Rainfall and self-selection patterns in Mexico-US migration

Consider an individual living in the home country, Mexico, and deciding whether to migrate to the host country, the US. Suppose his labor market performance depends on his skills (education levels). The wage equations in Mexico (subscript 0) and the US (subscript 1) can be written as follows.

where w_i (i = 0,1) is the wage, μ > 0 denotes the wage level with no schooling, δ > 0 is the return to schooling, s. The wage level with no schooling is higher in the US while Mexico has a higher return to schooling; thus I the author assumes μ₁ > μ₀ and δ₀ > δ₁ (Chiquiar and Hanson, 2005; McKenzie and Rapoport, 2010).

Earnings in Mexico are assumed to be affected by rainfall in Mexico through wage level with no schooling, μ, and return to schooling, δ, therefore, Eq. (1) becomes ln(w₀) = μ₀(R)+δ₀(R)s, where R denotes rainfall in Mexico that the individual experiences.

The author assumes ∂μ₀/∂R > 0: decreases in rainfall hurt the income of people with no schooling. Without well-developed insurance markets, uneducated people suffer a monetary loss in drought due to lower crop yield and impaired non-farm income. Furthermore, a higher return to schooling in drier years is assumed: ∂δ₀/∂R < 0. People who are more educated may self-select into job positions which are less vulnerable to natural disasters and diversify their income packages to avoid substantial losses arising which arise from climate fluctuations.

When |∂μ₀/∂R| _≫ |∂δ₀/∂R|, Mexican people at different education levels all suffer a decrease in income in Mexico in a dry year. When |∂μ₀/∂R| > |∂δ₀/∂R|, poorly educated people still experience an income loss as rainfall decreases, while it is possible that the most educated people are not negatively affected.

When regional rainfall in Mexico has no effects on migrants’ income in the US, an individual in Mexico will migrate to the US if

where C denotes migration costs. Migration costs in time equivalent units, π = C/w₀, are assumed to decrease with schooling (Chiswick, 1999; Chiquiar and Hanson, 2005; McKenzie and Rapoport, 2010). Without enough money, either from savings or credits, to pay for the upfront migration costs, Mexicans are not able to afford the migration trip even if condition (3) above is satisfied.

Migration costs mainly include upfront costs, such as transportation costs and coyote (smuggler) prices, and other costs, such as seeking help from networks and finding lodgings in the US. How rainfall influences these migration costs depends on its impacts on both the demand and supply in the market. In drier years, the lower-income of Mexicans suggests larger wage differentials and therefore stronger demand for migration services. More potential migrants and their higher willingness to pay for the movement lead to a higher and steeper downward sloping demand curve.

However, the manner by which the supplies of different migration costs change is ambiguous. The supplies of transportation services and existing networks in the US seem not to be affected by rainfall in the short run, since international train or flight services do not change greatly while networks in the US are mainly determined by historical migration. Stronger demand for them in drought may lead to higher prices of these services. Section 4 empirically shows that migrants traveling in drier years are less likely to contact and lodge from relatives in the US, implying not only higher monetary costs but also higher psychological costs. Besides, though the changes in the supply of coyote services are not clear, Section 4 also provides some evidence on higher coyote costs for illegal migrants who face lower rainfall levels.

Therefore, migration costs, C, are assumed to increase in drier years, yielding

where μ_π > 0, γ₁ > 0, γ₂ > 0. For simplicity, the effects of rainfall on migration costs are assumed to be identical across education levels. However, these effects would be larger for those who are less educated, since less-educated people are willing to pay more for the migration trips due to their larger changes in wage differentials in drought.

If the values of s satisfy

then individuals with s years of schooling are indifferent between staying in Mexico and migrating to the US. Viewing migration as an investment, the author sets A=lnw0+π<!-- π -->=μ<!-- μ -->0(R)−<!-- − -->δ<!-- δ -->0(R)s+eμ<!-- μ -->x−<!-- − -->γ<!-- γ -->1s−<!-- − -->γ<!-- γ -->1R$A=\ln \left(w_{0}\right)+\pi=\mu_{0}(R)-\delta_{0}(R) s+e^{\mu_{x}-\gamma 1 s-\gamma 1 R}$to capture the entire costs of the investment, including opportunity costs (income in Mexico); set B = μ₁ + δ₁s to denote the monetary return to the investment, namely migrants’ earnings in the US. To show the difference between them graphically, Figure 1 uses curved lines and straight lines to denote A and B, respectively.

