Appendix A: Alternative Independent Variables Considered
A number of domestic variables were considered when formulating the theory presented in this book, but were not included in the final hypotheses for the reasons outlined in this appendix. Indeed, throughout this project, colleagues, reviewers, and critical friends were kind enough to suggest the inclusion of a number of potential variables. The most notable among these included: the role of electoral politics in government intervention; the role of racism in government intervention; the presence of competing bidders; and the ownership structure of the acquirer. My aim was to create as parsimonious a theory as was possible on a complex subject. I therefore ultimately decided not to include variables that proved to be insignificant and/or whose inclusion as controls did not improve the explanatory power of the case studies or the fit of the statistical model.
Electoral Politics
I tested, but ultimately chose not to include, a variable on electoral politics. I initially considered this variable as a means of testing whether or not government interventions into foreign takeovers are correlated to the electoral calendar and/or are politicized to the advantage of politicians who are candidates in upcoming elections. Over the course of my case study research, however, I did not find electoral or partisan politics to have a significant impact on M&A interventions. This may be because in most cases it is difficult for a politician to use intervention of this type to “score electoral points” unless his or her constituents are already concerned about the geopolitical ramifications of the deal, or full of economic-nationalist zeal. Cases of intervention into cross-border M&A also rarely enter the public consciousness, and, even in cases of a formal veto, the general voting public is rarely brought into (or even aware of) the debate over intervention in the first place.
Let us consider the PepsiCo/Danone case, and the role played by the Franco-American clash over the Iraq War (see Chapter 3). This “clash” became a factor in government intervention as part of a wider geopolitical tension and rivalry between two allies (see Chapter 3, pp. 104–5). While intervention may have resonated in terms of electoral politics, the actions of the French government, even with its excellent “soundbites” on the issue, were more widely reported in the Financial Times and other financial outlets, such as Bloomberg, than in the French popular press. It would be difficult to determine what effect intervention had on a later election, or whether that played a role in the calculus of those involved in the French government – though I think it highly unlikely as a sole motivation, based on those I interviewed. Rather, the desire to balance US power in this instance was a genuine, albeit secondary, motivator of intervention, as was the healthy dose of economic nationalism that was the primary motivator of intervention at the time. Both of these factors may have been politically useful to some politicians, but I did not identify disingenuous instrumental advocacy of intervention for the purposes of electoral politics in this case. Even were that to be the case, it would be difficult for politicians to use intervention to their electoral benefit without the pre-existing condition of wider geopolitical concern or economic nationalism.
Indeed, the only case study where I found electoral politics to be of any real import was in the DPW case (see Chapter 4), where a handful of senators seemed to be raising the issue as part of an electoral strategy, aware that it would resonate with existing economic nationalist feeling within their particular constituencies. (Importantly, this was not true of all of the senators or of the other government stakeholders who came out against the deal). What the behavior of these few senators did contribute to, however, was the type of politicization that I argue makes this an outlier case. From my research, and based on those I interviewed, it became clear that this was historically the only extant case of M&A intervention to become this heavily politicized, making its dynamics unique. As discussed in Chapter 4 (pp. 164–6 and 172–86), this had negative consequences strategically (and politically) for the US, because it led to a strategic overreaction and, thus, overbalancing. I would have considered including levels of politicization of an individual transaction as a control variable if the frequency of such an occurrence were greater, but at this stage it does not appear generally significant across the history of government intervention into cross-border M&A, or across countries.
Racism
I also considered including racism as an explanatory variable. In my research, however, I came across only rare and isolated incidences of racist comments by individuals (see e.g., Chapter 3, p. 130 and Chapter 4, p. 175), and the individuals making these comments did not, themselves, seem to affect the nature or status of government action or intervention. Rather, greater scrutiny of the foreign investments emanating from particular countries seems to result from perceptions that they might be “threatening” to national security because of a recent rise in those countries’ relative power, combined with noticeable increases in the influx of FDI coming from them (see e.g., the discussion in the Introduction, pp. 7 and in Chapter 1; Graham & Marchick Reference Graham and Marchick2006; Meunier Reference Meunier2012; Tyson Reference Tyson1992). This is, however, different from racism, which though it may very well underlie threat perception in some outlier cases (see e.g., Chapter 4, p. 182), was not a consistent feature in the general debates over intervention and threat perception during the time period examined in this book.
