KATALYST – INNOVATION IN WOMEN’S ECONOMIC EMPOWERMENT MEASUREMENT

LEARNING FROM THE APPLICATION OF WEE INDEX

Linda Jones, Bidowra Khan, Olaf WeberNovember 2016

Executive Summary

Since its inception as a project in 2003, and throughout its three Phases, gender has been an integral cross-cutting area of Katalyst’s work. In order to achieve inclusive and balanced growth, Katalyst made conscientious efforts to leverage critical opportunities or entry points, and to facilitate gender mainstreaming within targeted sectors/value chains. During this time, Katalyst and the development industry have evolved their approach to gender, and specifically to women’s economic empowerment, and this is reflected in Katalyst’s development of a women’s empowerment index described in this paper.

Katalyst Bangladesh developed and applied a Women’s Economic Empowerment Index – the Katalyst WEE Index – to its Phase 3 women-targeted interventions in the vegetable and prawn sectors. In addition to providing information on the outcomes of Katalyst’s programming that targets women in selected sectors, the Katalyst WEE Index can be used for initiatives that integrate women into interventions or to a portfolio of interventions across sectors. The application of the index at Katalyst offers significant insights into both the impact of Katalyst interventions on women’s empowerment across dimensions and also how to apply the index in other programmes and contexts.

In designing the index, Katalyst built on the M4P WEE framework that has been adopted by many market systems programmes in recent years. In particular, the dimensions that it chose to measure – income including financial autonomy, access to inputs and training, decision-making at various levles, and workload – are relatively standard for market systems and women’s empowerment. Katalyst created a survey around these dimensions to gain a nuanced understanding of women’s empowerment.

The Katalyst WEE Index was first tested in a pilot stage and then rolled out in both sectors (prawn and vegetables) where Katalyst was implementing WEE targeted interventions. The pilot stage proved the validity of the index by testing it with treatment (beneficiary) and control (non-beneficiary) groups, and conducting statistical analysis across findings. The pilot also demonstrated that Katalyst interventions had impacted women’s empowerment across key dimensions.

The application of the Katalyst WEE Index offers significant insights into how to develop and apply an index for the comparative analysis of dimensions of women’s economic empowerment in market systems programmes more generally. These are described in this document with additional suggestions for next steps.

2

Table of Contents

1 INTRODUCTION.......................................................................................................................................... 4

1.1 KATALYST.......................................................................................................................................................41.2 BACKGROUND ON WOMEN’S ECONOMIC EMPOWERMENT AT KATALYST....................................................................41.3 STANDARD WEE FRAMEWORKS.........................................................................................................................51.4 KATALYST'S APPROACH TO THE MEASUREMENT OF WEE OUTCOMES.......................................................................61.5 THE KATALYST WEE INDEX...............................................................................................................................7

2 IMPLEMENTATION OF THE KATALYST WEE INDEX.......................................................................................8

2.1 PILOT OF THE KATALYST WEE INDEX.................................................................................................................82.2 ROLL-OUT OF THE KATALYST WEE INDEX............................................................................................................9

Homestead Vegetables......................................................................................................................................11Prawn Sector.....................................................................................................................................................12

3 LEARNINGS............................................................................................................................................... 13

4 NEXT STEPS............................................................................................................................................... 15

5 CONCLUSION............................................................................................................................................ 15

6 ANNEXES.................................................................................................................................................. 17

6.1 ANNEX 1: WEE INDEX QUESTIONNAIRE.............................................................................................................176.2 ANNEX 2: TECHNICAL DETAILS OF THE PILOT.......................................................................................................206.3 ANNEX 3: FURTHER ANALYSIS OF THE ROLL-OUT..................................................................................................22

3

1 INTRODUCTION

This paper reports on Katalyst’s innovative development and implementation of a Women’s Economic Empowerment Index – the Katalyst WEE Index. The WEE Index enables the programme to determine its level of achievement against key WEE indicators within and across interventions. The WEE Index was developed, tested and rolled out during the Katalyst’s final phase between 2013 and 2017 and is described in detail following introductory sections.

1.1 KATALYSTKatalyst is one of the largest market development projects in the world. Since its inception in 2003, Katalyst has achieved significant employment and income impact by increasing the competitiveness of farmers and micro, small and medium sized enterprises in selected urban and rural sectors in Bangladesh. It is a jointly-funded programme of the Swiss Agency for Development and Cooperation (SDC), the UK Government, the Canadian International Development Agency (CIDA) and the Embassy of the Kingdom of the Netherlands (EKN). It is implemented under the Ministry of Commerce (MoC) of the Government of Bangladesh by Swisscontact and GTZ International Services. It had two distinct phases. The first phase ran from 2003 to 2008 with a budget of CHF 25.6 million. The second phase started in 2008 and concluded in 2013 with a budget of CHF 50.67 million. The third phase initiated from March, 2013 onwards and will continue till March 2017 with a budget of CHF 32 million.

Katalyst follows a pro-poor market development approach in promoting economic growth. It focuses on sectors with high outreach to the poor, high growth potential and good opportunities to include the poor better in the socio-economic systems of the selected sectors. Katalyst partners with a wide range of private enterprises and the government in order to bring about systemic changes, leverage our resources, maximize impact and ensure sustainability.

1.2 BACKGROUND ON WOMEN’S ECONOMIC EMPOWERMENT AT KATALYST Since its inception as a project, gender has been an integral crosscutting area of Katalyst’s work. In order to achieve inclusive and balanced growth, Katalyst made conscientious efforts to leverage critical opportunities or entry points, and to facilitate gender mainstreaming within targeted sectors/value chains.

