The Women’s Empowerment in Agriculture Index

TL;DR: The Women's Empowerment in Agriculture Index (WEAI) as discussed by the authors measures empowerment, agency, and inclusion of women in the agricultural sector and comprises two subindexes: the first assesses empowerment in five domains, including (1) decisions about agricultural production, access to and decisionmaking power about productive resources, (3) control of use of income, (4) leadership in the community, and (5) time allocation.

About: This article is published in World Development.The article was published on 2013-12-01 and is currently open access. It has received 514 citations till now. The article focuses on the topics: Women's empowerment & Empowerment.

Summary

1. Introduction

• Habitat loss, degradation and fragmentation continue to threaten ecosystems worldwide (Tittensor et al. 2014).
• Five of the twenty Aichi Targets relate directly to the status of ecosystems (Convention on Biological Diversity 2014).
• They are recognized as having four essential elements: a biotic complex, an abiotic environment, the interactions within and between them, and a physical space in which these operate (Tansley 1935).
• The need for interdisciplinary expert skills in the identification and use of satellite remote sensing data is a central factor that has limited the uptake of this source of environmental information by ecologists and slowed the development of national, continental and global lists of threatened ecosystems.

2. Spatial distribution of ecosystems

• Accurate maps of the geographic distributions of ecosystems and how they change over time are fundamental components of most ecosystem risk assessment protocols (Figure 1; Nicholson et al. 2015).
• Ecosystems with small geographic range size are at greater risk of collapse from environmental catastrophes than those that are distributed over large areas (Keith et al. 2017, Murray et al. 2017a).
• Producing time series of ecosystem maps at spatial and temporal scales that underpin such estimates of change is a specialist task.
• The need for detailed knowledge of available data, advanced analytical methods, and an understanding of constraints and uncertainties of remote sensing has limited the availability of highly accurate and consistent maps that can be used operationally for ecosystem risk assessments.

3. Ecosystem processes and function

• The disruption of biotic and abiotic ecosystem processes can also be key pathways towards ecosystem collapse.
• To support identification of variables suitable for ecosystem risk assessments that can be monitored with satellite remote sensing, the authors reviewed 17 published case studies that have conceptualised or described the relationship between satellite remote sensing data and specific ecosystem responses that contribute to elevated risk of ecosystem collapse(Table 1).
• These data enabled an assessment of the changing ecological states of the lakes system over a 13 year period (Tebbs et al. 2015).
• Near- shore kelp forest ecosystems are also sensitive to changes in SST, and remotely sensed estimates of SST have been used to monitor functional decline of those systems (Table 2; Vergés et al. 2016).
• Sedimentation is a qualitatively different process of degradation in aquatic ecosystems.

4. Threatening processes

• Many ecosystem risk assessment protocols require extensive assessments of threatening processes.
• These include information on the location of threats, their extent, and their impacts on ecosystems and their component biota (Nicholson et al. 2015).
• Landscape- and seascape-scale perspectives on threats (eg deforestation, coastal development, pollution, fire, invasive species, disease, extreme weather events) are crucial to the application of risk assessments (Keith et al. 2017).
• Some risk assessment protocols also require assessments of ‘plausible threats’ (Nicholson et al. 2015).
• Remote sensing data is also becoming increasingly accessible to non-experts through the development of user friendly methods and online applications that allow straightforward access to remote sensing datasets (e.g., Ferrari et al. 2016b, Gorelick et al. 2017, Murray et al. 2017b).

5. Integrating remote sensing into ecosystem models

• Ecosystem models that elegantly represent salient ecological processes deliver the capacity to monitor and predict change, estimate risks of ecosystem collapse and explore alternative future management scenarios (Bland et al. 2017).
• Such models can be parameterised, initiated, or validated with remote sensing data.
• The eReefs model of the Great Barrier Reef, for example, consists of 1 km and 4 km resolution models that integrate three-dimensional hydrodynamic, sediment, biogeochemical and ecological data obtained across a spatial domain of several thousand square kilometres.
• EReefs utilises satellite remote sensing data (such as SST) to predict more than 40 biotic and abiotic variables across space and throughout the water column in near real-time and at high spatial resolution (Herzfeld et al. 2016).
• Remote sensing data therefore has great potential for parameterizing a wide variety of ecosystem models, and is increasingly being used to assess the skill of models at reproducing ecosystem dynamics.

6. Conclusions

• Utilizing data from existing remote sensing platforms, and integrating these with in situ monitoring programs, expert knowledge, clear conceptualization of ecosystem processes, and quantitative ecosystem models will be crucial to support the global deployment of ecosystem risk assessment protocols.
• The authors have suggested a structured pathway for the selection and use of remote sensing data for use in ecosystem risk assessment (Figure 2), which is founded upon a basic understanding of each ecosystem to be assessed.
• Ideally, remote sensing data should have spatial resolution and temporal resolutions that are fine enough to represent ecosystem dynamics and allow for the detection of rapid changes, correlate closely with appropriate in situ indicators of ecosystem degradation, and be capable of delivering deep time-series that allow ecosystem monitoring over several decades.
• Rapid degradation of ecosystems is occurring in nearly all biomes, and compiling a global list of threatened ecosystems as soon as possible will better enable an international response to global change and ensure the persistence of natural ecosystems into the 22nd century.

