Protocol: Do gender-based inequities in agriculture affect

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Protocol: Do gender-based inequities in agriculture affect food security and

nutrition outcomes? A mixed-methods systematic review

London, 08/05/2018

Hayaan Nur*, Helen Harris-Fry*, Bhavani Shankar**, Suneetha Kadiyala*

* London School of Hygiene and Tropical Medicine

** School of Oriental and African Studies

Contents

Background ............................................................................................................................................. 2

How increased gender equity might improve nutrition ....................................................................... 2

Why it is important to conduct this review ......................................................................................... 3

Objectives ............................................................................................................................................... 4

Methodology ........................................................................................................................................... 5

Inclusion and exclusion criteria .......................................................................................................... 5

Search Strategy ................................................................................................................................... 6

Description of methods used in primary research ............................................................................... 8

Criteria for determination of independence of findings ...................................................................... 8

Details of study coding categories ...................................................................................................... 8

Synthesis procedures and conventions ................................................................................................ 8

References ............................................................................................................................................. 10

Appendix 1: Sample Search Terms ....................................................................................................... 12

Appendix 2: Coding Form .................................................................................................................... 15

Appendix 3: Preliminary Time Frame .................................................................................................. 22

Appendix 4: Preliminary List of Experts .............................................................................................. 24

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Background Emerging evidence shows that agricultural interventions can improve both agricultural and nutritional

outcomes (Ruel, Quisumbing, & Balagamwala, 2017), indicating the potential for improvements in

agriculture to have multiple co-benefits for the health and wellbeing of rural populations. Realisation

of these benefits may be constrained by pervasive but highly heterogeneous gender-based inequities in

agriculture that limit women’s participation in and control over agricultural processes and outputs

(FAO, 2011).

Gender equity is defined as the ‘fairness of treatment for women and men, according to their respective

needs and interests. This may include equal treatment or treatment that is different but considered

equivalent in terms of rights, benefits, obligations and opportunities’ (ILO, 2007, p. 92). Gender

inequity therefore is gender-comparative, taking both women and men into account.

Women’s participation in agriculture is highly heterogeneous across contexts, but the Food and

Agriculture Organization estimate that women comprise an average of 43 percent of the agricultural

workforce in developing countries (FAO, 2011).Yet, women experience gendered inequities in many

agricultural domains, including limited control and access to land, livestock and inputs (seeds,

fertilisers, equipment and technology), work burdens (Johnston, Stevano, Malapit, Hull, & Kadiyala,

2018), and earnings (ILO, 2015). In addition to the potentially large societal costs to human wellbeing,

these gender-based inequities may be responsible for gender differentials in agricultural productivity

(Peterman, Behrman, & Quisumbing, 2014) and sub-optimal yields (Udry, Hoddinott, Alderman, &

Haddad, 1995). Constrained access to inputs and markets, and inefficiently gendering tasks and

responsibilities, might prevent women from participating in the most economically productive way. The

FAO (2011) estimates that, if women had the same access to productive resources as men, total

agricultural output in developing countries could rise by between 2.5 and 4 percent. Gender inequities

in agriculture may also leave women particularly vulnerable to climatic shocks, having limited access

to coping mechanisms, such as irrigation, improved seeds, credit and insurance, and alterative

livelihood strategies (Hagos & Holden, 2013).

How increased gender equity might improve nutrition Gender equity in agriculture may present a major opportunity to improve agriculture, food security,

nutrition, and health outcomes. It has been estimated that the increase in agricultural production due to

gender equity in agriculture could reduce the number of hungry people in the world by 12 to 17 percent

(FAO, 2011). There may be additional pathways through which gender equity in agriculture could

improve nutrition (see figure 1). For example, there is evidence that women tend to invest more income

on nutrition than men (Allendorf, 2007). Furthermore, gender equity might lead to an intra-household

allocation of foods that is more in line with the household members’ nutritional needs.

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Figure 1: The causal link between gender (in)equity and nutrition. On the gender-inequity side, structural inequities are

marked in orange, intra-household inequities in grey. The orange arrow and box relate to structural inequalities.

Sahn & Younger (2009), in their analysis of seven developing countries, find that most nutritional

inequality takes place at the intra-household level. Therefore, it is important to consider intra-household

inequities carefully. Nevertheless, wider, structural inequities may affect nutrition through the intra- or

extra-household level. For instance, an improvement in women’s land inheritance rights might lead to

an increased share of household land being held by women, and also redistribute land more equitably

between households.

Why it is important to conduct this review We aim to assess the potential for gender equity in agriculture to improve nutritional outcomes, and

identify the relative importance of different domains of gender inequity in agriculture in different low

and middle-income contexts. This analysis will help policymakers to: (1) prioritise policy responses to

address the gender gap in agriculture; and (2) increase the effectiveness of existing programs to improve

nutrition by targeting the most important domains of agricultural inequity. This information may also

further motivate and strengthen multi-sectoral approaches to improving agricultural productivity and

nutritional status of populations.

Currently, evidence on the impact of gender inequities in agriculture on food security, diets, and

nutrition outcomes is segregated across multiple disciplines and is not well synthesised. It is, therefore,

of limited use to policymakers. We identified six reviews that relate to gender inequities in agriculture

or nutrition.

