Tohoku University Research Center for Policy Design Discussion Paper

TUPD-2023-006

The Impacts of Climate Change on Farmers andIndigenous Peoples’ Consumption: Evidence from Panama

Ambar Lineth Chavez Espinosa
Graduate School of Economics and Management, Tohoku University

Akira Hibiki
Graduate School of Economics and Management, Tohoku University

May 2023

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The Impacts of Climate Change on Farmers andIndigenous Peoples’ Consumption: Evidence fromPanamaBy AMBAR LINETH CHAVEZ ESPINOSA 1 AND AKIRA HIBIKI2

Abstract
Climate change is a significant challenge faced by tropical developing countries. While efforts
have been made to support vulnerable socio-economic groups such as farmers and indigenous
peoples, little is known about how climate change affects these groups. This paper provides
empirical evidence by estimating the impact of weather shocks (high temperature,temperature shock, and flood) on households' total consumption and its components (food andnon-food consumption) in Panama. The study aims to explore the heterogeneity of weather
shocks' impacts, specifically between households of indigenous and non-indigenous peoples,and farmers and non-farmers, thus contributing to the literature on the effects of belonging toa minority and indigenous group when facing climate change impacts. By combining repeatedcross-sectional data from surveys on 17,650 households with gridded climate data and flood
events information for the years 1997, 2003, and 2008, the study examines if there aredifferences in the negative impact due to weather shocks between farmers and non-farmers orindigenous and non-indigenous households. The main findings are as follows: Firstly, higher
temperature, temperature shock, and flood reduce consumption and their negative impact onfood consumption is smaller than non-food consumption. Secondly, there are significant
differences in the negative impact of heavy rain shocks between farmers and non-farmers.
Furthermore, there are significant differences in the negative impact of weather shocks (higher
temperature, temperature shock, and flood) on non-food consumption between indigenous
and non-indigenous households, while there is no significant difference in total consumption
and food consumption. Thirdly, the negative impacts of weather shocks on the consumptionof poor households are less than those on the consumption of non-poor households. Hence,indigenous households are more vulnerable to climate change than farmers and poor
households.

Keywords: Climate change, Indigenous peoples, Farmers, Weather shocks, Consumption,
Vulnerability
JEL classification: D13, J15, O10, Q10, Q54

1

Graduate School of Economics and Management, Tohoku University,(E-mail: chavez.espinosa.ambar.lineth.s5@dc.tohoku.ac.jp)
2
Graduate School of Economics and Management, Tohoku University, (E-mail: hibiki@tohoku.ac.jp))

1

I.

Introduction
Tropical developing countries are especially vulnerable to the negative impacts ofclimate change due to high levels of poverty, inequality, and unfavorable climatic conditions.
The frequency of extreme weather events such as floods, storms, and droughts, along withchanges in temperature and precipitation patterns, exacerbate the challenges faced by
impoverished communities (Nishio, 2021). This study examines the economic implicationsof climate change on agricultural productivity, with a focus on the impact on indigenous
peoples who are among the poorest and most vulnerable to these changes. Discrimination,
exploitation, and environmental hazards are some of the challenges faced by indigenous
communities who may also be forced to migrate. In Panama, where 12% of the population isindigenous, 96.3% live in poverty, and their way of life and traditions are closely tied tonatural resources, making them especially susceptible to the effects of climate change.
Current literature suggests that climate change has a disproportionate impact on lowincome groups in various countries, including the African continent. Researchers haveexamined how farmers and poor households perceive negative trends in their consumption
following climate shocks (Azzarri & Signorelli, 2019; Call, et al., 2019). However, thesestudies tend to focus primarily on poverty as a key factor contributing to climate vulnerability
and do not take into account the potential influence of ethnic differences. Likewise, to identify
how diversification of crops, investing more time in off-farm jobs, and migration could
significantly influence the impact of climate change on poverty dynamics in rural households,
Gao & Mills, (2018) analyze the implementation of coping strategies. Furthermore, Amare,et al., (2018) document the use of positive and negative rainfall shocks during the wet season
to explain the negative impact on rural household consumption in Nigeria. Nevertheless, theinclusion of temperature shocks and rainfall shocks in either of both seasons could also affectagricultural productivity (Man, 2023).
While previous literature has focused on African countries, it is crucial to consider theunique context of Latin American countries, as they have their own cultural, social, andeconomic characteristics and a wide variety of weather conditions that may influence theperception of climate change. Despite increasing attention to climate change in Panama, there
is no empirical economic study that examines how households respond to temperature andprecipitation variations. Panama is an excellent case study as its agricultural sector has a low
GDP share but comprises 16% of the country's labor force. Farmers in this sector are
particularly vulnerable to weather variations, limiting their ability to invest in new technology
and adaptation. Additionally, the most affected ethnic group by poverty in Panama is its
indigenous peoples, who face significant social, political, and economic challenges, and rely
heavily on agriculture and natural resources.
We contribute to the existing literature by providing empirical evidence on the impactsof the weather shocks (higher temperature, temperature shock, flood, etc.) on consumption
and its composition (food consumption and non-food consumption) at the household level,
addressing the consumption trade-offs that households make in order to maintain their overall
welfare during periods of weather shocks. We especially, focus on the vulnerability ofindigenous peoples and farmers to the weather shocks. Our main findings are: Firstly, higher
temperatures, temperature shocks, and floods reduce consumption and such negative shockon food consumption is smaller than non-food consumption. Secondly, there are no significant
differences in the negative impact of the weather shocks between farmers and non-farmers.
2However, there are significant differences in the negative impact (higher temperature,temperature shock, and flood) on non-food consumption between indigenous households andnon-indigenous households, while there is no significant difference in total consumption andfood consumption. Thirdly, the negative impacts of the weather shocks on the consumptionof poor households are less than those on the consumption of non-poor households.
The rest of the paper is organized as follows: Section II presents the socio-economic
background of Panama’s farmers and indigenous peoples. Section III describes the literature
review. Section IV discusses the data and model. Section V presents estimation results.
Section VI concludes.

II.