Competing Bidders and Ownership Structure
Additional alternative explanations include the existence of a competing bidder (domestic or foreign) and the ownership structure of the acquirer. While I did not have the resources to include competing bidders as a variable in the statistical dataset, I do discuss their role in the case studies. In terms of direct government intervention on national security grounds, however, the only place where competing bidders realistically entered the equation were as interest groups lobbying against a particular acquirer, which is already accounted for in the control variable of interest group presence. Inclusion as an independent variable would, thus, run the risk of double-counting the effect of competing bidders on intervention.
Before finalizing my theory, I also tested the dataset and case studies for the impact of the ownership structure of the acquirer, and did not find it to be a significant variable, or to improve the fit of the model. There are a number of reasons for this, the most notable of which is that the ownership structure of a company alone may not necessarily account for the level of influence a foreign government actually has (or is perceived to have) over that company at a given point in time. The Chinese company Huawei, for example, is not a SOE, but is largely perceived to be heavily influenced by the Chinese government, especially in terms of its foreign acquisition strategy (US House 2012). Ultimate ownership of a company can also be difficult to fully discern from the outside, as it can easily be hidden through the use of shells, holding companies, and other means. As discussed in the case studies, ownership structure of the acquirer is an important factor. But it is also one that I believe is already accounted for in the independent variable examining geopolitical competition in each deal, and that is taken into account in the discussion of the specific national security concerns raised in each transaction (which can include state ownership of the acquirer). Having ownership structure as a separate variable could thus, again, potentially lead to double counting. To the extent possible, I did do some alternative tests for the impact of some ownership structures, such as SWFs, that might be seen as more likely to lead to interventions. But, again, this did not turn out to be statistically significant as an explanatory or control variable, and seemed to be overshadowed (and encompassed) by the variable of geopolitical competition in the case studies for each transaction in question. This may be because different SWFs have different agendas and remits, and the SWF (and state behind it) that is of geopolitical concern to one country may not be of concern to another. As discussed in Chapter 6, however, the one ownership structure that I think might be correlated to lower levels of intervention is that of institutional investors. Though testing the variables raised in Chapter 6 is beyond the remit of this particular book, it could potentially provide a further depth of understanding on this overall topic.
