metadata: authors: 0: author: Eberhard,Andreas
1: author: Varela,Gonzalo J.
2: author: Casal,Lucia
3: author: Ganz,Federico
count: World
docna: 0: docna: The Upstream Tariff Simulator (UTAS) : A
            Tool to Assess the Impact of Tariff Reform on Input Costs
            and Effective Protection across Sectors
docty: Policy Research Working Paper
subtopic: International Trade and Trade Rules,Business Cycles and Stabilization Policies,Common Carriers Industry,Food & Beverage Industry,Pulp & Paper Industry,Textiles, Apparel & Leather Industry,Plastics & Rubber Industry,Construction Industry,General Manufacturing,Trade and Multilateral Issues,Trade Policy,Rules of Origin,Industrial and Consumer Services and Products
teratopic: Macroeconomics and Economic Growth,Industry,International Economics and Trade
abstracts: cdata!: Increased international production
            fragmentation implies that firms at home rely on imported
            intermediates for production. In this context, tariff policy
            design needs to consider the impact downstream of changes in
            tariffs upstream. Policy makers embarking on tariff reforms
            need to answer questions such as: what is the impact of
            tariff changes on production costs downstream? What are the
            key input tariffs that could be reduced to lower production
            costs in priority sectors considering sectors' backward
            linkages? Or how will a tariff rationalization plan that
            focuses on tariff reductions in raw materials and
            intermediates affect effective protection across sectors?
            This paper presents the Upstream Tariff Simulator, a simple
            Microsoft Excel–based tool designed to help policy makers
            answer these questions, by combining information on tariffs
            and input-output structures and allowing alternative
            sectoral aggregations, and alternative market structures for
            input markets. It provides the underlying conceptual
            framework and a range of examples that show the insights
            that the tool can provide to policy makers when analyzing
            the impact of tariff reforms.
repnme: repnme: The Upstream Tariff Simulator (UTAS) : A
            Tool to Assess the Impact of Tariff Reform on Input Costs
            and Effective Protection across Sectors
display_title: The Upstream Tariff Simulator (UTAS)
            : A Tool to Assess the Impact of Tariff Reform on Input
            Costs and Effective Protection across Sectors
listing_relative_url: /research/2020/02/31800714/upstream-tariff-simulator-utas-tool-assess-impact-tariff-reform-input-costs-effective-protection-across-sectors
topicv3: Industrial Clusters and Value Chains
            m1326062 1226,Manufacturing Sectors m1326521 1281,Trade m1326924 698,Trade Policy and Integration m1327407 775,Trade Policy m1327640 776
publishtoextweb_dt: 2020-02-20T00:00:00Z
historic_topic: Macroeconomics and Economic Growth,Industry,International Economics and Trade
origu: EFI-SAR-MTI-MacroFiscal (ESAMU)
no_of_pages: 31
author_keys: 0: author_key: 1288250
1: author_key: 989943
2: author_key: 1683712
model_extractions: Policy Research Working Paper 9158 The Upstream Tariff Simulator (UTAS) A Tool to Assess the Impact of Tariff Reform on Input Costs and Effective Protection across Sectors _Andreas Eberhard-Ruiz Gonzalo J. Varela Lucia Casal Federico Ganz_ Macroeconomics, Trade and Investment Global Practice February 2020
Policy Research Working Paper 9158

# **Abstract**

Increased international production fragmentation implies that firms at home rely on imported intermediates for production. In this context, tariff policy design needs to consider the impact downstream of changes in tariffs upstream. Policy makers embarking on tariff reforms need to answer questions such as: what is the impact of tariff changes on production costs downstream? What are the key input tariffs that could be reduced to lower production costs in priority sectors considering sectors’ backward linkages? Or how will a tariff rationalization plan that focuses on tariff reductions in raw materials and intermediates affect effective protection across sectors? This paper presents the Upstream Tariff Simulator, a simple Microsoft Excel–based tool designed to help policy makers answer these questions, by combining information on tariffs and input-output structures and allowing alternative sectoral aggregations, and alternative market structures for input markets. It provides the underlying conceptual framework and a range of examples that show the insights that the tool can provide to policy makers when analyzing the impact of tariff reforms.

This paper is a product of the Macroeconomics, Trade and Investment 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 aeberhard@worldbank.org, gvarela@worldbank.org, lc944@cornell.edu, federicoganz@gmail.com.

_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 Upstream Tariff Simulator (UTAS): A Tool to Assess the Impact of Tariff Reform on Input Costs and Effective Protection across Sectors**<sup>*****</sup>

Andreas Eberhard‐Ruiz<sup>*</sup> Gonzalo J. Varela<sup>†</sup> Lucia Casal<sup>‡</sup> Federico Ganz<sup>§</sup> JEL: C67, F10, F17 Keywords: tariff policy, input‐output economics, simulator, effective protection

> *We thank Juan Pedro Gambetta provided excellent support throughout the preparation of the tool and background material. Valuable comments were received from Jakob Engel, Ana Margarida Fernandes, Alen Mulabdic, Alberto Portugal, Hans Timmer, and Erik von Uekxull, as well as from policymakers in workshops delivered in Kathmandu and Islamabad, specially to Ms Robina Ather, Chairperson of Pakistan’s National Tariff Commission and active supporter of this tool. We acknowledge financial support from the Trade Multidonor Trust Fund for Trade and Development without which this undertaking would not have been possible. * Eberhard is with the Social Protection and Jobs Group of the World Bank Group.

