Abstract
Selected Issues
Credit Growth and Economic Recovery in Europe: The Case of Hungary1
This paper reviews the relationship between real GDP growth and domestic bank lending to the private sector in Hungary after the global financial crisis (GFC), drawing on a cross-country analysis of European countries. The recessions that followed the GFC were deeper and lasted longer than the average recession. The recoveries have been characterized by tepid domestic bank lending to the private sector despite low interest rates. Hungary, like some other countries, even experienced a credit-less recovery. While it is difficult to disentangle the causality, this analysis concludes that: (i) both credit demand and supply were adversely affected by the GFC; (ii) key factors influencing credit developments include loan quality, deposit funding, and bank capital, as well as the macroeconomic environment; and (iii) lending by Hungarian banks to the private sector seems to be finally beginning to pick up.
A. Introduction
1. The 2008 GFC had a significant and permanent impact on real GDP and domestic bank credit growth in Europe, including Hungary (Figure 1). Before the GFC, most European countries experienced rapid economic and domestic bank credit growth. In many Central European and South Eastern European (CESEE) countries, it was fueled by expectations about convergence to average EU income levels and prospects of EU membership, including in Hungary, which became a member in 2004. This was conducive to foreign direct investment (FDI) and substantial inflows of EU funds, which boosted sentiments and employment prospects as well as the demand for credit. Domestic bank lending increased rapidly, fueled by banks’ easy access to foreign funding, as many parent banks faced saturated home markets. Much of the lending, however, was often channeled into real estate. The GFC shattered expectations, including on the perceived appropriateness of private sector debt levels (Bakker and Klingen, 2012). It took five years for CESEE countries, as a group, and six years for Hungary to reach pre-GFC real GDP per capita levels, as the private sector deleveraged. Domestic bank lending is just beginning to recover in Hungary, despite low nominal interest rates.
Europe and Hungary: GDP Growth and Bank Credit to the Private sector 1/
Citation: IMF Staff Country Reports 2017, 124; 10.5089/9781484300473.002.A001
Sources: BIS total credit statistics; IFS; and IMF staff calculations.Note: The pre-crisis (peak) trend is estimated up to year t=-3, and is extrapolated linearly thereafter.1/ Expansion peaks, associated with the GFC, occurred in either 2007 or 2008, for all European countries in the sample (Annex 1), except for Albania, Kosovo, and Poland, which avoided a post-GFC recession.2/ Unweighted average of the logarithm of real output or bank credit per capita; expansion peak year t=0, and 100 equals respective trend in t=7.Europe and Hungary: GDP Growth and Bank Credit to the Private sector 1/
Citation: IMF Staff Country Reports 2017, 124; 10.5089/9781484300473.002.A001
Sources: BIS total credit statistics; IFS; and IMF staff calculations.Note: The pre-crisis (peak) trend is estimated up to year t=-3, and is extrapolated linearly thereafter.1/ Expansion peaks, associated with the GFC, occurred in either 2007 or 2008, for all European countries in the sample (Annex 1), except for Albania, Kosovo, and Poland, which avoided a post-GFC recession.2/ Unweighted average of the logarithm of real output or bank credit per capita; expansion peak year t=0, and 100 equals respective trend in t=7.Europe and Hungary: GDP Growth and Bank Credit to the Private sector 1/
Citation: IMF Staff Country Reports 2017, 124; 10.5089/9781484300473.002.A001
Sources: BIS total credit statistics; IFS; and IMF staff calculations.Note: The pre-crisis (peak) trend is estimated up to year t=-3, and is extrapolated linearly thereafter.1/ Expansion peaks, associated with the GFC, occurred in either 2007 or 2008, for all European countries in the sample (Annex 1), except for Albania, Kosovo, and Poland, which avoided a post-GFC recession.2/ Unweighted average of the logarithm of real output or bank credit per capita; expansion peak year t=0, and 100 equals respective trend in t=7.2. This paper draws on a cross-country and bank-level analysis to help inform policy discussions on real growth and bank lending in Hungary (Antoshin et al, forthcoming). Section B compares Hungary’s recession and recovery paths to peers, as well as to normal and financial-crisis recoveries. Section C, using a bank-level cross-country panel, reviews how various bank fundamentals and selected macro factors have correlated with bank credit to the private sector. Section D focusses on the correlation between GDP growth and bank lending over the cycle. Section E describes the deleveraging that has taken place in Hungary, and most other CESEE countries, and examines recent trends in non-performing loans (NPLs), new lending based on transaction data, as well as lending surveys. Section F summarizes the main findings.
B. Was the Post GFC-Recovery in Hungary Different?2
3. This section compares Hungary’s recovery after the GFC to peers. Drawing on Jordà et al (2013), the local projection method, developed by Jordà (2005), is used to project recession and recovery paths. The following specification is used:
The dependent variable (y) is the cumulative change in key macroeconomic variables (real GDP per capita, real private-sector consumption per capita, real investment (GFCF) per capita, and real bank credit to the private sector per capita) from the beginning of each recession and recovery period included in the analysis.3 N and F are dummy variables indicating whether the recession and recovery episode was preceded by a financial crisis (F) or not (N).4 The control variables include: measures of excess credit accumulated during the expansion period
4. The coefficients ∅ and γ are used to construct the projection paths for “normal” non-financial and financial recession and recovery paths. Intuitively, ∅ and γ correspond to the average cumulative response of the dependent variable at each horizon (projection) period. They are plotted in the first column of Figure 2 below. The coefficients are derived from observations on a sample of 79 recession and recovery episodes across 35 advanced and large emerging-market countries (hereinafter referred to as the control group) that occurred from the beginning of the post-Bretton Woods era up to the eve of the GFC (1971–2006).5
Hungary and Emerging Europe: Performance Relative to Projection Paths
(cumulative percentage change from start of recession, percent)
Citation: IMF Staff Country Reports 2017, 124; 10.5089/9781484300473.002.A001
Sources: BIS total credit data; Hungarian authorities; IMF staff calculations.Hungary and Emerging Europe: Performance Relative to Projection Paths
(cumulative percentage change from start of recession, percent)
Citation: IMF Staff Country Reports 2017, 124; 10.5089/9781484300473.002.A001
Sources: BIS total credit data; Hungarian authorities; IMF staff calculations.Hungary and Emerging Europe: Performance Relative to Projection Paths
(cumulative percentage change from start of recession, percent)
Citation: IMF Staff Country Reports 2017, 124; 10.5089/9781484300473.002.A001
Sources: BIS total credit data; Hungarian authorities; IMF staff calculations.5. Hungary’s real GDP per capita since the GFC broadly followed the path of other CESEE countries. Hungary’s recession, however, was deeper because it was also affected by the European sovereign debt crisis due to its high public debt. It only caught up with peers in 2015. Compared to normal and financial-crisis recessions from which it usually takes two to three years to recover, the CESEE countries took on average about five years to reach pre-GFC real GDP per capita levels while Hungary took six years.6 Private consumption per capita declined more in Hungary than in peers. In contrast, investment per capita did much better than the average for CESEE countries. This is likely because of FDIs—with Hungary being an integrated part of the German supply chain (IMF, 2013)—but perhaps also because it was among the new EU member states that received the largest amounts of EU structural and cohesion funds per capita. The absorption of these funds was accelerated toward end-2015, as the 2007–2013 program period came to an end and these funds would have been lost if not used by end-2015. Like other CESSE countries, Hungary’s current account improved after the GFC, in part due to import compression, but also due to much stronger exports facilitated by sturdier external demand as the global recovery transpired.
