Tag: GDP
A Russian Sudden Stop Still a Major Risk
The Russian economy is facing serious challenges in 2015 even after the currency and stock market have strengthened on the back of (expectations of even) higher oil prices. Policy makers that ignore these challenges may be in for a rude awakening when more statistics on the real economy are now coming in. It is time that actions are taken to deal with Russia’s structural problems, mend ties with its neighbors that are also important economic partners, and refocus political priorities towards generating growth and prosperity for its population. In the long run, this is what creates the respect and admiration a great nation deserves.
Recent developments
The value of Russian assets, including shares and the currency, was more or less in free fall in the second half of 2014 and into the beginning of 2015. The annexation of Crimea and continued fighting in Eastern Ukraine and the associated sanctions contributed to a general loss of confidence in Russian assets, but the fall in international oil prices was an even more decisive factor (for a detailed account of the sanctions, see PISM (2015)).
Figure 1 shows how the stock market first took a big hit at the time of the invasion of Crimea, but then recovered before the massive downturn in mid-2014 as oil prices collapsed. The ruble followed a similar path, but with less volatility than the stock market, which is not too surprising given that the Central Bank of Russia (CBR) intervenes to stabilize the currency. However, the ruble had a short time of extreme volatility in mid to end-December when the uncertainty about the impact of financial sanctions was very high.
Figure 1. Oil price, Ruble and Stocks
Sources: CBR, US EIA, MICEX
Financial sanctions were particularly troubling since Russian companies, both private and state owned, have significant external debt that became increasingly hard to refinance. The magnitude of this external debt is also such that it is not a trivial matter for the government or central bank to handle despite the fact that public external debt is very low and international reserves are among the largest in the world. As a matter of fact, external debt was around $250 billion more than then the value of CBR’s international reserves at the peak, but the difference has come down somewhat to around $200 billion as external loans had to be paid back when new external funding was not available at attractive terms.
Sudden Stops
Before turning to the outlook for the Russian economy, a short discussion of sudden stops is warranted. “Sudden stops” is short for sudden stops or sharp reversals in international capital flows. Sudden stops and its effects on the real economy have been analyzed for some time now (see Calvo (1998) for an early contribution). Becker and Mauro (2006) concluded that sudden stops have been the most costly type of shock for emerging market countries in terms of lost GDP in modern history. In their study the average country that experienced a sudden stop had a cumulative loss of income of over 60 percent of its initial GDP before recovering back to its pre-crisis income level.
Sudden stops in capital flows have such large effects on the real economy because of the adverse effects reduced external funding has on imports. A first look at the accounting identity for GDP (GDP=Y=C+I+G+X-M) makes it hard to see how reduced imports can be a problem since imports (M) enter with a negative sign. This in itself suggests that reduced imports should increase GDP. However, imports are used for domestic consumption (C) or investment (I), two factors that enter the same identity with positive signs, which means that when they fall so does GDP. If this were the full story, the net effect on GDP from falling imports would be zero since the positive direct effect from imports would be exactly offset by reduced domestic consumption and investment.
Unfortunately the accounting identity does not make clear the dynamics that follow from this reduction in consumption and investment. For example, the foreign car (or machine) that is no longer imported and will not be sold, will also not require a domestic sales person, annual service, a parking space etc., so the eventual decline in consumption (or investment) will be much larger than the first round effect that is captured by a static accounting relationship. This is one reason why “improvements” in the trade balance stemming from the sudden decrease in imports is not necessarily a good thing for the economy.
Russia is also part of the international financial system with important capital flows both in and out of the country. As such, it is also subject to the risk that changes in sentiment and large capital outflows can affect imports and the real economy. For a time before the global financial crisis, net capital flows to Russia tended to be positive. However, this changed in 2009 and since then most quarters have been showing outflows.
