WEAI+ Mozambique Baseline Survey 2014-15:

Data Summary Report

Soybean Innovation Laboratory A collaboration for Sub-Saharan Africa

Jill L. Findeis, Fridah Mubichi, Maria da Luz Quinhentos, Nina Furstenau

MRA 8, University of Missouri-Columbia

December 2016 revised

1 | P a g e

Table of Contents

Acknowledgments 2

Executive Summary 3

Introduction 5

Country Overview 6

Geospatial Context 8 Survey Approach 11 Results: 14 Household profile 14 Hunger scale 17 Asset ownership 18 Productive capital assets 19 Labor assets and time allocation 21 Soybean uptake 24 Income from soybean and other crops 27 Intra-household decision-making 30 Reasons for decisions 34 Leadership and speaking up in public 34 Access to soya seed and current field practices 35 Concluding Observations 37 References 38 Appendixes Appendix A 41 Appendix B 42 Appendix C 45 Appendix D 54 Appendix E 55 Appendix F 58

2 | P a g e

Acknowledgments The team gratefully acknowledges the United States Agency for International Development and the Feed the Future Initiative for support of the Soybean Innovation Laboratory (SIL) which makes this work possible. The team also thanks the Instituto de Investigação Agrária de Moçambique (IIAM), and particularly Dr. Carlos Quembo, Dr. Magalhaes Miguel, Maria da Luz Quinhentos (MS) and Anacleta Mugabe, for their great work in administering the WEAI+ Mozambique Baseline Survey YR1. A special thanks is extended to all IIAM staff who contributed their valuable insights and time to the research. The hard work of the IIAM inspires us, and we deeply thank the IIAM Mozambique team. We also thank Dr. Kathleen Ragsdale and Dr. Lindsay Peterson of Mississippi State University (MSU) who designed the WEAI+ YR 1 survey instrument in collaboration with Dr. Dan Reynolds of Mississippi State University and with faculty at the University of Missouri-Columbia. We extend our thanks to Dr. Mary Read-Wahidi who has joined the Mississippi State University SIL MRA7 team. Additional thanks goes to Fridah Mubichi and Nina Furstenau who made major contributions to data analysis and the report. Our appreciation extends to Christina Brown, Laura Reiss and Amy Moum in the Division of Applied Social Sciences at the University of Missouri for their help in making the work in East Africa possible. We deeply thank Dr. Peter Goldsmith (University of Illinois at Champaign-Urbana), Principal Investigator of the USAID Feed the Future Soybean Innovation Laboratory (SIL), Courtney Tamimie (University of Illinois at Champaign-Urbana), Project Director for the Soybean Innovation Laboratory, and the many other individuals who comprise the SIL team. We also thank Chancellor Emeritus Dr. Brady Deaton and Dr. Marc Linit, Director of the MU College of Agriculture, Food and Natural Resources Experimental Station, for their unending support and advice. Finally, we thank the smallholders of Mozambique who took their valuable time to provide insights into Mozambique, agriculture and their households through their participation in the WEAI+ Mozambique Baseline Survey YR1. Their generosity and graciousness is inspirational.

3 | P a g e

Executive Summary

The WEAI+ Mozambique Baseline Survey YR1 was conducted to better understand gender equity and intrahousehold dynamics in rural Mozambique while also assessing the socioeconomic context of smallholder farm households. The WEAI+ survey also was conducted to understand engagement with and implications of soya and other legume uptake in Central and North Mozambique, regions that hold significant potential for these crops for adding protein to diets, enhancing incomes, and improving soil fertility. The WEAI+ Mozambique Baseline Survey YR1 used the Women’s Empowerment in Agriculture Index (WEAI) framework supplemented by a set of additional questions focused on soya and legumes. A total of 884 participants were interviewed in 9 villages in Central and North Mozambique in 2014-15. The villages are in Manica (3), Tete (3) and Zambezia/Nampula (3) Provinces. The survey was conducted as a collaboration among the Instituto de Investigação Agrária de Moçambique (IIAM), the University of Missouri (MU) and Mississippi State University (MSU). The same survey team was employed across the three regions and all received CITI Human Subjects training. Households within villages were randomly sampled to reduce selectivity bias. Whenever possible, both a woman and man were interviewed in each household, to allow understanding of intra-household differences in perspective. This report, the first in a series of reports and briefs on the WEAI+ Mozambique Baseline Survey YR1, provides a data summary for the WEAI+ conducted in Mozambique. The report first provides the geospatial context, and includes Google Earth satellite maps of each village site. The basic module structure of the WEAI is then described. This is followed by a summary of preliminary results related to Household Demographics and Hunger, Productive Assets, Labor Assets, Soya Uptake and Income, Intra-household Decision-making, Engagement with Extension, and Soya Production Practices. Differences across the three regions of the study are highlighted throughout the report. Results demonstrate that the population living in rural Mozambique villages is young, and hunger remains a challenge. While many rural Mozambican households have access to land for farming and grazing, productive capital is in the form of traditional hand tools and credit is largely unavailable. The lack of productive capital -- mechanized equipment and access to credit retards development. Cell phones have experienced rapid diffusion, with 63% of women reporting owning a cell phone themselves (26%) or with their spouse (37%). Women are less likely to speak up in public than men, a challenge that reduces wmen’s empowerment. Particularly troubling is that women are less likely to engage with extension personnel than men and many report being concerned about speaking up in public to extension agents. Many rural Mozambican women are highly engaged in farming but have neither the productive capital nor the training that would take them to the next level. Large regional differences in soybean uptake are documented for both men and women, with the Central region villages in Manica Province being least involved in soybean production. Many households in the Northwest and Northeast regions receive soybean income. Importantly, both women and men report being engaged in farming and marketing/selling of soya. Questions asked to respondents on practices used for soya – eg, spacing, planting -- showed very wide variation. One exception is that few use inoculants and few use other inputs on soya. Farmers report having seed to plant but significant proportions of men and women report lacking knowledge of where to buy improved seed.

4 | P a g e

Finally, the WEAI+ is found to be a powerful tool for opening up the ‘black box’ of the household, to begin to understand gender dynamics. For Mozambique, interesting gender-differentiated results are reported. First, regarding resources, many Mozambican women report that they have joint ownership of household and farm assets with a spouse, with more men reporting that they own individually. For decisions regarding purchase, sale and transfer of assets, her perceptions of her engagement declines further. Few – men or women -- have access to credit or engage in decisions related to credit. The Central region is found to have greater availability of informal credit. Second, many Mozambican men and women report making production decisions jointly with a spouse. Many Mozambican women also report having input into production decisions, more than expected based on other studies using the WEAI in other contexts including the WEAI+ survey conducted in Ghana. These results and findings related to input on income use and on motivations for decisions deserve much more analysis to understand why. Third, a majority of Mozambican women express significant concern with speaking up in public, including speaking up to extension personnel. The contact that women have with extension personnel also is observed to be low. Finally, Mozambican women are more likely to receive seed from relatives in their village than men, and express a willingness to try soya as an enterprise. Reducing constraints to their participation both as smallholders and as women is the next step.

5 | P a g e

Introduction The USAID-funded Feed the Future Soybean Innovation Laboratory (SIL) project in Mozambique covers nine village sites in Central and North Mozambique. The nine villages, located in five districts: Sussundenga, Angonia, Gurué, Lichinga and Malema, comprise the Mozambique Learning Laboratories where multiple research, extension/outreach and training activities take place. Examples of activities include distribution of Soy Success Kits, extension activities related to showing farmers – both men and women -- how to grow and market soya, and nutrition training to boost protein consumption by women and children in the villages where soya is grown. Participating villages were carefully selected by the Mozambique Institute for Agricultural Research (IIAM) in collaboration with the SIL team. Diversity in soil organic matter, temperature, and pH levels were taken into consideration.

Following site selection, a baseline (ex ante) survey – called the WEAI+ Mozambique Baseline Survey 2014-15 YR1 -- was conducted to establish baseline social and economic characteristics of households and individuals within households at the nine sites, both to understand women’s empowerment (ability of make choices) in rural Mozambique and to assess current practices related to soya and other legume production, consumption and market behaviors.

The Women’s Empowerment in Agriculture Index (WEAI) survey which is described below, is the basic framework used for the research; the WEAI survey framework allows calculation of an index that is used worldwide to understand the level of empowerment of women. Additionally, questions were added to supplement the WEAI survey to create what we refer to as the WEAI+. The supplementary questions relate to cultivation of crops, especially soya and other legumes. It is well known that legumes contribute to human health and soil health, twin goals for African development.

