The Robustness of Parametric Estimates of Pollution Abatement Costs on Manufacturing Plant Births Across U.S. States

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1 The Robustness of Parametric Estimates of Pollution Abatement Costs on Manufacturing Plant Births Across U.S. States Daniel L. Millimet Southern Methodist University Jeffrey S. Racine McMaster University Abstract The impact of environmental stringency on capital flows remains a hotly contested issue. Moreover, existing parametric estimates using counts of new foreign-owned manufacturing plant births provide counter-intuitive results: pollution abatement costs are only found to have an impact in non-pollution-intensive sectors. Applying recently developed nonparametric count data methods, we re-visit this issue through the analysis of panel data across U.S. states from While there are gains to the nonparametric approach, we continue to find a modest deterrent effect in non-pollution-intensive sectors only. JEL: C14, C52, F21, Q52, R12 Keywords: Foreign Direct Investment, Environmental Regulation, Nonparametric Econometrics, Count Data Models The authors wish to thank Arik Levinson and Wolfgang Keller for generously providing the data. Racine would like to gratefully acknowledge support from Natural Sciences and Engineering Research Council of Canada (NSERC: the Social Sciences and Humanities Research Council of Canada (SSHRC: and the Shared Hierarchical Academic Research Computing Network (SHARCNET: Corresponding author: Daniel Millimet, Department of Economics, Box 0496, Southern Methodist University, Dallas, TX Tel: (214) Fax: (214)

2 1 1. Introduction Over the past two decades, foreign direct investment (FDI) has risen dramatically relative to trade volumes (Ramondo (2005)). Consequently, understanding the factors affecting the spatial distribution of FDI flows is crucial, not only for local policymakers, but also for policymakers concerned with issues surrounding international trade, global environmental problems, and interjurisdictional competition. Specifically, it is vital to know if, and to what extent, FDI flows are influenced by environmental regulation (the so-called Pollution Haven Hypothesis (PHH)). If FDI flows are affected by environmental policies, numerous implications immediately follow. First, environmental policies may need to be placed under the umbrella of international trade agreements, or else unfettered discretion over environmental regulation may give policymakers an instrument by which to influence trade patterns and undermine trade agreements (e.g., Ederington & Minier (2003)). Second, international environmental treaties may require full participation by all countries or else risk being undermined by a relocation of polluting activities to countries opting not to participate. Finally, governments may be required to establish homogeneous environmental policies at the national level, as opposed to relegating decision making power to the state- or regional-level, or else interjurisdictional competition may lead to a race-to-the-bottom in environmental stringency (e.g., Fredriksson & Millimet (2002)). While the ramifications are profound, unfortunately the existing empirical evidence is mixed. On the one hand, List & Co (2000) and Keller & Levinson (2002) examine the spatial distribution of inbound US FDI across states, finding at least some evidence that location decisions are influenced by environmental stringency. Fredriksson, List & Millimet (2003) revisit the issue, controlling for political corruption as well as the potential endogeneity of environmental stringency. The authors continue to obtain an inverse relationship between state-level environmental stringency and inbound US FDI. However, important for the analysis presented herein, the effect is found to be non-linear. Xing & Kolstad (2002) analyze the spatial distribution of outbound US FDI in six industries across 22 host countries. Using SO 2 emissions as a measure of environmental stringency for the host countries, and treating this measure as endogenous, the authors find support for the PHH for the chemical and primary metals industry. Wagner & Timmons (2004) examine the spatial distribution of outbound German FDI in six industries across 55 host countries. Employing a dynamic panel data approach to control for agglomeration effects and utilizing survey data on business executives subjective assessment of host country environmental stringency, the authors

