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Consumption and credit constraints: a model and evidence from Ireland

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Abstract

After the onset of the financial crisis, consumption fell in many economies. This paper presents a small-scale DSGE model with occasionally binding credit constraints. Indebted households start facing credit constraints when the value of their main asset, housing, declines. As a response, they stop smoothing consumption and start deleveraging. Even households that only expect to face a credit constraint in the future deleverage. Using the Irish Household Budget Survey, we show that most Irish households continued to smooth consumption during the crisis. However, for highly indebted consumption smoothing is disrupted during the crisis. Households with leverage close to but below the standard loan-to-value ratio of 85% also seem to smooth consumption less than normal households. This is rational if they expect a further house price decline and therefore anticipate the need to deleverage in the near future. We interpret these results as evidence of credit constraints that arise from falling property prices.

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Notes

  1. This number is derived from real personal consumption expenditure from the FRED database and from average population growth in the 1930 s from the 1940 US Census.

  2. In a column on VoxEu, Gerlach-Kristen et al. (2013) present results on consumption decisions based on both micro- and aggregate data.

  3. The Household Finance and Consumption Network conducts a harmonised household survey across the euro area that is also compatible with the US Federal Reserve’s Survey of Consumer Finances. For a detailed description of data and results, see Eurosystem Household Finance and Consumption Network (2009, 2013). Irish data were collected only in the second wave 2013. For analyses of these data, see Lawless et al. (2015) and Staunton (2015).

  4. See O’Connell et al. (2013) for a comparison of real per capita consumption before and during the financial crisis in the Eurozone. Studies on Irish household consumption include Hogan and O’Sullivan (2007), Lydon and O’Hanlon (2012) and Gerlach-Kristen (2013, 2014).

  5. For an empirical analysis on Irish data over the period 1960–1991, see Roche (1995).

  6. Mankiw and Shapiro (1985) criticise the validity of some of these procedures for testing the PIH. For instance, they show that, if income has a unit root, the analysis in Flavin (1981) is biased towards rejection. Nelson (1987) finds evidence supporting the PIH.

  7. Barakova et al. (2014) use data from the National Longitudinal Survey of Youth to assess the impact of borrowing constraints and house price dynamics on the probability of homeownership during the US housing market boom between 2003 and 2007.

  8. In the earlier literature, some authors (Campbell and Mankiw 1990; Roche 1995) also assume that there are two types of consumers: a constant proportion of households is forward-looking optimising households and consumes their permanent income, while the remaining population consumes their current disposable income. Alternatively, Flavin (1985) proposes a specification of the consumption function that includes the unemployment rate, which is assumed to be a proxy for the proportion of liquidity-constrained households.

  9. This approach is also chosen by Mayer and Gareis (2013), who present a DSGE model for Ireland.

  10. Justiniano et al. (2015) model an occasionally binding borrowing constraint so as to reproduce the asymmetry of mortgage contract and the downward stickiness of mortgage debt observed in 2006–2007 US data. Benigno et al. (2009) analyse optimal monetary policy rules for “crisis” periods when the borrowing constraints bind and for “normal” periods when the borrowing constraint is slack. They conclude that optimal policy is nonlinear. For more details on methodological aspects and a comparison of alternative parameterised expectation algorithms, we refer the reader to Christiano and Fisher (2000).

  11. To solve the model, we employ a piecewise linear solution technique developed by Guerrieri and Iacoviello (2015a) and available online (https://www2.bc.edu/matteo-iacoviello).

  12. The dynamics in our model hold under more general conditions than in Mendoza (2010), where the amplification channel and asymmetric response are driven by an external finance risk premium and the debt deflation effect.

  13. In models assuming an ever-binding constraint, Eq. (3) holds with equality and Eq. (3a) is never invoked. We present an impulse response based on a model with an ever-binding constraint in Fig. 4.

  14. Normally, in the literature β is set equal to 0.9925, implying a steady-state real interest rate of 3% on an annual basis. We set the discount factor equal to 0.965, which means that households are more impatient. This value has a limited effect on the model dynamics, but guarantees an impatience motive large enough that the impatient agents are arbitrarily close to the borrowing limit. For the US economy, Iacoviello and Neri (2010) set this value for impatient households equal to 0.97, hence very close to our calibration.

  15. We acknowledge that, ideally, our model would assume that the borrowing constraint is not binding in the steady state, since it would be more intuitive to assume that households are not constrained in “normal” times. However, this is a shortcoming common to the literature on occasionally binding constraints. Besides Guerrieri and Iacoviello (2015b), see also Brzoza-Brzezina et al. (2015) and Benigno et al. (2009).

  16. The introduction of habit in consumption to modern DSGE models was initially proposed by Christiano et al. (2005). It causes that model is able to generate hump-shaped response of consumption to various shocks, as observed in the data.

  17. We have calibrated χ so that it matches the coefficient on the proxy for the lagged dependent variable—the 5-year-lagged consumption—that we estimate in Sect. 5. Clancy and Merola (2017) develop a DSGE model for Ireland, and they set χ = 0.8, close to the upper value of the interval [0.5, 0.9]. Our results are robust and results remain valid for alternative values of consumption persistence.