6. Lending to the private sector by Hungarian banks remained much more subdued than in other CESEE countries and even worse than in countries recovering from a financial crisis. Hungary did not have a financial crisis per the definition specified by Laeven and Valencia (2012), but Hungary is classified as a border line case. Shortly after the GFC, some Hungarian banks faced challenges accessing global financial markets. This in part motivated the arrangement agreed with the IMF in November 2008 (which expired in October 2010). Moreover, the rapid increase in NPLs, disputes and court cases between banks and particularly their household clients, as well as global discussions about tighter prudential regulation may have contributed to increased uncertainty that may have adversely affected banks’ willingness as well as their ability to lend. In December 2014, the resolution of MKB bank was initiated and it was successfully finalized in mid-2016. These factors may help explain why Hungary’s credit recovery path is close to that following a financial-crisis recession and recovery. Subdued bank lending may have had less impact on growth, since large reputable companies—particularly foreign subsidies but also Hungarian owned companies—financed themselves abroad (see Section E).
7. Hungary’s recovery path since the European sovereign debt crisis was then compared to peers controlling for differences in external demand. The “counterfactual dependent variable paths” were estimated in order to control for the extremely weak global demand environment that followed the GFC. First, a contemporaneous external demand variable based on actual data was included as a regressor in the standard equations described above to estimate its influence on the “typical” projection path. Secondly, this external demand variable was rescaled to reflect, on average, the external demand faced by European countries after the GFC. Thirdly, new counterfactual dependent variables were generated using the coefficients and values of the regressors from the first step. The counterfactual external demand values were then substituted for the observed external demand. These steps resulted in counterfactual dependent variables, which represented “what-if”‘ estimates of the dependent variables had the control group countries faced the same subdued external demand that European countries faced post-GFC. The standard regressions were then re-run with the new counterfactual dependent variables. The generated coefficients were used to construct the projection paths for non-financial and financial recession and recovery episodes plotted in the second column of Figure 2.
8. This model shows that since the European sovereign debt crisis, Hungarian real GDP growth per capita has recovered faster than other CESEE countries and even better than the typical recovery after a non-financial recession. Hungary’s private consumption per capita in this scenario did better than peers. Although still subdued compared to a normal recovery, it was largely within the confidence interval of a normal recovery. Investment was still estimated to have done better than peers, likely due to buoyant FDIs and EU funds, as mentioned above. Domestic bank lending, however, remained subdued under these assumptions. These results can in part be attributed to the fact that large reputable non-financial companies, being part of regional supply chains, had easy access to foreign financing, as suggested earlier.
C. Factors Influencing Bank Credit in Europe and Hungary7
9. A bank-level cross-country panel was used to assess how bank fundamentals and selected macro factors correlated with credit dynamics during 1999–2015. The unbalanced panel included 37 countries, as well as Hungary, and up to almost 8000 banks during the 1999–2015 period.8 Different specifications were tried using system generalized method of moments (GMM).9 The size of the bars in the chart represents the effect of a one-standard-deviation change in the respective regressor on credit growth in percentage points. Black filled bars indicate that these coefficients are statistically significant.
Europe: Determinants of Credit Growth 1/
(Standardized coefficients)
Citation: IMF Staff Country Reports 2017, 124; 10.5089/9781484300473.002.A001
Source: IMF staff estimations.1/ D stands for dummy.Europe: Determinants of Credit Growth 1/
(Standardized coefficients)
Citation: IMF Staff Country Reports 2017, 124; 10.5089/9781484300473.002.A001
Source: IMF staff estimations.1/ D stands for dummy.Europe: Determinants of Credit Growth 1/
(Standardized coefficients)
Citation: IMF Staff Country Reports 2017, 124; 10.5089/9781484300473.002.A001
Source: IMF staff estimations.1/ D stands for dummy.10. The presence of large foreign-owned banks in many CESEE countries may explain why regulatory capital and funding seemed less crucial for credit growth in these countries. The bank fundamentals include non-performing loan ratio (NPL) (-), customer deposits (+), bank equity index (+), and regulatory capital ratio (-), with the NPL ratio and customer deposits being the most important. As expected, the main macroeconomic influence on credit growth appears to be real GDP growth. It is a proxy for both demand and the ability to service the debt: as output increases, so did bank lending to the private sector. Inflation also had a positive although insignificant effect.
Bank credit
(Annual percentage change)
Citation: IMF Staff Country Reports 2017, 124; 10.5089/9781484300473.002.A001
Bank credit
(Annual percentage change)
Citation: IMF Staff Country Reports 2017, 124; 10.5089/9781484300473.002.A001
Bank credit
(Annual percentage change)
Citation: IMF Staff Country Reports 2017, 124; 10.5089/9781484300473.002.A001
11. In Hungary, similar factors have likely been in play. Unfortunately, the number of available observations for Hungarian banks in the sample were insufficient to use a similar specification for Hungary. The chart shows that real credit growth to the private sector of the Hungarian banks included in the sample (the black line) is within the confidence interval of the model. Moreover, lending by Hungarian banks in the sample is moderately lower than for peers after the GFC, although well within the confidence interval. It is thus possible that the same explanatory factors were also valid for Hungary. Figure 3 shows key indicators for the Hungarian banking system since 2000, although the definitions of the ratios have changed during this period. They show the lending boom, in part foreign funded, before the GFC; then a sharp increase in NPL and decrease in lending following the GFC; whereupon provisions increased, and cushions strengthened during the recovery, as the banks deleveraged.
Selected Indicators of the Hungarian Banking System, 2000–2016
Citation: IMF Staff Country Reports 2017, 124; 10.5089/9781484300473.002.A001
Sources: Hungarian authorities; IMF Financial Soundness Indicators Database; IMF, International Financial Statistics (IFS); previous IMF Staff Reports; and IMF staff calculations.Note: Definitions of ratios have changed and are not fully consistent over time.1/ Bank credit to the private sector as reported in IFS, deflated by CPI inflation.2/ Bank credit growth to households and non-financial firms (transaction data, i.e., adjusted for exchange rate changes).Selected Indicators of the Hungarian Banking System, 2000–2016
Citation: IMF Staff Country Reports 2017, 124; 10.5089/9781484300473.002.A001
Sources: Hungarian authorities; IMF Financial Soundness Indicators Database; IMF, International Financial Statistics (IFS); previous IMF Staff Reports; and IMF staff calculations.Note: Definitions of ratios have changed and are not fully consistent over time.1/ Bank credit to the private sector as reported in IFS, deflated by CPI inflation.2/ Bank credit growth to households and non-financial firms (transaction data, i.e., adjusted for exchange rate changes).Selected Indicators of the Hungarian Banking System, 2000–2016
Citation: IMF Staff Country Reports 2017, 124; 10.5089/9781484300473.002.A001
Sources: Hungarian authorities; IMF Financial Soundness Indicators Database; IMF, International Financial Statistics (IFS); previous IMF Staff Reports; and IMF staff calculations.Note: Definitions of ratios have changed and are not fully consistent over time.1/ Bank credit to the private sector as reported in IFS, deflated by CPI inflation.2/ Bank credit growth to households and non-financial firms (transaction data, i.e., adjusted for exchange rate changes).Non-Performing Loans
12. As expected, the NPL ratio was significant and negatively correlated with credit growth in the cross-country analysis, but its impact diminished during the recovery. Increasing NPLs is a symptom of problems in the economy that were not captured by a bank’s past credit policies. Initially they may trigger a change in risk perception and hence more conservative credit policies and thus adversely affect the willingness to lend. As provisions are expected and required, they absorb profits and perhaps even equity, hence hampering banks’ ability to lend. However, if the NPLs are appropriately and timely priced and provisioned, the NPL ratio should in principle not affect new lending. NPLs are a consequence of past credit policies, while a rational bank should make its new lending decisions based on its forward-looking projection of the expected net present value.