Socio-economic background
Panama presents fast economic growth that helps reduce poverty and inequality.
Nonetheless, the country was listed as the 4th most unequal in Latin America in 2018. Thiseconomic growth can be mainly attributed to three sectors: Construction; Wholesale, Retail,and Repair; and Transport and Communications. And even though agriculture is the secondlargest employer in Panama accounting for 16% of the total employment, its share within
GDP dropped from 4.6% in 2005 to 2.5% in 2015. Moreover, the demand for non-skilledworkers in the construction sector absorbed some of the labor released by agriculture(Hausmann, et al., 2016).
According to the Ministry of Environment of Panama, the variation in precipitation
provokes a change in sowing and harvesting periods, meanwhile, the temperature rise helps
the propagation of crop pests and diseases. As a result, the food safety of 52% of the rural
population is threatened (Ministerio de Ambiente, 2019).
Researchers measure the economic implications of climate change on agricultural
productivity at the national or regional level, which also aids in understanding the effects on
people's well-being, particularly those living in rural regions. However, climate change affects
nations around the world differently, with low-income countries being the most severely
impacted. And when examining how climate change is affecting countries with higher
inequality levels, it is discovered that the poor and vulnerable people are even more influenced(United Nations, 2016).
Indigenous peoples are particularly sensitive to climate change because they combine
several traits that no other socioeconomic group possesses. First, they are among the poorestof the poor, accounting for around 15% of the world's poorest people. Second, 70 million
indigenous peoples rely heavily on forests to meet their needs and despite only comprising
5% of the world's population, they are crucial to the sustainability of natural resources, where
they safeguard 80% of the planet's biodiversity and 22% of the Earth's surface. Due to theirextreme sensitivity to climate change, they encountered a substantial number of challenges,
and if forced to migrate, they face the risk of discrimination, exploitation, and environmental
hazards in their new locations. Furthermore, indigenous peoples' institutions and rights areusually ignored, which restricts their capacity to engage in decision-making, eliminates theirassessments, and prevents them from contributing any viable solutions for boosting their3

economy. Which in consequence could lead to the loss of their traditional knowledge,
practices, and ways of life (International Labour Office, 2017).
There are 7 indigenous groups in the Republic of Panama: The Ngäbe, Buglé, Guna,
Emberá, Wounaan, Bri bri, and Naso. According to official statistics, 96.3% of Panama's
indigenous population lives in poverty (84.8% in extreme poverty), and as of the 2010
national census, there were 417,559 indigenous peoples or 12% of the nation's overall
population. Panama’s indigenous people are mostly located in 5 different regions alongside
the country which are denominated as comarcas and comprise 1,7 million hectares. Each ofthese regions was recognized by different laws based on the constitutional rights of indigenous
peoples. Moreover, the religion, traditions, and lifestyle of the indigenous peoples of Panama
are highly connected to the use of natural resources. For instance, the main sources of income
for the Naso and other indigenous populations in Panama are farming, fishing, and agriculture.
Rivers are essential to the Emberá people's way of life and cosmology, whilst the Naso people
are leading a massive international movement to protect the biodiverse flora and fauna in thejungle where they have lived for centuries. Additionally, some goods, like cacao, have beenused in ceremonies and festivities throughout Central America for thousands of years and are
fundamental to their culture and lifestyle (Sarsanedas, 2014).
According to the Climate Change Vulnerability Index Report released by the Ministryof Environment of Panama, places with a large indigenous population have a vulnerability
value between 0.75 and 1, on a scale from 0 to 1, with 1 being assigned to the most vulnerable
locations. This index is derived by taking into account factors such as the degree of poverty,
the availability of public services, and infrastructure, as well as environmental and climatic
factors like the type of soil, the amount of precipitation, the average temperature, and thefrequency of floods and droughts. Most indigenous people live in areas that lack basic
infrastructure, have a high concentration of multifaceted poverty, and have enhanced
ecological sensitivity, which leads to a limited ability to adapt to climate change (Calderón,
et al., 2021).
The Intertropical Convergence Zone, where the Republic of Panama is located,
experiences heavy rainfall and high humidity from May to December. Except for the
Caribbean basin, where precipitation is frequent nearly all year, the dry season, which lasts
from January to April, features significantly less rainfall. The nation's water resources are asignificant economic asset. For instance, the optimal operation of the Panama Canal, which
contributes around 6% of Panama's GDP, depends on its network of watersheds. Additionally,hydropower accounts for 48% of Panama's electrical production (Perlman & Pava, 2019).
Extreme weather conditions are known to have a negative impact on the economy ofPanama. The government reported $102 million in losses in 2016 from drought-related
disasters, and $149 million in losses from flood damage in 2010, with the agriculture sector
being the most severely impacted. Given the lack of a structure for disaster risk management
and land use planning, together with the conditions of fast economic expansion andurbanization since the Panamanian administration of the Panama Canal in 2000, the risk fromweather disasters has increased. There were 2717 natural disasters in the Republic of Panama

4

from 1990 to 2013, of which 57% of them were floods, 17% were tropical storms or strong
winds, and 15% were landslides. (Gordon, 2014).

III.
Literature review
Due to low agricultural productivity in developing nations, the effects of abrupt changes
in temperature and precipitation on agricultural activities are particularly important. In
developing countries, the majority of farmers lack the necessary financial resources andphysical infrastructures, such as an irrigation system, adequate storage space, and farmmachinery, therefore their outcomes are largely dependent on weather variations (Call, et al.,2019). This scenario is common in Panama, where only 5% of farmers use technology toenhance their operations according to the 2010 Panama Agriculture Census.
There is an increasing number of studies that address the relationship between climatechange and consumption in African countries, where the combination of certain climate
conditions and the high presence of poverty has aggravated the climate-induced poverty trap.
Azzarri and Signorelli (2019) found that floods in countries of Africa South of the Sahara,
negatively affect farmers, more disproportionately smallholders, with a roughly 20% drop in
per capita expenditure. In addition, they found that droughts represent a better outcome inWest Africa and heat waves seem to be beneficial in Central Africa. On the other hand, somesmallholders in Uganda benefit in the short term when they experienced above-averagetemperatures, although after a decade crop yield declines (Call, et al., 2019). Adaptation
strategies are also implemented by some smallholders in Malawi where current and past
weather patterns lead households to devote more time to maize cultivation, adopting improved
maize seed varieties, while abandoning other potentially more remunerative opportunities
elsewhere. They also reduce the application of productivity-enhancing inputs, such as
fertilizer in response to adverse weather history (Sesmero, et al., 2017). These results suggestthat adaptation and coping mechanisms are not always in the same direction, while some
farmers might choose to use more technology and resources to keep producing the same kindof crops, others prefer to produce other varieties of crops which decreases the costs of some
inputs.
Letta, et al. (2018) exhibit that one standard deviation rise in temperature anomalies
reduces household per-adult total consumption growth by roughly 2.21% using a three-wave
household longitudinal dataset encompassing the years 2008 to 2013 in Tanzania. They
implement the interaction of weather shocks with poverty to provide insight into theheterogeneous impact of climate change in Tanzania, where most poor households depend on
agricultural activities and experience the direct effects of climate change. On the contrary, not
all farmers in Panama are poor, and not all the country's poor inhabitants work in agriculture.
Hence, our study examines heterogeneity using the interactions between climate variables anddummy variables for agriculture and indigenous households.
Although previous studies by Azzarri and Signorelli (2019) and Letta et al. (2018) haveexamined the effect of weather shocks on total consumption and food consumption in Africancountries, the changes in the ratio between food and non-food consumption have not been5

