This article examines inequality in income distribution in Argentina between 2014 and 2020 in a context of stagnation and economic crisis, which coincided with the outbreak of the Covid-19 pandemic. The determining factors of income distribution were analyzed based on a household survey, and a breakdown of the Gini coefficient was implemented to determine the factors that explained the increase in inequality. From a structuralist point of view, the retraction of formal employment, the expansion of the informal sector, and greater coverage of social protection policies were the central factors that explained the increased level of inequality at that time. Social transfers helped to mitigate inequality in the face of the Covid-19 pandemic.

The Covid-19 pandemic intensified the discussion regarding economic inequality. Shortly after its outbreak, some authors speculated that the virus would have an "equalizing" effect due to the fact that, based on the lessons learned from other pandemics and major wars, it was conjectured that the new disease could lead to processes of economic equality in the long term (Milanovic, 2020). However, evidence has shown that the pandemic has favored a significant concentration of income (World Inequality Lab, 2022).

In Latin America, Covid-19 broke out in a context of economic stagnation and followed the end of the commodity boom. Extensive literature already documents the reduction in economic inequality during this period (Birdsall et al., 2010; Economic Commission for Latin America and the Caribbean [ECLAC], 2014; Gasparini and Lustig, 2011; Gasparini et al., 2016, among others). However, as of 2014, all positive trends, which had been verified in terms of growth, income distribution and well-being, were partially reversed (ECLAC, 2022).¹

The Argentine case fits this description because, in the wake of the demise of the economic and social improvements of the early 2000s, the country faced economic stagnation, a crisis in the external sector, high inflation and, most recently, massive levels of indebtedness with multilateral organizations. This scenario worsened in 2020 due to the pandemic and the measures taken to restrict human mobility, which directly affected production, labor and commercial activities.

In peripheral economies, cycles of stagnation or slowdown in output have had specific implications on employment, income distribution and inequality. Productivity differentials between sectors and branches (conditions of production diversity typical of the periphery) result in relatively rigid income gaps, which deepen during recessions or periods of instability (Ffrench-Davs, 2015). The resulting occupational dynamics combine a progressive increase in the surplus labor force in the informal sector, open unemployment or forced downtime, leading to a decline in income in the informal sectors. The bottom line in terms of inequality would depend on the ability of the state to redistribute income through social protection and social security systems.

This article examines the changes in economic inequality in Argentine society before and after the outbreak of Covid-19. Although this phenomenon is typically monitored using statistical measurements (such as the Gini coefficient or entropy indexes), it should not be confused with a synthetic index. Understanding changes in inequality involves breaking it down based on the organization of the distribution matrix of socially valuable resources (in this case, income), considering that the organizational vectors may vary in their structural sense or relevance.

Changes in the inequality of income distribution during the period 2014-2020 are examined based on the micro databases of the Permanent Household Survey (EPH) of the Argentine National Institute of Statistics and Census (INDEC). A Gini coefficient distribution model was used to investigate inequality based on the role of different sources of income, the impact of each source on the observed change in the coefficient, and the factors that explain the differences. The role played by primary and secondary income distribution is resumed, making it possible to visualize the role played by labor market dynamics and social policy interventions in mitigating income distribution inequality.

This paper is structured as follows: after the introduction, the relevant background information is presented for the discussion of the proposed objective, followed by the specification of the source of the data and the methodology to be used. The results are subsequently presented in the analysis of the proximate determinants of the change in inequality. It concludes with a summary of findings and final reflections.

In economic literature, the topic of inequality acquired renewed academic and political interest (Atkinson, 2016). In the pioneering analyses by Kuznets in the mid-1950s, it was argued that there was an inverse relationship between the progress of economic development and inequality of income distribution based on certain empirical regularities. Modernization was meant to lead to a reduction in inequality. This hypothesis was based on a conventional assumption of dualistic models where, after an increase in the inequality resulting from the use of employment income for survival, the next stage would be one of real income growth and reduction in inequality (Kuznets, 1955).