13. Nevertheless, a high NPL ratio, even if fully provisioned, could hamper new lending. This may be the case if the ratio adversely affects funding costs, causes concerns about the evaluation of collateral and thus inadequate provisions, adverse market sentiment, or bank management’s pre-occupation with NPLs instead of new businesses. In the panel analysis, it is noticeable that the NPL coefficient becomes much less negative during recoveries, suggesting that if credit demand improves, then a high NPL ratio seems to be less of a concern. It is possible that during the recovery, most of the provisioning has already happened and is less of a potential constraint on equity to cover new risks.10 Furthermore, if the NPL is not written off even if fully provisioned by the bank, or if the loan is sold to a non-bank debt collection agency, the debtors may still be suffering. If the issue is not being resolved at the debtor level, it can still suppress their activity, including their demand for new loans to viable projects. In our view, these findings underpin standard recommendations to deal with NPLs of banks:
Timely provisions of NPLs is critical to not restrain future credit growth. This includes prudent conservative evaluation of collateral;
A credible strategy to resolve the stock of NPLs is important, as it helps remove many real or perceived lingering uncertainties, as recommended in IMF (2015); and,
Strengthen the debt recovery framework by making it more timely, efficient, predictable, and thus less costly for both borrowers and lenders. This is conducive for new lending, as it reduces the risk premium and perceived risks.
14. The Hungarian authorities have since the GFC taken numerous initiatives to address the high NPL ratio.11 One of the biggest challenges has been to deal with non-performing commercial real estate loans, while dealing with consumer loans has been particularly contentious. The NPL ratio trend has been successfully reversed since 2014. Moreover, new lending has reportedly very low NPL ratios.
Hungary: Real Private Credit Growth, NPL, and Real GDP
(Percent change, yoy)
Citation: IMF Staff Country Reports 2017, 124; 10.5089/9781484300473.002.A001
Sources: Hungarian Central Statistical Office; International Financial Statistics, IMF; and IMF staff calculations.1/ Change in stock of bank lending to the private sector deflated by CPI.Hungary: Real Private Credit Growth, NPL, and Real GDP
(Percent change, yoy)
Citation: IMF Staff Country Reports 2017, 124; 10.5089/9781484300473.002.A001
Sources: Hungarian Central Statistical Office; International Financial Statistics, IMF; and IMF staff calculations.1/ Change in stock of bank lending to the private sector deflated by CPI.Hungary: Real Private Credit Growth, NPL, and Real GDP
(Percent change, yoy)
Citation: IMF Staff Country Reports 2017, 124; 10.5089/9781484300473.002.A001
Sources: Hungarian Central Statistical Office; International Financial Statistics, IMF; and IMF staff calculations.1/ Change in stock of bank lending to the private sector deflated by CPI.Customer Deposits
15. Increasing customer deposits appear to facilitate bank lending. The customer deposit variable is significant with a positive sign in the bank-level cross-country analysis, although it is smaller during recessions. Obviously, less dependence on more volatile wholesale funding reduces vulnerabilities.12 In Hungary, following the GFC, the deposit growth of both households and non-financial companies decelerated, but new lending was less than amortizations. The loan-to-deposit ratio of firms and households thus declined from 140 at end-2008 to 83 percent at end-2016. This allowed Hungarian banks to also deleverage and reduce their dependence on wholesale funding from aboard—mainly from their parents.
Bank Capital
16. Bank regulatory capital, as percent of total assets, is associated with negative bank lending in the cross-country analysis, especially during recessions. This could imply that an increase in the regulatory requirement or perceived risks, makes banks less able or willing to lend. The more capital constrained banks may be inclined to observe higher capital requirements by deleveraging rather than raising new capital. This seems to be the case, probably during recessions when bank equity capital may be relatively more expensive, as corroborated by coefficient for the bank equity index. However, this variable is not significant for the CESEE sample, suggesting it was more important for parent banks. Banks not in need of increasing capital—likely the more conservative ones during the boom—probably had more restrictive credit standards. Hungarian banks have managed to increase their capital adequacy ratio since the GFC, but to a large extent by reducing their risk-weighted assets.
D. GDP Growth and Credit Growth in Europe and Hungary13
17. Bank credit is important for output growth, but perhaps less than often perceived.14 A statistically significant positive but modest correlation between economic growth and bank credit growth was found for 39 European countries during the 1999–2015 period. Annual data from the World Economic Outlook, International Financial Statistics, and BIS’ Total Credit to the Non-Financial Sector for 18 advanced European economies and 18 CESEE countries were used. A dynamic GMM panel estimator (Blundell and Bond, 1998) was used to estimate the relationship between real GDP growth and bank credit (Table 1, Appendix II).15 A 10 percent increase in domestic bank credit to the private sector correlates with real GDP increasing by about 0.6-0.7 percent. The main channel seems to be investment, as a 10 percent credit growth raises investment by 2 percent (Table 2, Appendix II). In general, these relationships did not change much during the recession and recovery periods.
Hungary: Real GDP, Investment, and Private Credit Growth
(Percent change, yoy)
Citation: IMF Staff Country Reports 2017, 124; 10.5089/9781484300473.002.A001
Sources: Hungarian Central Statistical Office; International Financial Statistics, IMF; and IMF staff calculations.1/ Change in stock of bank lending to the private sector (IFS) deflated by CPI.Hungary: Real GDP, Investment, and Private Credit Growth
(Percent change, yoy)
Citation: IMF Staff Country Reports 2017, 124; 10.5089/9781484300473.002.A001
Sources: Hungarian Central Statistical Office; International Financial Statistics, IMF; and IMF staff calculations.1/ Change in stock of bank lending to the private sector (IFS) deflated by CPI.Hungary: Real GDP, Investment, and Private Credit Growth
(Percent change, yoy)
Citation: IMF Staff Country Reports 2017, 124; 10.5089/9781484300473.002.A001
Sources: Hungarian Central Statistical Office; International Financial Statistics, IMF; and IMF staff calculations.1/ Change in stock of bank lending to the private sector (IFS) deflated by CPI.18. In some countries, the relationship between real GDP growth and domestic bank credit was more complex and sometimes negative after the GFC, showing signs of a “creditless” recovery.16 During the recovery period, however, the relationship between credit expansion and GDP growth was stronger when using the change in credit growth (credit impulse, Biggs et al, 2009) rather than credit growth. It was found that the credit impulse influenced GDP growth strongly and significantly during the post-GFC recessions and recoveries (Table 3, Appendix II).