3 2 find evidence supporting the PHH in the most pollution-intensive industries. Lastly, Dean, Lovely & Wang (2005) conclude that the spatial distribution of equity joint ventures across Chinese provinces is affected by environmental stringency measured in terms of effective levies on water pollution as well, but only from certain parent countries and then only in pollution-intensive industries. On the other hand, Henderson & Millimet (2007) revisit a portion of the analysis in Keller & Levinson (2002) using nonparametric techniques, finding the results to be quite sensitive to functional form. Specifically, environmental regulation plays a much smaller role in explaining the spatial distribution of the stock of inbound US FDI across US states; where the impacts remain statistically significant, the magnitudes are reduced. Moreover, the nonparametric methodology also indicates substantial heterogeneity in the effects of greater regulatory stringency across states and over time. List, McHone & Millimet (2004) find that the location decisions of new foreignowned plants across counties in New York State are unaffected by variation in regulatory stringency generated by the US Clean Air Act using a semi-nonparametric propensity score matching estimator. This result is consonant with the findings described above in Dean et al. (2005). While Dean et al. (2005) do find some evidence that pollution costs matter, the authors fail to find any evidence in support of the PHH when analyzing the location decisions of equity joint ventures originating from countries more distant (both geographically and culturally) from China. Eskeland & Harrison (2003) find little impact of variation in environmental costs on the distribution of FDI across industries into three of four developing countries; the authors do find modest evidence in support of the PHH for Côte d Ivoire. Finally, Javorcik & Wei (2004) uncover no effect of variation in environmental costs on the spatial distribution of FDI across Eastern Europe and the former Soviet Union. See Copeland & Taylor (2004) for a review of the literature. In light of the ambiguity that characterizes the literature to date, further assessment is vital. In this paper, we re-visit the analysis in Keller & Levinson (2002). There, the authors utilize state-level panel data on FDI inflows and FDI stocks to the US, as well as an innovative measure of relative pollution abatement costs (RAC), and find robust evidence that abatement costs have had moderate deterrent effects on foreign investment (p. 691). This conclusion stems from an analysis of three types of FDI: total employment at foreign-owned affiliates, total value of plant, property, and equipment (PP&E) at foreign-owned affiliates, and the number of new foreign-owned plants. The first two employment and PP&E are continuous stock measures, while the third is a count flow measure. As a result, Keller & Levinson (2002) use parametric panel data methods

4 designed for continuous outcomes to analyze the first two measures, and similar models for count data to analyze the final measure. Given the importance of this paper in the literature, as well as the evidence provided in Fredriksson et al. (2003) of potentially important non-linearities, assessing the sensitivity of Keller & Levinson s (2002) findings to the relaxation of parametric assumptions is warranted. To this end, Henderson & Millimet (2007) applied recently developed nonparametric methods applicable to continuous outcomes to assess the robustness of Keller & Levinson s (2002) conclusions with respect to the two stock FDI measures. As alluded to above, Henderson & Millimet s (2007) analysis yields three findings. First, while some of the parametric results are robust, modeling assumptions do matter and in crucial ways. Second, the impact of greater environmental stringency is heterogeneous across states and over time. Finally, where Henderson & Millimet (2007) do continue to find negative effects of abatement costs on the stock of inbound FDI, the effects are generally of smaller magnitude than documented in Keller & Levinson (2002). The shortcoming of Henderson & Millimet (2007) is that, at the time of their analysis, nonparametric methods designed to handle panel data with count outcomes were only in development. As such, Henderson & Millimet (2007) did not assess the robustness of Keller & Levinson s (2002) conclusions with respect to inbound FDI flows. This paper fills this gap by applying the nonparametric methodology developed in Hall, Racine & Li (2004). Not only is such an analysis warranted for completeness, but it is also necessary for three other reasons. First, the current analysis assesses whether significant differences arise with respect to stock versus flow measures of FDI. This distinction has been shown to be important in the analysis of trade and environmental issues (e.g., Chintrakarn & Millimet (2006) and the references therein). Second, the detrimental effect of environmental regulation on new foreign-owned plants found in Keller & Levinson (2002) is comparable in magnitude to their results obtained using the two continuous measures of FDI. Thus, assessing the robustness of these findings is equally necessary. Finally, contrary to expectations, Keller & Levinson (2002) find statistically significant effects of environmental regulation only when considering all manufacturing sectors; analysis of just pollution-intensive sectors yields insignificant results. It is important to know if this surprising finding is attributable to model misspecification. The results are surprising, indicating some deterrent effect of relative abatement costs on the flow of new foreign-owned plants. However, as in Keller & Levinson (2002), evidence consonant with the PHH only arises when analyzing non-pollution-intensive manufacturing industries. Nonetheless, 3