In developed countries, the increase in inequality over the last three decades has discredited Kuznets' hypothesis. In practical terms, it is agreed that growth in the share of the richest in the Income distribution would explain the increase in inequality. Unlike the inverted U predicted by Kuznets, the percentage of the rich fell in the first half of the 20th century and has increased since then (Atkinson, 2016; Atkinson et al., 2011). Piketty (2014, 2015) explains this result as the convergence of two regressive dynamics that operated to the detriment of equity: firstly, the process of concentration of wealth at the top as a result of the systematic increase in the rate of return on capital over the rate of growth since the last quarter of the twentieth century (Piketty, 2014); and secondly, the continuing gaps between income from the labor market, and significant inequality in pay between occupations and qualifications in the most dynamic economies (Piketty, 2015). Meanwhile, Milanovic (2016) combines technological factors, trade openness and policies (including tax systems and trade union action) in his explanation and proposes the notion of "Kuznets cycles" as waves in which increases and decreases in inequality can be observed.

In peripheral countries, the persistence of high levels of inequality led to the emergence of analytical contributions, which explicitly discarded the orthodox assumption of convergence and challenged the virtuous relationship between growth and reduction in inequality (Cortés and Salvia, 2019). In Latin America, structuralism addressed the dynamics of economic inequality based on the links between technological accumulation and innovation, the structure of the labor market and the resulting distribution of income. This point of view highlights the fact that, over time, the international insertion of peripheral economies affects the possibilities of improving inequality of income distribution (ECLAC, 2016; Pinto, 1976).

Thus, the question of structural heterogeneity is fundamental in explaining the inequality pattern. The concept aims to identify and explain how inter- and intra-sectoral asymmetries in production lead to disparities in productivity and remuneration between jobs available to the working population and, consequently, in income distribution (Di Filippo, 2009; Pinto, 1976). The reproduction of high levels of inequality operates based on a typical employment structure in peripheral countries: a) highly capitalized sectors, with productivity levels close to the international border, employing workers in regulated, relatively high-paying jobs; b) production sectors that have a low level of domestic consumption, less than the global average, but with a specific capacity to provide for the existence of regulated sectors of employment; and c) an extensive sector of micro-enterprises and establishments in the informal sector, easily accessible, with minimal income, characterized by self-employment, domestic work, rudimentary employment and precarious and/or informal regulation (Salvia, 2012).

Income distribution studies in Argentine society usually highlight a reduction in inequality in the 2000s (Beccaria and Maurizio, 2012; Gasparini et al., 2016; Judzyk et al., 2017). They emphasize the positive effect that the reduction in wage premiums associated with education would have had and highlight the importance of the demand for low-skilled labor (linked to the domestic market). Finally, they underline the reorganization of the intermediate levels of the occupational structure (Beccaria and Maurizio, 2012). At present, there is no research that systematically analyzes what happened to inequality of income distribution during the period of slowdown and crisis that began in 2014 and, in particular, the role of the Covid-19 pandemic in accentuating inequalities. This approach requires the addressing of the relationship between the business cycle and inequality of income distribution in peripheral countries. The economic instability typical of Latin American countries is a result of their high level of dependence on external financial flows and the evolution of the terms of trade (Ffrench-Davis, 2012). This instability intensifies structural heterogeneity by having a depressing effect on innovation and productivity. The cyclical behavior of peripheral countries Impacts occupational dynamics and, therefore, on inequality. During periods of stagnation or low growth, problems of productive absorption of the labor force are accentuated. Employment in dynamic sectors stagnates or grows below average, increasing various forms of underutilization: employment in the informal sector, public overemployment, underemployment or open unemployment. This, in turn, leads to a reduction in wages, particularly in the informal sector, due to increased pressures on supply.

However, the pattern of inequality of income distribution is not independent of the state's capacity for redistribution (i.e., the secondary distribution of income according to social policies (CFR. Esping-Andersen and Palier, 2011). These policies operate before or after market activity and play a vital role in the pattern of inequality, in terms of their capacity to cushion the effects of market forces; the main objective of the program is to reduce, compensate for or aggravate economic inequalities resulting from the primary distribution of income. Social security and protection systems constitute a central part of the distribution matrix, although their historical configuration in peripheral countries tended to reproduce structural fragmentation of the labor market (Filgueira, 2015). Furthermore, the extent of gaps in productivity and the volume of underutilization of the labor force had an impact on the fiscal space of the States, restricting the capacity to finance more extensive and sustainable redistribution systems (Tokman, 2006).

