1. Dilemma - problematise the trend to outsourcing and offshoring IT services.
2. Impact - list the conventionally understood negatives of outsourcing and offshore economic activity
3. The how do we explain the data?
4. Model behaviour using tools?
5. Possible experimental model? Process for behaviour change? Evidence to theory to evidence to theory
Data from 4.7 World Development Indicators: Structure of service imports (http://wdi.worldbank.org/table/4.7)
Plot the breakdown of Commercial service imports (Transport & Travel no longer tracked in this category) comprised of:
- "Insurance and financial services" and
- "Computer, information, communications, and other commercial services"
Statistical tools enable us to represent the data, makes sense of variation in the data, understand and infer meaning from the data. In drawing conclusions from the data we must balance concerns of validity, significance,
A wake-up call against the lazy application of statistical significance p-values ((Amrhein et al, 2019)). P-values should not be used in this dichotomist way of determining whether a result refutes a hypothesis or not.
- p > 0.05 (commonly taken to mean a hypothesis is NOT supported, but this is NOT what the value itself means)
- p > 0.05 (reread the p-value as it should be employed, to mean that there's a probability higher than 5% that what we observe could have arisen by chance)
You need to apply judgement when looking at data and use context when interpreting results.
The failings of traditional economics SD curve approaches is that it simply has no means of interpreting increasing income inequality and increasing dominance of market economy by firms.
"Motivate with facts, go to the models, return to the facts" (Carlin, 2019)
The failings of traditional economics SD curve approaches is that it simply has no means of interpreting increasing income inequality and increasing dominance of market economy by firms.
"Motivate with facts, go to the models, return to the facts" (Carlin, 2019)
