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Social Indicators Research

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22.05.2023 | Original Research

Working Poverty in Türkiye: A Dynamic Panel Analysis

verfasst von: Nursel Aydiner-Avsar, M. Burak Onemli

Erschienen in: Social Indicators Research | Ausgabe 1-3/2023

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Abstract

Employment is a key instrument to tackle poverty. However, working poverty continues to be a major issue and has caught significant attention especially in developed countries. This issue is of primary relevance also for middle-income developing countries such as Türkiye, but it has not yet been sufficiently investigated. This paper examines working poverty in Türkiye based on a dynamic analysis framework, using the 2007–2017 panel waves of the Survey of Income and Living Conditions. Specifically, it estimates the determinants of working poverty while controlling for state dependence and unobservable heterogeneity using the dynamic random effects probit model. Empirical findings show that the dynamic effects are both statistically and economically significant. This implies that working poverty is associated with a significant degree of state dependence in Türkiye. Hence, there is a need for direct policies to lift the working poor out of poverty. Among the individual, household and employment characteristics, education emerges as a key determinant of working poverty, supporting the predictions of the human capital theory. Additionally, in line with the predictions of the segmentation hypothesis, occupation, industry, firm size, and formal job status are all significantly associated with the probability of being working poor in Türkiye. With respect to household characteristics, the number of children in the household is positively related to the likelihood of working poverty, reflecting the burden on household needs. In contrast, having a retired person and increased work intensity at the household level help reduce the risk of being working poor, most likely through the income pooling effect.

Vorheriger Artikel Social Mobility from a Gender Perspective: Dynamics of Mothers’ Roles in Daughters’ Labor Market Performance

Nächster Artikel Exploring Social Capital Level in Regions with Large and Increasing Wealth Inequality: Lesson from Seoul, South Korea

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Based on the ILOStat database (accessed on 7 December 2022).

The share of employed individuals in extreme poverty was 26 percent in 2000 (ILO, 2019).

The term “working poor” is more common in the US, “in-work poverty” is mostly used in Europe. We use the term “working poverty” interchangeably with these two other alternative concepts throughout the paper.

Based on the ILOStat database (accessed on 7 December 2022).

Besides these explanations in the economics literature, there are also sociological explanations for working poverty. For example, cultural poverty theories in the sociology literature claim that poor people psychologically are not geared to take advantage of opportunities that they come across, leading to persistence of poverty across generations (Duncan, 1984). In a similar vein, individuals tend to marry people with similar socio-economic backgrounds leading to persistence in poverty (Crettaz, 2011).

See Crettaz (2013) and Lohmann and Marx (2018) for a comprehensive review of studies on working poverty.

Risk groups that are often mentioned but not systematically are ethnic minorities, people with fixed-term contracts or temporary jobs, those living in rural areas, those working in agriculture, and those disabled or having health issues. Risk groups for which conclusions change from country to country are young people, women, and self-employed workers (Crettaz, 2013).

We include studies on both working poverty and low pay while presenting the findings on developing countries as very few studies examine working poverty.

We discuss the most relevant studies, for our analysis, on the incidence of poverty since a comprehensive review of the poverty literature on Turkey is beyond the scope of this paper.

Vulnerability is defined as the risk that the expected income/consumption of the household will be below a certain threshold.

Appendix 1 presents the descriptive statistics for key explanatory variables employed in the empirical analysis. We do not discuss them here for the sake of brevity.

See Lohmann and Crettaz (2018) for an overview of alternative definitions of working poverty.

A household is considered income poor if the annual household equivalent income is below 40, 50, 60 or 70 percent of median household equivalent income in a given year. Defining poverty based on expenditure is not possible in our case as our data set does not provide expenditure data.

Our working poverty series ends in 2017. Overall poverty rate based on the 60 percent line was 21.3 percent in 2020 (Based on the latest TURKSTAT poverty news bulletin available at https://data.tuik.gov.tr/Bulten/Index?p=Gelir-ve-Yasam-Kosullari-Arastirmasi-2021-45581).

The primary reason for employing random effect models is to control and assess the effects of non-varying variables over time. Secondly, in general, the random effects approach is preferable in the micro panels since these panels involve a sample with numerous individuals randomly drawn from a vast population, and there is no interest in estimating the individual effects. On the other hand, in macro panels where the sample is fixed, estimating the individual effects may be interesting using the fixed effects approach. We choose to use the random effects model since we have a micro panel.

In a dynamic model, \({y}_{it}\) depends on its past values. In the presence of unobserved effects \(({\mu }_{i}\)), the process of the response variables needs to be initialized to account for how \({y}_{i}\) relates to the process before the observations are started. Then, the joint probability of \({y}_{i}\) conditional on \({X}_{i}\) can be reduced to a form that includes the joint probability of \({y}_{it}\) conditional on \({X}_{i}\),, \({y}_{it-1}\), the time-unvarying unobserved individual effects (\({\mu }_{i})\) and the vector of coefficients (\(\varphi )\). Under the given standard assumptions regarding error term and its components, the parameters are estimated from the likelihood function by the maximum likelihood technique. The likelihood function is integral over unobserved effects, and since these effects are assumed to be normally distributed, the integral over \({\mu }_{i}\) can be evaluated by the Gauss-Hermite quadrature. See Lucchetti and Pigini (2017) for further details.

GHK, proposed by Geweke (1989), Hajivassiliou and McFadden (1998), Keane (1994), is a simulation procedure for choice and transition probabilities in the limited dependent panel data models.

For example, Miranda (2007) finds that the Wooldridge method delivers estimators that can be subject to substantial bias and low precision, and the bias does not seem to decrease as number of individuals in panels get larger. Moreover, Akay (2009) suggests that the Wooldridge method works very well for panels with moderately long durations (longer than 5 -8 periods). However, in short panels (shorter than 5 periods), Hackman’s method gives more reliable results.

We also repeated our estimation by focusing only on full-time workers as the core of the labour market. Our findings are qualitatively the same. These results are not presented in the paper for the sake of brevity but are available upon request.

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Titel: Working Poverty in Türkiye: A Dynamic Panel Analysis
verfasst von: Nursel Aydiner-Avsar
M. Burak Onemli
Publikationsdatum: 22.05.2023
Verlag: Springer Netherlands
Erschienen in: Social Indicators Research / Ausgabe 1-3/2023
Print ISSN: 0303-8300
Elektronische ISSN: 1573-0921
DOI: https://doi.org/10.1007/s11205-023-03127-4

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