5 4 the nonparametric methodology does indicate some important non-linearities, as well as produces better out-of-sample forecasts than the parametric models. Thus, future research into the validity of the PHH would be well-served by not restricting the effects of environmental stringency to be homogeneous, delving deeper into the sources of such heterogeneity, and understanding why nonpollution-intensive manufacturing industries may be more sensitive to environmental costs than other sectors. The remainder of the paper is organized as follows. Section 2 discusses the estimation framework. Section 3 presents the data and results. Section 4 concludes. 2. Empirical Methodology 2.1. Parametric Count Models. Keller & Levinson s (2002) analysis of the count of new plants obtained by a state in a given year focuses mainly on the well known fixed effects negative binomial, where the unobserved effects are defined at the region level (rather than the state) since many states obtain no new plants during the sample period. For robustness, Keller & Levinson (2002) also estimate several fixed effects Poisson and zero-inflated negative binomial models, as well as experiment with different specifications. For more details, the reader is referred to Keller & Levinson (2002) Nonparametric Count Models. In the presence of covariates, count outcomes would perhaps most naturally be modeled by modeling a conditional probability and then constructing the conditional mode, i.e., that outcome which occurs with highest probability. However, the conditional probability (density) function involves a mix of continuous and discrete data, and Aitchison & Aitken (1976) refer to the simplest of such problems as parametrically awkward. Building on Aitchison & Aitken s (1976) seminal work, Hall et al. (2004) recently developed a nonparametric conditional density estimator that admits a mix of discrete and continuous data. This approach is well suited to modeling count data, and we briefly describe the estimator. For what follows, we use Xi c to denote a continuous variable, and write X i = (Xi c, Xd i ) Rq S d. Using the superscript d to denote a discrete variable, we consider an r-dimensional discrete random variable X d i, and let Xd is denote its sth component (s = 1,..., r). Assuming that Xd i has finite support, then without loss of generality we assume that the support of X d is is {0, 1, 2,..., c s 1}, so that the support of X d i is S d = r s=1 {0, 1, 2,..., c s 1}, where c s 2 is a positive integer (s = 1,..., r). Assuming that Y i, the count outcome, has finite support, then without loss of generality we assume that the support of Y d i is {0, 1, 2,..., c s 1}.

6 Let f( ) and µ( ) denote the joint and marginal densities of (X, Y ) and X, respectively. We estimate the conditional density g(y x) = f(x, y)/µ(x) by 5 (1) ĝ(y x) = ˆf(x, y)/ˆµ(x). The estimators of f( ) and µ( ) are given by (2) (3) ˆf(x, y) = n 1 ˆµ(x) = n 1 n K γ (x, X i )L λ0 (y, Y i ), i=1 n K γ (x, X i ), i=1 where γ = (h, λ), and K γ (x, X i ) = W h (x c, X c i )L(x d, X d i, λ), W h (x c, X c i ) = L(x d, X d i, λ) = q s=1 1 h s w ( x c s X c is h s ), r [λ s /(c s 1)] Nis(x) (1 λ s ) 1 Nis(x), s=1 N is (x) = 1(X d is xd s) is an indicator function that equals one when X d is xd s, zero otherwise, and L λ0 (y, Y i ) = L(y, Y i, λ). Data-driven methods of bandwidth selection such as cross-validation are required in applied settings, and we use likelihood cross-validation. This method involves choosing h 1,..., h q, λ 1,..., λ r by maximizing the log-likelihood function (4) L = n ln ĝ i (Y i X i ), i=1 where ĝ i (Y i X i ) = ˆf i (X i, Y i )/ ˆm i (X i ), and ˆf i (X i, Y i ) and ˆm i (X i ) are the leave-one-out kernel estimators of f(x i, Y i ) and µ(x i ), respectively. For further details, we refer the interested reader to Hall et al. (2004). For the application below, we proceeded as follows: (1) Conduct likelihood cross-validation to obtain the data driven bandwidths (i.e., determine the amount of smoothing appropriate for the data at hand). (2) Generate ĝ(y = y x i ) for y = {0, 1, 2,..., c s 1} and for i = 1, 2,..., n.