> † Varela is with the Macroeconomics, Trade and Investment GP of the World Bank Group.

> ‡ Ganz is with the Macroeconomics, Trade and Investment GP of the World Bank Group.

> § Casal is with Cornell University.
# 1) Introduction

Tariff policy reform is a policy lever for countries seeking to increase consumer welfare, boost productivity, and attract greater inward investment in the medium to long‐term. When asked how these outcomes materialize, economists typically distinguish between two channels. First, as protection levels on the production of final goods decline, tariff reform induces pro‐competitive forces among domestic producers, which now face tougher import competition, and incentivizes them to become more productive. Second, with lower tariffs, the costs of intermediate inputs decline, allowing firms to shift to new or better‐quality inputs, access better technology and integrate into increasingly important global value chains.

Although these benefits are widely acknowledged in academia, in policy‐making spheres, policy makers will often have to deal with short‐term adjustment costs, possible distributional consequences that may be politically sensitive, and other policy trade‐offs of tariff reforms. They will therefore want to consider these issues before embarking on a comprehensive tariff reform. Similarly, high levels of protection accorded to some sectors may make the sudden removal of tariffs unfeasible. Policy makers may hence seek more gradual reform approaches. In addition, many countries may have in place complex ‘cascading’ tariff structures intended to reconcile the desire for lower input tariffs with a policy of continued protection accorded to downstream activities. In this case, policy makers will be interested to know how different activities intertwine with a set tariff structure and how they affect effective protection levels across sectors.

1 This paper presents a Microsoft Excel‐based tool – “UTAS” (Upstream Tariff Simulator) to help policy makers assess the impact of tariff reforms on production costs and effective protection across a variety of sectors in a flexible and time‐efficient manner. The tool allows policy makers embarked on a tariff reform to answer key policy questions such as: What is the impact of tariff changes on production costs downstream? What are the key input tariffs that could be reduced to lower production costs in priority sectors taking into account sectors’ backward linkages? How will a tariff reform affect effective as opposed to nominal protection rates across the sectors of an economy?

Recent evidence shows that the consideration of such questions is important to maximize the potential benefits of a tariff reform.

First, a growing body of evidence shows that the productivity gains arising from lower tariffs on intermediate inputs are particularly large. In fact, two seminal studies by Topalova and Khandelwal (2011) and by Amiti and Konings (2007) show that, in India and Indonesia, the impact of trade reform on productivity was larger on account of the impact on the cost of inputs than due to the pro‐ competitive effects it unleashed through reductions of tariffs on final goods. Moreover, a reduction in tariffs on intermediate inputs can also play a very important role in boosting firms’ exports. Recent research shows, for example, that China’s well‐known export success story is at least in part attributable to cheaper imports of intermediates. Feng et al. (2016) find that Chinese firms that benefitted from lower tariffs on intermediate inputs after China’s accession to the World Trade Organization (WTO) exhibited stronger export growth than those that did not benefit from the decline in input tariffs. They also find that these effects were larger for firms that were not involved in any

> 1 Also, importantly, many developing countries rely heavily on trade taxes to pay for public expenditure, as customs duties and other trade‐related taxes provide a relatively easy and straightforward source of revenue. Policy makers will thus need to consider how much customs revenue a country can afford to lose in the short‐ run.

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form of international trade before the reform, which suggests that lower input tariffs can help firms to start exporting. This is also found in a paper by Bas (2012), who investigates the trade liberalization in Argentina during the early 1990s and demonstrates that firms producing in industries with the largest declines in input tariffs experienced the greatest increase in the probability of entering the export market. Moreover, Bas and Strauss‐Kahn (2015) show that Chinese firms that benefitted most from input tariff cuts accessed higher quality inputs to quality‐upgrade their exports, which is key to penetrate the most advanced markets around the globe that place strict quality requirements on imports. A thorough assessment of the cost implications of a tariff reform for firms should thus be an integral part of any trade policy analysis considering different reform options.

Second, with the growing importance of global manufacturing networks along global value chains, even low tariffs can have large effects on production costs, as parts and components comprising a product are produced in multiple locations crossing borders various times before they are assembled into the final product (Rouzet and Mirodout, 2013). Moreover, when value chains are global, it is not only the nominal values of tariffs that matter, but also their distribution between processed and unprocessed goods. In many developing countries, tariffs are lower on unprocessed than on processed goods. Diakantoni and Escaith (2014) discuss how the combination of such ‘tariff escalation’ and high levels of nominal duties can lead to a significant anti‐export bias and reduce firms’ incentive to export. On the one hand, this arises because steep tariff escalation increases firms’ returns on the domestic market relative to the international market. On the other, it stems from the fact that on the global stage exporting firms will be at a disadvantage compared to foreign competitors operating in a free trade environment with zero tariffs. Establishing changes to effective protection rates, which take into account the spread between tariff‐related upstream costs and the nominal protection accorded to a sector, is thus an essential aspect of trade policy analyses.

