Building Models And Analyzing African Iron And Steel Market For Chinese Iron And Steel Enterprises’ Possible Entry

The main objective of this thesis was to provide an understanding of internationalization, international market selection, the main factors influencing the intentional market selection and the main decision criteria entering into African market from Chinese Iron and Steel industry; and to provide them tools for analyzing African Iron and Steel Market. The method chosen to achieve this aim was mixed methods by using both quantitative and qualitative approaches. First, we started by reviewing literature on internationalization, international market selection, and international market selection methods. Secondly, we drew theoretical and conceptual framework from the frame of references, and built an interview guide to conduct our qualitative analysis. Thirdly, we conducted a qualitative research within Wuhan Iron and Steel (Group) Corporation (WISCO) to investigate Chinese Iron and Steel Industry’s main factors influencing Internationalization and International Market Selection, the main factors influencing International Market Selection entering into African Iron and Steel Market, and the main decision criteria when selecting African market. Fourth, the results of qualitative research within WISCO concerning the main factors influencing Chinese Iron and Steel Industry’s International Market Selection entering African Market has been set as hypotheses of the quantitative research. Fifth, we built a Model of Africa’s Demand for Iron and Steel Importation to test the generalization hypotheses, to analyze the causal relationships between African Iron and Steel Importation and its explicative variables, and to measure the influences of each explicative variable on African Iron and Steel Importation. Sixth, we built tools for analyzing African Iron and Steel Market taking account of the influences of each explicative variable on African Iron and Steel Importation; and analyzed African Iron and Steel Importation using the Tools we built. And finally, findings, conclusions and recommendations were given.The results of our qualitative analysis within WISCO reported that the international market selection of the Chinese Iron and Steel industry entering into Africa is primarily influenced by following external factors:Economic influences (GDP per capita, infrastructure and Exchange rate stability), Political and legal influences (political stability, trade barriers and general attitudes towards imports and direct foreign investment), and Market attractiveness (Market size, and growth rate, Profit and market potential); Concerning the influence of internal factors, the company’s international market selection into Africa in our case study has been primarily influenced by the Company capability profile factors (Resources, and Competitive advantage). Those factors are the same as the main factors considered by WISCO in his history of Internationalization. But the difference is that when considering African market, the main decision criteria of selection is Political Stability.We set the hypotheses that the main the factors influencing the international market selection of Chinese Iron and steel International Market Selection entering into African Iron and Steel Market are:economic factors and market attractiveness. Then we tested the existence of long term relationship between the five variables representative of those two factors (logarithm of iron and steel import, logarithm of GDP per capita, logarithm of infrastructure investment, the logarithm of the real effective exchange rate and the logarithm of the number of urban population).The results of our hypotheses testing brought us to confirm our hypotheses. The panel unit root tests and stationary tests were revealed that all variables have a unit root; in first differences, they are stationary, these variables are integrated of order 1 or I (1) and the level of the variables are I (1), so we can carry out the second part of the empirical analysis (Panel Cointegration tests) by taking the first difference of the variables, in order to check for the existence of a long-run relationship between them. The different cointegrations tests conducted between the dependent variable and the explanatory variables result in the confirmation of the fact that they are cointegrated, suggesting that there is a long-run relationship between the iron and steel importation data, GDP per capita, investment in infrastructure, real effective exchange rate and the number of urban population. Moreover also suggests that the specification of Error Correction Model (ECM) would be suitable for the representation of the theoretical relationship between the explanatory variables and iron and steel importation. The Pooled Mean Group (PMG) estimation method has been retained in comparison with the Mean Group (MG) method using Hausman tester which confirms that the first method provides consistent estimators of the model coefficients. The adjustment coefficient is (-φ= -0,586), significantly negative at 1% level of significance, Error Correction Modeling (ECM) specification is valid. Therefore we can consider the long-term relationship estimated in this model to perform our analysis. The Importations of iron and steel are positively correlated with all the independent variables of the model. All estimated coefficients are positive and significant at 1% level of significance. And finally, the usual determinants of importations, demand factor and factor price competitiveness, are significant in modeling of iron and steel importations and signs are consistent with expectations except the REER.As we tried to collect quantified data for the 3 factors (economic factors, political and legal factors, and market attractiveness), but for the political and legal factors, we didn’t find any quantified accurate data. The only data we found for these factors is a Political Stability and Absence of Violence/Terrorism percentile ranking from the World Bank’s Worldwide Governance Indicators (WGI). So we remove these factors from our modelBut through the analysis of our interviews and discussions with WISCO, we found that political stability is very important on account of it is the main decision criteria of WISCO’s international market selection entering to African Market, thus we decided to carry on an analysis of this variable and set it as the variable criteria for the preliminary screening as it is the main decision criteria for WISCO.Considering the above influences of the determinant of Demand factors, the Supply, and the above statement; we proposed to Chinese Iron and Steel industry Tools in order to analyze, compare and select African Iron and Steel Market. The Tools are Preliminary screen by political stability and Statistical final scoring model made by three steps.The preliminary screening was made by ranking the panel data by political stability variable as it is the main decision criteria of WISCO’s international market selection into the African Iron and Steel Market. We used Political Stability and Absence of Violence/Terrorism data of the Worldwide Governance Indicators (WGI),2014 Update. WGI’s Political Stability and Absence of Violence/Terrorism measures perceptions of the likelihood of political instability and/or politically-motivated violence, including terrorism. We sorted our 19 Africa countries of our study by Political Stability and Absence of Violence/Terrorism percentile value 2013 from the largest to smallest and selected the first 10m rank. The 10 countries that were selected to the next step of our study from our preliminary screening were:Mauritius, Seychelles, Zambia, South Africa, Senegal, Tanzania, Malawi, Mozambique, Togo and Morocco.And finally, results of the analysis of our final selection statistical inverse coefficient of variation scoring model taking consideration of the influences of our four variables from demand model and the potential supply of the market, our analysis of the final score Sp, can be concluded that the five countries that would be suggested to Chinese Iron and Steel companies are:first, Togo with a final score of 70.20, second, Tanzania with a final score 18.63, third, Morocco with a final score of 15.58, fourth, Senegal with a final score 14.57, and fifth, Mozambique with a final score 13.68.