Appendix B: Descriptive Statistics of Variables in MNLMs I–IV
Independent Variable | Average Change | 0 | 1 | 2 | 3 | |
---|---|---|---|---|---|---|
Security Community | 0→1 | 0.09112 | 0.16672 | −0.06863 | 0.01552 | −0.11361 |
Relative Military Power | Min.→Max. | 0.23133 | −0.26748 | −0.06653 | −0.12864 | 0.46266 |
+/−1/2 | 0.00373 | 0.00502 | 0.00115 | −0.00747 | 0.00129 | |
+/− s.d./2 | 0.06995 | 0.09528 | 0.02194 | −0.13989 | 0.02268 | |
Marginal Effect | 0.00373 | 0.00502 | 0.00115 | −0.00747 | 0.00129 | |
Resource Dependency | Min.→Max. | 0.21416 | −0.42832 | 0.13471 | 0.25855 | 0.25855 |
+/− 1/2 | 0.10910 | −0.21819 | 0.09054 | 0.10371 | 0.02394 | |
+/− s.d./2 | 0.03746 | −0.07492 | 0.03195 | 0.03451 | 0.00846 | |
Marginal Effect | 0.10954 | −0.21907 | 0.09377 | 0.10048 | 0.02482 | |
Nationalism | Min.→Max. | 0.14197 | −0.19701 | 0.16722 | 0.11671 | −0.08693 |
+/− 1/2 | 0.24762 | −0.33287 | 0.27597 | 0.21927 | −0.16237 | |
+/− s.d./2 | 0.04854 | −0.07087 | 0.05784 | 0.03923 | −0.02620 | |
Marginal Effect | 0.25897 | −0.37918 | 0.30942 | 0.20852 | −0.13876 | |
Pro-Globalization Sentiment | Min.→Max. | 0.28770 | 0.41447 | 0.15956 | 0.00138 | −0.57540 |
+/− 1/2 | 0.02696 | 0.02992 | 0.02401 | −0.00825 | −0.04568 | |
+/− s.d./2 | 0.02742 | 0.03044 | 0.02440 | −0.00837 | −0.04647 | |
Marginal Effect | 0.02636 | 0.02888 | 0.02385 | −0.00842 | −0.04431 | |
Economic Competitiveness | Min.→Max. | 0.09046 | −0.11453 | −0.06640 | 0.00145 | 0.17947 |
+/− 1/2 | 0.01765 | −0.02019 | −0.01512 | 0.00145 | 0.03385 | |
+/− s.d./2 | 0.01507 | −0.01720 | −0.01294 | 0.00126 | 0.02888 | |
Marginal Effect | 0.01739 | −0.01974 | −0.01504 | 0.00151 | 0.03327 | |
Inward Foreign Direct Investment | Min.→Max. | 0.11714 | −0.05466 | 0.12120 | −0.17962 | 0.11308 |
+/− 1/2 | 0.00062 | −0.00043 | 0.00068 | −0.00082 | 0.00057 | |
+/− s.d./2 | 0.03320 | −0.02253 | 0.03584 | −0.04387 | 0.03056 | |
Marginal Effect | 0.00062 | −0.00043 | 0.00068 | −0.00082 | 0.00057 | |
Interest Group Position | Min.→Max. | 0.07062 | 0.01497 | 0.12627 | −0.12145 | −0.01979 |
+/− 1/2 | 0.01774 | −0.00073 | 0.03549 | −0.02911 | −0.00565 | |
+/− s.d./2 | 0.01558 | −0.00065 | 0.03116 | −0.02554 | −0.00497 | |
Marginal Effect | 0.01774 | −0.00081 | 0.03549 | −0.02901 | −0.00567 | |
Pr(y|x) | 0.70996 | 0.16820 | 0.07593 | 0.04591 |
Security Community | Relative Military Power | Resource Dependency | Nationalism | Pro-Globalization Sentiment | Economic Competitiveness | Inward FDI | Interest Group Position | |
