The Minimum Wage Impact on Poverty in Mexico

doi:10.21203/rs.3.rs-5270649/v1

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The Minimum Wage Impact on Poverty in Mexico

https://doi.org/10.21203/rs.3.rs-5270649/v1

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This study contributes to the public debate on the effect of the minimum wage on poverty and provides the first estimate specifically for the impact of the minimum wage on multidimensional poverty in Mexico. We find that the elasticity of poverty to the minimum wage is between − 0.36 and − 0.58. This implies that between 2019 and 2022, the number of people in poverty decreased between 23.7% and 37.6% because of the minimum wage. In other words, of the 5.1 million people who escaped poverty, 4.1 million can be attributed exclusively to the minimum wage increase. Additional results indicate that minimum wage increases did not significantly impact the level of employment but did have an impact on labor income. As for social programs, the universal pension shows that a 10% increase in its funding reduces poverty by 13%; a 10% increase in basic education scholarships income contributes to a 3.5% reduction in income poverty.

Development Economics

Microeconomics

Social Policy

minimum wage

poverty

labor policy

social policy

mexico

The minimum wage has long been a subject of debate. Economists have held for decades that raising the minimum wage would inevitably involve a trade-off between job loss and wage increases. Nevertheless, recent evidence suggests that this is not always the case. As highlighted in economic literature, the impact of the minimum wage on income and employment hinges on various factors, including the monopsonistic nature of the labor market¹, law enforcement, the minimum wage level, among others.

The same principle applies to the minimum wage's impact on poverty. Numerous studies demonstrate that the minimum wage can alleviate poverty, while others suggest the opposite. The prevailing consensus in academic research indicates that this positive effect depends on whether the minimum wage's impact on family incomes can counterbalance any potential effects on employment. In cases where the minimum wage does not impact employment, it can have a positive effect on poverty reduction.

In August 2023, the National Council for the Evaluation of Social Development Policy (CONEVAL in Spanish) announced a substantial reduction poverty in Mexico, with 5.1 million fewer people living in poverty between 2018 and 2022. This marks a historic decrease in poverty within a short timeframe, particularly considering the severe economic repercussions of the COVID-19 pandemic. Poverty decreased from 41.9% of the country’s total population to 36.3%, a difference of 5.6 percentage points. During the same period, the minimum wage increased by 65.2%² in real terms.

To better understand this reduction, examining the substantial increases in the minimum wage in Mexico is crucial. Several recent studies, including those by Conasami (2019), Campos and Esquivel (2021), and Campos and Rodas (2020), have shown that the wage hikes during the period from 2018 to 2022 had no discernible impact on employment. Therefore, it is plausible that the wage increases played a significant role in reducing poverty. This paper seeks to evaluate this hypothesis empirically.

In Mexico, the official measurement of poverty is currently acknowledged as a multidimensional phenomenon that infringes upon people's rights and freedoms. As such, it is contingent not solely upon exceeding a minimum income threshold. Mexico holds the distinction of being the first country to employ a multidimensional approach to poverty measurement. This approach, in addition to income, considers various factors such as access to education, healthcare services, social security, nutrition, quality housing, basic services, and social cohesion as deprivations that must be mitigated to safeguard individuals from poverty.

The law has recognized the minimum wage in Mexico, since the promulgation of the 1917 Constitution, which is still in effect. Article 123 states that minimum wages "must be sufficient to satisfy the normal needs of a head of household in material, social, and cultural order, and to provide for the compulsory education of children."

It is crucial to recognize that during the 1980s and 1990s, the minimum wage in Mexico saw a decline of over 75% in its purchasing power. This erosion occurred because the wage increments during that period were consistently lower than the increase in prices due to a combination of its usage as a reference for fines, mortgages and fees up to 2016, as strategy to curtail inflation, and a deliberate mechanism to maintain low costs to attract foreign investment.

In 2017, marking the first time in decades, the minimum wage was increased beyond the inflation rate, albeit with relatively modest increments. It was from 2019 onwards that a substantial shift in wage policy materialized. Initially, the objective was to elevate the minimum wage to a level equivalent to the income poverty line. Furthermore, the minimum wage doubled as part of a regional development strategy and a measure to curb migration in the newly created Northern Border Free Zone (ZLFN in Spanish).