The WEAI+ Survey. The Women’s Empowerment in Agriculture Index (WEAI) survey that was used as the basic framework for the baseline work in Mozambique was developed for Feed the Future programs (FTF) by the International Food Policy Research Institute (IFPRI) and the Oxford Poverty and Human Development Initiative as a multi-dimensional tool to enable measurement of empowerment among women resulting from FtF initiatives (Alkire, et al, 2013).

The FTF initiative’s goal is to reduce global poverty and hunger by stimulating agricultural growth and improve the nutritional status of women and children, as targeted populations. Therefore, the WEAI provides a diagnostic tool to assess women’s empowerment

6 | P a g e

and control over their household, community and economy (Alkire, et al, 2012). Empowerment is defined as the ability to make choices, control or make decisions regarding credit and assets, as well as the ability to enjoy or benefit from their effort (Alkire et al, 2012). The WEAI is comprised of two sub-indices that assess state of empowerment in agriculture and gender parity within surveyed households in order to assess country, regional or program-level aggregate indices. The gender parity index (GPI) reflects the percentage of women who are equally empowered as the men in their households (Malapit, et al, 2014). Women are considered empowered when they achieve 80% - or four out of the five -- domains (Malapit, et al, 2014; Alkire, et al, 2013; Alkire, et al, 2012).

This report summarizes data collected in the 2014-15 WEAI+ Mozambique Baseline Survey YR1 conducted in Mozambique where soybeans show significant potential for feeding the future in Sub-Saharan Africa.

Country Overview

Mozambique is located in southeast Africa along the Indian Ocean, with an estimated population of 27.22 million in 2014 (data.worldbank.org).Mozambique borders on Malawi 1,569 km, South Africa 491 km, Swaziland 105 km, Tanzania 756 km, Zambia 419 km, and Zimbabwe 1,231 km (The World Factbook 2013-14).

Mozambique is large and largely rural. Mozambique’s north-south distance is roughly comparable to the north-south distance from Florida to Maine in the US. Understanding the significant size of the country -- and its rurality -- are important for understanding Mozambique.

Almost all (99.66%) of the population of Mozambique is African. Other populations include 0.06% European, 0.20% Euro-African and 0.08% Indian (The World Factbook, 2014).

7 | P a g e

In times of

scarcity, poor

rural people

have little to

buffer them

from food

insecurity.

The country's low

agricultural productivity

is the result of a lack of

appropriate

technologies and

supports.

… produce markets are

generally distant,

unreliable and

uncompetitive for

smallholder farmers,

who depend on

traditional farming

methods, low-yield seed

varieties and manual

cultivation techniques.

Alternative sources of

income outside

agriculture are few.

- IFAD Rural

Poverty Portal

FOOD INSECURITY

Almost 94% of the population is estimated to be below 60 years old; life expectancy at birth is 53 years.1 The estimated median age of the population in 2014 was 17 years and the estimated fertility rate was 5.21 children born per woman. The US Census Bureau International Data Base (2015) estimated the total fertility rate for Mozambique at 5.2 births per woman. Although the birth rate seems to have declined, Mozambique’s population is relatively younger compared to its neighbor, South Africa (Velkoff & Kowal, 2007). According to the World Bank (2015), 54.7% of Mozambique’s population in 2008 was below national poverty lines. Cunguara and Hanlon (2012) see poverty as an outcome of both historical and structural forces. Historically, Mozambique’s extended civil war displaced thousands and destroyed much of the previously existing social and communication infrastructure. Structurally, much of Mozambique’s political economy has undergone various structural changes over the last fifty years: first, an extended civil war displaced thousands; second, the Structural Adjustment Program by the World Bank created a transition from a command economy to a free market economy; and third, the 1992 General Peace Agreement for Mozambique allowed greater rural-urban migration.

Despite significant challenge, Mozambique had the strongest GDP growth rate in Sub-Saharan Africa over the last decade. Mozambique’s estimated GDP annual growth rate averaged around 7% compared to the region’s 4.2%. The World Bank Country Profile (2015) estimates 5.9% GDP growth in Mozambique due to improvements in trade, manufacturing, extractive industries, transport and communication, and electricity production. Regardless, much of this growth is still attributed to increased donor funding and infrastructure development within the country. About 81% -- 4 out of 5 -- of the population is employed in agriculture, which contributes almost 29% to the country’s annual GDP. Hence, smallholder farmers play a significant role in Mozambique’s development. Mozambique’s Ministry of Agriculture works in part through extension services to improve smallholder productivity. But as is true across much of Sub-Saharan Africa, extension services are spread thin. This is a particular challenge for a country as large and as rural as Mozambique. According to IFAD, ‘the country's low agricultural productivity is the result of a lack of appropriate technologies and supports. In

1 Appendix A includes Mozambique’s 2015 population pyramid.

8 | P a g e

addition, produce markets are generally distant, unreliable and uncompetitive for smallholder farmers, who depend on traditional farming methods, low-yield seed varieties and manual cultivation techniques. Alternative sources of income outside agriculture are few. In times of scarcity, poor rural people have little to buffer them from food insecurity.’ IFAD Rural Poverty Portal, http://www.ruralpovertyportal.org/country/home/tags/mozambique

Recent statistics from the World Bank show that agriculture’s contribution to Mozambique’s GDP declined by 1.6% between 2010 and 2014. Although much of this decline is argued to be attributed to severe droughts, floods, and other weather events over the last decade, poor market infrastructure and weak external links to facilitate access to farming inputs impede many smallholder farmers from thriving (Hanlon, 2007; Cunguara and Hanlon, 2012; De Braw, 2015; Kondylis, et al, 2015). At the same time, new developments in Mozambique’s infrastructure coupled with the widespread diffusion of cell technologies even into the rural countryside, show significant promise.

Geospatial Context Mozambique is Africa’s 16th largest country with 309,495 square miles (801,590 sq km) of land. In the north, there are rugged highlands which include the Angonia, Tete and Niassa highlands among others. The south is characterized by the Mashonaland plateau and Chimoio plateau. Annual rainfall is approximately 500-900mm and varies based on altitude. The average temperature is between 15-34C (59-93F), and also varies by altitude. Soil fertility is diverse in quality.

Appendix B includes average high and low temperatures and precipitation and average rainfall days by month for Sussundenga (Manica Province in the Central Region), Zambezia (termed the Northeast Region), and Tete (termed the Northwest Region).

The following section presents information on the Mozambique Learning Laboratories where multiple research, extension, and training activities take place. Both maps and Google Earth images of the surveyed villages are presented.

9 | P a g e

Nine villages. The WEAI+ Mozambique Baseline Survey YR1 covered nine villages spread across three regions. As shown on map 2 (following page), three villages are in Manica Province, just east of Zimbabwe, three are located in Tete Province (west of Malawi), and three are in Zambezia and Nampula Provinces (east of Malawi). Satellite imagery of each village is included in appendix C. The villages are all rural and depend on agriculture for their livelihoods. Smallholder farms are the domiant form of agricultural production.

The villages in Tete region are at least 304.8 meters (1,000 ft) higher in altitude than those in the Manica and Zambezia/Nampula regions. The highest elevation village of the nine is in Tete Province and measures 1,406 meters (4,613 ft) above sea level. The lowest elevation village is located in the Zambezia/Nampula region and is measured at 523 meters (1,715 ft) above sea level. Villages in Manica Province are between 621 meters (2,037 ft) and 898 meters (2,945 ft) above sea level.

10 | P a g e

11 | P a g e

Survey Approach

The WEAI+ survey instrument was designed by researchers at Mississippi State University (Dr. Kathleen Ragsdale, Dr. Lindsey Petersen, and Dr. Dan Reynolds) in collaboration with the University of Missouri research team. Collaborators from Catholic Relief Services in Ghana (CRS-Ghana) and the Instituto de Investigação Agrária de Moçambique (IIAM) provided key input, including contextualization to Ghana and Mozambique rural villages. Again, the WEAI format was used with modifications to create the WEAI+ instrument. Modifications included addition of sets of questions related to soya production, consumption and marketing. In the case of Mozambique, all questions were translated into Portuguese, the official language of Mozambique, prior to enumeration.

