Does economic globalization affect government spending? A meta-analysis

Definitions and images of globalization vary widely. In this paper, we focus on “economic globalization”, which is a much narrower concept than globalization per se, as the latter is a multifaceted concept consisting of different aspects of the economic, political and social sphere (e.g., Dreher et al. 2008a; Scholte 2008). Clark (2000, p. 86) prominently defines globalization as “the process of creating networks of connections among actors at multicontinental distances, mediated through a variety of flows including people, information and ideas, capital, and goods”. Various students of the globalization-spending relationship have developed a large number of indices to operationalize this multifaceted process of globalization. Herein, we restrict the analysis to the economic dimension capturing trade and financial openness. Brady et al. (2005, p. 922) conceptualize economic globalization as “the intensification of international economic exchange and the label for the contemporary era of international economic integration. Thus, [economic] globalization involves the current economic environment shaping welfare states and the heightening of concrete economic exchanges between countries.“

We follow accepted typologies of available economic globalization indicators, which distinguish between trade globalization, financial globalization and overall economic globalization, such that measures of the latter combine the trade and financial dimension (e.g., Squalli and Wilson 2011; Gräbner et al. 2018; Gygli et al. 2019). By far the most prominent trade globalization indicator is trade’s share in GDP (measured as the sum of imports and exports as percentage of GDP). In the dimension of financial globalization, researchers have adopted measures such as foreign direct investment (FDI) flows and indices for capital account liberalization. Furthermore, the globalization index of the Swiss Economic Institute (KOF) has emerged as the most popular overall economic globalization measure (Dreher 2006; Potrafke 2015). Our meta-data coding regarding the relevant economic globalization indicators follows the typology developed in Gräbner et al. (2018) along the dimensions of trade globalization, financial globalization and overall economic globalization (see the appendix for more details). Although our focus is on “economic globalization”, for the sake of simplicity the present paper generally refers to it as “globalization”.

A general consensus exists that “[w]e can no longer understand politics within countries—what we still conventionally call ‘domestic’ politics—without comprehending the nature of the linkages between national economies and the world economy, and changes in such linkages” (Milner and Keohane 1996, p. 3). In that context, academic researchers have proposed three competing theories. First, globalization may lead to substantial retrenchment in public spending. Second, globalization may cause government expenditures to expand markedly. Third, globalization nevertheless may have small (although not unidirectional) effects on government spending, but domestic factors may be more important determinants of public expenditures.

The first theory—globalization causes spending retrenchment—has been called the ‘efficiency’ hypothesis. In its simplest version, it stipulates that globalization reduces public expenditures because international competition effectively triggers a ‘race for the best location’ across nations, which fosters tax competition and leads to expenditure retrenchment as the more complete international integration of financial markets also penalizes deficit spending (e.g., Sinn 1997). The logic for capital mobility is that the greater potential for capital flight across borders has rendered financial market investors the ultimate arbiters of fiscal policymaking, as they effectively can ‘punish’ governments by selling-off government bonds (e.g., Streeck 2014). Hence, the simple version of the ‘efficiency hypothesis’ predicts that governments respond to globalization by introducing substantial cuts in public expenditures.

An extended version of the ‘efficiency’ hypothesis is more complex in the sense that it makes clear predictions about what kind of government expenditures will be affected negatively by globalization: here, the prediction is that globalization puts particular downward pressure on public expenditures for social protection and welfare, since the welfare state comes under attack owing to concerns that redistributive fiscal policies may hamper international ‘competitiveness’ (e.g., Alesina and Perotti 1997). While welfare spending is subject to globalization-induced fiscal consolidation pressures, the extended version of the ‘efficiency’ hypothesis concedes that other components of government spending nevertheless could be expected to respond positively to increasing international market integration; for example, public infrastructure or education spending may increase in response to globalization as governments try to make their locations more attractive to foreign investors, thereby ensuring international competitiveness (e.g., Schulze and Ursprung 1999). As different components of government spending can be expected to respond differently to globalization (in particular: spending on social protection and welfare vs. public infrastructure expenditures), it is not possible to test the extended ‘efficiency’ hypothesis by looking only at total government spending. Our meta-analysis will, therefore, consider different types of government spending.

