draft: do not cite without permission gender, agriculture

DRAFT: DO NOT CITE WITHOUT PERMISSION

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Gender, Agriculture, and Climate Adaptation in Ethiopia

Nicholas Reksten1 & Kevin McGee2

Draft for International Association for Feminist Economics Conference, Glasgow, June 27, 2019

Abstract

We explore the relationship between climate change, agricultural adaptation, and gender inequalities in

Ethiopia using the second and third waves of the Ethiopia Socioeconomic Survey conducted in the

2013/14 and 2015/16 growing seasons. While researchers and international organizations have raised

concerns about the differential impacts of climate change on men and women, little empirical work has

been published on the subject. In what we believe is the first country-level study on women and climate

adaptation, we test the hypothesis that farms and agricultural plots controlled by women are less likely

engage in adaptation behavior, such as increased irrigation, use of fertilizer, changes in livestock

management, and crop switching. Initial results support this hypothesis. The effect is large and

significant, and results are robust to several specifications of female control of the farm. We seek to

place these results in the context of social norms that have created gendered differences in agricultural

production and control in Ethiopia. Additionally, we include household-level data on the long-term

climate to understand the effect of changes in rainfall and temperature on adaptation. Understanding

these dynamics can lead to a more mindful inclusion of gender issues in climate response plans. The

paper ends with a discussion of the kinds of data that could provide additional analysis of these

questions, which we hope can be incorporated into future survey waves in Ethiopia and elsewhere. (JEL

Codes: O13; B54)

Introduction

The most recent report by the United Nations Intergovernmental Panel on Climate Change

(IPCC) has indicated that negative impacts from climate change are already being felt globally (IPCC,

2014). Ethiopia has already experienced an increase in temperature and a decrease in precipitation in

much of the country in recent decades, with the trend projected to continue and increasingly affect food

production (IPCC, 2014; Viste, Korech, and Sorteberg, 2013; Tadege, 2007). In 2017, agriculture

accounted for 34% of GDP in Ethiopia (World Bank, 2019), suggesting that the impact of climate change

on agriculture has the potential to significantly impact the economy both directly through changes in

farmers’ income and employment levels and indirectly through changes in food production that may

lead to shortages and/or food price inflation.

1 Assistant Professor, Department of Economics, University of Redlands 2 Economist, World Bank


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In response to changing climatic conditions such as more severe flooding or droughts, longer

and more severe heat waves, or desertification, farmers may attempt to adapt their production

practices and infrastructure to changing conditions, even without recognizing the threat of

anthropogenic climate change directly. Adaptation measures can include investments and practices

such as crop switching, irrigation use, fertilizer use, and increased investment in livestock (IPCC, 2014;

McCarthy, 2011; Deressa, Hassan, and Ringler, 2011). However, women may be less likely to engage in

such behaviors when they manage landholdings, making them disproportionately impacted by climate

change. Specifically, unequal distribution of income and assets could constrain the choices that women

are able to make (Skinner 2011). While there is a robust literature on potential links between gender

and climate change, little empirical work has been done on the subject outside of case studies.

This paper investigates the situation of women in agriculture in Ethiopia and the determining

factors of adaptation investments in agriculture using data from the Ethiopia Socioeconomic Survey’s

(ESS) second and third waves, collected in the 2013/14 and 2015/16 growing seasons. It will be

organized as follows. First, existing work on gender and climate adaptation, especially in the context of

Ethiopia, is discussed. Then, gender divisions in agriculture in Ethiopia are explored using the data.

After a brief discussion of the methodology for understanding differences in adaptation probability by

men and women, regression results are presented. Finally, the paper concludes with policy implications

and ideas for future work. In addition to contributing to the new and growing literature on women and

climate change adaptation in agriculture, this work aims to be a proof of concept, highlighting and

discussing what the data, in this case, cannot reveal about the topic of interest.

Climate Change Adaptation and Gender in Rural Agriculture

The United Nations Framework Convention on Climate Change’s (UNFCCC) 10th Conference of

Parties (COP) report emphasizes the need to mainstream gender into climate policy, acknowledging that

gender differences in adaptation measures can both make those measures more effective and prevent


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worsening gender inequalities (UNFCCC 2004). More broadly, a large literature has emerged calling for

gendered analysis of adaptation capabilities and behaviors (c.f. Nelson et al., 2002; Lambrou and Piana,

2006; UN Women Watch, 2009; Dankleman, 2010; Aboud, 2011; Skinner, 2011; World Bank, 2012; Carr

and Thompson, 2014). The extent of climate adaptation determines, in part, who incurs the costs of

climate change, with poorer and more vulnerable populations at greater risk and at risk from different

shocks (Lambrou and Piana, 2006; Carr and Thompson, 2014).