The sections of the WEAI+ Mozambique Baseline Survey YR1 include:

Module A Demographics

Module B.1 Dwelling characteristics (observational)

Module B.2 Dwelling characteristics (participant response)

Module C.1 Household demographics

Module C.2 Household hunger scale

Module D Key crop cultivation

Module E.1 Household decision-making

Module E.2 Household decision-making in production and income generation

Module F.1 Access to productive capital and credit – productive capital

Module F.2 Access to productive capital and credit - credit

Module G Access to agriculture/livestock/fisheries extension

Module H.1 Individual leadership and influence in the community

Module H.2 Group membership

Module I.1 Decision-making (who normally makes decisions)

Module I.2 Decision-making (control over activity participation)

Module J.1 Time allocation

Module J.2 Time allocation (activity record)

Module K.1 Soybean and other seed access

Module K.2 Soybean cultivation

Module L.1 Cultivation months (time record)

Module L.2 Income-generation months

12 | P a g e

The five domains in the initial WEAI survey were covered by the survey questions in the Mozambique WEAI+ to allow for comparability with other WEAI studies. The WEAI+ baseline survey conducted in Ghana for SIL used the same survey instrument by design. As described in a recent IFPRI discussion paper (Alkire, et al 2012), the five domains covered by the initial WEAI include:

Domain 1, Production: This dimension concerns decisions about agricultural production and refers to sole or joint decisionmaking about food and cash crop farming, livestock and fisheries, and autonomy in agricultural production, with no judgment on whether sole or joint decisionmaking was better or reflected greater empowerment.

Domain 2, Resources: This dimension concerns ownership of, access to, and decisionmaking power about productive resources such as land, livestock, agricultural equipment, consumer durables, and credit.

Domain 3, Income: This dimension concerns sole or joint control over the use of income and expenditures.

Domain 4, Leadership: This dimension concerns leadership in the community, here measured by

membership in economic or social groups and comfort speaking in public. Domain 5, Time: This dimension concerns the allocation of time to productive and domestic tasks and satisfaction with the time available for leisure activities. Alkire, et al 2012)

Topics related to the five domains are included in this baseline data summary report. Resources and decision-making related to resources are discussed first, both for women and men, to understand the context of rural Mozambique. Then, production is discussed, assessing participation in production, ownership and decision-making. Time allocation also is discussed. Income sources are presented next, followed by motivations for decision-making and leadership. Information on soya is included last.

Survey enumeration. Both male and female survey enumerators were trained by the research teams from the University of Missouri, Mississippi State University, and IIAM-Mozambique. Training took place in May 2014 in Chimoio, Mozambique. Each team member passed the Human Subjects Research online course offered by the Collaborative Institutional Training Initiative (CITI) Program. Enumerators who could understand local dialects were chosen to form the team, and a single team was used across all villages to enhance consistency across villages in survey enumeration. Survey enumerators from outside the nine villages were employed, since study participants may have concern about sharing personal information with local surveyors whom they know.

In each village, the (traditional) village chief (chef) and municipal chief

13 | P a g e

(chef du poste) were consulted prior to the survey, and permissions secured. Prior to visiting the village, Google Earth and Bing maps were made showing the landscape of each village, including village compounds. A random sample of households was developed based on the Google Earth/Bing Map imagery. Random sampling was used to reduce sample selection bias. All instruments and procedures were approved by the University of Missouri IRB. Following permissions being secured from the village chef/chef du poste, sampled households were asked if they wanted to participate in the survey. Consent procedures approved through IRB were used.

One adult female and one adult male decision-maker were asked to participate per household, if both present. If no adult male was part of the household, only the adult female was interviewed. Similarly, if no adult female was part of the household, only the male decision-maker was interviewed. When the household male or female decision-maker was not at home at the time of the interview, enumerators set up an appointment for a later time to return to the household to conduct the survey. Two appointments were made for this follow-up. The goal was to maximize the number of households in which both a male and female were interviewed, if both part of the household. Respondents were free to refuse to be interviewed, skip any questions and/or stop the interview at any time.

Within a household, female and male survey respondents received the same survey instrument. The woman was interviewed by a female. A male enumerator interviewed the male respondent. This makes it possible to gain insights into perceptions and knowledge – both from him and from her. Previous surveys that we have conducted among agricultural households in the US, China and Africa (Zhang 2011; Sevilla 2013; Smith and Findeis 2013), have used this approach, allowing the research team to better understand differences in responses across the household. Individuals were not interviewed with others present or nearby, again to avoid concern that responses could be shared with others.

14 | P a g e

Results

Household Profile. A total of 884 respondents in 9 villages were surveyed between August 2014

and April 20152. The sample was fairly evenly distributed across the three regions: 36% of respondents were from the Central (Manica) region, 29% from the Northeast (Zambezia/Nampula)3, and 35% from Northwest (Tete) Mozambique (table 1 below).

About 94% of respondents in the Manica area were married couple households, 5% were single female and 1% were single male. Similarly, the Zambezia/Nampula region reported about 94% of respondents as married couple households, 5% as single female and 1% as single male. In Tete, a region with matrilineal households, there were relatively fewer married respondents: about 81% reported being married, 18% reported being single female, and less than 1% were single male. The majority of respondents in the sample identified themselves as Christian (87%), 4% as Muslim and 9% as traditionalist.

Table 1: WEAI+ sample distribution by region, Mozambique 2014-15

Total respondents by region Frequency (n) Female (%) Male (%) Total (%)

Central (Manica) 316 33.55 38.44 35.80

Northeast (Zambezia/Nampula) 259 28.03 30.90 29.37

Northwest (Tete) 307 38.43 30.66 34.81

Total n=882 100% 100% 100%

McNair, et al (2015) reported a mean family size of 5.9 individuals in Tete and Manica provinces; our survey data indicate a smaller average household size of roughly 5 members. Household size ranged from one to 15 members across all surveys. Household member composition ranged from nuclear-type households with sons and daughters, to extended households with grandchildren, grandparents, in-laws, and others listed in the household profile.

Survey data suggest the villages have a very young population, consistent with expectations. Previously reported national census data (Knoema, 2015) showed 61% of household members in the 6-25 age range. WEAI+ data for Mozambique showed that about 22% are in the birth to 5 year age range, and another 36% were 6 to 15 years of age, inclusive. This young population is vulnerable from multiple perspectives (malnutrition, food security, disease) and improvements in agriculture can have significant benefits to them. Only 2.28% of household members were over 65 years, reflecting Mozambique’s low life expectancy. Figure 1 (on the following page) illustrates the reported age distribution of household members (n=4716).

2 Two of the survey respondents did not fill in a number of the questions and therefore their data are absent for many of the questions, resulting in 882 useable surveys. 3 The lower number of interviews in the Northeast region was due to one village having had a recent bad experience with a disease outbreak.

15 | P a g e

Figure 1: WEAI+ reported household member age by age group, Mozambique 2014-15

0%

5%

10%

15%

20%

25%

30%

35%

40%

0-5 Yrs 6-15 Yrs 16-25 Yrs 25-35 Yrs 36-45 Yrs 46-65 Yrs 65+ YrsPerc

ent

ho

use

ho

ld m

emb

ers

(%)

Reported age of household members (years)

Ethnic identity and language. Survey results show diverse ethnic and linguistic characteristics. Respondents were asked first to identify with an ethnic group and then to provide the language they speak at home. Table 2 provides comparisons by region of the language spoken by the respondent at home, using local names; Figure 2 provides the major ethnicities by region. In the Northwest (Tete), 92% of female respondents and 98% of male respondents report speaking Chechewa (Chichewa/Chewa). The Northeast is found to be largely Macua/Makua or Lomue/Lomwe. Finally, the Central village sites are Chiute/Ciute/Chute, Chimanhica and Shona, with a few others noted (table 2). Our experience in the Central sites, which are proximate to the Zimbabwe border, has shown a mix of ethnicities.

Table 2: Comparison of reported languages spoken at home by male and female survey respondents

Language spoken at home Central (Manica)

Northeast (Zambezia/Nampula)

Northwest (Tete)

% Female

% Male

% Female

% Male

% Female

% Male

Chimanhica 25.95 20.89 0 0 0 0

Chindau (Ndau) 1.27 1.27 0 0 0 0

Chiute/Ciute/Chute 56.33 59.49 0.76 0 0 0

Chechewa/Chichewa/Chewa 0 0 0 0 91.80 98.44

Lomue/Lomwe 0 0.63 65.91 70.87 0 0

Macua/Makua 0 0 33.33 29.13 0 0

Ngoni 0 0 0 0 6.56 0

Nhanja 0 0 0 0 1.09 0

Shona 16.46 17.72 0 0 0 0

Other 0 0 0 0 0.55 1.56

16 | P a g e

Education and Portuguese language. Education attainment in the rural population is very low. The WEAI+ data show gendered differences in education attainment and in ability to speak or read and write Portuguese. Women had lower education attainment than men. Almost 3 of every 5 women had less than a primary-level education which included no school at all. A total of 35% of women had a primary-level education and 5% had secondary education. About 34% of men had less than a primary-level education including no school at all, 53% had primary education, and 12% had secondary education.

Only 38% of women compared to 66% of men speak Portuguese. Fewer women (44%) said they could read and write in Portuguese compared to men (73%). Regional comparisons showed the highest number of Portuguese speakers, readers and writers live in Central Mozambique. Those in Northwest Mozambique reported the lowest ability to speak, read and write Portuguese.