19. Hungary experienced a creditless recovery, but alternative funding sources may have alleviated the strain. As previously mentioned, large non-financial manufacturing companies, often foreign-owned exporters, had access to foreign or intra-group financing.17 Moreover, EU funds, including generous advance payments and grants, may have eased the dependence on domestic bank credit to finance investments, which was confirmed in a survey by the European Investment Bank (EIB, 2017).18 This survey also found that while bank financing was the most important kind of external financing for investments, the share of surveyed Hungarian companies that rely on internal financing for investments was higher than the EU average. Moreover, the share of surveyed Hungarian companies that reported being finance-constrained was higher (13 percent on average, 17 percent for SMEs) than the EU average (5 percent).19
E. Hungary’s Balance Sheet Recession
20. Following the GFC and the European sovereign debt crisis, the private sector in Hungary, like most other CESEE countries, reduced its indebtedness. Figure 4 shows that, Hungarian households and non-financial companies—similarly to many other CESEE countries after the GFC—reduced their debt and built up savings rather than borrowing to invest and consume.20 This likely deepened the recession—and triggered a “balance sheet recession” (Koo, 2003 and 2011)—and slowed the recovery.21
Private Sector Balance Sheets in CESEE Countries, 2002–2016
Citation: IMF Staff Country Reports 2017, 124; 10.5089/9781484300473.002.A001
Sources: Eurostat (balance sheet data); IMF World Economic Outlook (nominal GDP); and IMF staff calculations.1/ Unconsolidated data including debt securities, loans, and other payables. 2016 Q3 are preliminary estimates.2/ Unconsolidated data including cash and deposits, debt securities, loans, equity and investment fund shares, and other payables. 2016 Q3 are preliminary estimates.3/ EU CESEE countries include Bulgaria, Croatia, Czech Republic, Estonia, Hungary, Latvia, Lithuania, Poland, Romania, Slovenia, and Slovakia.Private Sector Balance Sheets in CESEE Countries, 2002–2016
Citation: IMF Staff Country Reports 2017, 124; 10.5089/9781484300473.002.A001
Sources: Eurostat (balance sheet data); IMF World Economic Outlook (nominal GDP); and IMF staff calculations.1/ Unconsolidated data including debt securities, loans, and other payables. 2016 Q3 are preliminary estimates.2/ Unconsolidated data including cash and deposits, debt securities, loans, equity and investment fund shares, and other payables. 2016 Q3 are preliminary estimates.3/ EU CESEE countries include Bulgaria, Croatia, Czech Republic, Estonia, Hungary, Latvia, Lithuania, Poland, Romania, Slovenia, and Slovakia.Private Sector Balance Sheets in CESEE Countries, 2002–2016
Citation: IMF Staff Country Reports 2017, 124; 10.5089/9781484300473.002.A001
Sources: Eurostat (balance sheet data); IMF World Economic Outlook (nominal GDP); and IMF staff calculations.1/ Unconsolidated data including debt securities, loans, and other payables. 2016 Q3 are preliminary estimates.2/ Unconsolidated data including cash and deposits, debt securities, loans, equity and investment fund shares, and other payables. 2016 Q3 are preliminary estimates.3/ EU CESEE countries include Bulgaria, Croatia, Czech Republic, Estonia, Hungary, Latvia, Lithuania, Poland, Romania, Slovenia, and Slovakia.21. Net-financial assets of the Hungarian private sector continued to increase as the recovery gained traction. Net financial assets of households declined to about 41 percent of GDP in 2008, but have since increased to almost 87 percent of GDP (estimated, Q3 2016). This trend largely squares with the development of savings rates. Net-financial assets of non-financial companies declined around the GFC and European sovereign debt crisis, but have since also improved. This is corroborated with better earnings in recent years. The increase in financial assets suggest that numerous households and companies were not liquidity constrained. For these agents, sluggish demand and economic policy uncertainty after the GFC may have been more important for lowering their marginal propensity to consume and invest than liquidity constraints.
22. Both total credit and domestic bank loans to Hungarian households have declined since the sovereign debt crisis. Right after the GFC, credit to households as share of GDP continued to increase until the sovereign debt crisis hit, but mainly due to exchange rate effects of FX denominated loans. The deleveraging accelerated after the sovereign debt crisis. The share of domestic bank loans now accounts for about 70 percent of total household debt. The current debt-cap rules became effective January 1, 2015.22 Recent changes in the legal and regulatory framework are nurturing an emerging mortgage bond market.
Hungary: Debt of Households, 2000–16
(in percent of GDP)
Citation: IMF Staff Country Reports 2017, 124; 10.5089/9781484300473.002.A001
Sources: Eurostat’s Balance Sheet data; Magyar Nemieti Bank (MNB); World Economic Outlook (GDP); and IMF staff calculations.Note: Unconsolidated balance sheet data of households include debt securities, loans, and other payables 2016Hungary: Debt of Households, 2000–16
(in percent of GDP)
Citation: IMF Staff Country Reports 2017, 124; 10.5089/9781484300473.002.A001
Sources: Eurostat’s Balance Sheet data; Magyar Nemieti Bank (MNB); World Economic Outlook (GDP); and IMF staff calculations.Note: Unconsolidated balance sheet data of households include debt securities, loans, and other payables 2016Hungary: Debt of Households, 2000–16
(in percent of GDP)
Citation: IMF Staff Country Reports 2017, 124; 10.5089/9781484300473.002.A001
Sources: Eurostat’s Balance Sheet data; Magyar Nemieti Bank (MNB); World Economic Outlook (GDP); and IMF staff calculations.Note: Unconsolidated balance sheet data of households include debt securities, loans, and other payables 201623. The share of domestic bank loans as share of the total financial debt of Hungarian non-financial companies has declined from almost 25 percent in 2000 to about 15 percent at end-2016. The large reputable companies had access to foreign funding, as previously mentioned, and even domestic capital markets. Hungary’s capital markets, in particular the commercial bond market, remain relatively modest. Weaker companies with sparse collateral or short track record were facing immediate liquidity constraints after the GFC. In 2013, the MNB thus introduced the Funding for Growth Scheme (FGS), where the MNB provided inexpensive liquidity to banks to on-lend to micro, small, and medium-sized enterprises (SMEs) with an interest cap.23 The FGS did help many SMEs with working capital as well as new investments.24 The MNB decided to let the measure expire at end-March 2016, as market conditions improved. The Market-Based Lending Scheme was introduced in January 2016 and offers incentives to banks that commit to increase their lending micro companies and SMEs.
Hungary: Debt of Non-Financial Companies, 2000-16
Citation: IMF Staff Country Reports 2017, 124; 10.5089/9781484300473.002.A001
Sources: Eurostat’s Balance Sheet data; Magyar Nemzeti Bank (MNB);World Economic Outlook (GDP); and IMF staff calculations.Note: Unconsolidated and consolidated balance sheet data of non-financial companies include debt securities, loans, and other payables. 2016 Q3 un-consolidated data are preliminary estimates.Hungary: Debt of Non-Financial Companies, 2000-16
Citation: IMF Staff Country Reports 2017, 124; 10.5089/9781484300473.002.A001
Sources: Eurostat’s Balance Sheet data; Magyar Nemzeti Bank (MNB);World Economic Outlook (GDP); and IMF staff calculations.Note: Unconsolidated and consolidated balance sheet data of non-financial companies include debt securities, loans, and other payables. 2016 Q3 un-consolidated data are preliminary estimates.Hungary: Debt of Non-Financial Companies, 2000-16
Citation: IMF Staff Country Reports 2017, 124; 10.5089/9781484300473.002.A001
Sources: Eurostat’s Balance Sheet data; Magyar Nemzeti Bank (MNB);World Economic Outlook (GDP); and IMF staff calculations.Note: Unconsolidated and consolidated balance sheet data of non-financial companies include debt securities, loans, and other payables. 2016 Q3 un-consolidated data are preliminary estimates.24. Hungarian banks’ lending to households and non-financial companies contracted after the GFC, as NPLs increased. The NPL ratios have since reversed, first for firms and then for households. The NPL ratio of both households and non-financial corporations have declined during 2016, but more importantly, the shares of un-provisioned loans have also declined (Figure 5). Banks have cleaned up their portfolio of NPLs by selling them or writing them off. Moreover, real estate prices have improved substantially since 2014, hence permitting revoking provisions, which boosted banks profitability in 2016. The increasing NPL trend following the GFC has thus been broken, helped by numerous legal and prudential initiatives to strengthen the debt recovery frameworks and encourage out-of-court settlements, as well as generally improved market conditions.