This paper assesses whether the minimum wage had a substantial impact on the number of individuals experiencing multidimensional poverty. The results demonstrate that, indeed, because of the wage hikes between 2018 and 2022, the number of people living in poverty decreased between 23.7% and 37.6%. Of the 5.1 million individuals who emerged from poverty, 4.1 million can be directly attributed to the increases in the minimum wage.

Additional findings indicate that increases in the minimum wage had no significant impact on employment levels but did notably increase labor income, resulting in a substantial 21.3% increment. Furthermore, the study examined the influence of social programs on poverty reduction. The results suggest that, when accounting for endogeneity (the correlation between social programs and poverty), pensions, basic education scholarships, and remittances each have positive effects in reducing overall poverty, income poverty, and enhancing household income.

The structure of this research is as follows: First, it explains the data used and how variables of interest and controls were constructed. Second, it reviews the literature on the impact of the minimum wage on poverty. Third, it presents the results of the minimum wage's effect on poverty in Mexico for the period 2016–2022. Fourth, it analyzes the effects of increased labor income and employment. Fifth, it presents findings regarding the impact of the minimum wage and other social programs on poverty while considering endogeneity. Finally, the study concludes.

We used microdata from the National Survey of Income and Expenditure of Households (ENIGH in Spanish), conducted biennially by the National Institute of Statistics and Geography (INEGI in Spanish) for the years 2016, 2018, 2020, and 2022. The survey's primary objective is to provide statistical insights into the behavior of household income and expenditures, including their amounts, sources, and distribution. Additionally, it offers information about household members’ occupational and sociodemographic characteristics, as well as details on housing infrastructure and household equipment.

We estimate the multidimensional poverty for 2016–2022 using the methodology published by CONEVAL. However, there was an adjustment to calculate the indicators at the state level. In order to examine the variation in the minimum wage, we treat the 45 municipalities comprising the ZLFN as a distinct state.

At the state level, we calculate the total number of individuals in poverty, distinguishing between moderate and extreme levels, and those facing vulnerability due to low income or no access to basic services. We also determine the number of individuals who do not fall into the categories of poverty or vulnerability and those with at least one deficiency³ (lack of education, health services, etc.). In addition, we create variables to assess educational lag, access to healthcare services, access to social security, housing quality and space, access to basic housing services, and access to nutritious and high-quality food. This comprehensive approach allows us to analyze the multifaceted aspects of poverty and living conditions.

In addition, income at the individual level was deflated to June 2023 pesos and processed to obtain the monetary income, income from labor, income from transfers, and income from social programs. We distinguish income from social programs between those that continued from the 2012–2018 administration to the 2018–2024 administration (basic education scholarships, universal pensions, and support for the agricultural sector), other support for seniors, and other social programs. These individual incomes were then summed at the household level to obtain the per capita amount for each concept and the average at the state level. Additionally, we created variables to show the participation of each type of income in the total.

Sociodemographic controls calculated at the state level were: the total number of people and the distribution by sex, participation in the labor market, urban and rural localities, self-identified indigenous status, education levels (incomplete primary or less, complete primary or incomplete secondary, complete secondary or incomplete upper secondary, complete upper secondary and above), age groups (under 15 years, 15 to 19 years, 20 to 29 years, 30 to 39 years, 40 to 49 years, 50 to 59 years, 60 and older), the employed population, salaried workers, employment rate, and salaried employment.

In addition, A variable with the current minimum wage in each state in the month of August of each year, deflated to June 2023 pesos, was included.

The policy surrounding the minimum wage has consistently ignited intense debate among researchers examining its effects on employment and poverty. The evidence regarding the impact of the minimum wage on employment and poverty remains mixed. As highlighted in Neumark and Munguía (2020), the influence of the minimum wage depends on its relative level to the average wage, law enforcement, the sector's formality, and the number of affected workers. Additionally, it is influenced by the concentration of the labor market (Munguía, 2020).

When studying the impact of the minimum wage on poverty, it becomes evident that the results hinge on whether the minimum wage reduces employment and leads to a real increase in household income. In Latin American countries, it has been observed that the minimum wage decreases poverty (Sotomayor (2021), Gindling and Terrell (2010), Alaniz, Gindling, and Terrell (2011)). In all these cases, the real increase in household income surpasses any potential job losses. For the United States, Dube (2019) found that the minimum wage reduces poverty with an elasticity ranging from − 0.22 to -0.46.