Table 3: Education attainment and Portuguese language ability

Obs (n)

% Female

% Male

Total respondents surveyed by region n = 882 N = 471 N = 411

% less than primary school 59.24 34.31

% primary school 35.03 53.04

% secondary school 5.10 12.41

% college, vocational or technical 0.63 0.24

% able to speak Portuguese 882 38.30 66.26

% able to read and write Portuguese 691 44.13 72.81

0% 20% 40% 60% 80% 100%

Central (n= 316)

Northeast (n= 259)

Northwest (n= 308)

Percent who identify with ethnic group

Figure 2: Respondent's identified ethnic group by region, rural Mozambique

Yao

Nhanja

Lomue

Macua

Chechewa

Shona

Chute

Other

17 | P a g e

Hunger Scale

Overall, a total of 41% of respondents – roughly 4 of every 10 respondents – reported that there was a time in the past 4 weeks when there was no food in the house due to lack of resources. By gender, 42% of women reported this and 40% of men. Of those reporting no food, 1 in 4 reported their household experienced this 3-10 times during the previous 4 week period.

Variations in hunger is expected seasonally. The photo at the right shows a Mozambique village landscape in October when food is becoming scarcer. At the same time, annual shocks can happen with will result in even worse conditions. Mozambique can suffer from severe drought and also excessive rainfall/flood conditions.

As an example, roughly 1 in 3 respondents from the Northeast Mozambique villages -- interviewed in November 2014 during the depths of the hunger season -- reported no food in their household sometime in the last 4 weeks. In comparison, in Northwest Mozambique (Tate/Angonia region), respondents interviewed in April - May of 2015, reported an even greater prevalence of hunger, largely resulting from excessive rains that affected parts of this region and North Mozambique (Zambezia, Caba Delgado, etc.). In Tete/Angonia, about half (54%) of respondents reported no food in the house at some time in the previous 4 weeks. A total of 23% of Tete/Angonia respondents reported not having food 3-10 times over the last 4 week period. About one-third of Northwest respondents said that either they themselves and/or a household member slept hungry.

Asset Ownership

Dwellings. Dwellings in rural Mozambique include traditional wood huts with thatched roofs, dwellings of mud construction and red brick constructions. Overall, the majority (95%) of respondents reported owning their dwellings. They also report living in compounds. In Tete, 8% of female-headed households reported living in rented houses.

18 | P a g e

About 48% of houses can be described as having moderate wear-tear and 23% as poor. Some (13%) are described as excellent and few (3%) as very bad/poor. The rest are considered good. Most (89%) dwellings have earthen floors; mud plaster walls (70%) and thatched roofs (72%) are common (refer to

table 4). Additional data are summarized in appendix D.

Respondents reported owning a small number of small durables (eg, cooking pots, radio), usually reporting owning only 1-3 small durables, if any. As shown in table 5, 71% of married men and 84% of married women reported owning the house and associated structures with a spouse. About 27% of married males -- compared to 10% of married females -- said they themselves owned the house/structures, and could make these decisions alone.

Table 4: Dwelling characteristics reported by females, Mozambique

Roof

% houses

Thatch 71.61

Corrugated metal sheets 28.18

Other 0.21

Floor

Earth 88.56

Cement 11.23

Other 0.21

Exterior wall

Mud plaster 69.70

Mud brick 3.18

Concrete block 12.71

Other 14.41

Table 5: Reported productive capital ownership by couples, Mozambique 2014-15

Who owns:

Married females %

Married males %

Farming land

Self 9.97 21.78

Spouse 8.92 7.85

Self and spouse 79.53 69.04

Other household members 1.57 1.27

Grazing land Self 10.67 31.46

Spouse 2.67 6.74

Self and spouse 61.33 32.58

Other household members 25.33 29.21

19 | P a g e

Productive Capital Assets

About two-thirds of the sample, 68% of male respondents and 66% of female respondents, reported the household ‘owning’ land for farming. When married women and men were asked about farm land ownership, 69% of males and 80% of females said they owned the land with their spouse – ie, joint ownership. Another 22% of married males and 10% of married females reported owning land themselves. Very few married respondents owned farm land – as opposed to grazing land -- with other household members (see Table 5)4.

Land for grazing and livestock ownership. Comparatively few — only 92 males and 86 females—reported that the household has access to land for grazing, and even fewer owned the land. Of married females that responded, 61% said they held this land jointly with a spouse; 33% of married males reported joint holdings. Another almost third (31%) of married men said ‘self’ when asked about ownership of land for grazing, and 29% said with ‘other household member(s)’. Of married women, 11%

said ‘self’ and 25% -- 1 in 4 -- reported holding grazing land with ‘other household member(s)’ (table 5).

Perceptions of who owns large livestock differs by men and women, with more married women reporting joint ownership. This result calls into question if large animals – typically considered to be male owned – are in fact owned by the male or if joint ownership is more the reality.

When asked how many small livestock – goats, pigs and sheep -- the household owns, responses ranged from one animal to 11. Households also reported owning fowl – from as few as one to 10. Finally, only 14 men and 5 women responded that they own a pond for fishing/fishing equipment, indicating that only a few households in the nine villages use a fish pond/equipment as productive capital.

Cell phones and means of transportation. When respondents were asked if anyone in the

household currently owns a cell phone, 221 males and 233 females said yes. This is roughly half of the sample. Overall, 26% of married women said they themselves owned the cell phone or most of the phones owned by the household; another 37% said they shared ownership of the cell phone or phones with a spouse. Among married men, 64% reported self ownership of most and 30% said they owned with a spouse. When asked how many cell phones the household owns, 65% of 448 respondents said one, 25% reported two, 6% reported three, and 2% reported four cell phones. While many households in rural Mozambique now own cell phones, many still do not. Regardless, hardly any households interviewed in 6 of the 9 villages in 2008-09 reported owning cell phones, demonstrating their rapid diffusion in less than a decade. Importantly, many women in the 9 villages now have access to a cell phone and even consider that they jointly or solely own one (or more).

4 In this report, responses for ownership questions are reported (solely) for married males and females, since self and spouse ownership a key category.

20 | P a g e

Respondents also were asked if they owned means of transportation such as a bicycle, motorcycle or car. Around 82% of married males and 37% of married females reported that they – by themselves -- owned a means of transportation; another 14% of males and 18% of females reported owning transport (bicycle, motorcycle, etc.) jointly with their spouse. Overall, the men felt they owned most of the various means of transportation and could make decisions on selling, giving away or renting out. As noted above, slightly over one in three married women (37%) responded that they had self ownership of means of transport.

When asked how many means of transport the household owned, 80% of 507 respondents reported one, 15% reported two, and 4% said three. While many households now own one or more means of transportation, many still do not. Again, the numbers in the 2014-15 baseline are much higher than those recorded in 2008-09 in 6 of the 9 villages.

Table 6: Reported ownership of productive capital by type of household, Mozambique 2014-15

Who owns:

Married females %

Married males %

Cell phone

Self 26.19 64.35

Spouse 31.43 3.7 Self and spouse 37.14 29.63

Other household members 5.24 2.31

Means of transportation

Self 37.18 81.75

Spouse 43.16 2.38 Self and spouse 17.95 14.68

Other household members 1.71 1.19

Farm equipment. The fFarm implements in the villages tend to be traditional, with cultivation being manual. Most men and women in couple households reported sharing ownership of non-mechanized equipment with their spouse; 95% of married women and 86% of married men reported this. An example of a typical household farming implement used in rural Mozambique is shown in the photo to the right.

Comparatively few households (n=83) reported owning (any)

21 | P a g e

mechanized equipment for farming. Six unmarried women reported owning mechanized equipment. All married women who reported that the household owned mechanized equipment reported either owning with a spouse (58%) or that their spouse owned (43%).

Access to credit. Finally, to establish existing sources of productive capital and credit, we asked respondents to identify lending sources they had accessed or used over the last 12 months. Our survey data show that very few respondents took loans or borrowed cash/in-kind from NGOs, informal or formal lenders, friends or relatives, or group-based micro-finance lenders. For example, almost all respondents reported not taking a loan from an NGO: 95% did not in the Central region, 92% did not in the Northeast, and 94% did not in the Northwest. Very similar percentages are found for informal lenders, formal lenders, friends/relatives, or group-based micro finance. Not surprisingly, the lack of credit to farmers is a major impediment for development benefiting smallholders in the agricultural sector in Mozambique.

