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Minimum Wage and Ethnic-Gaps: Who are the Winners?

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Abstract

This paper attempts to answer whether minimum wage increases help to close ethnic-gap in Peru. To answer this question, I study the period 2004–2019 and estimate the effects of minimum wage increases based on nominal minimum wage, real minimum wage, and Kaitz index terms. Also, I use several econometric strategies, but mainly I rely the results on IV-OLS and IV-Probit. I find that the minimum wage has generally benefited Whites, Mestizos, and Andeans, but especially the latter, which has boosted the closing of ethnic-gap. Minimum Wage increases has benefited mainly the Andean people in the lowest income bracket, that is, on the most vulnerable population or dependent on changes in the minimum wage. However, I find also that the minimum wage also has benefited another specific group, the high-skilled mestizos, which indicates that while the gap is reduced on the lower side, it opens on the higher side. Finally, when I analyze the probability of being employed, having informal employment, and having stable employment, I find small and non-significant impacts in most of the estimates.

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Data Availability

Data availability on request.

Notes

  1. 539.4 is 140.48 current dollars, and 706 soles are 183.86 current dollars.

  2. 930 soles are 242.20 current dollars.

  3. 706 soles are 183.86 current dollars.

  4. The acronym of National Survey of Household I ENAHO due to its name in Spanish as Encuesta Nacional de Hogares.

  5. Identification by ethnic group is more complex than in other countries with well-marked ethnic groups, for example, the United States. Fernández and Fogli (2009) use the countries of origin of their ancestors as a proxy for ethnicity.

  6. The informality variable is presented in the ENAHO, a categorical variable that takes the value of 1 if the worker is informal and 0 if he/she is formal. INEI considers a worker informal if their work does not contribute to their affiliation to any public or private health care system. The first problem that arises with this measure is that it is lax and even differs from the monthly census that is carried out on companies through the Plantilla Eletronica and the second problem that arises with this variable is that it is available from the 2012 period onwards since from 2004—2011 the definition of informality differs from that of 2012, which is where additional questions are incorporated for a better definition of informality. So, using question P507, who are you in your workplace: Employer, Self-employed, Employee, Laborer, Unpaid family worker, Household worker, or other? I select the two types of workers who depend on the company and therefore have a salary, which is Employee and Laborer; I also select the age range from 18 to 65 years old, which are the considerable age ranges where a person enters the labor market and where he/she ends up retiring. Then, using the question P524B1, how much was your discount by law in S./?. This question is answered from 0 to more, where the first condition for the worker to be informal is that he/she has no discount by law since he/she would belong to an informal company or would be working in a formal company but informally. The second condition is based on question P419A1: Your workplace, yourself, a retiree, or a family member. Where the second condition is that the worker pays himself, whether he is retired or a family member, his membership dues. So based on these two conditions, I obtained my measure of informality for the period 2004–2019. This variable has a 90% correlation concerning the OCUPINF variable of the ENAHO, so it primarily captures the informality rate elaborated by the INEI.

  7. For this I use the ENAHO question P501 which is Last week you were working?

  8. For stable job, I use the people who responded that they were employed last week, according to question P501, and additionally condition it with the fact of having a permanent contract, which would represent job stability, and this is obtained from question P511A, under what type of contract are you under a permanent contract, fixed-term contract, probationary period, youth job training, apprenticeship contract, service contract, without a contract or other.

  9. There are some critics about the using of OLS pooled regression as main empirical strategy when using binary dependent variables, however, as find Gomila(2021) and Li et al.(2022), OLS is simpler and has a best estimator than Probit, but I included in the next part of this section, the analysis considering IV-Probit, to check robustness in the analysis.