Conversely, Neumark, Schweitzer, and Wascher (2005) and Neumark and Wascher (2010) found that the minimum wage has either no impact or slightly increases poverty, depending on the estimation model used, in the case of the United States. Arango and Pachón (2004) point out that in Colombia, poverty increases due to the minimum wage; their study suggests that the minimum wage had a negative impact on household employment, which outweighed the increase in real household income.

It is crucial to consider the mechanism by which the minimum wage affects poverty. If, as a result of the minimum wage increase, real household income rises with relatively minor job losses, then the minimum wage has a positive impact on reducing poverty.

In the context of Mexico, recent studies have not indicated negative employment impacts, but they have observed a significant increase in income (Conasami (2019), Campos and Esquivel (2021), and Campos and Rodas (2020)). Therefore, the hypothesis of this study posits that the minimum wage played a significant role in reducing poverty.

Finally, the sole recent study analyzing the minimum wage's effect on labor poverty is Campos and Esquivel (2022), which employ data from the National Survey of Occupation and Employment (ENOE in Spanish). Using synthetic controls, they found that labor poverty in the ZLFN decreased by 2.6 to 3 percentage points due to the minimum wage increase. Our work is unique in that it represents the first study conducted in Mexico that assesses the impact of minimum wage increases on multidimensional poverty, a more comprehensive measure of poverty. Furthermore, it examines the impact of various social programs on household income.

To assess the impact of minimum wage increases on poverty, we first propose a panel data model with two-way fixed effects⁴. Eq. (1) defines the model, which enables the identification of the minimum wage's effect on poverty while accounting for variations over time and across regions (states) and controlling for specific characteristics.

In essence, this model captures the relationship between changes in the minimum wage and changes in poverty over time and across diverse regions while considering various specific factors. Using panel data with fixed effects offers a robust approach for this analysis by effectively controlling and accounting for time and regional variations.

$$\:Povert{y}_{it}={\beta\:}_{1}+{\beta\:}_{2}{MW}_{it}+{\beta\:}_{3}{X}_{it}+{\gamma\:}_{i}+{\tau\:}_{t}+{\epsilon\:}_{it}$$

(1)

Where $\:Povert{y}_{it}$ represents the logarithm of the number of people in multidimensional poverty, extreme poverty, or income poverty⁵ in the state i and year t; $\:{MW}_{it}$ is the minimum wage in real terms; and $\:{X}_{it}$ is a vector encompassing all control variables, including logs of transfer amounts⁶, basic education scholarships, universal pensions, and other social program cash transfers, as well as the logs of total employment, the logarithm of the total population, the percentage of female employment, the percentage of the population in rural areas, the percentage of the indigenous population, the percentage of the population by education levels, and age groups. Finally, $\:{\gamma\:}_{i}$ represents fixed effects by states, $\:{\tau\:}_{t}$ denotes fixed effects by years, and $\:{\epsilon\:}_{it}$ is the standard error.

Table 1 presents the results employing Eq. (1). In the first column, we observe the impact of the minimum wage and social programs on poverty. Notably, the minimum wage exerts a highly significant effect on multidimensional poverty. The elasticity is -0.36, indicating that for every 10% increase in the minimum wage, poverty decreases by 3.6%.

As a result, the observed 65.2% increase in the minimum wage in real terms implies a substantial 23.7% reduction in poverty, after controlling for other factors. Furthermore, in the same table, it is noticeable that none of the social programs significantly impact the change in poverty. This could be attributed to a potential endogeneity issue, suggesting that a household's poverty status to some extent determines whether they receive a particular social program. Therefore, this estimation is revised in section 6 to determine the influence of social programs precisely.

In the second column, we estimate the impact of the minimum wage on extreme poverty. In this scenario, none of the variables have a significant impact. This outcome is not surprising, primarily because (a) the minimum wage primarily affected households that are employed in the formal sector, whereas households in extreme poverty typically have limited access to formal employment, and (b) extreme poverty, by definition, is more determined by the number of deprivations in other dimensions rather than total household income. In other words, there are additional vulnerability factors beyond income, the latter is the direct channel through which the minimum wage reduces poverty.

