Data and Operationalization

97 Hypothesis 2: An increasing number of reversals of institutional change will lead to lower FDI inflows over time.

Hypothesis 3: Countries that persistently face high intensity of institutional change will, on average, receive lower FDI inflows compared to more stable countries.

98 was logarithmically transformed using a technique to retain negative values used in Busse and Hefeker (2007).

𝐷𝐷𝐷𝐷_{𝑡𝑡𝑡𝑡} = ln (𝐹𝐹𝐷𝐷𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹_{𝑡𝑡𝑡𝑡}+�(𝐹𝐹𝐷𝐷𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹_{𝑡𝑡𝑡𝑡}²+ 1))

The data for the dimensions of the institutional change process is drawn from the World Governance Indicators (WGI) database, which was already used in time-series analyses before, testifying to its adequacy in portraying intra- and inter-country comparisons. The WGI also covers both de jure and de facto information on institutions by combining objective measures with expert survey data. Secondary databases like the WGI have the advantage that they draw from a variety of data sources, arguably averaging out some of the individual errors of the primary collection methods. Nevertheless, the quantitative measuring of institutions remains difficult (Voigt, 2013, 2018) and some concerns have been raised about the validity of the data (for an overview, see Thomas, M. A., 2010). Even if these problems of data validity and accuracy are critical, there is currently no feasible remedy to these shortcomings. The careful choice of data and the formulation of tentative conclusions seems to be the only way to address these issues at present.

Conceptually, I decomposed institutional change into the dimensions of intensity, volatility and persistency. The intensity of institutional change is proxied by the absolute change of an institutional indicator from one time period to the next (Variable: INT_CHANGE). I use the subcomponents of regulatory quality, voice and accountability, rule of law and government efficiency to reflect the formal institutional or regulative environment. The components control of corruption and political violence are excluded as they do not refer to structural institutional conditions.

𝐹𝐹𝐼𝐼𝐼𝐼_𝐶𝐶𝐶𝐶𝐶𝐶𝐼𝐼𝐶𝐶𝐶𝐶= |𝐹𝐹𝑤𝑤𝑤𝑤_𝑞𝑞𝑞𝑞𝑞𝑞𝐹𝐹𝑡𝑡− 𝐹𝐹𝑤𝑤𝑤𝑤_𝑞𝑞𝑞𝑞𝑞𝑞𝐹𝐹𝑡𝑡−1| where

𝐹𝐹𝑤𝑤𝑤𝑤_𝑞𝑞𝑞𝑞𝑞𝑞𝐹𝐹𝑡𝑡=(𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑡𝑡𝑟𝑟𝑟𝑟𝑟𝑟 𝑞𝑞𝑟𝑟𝑟𝑟𝑟𝑟𝑞𝑞𝑡𝑡𝑟𝑟 +𝑣𝑣𝑟𝑟𝑞𝑞𝑣𝑣𝑟𝑟 𝑟𝑟𝑎𝑎𝑎𝑎 𝑟𝑟𝑣𝑣𝑣𝑣𝑟𝑟𝑟𝑟𝑎𝑎𝑡𝑡𝑟𝑟𝑎𝑎𝑞𝑞𝑟𝑟𝑞𝑞𝑡𝑡𝑟𝑟 +𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟 𝑟𝑟𝑜𝑜 𝑟𝑟𝑟𝑟𝑙𝑙 +𝑟𝑟𝑟𝑟𝑣𝑣𝑟𝑟𝑟𝑟𝑎𝑎𝑔𝑔𝑟𝑟𝑎𝑎𝑡𝑡 𝑟𝑟𝑜𝑜𝑜𝑜𝑞𝑞𝑣𝑣𝑞𝑞𝑟𝑟𝑎𝑎𝑣𝑣𝑟𝑟) 4

To measure the volatility of institutional change, the simplest way would be to take the standard deviation. However, the variation around a mean would disregard the prevalence of reform reversal, i.e., a change of direction of the institutional indicator.

Moreover, as the standard deviation must be calculated over a period of

99 observations, it cannot be accessed in the context of year-on-year panel-regression.