Hungarian Banks’ Lending and NPLs to Households and Non-Financial Companies
Citation: IMF Staff Country Reports 2017, 124; 10.5089/9781484300473.002.A001
Hungarian Banks’ Lending and NPLs to Households and Non-Financial Companies
Citation: IMF Staff Country Reports 2017, 124; 10.5089/9781484300473.002.A001
Hungarian Banks’ Lending and NPLs to Households and Non-Financial Companies
Citation: IMF Staff Country Reports 2017, 124; 10.5089/9781484300473.002.A001
25. It is intrinsically difficult to disentangle whether lending has been subdued due to lack of demand or lack of supply, but lending surveys can offer a helpful clue. Figure 6 shows that both the demand for credit and the supply of credit contracted immediately after the GFC and the sovereign debt crisis, and have only gradually recovered. It seems that supply recovered before demand, at least for loans to households. Obviously, not just the average, but also the distribution matters, as different households and companies had different degrees of cushions to withstand the GFC.
Hungarian Banks’ Lending Surveys, 2002–2016
Citation: IMF Staff Country Reports 2017, 124; 10.5089/9781484300473.002.A001
Sources: Magyar Nemzeti Bank (MNB); and IMF staff calculations.Note 1: Lending surveys indicate the percentage of respondents reporting an increase or decrease in demand, their willingness to lend, or tightening of their lending standards compared to past period, weighted by their market share.Note 2: Quarterly data begins Q1 2009.1/ Change in credit stock deflated by CPI.2/ Bank credit growth based on transaction data, i.e., adjusted for exchange rate changes.Hungarian Banks’ Lending Surveys, 2002–2016
Citation: IMF Staff Country Reports 2017, 124; 10.5089/9781484300473.002.A001
Sources: Magyar Nemzeti Bank (MNB); and IMF staff calculations.Note 1: Lending surveys indicate the percentage of respondents reporting an increase or decrease in demand, their willingness to lend, or tightening of their lending standards compared to past period, weighted by their market share.Note 2: Quarterly data begins Q1 2009.1/ Change in credit stock deflated by CPI.2/ Bank credit growth based on transaction data, i.e., adjusted for exchange rate changes.Hungarian Banks’ Lending Surveys, 2002–2016
Citation: IMF Staff Country Reports 2017, 124; 10.5089/9781484300473.002.A001
Sources: Magyar Nemzeti Bank (MNB); and IMF staff calculations.Note 1: Lending surveys indicate the percentage of respondents reporting an increase or decrease in demand, their willingness to lend, or tightening of their lending standards compared to past period, weighted by their market share.Note 2: Quarterly data begins Q1 2009.1/ Change in credit stock deflated by CPI.2/ Bank credit growth based on transaction data, i.e., adjusted for exchange rate changes.26. New bank lending to households and particularly non-financial corporations seems to finally be picking up. The bank lending data used for the cross-country analysis are the change in stock of credit deflated by CPI inflation. This measurement can be misleading, since it does not consider changes in the amortization flows, sales of NPLs to non-banks, write-offs, closure of banks, as well as exchange rate effects. In recent months, new lending to particularly non-financial companies but also households have picked up when using transaction data (Figure 6). The reduced vulnerabilities and debt levels, as well as improved macroeconomic prospects underpin anecdotal reports that many banks are expecting to increase their lending during 2017.
F. Conclusion
27. Hungary, like most other European countries, experienced a deeper and longer recession following the GFC than what could have been expected from historical experience, but has recovered since 2013. The GFC triggered a deleveraging of private balance sheets, which suppressed the marginal propensity to consume and invest. Hungary experienced all the hallmarks of a so-called “balance sheet recession.” Moreover, Hungary, like some other European countries with high public debt, faced a double-dip recession related to the European sovereign debt crisis. The global nature of the GFC compounded the policy challenges to instigate the recovery. Hungary’s real GDP has recuperated and in 2014 was at par with the 2008 output level, benefitting from being part of the German supply chain as well as EU funds. Employment has also exceeded the pre-crisis level—even excluding those participating in the public work scheme—and companies are increasingly reporting labor shortages.
28. Hungary’s recovery has so far been creditless. Historically, creditless recoveries are not uncommon, particularly after periods of rapid credit growth.25 Hungary has shown that real growth and job creation can happen without rapid domestic bank credit growth. However, special conditions may have facilitated this process, since domestic bank credit has not been the only source of financing. The large reputable companies had access to foreign financing. Moreover, it is possible that relatively generous advance payments related to partially EU funded projects may have alleviated financial strains as the private sector deleveraged. The MNB took, like authorities in other countries (GFSR, 2013), initiatives to support SMEs—the most liquidity constrained companies. These initiatives seem to have had some positive impact (Endresz et al (2015) and László (2016)). Such programs, however, also entail risks that must be carefully managed. Numerous measures to address NPLs, including the conversion of FX denominated household loans during 2015, have likely also helped reduce uncertainties or substitute them with more manageable ones. Nevertheless, domestic bank lending to the Hungarian private sector remained subdued and only recently begun to recover as reflected in transaction data.
29. This study found that there is a significant positive correlation between real growth and bank lending, but that it is modest and could thus occasionally be overstated. One of the key lessons from the GFC must be that not all credit automatically provides sustainable growth and job creation. Credit-fueled booms, if based on exuberant and unrealistic expectations, will later be followed by painful adjustments. Or as Irving Fisher (1933, page 341) noted: “…over-confidence seldom does any great harm except when, as, and if, it beguiles its victims into debt.”
30. Finally, the study, underpins that: (i) timely provisioning; as well as (ii) resolving the inherited stock of NPLs; while, (iii) strengthening the debt recovery frameworks are critical for a sustainable recovery supported by new viable bank lending (IMF, 2015). In principle, fully provisioned NPLs should not hamper new lending. Nevertheless, lingering uncertainty about the evaluation of collateral can still hamper credit supply. In Hungary, the recent recovery of the real estate market and banks’ revocation of provisions may be an opportune time to review and bolster prudently conservative collateral evaluation policies.