Table 1

Impact of the Minimum Wage on Poverty
VARIABLES	(1)	(2)	(3)
VARIABLES	Total Poverty	Extreme Poverty	Income Poverty
Ln(MW)	-0.364***	0.0306	-0.702***
	(0.0733)	(0.165)	(0.0752)
Ln(Transfers)	-0.0144	0.00412	-0.191
	(0.0622)	(0.244)	(0.128)
Ln(Scholarships)	0.0419	0.192	0.00368
	(0.0518)	(0.149)	(0.0479)
Ln(Pensions)	0.0714	0.0470	-0.0718
	(0.0757)	(0.215)	(0.109)
Ln(Other Programs)	0.00123	0.0306	0.00560
	(0.0126)	(0.0440)	(0.0269)
Constant	8.681	-2.795	23.33**
	(5.567)	(18.94)	(8.982)
R²	0.996	0.984	0.991
Standard Robust Errors Clustered by States
* p < 0.01, p < 0.05, * p < 0.1

Control Variables: logs of total employment, logs of total population, percentage of female employment, percentage of population in rural areas, percentage of indigenous population, percentage of the population by education levels and age groups.

In the third column, we analyze the impact on income poverty. In this instance, the effect of the minimum wage is much more significant, as expected. For every 10% increase in the minimum wage, income poverty decreases by 7%.

Table 2 employs the same estimation model but for the multidimensional poverty rate (instead of the number of people in poverty). This allows us to assess the robustness of the results when changing the poverty variable from numbers to rates and facilitates the calculation of the number of people who exited poverty exclusively due to the minimum wage increases.

The findings closely mirror those in Table 1. The minimum wage demonstrates a positive and significant impact on both multidimensional poverty and income poverty rates. A 10% increase in the minimum wage results in a 0.7 percentage point reduction in the multidimensional poverty rate. This implies that, due to the minimum wage increase between 2018 and 2022, poverty decreased by 4.5 percentage points out of a total of 5.6 percentage points. In other words, of the 5.1 million people who escaped poverty, it can be asserted that 4.1 million did so solely due to the minimum wage increase.

Table 2

Impact of the Minimum Wage on the Poverty Rate
VARIABLES	(1)	(2)	(3)
VARIABLES	Total Poverty Rate	Extreme Poverty Rate	Income Poverty Rate
Ln(MW)	-0.0693***	-0.00827	-0.0627***
	(0.0205)	(0.00886)	(0.00668)
Ln(Transfers)	-0.0616**	-0.0262*	-0.0168**
	(0.0247)	(0.0138)	(0.00674)
Ln(Scholarships)	0.00817	0.00155	-0.00135
	(0.0132)	(0.00640)	(0.00420)
Ln(Pensions)	0.0480**	0.00595	-0.0112
	(0.0185)	(0.0132)	(0.00928)
Ln(Other Programs)	0.000765	0.00279	0.000176
	(0.00431)	(0.00244)	(0.00208)
Constant	2.747	-0.0750	2.603***
	(1.747)	(0.997)	(0.847)
R²	0.986	0.981	0.952
Standard Robust Errors Clustered by States
* p < 0.01, p < 0.05, * p < 0.1

Noteworthy results in Table 2 reveal that transfers appear to have some effectiveness in reducing poverty, including extreme poverty. The fact that remittances are more likely to reach households where individuals lack employment and have a higher number of deficiencies across dimensions may explain this effectiveness. These transfers also encompass non-universal pensions, which benefit households without access to formal employment. For the most part, social programs still do not exhibit a significant impact in this model. However as previously mentioned, these outcomes may be tied to an endogeneity issue.

In this section, we present the measurement of the impact of the minimum wage on household labor income and employment⁷. The model we employ is similar to the one discussed in Section 4. However, as employment can exhibit time-varying changes that are region-specific (time-space heterogeneity), it is imperative to employ a more robust model to ensure the accurate measurement of the minimum wage's effect on employment. For further details, refer to Allegretto et al. (2017) and Allegretto et al. (2011).

$$\:{Y}_{it}={\beta\:}_{1}+{\beta\:}_{2}{MW}_{it}+{\beta\:}_{3}{X}_{it}+{\gamma\:}_{i}+{\tau\:}_{t}+t\times\:{\varphi\:}_{i}+{\epsilon\:}_{it}$$

(2)

Equation (2) closely resembles Eq. (1), with the only difference being that the dependent variable $\:{Y}_{it}$ now pertains to either employment or household labor income. Additionally, we introduce the interactive term $\:t\times\:{\varphi\:}_{i}\:$to estimate the impact of the specific employment or income trend for each state. This modification is necessary due to heterogeneous time-varying changes in each region that can skew the estimation of the minimum wage in the canonical double fixed-effects regression. By accounting for these trends, we eliminate bias and accurately assess the impact of the minimum wage on employment and income.