By shedding light on how tariffs intertwine with an economy’s production structures across sectors, UTAS seeks to promote a more evidence‐based policy decision‐making process. It joins the set of trade analysis tools that the World Bank puts at the disposal of policy makers in client countries following the principles of accessibility, simplicity, and tractability.<sup>2</sup> The tool is MS Excel‐based, not requiring particular programming skills. It is straightforward to use and builds on a simple and transparent underlying trade model, where users can choose between different modeling assumptions and parameters. The data needed to run UTAS are relatively limited, requiring a country’s current tariff schedule, a proposed tariff schedule, and an input‐output table with a reasonable sectoral disaggregation.<sup>3</sup> The tool is also highly flexible to accommodate and combine different sector classification systems, which often make combining tariff schedules and sector‐specific production data a cumbersome and time‐consuming exercise.

The remainder of this paper explains how UTAS can assist policy makers in considering the impact of different trade reform options on tariff‐related costs for firms active in downstream activities and on effective protection rates across sectors. Section 2 outlines the overall objective of UTAS and how it allows policy makers to overcome a series of practical challenges when assessing the impact of tariff reform. Section 3 describes the analytical framework underlying UTAS. The structure of the tool and data requirements are discussed in section 4. Section 5 presents a series of policy examples that can be addressed with UTAS. Section 6 concludes.

> 2 Previous simulation tools include, for example, TRIST and SMART (Brenton et al., 2011).

> 3 I/O tables from GTAP are provided alongside the tool.

3
# 2) The purpose of UTAS

UTAS allows users to take full ownership of the tool and to develop it further to address varying country needs. It offers solutions to several challenges that typically complicate policy makers’ assessment of the impact of tariff reforms on production costs in a flexible and time‐efficient manner.

First, previous studies assessing the impact of tariffs on firms’ production costs and effective protection have generally relied on firm‐level survey or census data on manufacturing that allow disaggregating firms’ consumption of intermediate inputs. These data are then used to extract firms’ input cost shares and weigh input tariffs accordingly, and to subsequently attain firm‐level estimates of tariff‐related cost.<sup>4</sup> Most often, however, such highly disaggregated firm‐level data are not readily available, as they tend to include sensitive information and are expensive and time‐consuming to collect. The alternative pursued in UTAS is to build on economy‐wide input‐output (I/O) tables that provide a disaggregation of production costs by sector. Although more aggregated than firm‐level data, I/O tables are available in most countries and allow policy makers to attain a quick overview of varying input cost shares across sectors.<sup>5</sup> Yet the use of I/O tables is not without its challenges. Sector classifications of most I/O tables cannot be matched easily with available tariff‐schedules typically classified at a much finer product level on the basis of the Harmonized System (HS) tariff nomenclature of the WTO. UTAS overcomes this problem by drawing on pre‐defined concordance tables between HS codes and a range of commonly used I/O sector classifications (e.g. GTAP, ISIC or NAICS). A key function of UTAS is therefore to help users handling different sets of data to combine tariff schedules with I/O sectors’ input cost shares and to calculate tariff‐related input costs and effective protection rates for different sectors.

Second, a common problem to both firm‐level data and I/O tables is that it involves assigning weights to each input tariff according to a firm’s/industry’s _current_ reliance on that particular input. To date, most academic research has followed this approach (e.g. Amiti and Konings, 2007; Topalova and Khandelwal, 2011; Yu, 2014). The drawback of pursuing such a weighting approach is that it will understate the true cost impact of an upstream tariff reduction, as it does not consider substitution effects that could kick‐in, if tariffs were to be removed. Moreover, _new_ intermediates, which are used only after an input tariff is removed, will not be taken into account, as they will carry an effective weight of zero. Diakatoni and Escaith (2012) underline that the failure of taking into account substitution effects also puts the calculation of effective protection rate on weak theoretical grounding. In practice, a strategy to assess the magnitude of this bias could for example involve the use of a proxy I/O cost structure pertaining to a different country that is deemed less distorted by tariffs and is assumed to represent the sectors’ I/O structure in the absence of input tariffs. This implies assuming technological convergence across countries or across groups of countries with similar characteristics. By allowing for an easy switch between different I/O tables, UTAS enhances policy makers’ ability to carry out such sensitivity analyses and allows benchmarking results against the use of different I/O tables in a straightforward manner.

Third, studies that have calculated upstream tariff costs and effective protection rates have generally done so on the basis of direct input requirements alone. This omits the possibility of input tariffs affecting production costs indirectly through the value chain. To the extent that producers in upstream sectors are able to pass on changes in tariff costs to downstream producers, the true cost impact of a tariff on an upstream activity will be underestimated, if solely direct requirements are considered. UTAS automatically re‐calculates I/O tables that take into account direct and indirect requirements

> 4 An example is Amiti and Konings (2007) or Rahardja and Varela (2014).

> 5 GTAP has input output information on over 120 countries.