Katalyst’s first 10 years of practical experience demonstrated that gender mainstreaming is an approach/framework that, if aligned with commercial incentives of market systems, can both achieve income benefits and also positively influence the economic empowerment of women. For this to happen, women-specific constraints and opportunities related to market systems change have to be considered in relation to broader issues of empowerment; otherwise change accomplished during a project or intervention is at risk of being neither systemic nor sustainable.

4

Hence, in phase 31, Katalyst adopted a well-defined vision and framework to achieve women’s economic empowerment (WEE). The vision statement was:

“Increased number of women will achieve improved access to quality inputs, information, and knowledge and market linkage; and, as a result will have enhanced economic empowerment.”

Katalyst prioritized and operationalized a WEE framework that was fit for purpose in the creation of sustainable and systemic change through market facilitation. As a result, Katalyst has been on track to realize its vision that by 2017, 42000 women farmers/entrepreneurs will achieve increased competitiveness through improved access to quality inputs, information, and knowledge and market linkages. And as result of this, they will experience enhanced economic empowerment.

1.3 STANDARD WEE FRAMEWORKSBy 2011, with a growth in the M4P programme portfolio worldwide there was increasing demand for guidance on the integration of women into market systems programming. As a result, a Discussion Paper for Making Markets Work for the Poor: a Women’s Economic Empowerment Framework2 (the M4P WEE Framework) was commissioned as part of a multi-donor (DFID, SDC, Sida) effort to strengthen the M4P approach. The aim of the commissioned document was to marry WEE and M4P in one framework, and to spur dialogue and consensus-building on how to prioritise and operationalise women’s economic empowerment within M4P initiatives.3 The framework unpacked ‘women’s economic empowerment’ to derive a set of empowerment dimensions that are compatible with and necessary for sustainable systemic change brought about by a market systems approach. The framework reinforced the potential scalability and sustainability of market systems initiatives while taking women’s economic empowerment into account.

Since its publication in early 2012, the M4P WEE Framework has been adopted and adapted by programmes around the world: for example, the Market Development Facility in Fiji, Timor-Leste, Pakistan, Sri Lanka and Papua New Guinea, AIP-PRISMA in Indonesia, M4C in Bangladesh, ALCP in Georgia, Arab Women’s Enterprise Fund in Egypt, Jordan and Palestine, Kenya Market Trust and Financial Sector Deepening Zambia. Katalyst was one of the early adopters of the M4P WEE framework which was heavily consulted during the development of the phase 3 project document. Katalyst utilized the framework to define its target indicators which provide the basis for the Katalyst WEE Index described herein.

The global experience with the M4P WEE Framework contributed to the writing of an updated Women’s Empowerment and Market Systems (WEAMS) Framework in 20164 in which Katalyst’s innovative work in

1 Phase 3 ( 2013-2017)2 Jones, Linda (2012) Discussion Paper for an M4P WEE Framework: How can the Making Markets Work for tfhe Poor Framework work for poor women and for poor men? See Springfield Papers: https://beamexchange.org/resources/655/3 Jones, Linda (2012) Ibid4 Jones, L. (2016) Women’s Empowerment and Markets Systems: Concepts, practical guidance and tools. The BEAM Exchange. http://alcp.ge/pdfs/7e0e73ffd7c071a066013809d459af5f.pdf

5

indexing women’s empowerment was highlighted. The WEE indicators that were established in the M4P WEE Framework were reinforced as non-negotiable indicators in the WEAMS Framework:

1. Economic advancement – increased income and return on labour2. Access to opportunities and life chances such as skills development or job openings3. Access to assets, services and needed support to advance economically4. Decision-making authority in different spheres including household finances5. Manageable workloads for women

In addition to the M4P and WEAMS frameworks, the recommended approach for results measurement is the DCED results measurement standard5 viewed with a women’s empowerment lens.6 Pivotal to the DCED standard is the defining of indicators that capture the changes both at the individual / business level as well as wider changes in the market system. This is achieved in the above WEE indicators by assessing women’s behaviours (e.g., decision-making) as well as access to needed supports (e.g., services).

The following section describes how Katalyst’s approach builds on established best practice to measure WEE outcomes in its interventions.

1.4 KATALYST'S APPROACH TO THE MEASUREMENT OF WEE OUTCOMESKatalyst employs customized indicators, an innovative WEE Index, and quantitative and qualitative assessments to determine its impact on women’s economic empowerment.

The dimensions of empowerment chosen by Katatlyst are consistent with the original M4P WEE Framework7 and have been further customized/nuanced for intervention and activities (as is supported by the WEAMS Framework. Katalyst described its specific objectives as follow as indicated in the accompanying box.8

5DCED An Introduction to the DCED Standardhttp://www.enterprise-development.org/page/introduction-standard Viewed at April 17, 2016.6Markel, E. (2014) Measuring the Results of Women’s Economic Empowerment in Private Sector Development: a guideline for practitioners. The Donor Committee on Enterprise Development.http://www.enterprise-development.org/page/download?id=24337Jones, L. (2012) Women’s Economic Empowerment Framework for M4P Programs. Springfield Centre Paper (see Jones 2016 for a recent update)

8 Jones, L. and Weber, O. (2015) Report on Katalyst’s Wee Index Pilot Study. Katalyst.

6

These dimensions are then utilized as the indicators for the measurement of women’s economic empowerment (WEE).

