Abstract
This chapter explains the data source, the annual encuesta permanente de hogares, study time frame, and multivariate statistical model used to answer our research question concerning the determinants of economic informality. We utilize ten measures across three areas (social benefits, organizational practices, and firm characteristics) to proxy informality (the dependent variable) in Paraguay. We explain the use of nearly two dozen independent variables and provide descriptive statistics for both sets of variables (dependent and independent). The chapter ends with a presentation of our conceptual model for exploring the determinants of informality in Paraguay.
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Notes
- 1.
There may be differences in the data contained in the EPH 2017 available on the DGEEC website from our own data. This difference reflects the inclusion of Boquerón and Alto Paraguay in our data set which differs from that available on the website. The exclusion of these two departments on the website allows for comparison of previous years where these departments were omitted. Since our analysis is cross-sectional, we include all of the departments, and hence all available data, in our study.
- 2.
Income earners between 10 and 12 years of age comprise 0.1% of respondents in the analyses. Income earners between 13 and 14 years of age make up 0.6% of the respondents. While very small, we include these income earners in the analyses because the DGEEC recommends inclusion reflecting the labor reality on the ground; and in emerging markets with large informal sectors, youth are often included in the analyses because they may be economically active and important household contributors (see Pisani and Pagán 2004 for a labor study example that include those aged 12 and older that earned incomes in Nicaragua in their study of labor informality). The official work age in Paraguay for most employment is 18 years of age; this age limit, however, is rarely enforced.
- 3.
Logistic regression does not require meeting the primary statistical assumptions regarding linearity, normality, homoscedasticity, and measurement level (Pampel 2000).
- 4.
Nominal variables are categorical, such as male and female. Ordinal variables are ordered without a clear distinction of the measure in between, such as satisfied, somewhat satisfied, or very satisfied. Interval variables are ordered and measurable, such as the temperature outside. Ratio variables are ordered and possess an absolute zero, such as height and weight of a person.
- 5.
Very few (2.4%) respondents who received paid vacation days had fewer than ten.
- 6.
Tax rates for the PIT are subject to minimum income levels based upon the monthly minimum wage. The PIT tax rates are 0% (for annual income below 10 monthly minimum wages), 8% (for annual income between 10 and 120 minimum monthly wages), and 10% (for annual income above 120 minimum monthly wages) (Richter 2018). In 2019, the minimum monthly wage for most employment is 2,112,562 Guaraníes or about $350 US dollars.
- 7.
In Paraguay, the 71.8% of the self-employed are own account employees. 28.1% of self-employed employ two to five workers. Combined, nearly every self-employed respondent is part and parcel of a microenterprise.
- 8.
As age, the number of years old, is embedded in this variable of potential experience; age as a separate variable is not used in our analyses by convention (to avoid multicollinearity issues).
- 9.
We plan to conduct a comprehensive investigation into domestic servants in Paraguay at a later date.
- 10.
As such, professional as a variable is not included in the logistic regression analyses. Nonetheless, the professional category was grouped from high level government functionaries in the executive, legislative, and judicial branches, managers in the public and private sectors, scientists and intellectuals, and mid-level technicians and professionals. All others were grouped as non-professionals (office employees, service workers, food vendors, retailers, agricultural workers, artisans, machinists, unskilled workers, and so on).
- 11.
This variable is not included in the logistic regression analyses because it is captured in the log of earned income (in order to avoid issues of multicollinearity).
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Pisani, M.J., Ovando Rivarola, F.G. (2019). Informality Measures and Models. In: Understanding the Determinants of Economic Informality in Paraguay. Palgrave Pivot, Cham. https://doi.org/10.1007/978-3-030-24393-7_3
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