Therefore, I generate a variable that represents the sum of sign-changes of the institutional indicator over a time period (Variable: VOL_CHANGE). For example, if regulatory quality improves over five years, there will be no volatility in the process. If, instead, regulatory quality improves two years, then worsens for two years and again improves for the last year, two sign changes will be reported as a time-variant dummy variable. There are more sophisticated ways to explore the variability of time-series data, e.g., by use of autoregressive moving average models and uncertainty measures in the style of information entropy. However, exploring these lies out of the scope of this paper and the limited time series makes it difficult to justify the usage of some of these methods.

Persistency cannot be captured directly in the form of an indicator. In order to see if intense and volatile institutional change has an impact on the long-term distribution of FDI between countries, I will use a cross-section regression that averages both independent and dependent variables over the time dimension (between-estimator). Figure 3.5 plots this average of the INT_CHANGE variable over the observation period against the average FDI inflows. The size of rectangles reflects the institutional quality index based on the WGI data. While Figure 3.5 seems to support the relevance of the proposed intensity measure, the variance around the trend is very high. This is not surprising in the context of cross-country comparisons using secondary institutional data, but also reminds us to interpret the results with caution. Moreover, institutional quality and the intensity of institutional change seem to be only slightly correlated in the long-run, again suggesting that the intensity of institutional change is an independent factor.

100 Figure 3.5: INT_CHANGE on the X-axis and FDI inflows averaged over country in 2004-2017 on the Y-axis. Line is a linear trend. The size of the rectangles represents a measure of institutional quality taken from the same WGI data.

The regressions control for several economic and socio-political factors. Market size is generally found to be a significant predictor of FDI. As market size is both a function of general economic development and the purchasing power of citizens, I use Gross Domestic Product per Capita in constant $US (Variable: gdp_pc). As a proxy for macroeconomic uncertainty, I use the change of inflation rate based on the GDP deflator (Variable: inf_change). Other studies have used the nominal inflation rate, which may cause two issues⁵⁰. First, both extreme levels of inflation and moderate levels of deflation can be a serious problem for economic activity.

Second, it is the volatility of the inflation rate which is of interest for foreign investors as it is the second-moment and not the first-moment that generates

50 For instance, Busse and Hefeker (2007) had trouble finding significant results for the inflation rate predictor.

101 uncertainty on future price levels. For the structural model, the standard deviation of the inflation rate is used as a proxy for its volatility (Variable: inf_vola), which is not available in the panel model.

In terms of socio-political factors, I added a WGI index score as a control for institutional quality (Variable: wgi_qual), which is evidently important to assure that there are no spurious correlations between our institutional change variables and the dependent variable explained by other institutional effects. To avoid correlations with INT_CHANGE, the institutional quality index is based on the WGI rank data and the full six WGI dimensions are used. I also include an index capturing the static component of institutional constraints as it was used in previous studies to represent non-market uncertainty. This variable is taken from the Polity IV database and represents the strength of political veto players in a country (Variable:

pol_con). Further individual country effects are captured by the fixed-effects regression. Control variables were chosen beforehand and the specification is kept parsimonious to avoid unnecessary assumptions (Aguinis, Ramani & Alabduljader, 2018).

The correlation matrix in Table 3.2 shows no worryingly high values for the independent variables. The strongest correlation is found between GDP per capita and institutional quality, suggesting that these two variables may be collinear.

However, the correlation is not above 0.6 and the sample used here is large enough to compensate for this. Using the cross-section of this data, the variance inflation factor scores stay below 2.8, which is not indicative of problematic multicollinearity.

102 Table 3.2: Correlation Matrix (Essay 2).

Mean SD 1 2 3 4 5 6

FDI inflow 7.26 3.03 1

INT_CHANGE 2.31 0.60 -0.17 1

VOL_CHANGE 3.63 2.36 0.07 -0.19 1

gdp_pc 7.87 1.25 0.38 -0.13 0.11 1

inf_change -0.04 2.08 -0.02 0.02 -0.08 -0.02 1

pol_con 0.24 0.15 0.04 -0.09 0.04 0.01 -0.03 1

wgi_qual 3.46 0.65 0.25 -0.15 0.03 0.58 0.02 0.26

All correlation coefficients >0.06 are significant at the 1% level.