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Dollarization and the Dynamics of the Unobserved Economy:What have we learned from a decade of transition? Edgar L. Feige* *For presentation at CEFIR Seminar, October 15, 2003 Not for citation or quotation without prior consent of author [email protected] The Issues Many efforts to evaluate the transition process after a decade. (World bank, EBRD, IMF, etc). Virtually all evaluations are based on the “stylized facts” of observed GDP growth. Although all studies acknowledge the shortcomings of observed GDP as an indicator of the transition experience,they proceed to evaluate the impact of initial conditions, institutions and policies on the success or failure of transition based exclusively on observed GDP. The Thesis of this Paper Any evaluation of the transition process should be based on the best available information concerning the growth of total economic activity (TEA). TEA = Recorded Economy (Measured GDP) +Unrecorded Economy. Therefore, the first order of business is to review what we know about the unrecorded (unobserved) economy in transition. Warnings Avoid vague terms like “shadow economy” (Schneider and Enste). Avoid their emphasis on size of unrecorded economy and focus on growth of unrecorded sector. Be aware that different estimation methods: GDP discrepancies, Labor market discrepancies, AGI-PI discrepancies, CDM, Tanzi, ECM, MIMIC, measure different concepts and therefore should not be uncritically lumped together as if they were comparable. Be aware of different definitions of underground economies Definitions and Methods Matter Distinguish between “observed” and “unobserved” economic activity. Feige (1980) –”Observed sector of economy consists of those economic activities that are regularly caught in the net of our official statistical accounting mechanism.” (Recorded income) “Unobserved sector being the complement of the observed sector, consists of those activities that escape the purview of our current societal measurement apparatus.” (Unrecorded income) Definitions Total Economic Activity (TEA) = recorded + unrecorded income. Almost a quarter of a century has passed since this distinction was introduced, and now a consortium of national and international agencies has produced “The Handbook for the Measurement of Nonobserved Activities”(NOE) which presents a “systematic strategy for achieving exhaustive estimates of GNP.” Handbook definitions Takes account of “activities that are missing from basic data used to compile the national accounts because they are underground, illegal, informal, household production for final use, or due to deficiencies in the basic data collection system.” These comprise the NOE. Sets out methods to measure NOE. I will refer to “recorded economy” as comprising all economic activities that are in fact included in the published national accounts” , and “unrecorded economy” as those activities that should be, but are not fully included in national accounts” I use term “imputed unobserved income” (IUI) to describe the estimate of “non-observed” activity that is actually already included in the national accounts. We must require that new exhaustive measures of national accounts are consistent and transparent. Methods for Estimating Unrecorded Income Micro imputations to achieve exhaustiveness in national accounts (IUI) are very important, but they are also based on numerous (and sometimes questionable) assumptions. It therefore continues to be useful to employ macro indicator models to obtain independent estimates of the growth of the unrecorded economy. To date, transition literature has only employed variants of electric consumption