Data collection

The data for the dependent, main and control variables in this study were collected – or computed – from different sources. In this section I will identify the variables being measured and describe the methods used to obtain the data. All data was pre-existing or computed from pre-existing data.

Variables and measures

Table 1 summarizes the variables, measures, and data sources.

Number of FDI projects. The independent variable is the total number of Chinese (and U.S.) investment projects to Africa per host country over the period 2003-2011 as registered by MOFCOM (China) and fDi Markets (U.S.). Prior studies have used FDI flow (e.g. Buckley et al., 2007) or stock (e.g. Chen et al., 2016) to measure Chinese investments abroad. Although one could argue that FDI stock is generally a more important basis for requesting and deploying political capital for both companies and governments, FDI flow and stock are not very accurate measures of location choice for investment. The reason is that the results can be biased due to single large investments in especially the extractive industry. When using FDI flow or stock the results are therefore expected to be biased to countries with natural resources since the mining and oil industries require much higher investments than most other industries. With the resource-curse theory in mind, the use of FDI stock or flow could thus lead to bias towards more instable countries. There are good arguments for the use of both stock and number of FDI projects; however, the use of project numbers is generally regarded to be less biased and therefore I choose to use project numbers.

Table 1 Variables, measures and sources of data

Variable Description Measure Main or control

variable

Dependent Computed using data from fDi Markets and MOFCOM (2016)

POL Political stability Annual estimate of governance stability in host country

Main Data from ICRG, published by the PRS Group

INST Institutional maturity Annual estimate of governance of rule of law in host country

Main Data from worldwide

governance indicators database, World Bank (2017a)

31 The two main variables are IM (INST in the model) and PS (POL in the model). Prior studies have either used composite measures or two or more separate measures from the same database to measure the level of governance and institutions in host countries. For example, Buckley et al.

(2016) use both Government Stability and Rule of Law from the ICRG database to measure the quality of host countries’ institutions. Chen et al. (2016) look at “governance” using two measures from the Worldwide Governance Indicators: Rule of law and Political stability and absence of violence/terrorism. Child and Marinova’s (2014a) framework suggests to separate PS and IM.

Institutional maturity. Child and Marinova mention the Ease of Doing Business ranking from the World Bank as a measure for IM; however this ranking includes many more aspects of a good business environment than their description of IM, namely: ‘a situation in which a country’s institutions, such as its legal system and regulatory authorities, function in a transparent manner, adhering to clear rules that are applied in a universalistic manner to all citizens’ (2014: 353).

Therefore, I use “rule of law” from the WGI from the World Bank to measure the level of IM.

lnGDP Absolute market size Host country GDP at market prices (current USD)

Control Data from World Development Indicators (WDI) database,

Control Computed using data from ITC Trade map (2017)

INFL Host country

inflation rate

Host country annual inflation rate

Control Data from World Economic Outlook, International Monetary Fund (IMF, 2017) lnEXP Chinese (and U.S.)

exports to the host country

Total export per year Control Computed using data from ITC Trade map (2017)

lnIMP Chinese (and U.S.) imports from the host country

Total import per year Control Computed using data from ITC Trade map (2017)

lnIFDI Openness to FDI Ratio of inward FDI stock to host GDP

Control Computed using data from UNCTAD Stat (2017) and the WDI database, World Bank (2017b)

However, for a robustness check I replicate the measure using the Ease of Doing Business ranking from the World Bank as an alternative.

Political stability. PS is in prior studies often measured from its negative opposite: political instability or political risk. The much quoted study from Buckley et al. (2007) for example uses

“political risk” from the ICRG. The political risk rating from ICRG is composed of the following risk components: government stability; socioeconomic conditions; investment profile; internal conflict;

external conflict; corruption; military in politics; religious tensions; law and order; ethnic tensions;

democratic accountability; and bureaucracy quality (ICRG methodology at website PRS Group).

Since this composite political risk measure includes also measures for IM, I decided to focus on the component “government stability” only to measure PS.