In the context of agriculture, climate change can cause a number of impacts. Most directly,

changes in temperature and/or precipitation levels can impact crop yields (Burke and Emerick, 2016). In

the case of smallholder agriculture, that, in turn, can reduce what families have for subsistence and

income, and existing norms may dictate how the remaining resources are distributed within households.

Additionally, female-headed households who may already be disadvantaged could be more impacted by

this.

Common coping mechanisms in the face of such shocks include reducing consumption, asset

disposal, greater reliance on informal transfers, and, in some cases, migration (World Bank, 2012). In

agricultural settings, crop switching and/or changing farming techniques may be the primary coping

mechanisms. However, social norms or a lack of resources may prevent women from adopting

adaptation practices undertaken by men such as the greater utilization of oxen for plowing (Nelson and

Stathers, 2009). Climate-related conflict may also increase the number of female-headed households

who lack the resources to adapt (Omolo, 2011). Men and women may have different access to

information on climate change and adaptation measures, especially undermining the ability of female-

headed households to cope with climate change in the coming decades (FAO, 2010; Floro, Yesuf, and

Woldesenbet, 2016).

Importantly, gender interacts with other identity categories like socioeconomic status, age,

marital status, parental status, and ethnicity to produce the patterns of gendered impacts to climate


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change and differential adaptation (Carr and Thompson, 2014). Therefore, it is crucial in any

investigation to consider such factors beyond the notion of “add women and stir.” In doing so, the

analysis of gendered impacts of climate change can become more complete and nuanced.

Ethiopia presents an especially important context in which to explore these differences.

Agriculture accounted for roughly 34% of GDP and 68% of employment in 2017 (World Bank, 2019).

Agricultural products accounted for roughly two-thirds of total Ethiopian exports in 2017, with coffee

being the largest export crop (UN Comtrade, 2019). However, agriculture in Ethiopia is primarily

characterized by low productivity, small-scale mixed crop and livestock production. The low productivity

of farms then leaves farmers especially vulnerable to climate shocks and unable to adapt (Deressa,

Hassan, and Ringler, 2011). Climate change in Ethiopia has been characterized by a decline in already

low levels of average annual precipitation and an increase in average annual temperature of 1.65

degrees Celsius between 1955 and 2015 (Abebe, 2017). These trends are expected to continue into the

future (IPCC, 2014; Viste, Korech, and Sorteberg, 2013; Tadege, 2007). Importantly, these changes have

led to increasing food insecurity, even in the face of growing production of cereal crops (Abebe, 2017).

Adaptation behaviors in parts of Ethiopia in the face of climate change include planting trees,

soil and water conservation, switching crop varieties, crop diversification, changing the timing of

planting, intensive use of agricultural inputs, and investments in irrigation (Deressa, Hassan, and Ringler,

2011; Belay et al., 2017). There is also a greater reliance on informal transfers and social networks.

Additionally, the poor and women are more likely to supplement their income with the sale of fuelwood

and charcoal (World Bank, 2012).

There are important gendered dynamics in agriculture in Ethiopia. Women take part in most

activities in agriculture and food production and procurement, with tasks such as weeding, harvesting,

preparing storage containers, management of home gardens, raising poultry, bringing inputs to the field,

and acquiring water for some agricultural uses considered “women’s work.” However, in most of the


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country, there is a norm that women should not plow. Instead, they are typically in charge of cultivating

vegetable and fruit crops, which they also bring to market (Mogues et al., 2009). If they are the head of

the household, women are more likely to rent out their land (World Bank, 2012). Men tend to grow and

bring to market larger scale cash crops like coffee, teff, and khat (Mogues et al., 2009). Additionally, if

they are the head of the household, men tend to make decisions about crop selection, renting out land,

and selling livestock (World Bank, 2012).

Gender and Agriculture in Ethiopia

The paper uses data from waves 2 and 3 of the Living Standards Measurement Study-Integrated

Surveys on Agriculture (LSMS-ISA) known as the Ethiopia Socioeconomic Survey (ESS), which is nationally

representative.3 Only households outside of Addis Ababa are included here since we are interested in

the responses of households involved in agriculture. The survey questions are asked at several levels:

household/holder (N=3,765), individual (N=18,296), parcel (N=15,125), and field (N=22,915).4 A subset

of individuals manage fields (N=3,197), while others own livestock (N=3,765) and/or manage it (or

“keep” it, used here to differentiate from crop field managers; N=4,512). A basic analysis that

concentrates on the field and manager levels and then livestock holders and keepers can help shed light

on key aspects of where gender differences appear and do not in the context of agriculture.

