A Longitudinal Study of Polish Attitudes to Emigration: A Latent Markov Model Approach

Abstract

Latent class analysis can be viewed as a special case of model-based clustering for multivariate discrete data. When longitudinal data are to be analysed, the research questions concern some form of change over time. The latent Markov model is a variation of the latent class model that is applied to estimate not only the prevalence of latent class membership, but the incidence of transitions over time in latent class membership.In 2004, Poland joined the European Union, prompting a number of Poles to leave the country. To examine this event, a model-based clustering approach for grouping and detecting inhomogeneities of public attitudes to emigration from Poland was used. It focuses especially on latent Markov models with covariates, which additionally made it possible to investigate the dynamic pattern of Poles' attitudes to emigration for different demographic features. depmixS4, Rsolnp and LMest packages of R were used.

Implementing the OECD Anti-Bribery Convention

“…As in other countries, the digitalisation of financial markets and financial services brings a series of benefits to consumers, but also poses risks, which need to be taken into account and well managed. Concrete benefits include increased financial inclusion, convenient, fast, secure and timely financial transactions, tailored services and increased competition in the market (G20/OECD/INFE, 2018 [119]). At the same time, financial digitalisation can create new risks for consumers such as misuse of unfamiliar products by uninformed consumers; new types of fraud; security, privacy or confidentiality of data risks; inappropriate or excessive use of digital profiling to identify potential customers and exclude unwanted groups; rapid access to high-cost/short-term credit and other market practices that can reinforce behavioural biases or even financial exclusion (G20/OECD/INFE, 2018 [119]).…”

“…Concrete benefits include increased financial inclusion, convenient, fast, secure and timely financial transactions, tailored services and increased competition in the market (G20/OECD/INFE, 2018 [119]). At the same time, financial digitalisation can create new risks for consumers such as misuse of unfamiliar products by uninformed consumers; new types of fraud; security, privacy or confidentiality of data risks; inappropriate or excessive use of digital profiling to identify potential customers and exclude unwanted groups; rapid access to high-cost/short-term credit and other market practices that can reinforce behavioural biases or even financial exclusion (G20/OECD/INFE, 2018 [119]). These risks are especially relevant in a context of low digital literacy and for specific vulnerable populations (such as elderly populations or those with lower levels of education).…”