Written by: Luisa Blanco[*] and Cynthia L. Rogers
Tax competition and spillover models offer ambiguous predictions of tax haven impacts on non- tax havens. The implications of tax havens for less developed countries (LDCs), in particular, are not well understood and are little studied. This paper investigates the impact of tax havens on foreign direct investment (FDI) in non-tax haven LDCs. We investigate spillover and agglomeration factors by including measures of proximity to the nearest tax haven and the level of FDI inflows in the nearest tax haven. Employing cross sectional data of 115 LDCs from 1990 to 2006, we find evidence of spillovers from tax havens to nearby LDCs. FDI inflows in LDCs are positively and significantly related to FDI inflows in the nearest tax haven. Geographic diffusion has nuanced effects. LDCs at a medium distance to the nearest tax haven have higher levels of FDI than LDCs that are close to a tax haven. On the other hand, LDCs at a medium distance to a tax haven are less influenced by FDI spillovers compared with LDCs at closer proximity. Taken together, our results suggest that tax havens make good regional neighbors, but not good immediate neighbors for LDCs. These findings are robust to several model specifications, including one with spatially correlated error terms.
With the expansion of tax haven activities throughout the world has come an increasing concern regarding the causes and consequences of resulting international tax differentials. Indeed, tax havens have been touted as “parasitic” (Slemrod and Wilson, 2006) and “offshore pariahs” (Hampton and Christensen, 2002) that flourish at the expense of non-tax haven competitors. Such concerns have spurred anti-tax haven initiatives such as the Organization for Economic Cooperation and Development and European Union’s Harmful Tax Competition Initiative (Kudrle, 2008; Sharman, 2006; Ambrosanio and Caroppo, 2005; OECD, 1998), the United Nations proposed International Tax Organization (Horner, 2001), and the G20 Declaration on Strengthening the Financial System (G20, 2009), as well as national policies such as the proposed US Stop Tax Haven Abuse Act (Tanenbaum, 2009), and the Foreign Affiliates Rules in Canada (Huynh, Lockwood and Maikawa, 2007).
The potential negative impacts associated with tax haven activities are argued to be particularly harmful for Less Developed Countries (LDCs). Estimates of LDC annual revenue losses due to tax havens range from $15 billion (Gurtner, 2004) to $50 billion (Oxfam, 2000). Furthermore, several scholars predict that the anti-tax haven policies are unlikely to protect LDCs from the harmful impacts of tax haven activities (McLure, 2006; Altshuler, 2006; and Kudrle and Eden, 2003). The gloomy outlook for LDCs, however, focuses on the negative consequences of tax competition and overlooks the possibility that tax haven activities may benefit LDCs via positive spillovers (Altshuler, 2006). For example, Desai, Foley and Hines (hereafter DFH, 2006a) argue that multinational firms translate lower capital costs associated with tax haven activities into increased foreign investment in tax havens and non-tax havens. In addition, a rhome bias may arise from growing familiarity with regional investing opportunities, particularly those in LDCs which might otherwise remain unnoticed. Despite the controversy, understanding the mechanisms by which tax havens influence economic activity and capital investments in LDCs has been largely ignored in the literature.
We address this gap by analyzing the effect of tax havens in relation to foreign direct investment (FDI) inflows to LDCs using a large sample of LDCs from 1990 to 2006. Following DFH (2006b) we investigate regional agglomeration effects associated with tax haven activity.
In addition, we explicitly consider geographic diffusion of tax haven spillovers on non-tax haven LDCs by grouping LDCs according to their distance to the nearest tax haven. We find a significant regional agglomeration effect: FDI inflows in LDCs are positively associated with FDI inflows in the nearest tax haven. In terms of geographic diffusion, we find opposing effects. LDCs that are at a medium distance from a tax haven exhibit higher levels of FDI than LDCs that are close to a tax haven. On the other hand, increases in tax haven FDI inflows are estimated to have smaller spillovers for LDCs at a medium distance from a tax haven than for LDCs close to a tax haven. These findings are robust to several model specifications, including one with spatially correlated error terms.
Our findings suggest that tax havens may be good neighbors for LDCs and lend support to claims that positive spillovers may accrue to regional neighbors and are influenced by the level of regional agglomeration. Accordingly, our results are important for informing international tax policy discussions regarding appropriate responses to tax haven activities.
