metadata: authors: 0: author: Ferro,Esteban
1: author: Mare,Davide Salvatore
2: author: Miguel Liriano,Faruk
3: author: Patino Pena,Fausto Andres
4: author: Rodriguez Quezada,Maria Gabriela
5: author: Zeni,Federica
count: Dominican Republic,Central America
docna: 0: docna: The Effect of Carbon Taxes on Aggregate
            Productivity: The Case of the Dominican Republic
docty: Policy Research Working Paper
abstracts: cdata!: This paper examines the impact of
            implementing a carbon tax on aggregate total factor
            productivity in the Dominican Republic through the resource
            allocation channel. It incorporates energy
            inputs&#x2014;electricity and fuel&#x2014;into
            firms&#x2019; production functions, allowing predictions
            of potential changes in resource allocation due to the
            carbon tax. The theoretical implications of the model
            indicate that the carbon tax has a heterogeneous effect on
            firms&#x2019; input choices, contingent on the level of
            firms&#x2019; existing input market distortions.
            Moreover, the model suggests that in economies in which more
            productive firms are less distorted, the carbon tax can
            decrease aggregate total factor productivity. In contrast,
            when more productive firms are more distorted, the carbon
            tax can increase or decrease aggregate total factor
            productivity. Utilizing detailed firm-level data from 2009
            to 2018, covering up to 118,000 firms, this paper finds that
            a carbon tax is more effective when levied on fuels rather
            than electricity. For the majority of sectors in the sample,
            the paper finds that existing distortions in energy
            consumption are positively correlated with firm-level
            productivity. Moreover, the quantitative results show that
            the introduction of the carbon tax shifts the burden of
            market distortions from high productivity firms to low
            productivity ones, generating aggregate total factor
            productivity gains for most sectors in the Dominican
            Republic. Overall, this study underscores the importance of
            considering existing input market distortions when analyzing
            the impact of environmental taxes.
display_title: The Effect of Carbon Taxes on
            Aggregate Productivity: The Case of the Dominican Republic
listing_relative_url: /research/2024/10/34400144/effect-carbon-taxes-aggregate-productivity-case-dominican-republic
topicv3: Private Sector Development m1326060 1921,Business Taxation m1326300 1285,Sustainable Economic Growth m1326464 1100,Climate Change Mitigation m1327079 579,Energy and Climate Change m1327080 1756
publishtoextweb_dt: 2024-10-04T00:00:00Z
dois: 10.1596/1813-9450-10944
origu: Off of Sr VP Dev Econ/Chief Econ (DECVP)
no_of_pages: 45
author_keys: 0: author_key: 1078137
1: author_key: 1366794
2: author_key: 1753502
3: author_key: 1816473
4: author_key: NA
5: author_key: 2184674
model_extractions: Policy Research Working Paper 10944

# The Effect of Carbon Taxes on Aggregate Productivity The Case of the Dominican Republic

_Esteban Ferro Davide S. Mare Faruk Miguel Liriano Fausto Patiño Peña Maria Gabriela Rodriguez Quezada Federica Zeni_ Finance, Competitiveness and Innovation Global Practice October 2024
Policy Research Working Paper 10944

# **Abstract**

This paper examines the impact of implementing a carbon tax on aggregate total factor productivity in the Dominican Republic through the resource allocation channel. It incorporates energy inputs—electricity and fuel—into firms’ production functions, allowing predictions of potential changes in resource allocation due to the carbon tax. The theoretical implications of the model indicate that the carbon tax has a heterogeneous effect on firms’ input choices, contingent on the level of firms’ existing input market distortions. Moreover, the model suggests that in economies in which more productive firms are less distorted, the carbon tax can decrease aggregate total factor productivity. In contrast, when more productive firms are more distorted, the carbon tax can increase or decrease aggregate total factor productivity. Utilizing detailed firm-level data from 2009 to 2018, covering up to 118,000 firms, this paper finds that a carbon tax is more effective when levied on fuels rather than electricity. For the majority of sectors in the sample, the paper finds that existing distortions in energy consumption are positively correlated with firmlevel productivity. Moreover, the quantitative results show that the introduction of the carbon tax shifts the burden of market distortions from high productivity firms to low productivity ones, generating aggregate total factor productivity gains for most sectors in the Dominican Republic. Overall, this study underscores the importance of considering existing input market distortions when analyzing the impact of environmental taxes.

This paper is a product of the Finance, Competitiveness and Innovation Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at fzeni@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
The Effect of Carbon Taxes on Aggregate P roductivity: the Case of the

# Dominican Republic

Esteban Ferro<sup>1</sup> , Davide S. Mare<sup>1,2</sup> , Faruk Miguel Liriano<sup>1</sup> , Fausto Pati ̃no Pe ̃na<sup>1</sup> , Maria Gabriela Rodriguez Quezada<sup>3</sup> , and Federica Zeni<sup>∗1</sup>

> 1The World Bank, Washington, D.C., US

> 2University of Edinburgh, Edinburgh, UK

> 3Direcci ́on General de Impuestos Internos, Santo Domingo, DR

_JEL classification_ : D24; D61; H23; O47; Q58 _Keywords_ : climate change, carbon pricing, misallocation, TFP

> ∗Corresponding author: fzeni@worldbank.org. This paper’s findings, interpretations and conclusions are entirely those of the authors and do not necessarily represent the views of the World Bank, their Executive Directors, or the countries they represent. We are grateful to Daniel Navia Simon, Roberto Fattal Jaef, Nazim Tamkoc and participants to the DEC IG half-baked seminar and to the 2024 International Conference of the Financial Engineering and Banking Society for comments and suggestions. All errors and omissions are ours.
# **1 Introduction**

In 2015, in correspondence of the Paris Agreement, the Government of the Dominican Republic (DR) submitted its Nationally Determined Contribution (NDC) to the United Nations Framework Convention on Climate Change.<sup>1</sup> In its NDC, the Dominican Republic has pledged to reduce its greenhouse gas (GHG) emissions by 27% by 2030, relative to the levels recorded in 2010. Despite the effort to meet this commitment,<sup>2</sup> the DR is still an economy which relies heavily on fossil-fuel sources.<sup>3</sup> In similar contexts, countries have considered large-scale regulatory interventions, such as the introduction of a carbon tax.<sup>4</sup> The regulation would provide a strong price incentive for firms to reduce consumption of fossil-fuels and switch to lower carbon alternatives. While contributing to reducing emissions, the introduction of a carbon tax would also affect energy costs, which indicates that it would also have an impact on the allocation of factor inputs across firms.

In this paper, we analyze the impact of a hypothetical carbon tax on aggregate total factor productivity (TFP) through the resource allocation channel. We begin by developing a model in which a carbon tax affects firms’ consumption of energy inputs. In this model, the carbon tax is levied on firms’ total emissions from the use of electricity and fuel in the production process. Consequently, the introduction of this tax alters the cost of energy inputs, thereby changing firms’ demand for all factor inputs, modifying their emission intensities, and affecting aggregate TFP through resource reallocation.<sup>5</sup> Utilizing data from all formal firms in the Dominican Republic between 2009 and 2018, we calibrate the model at the sector level and quantify changes in firm-level emission intensities and aggregate sector TFP under two carbon tax scenarios. Our findings indicate that a carbon tax is more effective when levied on fuels rather than electricity. Additionally, this tax is likely to positively impact aggregate sector TFP for most industries by reducing distortions for high-productivity firms relative to low-productivity ones, thereby reallocating resources from the latter to the former within these sectors. Overall, the study highlights the importance of considering existing input market distortions when analyzing the introduction of environmental taxes.

