The Poverty Impact of Climate Change in Mexico

This paper examines the effects of climate change on poverty through the relationship between indicators of climate change (temperature and rainfall change) and municipal level gross domestic product, and subsequently between gross domestic product and poverty. The evidence suggests that climate change could have a negative impact on poverty by 2030. The paper proposes a two-stage least squares regression where it first regresses temperature and rainfall (along with geographic controls and state and year fixed effects) on municipal gross domestic product per capita for 2000 and 2005 The resulting gross domestic product per capita is used in a second equation to estimate municipal poverty on the same years. The authors then incorporate projections of temperature and rainfall changes by 2030 into the estimated climate-gross domestic product coefficients to assess the effects of climate change in economic activity and how this in turn will influence poverty. At the same time, they account for the potential adaptive capacity of municipalities through higher population densities and economic growth. Both would reduce poverty by 31.72 percentage points between 2005 and 2030 with changing climate. However, poverty could have been reduced up to 34.15 percentage points over the same period had there been no climate change. This suggests that climate change slows down the pace of poverty reduction. An alternative reading is that poverty is expected to increase from 15.25 percent (without climate change) to 17.68 percent (with climate change) by 2030. Given the existing population projections for 2030, this represents 2,902,868 people remaining in poverty as a result of climate change.

section_content: Global warming is expected to increase temperature and rainfall averages and their variability (IPCC, 2007). 2 Such changes are particularly worrying for developing countries because large portions of their populations rely on climate dependent activities, and have limited income to adapt. 

In response, economic analysis has attempted to estimate the possible impacts of climate change in developing societies. One strand of literature focuses on the link between income and climatic factors often looking at cross-country data, with most studies finding a negative relationship between income and temperature (Dell et al., 2009; Bansal, 2009). 

Another strand focuses on the effects of climate-related disasters on macroeconomic indicators or country-level variables, including GDP or its growth. Studies differ in their techniques, data and findings: Some encounter negative effects (Auffret, 2003), while others encounter positive effects (Skidmore and Toya, 2002). Loayza et al (2009) reconcile the seemingly contradictory results estimating the medium-term effects of climate-related disasters such as droughts, floods, storms (separately and simultaneously) on economic growth using a model with three main sectors (agriculture, industry, and services) and with the whole economy. Severe disasters have adverse effects regardless of type, but moderate floods, for instance, do increase industrial growth. 

All these studies of climate change effects or climatic disasters focus on local and economy-wide effects on output. However, it is known that output measures are an imperfect way to gauge society's overall well-being. Therefore, this paper will focus on the effects of climate change on poverty, as opposed to economic growth. 

A few studies have analyzed the possible impact of climate change on poverty in Mexico, some modeling the channels and the heterogeneity of impacts across income groups while others concentrate on poverty at the municipal level. Hertel et al. (2010) 3 use a global Computable General Equilibrium model (the Global Trade Analysis Project or GTAP) to mimic the effect of productivity shocks in agriculture (model the consumption and production for all commodities of the national economy), and then link those estimates to household data. They use three 2 Climate change is defined as the slow change in average temperatures and average precipitation predicted to result from the build-up of greenhouse gases in the atmosphere and not of climate variability. 3 scenarios of climate change on agricultural productivity (low, medium, or high productivity) 4 to evaluate the changes by 2030 on global commodity prices and the incidence on the poverty headcount rate (defined as the portion of the country's population living on less than $1 a day). The estimated relationships are then used to simulate the impact of the climate changes for the next 50 years. They find that average temperatures and precipitation are not significantly related to income. They assume that a negative relationship exists between per capita income and poverty, and so their conclusion is that Mexico appears to be less vulnerable to climate change than most other Latin American countries. However, the analysis is very crude. They do not estimate the poverty-output elasticity, and do not consider income and population projections which are accounted for in this paper. 

To assess the effects of climate change on poverty we propose a two-stage least squares regression (2SLS) where we first regress temperature and rainfall (along with municipal controls for elevation, longitude, latitude, distance from the municipality to the nearest road and state and year fixed effects) on municipal GDP per capita for 2000 and 2005, and then such predicted GDP per capita is used in a second equation to estimate municipal poverty on the same years. 

Changes in GDP per capita affect poverty and vice versa. This is why we use rainfall and temperature in a first stage to instrument GDP and hence circumvent the problem of endogeneity. In other words, we explain the variation in GDP per capita through differences in the climatic conditions of the municipality; and subsequently this serves to predict the effects of climate change on poverty. This exercise is carried out for poverty levels in the three officially-defined alternative measures of poverty for Mexico until last year: food The rest of this paper proceeds as follows. Section 2 introduces key definitions and data sources applied. Section 3 presents the empirical methodology followed by a discussion of the results in Section 4. Section 5 concludes.
section_name: Introduction

section_content: This study is carried out in 2,069 municipalities from all states in Mexico, representing 84%

of the total number of municipalities in the country. Missing municipalities are largely from Oaxaca and Puebla where climate model projections could not be fitted into smaller municipalities. Both states display pockets of high poverty (see Figure 3 ). However, their highly fragmented political geography, especially Oaxaca, it made unfeasible to analyze climate data (available at a resolution of 50 x 50 km approx) in some small municipalities. 

