Pollution Haven Effect through Input-Output Linkages

Transcription

1 Pollution Haven Effect through Input-Output Linkages H. Ron Chan University of Manchester February 2, 2015 PRELIMINARY - PLEASE DO NOT CITE OR CIRCULATE. Abstract How does environmental regulation affect trade flows? The pollution haven effect has hypothesized that tenser environmental regulation leads to a decrease in net exports, yet the empirical literature has found mixed results on its significance. This paper recovers a missing link on how environmental regulation affects non-pollution intensive industries through input-output linkages. When clean industries use dirty inputs intensively, they also experience a cost disadvantage, which leads to a decrease in exports. I model both channels and the intensive and extensive margins of trade using a heterogeneous firm model where each firm makes abatement choices and use materials as inputs. Using industry-level data from U.S. bilateral trade and pollution abatement cost and expenditure survey, I show that a 1% increase in pollution tax equivalent environmental regulation can cause a 0.035% drop in exports on average. Using input-output accounts I construct a measure on the intensity that an industry purchases pollution intensive inputs and show that it is negatively correlated with the industry export position. Keywords: Trade and environment, Firm heterogeneity, Pollution havens, Intermediate goods JEL Classifications: F12, F14, F18, L10, Q56 School of Social Sciences, University of Manchester, Arthur Lewis Building, Manchester, M13 9PL, United Kingdom. Contact: ron.chan@manchester.ac.uk. I would like to thank Maureen Cropper and Nuno Limão for their comments towards the early development of this paper. The remaining errors are of my own. 1

2 1 Introduction Two questions that motivate trade and environmental economists are how trade liberalization affects environment and how environmental regulation affects trade. 1 Pollution haven effect suggests that tightening environmental standard increases imports of pollution intensive ( dirty ) goods. Copeland and Taylor (1994) wrote the first paper to formulate the idea of pollution haven hypothesis in a Ricardian framework - the cost of producing a dirty output is relatively cheaper in countries with lower environmental standards and it forms the basis for comparative advantage of producing dirty goods. The fundamental idea of the effect is thus related to cost, and this paper illustrates and tests another channel on how regulation affects exports through input-output linkages. Non-pollution intensive firms may suffer a cost disadvantage and make relocation or withdraw export decisions if they required pollution intensive inputs in their production process. Therefore, the environmental regulation not only affects the export decisions for pollution intensive firms, but also affects clean industries that rely on dirty inputs where the input costs are higher due to the policy change. Literature that study the link between environmental regulation and trade largely ignore such input-output linkages between pollution intensive industries and industries which purchase these inputs. Most papers use data from the Current Industrial Reports: Pollution Abatement Costs and Expenditures (PACE) which report how much individual industries spent on reducing pollution. Those costs include payment to government agencies, maintenance and operation of pollution control equipments, environmental auditing and treatment or disposal of wastes. 2 PACE data give us a sense on how pollution intensive the industry is as well as symbolizing a tightening of environmental regulation which may be specific to certain industries. 1 A lot of scholars do not define these two properly and call both Pollution Haven Hypothesis. The question that studies how trade liberalization affects pollution is classified as the Pollution Haven Hypothesis by Copeland and Taylor (2004). The goal of the paper is to study the Pollution Haven Effect, which is how environmental standards affect trade flows. For readers that are interested in the Pollution Haven Hypothesis question, please refer to Antweiler et al. (2001) and Frankel and Rose (2005). Throughout the paper, I will refer the theory as pollution haven effect. 2 The PACE documents segregate those costs as capital expenditures and operating costs. The literature primarily uses operating cost as a proxy for control cost, since capital expenditure usually refers to investment that are geared towards pollution control which may not reflect either the pollution intensity or government control. 2

3 Using the information regarding pollution intensity in PACE, most papers estimate a net import equation with Pollution Abatement Operating Cost (PAOC) as the main control variable, along with a vector of other controls such as GDP and tariffs and industry and time fixed effects. 3 If the estimated coefficient (on PAOC) is positive, industries with higher pollution control costs, which are those who are more pollution intensive, observe a higher level of net imports, as predicted by the pollution haven effect. The time and industry fixed effects capture unobserved heterogeneity across time and industry that are time invariant (Levinson and Taylor, 2008). Following Copeland and Taylor (1994), several papers attempt to find evidence for the pollution haven effect using similar formulation illustrated above but their results are mixed. Levinson (1996) study the plant location decisions within US states and he did not find a significant response to cross-sectional (inter-state) difference in environmental regulation. Eskeland and Harrison (2003) look at foreign direct investment (in the form of multinationals) in developing countries and they could not find a economically significant and robust result such that foreign investors flee to developing countries where regulation is weak. Javorcik and Wei (2004) attempt to study whether pollution intensive industries move to countries with weaker environmental standards, while at the same time controlling for political factors that may hinder foreign direct investment. They could not find a significant result. Ederington et al. (2005) study an interesting relationship between footlooseness and pollution and they showed that industries with above-average transport cost do not respond well to the environmental regulation. Hanna (2010) compare foreign direct investments before and after the Clean Air Act Amendments in 1970 and she did not find that heavily regulated industries in US significantly move to developing countries. Levinson and Taylor (2008) take SIC 3-digit industry data and study US-Mexico and US-Canada trade, and they find a significant effect that pollution intensive industries tend to concentrate in Mexico. The key message of my paper is that such regression may not necessarily capture the pollution haven effect. Consider a very basic example with two industries in a home country. Industry A is dirty because the production process generates a lot of pollutants, and it has 3 Although I ignore in this illustration, most papers normalize the variables by their value added or costs of materials to make sure these are comparable across different industries which potentially have different sizes (Ederington et al., 2005). 3

