1. Priming the Pump. Does Aid Pave the Way for Investment?
Samuel Brazys, University College Dublin
Comments/suggestions welcome at The European Union’s Horizon 2020 research and innovation programme under grant agreement number (GLOBUS) provided generous funding support for this project.

2. Outline
Introduction/Theory
Data
Method
Results
Conclusions

3. Introduction/Research Question/Hypothesis
Does aid presage local FDI?
Hypotheses:
Proximity
Source Country Heterogeneity

4. Data
The data codes 9,684 greenfield or expansion FDI projects from 126 source countries from 2003 to 2017 with flows estimated at over $1,026 billion.
positive FDI inflows rather than net FDI inflows.
World Bank IBRD-IDA data records $612 billion in net FDI inflows from 2003 to 2016, compared to $1,008 billion in positive FDI inflows in the fDi Markets data.
Capital investment is estimated in the fDi Markets database for over 80% (7,920) of the project records.
Over time, the series’ annual values have a correlation coefficient of with a p-value of
“Aid Information Management System” (AIMS) datasets from Burundi, Democratic Republic of the Congo, Malawi, Nigeria, Senegal, Sierra Leone and Uganda
World and China – total Africa coverage
Collectively, these data cover 82,577 distinct project locations from 2000 to 2014

5. Method Knutsen et al. (2017) Quasi-experimental spatial-temporal
Active Aid
Inactive Aid
Non Aid
Difference-in-difference
Spatial cut-offs

6. Results – Overall Aid attracts local FDI (1) (2) (3) VARIABLES 25 km
(1)
(2)
(3)
VARIABLES
25 km
50 km
75 km
Active
-0.038
-0.137*
-0.377***
(0.052)
(0.072)
(0.066)
Inactive
-0.447***
-0.408***
-0.318***
(0.125)
(0.073)
(0.075)
Observations
1,371
R-squared
0.262
0.234
0.259
Baseline controls
YES
Year FE
Country FE
Difference in difference
0.409
0.271
-0.059
F test: active-inactive=0
9.244
7.191
0.364
p value
0.002
0.007
0.546

7. Results – Source Country
Source country co-location
EU & China
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
VARIABLES
US FDI
NonUS FDI
EU FDI
NonEU FDI
JP FDI
NonJP FDI
CN FDI
NonCN FDI
active_source
0.753***
0.558***
0.869***
0.857***
(0.075)
(0.033)
(0.051)
(0.034)
inactive_source
0.682***
0.587***
0.781***
0.793***
(0.032)
(0.041)
(0.036)
(0.031)
active_nonsource
-0.334***
0.019
-0.181***
-0.033
(0.054)
(0.052)
(0.045)
inactive_nonsource
-0.348***
-0.338***
-0.252***
-0.447***
(0.058)
Observations
1,371
1,246
R-squared
0.263
0.219
0.320
0.222
0.271
0.143
0.312
0.199
Baseline controls
YES
Year FE
Country FE
Difference in difference
0.071
0.014
-0.029
0.357
0.088
0.064
0.414
F test: active-inactive=0
0.793
0.035
0.338
26.085
2.305
0.987
1.774
32.815
p value
0.373
0.851
0.561
0.000
0.129
0.321
0.183

8. Pan-African (WB, China)
Results - Robustness
CHECK
Pan-African (WB, China)
Lagged Aid
Alternate Controls
Grid Cell Approach
Spatial Lag

9. Conclusions
Aid increases likelihood of FDI – by up to 40% – Aid works?
Source country aid & FDI goes together.
China & EU aid attracts other FDI
Future questions
Growth?
Welfare?
Equality?

10. Table SA1: World Bank and China Pan-African Results, Lags, and Alternative Controls
(S1)
(S2)
(S3)
(S4)
(S5)
(S6)
(S7)
(S8)
VARIABLES
WB and China
World Bank
China
Aid Lag 1
Aid Lag 2
Aid Lag 3
No Controls
Expanded Controls
active
-0.179***
0.059***
-0.174***
-0.124*
-0.096
-0.146**
-0.109
(0.014)
(0.075)
(0.085)
(0.079)
inactive
-0.216***
-0.358***
-0.220***
-0.407***
-0.406***
-0.426***
-0.364***
(0.018)
(0.073)
(0.072)
(0.081)
Observations
8,756
1,371
1,374
1,215
R-squared
0.331
0.345
0.234
0.233
0.216
0.240
Baseline controls
YES NO
Year FE
Country FE
Difference in difference
0.037
0.417
0.046
0.283
0.310
0.279
0.255
F test: active-inactive=0
2.845
4.300
7.612
7.842
7.560
5.275
p value
0.092
0.000
0.038
0.006
0.005
0.022

11. Table SA2: Grid Cell Approach
(S9)
(S10)
(S11)
VARIABLES
25 km
50 km
Inhabited Cells Only 50km
Active
-0.016***
-0.055***
-0.065***
(0.005)
(0.006)
(0.007)
inactive
-0.088***
-0.214***
-0.234***
(0.011)
(0.012)
Observations
17,902
17,875
14,208
R-squared
0.066
0.211
0.210
Baseline controls
YES
Year FE
Country FE
Difference in difference
0.071
0.159
0.168
F test: active-inactive=0
89.150
p value
0.000

12. Table SA3: Spatial Autoregressive and Spatial Error Models
(S12)
(S14)
VARIABLES
Spatial Lag
Spatial Error
active
-0.178**
-0.184**
(0.083)
(0.086)
inactive
-0.430***
-0.427***
(0.074)
(0.075)
Observations
1,371
Baseline controls
YES
Year FE
Country FE
p value rho/lambda
0.908
0.768
Difference in difference
0.252
0.243
Chi2 test: active-inactive=0
5.303
4.534
p value Chi2
0.021
0.033