  18. In Ireland in 2013 the average mortgage debt service to income ratio was 15.8. Concerning credit constraints, the share of constrained households was 18.4%. However, aggregate data may not accurately assess the risk exposure and the vulnerability of households. When we look at micro-data and individual characteristics, the situation may be different. The share of constrained households rises up to 31% for young households under 35 and up to 42.1% for single-parent families (Staunton 2015).

  19. All simulations are run in Dynare 4.3.3 (Adjemian et al. 2011), using the OccBin Toolkit developed by Guerrieri and Iacoviello (2015a).

  20. A recent and growing literature investigates the implications of using loan-to-value ratios to contain boom-bust cycles in credit and housing prices (e.g. Christensen and Meh 2011; Angelini et al. 2011; Lambertini et al. 2013).

  21. Using a county-level dataset, Mian and Sufi (2010b) and Mian et al. (2013) find that in those US counties that experienced a large increase in household leverage during the boom (2002–2005), consumption dropped more dramatically during the first phase of the recession (2007:Q3–2008:Q4) than in other counties.

  22. For Italy, this result might be explained by some peculiarities of the Italian credit market. Due to the existence of strong imperfections, households prefer to save in a precautionary manner or to rely on "informal networks" (i.e. help from parents or friends), rather than rely on credit markets.

  23. See Engelhardt (1996) for the USA and Berben et al. (2007) for the Netherlands. Kim and Chung (2016) find that house price decreases have a larger impact on movements in US macro-indicators (and consequently on US business cycle) than house price increases.

  24. Similarly, in a small open-economy DSGE model with occasionally binding collateral constraints, Benigno et al. (2012) find that government should intervene aggressively by subsidising the consumption of non-tradable goods in periods of stress, when the borrowing constraint is binding. In “normal” times, it is not optimal to intervene before the constraint actually binds.

  25. Income and consumption are reported for the household as a whole, not broken down by individual.

  26. The HBS reports expenditure, not consumption. This means that a household’s consumption jumps up if for instance a new car is bought. The consumption utility derived from the services of the car is not recorded in the data.

  27. For the economy as a whole, the savings rate computed from gross national disposable income and personal savings before stock appreciation is 4.2% in 1995, − 0.5% in 2000, 2.9% in 2005 and 3.8% in 2010..

  28. J-tests for the exogeneity of these instruments with respect to consumption do not reject by a wide margin.

  29. Kennedy and McIndoe-Calder (2012) report a somewhat lower average loan-to-value ratio between 50 and 80% for the years before the crisis.

  30. For the regressions in Sect. 5, we have run robustness tests by varying the leverage at origination from 75 to 100%. None of the main results changed. Results are available upon request.

  31. Kennedy and McIndoe-Calder (2012) report that most Irish mortgages are flexible-rate contracts. Nevertheless, since the speed of amortisation primarily depends on maturity, our measure of outstanding debt at the time of the HBS interview should be roughly accurate.

  32. We also tried including mortgage payments. This additional variable was significant only at the 5% level and did not change the main results. We also performed robustness checks that include housing expenditure in consumption, and the results are robust to this change in definition. Excluding all non-mortgage households does not change the main findings, either. Results are available upon request.

  33. The referee pointed out that high-consumption households might tend to have high leverage at origination. We hope to account for general consumption habits with the household-specific demographic and employment data. If there is spurious reverse causality left in our estimation set-up, this would bias the coefficients on leverage upwards. This in turn would imply that we underestimate the negative impact of credit constraints on consumption.

  34. Using leverage in logs and excluding non-mortgage households does not change the main findings. Results are available upon request.

  35. The referee has pointed out that we might underestimate leverage because of top-up loans. If so, \( \hat{\beta }^{L} \) is biased upwards, and in truth, the PIH would be rejected even more strongly.

  36. It is interesting to note that a Wald test for the equality of the income elasticities of households with medium and high leverage does not reject the null hypothesis (p value of 0.66). Precautionary deleveraging thus seems to have been as strong as that caused by actual credit constraints. This probably is one factor explaining why Irish consumption declined so sharply in the crisis.

  37. This time series starts in 1999. We therefore drop those mortgage households that moved to their current residence before that data. This means that of sample shrinks by 10%.

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Author information

Authors and Affiliations

  1. Swiss National Bank, Zurich, Switzerland

    Petra Gerlach-Kristen

  2. Research Department, International Labour Office, Geneva, Switzerland

    Rossana Merola

Authors
  1. Petra Gerlach-Kristen
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  2. Rossana Merola
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Corresponding author

Correspondence to Rossana Merola.