1.5 THE KATALYST WEE INDEXAs a response to external stakeholder9 concern for evidence that demonstrates a direct correlation between women’s economic empowerment and its market facilitation interventions, Katalyst researched ways to supplement documented qualitative outcomes with numeric data. In 2014, in order to deepen its understanding of the development impact of its interventions on women, Katalyst decided to develop a tool that would bring rigour to the measurement of WEE interventions. This rigour was to be achieved through the development of a tool that would be:

Structured: the structure for the tool was provided by the five WEE indicators adopted and adapted by Katalyst. This enforced consistency across measurement and reporting.

Flexible: the tool would allow for measurement to take place within a single sector (e.g., homestead gardening) or between two sectors (e.g., prawn cultivation and homestead gardening).

Comparative: outcomes for beneficiary (treatment) and non-beneficiary (control) groups who otherwise have similar conditions in life (e.g. PPI score, landholdings) could be assessed by the tool.

Comprehensive: the set of questions assessing WEE results would be multi-faceted and allow for follow-on qualitative assessments.

It was determined that the best approach to achieve this rigour was through the creation of a Women’s Economic Empowerment Index tool – the Katalyst WEE Index. In order to ensure the validity and rigour of such a tool, the services of a specialist in statistical analysis and impact measurement were sought to support the design and testing of the index – Professor Olaf Weber, University of Waterloo, Canada.10

The Katalyst WEE Index was developed and applied in two stages– a pilot and roll-out stage which are described in the next section.9 Donors of the project such as SDC and DFID.10 Professor Weber is an internationally respected impact measurement specialist who has explored impact in development contexts and programs.

7

1. Financial autonomy: Income earned from a particular activity or sector and control by women over the income from the target productive activity;

2. Access to input and services: Opportunities for women to participate fully in the productive activity and to upgrade their contributions and benefits through access to inputs, information skills building and market linkage;

3. Decision making authority: Women’s decision-making authority with regard to the productive activity;

4. Manageable workload: The negative, positive or neutral effect of involvement in this activity on women’s workload.

2 IMPLEMENTATION OF THE KATALYST WEE INDEXThe Katalyst WEE Index was first tested in a pilot stage and then rolled out in two sectors where Katalyst was implementing WEE targeted interventions11. The two sub-sections below describe the pilot briefly and the roll-out in more depth. This section is followed by a concluding section that summarizes the lessons learned as well as a recommended way forward.

2.1 PILOT OF THE KATALYST WEE INDEX The Katalyst WEE Index pilot was prepared and carried out in October and November 2015. The purpose of the pilot was twofold:

To test the validity of the index as a tool to compare changes in empowerment resulting from project interventions – that is, does the pilot study prove that the index tool itself is functional and can accurately capture changes across WEE dimensions?

To determine if, at the early stage of implementation of interventions, if outcomes are already measurable or if index only renders baseline information against which future findings can be compared.

The WEE Index was piloted in two major interventions in two sectors: homestead vegetable gardening and prawn cultivation.

The homestead gardening intervention in brief: The homestead vegetable gardening intervention addressed the lack of adequate information and access to quality inputs experienced by women homestead farmers located in remote areas of Bangladesh. In cooperation with three seed companies and one NGO partner, a network of access points was created to deliver information and seeds to women. Impact assessment had shown that women had realized increased yields and incomes12 from improved access to information and use of better inputs, but the index sought to go deeper and examine other aspects of empowerment.

The prawn cultivation intervention in brief: Prawn is a cash crop and normally a male-dominated sector. Women, however, play a very significant role as unpaid family labour in overseeing small-scale homestead prawn cultivation. The Katalyast intervention aimed to improve knowledge on modern

11 Targeted intervention: These interventions were designed to implement interventions with specifically addressing gender constraints within market systems and the majority of the target population is women.12 Per farmer/per harvesting year an income increase of BDT 5590 which is much higher than baseline income of BDT 3000.

8

In a nut shell, the Katalyst WEE index measures and assesses the impact of market systems

interventions on Women’s Economic Empowerment and makes statistical comparisons

across four dimensions of WEE: Financial autonomy, decision making, access to input and

information, manageable workload.

cultivation techniques and to promote usage of quality fish feed and aqua chemicals among women homestead farmers located in the southern and coastal belt of Bangladesh. As with homestead gardening, impact assessment had provided evidence of increased yields and incomes for participating women13 but did not explore other empowerment dimensions such as workload and decision-making.

Sampling for pilot: The total pilot sample included 130 respondents as illustrated in the table below, which was deemed statistically sufficient to test the validity of the tool and to determine early results. The control (non-beneficiary) group was selected based on its similarity to the treatment (beneficiary) group: the same level of poverty as measured by the Progress out of Poverty Index (PPI score of under 60) and other factors including size of land and education level of the respondent.

A pilot questionnaire was developed14, data entered and analysed. The questionnaire was based on the four dimensions of empowerment noted above, and scoring was assigned to each set of questions to determine levels of empowerment. Analysis examined both the development outcomes and the methodology and found that:

1. Through statistical analysis (chi-squared and regression analysis) the tool was found to be valid and statistically accurate, and can be applied again in the same or in other sectors in Bangladesh;

2. In other contexts, characteristics that validate the similarity between control and treatment groups could be different and would need to be verified by statistical analysis;

3. The interventions, which are in an initial stage, have begun to show direction towards impact and can be considered early indicators of change but, more importantly, as baseline against future measurement;

4. Especially significant differences between treatment and control groups were general decision-making across both sectors and access to skills development for project prawn farmer. Other differences were noted between treatment and control groups, as well as between sectors, details of which are provided in the next section.