4
and allows users to choose between original I/O tables that reflect either direct intermediate input requirements alone, or that take into account both direct and indirect requirements.<sup>6</sup> Moreover, it allows users to deviate from the perfect homogeneity assumption between imports and domestic inputs, underlying most studies on tariff‐related input costs available to date.<sup>7</sup> Assuming perfect homogeneity between imported and domestic varieties has two implications. First, domestic input producers are likely to compete with an infinitely elastic supply of imports and will therefore be unable to pass‐on higher tariff associated costs to upstream producers. Only then would calculating upstream tariffs on the basis of direct inputs alone appear to be the appropriate assumption. Second, the pass‐ through from changes in tariffs to changes in input costs is likely to be close to 100 percent. By allowing for differentiation between the domestic and imported varieties of the same product, UTAS thus allows considering scenarios where the pass‐through effect of input tariffs on production costs is incomplete. With this choice, UTAS therefore greatly increases the range of modeling scenarios that can be considered compared to previous applications.

# 3) Analytical framework

UTAS is an Excel‐based tool that allows policy makers to simulate the impact of tariffs on production costs across sectors in an economy and perform comparative static analysis for different tariff structures on the basis of a simple partial equilibrium framework.<sup>8</sup> It combines I/O data with detailed product‐level tariff data to establish average output tariff rates for each sector specified in the I/O table. To do this, UTAS draws on a series of pre‐set concordance tables that establish how product‐ level tariff schedules match with more aggregate sector‐level data of I/O tables. For each I/O sector, the tool then calculates an upstream tariff ( UT ) reflecting the total incidence of all intermediate input tariffs on sector i ’s production costs as follows: The variable w�� stands for the weight of upstream sector j in sector i ’s total production output and t� stands for the simple average of all ad‐valorem tariffs on products that belong to input sector j . The parameter β� measures the level of pass‐through of a tariff in sector j on output prices of sector j and can take values between 0 and 1. On the basis of equation (1), UTAS then establishes the effective protection rate ( EPR ) in each sector i . Effective protection rates measure how a sector’s output protection weighs up against the tariff costs it faces due to protection accorded to upstream sectors relative to a sector’s value added. They are calculated as follows: Equation (2) deducts from a sector i ’s nominal protection ( β�t� ) the additional production cost that producers face on account of upstream tariffs ( UT� ), where this difference is compared with the sector’s value added share in total output ( va� ). It therefore allows policy makers to gauge the net impact of an entire tariff schedule given the sector’s backward linkages.

> 6 This is done by calculating Leontief’s inverse.

> 7 An exception is a paper by Chevassus‐Lozza et al. (2013) who show that under imperfect competition and product differentiation the impact of lower input tariffs on the probability of firms entering export markets will differ between high and low productivity firms.

> 8 Notice that the partial equilibrium approach implies that UTAS considers sectors in isolation from each other. Substitution effects between sectors are not taken into account. UTAS therefore captures predominantly short‐term effects. Substitution effects that take longer to materialize and may kick in once firms start to respond to new price signals are not considered.

5
j j j = ij <!-- Start of picture text --> P, ;<br>Domestic Domestic<br>Demand Supply<br>P) =P; x (1+) 7»<br>Ge ah Qj<br>tL_{¥—<br>QP - Q = Imports;<br><!-- End of picture text --> j<l j j
ij <!-- Start of picture text --> i domesticDemand nes ”<br>domes inndomestic i varietyimportedDemand,<br>S<br>. Pr?im; = Pix (1+t)<br>> |<br>Pj<br>ip.tj>0 Qi”<br>Qe Qixjn0<br><!-- End of picture text -->

<!-- Start of picture text --> :<br>|<br><!-- End of picture text -->
|Output<br>Sector<br>|Output sector 1<br>|Output sector 2<br>||Output sector N<br>|
|---|---|---|---|---|
|inpursector?<br>|re<br>|ee<br>|e<br>||
|<br>Input sector2|<br> ee|<br>ee|e|ee|
|inputSector}|ee|ee|ee||
|<br>Total intermediate<br>consumption<br>|<br>J<br>IC,=Yint1C4;<br>|<br>J<br>IC,=>.IC2;<br>|<br>|J<br>ICy=>, en<br>|
|Totalvalueadded<br>|ee<br>|ee<br>|e<br>||
|<br>Total industryinput|<br>Output,|Output,|P||Outputy|
The North American Industry Classification System (NAICS) spans 385 sectors and is the standard used by the US Bureau of Economic Analysis (BEA) in producing its I/O tables for the US economy. The advantage of NAICS is that it allows users to simulate the impact of upstream tariffs on a very detailed sector disaggregation. The disadvantage is that few countries will have I/O tables based on NAICS. However, policy makers may want to benchmark the impact upstream tariffs using a foreign I/O table as basis for their simulations to better reflect the input cost structure of industries operating at the technology frontier and with limited tariff distortion. In such cases, the use of a US I/O table with an NAICS sector disaggregation may come in handy.

The concordance table that is used to match NAICS sectors with HS product‐level codes is based on work by Pierce and Schott (2009) is available for download under the following link: <u>http://faculty.som.yale.edu/peterschott/sub_international.htm.</u>

## **<u>ISIC Revision 3 sectors</u>**

The International Standard Industrial Classification System (ISIC) covers 145 different sectors and is a widely used standard to classify productive activities. In countries that use specific classification systems, concordance tables between national and ISIC industry codes are often readily available. Users may therefore want to use national I/O tables as a basis for their simulations by first converting them into ISIC Rev. 3 classification.