Additional information

The work was conducted when both the authors were staff members at the ESRI and the Trinity College in Dublin. The authors thank the Eurosystem Household Finance and Consumption Network (HFCN), Alan Barrett, Tim Callan, David Duffy, Stefan Gerlach, Matteo Iacoviello, Conor O’Toole, Margarita Rubio and seminar participants at the Deutsche Bundesbank, the Dutch National Bank, the ESRI, the National University of Ireland Maynooth, the Nottingham University and the UECE Conference in Lisbon for useful comments and suggestions. The authors are also indebted to Claire Burke and Brian O’Connell for help with the data and to Robert Kunst, an anonymous reviewer and an associate editor for their careful reading of the manuscript and their many insightful comments and suggestions. The authors, however, are responsible for any remaining errors. This paper is based on data from the Eurosystem Household Finance and Consumption Network, and the responsibility for all conclusions drawn from the data lies entirely with the authors. The views presented in this paper are the authors’ and do not necessarily represent those of the Swiss National Bank, the International Labour Organization and the Eurosystem Household Finance and Consumption Network.

Appendices

Appendix A: computation of permanent income and lagged consumption

Table 4 shows proxies for permanent real income by household tenancy, education and age. We compute this measure as the average over time (i.e. over HBS waves) of the average income within each population group. Mortgage owners, more educated households and middle-aged households tend to have the highest permanent incomes.

Table 4 Proxies for permanent income by age, tenancy and education level
Full size table

Table 5 presents proxies for lagged consumption by household tenancy, education and age. These are used as a proxy for the lagged dependent variable in Eqs. (12) and (13). It can be seen that consumption in 2004/2005, at the height of the boom, was much above the permanent income proxy.

Table 5 Proxies for lagged consumption by age, tenancy and education level
Full size table

Appendix B: computation of household leverage ratios

Here, we discuss how we compute the proxy for the leverage ratio of mortgage households. There are four main assumptions underlying this measure.

First, we assume a loan-to-value ratio of 85% at origination, so that the mortgage corresponds to 85% of the value of the property the household purchases. Thus, dorigination = LTVp horigination . We assume for simplicity that after the down-payment for the mortgage, the household does not have any assets but the house.

Second, we assume that the house value moves in unison with the general house price index presented in Fig. 6 in the main text.Footnote 37 We use the house price index in the quarter in which the interview was conducted and we denote it by p h t . For each household, we know how many years ago it last moved, though there is no information on quarters. We therefore assume that the move was exactly the number of years ago the household indicates, with no additional quarters. The general house price index at that point in time gives us the value at origination p horigination .

Third, we assume a fixed-rate mortgage contract with a maturity T of 28 years. The monthly payments made are constant over time and combine the interest payment, which declines as the remaining principal decreases, and an amortisation payment, which correspondingly rises over time. As there are no data available on mortgage rates at origination, we use the average mortgage rate in the quarter of the house purchase and denote it by mrate. We calculate the monthly payment as

$$ {\text{Monthly payment}} = \frac{{d_{\text{origination}} }}{{1 - {{\left[ {\frac{1}{{\left( {1 + mrate} \right)^{12T} }}} \right]} \mathord{\left/ {\vphantom {{\left[ {\frac{1}{{\left( {1 + m{\text{rate}}} \right)^{12T} }}} \right]} {m{\text{rate}}}}} \right. \kern-0pt} {mrate}}}}. $$

Fourth, we assume that each household only has one mortgage, so that the number of real-estate properties per household is \( h_{t} = 1 \).

Based on these assumptions, we compute the leverage ratio in the quarter of the HBS interview as

$$ \frac{{d_{t} }}{{p_{t}^{h} }} = {\text{lev}}_{t} {=} \frac{{d_{\text{origination}} - \sum {\text{monthly payment}}}}{{p_{t}^{h} }} = \frac{{{\text{LTV}}p_{\text{origination}}^{h} - \sum {\text{monthly payment}}}}{{p_{t}^{h} }}, $$

where we denote as \( \sum {\text{monthly payment}} \) the sum of the payments made since the origination of the mortgage.

To illustrate the calculation of the leverage ratio, let us consider the following example. A household bought its residence in 1999 at a price of 100, using a mortgage of 85. It is interviewed in the third quarter of 2009. Due to mortgage payments, its debt has by that time fallen to 66. At the same time, the general house price index has increased by 67%, so that the house price has moved from 100 to 167. Correspondingly, the leverage ratio is calculated as 66/167 = 39%. This is the number in the bottom-left cell in Table 6. If this household is interviewed a quarter later, in 2009Q4, its leverage ratio increases to 44% because of the rapidly falling house prices and in spite of the additional mortgage payments made.

Table 6 Proxies for leverage ratios used in Sect. 5
Full size table

The size of these numbers appears plausible. Lawless et al. (2015) report loan-to-value ratios by age group using Irish data from the second wave of the European Household Finance and Consumption network. These data refer to 2013 and show that households in their thirties typically had a leverage ratio above 100%. This number is smaller for older households, who bought at lower prices and had more time to amortise their debt. For households in their forties leverage was around 60%.

Table 7 shows which households were particularly likely to be highly leveraged. It can be seen that the younger the mortgage household, the more likely it is to be highly leveraged. There is also weak evidence that more highly educated households, small households, those with few children, those in rural areas and those unemployed are more indebted.

Table 7 Determinants of a high leverage ratio
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Gerlach-Kristen, P., Merola, R. Consumption and credit constraints: a model and evidence from Ireland. Empir Econ 57, 475–503 (2019). https://doi.org/10.1007/s00181-018-1461-4

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