---|---|---|---|---|---|---|---|---|
x= | 0.74877 | 5.51068 | 0.60780 | 0.49742 | 5.88792 | 4.84877 | 77.77100 | 6.73209 |
sd(x)= | 0.43479 | 17.37460 | 0.34215 | 0.18765 | 1.01634 | 0.85710 | 53.23630 | 0.87822 |
Independent Variable | Average Change | 0 | 1 | 2 | 3 | |
---|---|---|---|---|---|---|
Relative Military Power | Min.→Max. | 0.08911 | 0.17821 | −0.02880 | −0.14799 | −0.00143 |
+/− 1/2 | 0.00449 | 0.00786 | 0.00108 | −0.00897 | 0.00004 | |
+/− s.d./2 | 0.09847 | 0.17050 | 0.02546 | −0.19695 | 0.00099 | |
Marginal Effect | 0.00448 | 0.00785 | 0.00107 | −0.00897 | 0.00004 | |
Resource Dependency | Min.→Max. | 0.25547 | −0.49141 | −0.01954 | 0.44398 | 0.06697 |
+/− 1/2 | 0.09469 | −0.18939 | 0.02297 | 0.14820 | 0.01822 | |
+/− s.d./2 | 0.03127 | −0.06253 | 0.00978 | 0.04723 | 0.00552 | |
Marginal Effect | 0.08794 | −0.17589 | 0.02849 | 0.13208 | 0.01531 | |
Nationalism | Min.→Max. | 0.10227 | −0.19198 | 0.11596 | 0.08858 | −0.01256 |
+/− 1/2 | 0.18518 | −0.34529 | 0.19624 | 0.17412 | −0.02508 | |
+/− s.d./2 | 0.03502 | −0.06637 | 0.04018 | 0.02987 | −0.00367 | |
Marginal Effect | 0.18386 | −0.34863 | 0.21159 | 0.15614 | −0.01910 | |
Pro-Globalization Sentiment | Min.→Max. | 0.36010 | 0.60135 | 0.11742 | 0.00142 | −0.72019 |
+/− 1/2 | 0.01329 | 0.02258 | 0.00400 | −0.01351 | −0.01307 | |
+/− s.d./2 | 0.01385 | 0.02353 | 0.00417 | −0.01401 | −0.01369 | |
Marginal Effect | 0.01254 | 0.02134 | 0.00375 | −0.01364 | −0.01145 | |
Economic Competitiveness | Min.→Max. | 0.08383 | 0.10326 | −0.12758 | −0.04009 | 0.06441 |
+/− 1/2 | 0.02059 | 0.03340 | −0.03136 | −0.00983 | 0.00779 | |
+/− s.d./2 | 0.01757 | 0.02858 | −0.02675 | −0.00839 | 0.00655 | |
Marginal Effect | 0.02057 | 0.03376 | −0.03132 | −0.00982 | 0.00738 | |
Inward Foreign Direct Investment | Min.→Max. | 0.10046 | 0.20092 | −0.04167 | −0.12764 | −0.03161 |
+/− 1/2 | 0.00059 | 0.00118 | −0.00029 | −0.00076 | −0.00014 | |
+/− s.d./2 | 0.03069 | 0.06139 | −0.01449 | −0.03933 | −0.00757 | |
Marginal Effect | 0.00059 | 0.00118 | −0.00029 | −0.00076 | −0.00014 | |
Interest Group Position | Min.→Max. | 0.19701 | −0.29097 | 0.39364 | −0.10304 | 0.00037 |
+/− 1/2 | 0.05846 | −0.09037 | 0.11682 | −0.02656 | 0.00011 | |
+/− s.d./2 | 0.04971 | −0.07679 | 0.09932 | −0.02262 | 0.00009 | |
Marginal Effect | 0.05809 | −0.08963 | 0.11606 | −0.02656 | 0.00012 | |
Pr(y|x) | 0.77787 | 0.15374 | 0.06215 | 0.00625 |
Relative Military Power | Resource Dependency | Nationalism | Pro-Globalization Sentiment | Economic Competitiveness | Inward FDI | Interest Group Position | |
---|---|---|---|---|---|---|---|
x= | 6.26355 | 0.64045 | 0.49901 | 5.81662 | 4.90296 | 76.45350 | 6.73955 |