Figure 1 does not follow the convention in the literature as the straight line in the figure denotes the earnings in Mexico.

Figure 1

The relationship between threshold values and rainfall is defined by ∂s/∂R. Taking the derivative of R on both sides of Eq. (5), we can get

where (δ₁ − δ₀ + πγ₁) captures the increase in net benefits (wage differentials net of migration costs: B − A) from a migration trip when schooling increases by one unit. The right-hand side of Eq. (6), Δ, presents the decrease in net benefits (B − A) or the increase in the entire costs (A) of a migration trip when rainfall increases by one unit.

Mexicans with educational attainment around s_L may have high migration costs especially when p is sensitive to education levels. If migration costs and their dependency on education (γ₁) are large enough to make (δ₁ − δ₀ + πγ₁) > 0, more years of schooling give larger net benefits from migration, which is consistent with positive selection around s_L. Under these circumstances, it is very likely that ∂s/∂R < 0 when rainfall causes larger changes in migration cost (πγ₂) than in income in Mexico, in other words, Δ < 0. s_L moves to the right in drier years as shown in Figure 1(a). The effects of decreased rainfall on selection through higher migration costs outweigh the effects occasioned through lower Mexican income; then therefore, only those people who have more educated educational credentials with years of schooling greater than sL′<!-- ′ -->$s_{L}^{\prime}$will stick to their original decision because of their larger net benefits from migration. However, people with education levels between s_L and sL′<!-- ′ -->$s_{L}^{\prime}$change their decision because they lack enough adequate net benefits from migration to offset the negative effects of larger changes in migration costs than in income in Mexico. If income in Mexico is more sensitive to rainfall than migration costs, having Δ > 0 and ∂s/∂R > 0, s_L moves to the left in drier years as shown in Figure 1(b). When the decreases in income in Mexico outweigh the increases in migration costs in dry years, people with education levels between s_L and sL′<!-- ′ -->$s_{L}^{\prime}$switch from staying in Mexico to migrating to the US.

Unlike Cattaneo and Peri (2016)’s model that assumes higher temperature lowering savings, the author’s model here displays a possibility that rainfall affects migration costs, since savings are generally accumulated in previous years. As the failure of the credit and financial markets has plagued Mexico (Hanson, 2010), some people around s_L face tighter liquidity constraints in drier years. Even if the decreases in income in Mexico are larger than the increases in migration costs in drier years as shown in Figure 1(b), the actual threshold value of education may shift to the right as illustrated in Figure 1(c) when increased migration costs exceed savings. In Figure 1(c), people with education levels between s_L and sL′<!-- ′ -->′<!-- ′ -->$s_{L}^{\prime \prime}$may prefer and be able to make a migration trip in normal years, while in drier years they have to stay in Mexico because of higher upfront migration costs.

Similar reasoning can be applied to people with educational attainment around s_U, where a negative selection pattern is demonstrated. Their low migration costs suggest a high possibility that (δ₁ − δ₀ + πγ₁) < 0. It is very likely that income in Mexico for them is not significantly affected by rainfall, especially when |∂μ₀/∂R| > |∂δ₀/∂R|. Then, Δ ≈ −πγ₂ < 0 and ∂s/∂R > 0. Decreases in rainfall only bring higher migration costs, then s_U moves to the left in drier years as shown in Figure 1(a), as more years of schooling imply lower net benefits from migration. When rainfall has larger effects on income in Mexico than on migration costs, Δ tends to be positive and ∂s/∂R tends to be negative. s_U may move to the right in drier years as shown in Figure 1(b). People between s_U and sU′<!-- ′ -->$s_{U}^{\prime}$change migration decisions and prefer to move to the US in drier years.

Though investigating how the gap in average education levels between migrants and non-migrants changes with rainfall sheds light on selection, it is not quite appropriate to reveal how threshold values in the model and the education composition of migration flows change. Because rainfall tends to affect the migration decisions of people at margin, it is likely that changes in average education levels of migrants and non-migrants in the same direction will occur. For example, in Figure 1(c), people with years of schooling between s_L and sL′<!-- ′ -->′<!-- ′ -->$s_{L}^{\prime \prime}$do not have enough savings and consequently change their original migration decisions and stay in Mexico in dry years, leading to increases in the average level of education of both migrant and non-migrants groups, if we hold s_U unchanged. The gap in average education levels can be either enlarged or narrowed.