Far more fields are managed by men (83.3%) than women (16.7%), and the median size of a

female-managed field is far smaller (252.3 m2) than male-managed fields (559.1 m2). In most cases, the

fields are managed by the head of the household (95.3%), though this is true to a greater extent with

male managers (96.2%) than female (90.6%). In the case of 91.2% of fields, the head of the household is

the primary decision maker concerning crops to be planted, inputs to be used, and the timing of

3 Wave 1 of the survey did not include key questions of interest (such as the sex of the field manager), making the data unusable for this study. 4 All numbers here refer to wave 3 unless otherwise specified. The patterns discussed, however, hold for wave 2, as well.


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agricultural activities. In the case of 69% of fields, another member of the household is consulted

regarding these decisions, with the women co-managing 83% of the comanaged fields, far more than

men. Only 15% of fields have a second co-manager, and these are more likely to be male (67% of fields

with a second co-manager).

Differences in input use and the types of crops grown are present but not extreme. These are

detailed in table 1. Only 3.8% of fields are irrigated, though these fields are more likely to be managed

by men. Fifty-one percent of fields have some kind of fertilizer applied, and the female-managed fields

were actually slightly more likely to have fertilizer used. However, while 56.7% of fields have erosion

control, male-managed fields are more likely to have it than female-managed fields (58% to 48%,

respectively). Typically, fields will only be planted with a single crop (79%). A further 13.5% are planted

with 2 crops, and 5.9% are planted with 3. The average number of crops per field is identical between

male- and female-managed fields (1.3).

Table 1: Gender Differences in Field Improvements, Input Use, and Field Characteristics

Input/Improvement % of Total (N=22,915)

% male-managed

% female managed

t-statistic from difference of means test

Irrigated 3.81 4.01 2.91 3.24***

Use Fertilizer 50.91 50.48 53.07 -2.92***

Use Pesticide 3.17 3.27 2.65 -2.02**

Use Herbicide 9.45 9.82 7.49 -4.49***

Erosion control 56.81 58.45 48.72 11.11***

Mean # of crops 1.31 1.32 1.30 1.80

Note: * p<0.1; ** p<0.05; *** p<0.01 for difference in mean test between the two groups

Table 2 discusses patterns among crop managers, with significant differences emerging between

male and female managers. Male managers are much more likely to be the head of the household (94%

for men, 83% for women), while 13.5% of female managers are spouses (compared to just 0.69% of

male managers). The average manager makes decisions about 7.15 fields, with male managers

exercising control over more fields (7.73) on average than female managers (5.19). On average, 5.58

crop varieties are grown on these fields, with male managers overseeing a greater variety (5.88) than


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female managers (4.57). Male managers are more likely to oversee fields with improvements such as

irrigation, erosion control, or that use fertilizer, pesticide, or herbicide. There appears to be no

significant difference in the proportion of male and female managers that also hold livestock.

Crop switching and switching field improvements can be important coping mechanisms for

climate change, and, between planting in 2013 and 2015, 84% of male managers and 64.1% of female

managers changed the mix of crops that they grew, a significant difference. Additionally, 53% of male

managers and 38% of female managers changed the mix or number of improvements on fields that they

managed.

Table 2: Gender Differences in Manager Farm Practices

Practice Overall(N=3,197) Male managers (N=2,464)

Female managers (N=773)

t-statistic for difference of means

Mean # of fields 7.15 7.73 5.19 -11.78***

Mean # of crops grown

5.58 5.88 4.57 -8.86***

% any irrigated field 10.35 11.24 7.49 -2.92***

% used Fertilizer 72.04 74.27 64.67 -5.11***

% used Pesticide 9.20 10.22 5.86 -3.59***

%used Herbicide 24.47 26.65 16.89 -5.43***

% any Erosion control

70.32 72.98 61.26 -6.13***

% also have livestock 67.64 68.32 65.80 -1.28

% Crop switching 79.82 84.62 64.17 -12.43***

% Switching Improvements

49.73 53.10 38.42 -7.04***


While there are some important differences in the patterns of crops grown by men and women,

the distinctions are not incredibly stark, as shown in Table 3. Both women and men manage fields

growing the most important crops such as maize, sorghum, coffee, potatoes, teff, and enset (also known

as the false banana). Men are more likely to manage fields that grow sorghum, and teff, but women are

more likely to manage fields that grow enset (which, as noted by the World Bank (2012), can be useful


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as a drought-resistant food crop). In addition to sorghum and teff, male-managed fields are more likely

to be planted with wheat, sesame, and other pulse crops. In addition to enset, female-managed fields

are more likely to be planted with godere (a potato-like root vegetable), kale, gesho (similar to hops),

other vegetables, and other fruit.