II. Empirical and Theoretical Evidence of Tax Haven Impacts on Non-Tax Havens
This section presents a brief summary of the main threads in the literature related to tax haven activity. The impact of tax haven activity on non-tax haven countries is ambiguous from a theoretical and empirical perspective, particularly for LDCs.
As discussed by Dharmapala (2008), theoretical and empirical evidence suggests that tax haven activity has positive and negative spillover effects on non-tax haven countries. Negative effects arise from tax competition, where tax havens are likely to attract capital that would have otherwise gone to non-tax havens, if taxation policies were equal. Slemrod and Wilson (2006) present a theoretical model in which capital flows out of non-tax haven countries as national firms try to take advantage of reduced corporate taxes in tax havens. Tax havens not only offer low taxation, but are also likely to offer an environment friendly to foreign capital. This makes it easier for multinational corporations (MNCs) to locate operations in tax havens. Thus, the presence of tax havens in a given region will influence location decision of MNCs.
Non-tax havens may also be positively impacted if tax haven policies enable MNCs to significantly reduce capital costs by increasing foreign investment in tax havens. For instance, Dyreng and Lindsey (2009) estimate that American firms with activity in at least one tax haven face a tax rate on pre-tax income that is 1.5 percentage points lower on average than firms with no activity in tax havens. These reductions in capital costs allow MNCs to invest more in non- tax havens (DFH, 2006a; Hong and Smart, 2007). In fact, DFH (2006b) find a positive relationship between the establishment of tax havens and sales and investment growth in neighboring non-tax havens. In a related vein, Rose and Spiegel (2007) argue that proximity to offshore financial centers, which are likely to be tax havens, leads to greater competitiveness in the domestic banking sector and financial depth of nearby non-tax haven countries. They provide empirical evidence of this positive spillover in capital markets.
The ambiguity of potential tax haven impacts on LDCs, in particular, is not addressed in the literature. To the extent that LDCs have fundamentally different economic structures and challenges compared with developed countries, understanding the impact of tax haven activities on LDCs is important. For LDCs, it is expected that the negative and positive effects of tax havens work as mentioned above. Tax haven activity could reduce capital inflows to a nearby LDC as firms are attracted by the lower capital costs associated with tax haven operations. On the other hand, tax haven activity might increase capital inflows in nearby LDCs as MNCs increase investment in nearby countries generate agglomeration benefits. Furthermore, the savings in capital costs could translate into greater capital inflows to LDCs overall.
To address these issues, our analysis investigates the channels through which capital inflows in tax havens affect capital inflows in LDCs. Following the literature we consider agglomeration aspects as well as the role of proximity in the nature of spillover effects.
III. Empirical Methodology and Data
To investigate the tax haven consequences for LDCs, we estimate a country level model of FDI inflows using a sample of 115 non-tax haven LDCs selected on the basis of data availability (see Appendix, Table A1 for a list of countries included). We limit our data to LDCs for obvious reasons. Evidence suggests that the common characteristics and low tax rate policies of tax havens are associated with higher levels of FDI. As a result tax policies are suspected to be endogeneous in typical empirical investigations.
The mechanisms through which tax havens attract FDI are likely to be different from those of non-tax havens, and this justifies the exclusion of tax havens in our sample. Furthermore, it has been argued that because LDCs and developed countries do not share the same coefficients in FDI models (Bloningen and Wang, 2005) pooling them together is inappropriate. Thus, excluding developed countries from our sample allows us to model the impact of tax havens on FDI inflows to LDCs appropriately.
Our model captures two key aspects of tax haven spillover impacts: regional agglomeration and geographic diffusion. Our baseline specification is as follows:
The dependent variable is the average of the natural logarithm of total FDI inflows from 1990 to 2006 in US dollars(millions).[5 ] FDI inflows were obtained from the United Nations Conference on Trade and Development (UNCTAD) website. THFDI is the average of the natural log of FDI inflows between 1990 and 2006 in theclosest tax haven. We estimate distance to the nearest tax haven using Mayer’s and Zignago (2006) distance data. As listed in Appendix, Table A2, the tax haven countries used to calculate this variable include those identified by Dharmapala and Hines (2006) for which the distance to other countries is available.
The cross sectional approach is necessary given that our primary variable of interest, namely distance to the nearest tax haven, is time invariant. Notably, the highly volatile capital inflows are averaged over thesample period. Thus, our resulting estimates are of long run impacts of tax haven activity. A further benefit of the cross-section approach is that it allows for a larger sample of LDCs in the analysis compared with time-series approach which would impose added data requirements.