Table 3

Minimum Wage Effects on Labor Income and Employment
VARIABLES	Canonical Model		Controlled Model
	(1)	(2)	(3)	(4)
	Labor Income	Employment	Labor Income	Employment
Ln(MW)	0.290***	-0.0543***	0.327**	-0.0118
	(0.0489)	(0.0146)	(0.153)	(0.0673)
Ln(Transfers)	0.179**	0.00340	0.146	0.00465
	(0.0845)	(0.0244)	(0.108)	(0.0434)
Ln(Scholarships)	-0.0763**	-0.0236**	-0.0984	-0.0434
	(0.0299)	(0.0111)	(0.0764)	(0.0318)
Ln(Pensions)	-0.0966	-0.0221	0.0852	-0.0505
	(0.0833)	(0.0258)	(0.141)	(0.0473)
Ln(Other Programs)	-0.0291	-0.00142	-0.0549**	-0.00600
	(0.0172)	(0.00411)	(0.0252)	(0.00946)
Constant	11.46	-1.438	-22.47	-7.396
	(8.146)	(1.476)	(36.81)	(16.62)
R²	0.959	0.999	0.985	1.000
Regional trends	No	No	Yes	Yes
Standard Robust Errors Clustered by States
* p < 0.01, p < 0.05, * p < 0.1

Table 3 presents the effects of the minimum wage on labor income and employment. In columns (1) and (2), we estimate the canonical double fixed-effects model without considering the time spacial heterogeneity of each state. In columns (3) and (4), we address this heterogeneity by incorporating specific trends for each entity.

The results reveal a significantly positive impact of the minimum wage on labor income. In the preferred model (3), the elasticity is calculated at 0.327, indicating that for every 10% increase in the minimum wage, the average household labor income rises by 3.27%. This implies that the cumulative increase in the minimum wage between 2018 and 2022 led to a 21.3% boost in labor income, solely attributable to the minimum wage. This aligns with our findings on poverty reduction, where it becomes evident that income played a substantial role.

Conversely, in the case of employment, the canonical model indicates a significant elasticity of -0.0543, implying a decrease in employment. However, once we control for time and spatial heterogeneity, the elasticity loses significance, signifying that we cannot confidently assert that it differs from 0. Consequently, there is no discernible impact of the minimum wage on employment. These findings are consistent with those of Allegretto et al. (2017) and Allegretto et al. (2011).

The counterintuitive results concerning the effects of social programs suggest potential endogeneity issues because being poor can influence access to social programs. For instance, an individual in poverty is more likely to seek benefits from a social program compared to someone with higher income levels, even in cases where these programs are universal.

To mitigate the bias introduced by endogeneity, we propose a model that employs a system of equations solved through the generalized method of moments (SGMM). SGMM instruments the variables considered endogenous (in this case, social programs) using differences and lags. For example, when we examine the impact of universal pensions on poverty in 2022, one equation incorporates the change in the pension amount received in 2020. Simultaneously, in another equation, we estimate the change in the number of people in poverty between 2022 and 2020 using the income amount from the pension in 2022. In essence, we are comparing differences with levels and concurrently solving the system of equations.

The key advantage of this method is a substantial reduction in endogeneity, resulting in consistent and efficient estimated coefficients. For a more comprehensive understanding of the estimation method, we recommend referring to the works of Arellano and Bond (1991), Arellano and Bover (1995), and Roodman (2009) for their application in Stata.

