Multidimensional Poverty in Paraguay: Trends from 2000 to 2015


To meet Paraguay’s national development goals and the Sustainable Development Goals, policy makers require more information about poverty in the country. We propose a multidimensional poverty index (MPI) for Paraguay constructed using the Alkire–Foster dual-cutoff method for multidimensional poverty identification to complement existing national poverty measures based on income. Indicators, dimensions, weighting schemes, and cutoffs used in the Paraguayan MPI were determined based on national definitions of poverty and national and international development priorities. The MPI is estimated for the years 2000–2015 using national household surveys. From 2000 to 2015, the multidimensional poverty incidence in Paraguay declined by an average annualized rate of 9.2%, from 58% of the population in 2000 to 17% of the population in 2015. In 2015, 7% of the population is estimated to be living in multidimensional poverty, but not income poverty. This population would have remained invisible based on income poverty measures alone. This is the first MPI proposed for Paraguay that reflects the country’s national development priorities. The adoption of the MPI may assist policy makers in targeting previously invisible, vulnerable populations and assessing the impact of public policies on reaching the country’s development goals.

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  1. 1.

    See Lopez-Calva et al. (2015) and national statistics provided by the National Institute of Statistics (DGEEC).

  2. 2.

    Encuesta Permanente de Hogares (EPH).

  3. 3.

    Mexico was the first country to adopt an MPI. Information about their experience can be found in, and CONEVAL (Consejo Nacional de Evaluación de la Política de Desarrollo Social de México) (2010). See Angulo et al. (2016) for an MPI for Colombia, Ministerio de Desarrollo Social de Chile (2015) for an MPI for Chile, and INEC (2015) for the Costa Rican experience, among others.

  4. 4.

    This is followed by Ecuador and Bolivia with 36 and 31% of the population residing in rural areas, respectively.

  5. 5.

    Alternative approaches are rapidly growing. See Kakwani and Silber (2008), for a further discussion on dimensions of poverty.

  6. 6.

    Robust standard errors are calculated allowing for arbitrary forms of heteroskedasticity within a primary sampling unit.

  7. 7.

    Other potential sources of data are the “Encuesta Continua de Empleo” (ECE), 2002 and 2012 Censuses, and the “Encuesta de Ingresos y Gastos y Condiciones de Vida 2011/2012” (EIGyCV) (DGEEC 2010, 2011). The ECE is only representative of Asunción and urban Central, and provides limited data to monitor an adequate set of indicators of an individual’s experience of deprivations. The census would allow the exploration of the MPI at a very fine geographic level, but it too provides limited data to monitor an individual’s experience of deprivations. An additional shortcoming of censuses are that they are typically undertaken every 10 years and, thus, provide limited opportunities for continuous monitoring. The EIGyCV provides more information than the EPH that could be useful for the MPI, such as child’s health and nutrition, subjective well-being, trust in government institutions, and corruption, but it is also not suitable for monitoring multidimensional poverty, because it is only available for 1 year.

  8. 8.

    While the survey has been conducted annually, there have been some exceptions at the beginning of the survey. For example, the 1997 survey was undertaken between 1997 and 1998. Similarly, the 2000 survey was conducted between 2000 and 2001.

  9. 9.

    Judge and Schechter (2009) investigated data quality of several national household surveys, which included the EPH, and suggested data from the EPH are good quality.

  10. 10.

    This was the case for health insurance, sickness and accidents, television ownership, and internet access.

  11. 11.

    Table 5 in the appendix presents the share of missing values by indicator.

  12. 12.

    For reviews of multidimensional poverty in Latin America see Santos (2014), CEPAL (2013). For an MPI for the Latin America region see Santos et al. (2015). For global MPIs see Alkire and Santos (2010, 2014), UNDP (2010). For country specific MPIs see CONEVAL (Consejo Nacional de Evaluación de la Política de Desarrollo Social de México) (2010) for Mexico, Angulo et al. (2016) for Colombia, and Ministerio de Desarrollo Social de Chile (2015) for Chile. For more studies on multidimensional poverty, we recommend visiting the OPHI website at

  13. 13.

    Unfortunately the EPH does not provide information on work conditions for children younger than 10.

  14. 14.

    This formulation of \(\mathbf {R^{0}}\) was first introduced by Simpson (1943). For further information on this measure see Alkire (2012).