1. Doss (2013) reviews evidence on intra-household bargaining and resource allocation in

developing countries. She finds that there is sufficient evidence to conclude that empowering

women affects outcomes in general. She does not follow a systematic methodology and does

not analyse effects on nutrition or food security, or in rural settings specifically.

2. Harris-Fry, Shrestha, Costello, & Saville (2017) conduct a ‘conceptual review’ of the

determinants of intra-household food allocation to identify factors that have been proven or

hypothesised to affect intra-household food allocation. Confining their scope to South Asia,

they find that food allocation may be determined by relative differences in relative earned

income, bargaining power, food behaviours, social status, preferences and relationships within

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households. They find no high-quality evidence relating gender inequities in agriculture to the

intra-household allocation of foods.

3. Johnston et al. (2018) examine literature on time use in agriculture as an explanation for

undernutrition. They find a complex relationship between time use and nutrition and no

agreement regarding impacts. They find no studies that connect inequities in time-use with

nutrition.

4. Berti, Krasevec, & FitzGerald (2004) review the effectiveness of agriculture interventions in

improving nutrition outcomes. They find that most interventions increase food production, but

do not necessarily improve nutrition. Their discussion of gender inequities is limited to a

comparison of interventions that consider gender issues in some form with ones that do not.

They find that interventions that consider gender have a greater likelihood of being effective.

5. Meinzen-Dick, Quisumbing, Doss, & Theis (2017) review the effect of women’s land rights on

poverty reduction. Based mostly on observational studies and on limited analysis of causal

pathways, they find some evidence of a positive relationship. However, they do not discuss

effects on nutrition or food security.

6. Ruel, Quisumbing, & Balagamwala (2017), extending on Ruel & Alderman (2013), review the

evidence on nutrition-sensitive agriculture interventions on nutrition. They build on a tradition

of reviews generally finding mixed results (Harvey et al., 2014; Leroy & Frongillo, 2007;

Masset, Haddad, Cornelius, & Isaza-Castro, 2012; Webb, 2013; Webb Girard, Self, McAuliffe,

& Olude, 2012). While Ruel et al. (2017) find that nutrition-sensitive agriculture programs

improve a variety of nutrition outcomes, they and other reviews do not discuss gender in depth.

In summary, many reviews to date have not employed fully systematic approaches, and they have either

focused on the effects of gender inequities in agriculture on broader poverty-related outcomes, or they

have focused on the overall effects of agriculture interventions on nutrition without a gender equity

focus. We did not identify any reviews that investigated the effects of gender-based inequities in

agriculture on nutrition outcomes.

Objectives This systematic review is motivated by the question ‘does gender equity in agriculture improve

household food security and women and children’s nutrition outcomes?’ It assesses the plausibility of

this effect with evidence from correlational studies, and identifies direct evidence in experimental and

quasi-experimental studies. We will explore the mechanism behind this effect by extracting

intermediary outcomes and synthesising qualitative literature.

Primary objective:

To critically review the evidence of the impact of different gender inequities in agriculture on

women and child nutrition outcomes and household food security in low and middle-income

countries

Secondary objectives:

1. Review the mechanisms by which gender inequities may affect the above outcomes

2. Describe geographical differences in the impact of gender inequities in agriculture on women

and child nutrition outcomes and household food security

3. Identify any differences in the effects of agricultural inequities on nutritional outcomes between

boys and girls

4. Review the evidence of environmental effects on:

a. gender inequities in agriculture, and

b. the relationships between gender inequities in agriculture and food security and

nutrition outcomes

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Methodology

Inclusion and exclusion criteria Types of participants We will include all studies referring to rural contexts in low- and middle-income countries LMICs, as

defined by World Bank, (2018). We will use the year of the baseline data collection of studies to

determine whether a country was a low- or middle-income country at that point in time, as some

countries graduated from LMIC status. Studies that had their baseline data collected before 1987 will

be categories according to the 1987 categorisation (the first published by the World Bank).

To be considered in this review, there has to be an indication that at least one household member in the

majority of participating households is engaged in agriculture (ISIC divisions 01-03, United Nations,

2008). This includes household members engaging in agricultural labour (including food cropping and

cash cropping), sharecropping, land owning, subsistence farming, livestock, fisheries and forestry. The

study should include or refer to both men and women.

Exposures We will include studies that document exposures to variation in:

1. Gender inequity in land and livestock. At the structural level, these can be gender inequitable

inheritance and ownership rights and norms. At the household level, gender inequity refers to

inequities in the usufruct and statutory ownership, access, or use of land or livestock between

men and women within the household. In the qualitative literature, a wider framing of natural

resources and livestock ownership is allowed for, to include work on a higher conceptual level.

2. Gender inequity in labour markets. At the structural level, these are inequitable differences in

remuneration for labour, goods and services delivered by men and women, as well as inequities

in labour market opportunities. At the household level, these are inequities in earned income

and workforce participation.

3. Gender inequity in work burdens. At the structural level, these are gendered tasks and duties.

At the household level, this is the share of women’s work burden in the household relative to

men’s. In the quantitative literature, gender inequity in the work burdens is measured as the

inequity in total hours worked, time spent doing strenuous labour, or total energy expenditure.

Qualitative literature that conceptualises equity in time use or workloads more broadly is

allowed for. In this case, equity is distinct from equality. We do not consider an equal spread

of tasks ‘fair in the treatment of women and men’, if the total time burden and heavy labour are

distributed equally. Rather, the needs of women and men have to be met equally. For instance,

we consider it more equitable if pregnant women completely abstain from strenuous labour.

