Foreign Aid, Poor Data, and the
Fragility of Macroeconomic Inference
Lionel Roger, University of Nottingham
lexljrog@nottingham.ac.uk
Supervisors: Oliver Morrissey, Markus Eberhardt
Lionel Roger, UoN
28/10/2015
Aid Effectiveness?
Effectiveness
Lionel Roger, UoN
Harmfulness
28/10/2015
Aid Effectiveness
Savings
Investment
Foreign
Exchange
Economic
Growth
Public
Investment
Foreign
Aid
Lionel Roger, UoN
28/10/2015
Aid Harmfulness
Market
Distortions
Foreign
Aid
Investment
Economic
Growth
Corruption
Lionel Roger, UoN
28/10/2015
Cointegrated VAR
𝑔𝑑𝑝𝑡
Possible
Equilibria
𝑎𝑖𝑑𝑡
Figure 1: Pushing and pulling forces
Lionel Roger, UoN
28/10/2015
Juselius, Møller & Tarp (2014)
• Cointegrated VAR analysis for 36 African countries
• Individual model for each country
Effectiveness
Harmfulness
GDP
17
6
Investment
24
5
Either
27
10
Table 1: Summary of Results, Juselius, Møller & Tarp (2014)
Lionel Roger, UoN
28/10/2015
Data matters
~ x 3.3
~ x 2.5
Figure 2: GDP from 4 sources, normalised to 1965
Lionel Roger, UoN
28/10/2015
Data matters
Figure 3: Investment share from 4 sources
Lionel Roger, UoN
28/10/2015
Replication
• 4 sources of data
o Penn World Table versions 6.3, 7.1, 8.0 (Heston et. al, 2009, 2012; Feenstra
et al. 2015)
o World Development Indicators (The World Bank, 2015)
Replication
Alternative Datasets
PWT6
PWT7
PWT8
WDI
Inference
97%
67%
61%
77%
Consistent Coefficients
88%
63%
58%
63%
Reversed Coefficients
5%
28%
26%
12%
Effectiveness
26
18
13
6
Harmfulness
10
9
7
3
Sample
36
36
33
13
Table 2: Replication results
Lionel Roger, UoN
28/10/2015
Re-Specification
• Idea: “allow the data to speak freely”
• Sub-sample: 4 most and 4 least consistent countries
o Consistent: Burkina Faso, Cameroon, Gabon, Kenya
o Inconsistent: Benin, Lesotho, Mauretania, Togo
• Re-specification of country-specific models for each
dataset: 32 CVAR models
• Variable elements:
o Lag length: Lag-reduction test, Information Criteria, tests for
autocorrelation
o Equilibrium relations: Trace test, t-ratios of alpha-coefficients, roots of the
companion matrix, graphical analysis
o Extraordinary events: Inspection of residuals, institutional knowledge
(conflicts, cataclysms, historical events, etc.)
Lionel Roger, UoN
28/10/2015
Re-specification: Results
PWT6
PWT7
PWT8
WDI
Consistent Consistent
Coeff.
Inference
Effect.
Harmf.
Effect.
Harmf.
Effect.
Harmf.
Effect.
Harmf.
Burkina Faso
0
0
0
-
0
0
0
-
63%
4
Cameroon
0
0
0
0
0
0
0
-
79%
5
Gabon
0
0
0
0
0
0
0
0
71%
6
Kenya
+
+
+
+
+
+
+
+
58%
6
Benin
+
-
+
-
+
0
0
-
46%
4
Lesotho
+
+
-
-
+
0
0
-
13%
1
Mauretania
+
0
0
-
0
-
-
-
33%
0
Togo
+
0
+
-
+
-
+
+
25%
3
GDP
3
1
3
2
3
1
2
3
Investment
4
1
2
4
3
1
2
3
Either
5
1
3
5
4
2
2
5
Table 3: Results, Re-specified models
Lionel Roger, UoN
28/10/2015
Conclusions
• Macroeconomic data can vary a lot from source to
source
• The differences can matter a lot for the inference
• ~1/3 of Results change in qualitative manner with new
data
• Variation is exacerbated when models are allowed to
vary with data
• But: Most countries’ results remain stable
• Robustness checks should become standard
• Highlights importance of understanding beyond
statistical analysis
Lionel Roger, UoN
28/10/2015