Quantitative Method for International Business

Module Code: LUBS5901M01
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Module Title: Quantitative Method for
International Business
© UNIVERSITY OF LEEDS
Leeds University Business School Semester Two 2021/2022

Exam information:
There are 12 pages to this exam.
There will be 48 hours to complete this exam. We anticipate that this exam
should take students approximately
2 hours to complete.
This exam paper contains four questions.
Answer ALL questions.
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The deadline date for this assessment is 9am on Thursday 19th May 2022 (UK
time).
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this assessment.

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Module Code: LUBS5901M01
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Question 1.
In recent years, Multinational Enterprises’ (MNEs) strategies have been increasingly
impacted by an accelerating rate of change in the external environment. A research
project is carried out to investigate subsidiary performance amidst turbulence in China.
Subsidiary performance is expected to be dependent on choices that the MNE makes
when responding to environmental turbulence at the subsidiary level.
The researchers collected data from a questionnaire mail survey of senior managers
in MNE subsidiaries in China in 2015. They selected 400 foreign subsidiaries from a
list of clients or members provided by Shanghai Foreign Service Co., Ltd (SFSC),
China International Intellectech Corporation (CIIC), and the European Union Chamber
of Commerce in China. SFSC and CIIC are leading human resources service
providers in China with clients that were MNE subsidiaries including enterprises listed
in the Fortune 500. The geographical focus was foreign subsidiaries that have
investments in the Yangtze River Delta (Shanghai, Jiangsu and Zhejiang). This was
chosen because it represented the fastest growing and the most prosperous region
during China’s transition to a market economy, and one with potentially high variance
in terms of environmental turbulence.
The questionnaire was in English and sent to the senior managers of the 400 foreign
subsidiaries during the year 2015. 152 responses were received, yielding a response
rate of 38%. There are a diverse range of industries in the sample, including petroleum,
consumer goods, IT, industrial machinery, consulting, pharmaceuticals and
healthcare.
Define the population of the research project. Evaluate the applied sampling strategy
and the representativeness of the sample. Propose an alternative sampling strategy
and justify its effectiveness.
[20 marks]
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Question 2.
A firm is seeking to form an international joint venture (IJV) to achieve its international
expansion. The firm carefully selects four potential partners (A, B, C, and D) that have
complementary resources and assets. To further assess their suitability, the top
management team (TMT) collects quarterly financial information on the four potential
partners in the last 10 years. The boxplot is reported in Figure 2.1.
Figure 2.1 Boxplot of Return on Assets (ROA) of the potential partners
In addition, by analysing the data on IJVs within the industry, the TMT finds that the
longevity of the IJV (defined as the length of months an IJV survives) can be influenced
by the perceived trustworthiness of the partner, cultural distance, and the host-country
legal protection level. They visualise the industry data in Figures 2.2 to 2.4.
Accordingly, the TMT collects the information on those three factors for each of the
four potential partners and reports it in Table 2.1.
Figure 2.2 Scatter plot between Perceived trustworthiness and IJV longevity
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Figure 2.3
Scatter plot between Cultural distance and IJV longevity
Figure 2.4 Scatter plot between Legal protection level and IJV longevity
Table 2.1 The perceived trustworthiness, cultural distance, and legal protection level

Partner A Partner B Partner C Partner D
Perceived trustworthiness 40 30 55 50
Cultural distance 10 10 15 5
Legal protection level 2.0 1.5 2.5 3.0

Referring to the information provided, advise the firm which IJV partner to choose and
explain your answer.
[20 marks]
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Question 3.
A project examines the quality of institutions and their impact on firms’ strategies. The
study distinguishes two dimensions of institutional inefficiencies in a host country –
generalised and arbitrary – and explores their impact on the acquirers’ ownership
decisions in cross-border acquisitions (CBAs).
The institutional inefficiencies are defined as the problems in an institutional
environment that make it less effective. G
eneralised institutional inefficiencies are the
explicit problems in the rules of the game, which make the environment more difficult
for all firms to operate. And
arbitrary institutional inefficiencies are the problems in the
application of the rules of the game, arbitrarily privileging, or hindering firms.
Six hypotheses are developed:
Hypothesis 1. The higher the degree of generalised institutional inefficiencies
in a host country, the smaller the ownership acquired in CBAs.
Hypothesis 2. The higher the degree of arbitrary institutional inefficiencies in a
host country, the higher the ownership acquired in CBAs.
Hypothesis 3a. The acquirer MNEs’ CBA experience in the host region
weakens the negative relationship between generalised institutional
inefficiencies in a host country and the ownership acquired in CBAs.
Hypothesis 3b. The acquirer MNEs’ CBA experience in the host region
strengthens the positive relationship between arbitrary institutional inefficiencies
in a host country and the ownership acquired in CBAs.
Hypothesis 4a. For high-tech MNEs (in comparison to low-tech MNEs), the
negative relationship between generalised institutional inefficiencies in a host
country and the ownership acquired in CBAs is weakened.
Hypothesis 4b. For high-tech MNEs (in comparison to low-tech MNEs), the
positive relationship between arbitrary institutional inefficiencies in a host
country and the ownership acquired in CBAs is strengthened.
The hypotheses were tested using a sample of 5522 CBAs by firms entering emerging
economies. The results are reported in Table 3.1.
The variables involved in the analysis include:
Ownership acquired in CBAs, measured by the percentage – ranging from 10%
to 100% – of the equity acquired in the target firm;
Generalised institutional inefficiencies, measured by the mean value of the
firms’ perceived institutional inefficiencies, including tax administration,
corruption, political instability, business licensing and permit, and access to
finance, in that country and in that year;