Comparison Participants with high exposures to inequity in agricultural assets, labour markets and work burdens

will be compared to participants with low exposures to the former. We exclude comparisons between

female-headed households and male-headed households. We exclude studies that do not report an effect

of exposures to inequity alone. We will exclude studies that only report an effect of reduced inequities

coinciding with other exposures, for instance. Thus, an education intervention, for example, might

reduce gender inequities in earned incomes. We usually would still exclude a study on it, because the

effect on nutrition through earned incomes cannot easily be disentangled from the effect through child

feeding practices, for instance.

Outcomes To be included, studies must document associations with at least one of the four following outcomes:

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1. Undernutrition of women of reproductive age (aged 15-49 years). For inclusion, quantitative

literature has to measure undernutrition as the body-mass-index (BMI; kg/m2) and may use

mean BMI or cut-offs. Qualitative literature that characterises undernutrition more widely is

included.

2. Undernutrition of the children under 5 years of age. This is measured as stunting, wasting or

underweight (height-for-age z-scores, weight-for-height z-score or weight-for-age z-scores).

Qualitative work can characterise child undernutrition more widely.

3. Dietary quality of women of reproductive age. In the quantitative literature, this is typically

measured using the Minimum Dietary Diversity for Women (MDD-W; FAO and FHI 360,

2016) score or earlier versions of this score, but can be any standardisable measure of nutritional

adequacy calculated using individual dietary assessment that accounts for needs in some way

(e.g. using adequacy ratios, the cut-point method, or probability-based approaches).

‘Standardisable’ means that a z-score or a similar standardised measure can be calculated.

Qualitative studies which examine dietary quality in broader terms are included.

4. Dietary quality of the children under 5 years of age. This is typically measured using the

‘Minimum Dietary Diversity’ or ‘Minimum Adequate Diet’ indicators (WHO, 2010), but can

be any standardisable measure of nutritional adequacy. The same constraints to measurement

apply here, as for dietary quality of women.

5. Household food security. In the quantitative literature, food security is measured as (1)

household food expenditure or consumption in monetary terms (typically mean, per capita), (2)

the household share of food expenditures, i.e. proportion of total household expenditures spent

on food, (3) share of staple food expenditures, i.e. the proportion of total food expenditures

spent on staples; (4) household dietary diversity measured as any standardisable dietary

diversity score (typically HDDS;Swindale & Bilinsky, 2006), (4) the Household Food

Insecurity Access Scale (HFIAS; Coates, Swindale, & Bilinsky, 2007),. Qualitative literature

that measures food security in different terms is allowed for, as well.

Studies with at least one of these exposures and outcomes will be included. After these studies have

been selected, we will extract information on the following intermediate outcomes: agricultural

production, household income, women’s empowerment, household poverty, and economic inequity

between households. We will also extract information on climatic or environmental factors influencing

our exposures and the relationship between exposures and outcomes. We will also record other

intermediate outcomes on the impact pathways that emerge during the data extraction phase.

Types of studies Studies qualify for this review if they report original empirical evidence. This empirical evidence can

be qualitative or quantitative. The empirical evidence can document the association between exposures

and outcomes and causal impacts. We will exclude historical analyses. Studies written in English will

be included, and efforts will be made to include studies in other languages, based on the language

proficiencies of the investigators. There will be no exclusion based on publication date or the length of

follow-up. Published literature will be included, as well as unpublished studies that can be identified.

Search Strategy The search strategy will encompass a comprehensive literature and trial database search, the hand search

of selected journals, the backward tracking of citations and the consultation with experts.

The following databases and websites will be searched:

1. Bill and Melinda Gates Foundation Grants Database

2. BRIDGE

3. CAB Abstracts

4. Cochrane Central

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5. DfID Research for Development Outputs

6. EBSCO Discovery Service (including EconLit, IDEAS Repec and the World Bank e-library)

7. Eldis

8. Evaluation at FAO Library

9. FAO AGRIS

10. Gender Studies Database (EBSCO)

11. OpenTrials (which includes information from ClinicalTrials.gov, EU CTR, HRA, WHO

ICTRP, and PubMed)

12. Popline

13. Medline (Ovid) P

14. Scopus

15. The 3ie impact evaluation database

16. The AEA RCT registry

17. The IFPRI library

18. The World Bank IEG evaluations

19. UNICEF Evaluation Database

20. USAID Development Experience Clearinghouse

21. Web of Science

The following journals will be hand-searched:

1. American Journal of Agricultural Economics

2. Food Policy

3. Journal of Development Economics

4. Maternal and Child Health Journal

5. Public Health Nutrition

6. The Journal of Development Studies

7. The Journal of Nutrition

8. The Lancet

9. The Lancet Global Health

10. The proceedings of the Argiculture, Nutrition and Health Academy conference

11. The proceedings of the CSAE Conference

12. The proceedings of the NEUDC Conference

13. The World Bank Economic Review

14. World Development

We will perform the hand-search for papers published after 1995, since a trial search did not find a

substantive number of papers that would be included before this date. In case more than 20% of the

articles identified through the database search is published before 1995, we will revise this cut-off. In

case we do not identify any articles that are generally relevant to this systematic review within the last

five years of publication, we will discontinue the hand-search of this source.