method to estimate size and growth of unrecorded economy although the monetary methods are the most commonly used means of estimating the UG. Limited monetary data, and the problem of currency substitution has, to date, precluded estimates based on currency/deposit ratio method (Feige, 1986, 1989). ECM Approach Assumes that growth of electric consumption can be used as proxy for growth of TEA .Kaufmann-Kaliberda (1996, Johnson et al. (1997) Assumes unit or variable elasticity to estimate TEA. Then: Unrecorded economy= TEA-recorded Eilat-Zinnes (2000) use MEC approach that attempts to take account of variations in electric prices, electricity efficiency changes and changes in output composition. All ECM and MECM estimates require initial benchmark assumption about relative size of unrecorded sector. Electric consumption method ECM Simple unit and variable elasticity models Assumes that electric power consumption is best indicator of total economic activity Assumes that elasticity of electric consumption is best indicator of TEA Then difference between growth rate of electric consumption (TEA proxy) and recorded gdp yields approximate growth rate of unrecorded income. Shortcomings of simple ECM Many unrecorded activities may not require large amounts of electricity and may use other energy sources Efficiency of energy use may change over time due to technical progress, changes in industrial structure and changes in energy prices Johnson et.al. compensate for this by assuming different elasticities for different groups of countries Eilat/Zinnes use MECM –Try to first filter out effects of price changes, changes in industrial structure and private sector share of GDP and then use residual as proxy for TEA Table 1: Assumed Output Elasticity of Electric Consumption –Simple ECM Central and Eastern Europe “Energy efficient” 0.9 Baltic Countries “Energy neutral” 1.0 Former Soviet Union “Energy inefficient” 1.15 Initial condition problem All methods must assume initial starting values Big disagreement about initial conditions Russia (1989) Kaufman/Kaliberda Johnson assume Yu/TEA =12%:Alexeev Pyle study suggests 18%;EZ 14.7% and Kim 3.4% For some transition countries estimates range from 5 to 32.8% (Kazakhstan) Replication Efforts Importance: ECM models have been widely used to test hypotheses about the causes and consequences of unrecorded economy and to understand the transition process. Claims that observed fall in recorded output is false signal if Yu and Yo are substitutes To date: estimates only exist from 1990-1997 Most test are conducted using cross country regressions. Updates and extensions Employing new revised data sources we update ECM to 2001 Replication attempts using high and low start values Redo EZ procedure with GLS estimates Results produce many negative estiamtes of Yu/TEA Evaluation of What we Know about UG I attempt to replicate Johnson et al. I re-estimate simple ECM for 1990-2001 for 25 transition countries, but use Alexeev-Pyle (2002) initial values. Estimate MECM for all transition countries. Report all available estimates of IUI Compare above to Eliat Zinnes (MECM) results. Comparison of National Accounts and ECM Estimates of Unrecorded Income Average IUI/TEA NIPA Period Average Yu/TEA ECM-H Average Yu/TEA FUGLS FSU Armenia 1997-1999 20.1 19.7 26.0 Belarus 1999 10.7 22.2 21.2 Estonia 1997 10.2 32.4 33.6 Georgia 1997-1999 20.4 55.7 55.7 1999 18.9 20.5 16.2 1997-1998 12.1 64.4 65.2 Lithuania 1998 15.2 20.5 20.6 Moldova 1997-1998 15.7 58.9 58.0 Russian Federation 1997-1998 17.5 44.0 44.2 Ukraine 1999 16.7 57.1 56.6 Uzbekistan 1998 12.3 31.0 34.5 Kazakhstan Kyrgyz Republic Table 2a: Estimates of Initial Starting Values