The analysis uses five types of information:
section_name: Data

section_content: Historic data on GDP per capita come from INEGI for 2000, 2005 and 2010. The geographic location of the municipality affects productivity, which in turn impacts poverty. Therefore we account for each municipality s latitude, longitude and elevation above sea level in the analysis. 

Distance from the municipality to the nearest federal and state road are also used as a proxy measure of connectivity.
section_name: GDP and Geographic Data

section_content: Historic weather data were imputed to municipalities using a Variable Infiltration Capacity Model (VIC) for Mexico, developed by Liang et al. (1994) 6. The VIC model interpolates data using grids by: i) creating correlation matrices between existing rainfall stations and radar stations that indicate the presence of rain, but cannot capture its amount; ii) computing the corresponding means for both types of stations from the grids within the municipality; and iii) using those values multiplying the values of the core stations by the means ratio to scale them, through the reconstruction of a climatic model. 

The climatic variables employed for the study are annual average precipitation (in millimeters) and temperature, both estimated from daily records. In the Pacific, the states of Sonora, Sinaloa, Nayarit, Guerrero and central Jalisco exhibit the greater increases in temperature. Central Mexico appears less warm than coastal regions; although some desertic areas in Hidalgo and Queretaro display temperature increases (see Fig. 1 ).
section_name: Climate and Weather Data

section_content: Annual rainfall will change between -169 mm and +57mm. However, as Fig. 2 shows, almost the entire country will be facing a drier world by 2030-39. Desertic areas in states like Chihuahua, Nuevo Leon, Zacatecas and San Luis will become drier, as will the states of Chiapas, Tabasco, Veracruz and Puebla.
section_name: Climate Change Scenarios

section_content: The integration of income and consumption data from household surveys with census data has enabled the creation of municipal poverty maps in Mexico 9. We rely on such information for 2000 and 2005, using income poverty levels in three officially-defined (until 2011) alternative

Fourth Assessment Report. 8 Information about the basic characteristics of the emissions scenarios used can be found at the SRES Emissions scenarios. http://sdwebx. worldbank. org/climateportal/index. cfm. 9 Briefly, poverty mapping involves, first, discovering relationships between household characteristics and the welfare level of households as revealed by the analysis of a detailed living standards measurement survey;

and second, applying a model of these relationships to data on the same household characteristics contained in a national census to determine the welfare level of all households in the census. The resulting estimates of household welfare and poverty derived from the census are spatially disaggregated to a much higher degree than is possible using survey information (Elbers et al., 2004; Bedi, Coudouel and Simler, 2007). A household is considered food poor if its members income falls below the lowest income necessary to afford a minimum basket of food. A household is considered to be capacity poor if its members cannot afford to cover their basic expenses on food, health and education, according to an officially defined basket. Finally, a household is considered to be in asset poverty if its members cannot cover their expenses of food, health, education, dressing, home and public transportation. 

Average asset poverty in our sample is 49. 4% (national average is 47%). Some of the poorest states in the country include Guerrero, Michoacan, Chiapas, Oaxaca, Puebla and Veracruz. Northern and Central states are typically wealthier, though without excluding pockets of poverty inside some of them as Fig. 3 3 shows.
section_name: Poverty

section_content: Population projections come from the National Population Council in Mexico (CONAPO). We consider population dynamics at the municipal level between 2005 and 2030. The population census is usually available every decade for Mexico, and inter-censal population counts also provide inputs for updating projections.
section_name: GDP and Population Projections.

section_content: The projection of GDP follows the methodology applied by Malone, et al (2004) the "Gross Value Added" obtained by INEGI through a census of production, both available for 2030 at municipal level. We assume that all the municipalities within the state grow at the same rate.
section_name: GDP Projections.

section_content: We propose a two-stage least squares regression (2SLS) to assess the effects of climate change on poverty. In a first stage we regress temperature and rainfall (along with geographic controls for municipal elevation, latitude, longitude and proximity to roads, and state and year fixed effects) on municipal GDP per capita, and then such predicted GDP per capita becomes our main variable of interest in a second equation were we regress GDP against poverty. 

Changes in GDP per capita affect household poverty and vice versa; this is why, in fact, we use rainfall and temperature in a first stage to instrument GDP and hence circumvent the problem of endogeneity between GDP and poverty at a second stage. In other words, we explain the variation in per capita GDP through differences in the climatic conditions of the municipality; and this in turn serves to predict the effects of climate change on poverty.
section_name: Estimation Strategy

section_content: We propose the following model to assess the effects of temperature and rainfall (and their projections due to climate change) on municipal income for municipality (county) m at year t:
section_name: Climate Effects on Income

section_content: Changes in GDP per capita affect household poverty and vice versa. This is why, in fact, we use rainfall and temperature in equation 1 to instrument GDP and hence circumvent the problem of endogeneity between poverty and output. For empirical applications of rainfall as an instrumental variable for economic conditions, see also Pyndick (2011), Miguel et al. (2004),

and Dercon and Christiaensen (2007). 