We will use the following publications for backward-citation-tracking:

1. All included articles

2. Berti, Krasevec, & FitzGerald, 2004

3. Doss, 2013

4. FAO, 2011

5. Girard et al., 2012

6. Johnston et al., 2018

7. Leroy & Frongillo, 2007

8. Masset et al., 2012

9. Meinzen-Dick, Quisumbing, Doss, & Theis, 2017

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10. Ruel & Alderman, 2013

11. Ruel, Quisumbing, & Balagamwala, 2017

12. Webb, 2013

We will contact experts to identify any other relevant studies. A preliminary list of experts can be found

in Appendix 4 (Preliminary List of Experts).

We will search databases using the set of terms documented in Appendix 1 (Sample Search Terms).

Formatting will be adapted to the respective database.

Description of methods used in primary research We expect a mix of methods used in primary research. Since the field is multidisciplinary, we assume

that both qualitative and quantitative methods will be used. We anticipate that we will find both,

correlational, as well as causal evidence, and that most literature will not be experimental or quasi-

experimental studies.

Criteria for determination of independence of findings Multiple measures of the same exposure-outcome pair within one study or based on the same data will

not be regarded as independent. If we identify more than one publication or report on a single study, we

will choose the most prominent publication or report. This means, for instance, that we will prefer peer-

reviewed reports, edited and published reports, and reports with a higher citation count. If multiple

comparison groups are reported on, an equally weighted average will be created. If multiple estimates

of the same relationship are reported, we will choose the one preferred by the author of the paper. If

this is not clear, we will choose the one with the lowest risk of bias. If two papers report on two studies

of the same relationship based on the same data, we will choose the estimate with the lower risk of bias.

Details of study coding categories Details on the coding categories can be found in Appendix 2 (Coding Form).

Data Management EPPI reviewer will be used to manage references. Statistical analyses will be performed in Stata

(College Station, TX: StataCorp LP). Screening and risk of bias and study quality coding will be done

by Helen Harris-Fry and Hayaan Nur simultaneously (double screening and coding). Any conflicts in

their assessment will be resolved by the vote of Suneetha Kadiyala.

Missing data We will try to impute missing data. If data are missing, we will try to impute. In case the data cannot

be imputed, the authors will be contacted, with an answer period of at least one week.

Synthesis procedures and conventions Critical Appraisal We will report the results of the risk of bias assessment, both as an overall judgement per study, as well

as per bias domain or quality domain in an overview. The critical appraisal of quantitative studies

assesses risk of bias and employs a slightly modified version of the ROBINS I tool (Sterne et al., 2016).

The main changes we made are: the omission of the section ‘bias due to deviations from intended

interventions’, since this is not a review of interventions; and the addition of a section of bias in

instrumental variables study designs. The critical appraisal of qualitative studies assesses study quality

and is based on Lockwood, Munn, & Porritt (2015). We will exclude qualitative evidence that we deem

to be critically low quality. Further details can be found in Appendix 2 (Coding Form).

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Narrative synthesis The synthesis will be structured by exposure. We will use the correlational quantitative literature to

make an assessment of the plausibility of a relationship between gender inequities and nutrition. We

will then investigate whether causal quantitative literature supports this relationship. In the next step,

we will use the qualitative literature to explain the mechanisms behind the relationship, and intermediate

outcomes reported in the causal quantitative literature to triangulate these findings. We will then

integrate the results from each part of the synthesis. In this step, we will also integrate findings about

the structural-level, societal inequities that might undergird intra-household gender inequities.

Meta-analysis If the number of standardisable quantitative studies allows (minimum of two), we will conduct a meta-

analysis. The meta-analysis will follow the outline of the narrative synthesis. We will perform the meta-

analysis using random effects. We will only convert effect sizes if necessary, meaning that there is no

valid comparison possible without standardisation. All analyses will account for the nature of the

distribution of the relevant outcome, and we will present results as appropriate effects sizes with a

measure of precision.

Analysis of heterogeneity If the data warrants meta-analysis, we will report tests for heterogeneity of effects across studies, using

the I2 statistic and τ2 for reporting the between studies variance component.

Analysis of publication bias We will give provide a qualitative assessment of the likelihood of publication bias, comparing the

methods and results in unpublished included studies with the ones in published ones. If feasible, we

will assess potential publication bias and small-study effects using funnel plots and Egger tests.

Subgroup analysis We will analyse the heterogeneity of findings across geographical regions (according to the World

Bank-defined regions). For child nutritional and dietary outcomes, we will test for differences in effects

between boys and girls. Furthermore, if the data allow, we will assess the heterogeneity of findings

across different climatic and environmental conditions in a narrative style. We will analyse the

sensitivity of findings to the risk of bias or study quality.