During the last two decades , the focus of social policies increased throughout the entire region with the emergence of new instruments and the expansion of their coverage (Cecchini et al., 2014). Conditional transfer programs were mass-produced (Cecchini and Atuesta, 2017), and the elderly population covered by the pension system was expanded (Filgueira, 2015). In the Argentine case, in addition to implementing new conditional cash transfer policies (such as the Universal Child Allowance, AUH), different measures were implemented in the pension system that led to the expansion of coverage and the restructuring of the lowest pensions (Rofman and Oliveri, 2012).

Numerous studies show the effects of these interventions on living conditions and inequality. Several studies indicate positive effects on income levels, poverty reduction and inequality (Salvia et al., 2016; Garganta and Gasparini, 2017; Judzyk et al., 2017). At present, there is also a need for more research addressing the role of these types of intervention concerning inequality in the recent period.

The expansion of social interventions should also be considered in the context of a shorter period associated with the effects of Covid-19. As in other regions, economic activities, work routines and salaries were affected by the isolation measures implemented to control the spread of the disease, especially during 2020, with unequal effects on the population (Weller, 2020). The restrictions imposed in Argentina were no exception (Filgueira et al., 2020). As a result, the State faced the need to moderate the impact on households with exceptional productive and social labor policies. In this context, it is essential to simultaneously consider the role played by the labor market and social protection schemes on income distribution before and after the outbreak of Covid-19.

The study of economic inequality is based on microdata obtained from the EPH, which is collected every quarter by the INDEC (National Institute for Statistics and Census). The survey gathers information corresponding to 31 urban agglomerates with 100,000 inhabitants and more, making up around 63% of the country's total population. It includes information regarding employment and non-employment monetary income for all household members. Household surveys are the most frequent source of information for studying economic inequality because they permit the analysis of the behavior of different types of income, particularly income from non-registered activities. However, disadvantages include problems of sampling truncation (difficulties in capturing the upper and lower strata of the social structure) and the subsequent reduction in the sampling of the income of the richest (Cortés and Salvia, 2019).

The income reported by households is classified according to its origin: employment or non-employment. Employment income is then categorized according to the economic occupational sector in which it is generated as a proxy of the conditions of diversity in production and its expression in terms of employment (Salvia, 2012). A sectoral classification Initially proposed by the Regional Employment Program for Latin America and the Caribbean (1978) is reviewed and updated. This classification defines economic, and occupational sectors with indicators available in household surveys (the size of the establishment, the public or private nature of the economic units and the skills of the occupants). a) private, dynamic or modern formal sector: independent professional workers, employers and employees in establishments with more than five employees; b) public sector: employees in state establishments and employment assistance programs; c) micro informal sector: non-professional self-employed workers, micro-entrepreneurs, employees in companies with up to five workers and workers in private homes. Non-employment income is classified according to whether it originates from the system for social protection and social security policies (retirement and pensions, cash transfers or social assistance, unemployment insurance) or other sources (income and profits or transfers between private households). Reported income is classified on an individual level and then added to the household as a whole.

This research aims to identify the contribution of different sources of income to economic inequality. Using the Gini coefficient of per capita household income, a technique was employed that permits: a) the breakdown of the coefficient and the understanding of the contribution of each source of income to inequality; b) the breakdown of change over time and the understanding of the contribution of each source to the variation in total inequality. The model was Initially proposed by Lerman and Yitzhaki (1985), who resumed the study of the concepts of Kakwani (1977) and Shorrocks (1982). Formally, the per capita household Gini coefficient can be expressed as follows:

(1)

Where R_k is the correlation between the income distribution of a k source concerning the total income distribution; G_k is the coefficient of intra-inequality of each k source; and S_k is the share of each k source in total income. If the different sources of income of interest in this paper are identified, it follows that:

(2)

In (2), D_ISF represents the contribution to inequality generated by employment income from the formal private sector, D_ISP income from the public sector, D_ISI from the micro-informal sector; and the subsequent terms indicate non-employment income: D_IJYP is the contribution from retirement and pension income, D_ITI from transfers, social assistance and unemployment insurance, and D_IONL groups from other non-employment sources.

In addition to decomposing the Gini coefficient according to income sources, a change decomposition model was applied (Cortés, 2000). Based on (1), the change between two Gini indices (G_t, G_t+1) can be decomposed as follows:

(3)

The changes shall determine the contribution of a source to the change in inequality: in its correlation to the overall distribution (R_k), its share in total income (S_k) or its inequality (G_k); furthermore, the first and second order interactions between these factors must be added to this (those without analytical significance). Meanwhile, r_k, _k and s_k are the variation rates of the R_k, S_k and G_k components.