  10. Including father’s educational attainment as instrumental variable is a common instrument to controlling potential endogeneity, Hoogerheide et al. (2012) find that the biases are slow when using parents’ education on income regressions. For instance, we can see a variety of papers that analyze the parental education effects on different outcomes. Gong (2018) evaluates the effects of parental education on wages. Brunello and Miniaci (1999) fin that the once controlling endogeneity, the return of school increases from 4.8% to 5.6% in Italy. In a similar way for Sweden, Holmlund et al. (2011

    ). Nguyen et al. (2016) use father’s education attainment to analyze the effects of educational attainment on dementia risk. Block et al. (2017) evaluates the education attainment on the decision to start a business in European countries. Also, taking the average of the household’s education has been used in different settings such as Breierova and Duflo (2004). However, the parental education as instrumental variable is not perfect, and still suffer of some caveats, as mentioned in Li and Lu (2004).

  11. The bars that cross the point are 95% Confidence Interval, and this will be the same Confidence Interval for the rest of Figures. Also, those Figs. 5, and 7, 8, 9, 10, 11 consider the results from the Tables presented in the Annex.

  12. 730 soles are 190.11 current dollars.

  13. 680 soles are 177.09 current dollars.

  14. 1376 soles are 358.35 current dollars.

  15. 390 soles are 101.57 current dollars.

  16. 2365 soles are 615.92 current dollars.

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Acknowledgements

The authors thank the Editor Gary Hoover and the three anonymous referees for their helpful comments that improved the quality of the manuscript. Overall, the paper has been significantly improved in terms of both science and writing.

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Authors and Affiliations

  1. Universidad Nacional Mayor de San Marcos, Lima, Peru

    Carlos Chávez

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  1. Carlos Chávez
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Carlos Chavez written all the paper and made the analysis.

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Correspondence to Carlos Chávez.

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A previous version circulated as “Who wins and losses when there is an increase in the minimum wage? Closing the ethnic income gaps in Peru”.

Appendix 1

Appendix 1

Table 1

Table 1 Summary Statistics
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Table 2

Table 2 Minimum Wage’s effects on Workers in the Private Sector
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Table 3

Table 3 Minimum Wage’s effects on Workers in the Informal Private Sector
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Table 4

Table 4 Minimum Wage’s effects on Workers in the formal Private Sector
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Table 5

Table 5 Oaxaca-Blinder Decomposition
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Table 6

Table 6 Minimum Wage’s effects on Low and High Skill Workers in the Private Sector
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Table 7

Table 7 Minimum Wage’s effects on Low and High Skill Workers in the Informal Private Sector
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Table 8

Table 8 Minimum Wage’s effects on Low and High Skill Workers in the Formal Private Sector
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Table 9

Table 9 Minimum Wage’s effects on Vulnerable and Non-Vulnerable Workers in the Private Sector
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Table 10

Table 10 Minimum Wage’s effects on Vulnerable and Non-Vulnerable Workers in the Informal Private Sector
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Table 11

Table 11 Minimum Wage’s effects on Vulnerable and Non-Vulnerable Workers in the Formal Private Sector
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Table 12

Table 12 Minimum Wage’s effects on Job
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Table 13

Table 13 Minimum Wage’s effects on Informal Job
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Table 14

Table 14 Minimum Wage’s effects on Stable Job
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Table 15

Table 15 Minimum Wage’s effects on Low and High Skill Worker’s jobs
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Table 16

Table 16 Minimum Wage’s effects on Low and High Skill Workers’s Informal Jobs
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Table 17

Table 17 Minimum Wage’s effects on Low and High Skill Workers’s Stable Jobs
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Table 18

Table 18 Minimum Wage’s effects on Vulnerable and Non-vulnerable Worker’s jobs
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Table 19

Table 19 Minimum Wage’s effects on Vulnerable and Non-vulnerable Worker’s Informal Jobs
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Table 20

Table 20 Minimum Wage’s effects on Vulnerable and Non-vulnerable Worker’s Stable Jobs
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Chávez, C. Minimum Wage and Ethnic-Gaps: Who are the Winners?. J Econ Race Policy (2024). https://doi.org/10.1007/s41996-024-00136-4

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