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Table 3.1
Regression results

Model 1 Model 2 Model 3 Model 4
Est. p Est. p Est. p Est. p
Constant 18.937 .000 19.497 .000 19.830 .000 18.535 .000
(1.413) (1.203) (1.218) (1.501)
Transaction value -0.701 .001 -0.744 .000 -0.732 .000 -0.589 .004
(0.028) (0.207) (0.207) (0.205)
Acquirer assets -0.013 .000 -0.013 .000 -0.013 .000 -0.011 .000
(0.002) (0.002) (0.002) (0.002)
Cultural distance 3.185 .000 3.290 .000 3.317 .000 3.126 .000
(0.765) (0.679) (0.678) (0.763)
General inst. Inefficiencies -1.761 .009 -1.310 .058
(0.679) (0.691)
Arbitrary inst. Inefficiencies 2.840 .000 2.965 .000
(0.603) (0.601)
Acquirer CBA experience -0.302 .000 -0.365 .000
(0.041) (0.041)
Acquirer high-tech 5.863 .000 5.689 .000
(0.903) (0.689)
Generalised inst. Inefficiencies *
Acquirer CBA experience
0.073 .047
(0.027)
Arbitrary inst. Inefficiencies *
Acquirer CBA experience
0.209 .001
(0.039)
Generalised inst. Inefficiencies *
Acquirer high-tech
-2.286 .000
(0.043)
Arbitrary inst. Inefficiencies *
Acquirer high-tech
1.123 .103
(0.867)
Note. Dependent variable is ownership acquired, as a percentage. Standard errors are shown in parenthesis.

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Arbitrary institutional inefficiencies, measured by the variance of the firms’
perceived institutional inefficiencies, including tax administration, corruption,
political instability, business licensing and permit, and access to finance, in that
country and in that year;
Acquirer CBA experience in the region, measured by counting the total number
of CBAs that the acquirer MNE had completed in the target country’s region in
the past
;
Acquirer high-tech, a dummy variable based on the SIC code of the primary
business of the acquirer MNE – high-technology firms are coded as 1 and lowtechnology firms as 0;
Transaction value, measured by the logarithm of the total value of the deal;
Acquirer assets, measured by the financial value of the acquirer firms’ assets
(in millions of pounds);
Cultural distance, the differences of national cultures between acquirer and
target firms.
You are required to answer the following questions:
a) Write down the statistical models for Model 3.
[2 marks]
b) Suggest whether the results in Table 3.1 are consistent with any of the six
hypotheses. Please support your answer with detailed analysis (in terms of the
directions and statistical significances of the key explanatory variables).
[8 marks]
c) The CEOs in the emerging markets would like to know how the results of this project
can help them make ownership decisions in CBAs. How would you respond?
[12 marks]
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Question 4.
A research team is investigating the innovation performance of firms in emerging
markets and collects a sample of 47 firms. The dependent variable in the study is
innovation performance, measured by the number of patents. The impacts of the
following factors are examined:
R&D expenditure (the expenditure on R&D activities, in £k);
Foreign subsidiary (the number of foreign subsidiaries);
Foreign country (the number of foreign countries where the firm has
operations);
High-tech (dummy variable, 1 if the firm is a high-tech firm and 0 otherwise);
Firm size (the number of full-time employees);
Education (the average number of years in school for all full-time employees).
The researchers conduct a linear regression analysis. The results are reported as
follows:
Figure 4.1 Scatter plot of Foreign subsidiary and Innovation performance
Figure 4.2 Scatter plot of Foreign country and Innovation performance
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Figure 4.3 Residual histogram Figure 4.4 P-P plot
Figure 4.5 Scatter plot (𝑦̂ against 𝑒)
Table 4.1 Descriptive statistics
Table 4.2 Correlation
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Table 4.3
Model Summary
Table 4.4 Coefficients
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You are required to answer the following questions:
a) Evaluate the use of the linear regression model in explaining the causal
relationships between variables. Please include all the linear regression assumptions
in your answer.
[20 marks]
b) Discuss the practical implications of the study from the perspective of the managers.
[12 marks]
c) The research team categorises the dependent variable into a dummy variable, with
a value of 1 if the firm’s patent number is greater than median (i.e., 103) and a value
of 0 otherwise, and run a logistic regression. Write down the statistical model.
[3 marks]
d) The log-likelihood of the intercept-only model is -75.861 and the log-likelihood of
the unconstrained model in c) is -65.135. Given
𝜒02.05,6 = 12.59, would you recommend
that the variables in the logistic regression are useful to enhance our understanding
on emerging market firms’ innovation performance? Explain your answer.
[3 marks]