The concordance table that is used to match ISIC sectors with HS product‐level codes is based on the ‘HS Combined to ISIC Rev3’ concordance table of the World Bank Integrated Trade Solution (WITS) software available here: https://wits.worldbank.org/product_concordance.html.

# 4.2) The UTAS simulation modules

UTAS includes four simulation modules that allow users to seek answers to four different policy questions related to the impact of tariffs on production costs. We discuss each in turn.

The _Tariff ranking_ module allows users to establish which tariff lines of the HS tariff schedule have a particularly large impact on the cost structure in a particular sector. Based on the concordance tables of the respective sector classification chosen by the user, this tool generates a ranking of tariff lines at the HS 6‐digit product level, where a top‐ranked product implies that the prevailing protection accorded to this product has a particularly large cost impact on a selected output sector. The module is therefore intended to provide policy makers with a quick overview of how the tariff schedule and production costs intertwine across different sectors. It does not, however, quantify the impact of a particular tariff on sector costs. This is left to the modules described below.

Second, the _Sim I_ module allows users to select a specific output sector i and analyze how a change in tariffs of sector i ’s ten most important input sectors would affect sector i ’s production costs. This is done in steps. A baseline upstream tariff for the selected sector i is first calculated in line with equation (1). The user can then consider an increase or a reduction in the tariffs on sector i ’s ten most important intermediate input sectors. UTAS subsequently calculates a new upstream tariff and a new effective protection rate based on the revised/simulated tariff schedule. The difference in the baseline and simulated figures is then used to establish the overall impact of the reform on sector i .

Third, rather than focusing on one priority sector alone, the _SIM II_ module allows policy makers to examine how revising a tariff on one or several intermediate input sectors affects costs and effective protection rates across all output sectors. The tool calculates baseline upstream tariffs and effective protection rates for all sectors based on the data uploaded to UTAS. Users can then modify the prevailing tariff on specific input sectors. New upstream tariffs and effective protection rates are subsequently calculated based on the simulated tariff schedule for all output sectors. The difference between baseline and simulated values is then used to assess the overall cost impact of the reform across different sectors.

9
Fourth, at times policy makers will want to consider the impact of a change in tariffs defined at the product level. To this end, module _Sim III_ allows users to define an entirely new tariff schedule and simulate how the change in the tariff schedule affects costs and effective protection rates across sectors. The tool then calculates two sets of upstream tariffs: the first on the basis of the original tariff schedule and the second on the basis of the new/revised tariff schedule. The difference in the two sets of upstream tariffs is then used to assess the cost impact of the tariff changes in each sector.

# 5) Using UTAS for trade policy analysis

How do changes in tariffs affect production costs and effective protection rates across sectors? This question lies at the core of UTAS. An in‐depth understanding of the impact of tariff reform on downstream production costs and on effective rather than only nominal protection rates is important for countries seeking to boost their competitiveness. Yet while the academic literature provides important guidance on how intermediate input tariff reductions affect downstream firms’ performance in terms of export competitiveness, productivity or diversification, the available research is not always easy to translate into quantifiable estimates for policy purposes in a timely manner. In this section we discuss two practical examples to illustrate how UTAS can be used to assist policy makers in analyzing the implications of tariff reforms, followed by a series of additional considerations that should be considered when using UTAS for policy simulations.

## 5.1) Declining tariffs on intermediate inputs in textiles and apparel manufacturing in Nepal

Textiles and apparel are Nepal’s most important merchandise exports. Over the last decade, however, Nepal’s exports of textiles and apparel have stagnated. In 2017 they amounted to US$342 million compared to US$362 a decade earlier, according to figures from UN Comtrade. A key trade policy question therefore is how growth in textiles and apparel can be reinvigorated to boost the country ́s exports base. Key among possible measures is the reduction of tariffs and para‐tariffs on intermediate goods to reduce production costs of domestic firms and promote adoption of cheaper or better quality inputs from wherever they are most efficiently sourced. In parallel, this should be combined with a gradual reduction in these sectors’ output tariffs to lower effective protection levels accorded to these sectors, which will reduce the overall anti‐export bias and promote a greater outward orientation of firms active in these sectors.

We use UTAS to assess the impact that such a strategy would have on the textiles and wearing apparel sectors and rely on data from GTAP to account for the production structure in these sectors in Nepal. Data on import duties are taken from the UN TRAINS. For simplicity, all our simulations are based on the product homogeneity modeling framework of UTAS, which assumes a complete tariff pass‐ through on domestic prices.<sup>11</sup> The following policy questions guide our exposition. Screenshots of UTAS are included to facilitate a step‐by‐step replication of our results in UTAS.

### Q1: Which tariffs have the greatest impact on upstream production cost?

The _Tariff‐ranking_ module allows users to establish which tariff lines at the HS 6‐digt level have the largest impact on a particular sector. We use the module to determine the 20 tariffs with the highest impact for the apparel and textiles sectors, respectively. In the _Tariff‐ranking_ module this is done by clicking on the ‘Change Data’ icon and selecting the appropriate sector and the number of tariff lines to be considered in the analysis (see Figure 5).