> 1The Paris Agreement, adopted by 196 Parties in Paris in December 2015, is a legally binding international treaty that aims to limit global warning. It entered into force on November 4, 2016.

> 2Among other initiatives, in 2021 the DR signed a landmark agreement with the World Bank to subsidize reduction of emissions through preventing deforestation.

> 3The GHG footprint of the DR comes from the production and consumption of energy (from fossil-fuels for heating or lighting, for industrial processes, and transport) and partly from the agriculture sector (enteric fermentation and manure left on pasture). Aggregate information can be found on the International Energy Agency (IEA) website, while information at the economic sector level is reported in Sections 2 and 3.

> 4For the case of the DR, see for example the 2018 Collaborative Instruments for Ambitious Climate Action (CI-ACA) Dominican Republic or the 2020 carbon pricing instrument road map. A carbon tax entails imposing a levy on every unit of pollution emitted.

> 5A firm’s emission intensity is defined as the emissions produced by its consumption of an energy input per unit of output revenue.

1
The introduction of carbon taxes is advocated to reduce the carbon footprint of firms and households (Stavins [2020]; Metcalf [2019]). One argument in support of the introduction of this environmental tax is the provision of a direct economic incentive for reducing carbon emissions, alongside revenue generation for climate mitigation initiatives (Allan, Lecca, McGregor, and Swales [2014]). Additionally, these Pigouvian taxes facilitate the internalization of costs connected to adverse externalities arising from pollution, and can potentially drive innovation aimed at reducing carbon emissions (Goulder and Mathai [2010]). On the other hand, a typical concern is _carbon leakage_ ,<sup>6</sup> that is, the transfer of emissions from the local (regulated) jurisdiction to non-regulated ones as a result of the firms’ response to the policy. This concern is limited for small economies such as the DR, which contribute marginally to the global stock of carbon emissions generated worldwide. Other concerns regard the equity, distributional impact, and costs of the implementation of carbon mitigating policies, especially in developing countries. Additionally, the imposition of an environmental tax could bring negative impacts on economic growth, employment, and competitiveness (see, among others, Zhang, Guo, Zheng, Zhu, and Yang [2016]). Negative welfare effects could also arise as firms may pass on the increased energy costs to customers (Fabra and Reguant [2014], Marion and Muehlegger [2011]). Lastly, the efficacy of carbon taxes likely varies given institutional characteristics. For example, Allen, Barbalau, and Zeni [2023] argue that the development of financial market instruments contingent on carbon emissions shapes the optimal level of a carbon tax.

Numerous studies examine the effects of implementing carbon taxes from various perspectives.<sup>7</sup> K ̈oppl and Schratzenstaller [2023] offer an overview of their impact on diverse outcomes including macroeconomic effects, competitiveness, innovation, and distributional implications. The majority of these studies do not identify adverse effects of a carbon tax introduction on GDP growth, employment, competitiveness, or innovation. Some studies aim at uncovering the determinant of emissions and related firm choices (Haller and Murphy [2012]). Firm idiosyncratic characteristics are important in discerning the relationship between firm climate adaptation and performance. In this regard, Capelle, Kirti, Pierri, and Villegas Bauer [2023] provide direct evidence of the positive link between productivity-enhancing investments and lower emission intensity. Similarly, Lanteri and Rampini [2023] prove the importance of financing frictions in determining firm-level emissions. Yet the extant literature scarcely addresses the potential trade-off between TFP and environmental taxes (see, for example, Yamazaki [2022]) or attempts to model directly the change in firms’ production process brought by the introduction of such taxes. We fill this void by developing a theoretical framework that incorporates energy consumption as a crucial input within the production function. This

> 6See, among others, Fowlie and Reguant [2022].

> 7The World Bank [2023b] provides the latest information on carbon pricing schemes introduced around the world.

2
framework enables us to analyze the allocative efficiency consequences of implementing a hypothetical carbon tax at the firm level. Moreover, our model emphasizes the pivotal role of pre-existing distortions within the input market and their implications on the efficacy of such a policy measure.

In our paper, we extend the theoretical framework from Hsieh and Klenow [2009] to allow for differentiated energy inputs such as fuels and electricity and simulate the within-sector interactions of a carbon tax with existing market frictions. We hypothesize the introduction of a carbon tax levied on firms according to their total emissions from the consumption of fuels and electricity. A key insight gleaned from the model is the heterogeneous impact on firms of the carbon tax, contingent on the correlations between the energy distortions with the output and capital wedges, and the correlations between the energy distortions with firm productivity. Specifically, when more productive firms face lower distortions in the energy input markets and when there exists a positive correlation between the capital and output wedges with the energy wedges, the introduction of an environmental tax will decrease aggregate TFP. This occurs because introducing the carbon tax distorts higher-productivity firms more than lower-productivity ones, given that the former initially faced lower distortions. Consequently, resources are shifted from more productive to less productive firms, increasing misallocation and decreasing aggregate TFP. In contrast, if more productive firms face higher distortions or the correlations between the capital and output wedges with the energy wedges are non-positive, then the introduction of a carbon tax can either increase or decrease aggregate TFP.

Using detailed data on firms’ tax declarations and energy expenditures from the Direccion General de Impuestos Internos (DGII) of the Dominican Republic and aggregate information from the National Energy Information System (SIEN) between 2009 and 2018, we calibrate the model at the sector level. Using this data, we first provide a back-of-the envelope calculation of the revenues generated by the introduction of a carbon tax rate using plausible scenarios.<sup>8</sup> We find that, based on the most recent estimates in 2018, a tax of $110/tCO2 would generate carbon tax revenues approximately of $920 millions, or roughly 10% of total taxes collected in 2018.<sup>9</sup> On average, this represents roughly 20% of the total earnings reported by the firms in our sample in the same year, although there is substantial variation across sectors. The sectors with the highest carbon emissions footprint and that are also likely to be particularly impacted by the policy in the short term are transport, cement, and hospitality.

We conduct our main counterfactual exercise on the impact of a carbon tax in the DR accounting for input market frictions within sectors. We do not estimate frictions explicitly, but infer them from observed

> 8The estimated carbon prices are the output of an independent analysis on the DR, and are based on the REMIND-MAgPIE 3.0-4.4 model. For more information, see `https://www.ngfs.net/ngfs-scenarios-portal/explore` .

> 9The percentage of total taxes collected in 2018 is computed using an average exchange rate of 49.54 DOP/USD and 430,629 million DOP of total taxes collected in 2018.

