The large dataset collected by the Afrobarometer research project allows us to quantify popular perceptions of China’s economic and political engagement in Sub-Saharan Africa. Afrobarometer is a pan-African research institution measuring public attitudes toward democracy, economy, and society on a regular basis (Afrobarometer 2020). In the sixth round of the survey (2016) respondents were asked about their feelings concerning FDI and donor states. This data has not been often used despite the great interest in the topic and the important potential evidence for the future of foreign investments in developing countries.
4.1 Database
Data is collected through face-to-face interviews. The sampling is based on a random selection method with the aim of gaining maximum representation. The probability of including is proportional to the population size ‘wherever possible to ensure that larger (i.e. more populated) geographic units have a proportionally greater probability of being chosen into the sample’ (Afrobarometer 2016). The survey in Round six (2016) included interviews with more than 50,000 citizens in 36 African countries representing the views of more than three-fourths of the continent’s
population (Afrobarometer 2016). The high number of cases on the individual level allows us to gain a high external validity and thus the possibility of generalization with high explanation force.
To measure the amount of inward FDI on the macro level, we use data from the American Enterprise Institute (AEI), a U.S. conservative public policy think tank. As part of the China Global Investment Tracker, the AEI has counted Chinese investments and constructions since 2005. The Chinese Investment Tracker now includes 3,400 large transactions across energy, transportation, real estate, and other industries. The long period of FDI tracking is the most important advantage of this AEI data. Most other FDI databases, especially the official UNCTAD bilateral statistics, cover only several years of China’s foreign investments. Another positive aspect of this AEI data is their detailed, project-based tracking. This not only includes the amount of inward FDI; it also contains the Chinese company behind these investments and the sector in which these firms are active. With this valuable information, we are able to count FDI on the regional level. This disaggregation leads to a higher number of cases on the macro level and thus to better outputs.
4.2 Case Selection
The database used for this quantitative analysis includes 46,000 Observations in 32 African countries. These states are Benin, Botswana, Burkina Faso, Burundi, Cameroon, Cape Verde, Cote d'Ivoire, Ghana, Guinea, Kenya, Lesotho, Liberia, Madagascar, Malawi, Mali, Mauritius, Mozambique, Namibia, Niger, Nigeria, Senegal, Sierra Leone, South Africa, Sudan, Swaziland, Tanzania, Togo, Uganda, Zambia, Zimbabwe, Gabon and São Tomé, and Príncipe!. Four countries are excluded: Egypt, Tunisia, Algeria and Morocco. These countries are located in northern Africa and thus not fully comparable to the other states on the continent. In general, North African countries are economically more developed than countries below the Sahara (BBC 2004). Those countries are predominantly Arab and thus have many cultural differences as well. Collectively, these factors suggest that people in North African states have different perceptions of China and are thus not comparable. That is why this research focuses only on sub-Saharan Africa.
4.3 Operationalization Dependent Variables:
The explained or dependent variable measures the ‘attitude towards China’s growing economic and political influence’. The respondents were asked the following question: ‘In general, do you think that China’s economic and political influence on [your country] is mostly positive, or mostly negative, or haven’t you heard enough to say’? Respondents could choose among five different options to express their support or oppose China’s influence on their countries (5 = very positive/ 1= very negative). This categorical variable is used as the dependent variable for all regression models in this analysis.
Independent Variables:
The independent variable in the first hypothesis is the amount of inward FDI. It is the sum of all Chinese investments and construction projects from 2005, the first year of tracking, to 2016, the year the survey was published. We use three different measurements for inward FDI to strengthen the explanation force, as the amount of FDI must be set in relation to the country’s size. The first variable measures the amount of inward FDI relative to the country’s economic performance as measured by its gross domestic product (GDP). The amounts of FDI (in millions) have been divided by the amounts of the countries’ GDPs. The values describe how much FDI per one million GDP has flowed into the country. The second variable measures variable on the country level as well but relative to the state’s population (per capita).