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Appendix 1: Sample Search Terms Formatted for MEDLINE (Ovid)

1

( ( land adj5 right* ) OR landownership OR ( land adj5 own* ) OR ( land adj5 tenure ) OR

( property adj5 right* ) OR ( land adj5 tit* ) OR (land adj3 use*) OR ( asset* adj5 own* ) OR

( asset* adj5 right* ) OR ( livestock adj5 own* ) OR ( livestock adj5 right* ) OR ( land adj5

access ) OR ( property adj5 access ) OR ( poultry adj5 own* ) OR ( control* adj5 land ) OR

( control* adj5 asset* ) OR ( control* adj5 livestock ) OR ( inherit* adj5 right* ) OR ( inherit*

adj5 practic* ) OR ( farmland adj5 right* ) OR ( farmland adj5 own* ) OR ( farmland adj5

tenure ) OR ( farmland adj5 tit* ) OR ( farmland adj5 access ) OR ( farmland adj5 control )

OR (inherit* adj3 (asset* OR land OR farmland)) OR ((decision* OR power) adj3 ( land OR

livestock OR farmland OR asset* OR poultry ))).ti,ab,kw,sh.

2

( wage* OR remunerat* OR pay* OR paid OR salar* OR income* OR labor* OR labour*

OR employ* OR workforce OR ( ( vocation* OR job* OR occupation* OR profession* ) adj3

( opportunit* OR participa* ) ) OR (livelihood* adj3 opportunit*) OR ( ( intrahousehold OR "intra

household" ) adj5 bargain* ) OR ( ( intrahousehold OR "intra household" ) adj5 allocat* ) OR

( ( intrahousehold OR "intra household" ) adj5 ( decision* adj3 ( make* OR making OR made

) ) ) OR ( within adj3 ( household adj5 ( allocation OR bargain* ) ) ) ) .ti,ab,kw,sh.

3

( task* OR ( time adj3 use* ) OR activit* OR ( time adj3 allocat* ) OR workload* OR (

energ* adj3 expend* ) ).ti,ab,kw,sh.

4

(nutr* OR nourish* OR undernutrition OR undernourish* OR underweight OR bmi OR body

mass index OR anthropom* OR stunt* OR haz OR height-for-age OR length-for-age OR

wasting OR wasted OR WHZ OR WLZ OR WAZ diet* OR mdds OR mdd-w OR mddw OR dds

OR diversity score OR malnutrition OR caloric deficienc* OR malnourish* OR food security

OR food insecurity OR ( food adj5 expenditure* ) OR ( food adj5 consumption ) OR ( food

adj3 share* ) OR ( staple* adj5 share) OR hfias OR ( ( food OR calorie ) adj3 intake ) OR (

( intrahousehold OR intra household ) adj3 welfare ) ).ti,ab,kw,sh.

5

(( ( sex OR gender* OR female* OR woman OR women ) AND ( equit* OR equalit* OR

inequit* OR inequal* OR unequal* OR discriminat* OR power OR bargain* OR empower*

OR disempower*) ) OR (( sex OR gender* OR female* OR woman OR women ) adj5

power)).ti,ab,kw,sh.

6

(( ( sex OR gender* OR female* OR woman OR women ) adj3 (bias* OR parity) ) OR (effects

adj3 gender)).ti,ab,kw,sh.

7

( ( sex OR gender* OR female* OR woman OR women ) AND ( ( share OR gap OR distrib*)

adj3 ( land OR landownership OR property OR asset* OR livestock OR poultry OR inherit*

OR farmland OR wage* OR remunerat* OR pay* OR paid OR salar* OR income* OR

labor* OR labour* OR employ* OR workforce OR ( ( vocation* OR job* OR occupation*

OR profession* ) adj3 ( opportunit* OR participa* ) ) OR task* OR activit* OR workload* ) )

).ti,ab,kw,sh.

8

13

( ( ( intrahousehold OR intra household ) adj5 bargain* ) OR ( ( intrahousehold OR intra

household ) adj5 allocat* ) OR ( ( intrahousehold OR intra household ) adj5 ( decision* adj3 (

make* OR making OR made ) ) ) OR ( within adj3 ( household adj5 ( allocation OR bargain*

) ) ) AND ( sex OR gender* OR female* OR woman OR women )).ti,ab,kw,sh.

9

(1 or 2 or 3) and 4 and (5 or 6 or 7 or 8)

10

( afghanistan OR albania OR algeria OR angola OR argentina OR armenia OR armenian OR

azerbaijan OR bangladesh OR benin OR byelarus OR byelorussian OR belarus OR belorussian

OR belorussia OR belize OR bhutan OR bolivia OR bosnia OR herzegovina OR hercegovina

OR botswana OR brazil OR bulgaria OR Burkina Faso OR Burkina Fasso OR Upper Volta

OR burundi OR urundi OR cambodia OR Khmer Republic OR kampuchea OR cameroon OR

cameroons OR cameron OR camerons OR Cape Verde OR Central African Republic OR chad

OR china OR colombia OR comoros OR comoro islands OR comores OR mayotte OR congo

OR zaire OR Costa Rica OR Cote d'Ivoire OR Ivory Coast OR cuba OR djibouti OR

somaliland OR dominica OR Dominican Republic OR East Timor OR East Timur OR Timor

Leste OR ecuador OR egypt OR United Arab Republic OR El Salvador OR eritrea OR ethiopia

OR fiji OR gabon OR Gabonese Republic OR gambia OR gaza OR Georgia Republic OR

Georgian Republic OR ghana OR grenada OR guatemala OR guinea OR guiana OR guyana

OR haiti OR honduras OR india OR maldives OR indonesia OR iran OR iraq OR jamaica

