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Public Private
Partnerships in Sub-
Saharan Africa: A Policy
Framework Analysis
Christopher Jackson
Moore Conference 2016, College of Charleston
Department of Political Science
Advisor: Dr. Christopher Day
Panel 6
What is PPP and why
does it matter?
Brief Overview of PPP
➔Public Private Partnership
➔Means to provide infrastructure services
➔Particularly interesting for developing countries
Research
Question
PPP Policy Framework = PFA
Does the Adoption of Public
Private Partnership Policy
Frameworks (PFA) Increase
the Viability of
Infrastructure-Based PPP
Projects in Sub-Saharan
African Countries?
Existing
Literature
PFA = Success
What about Sub-Saharan Africa?
PPP Data in Sub-Saharan
Africa Very limited → affected variable selection
 World Bank endorsed PPP Database
 High internal validity; lower external validity
 Local reporting of data cannot have any accuracy
guarantees
 Drew from Quality of Government Database
Variables: Defining Viability
Independent Variable
Policy Framework Adoption
(PFA)
-Governmental policies
-Inclusion of segmented
policies
Dependent Variable*
Indicators of Viability (IOV)
1.Change in Project Number
from Year to Year (+∆Nx)
2.Total number of projects (Nx)
3.Multilateral Cooperation (MC)
*Variable constraints due to data limitations
Methodology
1.Logistic Regression
2.Linear Regression
3.Simple Percentage
Test
Why? -- verify claims on
PFA in SSA
Quantitative Methods
Summary of Proposed Causal Relationships
PFA –
Independent
Variable
IOV 1: ∆Nx –
Dependent
Variable
IOV 2: SA (Nx)-
Dependent
Variable
IOV 3: MC –
Dependent
Variable
PFA Occurs ∆Nx will be positive
after PFA (+∆Nx)
Total Nx in given
country is increased
Endorsement from
multilateral
organization
Absence of PFA ∆Nx will equal 0 or
be negative
Total Nx is
comparatively lower
No endorsement
from multilateral
organization
General Hypothesis: Hmain
If PFA occurs in a Sub-Saharan country, the viability of
PPP projects will be bolstered in that country.
33.1%
PFA has no effect on Validity
Hypothesis 3
Hmain
Accept the null hypothesis:
The adoption of a policy framework (PFA)
suggests no statistically significant effect on
PPP viability
Implications
PFA may be wasteful and inefficient
Literature on PPP and policy frameworks may not hold in
Sub-Saharan Africa
Why do we see this result?
Lack of transparency
Weakness of African legislative authority
Neoliberal tendency to replicate regimes
Question of the chicken or the egg?
Future
Research
Suggestions and Speculation
Based on H1 and H2 results, income
may have something to do with it:
chicken or the egg?
Adopt new approach: redefine
viability; if PFA isn’t responsible, what
is?
Why does it matter?
→ The global community wants to see
Sub-Saharan African development.
Answering the PPP question may
help to accomplish this goal.
Acknowledgements:
Dr. Christopher Day (Advisor)
Dr. Karyn Amira (Secondary Reader and
Quantitative Methods Support)
Q&A
For more information on datasets, statistical output, or PPP in
general, please contact me:
Christopher Jackson
jacksoncw@g.cofc.edu
704-609-2987
Photograph URLs
http://blogs.worldbank.org/publicsphere/files/publicsphere/ppp_mooc.jpg
http://businessfacilities.com/wp-content/uploads/2015/06/public-private.jpg
https://upload.wikimedia.org/wikipedia/commons/6/65/Sub-Saharan_Africa_definition_UN.png
http://images.indianexpress.com/2015/09/airport.jpg
Hypothesis 1
(H1)
If PFA occurs in a
country in year X, Nx
in that country will
increase (+∆Nx).
H1 Dataset Sample
Country
Unit of
Analysis
Financia
l
Closure
Year
Nx ∆Nx PFA* Income
Group
1**
Income
Group 2
Income
Group 3
Imf_gd
+
Angola 2001 1 0 0 0 1 0 .16
Angola 2003 1 0 0 0 1 0 .16
*PFA: 0 indicates absence of PFA; 1 indicates PFA occurred
**Income Group Key: Lower income; Income Group 2: Lower middle;
Income Group 3; Upper middle (0 indicates criteria not met; 1 indicates
criteria met)
+
imf_gd: Amount of debt owed to IMF as expressed as percent
of GDP; scaled to 0-1 scale for consistency
Hypothesis 2
(H2)
If PFA occurs in a
given country at any
point, the total
number of PPPPs in
that country will
increase
H2 Dataset Sample
Country
(Unit of
Analysis)
Nx1990-
2015
Nx1990-2015
Scaled
PF
A*
Income
Group
1**
Income
Group 2
Income
Group 3
Aid_crnio
scaled+
Angola 9 0.1 1 0 1 0 0.15
Benin 5 0.05 1 1 0 0 0.08
Botswana 3 0.02 1 0 0 3 0.62
*PFA: 0 indicates absence of PFA; 1 indicates PFA occurred
**Income Group Key: Lower income; Income Group 2: Lower middle; Income Group
3; Upper middle (0 indicates criteria not met; 1 indicates criteria met)
+
aid_crnio: rescaled to 0-1 scale; max value=13; min value=0
Hypothesis 3
(H3)
If PFA occurs,
there will be a higher
probability that MC
occurs as well
H3 Dataset Sample
Project (Denoted by Country) YMC* YPFA** YMC ≥ YPFA+
Benin 2002 2002 1
Burkina Faso 2001 2009 0
Cameroon 2000 2006 0
Cameron 2006 2006 1
*YMC=Year of Multilateral Cooperation
**YPFA=Year of PFA
+
YMC≥YPFA= Does MC occur after PFA? Yes=1; No=0

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FINAL PRESENTATION

  • 1. Public Private Partnerships in Sub- Saharan Africa: A Policy Framework Analysis Christopher Jackson Moore Conference 2016, College of Charleston Department of Political Science Advisor: Dr. Christopher Day Panel 6
  • 2. What is PPP and why does it matter?