3
output and input choices of firms, in a similar spirit as in Hsieh and Klenow [2009]. We find that for most economic sectors, the most productive firms face the highest levels of distortions. Consequently, for most of these sectors in which this relationship holds, our counterfactual examination predicts that the impact on aggregate sector TFP is projected to be positive. Furthermore, we show that the tax is more effective if it is levied on fuels - which are substantially more emission intensive - rather than electricity.

# **2 Theoretical Framework**

In this section, we build on the theoretical framework developed in Hsieh and Klenow [2009] to determine the impact of a carbon tax on aggregate TFP in presence of market distortions. We assume a single-sector economy in which the final good is produced from a set of intermediate goods in the economy. Producers of the intermediate goods act monopolistically and choose the optimal combination of labor, capital, electricity, and fuel to maximize profits. The production of the intermediate goods generates carbon emissions through consumption of fossil-fuel energy sources. Carbon emissions are taxed at a fixed rate which accounts for the social damage generated by releasing carbon in the atmosphere.

Denote _y_ as the final good produced by a representative firm facing a perfectly competitive output market. The firm combines output from a set of _m_ intermediate goods _{yi}i_ =1 _,...m_ using a Cobb-Douglas production technology: with _σ >_ 1. The firm solves the following profit maximization problem: with _p_ and _pi_ defined as the prices of the final good _y_ and of the intermediate good _yi_ , respectively, and satisfying the zero profit condition _py_ =<sup>�</sup><sup>_m_</sup> _i_ =1<sup>_piyi_.Eachintermediategood,</sup><sup>_yi_isproducedfromcapital,</sup><sup>_ki_,</sup> labor, _li_ , electricity, _xi_ , and fuel, _fi_ as: with the coefficients of output elasticities sufficing _βl_ + _βk_ + _βx_ + _βf_ = 1. Firm _i_ ’s quantity-based total factor productivity is denoted as _ai_ and represents how efficient firm _i_ is at transforming its demanded inputs into a unit of the intermediate good. The production of output _yi_ generates emissions from the consumption of 4
electricity and fuel, _exi_ and _efi_ , respectively, given by: with _ηx_ as the carbon content of the fuel mix used to generate electricity _xi_ , and _ηf_ as the carbon content of the fuel _fi_ used as direct input for producing _yi_ , both taken as given.<sup>10</sup> We assume that emissions _exi_ and _efi_ are taxed at a fixed rate _λ_ . Furthermore, we denote the cost of capital, labor, electricity, and fuel as _r, w, zx,_ and _zf_ respectively.

**Intermediate Good Producer’s problem.** Each intermediate good _i_ is produced by a monopolist which chooses the optimal combination of inputs _{ki, li, xi, fi}_ to maximize profits:

> <u>1</u> _−_<sup><u>1</u></sup> The first three terms in (5) are the same as in Hsieh and Klenow [2009]. The output price _pi_ = _y σ yi σ p_ is such that to maximize the representative producer’s profits in (2),<sup>11</sup> whereas the wedges _τyi_ and _τki_ are interpreted as distortions in the output and capital markets, which alter the optimal input choices of producer _i_ . Output distortions _τyi_ reflect government size restrictions, output subsidies or transportation costs, whereas capital distortions _τki_ are likely the result of credit constraints or subsidized access to credit.

The fourth and fifth terms represent the additional energy input costs. The wedges _τxi_ and _τfi_ are existing distortions in the electricity and fuel markets. In practice, firms facing higher (lower) wedges _τxi_ or _τfi_ have a higher (lower) cost of energy for each unit of production, resulting in under-consumption (overconsumption) of fossil fuels. Those wedges may capture information asymmetries regarding energy efficient technologies, bounded rationality, and/or managerial myopia or incentives to adopt energy efficiency measures (DeCanio [1993], Howarth, Haddad, and Paton [2000]). Importantly, the wedges _{τyi, τki, τxi, τfi}_ can be generally correlated within firms. For example, recent research in Iovino, Martin, and Sauvagnat [2021] has documented a positive relationship between subsidized access to debt financing and higher emissions intensity, whereas a work by Shapiro [2021] has shown that emissions intensive industries are more protected by import trade policies. In the case of the DR, the energy market suffers from significant energy losses due to poor infrastructure and electricity theft, which effectively increase operating costs. A compounding

> 10Note that _ηx_ and _ηf_ are kept constant across intermediate goods’ production functions _i_ = 1 _, . . . m_ . In the calibration section, we let _ηx_ and _ηf_ vary across industries and regions.

> 11That is, each producer _i_ acts monopolistically, anticipating the effect of its production choices on the intermediate good price _pi_ . This specification is taken from Hsieh and Klenow [2009].

5
effect is also the heavy reliance on imported fossil fuels, which introduces cost volatility. Additionally, a lack of investment in electrical infrastructure perpetuates poor service quality and ongoing dependence on subsidies, affecting firms heterogeneously.

Finally, the sixth term represents the total carbon tax expenditure incurred by the producer, which is given by the carbon tax _λ_ applied to total emissions from consumption of electricity and fuel respectively. This last term appears uniquely if the economy is subject to a carbon tax.

In summary, the allocation of resources across firms depends not only on the firms’ productivity, _ai_ , but also on the output, capital, and energy distortions they face. Hsieh and Klenow [2009] show that to the extent that resource allocation is driven by distortions rather than productivity, this will result in differences in the marginal revenue products of capital, labor, and energy. These marginal revenue products are as defined:

**Outcomes of interest.** Our model allows us to evaluate the implications of introducing the carbon tax _λ_ on firm-level emissions intensities, market structure, and aggregate TFP. To this end, we define producer _i_ ’s emission intensity from electricity and fuel as the total emissions produced by their consumption per unit of output revenues

Furthermore, we study the market structure leveraging firms’ market shares, which are defined as the share of firm _i_ ’s output revenue in aggregate output revenues: Lastly, aggregate TFP of the economy is defined by: with aggregate output, _y_ , as in (1) and where _K_ =<sup>�</sup><sup>_m_</sup> _i_ =1<sup>_ki_,</sup><sup>_L_=�</sup><sup>_m_</sup> _i_ =1<sup>_li_,</sup><sup>_X_=�</sup><sup>_m_</sup> _i_ =1<sup>_xi_and</sup><sup>_F_=�</sup><sup>_m_</sup> _i_ =1<sup>_fi_</sup> are the aggregate demand of capital, labor, and energy inputs in the economy 6
**Absence of distortions.** In what follows, we first want to assess the equilibrium effect of the tax _λ_ on the endogenous emissions intensities in (7), the market share in (8), and aggregate TFP in (9) respectively in absence of distortions, i.e. when the wedges equal 0 _{τyi_ = 0 _, τki_ = 0 _, τxi_ = 0 _, τfi_ = 0 _}_ for each _i_ = 1 _, . . . m_ . We prove in the Appendix the following:

**Proposition 1.** _In absence of distortions, producer i’s optimal emissions intensities, ξxi_<sup>_λandξ_</sup> _fi_<sup>_λ,in_(7)</sup> _are constant across firms and decrease with the carbon tax, λ, as:_

_where αx_<sup>_λ_=</sup><sup>_ληx/zxandα_</sup> _f_<sup>_λ_=</sup><sup>_ληf/zfaretheratiosoftheenergyinputs’taxrelativetotheenergyinputs’_</sup> _costs._

