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WEwork Collective: Endline Results and Conclusions

Author: Marta T. Grabowska

email: [email protected]

Executive Summary

a) Personal Development

i) No change in self-efficacy GSE scores, which is unsurprising given the very high

average scores at baseline.

Endline results and conclusion 2

ii) Improvement in speed of completing exercises 106 and 107; however, the change is

not unique to the treatment group and may have been caused by learning by doing.

iii) Much higher rate of nomination for public posts and contacting the local

representative, and a significant fall in applications for government assistance. The

effects are common to both treatment and control groups. More research may help

reveal whether the increase in nominations was the result of the election and why

government assistance applications fall significantly.

iv) Household expenses record-keeping increases substantially from baseline to endline,

although the result cannot be statistically attributed to the treatment.

b) Economic Activity

i) Respondents do fewer types of work on average in the endline, suggesting greater

specialisation.

The average time spent in wage employment over a week almost doubles; it also

increases for agriculture, but falls for housework. The change is not specific to the

treatment group – apart from wage work, which is more likely to be performed by the

treatment group at both baseline and endline.

ii) There is an overall increase in income from wage employment from $837.60 to

$1173.40. There is also a rise in average time spent in wage employment; in terms of

8-hour days, the rise is from 85.5 to 152.8. Proportionally, the increase in time

worked is higher than the increase in income, suggesting that respondents accept

lower-paid activities. Neither result is specific to treated individuals; however, the

results are sizeable and encourage further exploration.


iii) Overall, the number of businesses owned by women falls from 92 to 65, with

indications that businesses which are not sustained are less productive and their

owners take on more paid work instead. The characteristics of the sustained

businesses have also changed in a number of ways, including male employment,

ownership of assets and maintaining business records.

The analysis may be limited by the lack of data, as a relatively small proportion of the

women sampled have a business, even in the baseline.

iv) The number of respondents who plan to open a business in the future falls

significantly. Together with the findings in (iii), this suggests it may be worthwhile to

investigate why running businesses becomes less attractive to women.

v) The respondents had greater influence on average in all business-related decisions at

endline compared to baseline. Average scores at endline tend to be around 1.8, which

indicates respondents were more likely to be involved a lot in decisions made.

c) Financial Activity

There is no discernible change in the number of bank accounts opened or loan

applications. The project participants were significantly more likely to be members of

savings groups in the endline survey; however, the magnitude of this difference is

small and overall, the programme did not have significant effects on financial activity.

d) Social Networks

i) The size of the respondents’ social networks increased substantially between the

endline and the midline, including the number of people contacted by respondents,

the number of people contacting the respondents and the frequency of these contacts.

ii) The average trust in each contact listed remains constant, suggesting that networks

are not expanding by diffusing the quality of contacts.


iii) The proportion of family members in the network on average also declines

substantially, suggesting the networks may be expanding outside of the usual groups

contacted by the respondents.

iv) These results are not dependent on treatment status, implying that no clear treatment

effect can be identified; however, the effects are large and suggest the programme was

at least an important contributing factor to the changes in respondents’ networks.

e) Decision Making

i) Overall, the proportion of women involved in decision making increases for almost

every type of decision recorded in the survey. This is irrespective of treatment status –

the change occurs over time for both the treatment and control groups. The

percentage of women involved in the endline are high across the decision types – all

above 90% and most above 97%.

ii) Results also indicate a fall in the proportion of women who act as sole decision

makers. n More research would be required to establish what this indicates for the

position of the ii n respondents within the households.


Contents

Executive Summary.............................................................................................................2

a) Personal Development.........................................................................................2

b) Economic Activity...............................................................................................2

c) Financial Activity.................................................................................................3

d) Social Networks...................................................................................................3

e) Decision Making..................................................................................................4

1. Introduction..................................................................................................................8

a) Key Research Questions..........................................................................................8

b) Overview of Participants.........................................................................................9

2. Methodology..............................................................................................................11

a) The Experiment......................................................................................................11

b) Methodology..........................................................................................................13

3. Results by Topic.........................................................................................................18

a) Personal Development...............................................................................................18

i) Self-efficacy and self-confidence......................................................................18

ii) Cognitive Performance......................................................................................19

iii) Political Activity................................................................................................20

iv) Recording Household Expenses........................................................................22

b) Economic Activity.....................................................................................................23


i) Type of Employment.........................................................................................23

ii) Income and Employment...................................................................................26

iii) Current Business................................................................................................28

iv) Future Business..................................................................................................33

v) Business Decision Making................................................................................34

c) Financial Activity.......................................................................................................37

d) Social Networks.........................................................................................................39

i) Network Features: Size......................................................................................42

ii) Network Features: Frequency............................................................................43

iii) Network Features: Trust....................................................................................44

iv) Network Features: Composition by Relative.....................................................48

e) Decision Making........................................................................................................49

i) Decision Subject: Children and Education........................................................50

ii) Decision Subject: Health...................................................................................51

iii) Decision Subject: Spending, Business and Finance..........................................52

iv) Summary............................................................................................................54

4. Results Evaluation.....................................................................................................56

5. Conclusions and Recommendations for Future Research.........................................58

Appendix............................................................................................................................59


1. Introduction

“The WEwork Collective”, launched in 2015, is a unique programme within the

Cambodian context, combining transformational leadership training, focused business coaching,

and peer-led mentoring for over 250 rural women across 7 provinces for over 18 months. The

project is part of the innovative approach to women’s economic empowerment proposed by

WaterSHED, with support from the Bill & Melinda Gates Foundation’s Women and Girls at the

Center of Development (WGCD) Grand Challenge. The selected participants were asked a series

of detailed questions in three survey rounds to enable comprehensive post-treatment analysis of

changes over a range of outcomes.

This report summarises the findings of this analysis, categorised into 5 outcome

categories. An overview of the results is given in the Executive Summary. Section 1 of the report

provides an introduction to the programme, its aims and the participants. Section 2 is a summary

of the methodology of the programme and the analysis carried out. Section 3 provides a detailed

breakdown of results, including technical statistics and visual representation of the results.

Section 4 presents the self-reported changes participants experienced. Finally, section 5

concludes and provides recommendations for future research.

a) Key Research Questions

The analysis will look for changes over time in key indicators measured by the surveys.

The types of outcomes measured include: self-efficacy, intrahousehold decision making,

economic activities, professional reputation, income from wage work, opening and sustaining

businesses, business planning, financial activity, creating employment opportunities, professional

network size and characteristics, civic participation and cognitive performance. These outcomes


seek to measure the direct and indirect impact of increasing women’s economic participation in

the rural market for WASH and improving their skillsets through intensive training. The results

will contribute to the evaluation of the type of intervention offered by WEwork, outlining the

impact of combined training and mentorship on women’s empowerment and success in the

WASH market.

b) Overview of Participants

The analysis of baseline results enables us to identify several key features of the

participants who initially signed up for the programme. The respondents ranged in age from 16

to 75. They came from a range of educational backgrounds – literacy was high at 94%, but only

27% had completed secondary education. Majority (61%) were married, with the remaining

sample split fairly evenly between divorced, widowed and never married respondents. The

financial inclusion of the women was weak on average, with approximately one third having a

bank account, one quarter being a member of a savings group and one third making a loan

application in the previous year. On the other hand, economic activity was high. Majority of the

respondents worked in multiple activities, and 40% of households relied on combinations of

income from wage work, business and agriculture. The mean income earnt from wage work was

$832 in the year prior to interview. However, their professional networks were small; the median

woman exchanged advice about business and employment with 2 people.