In addition, the pilot allowed for refinement of the tool and its application for the roll-out phase, where the findings were also reinforced and expanded (see following sections).

2.2 ROLL-OUT OF THE KATALYST WEE INDEXFor the roll-out phase, the WEE Index was applied to a larger number of respondents in the homestead vegetable and prawn sector interventions (150 and 156 respectively) to assess a larger pool of households. The data collection was conducted as part of Katalyst’s standard impact assessment– to reduce duplication of effort in data collection. The incorporation of the index questions into the impact assessment resulted in greater scrutiny of the questions and their streamlining (see Annex One).

The quantitative analysis of multiple questions and answers for each empowerment indicator allowed the Monitoring and Results Measurement (MRM) team at Katalyst to assign a rating that effectively

13 Per farmer/per harvesting year an income increase of BDT 20,000 which is substantially higher than baseline income of BDT 3000. 14 See Annex Two for technical details of the pilot

9

indexed the result for that indicator overall. That is, if across multiple questions for one indicator (e.g., decision-making) a woman provided a majority of positive answers then the rating would be higher than for the responses of an informant who provided more negative answers. For example, in the case of access to information, women were asked about the source of information, the relevance of information in terms of usefulness etc. If a woman responded positively more than half the time (6-12 points) then she was rated as being able to obtain relevant information as per the following table.

Can obtain relative information easily 6-12 pointsCan obtain information somewhat relevant 4-5 points

Cannot obtain relative information <4 points

Each empowerment indicator was rated in a similar manner. Then the scores were collected for all the indicators. The score was then used to give a graphical distribution and then interpreted into percentage forms. For example, for the access to information indicator, within the sample, if the treatment group has 69 percent and control group has 21 percent respondents within the score range of 8 to 12, then the graphs for treatment and control (Fig:1 and Fig:2) will look like following (further graphical analysis for the roll-out is available in Annex Thre

Figure 1: Treatment

Figure 2: Control

10

69%

HOMESTEAD VEGETABLES

As noted in the pilot description, a recent impact assessment carried out by Katalyst had already shown that women benefitted in terms of increased yield and incomes as a result of the homestead gardening intervention.15 The application of the WEE index provided an opportunity to delve deeper into other dimensions of empowerment such as workload and decision making.

The homestead vegetable intervention had not benefitted from a baseline application of the WEE index so provides only a snapshot of evidence compared to a control group. The sample size for the application of the WEE Index in the targeted Katalyst homestead vegetable intervention was a treatment group of 100 respondents and a control group of 50.

The comparison of treatment and control groups resulted in the following findings around the WEE indicators utilized by Katalyst and described above. Although the data are only somewhat different between treatment and control groups for the most part, the data shows a consistent trend across indicators for greater empowerment across the treatment group.

Decision-Making: In decision-making, 79 percent of the treatment group self-reported as a key decision maker while 66 percent of the control group did so, exhibiting a difference of 13 percentage points. As we shall see in the next dimension, the quality of the decision-making is enhanced by improved access to information on vegetable cultivation on the part of the treatment group. Decision-making includes a range of spheres including productive and household activities. It has been demonstrated in women’s economic development projects16 that productive decision-making develops earlier, as women gain confidence in their knowledge and skills, and household decision-making strengthens over time.

15Per farmer/per harvest year, an income increase of BDT 5590 which is 85% higher than baseline income of BDT 300016 Personal communication: Dr. Olaf Weber, based on earlier impact research in Bangladesh.

11

21%

Access to inputs and information: A similar trend is noted in the case of access to inputs and information, where 97 percent of the treatment group women reported being satisfied with their access to information and inputs while only 82 percent of the control group did so. 17 This result suggests that the intervention was able to disseminate needed information to increase the knowledge of women homestead farmers on the benefit of using and purchasing quality inputs which were also provided..

Effect of workload and adding value to income: Equal numbers of women in treatment and control groups report that the increased workload is beneficial – that is 66 percent, although more women in the treatment group had increased income from homestead gardening (98% for the treatment group as opposed to 80% for the control group). However, more women in the treatment group report a negative impact on workload as compared to the control group (31% to 14%). While we would expect the treatment group have an increased workload with new techniques, the index reveals a possible negative outcome that requires further exploration and remediation. The value addition on income is 98% for treatment group as opposed to 80% for control group; meaning increased vegetable production has contributed in cost reduction from purchasing vegetable as well as added to disposable income when sold in the market.

Financial Autonomy: More women in the treatment group exhibied ‘financial autonomy’ – that is 98 percent as compared to 80 percent of the control group. Findings regarding financial autonomy were complemented with the findings from the decision-making section. In Katalyt’s field experience, financial autonomy is often a "lagging Indicator" of empowerment that requires more time to emerge. A study over time would help to draw more meaningful conclusions between control and treatment groups.

PRAWN SECTOR

A recent impact assessment carried out by Katalyst had already shown that women benefitted in terms of increased yield and incomes as a result of the intervention.18 During this impact assessment WEE index was applied to collect data on empowerment dimensions. As with the homestead vegetable intervention, the Katalyst WEE index was applied to draw comparisons between treatment (beneficiary) and control (non-beneficiary) groups who otherwise have similar conditions in life. The sample size for the application of the WEE Index in the targeted Katalyst prawn intervention was 107 in the treatment group and 49 in the control.