**Corollary 1.** . _Producer i’s market share, s_<sup>_λ_</sup> _i_<sup>_,in_(8)</sup><sup>_andaggregatetotalfactorproductivity,TFPλ,in_</sup> (9) _are the same as in a frictionless economy without a carbon tax._

Proposition 1 and Corollary 1 offer a useful set of predictions regarding the benchmark impact of the carbon tax absent distortions in the input and output markets. Equation 10 implies that each producer _i_ ’s emissions intensity _ξix_<sup>_λ_(</sup><sup>_ξ_</sup> _if_<sup>_λ_)isreducedbyahigheramountthehigherthefueltax</sup><sup>_α_</sup> _x_<sup>_λ_(</sup><sup>_α_</sup> _f_<sup>_λ_).Thismeansthat</sup> for a given carbon tax _λ_ , the higher carbon content of the energy source, _η_ , and the lower the unregulated price of the energy source, _z_ , the higher the effect of the tax on the producer _i_ ’s input choices. Importantly, such effect is constant across producers _i_ in the economy since each producer faces the same energy price.

Furthermore, Corollary 1 states that, absent distortions, we should not expect the tax to alter the market structure nor the efficient allocation of resources within the economy. Indeed, the unique effect of the tax _λ_ is that of reducing aggregate demand of fossil-fuel inputs _X_ and _F_ , and therefore total carbon emissions in such a way as to account for the negative externality.

Importantly, Proposition 1 also offers a testable implication to detect the presence of distortions in the energy markets. Specifically, absent distortions, and in absence of the carbon tax _λ_ = 0, the proposition implies that all firms should have the same baseline emissions intensities, which we denote as _ξxi_<sup>_∗_=(</sup> _σ−σ_ 1<sup>)</sup><sup>_<u>ηx</u>_</sup> _zx_<sup>_<u>βx</u>_</sup> and _ξfi_<sup>_∗_=(</sup> _σ−σ_ 1<sup>)</sup><sup>_<u>ηf</u>_</sup> _zf_<sup>_<u>βf</u>_.Consequently,accordingtothemodel,anyvariationinthebaselineemissionsintensities</sup> that we observe in the economy should be attributed to the presence of distortions.

We now move to the general case in which the economy presents distortions as represented by _V ar_ ( _τji_ ) _>_ 0 for _j_ = _y, k, x, f_ . We prove in the appendix the following

**Proposition 2.** _In presence of distortions, producer i’s optimal emissions intensities, ξxi_<sup>_λandξ_</sup> _fi_<sup>_λin_(7)</sup><sup>_,_</sup>

7
_are heterogeneous across firms and decrease with the carbon tax, λ, as:_

**Corollary 2.** . _Producer i’s market share, s_<sup>_λ_</sup> _i_<sup>_,in_(8)</sup><sup>_andaggregatetotalfactorproductivity,TFPλ,in_</sup> (9) _are different from the economy with distortions and no carbon tax._

Proposition 2 highlights that, setting _λ_ = 0, the presence of market distortions generate heterogeneity in emissions intensities across producers _i_ . Specifically, producers who experience a cheaper (more expensive) access to the output market and or a cheaper (more expensive) access to the energy market, as reflected in lower (higher) wedges _τyi, τxi_ , and _τfi_ respectively, have a higher (lower) emission intensity.

Second, we note that introducing a tax _λ_ would now reduce emissions intensities heterogeneously across producers. Specifically as we show in the appendix, the marginal effect of the tax on emissions intensities depends on the initial energy wedges _τxi_ and _τfi_ . The lower the wedges, the higher the marginal impact of the tax, and thus the relative decrease in the producer’s emissions intensity.

Corollary 2 implies that the impact of _λ_ on emissions intensities _ξxi_<sup>_λ_and</sup><sup>_ξ_</sup> _fi_<sup>_λ_willalsobereflectedinthe</sup> market structure, i.e., in each firm’s market share _s_<sup>_λ_</sup> _i_<sup>.AswedetailinthemodelAppendix,themarket</sup> share of producers _i_ with cheaper (more expensive) access to energy markets will be reduced (increased) respectively. This is an important feature of the tax as it reallocates resources from less energy constrained to more energy constrained firms.

Whether such alteration ultimately has a positive or a negative effect on aggregate TFP depends on the correlation between the energy distortions _{τxi, τfi}_ and the capital and output distortions _{τki, τyi}_ , as well as on the correlation between the energy distortions _{τxi, τfi}_ and firm quantity-based TFP, _ai_ . When _ai_ and _{τxi, τyi, τki, τfi}_ are jointly log-normal, following Hsieh and Klenow [2009], we derive in the Appendix an explicit expression for the aggregate TFP, which allows us to introduce the following:

**Corollary 3.** . _Let ai and {τxi, τyi, τki, τfi} be jointly log-normally distributed. Then a sufficient condition for the carbon tax, λ, to decrease aggregate TFP with respect to the distorted economy without a tax is that, for each energy wedge τji with j_ = _x, f :_

_Furthermore, under such condition, the higher the output elasticities of electricity and fuel βx and βf , the_ 8
_higher the reduction in aggregate TFP generated by the tax. If the inequalities in_ (12) _are not jointly verified, then the impact of the tax, λ, on aggregate TFP is ambiguous and depends on the magnitude of the tax, λ, relative to the energy prices, zf and zx, the output elasticities, βx and βf , and the correlation between the energy wedges, {τfi, τxi}, and {ai, τki, τyi} ._ Corollary 3 outlines a sufficient condition for a homogeneous carbon tax to negatively affect aggregate TFP in presence of distortions. Intuitively, when more productive firms face lower energy distortions, i.e. when _Corr_ ( _τji, ai_ ) _<_ 0, we can expect the tax to decrease aggregate TFP in equilibrium. This is because when _Corr_ ( _τji, ai_ ) _<_ 0, then firms that are more productive have cheaper access to energy markets, which implies that distortions tend to favour more productive firms over less productive ones. Introducing the tax would increase such distortions for more productive firms relative less productive firms in the economy. Vice versa, when _Corr_ ( _τji, ai_ ) _>_ 0, then we should expect that the effect of the tax on aggregate TFP is at least ambiguous, implying that the tax, _λ_ , has the potential to increase it. This is because the tax alleviates market distortions in favor of more productive firms in the economy. Lastly, the higher the output elasticities of energy, the stronger the positive/negative impact of the tax on aggregate TFP.