thoroughly investigated. Our study aims to bridge this gap by exploring the consumption
trade-offs that households make to maintain their overall welfare during times of weather
shocks.
Previous studies focus on the negative effects experienced in rural areas, even though
there is evidence that low-income inhabitants in urban areas of developing countries in Latin
America also struggle to cope with climate abnormalities. Desbureaux and Rodella (2019)
explored how droughts in metropolitan areas caused harm using data from 78 cities, including
Panama City, and they found that droughts have contributed to a decrease in the probabilityof an active worker being employed, as well as the number of hours worked. They impliedthat this could be a result of electric shutdowns, due to Latin American countries’ highdependence on hydropower, or it can be also explained by the increase in diarrhea, infections,and other conditions that are a consequence of the lack of drinkable water. Additional results
also reveal that there was an 8% decrease in labor incomes for informal workers during
droughts from 2005 to 2014. This study offers a more comprehensive understanding of thesituation in Latin American countries and although there are close socioeconomic similarities
among most of these countries, certain individual factors and weather patterns must be taken
into account to fully comprehend how extreme weather events affect each country's
population.
Our contribution is to understand the importance of preexisting socioeconomic factors
such as ethnicity when responding to climate change. We found that being a farmer or
belonging to an indigenous group in Panama increases susceptibility to the effects oftemperature and precipitation variability. This reveals that contrary to studies done in Africancountries, poverty itself has little influence on how vulnerable people are to climate change.

IV.
Data and Model
A. DataIn this study, we use Panama’s Living Standards Measurement Surveys (LSMS) whichwere collected and administrated by the World Bank and the Ministry of Economics andFinance of Panama. They provide repeated cross-sectional data of 4876 households in theyear 1997, 6032 in 2003, and 6742 in 2008. The objective of these surveys was to collectinformation on living conditions and determine an approximation of the poverty incidence inPanama. The questionnaires did not ask for the ethnicity of the surveyed, however, they ask
for the householder’s first language, which is a reliable proxy for indigenous ethnicity in thiscase (Fuentes Cordoba, 2019). After the year 1997, Panama presents several political divisionchanges, for that reason, corregimientos3 and districts from the years 2003 and 2008 were
aggregated to match the political divisions presented in 1997.
In the Living Standard Measurements Surveys, the definition of head of household is:“habitual resident of the house that’s recognized as the head by the other members of thehouse, given by the nature of his/her responsibilities, type of decisions that he/she has to take,3

Corregimientos are the administrative subdivisions of districts in Panama.

6prestige, relationship within the family, economic reasons, or due to social and cultural
traditions”. For that reason, we used household characteristics given their role and influenceon the household's social and economic position.

Table 1. Summary statistics
Variables
Consumption per capita (USD)

Obs
17650

Mean
2750.28SD
3019.72

Min
38.80

Max
90159.89

17588

913.31

711.232

6.49

12512.49

17588

1838.772542.06

10.12

81265.46

17650
17650
17650
17650
17650

4.07
48.52
0.75
8.51
1.55

2.46
15.81
0.43
4.56
1.09

1
15
0
0
0

25
101
1
18
10

17650
17650
17650
17650
17650

0.09
0.22
0.53
0.30
0.02

0.28
0.42
0.50
0.46
0.14

0
0
0
0
0

1
1
1
1
1

17650
17650
17650
17650

25.6
0.11
0.21
0.01

1.61
0.31
0.41
0.11

18.3
0
0
0

29.1
1
1
1

4

Food consumption per capita(USD)
Non-food consumption percapita (USD)
No. of members per household
Age of householder
Male householder
Householder years of education
Members working perhouseholdIndigenous ethnicity
Works in agriculture
Lives in an urban area
Lives in poverty
Lives in a corregimiento thatexperienced a floodMonthly average temperature
Temperature positive shock5
Precipitation positive shock
Precipitation negative shock

Table 1 displays that the number of household members has a maximum of 25 people,
this is common among indigenous households, in which a “typical” indigenous household
there is an average of two-room with 5-6 members per room. Additionally, the minimum age
for householders is 15 years old, there is a small number of cases where householders are lessthan 18 years old which is Panama’s legal majority of age. Thus, the distribution is mainly
concentrated around the mean age. The percentage of indigenous people in the sample is 9%,
which is close to the proportion of indigenous people in Panama’s total population during
these years (Central Intelligence Agency, 2022). To measure the poverty line each year,
LSMS uses consumption as an indirect measure of welfare, weighting total consumption by
the regional price index. The general poverty line is calculated based on the extreme poverty
line, and people below the extreme poverty line are individuals that can hardly meet their
4

All information for consumption per capita is provided in real value, 2010=100

Our study did not include a temperature negative shock variable as no negative temperature shock
was observed during the years of analysis when using 1 standard deviation.
5

7

minimum calorie requirements even if they spent their entire resources on food consumption.
The number of householders that work in the agriculture sector is 22% approximately, whichis also not far from the real total population percent that was reported around these years(Hausmann, et al., 2016). Additional summary statistics for subsamples of farmers andindigenous peoples are provided in Appendix Tables A2 and A3.
We obtain climate data from the Climatic Research Unit (CRU) at the University of
East Anglia Datasets. Since ground stations and satellite-based datasets have some drawbacks,
climate scientists developed different kinds of gridded data products, which are a result of theinterpolation among ground stations. Gridded datasets are usually used as a good source oftemperature data for economic analysis because they adjust for issues like missing station dataand elevation. (Letta, et al., 2018; Sesmero, et al., 2017). Nevertheless, like any other gridded
dataset it faces challenges when estimating precipitation. In comparison to temperature,
rainfall has a greater spatial variation therefore it is more difficult to interpolate. This issue isvery common in middle-income and developing countries because of the lack of stations
within a big number of gridded cells, although CRU provides a monthly global average ofinterpolated gridded temperature and precipitation data of over 4000 weather stations with aspatial resolution of 0.5° latitude by 0.5° longitude. Coordinates of corregimientos were used
to link these gridded weather data to households’ data. After comparing with other 2 different
weather data sources (satellite and ground stations), CRU provided the most consistent andreliable results for this analysis.
To assess the impact of weather on household consumption, we used monthly averagetemperature data from the year before each survey. We then grouped this data into different
temperature bins based on the distribution of monthly average temperatures for eachcorregimiento during the study period. The temperature bins we used were defined as follows:[18-22.9℃], [23-24.9℃], [25-26.9℃], and [27-29.1℃]. Daily temperatures in Panama can
exceed 30 degrees Celsius. However, since we used average monthly temperature data, themaximum monthly temperature was 29.1℃. The frequency of corregimientos experiencing a
range of temperatures is presented in Figure A1, as well as the spatial temperature distribution
for the study years, shown in Figure A2.
Since previous years' monthly average temperature and precipitation do not necessarily
account for historical variations or sudden weather changes. Similarly, to Amare, et al.
(2018) we constructed dummy variables for positive and negative weather shocks to capture
temperature and precipitation shocks as deviation from the 30-year average as follows:
̅
−𝑊