The second theory—globalization causes expanding public spending—can be called the ‘compensation’ hypothesis. It makes the theoretical prediction that more trade and greater financial openness lead to larger public sectors. The argument basically is about political incentives for expansionary spending policies, as citizens demand that policymakers compensate them directly for the increases in external risk and social polarization that are associated with international integration. Integration of international trade and financial markets may increase economic inequality and insecurity. In that context, governments have strong incentives to recompense their citizens. In his seminal paper on the ‘compensation’ hypothesis, Rodrik (1998, p. 1019) argues that “[i]f government spending played a risk-mitigating role, we would expect to see this primarily reflected in income transfer programs and in social security and welfare spending.” Hence, while the simple version of the ‘compensation’ hypothesis does not make specific predictions about which types of government spending will be affected by globalization, the extended version states that governments respond to globalization by building stronger safety nets and engaging in additional welfare spending. Both the ‘efficiency’ as well as the ‘compensation’ theory assigns special roles to social spending, although their extended versions clearly make opposing predictions regarding the unidirectional impact of globalization on social spending.

The third view is skeptical about theoretical claims that globalization produces strong and unidirectional effects on government spending, as suggested by both the ‘efficiency’ and the ‘compensation’ theory. Such skepticism rests mostly on theoretical arguments according to which other factors are more important determinants of government spending than globalization. A comprehensive survey would go beyond the scope of the present paper, but the third view generally stresses the importance of each nation’s own history in terms of institutions and culture (e.g., Navarro et al. 2004) as well as of the structure of the welfare state and the political system (e.g., Cusack 1997; Kim and Zurlo 2009). Empirical studies zooming in on the domestic determinants of government spending focus on explanatory variables such as partisan politics and democracy (e.g., Garrett 1998b; Kittel and Obinger 2003), fiscal decentralization (e.g., Rodden 2003; Ha 2008), the changing age structure of the population (e.g., Razin et al. 2002; Shelton 2008) and the state of the domestic economy (e.g., Stephens et al. 1999; Kittel and Winner 2005). Nevertheless, it is important to note that recognizing domestic influences on public spending does not necessarily imply that globalization has no effect at all: the impact of globalization on various components of government expenditures still may be non-zero, but smaller than suggested by either the ‘efficiency’ or the ‘compensation’ hypothesis and, moreover, not unidirectional across countries.

Numerous contributions to the literature have tried to test the foregoing competing theories of how globalization affects government spending—with inconclusive results (e.g., Rodrik 1998; Kittel and Obinger 2003; Brady et al. 2005; Busemeyer 2009; Epifani and Gancia 2009; Marshall and Fisher 2015; Chuaire et al. 2017). Thus far, academic research has not made an effort to synthesize and explore the published estimates of the globalization-spending relationship; the present article closes that gap in the literature by applying meta-analysis and meta-regression tools.

The systematic search and review of the literature for this paper began by tracking down all relevant journal articles addressing the relationship between globalization and government spending. By doing so, we included papers from a variety of academic fields, most prominently political science, sociology and economics. To search for papers, we first accessed (1) Google Scholar, (2) the EconLit database and (3) the Scopus database. We included the following keywords in the search process: “globalization + government spending”; “openness + government spending”¹ (we also substituted “government spending” by “public expenditures” and “government expenditures”, respectively). Furthermore, we followed up on the references cited in empirical studies and reviews of the relevant literature by screening article reference lists. The criteria for including existing studies in the meta-analysis sample are as follows:

Seventy-nine journal articles fulfilled the four criteria, yielding a total of 1182 estimates for the meta-study dataset. In the accompanying appendix, we present a table listing all of the 79 included studies. The estimates compiled from those studies comprise our total population of the relationship between globalization and government spending.

Not all of the relevant coefficients found in the globalization-spending literature are directly comparable, as the variables entered into the regressions are measured on different scales. For example, if some estimates are based on log-transformations of the underlying variable, but other estimates are not based on such a transformation, the size of the coefficients is difficult to compare. To illustrate the magnitude of the effect of globalization on government spending, we therefore need to make all estimates comparable. In the context of the present paper, partial correlation coefficients measure the strength and direction of the impact of globalization on government spending, while holding other factors constant. The two main advantages of the partial correlation coefficient are: first, it can meaningfully be compared across articles because it is unit free and bounded between − 1 and 1. Second, it can be calculated for a much larger set of estimates than other effect size measures. Formally, the partial correlation coefficient is given by:where t is Student’s t-statistic associated with the relevant regression coefficient and df denotes the degrees of freedom for that t-statistic (Stanley and Doucouliagos 2012, p. 25). The standard error of the partial correlation coefficient (ser) is given by \({\text{ser}} = \sqrt {\frac{{1 - r^{2} }}{df}}\), where r again is the partial correlation coefficient and df denotes degrees of freedom. Summary results for the partial correlations and associated confidence intervals (e.g., Schmidt and Hunter 2014) are available in the accompanying appendix.