> 11 Less restrictive assumptions are considered in the example of Section 5.2

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<!-- Start of picture text --> Product Inputs by Sector<br>Sector (Select)<br>Change Data Run Input-Output Calculations<br>Description HS6 Code Position<br>Select sector x<br>Select Bata, S$pysaSs$pSaa<br>Sector: | Tex - Textiles +<br>Top: 20 =<br>Leo20 _roy_|<br><!-- End of picture text -->

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| A 8 c dD | E F 6 H oon<br>1 [Product Inputs by Sector<br>2<br>3 Sector (Select) WAP - Wearing apparel<br>4 Change Data Run Input-Output Calculations<br>5 20<br>6 Description HS6 Code Position<br>7 Paints and varnishes; based on polyesters, dispersed or "3208.10" 1<br>8 Paints and varnishes; based on acrylic or vinyl polymers, :"3208.20" a<br>9 Paints and varnishes; based on polymers n.e.c. in headin{ "3208.90" 1<br>10 Paints and varnishes; based on acrylic or vinyl polymers, ("3209.10" 1<br>11 Paints and varnishes; (based on polymers other than acry "3209.90" 1<br>12 Paints and varnishes; (including enamels, lacquers and dis "3210.00" 1<br>13 | Ethylene polymers; monofilament, of which any cross-se "3916.10" 1<br>14 |Vinyl chloride polymers; monofilament, of which any cro "3916.20" 1<br><!-- End of picture text -->

<!-- Start of picture text --> 15 Plastics; monofilament, of plastics n.e.c. in heading no. 3"3916.90" 1<br>16 | Plastics; artificial guts (sausage casings) of hardened prot"3917.10" 1<br>17 Plastics; tubes, pipes and hoses thereof, rigid, of polymer "3917.21" 1<br>18 |Plastics; tubes, pipes and hoses thereof, rigid, of polymer "3917.22" :<br>19 Plastics; tubes, pipes and hoses thereof, rigid, of polymer "3917.23" zi<br>20| Plastics; tubes, pipes and hoses thereof, rigid, of plastics "3917.29" 1<br>21 | Plastics; tubes, pipes and hoses thereof, flexible, having "3917.31" 1<br>22 Plastics; tubes, pipes and hoses thereof, other than those "3917.32" 1<br>23 Plastics; tubes, pipes and hoses thereof, other than those "3917.33" t<br>24 Plastics; tubes, pipes and hoses thereof, n.e.c. in item no "3917.39" 1<br>25 | Plastics; tube, pipe and hose fittings (e.g. joints, elbows, {"3917.40" zs<br>26| Floor, wall or ceiling coverings; of plastics (excluding poly "3918.90" :<br>27 /Plastics; plates, sheets, film, foil, tape, strip, other flat shi"3919.10" 1<br>28 /Plastics; plates, sheets, film, foil, tape, strip, other flat shi"3919.90" 1<br>= a ir -hasins "2077 10" ————————<br>| UTAS | README | SIM_I | SIM_Il REGUL emg] © OUTPUT_TARIFFS_CALCULATIONS | (2)<br><!-- End of picture text -->
<!-- Start of picture text --> A A B E D E F G H I<br>1_Product Inputs by Sector<br>2<br>3Sector (Select) TEX - Textiles | |<br>SII change data| Run Input-Output Calculations<br>6 Description HS6 Code Position<br>7 Carpets and other textile floor coverings; knotted, of wo "5701.10" 1<br>8 Carpets and other textile floor coverings; knotted, of tex "5701.90" "i<br>9 |Carpets and other textile floor coverings; woven, (not tu'"5702.10" 1<br>10 Carpets and other textile floor coverings; woven, (not tu'"5702.31" 1<br>11_ Carpets and other textile floor coverings; woven, (not tu'"5702.32" 1<br>12 Carpets and other textile floor coverings; woven, (not tu'"5702.41" a<br>13. Carpets and other textile floor coverings; woven, (not tu'"5702.42" ay<br>14 Carpets and other textile floor coverings; woven, (not tu:"5702.49" 1<br>15 Carpets and other textile floor coverings; woven, (not tu'"5702.99" 1<br>16 Carpets and other textile floor coverings; tufted, of wool"5703.10" 1<br>17 Carpets and other textile floor coverings; tufted, of nylor"5703.20" 1<br>18 Carpets and other textile floor coverings; tufted, of man-"5703.30" 1<br>19 Textile floor coverings; felt tiles, (not tufted or flocked), )"5704.10" 1<br>20 Carpets and other textile floor coverings; of felt, (not tuf "5704.90" 1<br>21 Carpets and other textile floor coverings; n.e.c. in chapte "5705.00" 1<br>22 Carpets and other textile floor coverings; woven, (not tu’ "5702.51" 16<br>23 Carpets and other textile floor coverings; woven, (not tu’ "5702.52" 16<br>24 Carpets and other textile floor coverings; woven, (not tu: "5702.59" 16<br>25 Carpets and other textile floor coverings; woven, (not tu’ "5702.91" 16<br>26 Carpets and other textile floor coverings; woven, (not tu"5702.92" 16<br>2728 | Carpets and other textile floor coverings; woven, (not tu: "5702.39" 16<br>Competitionge assumption.<br>SIM_I Menu x<br>Select Data —— ————— a]<br>Simulation framework: (@ Homogenety © Differentiation<br>Gross output composition: Intermedia TEX - Textiles “<br>TRD - Trade<br>Goods (inputs) VOL - Vegetable oils and fats<br>" 3 \V_F - Vegetables, fruit, nuts<br>Services (inputs) WAP - Wearing apparel<br>Value Added WHT - Wheat<br>WOL - Wool, silk-worm cocoons<br>WTP - Water transport =<br>Upstream and output tariffs<br>BASELINE SCENARIO SIMULATED SCENARIO<br>Upstream tariff as % of gross output #N/A #N/A<br><!-- End of picture text -->
29 Composition of upstream tariff by largest contributing sectors 30, <!-- Start of picture text --> 29 Composition of upstream tariff by largest contributing sectors<br>30<br>Baseline Share in input<br>contribution to cost structure<br>31 # Sector in NAICS upstream tariff Baseline Tariff (US)<br>32 1 CRP - Chemical, rubber, plastic products 46.6% 8.3% 23.4%<br>3334 | 23 TEXNMM- -TextilesMineral products n.e.c. 43.1%8.3% 10.8%13.5% 2.6%16.6%<br>35 4 FMP - Metal products 1.6% 9.5% 0.7%<br>36 5 WAP - Wearing apparel 0.3% 19.0% 0.1%<br>37 6 LEA - Leather products 0.1% 11.9% 0.0%<br>38 rd COA - Coal 0.1% 5.0% 0.0%<br>39 | 8 P_C - Petroleum, coal products 0.1% 8.9% 0.0%<br>4041): 910 OMEOMF -- MachineryManufactures andn.e.c. equipment n.e.c. 0.0%0.0% 79%7.8% 0.0%0.0%<br><!-- End of picture text -->