𝑊

𝑖𝑡
Positive weather shock =1 if ( 𝑖𝑡−1
) >𝜎
𝑊 𝑆𝐷
𝑖

Negative weather shock=1 if (̅ 𝑖𝑡
𝑊𝑖𝑡−1 −𝑊
𝑊𝑖𝑆𝐷

) < −𝜎where 𝑊𝑖𝑡−1 is the previous year's temperature or precipitation during the wet or dry season
̅𝑖𝑡 is the historical average of temperature orat the location of household i for year t. 𝑊
precipitation during the wet or dry season for 30 years at the location of household i. 𝑊𝑖𝑆𝐷 is8the standard deviation of temperature and precipitation during the dry or wet season at thelocation of household i (calculated over 30 years).
A positive weather shock is defined when the monthly average temperature or precipitationof either the wet or dry season of the previous year was (σ) standard deviation above the longterm average of their respective season, while a negative weather shock is defined when themonthly average temperature or precipitation of either the wet or dry season of the previous
year was (σ) standard deviation below the long-term average of their respective season. To
account for the fact that longer-term changes in temperature can have a more sustained impact
on production and consumption, a value of (σ) = 1 is used for temperature shocks, whichenables the capture of the effects of gradual temperature changes that are particularly relevantto the agricultural sector. In addition, a value of (σ) = 1.5 is used for precipitation shocks
given the higher precipitation variability in tropical countries, thereby allowing for themeasurement of the impact of events that could have a severe and immediate effect on
agricultural production.
The information for floods used in this study is obtained by DesInventar.net which isa methodological tool that provides inventories for databases of damage, losses, and generaleffects of disasters. It contains the number of victims, people affected, and damage provokedby floods and other natural disasters in Panama within 1 year before each LSMS was taken(1997, 2003, 2008). To measure the impact of floods on income and consumption, manystudies use the frequency of floods within an area without considering if there were people
affected or not, accordingly, we first employ the dummy variable for floods being equal to
one if any flood happens in a corregimiento without taking into consideration the economic
losses or the number of people affected. The definition of people affected in DesInventar.net(2022) is “number of persons who suffer indirect or secondary effects associated with adisaster. These persons, different from “victims”, suffer the impact of secondary effects ofdisasters for reasons such as deficiencies in the provision of public services, the hampering oftrade and work, isolation, or their mental health may be affected”. Since the purpose of thisstudy is to provide evidence on how floods can affect households’ consumption and because
the presence of floods doesn’t necessarily imply an economic impact on inhabitants of certain
areas, in the main results we use a new dummy variable for floods considering the number ofpeople that were affected directly and indirectly, where “Lives in a corregimiento thatexperienced flood” is equal to one if the flood affected more than 10% of the population thatwas living in this corregimiento within one year before each LSMS was collected.
Monthly precipitation data may not be sufficient to denote the presence of floods insome regions since multiple criteria such as the total quantity of rainfall over a period of time,
the condition of the soil, the lack of vegetation, and the proximity to rivers must also be taken
into account. By incorporating a flood dummy variable, we can more accurately examine how
heavy rain affects consumption given pre-existing geological conditions of the area.

B. ModelTo explore the impact of flood, temperature, and precipitation on total consumption,
food consumption, and non-food consumption, we formulate the following equation as thebase model.
9𝑏log(𝑌𝑖𝑡 ) = 𝛽0 + 𝛽1 𝑋𝑖𝑡 + ∑ 𝛽2 𝑊𝑏𝑖𝑛𝑐𝑡+ 𝛽3 𝐹𝑖𝑡 + 𝛽4 𝑇𝑆𝑖𝑡 + 𝛽5 𝑃𝑛𝑠𝑖𝑡 + 𝛾𝐴𝑔𝑟𝑖𝑖 + 𝜃𝐼𝑛𝑑𝑖 +𝜇𝑑𝑡 + 𝑇𝑐 + 𝑢𝑖𝑐𝑡 (1)where 𝑌𝑖𝑡 denote outcome measures such as total consumption per capita, food consumption
per capita, and non-food consumption per capita. In addition, household characteristics suchas the number of members, age of the householder, number of members employed,householder education level, and occupation were also included ( 𝑋𝑖𝑡 ). Bin variables were𝑏
constructed using monthly average temperature (𝑊𝑏𝑖𝑛𝑐𝑡), while dummy variables were used
to indicate whether a household experienced a flood(𝐹𝑖𝑡 ), temperature shock(𝑇𝑆𝑖𝑡 ), negative
precipitation shock (𝑃𝑛𝑠𝑖𝑡 ) or was an agricultural household (𝐴𝑔𝑟𝑖𝑖 ) or belonged to an
indigenous ethnicity (𝐼𝑛𝑑𝑖 ). 𝜇𝑑𝑡 , 𝑇𝑐 and 𝑢𝑖𝑐𝑡 capture year by district effects, corregimiento
fixed effects, and the error term. The inclusion of fixed effects controls for the time-variant
heterogeneity across the districts.
Weather bin variables represent the number of months per year that the averagetemperature of a corregimiento was contained in a temperature band or “bin”. Averagemonthly temperature bin variables capture the relationship between temperature andconsumption per capita. They can provide insights into how consumption per capita responds
to variations in temperature across different temperature ranges. Temperature shocks, on theother hand, capture the short-term relationship between temperature and consumption percapita. They represent sudden, unexpected changes in temperature that may have an
immediate impact on consumption per capita.
The flood variable captures the event occurrence, whereas a positive precipitation shock
indicates a noteworthy deviation from anticipated precipitation levels. Although not aconclusive predictor of flooding, it can signify heavy rainfall events that saturate the soil,
leading to instability and a heightened risk of landslides and soil erosion. Conversely, a
positive precipitation shock can also signify an increase in the availability of water resources,
benefiting industries reliant on water, such as agriculture and hydroelectric power generation.
We use consumption as our dependent variable because it can account for individuals’
welfare differences. While income estimations sometimes are not provided for informal
workers or for those who rely on subsistence agriculture. Consumption measurements
provided by the LSMS account for the annual consumption of food (both purchased and nonpurchased, including self-consumption), housing (using an imputed value for self-owned
housing), durable consumer goods, consumer goods, and service expenses, basic services(such as water, gas, and electricity), as well as health and education expenditures.