In summary, while land is a plentiful resource in rural Mozambique, the very significant lack of productive capital severely constrains production. Traditional hand tools are the norm, and credit infrastructure is almost nonexistent. At the same time, cell phones are rapidly diffusing as a technology, and means for transportation are much more common than less than a decade ago. Thus, the networks of Mozambique’s rural villages are very likely to be larger in geographical space – more travel and greater communication with those outside the village. And yet, the capital constraints remain, severely restricting productivity and returns to existing resources. This is the case for both women and men.

Labor Assets and Time Allocation

Agriculture is by far the predominant work activity in Mozambique’s rural villages. As shown in table 7, food crop farming is universal among men and women. Cash crop farming also engages large percentages of both the male (83%) and female (78%) populations in the 9 villages.

In the three regions covered by the WEAI+ baseline survey, most households engage in field preparation in September thru October and into November. Activity lasts until the rains come. In Central Mozambique’s Manica Province, August is also are important time for field preparation. Both women and men participate. The field preparation period corresponds to the hunger season in Mozambique.

Table 7: Participant role in selected farming and income generating activities in past cropping season, Mozambique 2014-15

Table 7.

22 | P a g e

Following this, the rains come and the months of (roughly) March through July are a period of harvest. Marketing and selling of these crops then happens, as shown in the figures directly below.

The graphics in figures 3a, b, c show female engagement by region in production and marketing of crops other than soy; figures 4a, b, and c show participation -- by region -- for males. Both males and females engage in all aspects – field preparation, harvesting and marketing/ selling. Some respondents are found to work on others’ farms, either for a relative or for someone unrelated to them. Labor on someone else’s farm involved 10% or less of respondents at any one time, for each.

Figure 3a. Percent females involved in other (non-soy) crops, Northwest region

Table 7. Labor allocation, male and female, Mozambique villages 2014-15.

Activity respondent

participates in

Male % (n)

Female % (n)

Total %

Food crop farming

100 (408)

99.13 (461)

99.54

Cash crop farming

83.33 (408)

77.51 (458)

80.25

Livestock raising

72.48 (407)

71.96 (453)

72.21

Non-farm economic activity

40.55 (402)

30.21 (437)

35.16

Wage or salary

employment

26.87 (402)

17.28 (434)

21.89

Fish culture

6.87 (393)

3.95 (430)

5.35

23 | P a g e

Figure 3b. Percent females involved in other (non-soy) crops, Northeast region

Figure 3c. Percent females involved in other (nonsoy) crops, Central region

Figure 4a. Percent males involved in other (nonsoy) crops, Northwest region

24 | P a g e

Figure 4b. Percent males involved in other (nonsoy) crops, Northeast region

Figure 4c. Percent males involved in other (non-soy) crops, Central region

Soybean Uptake

Labor allocated to soybeans showed very interesting results. Figures 5a, b, c and 6a, b, c show large regional differences in farmer participation/engagement in soybean production and marketing/selling. In the Tete/Angonia villages, both women and men have high participation rates in soybean production and marketing/selling (refer to Northwest graphs below). Very importantly, the women of Tete/Angonia show high participation rates in not only field preparation and harvesting of soybean but also in the marketing/selling of soybeans. Almost half of women in the Northwest are engaged in field prep for soya, with a similar percent being involved in harvesting. A total of about 40% of women in this region participate in the marketing/selling function.

Rates of participation in the Northeast are roughly half of that in the Tete/Angonia region (Northwest), and rates of participation in soya in Manica Province (Central region) are very low at this time. The regional differences in soya production and marked and will be an important backdrop for further work in the SIL in Mozambique.

25 | P a g e

Figure 5a. Percent females involved in soybeans, Northwest region

Figure 5b. Percent females involved in soybeans, Northeast region

Figure 5c. Percent females involved in soybeans, Central region

26 | P a g e

Figure 6a. Percent males involved in soybeans, Northwest region

Figure 6b. Percent males involved in soybeans, Northeast region

Figure 6c. Percent males involved in soybeans, Central region

27 | P a g e

Income from Soya and Other Crops

The next set of figures build upon the findings for female and male time allocation, but show earnings by crop – maize, soybeans, other legumes, rice, and other crops. Results for women are included in figures 7a, b, c; results for men are included in figures 8a, b, c. Data for remittances, off-farm income, etc. are in appendix E.

Again, the importance of soybeans as an income source for the Tete/Angonia region (Northwest) is underscored. Additionally, the importance of soya for women farmers in the Northwest is again underscored. For example, in June 39% of women in the Northwest reported earnings from soybeans; 48% of men did.

Maize, other legumes (eg, common bean) and other crops outrank soya in the selected villages in the Northeast and Central regions of Mozambique. In the Northeast (Zambezia/Nampula), it is recognized that there communities in this region where soya has become very important in the crop mix. However, in the specific villages involved in the work under SIL, the effects are not as significant as could be the case. In Central Mozambique, earnings from soya are quite low.

28 | P a g e

Figure 7a. Percent females reporting income from crops, Northwest region

Figure 7b. Percent females reporting income from crops, Northeast region

Figure 7c. Percent females reporting income from crops, Central region

29 | P a g e

Figure 8a. Percent males reporting income from crops, Northwest region

Figure 8b. Percent males reporting income from crops, Northeast region

Figure 8c. Percent males reporting income from crops, Central region

30 | P a g e

Intra-Household Decision-Making

The WEAI+ baseline survey includes a large set of questions focused on the intersections between and among work, earnings and intra-household decision-making. The decision-making questions relate to level of input into decisions, who makes the decision (final say), and various motivations for decision-making. The data from these questions are very rich and complex. For the WEAI+, these questions are in the WEAI survey format, allowing comparability between the WEAI+ Baseline Mozambique YR1 2014-15 data set and other WEAI data sets, including the WEAI+ conducted in Ghana in the 2014.

Survey respondents were first asked if they had participated in particular work activities in the immediate past cropping season. These included 1) food crop farming, 2) cash crop farming, 3) livestock raising, 4) non-farm economic activities (eg, self-employment in a small business), 5) wage or salary employment, and 6) fishing. Following this, they were asked to provide their level of input for decisions related to each activity and use of income earned. The following sections include some of our findings.

Food crop farming. When asked if they had participated in food crop farming, almost all women (99%) and all men said they participated. When asked their perceived level of input in decision-making on food crop farming, results in table 8 show that significantly more men (42%) than women (17%) report providing input into all decision-making. About 43% of women and 36% of men provide input into most decisions. About 16% of women compared to 4 percent of men reported providing input into (only) a few or no decisions, when decisions are made.

When asked about level of input into decisions about use of income generated from food crop sales, 44% of males reported being involved in all decisions (table 8). In comparison only 17% of women believed they provided input into all decisions related to use of income from food crop sales. About 35% of men compared to 41% of women felt they participated in most decisions.

Cash crop farming. Most of the respondents – 78% of women and 83% of men (n=866) -- reported being involved in cash crop farming in the last cropping season. When asked to assess their level of input into crops primarily grown for sale, 14% of women and 44% of men felt they participated in all decisions. About 45% of women and 32% of men felt they participated in most decisions (table 8).

31 | P a g e

Table 8: Comparison between male and female input in crop farming decision making and use of income generated from crops.

Input on activity in immediate past cropping season

How much input did you have in making decisions about activity:

How much input did you have on use of income generated from activity:

Food crop farming Male % (n=408)

Female % (n=460)

Male % (n=408)

Female % (n=460)

No input 0.74 2.83 1.23 3.26 Few decisions 3.19 12.83 3.68 13.91

Some decisions 18.38 24.57 15.69 24.35 Most decisions 35.78 42.83 35.05 40.87

All decisions 41.91 16.74 43.87 16.74 No decisions made 0 0.22 0.49 0.87 Cash crop farming

(n=344) (n=358) (n=344) (n=356)

No input 1.74 1.96 1.16 1.97 Few decisions 2.62 12.85 2.91 13.20

Some decisions 19.48 26.26 15.99 26.97 Most decisions 32.27 44.69 32.85 43.26

All decisions 43.60 14.25 46.51 14.61 No decisions made 0.29 0 0.58 0

Further, when we asked respondents about input into decisions regarding use of income from sale of cash crops, a total of 700 individuals responded. Based on our results, significantly more men (47%) than women (15%) reported providing input into all decisions on use of income generated from sale of cash crops, and a total 43% of women compared to 33% of men said they were involved in most decisions. Overall, only 1% of males and only 2% of females perceived that they had no input into the use of the income that was generated from the sale of cash crops.

Raising livestock. Slightly over 70% of respondents (n=860) reported raising livestock in the immediate past cropping season. About 72% of both men and women were engaged in raising livestock. About 44% of men and 14% of women reported participating in all decisions related to raising livestock. When asked the level of input they perceive having on the use of income from livestock sales, 45% of men and 15% of women perceived participating in all decisions regarding livestock income. Significant differences were found.