References
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Appendix I. Country Groups
Countries with expansion peaks in 1971-2006 that are included in LP regression to derive projection paths.
| Euro area - advanced | Other European - advanced | Central, Eastern, and Southeastern European | Other | ||||
|---|---|---|---|---|---|---|---|
| 1 | Austria * | 1 | Denmark * | 1 | Albania | 1 | Argentina * |
| 2 | Belgium | 2 | Iceland * | 2 | Bosnia & Herzegovina | 2 | Australia * |
| 3 | Cyprus * | 3 | Israel * | 3 | Bulgaria | 3 | Brazil * |
| 4 | Finland * | 4 | Norway * | 4 | Croatia | 4 | Canada * |
| 5 | France * | 5 | Sweden * | 5 | Czech Republic | 5 | China,P.R.: Mainland |
| 6 | Germany * | 6 | Switzerland * | 6 | Estonia | 6 | China,P.R.:Hong Kong * |
| 7 | Greece * | 7 | United Kingdom * | 7 | Hungary | 7 | India * |
| 8 | Ireland * | 8 | Kosovo | 8 | Indonesia | ||
| 9 | Italy * | 9 | Latvia | 9 | Japan * | ||
| 10 | Malta * | 10 | Lithuania | 10 | Korea, Republic of * | ||
| 11 | Netherlands * | 11 | Macedonia, FYR | 11 | Malaysia * | ||
| 12 | Portugal * | 12 | Montenegro | 12 | Mexico * | ||
| 13 | Spain * | 13 | Poland | 13 | Philippines * | ||
| 14 | Romania | 14 | Singapore * | ||||
| 15 | Russian Federation * | 15 | South Africa * | ||||
| 16 | Serbia, Republic of | 16 | Turkey * | ||||
| 17 | Slovak Republic | 17 | United States * | ||||
| 18 | Slovenia |
Countries with expansion peaks in 1971-2006 that are included in LP regression to derive projection paths.
| Euro area - advanced | Other European - advanced | Central, Eastern, and Southeastern European | Other | ||||
|---|---|---|---|---|---|---|---|
| 1 | Austria * | 1 | Denmark * | 1 | Albania | 1 | Argentina * |
| 2 | Belgium | 2 | Iceland * | 2 | Bosnia & Herzegovina | 2 | Australia * |
| 3 | Cyprus * | 3 | Israel * | 3 | Bulgaria | 3 | Brazil * |
| 4 | Finland * | 4 | Norway * | 4 | Croatia | 4 | Canada * |
| 5 | France * | 5 | Sweden * | 5 | Czech Republic | 5 | China,P.R.: Mainland |
| 6 | Germany * | 6 | Switzerland * | 6 | Estonia | 6 | China,P.R.:Hong Kong * |
| 7 | Greece * | 7 | United Kingdom * | 7 | Hungary | 7 | India * |
| 8 | Ireland * | 8 | Kosovo | 8 | Indonesia | ||
| 9 | Italy * | 9 | Latvia | 9 | Japan * | ||
| 10 | Malta * | 10 | Lithuania | 10 | Korea, Republic of * | ||
| 11 | Netherlands * | 11 | Macedonia, FYR | 11 | Malaysia * | ||
| 12 | Portugal * | 12 | Montenegro | 12 | Mexico * | ||
| 13 | Spain * | 13 | Poland | 13 | Philippines * | ||
| 14 | Romania | 14 | Singapore * | ||||
| 15 | Russian Federation * | 15 | South Africa * | ||||
| 16 | Serbia, Republic of | 16 | Turkey * | ||||
| 17 | Slovak Republic | 17 | United States * | ||||
| 18 | Slovenia |
Countries with expansion peaks in 1971-2006 that are included in LP regression to derive projection paths.
Appendix II. Selective Regressions
GDP Growth and Bank Credit to the Private Sector: Recession and Recovery
Dynamic panel data; two-step system GMM estimator Sample of 39 European countries, estimation period: 1999-2015
Dummy takes the value of 1 during the recession period.
Dummy takes the value of 1 during the recovery period.
Volume of trading partners imports weighted by exports’ shares.
GDP Growth and Bank Credit to the Private Sector: Recession and Recovery
Dynamic panel data; two-step system GMM estimator Sample of 39 European countries, estimation period: 1999-2015
| (1) | (2) | (3) | |
|---|---|---|---|
| Full sample | AE | CESEE | |
| GDP growth rate (t-1) | 0.219** | 0.149** | 0.0967* |
| (0.085) | (0.066) | (0.060) | |
| Private sector credit growth | 0.0711*** | 0.0871** | 0.0558*** |
| (0.021) | (0.031) | (0.016) | |
| Private sector credit growth * | 0.0726 | −0.0249 | 0.0170 |
| Dummy recession1/ | (0.096) | (0.093) | (0.094) |
| Private sector credit growth * | 0.0308 | −0.0424 | −0.0581 |
| Dummy recovery2/ | (0.062) | (0.063) | (0.095) |
| Public consumption growth rate | −0.469* | 0.206** | 0.0280 |
| (0.251) | (0.090) | (0.051) | |
| Private sector credit-to-GDP ratio | −0.0129* | −0.00857 | −0.0405*** |
| (0.008) | (0.011) | (0.012) | |
| External demand 3/ | 0.253** | 0.216*** | 0.145** |
| (0.077) | (0.033) | (0.063) | |
| Log (VIX) | −0.00388 | −0.00760* | −0.0148* |
| (0.005) | (0.004) | (0.009) | |
| Dummy recession | −0.0281** | −0.0123** | −0.0369*** |
| (0.009) | (0.006) | (0.006) | |
| Dummy recovery | 0.000669 | 0.00920** | −0.000121 |
| (0.004) | (0.004) | (0.006) | |
| Constant | 0.0349** | 0.0267 | 0.0821** |
| (0.018) | (0.021) | (0.029) | |
| No. Obs. | 576 | 331 | 244 |
| No. countries | 39 | 21 | 18 |
| No.instruments | 20 | 21 | 20 |
| Autocorrelation test, p-value | 0.317 | 0.162 | 0.317 |
| Hansen test, p-value | 0.121 | 0.252 | 0.353 |
Dummy takes the value of 1 during the recession period.
Dummy takes the value of 1 during the recovery period.
Volume of trading partners imports weighted by exports’ shares.
GDP Growth and Bank Credit to the Private Sector: Recession and Recovery
Dynamic panel data; two-step system GMM estimator Sample of 39 European countries, estimation period: 1999-2015
| (1) | (2) | (3) | |
|---|---|---|---|
| Full sample | AE | CESEE | |
| GDP growth rate (t-1) | 0.219** | 0.149** | 0.0967* |
| (0.085) | (0.066) | (0.060) | |
| Private sector credit growth | 0.0711*** | 0.0871** | 0.0558*** |
| (0.021) | (0.031) | (0.016) | |
| Private sector credit growth * | 0.0726 | −0.0249 | 0.0170 |
| Dummy recession1/ | (0.096) | (0.093) | (0.094) |
| Private sector credit growth * | 0.0308 | −0.0424 | −0.0581 |
| Dummy recovery2/ | (0.062) | (0.063) | (0.095) |
| Public consumption growth rate | −0.469* | 0.206** | 0.0280 |
| (0.251) | (0.090) | (0.051) | |
| Private sector credit-to-GDP ratio | −0.0129* | −0.00857 | −0.0405*** |
| (0.008) | (0.011) | (0.012) | |
| External demand 3/ | 0.253** | 0.216*** | 0.145** |
| (0.077) | (0.033) | (0.063) | |
| Log (VIX) | −0.00388 | −0.00760* | −0.0148* |
| (0.005) | (0.004) | (0.009) | |
| Dummy recession | −0.0281** | −0.0123** | −0.0369*** |
| (0.009) | (0.006) | (0.006) | |
| Dummy recovery | 0.000669 | 0.00920** | −0.000121 |
| (0.004) | (0.004) | (0.006) | |
| Constant | 0.0349** | 0.0267 | 0.0821** |
| (0.018) | (0.021) | (0.029) | |
| No. Obs. | 576 | 331 | 244 |
| No. countries | 39 | 21 | 18 |
| No.instruments | 20 | 21 | 20 |
| Autocorrelation test, p-value | 0.317 | 0.162 | 0.317 |
| Hansen test, p-value | 0.121 | 0.252 | 0.353 |
Dummy takes the value of 1 during the recession period.
