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Two Stories of Wage Dynamics in Latin America: Different Policies, Different Outcomes

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Abstract

This article explores the variation in the wage distributions of two Latin American countries, Bolivia and Colombia, which have had different political and economic strategies in recent years. Using data from household surveys, a decomposition of the wage distribution in each country using functional principal component analysis is conducted. The results suggest that Bolivia, which has implemented state-centered policies, has experienced a general increase in wages, especially benefiting the least skilled workers and the informal sector. On the other hand, the wage distribution in Colombia, whose economic policy has leaned towards market-oriented principles, has become more concentrated around the median wage, mainly due to changes in formal sector wages.

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Notes

  1. Fortin et al. (2010) provide an extensive overview of several decomposition methodologies applied to wage distribution and inequality.

  2. The application section of Kneip and Utikal (2001) deals with the distribution of household income in the United Kingdom. Their approach is different from ours since they consider aggregate household income, regardless of its source (e.g., capital gains, unemployment subsidies, transfers...) while we focus on labor remuneration alone at the individual level.

  3. Both figures are in 2011 constant international dollars (PPP)

  4. The Latin American average unemployment rate for the period 2006-2015 was 7.5% (ILO 2017).

  5. In 2015, the average rate of informality in Latin America was 46.8% (ILO 2016).

  6. Colombia has free trade agreements with the United States, Mexico, the European Union, South Korea, Chile and the Andean Community of Nations (Bolivia, Ecuador and Peru), among others.

  7. Data for 2010 are missing because the INE did not conduct this household survey during that year.

  8. Although there are household surveys from Colombia for years prior to 2008, we do not use them because of methodological changes between the previous survey, the Encuesta Continua de Hogares (ECH), and the Gran Encuesta Integrada de Hogares (GEIH), which is the current format since 2008.

  9. In Appendix A, we calculate the amount of variation in monthly income attributable to the number of hours worked and to the hourly wage following Acemoglu et al. (2001). We find that by using hourly wages we can account for most of the variation in monthly labor income in both Bolivia and Colombia.

  10. See Hussmanns (2004) for a discussion of the definitions of informal job and informal sector.

  11. In Appendix B, we present statistics on the composition of the labor force by economic activity and the prevalence of informality by economic activity.

  12. See Chu et al. (2018) for further details on the nonparametric kernel density estimation under complex survey designs.

  13. This is equivalent to using a discrete univariate kernel function as in Aitchison and Aitken (1976)

    $$ l(x^{d_{s}},X^{d_{s}}_{it},\upsilon)=\left\{\begin{array}{ll}1-\upsilon& \text{if} X^{d_{s}}_{it}=x^{d_{s}}\\ \upsilon/(c_{s}-1)& \text{if} X^{d_{s}}_{it}=x^{d_{s}}\\ \end{array}\right., $$

    and setting υ = 0

  14. The bandwidths used for estimations are presented in Appendix C.

  15. Throughout this section, we omit the function arguments of \(\widehat {g}_{r}(X,X^{d})\) to simplify the notation.

  16. In Appendix D, we present the share of variation by category for the second and third component. When reading those results, it should be noted that those components account for a lower proportion of the total variation than the first component presented in the main body of the article.

  17. Due to the repeated cross-sectional structure of our data, we are unable to test the effect of firm heterogeneity as in Abowd et al. (1999).

  18. Canavire-Bacarreza and Rios-Avila (2017) and García-Suaza et al. (2014) explore the higher education wage premium in Bolivia and Colombia respectively.

  19. When using the second and third component, informal workers in Colombia have a higher share than formal workers. However, as the dynamic scree plot suggests, the combined share of total variation that can be explained by these two components is lower than the first component we are analyzing here.

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Acknowledgments

We would like to thank Maria Aristizábal-Ramirez, Juan Camilo Chaparro, David T. Jacho-Chávez, Fernando Rios-Avila, the editor, the anonymous referees, and the participants of the “Qué Investigas?” seminar at Universidad EAFIT and the 22nd Annual LACEA Meeting for their helpful comments and suggestions. We would also like to thank David T. Jacho-Chávez for sharing his R code with us, and the APOLO scientific computing team at Universidad EAFIT for their technical support for the estimations presented in this article.

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Appendices

Appendix A: Variation of Monthly Labor Income

In this appendix, we calculate the variation of monthly labor income that is attributable to changes in labor supply and to changes in hourly wages. In the spirit of Acemoglu et al. (2001), we compute an OLS regression of monthly labor income as a function of hourly wages and hours worked (joint explanation) and two OLS regressions in which we account for each factor separately. Tables 8 and 9 presents the R2 of these regressions.