Therefore, using person-year unbalanced panels, the author examines whether the effects of rainfall on migration probabilities differ by education levels. To provide more solid analysis, two separate samples for movement decision and migration status are used for the regression below

where M_i,c,s,y denotes movement decision or migration status in year y of individual i who is interviewed in community c and resides in state s in year y. Empirically, movement decision refers to the question of whether any actual migration trip to the US, longer than 1 month, in year y is made conditional on no migration experience in year y-1. If yes, then this dichotomous variable, M_i,c,s,y, is equal to unity; zero otherwise. Therefore, tourism trips are not regarded as migration experience. Since return trips are not clearly recorded in the MMP, the condition of no US experience in year y-1 is set to distinguish migration trips to the US from return trips to Mexico.

The MMP does not record migrants’ return trips to Mexico as clearly as their migration trips to the US. Using individuals’ location information in each life year makes it difficult to define migrants returning years. If migrants stay in the US in year y-1 but make a new migration trip to the US in year y, it is very likely that migrants firstly return to Mexico in year y and then travel to the US again in the same year. Therefore, with the condition of no US experience in year y-1, when migrants make new migration trips to the US every year continuously, those years are dropped in the corresponding sample.

The movement decision sample focuses on migration flows, while the migration status sample focuses on the migrant stock in the US. Both of them are worth studying. The difference between these two samples is mainly migrants’ years in the US, except for their traveling years. Conditional on staying in the US already, migrants make their location decisions without considering upfront migration costs. Therefore, switching from the movement decision sample to the migration status sample, the effects of rainfall through upfront migration costs should be smaller. As discussed above, rainfall may decrease savings to cover upfront migration costs or increase those costs. If migration costs never change, then lower rainfall levels strengthen liquidity constraints only through savings. In the migration status sample, more observations are not troubled by upfront migration costs or liquidity constraints; thus, the negative rainfall-driven effects of migration costs for poorly educated people on their migration decisions should be smaller than that in the movement decision sample. If rainfall deficits increase migration costs, especially those costs after arriving in the US, the rainfall-driven effects of migration costs should be larger in the migration status sample. In other words, migrants who are in the US and sensitive to migration costs may increase their return propensity in drier years if the costs of return trips to Mexico are trivial.

Though two definitions of dependent variables and two samples are used, independent variables are the same. E_i,y−1 denotes education-related variables. Generally, the author has years of schooling and its squared term in year y−1. In specifications that the author groups people based on their years of schooling, E_i,y−1 presents different educational categories. Coefficients on E_i,y−1 explain the relationship between migration probability and education, suggesting a selection pattern in migration.

Following Fishman and Li (2017), R_s,y is the rainfall in the main agricultural season, spring-summer season, in year y in the individual’s home state s in Mexico, measured in 100 mm units.

Mexico’s main agricultural outcome, Maize, has two major agricultural seasons: Spring-Summer (78.5% of total production) and Autumn-Winter (21.5%) (Secretariat of Agriculture, Livestock, Rural Development, Fisheries and Food (SAGARPA) 2008). The Spring-Summer season includes (i) planting period (April–June), (ii) growing period (July–August), and (iii) maturation and harvesting period (September–November).

The annual seasonal rainfall data from 1941 to 2012 at the state level are from the MMP and Comisin Nacional del Agua (Conagua).

The rainfall database has a few missing values. Corresponding observations are dropped. More localized rainfall indicators are not used due to the limitation and confidentiality requirements of the MMP data.

Although the effects of rainfall may last years, the author follows the literature and focuses exclusively on the effects in the short run (Feng et al., 2010; Cattaneo and Peri, 2016; Nawrotzki et al., 2013). In the robustness checks, controlling for rainfall in previous years yields consistent results. Table 1 displays the summary statistics of rainfall in Mexico at the state level. An average Mexican state has 791 mm rainfall in the spring (spring-summer season). Its probability of suffering drought is 14% when drought in each state is defined as one standard deviation below the historical average of that state.

In line with Orrenius and Zavodny (2005) and Cattaneo and Peri (2016), the interaction term between education and rainfall, E_i,y−1 × R_s,y, illustrates how the effects of rainfall on migration decision change with years of schooling and how rainfall shapes the relationship between migration probability and education.

X_i,y−1 consists of time-varying individual controls including age, marital status, number of children, and a dichotomous variable for previous migration experience. A dichotomous variable for agricultural jobs is also controlled for in some specifications. These individual characteristics, as well as years of schooling, are measured in year y-1 to avoid their causal relationship with rainfall.

