second quarter Russian GDP advanced 1.2% from a year earlier after a 1.6% rise in the previous

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1 Irina Sukhareva Could further financial deepening help to revive Russian economic growth? A Regional View Objectives Current year in Russia is characterized by weak dynamics of economic growth. In the second quarter Russian GDP advanced 1.2% from a year earlier after a 1.6% rise in the previous three months. Moreover, seasonally-adjusted growth rate to previous quarter is within the limits of statistically insignificant values ( %) during this year. Such slowdown of economy expansion is, mostly, a result of three negative tendencies. Two of them reflect the exhaustion of internal sources of growth: falling fixed-capital investments in conditions of shrinking business profitability and stabilization of retail trade which looks more fragile against the background of rising credit risks in the sector of bank retail lending. The third tendency stagnation of export of raw materials is mostly explained by external factors. To deal with internal sources of economic growth, one of the main possibilities to revive economic growth in Russia tensely discussed by policymakers (especially, during the recent Saint-Petersburg G20 summit 1 ) to enhance financial deepening is to make credit more easily available for businesses. Official position of government assumes the mechanism of reducing bank loan interest rates 2. It is supposed that this measure will result in investment-led growth through private sector investment launching. Given high level of regional heterogeneity (and its possible sources) in Russia (83 economically dissimilar regions), the principal objective of the research is to reveal effects of further financial deepening on accelerating economic growth in different Russian regions. In the context of policy implications of results, the research should also include a comparative analysis of stimulating development of different segments of financial sector. 1 According to the final summit speech of the President of Russia V. Putin on , it lies ahead to make additional analysis of the impact of financial sector reforms on economic growth. 2 Putin: the banking sector should reduce interest rate margin, Gazeta.ru, (in Russian)

2 The main hypothesis to be tested is high level of heterogeneity in finance-growth nexus in line with the predominance of positive effects of further financial development on fostering economic growth across Russian regions. Region production pattern will be considered as one of the main sources of heterogeneity. 3 As for comparative analysis of different ways to foster financial development, the null hypothesis consists in that it will be more effective to force financial deepening by increasing corporate lending than retail lending because the impact of the latter one mostly concentrates in increasing trade volumes. Testing of such hypotheses involves estimation of panel data regression models investigating interaction of financial deepening and GDP growth with taking into account possible ways to stimulate financial development and CART-technologies to assess a portrait of the Russian region in which proposed mechanisms of financial deepening could be successful for economic recovery. Practical contribution of research Conducting such an analysis for the panel of Russian regions might allow addressing the question on the relationship between financial deepening and regional growth. Research results can be applied for formation of future policy measures directed at stimulating economic growth. If regions with more developed financial systems exhibit higher levels of growth, then the government needs to put in place policies to enhance the financial deepening of Russian regions with underdeveloped financial markets. Revealing principal heterogeneity sources for financegrowth relationship across the regions could also help government to raise the effectiveness of financial deepening stimulus allowing to determine a subset of «successful» regions (with steady positive finance-growth nexus). In addition, the research also tries to bridge the gap of choosing effective policy measures to stimulate financial sector deepening. Assessment of the impact of stimulating different types 3 For example, manufacturing specialization of a region could be correlated with lower effect of financial deepening on fostering economic growth (Nili and Rastad (2007)).

3 of lending on economic growth in various Russian regions could add to accurate regional political tuning policy measures could have dissimilar effects in different types of regions. Literature review Interaction of financial development and economic growth, еspecially in periods of recession, is one of the key research questions in the context of macroeconomic and monetary policy optimization since the early 20 th century. First theoretical concepts belong to Bagehot (1873) and Schumpeter (1912). According to the majority of empirical studies financial deepening in parallel with more efficient banking system enhances economic growth (Caporale et al. (2009), Levine (2004)). Existing literature on the topic describes both substantial research results about main channels of financial deepening effect on economic growth acceleration and methodological issues of economic growth and financial development interaction estimation. The first category of research results assumes that financial development helps to accelerate economic growth through several channels 4 : facilitation goods and services exchange by payment services provision. Pagano (1993) presented a simple theoretical model of reducing transaction costs through financial development; collection of economic agent s savings and their effective allocation. Schumpeter (1912) was the first who revealed that banking sector could stimulate economic growth through the mechanism of financing effective investments. Henceforward this conclusion was echoed in many research papers (Cecchetti and Kharroubi (2012), Caporale et al. (2009)); liquidity risk reducing (Holmström and Tirole (1998) et al.); providing information about potential investment projects in the real sector of economy and their quality (Greenwood and Jovanovic (1990)); 4 Comprehensive and detailed overview of the main channels of financial deepening influence on economic growth could be founded in Levine (2004).

