Field Notes | Amara Hub

Where Uganda's Bank Credit Goes

Sat, 19 Sep 2026 09:00:00 +0000

Every month, the Bank of Uganda publishes a simple table. It shows how much money the country’s banks have lent and which parts of the economy received it. It is not a glamorous document but it tells a truer story about our economy than most speeches do.

We spent the past year looking at that table, and between July 2025 and July 2026, the total lent by Ugandan banks grew from 23.8 trillion to 28.1 trillion shillings. That is a great deal of new money. The interesting question is where it went.

Average share of all bank credit by sector, July 2025 to July 2026.

Averaged across the year, the biggest single share, about 25 percent of everything lent, goes to personal and household loans. That is more than any industry receives. It is people borrowing to pay school fees, to buy a phone or a motorcycle, to cover a hospital bill, to get through the weeks before the harvest comes in. Buildings and property take 18 percent. Shops and trade take 15 percent. Factories take 13 percent, while farming, the work that feeds Uganda and employs most of its people, gets 12 percent.

These are averages for the whole year, and the proportions barely moved from one month to the next. This is a signal of how banks lend, month after month, guided by where they believe the money will come back.

Let that sit for a moment. Agriculture employs most of Uganda’s workforce, yet our banks lend more than twice as much for personal spending as they lend to everyone who grows our food.

Three things are changing

Look across the whole year and the picture moves in three directions.

Personal borrowing is not going away, and it grew in step with everything else and kept its place at the top. This is simply how many Ugandan families now get by, and is not a passing phase.

For years the safe bet was to build and to trade, but that is now slowing. Lending to real estate and construction grew only 8 percent over the year, and lending to trade barely 10 percent, both well below the average. Real estate’s share of all lending has slipped from nearly 20 percent two years ago to 17 percent today. The old belief that the smart money is always in buildings and shops is slowly stopping being true.

Part of the explanation may lie outside this table altogether. Government borrowing from Ugandan banks has grown sharply in recent years, and the International Monetary Fund and Bank of Uganda have both pointed to this as a factor squeezing out private lending: banks can earn steady, low-risk returns by buying government securities instead of lending to a builder or a trader. This is one plausible reason private credit to sectors such as property and trade has grown more slowly. It is not the only explanation, and the two effects are difficult to fully separate in a table like this one, but it is a pattern worth watching alongside these numbers.

Lastly, and one to watch, is that the money is moving into the ground. Lending to mining and quarrying grew 264 percent in a single year. Lending to oil and gas, which was almost nothing two years ago, multiplied more than five times over. Lending for electricity and water also grew fast, up 220 percent, part of a wider push to build out power and infrastructure as the country prepares for oil production to begin. These are still small amounts today, but this is what the beginning of something looks like. The oil economy we have talked about for so long is no longer just talk. You can now watch the banks preparing for it, month by month, in this one table.

Growth in outstanding credit by sector, July 2025 to July 2026.

What about the farmers?

Farming did grow this year, by about 20 percent, slightly better than average. That is welcome, and worth acknowledging. But farming started from such a low base that its share of total lending barely moved, from 11.5 percent to 11.7 percent. Agriculture employs most of the country’s workforce, yet it continues to receive a modest share of formal bank credit relative to that role. Whether that gap should close, and how, is a question for policymakers, lenders and farmers to work through together.

What the table cannot tell us

Somewhere in that 12 percent for farming is a trader in Lyantonde and a family farming a few acres in the Lango subregion, but the table cannot see them, and neither, really, can we. A number this large should not be this anonymous.

At Amara Hub, our work has shown us how much economic activity, particularly the work women do on the farm and in the market, sits outside the formal records that shape national policy. This is not a criticism of the central bank’s data, which does exactly what it sets out to do. However, it is an observation about what any national, sector-level table currently leaves out. If lending data were also published by region and by the gender of the borrower, local leaders, researchers and lenders would have a fuller picture to work from. Until then, the table can only tell us how much was lent, but not to whom, or where.

About the data

Figures are drawn from the Bank of Uganda’s statistical portal, credit by sector, to July 2026.

Work on economic data, financial inclusion or regional policy and would like to explore this analysis further? Contact us via team (at) amarahub (dot) org.