4 to pay a lot of expenses to reach the standard set by the government. Industry B is also dirty in a sense that it uses a lot of dirty inputs, including output from Industry A, but it does not have to pay any upfront cost in reducing pollution like Industry A. If we use PACE data to define the dirtiness of the industry, Industry A will be dirty and Industry B will be clean. Now suppose that environmental standard is now tightened. Industry A has to pay and abate more after such regulation and it suffers a cost disadvantage. Industry B, on the other hand, also suffers a cost disadvantage. The increase in the prices of these dirty inputs, due to an increase of their marginal costs, also affect Industry B disproportionally because Industry B uses a lot of these inputs while other firms do not. If we have another foreign country with no environmental standards, both industries in this country will have a cost advantage, and according to the reasoning behind Copeland and Taylor (1994), we see an import of goods from both Industry A and Industry B in home country. If we ought to run the regression as illustrated above, the estimated coefficient on PAOC will simply be close to zero because we implicitly classify such change in the time fixed effect. Levinson and Taylor (2008) suggest that previous literature fails to identify the pollution haven effect due to lack of disaggregate data, endogeneity of environmental policy and unobserved heterogeneity. The consideration of input and output linkages can be seen as a remedy to the third point, while the use of 4-digit SIC industries level data and instruments can reduce the bias introduced by the first two. Usually scholars introduce fixed effects to control for such heterogeneity, but they cannot capture the input effect illustrated in the example above. Fixed effects can only capture time invariant industry characteristics but not the input effects which vary across time as regulation changes. More importantly, these input effects are driven by the policy change and therefore they could be correlated with the change in pollution abatement costs. Capturing this cost channel which also affects net imports is the first, and the most important contribution of my paper. The literature that study intermediate goods and pollution havens is relatively scarce. There are not many papers that study the relationship between intermediate goods and environmental regulation, McAusland (2004) presents a stylized model and models a two-stage production process where regulations happened in a form of product standards for intermediate goods producers with increasing returns in upstream market. She also implicitly assumes productivity of the final good increases with the pollution intensity of the intermedi- 4

5 ate goods, and she finds that emissions may increase after the emission standard is tightened when producers trade off environment for productivity and profits. Benarroch and Weder (2006) took a similar approach by considering endogenous environmental tax. Rather than looking at a change in product standards, they look at the effects of intra-industry trade on the pollution emissions. My paper is going to model the decision of a producer who needs to purchase inputs from another intermediate good producers. In other words, my model can capture the decision of intermediate good producers who may need to purchase inputs from other producers as well. The other contribution of my paper is to present the theory of pollution haven effects in a heterogeneous firm model, as pioneered by Melitz (2003). Most of the previous literature on trade and the environment are based on the Ricardian model (Copeland and Taylor, 1994; Levinson and Taylor, 2008). It compares unit cost in home and foreign countries and predicts trade flows based on each country s comparative advantages, as derived from the cross-country differences in pollution tax and production costs. 4 Holladay (2010) uses firmlevel data and he found that exports are less pollution intensive than domestically consumed goods. In Melitz (2003), exporting firms are more productive which may suggest that a linkage between pollution intensity and productivity. Kreickemeier and Richter (2014) study the trade in polluting goods in a Melitz (2003) framework and show that more productive firms choose to abate more, but they did not attempt to bring the model to testing. The main message of these heterogeneous firm models is that firms self-select themselves as exporters, depending on their productivity level. On the other hand, productivity also raises firm s motive in abating pollution because a firm with higher productivity has a higher profit margin. With more productive firms endogenously make themselves cleaner, the use of pollution abatement cost includes both a pollution cost and a productivity parameter on the right hand side. As productivity is positively correlated with export, it generates a positive bias on the coefficient. In other words, though theory predicts that pollution cost decreases export (which corresponds to a negative coefficient on the pollution cost), we may not observe a negative effect due to such extra productivity effect. Separating such effect is the second objective of my paper. Differing from the literature, I estimate an export 4 Antweiler et al. (2001), in the context of pollution haven hypothesis, consider the importance of factor endowments which can completely offset the Ricardian story, given that pollution intensive industries are usually capital intensive as well. 5