Table 3: Varieties of Crop Grown by Sex of Primary Field Manager

Crop % of fields planted (N=23,354)

% of male-managed fields planted (N=18,552)

% of female-managed fields planted (N=3,802)


Maize 14.56 14.42 14.78 -0.57

Sorghum 11.94 12.39 9.69 4.69***

Coffee 9.87 9.83 10.09 -0.49

Teff 8.75 9.18 6.60 5.15***

Enset 8.29 8.13 9.12 -2.02**

Wheat 6.25 6.40 5.50 2.10**

Barley 5.61 5.78 4.79 2.42

Chat 5.52 5.69 4.67 2.53**

Bananas 4.23 4.14 4.69 -1.53

Godere 3.66 3.34 4.98 -4.76***

Other pulse 3.52 3.67 2.80 2.63***

Kale 3.44 2.91 6.05 -9.72***

Kidney beans 3.41 3.33 3.83 -1.53

Other vegetables 3.17 2.90 4.56 -5.36***

Other fruit 3.13 3.03 3.64 -1.97**

Horsebeans 3.08 3.12 2.86 0.87

Gesho 2.51 2.40 3.09 -2.50**

Mangos 2.50 2.51 2.38 0.47

Avocado 2.44 2.40 2.59 -0.7

Millet 2.13 2.26 1.52 2.88***

Red peppers 2.01 2.05 1.81 0.96

Other root crops 1.79 1.79 1.83 -0.21

Field peas 1.78 1.83 1.55 1.2

Sweet potato 1.67 1.66 1.73 -0.3

Pumpkins 1.58 1.45 2.25 -3.63***

Sugarcane 1.39 1.43 1.18 1.23

Sesame 1.27 1.39 0.65 3.72***

Green pepper 1.14 0.95 2.07 -5.94***

Nueg 1.05 10.8 0.89 1.07

Garlic 1.03 0.86 1.91 -5.88***


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Table 4 shows the proportion of managers supervising a field with each variety of crop listed.

Here, the patterns are starker. While women manage fields growing a wide variety of crops, men are

more likely to supervise the growing of maize, sorghum, teff, coffee, enset (weakly significant), wheat,

barley, chat, bananas, horsebeans, other pulses, red peppers, mangos, millet, fieldpeas, sugarcane,

sesame, other oil seed, nueg, and chickpeas. Women are more likely to manage fields growing kale,

green peppers, and garlic.

Table 4: Proportion of Managers Supervising Fields with Crop Varieties Grown by Sex of Primary Manager

Crop % Overall (N=3,197)

% Male managers (N=2,464)

% Female managers (N=733)

t-statistic from difference of

means test

Maize 56.71 59.51 47.68 -5.71***

Sorghum 38.84 42.52 26.57 -7.85***

Teff 33.26 37.28 20.16 -8.73***

Coffee 30.92 32.45 26.02 -3.31***

Enset 29.27 30.06 26.84 -1.68*

Wheat 24.48 26.29 18.12 -4.54***

Barley 22.14 23.73 16.62 -4.09***

Chat 18.44 20.12 13.08 -4.32***

Bananas 17.69 18.78 14.31 -2.79**

Horsebeans 17.13 18.54 12.53 -3.80***

Kale 16.94 15.66 21.39 3.64***

Other vegetables 15.70 15.38 16.76 0.90

Other pulses 15.20 16.80 9.95 -4.55***

Other fruit 15.10 15.54 13.62 -1.27

Kidney beans 15.01 15.42 13.76 -1.10

Gesho 13.83 14.24 12.26 -1.36

Godere 12.92 12.78 13.62 0.60

Red pepper 11.34 12.33 8.17 -3.12***

Mangos 11.24 11.93 8.72 -2.42***

Avocados 11.06 11.52 9.40 -1.61

Millet 10.31 11.72 5.59 -4.82***

Fieldpeas 10.03 10.99 6.95 -3.20***

Pumpkins 9.47 9.13 10.76 1.33

Sweet potato 8.16 8.52 7.08 -1.24

Other root 8.10 8.40 7.08 -1.14


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Sugarcane 7.63 8.44 5.04 -3.04***

Green pepper 6.79 6.21 8.72 2.38**

Sesame 6.38 7.67 2.18 -5.35***

Garlic 6.29 5.68 8.45 2.71**

Other oil seed 5.85 6.69 2.72 -4.05***

Potatoes 5.48 5.68 4.77 -0.95

Nueg 5.42 6.21 2.86 -3.51***

Papaya 5.26 5.60 4.09 -1.61

Chickpea 5.11 5.96 2.32 -3.94***

Haricot beans 4.36 4.62 3.41 -1.42

Rapeseed 4.11 4.02 4.50 0.57

Other spice 3.89 3.81 4.22 0.50

Other cash crop 2.30 2.43 1.91 -0.83

Uncategorized 1.93 1.99 1.50 -0.86

Other cereal 1.46 1.46 1.36 -0.20


Tables 5 and 6 show differences in livestock ownership and management. Livestock holders are

overwhelmingly the head of household (95.2%), with similar proportion for male (96.0%) and female

(93.3%) holders. On average, male holders have more of every type of livestock other than poultry.