sd(x)= | 18.28520 | 0.35198 | 0.19037 | 1.03735 | 0.85326 | 51.37000 | 0.85166 |
Independent Variable | Average Change | 0 | 1 | 2 | 3 | |
---|---|---|---|---|---|---|
Relative Military Power | Min.→Max. | 0.47126 | −0.77529 | −0.05137 | −0.11587 | 0.94253 |
+/− 1/2 | 0.00994 | 0.01143 | −0.00614 | −0.01373 | 0.00845 | |
+/− s.d./2 | 0.21897 | 0.31222 | −0.11533 | −0.32261 | 0.12572 | |
Marginal Effect | 0.00990 | 0.01136 | −0.00613 | −0.01367 | 0.00844 | |
Nationalism | Min.→Max. | 0.22795 | −0.45591 | 0.33595 | 0.10897 | 0.01098 |
+/− 1/2 | 0.48887 | −0.94099 | 0.94437 | 0.03336 | −0.03675 | |
+/− s.d./2 | 0.06438 | −0.12876 | 0.06902 | 0.04839 | 0.01135 | |
Marginal Effect | 0.31879 | −0.63758 | 0.30040 | 0.26355 | 0.07363 | |
Pro-Globalization Sentiment | Min.→Max. | 0.18695 | −0.37391 | 0.22093 | 0.09266 | 0.06032 |
+/− 1/2 | 0.04427 | −0.08854 | 0.03934 | 0.02949 | 0.01971 | |
+/− s.d./2 | 0.04088 | −0.08175 | 0.03601 | 0.02736 | 0.01838 | |
Marginal Effect | 0.04255 | −0.08510 | 0.03539 | 0.02937 | 0.02034 | |
Economic Competitiveness | Min.→Max. | 0.06814 | −0.12960 | 0.00236 | −0.00668 | 0.13393 |
+/− 1/2 | 0.01770 | −0.03365 | 0.00074 | −0.00175 | 0.03466 | |
+/− s.d./2 | 0.01514 | −0.02879 | 0.00064 | −0.00150 | 0.02965 | |
Marginal Effect | 0.01762 | −0.03350 | 0.00075 | −0.00175 | 0.03450 | |
Inward Foreign Direct Investment | Min.→Max. | 0.40968 | −0.74522 | 0.45621 | −0.07415 | 0.36316 |
+/− 1/2 | 0.00191 | −0.00343 | 0.00108 | −0.00039 | 0.00274 | |
+/− s.d./2 | 0.12554 | −0.22773 | 0.08291 | −0.02334 | 0.16816 | |
Marginal Effect | 0.00191 | −0.00343 | 0.00108 | −0.00039 | 0.00274 | |
Interest Group Position | Min.→Max. | 0.49825 | 0.96866 | −0.99650 | 0.01125 | 0.01659 |
+/− 1/2 | 0.15071 | 0.30143 | −0.20371 | −0.02469 | −0.07303 | |
+/− s.d./2 | 0.14223 | 0.28446 | −0.18840 | −0.02437 | −0.07169 | |
Marginal Effect | 0.11195 | 0.22390 | −0.10172 | −0.03229 | −0.08989 | |
Pr(y|x) | 0.85855 | 0.02041 | 0.03871 | 0.08233 |
Relative Military Power | Nationalism | Pro-Globalization Sentiment | Economic Competitiveness | Inward FDI | Interest Group Position | |
---|---|---|---|---|---|---|
x= | 3.26682 | 0.49271 | 6.10042 | 4.68725 | 81.69770 | 6.70988 |
sd(x)= | 14.24750 | 0.18105 | 0.92834 | 0.85651 | 58.81650 | 0.96164 |
Independent Variable | Average Change | 0 | 1 | 2 | |
---|---|---|---|---|---|
Intervention Type | Min.→Max. | 0.49975 | −0.74962 | 0.44900 | 0.30063 |
+/− 1/2 | 0.37102 | −0.55653 | 0.26882 | 0.28771 | |
+/− s.d./2 | 0.36460 | −0.54690 | 0.26414 | 0.28276 | |
Marginal Effect | 0.41042 | −0.61563 | 0.29701 | 0.31862 | |
Pr(y|x) | 0.32086 | 0.28953 | 0.38961 |
Intervention Type | ||
---|---|---|
x= | 0.65550 | |
sd(x)= | 0.97861 |
Appendix C: MNLM III and Resource Dependency