𝑏𝑏log(𝑌𝑖𝑡 ) = 𝛿0 + ∑ 𝛿1 𝑊𝑏𝑖𝑛𝑐𝑡
× 𝜋𝑖 + ∑ 𝛿2 𝑊𝑏𝑖𝑛𝑐𝑡+ 𝛿3 𝐹𝑖𝑡 × 𝜋𝑖 + 𝛿4 𝐹𝑖𝑡 +𝛿5 𝑇𝑆𝑖𝑡 × 𝜋𝑖 + 𝛿6 𝑇𝑆𝑖𝑡 + 𝛿7 𝑃𝑛𝑠𝑖𝑡 × 𝜋𝑖 + 𝛿8 𝑃𝑛𝑠𝑖𝑡 + 𝛿9 𝑋𝑖𝑡 + 𝛾𝜋𝑖 + 𝜇𝑑𝑡 + 𝑇𝑐 + 𝑢𝑖𝑐𝑡 (2)10For alternative specification to capture the heterogenous effect of flood and weather
variables by minority/non-minority and farmer/non-farmer, we used interaction terms of thedummy variable of interest (farmer, indigenous or poor), 𝜋𝑖 , with the weather variables inequation (2).
V.

Results
The baseline estimates from equation (1) are reported in Table 2. We find thattemperatures between 27°C and 29.1°C have a significant negative influence on households'
total consumption per capita, food consumption per capita, and non-food consumption percapita by 3.8 %, 4.4 %, and 3.3 %, respectively. Temperature shocks are significant with anegative sign for total consumption per capita and non-food consumption per capita, while itis not significant for food consumption per capita. They reduce non-food consumption to
avoid a decrease in food consumption since food is more important than non-food. On theother hand, floods are significantly negative for total consumption per capita, foodconsumption per capita, and non-food consumption per capita. It should be noted that thenegative impact on non-food consumption per capita is larger than food consumption percapita. Households reduce their non-food consumption to lessen the negative impact on foodconsumption due to flood shocks, similar to temperature shocks. However, due to the larger
negative impact of flood shocks on food consumption, households are unable to fully offset
its effects despite reducing their non-food consumption.

Table 2. Effect of climate change on households’ consumptions
Log total
Log food
consumption
consumption
per cap (USD) per cap (USD)
Bin temperature 23°_24.9°C-0.0044-0.0071(0.0094)(0.0097)
Bin temperature 25°_26.9°C-0.0080-0.0082(0.0067)(0.0072)
Bin temperature 27°_29.1°C-0.0386***-0.0441***(0.0100)(0.0104)
Temperature shock-0.1503*-0.0602(0.0794)(0.0781)
Precipitation negative shock
0.00220.0683(0.1419)(0.1260)
Lives in a town that
-0.1916***-0.1047**experienced a flood(0.0484)(0.0500)
Farmer
-0.0410*-0.0258(0.0222)(0.0222)
Indigenous-0.2790***-0.1916***(0.0235)(0.0257)
No. of members per household -0.1597***-0.1334***(0.0025)(0.0026)
Male householder
0.1078***0.0953***(0.0103)(0.0106)
Age of householder
0.0249***0.0124***
11

Log non-foodconsumption
per cap (USD)-0.0097(0.0113)-0.0113(0.0079)-0.0334***(0.0122)-0.2553***(0.0978)-0.0815(0.1780)-0.2553***(0.0582)-0.0802***(0.0279)-0.3935***(0.0286)-0.1788***(0.0030)0.1114***(0.0124)0.0323***

Age of householder^2Householder years ofeducation
Members working perhouseholdLives in an urban area
Observations
R-squared
Adjusted R-squaredF
df_m(0.0015)-0.0002***(0.00001)
0.0483***(0.0013)0.0751***(0.0050)
0.1978***(0.0162)
17650
0.704
0.695
72.32***
541(0.0016)-0.0001***(0.00001)
0.0242***(0.0013)0.0473***(0.0049)
0.1161***(0.0167)
17588
0.525
0.510
32.5***
541(0.0019)-0.0002***(0.00001)
0.0645***(0.0016)0.0957***(0.0060)
0.2555***(0.0201)
17588
0.724
0.716
87.20***
541

Robust standard errors are in parentheses. * p < 0.1, ** p < 0.05, *** p < 0.01.

Table 3 presents the findings on the heterogeneous impacts between farmers and nonfarmers. The analysis indicates that temperature shocks and flood variables have a significant
negative effect, while their interaction terms with the farm's dummy variable are insignificant.
Our results show that floods decrease total consumption, food consumption, and non-foodconsumption, but we do not observe any statistical differences in the impacts between farmers
and non-farmers. Notably, the negative impacts on total consumption and non-foodconsumption are larger than those on food consumption because of the same reason as is in
the case of Table 2. The bin variable of 27~29.1 ℃ is significant with the negative sign. This
indicates that higher prices caused by a reduction in crop production due to higher temperature
reduces not only food consumption but also non-food and total consumption. It is worth
mentioning that the interaction terms of this bin variable with the farmer’s dummy variable
for food expenditure are significantly positive indicating that such an impact on the farmer’s
food expenditure is smaller. This could be due to a variety of factors, such as greater accessto food through agricultural production, the ability to store food for longer periods, and thedecreased sales of their crop production to consume them by themselves.

Table 3. Heterogenous effects of climate change (farmers and non-farmers)
Log total
Log food
Log non-foodconsumption
consumption
consumption
per cap (USD) per cap (USD) per cap (USD)
Bin temperature 23°_24.9°C-0.0003-0.0050-0.0028(0.0095)(0.0098)(0.0114)
Farmer # Bin temperature-0.0163***-0.0081-0.0258***
23°_24.9°C(0.0062)(0.0066)(0.0075)
Bin temperature 25°_26.9°C-0.0056-0.0068-0.0068(0.0069)(0.0074)(0.0081)
Farmer # Bin temperature-0.0056-0.0040-0.0101**
25°_26.9°C(0.0039)(0.0041)(0.0048)
Bin temperature 27°_29.1°C-0.0401***-0.0487***-0.0295**(0.0103)(0.0107)(0.0124)
12

Farmer # Bin temperature
27°_29.1°C
Temperature shock

0.0059(0.0059)-0.1591**(0.0811)
Farmer # Temperature shock
0.0291(0.0334)
Precipitation negative shock
0.0059(0.1485)
Farmer # Precipitation negative -0.0256
shock(0.0750)
Lives in a town that
-0.1926***
experienced a flood(0.0492)
Farmer # Lives in a town that
0.0158experienced a flood(0.0829)
No. of members per household -0.1599***(0.0025)
Male householder
0.1079***(0.0103)Age of householder
0.0248***(0.0015)
Age of householder^2-0.0002***(0.00001)
Householder years of
0.0483***
education(0.0013)
Members working per
0.0754***household(0.0050)
Lives in an urban area
0.2000***(0.0162)
Observations
17650
R-squared
0.705
Adjusted R-squared
0.695
F
71.61***
df_m
547

0.0155***(0.0060)-0.0746(0.0800)
0.0310(0.0331)0.0603(0.1290)-0.0170(0.0741)-0.0961*(0.0499)-0.0467(0.0978)-0.1336***(0.0026)
0.0953***(0.0106)
0.0124***(0.0016)-0.0001***(0.00001)
0.0242***(0.0013)0.0476***(0.0049)
0.1182***(0.0167)
17588
0.526
0.511
32.29***
547-0.0088(0.0073)-0.2552**(0.0998)
0.0051(0.0417)-0.0480(0.1897)-0.0840(0.0964)-0.2530***(0.0600)
0.0062(0.0998)-0.1791***(0.0030)0.1116***(0.0124)0.0323***(0.0019)-0.0002***(0.00001)
0.0645***(0.0016)
0.0960***(0.0060)
0.2582***(0.0201)
17588
0.725
0.716
86.09***
547

Robust standard errors are in parentheses. * p < 0.1, ** p < 0.05, *** p < 0.01.