# **3 Construction of the Dataset**

The empirical exercises carried out in this paper combine firm-level data and an energy balance matrix to estimate emissions at the firm level in the Dominican Republic. The first source of firm-level information is the tax forms from the Direccion General de Impuestos Internos (DGII), which compiles tax declarations for the universe of formal firms required to file taxes in the Dominican Republic between 2007 and 2021. The number of firms each year averages around 90,000, ranging from 55,000 in 2007 to close to 129,000 in 2021. Through these tax declarations, we have the necessary information to track and compute several firm performance variables, including sales, production, value added, and productivity. Also key to our analysis is firms’ expenses and liabilities, as this will be the conduit through which a CO2 tax affects firms. The database also includes relevant firm characteristic variables such as the economic sector of activity and geographical location. We complement this information with data from the National Energy Information System (SIEN). SIEN reports energy consumption and energy generation by type of source and economic activity, as well as CO2 emissions by aggregate sectors. SIEN also details how energy is used in the main areas of consumption. This is complemented by data on supply flows, including supply, transformation centers, and final energy consumption. With this information, it is possible to estimate total emissions 9
related to purchase of energy by fuel type and by economic activity. Aggregate information from SIEN is provided between 2009 and 2018, whereas firm-level information from the DGII is available between 2007 and 2021. Our analysis refers to the combined dataset relative to the period 2009–2018. Summary statistics at the activity level are reported in Appendix B.1.

**Estimation of CO2 emissions.** Form 606 provides the link between DGII firm data and SIEN emissions data, which enables us to estimate energy consumption and CO2 emissions by firm. Form 606 is a monthly report submitted by companies to DGII that provides information on the purchase of goods and services that include a tax receipt number (NCF). To quantify the energy consumption and CO2 emissions by firm, we leverage on the information on purchases of different fuels for energy generation as well as electrical energy purchases. The energy activities reported in Form 606 are displayed in Appendix B.2.

**Emissions from Electricity Consumption.** A firm’s expenditure in electricity is equated to a firm’s expenditure reported in Form 606 in sectors 4011 (electric energy generation), 4012 (transportation of electric energy), and 4013 (distribution and sale of electric energy). Using a firm’s location, it is possible to assign a price per KWh to each firm based on the average price reported by the Dominican Republic’s electricity distribution companies: EDESUR, EDENORTE, and EDEESTE (EDEs). Each of the EDEs serves a specific region in the country, and there is variation in the average electricity price across regions. Dividing the firm’s electricity expenditure by its location’s average electric price, we estimate the firm’s energy consumption in kilowatt-hour (kWh). To estimate CO2 firms’ emissions from electricity consumption, it is necessary to estimate a conversion factor from kWh consumed to kilogram (kg) of CO2 emissions. BNE provides information on the total Gigawatt hour (GWh) of electricity produced, with the SIEN representing nearly 93.3 percent of the 21.7 thousand GWh of electricity generated, isolated systems accounting for 5 percent, and auto producers for the remaining 1.7 percent. Dividing the total electricity generated in GWh by the total emissions of CO2 in kg, we obtain the conversion factor needed to transform firms’ electricity consumption into firms’ emissions of CO2.

**Emissions from Fuel Consumption.** In addition to electricity consumption, many firms produce emissions through consumption of different types of fossil fuels in their production and transportation processes. Ideally, we would want to know a firm’s expenditure on each type of fuel so that we can perform a similar exercise as with electric consumption to estimate firms’ emissions from fuel consumption; however, the information provided in Form 606 is aggregated by energy sector as described in Appendix B.2. To estimate emissions from fuel consumption, we compare the International Standard Industrial Classifications (ISICs)

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<!-- Start of picture text --> 17 o94<br>|<br>6 0.56<br>0.44 0.42 0.42<br>4 0.35 0.33<br>O22<br>2<br>0.08 O12<br>0.040.030.0299 0.02 001<br>0<br>Micra Small Medium Large<br>HE Number offirms HE Production<br>ME Energy Expenditure EE CO? Emissions<br><!-- End of picture text -->
Financial Services Utilities Serv.

Manuf. Textiles and apparel Professional services Other services Other Manuf. Agro and Fish Construction Real estate services Mining Manuf. Chemicals & plastics Manuf. Food and tobacco Wholesale & Retail Hospitality Manuf. Cement Transport and Storage <!-- Start of picture text --> 0 2,000 4000 6,000<br>KT of CO2<br><!-- End of picture text -->
of resources within sectors. In practice, we observe substantial variation in electricity and fuel emissions across and within sectors. Among the most intensively emitting sectors based on their consumption of electricity are water distribution, postal services, and agriculture, whereas the most intensively emitting sectors based on their consumption of fuels are air transport, land transport, and the manufacturing of cement (see Figure 3 and Figure 4).

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**Figure 3.** Electricity Emission Intensity

<!-- Start of picture text --> Water Distribution<br>Posta l services<br>Agriculture<br>Air transport<br>Manu f .-Pla stics<br>Manuf.-Chemicals<br>Retail<br>Manu f .-Apparel<br>Manu f .-Machinery<br>Construction<br>Water transport<br>Storage/wa rehouse<br>Manu f .-Me tal products<br>Land t ransport<br>Wholesale<br>Manuf.-Cement<br>Manu f .- Editing and recording<br>Manu f .-O thers<br>Vehic l e/ f u el sales<br>M anuf.-Textiles<br>0 .2 .4 .6 .8 1<br><!-- End of picture text --> Note: The bar plot shows the average emissions intensity from electricity consumption by economic sector. Emissions intensity is computed as total emissions by electricity consumption divided by value added. Data are from DGII and SIEN and are relative to 2018. Dotted blue lines refer to standard deviations within sectors.

**Figure 4.** Fuels Emission Intensity

<!-- Start of picture text --> Air transport<br>Land t ransport<br>Manu f.-Cement<br>Water tran sport<br>Stora ge/warehouse<br>Agriculture<br>Posta l services<br>Ma nuf.-Textiles<br>M anuf.-Chemicals<br>Manuf.-Metal products<br>Vehicle/fue l sales<br>C onstruction<br>Manuf.-Plastics<br>Manuf.-Machinery<br>Wholesale<br>Manuf . -Apparel<br>Manuf.-Others<br>Manuf.-Editing and recording<br>Water Distribution<br>Retail<br>0 10 20 30<br><!-- End of picture text --> Note: The bar plot shows the average emissions intensity from fuels consumption by economic sector. Emissions intensity is computed as total emissions by fuels consumption divided by value added. Data are from the combined sources DGII and SIEN and are relative to 2018. Dotted blue lines refer to standard deviations within sectors.

**Existing carbon pricing and fuel taxes.** Our methodology for estimating emissions at the firm level lies on the assumption that firms within sectors face the same price of fuel and electricity. While this may be a strong assumption in some countries, it does seem to apply fairly well in the case of the firms in our sample. Fuel prices are announced weekly by the Ministry of Industry, Commerce, and SMEs (MICM);

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there is a slight price differential for electricity prices based on firms’ locations on the island.

Liquid fuels in the DR are priced according to an import parity price rule, whereby the Ministry for Industry, Commerce, and SMEs (MICM) updates weekly the official prices to match the supply cost (import parity plus commercialization margins), an excise-specific tax (Ley 112-00), and an ad-valorem tax (set at 16 percent by Ley 495-06). A similar system applies to natural gas prices. According to the The World Bank [2023a], this pricing system does not provide subsidies for liquid fuels on a regular basis, which are common in other countries in the region. However, since March 2022, the Dominican government has implemented a policy of extraordinary fuel subsidies to curb inflation, resulting in frozen prices for premium gasoline at 293.60 pesos per gallon and regular gasoline at 274.50 pesos per gallon. The government also provides subsidies to shield consumers from high electricity costs, which amounted to US 1.5 million in 2024 (about 1.2 percent of GDP). In 2021 and 2022, government spending on electricity subsidies increased by 63% and 52% due to rising global fuel prices.