|29|Comp|osition of upstream tariff by largest contri|buting sectors|||
|---|---|---|---|---|---|
|30||||||
||||Baseline<br>contribution to||Share in input<br>cost structure|
|31|#|Sector in NAICS|upstream tariff|Baseline Tariff|(US)|
|32|1|CRP - Chemical, rubber, plastic products|46.6%|8.3%|23.4%|
|33<br>||2|TEX - Textiles|43.1%|10.8%|16.6%|
|34|3|NMM- Mineral products n.e.c.|8.3%|13.5%|2.6%|
|35|4|FMP - Metal products|1.6%|9.5%|0.7%|
|36|5|WAP - Wearing apparel|0.3%|19.0%|0.1%|
|37|6|LEA - Leather products|0.1%|11.9%|0.0%|
|38<br><br>|rd<br>|COA-Coal<br>|0.1%<br>|5.0%<br>|0.0%<br>|
|39<br>||8|<br>P_C - Petroleum, coal products|0.1%|8.9%|0.0%|
|40<br>:|9|OME-Machineryandequipmentn.e.c.|0.0%|79%|0.0%|
|41)|10|<br>OMF-Manufacturesn.e.c.|0.0%|7.8%|0.0%|

|31|#|Sector in NAICS|Baseline<br>contribution to<br>upstream tariff|Baseline Tariff|Share in input<br>cost structure<br>(US)|
|---|---|---|---|---|---|
|32<br>||1|TEX-Textiles|37.2%|10.8%|16.4%|
|33<br>||2|<br>NMA-Mineralproductsn.e.c.|18.8%|13.5%|6.7%|
|34|=|<br>FMP - Metal products|17.0%|9.5%|8.5%|
|35|4|CRP = Chemical, rubber, plastic products|12.6%|8.3%|7.2%|
|36|5|PFB - Plant-based fibers|10.5%|4.3%|11.7%|
|37<br><br>|6<br>|MVH-Motorvehiculesandparts<br>|27%<br>|24.8%<br>|0.5%<br>|
|38<br>|<br>|7<br>|<br>IS - Ferrous metals<br>|0.9%<br>|10.7%<br>|0.4%<br>|
|39|8|COA-Coal|0.2%|5.0%|0.2%|
|40)|9|<br>P_C-Petroleum,coalproducts|0.1%|8.9%|0.0%|
|41<br>||10|<br>OME-Machineryandequipmentn.e.c.|0.0%|7.9%|0.0%|
<!-- Start of picture text --> 16 Upstream and output tariffs<br>7 BASELINE SCENARIO SIMULATED SCENARIO CHANGE IN P.P.<br>19 Upstream tariff as % of gross output 3.1% 1.6% -1.6%<br>21 Upstream tariff as % of total inputs cost 4.2% 2.1% -2.1%<br>23 | Upstream tariff as % of total goods inputs cost 9.3% 47% -47%<br>24<br>25<br>26 | Output tariff (average tariff on WAP - Wearing apparel) 19.0% 19.0% 0.0%<br>27 Rate of Effective Protection 647% 71.1% 6.4%<br>28<br>29 Composition of upstream tariff by largest contributing sectors Reset<br>30<br>Baseline Share in input<br>contribution to cost structure Simulated<br>31 # Sector in NAICS upstream tariff Baseline Tariff (US) Tariff Change in p.p.<br>32 1 CRP - Chemical, rubber, plastic products 46.6% 8.3% 23.4% 4% 4%<br>33 2 TEX - Textiles 43.1% 10.8% 16.6% 5% -5%<br>34 3 NMM - Mineral products n.e.c. 8.3% 13.5% 2.6% 7% 7%<br>35 4 FMP - Metal products 1.6% 95% 07% 5% -5%<br>36 5 WAP - Wearing apparel 0.3% 19.0% 0.1% 19% 0%<br>37| 6 LEA - Leather products 0.1% 11.9% 0.0% 6% 16%<br>38 7 COA - Coal 0.1% 5.0% 0.0% 3% -3%<br>39 8 P_C - Petroleum, coal products 0.1% 8.9% 0.0% 4% -A%<br>16 Upstream and output tariffs<br>7 BASELINE SCENARIO SIMULATED SCENARIO CHANGE IN P.P.