The proximate determinants of economic inequality include transformations in the labor market and the coverage of social protection and social security policies. In addition, it is essential to briefly characterize the 2014-2020 period: a phase of stagnation and recession before the outbreak of the pandemic and another stage that concentrates on the effects of the economic and health crisis.

Between 2014 and 2017, there was a succession of short cycles of expansion and contraction of output, a meager balance of growth (GDP grew only 3% and GDP per capita did not increase) and the consolidation of an inflationary regime that eroded actual remuneration. As of 2016, the change in economic policies also contributed to the deterioration of the employment income circuit.² At the same time, social protection and social security continued to grow³ with the consolidation of previous interventions and limited innovations. The most notable measures were the regularization by the law of the updating of the AUH and pensions, the introduction of a new moratorium in 2014, the inclusion of the so-called Historical Reparation⁴ and the implementation of a non-contributory pension scheme (Pensión Universal para el Adulto Mayor [PUAM] Universal Pension for the Elderly) in 2016 (Rottenschweiler, 2020).⁵

As of 2018, a significant outflow of capital led to a sharp devaluation, accentuating the previous inflationary and recessionary effects. This worsened the loss of wage purchasing power. GDP decreased by 4.5% in a biennium; In 2019, year-on-year inflation exceeded 50% and poverty reached 35.3% (the highest figure in a decade). The outflow of capital initiated a new cycle of indebtedness with the International Monetary Fund in the amount of USD $44,867,000. The capacity to generate employment (unemployment exceeded 10%) and protected employment deteriorated. The purchasing power of transfers and pensions also declined, although to a lesser extent than employment income, due to their regular updating (Arcidiácono and Bermúdez, 2020; Rottenschweiler, 2020).

The arrival of Covid-19 resulted in a 9.9% decrease in the GDP for the Argentine economy due to the measures aimed at containing the pandemic. The economic activities most affected by confinement fell much more than the national average, such as hotels and restaurants (-49.2%), social and personal services (-38.9%) and construction (-22.6%). These activities are extremely labor-intensive and, in particular, over-represented in the informal sector. In this context, the activity rate dropped by almost eight percentage points, not only because of what happened to informal workers but also because of a 1.9% reduction in the number of registered workers. To cushion the impact of this crisis, the authorities implemented a series of social protection measures: an Emergency Family Income (IFE) for households of informal or unemployed workers and special vouchers for AUH recipients, retirees and pensioners.

Table 1 reflects different aspects of the deterioration in employment during the different sub-periods: a) since 2014, open unemployment rose uninterruptedly until 2020 (11.5%); b) salaried employment in the formal private sector lost relative share in the economic-occupational structure; c) the micro informal sector maintained its high share of the total labor force, although it also fell in 2020 due to the effects of the pandemic on the sector; and d) the public sector increased its capacity to absorb employees, which was maintained during 2020 in a context of increasing underutilization of the labor force.

In addition, the drop in formal sector salaried employment and the increase in open exclusion led to an increase in employment income gaps between labor market insertions. Table 2 presents median hourly employment income ratios by main occupation category to examine their distance from overall income.

During the period, the advantages related to the remuneration of formal independent employment, state employment and, to a lesser extent, legal salaried employees were maintained. Furthermore, these advantages increased for public and private formal employees when the pandemic disrupted the labor scenario due to the mechanisms for updating salaries and preserving this type of employment. Meanwhile, the general deterioration of pay coexists with the persistent failure to increase pay for informal workers, which got worse, especially over the last year (0.71).⁶

Table 3 presents data regarding the levels of coverage of social protection and social security policies in households. On the one hand, between 2014 and 2017, the share of households linked to social protection increased (from 44.5% to 50.1%) due to the extension of pension and retirement coverage promoted by public policies. On the other hand, the proportion of households only receiving other types of social policy transfers remained around 10% until the outbreak of the Covid-19 pandemic in 2020 (17.8%). Increases in this category are evidence of the government's efforts to compensate for the reduction in income in the labor market, especially in the informal employment sector. Overall, the studied years confirm the increased importance of social protection in household income (Kaplan and Delfino, 2021).