of Unrecorded Income (% TEA) FSU Kaufmann /Kaliberda; Johnson et.al. - 1989 Alexeev / Pyle 1989 Eilat / Zinne s 1990 Feige/ Urban Low 1989 Feige / Urban High 1989 Kim 1989 The Baltics Estonia 12.0 22.1 19.9 12.0 22.1 1.5 Latvia 12.0 22.1 12.8 12.0 22.1 1.8 Lithuania 12.0 22.1 11.3 12.0 22.1 5.1 Belarus 12.0 28.6 15.4 12.0 28.6 3.3 Moldova 12.0 28.6 18.1 12.0 28.6 8.2 Russian Federation 12.0 18.0 14.7 12.0 18.0 Ukraine 12.0 25.3 16.3 12.0 25.3 6.6 Armenia NA NA 23.4 12.0 32.8 8.6 Azerbaijan 12.0 32.8 21.9 12.0 32.8 9.8 Georgia 12.0 32.8 24.9 12.0 32.8 11.3 Western FSU 3.4 The Caucasus ECM Estimates Russian Federation 50 percent 40 30 20 10 0 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 year Johnson Original Results ECM - High Start Values ECM - Low Start Values Some Bad results Poland 30 20 percent 10 0 -10 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 -20 -30 year Johnson Original Results ECM MECM Estimates percent Russian Federation 50 45 40 35 30 25 20 15 10 5 0 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 year EZ FUEZ FUGLS MECM Replication percent Poland 30 25 20 15 10 5 0 -5 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 -10 -15 -20 year EZ FUEZ FUGLS Comparison of Recorded Growth, Growth of the Unrecorded Economy and the Rate of Growth of Total Economic Activity: FSU- 1989-2001 Average Growth Average Growth Average Growth Recorded GDP Unrecorded Economy Total Economic Activity Estonia -0.9 -6.1 -2.3 Latvia -1.8 -8.9 -3.6 Lithuania -2.7 -8.0 -4.1 MEAN FOR GROUP: -1.8 -7.7 -3.3 Belarus -0.5 -12.5 -3.2 Moldova -7.4 -2.8 -7.9 Russian Federation -3.6 5.7 -1.5 Ukraine -6.1 3.2 -3.0 MEAN FOR GROUP: -4.4 -1.6 -3.9 FSU The Baltics Western FSU Continued The Caucasus Armenia -1.3 -12.7 -4.5 Azerbaijan -3.7 3.7 -1.1 Georgia -6.5 -0.3 -4.8 MEAN FOR GROUP: -3.8 -3.1 -3.5 Kazakhstan -1.7 -12.5 -4.4 Kyrgyz Republic -2.9 8.7 1.8 Tajikistan -4.9 3.6 -2.0 Turkmenistan -1.1 2.8 -2.1 Uzbekistan 0.3 0.2 0.0 MEAN FOR GROUP: -2.1 0.6 -1.3 MEAN FOR FSU: -3.0 -2.4 -2.8 Central Asia Comparison of Recorded Growth, Growth of the Unrecorded Economy and the Rate of Growth of Total Economic Activity: CEE- 1989-2001 CEE Croatia Average Growth Average Growth Average Growth Recorded GDP Unrecorded Economy Total Economic Activity 3.7 -0.2 -0.7 Czech Republic 0.4 10.5 0.7 Hungary 0.8 -4.4 -0.4 Poland 2.2 -78.0 -0.7 Slovak Republic 0.7 -2.3 -1.2 Slovenia 2.0 16.2 3.8 MEAN FOR GROUP: 0.9 -9.0 0.3 Albania 1.5 15.7 6.7 Bulgaria -2.0 -3.8 -2.7 Macedonia -1.5 6.7 0.8 Romania -1.5 -247.5 -4.2 MEAN FOR GROUP: -0.9 -57.2 0.2 -28.3 0.3 MEAN FOR CEE: 0.2 Relationship between unrecorded and recorded 5) Yu = 50.6 -.38 Yo - .05 Yo x D R2 =.87 (36.5) (-16.1) (-4.1) N=318, Adj The corresponding MECM equation employing the FUGLS estimate of Yu is: 6) Yu = 47.6 - .34 Yo - .05 Yo x D N=298, Adj R2 =.85 (31.4) (-13.1) (-4.1) Evolution of Recorded and Unrecorded Income-ECM Estimates Russian Federation Evolution of Recorded and Unrecorded Economy ECM-H 100.0 80.0 60.0 40.0 20.0 0.0 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 Recorded Unrecorded Bad News Poland Evolution of Recorded and Unrecorded Economy ECM-H 120.0 100.0 80.0 60.0 40.0 20.0 0.0 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 Recorded Unrecorded Observations Size of unrecorded sector is highly dependent on initial benchmark assumptions. What is important for transition countries is growth of recorded and unrecorded economy ECM approach produces negative values for size of unrecorded economy for several countries. It can not be regarded as a reliable means of estimating the size of UG in transition May still be useful to estimate dynamics –growth rates in unrecorded