Dummy takes the value of 1 during the recovery period.
Volume of trading partners imports weighted by exports’ shares.
Private Gross Fixed Capital Formation (GFCF) and Bank Credit to the Private Sector
Dynamic panel data; two-step system GMM estimator Sample of 39 European countries, estimation period: 1999-2015
Private Gross Fixed Capital Formation (GFCF) and Bank Credit to the Private Sector
Dynamic panel data; two-step system GMM estimator Sample of 39 European countries, estimation period: 1999-2015
| (1) | (2) | (3) | |
|---|---|---|---|
| Full sample | AE | CESEE | |
| GFCF growth rate (t-1) | 0.168* | 0.224** | 0.0948 |
| (0.098) | (0.083) | (0.160) | |
| Private sector credit growth | 0.200** | 0.181** | 0.149* |
| (0.103) | (0.069) | (0.092) | |
| General government balance ratio (% GDP) | 0.493 | 0.664* | 0.162 |
| (0.442) | (0.370) | (0.424) | |
| Private sector credit-to-GDP ratio | −0.126** | −0.0743*** | −0.324** |
| (0.041) | (0.019) | (0.147) | |
| Interest rate (policy rate) | −1.783*** | −1.176** | −1.618** |
| (0.515) | (0.507) | (0.580) | |
| External demand | 0.725** | 0.718*** | 0.416* |
| (0.236) | (0.154) | (0.238) | |
| Log (VIX) | −0.0438 | −0.00832 | −0.131** |
| (0.029) | (0.016) | (0.048) | |
| Constant | 0.223** | 0.0793 | 0.562*** |
| (0.105) | (0.067) | (0.162) | |
| No. Obs. | 526 | 302 | 232 |
| No. countries | 38 | 21 | 17 |
| No. instruments | 18 | 18 | 17 |
| Autocorrelation test, p-value | 0.123 | 0.970 | 0.015 |
| Hansen test, p-value | 0.188 | 0.301 | 0.343 |
Private Gross Fixed Capital Formation (GFCF) and Bank Credit to the Private Sector
Dynamic panel data; two-step system GMM estimator Sample of 39 European countries, estimation period: 1999-2015
| (1) | (2) | (3) | |
|---|---|---|---|
| Full sample | AE | CESEE | |
| GFCF growth rate (t-1) | 0.168* | 0.224** | 0.0948 |
| (0.098) | (0.083) | (0.160) | |
| Private sector credit growth | 0.200** | 0.181** | 0.149* |
| (0.103) | (0.069) | (0.092) | |
| General government balance ratio (% GDP) | 0.493 | 0.664* | 0.162 |
| (0.442) | (0.370) | (0.424) | |
| Private sector credit-to-GDP ratio | −0.126** | −0.0743*** | −0.324** |
| (0.041) | (0.019) | (0.147) | |
| Interest rate (policy rate) | −1.783*** | −1.176** | −1.618** |
| (0.515) | (0.507) | (0.580) | |
| External demand | 0.725** | 0.718*** | 0.416* |
| (0.236) | (0.154) | (0.238) | |
| Log (VIX) | −0.0438 | −0.00832 | −0.131** |
| (0.029) | (0.016) | (0.048) | |
| Constant | 0.223** | 0.0793 | 0.562*** |
| (0.105) | (0.067) | (0.162) | |
| No. Obs. | 526 | 302 | 232 |
| No. countries | 38 | 21 | 17 |
| No. instruments | 18 | 18 | 17 |
| Autocorrelation test, p-value | 0.123 | 0.970 | 0.015 |
| Hansen test, p-value | 0.188 | 0.301 | 0.343 |
GDP Growth and Credit Impulse: Recession and Recovery
Dynamic panel data; two-step system GMM estimator Sample of 39 European countries, estimation period: 1999-2015
Dummy takes the value of 1 during the recession period.
Dummy takes the value of 1 during the recovery period.
Volume of trading partners imports weighted by exports’ shares.
GDP Growth and Credit Impulse: Recession and Recovery
Dynamic panel data; two-step system GMM estimator Sample of 39 European countries, estimation period: 1999-2015
| (1) | (2) | (3) | |
|---|---|---|---|
| Full sample | AE | CESEE | |
| GDP growth rate (t-1) | 0.300*** | 0.198** | 0.300*** |
| (0.030) | (0.066) | (0.040) | |
| Change in private sector credit growth | −0.0218 | −0.0244 | −0.0116 |
| (0.023) | (0.025) | (0.015) | |
| Change in private sector credit growth * | 0.0888** | 0.109* | 0.118** |
| Dummy recession1/ | (0.031) | (0.058) | (0.060) |
| Change in private sector credit growth * | 0.0739** | 0.0537** | 0.0957*** |
| Dummy recovery2/ | (0.029) | (0.028) | (0.029) |
| Public consumption growth rate | 0.0838* | 0.428*** | 0.0152 |
| (0.050) | (0.099) | (0.063) | |
| Private sector credit-to-GDP ratio | −0.0204*** | −0.001 | −0.0249* |
| (0.006) | (0.004) | (0.014) | |
| External demand 3/ | 0.237*** | 0.236*** | 0.265*** |
| (0.027) | (0.057) | (0.051) | |
| Log (VIX) | −0.00899*** | −0.00926** | −0.0104 |
| (0.003) | (0.004) | (0.009) | |
| Dummy recession | 0.00266 | 0.00817** | −0.00125 |
| (0.003) | (0.003) | (0.005) | |
| Dummy recovery | −0.0222*** | −0.0103 | −0.0324** |
| (0.006) | (0.007) | (0.012) | |
| Constant | 0.0491*** | 0.0209 | 0.0556* |
| (0.012) | (0.016) | (0.029) | |
| No. Obs. | 576 | 325 | 250 |
| No. countries | 39 | 21 | 18 |
| No. instruments | 19 | 21 | 19 |
| Autocorrelation test, p-value | 0.358 | 0.220 | 0.514 |
| Hansen test, p-value | 0.651 | 0.294 | 0.355 |
Dummy takes the value of 1 during the recession period.
Dummy takes the value of 1 during the recovery period.
Volume of trading partners imports weighted by exports’ shares.