Most respondents lived in acceptable housing conditions, with only 4% living in houses

with mud floors. Moreover, within their households, 92% of people used a latrine for defecation

regularly and 267 households owned a functioning pour-flush toilet. The majority (96%) of

households either treated water before drinking, or bought bottled water. The women were fairly


well involved in decision making within the household, participating in 89% of the decisions

made. Majority (92%) had also actively participated in civic life in their communities, with 57%

holding a public post. The average of the women’s self-reported Generalized Self-Efficacy Scale

(GSE) scores was 33, higher than the international average of 29.

These indicators do suggest the women who signed up for the programme were unique in

the context of rural Cambodia. The literacy rate for adult women in Cambodia was around 75%

in 2014, considerably lower than in our sample.1 The percentage of adult females with a bank

account in 2014 was 20.5 – lower than in our sample.2 Similarly, substantially more of the

WEwork women work for a wage or salary – the national average is 40% of women in work, 3

whereas in our sample the proportion in wage work is 78%. In terms of WASH, only 2/3

households in Cambodia have access to an improved water source and 46% have a sanitation

facility.4 As of 2015, 60% of rural Cambodians were openly defecating, compared to under 10%

in our sample.5 These differences affect the potential for scalability of the project as the women

perform substantially above the rural Cambodian average in a range of development indicators.

Therefore, we cannot know how a more representative sample of rural women would perform as

a result of this programme. However, this is not necessarily a compromise for this study – rather

a point to highlight in future attempts to recreate the programme.

1 National Institute of Statistics, Directorate General for Health and ICF International. (2015). 2014 Cambodia Demographic and Health Survey Key Findings. Rockville, Maryland, USA: p.2

2 World Bank Development Indicators, The World Bank3 Ibid.4 2014 Cambodia Demographic and Health Survey Key Findings: p.25 World Bank Development Indicators, The World Bank


2. Methodology

a) The Experiment

The Sample and Data Collection

The WEwork initiative recruited women from 7 rural provinces in Cambodia. The

recruitment process consisted of meetings in villages in the target provinces, describing the

programme and giving women opportunity to sign up. The 299 women interested in joining

the programme were asked to complete the baseline survey, which was carried out between

February and April, 2016. The second round was carried out in September and October 2016,

with the 228 women who remained in the programme from baseline, as well as 10 new

participants. Finally, the endline survey was conducted in May and June 2017 with 280

participants.

As in any experiment, not all participants who initially signed up to WEwork remained

until the end of the project. Some attrition was random, with numerators unable to reach

participants on the day of the survey for largely random reasons – the details of this are

summarised in the master list for both midline and endline. Overall, 273 of the initial

respondents can be compared at both baseline and endline, which allows for an investigation

of treatment effects. Of these, 44 dropped out of the programme after the baseline survey and

therefore provide a form of counterfactual for the experiment.

The Training Programme

Four training workshops were held for the participants, each one lasting two days. The

training focused on personal and business skills, financial literacy and WASH knowledge. Table

1 provides a summary of the topics covered by the training.


Table 1

Personal

Gender: roles, inequality, challenges Motivation from female role models Involving men in success Self-confidence and fears Vision and goal setting Action planning Community Facilitation Skills Problem Solving Decision Making Conflict Resolution

Business

Markets and supply and demand Steps to starting a business Business planning – marketing,

budgeting Sales skills Business problem solving People and team management Time management, productivity

WASH

WASH knowledge – i.e. open defecation, drinking water, bodily hygiene

WASH product types – toilets and shelters, water purifiers, hand washing station

WASH opportunities – for women, as suppliers or producers, sales agents and key persons etc.

Educating the community about WASH

Financial

Separating personal and business finances

Tracking expenses and income Savings Budgeting Cash management Accessing credit, managing debt

The topics covered by the training illustrate the approach taken by WaterSHED – the

skills taught were wide ranging and complementary, covering the personal and professional skills

needed for economic and personal empowerment. Previous literature suggests that such

multidimensional approaches maximise the chance for training programmes to succeed.6 In

addition, the participants attending the workshops received a per diem to compensate for travel

expenses and food costs. However, in contrast to many programmes in developing countries, no

additional motivational payment was given; instead, the per diem was presented as a scholarship

6 See: Buvinic, M. & Furst-Nichols, R. (2016) or Pereznieto, P. (2016)


and the training as an opportunity to learn new skills. This implies that continued participation

required personal ambition and motivation and so the results will not be affected by the

attendance of persons seeking only financial reward. The workshops did additionally include

promotion of WaterSHED Ventures products – La Bobo and CWP – and opportunity to purchase

these for private use or resale; however, this wasn’t technical assistance specifically and the

intervention was mainly focused on the training.

b) Methodology

This section will outline the methodology used in this study for identifying effects of

treatment. The details are somewhat technically demanding but they may be of interest to data

analysts and are therefore included here for completeness. However, readers not interested in

details of methodology may wish to skip this section.

This study aims to identify the effects of participation in WEwork on key outcome

variables. These outcomes were measured in surveys conducted before, during and after the

project, which enabled us to compare two differences: between the pre-treatment and post-

treatment outcomes, and between the treatment and control group. This is the difference-in-

difference (DiD) estimator. However, before the DiD estimator is discussed, we need to evaluate

whether the treatment and control groups are balanced over other variables. Balance is required

to credibly argue that the control group is a valid counterfactual to the treatment group, and it is

the only formal test of parallel trends we are able to carry out in the absence of further pre-

treatment surveys. Balance in this case means no significant differences across different

characteristics between respondents. The assignment mechanism in this study was not random;

the control group consists of women who dropped out of the project and therefore received no


treatment. This made it imperative to test formally for differences between the treatment and

control in order to ascertain whether the control group offers a useful counterfactual to the

treatment group.

The t-tests carried out, as reported in the Appendix, show that the control and treatment

groups are unbalanced on a range of variables. Both the equal and unequal variance tests find

hours spent doing housework and self-efficacy scores to be significantly different between the

control and treatment groups, with the control scoring lower in self-efficacy and spending less

time on housework. In addition, the standard t-test also reports wage-work, toilet use and literacy

levels to be higher for the treatment group, whereas Welch’s test reports that members of the

treatment group have more jobs and contact more people for advice. Although Welch’s test is

likely to perform better given the difference in sample sizes, the results from the standard t-test

were also used to inform the set of control variables necessary for regression analysis. In the DiD

analysis, the set of control variables used was whether respondent worked for a wage in past 12

months, had a toilet, the number of people they contacted for advice and hours spent on

housework in the last 7 days.

