May 23, 2012

Self-employment accounts for a large share of female employment in most developing countries

But:◦ Most female-owned firms are very small in scale, with low

earnings◦ In much of South Asia and the Middle East, the majority

of women are not even employed at all.

Key questions:1. Can business training (alone or with a grant) raise

the incomes of low-earning women;2. Can it allow women outside the labor force to

start new businesses.

A lot of emphasis has been on capital as the constraint to female microenterprise growth; hence the attention given to microfinance.

But:◦ In recent experiments in Sri Lanka and Ghana,

we’ve found physical capital alone has not been enough to raise incomes of subsistence-level female-owned businesses.

◦ Recent microfinance experiments also shown very modest results in this regard – although have had some success in getting new businesses started.

Conduct randomized experiments in Sri Lanka to test impact of business training on 2 different groups of women:◦ Self-employed with low levels of income◦ Out of the labor force but interested in entering.

Use the ILO’s SIYB training program, which is the most commonly used worldwide.

Look at impact of training alone, as well as training + grants.

Measure outcomes at 4 points in time post-training: increases power + look at trajectories.

Last couple of years have seen a number of randomized experiments on business training in developing countries

Most are with microfinance clients, focus on existing business owners, are often with customized training programs, and have only single snapshot follow-up.

Karlan and Valdivia – Peru – improvements in business practices, no sig. improvements in sales, profits or employment; maybe higher sales in bad months.

Drexler et al. – Dominican Republic – compare two programs. Find simpler “rules of thumb” program improved practices and sales in bad months, no sig. impact on average sales, and profits not looked at.

Berge et al – Tanzania – for females weak improvements in business practices, no impact on business outcomes (males they get improvements).

Bruhn and Zia – Bosnia – improvements in business practices, but no increases in business profits or survival rates.

Gine and Mansuri – Pakistan – women improve business knowledge, but show no improvements in outcomes

Urban labor force participation rate for 20-40 year old women only 38% (vs >90% for men)

28% of those in paid work are self-employed◦ Median profits only 5000 Rs (US$43)/month◦ Only 5% have any paid workers

• Identify two groups of women in districts in and around Colombo and Kandy. Listing in 142 GNs in 10 DS divisions. – Age 25-45 yrs– Current enterprises: > 20 hrs per week in self

employment, sector other than seasonal agriculture/fisheries, monthly profits =< 5000 Rs ($43).

– Potential enterprises: planned to enter self-employment in next year, able to identify the nature of the proposed business, unmarried/married with no kids/married with kids > 5 yrs of age/if < 5 yrs of age had someone to look after the kids.

• Selected sample of 628 current enterprises and 628 potential enterprises equally distributed across 10 DS divisions.

◦ Typical industries are tea shops, beauty shops, bag and mat manufacturing, tailoring, sewing, fruit & vegetable sales, making and selling lunch packets.

◦ 36 years old, married, with 10 yrs of education, running the business for 6.5 yrs.

◦ Mean monthly business income SLR 4000 (US$34).◦ This is about 1/4th of HH income◦ Low business practices score at baseline (mean is 4.6

out of 29). Only 17% keep written records, only 4% done any

advertising in last 6 months, only 9% have sales target fro next year, only 3% have budget of what costs for next year likely to be.

◦ Only 18% have done any business related training – and of this mainly technical training

◦ Only 18% have never worked before, but only 8% have previously been in SE

◦ 50% have taken some concrete steps towards opening a business in the past year.

◦ 2 yrs younger in age than current group, but otherwise similar in terms of education, digitspan recall, raven tests, attitudes towards risk, and number of children.

◦ Monthly HH income about Rs 1100 less than current.

◦ Less likely to own fridge or sewing machine (assets that have business potential)

Randomly selected 400/628 in each group to be offered business training◦ Half of these were also selected to receive a grant of

15,000 Rs (US$129) conditional on finishing the training.◦ At the time of being offered training, individuals were told

that half of those who completed the training would be randomly chosen to receive a grant of this size.

Randomization stratified on D.S., and other key variables.◦ Current enterprises: children to look after; baseline profits◦ Potential enterprises: taken steps to opening business;

whether had ever worked before As a result of randomization, treatment and control

groups balanced on baseline characteristics.

ILO Start and Improve Your Business (SIYB) program◦ Designed to meet needs of small-scale entrepreneurs in

developing countries◦ Started in Eastern Africa in 1977◦ Global outreach of 1.5 million trainees, implemented in

over 95 countries◦ Use three packages:

Generate Your Business (GYB) – 3 days on generating idea for business

Start Your Business (SYB) – 5 days on main aspects needed to start a business – what to sell, pricing, organizing staff, equipment and inputs, legal form, etc.

Improve Your Business (IYB) – 5 day course which helps existing business owners develop their business – modules on marketing, buying, costing, stock control, record-keeping, and financial planning.

• Potentials: 3 day GYB + 5 day SYB. • Currents: 1 day Refresher GYB + 5 day IYB• Both groups got 1 day technical training – exposure to,

and training in, some relatively high return sectors which are socially acceptable for women. 2-3 options available at each training location.

• Training provided by SLBDC, which has 8 years of experience delivering this content to local market & university-educated trainers.

• Cost to us of training was around $130 per individual trained.

• Course was offered to participants for free + attendance payment of 400 Rs per day to cover transport and opportunity cost of training.

Current: 279 (69.8%) of the 400 offered treatment attended training and 268 (67%) completed training.◦ Married, more educated women running young firms

more likely to attend.◦ Opportunity cost of time matters – less likely to

attend if more profitable, work more hours, have more wealth.