The estimation process involves identifying two simultaneous equations:

$$\:{Poverty}_{it}={\beta\:}_{1}+{\beta\:}_{2}{MW}_{it}+{\beta\:}_{3}{Z}_{it}+{\beta\:}_{4}{X}_{it}+{\gamma\:}_{i}+{\tau\:}_{t}+{\epsilon\:}_{it}$$

(4)

$$\:\varDelta\:{Poverty}_{it}={Poverty}_{it}-{Poverty}_{it-1}={\beta\:}_{2}\varDelta\:{MW}_{it}+{\beta\:}_{3}{\varDelta\:Z}_{it}+{\beta\:}_{4}\varDelta\:{X}_{it}+{\gamma\:}_{i}+{\tau\:}_{t}+{\epsilon\:}_{it}$$

(5)

The variables in Eq. (5) remain the same as those in Eq. (1). However, the variables $\:{Z}_{it}$ represent the endogenous variables, which consist of the logarithms of scholarships, pensions, and other social programs. On the other hand, $\:{X}_{it}$ comprises the exogenous variables. Therefore, the $\:{Z}_{it}\:$variables are instrumented using their differences. Eq. (5) portrays difference equations, denoted by ∆, which indicate differences between periods. In the equation $\:{\varDelta\:Z}_{it}$, it is instrumented by its levels.

Table 4

Minimum Wage and Social Programs Impact on Poverty Controlling by Endogeneity
	(1)	(2)	(3)	(4)	(5)
VARIABLES	Poverty	Poverty Rate	Income Poverty	Labor Income	Employment
Ln(MW)	-0.577**	-0.129*	-0.792***	0.462**	-0.0479
	(0.284)	(0.0770)	(0.195)	(0.185)	(0.0833)
Ln(Transfers)	-0.768***	-0.270***	-0.129	0.455***	0.0529
	(0.221)	(0.0605)	(0.0841)	(0.120)	(0.0368)
Ln(Scholarships)	0.176	0.0943	-0.352*	-0.142	-0.148**
	(0.187)	(0.0583)	(0.214)	(0.134)	(0.0599)
Ln(Universal Pensions)	-1.295**	-0.324**	0.343	0.257	0.0545
	(0.563)	(0.148)	(0.640)	(0.347)	(0.197)
Ln(Other Programs)	0.0514	0.113	-0.262	-0.138	-0.109
	(0.286)	(0.0766)	(0.184)	(0.163)	(0.0727)
Constant	-6.229	-0.252	12.15	8.663	0.678
	(14.33)	(4.207)	(10.65)	(8.926)	(3.670)
AB Test AR(1)	0.146	0.0767	0.0392	0.151	0.0300
AB Test AR(2)	0.323	0.423	0.178	0.297	0.126
Hasen Overid	0.294	0.683	0.355	0.323	0.191
Sargan Overid	0.352	0.675	0.228	0.309	0.0827
Standard Robust Errors Clustered by States
* p < 0.01, p < 0.05, * p < 0.1

Endogenous Variables: Pensions, scholarships, and other social programs.

For all tests, the probability of accepting the null hypothesis is reported. To address the issue of autocorrelation, the probability of the AB AR(2) test must be greater than 0.10. To prevent model overidentification, the Hansen and Sargan tests must also yield probabilities greater than 0.10.

Table 4 presents the effects of the minimum wage and social programs on various poverty-related indicators, including the number of people in poverty, the poverty rate, income poverty, labor income, and employment. Essentially, we replicate all the previous estimations using a model that effectively addresses endogeneity. It is worth noting that the only variables assumed to be endogenous are the social programs because the minimum wage is regarded as an exogenous public policy tool, universally impacting all individuals⁸.

The impact of the minimum wage remains consistently robust in reducing poverty. A 10% increase leads to a 5.8% reduction in overall poverty and a 7.9% decrease in income poverty. After controlling for endogeneity, the results align closely with those observed in the labor market section: the minimum wage significantly enhances household labor income (an increase of 4.6% for every 10% raise in the minimum wage) and does not significantly impact on employment.

Notably, in Table 4, once we account for the endogeneity of social programs, they emerge as significant contributors to poverty reduction. Universal pensions, in particular, has an exceptionally significant impact on poverty and the poverty rate. For every 10% increment in the amount received from the pension, poverty is diminished by a substantial 12.9%. While not directly impacting multidimensional poverty, basic education scholarship programs contribute to a 3.5% reduction in income poverty for every 10% increase in scholarship income.

Transfers, predominantly comprising remittances and non-universal pensions, also play a pivotal role in diminishing multidimensional poverty and the poverty rate while increasing household income. These transfers exhibit elasticities of -0.77, -0.27, and 0.46, respectively.