However, there are also power considerations which may compromise the validity of the

DiD approach. The power of the design is the probability that, for a given effect size and a given

statistical significance level, we will be able to reject the hypothesis of zero effect.7 Due to the

assignment mechanism, the control group is significantly smaller than the treatment group. This

has important power implications for estimates of treatment effects. The minimum detectable

effect (MDE) size for a given power (κ), significance level (α), sample size (N) and proportion of

subjects allocated to the treatment group (P) is:8

7 Duflo, E., Glennerster, R. & Kremer, M. (2007). Using Randomization in Development Economics Research: A Toolkit. CEPR Discussion Paper Series, 6059: p.28

8 Ibid.; p.29


MDE= (t1−κ+t α )∗√ 1P(1−P) √ σ2

N

This illustrates the trade-off between power and P. MDE is minimised at the point where

P is 0.5; above this point, the MDE increases, thus increasing the probability of falsely

concluding the treatment has no effect. The P in this study is approximately 84%, which means

to achieve power of 80% (a good benchmark) the MDE would be very high relative to a sample

with a split closer to half and half. Indeed, this foreshadows Section 3, in which DiD analysis

will find few outcomes attributable to treatment. Therefore, in the proceeding analysis it is

necessary to bear in mind that the compromises of the dataset – namely lack of randomisation

and low power – will make it very difficult to detect a treatment effect.

In order to deal with this challenge, two methods will be used to ascertain the impact of

the programme: DiD and simple t-tests for the difference in mean outcomes for the treatment

group at baseline and endline, assuming equal variances. The t-tests will identify the outcomes

which change over time, which will provide us with a list of measures the programme may have

affected. The DiD will test whether we can have confidence in attributing the changes to the

programme (despite the low power of the test). Together, the two methods will provide a cross-

validated list of outcomes and good grounding for future researchers, who may choose to employ

more powerful identification tools.

To conclude this section, the DiD estimator is briefly presented. The potential outcomes

framework can be written as follows:

(1) y0¿=μ0+αi+ γt+u0¿¿¿

(2) y1¿=μ1+α i+ γt+u1¿¿ ¿

For those who are treated, we can write the first differences as:


(3) ∆ y1¿= y1¿− y0 i,t−1=μ1−μ0+γ+u1¿−u0i ,t−1¿¿¿

and for the untreated, the first differences are:

(4) y0¿= y0¿− y0i,t −1=γ+u0¿−u0i,t−1¿¿¿

As a result, looking at first differences allows us to purge the time-invariant selection on

unobservables, which means unconfoundedness holds. We are assuming that the time trend does

not depend on treatment status and that time-varying error terms in the potential outcomes are

independent of assignment – these are strong assumptions. The key is the parallel trends

assumption – whether the treatment and control groups were really following the same path prior

to treatment. We cannot test this formally given no pre-baseline data; however, identifying the

unbalanced variables and including them in the DiD estimator is the strongest approach available

for meeting the parallel trends requirement.

The final consideration for DiD estimation is in inference; namely, estimating correctly

the standard errors. It is hard to assume that the errors meet Gauss-Markov assumptions;

individual outcomes are likely to be correlated by area and shocks may be correlated over time.

The standard solution to this problem which was employed in the study was clustering of errors.

This is done by province, which is the most likely source of common trends as both training-

specific shocks and governance shocks were likely to occur at the provincial level. The

regressions were additionally estimated using robust errors; however, the analysis which follows

uses the clustered error estimates. Formal regression tables can be found in project files upon

need.


3. Results by Topic

a) Personal Development

i) Self-efficacy and self-confidence

The WEwork training and mentorship programme aimed to improve self-efficacy among

participants. Each round of the survey asked 10 questions relating to confidence and problem

solving, based on the Generalized Self-Efficacy Scale (GSE) methodology. The maximum score

on the scale is 40 and the minimum is 10; higher scores indicate higher confidence and self-

efficacy. At baseline, the WEwork women had an average score of 33 on the GSE, considerably

higher than the mean score of 29 from various international surveys. This implies firstly that the

sample of participants may be exceptional in comparison to the population, and secondly, that the

ability of the programme to improve scores is somewhat limited.

An analysis of the baseline characteristics of the women who remained in the programme

and those who dropped out indicates that the former have significantly higher GSE scores. The

participants’ mean score was 33.4 whereas the score of individuals who dropped out was 31.2; a

t-test shows the means are significantly different at the 1% confidence level.

The results of the measures collected for self-efficacy do not indicate significant changes

overtime, which is in line with the high scores observed in baseline results. Figure 1 shows that

for 50% of women in the treatment group the scores did not change from baseline to endline. An

additional t-test on the GSE score reveals no significance difference in means between the base

and endline.9

9 t = 0.9641

Figure 1


Estimations of treatment effects identify a few significant results, though for the majority

of self-efficacy measures there is no effect of treatment. For q97: “It is easy for me to stick to

my aims and accomplish my goals” and q102: “When I am confronted with a problem, I can

usually find several solutions” treatment has a significant and positive effect. However, for these

two variables, the post-treatment results are significantly lower as indicated by the “post”

dummy; therefore, the treatment effect is best interpreted as dampening the effects of the fall, as

opposed to increasing the scores from baseline.

ii) Cognitive Performance

In addition to self-efficacy, the questionnaire included simple cognitive exercises to test

for improvements in this area. There is no significant evidence that following treatment,

participants in the programme perform better than the control group at the designated cognitive

tests. The number of participants who answered the “race” question correctly did not change

between the baseline and endline. Conversely, project participants did considerably better at

q95 q96 q97 q98 q99 q100 q101 q102 q103 q1040%

10%20%30%40%50%60%70%80%90%

100%

Differences in GSE Score Components

scores in base and endline same scored less in endline than baselinescored more in endline than baseline

Components of GSE

Prop

ortio

n of

Res

pond

ents


endline in both arrow exercises; the mean answer time fell by 2.6 seconds and 1.6 seconds on

average. However, the control group also performs significantly better, suggesting an element of

learning-by-doing from the questionnaire. Overall, our cognitive measures may have lacked the

sensitivity to detect changes in cognitive abilities. In addition, the intervention may not have

been sufficient to make a serious change to cognitive performance, and indeed the ability to

improve cognitive performance in adults is debated in behavioural literature.10

iii) Political Activity

T-tests for difference in means: * 10% significance, *** 1% significance

Another measure of personal development is whether participation in the programme has

made women more active politically, as a proxy for increased leadership and better

communication. The majority (approximately 60%) of women in the WEwork collective held a

public post at baseline and this ratio persisted to the endline, as shown by Figure 2. However,

despite the lack of change in the sample overall, a comparison between the treatment and control

10 See for example, Pfeiffer, F. & Reuss, K. (2008). Age-dependent skill formation and returns to education. Labour Economics, 15(4)

Occupie

s Pub

lic Pos

t

Nomina

ted fo

r Pub

lic Pos

t ***

Govern

ment A

ssista

nce *

**

Inform

ation

Cam

paign

Contac

t Rep

resen

tative

***

Notify

of Prob

lems *

Commun

ity W

orks *

**0

0.2

0.4

0.6

0.8

Change in Public Participation

base end

Prop

ortio

n of

Res

pond

ents

Figure 2


groups reveals a positive effect of the treatment. The effect is significant only at the 10% level;

however, it does indicate the treatment may have contributed to more women obtaining public

posts, with untreated individuals dropping out.