The comparison of treatment and control groups resulted in the following findings around the WEE indicators utilized by Katalyst and described above. The data show greater variations between treatment and control groups in the prawn intervention than in the homestead vegetable intervention and is likely a factor of being in a new sector (see discussion in lessons learned below).

17 Note that this question needs to be more fully explored in qualitative research to ensure the self-reporting of women reflects the reality of access to a range of information.18 Per farmer/per harvesting year an income increase of BDT 20,000 which is substantially higher than baseline income of BDT 3000.

12

Decision-Making: In decision-making, 61 percent of the treatment group self-reported as a key decision maker while 44 percent of the control group did so, exhibiting a difference of 17 percentage points. As described above, the quality of decision-making would be enhanced by improved access to information on prawn production on the part of the treatment group.

Access to inputs and information: A much stronger trend is noted in the case of access to inputs and information, where 69 percent of the treatment group women reported being satisfied with their access to information while only 21 percent of the control group did so.19 Since prawn production is a relatively new activity for women in the region and women are not usually targeted by input companies or traders, this disparity illustrates that the Katalyst intervention has very successfully provided access to inputs and information that are not otherwise available.

Effect of workload and adding value to income: Equal numbers of women in treatment and control groups report that the increased workload is beneficial – that is 79 percent. However, more women in the treatment group report a positive impact of increased workload on income as compared to the control group (92% to 79%).

Financial Autonomy: Somewhat more women in the treatment group exhibit ‘financial autonomy’ – that is 86 percent as compared to 81 percent of the control group. As was described in the homestead vegetable section, financial autonomy is a lagging indicator. Moreover, since prawn cultivation is considered a cash crop for the household, the income is likely to be used as general household income rather than women’s own income. Hence control over income is likely to occur at a slower pace and would also benefit from a longitudinal study.

3 LEARNINGSThe design and implementation of the Katalyst WEE Index has provided early insights into achievements around WEE at Katalyst as described in the pilot and roll-out sections above. Even more importantly, the application of the Katalyst WEE Index offers significant insights into how to develop and apply an index for the comparative analysis of dimensions of women’s economic empowerment in market systems programmes:

o In the Katalyst pilot and roll-out the index was run to compare WEE dimensions between treatment and control groups. However, applying the Katalyst WEE Index to collect baseline data at the start of an intervention and then annually as part of a household survey/impact assessment would be ideal in understanding the impact of interventions longitudinally. Ideally, Katalyst or other programmes would carry out such a study at baseline, 12-24 months into the project and then after 2-3 more years.

o Combining the pilot WEE Index with the roll-out in the annual household survey led to streamlining of questions and allowed for data to be collected quantitatively. These questions

19 Note that this question needs to be more fully explored in qualitative research to ensure the self-reporting of women reflects the reality of access to a range of information.

13

focused on specific aspects of empowerment, even within the targeted WEE dimensions. More questions could be added to the survey to derive a more nuanced understanding of women’s empowerment, or findings can be further elaborated through qualitative research such as focus group discussions. The Katalyst WEE Index is a tool in the MRM toolkit and if applied elsewhere, it is up to other programmes to determine the extent of its use and other complementary tools.

o Following on the previous point, qualitative WEE assessment can be instrumental in elaborating on underlying meanings of self-reported data. For example, if women in control groups are ‘satisfied’ with their level of knowledge, they may not be aware of gaps in this knowledge that have been narrowed for other women through training.

o When interventions are in new sectors for women (e.g., prawn) in comparison to established sectors for women (e.g., vegetable), we can probably expect greater disparity between control and treatment groups. That is, if a group of women have learned prawn fishing which is non-traditional, the difference in terms of their capacity and benefit will be much greater between control and treatment groups than in vegetables where all women have some knowledge and capacity.

o The use of control and treatment groups offers a form of comparison or benchmarking. Benchmarking can also happen longitudinally by tracking the change in WEE dimensions over time. It may also be possible to use secondary data – such as national statistics on income or involvement in extension groups – to create a comparative benchmark of women in interventions versus the population in general. This in turn can strengthen the attribution of facilitative activities.

o Like other monitoring and evaluation tools, the Katalyst WEE Index could be applied more broadly to other phases of a project life cycle and support project management. For example, if the WEE Index is run at baseline, it can provide valuable information for systemic change area selection and intervention design. If run part way through an intervention, it can indicate early signs of positive or negative change that can be encouraged or diminished.

o Although applied in targeted interventions at Katalyst, the WEE Index could be applied in integrated interventions to determine the impact on women’s empowerment. Women in the intervention could still be compared to women who are not participants (treatment and control as above).

o The Katalyst WEE Index is valid for a context when certain characteristics between control and treatment groups are selected to identify like groups. For example, in Bangladesh level of poverty (as determined by PPI score), size of landholding and level of education of the target women had to ‘match’ in order to compare the two sets of women. The use of these characteristics as identifiers of like groups was validated by chi-square and regression analysis during the pilot phase. Other contexts would need to identify and validate its target characteristics. In this way, a programme cannot only monitor for change but to also verify attribution.

o In applying the Katalyst WEE Index, it is helpful to understand the context and situation of interviewees. For example, women enumerators may receive the most open answers from women, and women may be less guarded if male or other senior family members (e.g., mother-

14

in-law) are not present. This understanding can also assist in appropriate phrasing of sensitive questions to enable the greatest insight.

o As noted in the Katalyst analysis for vegetable and prawn, not all dimensions of women’s empowerment are achieved equally or at the same speed. A longitudinal application of the Index, and across contexts, could provide very interesting learning around the process and even sequencing of women’s empowerment.