  • 3. Brief Overview of PPP ➔Public Private Partnership ➔Means to provide infrastructure services ➔Particularly interesting for developing countries
  • 4. Research Question PPP Policy Framework = PFA Does the Adoption of Public Private Partnership Policy Frameworks (PFA) Increase the Viability of Infrastructure-Based PPP Projects in Sub-Saharan African Countries?
  • 5. Existing Literature PFA = Success What about Sub-Saharan Africa?
  • 6. PPP Data in Sub-Saharan Africa Very limited → affected variable selection  World Bank endorsed PPP Database  High internal validity; lower external validity  Local reporting of data cannot have any accuracy guarantees  Drew from Quality of Government Database
  • 7. Variables: Defining Viability Independent Variable Policy Framework Adoption (PFA) -Governmental policies -Inclusion of segmented policies Dependent Variable* Indicators of Viability (IOV) 1.Change in Project Number from Year to Year (+∆Nx) 2.Total number of projects (Nx) 3.Multilateral Cooperation (MC) *Variable constraints due to data limitations
  • 8. Methodology 1.Logistic Regression 2.Linear Regression 3.Simple Percentage Test Why? -- verify claims on PFA in SSA Quantitative Methods
  • 9. Summary of Proposed Causal Relationships PFA – Independent Variable IOV 1: ∆Nx – Dependent Variable IOV 2: SA (Nx)- Dependent Variable IOV 3: MC – Dependent Variable PFA Occurs ∆Nx will be positive after PFA (+∆Nx) Total Nx in given country is increased Endorsement from multilateral organization Absence of PFA ∆Nx will equal 0 or be negative Total Nx is comparatively lower No endorsement from multilateral organization
  • 10. General Hypothesis: Hmain If PFA occurs in a Sub-Saharan country, the viability of PPP projects will be bolstered in that country.
  • 11.
  • 12. 33.1% PFA has no effect on Validity Hypothesis 3
  • 13. Hmain Accept the null hypothesis: The adoption of a policy framework (PFA) suggests no statistically significant effect on PPP viability
  • 14. Implications PFA may be wasteful and inefficient Literature on PPP and policy frameworks may not hold in Sub-Saharan Africa
  • 15. Why do we see this result? Lack of transparency Weakness of African legislative authority Neoliberal tendency to replicate regimes Question of the chicken or the egg?
  • 16. Future Research Suggestions and Speculation Based on H1 and H2 results, income may have something to do with it: chicken or the egg? Adopt new approach: redefine viability; if PFA isn’t responsible, what is? Why does it matter? → The global community wants to see Sub-Saharan African development. Answering the PPP question may help to accomplish this goal.
  • 17. Acknowledgements: Dr. Christopher Day (Advisor) Dr. Karyn Amira (Secondary Reader and Quantitative Methods Support)
  • 18. Q&A For more information on datasets, statistical output, or PPP in general, please contact me: Christopher Jackson jacksoncw@g.cofc.edu 704-609-2987
  • 20. Hypothesis 1 (H1) If PFA occurs in a country in year X, Nx in that country will increase (+∆Nx). H1 Dataset Sample Country Unit of Analysis Financia l Closure Year Nx ∆Nx PFA* Income Group 1** Income Group 2 Income Group 3 Imf_gd + Angola 2001 1 0 0 0 1 0 .16 Angola 2003 1 0 0 0 1 0 .16 *PFA: 0 indicates absence of PFA; 1 indicates PFA occurred **Income Group Key: Lower income; Income Group 2: Lower middle; Income Group 3; Upper middle (0 indicates criteria not met; 1 indicates criteria met) + imf_gd: Amount of debt owed to IMF as expressed as percent of GDP; scaled to 0-1 scale for consistency
  • 21. Hypothesis 2 (H2) If PFA occurs in a given country at any point, the total number of PPPPs in that country will increase H2 Dataset Sample Country (Unit of Analysis) Nx1990- 2015 Nx1990-2015 Scaled PF A* Income Group 1** Income Group 2 Income Group 3 Aid_crnio scaled+ Angola 9 0.1 1 0 1 0 0.15 Benin 5 0.05 1 1 0 0 0.08 Botswana 3 0.02 1 0 0 3 0.62 *PFA: 0 indicates absence of PFA; 1 indicates PFA occurred **Income Group Key: Lower income; Income Group 2: Lower middle; Income Group 3; Upper middle (0 indicates criteria not met; 1 indicates criteria met) + aid_crnio: rescaled to 0-1 scale; max value=13; min value=0
  • 22. Hypothesis 3 (H3) If PFA occurs, there will be a higher probability that MC occurs as well H3 Dataset Sample Project (Denoted by Country) YMC* YPFA** YMC ≥ YPFA+ Benin 2002 2002 1 Burkina Faso 2001 2009 0 Cameroon 2000 2006 0 Cameron 2006 2006 1 *YMC=Year of Multilateral Cooperation **YPFA=Year of PFA + YMC≥YPFA= Does MC occur after PFA? Yes=1; No=0