As far as carbon taxes are concerned, the DR does not have an explicit carbon taxation mechanism, which varies within or across sectors, but a system of excise taxes applied at the fuel level. Gasoline, diesel, and petrol are the fuels subject to comparatively higher tax rates, whereas LPG and coal for electricity generation are tax-exempt. Revenues from fuel taxes are low (1.5 percent of GDP on average between 2005 and 2022) and should be raised considerably to help meet the decarbonization goals.

We now take the model to the data. We first set the tax level _λ_ = 0, reflecting the fact that the Dominican Republic does not currently have a carbon tax, and estimate output elasticities, firm-level productivities _ai_ as well as wedges _{τxi, τyi, τki, τfi}_ across different sectors. We then use these estimates to evaluate the effects of different taxes _λ_ on our outcomes of interest.

# **4 Model Calibration**

The theoretical model indicates that existing distortions would predict a heterogeneous response to the carbon tax and an ambiguous effect on aggregate TFP, depending on the correlation of wedges and firmlevel productivity. In order to quantify the potential gains/losses introduced by different carbon taxes _λ_ , we infer the wedges _{τ_ ˆ _ji}j_ = _k,y,x,f_ and quantity-based productivity _a_ ˆ _i_ using firm-level data.

In particular, wedges are identified using the following equations that arise from the first-order conditions 15
of firm _i_ ’s profit maximization problem.

The productivity _a_ ˆ _i_ is also inferred from the data and equal to: <u>1</u> with _κs_ = _w_<sup>_βl_</sup> _∗_ ( _py_ )<sup>_−_</sup> _σ−_ 1 _/p_ . Note that, as in Hsieh and Klenow [2009], the sector-level constant _κs_ is not observable. However, the constant does not affect changes in market share with respect to the baseline economy or changes in aggregate sector TFP, so it can be normalized to 1.

To estimate these wedges and _a_ ˆ _i_ , we follow a similar approach to Hsieh and Klenow [2009]. This requires measuring firms’ output, capital, labor, fuel expenditures, and electricity expenditures from the data, as well as calibrating factor prices, the elasticity of substitution between varieties, and the output elasticities of inputs. As explained in the previous section, we measure firms’ expenditures on oil and electricity using firm fuel and electrical energy purchases, deflated by sectoral GDP deflators reported by the Dominican Republic’s Central Bank. Also, we calculate firms’ value added, _psiysi_ , as the difference between gross revenue and intermediate inputs. We use aggregate sector value-added deflators, provided by the Dominican Republic’s Central Bank, to deflate our estimate of firms’ value-added. The capital input, _ki_ , is measured as the book value of fixed assets, which we deflate using the aggregate sector gross revenue deflators. As Hsieh and Klenow [2009] we control for differences in human capital and rent sharing across firms by using firm wage bill as the labor input _li_ deflated by the intermediate input industry deflator.

To calibrate factor prices, we assume that the price of capital is _r_ = 0 _._ 1 and the price of labor is _w_ = 1.<sup>12</sup> We set the elasticity of substitution between varieties _i_ to _σ_ = 3, implying a 50 percent markup reflected in firms’ output price, _p_<sup>_∗_</sup> _i_<sup>.The output elasticities of inputs are estimated at the 2-digit ISIC sector level using a</sup>

> 12We abstract from making assumptions on fuel ( _zo_ ) and electricity ( _zx_ ) input prices that might bias results, as these prices likely vary at the firm-level and we do not observe them. Furthermore, since the calculation of fuel and electricity input distortions requires only information on fuel ( _zoo_<sup>_∗_</sup> _i_<sup>)andelectricity(</sup><sup>_zxx∗_</sup> _i_<sup>)inputexpenditures,andnotofinputprices,this</sup> approach does not affect our computations.

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linear regression of value added on the four input factors, with the following constraint: _βl_ + _βk_ + _βx_ + _βf_ = 1. In this manner, we assume that firms’ production function is constant returns to scale.

There is substantial data attrition in the estimation of the production function. On average, in a given year, only 21 percent of firms provide sufficient information to DGII in order to estimate the production function. The data required includes: total sales, total intermediate inputs, finished product and intermediate input inventories, wage bill, electricity expenditure, and fuel expenditure. Furthermore, for some 2-digit industries, we obtain negative elasticities for some factor inputs. Industries with negative elasticities are dropped from our analysis.<sup>13</sup> As a result of this data attrition, the sample of firms used for our quantitative analyses is substantially smaller, representing only 5.3 percent of firms in 2018. However, this sample still accounts for a substantial share of economic activity and firm emissions, representing 31.6 percent of value added and 27.6 percent of total firm emissions, as displayed in Table 1.

# **5 Existing Misallocations in the Dominican Republic**

This section presents evidence of allocative inefficiencies of factor inputs across firms in the Dominican Republic, including fuel and electricity inputs. Figure 5 displays large dispersion in marginal revenue products of inputs, computed as in (6). This reflects the variation in the estimated wedges _{τ_ ˆ _ji}j_ = _k,y,x,f_ , including electricity and fuel inputs, which suggests the presence of energy frictions in the Dominican Republic. Furthermore, there is also substantial dispersion of firm revenue-based total factor productivity in the Dominican economy. As derived in the Appendix A, revenue-based TFP is defined as: implying that heterogeneity in _tfpri_ reflects the combined variation in the estimated wedges _{τ_ ˆ _ji}j_ = _k,y,x,f_ . As explained by Hsieh and Klenow [2009], in the absence of distortions, revenue TFP would equalize across firms. Furthermore, differences in the input quantities demanded by firms would only depend on quantitybased TFP, so that firms that are more productive capture a larger share of factor inputs. Conversely, the presence of distortions may cause factors to be allocated not towards more productive firms (higher _ai_ ), as demanded input quantities would also depend on distortions. In turn, this can lead to a lower level of aggregate TFP, as the most efficient firms would not necessarily capture the largest shares of factor inputs in comparison to the frictionless economy.

> 13Service industries with a 2-digit ISIC code greater than 64 are excluded from our analysis as these are sectors with very low emissions. These sectors include financial services, real estate services, professional services, public administration, education services, health services, social services, arts and entertainment, and private home services.