Livestock holdings of men are, on average, worth about twice as much as livestock holdings of women.

This pattern is even starker for median holding, with the median value of male holdings at 11,830 birr

(about US$407) and the median value of female holdings at 3,065 birr (about US$105).

Table 5: Gender Differences in Livestock Ownership

Livestock type (mean # kept) /value

Overall(N=3,765) Male holders (N=2,795)

Female holders (N=970)


# of large ruminants 3.63 4.23 2.02 3.91***

# of small ruminants 4.36 4.93 2.86 5.39***

# of camelids 0.22 0.26 0.12 1.94*

# of poultry 2.83 3.23 1.76 8.20***

# of equines 0.53 0.63 0.26 9.66***

# of beehives 0.30 0.39 0.06 4.23***

Mean Value of livestock owned (birr)

16328.42 19118.65 8831.21 5.21***



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Similar patterns hold among those managing, or keeping, livestock. Women participate more in

keeping livestock, but, again, they are much more likely to keep poultry than other kinds of animals,

relative to male keepers. Male livestock keepers are more likely to be either the head of the household

(72.2%) or the son of the head (23.9%), whereas female keepers are most likely to be the head’s spouse

(55.1%) than the head of the household (27.9%). A further 12.9% are daughters of the head of

household.

Table 6: Gender Differences in Livestock Keeping

Livestock type (mean # kept)

Overall(N=4,512) Male keepers (N=2,465)

Female keepers (N=2,047)


# of large ruminants

3.02 4.23 1.58 6.41***

# of small ruminants

3.63 4.15 3.01 4.15***

# of camelids 0.18 0.28 0.07 4.07***

# of poultry 2.35 1.39 3.53 -16.24***

# of equines 0.44 0.56 0.30 9.26***

# of beehives 0.25 0.42 0.04 6.66***


These descriptive statistics illuminate key aspects of agricultural practices and differences by

gender in Ethiopia. Most decisions regarding the types of crop to grow and inputs to use are made by

the head of the household, while is more likely to be male. Most livestock is owned by men with the

exception of poultry, and the value of male holdings is significantly higher. Men are more likely to

manage fields with improvements, and, while a wide variety of crops is managed by both sexes, with

seemingly few prohibitions on female management of certain varieties, men are more likely to oversee

fields growing key crops like maize, teff, and coffee. Crucially in the framework of climate adaptation,

men are more likely to switch crops and improvements between growing seasons. Generally, men

appear to control more assets and have more flexibility than women in agriculture.


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Predictors of Adaptation

To investigate the question of whether gender matters for adaptation decisions more directly,

we use series of probit regressions to understand the probability that a field has been improved in a way

that makes it better able to withstand climate shocks and then to understand factors that are associated

with switching crops or improvements on fields at the manager level. Field improvements, as above,

include the use of irrigation; the use of fertilizer, herbicides, or pesticides; or the use of erosion control.

Table 7 lists the variables used in the field-level regression and their summary statistics, while table 8

does so for variables used in the manager-level regressions. Independent variables include the sex of

the field manager, the age of the field manager, the size of the field, and whether the field is growing

cash crops or food crops. Standard errors are clustered at the regional level.

In order to understand the affect that climate change may have on different adaptation coping

mechanisms, we incorporate three climate variables into our models. We use data on daily rainfall

estimates from the Africa Rainfall Climatology version2 (ARC2) available from the National Oceanic and

Atmospheric Administration (NOAA). The ARC2 dataset contains daily precipitation estimates between

1983 and the present with a spatial resolution of 0.1⁰. Daily precipitation estimates were extracted from

ARC2 at the geolocation of each ESS3 household and then aggregated to an annual total rainfall for

every year between 1983 and 2016. We also incorporate information on air temperature from the

Modern-Era Retrospective analysis for Research and Applications Version 2 (MERRA2) from the National

Aeronautics and Space Administration (NASA). The MERRA2 data set contains hourly air temperature

estimates between 1980 and the present with a spatial resolution of about 0.5⁰. The hourly temperature

estimates were extracted at the geolocation of each ESS3 household, after which two aggregate annual

measures were calculated: (1) the number of growing degree days (GDD) 5 and (2) the number of days

5 Growing degree days are determined for each period through the following formula: 𝐺𝐷𝐷 =

𝑇𝑀𝑎𝑥−𝑇𝑀𝑖𝑛

2− 𝑇𝑏𝑎𝑠𝑒.