activity Therefore we must move beyond simple cross country tests and move to richer panel data testing CDM Approach De facto dollarization of many transition countries implies that foreign currency in circulation (FCC) is an important component of the effective currency supply and foreign currency deposits (FCD) are an important component of the effective supply of deposits. I have now constructed estimates of FCC in transition countries based on CMIR data. Roughly 50% of US dollars are held abroad. Mostly in Latin America and transition countries. For transition countries I use (FCC+LCC)/(FCD+LDD) =(total currency/total deposits) as the appropriate C/D ratio. Since currency substitution will effect both denominator and numerator, variations in C/D are assumed to largely reflect changes in unrecorded sector which primarily uses cash as medium of exchange to avoid a paper trail and hence reduces probability of detection. The US Currency Enigma • $580 billion of US currency in $1-$100 bills. • $2200 per person$160 in 1961. • $5500 per family • 65% in $100 bills • 14 $100 bills per person • 70 notes per person Key Issues • Who holds all of this currency? – Households? – Firms? – The Underground Economy? – Residents of other countries? • How much of America’s currency is held abroad? • If Abroad, where is US currency located? • What are the implications of US currency held overseas? Allocation Of Currency Survey Estimates 80 70 60 50 Households Firms Underground Missing 40 30 20 10 0 Percent Table 1 Countries with highest estimated Currency Substitution Index (CSI) Country Currency Substitution Index Kazakhstan 95 Cambodia 94 Belarus 88 Russia 87 Azerbaijan 80 Argentina 80 Nicaragua 79 Uruguay 77 Bolivia 75 Georgia 74 Mongolia 73 Ukraine 72 Latvia 70 El Salvador 69 Peru 64 Dominican Republic 62 Costa Rica 62 Saudi Arabia 62 Bahrain 57 Estonia 57 Romania 54 Turkey 54 Kyrgyz Republic 52 Venezuela 51 Armenia 51 De facto Dollarization Estimates employing various methods suggest that roughly 50% of US currency is held abroad, roughly $290 billion However the location of dollars was previously unknown. I have now attempted to determine the location of US currency held abroad employing CMIR data. Unofficial Dollarization Behavioral adaptation of using FCC as substitute for services of domestic money Currency substitution refers to the use of foreign currency as a medium of exchange and unit of account Asset substitution refers to use of foreign denominated monetary assets as stores of value Why do we care how much cash is abroad? Required for estimation of domestic money supply Improves conduct of monetary policy BOP estimates of cross border flows Better monetary estimates of underground economy Seigniorage Counterfeit detection and deterrence Estimated Distribution of US Currency Overseas Distribution of US Currency Abroad Asia 22% CEE 2% Africa 1% Middle East 10% Latin America 23% FSU 40% North America 2% Dollarization Present two measures of dollarization. FCC per capita and Currency Substitution Index (CSI)=FCC/(FCC+LCC) For Russia my estimates suggest that per capita dollar holdings amount to $903 only exceeded by Kazakhstan with $1024 and Latvia with $1209 – Unfortunately, the Russian Central Bank did not participate in CEE/IMF effort to monitor Euro introduction. Therefore, we have an incomplete picture of FCC in circulation, since euro holding estimates are not available. Alternative Measures of de facto Dollarization –Select FSU Countries CDI= (FCC+FCD) / (M2+FCC) DI=FCD / M2 CDI Trend DI Trend Belarus 57.2 52.8 ↑ ↑ Ukraine 37.8 19.4 ↑ ↑ Moldova 27.3 27.3 ↑ ↑ Russia 73.5 24.5 ↑ ↑ Inter-temporal Correlations between Comprehensive