Lionel Roger, UoN 28/10/2015 Foreign Aid, Poor Data, and the Fragility of Macroeconomic Inference...

Lionel Roger, UoN 28/10/2015

Foreign Aid, Poor Data, and the Fragility of Macroeconomic

Inference

Lionel Roger, University of [email protected]

Supervisors: Oliver Morrissey, Markus Eberhardt


Aid Effectiveness?

Effectiveness

Harmfulness


ForeignAid

Aid Effectiveness

Savings

InvestmentEconomic

GrowthForeign

Exchange

Public Investment


Aid Harmfulness

ForeignAid

MarketDistortions

InvestmentEconomic

Growth

Corruption


Cointegrated VAR

𝑎𝑖𝑑𝑡

𝑔𝑑𝑝𝑡

Pulling forcesPushing fo

rces

Possible Equilibria

Figure 1: Pushing and pulling forces


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)


Data matters

Figure 2: GDP from 4 sources, normalised to 1965

~ x 3.3

~ x 2.5


Data matters

Figure 3: Investment share from 4 sources


Replication

• 4 sources of datao 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


Re-Specification

• Idea: “allow the data to speak freely”• Sub-sample: 4 most and 4 least consistent countries

o Consistent: Burkina Faso, Cameroon, Gabon, Kenyao 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

autocorrelationo Equilibrium relations: Trace test, t-ratios of alpha-coefficients, roots

of the companion matrix, graphical analysiso Extraordinary events: Inspection of residuals, institutional

knowledge (conflicts, cataclysms, historical events, etc.)


Re-specification: Results

PWT6 PWT7 PWT8 WDIEffect. Harmf. Effect. Harmf. Effect. Harmf. Effect. Harmf.

Consistent Coeff.

Consistent Inference

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


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 Foreign Aid, Poor Data, and the Fragility of Macroeconomic Inference...

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