Why \”More Women in the Pipeline\” Keeps Failing as a Strategy

A strategy built on putting more women into the pipeline assumes the pipe is short of women. On that question AVODA’s own figures are gender-blind. In Rukungiri district, 320 women’s groups applied in one funding round to UWEP, the government’s interest-free fund for women’s enterprise groups, and 24 were supported. A 2022 review by Publish What You Fund and the Civil Society Budget Advocacy Group reported that the national fund reaches only 10 to 15% of the groups that apply. In Uganda the pipe is full at the intake end, and the women leave it further down, at stages few programs count.

Key Takeaways

The pipeline frame comes from American technology careers, where too few women entered technical tracks. In sub-Saharan Africa, the World Bank found women more likely than men to be entrepreneurs, the only region where that holds.
Uganda’s public fund for women turns away most applicants: 24 of 320 groups in one district. Adding applicants to a queue with fixed capacity lengthens the queue and leaves the number served where it was.
Women leave at later stages. Across African venture deals tracked by Africa: The Big Deal since 2019, female-led startups held 13% of deals and 5% of capital, and startups with a female CEO took 5% of Series B or C deals.
Programs count intake because intake arrives inside the grant period. Exits arrive after the report is filed, so they stay invisible.
The Finishing Count follows one cohort of women through five stages, records the reason each one left, and moves money to the stage with the largest exit.

Where the pipeline idea comes from

Women in the Technology and Innovation Ecosystem: From Movers to Makers in America (CRC Press, 2026), edited by Manleenjot Kaur, Melissa Hyunh and Tilottama Singh, uses the word in its familiar sense. Chapter 8, by Rashmi Singh, Anuja Shukla and Sushil Kumar, praises coding programs for girls for “creating a pipeline of future professionals.” Chapter 7 lists “mentorship pipelines” among the reforms it proposes and calls mentorship the infrastructure of “a sustainable and high-performing pipeline of women innovators.”

In its American setting the frame has a fair basis. Chapter 7 reports that women made up 8% of the US STEM workforce in 1970, and the entry problem there was real for decades. The chapter also reports a surge at the front of the pipe: 47% of new US entrepreneurs in 2022 were women, up from 29% before the pandemic, credited to the World Economic Forum. I traced that figure before using it. The survey behind it belongs to Gusto, a payroll company, and Bloomberg’s report of it gives a slightly different picture: almost half of 2021’s new entrepreneurs were women, up from 28% in 2019. The direction holds and the year is one off.

The more useful passage is the book’s own audit of American programs. Section 7.13 reviews federal and private initiatives for women innovators and finds that the National Science Foundation’s ADVANCE program had “more institutional success in bringing more women into the academy than helping women to bridge out” into commercial industry. It describes the private funds as “heavily weighted toward providing a portion of seed funding,” with little support for scaling. Section 7.14 adds that numerical representation is a first step and guarantees little on its own. So the book that recommends pipelines also records, in its own pages, the pattern this essay is about: programs got women in more successfully than they carried them through.

Uganda’s pipeline is already full

The pipeline frame transfers poorly to Uganda because the shortage it assumes sits elsewhere. The World Bank’s 2019 report Profiting from Parity found that almost 50% of women in Africa’s non-agricultural labor force are entrepreneurs, and that sub-Saharan Africa is the only region in which women are more likely than men to be entrepreneurs. Women here start businesses. They start them at a rate the American programs in the book were built to reach.

What happens after the start is the gap. The same report found women-owned firms in its 14-country sample earning 34% less in monthly profit than men’s, on average. The two Ugandan micro-enterprise samples in its figure showed gaps of 30% and 31%. A World Bank working paper on firms in and around Kampala, by Francisco Campos and colleagues, cites Uganda Bureau of Statistics data from 2006: 6% of women operated in male-dominated sectors, against 34% of men. The other 94% of women’s businesses sit in mixed or female-dominated trades, and in the paper’s own sample, women in female-dominated trades made less than half the crossovers’ monthly profit.

The public fund shows the same shape from the other side. UWEP lends interest-free revolving credit to women’s groups. The number of groups funded rose from 2,334 in 2016/17 to 4,041 in 2020/21, and 73% of the money due had been recovered, according to the 2022 review. Those are the measures the review reports: groups funded and shillings returned. Meanwhile the fund serves 10 to 15% of applicant groups. Any campaign that brings more women to the application stage adds to a queue that already holds, by my arithmetic from that funding rate, seven to ten applicant groups for every place. The number of groups served stays where the budget puts it.