Figure 2. Private Sector Capital Outflows Continue (Q1 2015 in red)
Source: CBR
The speed of outflows picked up dramatically in 2014, reaching more than $150 billion for the year. The general picture of outflows has continued in the first quarter of 2015, with outflows of around $35 billion (which for comparison is twice the $17.5 billion IMF package that was agreed for Ukraine in March 2015). Although Russia still has resources to support a high level of imports, the more capital that leaves, the less money there is to spend and invest in the country.
The Outlook
Everyone knows that Russia generates most of its export revenues from natural resources in general and from oil more specifically. The fact that the health of the economy is closely related to international oil prices is no secret either and Figure 1 showed the tandem cycle of oil prices, the ruble and the stock market. But how important is oil prices as a determinant of GDP growth? This is of course a big question that requires sophisticated thinking and modeling to figure out at a more structural level. But if we are just looking for a back of the envelope estimate, a simple regression of growth of oil is potentially interesting. Perhaps somewhat surprisingly, oil price growth has very high explanatory power: regressing annual changes in GDP per capita in real dollar terms on annual changes in real oil prices (and a constant) for the period 1998 to 2014 generates an R2 of 0.64! Not bad for a one variable macro “model” of the Russian economy. The coefficient on real changes in oil prices is estimated to be 0.15 and hugely significant and the intercept, which could be interpreted as the underlying growth rate in this “model”, of 2.4%.
Using the same IMF data on the real oil price for the first three months of 2015 and comparing that to the average oil price for the full year 2014 implies a drop in the real oil price of 46 percent. Using this oil data as the forecast for all of 2015 and plugging this into the estimated equation suggests that the oil price drop in itself would be associated with a decline in income of almost 7 percent. Adding back the underlying growth rate of just over 2 percent still means a negative growth rate of almost 5 percent in 2015, without even starting to think about sanctions, capital flows or structural problems.
However, there is more data that points in the directions of the economic troubles that lay ahead in 2015, which is trade data. We just discussed the importance of sudden stops and associated drops in imports in explaining large drops in output in emerging markets. Figure 2 already showed the continued capital outflows, and Figure 3 provides a scatter plot of changes in imports and GDP growth. Over the years, Russia has displayed a strong positive correlation between import growth and GDP growth that is in line with the description of sudden stop dynamics.
Figure 3. Imports and GDP Growth (Q1 2015 in red)
Source: Author’s calculations based on CBR and the Federal State Statistics Service (GKS) data
Figure 3 shows the import change in Q1 2015 (i.e., Q1 in 2015 compared to Q1 2014) as a red diamond and puts it on the linear regression line of past observations to get the implied GDP growth number for Q1 2015. First of all, the 36 percent drop in imports is at an all time high for the decade and at roughly the same level as in the worst quarter of 2009 in the global financial crisis. The implied drop in GDP is 10.5 percent (compared with a drop of 9.5 in the worst quarter of 2009). Again, this is not a formal model to generate GDP forecasts, but it is certainly a signal that suggests that the Russian economy has problems to deal with.
Concluding Remarks
The IMF (2015) just released its latest forecast for Russia together with the other countries of the world. The projection for 2015 is a decline of real GDP of 3.8 percent, which is not a great growth number by any means but less negative than what was discussed at the end of 2014. The Economist (2015) in its latest issue is also quoting a banker who says that the situation is not as bad as was previously imagined. The upward revisions have also led to statements among policy makers that seem to suggest that the problems for the Russian economy are behind the country.
Although the free fall associated with the sharp drop in oil prices is halted, recent data on capital flows and imports suggest that the problems for the Russian economy are far from over. If oil prices stay at current levels, capital outflows continue, and imports remain as suppressed as they were in the first quarter, the fall in GDP may be in the same order as in 2009. At that time GDP declined by 8 percentage points, or more than twice the recent forecasts for 2015.
Russian policy makers need to make serious structural reforms and mend ties with its important economic partners near and far to put the country on a more healthy growth trajectory. Simply praying for increasing oil prices is not enough; it is time that Russia becomes the master of its own economic faith.