Table 4 presents the results of the analysis of the heterogeneous impacts between
indigenous and non-indigenous households. The results for the effects of temperature shocks
and flood are similar to those in Table 3, indicating that they significantly reduce consumption
and that there is no difference between indigenous households and non-indigenous households
except for the impact of temperature shock on non-food consumption. Since indigenous
households are generally poor, the share of their food consumption is higher. This is why they
reduce non-food consumption to avoid a reduction in food consumption. We also find that thebin variable of 27~29.1°C is significantly negative for all types of consumption and theinteraction term of the bin variable with the indigenous dummy variable is insignificant forfood consumption but significantly negative for non-food consumption. This is because
indigenous households are likely to reduce non-food consumption to avoid a reduction in food13

consumption as is in the case of temperature shock. Interestingly, precipitation negative shock
capturing the impact of drought is not significant but the interaction term of the shock with
the indigenous dummy variable for non-food is significantly negative only for non-foodconsumption but not for other consumption. This may be because indigenous households
cannot adapt to the drought because of a lack of financial resources to mitigate its negative
impact.
We examined the effect of heavy rainfall by substituting our flood variable with a
precipitation positive shock in Tables A1 and A2. The results demonstrate that farmers
experience larger negative impacts on their consumption, while indigenous peoples do notshow any significant differences in the impact of heavy rainfall.

Table 4. Heterogenous effects of climate change (indigenous and non-indigenous peoples)
Log total
Log food
Log non-foodconsumption
consumption
consumption
per cap (USD) per cap (USD) per cap (USD
Bin temperature 23°_24.9°C-0.0009-0.0088
0.0001(0.0098)(0.0100)(0.0116)
Indigenous # Bin temperature-0.0328***-0.0194-0.0512***23°_24.9°C(0.0120)(0.0127)(0.0152)
Bin temperature 25°_26.9°C-0.0074-0.0118-0.0058(0.0070)(0.0075)(0.0083)
Indigenous # Bin temperature
0.00290.0113*-0.0075
25°_26.9°C(0.0061)(0.0068)(0.0074)
Bin temperature 27°_29.1°C-0.0361***-0.0474***-0.0231*(0.0104)(0.0107)(0.0125)
Indigenous # Bin temperature-0.0163-0.0005-0.0338**27°_29.1°C(0.0125)(0.0128)(0.0154)
Temperature shock-0.1343*-0.0601-0.2023**(0.0807)(0.0793)(0.0994)
Indigenous # Temperature shock-0.02430.0293-0.1411**(0.0537)(0.0570)(0.0662)
Precipitation negative shock 1.5
0.00760.0709-0.0668(0.1419)(0.1259)(0.1784)
Indigenous # Precipitation-0.0840-0.1064-0.3061**
negative shock 1.5(0.1187)(0.1297)(0.1454)
Lives in a town that experienced a -0.1921***-0.1156**-0.2303***flood.
(0.0479)(0.0491)(0.0586)
Indigenous # Lives in town that
0.01150.0577-0.0958
experienced flood(0.0905)(0.1075)(0.1020)
No. of members per household-0.1599***-0.1334***-0.1793***(0.0025)(0.0026)(0.0030)
Male householder
0.1073***0.0949***0.1108***(0.0103)(0.0106)(0.0124)
Age of householder
0.0249***0.0124***0.0323***14

Age of householder^2Householder years of education
Members working per household
Lives in an urban area
Observations
R-squared
Adjusted R-squaredF
df_m(0.0015)-0.0002***(0.00001)
0.0484***(0.0013)0.0755***(0.0050)
0.1999***(0.0162)
17650
0.705
0.695
71.71***
547(0.0016)-0.0001***(0.00001)
0.0241***(0.0013)0.0474***(0.0049)
0.1160***(0.0167)
17588
0.526
0.510
32.16***
547(0.0019)-0.0002***(0.00001)
0.0647***(0.0016)
0.0967***(0.0060)
0.2609***(0.0201)
17588
0.725
0.716
87.13***
547

Robust standard errors are in parentheses. * p < 0.1, ** p < 0.05, *** p < 0.01. District, year, district x year
fixed effects, and corregimiento fixed effects are included

In our study, we aimed to investigate whether poverty is the primary determinant ofvulnerability to climate change. We used interaction terms of a poverty dummy variable withweather variables to evaluate the heterogeneous effects of weather on poverty. Our results,
presented in Table 5, demonstrate that poor households with temperature shocks have asmaller negative impact on total and food consumption compared to Letta et al.'s (2018) study.
However, the negative impact of precipitation shocks is larger on poor households, similar towhat we found in Table 4. Since our poverty dummy variable reflects the effect perceived bypoor farmers and indigenous peoples, we further analyzed the impact of climate change on
non-farmers and non-indigenous households in Table A3. This allows us to account for the
impact on households that do not directly depend on agricultural activities. The results showthat these households do not have significant impacts from monthly temperature variations.
Temperature shocks have a significant negative impact on non-agricultural non-indigenous
households' consumption, plausibly due to higher food prices making it more difficult toafford sufficient quantities of food and other goods. However, poor households experience
less negative impact from temperature shocks and floods than non-poor individuals, possibly
due to some poor households having greater access to government assistance for severe floods
and food fairs in their communities when prices increase. Overall, our study suggests thatpoverty in Panama does not necessarily render individuals vulnerable to temperature
variations, and negative precipitation shocks do not seem to have a significant negative impact
on consumption, contrary to previous literature's suggestions.