The cycle of economic stagnation and crisis aggravated by the Covid-19 pandemic has been correlated with a simultaneous decrease and worsening of income distribution. On the one hand, figure 1 indicates the relative stability of inequality during the 2014-2017 period and the sustained increase since the 2018 crisis.

The decomposition allows us to recognize the contribution of income sources to total inequality (see Table 4). First, the relative contribution of labor and non-employment household income in determining the per capita Gini coefficient changed during the studied period. Between 2014 and 2020, there was a reduction in the contribution of employment income to inequality of almost nine percentage points, i.e., a decrease in the capacity of these sources of income to determine the index. Conversely, non-employment revenue increased its relative contribution: in 2014, it accounted for 18.6% and by 2020, 27.4%.

At the same time, changes between the different sources of employment are highlighted. First of all, the contribution of the formal private sector decreased (-5.5 percentage points) due to the fall in this type of employment in recent years. In contrast, the public sector behaved erratically, although its relative contribution to inequality was maintained between peaks. Secondly, there was a decrease in the contribution of income from the micro-Informal sector (-2.7 percentage points) due to the impoverishment of this sector. Therefore, although the income from these economic-occupational sectors contributed positively to inequality, it did so to a lesser extent in 2020 than in 2014.

In contrast, the income for the pension system increased its contribution to inequality by 7.2 percentage points due to the aforementioned extension of its coverage. By 2020, pensions were more unequal than in 2014.⁷

The other sources also contributed to an increase in inequality (2.7 percentage points).

In summary, over the entire period, employment income reduced its contribution to inequality (almost 2 points of the Gini coefficient). Between 2014 and 2019, all labor sources contributed to this decrease. However, the Covid-19 scenario presented some unusual features: total inequality did not report significant changes, the public sector resumed a role of pro-inequality and the role of social policy income grew rapidly.⁸ Non-employment income sources also systematically contributed to greater inequality (around 4 points of the Gini coefficient), except for monetary transfers (such as the AUH or the IFE), which contributed to its morigeration, especially during the Covid-19 scenario.

What factors explain movements in the Gini coefficient, both in composition and magnitude? Based on equations (2) and (3), the underlying factors are analyzed during three windows of observation: 2014-2019, 2019-2020 and, in summary, the 2014-2020 cycle as a whole.

Between 2014 and 2019, employment income contributed to a reduction in inequality, primarily because it decreased its relative share of household income (s) and secondarily because the source decreased its correlation with overall distribution (r) (see table 5). However, employment income therein became more unequal (g). These results confirm that the regressive conditions that operated in the occupational dynamics and their effects on employment income decreased the importance of this source in the distribution.

Non-employment income increased its contribution to overall inequality due to a twofold process: an increase in its incidence in household income (s) and an increased correlation with overall distribution (r). This last aspect would also account for the relative worsening of households receiving employment income compared to those receiving non-employment income. Retirement and pension income played the leading role, followed by rent, profits and interest.

Between 2019 and 2020, the overall index remained the same. Still, the changes linked to the decrease in the relative share of employment income in overall inequality (s) persisted, despite the growth of intra-inequality operating in the opposite direction (g) (see Table 6). There are also some specific features. The most significant decrease in participation was observed in income from the formal private sector (s), which in turn became more unequal (g). The involvement of the micro-informal sector also decreased (s), hence its contribution to the reduction in inequality. On the other hand, the public sector positively contributed to inequality because its share of total income (s) increased.

Concerning non-employment income, the trend of increasing participation in total income (s) was confirmed, although it had a lower correlation with general distribution (r). Cash transfers, reinforced during the pandemic, accounted for a reduction of almost half a point in the Gini coefficient. The intensification of their effect was caused by a significant increase in their share of the overall distribution (s).⁹ On the other hand, inequality between non-employment sources decreased (g).

In short, between 2014 and 2020, employment income contributed to a reduction in inequality in a context of declining income, mainly because, being a source that contributes to inequality, its share in total income (s) and its correlation with the general distribution (r) decreased (see Table 7). The former is related to what happened with participation in the formal private sector and, to a lesser extent, in the micro informal sector, while the negative correlation is linked exclusively to the behavior of the micro informal sector and indicates a deterioration of the sector. The internal inequality (g) of these sources also increased, which is explained by the behavior of formal income. In other words, had it not been for their deterioration, formal employment income would have contributed to greater overall inequality. On the other hand, public sector income contributed to greater inequality due to its increased share in distribution (s) and its increasing correlation with distribution (r).

