Abstract
Working poverty is becoming an increasingly common phenomenon. Prior quantitative research has painted a representative but narrow picture of the contours of in-work poverty, while ethnographic case studies have provided a nuanced account of the mechanisms shaping the experiences of workers in specific low-wage labor markets. However, none of these studies provides an account that, at the same time, covers the main theories explaining working poverty, is based on a representative population sample, and is attuned to the proximate interactional dynamics shaping poverty risks. The current study employs a concurrent mixed-methods design combining information from repeated cross-sectional nationally representative surveys with in-depth interviews, and argues that this design is ideal for explaining the factors shaping the likelihood of in-work poverty.
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Source: IIS, 1991–2015. Note: Models for all five periods control for the following variables (with the category used for calculating the probabilities listed in parenthesis): household structure (couples with 1–2 children = 1), minority status (Mizrachi = 1), age group (55–65 = 1), the number of earners in the household (= mean), education (post-secondary non-academic = 1), occupation (craft = 1), industry (agriculture = 1) and self-employment status of household head (= 0)
Source: IIS, 1991–2015. Note: Models for all five periods control for the following variables (with the category used for calculating the probabilities listed in parenthesis): age group (55–65 = 1), the number of earners in the household (= mean), minority status (Mizrachi = 1), education (post-secondary non-academic = 1), occupation (craft = 1), industry (agriculture = 1) and self-employment status of household head (= 0)
Source: IIS, 1991–2015. Note: Models for all five periods control for the following variables (with the category used for calculating the probabilities listed in parenthesis): household structure (couples with 1–2 children = 1), age group (55–65 = 1), minority status (Mizrachi = 1), education (post-secondary non-academic = 1), occupation (craft = 1), industry (agriculture = 1) and self-employment status of household head (= 0)
Source: IIS, 1991–2015
Source: IIS, 1991–2015. Note: Models for all five periods control for the following variables (with the category used for calculating the probabilities listed in parenthesis): household composition (couples with 1–2 children = 1), age group (55–65 = 1), the number of earners in the household (= mean), education (post-secondary non-academic = 1), occupation (craft = 1), industry (agriculture = 1) and self-employment status of household head (= 0)
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Availability of data and materials
Data from the Israeli Income Survey is publically available at: https://www.cbs.gov.il
Notes
Nevertheless, there is a large variety in poverty rates which we do not discuss in this paper among immigrant groups from different countries of origin.
We also documented age by distinguishing between households the age of whose head was 18–34, 35–54, and 54–65.
We also included information about self-employment status (1 = at least one adult person in the household is self-employed) and occupation in the models. Occupation was defined on the basis of single-digit ISCO88, distinguishing between managers, professionals, associate professionals/technicians, clerks, salespersons, agricultural workers and craftsmen (including operators), which left elementary/non-skilled manual laborers as the reference category. We also included a dummy variable indicating missing information on occupation.
For both industry and ethnicity, assignment of household head status was based on labor supply (i.e., the person with the highest number of working hours in the household), and subsequently (in cases of identical hours of work) on level of salary. In the rare instances where both members of a couple had identical hours of work and salary we randomly assigned headship status. Because self-employed workers’ hours of work and income are not reported in the Israeli surveys, in households where there was at least one self-employed worker, we designated household head status differently. In such cases, we designated the household head according to relation to household head variable.
The quantitative analysis also documented differences by occupation, with laborers and workers in sales occupations facing a higher likelihood of poverty than those in other occupations.
Figure 4 was produced by using IIS data for the same period as all other figures (i.e., 1991–2015). Because the goal of the figure is to illustrate on the baseline predicted level of working poverty during the entire period, without a reference to group difference, the estimation was based on yearly logistic models containing no predictors.
One should not conclude that there is no discrimination among the Jewish population based on ethnicity. However, such discrimination is manifested in higher level jobs, rather than in those held by the low-income participants in this research.
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Research presented in this paper was supported by funding received from the German-Israeli Foundation for Scientific Research and Development (Grant # G-1221–374.4).
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Appendix
Appendix
Odds ratios (with standard errors in parentheses) from logistic models predicting the likelihood of in-work poverty among households in Israel, 1991–2015.