4 monitoring investment and carrying out corporate governance (Barajas et al. (2013)); investors base diversification and improving possibilities to hedge risks with the help of stock market development (Levine and Zervos (1998)). But there are two sides to the coin. Some authors argue that the role of financing sector in stimulating economic growth was generally overestimated (Lucas (1988), Robinson (1952)), or sufficiently differs across regions (Barajas et al. (2013) et al.) Regional heterogeneity of financial development effects on economic growth could be strongly linked with inflation dynamics, financial deepening degree or regional production pattern. Rioja and Valev (2004) show that statistically significant positive finance-economic nexus appears only in countries with intermediate level of financial depth and becomes weaker in countries with relatively high one. Nili and Rastad (2007) point out that oil-exporting regions tend to have lower level of financial depth than oil-importing ones and then weaker impact of financial development on economic growth 5. Easterly et al. (2000) empirically reveal that interaction function of finance-economic nexus could be convex and non-monotone. That is, when private sector credit to GDP ratio in a country exceeds some threshold (in this case, 100% of GDP) economic growth volatility begins to rise. Threshold hypotheses are also confirmed in a set of research papers (U-shaped form of finance-growth nexus: Cecchetti and Kharroubi (2012), Arkand, Berkes, Panizza (2011)). As for methodological issues of estimation of economic growth and financial development interaction, the main empirical approach to address this question consists in applying cross-sectional and panel regressions techniques to reveal sign and extent of financial deepening effect on economic growth (Levine and Zervos (1998) et al.) Measures of financial depth are complemented by a group of control factors representing other significant determinants of economic growth and convergence effects (established by Solow (1956)). 5 Klein (2010) adds to negative characteristics of oil-exporting regions their dependency on oil-sector shocks.

5 Recent research tends to take into account the dynamic interactions between finance and growth and to test the hypothesis of their two-way causality. A part of financial development dynamics could be caused by economic expansion increasing demand for financial services. Theoretical background for such models was provided by Greenwood and Jovanovic (1990). Latest empirical studies which try to catch endogeneity effect are based on dynamic panel methodologies with Generalized Method of Moments (GMM) estimator (Rousseau and Wachtel (2000), Beck and Levine (2004)) and sometimes with Arellano-Bond techniques (Barajas et al. (2013)). Older studies used instrumental variables estimations to address the same issue of endogeneity (Beck, Levine and Loyaza (2000)) or included initial values of financial depth into regression (acceptable for long time periods of analysis). Existing empirical studies use wide range of indicators to approximate financial depth: liquid liabilities to GDP ratio, M2 to GDP ratio, private sector credit to GDP, Commercial vs. Central Bank indicator (commercial bank assets divided by commercial bank plus central bank assets, Rioja and Valev (2004)) to measure banking sector development and market capitalization to GDP and value of shares traded to GDP (or to total market capitalization) to reflect stock market activity (Barajas et al. (2013)). For regional-level analysis in Russia it will be possible to take two of mentioned measures due to data scarcity private credit by banks to GDP and Commercial vs. Central Bank indicator. According to Shatkovskaya (2007), retail and corporate lending dynamics in Russian regions is highly correlated with their regional production patterns. The author also argues that Russian regional banks are characterized by much bigger share of retail lending in total loan portfolio than metropolitan areas (Moscow and Saint-Petersburg regions). In this context it s reasonable to provide an additional analysis of corporate and retail lending on regional economic growth in Russia separately.

6 Methodology Data 1. Quarterly financial (bank industry) factors were collected from the Bank of Russia regions database called OLAP ( for the period from 1q 2001 till 2q Bank-data covers: institutional characteristics (number of banks in force, assets concentration indicators and financial activities results etc.); foreign exchange operations (aggregate data for regions); separate resource base items (corporate, retail and government deposits, issued certificates of deposit, saving certificates, bonds and bills); separate assets items (corporate and retail loans including overdue loans, bonds and bills assets); separate operating indicators (interbank lending volumes, hypothec lending characteristics). 2. Yearly macroeconomic factors for the Russian regions were collected from Federal State Statistics Service databases (Central Base of Statistical Data: United Interdepartmental Statistical Information System: within the period from 1996 till Macroeconomic-data covers a wide range of indicators in several areas: gross regional product indicators (GRP: aggregate volumes, per capita ratio, output structure by branches, etc.); consumer price index; housing and communal services; employment and wages; public health services; fixed-capital investments; innovations; foreign direct investment; culture; population; education; environment; entrepreneurship; living standards. Estimation According to the literature review, we specify the aggregate form of finance-economic nexus regression:

7 g it 1 fit 2 fit * HSit 3 yit 1 4X it it, where g it growth rate of GRP per capita 6 in period t (Levine (2004)), y it GRP per capita of region i in period t, f it financial depth measure (private sector credit to GRP ratio, corporate and retail lending to GRP ratios, Commercial vs. Central Bank indicator), 3 reflects the convergence effect. It assumes that y it-1 should have a negative impact on the rate of economic growth; X it control variables set, that includes secondary school enrollment measures (level of human capital impact), FDI inflows by region to GRP ratio (analogous to Beck and Levine (2004), Barajas et al. (2013)), CPI, ratio of federal subjects executive authorities expenditures to GRP ratio (analogous to Arcand et al. (2011)) and dummies for crisis periods (to eliminate crisis impact) and metropolitan regions (Moscow and Saint-Petersburg, Shatkovskaya (2007)); HS it potential heterogeneity sources: level of financial depth (Rioja and Valev (2004)), income level and production pattern (shares of main branches output in total output, Barajas et al. (2013)), institutional characteristics (Arcand et al. (2011)). it error term (individual effect and idiosyncratic shock). i it i = 1, 2, 83 (number of federal subjects of the Russian Federation). T = years from 2001 to The described regression couldn t be estimated with the help of traditional panel data assessment methods (fixed effects, random effects approaches and, in addition, pooled OLS approach) because of inclusion of lagged dependent variable (y it-1 ). Achieving consistent estimation requires applying instrumental variables methods (elaborated especially for dynamic panel regression estimation by Arellano, Bond (1991) and recently developed by Windmeijer (2005) 7 ), within the framework of Generalized method of moments (GMM-estimator) in consequence of fixed effects estimator inconsistency. 6 In order to verify the robustness of results it s supposed to use other approximations of growth: industrial production growth (Rajan and Zingales (1998)) and GDP per worker (Cecchetti and Kharroubi (2012)). 7 It is finite sample correction procedure in order to obtain robust standard errors.

8 Testing hypothesis about financial depth threshold in the Russian regions could be done by the following approaches: inclusion of a parameter (f it ) 2 in the right side of equation (1) with the expected negative sign. Lind and Melhum (2010) test could also be conducted here in order to control the possibility of an inverted U interaction function of finance-growth nexus; semi-parametric estimation to compare different variants of the model g f ( f ) X y ) through the approximation of the ( it it 2 it 3 it 1 it functional space (Arcand et al. (2011)). To form a portrait of the region in which further financial deepening could be successful for economic recovery we plan to employ Classification and Regression Tree (CART) methodology. According to Manasse and Roubini (2008), this technique is one of the effective ways to classify objects by one or a set of characteristics. With the help of CART that supports computer-intensive data mining technique it s possible to identify a set of explanatory variables and their critical values with taking into account their interactions by stated object characteristic «successful» and «unsuccessful» region. Successful region could be determined with respect to panel regression estimation results obtained at the first step. If region s coefficient of determination (R 2 ) surpasses 50%, the region will fall in «successful» category and vice-versa. A set of explanatory variables which play a role of main regional macroeconomic and financial characteristics should be widened in that case. Besides initial set of control it could include bank efficiency measures (financial activities results, Arcand et al. (2011)), credit risk indicators (share of overdue loans in total loans) and some regional institutional characteristics. Besides the predominance of significant and robust positive effect of financial deepening, expected research outputs are: relatively low impact of financial development on economic growth for regions with high share of manufacturing and extractive industries output in total output.

9 According to Shatkovskaya (2007), the prevalence of large holdings (a characteristic of mentioned industries) in a region sufficiently hampers bank lending development because loan demand of such companies is covered by intracompany transfers. weak positive impact of retail lending on regional economic growth because the impact of this segment of lending could mostly concentrate in increasing trade volumes without affecting industrial production sector. In addition, enhancing trade volumes could be caused by import volumes boosting which in turn could dampen economic growth. So, policy measures to force financial deepening by increasing corporate lending will be more effective than retail lending or total private loan portfolio stimulus. Preliminary results Several simple specifications of finance-growth models based on static panel data were evaluated. The impact of financial depth (private sector credit-to GRP ratio) is not stable reflecting necessity to control for potential heterogeneity and endogeneity effects. Significant control factors are secondary school enrollment measure (percentage of children enrolled in secondary schools, % of total number of children of eligible age) and FDI inflows by region to GRP ratio. Bibliography 1. Arcand J.L., Berkes E., Panizza U. (2011): Too Much Finance? // Unpublished. (Washington: International Monetary Fund). 2. Bagehot W. (1873), Lombard Street: A Description of the Money Market // History of Economic Thought Books, McMaster University Archive for the History of Economic Thought. 3. Barajas A., Chami R., Yousefi S.R. (2013): The Finance and Growth Nexus Re- Examined: Do All Countries Benefit Equally? // IMF Working Paper, 13(130).