Conversely, there was a significant increase11 in the number of participants nominated for

public positions from base to end; the proportion increased from 5% to 20% as shown in Figure

2. However, a comparison between the treatment and control groups reveals no effect of

treatment; instead, the rise in nominations is mopped up by the dummy for endline. An increase

in public nominations may well be more related to the elections which took place in Cambodia

around the time of endline questionnaires – these elections could have acted as a common shock.

However, the elections took place in June, and public nominations increased for participants in

both May and June interviews. Therefore, it is reasonable to propose the elections do not explain

the increase in public post nominations.

All the remaining measures of political activity (apart from participation in information

campaigns) similarly show no conclusive effect of treatment, but do show significant changes

overtime. Only 14% of participants had taken action to obtain assistance from government

programmes in the endline, compared to 50% at baseline. Similarly, the proportion participating

in public projects dropped from 85% to 70%. Conversely, the proportion contacting a local

representative increased from 48% to 80%.12 Together these statistics indicate a change in the

way participants engage with authorities and the community, gaining public positions and

contact with local representatives. Although these changes cannot be accredited to the

programme beyond doubt, the shifts are sufficiently large to indicate the WEwork programme

was very likely to contribute to the changes.

11 t-value: -5.7312 t-values: 9.64, 3.83, -7.97, respectively


iv) Recording Household Expenses

Finally, the WEwork training encouraged greater control over one’s life; therefore, it is

important to analyse whether the treatment affected household expense record keeping. A simple

t-test shows a substantial increase in the proportion of households which do track their expenses,

from 5.7% to 47.2%.13 The DiD results suggest, however, that this effect cannot be attributed to

treatment as the rise is captured by the “post” dummy and the treatment status, with no treatment

effect identified. An additional panel logistic regression confirms that keeping records of

expenses does increase over time and is greater among participants who work in agriculture and

are financially active. Conversely, keeping records is less likely among older participants. These

results imply strongly that an improvement in record keeping occurred over time; however, this

cannot be attributed statistically to the treatment. Given, however, that this was a focus point of

the training programme, it would be reasonable to assume that the change is related to WEwork.

b) Economic Activity

i) Type of Employment

13 t-value: -10.78

Figure 3


Base Mid End0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Change in Composition of Work

All threeWage and BusinessAgriculture and BusinessAgriculture and WageOnly BusinessOnly Wage WorkOnly Agriculture

Figure 4


The training provided by the WEwork programme aimed to improve the work and

business related skills of the participants; therefore, outcomes related to economic activity levels

are very important for measuring the effectiveness of the project. Firstly, it is important to

investigate whether there has been a change in the type of economic activities the respondents

engage in. Figures 3 and 5 depict the outcomes of two measures used to proxy the type of work

respondents do: the former illustrates the percentage of respondents who work in each job type

and the latter the average number of hours respondent spent working in the job type in the week

prior to interview. Figure 4 on the other hand illustrates the proportion of participants who work

in each of the combinations of work types. Figure 3 depicts a significant fall from baseline to

endline in the percentage of respondents engaging in each type of work and the decline in mean

is confirmed by t-tests for wage and agricultural work.14 Nevertheless, the results indicate a fall

in the level of activity diversification, suggesting women have been able to increasingly

14 t-values: 2.02 and 4.59, respectively

Wage Work

Agriculture

Business

Housework

0 5 10 15 20 25

Work Type, in last 7 days

Baseline Endline

Hours Spent Last Week, by Work Type

Figure 5


specialise in their work. This finding is confirmed by results presented in Figure 4 where by the

endline there is a rise in the proportion of women doing just one type of work and a decline in

women working in all work types.

Results illustrated in Figure 5 confirm the findings from Figure 3, as respondents are

shown to be spending more time in both agricultural and wage work activities. While the average

number of hours spent in agriculture increased by a substantial 25%, the time spent in wage work

almost doubled. T-tests confirm for both agriculture and wage work that the change in means was

significant,15 whilst no change in average time spent on non-agricultural business activities could

be detected. In addition, the mean number of hours spent on housework also falls significantly,16

indicating that respondents may have been able to spend more time in economic activities than

performing housework.

Testing these results using DiD methodology proposes a new pattern for some of the

work activities. The treatment status is highly significant for wage work in the last 12 months,

which suggests that rather than the change being from baseline to endline, there is a difference

between the control and treatment groups. The regressions do not detect a change for the

remaining activities over the 12 month period. Conversely, testing the differences over the last 7

days shows a significant change from the baseline to endline, confirming the previous

interpretation. This change is not differentiated by group and therefore the respondents of both

the treatment and control group spend more time in wage work on average. Overall, for both the

treated and untreated individuals there were interesting changes in activity levels from the

baseline to endline with less activity diversification and significantly more time devoted to wage

15 t-values: -1.86 and -6.13 16 t-value: 2.17


work. In addition, the treatment group were significantly more likely to engage in wage work in

both rounds of the survey.

Finally, there is a significant effect of treatment identified by the dummy indicating

whether the respondent is working in a position of local authority. The treated individuals in the

endline were 14.4% more likely to have such a role, at the 5% level of significance. This result

speaks directly to the findings in section a.iii), offering additional evidence that treatment

improves the political activity of respondents.

ii) Income and Employment

One

of the

key

aims of WEwork is to provide its participants with the skills necessary to improve their incomes

and raise employment levels. Figure 6 presents the change over time in the number hours

respondents worked for a wage (combined into 8-hour full days for clarity) and the full income

gained from wage employment (scaled down by a factor of 10). Overall, average incomes

increased substantially, as shown by Figure 6: mean income rises from $837.60 to $1173.40. T-

Baseline Endline50

70

90

110

130

150

170

Full Days Worked

Full Income ($10)

Average Income per 5 days

Income from Employment

days

/$

Figure 6


tests reject the null of no change in mean at the 1% level of significance, indicating that we can

be very confident of the change.17 This is a substantial result, although DiD analysis does not

identify this as a treatment effect; instead, the dummy “post” is significant at the 5% level. Given

the substantial increase, it is possible that those who earn the least at baseline drop out of wage

work, introducing a downward bias to average income. To test this possibility, the sample was

limited to only the individuals who engaged in wage work in both survey rounds. The t-test again

indicates a significant increase in mean income, though mean at base is higher at $927. 18

Additional regression analysis on the panel using fixed effects reveals that income depended

strongly on the survey round, increasing by $139 over time at 1% level of significance. In

addition, each increase in the highest level of completed schooling increased income by $5 and

having a job in WASH increased income by a substantial $270, both significant at 10%.

