Additional Results and Robustness

3.4.1 Alternative Measures of Liquidity Provision

We have so far captured the effect of liquidity provision by the Eurosystem through TARGET2 net liabilities. TARGET2 net liabilities reflect liquidity provision by the Eurosystem as long as the latter meets asymmetric funding needs of banking systems across national borders of the euro area. This is the case during the period under review because the banking systems in distressed countries lost access to private inter-bank markets as a whole.

To demonstrate that our approach is sensible, we replace TARGET2 net liabilities in Equation (3.1) with the refinancing operations of banks in the GIIPS countries with their respective national central banks (NCBs). The refinancing operations give us the most direct measure of liquidity provision by the Eurosystem to the financial sys-tem. The drawback is that we do not capture liquidity provision by NCBs outside the common monetary policy framework, such as Emergency Liquidity Assistance (ELA) Cour-Thimann (2013). For the BELL countries, we set the amount of refinancing at zero. Consistent with the treatment of the TARGET2 net liabilities, we include the volume of refinancing operations relative to 2007 GDP in the regression equation.

We present the results of this exercise in Table 3.6. We show the results for the six dependent variables (real and nominal unit labor costs, real and nominal wages, labor productivity and prices) from our preferred specification, which includes all control variables simultaneously. The results are qualitatively and quantitatively very similar to our main result. The effect of the interaction between central bank refinancing op-erations and financial vulnerability is negative and highly significant in the regression using real unit labor costs and real wages. Also, the effect remains positive and highly significant in the regression using prices as the dependent variable.

All in all, the results from this robustness test suggest that TARGET2 net liabilities do indeed capture, first and foremost, the effect emanating from liquidity provision by the Eurosystem.

We have so far assumed that the central banks of the BELL countries were unable to support their financial system because they committed to defending the fixed exchange rate peg to the euro. However, insofar as foreign currency reserves were sufficiently high, the BELL central banks potentially had scope to provide liquidity support to the financial system. If this is the case, our previous results would be misleading. We test for this alternative by replacing in Equation (3.1) the TARGET2 net liabilities with

Chapter 3: Liquidity Provision, Financial Vulnerability, and Internal Adjustment to a Sudden Stop

sheets over the adjustment horizon, that is, since the liquidity shock. The idea is that central bank operations providing additional liquidity support increase the asset side of the central bank balance sheet.

In Table 3.7, we present the results of the regressions using the size of the central bank balance sheet (scaled by 2007 GDP) as a measure of liquidity provision. Our results are qualitatively the same as in our benchmark model. In line with the findings of the baseline regression, the effect is negative and statistically significant for real unit labor costs and real wages, and positive and significant for prices. The point estimates are somewhat smaller compared to the baseline regressions in Tables 3.3 to 3.5.

3.4.2 Reverse Causality

A potential concern with respect to our analysis so far is the fact that liquidity provi-sion by the Eurosystem might not be exogenous to the adjustment dynamics. If this is the case, reverse causality might bias our findings.¹²

We address the issue of reverse causality in two ways. In a first exercise, instead of using the contemporaneous TARGET2 net liabilities, we use TARGET2 net liabilities lagged by four quarters. We argue that liquidity provision by the Eurosystem one year ago should not be determined by today’s stance of adjustment at the sector level.

The results of this test, presented in Table 3.8, are similar to our benchmark results.

The interaction effect is insignificant in the regressions for nominal unit labor costs and nominal wages, but negative and highly significant in the real wages and real unit labor costs regression, and positive and highly significant in the price regression. However, the interaction effect now becomes significant in the labor productivity regression (at the 5% level). The point estimate of the interaction effect in the price, real wages and real unit labor costs regressions is similar in size to the effect in the baseline regression.

We take this as an additional indication against reverse causality.

The second approach we take to address the issue of reverse causality consists of replacing the TARGET2 net liabilities with a dummy variable indicating whether or not a country was a member of the euro area at the time of the sudden stop.

In our case, prior to the Baltic countries’ EMU membership, this is identical to an indicator variable taking the value of one for the GIIPS countries and zero for the BELL countries. The main advantage of this “GIIPS dummy” is that it is clearly exogenous and predetermined. We assume that euro area membership captures, first and foremost, the effects emanating from access to liquidity provided by the Eurosystem. The results

12We again emphasize here that reverse causality or endogeneity is not an issue for our measure of dependence on external finance because it is calculated based on a period preceding our estimation period.

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are presented in Table 3.9. All our previous results are confirmed by this exercise.