For those cases in which security community = 0, the descriptive statistics for resource dependency are as follows:
Descriptive Statistics | |||||
---|---|---|---|---|---|
N | Minimum | Maximum | Mean | Std. Deviation | |
Resource Dependency | 51 | .14 | 1.82 | .5105 | .29308 |
Valid N (listwise) | 51 |
Resource Dependency | |||||
---|---|---|---|---|---|
Frequency | Percent | Valid Percent | Cumulative Percent | ||
Valid | .14 | 3 | 5.9 | 5.9 | 5.9 |
.19 | 1 | 2.0 | 2.0 | 7.8 | |
.20 | 1 | 2.0 | 2.0 | 9.8 | |
.21 | 3 | 5.9 | 5.9 | 15.7 | |
.22 | 1 | 2.0 | 2.0 | 17.6 | |
.31 | 2 | 3.9 | 3.9 | 21.6 | |
.33 | 1 | 2.0 | 2.0 | 23.5 | |
.35 | 1 | 2.0 | 2.0 | 25.5 | |
.36 | 3 | 5.9 | 5.9 | 31.4 | |
.38 | 3 | 5.9 | 5.9 | 37.3 | |
.39 | 7 | 13.7 | 13.7 | 51.0 | |
.55 | 2 | 3.9 | 3.9 | 54.9 | |
.59 | 8 | 15.7 | 15.7 | 70.6 | |
.59 | 1 | 2.0 | 2.0 | 72.5 | |
.60 | 2 | 3.9 | 3.9 | 76.5 | |
.61 | 2 | 3.9 | 3.9 | 80.4 | |
.62 | 1 | 2.0 | 2.0 | 82.4 | |
.63 | 2 | 3.9 | 3.9 | 86.3 | |
.76 | 2 | 3.9 | 3.9 | 90.2 | |
1.00 | 1 | 2.0 | 2.0 | 92.2 | |
1.01 | 1 | 2.0 | 2.0 | 94.1 | |
1.03 | 1 | 2.0 | 2.0 | 96.1 | |
1.03 | 1 | 2.0 | 2.0 | 98.0 | |
1.82 | 1 | 2.0 | 2.0 | 100.0 | |
Total | 51 | 100.0 | 100.0 |
For those cases in which security community = 0 and the dependent variable = 3, however, the descriptive statistics change dramatically:
Descriptive Statistics | |||||
---|---|---|---|---|---|
N | Minimum | Maximum | Mean | Std. Deviation | |
Resource Dependency | 6 | .14 | .60 | .4652 | .17938 |
Valid N (listwise) | 6 |
Resource Dependency | |||||
---|---|---|---|---|---|
Frequency | Percent | Valid Percent | Cumulative Percent | ||
Valid | .14 | 1 | 16.7 | 16.7 | 16.7 |
.36 | 1 | 16.7 | 16.7 | 33.3 | |
.55 | 2 | 33.3 | 33.3 | 66.7 | |
.59 | 1 | 16.7 | 16.7 | 83.3 | |
.60 | 1 | 16.7 | 16.7 | 100.0 | |
Total | 6 | 100.0 | 100.0 |
Appendix D: Descriptive Statistics of Dataset Variables: Frequencies
Statistics | |||||||||
---|---|---|---|---|---|---|---|---|---|
Intervention Type | Security Community | Relative Military Power | Resource Dependency | Nationalism | Pro-Globalization Sentiment | Economic Competitiveness | Inward Foreign Direct Investment | ||
N | Valid | 209 | 209 | 209 | 209 | 209 | 209 | 209 | 203 |
Missing | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 | |
Mean | .66 | .76 | 7.45 | .62 | .49 | 5.89 | 4.88 | 77.77 | |
Std. Deviation | .98 | .43 | 22.96 | .35 | .19 | 1.01 | .87 | 53.24 | |
Variance | .96 | .19 | 527.15 | .12 | .04 | 1.02 | .75 | 2834.11 | |
Minimum | .00 | .00 | .00 | .10 | .20 | 2.54 | 2.91 | −25.03 | |
Maximum | 3.00 | 1.00 | 158.86 | 1.83 | .76 | 7.97 | 6.94 | 164.53 | |
Percentiles | 25 | .00 | 1.00 | .08 | .38 | .37 | 5.28 | 4.17 | 31.96 |
50 | .00 | 1.00 | .47 | .59 | .47 | 6.25 | 5.01 | 70.69 | |
75 | 1.00 | 1.00 | 4.78 | .72 | .71 | 6.58 | 5.52 | 99.44 |