OR jordan OR kazakhstan OR kazakh OR kenya OR kiribati OR korea OR kosovo OR

kyrgyzstan OR kirghizia OR Kyrgyz Republic OR kirghiz OR kirgizstan OR Lao PDR OR

laos OR lebanon OR lesotho OR basutoland OR liberia OR libya OR macedonia OR

madagascar OR Malagasy Republic OR malaysia OR malaya OR malay OR sabah OR sarawak

OR malawi OR mali OR Marshall Islands OR mauritania OR mauritius OR Agalega Islands

OR mexico OR micronesia OR Middle East OR moldova OR moldovia OR moldovian OR

mongolia OR montenegro OR morocco OR ifni OR mozambique OR myanmar OR myanma

OR burma OR namibia OR nepal OR Netherlands Antilles OR nicaragua OR niger OR nigeria

OR muscat OR pakistan OR palau OR palestine OR panama OR paraguay OR peru OR

philippines OR philipines OR phillipines OR phillippines OR Papua New Guinea OR romania

OR rumania OR roumania OR rwanda OR ruanda OR Saint Lucia OR St Lucia OR Saint

Vincent OR St Vincent OR grenadines OR samoa OR Samoan Islands OR Navigator Island

OR Navigator Islands OR Sao Tome OR senegal OR serbia OR montenegro OR seychelles

OR Sierra Leone OR Sri Lanka OR Solomon Islands OR somalia OR sudan OR suriname OR

surinam OR swaziland OR South Africa OR syria OR tajikistan OR tadzhikistan OR tadjikistan

OR tadzhik OR tanzania OR thailand OR togo OR Togolese Republic OR tonga OR tunisia

OR turkey OR turkmenistan OR turkmen OR uganda OR ukraine OR uzbekistan OR uzbek

OR vanuatu OR New Hebrides OR venezuela OR vietnam OR Viet Nam OR West Bank OR

yemen OR zambia OR zimbabwe ).ti,ab,kw,sh.

11

( ( ( developing OR less* developed OR under developed OR underdeveloped OR middle

income OR low* income OR underserved OR under served OR deprived OR poor* ) adj2 (

countr* OR nation* OR population* OR world OR state* ) ) OR ( ( developing OR less*

developed OR under developed OR underdeveloped OR middle income OR low* income ) adj2

( economy OR economies ) ) OR ( low* adj2 ( gdp OR gnp OR gross domestic OR gross

national ) ) OR ( low adj3 middle adj3 countr* ) OR ( lmic OR lmics OR ( third adj2 world

) OR lami countr* OR transitional countr* ) OR Africa OR (Africa* NOT African American)

OR asia* OR Latin America* OR South America* OR Central America* OR caribbean OR

oceania* OR Middle East* OR mena ).ti,ab,kw,sh.

12

14

10 OR 11

13

9 AND 12

15

Appendix 2: Coding Form

Bibliographic information

Author

Title

Year

Publication type Journal Article/Other peer-reviewed

material/Other (specify type and

published/unpublished)

Series Name

Language

General Information

Country of study

Funding source of study

Potential conflict of interest from funding?

(Based on own judgement if no declaration is

provided)

Yes/No/Unclear

Study Eligibility

Types of participants

Low or middle income country? Yes/No (Exclude)/Unclear

Geographic region East Asia and Pacific/Europe and Central Asia/

Latin America & the Caribbean/MENA/South

Asia/Sub-Saharan Africa

One household member in primary sector in

majority of cases? (If assumptions have to be

made, describe them)

Yes/No (Exclude)/Unclear

Exposures

Gender inequity in land or livestock Yes/No/Unclear

Gender inequity in work burden Yes/No/Unclear

Gender inequity in labour markets (wage gap,

opportunities, earned income or participation)

Yes/No/Unclear

Exclude if all three “No”

Is the study an interventionist study? Yes/No/Unclear

Outcomes

What are the outcomes documented in the study

(including those that are reported but not

included in our study)?

Is at least one outcome: nutrition or dietary

quality of women, nutrition or dietary quality of

children, or household food security?

Yes/No(Exclude)/Unclear

If the study is quantitative: Is an eligible metric

used to assess the outcome?

(women: BMI, dietary diversity, dietary

adequacy;

children: HAZ, WHZ. WAZ, dietary diversity,

dietary adequacy;


16

HH: food expenditure, food share, staples share,

HDDS, HFIAS)

Type of Study

Is the study empirical? (reports original data, i.e.

qualitative or quantitative data directly on

women/ children/ HHs).

No literature research/reviews


Is the study quantitative? Yes/No/Unclear

Is the study qualitative? Yes/No/Unclear

Does the study try to establish a causal or

correlational link between exposures and

outcomes?

Causal/Correlational/Both/Unclear/Other

(describe)

If the study is excluded: Should references be

tracked?

Yes/No/Unclear

Study details

Description of the exposure measure

Description of any evidence presented that the

exposure is (driven by) a structural phenomenon

Description of the outcome measure Maternal BMI, maternal dietary

diversity, maternal dietary adequacy;

HAZ, WHZ, WAZ, child dietary

diversity, child dietary adequacy;

HH food expenditure, HH food share,

HH staples share, HDDS, HFIAS)

Measure of effect Treatment effect: ITT/ATT/ATE/Other(specify)

Correlation: Conditional/Unconditional (specify)

Description of the measure of effect

Start date of the study (baseline)

If intervention-based: Description of the

intervention

Start date of the intervention/ change in exposure

Description of the method of analysis

Sampling methodology

Sample size

If a representative sample: Of which population

was the sample representative?

