘Dodging the grabbing


hand: The determinants of unofficial activity in 69 countries’, Journal of Public Economics,76, pp. 459–93.

Grossman, G. (1991). ‘Wealth estimates based on the Berkeley-Duke Émigré Questionnaire:A statistical compilation’, Berkeley-Duke Occasional Papers on the Second Economy in theUSSR, No. 27, May.

Johnson, S. and D. Kaufmann (2001). ‘Institutions and the underground economy’, in O. Havrylyshynand S. Nsouli (eds.), A Decade of Transition: Achievements and Challenges, Washington, DC:International Monetary Fund.

Johnson, S., D. Kaufmann and A. Shleifer (1997). ‘The unofficial economy in transition’,Brookings Papers on Economic Activity, 2, pp. 159–221.

Johnson, S., D. Kaufmann and P. Zoido-Lobaton (1998). ‘Regulatory discretion and the unoffi-cial economy’, American Economic Review, 88(2), pp. 387–92.

Kaufmann, D. and A. Kaliberda (1996). ‘Integrating the unofficial economy into thedynamics of post-Socialist economies: A framework for analysis and evidence’, inB. Kaminski (ed.), Economic Transition in Russia and the New States of Eurasia, London:M.E. Sharpe, pp. 81–120.

Lacko, M. (2000). ‘Hidden economy – An unknown quantity: Comparative analysis of hiddeneconomies in transition countries, 1989–95’, Economics of Transition, 8(1), pp. 117–149.

Levine, H. (1983). ‘Possible causes of the deterioration of Soviet productivity growth in theperiod 1976–80’, in Soviet Economy in the 1980’s: Problems and Prospects, Joint EconomicCommittee, Congress of the United States, Washington, DC: US Government PrintingOffice, pp. 153–168.

May, J., W. Pyle and P. Sommers (2002). ‘Does governance explain unofficial activity?’Applied Economics Letters, 9, pp. 537–39.

Millar, J. (ed.) (1987). Politics, Work, and Daily Life in the USSR: A Survey of Former SovietCitizens, Cambridge, MA: Cambridge University Press.

Narodnoe khoziaistvo SSSR v 1980 godu, Moscow: Finansy i statistika.Noren, J. and L. Kurtzweg (1993). ‘The Soviet economy unravels: 1985–91’, in The Former

Soviet Union in Transition, v. 1, Joint Economic Committee, Congress of the United States,Washington, DC: US Government Printing Office, pp. 8–33.

Ofer, G. and A. Vinokur (1992). The Soviet Household Under the Old Regime: Economic Conditionsand Behavior in the 1970s, Cambridge, MA: Cambridge University Press.

Reed, J. (1995). ‘The great growth race’, Central European Economic Review, December –January, pp. 8–10.

Rosser, J. M. Rosser and E. Ahmed (2000). ‘Income inequality and the informal economy intransition economies’, Journal of Comparative Economics, 28, pp. 156–171.

Rutgaizer, V. (1992). ‘The shadow economy in the USSR’, Berkeley-Duke Occasional Papers onthe Second Economy in the USSR, 34, February.

Schneider, F. and D. Enste (2000). ‘Shadow economies: Size, causes, and consequences’,Journal of Economic Literature, 38, pp. 77–114.

Smith, P. (1997). ‘Assessing the size of the underground economy: The statistics Canadaperspective’, in O. Lippert and M. Walker (eds.), The Underground Economy: Global Evidenceof Its Size and Impact, Vancouver, Canada: The Fraser Institute, 1997, pp. 11–36.

Treml, V. (1992). ‘A study of labor inputs into the second economy of the USSR’, Berkeley-Duke Occasional Papers on the Second Economy in the USSR, No. 33, January.

World Bank (1993). ‘Statistical Handbook 1993: States of Former USSR’, Studies of Economiesin Transformation, Paper No. 8, Washington, DC: The World Bank.


App

endi

x

Tab

le A

1. R

egre

ssio

ns o

f un

offi

cial

eco

nom

y (a

s %

of

GD

P) o

n go

vern

ance

ind

icat

ors

and

Sovi

et d

umm

y

17 t

rans

itio

n co

untr

ies

15 t

rans

itio

n co

untr

ies

(exc

ludi

ng B

elar

us a

nd U

zbek

ista

n)

John

son

et a

l.A

utho

rsJo

hnso

n et

al.

Aut

hors

Gov

Sov

R2

Gov

Sov

R2

Gov

Sov

R2

Gov

Sov

R2

Leg

al

Safe

guar

ds

OL

S−0

.036

(−

1.61

)0.

033

(0.3

1)

0.31

0−0

.044

(−

2.38

)*0.

115

(1.2

7)0.

605

−−−− 0.0

55

(−−−−3.

26)*

* 0

.033

(0

.42)

0.64

8−0

.055

(−

3.67

)**

0.11

5 (1

.57)

0.77

5

Cri

me

and

co

rrup

tion

OL

S−0

.035

(−

1.72

)0.

031

(0.2

9)

0.32

5−0

.040

(−

2.36

)*0.