The base temperature we use in this study is 8 degrees Celsius. The maximum temperature is capped at 30


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with a maximum temperature above 30⁰C (HOT). GDD is a measure of plant growth (heat absorption)

and is generally positively associated with crop output. HOT captures days with temperatures that are

damaging to many crops and thus is generally negatively associated with crop output

For all three climate indicators (rainfall, GDD, and HOT), we calculate the mean over the period

from the start of the series (1983 for ARC2 and 1980 for MERRA2) through 1998 and then the deviation

from that mean in 2014 to capture the climate conditions between the two observed growing seasons in

waves 2 and 3. Tables 7 and 8 show that 2014 was an unusually cool and wet year for the country,

making it not ideal for studying adaptation to a hotter and wetter climate. But, as in any large area,

significant variation in climate conditions exists that can be exploited in the regression models.

Table 7: Summary Statistics; Field level

Variable Mean (Std. Deviation)

Any field improvements (dummy; 1 if yes) 0.76 (0.42)

Number of field improvements on field (count) 1.23 (0.91)

Manager sex (dummy; 0 if female, 1 if male) 0.83 (0.37)

Manager age (years; age-squared included in regression) 47.54 (14.28)

Field size (square meters; log taken in regression) 1593.61 (28474.8)

Cash crops (dummy; 1 if grown on field) 0.19 (0.40)

Food crops (dummy; 1 if grown on field) 0.91 (0.29)

Rainfall: 2014 deviation from 1983-1998 mean (mm) 163.27 (153.41)

HOT days (>30 degrees C): 2014 deviation from 1980-1998 mean -0.76 (7.86)

Growing degree days: 2014 deviation from 1980-1998 mean 76.18 (86.87)

Total Observations 22,558

degrees (at which point the yields of most crops begin to decline). A higher number of growing degree days implies that crop yields will be higher.


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Table 8: Summary Statistics; Manager level

Variable Mean (Std. Deviation)

Improvements on any fields? (dummy; 1 if yes) 0.88 (0.23)

Number of improvements on any fields 2.54 (1.25)

Any crop switching on fields between waves 2 and 3 0.80 (0.40)

Switching improvements between waves 2 and 3 0.50 (0.50)

Manager sex (dummy; 0 if female, 1 if male) 0.77 (0.42)

Manager age (years; age-squared included in regression) 46.71 (15.02)

Number of fields managed 7.14 (5.24)

Cash crops (dummy; 1 if grown on a managed field) 0.54 (0.50)

Food crops (dummy; 1 if grown on a managed field) 0.99 (0.87)

Rainfall: 2014 deviation from 1983-1998 mean (mm) 150.46 (156.80)

HOT days (>30 degrees C): 2014 deviation from 1980-1998 mean -0.299 (-328.26)

Growing degree days: 2014 deviation from 1980-1998 mean 83.70 (95.54)

Total Observations 3,176

The final regressions take the following form:

𝑃𝑟𝑜𝑏(𝐼𝑚𝑝𝑓 = 1|𝑋) = Φ(β′𝑋 + 𝜖𝑓)

Where

𝐼𝑚𝑝𝑓 = {1 𝑖𝑓 𝑡ℎ𝑒 𝑓𝑖𝑒𝑙𝑑 ℎ𝑎𝑠 𝑎𝑛 𝑖𝑚𝑝𝑟𝑜𝑣𝑒𝑚𝑒𝑛𝑡, 𝑐𝑟𝑜𝑝𝑠 𝑎𝑟𝑒 𝑠𝑤𝑖𝑡𝑐ℎ𝑒𝑑, 𝑖𝑚𝑝𝑟𝑜𝑣𝑒𝑚𝑒𝑛𝑡𝑠 𝑎𝑟𝑒 𝑠𝑤𝑖𝑡𝑐ℎ𝑒𝑑

0 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒

Additionally, Φ is the standard normal cumulative distribution, 𝑋 is the vector of independent variables

that would impact the probability that a field has been improved, 𝛽′ is the parameter estimates, and 𝜖 is

the random error term.