Appendix E: Bivariate Correlations of Dataset Variables
Statistics | |||||||||
---|---|---|---|---|---|---|---|---|---|
Security Community | Relative Military Power | Resource Dependency | Nationalism | Pro-Globalization Sentiment | Economic Competitiveness | Inward Foreign Direct Investment | Interest Group Position | ||
Security Community | Pearson Correlation | 1 | .104 | .182** | −.002 | −.117 | .126 | −.043 | .024 |
Sig. (2-tailed) | .135 | .008 | .973 | .092 | .070 | .544 | .732 | ||
N | 209 | 209 | 209 | 209 | 209 | 209 | 203 | 209 | |
Relative Military Power | Pearson Correlation | .104 | 1 | .370** | −.285** | .084 | .125 | −.244** | .086 |
Sig. (2-tailed) | .135 | .000 | .000 | .226 | .071 | .000 | .214 | ||
N | 209 | 209 | 209 | 209 | 209 | 209 | 203 | 209 | |
Resource Dependency | Pearson Correlation | .182** | .370** | 1 | −.529** | −.058 | −.153* | −.374** | −.039 |
Sig. (2-tailed) | .008 | .000 | .000 | .401 | .027 | .000 | .574 | ||
N | 209 | 209 | 209 | 209 | 209 | 209 | 203 | 209 | |
Nationalism | Pearson Correlation | −.002 | −.285** | −.529** | 1 | .364** | .486** | .389** | .329** |
Sig. (2-tailed) | .973 | .000 | .000 | .000 | .000 | .000 | .000 | ||
N | 209 | 209 | 209 | 209 | 209 | 209 | 203 | 209 | |
Pro-Globalization Sentiment | Pearson Correlation | −.117 | .084 | −.058 | .364** | 1 | .625** | .369** | .801** |
Sig. (2-tailed) | .092 | .226 | .401 | .000 | .000 | .000 | .000 | ||
N | 209 | 209 | 209 | 209 | 209 | 209 | 203 | 209 | |
Economic Competitiveness | Pearson Correlation | .126 | .125 | −.153* | .486** | .625** | 1 | .065 | .670** |
Sig. (2-tailed) | .070 | .071 | .027 | .000 | .000 | .357 | .000 | ||
N | 209 | 209 | 209 | 209 | 209 | 209 | 203 | 209 | |
Inward Foreign Direct Investment | Pearson Correlation | −.043 | −.244** | −.374** | .389** | .369** | .065 | 1 | .385** |
Sig. (2-tailed) | .544 | .000 | .000 | .000 | .000 | .357 | .000 | ||
N | 203 | 203 | 203 | 203 | 203 | 203 | 203 | 203 | |
Interest Group Position | Pearson Correlation | .024 | .086 | −.039 | .329** | .801** | .670** | .385** | 1 |
Sig. (2-tailed) | .732 | .214 | .574 | .000 | .000 | .000 | .000 | ||
N | 209 | 209 | 209 | 209 | 209 | 209 | 203 | 209 |
** Correlation is significant at the 0.01 level (2-tailed).
* Correlation is significant at the 0.05 level (2-tailed).
Appendix F: Negative Case Selection
In Chapter 6, four negative cases were chosen for study on the basis of insights from Mahoney and Goertz's “Possibility Principle” (see Mahoney & Goertz Reference Mahoney and Goertz2004; Skocpol Reference Skocpol and Skocpol1984). These scholars’ approaches needed to be slightly adapted, because of the probabilistic nature of this study's hypotheses and the use of continuous independent variables.
