Susanne: World Recession Susceptibility Analysis This presentation covers my journey in making Susanne, to be found here: http://susanne.bitballoon.com/. This is a statistical computation and semantic web project, and I sort of went the extra mile. We find that export and banking economic variables best predict recession susceptibility, which can be gauged with up to 92% accuracy using SVM classifiers. This is using World Bank data and R tooling. The website is made using D3.js and AngularJS, and does not use a backend.

1. Making Susanne
THE WORLD RECESSION SUSCEPTIBILITY ANALYSIS WEBTOOL
Aditya Gupta (2011009)
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2. Overview
 Objective and Motive
 Process
 Data
 Data Collection
 Data Cleanup
 Dependent Variable
 Analysis
 Regression, Classification
 Clustering and Results
 Further Work
 Presentation
 Drawbacks
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3. Objective
Key Questions
 To study what economic
composition makes economies
more susceptible to
Global Recession
 By how much? How significantly?
 Can we predict recession impact?
 Studying similarity of economies
 How to quantify
 Recession impact
 Susceptibility
Key Goals
 Empirically ascertain significance
and impact of certain economic
traits vis-à-vis expert opinions
 Develop a powerful model to
predict recession susceptibility
 Present a global and intuitive view
across parameters and their
significance, i.e., Susanne.
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4. Why? 4
 Recessions Hurt
 2008 – 2010
 Unemployment and layoffs
 Austerity
 Bearish Markets
 Inflation
 Bank Runs

5. Data
A BIG PROBLEM IN ITSELF
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6. Data Collection
 60+ economic variables linked to Recession
 Allegedly, as per sources like Economist, Forbes, World Bank, WWW
 Or as per our suspicion
 Preferable country-specific ratios (Normalized and Structural Information)
 13 years, starting from 2000 to 2013
 210 Countries
 2500 rows of 60+ columns, each row is a country-year identified
 Source:
 World Bank (OECD National Account File): http://data.worldbank.org/
 United Nations Comtrade Database, International Monetary Fund, Direction
of Trade Database, Balance of Payments Database, and more.
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7. Simple Enough?
 No, its blistering gunk.
 Only 86 out of ~2500 rows have
complete data (no NA’s)
 Non-normalized values
 What now?
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8. Data Cleanup
 Manually add values for nearly complete columns
 Compress and remove years 2008-2010
 Observed Class Variables – not Causal
 Remove countries with almost no data (Afghanistan and 30 others)
 Down to 2100 rows
 Drop columns if:
 Significant, and have very little data available
 Year values for super-specific variables like
“Merchandise Exports to Scandinavian Countries as % of exports”
 We surely don’t have this for most countries, especially those like Albania
 Insignificant
 Determined from Regressional Analysis (MLR) p-values
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9. Still too many missing values
 What to do?
 Drop row (done)
 Weighted expansion of row (did not consider)
 Infer a value
 Average value for parameter for country
over 13 years
 Result: Fixed 5000 out of 70K cells
 Still only 86 full rows
 Why?
 No value for property exists for a country at
all…
 Solution: Global Average – the sacrilege!
 Awful. Pull towards the mean, misclassify.
 But no directional bias.
 Trade-off: Unlocks a world of data
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For a specific country…
Year V1 Pred V2 Pred
2001 15 15 15 15
2002 17 17 17 17
2003 15 15 19 19
2004 16 16 21 21
2005 ? 16 ? 18
Avg 16 18

