Abstract
This paper empirically investigates patterns in the use of credit and their temporal evolution against socio-economic and behavioural traits of borrowers. Debt holder segments were identified from data contained in three waves (2011, 2013 and 2015) of the biennial panel study of Polish households—Social Diagnosis. Analysis supported claims for a differential role of socio-economic characteristics and behavioural factors in evolution of segments of credit users. The analysis conducted with latent transition modelling confirmed intertemporal stability of borrowing patterns. At the same time, it was revealed that: (1) some groups of borrowers—mortgage holders in particular—were likely to stay in their respective groups, while others—especially those borrowing from outside the banking sector and those indebted for other purposes—were more likely to transition; (2) mortgages and loans for household run business were strongly linked to household socio-economic characteristics; (3) loans for durables, renovation and, most notably, consumption were less driven by age of the household head, whereas the ability to manage income was clearly pertinent for transition to those groups; (4) the group of overindebted consumers, although not particularly large, was characterized by high probability of remaining indebted with very low chances of escaping debt.
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Source: Own calculations based on data from National Bank of Poland, Polish Central Statistical Office, The Social Diagnosis Survey
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Notes
The distinction between household and individual decisions in credit market is extremely difficult. This article, although based on household level data on value, sources and objectives for debt, also refers to a set of household head characteristics to define credit market behaviour. Consequently, throughout this article, the term consumer refers to the household head, who is responsible for household level financial decisions.
Karlsson et al. (1997) report an experiment showing that propensity to finance with credit increases when the motive for consumption deviates from that of saving.
More detailed description of latent class modelling can be found in Muthén (2004). Its advantages over other segmentation techniques are well described in Vermunt and Magidson (2002). Estimation of latent class models is performed with a maximum likelihood estimator following the EM algorithm, in which the information about latent class membership is considered missing and thus, is derived from the data (Muthén et al. 1999).
A stepwise approach was employed for estimation of the latent transition model allowing description of classes to be estimated in the first step. Transition (\(\beta_{p,k,t - 1}\)) and socioeconomic covariates (\(\alpha_{j,k}\)) were only then introduced. In order to maintain the latent character of class membership and to avoid a downward bias in estimates of error terms (Di Mari et al. 2016), we multiply imputed class membership following the latent class membership probabilities obtained in the first step. For this purpose we used a set of 10 multiple imputations.
Using BIC, it is possible to maintain a balance between model complexity (number of latent classes) and its parsimony. Models with more classes offer a better fit to the data, yet additional complexity should be penalized because it might lead to identification of spurious relationships resulting from noise in the data.
Due to the very large number of observations it is highly unlikely that the effect observed by Lukočienė et al. (2010) was present. They stated that the smaller the sample size, the less likely it was to find the correct number of classes.
There is a caveat to interpretation of significance in latent class analysis. If a significant parameter indicates that transition to a particular class increases (decreases), since all transition probabilities sum to 1, it is also implied that transition to all other classes decreases (increases). In this kind of specification lack of significance only points to that particular factor not playing differential role in the transition to a particular class with respect to the reference class (NON-ACTIVE in our case). Nevertheless, it does not mean that the factor is completely irrelevant for transition to other classes.
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Białowolski, P. Patterns and evolution of consumer debt: evidence from latent transition models. Qual Quant 53, 389–415 (2019). https://doi.org/10.1007/s11135-018-0759-9
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DOI: https://doi.org/10.1007/s11135-018-0759-9