Nonfarm work activities. Based on a sample of 839 respondents who answered this question, 30% of women and 40% of men responded that they were engaged in non-farm economic activities in the immediate past cropping season. When asked about participation in decision-making related to non-farm economic activity, 29% of both men and women felt they participated in most decisions; 17% of women and 37% of men perceived participating in all decisions. When asked about participation in decision-making on the use of income generated from small businesses and other non-farm economic activity, 32% of women and 29% of men reported having input in most decisions related to this income.

32 | P a g e

Wage or salary employment. When we asked respondents if they had participated in wage or salary employment in the immediate past cropping season, the majority (78%) said they had not. Only about 17% of women in the villages and 27% of men participated in wage or salary employment.

Fishing or fishpond culture. The majority (95%) of surveyed respondents did not participate in fishing or fishpond culture. However, of the few men and women who did, only 24% felt they were involved in most decisions compared to 18% who felt they had no input in the activity. When asked about their input on use of income from fishing/fishpond culture, only about 11% of women and 17% of men reported being involved in all decisions. Only 15% of women and 19% of men perceived they had no input in decisions related to use of this income.

The influence of relatives external to the household. To further determine household decision-making in production and in use of income, we asked respondents if their relatives or their spouse’s relatives (other than those listed in their households) assist in agriculture, non-farm economic activities or their employment, and if they have influence. Only 9.57% (n=867) said they had relatives -- other than those listed in their households – who help in food crop farming. When asked ‘how much input did your’ relatives have on your decisions regarding food crop farming, only 85 responded. Of the 85, about 35% of males and 23% of females responded that their external relatives are involved in all decisions. Figure 9 illustrates this. When asked if their spouse’s relatives (other than those listed in their household) help with food crop farming, only about 6% (n=864) said their spouses relatives helped with food crop farming.

Figure 9: Comparison of male-female responses on input of relatives - food crop farming

0%

5%

10%

15%

20%

25%

30%

35%

40%

No input Few decisions Some decision Most decision All decision No decisionmade

How much input do external relatives have on your decision-making on food crop farming

(n=85)

Male Female

33 | P a g e

Reasons for Decisions

To establish motivations in decision-making, we asked respondents if their actions 1) are determined by the situation (they really don’t have an option), b) are partly because they will get in trouble with someone if they behave differently, c) so others won’t think poorly of them, or d) they do what they do because they personally think that it is the right thing to do. Respondents could have several reasons for their actions.

Overall, respondents reported as having an option, in other words, their actions were not determined because they did not have an option. For example, when 869 respondents were asked if their actions in agricultural production were determined by the situation/not having the option, most women (91%) and men (92%) reported that their actions were not determined by the situation or not having options. Only 5 percent of women (n=459) and 1 percent of men (n=408) felt they had no option when it came to agricultural production. Similar percentages are observed for getting inputs for agricultural production and types of crops to grow. For taking crops to market, 89% of males voiced not being limited, and 84% of women did. Some women (10%) reported that the market decision not made.

Leadership and Speaking Up in Public

To examine individual leadership and influence in the community, we asked respondents a set of WEAI questions related to whether the individual feels comfortable speaking up in public on various topics. Topics included 1) speaking up in public to help make decisions on infrastructure (small wells, roads, water supplies, etc.), 2) speaking up in public to ensure proper payment of wages for public works or other similar programs, 3) speaking up in public to protest misbehavior of authorities or elected officials, and 4) speaking up in public to ask Agricultural Extension Agents questions about agricultural practices, policies or decisions that affect the respondent.

Women are found to be less comfortable than men when speaking up in public, in general. Speaking up in public to ask Agricultural Extension Agents questions about agricultural practices, policies or decisions is of particular interest. Women generally had much more difficulty speaking up with Agricultural Extension Agents than did men. A total of 35% of the male respondents felt ‘very comfortable’ speaking up with Agricultural Extension Agents compared to 12% of women. About 20% of women respondents reported that they are ‘not comfortable’ speaking up with Agricultural Extension Agents (refer to figure 10).

Overall (men and women combined), 36% of respondents in the Central region, 25% of respondents in the Northeast and 9% of respondents in the Northwest (Tete/Angonia) felt ‘very comfortable’ speaking with Agricultural Extension Agents. Almost 38% of respondents in the Northwest (Tete/Angonia), 27% of respondents in Northeast and 18% of respondents in the Central region reported that they had ‘great difficulty’ speaking with Agricultural Extension Agents about agricultural practices, policies or decisions.

Part of the problem may be lack of familiarity with Agricultural Extension Agents: when respondents were asked if they had met with an Agricultural Extension Agent or Livestock/Fisheries Extension Agent in the last 12 months, 73% of 400 men and 74% of 459 women said no. When asked if, in the last 12 months, they had ever been given seed by an Agricultural Extension Agent, 76% of 399 men and 72% of 457 women responded no.

34 | P a g e

Figure 10: Comparison of men’s and women’s comfort level in speaking up to Agricultural Extension Agents on agriculture practices, policies or decisions that affect them

5.94

17.08

19.31

22.52

35.15

19.91

36.76

16.63

14.22

12.47

0 10 20 30 40

No, not comfortable

Yes, but with great deal ofdifficulty

Yes, but with little difficulty

Yes, fairly comfortable

Yes, very comfortable

Female Male

Access to Soya Seed and Current Field Practices d

Access to soya seed. About half of male and female respondents – or one out of every two farmers -- reported that they have grown soya, even if it was several years ago. Roughly the same percentages said they themselves had grown soya. A majority (84% of 407 males and 77% of 457 females) reported that they believed that they would not need to be shown how to better grow soya by an Agricultural Extension Agent or other trained person before they would try it, but over half of males and 32% of females said they would have to see others grow soya successfully before they would try it.

In total, 66% of males and 70% of females said they did not know where to buy soya seed that grows well in their area. In comparison, when asked about common bean seed, 61% of males and 57% of females, said they know where to buy bean seed that grows well in their area (table 10). Most of the respondents (66% of males and 61% of females) reported that no groups or organizations give out free soya seed locally (see table 11).

35 | P a g e

Soybeans by region. Farmers in Tete/Angonia were the most familiar with soybeans. For example,

68% of respondents in the Northwest compared to only 37% in the Manica region and 46% in Zambezia/Nampula region, said a member of their household had tried to grow soybeans.

Among survey respondents, 65% in the Northwest (Angonia/Tete), 29% in the Central region (Manica), and 45% in the Northeast (Zambezia/Nampula) answered that they themselves had tried to grow soya. In the Northwest, only 30% of respondents said they would require a demonstration before trying soya.

Table 10: Survey questions on access to soya Men %

Women %

Yes No Yes No

Have you yourself grown soybean? 47 53 46 54

Would you have to be shown how to grow before trying soybean?

16 84 23 77

Would you have to see others grow soybean successfully before trying it?

55 45 32 68

Does any group give out free soybean seed locally? 34 66 39 61

Do you know where to buy soybean seed that grows well in your area?

34 66 30 70

Do you know where to buy bean seed that grows well in your area?

61 39 57 43

Table 11: Survey question: Have you been offered any kind of FREE improved seed?

Percentage who received free or improved seed from source

Source of free improved seed Women % (n=240)

Men % (n=204)

From a relative in your village? 46 29

From a relative in another village? 18 26

From a relative living/working in an urban area? 0.05 1

From a friend in your village? 3 1

From a friend in another village? 1 0.05

From a friend living/working in an urban area? 1 1

From an agricultural extension agent? 15 20

From a farmer’s association? 1 2

From a local market? 3 3

From an aggregator? 1 0.05

From a seed company? 0.05 0

From an NGO? 10 15

36 | P a g e

In all three regions—49% in the Northwest, 35% in the Central, and 44% in the Northeast -- respondents said that they would need to see others succeed before they would try improved soybeans.

There was strong interest across the three regions—89% in the Central region, 91% in the Northeast, 91% in the Northwest—in trying free improved soya. Those percentages declined to 58%, 48%, 39%, respectively, when asked if they were ‘very interested’ in purchasing improved soybeans.

Lack of money, lack of information. When asked, ‘What is the one factor most likely to prevent you from purchasing new improved soybean seeds,’ 55% of 391 male respondents said lack of money and 45% said lack of information about where to buy the seed. Of 447 females, 62% reported lack of money and 38% said lack of information.

When asked if they themselves had ever been given—free of charge—any kind of improved seed (soya, common bean, etc.) from a relative in their village, 39% of respondents said yes. In total 22% received improved seed from a relative in another village, 17% from an agricultural extension agent, and 3% from a local market. Women were more likely than men to receive free improved seed from relatives within the village; men were more likely to receive from relatives outside the village than women.