Non-employment income contributed to an increase in inequality due to a rise in the share of total income (s) and its higher correlation (r). This effect was due to pensions and other non-employment income. Income transfers increased their share(s) and thus reinforced their role in reducing inequality, particularly since the outbreak of the pandemic.

This paper aims to examine the dynamics of economic inequality in Argentine society in a context of stagnation, wage growth and crisis aggravated by the emergence of Covid-19. Argentine society (together with the rest of the region) witnessed a partial reversal of the socioeconomic improvements achieved at the beginning of the century. In particular, labor market imbalances deepened. Structuralist theory suggests that the loss of dynamism in the formal sectors translates into a greater relative level of unemployment and employment growth in the informal sector, with negative consequences for wages in this type of activity and, consequently, for income distribution. The final results regarding inequality are not independent of what happens with the secondary distribution of income, i.e., with social protection and social security policies. However, these effects will also have to be processed in an unprecedented framework caused by the consequences of the pandemic on the labor market, the State's capacity for intervention and, in the Argentine case, a persistent regime of high inflation.

The analysis shows that the cycle of stagnation and crisis increased structural problems compatible with the previous arguments. Between 2014 and 2020, employment in the formal sector decreased, pay gaps increased (i.e., there were more significant sectoral asymmetries in the employment structure), open employment exclusion increased and the impoverished and growing informal sector was consolidated. On the other hand, the public sector retained a countercyclical role in absorbing employment. Likewise, the relevance of social protection and social security schemes increased and there was sustained growth in coverage. This was in response to different public policies (especially during the Covid-19 period). Moreover, the relative indexation of these public transfers during high inflation seems to have favored greater functional importance in household income and has given it a renewed role in the matrix of distributive inequality during the conditions of economic and labor retrenchment of Covid-19.

The general decline of the period is reflected both in the successive increases in the Gini coefficient and the set of variations in the income distribution profile. On the one hand, the gradual decrease in the value of wages and the structural importance of the formal sector is a distinctive feature of these years and its equivalent can be found in an ever-decreasing contribution to the mass income distribution among the population. Formal sector income contributed negatively to inequality by becoming less relevant, even though it became more unequal within the formal sector. On the other hand, the micro-Informal sector's income also negatively contributed to the Gini. This seems to be a process of "equity by impoverishment" (Cortés and Rubalcava, 1991) rather than a virtuous process of inequality reduction. Likewise, the decrease in the relevance of the micro-informal sector's income was paralleled by an increase in the relative and absolute weight of the sector's workers in the employment structure. In other words, an absolute impoverishment of this sector would have been consolidated in a regressive economic context. In contrast, income from the public sector, despite its increasing deterioration until 2019, gained importance and contributed positively to an increase in inequality.

As far as state action for income distribution is concerned, there are two distinct types of behavior. On the one hand, conditional cash transfers (such as the AUH or the IFE during the Covid-19 pandemic) had a negative (albeit limited) effect on inequality, which deepened during the health crisis due to its greater weight on the income structure. Meanwhile, retirement and pension income played a decisive, positive role with regard to the increase in inequality due to surges in inflation, with increasingly impoverished employment income, as a result of the aforementioned mechanisms for updating assets. In addition, the way in which this income was re-composed based on the 2016 legal provisions favored pensioners at the highest levels.

In general, the period demonstrates a process of socioeconomic deterioration expressed in a balance of increased inequality in the distribution of income. The overall balance is that those in informal employment became poorer, while more and more employed people depended on this type of employment. Wage gaps between employment sectors increased, thus preserving the structural asymmetries associated with productive heterogeneity. This coincides with increased coverage and fragmentation of social policy initiatives, which, however, need to be Increased to reduce inequality.

The analysis presented concerning the cycle of stagnation and crisis exacerbated by the Covid-19 pandemic raises several questions regarding the evolution of inequality in the immediate future. Even when experiencing a cycle of significant recovery, it is well known that not all growth positively affects well-being: it must be accompanied by progressive improvements in income distribution. In this regard, the background information examined and the possible changes that the pandemic triggered in the labor market question the potential for growth to absorb the labor force productively. In this context, there is an urgent need to implement economic policies that favor employment in the formal sector, strengthen the micro-Informal sector's productive conditions and support an improvement in incomes without exacerbating the current levels of inequality.