7 6 (3) The conditional mode of Y, y m i is defined as y m i : g(y = y m i x i ) = max y {0,1,2,...,c s 1} g(y = y x i). The estimated conditional model is obtained simply by replacing the unknown g( ) with its kernel estimate given in (1). (4) Call the estimated conditional mode ŷ m i, i = 1, 2,..., n. (5) Having estimated the conditional mode, we can then proceed to assess how the conditional mode (or the conditional probability of any other count) changes as one or more of the covariates change. 3. Analysis 3.1. Data. The data come directly from Keller & Levinson (2002); thus, we provide only limited details. The data cover the 48 contiguous U.S. states from , omitting 1987 and 1989 due to missing data. The three dependent variables are the count of new foreign-owned plants in all manufacturing sectors (denoted as Total), the number of new plants in the chemical sector (denoted as Dirty), and the number of new plants in all other manufacturing sectors (denoted as Clean). As argued in Keller & Levinson (2002), the chemical sector is analyzed separately given that FDI in these industries is more likely to be responsive to differential abatement costs given the pollutionintensive nature of production. The independent variable of primary interest is Levinson s (2001) index of state-level RAC, defined as the ratio of actual state-level abatement costs to predicted state-level abatement costs, where the predicted value is based on the industrial composition of the state. Consequently, higher values indicate greater pollution control costs. The index varies over time and across states. Other continuous control variables include: market proximity (a distance-weighted average of all other states gross state products), population, unemployment rate, unionization rate, average production-worker wages across the state, total road mileage, land prices, energy prices, and tax effort (actual tax revenues divided by those that would be collected by a model tax code, as calculated by the Advisory Commission on Intergovernmental Relations). The discrete control variables are regional and time effects. All continuous variables are expressed in logarithmic form with the exception of the unemployment and unionization rates Results. For detailed results from parametric count data models, we refer the reader to Keller & Levinson (2002). However, for comparison, we note that Keller & Levinson (2002) find a negative

8 and statistically significant effect of RAC on the count of total new manufacturing plants using a fixed effects negative binomial or Poisson model; the impact on new plants in the dirty sector is negative, but statistically insignificant at conventional levels. Prior to assessing the nonparametric estimates of the effects of RAC on plant counts, Table 1 displays the bandwidths. Examination of the bandwidths is useful in that they provide information 7 about the degree of smoothing, as well as the relevancy of the different covariates. Two issues to note. First, the first column of the table displays the estimated bandwidth associated with the dependent variable, ˆλ 0, something that does not arise in nonparametric analyses of continuous outcomes. Second, ˆσ here refers to the standard deviation of the variable in question a bandwidth that exceeds, say, 3ˆσ effectively removes the (continuous) variable in question from the resulting estimate (i.e., it is said to be smoothed out by the fitting criterion). Turning to the actual bandwidths, three salient findings emerge. First, the three estimates of ˆλ 0 are close to zero, indicating very little smoothing across observations with different counts. This suggests that a parametric model with constant coefficients across state-year cells is likely to be misspecified. Second, the bandwidth on RAC is extremely large in the analysis of the dirty sector; close to zero in the other two models. This implies that the count of new plants in pollutionintensive sectors is unrelated to RAC, whereas it is in non-pollution-intensive sectors. Finally, many of the remaining covariates are found to be irrelevant in at least some of the models. For the continuous variables, the bandwidths for wages, energy prices, and tax effort are extremely large in all three models, implying that these variables are smoothed out. For the discrete variables, the bandwidths on the time effects are close to unity in all three models, implying this variable also has little explanatory power. In fact, while RAC, population, road mileage, unionization rate, and unemployment rate play a role in explaining new plant counts in the clean sector, only market proximity and region play a meaningful role in explaining the location of new plants in the dirty sector. In terms of the effects of RAC on new plant counts, Figure 1 plots the conditional probability associated with different counts against RAC (holding all other variables fixed at the median, except region which is fixed at the mode). The top panel displays the results for the dirty sector, and simply confirms what one learned from the bandwidths: the conditional probability is unrelated to RAC. The middle panel displays the results for the clean sector. The plot indicates some effect of RAC, as well as evidence of non-linearity. For example, the probability of receiving zero new plants