6 equation rather than an import equation, based on the decision of U.S. firms which face U.S. regulations. Besides the attempt to show how pollution intensity affects export, I will also study whether this effect varies with export partners. As pointed out in Javorcik and Wei (2004), although the above papers are all studies of pollution havens, they do not coincide in their research questions. A number of studies focus on the industry dimension (Eskeland and Harrison, 1997; Keller and Levinson, 2002; Ederington et al., 2005; Hanna, 2010). They look primarily on whether pollution intensive industries have a higher imports as in equation (1). Pollution haven effect also postulates that we observe an increase in imports from countries with weaker environmental standards following a policy domestically. Few other studies look at the location dimension (Levinson, 1996) and only some managed to study both dimensions at the same time (Javorcik and Wei, 2004; Levinson and Taylor, 2008). The difficulty of studying the pollution haven effect in the location dimension is the unobserved heterogeneity across countries. Even we ought to believe that pollution intensities of industries are roughly similar in all countries, we cannot observe the policy changes inferred from their pollution control costs. These time-varying covariates can cause bias on the estimates since these policy changes determine the bilateral trade flows. I will start by looking at the industry dimension and start building up by adding location dimension and see how results change. The paper will first start by developing a simple partial equilibrium model where firms are heterogeneous in productivity as well as polluting in nature. In additional to input choices, firms also have to endogenously decide on the resources for pollution abatement. Although environmental regulation comes with many different forms in the real world, I model environmental regulation as a single pollution tax for each unit of pollution that the firm emits, and the magnitude of the pollution tax will act as a proxy for environmental regulation. Then, I will discuss how pollution tax is going to affect the intensive and extensive margins. In Ricardian models all firms are identical therefore there either exists some exogenous exit conditions or studies focus on a representative firm. Not only does pollution tax affect the intensive margin, it also affects the extensive margin following an increase in pollution tax and a decrease in the profit margin. I also show how the pollution abatement cost measures, which are consistently used by the literature, can be justified as a right hand side variable 6

7 on pollution tax. The pollution cost term includes not only the pollution tax component that the paper wants to capture, but also a mark-up term which is positively related to export market size and average productivity. This additional term can explain why there is no result for pollution haven effects by just running trade flows on pollution cost, since this mark-up is going to affect export positively. I use trade data from the database made available by Robert Feenstra as well as industry characteristics from NBER-CES database (Feenstra 1996; 1997) to make some empirical estimation on my model. After controlling for unobserved industry characteristics using industry fixed effects, I found that a 1% increase in pollution tax results in a 0.06 to 0.07% decrease in exports by looking at aggregate exports over time. I test the same hypothesis on different country groups and I found that the size of the effect is bigger for developing countries-bound exports. I cannot find a significant effect via the intermediate good linkage only using industry level data. The effect seems to be too large, hence I proceed to estimate the model using countryindustry-time data and use country-time fixed effects and industry-region fixed effects to control for country s import barriers as well as unobserved industry characteristics that imply greater trade to a certain region respectively. In this framework, the estimated effect of environmental regulation on exports is %, and surprisingly the effect of the intermediate goods is also huge and statistically significant, which cannot be found in previous setup where all exports are pooled in one. The rest of the paper is organized as follows. Section 2 discusses the basic setup of the model and derives equilibrium exports. Section 3 builds an intermediate step between the theory and the empirics by outlining how environmental regulation affects the aggregate exports. Section 4 briefly describes the source of data. Section 5 outlines the estimation framework and strategies. Section 6 presents results from the empirical analysis. Section 7 concludes the paper with a short summary. 7

8 2 Theory 2.1 Production and Pollution Consider a partial equilibrium model where there are multiple industries in the home economy. A producer in each industry produces a differentiated good and the producers engage in a monopolistic competition. Each producer has to pay a fixed cost f E upon entering the market. They will draw their productivity λ from a probability distribution function f (λ) which have a support [λ, λ], where λ > 0. In other words, producers are heterogeneous in their productivity level λ. There are two economies in the model but we are going to focus on producers in the home market, who are deciding whether to exit, export or not to export. We can imagine the foreign market being a mirror image of the home market, or there only exists consumers who are demanding domestically produced goods. To produce the output, each firm uses two kinds of inputs: labor l and materials m. Labor is available at a unit cost of w (which is exogenously fixed and normalized to 1 thereafter), and the unit cost for materials is c. Conditional on the drawn productivity level λ i, the firm has a production function R(l, m; λ i ) = λ i ( m β ) β ( ) l 1 β (1) 1 β Here I model intermediate goods as an input to a firm, without explicitly model the interaction of two industries. All linkages will be acting through the cost of materials c. To capture different empirical regularities of productivity sorting 5, each producer has to bear a fixed cost of f if they produce positive amount of R. They also have an option to export to the foreign market, given that they pay the fixed cost of exporting f X. 6 There is a iceberg transport cost τ > 1 so their effective price in the foreign market is p f i = τp i, where p i is the price charged in the domestic market. The differentiated good also emits pollution. The amount of pollutant z that the producer 5 A lot of empirical evidences point to the fact that more productive firms do export more. In studying trade liberalization in Chile, Pavcnik (2002) is the first to document the existence of productivity heterogeneity among plants. Eaton et al. (2008) and Helpman et al. (2008) fit firm level data with models of monopolistic competition with productivity heterogeneity and very significant results are found. 6 Here we can interpret the fixed cost oft exporting as costs of marketing, building up foreign relationship, alternating product standards and other logistics investments, in order to facilitate the product to be sold in the foreign market. 8