120

(1.3

5)

0.60

4−−−− 0

.051

(−−−−

3.28

)**

0.0

38

(0.4

9)0.

650

−0.0

51

(−3.

45)*

*0.

126

(1.6

9)0.

760

Tax

bur

den

OL

S−0

.039

(−

1.09

)0.

074

(0.6

7)0.

247

−0.0

53

(−1.

74)

0.15

4 (1

.63)

0.

544

−−−− 0.1

14

(−−−−4.

67)*

* 0

.010

(0

.16)

0.76

5−0

.114

(−

4.83

)**

0.10

1 (1

.63)

0.83

7

Reg

ulat

ion†

OL

S−0

.118

(−

3.56

)**

0.14

6 (2

.38)

*0.

648

−0.1

16

(−3.

76)*

*0.

236

(4.1

4)**

0.76

0−−−− 0

.113

(−−−−

3.53

)**

0.1

65

(2.7

2)*

0.69

1−0

.113

(−

3.61

)**

0.24

8 (4

.15)

**0.

774

Leg

al

effe

ctiv

enes

sO

LS

−0.0

68

(−1.

37)

0.06

1 (0

.58)

0.28

0−0

.079

(−

1.86

)0.

154

(1.7

0)

0.55

6−−−− 0

.110

(−−−−

2.83

)* 0

.067

(0

.85)

0.60

1−0

.109

(−

2.85

)*0.

159

(2.0

4)0.

715

Not

es: N

umbe

rs i

n pa

rent

hese

s ar

e t-

stat

isti

cs; n

umbe

rs u

nder

‘Gov

’ are

coe

ffic

ient

s on

gov

erna

nce

vari

able

s; ‘S

ov’ i

s a

dum

my

vari

able

for

form

er S

ovie

t re

publ

ics.

* Si

gnif

ican

t at

5%

lev

el; *

* Si

gnif

ican

t at

1%

lev

el.

Res

ults

in

bold

are

tho

se r

epor

ted

in

John

son

et a

l. (1

997)

.†

For

‘reg

ulat

ion,

’ no

obse

rvat

ions

for

Kaz

akhs

tan

and

Uzb

ekis

tan.

Sour

ces:

See

not

e in

Tab

le 7

. All

gove

rnan

ce d

ata

are

for

1995

.


Table A2. Regressions of unofficial economy (as % of GDP) on governance indicators

Table A3. Regressions of unofficial economy (as % of GDP) on World Bank governance indicators

Lacko Authors

Gov R2 Gov R2

Legal safeguards −0.015 (−3.24)** 0.428 −0.060 (−4.16)** 0.552Crime and corruption −0.013 (−3.10)** 0.407 −0.055 (−4.08)** 0.543Tax burden −0.023 (−3.15)** 0.415 −0.086 (−3.52)** 0.470Regulation† −0.032 (−2.17)* 0.282 −0.141 (−3.11)** 0.446Legal effectiveness −0.031 (−2.85)* 0.354 −0.124 (−3.52)** 0.470

Note: Lacko produced estimates for the ratio of the hidden economy to the official GDP. Since we andJohnson et al. (1997) used the ratio of the hidden economy to total GDP as our dependent variable, we adjustLacko’s estimates according to the following formula: L/(1 + L), where L refers to Lacko’s estimate of theratio of the hidden economy to official GDP. We thus convert her estimates into the ratio of the hiddeneconomy to total GDP. She produces estimates for 20 countries. The above regressions are, however, justrun for the 16 countries for which we both have estimates. These are the seventeen countries listed in Table2 minus Moldova.

17 transition economies 15 transition economies

Johnson et al. Authors Johnson et al. Authors

Governance Indicator Gov R2 Gov R2 Gov R2 Gov R2

Voice and accountability −0.084 (−1.54)

0.136 −0.151 (−2.97)**

0.370 −0.200 (−4.31)**

0.588 −0.251 (−5.26)**

0.680

Political instability −0.167 (−2.59)*

0.309 −0.226 (−3.70)**

0.477 −0.225 (−4.22)**

0.578 −0.265 (−4.31)**

0.588

Government effectiveness −0.115 (−1.64)

0.153 −0.180 (−2.64)*

0.318 −0.209 (−3.33)**

0.460 −0.249 (−3.47)**

0.481

Regulatory burden −0.077 (−1.32)

0.105 −0.143 (−2.58)*

0.307 −0.228 (−4.45)**

0.604 −0.278 (−5.07)**

0.664

Rule of law −0.139 (−1.79)

0.176 −0.217 (−2.92)*

0.362 −0.270 (−4.07)**

0.561 −0.319 (−4.23)**

0.579

Graft −0.175 (−2.59)*

0.309 −0.239 (3.78)**

0.488 −0.244 (−4.51)**

0.611 −0.286 (−4.59)**

0.618

Notes: Numbers in parentheses are t-statistics; numbers under ‘Gov’ are coefficients on governance variables.* Significant at 5% level; ** Significant at 1% level.May et al. (2002) first noted the relatively poor fit between these governance variables and the unofficialeconomy estimates of Johnson et al. (1997).Source: World Bank.

Documents

Alexeev and Pyle 2003