Similarly, mean full days worked rose from 85.5 to 152.8, and the “post” dummy is

significant at the 1% level. Although this change cannot be attributed to the treatment

statistically, it is sizeable and suggests at least some positive contribution from joining the

WEwork network. However, because the increase in number of days worked is proportionately

larger than the increase in income, as illustrated by Figure 6, on the average the daily wage

decreases. This is not necessarily a problematic finding; instead, it indicates greater economic

activity, despite lower marginal reward. On average the participants do 153 full days of work,

which is less than half the year and indicates further capacity for expansion of employment. In

addition, the training aimed to increase the skillset of the participants, making them more

productive; a fall in average daily wage indicates this attempt was unsuccessful. However, the

fact that participants are under-employed and the decreasing marginal returns to work could well

17 t-value: -3.5218 t-value: - 2.28


be indicative of rural job market realities, especially as women’s economic activities remain

highly diversified. The question of whether training can succeed at raising employment levels

and wages despite of local conditions, or whether results will be determined by local capacity,

would be an interesting one to explore in the future.

iii) Current Business

Figure 7

Figure 8


Aside from economic activity as wage work, the training programme included multiple

sessions focusing on business-specific skills; therefore, it is important to measure what happens

to the number of businesses and how they are run from baseline to endline. In addition,

researchers around the world are investigating issues related to rural businesses in developing

countries, such as: how important are small medium enterprises (SMEs)? What are their main

constraints? What is the impact of women entrepreneurs on SME development? Can SMEs be

sources of innovation?19

The results of WEwork can speak directly to some of these questions. First, the number

of respondents who owned or part-owned a business fell from 92 at baseline (76 in the treatment

group and 16 in the control group) to 65 at endline (55 and 9 in the treatment and control groups

respectively). Of the 65 endline businesses, 7 were new (or not reported at baseline), 57 were

sustained from baseline to endline (49 in treatment and 8 in control group) and 39 were dropped

(33 in treatment and 6 in control group). There is no significant difference between the treatment

and control groups which may suggest that factors other than the programme influenced whether

the business carried on or shut down between the base and the endline.

Figure 7 provides a summary of some of the differences in characteristics between

sustained and dropped businesses at baseline. Some of the differences seem very significant.

Approximately twice as many sustained businesses as dropped businesses hired employees, held

inventories, introduced new products or services and had buildings, land or a vehicle in the 12

months preceding the survey. These indicators suggest that on average, the businesses which

were not sustained were less productive – they did not generate employment, expand their

19 Literature review on the topic: Rama, M. & Li, Y. (2013). Firm Dynamics, Productivity Growth and Job Creation in Developing Countries The Role of Micro- and Small Enterprises. Background Paper for The World Development Report, The World Bank


activity range or have as much physical capital. Therefore, the closing of such businesses may

have been an improvement on overall efficiency. The types of businesses the women owned or

co-owned are shown in Figure 8, for those types specified by 2 or more respondents. The chart

indicates that grocery selling businesses were most common in the baseline, followed by food

selling, tailoring and construction material. Grocery selling businesses were likewise the most

common in the endline survey; however, they were equal to businesses selling cement products –

including toilet and water jar suppliers – which increased from 6 to 15 such businesses.

Conversely, the number of food selling businesses declined substantially between the baseline

and the endline.

Given this change in the number of businesses it is important to investigate what

activities the women whose businesses shut down did instead. The percentage reporting work in

agriculture and wage work falls, in line with results presented in section 3b.i). On the other hand,

the variables “hours spent in wage work in last 7 days”, “number of 8 hour days worked in wage

employment in last 12 months” and “annual wage income” all rise significantly,20 indicating

those who no longer owned businesses shifted to more wage work. Together with the indicators

that the businesses which shut down were likely to have been less productive, these results

suggest that it was more profitable for some respondents to move to more wage work compared

to running a business.

Second, it is important to test whether treatment had an effect on the characteristics of the

businesses owned by women which were sustained. This limits the sample to 57 businesses, 49

in the treatment and 8 in the control groups. In terms of employment generation, there was no

significant change in whether households hired employees, but the findings from t-tests show a

decline in the number of full-time male workers hired from 1.6 to 0.47 and a rise in part-time

20 t-values: -2.25, -3.21 and -1.98, respectively


male workers hired 0 to 0.57 on average.21 No significant change in female employment was

detected and on average it was low compared to male – under 1 in 5 businesses hired full or part-

time female employees in either time period. Regression analysis indicates the fall in male full-

time employment was specific to the endline survey and did not depend on treatment status, and

detects no significant treatment impact on the remaining measures of employee hiring.

In terms of business assets, t-tests indicate a substantial change in number of businesses

owning buildings and land, from 33% to 74%, and the value of these from $8000 to $19000, on

average.22 However, no change in ownership of vehicles or physical capital could be detected by

the t-tests. The decomposition into treatment effects reveals some additional information. There

was a significant positive change in land and building ownership between baseline and endline;

however, the treated group saw a smaller rise in the endline than the control group. Similarly,

there is indication of an increase in vehicle ownership from baseline to endline, irrespective of

the treatment status. Within the treatment group at both periods, the tools, equipment and

machinery were also significantly more valuable. Overall, the results together imply a positive

increase in the proportion of businesses owning land and buildings; however, this cannot be

credited to treatment conclusively.

Finally, the t-tests indicate that the proportion of businesses with an inventory stock fell

from 81% to 60%, the number of businesses keeping accounts rose from 7% to 18%, but

knowledge of profits deteriorated.23 Regressions suggest that the difference in inventories is

significant between the treatment and control groups, but not over time. Conversely, the rise in

bookkeeping was significant over time, but not between groups. However, the regressions

identify no effect on knowledge of profit attributable to treatment or time period. Therefore, the

21 t-values: 1.80 and -3.1022 t-values: -4.26 and -1.9723 t-values: 2.28, -1.62 and 2.65, respectively


evidence remains inconclusive on whether these characteristics were influenced by the treatment,

and in which direction. There is also no evidence in t-tests or regressions of changes in

introducing new services, or the location of the business’s activities. To summarise, some of the

characteristics of the sustained, woman-owned businesses – namely, male employment, assets,

and keeping accounts – do seem to change over time. However, there is an issue of identification

of causality of this change, which may be related to the small sizes of treatment and control

groups. Further research could clarify some of the patterns observed.

iv) Future Business

There has been a significant decline in the proportion of respondents who plan to open a

business from baseline to endline, as evidenced by both the t-test (decline from 73% to 37%)24

and the DiD regression. The difference in means is significant at 1% and the “post” dummy is

negative and significant at the 5% level. In addition, the respondents who do plan to open a

business were on average further behind in their planning stage at endline. The reasons for this

shift, in addition to the decline in the number of businesses, could be investigated by further

research – is self-employment not attractive to the participants, and if so, why?

v) Business Decision Making

24 t-value: 7.55

Involved a Little Involved a LotNot involved Involved a Little Involved a Lot

Figure 9: Decision to Hire an Employee

EndlineBaseline


Finally, it is important to test whether treatment has had any effects on the participants’

ability to contribute to the decisions made about household businesses, regardless of whether the

woman considers herself an owner. The survey asked about decisions made regarding (1) hiring

employees, (2) expanding the business geographically, (3) introducing new services, (4)

investing in capital, (5) investing in inventory and (6) when the business operates. There were

between 17 and 58 such decisions made at baseline and 17 and 32 at endline, which is

considerably fewer. The respondents’ ability to affect the decisions made are measured using

overall t-tests on means and DiD estimation on samples where the decision was made by the

household in both baseline and endline surveys.

Firstly, there is evidence to suggests that participants became on average more involved

in the decision making in all the categories apart from (3). T-tests on differences in means reject

the null at the 1% level of significance for questions (1), (4) and (5).25 The remaining tests reject

the null at the 10% level, which means considerably less confidence in rejection of the null;

however, it is nonetheless a suggestion of greater influence over the future of the business.26 The

involvement in decision making is categorised as 0 – not at all involved, 1 – involved a little and

2 – involved a lot. The means at endline tend to be around 1.8; therefore, on average, respondents

in the endline were more likely to be involved in the decisions a lot.