We construct three dummy variables that account for different levels of proximity to the closest tax haven: CLOSE, MEDIUM, and FAR. The categories were chosen to form three equal size groups as a logical startingpoint since we have no a priori reason to impose a particular functional form on the proximity variables.[8 ] Because the proximity measures are not normally distributed, dividing the sample based on deviations fromthe mean resulted in a vastly different number of countries across the categories.Our model specifications omit the CLOSE dummy making this group the benchmark. The distance dummies are also interacted withTHFDI to capture geographic diffusion of spillovers. A positive (negative) estimated coefficient on the interaction term would indicate that greater FDI inflows in the nearest tax haven are associated with greater(lower) FDI inflows for countries in that distance group compared with the omitted group (countries that are in the close range).
The vector X represents a set of control variables identified in previous empirical analyses as important determinants of FDI. The baseline model includes typical control variables such as the initial level of realGDP, population, exchange rate, and trade openness.
Averages of these variables were estimated using the available observations for each country from 1990 to 2006 (all in natural logs). We also control for geographic conditions by including the country area (in naturallogs), a landlocked dummy, and regional dummies (Africa, America, Asia, Europe, and Pacific).
We consider other factors for robustness purposes. Institutional characteristics are also important control variables. We extend our basic model by adding three dummies that account for different dimensions of institutions: high corruption, British legal origin, and English language dummies. We alsoconsider including an indicator that accounts for the degree of fiscal freedom. As a final robustness check, we estimate models using the net FDI inflows instead of gross FDI inflows as the dependent variable specifiedin equation 1. A description of all the variables used in this analysis and their sources are presented in Table 1. Summary statistics are given in Table 2.
The model is first estimated using the ordinary least squares (OLS) estimator with White’s robust standard errors. We also estimate a model that considers structural instability in the form of non-constant errorvariances for the purpose of robustness. The spatial error model corrects for the potential bias resulting from the possibility that FDI inflows in one country may be dependent on the FDI inflows of nearby countries.The spatial error model is recommended when spatial dependence is expected in the disturbance term because the OLS estimator is no longer efficient and provides biased standard errors. (Anselin, 1988, 1999).
The error term in the spatial error model is specified as follows:
where λ represents the coefficient for the spatially correlated error and W is an NxN symmetric matrix that represent proximity between country i and country j. Following Bloningen et al. (2007) the W matrix isconstructed where the inverse of the distance from country i to country j is used for all non-diagonal terms. These are weighted so that one element of the matrix is equal to 1 for the two countries with the shortestbilateral distance and all the other elements of the matrix are decreasing as distance increases.[14 ] The spatial error model is estimated with the maximum likelihood estimator (MLE) and W is normalized so that eachrow sums to unity.
The Wald test will be used to determine whether errors are spatially correlated.
IV. Empirical Results
a. Estimation with Ordinary Least Squares
Estimates from our baseline model are shown in column 1 of Table 3. The estimated coefficients of the majority of the control variables (initial GDP, population, openness, exchange rate, land area, and landlockeddummy) are significant at the one percent level and have the expected signs. As a group, the regional dummy coefficient estimates are jointly significant, but only countries in the American region observe higherlevels of FDI relative to countries in the African region (the omitted category).
The estimates using this specification suggest that the impact of tax haven activities on FDI inflows to LDCs comes from both agglomeration and geographic diffusion effects. In termsof the agglomeration effect, we find that FDI inflows to tax havens have a positive and significant effect (at 1 percent level) on LDC FDI inflows. In relation to geographic diffusion, the positive coefficient for the MEDIUM dummy suggests that LDCs within amedium distance of a tax haven experience higher levels of FDI inflows than those that are closest, ceteris paribus. The coefficient on the FAR dummy is smaller and is not statistically different from zero suggestingthat the more distant LDCs don’t benefit more than the closest LDCs do. Taken together this suggests that regional a premium accrues to the LDCs within a medium range of a tax haven.
The estimated coefficient associated with the interaction term, THFDI*MEDIUM, is negative and statistically significant at the 5 percent level. This indicates that increases in tax haven FDI inflows are associated withsmaller FDI spillovers to LDCs within a medium distance of a tax haven. The distance interaction dummy for countries in the FAR category is also negative, but is smaller in absolute size and not statistically significant.In other words, tax haven spillovers are higher for those LDCs with the closest proximity to a tax haven.