When asked if they themselves had ever purchased any kind of improved seed (soybean, bean, etc.) from a relative in their village, 39% of all of the respondents said yes, 14% said they purchased improved seed from a relative in another village, 13% said they purchased improved seed from a friend in their village, and 22% they purchased improved seed from a local market. Table 13 further illustrates where men and women purchase new and improved seed.

Table 13: Survey question: Have you PURCHASED any kind of improved seed?

Percentage who purchased improved seed from source

Source of purchased improved seed Women % (n=239)

Men % (n=243)

From a relative in your village? 45 33

From a relative in another village? 16 12

From a relative living/working in an urban area? 1 1

From a friend in your village? 9 16

From a friend in another village? 3 2

From a friend living/working in an urban area? 0 0

From a farmer’s association? 0 0

From a local market? 20 25

From an aggregator? 0 5

From a seed company? 3 3

From an NGO? 0 1

37 | P a g e

Input use on soybeans. When asked if inoculum was used on soybeans at planting time, 85% of the 207 males and 92% of the 288 females who responded to the question said no. If they did use inoculum, the majority (75% of males and 76% of females) obtained it from an Agricultural Extension Agent.

Almost all (89%) male respondents reported that they did not use phosphorus fertilizer on their soybeans at any time before harvest. A total of 94% of females responded that they did not use. None of the respondents reported using pesticides on soybeans. Clearly, input utilization on soya is low.

Planting practices. Soybeans are planted by hand in rural Mozambique. When asked about how far apart each hill of soybeans was planted, a wide variety of answers were given, ranging from 0.2 meters to 100 meters, indicating a lack of understanding of the question. This also was the case when asked how far apart each row of soybeans is planted—with answers ranging between 0.5 meters and 50 meters between rows.

When asked how many soya seeds per hill they planted, 94 males and 117 females responded with the following: 61% of males and 52% of females said 3 seeds per hill, 22% of males and 27% of females said 4 seeds per hill, 11% of males and 11% of females said 5 seeds per hill.

Finally, when asked how many soya seeds were planted per foot if they planted them in rows, 99 males and 93 females responded as follows: 33% of males and 32% of females said 3 seeds per foot, 24% of males and 27% of females said 2 seeds per foot, 19% of males and 14% of females responded 4 seeds per foot.

Concluding Observations

The WEAI, supplemented by questions on soy and other legumes to create the WEAI+, is found to be a powerful tool for opening up the ‘black box’ of the household to begin to understand intra-household gender dynamics.

For rural Mozambique, interesting gender-differentiated results are found. First, regarding resources, many Mozambican women report joint ownership with a spouse of household and farm assets; a greater percentage of men report that they own the asseets as individuals. For decisions regarding purchase, sale and transfer of assets, her perceptions of her control is lower. In any case, few – men or women -- have access to formal or informal credit or engage in decisions related to credit. Second, many Mozambican men and women also report making farm production decisions jointly with a spouse, and both women and men engage in food crop farming as cash crop farming. Mozambican women often report providing input into production decisions, more than expected based on other WEAI studies including the WEAI+ survey conducted in Ghana (Ragsdale and Read-Wahidi, 2015). These results and findings related to input on income use decisions by gender and on motivations for decisions deserve much more analysis to understand why. Third, a majority of rural Mozambican women express

Table 14: Where inoculum was obtained

Females %

(n=25)

Males %

(n=28)

From a relative in your village?

4 45

From a relative in another village?

0

From a friend in another village?

4 0

From an agricultural extension agent?

76 75

From an aggregator? 0 7

From an NGO? 4 0

38 | P a g e

significant reticence to speak up in public, including speaking up to extension personnel with whom they are supposed to work. Further, contact that Mozambican women have with extension personnel also is low.

The WEAI+ Mozambique survey found substantial interest in soybeans. In Tete and Zambezia/Nampula about half of survey respondents had tried soybeans in the past; soya uptake by households was observed to be less in the Manica Province region near Sussundenga. Over the past decade, soya production in Mozambique has been the focus of several important initiatives, including Bill & Melinda Gates Foundation support of a Technoserve-IITA collaboration in the North, and Lozane Farms in Tete where seed scaling takes place (Walker and Cunguara 2016). In Tete, survey results show that many women as well as men participate in soya production; interestingly and importantly, many women are not excluded from marketing/selling of soya. Both women and men report income from soya. About 50% of households in the Tete and Zambezia/Nampula villages report income from soya post harvest.

The WEAI+ Mozambique Baseline YR 1 Survey results suggest that smallholder farm women in Mozambique can and do participate in soybean value chains; additional efforts to ensure their continued inclusion could have important pay-offs. The Soy Uptake and Network Survey (SUNS), now in the field, is expected to provide additional evidence of women’s role in soya uptake, production, marketing and the generation of income from this emerging and important crop.

References

Alike, S., Malapit, H., Meinzen-Dick, R., Peterman, A., Quisumbing, A., Seymour, G., & Vaz, A. (2013). Instructional Guide on the Women’s Empowerment in Agriculture Index. UF Initiative, Éd.

Alkire, S., Meinzen-Dick, R., Peterman, A., Quisumbing, A., Seymour, G., & Vaz, A. (2012). The Women’s Empowerment in Agriculture Index. IFPRI Discussion Paper 1240. Washington, D.C.: International Food Policy Research Institute. Accessed from http://ebrary.ifpri.org/cdm/ref/collection/p15738coll2/id/127557 on 12/1/2015

Britannica On-line Encyclopedia –accessed from http://www.britannica.com/place/Mozambique on 12/2/2015

Cunguara, B., & Hanlon, J. (2012). Whose wealth is it anyway? Mozambique's outstanding economic growth with worsening rural poverty. Development and change, 43(3), 623-647.

Chaguala, P. A., Nobela, L., Aline, S., Maria, R., Buque, I., Tenywa, J. S., ... & Santos, L. (2011). Maize response to macro-and micro-nutrients in two different agro-ecological zones in Mozambique. In 10th African Crop Science Conference Proceedings, Maputo, Mozambique, 10-13 October 2011 (pp. 611-615). African Crop Science Society. Accessed from http://www.acss.ws/Upload/XML/Research/845.pdf on 12/4/2015

De Brauw, A. (2015). Gender, control, and crop choice in northern Mozambique. Agricultural Economics, 46: 435–448. doi: 10.1111/agec.12172

39 | P a g e

Famba, S. I. (2011). The challenges of conservation agriculture to increase maize yield in vulnerable production systems in Central Mozambique. PhD dissertation.

Folmer, E.C.R., Geurts, P.M.H., Francisco, J.R. (1998) Assessment of soil fertility depletion in Mozambique, Agriculture, Ecosystems & Environment, Volume 71, Issues 1–3, 1 December 1998, Pages 159-167, ISSN 0167-8809, http://dx.doi.org/10.1016/S0167-8809(98)00138-8. Accessed from http://www.sciencedirect.com/science/article/pii/S0167880998001388 on 12/4/2015

Grabowski, P. P., & Kerr, J. M. (2014). Resource constraints and partial adoption of conservation agriculture by hand-hoe farmers in Mozambique. International Journal of Agricultural Sustainability, 12(1), 37-53.

Hanlon, J. (2007). Is poverty decreasing in Mozambique. Paper presented at the Inaugural Conference of the Instituto de Estudos Sociais e Económicos (IESE). September 19.

Kondylis, F., Mueller, V. & Zhu, S. (2015), Measuring agricultural knowledge and adoption. Agricultural Economics, 46: 449–462. doi: 10.1111/agec.12173

Malapit, H. J., Sproule, K., Kovarik, C., Meinzen-Dick, R., Quisumbing, A., Ramzan, F., Hogue, E., Alkire, S., (2014). Measuring progress toward empowerment: Women's empowerment in agriculture index: Baseline report. Washington, D.C.: International Food Policy Research Institute (IFPRI). Accessed from http://ebrary.ifpri.org/cdm/ref/collection/p15738coll2/id/128190 on 11/11/2015

McNair, W. E., Lambert, D. M., & Eash, N. S. (2015). Conservation agriculture practices and participation in Manica and Tete Maize Markets, Mozambique. African Journal of Economic and Sustainable Development, 4(1), 49-73.

Mozambique Institute of Agronomic Research – Soil types map (1972): Accessed from http://eusoils.jrc.ec.europa.eu/esdb_archive/EuDASM/Africa/images/maps/download/afr_epdm.jpg on 12/4/2015

Narayan-Parker, D., & Petesch, P. L. (Eds.). (2007). Moving Out of Poverty, 1: Cross-disciplinary Perspectives on Mobility (Vol. 1). World Bank Publications.