9 emits is some fraction of the production output R, z = a(θ)r(l, m; λ i ) (2) θ is the abatement level (which is constrained between zero and one) that the producer can choose, where a ( ) < 0. θ is expressed as a fraction of the production R that the producer is willing to give up in order to reduce pollution. The final output y i depends on the amount of abatement that the firm chooses: the more resources the producer put in reducing pollution, the smaller fraction of the production that can be sold. y i = (1 θ)r(l, m; λ i ) (3) I assume that the government charges a pollution tax t for each amount of pollutant z. I assume that there is no fixed cost of emitting pollution For computational convenience, I will follow Levinson and Taylor (2008) and adopt a parametric assumption on the functional form of a( ): a(θ) = (1 θ) 1/α (4) Here I assume that the abatement technology is identical for all producers - the productivity parameter only determines how efficient the firm produces the pre-abatement output. This form will be very useful in simplifying our analysis. By substituting (4) and (2) into (3), we get y i = z α (R(l, m; λ i )) 1 α = z α [ λ i ( m β ) β ( ) ] l 1 β 1 α (5) 1 β Here we get the usual result of treating the pollution z as a third input (environmental service) with t as a unit cost of such service. In my model, firms endogenously choose the abatement level θ and the amount of inputs l and m. Equivalently, each firm can be seen as implicitly choosing the level of pollution z to emit on top of the two inputs. For computational and analytical convenience, I will interpret the amount of pollution as a choice variable rather than abatement level from this point onwards. 9

10 2.2 Consumption Consumers have constant-elasticity-of-substitution (CES) preferences over the differentiated goods. Through standard exercises of utility maximization, I can derive the domestic demand of variety i which can be written as, y i = Ap σ i (6) The foreign demand, which equivalently gives us exports, is defined analogously x i = A (p f i ) σ = A τ σ p σ i (7) where σ 1/(1 ρ) > 1 is the elasticity of substitution between any two differentiated goods. A can be broken down into A = EP σ 1 where E and P are home country (industry) expenditure and price index on this industry respectively, with A is defined analogously. P takes into account the general equilibrium effect. A change in policy will result in a change in price index through both (i) a change in the price each producer charges and (ii) the composition of (domestic and foreign) producers. The sign of the general equilibrium effect will be ambiguous in our policy exercise, and potentially it can be taken care by industrycountry fixed effects, which will be described in more details later in the paper. 2.3 Input Demands, Price and Output in Equilibrium Under monopolistic competition with CES utilities, each differentiated good producer charges a constant markup over her own marginal cost. To determine the marginal cost and derive each firm s profit function, it is important to solve for the cost minimization problem for each firm. The producer of variety i minimizes the cost given her output reaches a certain fixed target q, and recalling that wage rate i 1, min l,m,z l + cm + tz s.t. zα [ λ i ( m β ) β ( ) ] l 1 β 1 α q (8) 1 β By solving (8), we can show that the marginal cost is equivalently MC i = K 1 t α (λ 1 i c β ) 1 α where K α α (1 α) 1 α. Given the marginal cost, the differentiated good producer will 10

11 charge the price 7 p i = (Kρ) 1 t α (λ 1 i c β ) 1 α (9) Using the demand function (6), we can derive the post-abatement output y i, y i = A(Kρ) σ λ σ(1 α) i t σα c σ(1 α)β (10) which is increasing in the size of the economy A, productivity, and decreasing in the pollution tax and cost of materials. We can also derive the pollution intensity of the producer. The input demand for environmental service, in other words, the total pollution emitted by the producer can be written as z i = αaρ σ K σ 1 λ (1 α)(σ 1) t α(σ 1) 1 c β(1 α)(σ 1) (11) Using (10) and (11), the pollution intensity of the producer is given by, z i = α [ 1 y i K λ c β ] 1 α (12) t and the intensity is decreasing in productivity, as an indirect evidence found in Holladay (2010) that exports are generally less pollution intensive, compared to non-exports. The key of driving this result is that productivity impacts the production efficiency of the preabatement output R. In other words, productivity in this model can be viewed as an inputaugmented productivity. As the productivity goes up, it is more efficient to produce using the two inputs so the firm will optimize by substituting away from the third input, as the use of environmental service goes down, the firm emits less pollution. From (3) and some derivations, each producer (non-exporter in this case, with A replaced by A + A τ 1 σ for exporters) devotes θr = (1 α)aρ σ K σ 1 λ (1 α)(σ 1)+1 i t (σ 1)α c β[(1 α)(σ 1)+1] A(Kρ) σ λ σ(1 α) i t σα c σ(1 α)β = y i [(1 α)k 1 λ α t α c αβ 1] (13) 7 The profit-maximizing price is determined from the condition MR = MC, where MR = p i (1 σ 1 ). By arranging and the identity σ = 1/(1 ρ), we get p i = MC/ρ 11