The government stability component from ICRG is an assessment of both of the government’s ability to carry out its declared program(s), and its ability to stay in office. It includes the following three subcomponents: government unity, legislative strength, and popular support. The risk rating assigned is the sum of the three subcomponents, each with a maximum score of four points and a minimum score of 0 points. A score of 4 points equates to Very Low Risk and a score of 0 points to Very High Risk. The measure government stability (POL) therefore fits Child and Marinova’s (2014a) description of PS, namely: a country which governance system enjoys popular legitimacy, in which changes in government are orderly, and in which the policies of different governments exhibit substantial continuity (2014: 353). As a robustness check I replicate the measure using “political stability” from the WGI from the World Bank.

Control variables

I control for the standard variables that have been included as controls in prior research and that apply to my sample, including: market size, natural resource endowments, inflation, existing trade relations, and how welcoming the host country is towards FDI in general.

Market (GDP). The data are obtained from the WDI using GDP at market prices (current USD).

Natural resources (NREXP). Amighini et al. (2011) refer to Buckley et al. (2007), Cheung and Qian (2009), and Kolstad and Wiig (2012) and state that the results of these studies show that Chinese investments are motivated by the need to satisfy their growing demand for primary resources, especially for investments going to developing countries. However, Buckley et al. (2007) and Cheung and Qian (2009) actually find that natural resources are insignificant, and Kolstad and Wiig

33 (2012) find only an interaction effect for natural resources and weak institutions in the host country.

Despite these results, the popular assumption is still that Chinese firms are attracted to countries with natural resources, especially in developing regions. I use Buckley et al.’s (2007) measure for natural resource endowment, namely: the ratio of ore and metal exports to merchandise exports of the host country and add the categories precious stones and mineral fuels and oils (in order to include gold, silver, diamonds, oil and gas).

Inflation (INFL). The conventional idea is that macroeconomic instability (proxied by high inflation) is a deterrent for foreign investors. I use the World Economic Outlook to measure the inflation rate, measured as the percentage change in consumer price index.

Trade (EXP and IMP). Research has shown that trade and FDI have a strong relationship (Blomström

& Kokko, 1997; Wells, 1983). FDI could be in support of trade or trade could be a substitute for exports for example (see springboard theory by Luo & Tung, 2007). For the export to and import from African countries of China and the U.S. I use data in current dollars from the International Trade Centre (ITC) Trade map.

Welcoming to FDI (IFDI). Traditional FDI theory assumes that the more open a country is to international investment, the more attractive it is likely to be as a destination for FDI (Chakrabarti, 2001). I am using the same proxy for the openness of the host economy to international investment as Buckley et al. (2007), namely the ratio of inward FDI stock to host GDP. I do this by excluding respectively Chinese and U.S. FDI to the total FDI received by each host country.

I excluded Buckley et al.’s (2007) measures for cultural proximity, geographical distance, exchange rate and patent registration; and Amighini et al.’s (2011) measures for telephone mainlines, secondary education enrollment, research and development (R&D) expenditures and geographical distance. First of all, geographical distance is not very relevant for this study since the distance to the African host countries is not significantly different for both the U.S. and China. Cultural proximity is defined by Buckley et al. (2007) as when the percentage of ethnic Chinese of the total population is more than one per cent. However, when comparing between the cultural proximity of Chinese investors with U.S. investors African host countries then there are many other factors that play a role, like for example the enormous influence of U.S. food and pop culture in the world and number of African students that study in the U.S. versus in China. Coming from an anthropological background I find the concept of cultural proximity too complex to capture in a single or few measures. Amighini et al. (2011) use the number of telephone mainlines as a proxy to

indicate the availability of infrastructures and communication facilities in the host country.

However, in most African countries mobile phone connection is much more developed than landline connection (Kefela, 2011). Furthermore, the lack of road, rail and telecom infrastructure in many African countries is actually an important factor that attract Chinese (and other) investors rather than withhold them. Secondary education enrollment, R&D expenditures and patent registration are proxies for strategic assets that could be obtained when investing in host countries with high levels of human capital and advanced technologies. I decided to not include these measurements since strategic asset seeking motives are associated with investments in advanced markets and not in Africa (see for example Child and Rodrigues (2005)).