9 8 Table 1. Bandwidth Summary FDI RAC Market Population Unemployment Unionization Wages Proximity Rate Rate Total e e e e+05 Dirty Industries e e e e e e+08 Clean Industries e e e+07 ˆσ e e Road Land Energy Tax Region Year Mileage Values Prices Effort Total 1.1e e e e Dirty Industries 7.5e e e e Clean Industries 2.2e e e e ˆσ 4.8e e Table 2. Model Summary Nonparametric Parametric CCR Total CCR Dirty CCR Clean McFadden Total McFadden Dirty McFadden Clean first decreases marginally as RAC rises from below unity to unity; above unity, the probability increases as RAC increases. In terms of magnitude, a one-point increase in RAC (from one to two) is associated with an approximately 10% rise in the conditional probability (from about 60% to 70%). The conditional probability of receiving one new plant also exhibits some non-linearity, although not as severe. Finally, the conditional probabilities of receiving two, three, or four new plants are essentially monotonically decreasing with RAC. The bottom panel displays the results for all manufacturing sectors. The plot indicates some effect of RAC, as well as more extreme non-linearity. As in the clean sector, the probability of receiving zero new plants first decreases and then increases with RAC. However, the magnitude of the effect is much greater than in the middle panel; a one-point increase in RAC (from one to two) is associated with an approximately 35% rise in the conditional probability (from about 35% to 70%). Interestingly, Keller & Levinson (2002) obtain a 37% fall in the probability of receiving a new plant as a result of a one-point rise in the Levinson index. Thus, while Henderson & Millimet

10 9 (2007) found smaller effects of RAC on inbound FDI stocks using nonparametric (as opposed to parametric) methods, there is less difference with respect to inbound FDI flows. 1 In addition, the conditional probability of receiving one new plant also exhibits some nonlinearity, although not as severe. In particular, the conditional probability is virtually constant up until RAC rises to a value of roughly 1.7, and then exhibits a relatively sizeable decline thereafter. 2 Finally, the conditional probabilities of receiving two, three, or four new plants also exhibit a slight inverted U-shape with respect to RAC. While the results are similar to Keller & Levinson (2002) to a degree, the non-linearities suggest some misspecification of the parametric model. To quantify this, we undertake two steps. First, Table 2 presents a summary of the actual versus predicted counts for each nonparametric model. CCR refers to the correct classification rate, which is the proportion of actual counts that are correctly predicted by the model, while McFadden is McFadden, Puig & Kirschner s (1977) measure of goodness-of-fit for count models. 3 In all cases, the nonparametric model outperforms the corresponding parametric model. Table 3. Model Summary In-Sample Hold-Out Sample Nonparametric Parametric Nonparametric Parametric CCR Total CCR Dirty CCR Clean McFadden Total McFadden Dirty McFadden Clean Second, we assessed out-of-sample performance by randomly selecting a sub-sample of 700 observations, re-estimating the parametric and nonparametric models, and forecasting out-of-sample. While this is only one of many possible shuffles of the data, the out-of sample performance of the nonparametric models were consistent with their in-sample performance, indicating that the better in-sample performance of the nonparametric models using the entire sample relative to commonly used parametric models indeed reflects the fact that the nonparametric models are more faithful to the underlying data generating process. Specifically, Table 3 indicates a higher CCR for the 1 Note, however, that while the change from an index value of unity to two yields effects comparable to Keller & Levinson (2002), a one unit change from other values of the index does not necessarily agree with Keller & Levinson (2002). 2 Less than 6% of the sample has a value above For a binary outcome, it is p 11 + p 22 p 2 21 p 2 12 where p ij is the ijth entry in the 2 2 confusion matrix expressed as a fraction of the sum of all entries, while for multinomial counts it is the obvious extension.

11 10 nonparametric model in all cases except for the hold-out sample of the dirty sector. The nonparametric model outperforms the corresponding parametric model based on McFadden s performance metric in all cases. 4. Conclusion The impact of environmental stringency on capital flows remains an open, but critically important, issue to both academics and policymakers. Using recently developed nonparametric count data methods to analyze the location decisions of new foreign-owned manufacturing plants across U.S. states from , we find a modest deterrent effect of relative abatement costs on the probability of receiving a new non-pollution-intensive plant. However, there is no effect of pollution-intensive plants. Perhaps surprisingly, given previous findings in Fredriksson et al. (2003) and Henderson & Millimet (2007), these results are consonant with the parametric analysis in Keller & Levinson (2002). Nonetheless, the nonparametric methodology supports these previous studies in highlighting the fact that the impact of environmental regulations on capital flows is heterogeneous across time and location. Future empirical analyses of the PHH would be well-served by allowing for such heterogeneity, as well as attempting to understand the sources of these differences.