12 I can then derive θ, the fraction of the resources spent on abatement: [ 1 θ = 1 λ α 1 α c β ] α (14) t Aligning with the result for pollution intensity, θ also increases with the productivity level λ. It is positive if and only if 1 α c β λ 1 α t < 1 λ > α c β 1 α t L (15) In other words, for firms that are not so productive (with productivity below L), they will not spend any resources in pollution abatement. We will not observe any data on pollution abatement costs for these firms. 2.4 Profits and Exporting Decision Given prices and outputs, we can derive the domestic profit function, π i = 1 σ AKσ 1 [t α (λ 1 i c β ) 1 α ] 1 σ f (16) and it is increasing in the productivity parameter λ i. As in Melitz (2003) and others, such one-to-one relationship between profit function and productivity allows us to concentrate on the productivity space to study the difference in profits as profit is strictly increasing in productivity in this model. Due to the presence of the fixed cost f, there also exists a cutoff productivity λ in which the producer is indifferent between producing or shutting down the firm. If the producer has a productivity above the cutoff, she will produce; otherwise, the firm will be shut down. How about the exporting decision? Since the profits earned in the domestic market and the foreign market are additively separable, as long as the fixed cost of exporting exceeds the additional profit that the firm earns in the foreign market, the firm will choose to export. The profit earned in the foreign market can be written as, [ πi = 1 σ A τ 1 σ K σ 1 1 ( λi t α c β ) 1 α ] σ 1 f X (17) 12

13 Define λ to be the cutoff productivity that the producer is indifferent between exporting or not exporting. λ is implicitly defined below by setting π i = 0. [ 1 σ A τ 1 σ K σ 1 1 t α ( λ ) ] 1 α σ 1 = f X (18) c β λ determines the entry (to the foreign market) margin. Not surprisingly, it is affected by the size of the foreign market, the transport cost, the cost of materials, the regulation (imposed as a form of cost), the fixed cost of exporting, as well as other industry-importer characteristics that absorbed in the price index within A. When the margin increases, it implies that the firm has to be very efficient in order to find herself profitable to enter the foreign market. A note that such cutoff productivity may not exist. Even for the most productive producer (with λ = λ), it can turn out that 1 σ A τ 1 σ K σ 1 λ (1 α)(σ 1) t α(σ 1) c β(1 α)(σ 1) f X < 0, especially when the foreign market is very small (i.e. A is small) or the transport cost is very high. 2.5 Aggregation In equilibrium, there are M and M producers in the market that are producing and exporting positive output respectively, and both are large. By the Law of Large Numbers, we can take the probability distribution of productivity to be the distribution of all the producers ex post. Following Melitz (2003), I define µ(λ) and g(λ) to be the distribution of all surviving firms and exporters respectively: f (λ) if λ λ 1 F( λ) µ(λ) = 0 otherwise f (λ) if λ λ 1 F(λ g(λ) = ) 0 otherwise (19a) (19b) 13

14 I can then define the aggregate export X using the above g( ) function, X = = λ λ λ λ M x(λ)g(λ)dλ M A τ σ (Kρ) σ λ σ(1 α) i t σα c σ(1 α)β g(λ)dλ (20) 3 Predictions from the Model To move from theory to estimation, we can make use of the theory and derived conditions outlined in the earlier section to study to effect of environmental regulation on the aggregation exports X. I can use comparative statics and focus on one industry to answer the following questions: Is the effect bigger for a more pollution intensive industry? Is the effect bigger for a more material intensive industry? Previous literature assume all firms are identical and focus the problem on the representative firm, therefore they make no attempt to control for the productivity of the firm. As they lead to different predictions on trade flows (environmental and material costs decrease exports while average productivity increases exports), it is therefore very important for researchers to control for this in the estimation equation, which I will discuss it in the next subsection. Then, I separate the effects into extensive and intensive margins. For the extensive margin, I study the effect of environmental regulation on the productivity cutoff λ. As I mentioned earlier, when λ increases, it means that there are fewer firms exiting since the exporting firms have their productivity level on the support [λ, λ]. It also affects the number of firms M at the equilibrium. That is not the only effect, however, as each exporter will respond by changing their own production (and hence their exports) when they observe a different t, which is the intensive margin. 3.1 Where does the "Pollution Cost" variable fit in? The literature unanimously use pollution abatement operating cost (PAOC) to proxy for pollution cost, and I will justify the use of such measure in determining trade flow. In the 14