To test whether this shift was related to participation in the programme, DiD analysis was

conducted. This limits the samples substantially – only one household made decision (3) at both

endline and baseline, for example. Of the remaining decisions made, no effects of treatment can

be identified for (1), (2) or (4); however, decisions 5 and 6 both identify significant effects. For

(5), the ability to influence decisions falls in the “post” period overall by 0.3; however, the

25 t-values: -3.28, -1.88 and -2.85, respectively26 T-values: -1.44, -1.49


coefficient on the interaction term for “post” and “treatment” is significant and positive at 0.56,

thus balancing out the fall. For decision (6), there is no treatment effect, but the coefficients on

“treatment” and “post” separately are significant, indicating that the treatment group already had

greater influence over decisions made and all respondents were able to influence decision (6)

more by the endline.

These regression results do not fully confirm the observations from t-tests, which is likely

to be caused by sample considerations – the sample was limited firstly by household having to

have a business and secondly by the decisions made about the household business. However, of

the sample that remains, there are some identifiable shifts in the ability of WEwork women to

contribute to household business decision making. Figure 9 represents this visually: by the

endline, no respondent is not involved in the decision. While in the baseline, majority of women

were involved only a little, by the endline the majority are involved in the decision a lot.

Although it is difficult to identify this as a treatment effect and there are considerable sample

restrictions, the change is considerable and indicates improvements in the participants’ position

within the household. This analysis will contribute to section 5, which will look at intra-

household decision making more broadly.


c) Financial Activity

One of the aims of the WEwork programme was to enable women to become more

financially active, by either opening bank accounts, joining savings groups or making loan

applications. At baseline, 95 participants had a bank account, 79 were members of a savings

group and 97 had applied for a loan. These figures are represented as a proportion of the total in

Figure 10. This simple comparison suggests that there had not been a major change in financial

market participation among the respondents. At both baseline and endline, around 35% of

participants have a bank account and the proportion joining savings groups remains at around

30%; conversely, the proportion applying for loans appears to fall from 36% to 32%.

Baseline Endline Baseline Endline Baseline Endline0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%Financial Activity

No Yes

Prop

ortio

n of

Res

pond

ents

Bank Account Savings Group Loan Application

Figure 10


However, regression analysis does not confirm this pattern. There is no detectable effect

of treatment on number of bank accounts or loan applications; however, there is an effect of

magnitude 0.106 of treatment on participants joining savings groups, significant at the 5% level.

In addition, being a member of the treatment group was a significant factor in loans applications,

which suggests that WEwork members were on average more financially active than those

respondents who dropped out. However, the magnitudes of the treatment effects remain low and

Figure 10 demonstrates that no substantial improvements in financial activity were achieved. T-

tests of these patterns within the treatment group alone indicate that there is no significant

difference between the baseline and endline in any of the financial indicators. Therefore, there is

no clear pattern of higher activity after treatment; though the fact that savings group participation

is higher compared to counterfactual suggests a possible direction for future studies.

However, it is possible to model the factors which determine whether the respondent

consumes financial products using panel logistic regressions. Respondents are significantly more

likely to have a bank account, join a savings group and make a loan application if their husbands

do so, and the older they get. Having a bank account and joining a savings group additionally

increases with the number of contacts the respondent has. Education also contributes positively

to joining savings groups, whilst members of savings groups are more likely to apply for loans.

These results provide some interesting insights as to why some women are financially included

while others are not, inviting further research into the supply and demand forces operating in the

rural financial markets.


d) Social Networks

Figure 12

0.0

5.1

.15

.2.2

5P

ropo

rtion

of R

espo

nden

ts

0 5 10 15 20Number of Contacts In

baseline endline

Distribution of Contacts In0

.05

.1.1

5.2

.25

Pro

porti

on o

f Res

pond

ents

0 5 10 15Number of Contacts Out

baseline endline

Distribution of Contacts Out

Figure 11


0.1

.2.3

0.1

.2.3

0 5 10 15 0 5 10 15 0 5 10 15

Kampong Cham Kampong Speu Takeo

Kampong Chhnang Pursat Battambang

baseline endline

Pro

porti

on o

f Res

pond

ents

Number of Contacts Out

Graphs by province id

Number of Contacts Out (by Province)

Figure 14

0.2

.40

.2.4

0 5 10 15 20 0 5 10 15 20 0 5 10 15 20

Kampong Cham Kampong Speu Takeo

Kampong Chhnang Pursat Battambang

baseline endline

Pro

porti

on o

f Res

pond

ents

Number of Contacts In

Graphs by province id

Number of Contacts In (by Province)

Figure 13


Figure 16: Kampong Chhnang, endline

Key: Dark Blue: WEwork participants Light Blue: contacts Edge width: frequency of contact

Figure 15: Kampong Chhnang, baseline


i) Network Features: Size

The number of people the respondents report contacting or being contacted by at the

endline is significantly higher than at the baseline. This is illustrated in Figures 11-14 and in t-

tests carried out on the differences in the degree in and degree out between baseline and endline.

T-tests for degree in and out both reject the hypothesis of no difference between base and end at

the 1% significance level.27 Figures 11 and 12 plot the densities of degree in and out respectively

with the blue line corresponding to the baseline and the red line to the endline. The densities

were fitted using Gaussian kernels. Both Figures show a similar shift to the right of the density

curves, illustrating the rise in the mean number of contacts. The modal degree in at base was

around 1, with the majority of data clustered in the 0-2.5 range; conversely, the modal point in

the endline is around 4. The endline graph is also flatter and denser in the right tail, indicating

less clustering of data around the centre. Figure 12 demonstrates an almost identical pattern for

degree out, though the baseline graph is even denser around the mode. Finally, Figures 13 and 14

show the same pattern of rightward shift and flattening of the endline curves holds at the

provincial level also; the growth of number of contacts was widespread.

DiD estimations also indicate, at the 1% level of confidence, that the sizes of networks

observed in the endline were significantly greater than the sizes observed in the baseline. In

addition, the treatment status was significant at the 5% level for degree in, suggesting that the

treated individuals were better connected prior to treatment. However, the regressions were not

able to identify a treatment effect; instead, the networks increase for both, the participants and

the control group. This result could be due to the respondents’ increased ability to name contacts

when questioned repeatedly, though the scale of the increase suggests this is unlikely to be the

result of measurement alone. It is more likely that effects from treatment spread to the control

27 T-values: -9.51 and -11.85


group – respondents are likely to communicate with one another on various issues, and the

control group had been introduced to the experiment before dropping out.

Finally, the increase in size of networks can be illustrated with network graphs. Figures

15 and 16 show the stark difference in the size of networks recorded for Kampong Chhnang. The

dark blue nodes indicate WEwork members, whereas the light blue nodes are their contacts. The

increase in the number of nodes from baseline to endline and the number of edges is clear.