12 11 References Aitchison, J. & Aitken, C. G. G. (1976), Multivariate binary discrimination by the kernel method, Biometrika 63(3), Chintrakarn, P. & Millimet, D. L. (2006), The environmental consequences of trade: Evidence from subnational trade flows, Journal of Environmental Economics and Management 52, Copeland, B. & Taylor, M. S. (2004), Growth, and the environment, Journal of Economic Literature 42, Dean, J. M., Lovely, M. E. & Wang, H. (2005), Are foreign investors attracted to weak environmental regulations: Evaluating the evidence from china, Unpublished manuscript, Syracuse University. Ederington, J. & Minier, J. (2003), Is environmental policy a secondary trade barrier? an empirical analysis, Canadian Journal of Economics 36, Eskeland, G. S. & Harrison, A. E. (2003), Moving to greener pastures? multinationals and the pollution haven hypothesis, Journal of Development Economics 70, Fredriksson, P. G., List, J. A. & Millimet, D. L. (2003), Corruption, environmental policy, and fdi: Theory and evidence from the united states, Journal of Public Economics 87, Fredriksson, P. G. & Millimet, D. L. (2002), Strategic interaction and the determination of environmental policy across us states, Journal of Urban Economics 51, Hall, P., Racine, J. S. & Li, Q. (2004), Cross-validation and the estimation of conditional probability densities, Journal of the American Statistical Association 99, Henderson, D. & Millimet, D. (2007), Pollution abatement costs and foreign direct investment inflows to u.s. states: A nonparametric reassessment, Review of Economics and Statistics 89, Javorcik, B. S. & Wei, S.-J. (2004), Pollution havens and foreign direct investment: Dirty secret or popular myth?, Contributions to Economic Analysis & Policy 3, Keller, W. & Levinson, A. (2002), Pollution abatement costs and foreign direct investment inflows to u.s. states, Review of Economics and Statistics 84, Levinson, A. (2001), An industry-adjusted index of state environmental compliance costs, in C. Carraro & G. Metcalf, eds, Behavioral and Distributional Effects of Environmental Policy, University of Chicago Press, Chicago. List, J. A. & Co, C. Y. (2000), Environmental regulations on foreign direct investment, Journal of Environmental Economics and Management 40, List, J. A., McHone, W. W. & Millimet, D. L. (2004), Effects of environmental regulation on foreign and domestic plant births: Is there a home field advantage?, Journal of Urban Economics 56, McFadden, D., Puig, C. & Kirschner, D. (1977), Determinants of the long-run demand for electricity, Proceeedings of the American Statistical Association (Business and Economics Section) pp Ramondo, N. (2005), Size, geography, and foreign direct investment, Unpublished manuscript, Department of Economics, University of Texas. Wagner, U. & Timmons, C. (2004), Agglomeration effects in foreign direct investment and the pollution havens hypothesis, Unpublished manuscript, Department of Economics, Duke University.

13 12 Xing, Y. & Kolstad, C. D. (2002), Do lax environmental regulations attract foreign direct investment?, Environmental and Resource Economics 21, 1 22.

14 13 Dirty Conditional Density Pr(y=0 X) Pr(y=1 X) Pr(y=2 X) Pr(y=3 X) Pr(y=4 X) Index Clean Conditional Density Pr(y=0 X) Pr(y=1 X) Pr(y=2 X) Pr(y=3 X) Pr(y=4 X) Index Total Conditional Density Pr(y=0 X) Pr(y=1 X) Pr(y=2 X) Pr(y=3 X) Pr(y=4 X) Index Figure 1. Plots of the Conditional Probability Versus the Levinson Index of Relative Abatement Costs. Note: Higher values of the index correspond to more stringent environmental regulations.

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