1991–1995 | 1996–2000 | 2001–2005 | 2006–2012 | 2013–2015 | |
|---|---|---|---|---|---|
Education (reference = Secondary education or less) | |||||
Post-secondary non-academic | 0.85 (0.08) | 0.79*** (0.05) | 0.80*** (0.04) | 0.94 (0.04) | 0.94 (0.07) |
Post-secondary academic | 1.07 (0.09) | 0.77*** (0.05) | 0.60*** (0.03) | 0.62*** (0.03) | 0.63*** (0.06) |
Household structure (reference = single headed households) | |||||
Singles | 0.95 (0.10) | 0.74*** (0.06) | 0.48*** (0.03) | 0.34*** (0.02) | 0.36*** (0.04) |
Childless couples | 0.95 (0.13) | 0.88 (0.09) | 0.69*** (0.05) | 0.43*** (0.03) | 0.42*** (0.05) |
Couples with 1–2 children | 1.52*** (0.15) | 1.39*** (0.11) | 1.07 (0.06) | 0.85*** (0.04) | 0.79* (0.08) |
Couples with 3 + children | 2.01*** (0.21) | 1.96*** (0.16) | 1.84*** (0.11) | 2.05*** (0.10) | 1.83*** (0.17) |
Number of earners | 0.10*** (0.01) | 0.11*** (0.01) | 0.12*** (0.01) | 0.12*** 0.00 | 0.15*** (0.01) |
Minority (reference = Israeli Arabs) | |||||
Mizrachim | 0.50*** (0.04) | 0.46*** (0.03) | 0.40*** (0.02) | 0.31*** (0.01) | 0.28*** (0.03) |
Ashkenazim | 0.40*** (0.04) | 0.34*** (0.03) | 0.34*** (0.02) | 0.30*** (0.02) | 0.30*** (0.03) |
Israeli born | 0.54*** (0.07) | 0.64*** (0.05) | 0.58*** (0.04) | 0.42*** (0.02) | 0.35*** (0.03) |
Recent immigrants | 0.79* (0.08) | 0.58*** (0.04) | 0.50*** (0.03) | 0.36*** (0.02) | 0.31*** (0.03) |
Age (reference = 18–34) | |||||
Ages 55–65 | 0.37*** (0.05) | 0.51*** (0.05) | 0.52*** (0.04) | 0.56*** (0.03) | 0.35*** (0.04) |
Ages 35–54 | 0.63*** (0.04) | 0.56*** (0.03) | 0.65*** (0.02) | (0.02) 0.56*** | 0.46*** (0.03) |
Self-employed | 1.84* (0.49) | 0.9 (0.06) | 1.24*** (0.06) | 1.04 (0.05) | 1.23* (0.10) |
Occupation (references = unskilled laborers) | |||||
Managers | 0.03*** (0.01) | 0.05*** (0.01) | 0.08*** (0.01) | 0.07*** (0.01) | 0.09*** (0.02) |
Professionals | 0.12*** (0.019) | 0.16*** (0.02) | 0.20*** (0.02) | 0.16*** (0.01) | 0.18*** (0.03) |
Associate professionals | 0.21*** (0.02) | 0.33*** (0.03) | 0.34*** (0.02) | 0.32*** (0.02) | 0.21*** (0.03) |
Clerical | 0.20*** (0.02) | 0.32*** (0.03) | 0.33*** (0.02) | 0.32*** (0.02) | 0.40*** (0.06) |
Service and sales | 0.62*** (0.06) | 0.65*** (0.05) | 0.68*** (0.04) | 0.65*** (0.04) | 0.59*** (0.08) |
Skilled agricultural | 0.90 (0.23) | 0.70 (0.18) | 0.81 (0.17) | 0.61** (0.11) | 0.64 (0.22) |
Craft | 0.36*** (0.03) | 0.45*** (0.03) | 0.48*** (0.03) | 0.45*** (0.03) | 0.42*** (0.06) |
Missing | 0.08*** (0.03) | 0.10*** (0.06) | 0.08*** (0.02) | 0.15*** (0.02) | 0.52*** (0.06) |
Industry (reference = other) | |||||
Manufacturing | 0.67 (0.15) | 0.86 (0.20) | 0.84 (0.16) | 0.85 (0.13) | 0.74 (0.09) |
Service | 0.74 (0.16) | 0.98 (0.22) | 1.02 (0.18) | 1.07 (0.17) | 0.95 (0.25) |
Other | 3.01* (0.16) | 0.89 (0.22) | 3.86*** (0.19) | 0.43 (0.17) | 0.95 (0.32) |
N | 18,681 | 40,773 | 48,309 | 66,747 | 18,680 |
Pseudo R2 | 0.25 | 0.249 | 0.265 | 0.31 | 0.297 |
BIC | 1,877,118 | 2,875,754 | 3,601,590 | 5,884,533 | 2,791,187 |
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Levanon, A., Lavee, E. & Strier, R. Explaining the Factors Shaping the Likelihood of Poverty Among Working Families by Using a Concurrent Mixed Method Design. Soc Indic Res 157, 1089–1109 (2021). https://doi.org/10.1007/s11205-021-02689-5
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DOI: https://doi.org/10.1007/s11205-021-02689-5