15 model above, θ R(l, m; λ i ) is the amount of output that each producer devotes to pollution abatement, and the PAOC per value of shipments for each firm can be defined as, PAOC per value of shipments = θr MC py [( = ρ λ 1 α ) t α α c β 1] (21) The binding condition (15) that keeps θ > 0 also ensures that PAOC per value of shipments is positive. However, if the binding condition is not satisfied, we will not be able to observe any value of PAOC, which is the design of the survey report that focuses on the heavy polluting industries only. 8 This measure is increasing in pollution tax (stringency of environmental regulation) and productivity of the producer, at the same time it is decreasing in the material cost. It justifies the usage of PAOC per value of shipments in the literature. 9 Since I do not have firm level data on the above measure, as most papers do, I will use industry (4 digit level 1972-based SIC, see Section 4) level measure, defined as the total pollution control cost per shipment of the industry, POLCOST. It can be defined over distribution of all surviving firms µ(λ): ( ) 1 α t α A λ POLCOST = ρ α c β A λ λ (σ 1)(1 α)+α i λ λ λ (σ 1)(1 α) i µ(λ)dλ + A τ 1 σ µ(λ)dλ + A τ 1 σ λ λ (σ 1)(1 α)+α i λ λ λ (σ 1)(1 α) i λ µ(λ)dλ ρ µ(λ)dλ The PAOC measure can be justified as a proxy for (t/c β ) α. If we compare (22) to individual firm s θ (14), we can see that that productivity enters as an aggregate measure rather than (22) as a single λ in (14). Other than the productivity measure in the equation (the fraction is decreasing in the average productivity of the industry), this aggregation term is also affected by the size of exporting countries, trade costs τ, interacting with the productivity distribution of the surviving firms. These are also determinants of exports, with most of 8 I try my best to recover the PAOC data by algebra from the survey if only one industry out of a 3-digit SIC sector is missing. Say the survey reported the total of sector 201 as well as industries 2011, 2013 and 2017, and from the list of SIC industries, there are 4 industries associated with sector 201 In earlier PACE surveys, many industries are reported as low pollution with less than 0.05 million USD of pollution operating cost. I can only assign missing values for industries that the survey did not report and cannot be computed. 9 Literature also use value added or cost of materials to create the pollution cost measure (Ederington et al., 2005; Levinson and Taylor, 2008). Although results do not change qualitatively in their papers, the use of value added implicitly assumes producers do not use intermediate goods in their production process. As focusing on intermediate goods is our main focus of the paper, I will use value of shipments as the denominator of such measure. The use of value added in their empirical section do not change my results as well. 15

16 them affect exports positively. Consider two industries that are almost identical in terms of the regulations and input decisions that they are facing, if one industry has more productive firms than the other (left-skewed productivity distribution), the observed pollution cost measure will be higher for this country since more productive firms spend more to reduce pollution. This variation will pollute our pollution cost measure as this form of variation is not capturing the variation in terms of environmental regulation. The productivity, trade costs and the distribution of firms enter into the right hand side as well as the left hand side of the estimation equation. For example, when there are some (unobserved) shocks that impact firm s distributions in some industries, it will affect the aggregate exports as well as the pollution cost measure, causing a correlation between the pollution cost and the error term which will bias my estimates. It is thus important to control for industry-by-time unobserved effects as well. 3.2 Extensive and Intensive Margins Equation (18) implicitly defines λ. As mentioned in previous section, in a partial equilibrium framework, what determines entry and exit is the change in cost of materials and the regulation. Now consider an increase in pollution tax t, reflecting strengthening the regulation on pollution. The key in this paper is that pollution tax is going to affect the cost of materials as well. Imagine Producer A who buys output from another Producer B as an intermediate input for production of good A. When the pollution tax increases. Producer B s marginal cost increases and it also increases the price charged by Producer B. It implies that Producer A has to pay more for the materials, due to the fact that pollution tax increases. After taking logarithm on both sides and grouping terms, we get ln(λ ) 1 α = γ 0 + α ln t + β(1 α) ln c = γ 0 + α ln t + β ln c (23) where t = tc β is the term captured by the pollution cost measure. Equation (23) illustrates the importance of controlling for c. If we ignore the impact on the cost of materials, i.e. ln c/ ln t = 0, we get the result that pollution tax increases the export productivity 16

17 cutoff. This is our usual result - when there is an increase in pollution tax, the export decreases more for industries that are more pollution intensive, i.e. when α is higher. I call this the pollution intensity effect. The intuition is that pollution tax represents a bigger share in the cost of pollution intensive firms. When there is an increase in the pollution tax, these producers in the pollution intensive industry are greatly affected in terms of their marginal costs. It implies that the profit margin that they can earn in the foreign market shrinks, while the fixed cost of exporting remains the same, this decreases their incentives of exporting. The marginal firms, which have their productivity parameters close to the cutoff λ will now stop exporting and hence this decreases the aggregate exports by exiting. However, now there is the second term due to the cost of materials, the intermediate goods effect, which acts in the same direction direction as the pollution intensity effect. It increases the effect on extensive margin since ln c/ ln t > The intermediate goods effect is determined jointly by the material intensity of the industry β as well as how much the cost is being affected ( ln c ln t ). The intuition of the direction is the same as the one above. When the industry is more material intensive and the cost of materials is much affected by the pollution tax, these industries are also greatly affected in terms of their profit margins, hence results in a reduction in exports. If we ignore the intermediate goods effect, the estimate is going to be biased. As argued in the introduction, if material intensity and pollution intensity are negatively correlated, then the coefficient on pollution cost will be biased downwards. λ characterizes the extensive margin. Following an increase in pollution tax, the marginal firm finds it no longer profitable to export (since revenue decreases while the fixed cost of exporting stays the same) and it will choose to exit the foreign market. Besides pollution tax and cost of materials that can affect the export market participation decision, there are also a number of factors that influence that decision. I will separately control for tariffs which act as a big portion of the trade cost facing the exporters. If we are pooling all countries together in our sample, the exporter s size as well as the fixed cost of exporting (I have assumed that it is constant over time) will be captured and controlled by time fixed effects. The extensive margin also depends a lot on the prior distribution of firm as well as the probability of entry 10 Here I did not model the intermediate good firm explicitly but we can think of an intermediate good firm as having the same problem as a final good producer. Monopolistic competition implies a positive relationship between the cost (t) and the price (c) of the intermediate good, while other form of competitions also feature this. 17