Remaining provinces exhibit the same pattern and graphs are presented in the Appendix. Overall,

the increase in the number of contacts is beyond dispute, and the treatment was very likely to be

a contributing factor.

ii) Network Features: Frequency

Given the increase in network size, it is unsurprising that the number of times the

respondents contact and are contacted by each person also increases on average. This is indicated

by both, t-tests and regression results. The mean increase in the frequency of contacts in is 1.4,

whereas for contacts out it is 1 – that is, on average, each respondent contacts the member of

their network an extra 1.4 times and is contacted by them 1 more time.28 Similarly to network

size, there is no significant distinction in this effect between the treatment and control group. The

width of edges in Figures 15 and 16 represents the frequency of contact between nodes, and the

graphs illustrate that in addition to an increase in the number of nodes there is an increase in

contact frequency also.

28 t-values: -9.28 and -6.62


iii) Network Features: Trust

The respondents were asked how much, on a scale of 0 – 100%, they trust the contacts they

listed, which allows us to proxy for the quality of the network in addition to its size. The results

of t-tests and regressions indicate that average trust in listed contacts did not change

significantly, with mean trust at 77.8 in the baseline survey and 76.6 in the endline survey.

Therefore, instead of the growth of the network leading to the thinning out of contact quality, we

observe that participants were able to increase their number of contacts whilst retaining their

trust in the individuals contacted. As before, the effect varies by time, and no treatment effects

can be identified.

Figures 17 and 18, and 19 and 20 illustrate mean trust in Battambang and Kampong

Cham provinces respectively using different measures. In the former, the edges increase in

thickness and opacity as percentage trust increases. We can see that the number of nodes and

edges increases from baseline to endline; however, the edges do not become thinner and less

opaque. The pattern is repeated in other provinces, and the graphs are provided in the Appendix.

Figures 19 and 20 highlight in red the edges for contacts trusted 100%, which seem to actually

increase in Kampong Cham province. The graphs together show that increasing network size did

not force participants to compromise on trust levels.


Figure 18: Battambang, endline

Figure 17: Battambang, baselineKey:

Node size: larger = WEwork participantEdge opacity: trust( %)Edge width: trust (%)

Figure 19: Kampong Cham, baseline

Figure 20: Kampong Cham, endline

Key:

Edge colour: red if trust = 100%


Figure 21: Kampong Speu, baseline

Figure 22: Kampong Speu, endline

Key:

Edge colour: red = nonrelative blue = relativeEdge width: frequency


iv) Network Features: Composition by Relative

Finally, it is important to test whether along with an increase in the size of the network

there was also a change in the composition of networks. One of the contact details collected in

both rounds of the survey was the relationship to the respondent. The proportion of relatives in

the participants’ networks is an important indicator of whether the project succeeded at

developing the

participants’ confidence in communication. The data indicate a significant fall in the proportion

of relatives in the networks, for both treatment and control groups. T-test rejects the null of no

difference between the baseline and midline mean proportion of relatives at the 1% level of

significance.29 At baseline, the mean proportion of family members in the contact list was a high

72%, whereas by the endline this had fallen to 48%. Figures 21 and 22 illustrate this pattern for

Kampong Speu (with remaining provinces in the Appendix). The red edges show contact

between non-relatives. There is a substantial, visible increase in the number of red edges from

Figure 21 to 22, indicating that the respondents’ networks increased in size and in their outreach

beyond the family.

29 t-value: 7.66


e) Decision Making

Finally, the surveys asked respondents about the decisions their households made, who

made these decisions and how much involvement the respondents were able to have. These

measures proxy the position of the women within the household and their skills in

communication. Given that the programme worked to build women’s confidence and skills in

communication, it is important to analyse whether any changes attributable to the training can be

detected with respect to the women’s authority within their households. Three measures will be

analysed: whether the respondent was involved a lot or a little, whether the respondent was

involved in the final decision making and whether the respondent was a sole decision maker.

These measures were calculated over 15 decisions, which will be categorised and analysed in

three sections.

Figure 23


i) Decision Subject: Children and Education

The respondents were asked about their involvement in decision making regarding (1)

which grade of schooling the children should complete, (2) which school they should attend, and

(5) how many children to have. These are numbered correspondingly in Figure 23. These

decisions were made by a large proportion of participants at both endline and midline – from

25% to 50%. The results of t-tests for change in mean indicate that fewer women were the sole

decision makers in the endline for both (1) and (5), with the mean percentage of sole decision

makers at 32% at baseline for both decisions falling to 22% and 18% respectively. Conversely,

for (5) the percentage of respondents who were involved in the decision rises from 90 to 98%,

meaning almost every woman contributed to deciding the number of children she should have.30

This is an important measure of empowerment and therefore a positive result. The percentage of

sole decision makers proxies for the respect women have in their families; however, for decisions

about children, which impact both partners, the fact that fewer women make the decisions alone

could indicate a better communication process between spouses.

The results of regression analysis indicate that irrespective of treatment status, the

percentage of women involved in decision (1) rose by 20% and for (2) by 17%. However, for (2)

there were significantly fewer sole decision makers at endline, with the coefficient on post

indicating a 32.5% decline. However, this does vary by treatment status, as treated individuals at

endline were more likely to make the decision alone and for the treated, the number of sole

decision makers rose by 4%. The results for involvement in the decisions are shown in Figure 23.

The radius expands in all decisions from baseline to endline; however, the expansion is small for

(1) and (2). Overall, the results for questions relating to children and their education are mixed.

Respondents were less likely to be sole decision makers at endline (apart from treated individuals

30 t-values: 1.97, 1.95 and -2.31, respectively


for (2)), indicating that more negotiation took place. By contrast, women were more likely to be

involved in decisions (1) and (5), regardless of treatment, which could indicate an improvement

in their position within the household.

ii) Decision Subject: Health

The responses for decisions made on (3) obtaining healthcare for the respondent, (4)

obtaining healthcare for children, and (6) which contraceptive method to use. Over half of the

respondents made decisions (3) and (4) at baseline and endline, whereas around a quarter made

decision (6). The results of t-tests indicate more women were involved in the making of all three

decisions. The percentage of women involved in the decision about obtaining healthcare for

themselves is high, rising from 91% to 96%. Similarly for healthcare for children, the percentage

of women involved in the decision rises from 91% to 97%, and for contraceptive method the

change is from 90% to 99%.31 This increase is demonstrated by the outward shift of the radius in

Figure 23. These results indicate both, a positive trend over time, and a high starting point at

baseline, which is a positive finding given the importance of women having a voice in healthcare

decisions.

Regression analysis identifies no significant differences between the treatment and

control groups or the baseline and the endline for decision (6). Conversely, for (3) and (4) there

is a significant difference between the treatment and control groups. For (3), the treatment group

were 9% less likely to be involved a little in the decisions, but correspondingly 9% more likely to

be involved a lot. For (4), this is even higher, with 20% more respondents from the treated group

involved a lot and 21% fewer involved a little. This result helps us understand the characteristics

of respondents who remained in the treated group, versus those who did not participate in the

31 t-values: -2.19, -2.23 and -2.31, respectively


training. Finally, regression confirms that irrespective of treatment status there was a significant

20% increase in number of respondents involved in decision (4); however, significantly fewer

women in the treatment group and in the endline period made decision (3) alone. Overall, the

results do not indicate a significant change between the treatment and control group overtime;

however, they do indicate that respondents were significantly more involved in decision making

at endline which is a positive finding giving the importance of control over healthcare.

iii) Decision Subject: Spending, Business and Finance

Finally, the respondents were asked about a set of questions relating to decisions relating

to financial matters. Firstly, for decisions (7) spending on food and (8) spending on major

household assets, significantly more respondents were involved in the decisions made, and their

involvement was greater. This is illustrated in Figure 23. Decisions on these topics were made in

50% to 75% of the respondents’ households. The t-tests show a significant increase in the

percentage of respondents involved in both decisions, rising from 90% to 97% for (7) and 85%

to 96% for (8). Significantly fewer decisions were made solely by the respondent for (7),

according to both t-tests and regression analysis.32 Conversely, there is a negative coefficient on

“post” dummy for sole decision making for (8), but a positive effect of treatment helps to balance

this effect and there is no significant change in percentage of respondents taking decision (8)

alone. Finally, for (8), there is a significant 7% fall in number of respondents involved in

decision a little, and a corresponding 6% increase in percentage involved in the decision a lot.33

This is a positive change, despite the fact that it cannot be attributed to treatment formally.

