The income compensation required to lift Mexican households to their beginning-of-the-year level of welfare is $53.24 Mexican pesos per family every two weeks. ENIGH reports a total of 33,814,132 households in México. Thus, public officials would need to spend $48.6 billion pesos ($2.4 billion dollars) for one year if they attempted to alleviate the poverty effects of the recent food price increases. In Figure 1, we consider state bounders to determine the cost of alleviating their food insecurity level and the criteria for allocating aid. This initial estimation can be effective for both a top-down federal government program or individual state-level programs, where the former could try to maximize the number of recipients given a budget constraint, and the latter could identify the amount of aid required within its political borders. In Figure 2, we broaden our perspective to the national level and review household demographic characteristics to assess the provision of targeted aid (e.g., urban vs. rural and female vs. male dimensions). Considering demographic characteristics can help further narrow down households with the most need for aid, and existing evidence in the U.S. indicates that poverty and food insecurity depend on demographic factors (Gundersen et al., 2020; Gundersen and Ziliak, 2022; Landry et al., 2022).
We use our previously described formulation to simulate the repercussions of food price increases across each state and determine the amount of money the Government of Mexico would need to distribute per state to alleviate the anticipated poverty. Figure 1 presents a geographic view of the poverty effects caused by the recent food price escalations across Mexico. Our analysis reveals substantial geographic heterogeneity in the level of income compensation required to lift Mexican households to their pre-price escalation period (e.g., the beginning of the year). Specifically, Figure 1 distinguishes Mexican states that require the least income compensation, where limited spending can bring the most effectiveness, and where a policy evaluation would be the costliest. For example, the state of Chihuahua requires between $40.70 and $42.74 pesos per family every two weeks, the state with the lowest need. A food poverty alleviation policy for the state of Chihuahua requires an annual price tag of $1.20 billion pesos ($62.17 million dollars).
In contrast, a policy for Michoacán with a similar number of households as Chihuahua has a price tag of $2.17 billion pesos ($108.32 million dollars). The policy cost in Michoacán is almost double that of the policy in Chihuahua. Noticeably, poor states mostly located in the Southern part of the country require relatively lower amounts of income compensation compared to wealthier states in the West-Center part of the country, suggesting differentials in the cost of food and household preferences drive the income compensation heterogeneity. For instance, the state of Chiapas, arguably the poorest in México, requires between $43.20 and $45.94 pesos per family every two weeks. Income compensation in Chiapas would cost $1.53 billion pesos ($76.9 million dollars) over a year, which is 29% lower than what families in the state of Michoacán would require.
Calculations in Figure 1 demonstrate two paths to effectively tackle the poverty effects raised by the recent price escalation in 2021. On the one hand, policymakers can allocate resources first to states that require the least income compensation (such as Chihuahua, Chiapas, and Tamaulipas) and move to the states that require the most income compensation (México City and the states of Jalisco and Michoacán). Such an approach is optimal since, given a budget constraint, policymakers would reach the most households. On the other hand, a bottom-top approach would have a similar optimal allocation if low-income households are chosen initially. Most states requiring the least income compensation are in the country’s southern parts, where arguably the poorest families are.
Even though our calculations in Figure 1 are a comprehensive effort to direct policy efforts to alleviate the poverty effects caused by recent price escalations, our calculations ignore demographic factors that shape poverty effects within each state. Figure 2 fills this gap by calculating the welfare implications across combinations of income status (poor vs. non-poor), location characteristics (rural vs. urban), and whether a female leads the households or not. Figure 2 reveals that households led by females are more severely impacted by the recent food price escalation than their male counterparts. In addition, our findings reveal that the more urban the location and the poorer the family, the less income compensation the female-led household requires. Figure 2 reveals a clear heterogeneity in needs based on preferences and characteristics.
Who is most affected by food price increases is not theoretically obvious. Engel’s Law demonstrates that as household income increases, the income allocations to food decrease as a percentage of the household’s overall budget. But the absolute amount of income spent on food often continues to rise in conjunction with increasing household income. Our analysis indicates that non-poor households are more affected by the recent price increases, and there appears to be an urban/non-urban divide in our welfare calculations. The poor/non-poor and urban/non-urban dimensions were first considered by Wood, Nelson and Nogueira (2012), where the authors found that the welfare effects for non-poor households are higher than those for poor households. Our results reveal an ambiguous difference in welfare effects between rural and urban households, with rural households possibly off-setting some of the consumer side losses from increased food prices with increases in product sales value or diminishing the influence of these food price increases by on-farm consumption. The difference in welfare effects between rural and urban households is not statistically significant in our study or Wood, Nelson and Nogueira (2012). Therefore, Figure 2 suggests that Mexico’s public policy should target the country’s vulnerable population by considering household characteristics such as household head gender, income level, and the urban/rural divide. For instance, Figure 2 suggests that female-led households require additional monetary needs than male-led households.