4 NEXT STEPSThe statistical analysis and results described above were derived from applying the Katalyst WEE Index during impact assessment, and then analysing the data separately. The analysis demonstrates that the four WEE dimensions used by Katalyst provide information about women’s empowerment that is useful for evaluation and also in making programme decisions. Moreover, based on qualitative evidence over the years, it is likely that the quantitative evidence captured to date will shift and become stronger over time illustrating greater impact across all four dimensions of the index.

As Katalyst is in the final year of its final phase, it would be ideal if other programs can adopt this index and move it forward. For example, the Katalyst WEE Index could be:

1. Adopted by other Swisscontact programmes to further test and develop, and utilize in new ways to manage projects, to monitor over time, and to determine attribution;

2. Shared beyond Swisscontact for the benefit of WEE programmes globally, while maintaining the Swisscontact or Katalyst branding;

3. The methodology can be standardized with concrete guidelines such as toolkit and ‘how-to’ directions to allow for easier and more widespread application;

4. Both this brief and the methodology could be published for greater adoption and adaptation in market systems programmes.

5 CONCLUSION In Phase 3, Katalyst prioritized and refocused its approach for operationalising and measuring women’s economic empowerment in order to achieve and demonstrate tangible impact. This result was a targeted set of women’s interventions and empowerment dimensions that could be measured and reported.

Katalyst explored possible entry points and mechanisms to mainstream gender in its market systems/agri-value chains and decided upon targeted interventions. From earlier qualitative assessment, and now with the aid of the WEE index that, Katalyst was able to establish that its market facilitation approach has empowered women, but that this empowerment can be uneven and timebound. That is, Katalyst work shows that there is a non-linear yet positive influence over the selected dimensions of empowerment; for example, there is striking progress observed among beneficiary groups in certain

15

dimensions (e.g. access to information and inputs), but other dimensions improve more slowly over time (i.e. financial autonomy) as they are influenced by other socio-economic variables.

With this paper, Katalyst’s experience to achieve and measure ‘economic empowerment for women’ contributes to wider market development initiatives in the local as well as the global contexts.

16

6 ANNEXES6.1 ANNEX 1: WEE INDEX QUESTIONNAIRE

Intervention CodeRespondent’s NameUpazilaDistrictFarmer Type (Treatment/Control)

1. Financial Autonomy and Decision-making in the production process (Select one only – the main decision maker for each activity

1.1 Decisions made for expenditures from the productive income on

Indicator (Who decides on the following tasks Wife(Score 2)

Husband or other household member(Score 0)

Joint with husband or other household member(Score 1)

Oneself (clothing and other necessities 2 0 1

Family consumption/nutrition ( food related expenditure

2 0 1

Household health and medication 2 0 1Children ( education, clothing and other necessities) 2 0 1Household renovation and improvement 2 0 1Household assets (TV, Mobile phone, Tube well, Livestock, motorcycle, jewelry, gold, etc.)

2 0 1

Investment in own business (either the targeted activity or other activities)

2 0 1

Savings 2 0 1Wedding/Social events/entertainment 2 0 1Lend money to friends or relatives 2 0 1Purchase land/pond/house 2 0 1

1.2 Decision-making in the production process (agricultural and non-agricultural)

17

Indicator (Who decides on the following tasks) Wife(Score 2)

Husband or other household member(Score 0)

Joint with husband or other household member(Score 1)

What you will produce (e.g., crop, handicraft, other 2 0 1Production costs? 2 0 1How the product is sold (e.g., sales at market, sales to neighbor, use at home)?

2 0 1

Transportation costs and other cost of sales? 2 0 1How much to sell (versus consume) 2 0 1The asking price? 2 0 1How to spend the income? 2 0 1

2. Access to inputs and information for specified productive activity and information & knowledge for productive activities for both agricultural and non-agricultural (Choose one)

Indicator Score2.1 Are you able to access the inputs that you need for this productive activity?

Completely satisfied – 3 3

Somewhat satisfied – 2 2

A little satisfied – 1 1

Not satisfied – 0 0

2.2 Are you able to get the information that you need related to your productive activity?

Completely satisfied – 3 3

Somewhat satisfied – 2 2A little satisfied – 1 1

Not satisfied – 0 0

2.3 How do you access inputs:

Through a family member or close friend 1From a community member (including lead farmer) or community group 2From a supplier directly (including government, NGO or business) 3

2.4 How do you access information:

18

Through a family member or close friend/ পরি�বারে�� সদস্য অথবা ঘরি ষ্ঠ বনু্ধ দ্বা�া 1From a community member (including lead farmer) or community group / সমারে�� সদস্য বা সামারি�ক দল দ্বা�া (নে তা কৃষক ও প্ররে�া�্য)

2

From a supplier directly (including government, NGO or business) 3

3. Effect on women’s workload / মহি�লাদে�র কাদের উপর চাপ

Q3.1 Are there negative impacts on your workload from engaging in this activity (count number of women with no negative impacts for scoring above)?

Yes 1No 2Don’t know 3

Q3.2 Do you feel that the amount of work in this activity is worth it to your income and household well-being?