A_s, B_y, and C_c capture state fixed effects (FE), year FE, and community FE, respectively. They control for economic conditions and border enforcement studied by Orrenius and Zavodny (2005), community-level networks discussed by McKenzie and Rapoport (2010), migration policy changes, such as the Immigration Reform and Control Act (IRCA),

For more details about IRCA, see Li (2016).

Errors are clustered at the state × year level.

Table 2 displays the summary statistics of characteristics of observations, person-years, in both the movement decision (travel) and migration status (in the US) samples. These two samples give similar facts. Regarding years of schooling, migrants are largely from the category “Primary.” The frequencies of people from Primary, Middle, and High categories are larger for migrants, suggesting intermediate selection. In addition, migrants tend to be unmarried, younger, and experienced with migration. Above 50% of migrants hold illegal documents.

Many migrants may return to Mexico or transit back and forth across the Mexico-US border. Analysis of return decisions of migrants who already stay in the US should provide some information on changes in wage differentials and migration costs other than upfront costs as rainfall changes.

To study migrants’ return decisions, the author replaces the dependent variable in Eq. (7) with a dichotomous variable for migrants’ return trips to Mexico and expect opposite signs of coefficients on rainfall related variables. Return decision (Return_i,c,s,y) is unity if migrant i from Mexican community c and state s returns from the US to Mexico in year y; zero otherwise. Since the MMP only has individuals’ annual location information but no precise records on migrants’ return trips, the author constructs two samples using different definitions of a return trip. In the first sample, the dichotomous variable for return in year y is unity if the migrant’s location is in the US in year y but is in Mexico in year y + 1, zero for locations in the US for continuous years. In the second sample, Return_i,c,s,y has the value of unity in year y if the migrant’s location is in the US in year y − 1 while is in Mexico in year y, conditional on no new migration trips to the US in year y − 1; zero if migrants have locations in the US for continuous years. The “Return (Leave)” and “Return (Arrive)” are used to denote the corresponding samples for these two different dependent variables, respectively. Both samples drop migrants’ years when (1) migrants’ locations are in Mexico but return trips are not found, or when (2) migrants make new migration trips to the US every year continuously. The second sample has a larger sample size.

For example, if the location information for a migrant is the US in year 2000 and 2001 but Mexico in year 2002: (1) the first sample has Return=0 and Return=1, while year 2002 is dropped; (2) the second sample has Return =0, _{2000 2001 2000}Return₂₀₀₁ =0, and Return₂₀₀₂ =1.

Before presenting the relationship between selection on schooling and rainfall, the author studies the increasing migration costs in drier years as given in Tables 3 and 4. Using limited data in the MMP on migration costs, Table 3 displays a strong negative correlation between rainfall and illegal migration costs; Table 4 suggests fewer benefits from networks in the US and tighter liquidity constraints in years with lower rainfall levels.

Illegal migration costs in Table 3 include (1) monetary costs of hiring a coyote (smuggler) to lower apprehension probability and (2) extra costs associated with unpopular border crossing sectors. Controlling for community FE, state FE, and year FE, column (1) in the first panel regresses non-zero coyote costs of illegal trips to the US on rainfall (R_s,y) in migrants’ hometowns. When rainfall decreases by one standard deviation (439 mm), coyote costs increase by about 35 dollars, 7% of the average coyote costs.

For illegal migrants who cross the border several times in 1 year, their coyote costs of the latest cross are used. Considering the possible effects of rainfall on exchange rates, using coyote costs in pesos give consistent results.

Conducting a similar analysis in the second panel in Table 3, the dependent variable is changed to a dichotomous variable for the most common cross sector for illegal crossings. It is set at unity if migrants choose Tijuana in Baja California del Norte to cross the border, zero otherwise.

About 40% of illegal crossings in the sample took place in Tijuana. In addition, migrants usually would not change their cross sectors within a year.

Furthermore, focusing on migrants’ interactions with their connections or relatives, Table 4 also suggests higher migration costs in drier years for both legal and illegal migrants. The dependent variables in columns (1)–(3) are dichotomous variables for lodging from relatives during their stays, contacting relatives in the US, and receiving financial help for their US trips, respectively. Regressing them on rainfall in Mexico, columns (1) and (2) indicate that lower rainfall levels decrease migrants’ possibilities to lodge from or contact their relatives in the US. Migrants travel in drier years, especially those less educated, experience more difficulties in utilizing networks in the US and hence higher migration costs, such as rental, information, and job search costs. Column (3) reveals tighter liquidity constraints since lower rainfall levels increase the probabilities that less-educated migrants receive financial help from friends, relatives, and community members.