TABLE 5. Heterogenous effects of climate change (poor and non-poor)
Log total
Log food
consumption
consumption
per cap (USD) per cap (USD)
Bin temperature 23°_24.9°C-0.0058-0.0119(0.0081)(0.0090)
Poor # Bin temperature 23°_24.9°C -0.0025
0.0070(0.0046)(0.0059)
Bin temperature 25°_26.9°C-0.0119**-0.0136**
15

Log non-foodconsumption
per cap (USD)-0.0077(0.0101)-0.0135**(0.0059)-0.0148**

Poor # Bin temperature 25°_26.9°C
Bin temperature 27°_29.1°C
Poor # Bin temperature 27°_29.1°C
Temperature shock
Poor # Temperature shock
Precipitation negative shock
Poor # Precipitation negative shock
Lives in town that experienced
flood=1
Poor # Lives in town thatexperienced flood
No. of members per household
Male householder
Age of householder
Age of householder^2Householder years of education
Members working per household
Lives in an urban area
Observations
R-squared
Adjusted R-squaredF
df_m(0.0057)
0.0025(0.0026)-0.0346***(0.0085)0.0071*(0.0041)-0.1196*(0.0639)0.0422*(0.0238)
0.0259(0.1057)-0.0998*(0.0600)-0.1966***(0.0427)0.0446(0.0485)-0.1080***(0.0021)0.0718***(0.0091)
0.0168***(0.0013)-0.0001***(0.00001)
0.0364***(0.0011)0.0439***(0.0041)
0.1179***(0.0135)
17650
0.783
0.776
112.93***546(0.0066)
0.0076**(0.0033)-0.0434***(0.0094)
0.0148***(0.0049)-0.0617(0.0664)0.0819***(0.0273)
0.1244(0.1022)-0.1292*(0.0660)-0.1118**(0.0468)
0.0492(0.0605)-0.0889***(0.0024)0.0657***(0.0097)
0.0057***(0.0015)-0.00001***(0.00001)
0.0138***(0.0012)0.0207***(0.0043)0.0459***(0.0149)
17588
0.612
0.599
48.82***
546(0.0071)-0.0024(0.0034)-0.0255**(0.0108)-0.0037(0.0053)-0.1784**(0.0878)-0.0188(0.0327)0.0250(0.1436)-0.2383***(0.0847)-0.2413***(0.0540)-0.0054(0.0662)-0.1215***(0.0026)
0.0694***(0.0111)0.0234***(0.0016)-0.0002***(0.00001)
0.0518***(0.0014)0.0615***(0.0051)0.1732***(0.0173)
17588
0.786
0.779
115.42***546

Robust standard errors are in parentheses. * p < 0.1, ** p < 0.05, *** p < 0.01. District, year, district x year
fixed effects, and corregimiento fixed effects are included.

VI.
Conclusion
The empirical evidence on the vulnerability of indigenous and farmer households isimportant to consider effective solutions. Indigenous peoples of Panama who depend heavily
on agriculture view the effects of climate change differently from other socioeconomic groups.
They are one of the groups in Panama most at risk from climate change due to their cultural16

dependence on natural resources, lack of government support, and several social adversities.
Also, farmers encounter numerous difficulties while attempting to adapt to and execute theusage of technology at a time when agricultural productivity is continuously declining andpreventing them from escaping the poverty cycle. However, few previous studies havediscussed these issues. Therefore, in this study, we explore how the weather shocks(temperature shock, precipitation shock, flood, etc.) affect consumption and its composition(total consumption, food consumption, and non-food consumption) and how heterogenous theimpact of the shocks is between farmers and non-farmers, indigenous people and nonindigenous people, and poor household and non-poor households.
Our main findings are threefold. Firstly, higher temperatures, temperature shock, andfloods reduce consumption and such negative shock on food consumption is smaller than nonfood consumption. This is because households try to mitigate the negative impact on foodconsumption by reducing non-food consumption since food is more important. Secondly,
there is no significant difference in the negative impact of the weather shocks between farmers
and non-farmers. However, there are significant differences in the negative impact (higher
temperature, temperature shock, and flood) on non-food consumption between indigenous
households and non-indigenous households, while there is no significant difference in total
consumption and food consumption. This may occur since indigenous households, who arepoorer, would like to shield their food consumption by reducing non-food consumption more.
Therefore, indigenous households are vulnerable to weather shocks, though farmer’shouseholds are not. Thirdly, the negative impacts of the weather shocks on the consumptionof poor households are less than those on the consumption of non-poor households. This is
likely to be because of the financial support from the government which targeted poor
households’ functions effectively.
From the above evidence, indigenous households are more vulnerable to climate change
than farmers and poor households. Therefore, more attention should be paid to indigenous
households for public support to reduce the risk of climate change.

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19

VII.

Appendix

Table A1. Heterogenous effects of climate change (farmers and non-farmers)

Bin temperature 23°_24.9°C
Farmer # Bin temperature
23°_24.9°C
Bin temperature 25°_26.9°C
Farmer # Bin temperature
25°_26.9°C
Bin temperature 27°_29.1°C
Farmer # Bin temperature
27°_29.1°C
Temperature shock
Farmer # Temperature shock
Precipitation negative shock
Farmer # Precipitation
negative shock
Precipitation positive shock
Farmer # Precipitation positiveshock
All controls
Observations
R-squared
Adjusted R-squaredF

Log total
consumption
per cap (USD)-0.0005(0.0096)-0.0132**(0.0063)-0.0068(0.0069)-0.0054(0.0040)-0.0423***(0.0103)0.0101*(0.0060)-0.1578*(0.0815)
0.0112(0.0344)-0.0054(0.1504)-0.0599(0.0752)-0.0548**(0.0273)-0.0648**(0.0280)
YES
17650
0.705
0.695
71.45***

Log food
consumption
per cap (USD)-0.0048(0.0099)-0.0046(0.0067)-0.0082(0.0074)-0.0030(0.0041)-0.0502***(0.0107)0.0203***(0.0061)-0.0638(0.0802)0.0054(0.0344)0.0691(0.1305)-0.0582(0.0746)-0.0145(0.0267)-0.0946***(0.0289)
YES
17588
0.526
0.511
32.28***

Log non-foodconsumption percap (USD)-0.0026(0.0114)-0.0228***(0.0076)-0.0080(0.0082)-0.0098**(0.0048)-0.0321***(0.0124)-0.0047(0.0075)-0.2438**(0.1014)-0.0121(0.0428)-0.0333(0.1946)-0.1222(0.0967)-0.0477(0.0338)-0.0644*(0.0351)
YES
17588
0.725
0.716
85.72***

Robust standard errors are in parentheses. * p < 0.1, ** p < 0.05, *** p < 0.01. District, year, district x year
fixed effects, and corregimiento fixed effects are included. These results include a precipitation positive
shock variable instead of a flood dummy variable.