Yes 1No 2Don’t know 3

4. Financial Autonomy: Relative importance of productive activity to household income

Name of the Activity/

Family Member(s) Engaged/

Annual Income from the Activity (BDT) /

Is this the productive activity we are covering in this intervention /

Self

If Other, please mention (e.g., husband, father, mother in law)

Yes No

1st most productive Activity in terms of HH income

1 1 2

2nd most productive Activity in terms of HH income

2 1 2

3rd most productive Activity in terms of HH income

3 1 2

4th most productive Activity in terms of HH income

4 1 2

Intervened productive Activity, If not one of the top four (if not exact amount – is it somewhat less or much less than the main productive

5 1 2

19

activities listed above)

6.2 ANNEX 2: TECHNICAL DETAILS OF THE PILOT Sectors: The WEE Index was piloted in two sectors: homestead vegetable gardens and prawn cultivation.

Sampling: The total sample included 130 respondents, as illustrated in the table below.

Sector Treatment control TotalPrawn 25 25 50Vegetable 40 40 80Total 65 65 130

Non-project participants (control group) live near the project but those selected were not so close as to contaminate results. They were selected randomly regardless of their involvement in a sector. The control group had a similar PPI score (under 60) as the treatment group. The two sets were from:

i. Vegetable gardens (northern and southern Bangladesh )ii. Prawn cultivation (southern Bangladesh)

Questionnaire: The questionnaire was the same for both the control and treatment groups so that data could be compared. Most of the questions were quantitative and answerable by a single digit or letter. The questionnaire for the pilot stage was more lengthy than that used in the roll-out

Data Entry: A format was provided for data entry to allow for comparison within and among sectors, and between treatment and control groups. This was completed and sent to the impact measurement team for analysis, along with the support of a statistician from the University of Waterloo in Canada. The next section summarizes the results of analysis.

Findings from the Pilot: The analysis suggests that interventions have a significant effect on productive decision making authority. Women in the treatment group have a significantly higher productive decision making authority than those in the control group. That is, as women gain skills and knowledge, they are better able to make decisions around how to handle production and do not need to seek out this information. This can be measured by the program and be directly attributed to it with careful monitoring and evaluation.

However, the effect does not appear to hold for the treatment group regarding financial decision making. On the one hand, it is possible that, as more income comes into the household, women do not gain increased control over income or they actually lose ground in this area – however, it may be too early to draw this conclusion. Many studies suggest early positive impacts regarding decision making around the particular productive issues they target (e.g., decision making). Thus, in this case, an effect can be expected for decisions centered on the target activity and not on more general decisions in the early stage. A study over time would help understand where treatment and control group women started and where they are now in terms of financial control. Furthermore, as is the experience with the

20

researchers, greater financial autonomy comes later so this could still improve over time. Even if the decision making and women’s empowerment is moving in a positive direction, we have to be consider the general societal direction and issues of attribution.

Interestingly, being active in one of the target sectors is mainly influenced by the age of the women (the higher the age, the higher the likelihood to be active in the target sector) and the size of land owned – as found in the statistical analysis described below. That is, women who own smaller sizes of land are more likely to be active in the target sector, and women with smaller landholdings a lower PPI were targeted.

The main factors that influence the total household income of the participants are the size of land owned, education and the PPI20 score. All three factors have a positive impact on the income. Hereby, the effect of the size of land is mainly valid for the prawn sector while the effect of the PPI score has a higher impact on the vegetable sector:

For the prawn sector, the size of land owned and the total income have a significant positive impact on the likelihood to be active in the sector. Women active in the prawn sector own more land and have a higher income than those that are in other sectors. Furthermore, the size of land owned has a significant positive impact on the total income in the prawn sector and in the vegetable farming sector since as income from prawn farming increases, larger ponds are acquired.

For the vegetable farming sector, the PPI has a significant impact on the total household income. The higher the PPI score, the higher is the total income. This is not valid for the prawn sector. However, it was also found that women with smaller land benefit more from the intervention in vegetable farming – suggesting that poverty targeting by the programme was effective.

Analysis was conducted to ensure that identified project outcomes were not a result of non-project variables such as PPI score, size of land, education of woman respondent, - these differences were controlled for in the treatment and non-treatment groups. Especially significant differences between treatment and control groups were general decision-making across both sectors and access to skills development for project prawn farmer. Other differences were noted between treatment and control groups, as well as between sectors, details of which are provided in the next section.

The WEE Index Pilot reveals limited benefits to project participants at this early stage of the interventions. However, the pilot revealed (through statistical analysis) that the approach itself is valid. The questionnaire, data collection and entry, the indexing analysis and other statistical analysis are all viable and represent a valid methodology. Through cross-checking, Chi 2 tests and regression analysis, the findings have been proven to be solid and not contradictory.

At this stage of the interventions, the application of the index serves best as a baseline with periodic comparative measurement. As women-targeted interventions are new in these sectors, then results are too early to be conclusive. With comparative data over time, we can compare

20 The Progress out of Poverty Index (PPI) utilizes non-income indicators to determine poverty ranking and is widely used as a more accurate indication of poverty than income. The PPI has been utilized by Katalyst in the identification of poor households in its project area, and was therefore a useful benchmark for selecting the control group.

21

results and draw more final conclusions. In order for this data to be valid, we must ensure that the control sample is not contaminated. Control group respondents that are selected from the same sub-district as the treatment group might benefited from systems change (e.g. availability of better and more affordable seed from private sector partners) and, by observing others in the sub-district, improved their vegetable production and incomes. Therefore, a counterfactual from other sub-districts or even districts with households with similar PPIs and other variables might be needed to provid stronger evidence following the baseline.