This instrumented log of GDP per capita through temperature and rainfall in equation 1 becomes our main regressor of interest in the OLS poverty model of equation 2; in other words, once instrumented output will be used as explanatory variables in the poverty equation to compute the relevant parameters. 

In equation 2 our dependent variable is headcount municipal poverty, and is constructed as follows:

where z is the poverty line, c is current income for household h, N is the total population size, and the total sum T is taken only on poor households ordered from bottom to top: c1, c2,. . .,cT. So P is equal to the share of the population which is poor. In equation 2, GDP is already weighed by the population for each municipality m. The term G mt represents the same municipal geographic characteristics as in model 1 (i. e., latitude, longitude, elevation and proximity to roads). 

5 Results.
section_name: Climate-Induced GPD Effects on Poverty

section_content: In this section we report the estimates of the cross-sectional two-stage least squares (2SLS) regression between poverty and per capita income, which was instrumented through temperature and rainfall in the first stage of the regression. 

The results of the climate-income relationship are presented in the lower panel of Table 1. 

There is a non-linear relationship between temperature and rainfall and per capita output. In our most complete specification (column 2), a 1mm increase in average precipitation reduces per capita income by 2. 72 percent on average; and a 1 Celsius degree increase reduces municipal per capita income by 5. 60 percent on average. 

The estimation also employs municipal controls that act as fixed effects in the model, such as geographic controls for location and elevation (latitude, longitude, elevation above sea level) and connectivity of the municipality (distance to the nearest primary and secondary road). 

Tests were developed to validate the use of the proposed instrumental variable model (2SLS). 

We tested the strength of our instruments. The common rule of thumb for models with one endogenous regressor is that the F-statistic against the null that the excluded instruments are irrelevant in the first-stage regression should be larger than 10. Our general test confirms the validity or joint significance of the model. We also confirmed the exogeneity of our instruments (i. e., the assumption that the instruments are not correlated with the error term in the equation of interest) through the Kleibergen-Paap test ( 2006 ) and Hansen J statistic tests.
section_name: Regression Results on Output

section_content: The relationship between per capita income and poverty is explored in the second-stage regression of municipal poverty in 2000 and 2005 against the predicted log of GDP per capita from equation 1. We added geographic fixed effects (latitude, longitude and elevation above sea level). 

We present estimates between per capita income and asset poverty (results for other poverty measures are available on request) in the upper panel of Table 1. As expected GDP growth decreases poverty. Our study finds poverty elasticity to growth of -1. 74%. In other words that, when average per capita GDP increases by 1 percent, the proportion of municipal poverty declines by around 1. 74 percent. Past studies have found a growth elasticity of poverty ranging from -2. 59 to -0. 69 (For a review see Székely and Foster, 2001).
section_name: Regression Results on Poverty

section_content: This section provides estimates of the poverty impacts of climate-driven changes in output. 

Thus far we have estimated the impact of historic temperature and precipitation on GDP per capita in each municipality. Now, we use IPCC's temperature and rainfall projections for 2030 tailored for Mexico to build a different climate vector for each municipality, which then helped to obtain the percent change in output induced by climate change. 

To calculate the effects of global warming on GDP in the study areas, we calculate the difference in GDP per capita at projected temperature and rainfall scenarios from the predicted GDP at the historical mean. Predicted impacts of climate change on output result from combining predicted coefficients in model ( 1 ) with climate projections. 

Our estimates suggest that global warming is expected to decrease output (GDP per capita) in the sample by 1. 45 percent, with municipality-specific estimates ranging from -2. 25 to +5. 86%. 

How much will such output declines due to climate change affect poverty? To obtain the poverty impacts of climate change by 2030, we have projected changes on temperature and rainfall and then obtained their effect on GDP in equation ( 1 ). Then we simulate poverty rates in equation ( 2 ) for the new projections of GDP per capita under climate change in 2030. 

In our sample, 48. Note: Poverty estimates at regional and state levels are population-weighted.
section_name: Simulated Impacts of Climate Change on Per Capita GDP and Poverty

section_content: Climate change will increase temperature and rainfall variability, which combined will reduce income in Mexico: GDP per capita for our sample of municipalities in Mexico will vary by 1. 45 percent by 2030 given projected changes in rainfall and temperature. 

It is hard to anticipate if output losses (due to climate change) will increase poverty in the study regions. Some municipalities will experience important readjustments in their productive composition and population mobility will modify the outlook. Households can adapt to changing climate conditions through spatial mobility and increased mean per capita output due to economic growth, so we account for both of these mechanisms over the next 25 years. 

The main impact of climate change on poverty is a reduction of the gains that would result from improved spatial mobility and increased mean per capita output due to economic growth adaptive capacity. This is based on the fact that higher population densities and prosperity reduce poverty. Our estimates suggest that population and economic growth reduce
section_name: Conclusion.

section_content: and insurance. 

Finally, the analysis presented here provides average estimates of income and poverty increases associated with temperature and rainfall changes. It is also necessary to assess the distributional impact of climate change across population groups.