  15. 15.

    Recall from Sect. 3.1 that estimates of standard errors and confidence intervals incorporate survey design and are robust to arbitrary forms of heteroskedasticity that allow within cluster correlation.

  16. 16.

    According the Paraguayan Household Surveys, \(94\%\) of the employed population of San Pedro worked in informal sectors and obtained an average of 5.4 years of schooling. In Central, \(65\%\) of the employed population worked in informal sectors and obtained 8.3 years of schooling, on average.

  17. 17.

    The 2015 Paraguayan Household Survey is the first survey representative of 15 of the 17 departments in Paraguay, since the 2003 survey. Figure 14 in the appendix presents the MPI by the individual departments. This information could be used by policy makers to target departments where the MPI is highest.

  18. 18.

    The extreme poverty line is based on a food basket and only depends on food prices. All things being equal if food prices grow faster than the general CPI, the poverty line increases and incorporates more people into poverty. However, rural households may be net sellers or consumers of food products, making it difficult to determine if food prices will increase or decrease poverty. Lopez-Calva et al. (2015) present a decomposition of changes in extreme poverty and suggest rising food prices were contributing to poverty increases in Paraguay between 2003 and 2011.

  19. 19.

    A decomposition by individual deprivation indicators is found in Fig. 15  in the appendix.


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Correspondence to Paul A. Ervin.



See Tables 5, 6, 7 and Figs. 9, 10, 11, 12, 13, 14 and 15.

Table 5 Missing values of the pooled sample
Table 6 Means and standard deviations for all years
Table 7 Tetrachoric correlations matrix for pooled sample
Fig. 9

Multidimensional poverty intensity (A)

Fig. 10

Indicators’ deprivation headcount ratios and their 95% confidence intervals in the health, water, and sanitation dimension. a Uncensored water source deprivation indicator, b censored water source deprivation indicator, c uncensored water supply deprivation indicator, d censored water supply deprivation indicator, e uncensored sanitation deprivation indicator, f censored sanitation deprivation indicator, g uncensored kitchen and cooking fuel deprivation indicator, h censored kitchen and cooking fuel deprivation indicator, i uncensored healthcare deprivation indicator, j censored healthcare deprivation indicator

Fig. 11

Indicators’ deprivation headcount ratios and their 95% confidence intervals in the housing and basic goods and services dimension. a Uncensored housing materials deprivation indicator, b censored housing materials deprivation indicator, c uncensored people per room deprivation indicator, d censored people per room deprivation indicator, e uncensored durable goods deprivation indicator, f censored durable goods deprivation indicator, g uncensored electricity deprivation indicator, h censored electricity deprivation indicator, i uncensored telephone deprivation indicator, j censored telephone deprivation indicator, k uncensored access to information deprivation indicator, l censored access to information deprivation indicator

Fig. 12

Indicators’ deprivation headcount ratios and their 95% confidence intervals in the education dimension. a Uncensored delayed education deprivation indicator, b censored delayed education deprivation indicator, c uncensored literacy deprivation indicator, d censored literacy deprivation indicator, e uncensored early dropout deprivation indicator, f censored early dropout deprivation indicator

Fig. 13

Indicators’ deprivation headcount ratios and their 95% confidence intervals in the employment dimension. a Uncensored under- or unemployed deprivation indicator, b censored under- or unemployed deprivation indicator, c uncensored salary deprivation indicator, d censored salary deprivation indicator, e Uncensored child labor deprivation indicator, f censored child labor deprivation indicator, g uncensored work or study deprivation indicator, h censored work or study deprivation indicator

Fig. 14

MPI by Departments 2015. a MPI by department (2015), b map of MPI by department (2015)

Fig. 15

Contribution of indicator to the multidimensional adjusted (MPI) poverty headcount ratio. Note: 2002 and 2011 due to missing data. The 2000 survey was administered in October 2000–February 2001

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Ervin, P.A., Gayoso de Ervin, L., Molinas Vega, J.R. et al. Multidimensional Poverty in Paraguay: Trends from 2000 to 2015. Soc Indic Res 140, 1035–1076 (2018).

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  • Paraguay
  • Multidimensional poverty index
  • Alkire–Foster method
  • Poverty
  • Latin America

JEL Classification

  • I32
  • D63