18 - which can all be captured by industry fixed effects, assuming that they are not changing over time. For the intensive margin, using the foreign demand in equation (7), our equilibrium conditions and taking logarithm on both sides and grouping terms, we can write ln x i = γ 1 σα ln t σ(1 α)β ln c = γ 1 σα ln t σβ ln c (24) Like the extensive margin, both pollution tax and material costs decrease exports of each exporter through the profit margin story described above. Altogether, the increase in marginal costs (which include both costs of purchasing materials and paying for pollution abatements) leads to an increase in firms price which is equivalently a decrease in foreign demand of the variety. 3.3 Implications on Aggregate Exports I define a measure of average productivity of exporters ϕ by ϕ = λ λ λ σ(1 α) i g(λ)dλ (25) Using equation (20) and (25) and taking logs on both sides, we can write the aggregate exports as ln X = φ 0 + ln M + ln ϕ σ ln τ σα ln t σβ ln c (26) First two terms (ln M, ln ϕ) reflect the extensive margins that I discussed earlier, while the last two terms reflect the intensive margins. Recall that M is the number of exporters and ϕ is the average productivity. As both costs increase, this decreases firms incentives to export as their profit margin lowers which leads to in an increase in cutoff productivity λ. As a result, the number of exporters M will decrease while the average productivity ϕ increases due to selection effect. Through such selection effect, unproductive firms are forced to exit from the foreign market and hence it boosts the average productivity. As higher productivity increases profit margins, it increases the (aggregate) exports. More importantly, the average productivity of the exporting firms increases due to the strengthening 18

19 in environmental regulation which also promotes exports. 4 Data The U.S. bilateral imports and exports data are downloaded from the Center of International Data (CID) in Robert Feenstra s website (Feenstra, 1996; 1997). They are aggregated to the 4-digit Standard Industrial Classification (SIC, 1972-basis) codes and this will serve as my definition of an industry as well. The imports value and are customs values of imports and both imports and exports are denominated in nominal dollars. The freight costs (defined as the ratio of CIF value of imports to the custom value of imports) will be used as a proxy of trade costs. A note that both trade cost measures are not totally relevant in this context because I am estimating an export equation, however, as I have pooled exports to all countries together into aggregate exports, the magnitude of the trade cost measures can act as a proxy to the trade cost of the industry which is likely to affect the other direction of trade flow as well. Data on Pollution Abatement Cost are gathered from the Current Industrial Reports: Pollution Abatement Costs and Expenditures Survey (PACE). The survey started in 1973 and discontinued in 1994, then resumed in 1999 and 2005 in non-regular basis. Following the literature, I use pollution abatement operating cost to act as a proxy for environmental cost that the firm faces. It includes costs of operating and maintaining pollution control equipments as well as possible payments to the government. It differs from the pollution abatement capital expenditure which are upfront costs of setting up long term investments like purchasing a new equipment. The capital expenditure in PACE may not necessarily reflect the environmental stringency or the industry pollution intensity at a particular time as it can be part of the long term investment decision made by the firm. I restrict the time frame from 1973 to 1988 as the classification of industries changes from 1972-based SIC to 1987-based SIC for the data beyond Though there are concordance tables available 11 that researchers can use, they are based on trade and production in 1988 which may not match exactly the data beyond The PACE data are not available 11 For instance, Feenstra provided a table that match 1987 MSIC industries to 1972 SIC industries. 12 This point is also emphasized in Levinson and Taylor (2008), which restrict the timeframe to be