32 t-values: -2.77, -4.23 and 2.91, respectively33 t-values: 2.25 and -1.84


Secondly, the respondents were asked about decisions regarding (12) taking out loans and

(13) how to use loans. Around 50% of respondents had these decisions made in their households.

There were no significant changes identified by either t-tests or regression for (13), and the mean

percentage of respondents involved in the decision making remained high at around 90%. The

lack of significant changes is evident in Figure 23 as the radius does not expand significantly for

these questions. For (12), there was a significant increase form 90% to 95% in respondents

involved in the decision making;34 however this was not confirmed by regression analysis and no

treatment effects could be identified. Therefore, the respondents are relatively highly involved in

financial decision making, though treatment made limited difference to this involvement.

Finally, the respondents were asked about a range of household income and business

decisions: (9) selling household assets, (10) saving household income, (11) which crops to grow,

(14) to migrate for employment and (15) to open a new business. Decisions (9)-(11) were made

in over 50% of households, whereas decisions (14) and (15) in a small range of around 20% of

the sample. In all these decisions, significantly more respondents were involved in the decision

making at endline than at baseline. For decision (9), the change was from 87% to 97%; for

decision (10) there was a very large increase from 44% to 99%; for (11) the change was from

87% to 93%; for (14) there was a rise from 80% to 95%; finally for (15) the change was from

84% to 99% or respondents.35 These changes are illustrated in Figure 23.

In addition to higher involvement in the decision, there was a rise in respondents

involved a lot and a fall in respondents involved a little in decisions (9) and (11), which is

confirmed by both t-tests and regression analysis. Decision (10) saw a large rise of 45% in

respondents being involved in the decision making; however, there was also a large fall in the

34 t-value: -1.8935 t-values: -3.26, -15.15, -2.08, -1.89, -2.17 and -3.76, respectively


percentage of women taking the decision alone from 45% to 32% - though this pattern is not

identified by regression analysis.36 For the remaining decisions, the changes identified by t-tests

on average involvement were the only discernible effects, as none of the regressions identify any

treatment effects.

iv) Summary

Overall, the pattern which emerges indicates the respondents were significantly more

involved in household decision making, though this cannot be attributed to treatment alone as

both the treatment and control groups followed this trend. However, this result is very consistent

and does indicate that some aspect of the interview process and the training was likely to

influence the confidence of respondents, and in turn, their position in the household. Conversely,

for many of the decisions measured a smaller percentage of respondents than previously were

sole decision makers. One of the interpretations of this result could be that trust in the

respondents and their position in the household had not improved. Alternatively, a greater

number of decision makers could indicate that respondents have greater confidence in

communication and negotiation among family members, and are able to ask for assistance when

they need it. Further studies could investigate further the reasons behind these observed trends.

36 t-values: -2.31, 2.15, -1.86, 2.19, and 2.65


4. Results Evaluation

Finally, the participants were asked to assess their experience of change in the 10 areas

illustrated in Figure 24, from no change to a large change. This self-assessment provides a useful

comparison for the quantitative data presented in Section 3. Overall, only 2 areas saw fewer

participants reporting no change – how income is earnt from employment (no change for 60%)

and how the household business is ran (80% report no change). However, the data indicated that

some significant changes did take place in this area, with women spending more time in wage

Figure 24


employment and several businesses shutting down, with remaining ones seeing their

characteristics alter. Conversely, for the level of self-confidence, the vast majority (90%) report a

change, and for 82% of participants the change was medium-sized or large. Yet, the self-efficacy

measurements detect no change between baseline and endline. The disparities could be caused by

two reasons: the respondents were not sufficiently self-aware to report a change, or our data was

not meaningfully measuring these aspects of their lives. It would be difficult to argue that only

one of these reasons was true; however, it is important to consider how much we trust the data.

On the other hand, remaining indicators largely correspond to the findings of Section 3. The

women report being more busy, and we know they often spend more time in wage work. Around

40% also report changes in relationships with household members, which broadly corresponds to

changes in decision making and inclusion. A large proportion also report changing relationship

with village members which corresponds to the finding of more public position nominations and

larger networks. Therefore, the reliability of the data is very unlikely to be the only cause of the

inconsistencies. These findings allow us to draw lessons on both – the scale of self-awareness of

the participants, and the meaningfulness of some of the indicators.

Finally, there are some data considerations which need to be acknowledged in light of the

results. For a large number of the indicators, there is a change over time, although we rarely

detect a change attributable to treatment – this could be a consequence of the lack of power. In

addition, there may have been a degree of learning through the questionnaire (perhaps for

network data, or the cognitive exercises) and learning how to answer the questions thanks to

treatment (keeping better track of incomes and decisions made) – both could bias our results.

However, these considerations affect any survey data collection process. Overall, it is reasonable


to assume that some changes may be biased upwards or downwards due to measurement

difficulties; however, this bias is unlikely to invalidate a significant proportion of the results.

5. Conclusions and Recommendations for Future Research

The results presented in Section 3 are summarised in the Executive Summary; therefore,

the conclusion leaves space for final recommendations for future studies. As mentioned in

previous sections, the methodology of the study could be improved further in future attempts to

reproduce or scale up projects of this form. These improvements would include greater

randomisation during project design and recruitment of more representative samples of women,

thus establishing a stronger counterfactual for the experiment which would increase the power to

detect changes. In addition, conducting the survey was highly informative with respect to which

measures were meaningful and future researchers may choose to try different combinations of

indicators for self-efficacy and cognitive performance.

The results reveal several areas of interest for future research. Given many of the women

reported experiencing changes in self-confidence and control over their lives, there is cause for

further behavioural research – despite the lack of results in cognitive and non-cognitive skills

measured. With respect to economic activities, significant changes were reported in terms of

income, wage work and changes in women’s businesses and analysing further the rural job

market from the female perspective could shed light on rural jobs and businesses. The non-result

in the financial sector implies further research into financial product demand and supply could be

of interest, especially with the growing body of evidence which points to effectiveness of mobile


money and savings groups in women’s empowerment projects. The promising results from the

network analysis support the cause for further investigation, including collecting more

information about the people the women contact, including their occupation and position within

the village. Finally, though the women’s involvement in decision making improved substantially,

it is important to analyse whether this meaningfully impacted their empowerment within the

household.

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