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References
- Becker, T., and P. Mauro (2006), “Output drops and the shocks that matter”, IMF Working Paper, WP/06/197
- Becker, T. (2014), “A Russian Sudden Stop or Just a Slippery Oil Slope to Stagnation?”, BSR Policy Briefing 4/2014, Centrum Balticum
- Calvo, G. (1998), “Capital Flows and Capital-Market Crises: The Simple Economics of Sudden Stops,” Journal of Applied Economics, Vol. 1, No. 1, pp. 35–54.
- Economist, The (2015), “Russia and the West: How Vladimir Putin tries to stay strong”, April 18-24 issue
- IMF, (2015), World Economic Outlook, April
- PISM, (2015), “Sanctions and Russia”, Polski Instytut Spraw Międzynarodowych, (The Polish Institute of International Affairs)
Disclaimer: Opinions expressed in policy briefs and other publications are those of the authors; they do not necessarily reflect those of the FREE Network and its research institutes.
Baltic Shadow Economies
This policy brief summarises the results and implications of a recent study of the size and determinants of the shadow economies in Estonia, Latvia, and Lithuania. The results suggest that the shadow economy in Latvia in 2010 is considerably larger than in neighboring Estonia and Lithuania. While the shadow economy as a percentage of GDP in Estonia contracted from 2009 to 2010, it expanded in Latvia and Lithuania. An important driver of shadow activity in the Baltic countries is the entrepreneurs’ dissatisfaction and distrust in the government and the tax system. Involvement in the shadow economy is more pervasive among younger firms and firms in the construction sector. These findings have a number of policy implications, which are discussed at the end of this brief.
Background and Aims
Anecdotal evidence suggests that the shadow economies in the Baltic countries and other emerging Central and Eastern European countries are substantial in size relative to GDP. This is an important issue for these countries because informal production has a number of negative consequences.
First, countries can spiral into a ‘bad equilibrium’: individuals go underground to escape taxes and social welfare contributions, eroding the tax and social security bases, causing increases in tax rates and/or budget deficits, pushing more production underground and ultimately weakening the economic and social basis for collective arrangements. Second, tax evasion can also hamper economic growth by diverting resources from productive uses (producing useful goods and services) to unproductive ones (mechanisms and schemes to conceal income, monitoring of tax compliance, issuance and collection of penalties for non-compliance). Third, informal production can constrain entrepreneurs’ ability to obtain debt or equity financing for productive investment because potential creditors/investors cannot verify the true (concealed) cash flows of the entrepreneur. This can further impede growth. Finally, shadow activities distort official statistics such as GDP, which are important signals to policy makers.
The aim of our study is to measure the size of the shadow economies in Estonia, Latvia, and Lithuania, and to analyse the factors that influence participation in the shadow sector. We use the term ‘shadow economy’ to refer to all legal production of goods and services that is deliberately concealed from public authorities. The study also makes a methodological contribution by developing an index of the size of the shadow economies as a percentage of GDP. It is foreseen that the index will be published regularly.
Although an index invites comparisons, and maybe even ‘competitions’ between countries, the purpose here is not to create a ‘Baltic championship’ on shadow economies. The index should primarily be seen as a tool to promote discussion on the size and role of the shadow economy and to provide a metric which can be used to measure the degree of success in fighting the shadow economy.
Method of Measuring the Shadow Economies
Estimates the size of the shadow economies are derived from surveys of a stratified random sample of entrepreneurs in the three countries (591 in Latvia, 536 in Lithuania and 500 in Estonia). The rationale for this approach is that those most likely to know how much production or income goes unreported, are the entrepreneurs who themselves engage in the misreporting and shadow production.
Survey-based approaches face the risk of underestimating the total size of the shadow economy due to non-response and untruthful response given the sensitive nature of the topic. We minimise this risk by employing a number of surveying and data collection techniques shown in previous studies to be effective in eliciting more truthful responses (e.g., Gerxhani, 2007; Kazemier and van Eck, 1992; Hanousek and Palda, 2004).