The benchmark estimation suggests a nuanced relationship between proximity to a tax haven and FDI spillovers. LDCs within a medium distance of a tax haven have higher FDI inflows but smaller spillover effects. We estimate the net effect by using the mean THFDI values and the estimated coefficients for this variable, the tax haven proximity dummies, and the distance interaction terms. LDCs within a medium distance of a tax haven are estimated to have around 1.35 million ($US ) higher FDI inflows than LDCs which are close to a tax haven and 1.25 million ($US) higher FDI inflows than LDCs at a far distance. These static estimates suggest that close proximity to a tax haven is associated with lower FDI inflows, and being too close to a tax haven is not beneficial. In other words, from the non-tax haven LDC perspective, tax havens are good regional neighbors, but not good immediate neighbors.
For a dynamic perspective, we consider the net tax haven spillover effect on total FDI inflows to LDCs by simulating a one standard deviation increase in FDI inflows in the nearest tax haven. As shown in Table 5 row 1, the estimated impacts are increases of 1.77, 1.01, and 1.33 million ($US) in total FDI inflows for a LDC within the close, medium and far range of a tax haven, respectively. Thus, these estimates suggest that the net effect of the tax haven spillover is greater for LDCs that are close to a tax haven than for LDCs in the medium and far ranges. It also suggests a “U-shaped” geographic diffusion of tax haven spillover effects on FDI.
Omitted variable bias is a legitimate concern in our estimation since our results could be driven by other underlying causes that are related to tax haven activities. To address this concern, we introduce importantinstitutional and fiscal policy variables in our models. Institutional features are captured with dummy variables for countries with high corruption, with British legal origins, and with English as the official language.Results obtained by including the institutional variables in our benchmark model are shown in Table 3, column 2. The estimated coefficients of the variables that account for the tax haven spillover effect arestatistically significant at the 1 and 5 percent level, and their magnitudes are very similar to our previous estimates. The coefficients for the three institutional variables are all insignificant, and the F-test shows that,as a group, these variables do not contribute significantly to the model.
To account for fiscal policies, we also include the fiscal freedom index. Estimates obtained by adding the fiscal freedom index to the benchmark model are shown in column 3 of Table 3, where the sample is reducedto 111 countries due to data unavailability. The estimated coefficient for fiscal freedom is statistically insignificant and our previous results are robust to the inclusion of this variable. We conclude that these obviouspotential sources of omitted variable bias are not driving our results.
Another source of concern is the specification of our dependent variable. Specifically, proximity to a tax haven might be associated with FDI outflows in a specific country, which would not be captured using ameasure of FDI inflows. Accordingly, we estimate our benchmark models using average net FDI inflows as the dependent variable for robustness purposes. Estimates obtained when using the average of the naturallog of net FDI inflows are shown in Column 4 of Table 3. Our previous results are robust: the estimated coefficients for the variables of interest continue to be significant at the 1 and 5 percent level and themagnitudes of the coefficients are similar to those found before. Using the mean value of THFDI, LDCs within a medium distance to a tax haven have estimated net FDI inflows of 1.15 and 1.06 million ($US) more thanLDCs within a close and far distance to a tax haven, respectively. As shown in Table 5, row 2, a one standard deviation increase in THFDI is associated with an estimated 1.72, 1.02, and 1.58 million ($US) increase in net FDI inflows for LDCs within a close, medium and far range of a tax haven, respectively. Similar to our previous findings, the estimates using net FDI inflows as the dependent variable are robust to the inclusion of the institutional variables. These estimates are not included for the sake of brevity, but are available upon request.
b. Estimation with Spatial Correlation
Table 4 shows the results of using MLE estimation allowing for spatial correlation of the error term. Estimates for the basic spatial error model are shown in Column 1. We find no evidence of a spatially correlated errorterm.These estimates are very similar to the OLS estimates: THFDI is associated with greater FDI inflows, LDCs within a medium distance of a tax haven have higher levels of FDI inflows and have a smallerestimated THFDI spillover effect than LDCs that are close to a tax haven. Given that we find no evidence of spatially correlated error term, the estimates of the OLS are efficient and appropriate.
