experienced the smallest depreciation in the crisis. Again, we cannot pick out a single country which experienced a severe depreciation and has a significant share of FX mortgages.

**Table 4.5. Household Use of Credit and Crisis Impact**

|Dependant variable|_Cut consumption_|_Cut services_|_Sold assets_|
|---|---|---|---|
|Model|(1)|(2)|(3)|
|Credit card|0.014|0.018|0.005|
||[0.0126]|[0.0146]|[0.00460]|
|Mortgage|0.0305***|0.0828***|0.0170*|
||[0.0118]|[0.0186]|[0.00898]|
|Job loss|0.0738***|0.129***|0.0173***|
||[0.0157]|[0.0139]|[0.00599]|
|Closed business|0.017|-0.016|0.005|
||[0.0197]|[0.0186]|[0.00412]|
|Wage reduction|0.0857***|0.0786***|0.003|
||[0.00928]|[0.00989]|[0.00331]|
|Less remittance|0.0349**|0.0806***|0.0172***|
||[0.0173]|[0.0169]|[0.00654]|
|Income|0.001|-0.0273***|-0.001|
||[0.00998]|[0.0105]|[0.00244]|
|Self employed|-0.006|0.020|0.006|
||[0.0158]|[0.0137]|[0.00528]|
|University degree|-0.0382***|-0.0559***|-0.002|
||[0.0130]|[0.0126]|[0.00409]|
|Method|Probit|Probit|Probit|
|Pseudo R2|0.08|0.08|0.05|
|Country fixed effects|yes|yes|yes|
|# Households|13,838|13,838|13,838|
|#countries|21|21|21|

Source: LITS survey and authors‟ calculation. Note: The dependent variables in this table are Cut consumption, Cut services and Sold assets. Cut consumption is 1 if the household reduced its consumption of goods in 2009 or 2010 and 0 otherwise. Cut services is 1 if the household reduced its use of health, utilities or telecommunication services in 2009 or 2010 and 0 otherwise. Sold assets is 1 if the household sold off an asset in 2009 or 2010 and 0 otherwise. The variable Job loss is 1 if any household member lost his job since 2008, and 0 otherwise. Closed business is 1 if a family business was closed since 2008, and 0 otherwise. Wage reduction is 1 if a family member's wage income was reduced since 2008, and 0 otherwise. Less remittance is 1 if remittances from abroad to the family were reduced since 2008, and 0 otherwise. All other variables are as defined in tables 3.3.1 and 3.3.2. All models report marginal effects of profit estimations and include country fixed effects. Observations are weighted according to sample weighting in the LITS survey. Standard errors are reported in brackets and are adjusted for clustering at the country level. ***, **, * denote significance at the 0.01, 0.05 and 0.10-level.

Our analysis above suggests that debt overhang in Eastern Europe is a concentrated phenomenon: Few households have mortgage debt. But those households that do have mortgage debt have reduced their consumption and investment substantially. Which countries are most likely to suffer from such mortgage debt overhang? Figure 4.2.a plots the change in GDP per country in 2009 against the share of households with mortgage debt. The figure suggests that mortgage debt overhang is hardly an issue in the region. Estonia is the only country which experienced a sharp contraction in GDP in 2009 and boasts a high incidence of mortgage credit (17%).

As mentioned above the use of credit cards seems to have had little impact on household consumption and investment during the crisis. Thus it is questionable whether the high levels of credit card use actually influence economic activity. Figure 4.2.c plots the change in GDP per country in 2009 against the incidence of credit card use. The figure suggests that Estonia, Latvia and Ukraine are most likely to be affected by consumer credit. These countries experienced 33