10. Dependent Variable
 Goal: Capture Recession Impact between 2008-10
 Technical Definition: Absolute Growth Rate
 Problem: 15% to 1%, still not recession
 Average Growth Rate drop between 2008 – 2010?
 Problem: 1% drop for UK (at 0.5%) vs. AFG (at 15%)
 Percentage Drop in Growth Rate
 Problem: 0.1% to 3%, 3000% change!
 Solution?
 Drop.SD: Drop in Growth Rate in number of Standard
Deviations over 2008 – 2010.
Variance in Growth rate over last 20 years.
 Variance: Lot of manual data collection
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11. Distribution and Discretization of
Drop.SD
 Corresponded well with web information about
countries that “avoided recession” and those
“hit worst”
 21% of the countries labelled as unsusceptible
 Less than -0.25 Drop in GDP during 2007-2010
 Middle 36% labelled as relatively unaffected
 between -0.25 to 0.75 SD GDP Drop in Recession
 Highest 43% adversely affected
 >0.75 SD drop in GDP
 How good was this division?
 lm R-squared rose from 41.58% to 44.14% (no loss in
predictive power, i.e. reasonable classification)
 Good split. Most countries were affected horribly.
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Other Options
• Equal Density Split
• Maximise Classification Accuracy
• Purely Contextual

12. Analysis
AFTER WE FINALLY HAVE 2099 COMPLETE ROWS OF DATA
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13. Classification: Can we predict
Recession Susceptibility?
 Assumption (non trivial)
 Drop.SD correctly represents Recession Impact in 08-10
 Recession Impact in 08-10 correctly represents Recession Susceptibility
 Can’t do better but guess a few things.
 SVM has 92% accuracy. Seems like it.
 Caveat:
 Bootstrap Analysis: Training Data = Test Data
 Workaround (can’t generate new countries or years):
 5-Fold Cross Validation
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14. Important Variables and their
Impact
 Using Multilinear Regression (MLR),
 for each Economic Variable, we get
 degree
 and direction
of impact on Drop.SD
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15. Clustering
 Motivation:
“Are we brute-engineering a predictor, or is there an actual underlying
economic structural pattern of recession-susceptibility?”
 70% accuracy (consistent clusters)with k-means, using multiple k
values.
 You decide.
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16. Aside: Some nifty R snippets
 Populating Missing Values smartly:
 d7.agg <- aggregate(d7.norm[,5:48], by
= list(Country.Name =
d7.norm$Country.Name, Time =
d7.norm$Time), FUN = function(x) {
if(anyNA(x)) { mean(na.omit(x)) } else
{ mean(x) } })
 Class labelling:
 Rec.Affect <- ifelse
(CompleteDataFinal$Drop.SD. >= 1, 1,
ifelse(CompleteDataFinal$Drop.SD. >= -
0.25, 0, -1)).
 SVM (predict Recession Susceptibility)
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> model <- svm(as.factor(Rec.Affect) ~
., data = CompleteDataFinal[,
c(6:46)])
> predictions <- predict(model,
CompleteDataFinal[,c(6:46)])
> table(pred = predictions, true =
Rec.Affect)
true
pred -1 0 1
-1 383 8 7
0 33 675 41
1 15 61 876
> t <- table(pred = predictions, true
= Rec.Affect)
> (t[1,1] + t[2,2] + t[3,3])/sum(t)
[1] 0.9213911

17.  Allowing similarity checks between
Economies
 Overall
 Over Economic Categories
 Using a Semantic Web compliant
Cardinality Checks and Ontology
 Classify Economic Variables into
one or more of:
 Central Government
 Economic Structure
 Net Exports
 Banking
 Manufacturing
 GDP
 Discretize them into , and over:
 Value
 And Impact
 Using middle 80 percentile cut-offs
Further Work - Accessibility 17

18. Presentation
THE WEBTOOL, I.E, SUSANNE
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19. A Knowledge Map (Ontology) 19

20. Ontology (2) 20
• About 3500 Nodes
• And 7000 Edges

21. Backend 21

22. Frontend 22
 Takeaway:
 Single Page Architecture
 Global State:
 (a) Country
 (a) Year
 (a) Property
 Multiple Interesting Views:
 Map
 Country
 Property
 And 2-way state
integration and state-
update
 thanks, angular

23. Search (Tutorial) 23

24. Search (2) 24

25. Search (3) 25

26. Search (4) 26

27. Search (5) 27

28. Search (6) 28

29. Search (7) – and the last one,
breathe.
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30. Demo
http://susanne.bitballoon.com/
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31. Thank You
Sincerely
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