Cash crops are defined as those sold primarily at markets and for export, and they include the

following: coffee, sugarcane, chat, sesame, and rapeseed in addition to other cash, oil seed, and spice

crops. Food crops are used primarily for consumption and subsistence (though there may be some

crossover between the two categories). Here, food crops include: maize, barley, millet, sorghum, teff,

wheat, garlic, chickpeas, haricot verts, horse beans, field peas, kidney beans, nueg, red peppers, green

peppers, bananas, mangos, papaya, potatoes, pumpkins, sweet potatoes, godere, enset, gesho, and

avocados, in addition to other root vegetables, fruits, pulses, and vegetables. Because crops are


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classified as either cash or food, only the cash crop dummy is included in the regression. Standard

errors are clustered at the regional level.

Table 9 presents the results from the field-level regressions with four models. Model 1 uses

region fixed effects instead of climate data. Model 2 includes the rainfall variable, Model 3 includes the

deviation in the number of HOT days, and Model 4 includes the deviation in the number of GDD.

Importantly, fields managed by men are more likely to have improvements in each case (that

is, the marginal effect is positive). The effect is small, however, ranging from 3.1% in Model 1 to 4.5% in

Model 2. The climate variables are each significant at the 1% level, but the effect of each is small. For

each millimeter positive deviation from the mean, the probability of having an improvement increases

by 0.02%.6 Fields located in areas with more very hot days were more likely to have improvements,

though, again, the marginal effect is small. An additional day above 30 degrees Celsius increases the

probability of an improvement by 0.6%. Similarly, having an additional growing degree day increases

the probability by 0.09%. The effect of age is even smaller, though significant. The size of the field

matters, with a 1% increase in the size being associated with a 2.4%-3.7% increase in the probability that

it will have an improvement. Fields where cash crops are grown are about 11% less likely to have some

kind of improvement.

6 Note: when the absolute value of the deviations from the mean was included instead, it was found to be insignificant, suggesting that it is the positive deviation from the mean that matters and not simply being further from the mean (either wetter or dryer).


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Table 9: Marginal Effects, Field Improvements, Field Level

Variable Marginal effect (std. error)

Model 1 Model 2 Model 3 Model 4

Manager sex 0.031 (0.015)** 0.045 (0.017)*** 0.043 (0.017)*** 0.044 (0.017)***

Age 0.008 (0.002)*** 0.009 (0.003)*** 0.009 (0.003)*** 0.009 (0.002)***

Age squared -0.00007 (0.00002)***

-0.00008 (0.00003)***

-0.00008 (0.00002)***

-0.00007 (0.00003)***

Log field size 0.024 (0.004)*** 0.037 (0.005)*** 0.035 (0.005)*** 0.033 (0.005)***

Cash crop dummy -0.11 (0.017)*** -0.147 (0.02)*** -0.148 (0.02)*** -0.142 (0.020)***

Rainfall deviation -- 0.0002 (0.0001)** -- --

HOT day deviation -- -- 0.006 (0.0002)*** --

GDD deviation -- -- -- 0.0009 (0.0002)***

Region fixed effects?

Y N N N

Wald 𝝌𝟐 342.89 133.87 135.71 134.53

N 22,558 22,558 22,558 22,558

Note: * p<0.1; ** p<0.05; *** p<0.01

Tables 10 and 11 present results at the manager level for crop switching and improvement

switching, respectively.7 Here, manager sex matters much more. Male managers are between 12% and

13.4% more likely to switch crops between the two survey waves, and they are between 13.2% and

13.8% more likely to switch improvements between the two waves than female managers. Older

managers are more likely to switch in either case, too. Those managing more fields are (predictably)

more likely to switch crops between waves, though the number of fields managed is not a predictor of

improvement switching. Those growing cash crops are anywhere from 5.5 to 7.4% more likely to switch

crops and between 5.2% and 7.3% more likely to switch improvements.

7 It should be noted that, likely because 88% of managers oversee fields with improvement switching, the models presented in table 11 are a relatively poor fit, with low 𝜒2 statistics.


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Table 10: Marginal Effects, Crop Switching, Manager Level