We estimate the net effect of a one standard deviation increase in THFDI using the estimates for the spatial correlation model shown in Column 1 of Table 4. As shown in row 3 of Table 5, an increase on THFDI of one standard deviation leads to FDI inflows increases of 1.78, 1.00 and 1.36 million ($US) for LDCs in the close, medium and far range of a tax haven, respectively. Thus, in this estimation, the spillover effect of THFDIinflows is again estimated to be greater for those LDCs within the closest distance to a tax haven.
Table 4, column 2 shows the estimates obtained using the spatial error model that controls for institutions (including the high corruption, British legal origins, and English language dummy variables). Again, we find no evidence of a spatially correlated error term and previous results are robust. Column 3 of Table 4 shows that the estimates using the net FDI inflows as dependent variable in the spatial error model. Again, there is no evidence of a spatial correlation in the error term and the estimated coefficients are significant and of similar magnitude to those found before (c.f. column 4, Table 3). Using these estimates, suggests that a onestandard deviation increase in THFDI leads to a 1.69, 1.03 and 1.61 million ($US) increase in FDI inflows for LDCs within a close, medium and far distance of a tax haven, respectively (see Table 5, row 4). Again, thespillover effect of tax haven FDI inflows is greater for those countries within the closest range of a tax haven.
To summarize, there are three interesting results. First, we find evidence of a positive agglomeration effect of tax havens on LDCs: higher FDI inflows in the nearest tax haven are associated with greater FDI inflows in LDCs. Second, in terms of the geographical diffusion effect of tax haven proximity, we find that countries within a medium distance of a tax haven have higher levels of FDI than LDCs in close proximity of a tax haven. This supports the argument that tax competition effects are more salient for LDCs in close proximity to a tax haven. Third, considering the interaction between agglomeration and geographical diffusion effects suggests a positive net effect of tax haven proximity. Estimates indicate that LDCs within a close range of a tax haven benefit more from increases in FDI inflows in the nearest tax haven compared with LDCs more distant from a tax haven.
The empirical analysis developed in this paper suggests that tax havens have a neighborhood effect on the FDI inflows to LDCs during the 1990-2006 period. We find that the overall spillover effect of tax havens isgreater for LDCs with a tax haven nearby. While there is evidence of opposing spillover effects of tax haven FDI inflows and proximity, the overall effect indicates that increases on FDI in the nearest tax haven willlead to greater FDI inflows for those LDCs within close proximity to a tax haven. This finding is relevant since it runs contrary to the popular argument that tax haven activity is likely to be especially damaging to LDCs. Our analysis indicates that there is a significant positive spillover from tax havens to LDCs, and this should not be ignored in the discussion of international taxation policy.
Our results support the predictions of spillover models. Like DFH (2006b) we identify the importance of regional agglomeration associated with tax haven activities. As a point of departure from DFH (2006b) our analysis also highlights the geographic diffusion of the spillovers from an LDC perspective. In terms of our sample country, increases in FDI inflows to the tax haven Belize will have the greatest impact on the LDCs thatare in close proximity, including El Salvador, Guatemala, Honduras, and Nicaragua. Likewise an increase in FDI inflows to the tax haven Switzerland will have greatest benefit for LDCs in close proximity, includingCroatia, Slovakia, Hungary, Bosnia, and Herzegovina. On the other hand, since countries located at a medium distance from the nearest tax haven are expected to have higher levels of FDI, Ghana and Senegal are likelyto face less competition with the nearest tax haven, Liberia, compared with Guinea and Sierra Leone, which are close to Liberia.
Our results warrant a few caveats. In contrast to typical tax haven studies where exogeneity of FDI is problematic, the FDI inflows to tax havens are likely to be exogenous to FDI inflows of non-tax haven LDCs. Even iftax havens are drawn to areas with an agglomeration of FDI activity (DFH 2006b), this activity is not likely driven by non-tax haven LDC activity. To the extent that it affects our results, we argue that it may not be thetax haven per se that drives the spillovers, but the effect of being in a FDI-rich region. We mitigate omitted bias concerns by including regional dummies, correcting for spatial error correlation, and includinginstitutional features as well as an index of fiscal-freedom. Omitted variable bias, however, may still be present. In addition, the exact form of spatial diffusion of tax haven impacts is unknown. The precise nature of geographic diffusion warrants further investigation.
Notably, our general conclusions are based on a single outcome measure, FDI inflows. To the extent that FDI inflows are related to growth outcomes (Hansen and Rand, 2006), our results suggest that there is a positiveneighborhood effect for LDCs. The importance of proximity to tax havens, however, is likely to have differential impacts on other outcome measures, particularly those that more closely capture welfare measures. For such outcomes, it is possible that tax competition forces could be more important than spillover influences for LDCs. According, our analysis provides a starting point for such future investigations.