Dollarization Index (CDI) and the IMF Dollarization Index (DI) CDI-DI Period Russia 0.222 93-2001 Belarus 0.997 92-2001 Ukraine 0.596 92-2001 Currency (CSI) and Asset Substitution (ASI) Indices Western FSU Western FSU CSI ASI CSI Trend ASI Trend Belarus 34 194 ↑ ↑ Moldova NA 78 NA ↑ Russia 87 51 ↑ ↑ Ukraine 64 50 ↑ ↑ Cross Country CorrelationsDollarization - Currency and Asset Substitution Indices Region CSI-ASI CSI-CDI CSI-DI ASI-CDI ASI-DI FSU* 0.312 0.763 0.113 0.628 0.692 CEE 0.021 0.431 0.347 0.886 0.932 FSU*+CEE 0.355 0.748 0.218 0.711 0.695 Inter-temporal Correlations CSI-ASI Belarus 0.540 Russia -0.039 Ukraine 0.534 ASI-CDI ASI-DI 0.39 0.961 0.952 0.929 -0.10 0.279 0.988 0.938 0.783 0.942 CSI-CDI 0.455 CSI-DI .275 Observations: CDI and DI give quite different pictures of extent of dollarization CDI and DI are correlated over time but relationship shows significant country differences DI is better measure of asset substitution than currency substitution Relationship between currency substitution is more complex and variable than is usually thought to be the case, both over time and between countries Implications of de facto dollarization De facto dollarization is typically a rational response to agents loss of confidence in the domestic currency, often resulting from inflation, currency devaluations and/or currency confiscations. May also be due to growth of underground economy De facto dollarization results in loss of seigniorage It thwarts the monetary authority from pursuing inflationary finance Inhibits its effectiveness in controlling exchange rates. Efficiency gains from portfolio diversification Reduced incentives for inflation finance and capital flight Lowers costs of tax evasion and reduces ability of fiscal authority to command resources from the private sector. Growth of UG distorts macro information system making policy formulation more difficult Table 3: Comparison of Recorded Growth, Growth of the Unrecorded Economy and the Rate of Growth of Total Economic Activity: CDM Composite Estimates FSU Average Growth Average Growth Average Growth Recorded GDP Unrecorded Economy Total Economic Activity The Baltics Estonia -1.69 1.69 -1.47 Latvia -3.06 2.13 -2.31 Lithuania -3.32 -4.32 -3.85 MEAN FOR GROUP: -2.69 -0.17 -2.54 Belarus -1.27 -2.55 -1.95 Moldova -9.01 -2.40 -8.33 Russian Federation -4.08 11.84 0.70 Ukraine -7.95 6.90 -1.89 MEAN FOR GROUP: -5.58 3.45 -2.87 3.31 7.71 4.53 Azerbaijan -5.08 11.28 0.13 Georgia -7.51 2.00 -4.47 MEAN FOR GROUP: -3.09 7.00 0.06 -3.04 4.89 -0.44 0.35 8.89 2.13 Tajikistan -2.83 -6.08 -4.49 Turkmenistan -3.88 7.88 -0.21 0.16 -1.91 -0.82 MEAN FOR GROUP: -1.85 2.73 -0.77 MEAN FOR FSU: -3.26 3.20 -1.52 Western FSU The Caucasus Armenia Central Asia Kazakhstan Kyrgyz Republic Uzbekistan Table 3: Comparison of Recorded Growth, Growth of the Unrecorded Economy and the Rate of Growth of Total Economic Activity: CDM Composite Estimates CEE Average Growth Average Growth Average Growth Recorded GDP Unrecorded Economy Total Economic Activity EU Border Countries Croatia 2.83 4.14 2.93 -0.02 19.01 1.59 Hungary 0.53 -0.94 0.11 Poland 2.38 1.35 1.98 Slovak Republic 0.45 1.92 -0.09 Slovenia 4.25 6.42 4.27 MEAN FOR GROUP: 1.74 5.32 1.80 Albania 1.05 9.45 3.54 Bulgaria -2.44 8.80 -0.39 0.33 11.96 2.60 Romania -2.03 -1.06 -1.96 MEAN FOR GROUP: -0.77 7.29 0.95 0.73 6.11 1.46 Czech Republic The Balkans Macedonia MEAN FOR CEE: Observations Table 3 reveals that for FSU average rate of growth of recorded economy was –3.26% while Unrecorded economy grew by 3.20%. Therefore TEA fell by – 1.52% not, -3.26 as reflected in GDP growth. For CEE GDP growth =.73% but unrecorded growth was 6.11% therefore TEA grew by 