If non-representative sample: Describe the

participants

% missing values/ attrition

Results

(Statistics will be extracted with a separate table)

Describe the results for the association between

the exposure and outcome. In case there is more

than one pair, report separately.

Is there any evidence that this relationship is

influenced by climatic or environmental change?

In case there is more than one pair, report

separately.

17

Describe the results for the association of the

exposure on any intermediary outcome

(agricultural production, household income,

women’s empowerment, household poverty,

economic inequity between households, or

other).

In case is more than one pair, report separately.

Are there any adverse effects of the intervention/

change in exposure reported in the study?

Describe.

Risk of bias

For quantitative evidence:

The following confounders should be addressed in most studies

Household income, poverty or other appropriate measure of economic status,

Land ownership

Household size / composition,

Caste/ ethnicity/ religion/ gender attitudes

Specify a target randomised trial

Unit level of randomisation Individual/community/country/other(describe)

Participants

Experimental intervention

Comparator

Specify the outcome which is being

assessed for risk of bias

In case of multiple analyses being

presented, specify the numeric result

and/or reference (e.g. to a table, figure

or paragraph) that uniquely defines the

result being assessed

Complete the following item for each confounding domain (i) listed above, or (ii) relevant to

the setting or this particular study, or (iii) which the study authors identified as potentially

important

Confounding domain

Measure of confounder

Is there evidence that controlling for this

variable was unnecessary?

Is the confounding domain measured

validly and reliably by this variable?

Bias due to confounding Description Response options

Y = Yes

PY = Probably Yes

PN = Probably No

N = No

NI = No Information

NA = Not applicable

1.1 Is there potential for confounding in

this study?

Y/PY/PN/N

If Y/PY: determine whether there is a need to assess time-varying confounding

18


Y = Yes

PY = Probably Yes

PN = Probably No

N = No

NI = No Information

NA = Not applicable

1.2 Was the analysis based on

panel/time-series data?

(If N/PN, answer question 1.4 to 1.6.

If Y/PY, go to question 1.3)

NA/Y/PY/PN/N/NI

1.3 Were changes in the exposure likely

to be related to factors that are

prognostic for the outcome?

(If N/PN, answer questions 1.4 to 1.6.

If Y/PY, answer questions 1.4 to 1.8)

NA/Y/PY/PN/N/NI

Relating to baseline confounding

1.4 Did the authors use an appropriate

analysis method that controlled for all

important confounding domains?

NA/Y/PY/PN/N/NI

1.5 If Y/PY to 1.4:

Were confounding domains that were

controlled for measured validly and

reliably by the variables available in this

study?

NA/Y/PY/PN/N/NI

1.6 Did the authors control for any post-

exposure variables that could have been

affected by the intervention/exposure?

NA/Y/PY/PN/N/NI

Relating to baseline and time-varying confounding

1.7 Did the authors use an appropriate

analysis method that controlled for all

important confounding domains and for

time-varying confounding?

NA/Y/PY/PN/N/NI

1.8 If Y/PY to 1.7:

Were confounding domains that were

controlled for measured validly and

reliably by the variables available in this

study?

NA/Y/PY/PN/N/NI

Risk of bias due to confounding

judgement

Low/Moderate/Serious/Critical/NI

Selection Bias

2.1 Was the inclusion of participants

into the study based on participants’

characteristics?

Y/PY/PN/N/NI

2.2 If Y/PY to 2.1:

Were these characteristics likely to be

associated with the exposure?

NA/Y/PY/PN/N/NI

2.3 If Y/PY to 2.1 and 2.2:

Were the post-exposure variables that

influenced selection likely to be

influenced by nutrition/ food security or

a cause of nutrition/ food security?

NA/Y/PY/PN/N/NI

Risk of selection bias judgement Low/Moderate/Serious/Critical/NI

Bias in the classification of exposures

19


Y = Yes

PY = Probably Yes

PN = Probably No

N = No

NI = No Information

NA = Not applicable

3.1 Were the groups exposed to gender

inequity clearly defined?

Y/PY/PN/N/NI

3.2 Could the classification of gender

inequity have been affected by

knowledge of the outcome or risk of the

outcome?

Y/PY/PN/N/NI

Risk of bias due to the classification of

exposures judgement


Bias due to missing data

4.1 Were outcome data available for all,

or nearly all, participants?

Y/PY/PN/N/NI

4.2 Were participants excluded due to

missing data on the exposure?

Y/PY/PN/N/NI

4.3 Were participants excluded due to

missing data on other variables needed

for the analysis?

Y/PY/PN/N/NI

4.4 If PN/N to 4.1 or Y/PY to 4.2 or 4.3:

Are the proportion of participants and

reasons for missing data similar across

all degrees of exposure?

NA/Y/PY/PN/N/NI

4.5 If PN/N to 4.1 or Y/PY to 4.2 or 4.3:

Is there evidence that results were robust

to the presence of missing data?

NA/Y/PY/PN/N/NI

Risk of bias due to missing data

judgement


Bias in measurement of outcomes

5.1 Could the outcome measure have

been influenced by the knowledge of the

exposure to gender inequity?

Y/PY/PN/N/NI

5.2 Were the outcome assessors aware

of the study goal?

Y/PY/PN/N/NI

5.3 Were the methods of outcome

assessment comparable across different

exposures to gender inequity?

