1.
ASSESSING THE INFLUENCE OF CHINA-
AFRICA INFRASTRUCTURE INTERACTION ON
AFRICAN LISTED FIRMS
BY
LAWAL RODIAT YETUNDE: 676091
DEPARTMENT: FINANCE AND MANAGEMENT STUDIES

2.
Introduction
 Infrastructure hindering economic development in Africa
 For sustainable economic development: sizeable investment,
infrastructure and viable industry needed
 United Nations 2015 target: Industry, Innovation and
Infrastructure
 World bank 2017 predictions 1.7% growth rate
 Therefore, Africa needs a sizeable investment to infrastructure
gap

3.
Motivation
 Significant intervention by the traditional donors
 Alternative financial tools by the Chinese targeted towards infrastructure
development
 In Africa statistics available show:
 Chinese FDI increased rapidly from $0.5 billion in the year 2003 to
$43 billion in 2017 (Guillon & Mathonnat, 2019; UNCTAD, 2019)
 official development assistance projects increased 32 times from $0.3
billion recorded in 2000 to $15 billion in 2018 (Guillon & Mathonnat,
2019; Dahir, 2018).
 Chinese investment generated heated debate among scholars: win-win
partnership, debt trap, resource motive, political control
 Need for empirical testing: Prior emprics focused on macro aspect and
found mixed results
 Need to focus on micro aspect to assess the real benefits behind Chinese
investment.

4.
Research Questions
 What drives the interaction between China and Africa?,
 To what extent does China-Africa interaction affect the
performance of listed firms in Africa? and,
 What is the comparison between the performance of firms
that interact and firms that do not interact?.

5.
Contributions
 Firstly, we focus mostly on micro perspectives of China-Africa
engagement.
 Secondly, we use corporate governance variables as measures
of the interaction and develop the classification as interaction
through:
directorship
foreign ownership
 Thirdly, we empirically analyze the performance of listed
African firms that interact with the Chinese and the firms that
do not interact.

6.
Literature Review
 Concept: The role of FOCAC, Forms of investment, Agents,
How the relationship works
 Theories
Resource dependency and internalisation theory versus
agency theory
Structure-conduct-performance model (SCP), efficiency
model and firm effect model
 Empirical evidence: Positive (Ujunwa, Nwakoby & Ugbam,
2012; Peck-Ling, Aik & Lim, 2016), Negative (Madura, 2010;
Mustapha, 2011; Miletkov, Poulsen & Babajide, 2014) and No
relationship (Rose, 2007; Muraveyev, 2017).

7.
Methodology
 Data and sample
 Empirical strategy
 PERF = bo + b1 X + b2 INT + e (1)
 Heckman two-steps model
Selection equation:
INT= bo + b1 Z+ b2 GDP + b3 PRI + v (2)
Outcome equation
PERF = bo + b1 X + b2 INT + b3 IMR + b4 CV + u (3)

8.
Results
Model 1: Market value added
Heckman first stage: Determinants of Firm interaction
Variables Coefficient
PCD_lag1 1.880***
(0.178)
Size_lag! 0.016*
(0.010)
PRI 0.516***
(0.083)
Year dummy YES
Industry dummy YES
Country dummy YES
Observations 4024
Standard errors in parentheses
*** p<0.01, ** p<0.05, *<0.1

9.
Heckman second stage: Firm interaction on MVA
Model 1 (1a) (1b) (1c)
VARIABLES MVA MVA MVA
Firm interaction lagged 0.101** 0.110*** 0.090**
(0.040) (0.040) (0.041)
Presence of Chinese director 0.169*
(0.093)
Firm ownership 0.001*
(0.001)
Lambda 0.190*** 0.193*** 0.086**
(0.071) (0.071) (0.039)
INT*PCD 0.153
(0.093)
INT*FOWN 0.001*
(0.001)
Constant -0.492** -0.515** -0.264
(0.217) (0.218) (0.176)

10.
Alternative measure of firm interaction
Interaction through director
Heckman first stage :
Variables Coefficients
Legal origin 5.262*
(3.165)
Liquidity lagged 0.163***
(0.038)
Growth lagged 1.126***
(0.279)
Primary industry 0.666***
(0.124)
Year dummy YES
Industry dummy YES
Country dummy YES
Observation 4024
Standard errors in parentheses
*** p<0.01, ** p<0.05, *<0.1

11.
Heckman second stage and OLS: PCD on MVA
Variables Heckman second stage OLS
PCD lagged 0.026
(0.080)
0.072
(0.075)
Lambda 0.003
(0.102)
Constant 0.758*
(0.452)
0.338
(0.208)
R-squared 0.626
Observations 4,024 79
Standard errors in parentheses
*** p<0.01, ** p<0.05, *<0.1

12.
Interaction through firm ownership
Heckman first stage
Variables Coefficients
Legal origin 5.304*
(3.128)
Liquidity lagged 0.136***
(0.041)
MVA lagged 0.444*
(0.254)
Primary industry 1.055***
(0.131)
Year dummy YES
Industry dummy YES
Country dummy YES
Observations 4024

13.
Heckman second stage: Fown on MVA
Variables MVA
Firm ownership lagged 0.031
(0.066)
Lambda -0.339*
(0.188)
Constant 0.224
(0.633)
Observations 4024
Standard errors in parentheses
*** p<0.01, ** p<0.05, *<0.1

14.
Comparison of Performance
Model 1: MVA
Two-sample t-test
Variable Obs Obs Mean 1 Mean 1 T-value P-value
MVA: 0
1
4304 223 0.211 0.211 -0.05 0.971

15.
Model 2: ROA
Two-sample t-test
Variable Obs 1 Obs 2 Mean 1 Mean 2 T-value P-value
ROA: 0 1 4304 223 0.064 0.083 -2.5 0.013

16.
Model 3: Labour productivity
Two-sample t-test
Variable Obs 1 Obs 2 Mean 1 Mean 2 T-value P-value
LP: 0 1 4304 223 8.606 9.65 -2.95 0.003

17.
Conclusion
Based on the market value added results and
performance comparison results,
 The study concludes that the study concludes that China-Africa
infrastructure interaction affect non-financial firms’ market value added
positively in Africa.
More firms should be encouraged to interact in
China-Africa infrastructure projects, a policy
implication for African government.

18.
Thank you for listening