In the MMP, about 90% of migrants with financial help report that friends, relatives, and community members are the sources, while the rest of them receive help from employers or banks.

However, the author admits that it is difficult to identify a solid relationship between migration costs and rainfall in the absence of availability of more detailed data on total migration costs, including money spent on legitimate procedures and transportation. If total migration costs, rather than just coyote costs in illegal migration trips, increase by 7% as rainfall decreases by one standard deviation as shown in Table 3, migration trips become less profitable and more difficult if liquidity constraints bind. Therefore, the costs channel through which rainfall affects migration decision and selection cannot be ignored.

Regarding the relationship between rainfall and selection on schooling, Eq. (7) has been fitted to the MMP data. Results are reported in Table 5. The first three columns are from the movement decision (travel) sample, while the remaining three columns are from the migration status (in the US) sample.

Unweighted specifications are used because sample weights are at the community level, while community FE are controlled for. Weighted regressions give consistent results.

In column (1) of Table 5, the inverted-U-shaped relationship between migration probability and years of schooling suggests that migrants are more likely to be from the middle of the education distribution. This relationship is affected by rainfall as the interaction terms have significant coefficients in column (2). Graphically presenting these results in Figure 2(a), the solid line and dashed line show individuals’ probabilities of migrating versus years of schooling at average (791 mm) rainfall level and at one-standard-deviation below average (352 mm) rainfall level, respectively.

For simplicity, the values of other control variables are set to zero in Figure 2, since they only contribute to the parallel movements of lines.

Figure 2

The selection on education in a normal year with average rainfall level tends to be intermediate toward negative since the inverted-U shape shows higher migration probability for relatively poor people. When rainfall decreases, the less dispersed curve indicates increased migration probabilities of people with about 5–12 years of schooling, but decreased migration probabilities of others.

Column (3) of Table 5 provides consistent results by grouping people into different educational categories. Results are transformed into solid and dashed lines in Figure 3(a), which represent the coefficients on educational categories and their changes caused by Rainfall decreasing by one standard deviation, respectively. Compared to Primary, which is the benchmark, people in categories Middle and High are more likely to migrate, while people in categories No school, College, and Grad are less likely to migrate. In drier years, people in categories Middle and High increase their migration probabilities, while those in categories No school, Primary, and Grad lower their probabilities. The dashed line provides a less dispersed curve.

Figure 3

These changes in the selection are consistent with Figure 1(c). Decreases in rainfall lead to lower migration probabilities of the least educated people. In drier years, these people may experience larger increases in migration costs than increases in wage differentials. As migration becomes less attractive to them, they may give it up voluntarily. When the opposite happens, tighter liquidity constraints should be the main factor impeding their movement. If migration costs, including upfront costs, are not affected by rainfall, only declines in savings could lead to the reinforced positive selection on education among those identified as poorly educated people, as shown in Table 5, and Figures 2 and 3. However, if we believe that rainfall in the spring does not affect Mexicans’ savings, which are largely likely to have been accumulated in previous years, in Table 5, higher upfront costs as shown in Table 3 are the only reason for tighter liquidity constraints as shown in column (3) of Table 3.

In addition, given the fact that people in the Middle and High categories are more likely to migrate in drier years, we know that at least some people have larger increases in wage differentials than increases in migration costs. Besides, liquidity constraints are not hindering their movement.

As for people who are highly educated, such as those with graduate degrees, their lower migration propensities in drier years may mainly result from higher migration costs, especially those do not decline as education levels increase since their income in Mexico may not be sensitive to rainfall. Another possible explanation is that their income in Mexico may increase as rainfall decreases since the prices of unskilled labor may decline.

Using the migration status sample to study people’s location choices every year, columns (3)–(5) of Table 5 and corresponding figures give stronger results than the movement decision sample. Both the magnitudes of coefficients on variables of interests and the magnitudes of gaps between solid and dashed lines in Figures 2 and 3 are larger for the migration status sample. If the costs of migrants in the US returning to Mexico are negligible, the difference between these two samples mainly represents years spent by migrants in the US, during which upfront migration costs or liquidity constraints should be excluded from their concern. Assuming other migration costs do not change by rainfall, in drier years at least those poorly educated migrants in the US should have stronger intentions to stay and present smaller gaps between lines for the migration status sample. However, this contradicts the fact that larger gaps between lines in Figures 2 and 3, implying their higher return rate. A good explanation is that migration costs, other than upfront costs, increase as rainfall decreases. It is quite possible that upfront costs only make up a small portion of total migration costs, while the rest are more sensitive to rainfall.