These approaches include framing the survey as a study of satisfaction with government policy, gradually introducing the most sensitive questions after less sensitive questions, phrasing misreporting questions indirectly, e.g., asking entrepreneurs about the shadow activity among ‘firms in their industry’ rather than ‘their firm’, and, in the analysis, controlling for factors that correlate with potential untruthful response, such as tolerance towards misreporting. We aggregate entrepreneurs’ responses about misreported business income, unregistered or hidden employees, as well as unreported ‘envelope’ wages to obtain estimates of the shadow economies as a proportion of GDP.
There are three common methods of measuring GDP: the output, expenditure and income approaches. Our index is based on the income approach, which calculates GDP as the sum of gross remuneration of employees (gross personal income) and gross operating income of firms (gross corporate income). Computation of the index proceeds in three steps: (i) estimate the extent of underreporting of employee remuneration and underreporting of firms’ operating income using the survey responses; (ii) estimate each firm’s shadow production proportion as a weighted average of the two underreporting estimates with the weights reflecting the proportions of employee remuneration and firms’ operating income in the composition of GDP; and (iii) calculate a production-weighted average of shadow production across firms. Taking weighted averages of the underreporting measures rather than a simple average is important for the shadow economy index to reflect a proportion of GDP.
Size of the Shadow Economies
Table 1 indicates that the shadow economy as a proportion of GDP is considerably larger in Latvia (38.1%) compared to Estonia (19.4%) and Lithuania (18.8%) in 2010. Only Estonia has managed to marginally decrease the proportional size of its shadow economy from 2009 to 2010 – a statistically significant decrease of 0.8 percentage points. In contrast, the proportional size of the shadow economies in Lithuania and Latvia has increased by an estimated 0.8 and 1.5 percentage points, respectively.
Table 1. Shadow economy index for the Baltic countries
Note: This table reports point estimates and 95% confidence intervals for the size of the shadow economies as a proportion of GDP. The third column reports the change in the relative size of the shadow economies from 2009 to 2010.
Form of Shadow Activity
Figure 1 illustrates the average levels of underreporting (business profits, number of employees and salaries) in each of the countries in 2009 and 2010. The average levels of underreporting in all three areas are in the order of two to three times higher in Latvia compared to Lithuania and Estonia. In Latvia and Lithuania, the degree of underreporting of business profits and salaries (‘envelope’ wages) is approximately twice as large as the underreporting of employees. The exception to this trend is the relatively low amount of underreported business profits in Estonia, likely to be a result of low corporate tax rates. Bribery in Latvia and Lithuania constitutes a similar fraction of firms’ revenue, approximately 10%, whereas in Estonia bribery is less pervasive and constitutes around 6% of firms’ revenue.
Figure 1. Simple averages of underreporting and bribery among Estonian (EE), Lithuanian (LT) and Latvian (LV) firms in 2009 and 2010.
Determinants of Involvement in the Shadow Economy
The literature on tax evasion identifies two main groups of factors that affect the decision to evade taxes and thus participate in the shadow economy. The first set emerges from rational choice models of the decision to evade taxes. In such models individuals or firms weigh up the benefits of evasion in the form of tax savings against the probability of being caught and the penalties that they expect to receive if caught. Therefore the decision to underreport income and participate in the shadow economy is affected by the detection rates, the size and type of penalties, firms’ attitudes towards risk-taking and so on. These factors are likely to differ across countries, regions, sectors of the economy, size and age of firm, and entrepreneurial orientation (innovativeness, risk-taking tendencies, and pro-activeness).
Empirical studies find that the actual amount of tax evasion is considerably lower than predicted by rational choice models based on pure economic self-interest. The difference is often attributed to the second, broader, set of tax evasion determinants – attitudes and social norms. These factors include perceived justice of the tax system, i.e., attitudes about whether the tax burden and administration of the tax system are fair. They also include attitudes about how appropriately taxes are spent and how much firms trust the government. Finally, tax evasion is also influenced by social norms such as ethical values and moral convictions, as well as fear of feelings of guilt and social stigmatisation if caught.
