A multilevel country panel analysis of the effects of debt on national economic growth using Reinhart and Rogoffs data Andrew Bell Ron Johnston and Kelvyn Jones andrewbellbristolacuk ID: 341342
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Slide1
Stylised fact or situated messiness? A multilevel country panel analysis of the effects of debt on national economic growth, using Reinhart and Rogoff’s data
Andrew Bell, Ron Johnston and Kelvyn Jonesandrew.bell@bristol.ac.ukNCRM Research Methods Festival, July 2014
School of Geographical SciencesSlide2
OutlineReinhart and Rogoff - Growth in a time of debt
Herndon et al’s critiqueWhat is missing?Our analysisRandom coefficients modelMultilevel distributed lag modelSlide3
Key methodological pointThe world is complex, and needs realistically complex models to represent itAiming for an average effect, or ‘stylised fact’ can be very misleading when relationships are heterogeneous over time or spaceSlide4
Growth in a Time of Debt (2010)Amer
Econ Rev 100(2) 573-78Reinhart and Rogoff argue for a threshold debt value at 90% of GDP, after which growth dramatically declines in rich countries.Entirely descriptive – no statistical modelSlide5
InfluenceA key citation and influence for those in favour of austerity budgets“As Rogoff
and Reinhart demonstrate convincingly, all financial crises ultimately have their origins in one thing.” (George Osborne, 2010)“conclusive empirical evidence that gross debt …exceeding 90 percent of the economy has a significant negative effect on economic growth.” (Paul Ryan, 2013, p78)Slide6
Herndon et al’s critiqueCamb J Econ
, 2014, 38(2), 257-279Find three key flawsAn excel spreadsheet error deleting five countries at the top of the alphabetWeighting by country, not by country-yearExclusion of certain data points It seems that the combination of the second two are what produced the apparent threshold effectSlide7
Herndon et
al’s critique
When corrected, change is much less extreme – no threshold – growth declines gradually with debt
But still an apparent relationship – growth declines with debt.Slide8
Herndon et
al’s
critiqueSlide9
What is missing?‘Stylised fact’ of a single un-varying effect is too simplisticWhy should the effect of debt be the same in Japan as in the USA?Assumption that debt leads to growth and not vice-versaSlide10
Increase in Deficit
More
govt
borrowing
Interest rates up
Growth reduced
Government spends to stimulate growth
Reduced government revenue
Increase in Debt
Investors wary of govt ability to make repayments
Investor flight
Direction of causalitySlide11
Our reanalysis – 2 partsMultilevel model that allows the growth-debt relationship to vary between countries (random slopes model)Multilevel ‘distributed lag model’ that gives evidence of direction of causality
does growth go up after debt, or before?Slide12
Random slopes model
Run in
MLwiN
Model additionally run using RR’s 4 groupings (instead of a linear effect) – results substantively similarSlide13
Random slopes model
Average effect of debt (within and between effects separated – see Bell and Jones 2014)
Run in
MLwiN
Model additionally run using RR’s 4 groupings (instead of a linear effect) – results substantively similarSlide14
Random slopes model
Average effect of debt (within and between effects separated – see Bell and Jones 2014)
Varying effects of debt across countries
Run in
MLwiN
Model additionally run using RR’s 4 groupings (instead of a linear effect) – results substantively similarSlide15
Random slopes model
Average effect of debt (within and between effects separated – see Bell and Jones 2014)
Varying effects of debt across countries
Run in
MLwiN
Model additionally run using RR’s 4 groupings (instead of a linear effect) – results substantively similar
Occasion-level variance (that is, volatility) varies with debtSlide16
Random slopes model
Average effect of debt (within and between effects separated – see Bell and Jones 2014)
Varying effects of debt across countries
Occasion-level variance (that is, volatility) varies with debt
Year controlled in all parts of model
Run in
MLwiN
Model additionally run using RR’s 4 groupings (instead of a linear effect) – results substantively similarSlide17
Distributed lag model
From http://www.nextnewdeal.net/rortybomb/guest-post-reinhartrogoff-and-growth-time-debt
Regress multiple leads and lags of debt on growth
Can plot these in an ‘impulse response’ graph
See whether a change in growth or a change in debt happens firstSlide18
Distributed lag model
From http://www.nextnewdeal.net/rortybomb/guest-post-reinhartrogoff-and-growth-time-debt
Dube
(2013) – reanalyses RR’s data, finds evidence direction is mainly in the direction from growth to debt, not from debt to growth
But is this the same for all countries?
Use the multilevel logic of previous model to allow causal direction to vary by country…Slide19
Multilevel distributed lag model
.
Run in Stata using the
runmlwin
command (code
available
on request
)Slide20
Results (1)Average effect (the “stylised fact”) now not significantLots of variation between countries
No evidence of a relationship between growth and debt in the UK
Australia
Greece
Ireland
Japan
UK
US
0
1
2
3
4
5
6
0
70
140
210
Predicted Growth (%GDP)
Debt:GDP ratioSlide21
Results (2)
Higher level-1 variance at debt ratios greater than 90%Suggests debt is associated with volatility in economic growthSlide22
Results (3)
In most countries, a change in debt occurs after a change in growthSuggests low growth causes debt, rather than debt causing growth.
Some variation – e.g. less clear directionality in Ireland.Slide23
ConclusionsSubstantive: The relationship between growth and debt is highly variable;
The average effect (‘stylised fact’) is not significant, although volatility in growth does appear to be higher at debt:GDP ratios over 90%;The causal direction is predominantly from growth to debt, not debt to growthMethodological: Stylised facts are often too simplistic – the world is complex and messy, and our statistical models should aim to reflect that complexity.Slide24
For more informationBell, A; Johnston, R; Jones K (2014) Stylised fact or situated messiness? The diverse effects of increasing debt on national economic growth. Journal of Economic Geography
, online, DOI: 10.1093/jeg/lbu005Bell, A and Jones, K (2014) Explaining fixed effects: Random effects modelling of time-series cross-sectional and panel data. Political Science Research and Methods
, online, DOI: 10.1017/psrm.2014.7Paper showing the advantages of using a multilevel/random effects model, rather than fixed effects models or other alternatives