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**Table 1**

Share of economic activity by calibration model sample (Year 2018)

|**Economic activity**|**PY**|**Firms**|**Electricity**<br>**Expendi-**<br>**ture**|**Fuel**<br>**Expen-**<br>**diture**|**Total**<br>**emis-**<br>**sions**|**Electricity**<br>**emissions**|**Fuel**<br>**emis-**<br>**sions**|
|---|---|---|---|---|---|---|---|
|Agriculture|66.7%|8.4%|62.4%|45.0%|73.3%|88.3%|66.5%|
|Manuf.-Textiles|28.9%|12.0%|60.7%|19.2%|53.0%|79.9%|26.0%|
|Manuf.-Apparel|24.0%|12.7%|73.7%|50.1%|92.7%|97.2%|66.7%|
|Manuf.-Editing and recording|71.5%|14.6%|58.4%|50.2%|74.0%|76.9%|66.7%|
|Manuf.-Chemicals|70.1%|17.5%|65.9%|60.1%|91.1%|95.5%|87.0%|
|Manuf.-Plastics|68.3%|30.6%|72.7%|58.5%|92.3%|95.9%|77.1%|
|Manuf.-Cement|75.5%|22.4%|73.7%|64.8%|86.5%|97.3%|85.7%|
|Manuf.-Metal products|90.0%|11.8%|40.3%|41.7%|54.5%|54.1%|55.7%|
|Manuf.-Machinery|23.6%|13.8%|44.6%|24.7%|45.0%|57.7%|32.6%|
|Manuf.-Others|57.3%|11.7%|61.0%|28.2%|66.4%|80.6%|37.3%|
|Water Distribution|33.7%|9.6%|61.5%|15.5%|78.1%|80.6%|20.2%|
|Construction|72.1%|5.6%|34.3%|31.0%|59.6%|62.8%|57.0%|
|Vehicle/fuel sales|75.5%|10.7%|52.8%|46.6%|77.8%|84.4%|74.1%|
|Wholesale|53.2%|11.5%|57.4%|50.9%|87.4%|91.9%|81.0%|
|Retail|50.7%|10.9%|57.9%|30.9%|90.9%|92.7%|49.2%|
|Land transport|27.6%|5.6%|38.2%|24.3%|46.1%|70.8%|46.0%|
|Water transport|40.3%|8.2%|51.7%|47.5%|88.3%|96.3%|88.1%|
|Air transport|7.1%|5.5%|50.6%|9.6%|18.1%|94.6%|17.8%|
|Storage/warehouse|47.9%|10.1%|46.8%|6.7%|13.6%|86.8%|12.4%|
|Postal services|44.7%|12.5%|44.4%|39.2%|82.5%|84.4%|74.2%|
|**Total (2018)**|31.6%|5.3%|8.8%|39.9%|27.6%|13.4%|42.0%|

Note: This table reports the share of economic activity attributed to the calibration sample for those industries with only positive output elasticities of factor inputs and with an ISIC sector code below or equal to 64. Among the sectors omitted are financial services, real estate services, professional services, public administration, education services, health services, social services, arts and entertainment, and private home services. The economic activity variables considered from left to right are: value added (PY), number of firms, electricity expenditure, fuel expenditure, total emissions, electricity emissions, and fuel emissions.

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<!-- Start of picture text --> 3<br>~~<br>fi/if \\ \\<br>A fi A \<br>/ / LY \<br>o|§ —— _b4A NY ‘ Tf<br>15 -10 5 0 5 10<br>— MRP Labor — MRP capital<br>——— MRP Electricity ©———— MRP Fuels<br><!-- End of picture text -->

<!-- Start of picture text --> 2<br>2<br>fat A<br>05<br>9<br>“10 o 10 20<br>———_ In(TFPR_i) ——— In(a_i)<br><!-- End of picture text -->
**Table 2**

Share of industries with positive correlations between firm distortions and firm quantity-based TFP

|**Year**|**Capital**|**Electricity**|**Fuels**|
|---|---|---|---|
|2009|83.3%|64.3%|53.3%|
|2010|42.1%|71.4%|64.3%|
|2011|38.9%|94.1%|85.7%|
|2012|44.4%|68.8%|66.7%|
|2013|31.6%|56.3%|53.3%|
|2014|44.4%|76.5%|60.0%|
|2015|50.0%|64.7%|80.0%|
|2016|38.9%|76.5%|75.0%|
|2017|42.1%|83.3%|62.5%|
|2018|36.8%|64.7%|75.0%|

Note: Each column shows the share of industries that report a positive correlation between quantity-based TFP, _ai_ , the column’s respective distortion, _τji_ , where _j_ = _k, x, f_ .

**Estimation of aggregate tax revenues.** In a first exercise, we depart from the theoretical model to give a back-of-the-envelope estimate of the revenues collected by the Dominican Republic for different levels of the carbon tax. Such figure may be useful to assess the potential relevance of a carbon tax on the government budget and public spending. The total revenues collected in each sector by the regulator are estimated as:<sup>14</sup>

with _e_ ˆ _ix_ and _e_ ˆ _if_ representing the historical emissions from electricity and fuel, respectively, as summarized in the data section. For the choice of the tax levels, we refer to two scenarios consistent with the First NGFS Comprehensive Report. In the first scenario (National Determined Contributions - NDCs) the carbon price is calibrated to include all pledged targets, even if they are not yet backed up by effective policies. For the Dominican Republic, this implies a carbon price of 52.31 USD/tCO2 by 2025. The second scenario (Net Zero 2050) assumes a more ambitious goal of limiting global warming to 1.5°C and reaching net zero emissions by 2050. This would imply a carbon price of around 109.13 USD/tCO2 by 2025.<sup>15</sup> Figure 7 shows the carbon tax revenues across different sectors upon the introduction of a 110$/tCO2 tax applied to electricity and fossil-fuel bills. The blue bars refer to cumulative revenues across the two fuel sources. The five most polluting sectors are transport and storage, cement and lime, other manufacturing,

> 14This amounts to evaluating the impact of the tax in a static setting where each producer _i_ cannot adjust its production decisions in response to the tax _λ_ .

> 15The estimated prices are the output of an independent analysis on the DR, and are based on REMIND-MAgPIE 3.0-4.4 model. For more information, see `https://www.ngfs.net/ngfs-scenarios-portal/explore` .

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**Figure 7. Carbon Tax Revenues**

<!-- Start of picture text --> 300<br>200<br>USD millions<br>100<br>0<br><!-- End of picture text --> Note: The plot shows in $ millions the total carbon tax revenues generated by a $110/tCO2 tax applied to the firms’ combined fuels and electricity bills sorted from the least polluting to the most polluting sector. Data refer to 2018.

other commercials, and hotels, bars, and restaurants, respectively. We estimate that the cumulative tax revenues amount to $920 millions. This corresponds to 20 percent of the firms’ total earnings and about 3 percent of their reported total income. A $52/tCO2 carbon tax would generate, on the other hand, cumulative revenues of about $434 millions. Documenting such figures is important to provide a sense of the impact of the regulation. However, as discussed, it is important take into account firm-level adjustments to the policy, as well as equilibrium adjustments in the input markets. We now perform counterfactual exercises on our model to derive the effect of the tax on our outcomes of interest, which are firm-level emissions intensities across fuel sources, and TFP across sectors.

**Effect of the tax on emissions intensities.** Following Proposition 2, the new emissions intensities become:

with _ξ_<sup>ˆ</sup> _xi_ and _ξ_<sup>ˆ</sup> _fi_ the historical emissions intensities, summarized in the Data Section, and _τ_ ˆ _ix_ and _τ_ ˆ _if_ the estimated historical price wedges in the electricity and fuel markets, summarized in the calibration Section.<sup>16</sup>

> 16Expression (17) holds true taking energy prices _zx_ and _zf_ as fixed at the historical benchmark. Lower (higher) prices would result in an overestimation (underestimation) of the tax effect respectively.