Migrants in the US who are from the High category prefer to stay in the US when there is a reduction in rainfall probably because they benefit more from migration as rainfall decreases.

People in this category display higher incentives to migrate as rainfall decreases in the movement decision sample. As upfront costs being absent, the trend is stronger due to smaller changes in migration costs for migrants in the US than migrants traveling with upfront costs.

The comparison between results from those two samples for people with graduate degrees is unclear because of their small proportion in samples. Migrants in the US increasing their intention to return in drier years may reflect that upfront costs are not significant compared to other costs, such as money and time on searching jobs.

The analysis mentioned above shows that the intermediate selection pattern is reinforced when rainfall decreases. Furthermore, this is more obvious for the migrant stock than migration flows, suggesting higher migration costs as rainfall decreases. The return analysis mentioned in Section 4.3 uses migrants’ years in the US and provides supporting results.

Table 6 presents how migrants’ return decisions are determined by rainfall levels in Mexico. Two definitions for migrants’ returns and corresponding samples are used as mentioned in the data section. They give consistent results. In columns (1) and (3), coefficients on School, School Squared, and their interaction terms with Rainfall have expected signs, which are the opposite of the signs of coefficients in the migration decision regressions in Table 5. Figure 4 uses two lines to display the relationship between return decisions and years of schooling at different rainfall levels. In a normal year with an average rainfall level (791 mm), migrants with fewer years of schooling are more likely to return. However, in a drier year, migrants with about 4–12 years of schooling tend to stay, while migrants with no years of schooling (Figure 4(b)) or more than 12 years of schooling are more likely to return to Mexico. Columns (2) and (4) of Table 6 confirm that by using different educational categories. The original selection on education in return migration is also reinforced in drier years.

Figure 4

The following robustness checks provide consistent results, though some of the result tables are not reported in the present article.

Mexicans who work in the agriculture sector may be more sensitive to rainfall. Controlling for the occupational dichotomous variable, Agriculture, and dropping observations without a job does not bring about any significant change in results. Agricultural workers may experience larger changes in wage differentials as well as migration costs when rainfall levels change. Distinguishing agricultural workers from nonagricultural workers, Tables A4 and A5 in Appendix show how rainfall affects migration selection on education in those subsamples, respectively.

The corresponding summary statistics are shown in Table A3 in Appendix.

Rainfall in previous years may have continuous effects on later migration decisions. Controlling for rainfall in previous years, for example, comparison of rainfall in the last year with rainfall during the preceding 8 years, provides consistent results.

Applying the Cox proportional hazard model to conduct migration duration analysis, Table A6 in Appendix indicates that migrating in drier years results in longer durations of stay in the US. This is consistent with higher migration costs as rainfall decreases.

Since the MMP asks respondents about their migration history to get retrospective data, recall errors may bias results. Limiting the author’s samples within the most recent 10 years, or 5 years, or from years after 1987 when Immigration Reform and Control Act (IRCA) was in the act in the US, regressions provide highly consistent results.

One may be concerned about the possible causal effects of rainfall on education, though most of the people in the sample finish their education, owing to the fact that the average years of schooling is about 5. Using the main samples and regressing years of schooling (E_i,y−1) on rainfall (R_s,y), Table A7 in Appendix shows that rainfall levels in the current year have no significant effects on education levels in the previous year when individual characteristics and different types of FE are controlled according to different specifications. In addition, limiting the study to people with a job or people whose ages are above 6 years combined with their years of schooling creates subsamples consisting of individuals who barely change their education levels. Employing those subsamples and repeating the empirical analysis give consistent results.

Though controlling for temperature may give a good robustness check, availability of data on state-level temperature is quite limited. The Conagua only provides data on temperature after 1985, while the main samples of the author cover years 1941–2012. National Oceanic and Atmospheric Administration (NOAA) has earlier records from different temperature stations, which were established in different years, presenting too many missing values for some states in Mexico. Using the temperature records from the stations, which are closest to capitals of states, brings measurement errors as well. With the limited data from NOAA, controlling for state-level temperature in regressions does not significantly alter the author’s results.