From a policy perspective, we support Altshuler’s (2006) suggestion that the limited ability of LDCs to combat tax haven policies in other countries need not doom LDCs to a bleak future. That is, if indeed the net overall benefits to multinational firms result in greater capital investments in non-tax havens, including LDCs, then there is a potential that LDCs may fare better with the tax haven crumbs that spill over to them than they would in a world with less tax haven activity. Thus, the nuanced role of distance is important to consider. Our results suggest that from an LDC perspective, there is positive tax haven neighborhood effect.
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[*]Acknowledgement: We thank Rossitza Wooster for her helpful comments. We also thank Michelle Isenhouer and Josephine Huang for excellent research assistance. Send correspondence to Luisa Blanco firstname.lastname@example.org.
See Kudrle andEden(2003), McLure (2006), Oxfam(2000) and Gurria (2009) for further discussion.
Whereas, DFH (2006b) consider firm level demand for tax haven operations, we investigate the link between tax haven activity and FDI inflows from a country level (LDC) perspective.
Non-tax havens are those countries which are not designated as a tax haven by Dharmapala and Hines (2006). LDC designation follows the World Bank’s (2007) classification.
De Mooij and Ederveen (2003) and DFH (2004) discuss empirical analyses of FDI response to tax rates. Hines (2005) documents the conspicuous degree to which tax havens attract FDI. Blanco and Rogers (2010) discussthe endogeneity of tax haven policies in terms of growth outcomes.
Averages of the natural logarithmofFDI were constructed withthe available observations from1990to 2006.
FDI inflows can be considered as a proxy measure for country growth: Chowdhury and Mavrotas (2006), Hansen and Rand (2006), and Sylwester (2005) show that greater FDI inflows are associated with greater economic growth in LDCs.
Mayer and Zignago (2006) use the great circle formula to calculate the distance from the most important cities and agglomerations of one country to another. See details athttp://www.cepii.fr/distance/noticedist_en.pdf. Our approach is similar to that of Rose and Spiegel (2007).
See Table 1 for an explanation on how these dummies were constructed. Note that we experimented with four categories using natural breaks and quartiles. The results presented are qualitative the same.
The standard deviation of thedistance tothenearest tax haven category is almost 75 percent of its mean value.
Blonigen (2005) presents a comprehensive review ofliterature relatedtothe determinants of FDI.
Although several empirical analyses use the current level of GDP as a control variable, we use the initial level of GDP because FDI and GDP may be simultaneously determined. See Chowdhury and Mavrotas (2006), Hansen and determination of FDI and GDP growth.
Rand (2006) and Sylwester (2005) and Aharonovitz and Miller (2010) for a discussion of the simultaneous
Weomit the dummy forAfricancountries.
Blonigen et al. (2007) empirically analyze the determinants of FDI and find that there is spatial interdependence. They use a spatial lag model since they are interested in determining the existence and the natureof spatial interdependence. We do not use a spatial lag model because we are mainly interested in correcting for potential bias. Refer to Kelejian and Prucha (1999) and Kapoor et al. (2007) for a discussion of theproblems that arise when errors are spatially correlated.
The W matrix is constructed using the distance between the most important cities and agglomerations in countries (provided by Mayer and Zignago, 2006). The W matrix is a symmetric matrix since the diagonal terms, which represent the distance of one country to itself, are equal to zero. See Blonigen et al. (2007) for further explanation.
Our estimates show robust standard errors since the estimator of variance uses the Huber/White estimator instead of the traditional calculation.
Refer to LeSage (1999) for a discussion on how to estimate the spatial error model and how to test for the presence of spatially correlated errors.
[17 ]Using the Wald statistic, we fail to reject the hypothesis that the error term is not spatially correlate
Previously published by the Pepperdine University – School of Public Policy , April 2010
JEL classification codes: H87, F21
Keywords: Tax havens, Foreign Direct Investment, Less Developed Countries, Spatial Econometrics
Ever-increasing levels of foreign direct investment, the rising R&D intensity of multinational firms, and the growing volume of world trade between related parties together imply that the demand for tax havenoperations is likely to increase over time, as are the concerns of non-haven policymakers. (Desai, Foley and Hines, 2006b, p. 530)