Finally, the remaining social programs fail to impact poverty significantly, potentially because their objective differs from this. For instance, the "Jóvenes Construyendo el Futuro" (JCF) program aims to boost youth labor force participation, which may not necessarily align with households in poverty.

Poverty reduction stands out as one of the most crucial objectives for many countries. Governments, certain private enterprises, and international organizations consistently collaborate in their endeavors to decrease the number of individuals living in poverty.

In the case of Mexico, various strategies have been implemented over time to alleviate poverty. These strategies have included targeted social programs, conditional cash transfers, and other approaches. In the past five years, the Mexican government has substantially transformed its social and labor policies. Social policy has evolved towards universality, accompanied by considerable increases in transfer amounts. Concurrently, labor policy has undergone significant changes, such as nearly doubling the real minimum wage, outlawing outsourcing, promoting union democratization, and several other transformative measures.

This study underscores the efficacy of both social programs and the minimum wage policy in diminishing poverty. In concentrated labor markets, such as those in Mexico, the increase in the minimum wage is unlikely to result in job losses, while it substantially augments the income of the poorest households.

The hike in the minimum wage between 2018 and 2022 has lifted 4.1 million people out of poverty. The poverty elasticity concerning the minimum wage, at -0.36, translates to a 3.6% reduction in poverty for every 10% increment in the minimum wage. This is attributed to the minimum wage's limited impact on employment but its significant influence on household labor income.

Once endogeneity is considered, the universal pension program exhibits a remarkably significant role in poverty reduction. For every 10% increase in the program's funding, poverty decreases by 13%, although the impact is amplified due to the larger increase in the minimum wage.

Lastly, it is crucial to acknowledge the limitations of these public policies. The minimum wage has yielded positive effects by reducing poverty and enhancing income and consumption within the domestic market. Nonetheless, it cannot be endlessly increased. While there is room for growth, an optimal point must be reached to ensure that jobs are preserved and workers receive a fair wage. Concerning universal pensions, fiscal constraints are the primary limiting factor. The pension amount cannot be perpetually increased, particularly as the senior population expands each year unless accompanied by targeted policy measures focused on its financing.

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A monopsony is a market structure where there exists only one buyer or demander instead of multiple buyers. As a result, this market demonstrates lack of perfect competition. In the context of the labor market, monopsony doesn't necessarily imply the presence of a single employer but can be attributed to various market imperfections, such as asymmetrical negotiation power between employers and workers, differing worker preferences, limited labor mobility, and the illegal exploitation of workers, among other factors.
We use a weighted average minimum wage, accounting for the existence of two wage zones with different minimum wage values.
In Mexico, poverty is measured using a multidimensional indicator. A person is considered to be in poverty (or extreme poverty) when their household's per capita income falls below the poverty (or extreme poverty) line and they experience at least one (or three, in the case of extreme poverty) deprivation. A person whose household per capita income is above the poverty line but who still experiences at least one deprivation is classified as vulnerable due to deprivations. Alternatively, individuals are considered vulnerable due to income when they have no deprivations but live in a household with a per capita income below the poverty line. Those who do not fall into any of these categories are classified as non-poor and non-vulnerable.
As previously mentioned, to account for the variation in minimum wage, the ZLFN, which encompasses the entire state of Baja California, is considered as a separate federal entity.
We also estimate the impacts on the rates.
The income categories P032 to P041 of the ENIGH encompass items such as labor retirements and non-universal pensions, insurance compensations for accidents and job separations, non-social program-related scholarships, donations, and remittances.
We consider only total employment because the ENIGH is not designed to provide a classification of formal and informal, young adults and female employment according to the ENOE criteria.
Transfers are incorporated into the model as an exogenous variable. These transfers encompass scholarships that do not belong to federal programs, remittances, pensions, compensations, and donations. In Annex Table A1, the same estimates are provided with the transfer variable considered as endogenous, and the results have no significant changes.

The authors declare no competing interests.

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The Minimum Wage Impact on Poverty in Mexico

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Abstract

1. Introduction

2. Database

3. Literature Review

4. Results: The Impact of the Minimum Wage on Poverty

5. Labor Market

6. Endogeneity

7. Conclusion

References

Footnotes

Additional Declarations

Supplementary Files

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