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.