KNOEMA-National Statistics Office of Mozambique – Accessed from http://knoema.com/MNSORS2012Nov/regional-statistics-of-mozambique-2015 on 12/5/2015

Ragsdale, K. & Read-Wahidi, M. (2015). Gender Equity & Soybean Uptake in Northern Ghana WEAI+ Prefliminary Results. Special Report on WEAI+ 2014 Implementation, December 2015.

Sevilla, L. (2013). Social Networks and the Exchange Economy in Rural Mozambique: A study of off-farm

labor and crop marketing behaviors. PhD dissertation, The Pennsylvania State University, University Park.

Smith, R. A., & Findeis, J. L. (2013). Exploring audience segmentation: investigating adopter categories to

diffuse an innovation to prevent famine in rural Mozambique. Journal of Health Communication, 18(1), 6-19.

40 | P a g e

Smith, R. A., Barclay, V. C., & Findeis, J. L. (2011). Investigating preferences for mosquito-control technologies in Mozambique with latent class analysis. Malaria Journal, 10(1), 1.

The World Factbook 2013-14. Washington, DC: Central Intelligence Agency, 2013 – Accessed from https://www.cia.gov/library/publications/resources/the-world-factbook/geos/mz.html on 11-23-2015 United States Census Bureau, International Programs - Accessed from http://www.census.gov/population/international/data/idb/region.php?N=%20Results%20&T=13&A=separate&RT=0&Y=2015&R=102&C=MZ on 11-23-2015

United States Census Bureau, International Programs - Accessed from

http://www.census.gov/population/international/data/idb/region.php?N=%20Results%20&T=13

&A=separate&RT=0&Y=2015&R=102&C=MZ on 11-23-2015

Velkoff. V.A., & Kowal, P. R., (2007). U.S. Census Bureau, Current Population Reports, P95/07-1, Population Aging in Sub-Saharan Africa: Demographic Dimensions 2006, U.S. Govt. Printing Office, Washington, D.C - Accessed from https://www.census.gov/prod/2007pubs/p95-07-1.pdf on 11/23/2015

Walker, T. & Cunguara, B. (2016). Taking Stock of Soybean R&D and USAID’s Feed the Future Program in Mozambique. Modernizing Extension and Advisory Services (MEAS) Project Report. March 17.

Weather and Climate - Accessed from - https://weather-and-climate.com/average-monthly-Rainfall-Temperature-Sunshine, Cuamba, Mozambique on 12/2/2015

World Bank -Country Profile (2015) – Accessed from http://www.worldbank.org/en/country/mozambique/overview on 11-23-2015

World Bank Data – Mozambique World Development Indicators – Accessed from http://data.worldbank.org/country/mozambique#cp_wdi on 11-23-2015

Zhang, Lin. (2011) Rural Labor Mobility, Agricultural Technology Diffusion, and Agricultural Land Leasing in the Process of Rural Economy Development: A multi-agent simulation modeling approach representing dynamic household decision-making in south China. PhD dissertation, The Pennsylvania State University, University Park.

41 | P a g e

Appendix A: Population Pyramid, Mozambique

Population pyramid of Mozambique based on 2015 population estimates.

Sourced from https://www.cia.gov/library/publications/resources/the-world-factbook/geos/mz.html

42 | P a g e

Appendix B. Temperature and Rainfall in Sussundenga (Central), Zambezia

(Northeast) and Tete (Northwest) Regions

Source: http://www.worldweatheronline.com/sussundenga-weather-averages/manica/mz.aspx

43 | P a g e

Source: http://www. worldweatheronline.com/zambezia-weather-averages/cabo-delgado/mz.aspx

44 | P a g e

Source: http://www.worldweatheronline.com/tete-weather-averages/tete/mz.aspx

45 | P a g e

Appendix C. Google Images for Mozambique Village Sites

Image: Village 1 – Northwest region (Elevation = 3924 ft.)

46 | P a g e

Image: Village 2 – Northwest region (Elevation = 4116 ft.)

47 | P a g e

Image: Village 3- Northwest region (Elevation = 4631 ft.)

48 | P a g e

Image: Village 4- Central region (Elevation = 2945 ft.)

49 | P a g e

Image: Village 5 – Central region (Elevation = 2037 ft.)

50 | P a g e

Image: Village 6 –Central region (Elevation = 2255 ft.)

51 | P a g e

Image: Village 7 – Northwest region (Elevation = 1715 ft.)

52 | P a g e

Image: Village 8 - Northwest region (Elevation = 2395 ft.)

53 | P a g e

Image: Village 9 - Northwest region (Elevation = 2636 ft.)

Appendix D. Dwelling and Water Source by Region

Table D.1: Reported state of dwelling by region

Accessed state of dwelling Central % Northeast % Northwest %

Excellent 12.66 11.36 14.84

Good 15.19 11.36 10.99

Moderate wear 46.84 53.03 45.6

Poor 22.15 20.45 25.27

Very bad 3.16 3.79 3.3

Table D.2: Reported main source of water for household by region

Household main water source Central %

Northeast % Northwest %

Borehole 51.27 37.88 12.09

River/pond 24.68 34.09 27.47

Well 22.78 28.03 60.44

Public tap 1.27 0 0

Page | 55

Appendix E. Income from other activities by region

Percent females earning income from other activities, Northwest region

Percent females earning income from other activities, Northeast region

Page | 56

Percent females earning income from other activities, Central region

Percent males earning income from other activities, Northwest region

Page | 57

Percent males earning income from other activities, Northeast region

Percent males earning income from other activities, Central region

Page | 58

Appendix F. Decision-making supplements

Table F.1.: Comparison between male- female engagement in agriculture production and marketing of crops based on WEAI+ survey data

Who decides on agricultural production Male % Female % Total %

Husband 22.6 11.35 16.65 Wife 3.69 17.47 10.98

Joint with spouse 73.22 69.65 71.33 Someone else in household 0.25 0.22 0.23

Joint with someone else 0 0.66 0.35 Joint with someone outside household/other 0.25 0.22 0.23

Decision not made 0 0.22 0.12 Who decides on what inputs to buy for agriculture production Male % Female % Total %

Husband 21.87 13.29 17.32 Wife 2.7 11.98 7.62

Joint with spouse 70.76 66.23 68.36 Someone else in household 0.25 0.44 0.35

Joint with someone else 0 0.44 0.23 Joint with someone outside household/other 0.49 0.44 0.46

Decision not made 3.93 7.19 5.66 Who decides on type of crops for agriculture production Male % Female % Total %

Husband 19.9 9.80 14.55 Wife 2.95 16.99 10.39

Joint with spouse 76.41 70.81 73.44 Someone else in household 0.25 0.44 0.35

Joint with someone else 0 0.87 0.46 Joint with someone outside household/ other 0.49 0.44 0.46

Decision not made 0 0.65 0.35 Who decides when or who would take crops to the market Male % Female % Total %

Husband 16.71 10.48 13.41 Wife 6.88 13.76 10.52

Joint with spouse 68.55 62.88 65.55 Someone else in household 0.49 0.22 0.35

Joint with someone else 0.25 0.87 0.58 Joint with someone outside household /other 0 0.00 0.00

Decision not made 7.13 11.79 9.6

Page | 59

Table F.2: Comparison between male- female responses on why they engage in various agricultural

activities in rural Mozambique

Regarding agriculture production I do what I do because I personally think it is the right thing to do.

Male % Female % Total %

Never true 4.9 33.48 20.00

Not very true 1.96 7.22 4.74

Somewhat true 7.6 3.50 5.43

Always true 85.54 55.58 69.71

Decision not made 0 0.22 0.12

Regarding getting inputs for agricultural production I do what I do because I personally think it is the right thing to

do.

Male % Female % Total %

Never true 5.17 32.82 19.77

Not very true 1.72 4.19 3.02

Somewhat true 5.67 6.17 5.93

Always true 83.99 51.10 66.63

Decision not made 83.99 51.10 66.63

Regarding the types of crops to grow for agricultural production I do what I do because I personally think it is the

right thing to do.

Male % Female % Total %

Never true 4.9 33.41 19.93

Not very true 1.23 5.05 3.24

Somewhat true 6.13 5.49 5.79

Always true 87.75 55.16 70.57

Decision not made 0 0.88 0.46

Regarding taking crops to the market (or not), I do what I do because I personally think it is the right thing to do.

Male % Female % Total %

Never true 4.42 30.84 18.35

Not very true 0.98 6.17 3.72

Somewhat true 4.91 3.96 4.41

Always true 83.29 48.68 65.04

Decision not made 6.39 10.35 8.48