In line with standard techniques for meta-regression analysis (e.g., Stanley and Doucouliagos 2012), we adopt the assumption that the ith estimate of the globalization-spending partial correlation coefficient from study j, denoted r_ij, is influenced not only by sampling error (\(\varepsilon_{ij}\)), but by a vector of variables (\(Z_{kij}\)) consisting of characteristics that capture differences in the underlying effect of globalization on government spending. The meta-regression model can thus be written as follows:By estimating Eq. (4), we can account simultaneously for publication selection bias (the standard error SE is still included) and for the factors that might explain excess heterogeneity. We estimate Eq. (4) by WLS with the inverse of the variances as optimal weights. Stanley and Doucouliagos (2017) argue that the WLS estimator is preferable to other standard estimators that can be applied in meta-regressions. WLS is preferred since the estimates do not have the same variances and because it is important to assign more weight to those estimates that are more precise. However, we also will conduct robustness checks based on applying different estimators. As most of the studies in our meta-study database report several estimates, we also correct for potential within-study dependence by clustering the standard errors obtained from the meta-regression model at the study-level.

A few preliminary remarks are in order regarding the interpretation of the coefficients in the estimates of multivariate meta-regression models presented below. The models always omit one category (as the reference category) from each group of mutually exclusive and jointly exhaustive dummy variables (e.g., government spending or economic globalization measures). That is necessary to avoid perfect multicollinearity implying that the intercept \(\beta_{0}\) cannot be interpreted as the “true” effect of globalization on government spending because it incorporates the effects of the reference groups. Other specifications would yield different estimates of the intercept term. Our specification provides an estimate of the impact of trade globalization on total government spending, based on data from advanced countries only—with all variables entered in their levels.

Note that the choice of the omitted (reference) categories in no way influences any of the other estimated coefficients, but it shifts the empirical model’s intercept \(\beta_{0}\).⁹ Hence, the coefficients of the moderator variables from each group of mutually exclusive and jointly exhaustive dummy variables allow us to make predictions regarding the impact of economic globalization on government spending based on the chosen reference categories. For example, the estimated average partial correlation of social spending compared to total government spending (which is the reference category) can be predicted by adding up the value of the intercept \(\beta_{0}\) and of the social spending coefficient. Also be aware that we adopt a general-to-specific estimation approach, which is the suggestion of prominent guidelines for meta-analysis (e.g., Stanley and Doucouliagos 2012, p. 105). In particular, we eliminate the variable that returns the largest p value when all coded moderators in Table 2 are entered and repeat that step until the p values of all variables are smaller than 0.1.¹⁰ The advantage of the general-to-specific approach is “that model construction proceeds from a very general model in a more structured, ordered and (statistically valid) fashion, and in this way avoids the worst of data mining” (Charemza and Deadman 1997, p. 78).

Table 3 shows the general-to-specific modeling results from the multivariate meta-regression analysis.¹¹ Column (1) applies WLS with the standard errors clustered at the study level. The social spending variable returns a negative and statistically significant coefficient, indicating that—compared to estimates entering total government spending as the dependent variable (which is excluded as the reference category in the group of government spending measures)—estimates that model social spending alone report larger negative effects of globalization on total government spending. Indeed, our model predicts that globalization has a small-to-moderate negative impact on social spending of − 0.1 (obtained by adding the social spending coefficient to the intercept). While the finding of a negative and statistically significant coefficient on the social spending variable in column (1) clearly contradicts the extended version of the ‘compensation’ hypothesis, which predicts that globalization has a differential impact on various components of government spending and, in particular, will push up spending on social protection to compensate for the greater risks associated with international market integration (e.g., Rodrik 1998), it lends support to the extended version of the ‘efficiency’ hypothesis, which predicts the opposite, namely that social spending will be under more pressure from globalization than other components of public expenditures (e.g., Schulze und Ursprung 1999; see also Sect. 2.2). Notably, however, the size of the impact of globalization on social spending predicted by the model estimated in column (1) is small-to-moderate when its interpretation is based on the guidelines proposed by Doucouliagos (2011), which suggest that a partial correlation coefficient between zero and 0.07 must be considered to be small. In other words, the regression results do not predict a large negative impact of economic globalization on social spending, on average, but rather small-to-moderate downward pressure.

In fact, no government expenditure variable other than social spending carries statistical significance. The coefficients of the financial globalization and the overall economic globalization variables, however, are both positive and statistically significant. Those findings suggest that, compared to models that control for trade globalization, models that include financial globalization or overall economic globalization report larger (i.e., more positive) relationships between globalization and government spending. The meta-regression results suggest that the choice of the economic globalization variable indeed does matter and the ‘compensation’ effects reported in the literature tend to be stronger when the dimension of financial globalization is considered.