The values describe how much FDI per one million inhabitants has entered the country. To create a more detailed image of where FDI goes, this analysis attempted to specify the exact placements of these Chinese investments on the regional level.
Through extensive research, we were able to disaggregate approximately 300 Chinese investments included in the AEI database and link them to specific regions that are included in the Afrobarometer survey (details in Appendix). On the regional level, the FDI amounts are not set in relation to the province’s economic force or their population. On this level, GDP is not reliable – not least because the included regional borders of several African states have been redrawn.
On the individual level, four independent variables are used. For the second hypothesis, the explaining variable measures the personal professional situation – if respondents have low- or high-skill jobs. A question asked of the respondents is
‘What is your main occupation’? The answers were categorized in line with the classifications of the United Nations Labour Office (ILO), including four different skill levels. The independent variable for hypothesis 3 measures the level of education. This happens with a question in the Afrobarometer survey that asks the citizens about their highest education level. Similar to skill level, the education variable is divided into four categories. The independent variable for hypothesis four classifies the respondents’ living environment – whether they live in rural or urban areas. This dichotomous classification was made by the interviewer (all interviews were conducted face-to-face). The explaining variable for the fifth hypothesis measures the respondents’ individual living conditions. In the Afrobarometer survey, people were asked how they describe their own present living conditions. !Their answers are classified into five different categories where 1 means
‘very bad’ and 5 ‘very good’. The last independent variable is the respondents’
perceptions of economic change in their countries. The question in the survey asks
‘How do you rate economic conditions in this country compared to twelve months ago’? Respondents could choose between five options where 5 means ‘much better’
and 1 ‘much worse’. It is clear that a change in only twelve months might be difficult to determine. However, people’s perceptions are likely based on a general feeling about economic performance in their country, so the time period does not functionally matter.
Control variables:
To control whether our independent variables are in fact relevant in shaping people’s perceptions of China’s growing political and economic influence, several control variables are included in the analysis on both the micro and macro levels. On the individual level, we include two important variables that were tested already by Hanusch (2012). He found strong evidence that respondents concerned about human rights have less favorable attitudes toward China. For democracy, his results are somewhat weaker. The individual support for human rights is measured with the following questions: do people think that every country has the duty to guarantee free elections and prevent human rights abuses? Further, respondents were asked if they think that a country should sanction other countries that abuse human rights.
The answers are categorized into four groups. The support for democracy is measured with a question asking if democracy is preferable to any other kind of government or if under certain circumstances another form of government is better.
This variable contains three categories where a value of 3 signifies the highest support for democracy. On the country level, four control variables are included in the regression model. The first control is GDP per capita, which controls whether the economic performance of a country influences how people perceive China’s engagement in their country. The second control is the Human Capital Index (HCI).
It measures the degree of development in a country. The HCI is based on the three pillars: child survival, level of education, and health. The index ranges between 0 and 1 (Worldbank 2020b). An important factor influencing people’s attitudes can also be social inequality. This variable is measured by the Gini Index, which indicates the degree of inequality in income distribution. The index describes how much of the total income of an economy is attributable to a certain proportion of the population (FU Berlin 2020). The final macro control variable is the Polity IV index measuring the level of democracy in a country.
4.4 Method
For this research, multilevel regression models were calculated to test the hypotheses. This allows the calculation of the effects of predictors on different levels:
micro and macro. The multi-level regression gives the great advantage of performing both simultaneously (University of Bristol 2019). In our case, the dependent variable is on the individual level – a precondition of a multi-level regression – while the level of the independent variables vary. For hypothesis 1, the predictors are on a macro level, while the predictors are on the individual level for hypotheses 2 through 6.
However, as both levels of analysis are highly connected to our research question, the individual and macro levels are not regarded separately. To see whether the amount of FDI affects the impacts of our micro-level socio-economic variables on people’s perceptions of China, separate models for calculating interaction effects were formulated.