The Caregiving Typology

Thu, 16 Jul 2026 09:00:00 +0000

In January, fifteen women spent two weeks documenting their daily lives to contribute to our photovoice research on climate and care. Their pictures showed longer walks for water, fuelwood trips that take whole mornings, more childcare during drought months, and routines women skip because no one else will cook at home.

When the photographs were presented at a community baraza, 173 people attended. A local government official summed up the discussion: the community has always been asked to support climate adaptation and restoration, but nobody has accounted for what that asks of the women caring for every household in it.

That left us with a familiar problem. We had strong qualitative evidence about who these caregivers are, and a monitoring panel of 80 households doing structured monthly interviews on KoboToolbox and producing numbers about workloads and income. The two didn’t connect. Stories couldn’t answer “how many?” Our panel average couldn’t answer “who?” Local government officials asked us both these questions.

The methodology we borrowed

Quicksand, a design research studio, faced the same problem at national scale in their Digital Personas work. Instead of clustering or training a classifier, they wrote plain rules by hand that assign every survey respondent to a qualitatively-derived persona.

We applied the same approach to our 80-household panel.

Five Ways to Carry a Household

From the photovoice themes and the baraza records, we defined five caregiver types. They are built on three variables our fieldwork showed to be decisive: whether care is shared within the household, whether the caregiver earns from a food enterprise, and whether sharing is possible at all in her household.

Figure 1: Flowchart of the five ordered typing rules.

T1 — Caregiver-entrepreneur with shared care. Care redistributed and income earned. The full CARES pathway.
T2 — Constrained entrepreneur. Earning, but with no reduction in care load. A double burden.
T3 — Transitioning household. Men now doing regular care work like collecting water, cooking some evenings, but no enterprise yet.
T4 — Sole carer under compound load. No adult man in the household, plus dependants needing daily care. Redistribution within the household is not an option. These households are who the shared care pod is for.
T5 — Full-load caregiver. An adult man present; care still done by the woman alone. This is the group the programme is trying to shrink.

What it changes

Prevalence. We can now show how many households fall into each type and track how this changes over time. After six months, the number of full-load caregivers in our project, women carrying all the household’s care responsibilities alone, fell from 45 households to 22 as care was shared more equally and additional support reached them.

Movement. The transition matrix shows how individual households are changing, rather than only presenting overall averages. In our study, seventeen households moved from full-load caregiving into the transitioning group, while six completed the full pathway. However, eleven sole caregivers did not move at all. Household dialogue cannot redistribute care when there is no one else in the household to share it with. This provides measurable evidence of the need for community-based care services and support.

Disaggregation. We can break down any measure by household type. In our study, constrained entrepreneurs spent an average of 8.9 hours a day on care work, the second-highest level after sole caregivers. Photovoice participants had already explained that earning an income does not automatically reduce care responsibilities. The typology helps us measure that pattern.

Advocacy. The characteristics used to define the household types, such as household composition and the number of dependants, are also available in census and Demographic and Health Survey data. This allowed us to estimate how many caregivers across our study population may face similar conditions, use those estimates to make realistic requests to local government, and then record and follow up on the commitments made.

Caveats

The household types are not permanent labels since families can move from one type to another, and the matrix will track movement in both directions. The boundaries between the types are based on our researchers’ judgement, not on fixed statistical cut-off points. With only 80 households, the study can describe patterns but cannot make broad statistical claims. Also, correlations in the data show that two things may be linked, but they do not prove that one caused the other. The photographs, diaries and dialogue records help explain what is actually happening.

Get the working paper

Our paper explains the methodology, including the four open KoboToolbox forms, the analysis process, the statistics used and their limitations, and how the findings are linked to the wider population. The forms and the script used to classify households are also openly available for others to adapt and reuse.

If you work on care, climate or livelihoods programs and would like to test this approach or challenge where we have drawn the boundaries between household types, contact us via team (at) amarahub (dot) org.

Small Notes on Big Questions

Sun, 05 Jul 2026 12:00:00 +0000

At Amara Hub, some of our most important work begins as a question, a sketch, a conversation, or an idea that we are not yet ready to fully define.

This is our space to share some of that thinking on what we are exploring, what we are trying to understand, and what we may eventually shape, support, challenge, or bring into the world.

These are internal notes from unfinished journeys.

We are sharing them because sometimes the work begins before the funding, before the perfect plan, and before we fully know where it will lead.

It begins with deciding that a question is worth taking seriously, and then following it far enough to see what becomes possible.