Y/PY/PN/N/NI

5.4 Were any systematic errors in

measurement of the outcome related to

exposures to gender inequity?

Y/PY/PN/N/NI

Risk of bias in measurement of

outcomes judgement


Bias in selection of the reported results

Is the reported effect estimate likely to be selected on the basis of the results, from…

7.1 … multiple outcome measurements

within the outcome domain?

Y/PY/PN/N/NI

7.1 … multiple analyses of the

exposure-outcome relationship?

Y/PY/PN/N/NI

7.3 … different subgroups? Y/PY/PN/N/NI

20


Y = Yes

PY = Probably Yes

PN = Probably No

N = No

NI = No Information

NA = Not applicable

Risk of bias due to the selection of

reported results judgement


IV Methods Bias

7.1 If an IV estimate was used

(including fuzzy RDDs), is there

evidence that the exclusion restriction

did not hold?

NA/Y/PY/PN/N/NI

7.2 If an IV estimate was used

(including fuzzy RDDs), is there

evidence that the instrument was

relevant (F-statistic >10)?

NA/Y/PY/PN/N/NI

Risk of IV-bias judgement Low/Moderate/Serious/Critical/NI

Overall bias

Risk of bias judgement Low/Moderate/Serious/Critical/NI

For qualitative evidence:

Description Response

Y = Yes

PY = Probably Yes

PN = Probably No

N = No

NI = No Information

NA = Not applicable

1. Is there congruity between the stated

philosophical perspective and the

research methodology? Consider if the

researcher has justified the study

design.

NA/Y/PY/PN/N/NI

2. Is there congruity between the research

methodology and the research question

or objectives?

NA/Y/PY/PN/N/NI


methodology and the sampling

methods? Can tell if we know who was

selected, why, and if any people

refused to participate

NA/Y/PY/PN/N/NI


methodology and the methods used to

collect data? Can tell if methods are

described, justified, modifications

made clear, form of data made clear

(tape recordings or notes?), and data

saturation mentioned.

NA/Y/PY/PN/N/NI


methodology and the representation

and analysis of data? Can tell if there

NA/Y/PY/PN/N/NI

21

is an in-depth description of analysis

methods, including how themes were

derived from the data if thematic

analysis, whether the researcher

explains how data presented were

selected from the original sample, if

data are sufficient


methodology and the interpretation of

results? Can tell if findings are

explicit, there is adequate discussion of

the evidence for and against

researcher’s arguments, if the

researcher has discussed the credibility

of their findings (e.g.

triangulation/multiple analyses) and if

findings are discussed in relation to

original research question

NA/Y/PY/PN/N/NI

7. Is there a statement locating the

researcher culturally or theoretically?

NA/Y/PY/PN/N/NI

8. Is the influence of the researcher on the

research, and vice- versa, addressed?

Can tell if researcher has examined

their own role and potential bias in

formulation of questions, data

collection, analysis, and response to

events during the study

NA/Y/PY/PN/N/NI

9. Are participants, and their voices,

adequately represented?

NA/Y/PY/PN/N/NI

10. Is the research ethical according to

current criteria or, for recent studies,

and is there evidence of ethical

approval by an appropriate body?

NA/Y/PY/PN/N/NI

11. Do the conclusions follow from the

analysis, or interpretation, of the data?

NA/Y/PY/PN/N/NI

Overall quality appraisal High quality/Medium quality/Low

quality/Critical/NI

Extractor information

Name

Date

Place

Extracted references for screening

22

Appendix 3: Preliminary Time Frame

12-Feb 19-Feb 26-Feb 05-Mar 12-Mar 19-Mar 26-Mar 02-Apr 09-Apr 16-Apr 23-Apr 30-Apr 07-May 14-May

Review activities

Develop protocol Feedback and

finalisation of protocol Run preliminary

searches, refine as

needed, and run the

search

Filter results

Reconcile discrepancies

Data extraction

Quality review Write summary results

for Lancet series Incorporate feedback

from inequities group Incorporate feedback

from wider Lancet

group Full paper - including

meta-analysis if

appropriate

(Continued on next page)

23

21-May 28-May 04-Jun 11-Jun 18-Jun 25-Jun 02-Jul 09-Jul 16-Jul 23-Jul 30-Jul 06-Aug

Review activities

Develop protocol Feedback and finalisation of

protocol Run preliminary searches,

refine as needed, and run the

search

Filter results

Reconcile discrepancies

Data extraction

Quality review Write summary results for

Lancet series Incorporate feedback from

inequities group Incorporate feedback from

wider Lancet group Full paper - including meta-

analysis if appropriate

24

Appendix 4: Preliminary List of Experts

1. Agnes Quisumbing

2. Amy Webb-Girard

3. Cheryl Doss

4. Chrisopher Udry

5. Deborah Johnston

6. Eileen Kennedy

7. Esther Duflo

8. Keera Allendorf

9. Lawrence Haddad

10. Marie Ruel

11. Naila Kabeer

12. Patrick Webb

13. Paulina Rossi

14. Peter Berti

15. Ruth Meinzen-Dick

16. Somebody at DFID

17. Somebody at the FAO

18. Somebody at the World Bank

19. Somebody at WHO

20. Virginie Le Masson

Documents

Protocol: Do gender-based inequities in agriculture affect