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Predicting Macroeconomic Trends
Through Real-Time Mobile Data Collection
Author: Jon Gosier
D8A Group, LLC
Conducted on Behalf of Market Atlas, LLC
Telephone: (+1) 520-301-7906; jon@d8a.com
Ackno...
Does real-time consumer
spending data predict
macro-economic
trends?
Jon D. Gosier
Lead Researcher
Justin Mahwikizi
Market Atlas, CEO
Akin Sawyerr
Market Atlas, CSO
Ahmed M. Maawy
Assistant R...
The ultimate goal of this project is to see if there are strong correlations that
can be found between real-time consumer ...
Methodology
Real-Time Consumer Spending (RTCS) is
the blanket term we created to refer to
all statistically relevant data collected
fr...
Mombasa, Kenya
* Surveys via Mobile Phone and SMS
* Accounted for pricing bias
* Collected sales data from vendors
instead of consumers
*...
* Cellphone Vendors
* SIM Card Resellers
* Fruit Vendors
* Meat Vendors
* Grain/Rice Vendors
* General Store
* Clothing Ve...
* data.worldbank.org
* tradingeconomics.com
Other Data Sources
Findings
Vendor Type NOV-14 DEC-14 Trend Change % Over Inflation
Clothes 200 211 ↑ 5.5% ↓
Everything 8.16 7.54 ↓ -7.59% ↓
Grains/Ric...
Kenya Macroeconomic Data (Annual)
2005 2007 2009 2011 2013 Trend
GDP ($ billions) $18.7 $31.9 $37.0 $41.9 $55.2 ↑
GDP (2-y...
Kenya Macroeconomic Data (Monthly)
NOV-14 DEC-14 Trend
Inflation Rate 6.43% 6.09% ↓
Food Inflation 8.16% 7.54% ↓
Consumer ...
Findings
1- Because interest rates were up (↑) for the year,
and inflation was down (↓) month on month
between November and...
2- Some products seemed to defy expectations.
Clothing, Grains/Rice, and Fruit all moved volumes
at rates higher than the ...
3- Historic performance of the Kenyan Stock
Market and rates of inflation seem to follow
similar patterns.
Findings
Kenya I...
Future Research
* If RTCS and Inflation show strong a correlation to one
another, and Inflation and the stock market show co...
Predicting Macroeconomic Trends
Through Real-Time Mobile Data Collection
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Predicting Macroeconomic Trends Through Real-Time Mobile Data Collection [Slides]

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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

Published in: Data & Analytics
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Predicting Macroeconomic Trends Through Real-Time Mobile Data Collection [Slides]

  1. 1. Predicting Macroeconomic Trends Through Real-Time Mobile Data Collection
  2. 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. 3. Does real-time consumer spending data predict macro-economic trends?
  4. 4. Jon D. Gosier Lead Researcher Justin Mahwikizi Market Atlas, CEO Akin Sawyerr Market Atlas, CSO Ahmed M. Maawy Assistant Researcher Project Team
  5. 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. 6. Methodology
  7. 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. 8. Mombasa, Kenya
  9. 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. 10. * Cellphone Vendors * SIM Card Resellers * Fruit Vendors * Meat Vendors * Grain/Rice Vendors * General Store * Clothing Vendors Data Providers
  11. 11. * data.worldbank.org * tradingeconomics.com Other Data Sources
  12. 12. Findings
  13. 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. 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. 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. 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. 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. 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. 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. 20. Predicting Macroeconomic Trends Through Real-Time Mobile Data Collection

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