3.4.3 International Liquidity Flows through Global Banks

An implicit assumption in the analysis so far is that banks in the BELL countries could not tap the liquidity provided by the Eurosystem. This assumption holds true only for truly domestic banks in the BELL countries. However, some large European banks have a presence in the BELL countries either through affiliates or subsidiaries (Ce-torelli and Goldberg, 2011; De Haas et al., 2012). These large European banks might have channeled liquidity provided by the Eurosystem to the BELL countries via their internal capital market. For this reason, we take the full specification and additionally add a variable capturing the cumulative change in the private financial account for the BELL countries (relative to 2007 GDP). Any capital inflow through banks, other financial intermediaries and private investors increases the private financial account.¹³ Hence, the private financial account gives a complete picture of private liquidity flows into the BELL countries. We allow this variable to affect internal adjustment condi-tional on financial vulnerability.

We show the results in Table 3.10. First, our key results are not affected by this exercise. Second, except for nominal wages, the effect of the interaction between the cumulated private financial account and the measure of financial vulnerability is always insignificant. Hence, additional external liquidity inflows, for example through large European banks, did not differentially affect the adjustment path since the sudden stop.

3.4.4 Alternative Measures of Financial Vulnerability

We replace our baseline measure of financial vulnerability with the share of tangible assets in total assets at the sector level (averages over the period from 2000 to 2007).¹⁴ Sectors with a better supply of tangible assets that can be posted as collateral to obtain external financing should be less vulnerable to the liquidity shock (Manova, 2013). We include the inverse of asset tangibility in the regression equation to facilitate comparison with our baseline measure of financial vulnerability.

13Controlling for this channel seems particularly important in the BELL countries, especially towards the end of the sample when private capital flows reversed. We do not add the private financial account alongside the TARGET2 net liabilities for the GIIPS countries because TARGET2 net liabilities and the private financial account are almost perfectly negatively correlated for the GIIPS countries. That is, whenever there was a capital outflow from the GIIPS countries, the TARGET2 net liabilities increased to offset the outflow. In this respect, for the GIIPS countries the private financial account does not contain additional information above and beyond the TARGET2 net liabilities.

Chapter 3: Liquidity Provision, Financial Vulnerability, and Internal Adjustment to a Sudden Stop

According to the results in Table 3.11, replacing the financial vulnerability measure based on MFI loan growth with the measure of financial vulnerability based on the share of tangible and liquid assets in total assets does not affect our main finding. In fact, both statistical significance and the size of the interaction effect are very similar to the baseline results presented above.

We also conducted a host of robustness tests directly concerning our baseline mea-sure of financial vulnerability. We used a dummy variable indicating whether a sector is above-average in terms of financial vulnerability, we ranked the sectors according to the financial vulnerability measure, and we changed the window over which we calcu-late the growth rate in borrowings from MFIs. We give a compact presentation of the findings in Table 3.12. In general, these sensitivity tests confirm our previous findings.

All in all, we conclude from the results presented in this section that our findings are not very sensitive to the exact specification of the external financial vulnerability measure based on growth in MFI loans. Also, our results are robust against the use of alternative indicators of financial vulnerabilities proposed in the literature.

3.4.5 Controlling for Country-Sector Specific Effects

So far, we have identified the differential effect of liquidity provision on the adjustment dynamics from the variation in financial vulnerability across sectors within and between countries. We explicitly included the variation between countries for identification purposes because we aim to understand the different adjustment dynamics in the GIIPS versus the BELL countries. A drawback of this strategy is that we cannot rule out that structural, time-invariant country-sector specific characteristics might bias our results.

To address this issue, we re-estimate our baseline specification given in Equation (3.1) but add a full set of (time-invariant) country-sector specific fixed effects. The regression equation now reads:

∆_tlog(Y_ikt) =α_iτ+α_it+α_kτ+α_kt+α_ik+γ[F V_k×LP_it] + [F V_k×X_it] +ε_ikt (3.7) The country-sector specific fixed effects αik capture all observed and unobserved country-sector specific structural characteristics. The parameter on the interaction between liquidity provision and financial vulnerability, γ, is now identified only from the within-country variation in financial vulnerability across sectors. Given that we now use the within-country-sector variation for identification, we base our inference on standard errors clustered at the country-sector level.¹⁵ In this way, we explicitly

15Using clustered standard errors at the country-sector level in the baseline regression does not change our main finding.

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control for potential error correlation within the country-sector pairs over time (see, e.g., Jiménez et al., 2014).

The results are shown in Table 3.13. All in all, the results suggest that our findings are robust to controlling for structural country-sector specific effects.

3.4.6 Controlling for Adjustment in Labor Productivity

Our results so far show that liquidity provision by the Eurosystem reduced