Potentials: 282 (70.5%) of the 400 offered treatment attended training and 261 (65.3%) completed.◦ More able, older women more likely to attend◦ Take-up lower in Colombo than elsewhere

Follow-up surveys are at 3-4 months; 7-8 months; 15-16 months; and 24-25 months after training.

Attrition rates low – getting 580-590 out of 624 in follow-up rounds (92-94%). Attrition unrelated to treatment status in current enterprises, slightly lower for trained in potential sample but results robust to this

Measure:◦ Business outcomes, including profits, sales, capital-

stock◦ Business practices

On current enterprises◦ On business practices◦ On business outcomes

On potential enterprise owners◦ On whether they start-up a business◦ On how well these businesses do

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0 20000 40000 60000 80000 100000Real Monthly Profits in Round 2

Control CashTraining only

4 months after training + grant

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0 20000 40000 60000 80000Real Monthly Profits in Round 5

Control CashTraining only

25 months after training and grant

Table 4: Impact on Firm Performance for Current EnterprisesRound 2 Round 3 Round 4 Round 5

Truncated Truncated Truncated Truncated TruncatedLevels Levels Logs Levels Levels Levels Levels

Panel A: Monthly ProfitsITT EffectsAssigned to Cash if finish Training 724.9 1,207** 0.168** 1,758* 1,910** 432.5 169.9

(839.9) (593.0) (0.0716) (932.6) (898.5) (1,123) (1,099)Assigned to Training only -695.7 -171.3 0.0240 11.75 -76.47 -460.3 -760.6

(920.7) (626.2) (0.0752) (889.5) (912.4) (1,148) (1,241)

All rounds pooled

Panel B: Monthly SalesITT EffectsAssigned to Cash if finish Training 5,171 4,436 0.143 6,818* 3,284 3,079 2,129

(4,686) (3,500) (0.0932) (4,020) (5,366) (6,534) (6,482)Assigned to Training only -2,941 -1,786 -0.0414 -1,718 -1,519 -3,884 -2,248

(4,422) (3,512) (0.0967) (3,845) (5,386) (5,993) (7,177)Panel C: Capital StockITT EffectsAssigned to Cash if finish Training 17,221** 10,379*** 0.155** 9,535* 7,270 12,195* 11,374**

(7,815) (3,583) (0.0691) (4,893) (4,932) (6,379) (5,760)Assigned to Training only -700.2 -490.7 -0.0671 -3,476 -278.1 -4,452 3,389

(5,616) (3,338) (0.0629) (4,192) (4,596) (5,921) (6,474)

TOT impacts:◦ Cash + Training: 29 p.p. increase at R2, 2 p.p. in R4

and R5◦ Training only: 12.2 p.p. increase at R2, -2 p.p. in R5.

Have sped up entry – so impact evaluations which looked only in the first year would think big impact on business start-up, but by 25 months no significant impact on levels of start-up.

What about who runs a business? ◦ Model predicts we should see selection on ability and

wealth

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Cash+Training Training onlyControl group

So even though levels of business ownership are the same, interventions have changed who owns a business – which makes evaluating the impact of the training and grants on the business less straightforward.

Two approaches:◦ Naïve experimental approach – estimate impacts

via OLS – since selection seems to be that interventions bring in poorer and less analytically able, this might be argued to be lower bound.

◦ Use generalized propensity score and run weighted regression to compare like with like.

Table 7: Impacts on Total Work Income and Business Outcomes for Potential Group

Business PracticesR2 and R3 R4 and R5 R2 and R3 R4 and R5 R4 and R5

Panel A: Experimental ITT EstimatesAssigned to Cash if finish Training 266.7 696.7 -161.0 804.7 0.999**

(556.5) (728.5) (741.7) (830.2) (0.489)Assigned to Training only 211.5 1,494* 484.9 2,244** 0.870

(545.4) (773.9) (785.3) (975.9) (0.559)

Observations 1,175 1,119 615 675 676Firms 601 585 359 393 394p-value for testing treatment equality 0.920 0.327 0.398 0.165 0.819Control group mean 3516 4940 5001 5209 8.33

Panel B: Generalized Propensity Score Reweighted Estimates to account for selection into who operates a businessAssigned to Cash if finish Training 59.12 767.2 1.173**

(692.6) (846.0) (0.502)Assigned to Training only 374.3 2,171** 0.971*

(772.0) (1,072) (0.567)Observations 590 651 652Firms 345 380 381p-value for testing treatment equality 0.6702 0.2127 0.7282

Total Work Income ProfitsBusiness outcomes

Training alone not enough to get subsistence businesses run by women to grow◦ Consistent with results from other business training

studies◦ Also consistent with work on capital grants◦ Adding capital gives temporary boost in profitability,

but appears to be relatively short-livedReally hard to get these subsistence-level firms to

grow Policy options:

More intensive one-on-one mentoring e.g. Valdivia – but expensive.

Address constraints to participation in wage work, with labor market failures potentially reason these women operating business in the first place.

Potential enterprises◦ Results more encouraging for ability of business

training to help women start businesses more quickly, and make these businesses more profitable This is a group existing business training studies

haven’t focused on Consistent with microfinance studies which have

found some impact on business start-up

=> Easier to get women to start-up subsistence businesses than it is to get these businesses to grow.

Results show the importance of tracing out the trajectory of impacts◦ Single follow-up survey would miss much of the story.

Importance of looking at impacts on different subgroups of interest◦ Potential vs Current firm samples◦ Arguably learn more about firm growth constraints by

taking a sample of general population than by taking microfinance clients.

Issue of content when comparing evaluations◦ “business training” varies a lot in curricula, cost,

number of hours, etc. across studies, making difficult to compare.