10 4. Beck T., Levine R. (2004): Stock Markets, Banks, and Growth: Panel Evidence // Journal of Banking and Finance, 28, pp Beck T., Levine R., Loayza N. (2000): Finance and the Sources of Growth // Journal of Financial Economics, vol. 58, pp Caporale G.M., Rault C., Sova R., Sova A. (2009): Financial Development and Economic Growth: Evidence from Ten New EU Members // DIW Berlin Discussion Papers, October. 7. Cecchetti S.G., Kharroubi E. (2012): Reassessing the Impact of Finance on Growth // BIS Working Papers, No. 381, July. 8. Easterly W., Islam R., Stiglitz J. (2000): Shaken and Stirred, Explaining Growth Volatility // Annual Bank Conference on Development Economics. World Bank. Washington D.C. 9. Holmström B., Tirole J. (1998): Private and Public Supply of Liquidity // Journal of Political Economy, 106, pp Greenwood J., Jovanovich B. (1990): Financial Development, Growth and the Distribution of Income // Journal of Political Economy 98(5), Khan M., Senhadji A. (2000): Threshold Effects in the Relationship between Inflation and Growth // IMF Working Paper No. 01/ Klein N. (2010): The Linkage between the Oil and the Non-oil Sectors A Panel VAR Approach // IMF Working Paper No. 10/ Levine R. (2004): Finance and Growth: Theory and Evidence // NBER Working Paper No (Cambridge, Massachusetts, NBER). 14. Levine R., Zervos S. (1998): Stock Markets, Banks, and Economic Growth // The American Economic Review 88(3), Lind J.T. and Melhum H. (2010): With or Without U? The Appropriate Test for a U- Shaped Relationship // Oxford Bulletin of Economics and Statistics, 72(1), Lucas R.E. (1988): On the mechanics of economic development // Journal of Monetary Economics, 22(1), 3-42.

11 17. Manasse P. and Roubini N. (2008): «Rule of Thumb» for Sovereign Debt Crises // Journal of International Economics, 78, Nili M., Rastad M. (2007): Addressing the Growth Failure of the Oil Economies: The Role of Financial Development // The Quarterly Journal of Economics and Finance, vol. 46, pp Pagano M. (1993): Financial Markets and growth: an overview // European Economic Review, 37, pp Rajan R.G., Zingales L. (1998): Financial Dependence and Growth // American Economic Review, 88, Rioja F., Valev N. (2004): Does One Size Fit All? A Reexamination of the Finance and Growth Relationship // Journal of Development Economics, 74(2), Robinson J. (1952): The Generalization of the General Theory // in «The Rate of Interest and Other Essays», MacMillan. 23. Rousseau P.L., Wachtel P. (2000): What is Happening to the Impact of Financial Deepening on Economic Growth? // Economic Inquiry, Vol. 49, No.1, pp Schumpeter J.A. (1912): A Theory of Economic Development // Harvard University Press. 25. Shatkovskaya T. (2007): Development of the Russian regional markets of bank services in // Banking (Bankovskoe delo), 12 (in Russian) 26. Solow R. (1956): A Contribution to the Theory of Economic Growth // The Quarterly Journal of Economics, Vol. 70, No. 1, pp Windmeijer F. (2005): A finite sample correction for the variance of linear efficient twostep GMM-estimators // Journal of Econometrics, 126(1), pp Alternative/additional sources of funding Financial sector forecasting methodology and practice and are correlated with research author (Irina Sukhareva) job duties at the Center for Macroeconomic Analysis and Short-Term Forecasting (CMASF). Therewith, the analysis of financial deepening and economic growth nexus is not included in research author job duties list. Consequently, CMASF can not be considered as

12 additional resource of research funding. At the same time, the research is also not financed by other charities and organizations. Thereby, EERC is singular potential source of funding for all of the research stages expenses. Research timetable Research stage Execution period An enhanced theoretical and empirical From December 2013 till March 2014: literature review on finance-growth Country-level: December-January relationship theory and estimation Regional-level: in March methodology Methodology: specification of econometric From March 2014 till a April 2014 equations for finance-growth nexus across Russian regions Building a database of financial and During May 2014 macroeconomic data on Russian regions (with yearly data over the period from 2001 to 2012) Empirical outcomes (econometric estimation From June 2014 till July 2014 of different panel data regressions) Defining a portrait of «successful» region From August 2014 till September 2014 (with steady positive finance-growth nexus) with the help of CART-methodology Analysis, description and interpretation of During October 2013 achieved results Practical recommendations for monetary During November 2013 authorities Making final research report During December 2013