This deck outlines a study conducted in Mombasa, Kenya where real-time consumer data collection techniques (also known as known as big data, real-time data, crowdsourced data or open source data) were used to investigate hypotheses about macroeconomic trends. It concludes that there are many reasons to feel confident that these techniques may serve as sufficient alternatives for economic forecasts in countries where traditional means of microeconomic data collection are sparse due to poor infrastructure and other circumstance. Further research is needed to verify the repeatability of these findings and the methods soundness statistically. The full paper can be found here - http://www.slideshare.net/jongos1/predicting-macroeconomic-trends-through-realtime-mobile-data-collection

1. Predicting Macroeconomic Trends
Through Real-Time Mobile Data Collection

2. Author: Jon Gosier
D8A Group, LLC
Conducted on Behalf of Market Atlas, LLC
Telephone: (+1) 520-301-7906; jon@d8a.com
Acknowledgements: Ahmed Maawy, Research Assistant;
Akin Sawyerr, CSO, Market Atlas; Justin Mahwikizi, CEO, Market Atlas;
Appfrica and D8A Group LLC;
and the John S. and James L. Knight Foundation

3. Does real-time consumer
spending data predict
macro-economic
trends?

4. Jon D. Gosier
Lead Researcher
Justin Mahwikizi
Market Atlas, CEO
Akin Sawyerr
Market Atlas, CSO
Ahmed M. Maawy
Assistant Researcher
Project Team

5. The ultimate goal of this project is to see if there are strong correlations that
can be found between real-time consumer spending patterns and macro-
economic trends and market fluctuations in African countries.
If successful, our methodology will present a new way investment decisions
can be made as it relates to Africa and other emerging market countries
which suffer from poor private sector visibility and financial infrastructure.

6. Methodology

7. Real-Time Consumer Spending (RTCS) is
the blanket term we created to refer to
all statistically relevant data collected
from a sample of data providers to
represent larger population trends.
This first half of the experiment was
limited to collecting RTCS data and using
it to test our hypotheses about
macroeconomic trends. The second half
will focus on finding strong correlations.
Methodology

8. Mombasa, Kenya

9. * Surveys via Mobile Phone and SMS
* Accounted for pricing bias
* Collected sales data from vendors
instead of consumers
* Tested correlations against macro-
economic indicators like Gross Domestic
Product, Purchasing Power Parity,
Inflation, Foreign Direct Investment,
Debt and others.
Methodology

10. * Cellphone Vendors
* SIM Card Resellers
* Fruit Vendors
* Meat Vendors
* Grain/Rice Vendors
* General Store
* Clothing Vendors
Data Providers

11. * data.worldbank.org
* tradingeconomics.com
Other Data Sources

12. Findings

13. Vendor Type NOV-14 DEC-14 Trend Change % Over Inflation
Clothes 200 211 ↑ 5.5% ↓
Everything 8.16 7.54 ↓ -7.59% ↓
Grains/Rice 900 910 ↑ 1.11% ↓
Meat 450 390 ↓ -13.33% ↓
Fruit 19000 21774 ↑ 14.6% ↑
Cell Phones 44 27 ↓ -38.64 ↓
SIM Cards 300 100 ↓ -66.66% ↓
All 2986.02 3345.65 ↓ -15.00% ↓
Kenya Microeconomic RTCS Data (Monthly)

14. Kenya Macroeconomic Data (Annual)
2005 2007 2009 2011 2013 Trend
GDP ($ billions) $18.7 $31.9 $37.0 $41.9 $55.2 ↑
GDP (2-yr change) 25.5% 70.59% 15.99% 13.24% 31.74% ↑
GDP (growth rate) 5.91% 6.99% 2.74% 4.42% 4.69% ↓
GDP (per capita) $523.61 $721.46 $771.29 $816.44 $994.31 ↑
Real Interest Rate 7.6% 5.0% 2.8% 3.8% 10.9% ↑
Consumer Price
Index
72.57 80.24 102.09 121.17 140.11 ↑
Inflation (consumer
prices annual %)
10.3% 9.8% 9.2% 14.0% 5.7% ↓

15. Kenya Macroeconomic Data (Monthly)
NOV-14 DEC-14 Trend
Inflation Rate 6.43% 6.09% ↓
Food Inflation 8.16% 7.54% ↓
Consumer Price
Index
151.92 pts 151.85 pts ↓
CPI (% change) -0.21% -0.05% ↓

16. Findings
1- Because interest rates were up (↑) for the year,
and inflation was down (↓) month on month
between November and December, traditional
economic patterns would suggest consumer
spending should also be down.
Our observations showed that it was.

17. 2- Some products seemed to defy expectations.
Clothing, Grains/Rice, and Fruit all moved volumes
at rates higher than the percentage change in
inflation.
One might conclude that this is because these are
‘essentials’ that people will buy regardless of
economic trends.
Findings

18. 3- Historic performance of the Kenyan Stock
Market and rates of inflation seem to follow
similar patterns.
Findings
Kenya Inflation Rate Kenya Stock Market

19. Future Research
* If RTCS and Inflation show strong a correlation to one
another, and Inflation and the stock market show correlated
patterns, can RTCS serve as leading indicator for market
trends?
* If there is a causal link between RTCS and Inflation, is there a
link with other macroeconomic indicators?
* Why do fruit, clothing, and grains/rice defy trends that other
industries do not?

20. Predicting Macroeconomic Trends
Through Real-Time Mobile Data Collection