First, Mahoney and Goertz's Possibility Principle offers some “ideal” guidelines for case selection, but these are not always practical or practicable for all forms of inquiry. The Possibility Principle posits that “only cases where the outcome of interest is possible should be included in the set of negative cases; cases where the outcome is impossible should be relegated to a set of uninformative and hence irrelevant observations” (Mahoney & Goertz Reference Mahoney and Goertz2004, 653). Yet, this methodology is most useful for those using typological theory or Boolean algebra, rather than the type of probabilistic theory employed in this study (Mahoney & Goertz Reference Mahoney and Goertz2004, 654).
Furthermore, their approach is ideal for research that primarily employs dichotomous variables, which this work does not – and their method for selecting “relevant cases” on the basis of continuous variables is problematic. They argue that “the analyst” should examine the range of each independent variable from the minimum to the maximum, and for each variable “must [then] decide and justify the exact threshold or cutoff point at which the outcome is considered possible” (Mahoney & Goertz Reference Mahoney and Goertz2004, 659). “In practice,” they claim,
one often sets this threshold at a fairly high level (e.g., >.50 [assuming a variable that is continuous from 0 to 1]) to ensure that at least one variable is clearly present in all cases. Under some circumstances, however, the analyst may be better served by intentionally setting the threshold at a lower level. This is especially true if the analyst has good reason to believe that the higher threshold will exclude too many cases as irrelevant
In other words, the application of the principle to continuous variables requires that the author make a subjective choice about the level at which each variable can be considered to have reached a threshold beyond which the hypothesized outcome will be affected. Such subjective choices are always open to counterargument, and are even more difficult to make when more than one outcome must be taken into consideration. This author set the threshold first at the median value of each independent variable (because the variables used in this dataset do not all have a 0–1 range, this was considered the closest approximation to the “>.50” advised level), and then again at the average value of each. Each threshold is high, but still only a negligible number of cases could be dismissed as “irrelevant” in this manner. This is likely because the population of cases has already been well defined by sector and size. Setting the threshold any higher would unnecessarily exclude cases as “irrelevant” that might not be.
Furthermore, Mahoney and Goertz argue that irrelevant cases should be extracted from the sample of cases studied, primarily because their inclusion increases the potential that the true significance of the relationship between the variables will be hidden, or deflated (Mahoney & Goertz Reference Mahoney and Goertz2004, 654). Yet, if the relationship still shines through with a certain degree of clarity, this is not necessarily as great a problem as if the relationship were inflated. In other words, “irrelevant” cases might make the researcher's job harder, but this certainly doesn't detract from any significant relationships between the dependent and independent variables that are found. It only implies that those relationships might be even stronger than they appear. Finally, by asking the researcher to make subjective decisions that affect the “relevant”/“irrelevant” divide within a case universe, Mahoney and Goertz's approach may actually hide some vital and interesting observations that can be drawn from a population of “negative” cases. Populations that haven't been parsed in such a manner may offer valuable insights both for a particular hypothesis, and for the building of the greater theory surrounding it.
What is important, however, is the basic argument on the part of Mahoney and Goertz that negative cases should be selected on the basis that they exhibit similar values on the independent variables to “positive cases,” and that the positive outcome was therefore “possible” in these cases (see Mahoney & Goertz Reference Mahoney and Goertz2004, 653–4). Thus, having excluded the “irrelevant” using possibility principle, the key is to make a determination concerning which cases within the population are most relevant. As the approach taken within this study is a probabilistic one, the threshold values set earlier can be used in conjunction with qualitative data on the variables to show which cases were most likely to have had a positive outcome (i.e., which had the highest presence of economic nationalism and/or geopolitical competition concerns). A random sample of four was chosen from among these cases. In summary, then, each of the four cases discussed in Chapter 6 could have resulted in a positive outcome (i.e., bounded or unbounded intervention), because of the presence of a high level of economic nationalism and/or geopolitical competition, but did not, and can therefore be considered “relevant” for the purposes of hypothesis testing according to the Possibility Principle.