20 in 1987 because the survey was not conducted. I also exclude PACE in 1979 because of the significant difference in the reporting that lead to an exclusion of many observations. 13 The trade data and pollution abatement cost data are then matched and merged with the dataset on the corresponding industry characteristics. Industry data are downloaded from the NBER-CES Manufacturing Industry Database (Bartelsman et al., 2000). The database have information on the total payroll, value added, cost of materials, value of shipments, real structures capital stock, etc. To locate the input-output linkages, I use the direct requirement table from the 1982 Benchmark Input-Output Accounts from the U.S. Department of Commerce, Bureau of Economic Analysis on their web. 14 The direct requirement table publishes the input coefficients to each industry. For instance, to produce one dollar of output, Industry Y requires 50 cents of commodity in Industry Z, in this case, the input coefficient is In order to use the 1982 table to look at the use of intermediate goods, it is necessary for me to convert the (1972-based) SIC 4-digit industry aggregation into I-O industry classifications in For more than 90% of manufacturing industries, the I-O industry classifications directly correspond to one or more SIC-based industries so once it has been aggregated up. Industries are dropped when the clear classifications are not available. 16 Table 1 lists summary statistics for some of the variables described above. 5 Empirical Framework Most of the literature on pollution haven effect look at net import as the dependent variable. However, the use of pollution abatement cost of U.S. firms does not give an accurate measure of environmental policy stringency abroad. In the model, the export decision of the firm 13 The 1979 PACE reports pollution cost by media (air, water and solid waste) and it only reports the biggest polluters in each of these three medias. In other words, if an industry pollutes a lot in air but not so much in water, the aggregate pollution cost cannot be computed due to missing data. Most of the papers explicitly exclude data in 1979 too (e.g. Ederington et al., 2005; Levinson and Taylor, 2008). 14 The direct requirement table is not available for 1977 and 1972 Benchmark Input-Output Accounts Benchmark Input-Output Accounts did publish the direct requirements table but the aggregation is based on 1987 SIC codes which are not matched in this paper due to reasons below. 15 The data also showed heavy dependence among the industries, with most industries requiring at least 40 commodities (Young, 1991). 16 For example, Industrial Inorganic Chemicals, nec" (SIC 2819) shows up in two I-O codes as (Industrial Inorganic Chemicals, nec) as well as part of (Primary Aluminium, with SIC 3334). In this case, both SIC 2819 and SIC 3334 are not used in the analysis. 20

21 depends on its own pollution cost - there is no reason to believe that their pollution costs are going to have any systematic relationship with the pollution costs faced by US firms. Most paper studies imports or inbound foreign direct investments and they use US pollution costs to approximate for the environment regulation (Keller and Levinson, 2002; Ederington et al., 2005). While there are good reasons to believe that US pollution costs are going to negatively impact the inward FDI decision, the export decision should depend on the pollution costs and regulation of the production origin. Without proper information on the pollution costs for all countries, I decide to focus on the determinants on exports only, which my model predicts there is a negative relationship between pollution cost and exports. In Section 3.1, I showed that (log of) pollution cost and material cost affect linearly on ln λ (the cutoff productivity), however, they will enter non-linearly in ln M and ln ϕ as both changes on M and ϕ depend on the distribution of productivity which is unknown to researchers. While I can control for firm s productivity by using some indirect measures, it is not possible for me to control for the number of exporters on the right hand side of the estimation equation. I will try to control separately for the productivity of the industry by using the wage share of nonproduction workers. Nonproduction workers have long been linked with more efficient process in production (Feenstra and Hanson, 1996) and different papers have also employed this measure in the estimation of outsourcing and trade (see Feenstra and Hanson, 1999). Helpman et al. (2004) use nonproduction workers to proxy for scale economies in their study of firm; s tradeoff of exports and FDI. Without order proper measure of productivity of exporters, this serves as an imperfect proxy for industry productivities that are changing over time. For exports from industry i at time s, I am going to employ a fixed effects panel estimation of the following equation: ln X is = β 0 + β 1 ln POLCOST is + β 2 ln MATCOST is + β 3 PROD is + β 4 ln TRADECOST is + φ s + φ i + u is (27) where φ s and φ i are the time and industry fixed effects respectively. Industry fixed effects can be used to control for unobserved industry heterogeneity, especially the industryspecific distribution of productivity, which I am going to assume that it is constant over time. Noting that entry and exit is affected by environmental and material costs (which in 21

22 turn affect the extensive margin), this motivates me to estimate the right hand side nonparametrically or using some flexible functional form. As the main goal of the paper is to try to find a piece of evidence for pollution haven effect, this motivates us to use a second-degree series estimation on the nonparametric portion of the right hand side, where we can easily articulate the relationship between pollution cost (POLCOST) and exports, and how it is affected by (MATCOST). I utilize two measures of MATCOST in the estimation. The first measure is from the cost of materials measure in the NBER-CES Manufacturing Database (labelled as Cost of Materials (ASM)" in the subsequent section of the paper, since the NBER-CES database is compiled from the Census of Manufactures as well as Annual Surveys of Manufactures). It includes cost of supplies and other raw materials, including expenditures on electricity energy. I deduct the cost of materials by the energy cost, and then scale this by the value of shipments to get a unit measure of cost. However, this does not directly capture the input linkage story demonstrated above, therefore I have created another measure of cost of materials. In order to capture the cost of intermediates that are being affected by the regulation spillover effects on other manufacturing industries, the second measure utilizes the direct requirement table from the Benchmark Input-Output Accounts in Denoting the input coefficient of commodity j used by industry i by d ij, I construct the cost by applying the following formula: [ ] Cost of Materials (I/O) is dij ln POLCOST js j (28) Tables 2 to 4 list the 10 most pollution intensive and material intensive industries according to the above constructions. As discussed earlier, some industry specific shocks may be causing some correlations between the error term and our variables of interest. After presenting results using industryby-time (by pooling all countries together) data, I will present another set of results by including the country dimension to the data as well. This approach has two distinct advantage over the first one. First, it can also allow for different fixed effects to control for some other country-by-industry or country-by-time unobserved effects. Since I do not observe the trade barriers (primarily for imports) for each individual country and some industries 22