GDP Growth and Credit Impulse: Recession and Recovery
Dynamic panel data; two-step system GMM estimator Sample of 39 European countries, estimation period: 1999-2015
| (1) | (2) | (3) | |
|---|---|---|---|
| Full sample | AE | CESEE | |
| GDP growth rate (t-1) | 0.300*** | 0.198** | 0.300*** |
| (0.030) | (0.066) | (0.040) | |
| Change in private sector credit growth | −0.0218 | −0.0244 | −0.0116 |
| (0.023) | (0.025) | (0.015) | |
| Change in private sector credit growth * | 0.0888** | 0.109* | 0.118** |
| Dummy recession1/ | (0.031) | (0.058) | (0.060) |
| Change in private sector credit growth * | 0.0739** | 0.0537** | 0.0957*** |
| Dummy recovery2/ | (0.029) | (0.028) | (0.029) |
| Public consumption growth rate | 0.0838* | 0.428*** | 0.0152 |
| (0.050) | (0.099) | (0.063) | |
| Private sector credit-to-GDP ratio | −0.0204*** | −0.001 | −0.0249* |
| (0.006) | (0.004) | (0.014) | |
| External demand 3/ | 0.237*** | 0.236*** | 0.265*** |
| (0.027) | (0.057) | (0.051) | |
| Log (VIX) | −0.00899*** | −0.00926** | −0.0104 |
| (0.003) | (0.004) | (0.009) | |
| Dummy recession | 0.00266 | 0.00817** | −0.00125 |
| (0.003) | (0.003) | (0.005) | |
| Dummy recovery | −0.0222*** | −0.0103 | −0.0324** |
| (0.006) | (0.007) | (0.012) | |
| Constant | 0.0491*** | 0.0209 | 0.0556* |
| (0.012) | (0.016) | (0.029) | |
| No. Obs. | 576 | 325 | 250 |
| No. countries | 39 | 21 | 18 |
| No. instruments | 19 | 21 | 19 |
| Autocorrelation test, p-value | 0.358 | 0.220 | 0.514 |
| Hansen test, p-value | 0.651 | 0.294 | 0.355 |
Dummy takes the value of 1 during the recession period.
Dummy takes the value of 1 during the recovery period.
Volume of trading partners imports weighted by exports’ shares.
Prepared by Tonny Lybek. This note draws on an ongoing cross-country analysis of credit growth and economic recovery in Europe after the global financial crisis performed by an EUR team comprising Sergei Antoshin, Marco Arena, Tonny Lybek, John Ralyea, and Etienne Yehoue under the supervision of Nikolay Gueorguiev. The paper also benefitted from insightful comments posed by participants at a seminar held at the Magyar Nemzeti Bank (MNB, the Hungarian Central Bank) on March 7, 2017.
In the cross-country paper, this analysis is performed by John Ralyea (EUR). For details on the data and methodology, see Antoshin et al (forthcoming).
Per capita was used, since some countries have experienced noticeable changes in their population since 1999.
A distinction is made between normal recessions and recoveries and those related to a financial crisis, i.e. those having had a systemic banking crisis as defined by Laeven and Valencia (2012). Two main conditions must be met to qualify for a systemic banking crisis. First, (Laeven and Valencia (2012, page 4): “Significant sign of financial stress in the banking system (as indicated by significant bank runs, losses in the banking system, and/or bank liquidations.” Secondly, “Significant banking policy intervention measures in response to significant losses in the banking system.” This definition implies that some countries may not have had a systemic banking crisis, if they have not met three of the six sub-conditions being “significant,” although they may still have experienced severe financial tensions, like Hungary.
28 separate regressions were run (7 regressions for each dependent variable) with a projection horizon of 7 years, consistent with the post-GFC period from 2009–2015. The sample episodes include 20 recession and recovery periods in European countries. Out of the total episodes, 64 were classified as non-financial and 15 as financial recessions. The Bry and Boschan (1971) algorithm was used to date business cycles across countries.
Poland avoided a recession, while Croatia’s experienced a prolonged recession and has not yet reached pre-crisis real GDP.
In the cross-country paper, this analysis is performed by Etienne Yehoue. For details on the data and methodology, see Antoshin et al (forthcoming).
The annual macro data are from the World Economic Outlook data base, while the individual bank data are from FitchConnect. Unconsolidated balance sheets were used unless only consolidated balance sheets were available. Extreme values were removed from the sample. The key variable for credit growth was provided by FitchConnect, but the high degree of variability, even after removing outliers, suggest that the results are only indicative.
The GMM methodology helps mitigate endogeneity issues, while the larger dataset alleviates multicollinearity challenges. Even with the use of several instrument variables, it is obviously difficult to disentangle causality. See, for instance, Everaert et al (2015).
Unfortunately, sufficient data were not available to use the un-provisioned part of NPLs instead of the gross NPL ratio. This would be akin to use the Texas ratio, which is defined as the value of the lender’s non-performing assets divided by the sum of its tangible equity and loan loss reserves.
Among the recent measures for households, the conversion of FX denominated loans during 2015 reduced the uncertainty for many borrowers. A Personal Bankruptcy Law became effective in September 2015 for mortgages and in October 2016 for all household loans. While not used much—about 650 cases were in process at end-February 2017—it facilitated ending the moratorium on evictions. In 2016, the MNB also published its recommendations on resolving defaulting mortgage loans. In 2014, the MNB established an asset management company for non-performing project loans to commercial real estate (MARK). It has been sold to the private sector (the transaction expected to be finalized in June 2017), given improved market conditions. Effective July 1, 2017, a special capital charge (systemic risk buffer) will be applied to banks with an extraordinary large share of non-performing commercial real estate loans. Details about these and the many previous initiatives can be found in various issues of the MNB’s Financial Stability Reports and Macro Prudential Reports.
However, in periods with very low wholesale interest rates, banks face a trade-off as a large retail funding base may be costlier but more reliable.
In the cross-country paper, this analysis is performed by Marco Arena. For details on the data and methodology, see Antoshin et al (forthcoming).
There are two strands of literature. One arguing that increased credit and financial deepening facilitate growth, which indeed is the case if agents are liquidity constrained. The other is concerned about over-indebtedness, when credit is extended to unsustainable projects and borrowers with insufficient cushions, hence adversely affecting growth, when balance sheets adjust.
The complex specification cannot be estimated on individual countries given the limited number of observations.
The definition of “creditless” here refers to domestic banks lending.
IMF (2009, Box 3.2) found that (page 116): “… disruptions to the supply of credit may not matter much for firms that are highly dependent on outside funding if they produce goods that are highly tradable.”
The EIB survey was based on phone interviews with 476 Hungarian companies during July – October 2016. The results were weighted by the value-added of the firms.
The cross-country analysis assessed if various manufacturing sectors that typically are more dependent on external financing had suffered more in terms of value-added and investments after the GFC. The results, however, including for Hungary, were inconclusive.
Figure 3 is not adjusted for revaluation effects, the data are unconsolidated, meaning intra-company loans are not netted out, and country specific conditions obviously affect the perceived “optimal” debt level, as for instance discussed by Jarmuzek and Rozenov (2017).
The MNB (2016, chapter 5) estimates that: (i) excess credit during the credit boom (2002–2008) increased annual real GDP by about 0.4 – 0.8 percent; and, (ii) the deleveraging after the GFC reduced annual real GDP growth by about 1.1 – 1.4 percent during 2009–2015.
For details on the MNB’s macroprudential policies, see the MNB’s Macroprudential Report, October 2016.
After the GFC, many countries introduced measures to support both credit supply and demand (GFSR, 2013, Table 2.3).
At end-March 2017, nearly 40,000 micro and SMEs had received loans during the various phases of the FGS. For more information on its impact, see, for instance, Endresz et al (2015) and László (2016).
For instance, Claessens, Kose and Terrones (2009 and 2011, page 26) conclude that: “… recessions accompanied with financial disruption episodes, notably house price busts, tend to be longer and deeper while recoveries combined with rapid growth in credit and house prices tend to be stronger.” Abiad, Dell’Ariccia, and Li (2011) find that creditless recoveries are significantly deeper and longer after financial crises. Jordà et al. (2013) argue that financial indicators, like credit, tends to amplify the business cycle, and (Jordà et al., 2014) that the recovery path is even worse when the credit-fueled crisis coincides with elevated public debt. Taylor (2015) confirms that one in four recessions are caused by financial crises and that these recessions are deeper and longer, with inflation subdued and credit recovery slow.