Where women leave

If entry is the strong stage, the useful question is which stage is weak, and the venture data answers it most clearly because investors record every round. Africa: The Big Deal tracks African startup funding by founder gender. Its data, reported by Techloy in June 2025, shows female-led and co-founded startups accounting for 28% of pre-seed deals. By Series B or C, startups with a female co-founder made up 14% of deals, and those with a female CEO 5%, across 106 late-stage rounds. Since 2019, female-led startups have landed 13% of deals and 5% of the capital. In 2024 alone, Africa: The Big Deal counted $48 million for female CEOs, 2% of the year’s total.

The same thinning shows up in small firms. Women enter in large numbers, a smaller share reaches the profitable sectors, and a smaller share again reaches the loans and rounds that pay for growth. Each stage has its own exit and its own cause, and a single intake count blends them into one reassuring number.

AVODA’s own headline counts, 1,247+ applied and 600+ trained, are intake figures too, and our one published later-stage number, the drop-out split from 99 alumni in Who Actually Finishes, is self-reported and carries no gender breakdown.

Why intake keeps winning the results framework

Intake wins because of timing. A program funded for two years can report applications in month two, enrollments in month three and graduates in month nine. Survival at twelve months, a first hire, a second loan, a supplier contract with a supermarket: these arrive in month twenty or later, often after the final report is due. A program officer who wants a strong annual report counts what will exist by the reporting date.

Once a count becomes the headline, the program bends toward it. Recruitment gets budget because recruitment moves the number. Follow-up gets whatever is left. A results framework that rewards “women reached” will, over a few cycles, produce a program very good at reaching women, which is a real skill and a smaller one than the mission described.

The cost of this falls on the founder. A woman who joins a cohort, takes the training and then stalls at her first wholesale order appears in the report as a success. Her exit happened, and nobody wrote it down.

The Finishing Count

A program lead or funder who wants to see where women leave needs five numbers for one cohort and a column of reasons beside them. The Finishing Count puts those five numbers where the single intake count used to sit.

Applied. Every woman or group that submitted an application.
Admitted. Those who entered the program or received the loan.
Trading at twelve months. Still selling a year after admission, confirmed by a sale, a receipt book or a buyer.
First step past survival. A first paid employee, a registration, a formal buyer, or a move into a higher-margin product line. Each program picks one and keeps it.
Second capital. A repeat loan, a larger loan or outside investment, with the amount recorded.

For every woman who leaves between two stages, the count records one reason in her own words where possible: childcare, a failed order, a family claim on the cash, a lender’s collateral rule, a decision to stay small. The reason column matters as much as the counts, because a chosen exit and a forced one look identical in a headcount.

Three rules govern its use. Count every stage for the same cohort, so the numbers describe one group of women moving through time. Put the stage with the largest exit in the headline of next year’s framework. And move at least one budget line from intake to that stage before the framework goes to the funder.

Here is an invented cohort, drawn from no real program, to show the arithmetic: 400 applied, 60 admitted, 41 trading at twelve months, 9 past survival, 3 with second capital. The large losses sit at admission, which the budget sets, and between survival and growth. For this program, more recruitment money would buy a longer queue, and the next budget line belongs to the step after survival.

Crossing into metal, carpentry and foundry work

Women in and around Kampala who crossed into male-dominated sectors, such as metal fabrication, carpentry or foundry work, earned as much as men in the same sectors, Campos and his colleagues found in the paper cited above. Against women who stayed in catering, hairdressing or tailoring, their firms had about three times the revenue and more than double the profit. A significant majority of the women who stayed believed they earned the same as the crossovers or more. The authors identify information as the first hurdle and point to a Kenyan training trial in which women shown earnings data and a video of female car mechanics were nine percentage points more likely to prefer a male-dominated course and five points more likely to enroll in one. On this reading the pipeline matters a great deal: the question is which pipe a woman enters, and a cheap information campaign moves her into a better one.

I concede the point. Sector of entry is a real lever, and in some places the entry pipe really is narrow. The 2022 Uganda review reports female participation of about 25% in technical trades at vocational institutes. For metalwork, electrical installation or software, recruiting women at the front is sound work.

The same paper, read to the end, also supports the Finishing Count. Its authors write that information alone is unlikely to suffice. What distinguished the crossovers was exposure to the sector through family and friends, male role models, and active participation in a business before owning one. Their recommendations are mentors from a woman’s own network and apprenticeships in businesses run by people she trusts. Those supports carry a woman through entry and into operation. A strategy that counts only how many women chose the course would miss every one of them.