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**Figure 8.** Impact of the Tax on Average Emission Intensity

<!-- Start of picture text --> W ater Distributio<br>Postal services<br>Agriculture<br>Air transport<br>Manuf.-Plastics<br>Manuf.-Chemicals<br>Retail<br>Manuf.-Apparel<br>Manuf.-Machinery<br>Construction<br>Water t ransport<br>Storage/warehouse<br>Manuf.-Metal products<br>Land transport<br>Wholesale<br>Manuf.-Cement<br>Manuf.-Editing and recording<br>Manuf.-Others<br>Vehicle/fuel sales<br>Manuf.-Textiles<br>0 .1 .2 .3<br>A ir transport<br>Land transport<br>Manuf.-Cement<br>Water transport<br>Storage/warehouse<br>Agriculture<br>Postal services<br>Manuf.-Textiles<br>Manuf.-Chemicals<br>Manuf.-Metal products<br>Vehicle/fuel sales<br>Construction<br>Manuf.-Plastics<br>Manuf.-Machinery<br>Wholesale<br>Manuf.-Apparel<br>Manuf.-Others<br>Manuf.-Editing and recording<br>Water Distribution<br>Retail<br>0 2 4 6<br><!-- End of picture text --> Note: The bar plots show the baseline emissions intensities (in blue) against the counterfactual emissions intensities deriving from the introduction of a carbon tax representing 10% (red bar) and 50% (green bar) of the baseline energy prices respectively. The top plot refers to the case of electricity. The bottom plot refers to the case of fuel.

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Figure 8 compares the new average emissions intensities against the computed emissions intensities (see Figure 3 and Figure 4) across different fuel sources and sectors. We have chosen to plot two levels of the carbon tax which correspond to an increase of 10% (red bar) and 50% (green bar) of the baseline energy prices respectively. As observed, there is an heterogeneous response to the tax across sectors and fuel sources. As predicted by the model, the tax is far more effective if applied on fuels than on electricity. This because the carbon content of the fuel sources are much higher than that of the fuels used to produce electricity. Particularly interesting is the case of air transport, whose average emissions intensity is reduced by more than 90% in the case of a fuel tax increasing fuel prices of 50%. After the tax, according to the model, the representative firm in this sector will be less polluting than the land transport or cement sectors. Clearly, as our model abstracts from the opportunity to change the production technology, the ultimate costs of such tax have to take into account the availability of greener technologies.

**Effect of the tax on aggregate TFP.** To derive the counterfactual effect of the tax on aggregate sector TFP, we make use of the Corollary 3’s assumption that the wedges _{τ_ ˆ _iy,_ ˆ _τix,_ ˆ _τik,_ ˆ _τif }_ as well as the firm-level productivity _ai_ follow a log-normal distribution. Under such assumption, we show in the Appendix that the logarithm of aggregate TFP subject to a tax _λ_ is:

ˆ with _ai_ the estimated historical firm-level productivity, _σ_ = 3 set as in the calibration section, _m_ equal to ˆ _λ_ the historical number of firms in the sector, and the total revenue productivity _tfpri_<sup>estimatedfromthe</sup> historical one using the formula provided in the appendix. We compare the expression in equation (18) with the historical total productivity computed as in equation (18) assuming _λ_<sup>ˆ</sup> = 0.

Figure 9, y-axis, shows the percentage changes in aggregate sector TFP generated by a tax _λ_ that increases the energy prices by 10 percent (left plot) and by 50 percent (right plot), respectively. These are compared against the baseline aggregate sector TFP as estimated from our model. In line with the model prediction and with the evidence that the majority of sectors have energy wedges that are positively correlated with firm-level productivity, we conclude that the impact of the tax is primarily positive. Specifically, with the exception of a few sectors, among them agriculture and metal manufacturing, the majority of sectors are positively impacted by the tax, given our estimated frictions. Clearly, the higher the tax, the higher the positive/negative impact on productivity, as illustrated by the differences between the top and bottom plots.

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<!-- Start of picture text --> 05 #Manuf-Others<br>Air transport<br>04<br>*Postal services<br>03 * Construction<br>o<br>bao)<br>Cc<br>oo<br>Oo 02 Water transport<br>a + eManut Chemicals<br>2 *Retail Wholesale<br>a , Land transport +<br>— +Manuf-~Cement<br>017 #Manuf-Machinery *Vehiclefuel sales<br>+ Storage/warehouse<br>Distibuti<br>.<br>Water Manuf-Apparel<br>i 1<br>Manut.= i<br>#Manut-Metal products<br>Agriculture<br>-.04<br>0 500 1000 1500 2000<br>logTFP<br><!-- End of picture text --> <!-- Start of picture text --> 12<br>#Manuf.-Others<br>|<br>Air transport<br>08 * Construction<br>*Postal services<br>o<br>4c 06<br>ee<br>O<br>Soo -4 swaitblaandenemicals7 .<br>o. *Manuf.-Machinery; *Retail Wholesale<br>+Land transport .<br>02 AWareh #Vehiclefuel sales<br>ateranut-Apparel oft atin arehouse<br>0-4 Wanut-Etitingmarut= and recording<br>Manuf.-Metal products<br>Agriculture<br>-.02<br>0 500 1000 1500 2000<br>logTFP<br><!-- End of picture text -->
# **7 Conclusion**

In this paper we study the distributional effects at the sector level of the introduction of a hypothetical carbon tax in the Dominican Republic. We first extend the theoretical framework in Hsieh and Klenow [2009] and introduce the consumption of energy and fuels as production inputs. We then take the model to the data using information on the universe of formal firms in the Dominican Republic between 2009 and 2018.

We provide two main sets of results. First, the theoretical model provides a framework to understand the effect of the introduction of a hypothetical carbon tax. A key insight gleaned from the model is the heterogeneous impact on firms, contingent on the correlation between distortions in the input market and firm-level productivity. Specifically, when more productive firms encounter fewer distortions and there exists a positive correlation between the capital and output wedges with the energy wedges, the introduction of an environmental tax can lead to a reduction in aggregate TFP. If these correlations do not hold, then aggregate TFP can either increase or decrease.

Second, our empirical analyses shed light into the anticipated consequences of implementing a carbon tax in the Dominican Republic. We find that the most productive firms across various economic sectors face the highest levels of distortions. Consequently, the introduction of a carbon tax could potentially elevate aggregate TFP in these sectors. Through a counterfactual examination where we hypothesize different levels of the carbon tax, we demonstrate a notable decrease in emission intensity, particularly for fuel consumption. Moreover, the impact on aggregate TFP is projected to be positive, as the most productive firms tend to be burdened by the highest wedges in the majority of sectors.

Our study presents some limitations that may be the focus of future research. The theoretical model does not capture the dynamic adjustment of firms following the introduction of the carbon tax. Firms may upgrade their technology to face the challenge posed by the introduction of the tax. The carbon tax may also be levied differently, affecting industrial factors heterogeneously. We are also not able to differentiate between energy sources to include renewable and clean energy production which would not be affected by the carbon tax. These may be useful starting points for future work.

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