Manager sex 0.134 (0.016)*** 0.120 (0.16)*** 0.123 (0.016)*** 0.124 (0.016)***

Age 0.019 (0.003)*** 0.018 (0.002)***

0.018 (0.003)*** 0.018 (0.003)***

Age squared -0.0002 (0.00002)***

-0.0001 (0.00003)***

-0.0001 (0.00003)***

-0.0001 (0.00003)***

Number of fields 0.022 (0.002)*** 0.025 (0.003)***

0.025 (0.003)*** 0.024 (0.003)***

Cash crop dummy 0.055 (0.016)*** 0.074 (0.016)***

0.071 (0.016)*** 0.067 (0.017)***

Rainfall deviation -- -0.0001 (0.00005)**

-- --

HOT day deviation -- -- -0.0009 (0.001) --

GDD deviation -- -- -- -0.0002 (0.0010)**


Y N N N

Wald 𝝌𝟐 460.15 334.11 338.97 346.88

N 3,176 3,176 3,176 3,176

Note: * p<0.1; ** p<0.05; *** p<0.01

Table 11: Marginal Effects, Improvement Switching, Manager Level



Manager sex 0.138 (0.024)*** 0.132 (0.024)*** 0.132 (0.025)*** 0.133 (0.024)***

Age 0.014 (0.004)*** 0.013 (0.004)*** 0.013 (0.004)*** 0.013 (0.004)***

Age squared -0.0001 (0.00004)***

-0.0001 (0.00004)***

-0.0001 (0.00004)***

-0.0001 (0.00003)***

Number of fields 0.003 (0.003) 0.003 (0.003) 0.004 (0.002) 0.004 (0.003)

Cash crop dummy 0.052 (0.025)** 0.073 (0.024)*** 0.072 (0.024)*** 0.076 (0.024)***

Rainfall deviation -- -0.0001 (0.00009)

-- --

HOT day deviation -- -- 0.004 (0.002)** --

GDD deviation -- -- -- 0.0003 (0.0001)*


Y N N N

Wald 𝝌𝟐 88.26 74.82 79.96 79.57

N 3,176 3,176 3,176 3,176

Note: * p<0.1; ** p<0.05; *** p<0.01


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The impacts of the climate variables are, again, small, and the significance is uneven. In the case

of crop switching, a larger deviation from the mean makes switching less likely by a very small amount.8

For improvement switching, the rainfall variable is insignificant. The number of very hot days is

insignificant for crop switching, though an additional very hot day is associated with an increase in

probability of improvement switching of 0.04%. Having more GDD is associated with a slightly lower

probability of crop switching but a slightly higher probability of improvement switching. In all cases,

however, the effect is small.

Discussion and Conclusion

This investigation reveals some interesting differences and similarities between male and female

decisionmakers in agriculture. Most decision makers (i.e. crop managers) are the head of the

household, meaning that this analysis can essentially speak to differences in behaviors between male-

and female-headed households. Overall, male manager and heads of household show access to more

resources (more fields managed, more livestock owned and managed at higher values), and more

flexibility (more likely to switch crops). The fields managed by men are more likely to be improved or to

have improvements added. Both men and women grow a wide variety of crops, but men are more likely

to manage fields with a greater variety, and they are more likely to grow key cash crops. Men are more

likely to own livestock of any type aside from poultry. This suggests that farms and households run by

women will be at a relative disadvantage in a warming climate unless interventions are targeted.

While the climate variables included in regressions here had relatively small effects on adaptation

measures, they were often significant and show promise in understanding variation as climate change

worsens in the future. Additionally, 2014 was not an ideal year between waves as it was cooler and

8 In the case of a model with the absolute value of the deviation from the mean, the rainfall variable was negative and significant at the 10% level.


19

wetter than many other recent years have been in Ethiopia, meaning that additional adaptation

behaviors were not likely implemented on large scales.

As climate change begins to manifest more visibly around the world, it will likely impact the

most vulnerable and least flexible populations to the greatest degree. Female farmers in Ethiopia are

less able to withstand variation and change in the climate through established coping mechanisms.

Other mechanisms not tested here, such as using assets from informal networks or migration, may play

a larger role from them, but women may have less access to these, too. More broadly, gains that have

been made in recent years in reducing poverty, stabilizing agricultural output and prices, and promoting

greater equality between men and women may first be slowed and eventually rolled back in the face of

a changing climate that sees more heat waves, more droughts, and more flooding reduce crop yields

and kill livestock.

This study also strongly suggests that better data collection is desperately needed to understand

how climate adaptation unfolds (or does not), to test hypotheses from theoretical work, and to

understand how generalizable findings from case studies are. Surveys need to ask questions specifically

about climate adaptation behaviors to better understand local variations in coping mechanisms.

Additionally, understanding which resources are available to inform about climate change and promote

adaptation and who has access to those is necessary. Comprehensive time use data should be collected

that includes time spent on non-agricultural household activities. More detailed data on the timing of

crop planting should be collected (the ESS only asks about the month of planting, making a detailed

analysis impossible). More frequent data collection would enable the effects of climate change on crop

yields to be more thoroughly analyzed. Though humanity is only beginning to feel the effects of

anthropogenic climate change, an early understanding of the factors that shape responses to it will be

crucial as impacts are felt ever more severely in the coming decades.


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