Table 8 Variation of monthly labor income explained jointly by hours worked and hourly wages (Column (1)) and Variation of monthly labor income explained separately by hours worked and hourly wages (Columns (2) and (3) respectively) - Bolivia
Table 9 Variation of monthly labor income explained jointly by hours worked and hourly wages (Column (1)) and Variation of monthly labor income explained separately by hours worked and hourly wages (Columns (2) and (3) respectively) - Colombia

Appendix B: Additional Labor Market Statistics

B.1 Hours Worked

In 2006, the average Bolivian worked for 49.2 hours a week. Between 2006 and 2009, the number of hours worked diminished, but then it rose again in 2011 and 2012. In the last years of our period of study, the average number of hours worked remained fairly stable around 48 hours (the maximum allowed by the law) (UDAPE 2017).

In Colombia, the average number of weekly hours worked in 2008 was 47.3. Between 2008 and 2015, the average number of hours worked has decreased, reaching 45 hours in 2015 (Organisation for Economic Co-Operation and Development 2019). In spite of the reduction in the last years, the number of weekly hours worked in both Bolivia and Colombia is considerably larger than the average number of hours worked a week in OECD countries (37.6 in 2015) (Organisation for Economic Co-Operation and Development 2019).

Fig. 15
figure 15

Average Weekly Hours Worked: Bolivia (2006-2015) and Colombia (2008-2015). Sources: Bolivia UDAPE (2017); Colombia Organisation for Economic Co-Operation and Development (2019)

B.2 Urban Labor Force by Economic Activity

Figure 16 depicts the composition of the urban labor force by economic industry in both Bolivia and Colombia at the start and at the end of our period of study. The national statistics institutes of both countries use adaptations of the ISIC classification (UN, 2002) to define economic activity. Using this division, we define four broad sectors: Agriculture and Mining, Manufacturing, Retail and Service.

Fig. 16
figure 16

Labor force by economic activity for Bolivia (top) and Colombia (bottom). The categories are defined as follows: Agriculture includes mining, Manufacturing includes construction and electricity, water and natural gas supply. Retail includes Hotels and Restaurants, and Services includes transportation, communication, real estate, social services, financial services, business activities and public administration. Sources: Bolivia: INE (2017); Colombia: DANE (2017)

From Fig. 16, we see that the composition of the urban labor market by industry is fairly similar and has not varied much during our period of study. Approximately 40% of the labor force is in the service sector, followed by retail and manufacturing, with approximately 30% and 25% of the labor force working in these industries respectively.

B.3 Urban Informality by Economic Activity

In this subsection, we restrict our analysis to the urban informal sector. In Fig. 17 we present the composition of the informal labor force by industry in 2015, the last year of our sample. When considering only the informal sector, retail becomes the most common economic activity, whereas services, the most common economic activity for the full urban labor market, is the second most common industry for informal workers.

Fig. 17
figure 17

Informal labor force by economic activity for Bolivia (top) and Colombia (bottom) in 2015. The categories are defined as follows: Agriculture includes mining, Manufacturing includes construction and electricity, water and natural gas supply. Retail includes Hotels and Restaurants, and Services includes transportation, communication, real estate, social services, financial services, business activities and public administration. Sources: Bolivia: Own calculations based on INE (2017); Colombia: DANE (2017)

We also calculate the proportion of informal workers by industry in Fig. 18. More than two-thirds of the workers in the retail sector in both Bolivia and Colombia are informal workers and in both countries, the service sector has the lowest incidence of informality. Nevertheless, informality accounts for more than 35% of the workers in that sector.

Fig. 18
figure 18

Share of informal labor by economic activity for Bolivia (top) and Colombia (bottom). The categories are defined as follows: Agriculture includes mining, Manufacturing includes construction and electricity, water and natural gas supply. Retail includes Hotels and Restaurants, and Services includes transportation, communication, real estate, social services, financial services, business activities and public administration. Sources: Bolivia: Own calculations based on INE (2017); Colombia: Own calculations based on DANE (2017)

Appendix C: Bandwidth selection

Table 10 Bandwidths used in the estimations for Bolivia. ln(w): logarithm of real hourly wage, in 2010 USD; Educ: Educational attainment; Sec: Economic Sector
Table 11 Bandwidths used in the estimations for Colombia. ln(w): logarithm of real hourly wage, in 2010 USD; Educ: Educational attainment; Sec: Economic Sector

Appendix D: Share of Variation by Category - Second and Third Components

Table 12 Share of variation accounted for by each category of the discrete variables - Second Component
Table 13 Share of variation accounted for by each category of the discrete variables - Third Component

Appendix E: Yearly Deformations

Fig. 19
figure 19

Total deformation \(\tilde {g}_{r}(\widehat {\theta }_{t;r}-\widehat {\theta }_{1;r})\) for the first component in different years - Bolivia (top) and Colombia (bottom)

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Canavire-Bacarreza, G., Carvajal-Osorio, L.C. Two Stories of Wage Dynamics in Latin America: Different Policies, Different Outcomes. J Labor Res 41, 128–168 (2020). https://doi.org/10.1007/s12122-019-09296-x

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