Our study uses regression analysis to identify the factors that are statistically related to firms’ involvement in the shadow economy. The results indicate that the size of the shadow economy is smaller in Estonia and Lithuania relative to Latvia, after controlling for a range of factors.
Tolerance towards tax evasion is positively associated with the firm’s stated level of income/wage underreporting. Satisfaction with the tax system and the government is negatively associated with the firm’s involvement in the shadow economy, i.e. dissatisfied firms engage in more shadow activity, satisfied firms engage in less.
This result is consistent with previous research on tax evasion, and offers an explanation of why the size of the shadow economy is larger in Latvia than in Estonia and Lithuania; namely that Latvian firms engage in more shadow activity because they are more dissatisfied with the tax system and the government as illustrated in Figure 2. Analysing each of the four measures of satisfaction separately we find that shadow activity is most strongly related to dissatisfaction with business legislation, followed by the State Revenue Service, the government’s tax policy, and finally the government’s support for entrepreneurs.
Figure 2. Average satisfaction of firms with the tax system and government in 2010.
Note: These questions use a 5-point scale: 1=“very unsatisfied”; 2=“unsatisfied”; 3=“neither satisfied nor unsatisfied”; 4=“satisfied”; and 5=“very satisfied”. SRS is State Revenue Service.
Another strong determinant of involvement in the shadow economy is firm age, with younger firms engaging in more shadow activity than older firms. This effect dominates relations between firm size and shadow activity. A possible explanation for the relation is that young firms entering a market made up of established competitors use tax evasion as a means of being competitive in their early stages. The regression results also provide some evidence that after controlling for other factors, firms in the construction sector and firms that have a pro-active entrepreneurial orientation tend to engage in more shadow activity.
Policy Implications
First, the relatively large size of the shadow economies in the Baltic countries, and their different expansion/contraction trends, cause significant error in official estimates of GDP and its rates of change, because although statistics bureaus in each of the countries attempt to include some of the shadow production in GDP estimates they do not capture the full extent. Not only is GDP used in key policy ratios such as government deficit to GDP, debt to GDP, but also the rate of change is used as a key indicator of economic performance and therefore guides policy decisions. When the shadow economy is expanding (as in Latvia and Lithuania) official GDP growth rates underestimate true economic growth and when the shadow economy is contracting (as in Estonia) official GDP growth rates overstate true economic growth. At a minimum, policy makers need to be aware of these biases in official statistics, but ideally, statistical bureaus would implement more rigorous methods to estimate and incorporate shadow production in official statistics.
Second, our results suggest that to reduce the size of the shadow economies in the Baltic countries by encouraging voluntary compliance, a key factor that needs to be addressed is the high level of dissatisfaction with the tax system and with the government. Addressing this issue could involve actions such as making tax policy more stable (less frequent changes in procedures and tax rates), and increasing the transparency with which taxes are spent.
Finally, our estimates of the size of the shadow economies suggest that there is significant scope for all three governments to increase their revenues by bringing production ‘out of the shadows’. Investment in programs aimed at reducing the size of the shadow economies could be rather profitable for the Baltic governments, because even a small influence on entrepreneurial behaviour could result in significant revenue increases.
References
- Gerxhani, K. (2007) “‘Did you pay your taxes?’ How (not) to conduct tax evasion surveys in transition countries”, Social Indicators Research 80, pp. 555-581.
- Hanousek, J., and F. Palda (2004) “Quality of government services and the civic duty to pay taxes in the Czech and Slovak Republics, and other transition countries”, Kyklos 57(2), pp.237-252.
- Kazemier, B., and R. van Eck (1992) “Survey investigations of the hidden economy”, Journal of Economic Psychology 13, pp. 569-587.
- Schneider, F., A. Buehn, and C.E. Montenegro (2010) “Shadow economies all over the world: New estimates for 162 countries from 1999 to 2007”, World Bank Policy Research Working Paper 5356.
Disclaimer: Opinions expressed in policy briefs and other publications are those of the authors; they do not necessarily reflect those of the FREE Network and its research institutes.