For future benchmarking, it was proposed to start the evaluation phase early and to evaluate the impact of the treatment over time. Ideally, Katalyst or other programmes would carry out such a study at baseline, 12-24 months into the project and then after 2-3 more years. We would expect that treatment groups advance in the initial stages and that, if systemic change has been successful, the gap would narrow (as we see with prawns and vegetables).

22

6.3 ANNEX 3: FURTHER ANALYSIS OF THE ROLL-OUTPRAWN ANALYSIS

The variables being tested were:

T1: Decision Making

T2: Access to Information

T3a: Effect of Workload

T3b: Added Value of Workload

T4: Financial Autonomy

Two group t-test have been conducted to tests differences between the treatment group and the control group. Non-parametric tests suggested the same significance.

The results are presented in Table 1.

Table 1: T-tests for the empowerment variables

Variable Meantreatment Meancontrol P-valueT1: Decision Making 19.4 15.7 .007T2: Access to Information 8.0 6.2 <.00001T3a: Effect of Workload 1.9 2.0 .11T3b: Added Value of Workload 1.1 1.0 .16T4: Financial Autonomy 8.2 7.9 .66

The results suggest that the treatment group has higher values for all empowerment variables T1 to T4. Moreover, T1: Decision Making and T2: Access to information show a highly demanding statistical difference. The following Figure 1 to Figure 5 show that the data is relatively normally distributed. Because the variables T3a and T3b consist of only a few categories, we used a Chi 2 test to test for differences between two categorized groups. The results suggest a significant difference for T3a: Effect of Workload (p = .049), but not for T3b: Added Value of Workload. With regard to T4: Financial Autonomy, the control group’s mode is the value 9 that has a much higher percentage as the other values. Generally, the T4 distribution of the treatment groups is more even.

23

05

1015

0 10 20 30 40 0 10 20 30 40

Control TreatmentP

erce

nt

T1-Decision MakingGraphs by Group

Figure 1: Decisions Making

010

2030

40

0 5 10 15 0 5 10 15

Control Treatment

Per

cent

T2- Access to InfoGraphs by Group

Figure 2: Access to Information

24

020

4060

80

-2 0 2 4 -2 0 2 4

Control TreatmentP

erce

nt

T3a-Effect of WorkloadGraphs by Group

Figure 3: Effect of Workload

050

100

0 1 2 3 4 0 1 2 3 4

Control Treatment

Per

cent

T3b- Workload adds valueGraphs by Group

Figure 4: Workload Adds Value

25

050

0 5 10 15 0 5 10 15

Control TreatmentP

erce

nt

T4- Financial AutonomyGraphs by Group

Figure 5: Financial Autonomy

Figure 6: Financial Autonomy Overall the results suggest a positive impact of the treatment on the empowerment variables T1 to T4.

VEGETABLE

In order to analyze the current empowerment data and to calculate weights for the different indicators we created a data set that included both the data for the treatment and the control group.

The variables that have been used are:

• T1: Decision Making • T2: Access to Information • T3a: Effect of Workload • T3b: Workload adds value to Income • T4: Financial Autonomy

The report suggests that there are positive trends across all indicators and the improvement is especially notable for access to T2: Access to information and T1: Improved decision making, but is not significantly different for workload and value of workload. The following Figure 1 to Figure 5 show that the data is relatively normally distributed.

26

Figure 7: Decisions Making

27

Graphs by treatFrequency

T1: Decision Making

TreatmentControl

403020100403020100

40

30

20

10

0

Graphs by treatFrequency

T2: Access to Information

TreatmentControl

151050151050

60

40

20

0

Figure 2: Access to Information

Figure 3: Effect of Workload

28

Graphs by treatFrequency

T3a: Effect of Workload

TreatmentControl

4321043210

60

40

20

0

Figure 4: Workload Adds Value

29

Graphs by treatFrequency

T3b: Workload adds value to IncomeTreatmentControl

32103210

100

50

0

Figure 5: Financial Autonomy

To calculate the regression analysis we used the median of all participants, including the control group, for their empowerment to create two groups. The median was 33. Consequently, the empowered group has values higher than 33. Then we conducted a linear regression analysis to calculate the weights of the variables T1 to T4 as independent variables for predicting empowerment. The regression was significant (p < .0001) and its explanation of variance is r2 = .45. Both values indicate a good ability of the function to predict empowerment. Tests for variance inflation and for the effect of the correlation between the independent variables were also negative. The coefficients and the significance of the individual variables are presented in Table 2. The coefficients indicate that empowerment changes by the value of the coefficient if the independent variable is changed by one unit. For instance, empowerment increases by 0.0469556, if T2: Decision making is increased by one unit.

Table 2: Regression coefficients and significance

Variable Coefficient SignificanceT1: Decision Making 0.0469556 <.0001T2: Access to Information 0.1190395 <.0001T3a: Effect of Workload 0.0881741 0.241T3b: Workload adds value to Income 0.5581571 0.143T4: Financial Autonomy 0.08053 <.0001Constant -2.445874 <.0001

30

Graphs by treatFrequency

T4:Financial Autonomy

TreatmentControl

151050151050

50

0

The results presented in Table 1 indicate that the five variables have a positive impact on empowerment. However, ‘Effect of Workload’ and ‘Workload adds value to income’ were not significant. The reason for this is that both variables do not vary. Only very few participants respond to them negatively.

31