Our Impacts

We are data nerds. We believe, as a matter of integrity and intellectual honesty, that organizations claiming to help others should support those claims with convincing quantitative evidence of impact. We rely on such evidence in law, public policy, medicine, engineering, and other disciplines that affect our daily lives. We should hold well-intentioned charities to the same standard.

The data we've collected since 2014 show partner and control villages on divergent development paths, with the former outperforming the latter. Below we graph partner v. control villages over time in terms of changes in a overall development score and categorical scores for health, education, business, lifestyle, agriculture, and livestock.

Do these graphs reveal statistically significant impacts? Find out below.

% Change in Overall Development: Partner Villages v. Control Villages (higher % is better)
% Change in Health Burden: Partner Villages v. Control Villages (lower % is better)
*Scores based on # of waterborne illnesses, malaria cases, maternal deaths, and infant deaths per capita.
% Change in Local Education: Partner Villages v. Control Villages (higher % is better)
*Scores based on school enrollment and national exam passage rates per capita.
% Change in Business Activity: Partner Villages v. Control Villages (higher % is better)
*Scores based on # of agriculural and non-agricultural village businesses per capita.
% Change in Lifestyle Upgrades: Partner Villages v. Control Villages (higher % is better)
*Scores based on # of roofs with iron sheets, TVs, motorcycles, and smartphones per capita.
% Change in Agricultural Production: Partner Villages v. Control Villages (higher % is better)
*Scores based on # of 60 kg bags of maize produced per capita.
% Change in Livestock Holdings: Partner Villages v. Control Villages (higher % is better)
*Scores based on # of goats and cows per capita.

Evaluation Results

Our most recent impact evaluation occurred in early 2019. Our approach wasn't fancy or expensive. Instead, it tested a foundational assumption that financing village-led projects without any sort of preconceived development agenda would allow local communities to illuminate development pathways obscured from the outside. In other words, we funded a bunch of projects, collected a bunch of development indicators, and tested whether anything had changed.

Did communitites improve and, if so, how? Here's what we found. For starters, while control villages exhibited a 19% increase in overall development score over four years, partner villages posted a whopping 73% increase during the same timeframe. This result was statistically significant at the 1% level. The table below reveals which of the 25 development data metrics we use contributed to this development surge by treatment villages.

Metric Boys in nursery Girls in nursery Goat assets Waterborne illness Infant deaths Agri biz Other biz Homes w/ metal roofs
% change +50% +67% +83% -64% -100% +81% +100% +50%
impact per village +7 boys +12 girls +45 goats -109 cases -2 deaths +13 biz +8 biz +13 homes
p-value p<0.05 p<0.01 p<0.01 p<0.01 p<0.05 p<0.01 p<0.01 p<0.05
total impact (all villages) +133 boys +228 girls +855 goats -2071 cases -38 deaths +247 biz +152 biz +247 homes
*Table shows development impacts (changes in treatment v. control villages) after two projects and a total investment of $7,000 per treatment village, on average. Not shown are small but statistically significant increases in motorcycles (+2 per village) and TVs (+2 per village). In the table above, % change is based on 2014 treatment village average.

How We Got These Results

Using community surveys spanning 2014 (baseline) to 2018, across 41 projects and 56 villages, we tracked the following 25 development indicators pertaining to health, education, business, lifestyle, agriculture, and livestock, both in partner villages and control villages that want to partner with Village X. We chose these indicators because they are easy to collect and indicative of village development trends. We then applied a difference-in-differences analysis to detect our model’s impacts, controlling for village population, number of households, and surrounding district.

# of waterborne illnesses
# of malaria cases
# of maternal deaths
# of infant deaths
enrollment: # of boys/girls in nursery, primary, secondary, and tertiary
test scores: # of boys/girls passing PLSCE (end of primary exam) and # of boys/girls passing MSCE (end of secondary exam)
# of agricultural businesses
# of non-agricultural businesses
# of TVs
# of motorcycles
# of steel roofs
# of smartphones
# of (60 kg) bags of maize
# of goats
# of cows

Project profiles on this website have graphs showing how development scores for a given village change over time, including an overall village development score and scores for each of the sub-categories set forth above. These scores are not precision instruments. Instead, they capture village development trends over time.

Want to take a deeper dive? Checkout our dataset here. It's part of our 100% tranparency guarantee.

Next Steps

We are not satisfied with our impact evaluation. It's a good start, but we can do better. In particular, we could track how projects we finance affect individual household spending within a village. How do families modify their spending in response to village-led projects? Do the projects make families wealthier? We could check our data against development trends revealed by satellite imagery.

Answering these questions requires overcoming two challenges: (1) our current portfolio of partner villages is not large enough (we need at least 100 per year); and (2) we do not possess a large monitoring and evaluation budget. We plan to scale our operations to overcome the first challenge and to partner with academic researchers to overcome the second one.

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