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Can Social Capital Affect Subjective Poverty in Europe? An Empirical Analysis Based on a Generalized Ordered Logit Model

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Abstract

In a previous exploratory analysis of the 2009 EU-SILC survey and the Eurostat statistics database, the authors tried to reveal to what extent self-perceived poverty in Europe is associated with specific household socioeconomic characteristics and particular aspects of household/community social capital endowment, by means of a multiple correspondence analysis. Such an analysis has appeared to be a useful tool to disclose the primary risk factors of family poverty status and, in particular, it showed that self-perceived poverty (measured by the proxy variable “ability to make ends meet”) is strongly associated not only with household socioeconomic characteristics, but also with the indicators commonly recognized as elementary proxies of household/community social capital endowment. The aim of the present paper is to capture the effect of social capital on household subjective poverty. More precisely, a generalized ordered logit model is estimated, in order to highlight to what extent: (a) self-perception of poverty in Europe is affected by the respondent/household socioeconomic characteristics and by household/community social capital endowment; (b) probabilities corresponding to response categories vary according to different levels of predictors; (c) differences among European countries in terms of self-perception of poverty may be related to different levels of social capital endowment. The results are very encouraging and confirm that social capital could be used by local and central governments as a further key function, in addition to the traditional socioeconomic ones for planning poverty reduction policies.

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Notes

  1. Here the focus is on subjective poverty rather than on the wider concept of happiness, which extends beyond pure economic factors and which according to Diener et al. (1999) is a broad category of phenomena that includes people’s emotional responses, domain satisfactions and global judgments of life satisfaction. The term “economics of happiness” is used to refer to studies on aspects of life satisfaction and on their links with different domains of life, including social capital (Diener et al. 1985; Pradhan and Ravaillon 2000; Mc Bride 2001; Frey and Stutzer 2002; Van Praag et al. 2003; Yip et al. 2007; Dolan et al. 2008; Pedersen and Schmidt 2011; Rodriguez-Pose and von Berlepsh 2014).

  2. EU-SILC is the Eurostat project on Income and Living Conditions which involves all the European Union state members. It provides two types of data, cross-sectional and longitudinal over a four- year period (EU-SILC uses a 4-years rotational design).

  3. Some of these variables are not statistically significant and/or have too many missing values and thus they have not been included in the generalized ordered logit models discussed in Sect. 3. They are: low work intensity status, branch of activity, risk of poverty, health, reasons for changing dwelling, work intensity status, alimonies received.

  4. Despite some shortcomings mainly due to the impossibility of measuring all components of social capital, EU-SILC cross-sectional survey and the Eurostat statistic database represent together an important reference source for comparative studies aiming at measuring the effect of social capital on household economic well-being, especially because they provide comparable and high quality cross-sectional indicators for all EU member states. Therefore the EU-SILC survey and the Eurostat statistic database represent an irreplaceable decision-making tool to assess suitable policies aiming at poverty reduction in Europe.

  5. Family workers are persons who help another member of the family to run an agricultural holding or other business, provided they are not considered as employees.

  6. In order to verify this assumption the autofit option of the gologit2 procedure of Stata software has been employed (Williams 2006). This procedure does a series of Wald tests on each variable to see whether its coefficients differ across equations.

  7. The model reduces to the ordinal logit one when the beta coefficients are the same for each j category and the so called proportional odds assumption is satisfied.

  8. The only exception occurs for the class 65–79 of the variable Age; nevertheless we decided to hold this class distinct from the last one, the unique with a positive effect on the odds.

  9. As a matter of fact, it is worth noting that if the dependent variable had only three response categories (with great difficulty or with difficulty; with some difficulty or fairly easily; easily or very easily), the overall percentage of correctly predicted values increases of almost 45–50 % compared to the model with six responses. However, as in such a reduced scale important details are lost, we decided to keep the original six-point scale.

  10. Note that the scale of the vertical axis is showed on the left for the blue lines and on the right for the red ones.

  11. A possible explanation is that among the proxies variables used to form the Social Behavior index there are those referred to rate of crime and perceived violence and vandalism, which in a previous analysis (Guagnano et al. 2013) showed opposite associations with subjective poverty.

  12. The only predictors with invariable effects are: classes 25–29, 60–64 and 65–79 of the variable Age; the categories Medium educational qualification, Payment of a rent for accommodation and all the categories of the following variables: Marital status; Employment status (excepted for inactive); Household type (from the fifth categories until the second last).

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Acknowledgments

The present work has been developed within the research “Perception of poverty. Individual, household and social environmental determinants” led by Isabella Santini at SAPIENZA University of Rome, partially supported by 2010 Italian M.I.U.R. grants (Prot. C26A10WW49).

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  1. Department of Methods and Models for Economics, Territory and Finance, SAPIENZA University of Rome, Via del Castro Laurenziano, 9, 00161, Rome, Italy

    Giuseppina Guagnano, Elisabetta Santarelli & Isabella Santini

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  1. Giuseppina Guagnano
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Correspondence to Giuseppina Guagnano.

Appendices

Appendix 1

See Table 2.

Table 2 Respondent and household socio-economic characteristics (Source: EU-SILC 2009)
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Appendix 2

See Table 3.

Table 3 Social capital indicators
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Appendix 3

See Table 4.

Table 4 Parameters estimates from partial proportional ordered logit model comparing probabilities for Ability to make ends meet to be beyond a given category j versus to be at or below category j
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Guagnano, G., Santarelli, E. & Santini, I. Can Social Capital Affect Subjective Poverty in Europe? An Empirical Analysis Based on a Generalized Ordered Logit Model. Soc Indic Res 128, 881–907 (2016). https://doi.org/10.1007/s11205-015-1061-z

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