A tower left at its foundation

Who answers for a tower left standing at its foundation? Jesus put the builder’s question in Luke 14:28, asking who would build a tower without first sitting down to “count the cost, whether he has enough to complete it.” The parable concerns the cost of discipleship, and its logic reaches any work begun in public. Two premises from the evidence above meet it here. Intake gets counted because it arrives inside the funding cycle, and exits go uncounted because they arrive after it. A better indicator is the reasonable response, and the Finishing Count is one. Yet an indicator leaves the incentive where it was: whoever designs the next program is still rewarded for starting things. The parable moves the obligation from the report to the builder. A program that admits 400 women owes them the means to finish, and a funder who pays for the foundation answers for the walls, whatever the results framework happens to ask.

Frequently Asked Questions

What does “more women in the pipeline” mean?

It is a strategy that raises the number of women at the entry point of a career or business track, through recruitment, outreach or application drives. It assumes the main shortage is at entry. That assumption fits some US technical careers. In Uganda, where women already start businesses at high rates, the larger losses come at later stages.

Why do women-led businesses in Uganda stay small?

The World Bank found Ugandan women’s micro-enterprises earning 30 to 31% less monthly profit than men’s in two samples. Research in Kampala found only 6% of women in male-dominated, higher-margin sectors against 34% of men. Sector placement, access to larger loans and later-stage capital each contribute, and the mix differs between groups of women.

How much funding do women-led startups in Africa get?

Africa: The Big Deal reports that female CEOs attracted $48 million in 2024, about 2% of the total. Since 2019, female-led startups have held 13% of deals and 5% of capital. Their share falls as rounds grow: 5% of Series B or C deals went to startups with a female CEO.

What is the Finishing Count?

The Finishing Count is a tracking method for women’s enterprise programs. It follows one cohort through five stages: applied, admitted, trading at twelve months, a first step past survival, and second capital. It records why each woman left, then moves money and the headline indicator to the stage with the largest exit.

Should programs stop recruiting women?

Recruitment still matters where entry is genuinely narrow, such as technical trades at Ugandan vocational institutes, where female participation is about 25%. Where applications already exceed places many times over, as with the public women’s fund, extra recruitment lengthens the queue. Programs should fund the stage where their own Finishing Count shows the largest loss.

Next Step

A results framework is cheapest to change before anyone signs it. While your next one is still in draft, bring last cycle’s participant list to a strategy call with me. In one session we will fill the five stages of the Finishing Count together and find the stage where your money will do the most, which can save a year of funding the wrong end of the pipe.

Sources and Evidence

Kaur, Hyunh and Singh (eds.), Women in the Technology and Innovation Ecosystem: From Movers to Makers in America, CRC Press, 2026, DOI 10.1201/9781003604402 (Routledge series page): the book; chapters 7 and 8 quoted and checked.
Bloomberg via Employee Benefit News, “Half of new U.S. entrepreneurs are women, leading a creation boom” (June 2022): Gusto survey, almost half of 2021’s new entrepreneurs women, up from 28% in 2019; used to check the book’s 47% figure.
World Bank, Profiting from Parity: Unlocking the Potential of Women’s Businesses in Africa, executive summary (2019): women more likely than men to be entrepreneurs in sub-Saharan Africa; 34% average profit gap; Uganda micro samples at 30% and 31%.
World Bank, Profiting from Parity publication page: women-owned firms post profits 34% lower, with fewer employees and lower sales.
Publish What You Fund and CSBAG, Assessing National Funding for Women’s Economic Empowerment in Uganda (September 2022): UWEP funds 10 to 15% of applicant groups; Rukungiri 320 applied, 24 supported; groups funded 2,334 to 4,041; 73% recovered; 25% female participation in technical trades.
Campos, Goldstein, McGorman, Munoz Boudet and Pimhidzai, “Breaking the Metal Ceiling: Female Entrepreneurs Who Succeed in Male-Dominated Sectors,” World Bank Policy Research Working Paper 7503 (2015): the hostile witness; crossovers earn as much as men, with about three times the revenue and more than double the profit of women in female-dominated trades; 6% of women against 34% of men in male-dominated sectors; mentors and apprenticeships.
Techloy, “Why Are the African Women-Led Startups Facing a Funding Disparity?” (June 2025), reporting Africa: The Big Deal: 13% of deals and 5% of capital since 2019; 28% of pre-seed deals; 14% and 5% at Series B or C.
Africa: The Big Deal, 2024 funding by founder gender (January 2025): $48 million to female CEOs in 2024, 2% of the total.
Luke 14:28 to 30 (ESV), Bible Gateway: “count the cost, whether he has enough to complete it.”