Furthermore, the results in column (1) of Table 3 indicate that, on average, estimates that rely on samples of developing countries find stronger relationships between economic globalization and government spending than those based on samples of advanced countries. That finding supports the argument that the composition of the country group moderates the impact of globalization on government spending in the sense that the ‘compensation’ mechanism, on average, is stronger in developing countries than in advanced countries. Note again, however, that the average ‘compensation’ effect in developing countries predicted by the model estimation in column (1) is rather small. Based on our categorization of reference groups, the model predicts that globalization has a small positive impact on total government spending in developing countries (− 0.05 + 0.12 = 0.07).

Furthermore, the results in model (1) of Table 3 suggest that the choice of functional form influences the reported partial correlations. The omitted reference category is the level level specification, i.e., when the spending and globalization variables both are entered in levels. It can be seen that estimates using functional forms other than level level report larger negative relations between globalization and government spending. Garrett (2001, p. 21) argues in favor of “an important analytic difference between the extent to which a given country is integrated into international markets in a given period of time (i.e., the level of integration) and the rate at which integration has increased between two periods (i.e., changes in market integration). The changes measure is much closer to conventional understandings of globalization, but the levels measure has been used in the best econometric work on the globalization and government spending relationship around the world.”

Our meta-regression analysis shows that the evidence reported in the existing literature indeed leans meaningfully more towards supporting the ‘compensation’ hypothesis when both the government spending and the globalization variable are specified in levels. In particular, the model in column (1) of Table 3 predicts a medium-sized negative impact of globalization on total government spending (–0.05 + –0.11 = –0.16) when the model is specified in first differences, which rejects the predictions of the ‘compensation’ theory clearly.

Several additional findings from column (1) of Table 3 merit highlighting. First, estimates based on OLS estimators lean more towards supporting the ‘compensation’ hypothesis than other econometric specifications. In contrast, the estimated coefficients for Random Effects and other estimators (e.g., IV, seemingly unrelated regressions) are statistically insignificant. Second, entering country fixed effects moderates the impact of globalization on spending: if country dummies are included, the reported partial correlation tends to lean more towards the side of zero. Third, we find that estimates published in economics journals report stronger (i.e., more positive) relationships between globalization and government spending. In other words, the economics literature reveals a tendency for reporting positive globalization-spending effects. Fourth, scholars who have contributed to the relevant literature previously report partial correlations that tend to be more negative. Fifth, three regressors seem to moderate the impact of globalization on government spending. On the one hand, the two macroeconomic controls (GDP growth and unemployment) return statistically significant coefficients, suggesting that empirical researchers wanting to model the relationship between globalization and government spending should pay particular attention to macroeconomic controls. On the other hand, estimates based on models in which terms-of-trade risk also is controlled for report larger negative effects of globalization on government spending. The coefficients on democracy are significant in three of the four models reported in Table 3. Finally, we can ask whether the evidence for publication selection bias reported in Table 1 is confirmed when we control for additional moderator variables. Indeed, the standard error coefficients in Table 3 have the same signs as the coefficients reported in Table 1; the size of those coefficients is similar. That finding can be interpreted in the sense that the multivariate meta-regression analysis confirms that some publication selectivity bias exists in the globalization-spending literature, but the magnitude of the bias is relatively small (Doucouliagos and Stanley 2013, p. 320). Finally, our meta-regression results do not find substantial evidence for time-varying effects in the globalization-spending relationship: the general-to-specific approach eliminates the continuous variable capturing the mean year of the data’s underlying structure because the coefficient on that variable is estimated to have very low precision.¹²

In columns (2)–(4) of Table 3, we test the robustness of the meta-regression results reported in column (1). We do so both by applying different estimators and by transforming the dependent variable. More specifically, the Random Effects model in column (2) introduces an additional between-study variance term to account for differences in the globalization-spending estimates that go beyond sampling error and differences captured by the moderator variables (e.g., Schmidt and Hunter 2014). The robust regression estimator applied in column (3) downweighs observations with larger absolute residuals and, hence, is less fragile to the influence of outlier observations. Finally, column (4) uses Fisher’s z-transformed partial correlation coefficients to account for the distribution of the partial correlations potentially not being normal when their values are close to − 1 and + 1. The results reported in columns (2)–(4) show that our baseline results largely remain unaffected when those robustness checks are introduced. The sizes of some of the coefficients and their standard errors are subject to variation. However, two cases arise in which a variable whose coefficient was estimated to be significant in column (1) turns out not to be significant in all three additional models: the coefficients on both overall economic globalization and democracy lose their significance when we apply robust regression methods. Besides that, the results in columns (2)–(4) of Table 3 indicate robustness.