20

Table A2. Heterogenous effects of climate change (indigenous and non-indigenous peoples)

Bin temperature 23°_24.9°C
Indigenous # Bin temperature
23°_24.9°C
Bin temperature 25°_26.9°C
Indigenous # Bin temperature
25°_26.9°C
Bin temperature 27°_29.1°C
Indigenous # Bin temperature
27°_29.1°C
Temperature shock
Indigenous # Temperature shock
Precipitation negative shock
Indigenous # Precipitation
negative shock
Precipitation positive shock
Indigenous # Precipitation
positive shock
All controls
Observations
R-squared
Adjusted R-squaredF

Log total
consumption
per cap (USD)-0.0012(0.0098)-0.0301**(0.0119)-0.0083(0.0071)0.0019(0.0060)-0.0378***(0.0103)-0.0109(0.0127)-0.1452*(0.0816)-0.0454(0.0539)-0.0035(0.1453)-0.1399(0.1204)-0.0576**(0.0268)-0.0750(0.0638)
YES
17650
0.704
0.695
71.69***

Log food
consumption
per cap (USD)-0.0089(0.0100)-0.0180(0.0127)-0.0125*(0.0075)0.0106(0.0067)-0.0482***(0.0107)0.0029(0.0130)-0.0653(0.0797)
0.0090(0.0570)0.0772(0.1285)-0.1465(0.1319)-0.0211(0.0262)-0.0632(0.0680)
YES
17588
0.526
0.510
32.23***

Log non-foodconsumption
per cap (USD)-0.0006(0.0117)-0.0473***(0.0152)-0.0076(0.0083)-0.0071(0.0073)-0.0259**(0.0125)-0.0260*(0.0157)-0.2113**(0.1017)-0.1607**(0.0663)-0.0624(0.1847)-0.3814***(0.1474)-0.0524(0.0334)-0.1159(0.0783)
YES
17588
0.725
0.716
86.96***

Robust standard errors are in parentheses. * p < 0.1, ** p < 0.05, *** p < 0.01. District, year, district x year
fixed effects, and corregimiento fixed effects are included. These results include a precipitation positive
shock variable instead of a flood dummy variable.

21

Table A3. Heterogenous effects of climate change (poor and non-poor)

Bin temperature 23°_24.9°C
Poor # Bin temperature
23°_24.9°C
Bin temperature 25°_26.9°C
Poor # Bin temperature
25°_26.9°C
Bin temperature 27°_29.1°C
Poor # Bin temperature
27°_29.1°C
Temperature shock
Poor # Temperature shock
Precipitation negative shock
Poor # Precipitation negativeshock
Lives in town that experienced
flood=1
Poor # Lives in town thatexperienced flood=1
Observations
R-squared
Adjusted R-squared

Log total
consumption
per cap (USD)-0.0012(0.0134)-0.0019(0.0060)-0.0054(0.0120)0.0024(0.0034)-0.0111(0.0154)0.0037(0.0054)-0.2766***(0.1001)
0.0794**(0.0322)-0.1459(0.1526)-0.0009(0.0973)-0.1785***(0.0484)
0.1884***(0.0591)
13028
0.692
0.680

Log food
consumption
per cap (USD)-0.0081(0.0147)
0.0066(0.0084)-0.0037(0.0135)
0.0064(0.0048)-0.0259(0.0173)
0.0084(0.0070)-0.2547**(0.1135)
0.0904**(0.0398)-0.0250(0.1216)-0.0688(0.1054)-0.0702(0.0513)0.2431***(0.0741)
12981
0.495
0.474

Log non-foodconsumption
per cap (USD)-0.0012(0.0153)-0.0082(0.0075)-0.0085(0.0134)-0.0015(0.0042)0.0024(0.0177)-0.0010(0.0067)-0.3193***(0.1226)
0.0778*(0.0421)-0.2994(0.2223)
0.0326(0.1497)-0.2306***(0.0617)0.0832(0.0890)
12981
0.691
0.679

Robust standard errors are in parentheses. * p < 0.1, ** p < 0.05, *** p < 0.01. Results are provided excluding
farmers and indigenous peoples.

22

Table A4. Summary statistics (Farmers)
Variables
Consumption per cap (USD)
Food consumption per cap(USD)
Non-food consumption per cap(USD)
No. of members per household
Age of householder
Male householder
Householder's years of education
Members working per household
Indigenous ethnicity
Lives in an urban area
Lives in poverty
Lives in a corregimiento thatexperienced a floodMonthly average temperature 1
year
Temperature positive shock
Precipitation positive shock
Precipitation negative shock

Obs
3942
3932

Mean
1361.
625.02SD
1608.99510.94

Min
38.80
12.63

Max
28312.11
5630.753932

734.751291.02

10.47

27686.11

3942
3942
3942
3942
3942
3942
3942
3942
3942

4.68
48.83
.95
5.39
1.82
0.21
0.07
0.62
0.02

3.0
14.91
0.20
3.46
1.13
0.41
0.26
0.48
0.13

1
16
0
0
0
0
0
0
0

24
95
1
18
10
1
1
1
1

3942

25.27

2.04

18.3

29.1

3942
3942
3942

0.19
0.27
0.03

0.39
0.44
0.17

0
0
0

1
1
1

Table A5. Summary statistics (indigenous peoples)
Variables
Consumption per cap (USD)
Food consumption per cap(USD)
Non-food consumption per cap(USD)
No. of members per household
Age of householder
Male householder
Householder years of education
Members working per household
Works in agriculture
Lives in an urban area
Lives in poverty
Lives in a corregimiento thatexperienced a flood
Av. monthly temperature 1 year
Temperature positive shock
Precipitation positive shock
Precipitation negative shock

Obs
1520
1513

Mean
725.69384.18SD
856.15368.55

Min
38.80
11.35

Max
12921.52
3706.411513

339.06580.2110.1210638.11

1520
1520
1520
1520
1520
1520
1520
1520
1520

6.97
45.76
.84
4.31
2.06
0.55
0.15
0.86
0.04

3.77
14.11
0.36
4.33
1.48
0.49
0.35
0.34
0.18

1
16
0
0
0
0
0
0
0

25
90
1
18
10
1
1
1
1

1520
1520
1520
1520

24.43
0.25
0.19
0.07

2.50
0.43
0.39
0.25

18.3
0
0
0

28.7
1
1
1

23

Figure A1. Frequency of monthly average temperature by corregimientos in 3 years period.
5604 5664

6000

Monthly occurance

5000
4000
2634

3000

2839

2000
1000

534

64

875

1041
651

94

0

Average temperature per month (C)Figure A2. Annual average temperature (°C) years 1997, 2003, and 2008, in eachcorregimiento.

Note: 631 corregimientos are considered based on Panama’s 2010 census.

24Figure A3. Frequency of floods in years 1997, 2003, and 2008 by the proportion of people
affected in each corregimiento.

Figure A4. Percentage of farmer households by corregimiento (LSMS’s 1997, 2003,2008)25

Figure A5. Percentage of indigenous households by corregimiento (LSMS’s 1997,
2003,2008)

Figure A6. Percentage of poor households by corregimiento (LSMS’s 1997, 2003,2008)26