1.46% Russia and Ukraine showed greater overall growth due to growth in UG. Tajikistan overall growth was lower than recorded growth due to decline in UG. Table 4. Estimate of Growth of Recorded (REC) and Total Economic Activity (TEA) (Recorded and Unrecorded Over the Decade of Transition. FSU Base year REC 2000 Index TEA 2000 Index The Baltics Estonia 1989=100 79.8 83.6 Latvia 1989=100 64.2 73.4 Lithuania 1989=100 65.5 62.2 69.8 73.1 MEAN FOR GROUP: Western FSU Belarus 1989=100 84.1 78.1 Moldova 1989=100 32.1 36.2 Russian Federation 1989=100 61.8 104.1 Ukraine 1989=100 38.6 79.6 54.2 74.5 MEAN FOR GROUP: The Caucasus Armenia 1992=100 127.2 139.8 Azerbaijan 1989=100 51.1 98.0 Georgia 1989=100 34.5 53.4 70.9 97.0 MEAN FOR GROUP: Central Asia Kazakhstan 1989=100 69.4 90.6 Kyrgyz Republic 1993=100 99.1 109.0 Tajikistan 1992=100 77.6 71.4 Turkmenistan 1992=100 65.9 89.9 Uzbekistan 1989=100 100.5 89.7 MEAN FOR GROUP: 82.5 90.1 MEAN FOR FSU: 70.1 83.9 Table 4. Estimate of Growth of Recorded (REC) and Total Economic Activity (TEA) (Recorded and Unrecorded Over the Decade of Transition. CEE Base year REC 2000 Index TEA 2000 Index EU Border Countries Croatia 1992=100 123.9 125.44 Czech Republic 1989=100 98.7 118.31 Hungary 1989=100 104.5 100.28 Poland 1989=100 127.2 123.59 Slovak Republic 1989=100 102.7 98.15 Slovenia 1992=100 139.5 139.36 116.1 117.52 MEAN FOR GROUP: The Balkans Albania 1989=100 102.9 141.50 Bulgaria 1989=100 74.8 93.03 Macedonia 1992=100 101.6 117.91 Romania 1989=100 78.3 78.80 89.4 107.81 105.4 113.64 MEAN FOR GROUP: MEAN FOR CEE: Table 5. Relationship Between the Recorded and Unrecorded Economies and their Volatility.- CDM Estimates Correlation coefficient Regression coefficient FSU Standard deviation Standard deviation Recorded economy Unrecorded economy The Baltics Estonia -0.62 -1.62 8.48 22.02 Latvia -0.32 -0.49 12.66 18.91 0.03 0.06 9.56 17.99 -0.30 -0.68 10.23 19.64 Belarus -0.01 -0.03 7.99 18.04 Moldova -0.62 -1.49 12.32 29.42 Russian Federation -0.08 -0.22 6.59 18.54 0.00 0.00 8.13 8.18 -0.18 -0.44 8.76 18.54 Armenia -0.02 -0.03 7.43 15.09 Azerbaijan -0.41 -0.57 13.05 18.27 0.00 0.00 17.32 26.88 -0.14 -0.20 12.60 20.08 Kazakhstan -0.05 -0.17 7.04 22.89 Kyrgyz Republic -0.77 -2.90 10.21 38.67 Tajikistan NA NA 13.09 NA Turkmenistan NA NA 16.19 NA Uzbekistan NA NA 4.97 NA MEAN FOR GROUP: -0.41 -1.54 10.30 30.78 MEAN FOR FSU: -0.24 -0.62 10.34 21.24 Lithuania MEAN FOR GROUP: Western FSU Ukraine MEAN FOR GROUP: The Caucasus Georgia MEAN FOR GROUP: Central Asia Table 5. Relationship Between the Recorded and Unrecorded Economies and their Volatility.- ECM Estimates Correlation coefficient Regression coefficient CEE Standard deviation Standard deviation Recorded economy Unrecorded economy EU Border Countries Croatia -0.32 -1.60 5.11 25.87 Czech Republic -0.82 -8.28 4.53 45.83 Hungary -0.38 -0.58 5.14 7.86 Poland -0.19 -6.78 5.96 208.91 Slovak Republic -0.27 -4.72 6.67 116.34 0.29 45.35 0.85 133.74 -0.28 3.90 4.71 89.76 Albania 0.13 0.23 12.47 21.12 Bulgaria 0.01 0.05 5.97 23.41 Macedonia -0.04 -0.11 4.87 14.06 Romania -0.13 -1.17 6.18 56.73 MEAN FOR GROUP: -0.01 -0.25 7.37 28.83 MEAN FOR CEE: -0.17 2.24 5.78 65.39 Slovenia MEAN FOR GROUP: The Balkans Summary Evaluation of transition experience requires estimates of TEA. IUI estimates are worthwhile but still incomplete and still appear to underestimate the size and growth of unobserved sector. ECM results appear much less reliable than CDM results. Unrecorded and recorded economies are negatively correlated suggesting that substitution effects dominate income effects. The unrecorded sector serves as a buffer for the recorded sector and is counter-cyclical and more volatile.