Chapter 3 Research Methodology
3.5 Sample and Data
This section discusses the sample markets in the study, firms and sources of data used in the study.
3.5.1
Sample Markets and Firms
As discussed in Chapter One, the purpose of this study is to measure IC efficiency and compare it between developed, emerging and frontier countries. The purpose of this comparison is to determine if economic development plays any role in the performance of IC. Previous studies on IC produce quite divergent results. Some studies, Vishnu and Kumar Gupta (2014) and Chen et al. (2005) report a significant positive relationship between IC efficiency and firm performance in emerging markets, whereas Firer and Williams (2003) report no relationship. Similarly, Tan et al. (2007) report a significant positive relationship between IC and firm performance in developed markets whereas W. H. Su and Wells (2015) and Joshi et al. (2013) find no conclusive results in the Australian developed economy. Similar results are documented for the under-developed markets. These mixed results can be attributed to at least three reasons. First, there is no study in the literature that includes different types of market (developed, emerging and frontier) to look at the bigger picture. There is a gap in the literature whether economic development plays any significant role in the efficiency of IC or if IC can perform efficiently in any given scenario. Second, the existing published studies on IC rely on static measures such as OLS or FE to estimate the relationship between IC and firm performance. In other words, previous studies ignore the dynamic relationship between IC and firm performance (see chapter 4). Third, most studies use the original version of VAIC model, which suffers from criticism of its construction.
To address the first gap in the literature, we expand the study’s scope to three types of market, i.e., developed, emerging and frontier markets. As per the MSCI index, countries are divided into three categories, i.e., developed, emerging and frontier countries22. Five countries from each region are
selected based on their GDP per capita23. GPD per capita24 is applied as the first criterion in sample
selection because the IC efficiency is associated with GDP per capita where countries with a good
22 This list of categories is available from https://www.msci.com/market-cap-weighted-indexes.
23 Previous researchers who used multiple countries for comparison have resorted to random selection of the
countries (Kwan, 2003; De Jong et al., 2008; Young et al., 2009; T. Chen, 2013; Gigante, 2013; Berzkalne & Zelgalve, 2014);
GDP performance exhibit greater efficiency of IC (Navarro et al., 2011). Cañibano et al. (2000) argue that most manufacturing economies are quickly replaced by knowledge-based economies that ultimately increases the importance of IC. We apply the Knowledge Economy Index (KEI) as the second criterion in sample selection. KEI scores for each country are from the World Bank development indicators. Countries with higher GDP per capita as well as KEI (see Table 3.2) from each region (developed, emerging and frontier) are selected for the sample. The markets included in our study sample are presented in Table 3.2.
Table 3.2 Sample Markets from Developed, Emerging and Frontier Countries
Developed Markets Emerging Markets Frontier Markets
Market GDP KEI Market GDP KEI Market GDP KEI
Australia 67.46 8.88 China 6.80 4.37 Argentina 14.76 5.43 Austria 49.05 8.61 Malaysia 10.51 6.10 Nigeria 3.01 2.20 Netherlands 47.61 9.11 Russia 14.61 5.78 Pakistan 1.29 2.45 Singapore 55.18 8.26 South Africa 6.61 5.21 Saudi Arabia 25.85 5.96 Sweden 58.26 9.43 Turkey 10.94 5.16 Ukraine 3.90 5.73
Note: GDP is GDP per capita (amounts are in US$ 000) and KEI is the knowledge economy index. All data are sourced from World Bank Development Indicators 2013.
The next step is to select firms from each market. Firer and Williams (2003) and Zéghal and Maaloul (2010) argue that IC is necessary for firms in every sector hence it should be studied across all sectors. Although IC is important for all types of firm such as small or big, public or private (Kolachi & Shah, 2013), one advantage in selecting publicly listed firms is that data for listed firms are available publicly. Another advantage is that since the annual reports of publicly listed firms are always audited by reliable sources, it increases the reliability of the results (Chen et al., 2005). Based on Kolachi and Shah (2013) argument that IC is important for big firms with as many as 500,000 employees as well as for small firms with 50 employees, we select all publicly listed firms frim in the 15 markets. The study time period is 10 years (2005 to 2014) since Wintoki et al. (2012) argue that a panel data study of fewer than 10 years may produce biased results. The time period is specifically chosen to encompass the 2008 global financial crisis that provides a basis to analyse the role of IC in the performance of firms pre and post a financial crisis.
One of the limitations of the VAIC model is that it does not work for the companies with negative value added or losses (Firer & Williams, 2003). Pulic (1998) argues that since firms with negative income do not add any value, their IC efficiencies cannot be calculated. Thus, following previous studies (Shiu, 2006; Ting & Lean, 2009; Zéghal & Maaloul, 2010) we drop from the study firms with negative value added or negative operating profits. Firms in our sample should have at least four
years of data; firms with fewer than four years of data were deleted from the sample. There were 11,189 listed firms in the study time period but after carefully reviewing that the firms in the sample met all the above criteria, there were 7,117 listed firms left. Table 3.3 presents the markets list of firms in the sample.
Table 3.3 The Markets List of Firms in the Study Sample
Developed Markets Emerging Markets Frontier Markets Market Firms Market Firms Market Firms
Australia 571 China 2536 Argentina 74
Austria 75 Malaysia 874 Nigeria 83
Netherlands 96 Russia 689 Pakistan 215
Singapore 598 South Africa 256 Saudi Arabia 132
Sweden 290 Turkey 280 Ukraine 348
3.5.2
Data sources
This current study uses a monetary measure, i.e., the VAIC model to calculate the IC efficiency, quantitative performance measures such as ROA and ROE, and annual reports data to measure the variables. We obtained firms’ financial data from the Bloomberg database for the years 2005 to 2014. We also obtain country level data, such as GDP, and other country statistics from the World Bank development indicators 2013.
3.5.3
Data Transformation (Natural Logarithm)
The study’s scope is expanded over three major markets, i.e., developed, emerging and frontier markets, and covers all publicly listed firms. Therefore, varying size of the firms is expected. Another unique characteristic of the dataset in our study is that it includes more percentage form ratio variables such as ROA and ROE as dependent variables and efficiencies such as HCE and SCE as independent variables. Charbaji (2011) argues that ratio variables increase skewness in the data so one should log transform the data for better statistical analysis. Similarly, Osborne (2005) claims that log transformation improves data distribution for statistical testing. The author also argues that all data points remain in the same relative order as they were before transformation. Gujarati (2012) states that log transformation is popular in econometric analysis that measures the rate of change of the slope coefficient (β) Y against the X variable. However, one precautions is that if there are negative values in the dataset then log transformation might not be useful since a natural log of a negative number is not defined. Since firms with negative operating profits or equity were deleted
from our sample, following (Osborne, 2005; Charbaji, 2011; Gujarati, 2012), we take natural logarithms of the variables to increase the efficiency of the econometric analysis.
3.5.4
Data Analysis
We measure the IC efficiency scores for firms in each market with MS Excel and SPSS (version 22) to perform the descriptive analysis. Next we use STATA (version 12) to estimate the static models (OLS & Fixed-Effect) as well dynamic panel data estimator such as system GMM. All diagnostic tests such as unit root, heteroscedasticity and autocorrelation are performed in STATA.