Figures

Table 9.5—Uganda pilot WEAI

Table A.1—Five domains of empowerment indicator definitions

Table 9.1—Bangladesh pilot WEAI

Table 10.1—Tabulations between empowerment and individual and household’s characteristics— Bangladesh pilot

Table 9.4—Guatemala 5DE, decomposed by dimension and indicator

Table 10.4—Tabulations between empowerment and answers to decisionmaking and autonomy questions—Guatemala pilot

Table 10.6—Tabulations between empowerment and answers to decisionmaking questions— Uganda pilot

Table 2.1—The domains, indicators, and weights in the Women’s Empowerment in Agriculture Index

Table 9.3—Guatemala pilot WEAI

Figure 9.1—Contribution of each indicator to disempowerment in Bangladesh sample

Table 10.5—Tabulations between empowerment and individual and household characteristics— Uganda pilot

Table 10.2—Tabulations between empowerment and answers to decisionmaking and autonomy questions—Bangladesh pilot

Table 9.6—Uganda 5DE, decomposed by dimension and indicator

Table A.1—Five domains of empowerment indicator definitions

Table 11.1—Empowerment patterns, by household

Figure 9.3—Contribution of each indicator to disempowerment in Uganda sample

Table 9.2—Bangladesh 5DE, decomposed by dimension and indicator

Table 10.3—Tabulations between empowerment and individual and household’s characteristics— Guatemala pilot

Table 10.2—Tabulations between empowerment and answers to decisionmaking and autonomy questions—Bangladesh pilot

Figure 9.2—Contribution of each indicator to disempowerment in Guatemala sample

Frequently asked questions

Q1. What are the contributions in "The women’s empowerment in agriculture index" ?

In this paper, the authors proposed a method to solve the problem of the problem.v ǫ 

Q2. What is the way to capture seasonality in multipurpose household surveys?

In large-scale multipurpose household surveys, a more accurate short time-use module could be used for both time-use questions, and survey administration could be staggered to better capture seasonality. 

Q3. Why did the pilot sample only female-only and dual-adult households?

Because the objective of the survey was to produce empowerment measures for women, and for women in relation to men in their households, the pilot sampled only female-only and dual-adult households (that is, those with male and female adults). 

Q4. What is the cost-effective way to obtain information on some indicators used in the WEAI?

Short of doing biometric measurements (for example, to detect agitation or nervousness), self-reports may be the most cost-effective and feasible way of obtaining information on some indicators used in the WEAI as it is administered in a field setting. 

Q5. What percentage of the women in the sample live in households that did not use any source of credit?

A very large proportion of the women in the sample live in households that did not use any source of credit: 50.0 percent in Bangladesh, 70.3 percent in Uganda, and 74.0 percent in Guatemala. 

Q6. How many percent of women are less empowered?

Of the 45.6 percent of women who are less empowered, the empowerment gap between them and the males in their households is 22.4 percent. 

Q7. What are the indicators proposed for tracking gender equality?

The indicators proposed for tracking MDG3 (ratios of girls to boys in primary, secondary, and tertiary education; the share of women in wage employment in the nonagricultural sector; and the proportion of seats held by women in national parliament), although useful for characterizing progress toward gender equality, are proxy or indirect indicators and thus do not provide direct measures of empowerment as experienced by individuals. 

Q8. Why is there a need for a tool to measure and monitor the impact of agricultural interventions?

Because most indexes and indicators used in monitoring development progress on gender equity have little coverage of the agricultural sector, whereas many agriculture-related indicators are gender-blind, there is a clear need for a tool to measure and monitor the impact of agricultural interventions on empowerment of women within the agricultural sector (Kishor and Subaiya 2008; Malhotra and Schuler 2005). 

Q9. How many factors are used to determine the extent to which an individual feels he or she can?

The answer scale for the question regarding the extent to which the individual feels he or she can participate in decisions is 1 = not at all, 2 = small extent, 3 = medium extent, and 4 = to a high extent. 

Q10. What countries were selected to represent Africa south of the Sahara?

Based on the scope and time line envisaged for development of the index and the experience of IFPRI in field research within the focus countries, Bangladesh was selected to represent South Asia, Guatemala to represent Latin America, and Uganda to represent Africa south of the Sahara. 

Q11. What is the evidence to support the clustering of exchange rights?

Based on the results of EFA performed by asset, there is strong empirical evidence to support the clustering of the exchange rights (to sell, to give, and to rent), and there is some empirical evidence to support the clustering of those exchange rights and the right to buy. 

Q12. What can be used to assess the state of empowerment in agriculture?

the WEAI or adaptations of it can also be used more generally to assess the state of empowerment and gender parity in agriculture, to identify key areas in which empowerment needs to be strengthened, and to track progress over time. 

Q13. What is the need for the time-use data to reflect women’s achievements across seasons?

The need for the time-use data to reflect women’s achievements across seasons is, of course, of paramount importance when the time-use data are interpreted as accurate at the individual level as in the case of the WEAI. 

Q14. What is the relationship between women’s empowerment in agriculture and other domains?

Table 10.4 shows that in Guatemala, there was a clear association between women’s empowerment in agriculture and empowerment in other domains: greater decisionmaking and autonomy about minor household expenditures, serious health problems, protection from violence, religious faith, their own daily tasks, and use of family planning. 

Q15. How many women in the top three wealth quintiles were not yet empowered?

It is striking that on average, 69 percent of women in the top three wealth quintiles were not yet empowered (including 67 percent of the richest 20 percent), indicating that wealth is an imperfect proxy for women’s empowerment in agriculture. 

Q16. How many indicators are used to determine if a woman is empowered?

All in all, a woman or man is defined as empowered in 5DE if she or he has adequate achievements in four of the five domains or is empowered in some combination of the weighted indicators that reflect 80 percent total adequacy or more. 

Q17. Why were household-level indicators included in the pilot?

These household-level indicators were included in the pilot largely for validation purposes, that is, to test whether the evaluation of empowerment changes depending on how or to whom the question is posed or to examine whether responses to questions are influenced by household attributes such as wealth but are not included in the Women’s Empowerment in Agriculture Index (WEAI) itself.