<br>19 Upstream tariff as % of gross output 4.2% 2.9% -1.3%<br>21 Upstream tariff as % of total inputs cost 4.8% 3.3% -1.5%<br>23 Upstream tariff as % of total goods inputs cost 6.6% 4.5% -2.1%<br>24<br>25<br>26 Output tariff (average tariff on TEX - Textiles) 10.8% 10.8% 0.0%<br>27 Rate of Effective Protection 51.7% 61.9% 10.2%<br>28<br>29 Composition of upstream tariff by largest contributing sectors Reset<br>30 P<br>Baseline Share in input<br>contribution to cost structure Simulated<br>3132 #1 TEXSector- Textilesin NAICS upstream37.2% tariff Baseline10.8% Tariff 16.4%(US) Tariff11% Change0% in p.p.<br>33 | 2 NMM - Mineral products n.e.c. 18.8% 13.5% 67% 7% 7%<br>34 2 FMP - Metal products 17.0% 9.5% 8.5% 5% -5%<br>35 4 CRP - Chemical, rubber, plastic products 12.6% 8.3% 7.2% 4% -4%<br>36, 5 PFB - Plant-based fibers 10.5% 43% 117% 2% -2%<br>37 6 MVH - Motor vehicules and parts 27% 24.8% 0.5% 12% -12%<br>38 a LS - Ferrous metals 0.9% 10.7% 0.4% 5% -5%<br>39 8 COA - Coal 0.2% 5.0% 0.2% 3% -3%<br>40 9 P_C - Petroleum, coal products 0.1% 8.9% 0.0% 4% 4%<br><!-- End of picture text -->
<!-- Start of picture text --> Sector Output Tariff UpstreamTariff (% UpstreamTariff (% total UpstreamTariff (% RateEffectiveof SimulatedTariff [Changeinp.p. UpstreamTariff (% Changeinpp. RateEffective of Change p.p. in<br>0 gross output) input cost) input goods _ Protection |goods cost) Protection<br>ai | [1 POR-Paddyrice 30% 14% 7% 87% 33% 30% = 14% 77% 86% 93%<br>1%) |2 WHT- Wheat 75% 19% 14% 1% 75% Aya tM (Ctrl) ~]19% 7.4% 7.2% 7.5%<br>8 [3 GRO-Cereal grains nec. 68% 10% 60% 70% 639% 68% uz 10% 60% 70% 63%<br>14.) |4 — VF- Vegetables, fruit, nuts 89% 10% 66% 85% 92% 89% 0.0% 10% 66% 85% 8.2%<br>6 (5 OSD-Cilseeds 89% 19% 639% 85% 36% 89% 0% 13% 00% 69% 0% 85% 00% 36% 00%<br>761?813,20) |6|?(8|9|10 —— C_B-PFB-Plant-basedOCR-Cropsnec. CTL-Bovine QAP- SugarAnimal cane,catlle,products sugarfibers sheepnec. beetand goats, horses 88%43%92%74%7% 10%05%12%40%24% 58%38%8%97%12% 78%54%88%98%72% 34%44%94%58%7% 8.8%43%92%7.4%21% 0%0%o.0%0%0% 05%2%4.0%24%10% 0.0%0.0%0.0% 58%38%8%97%72% 00%00%0.0% 78%54%88%98%72% 0%00%00% 3.4%44%94%5.8%79% 0%0%0%<br>21)22) |! AMK-RawMilk 00% 22% 50% 78% “38% 0% 0% 122% 5.0% 78% 0 -38%<br>23 | — WOL- Wool, silk-worm cocoons 00% 48% 51% 58% 855% 0.0% 0.0% 4.8% 0.0% 51% 00% 58% 00% -85.5% 0%<br>24 |13 FRS-Forestry 58% a1% 08% 52% 65% 5.8% 0.0% ow 08% 52% 65%<br>25 |14 FSH-Fishing 83% 67% 23% 126% 35% 83% a.0% 6.7% 12.3% 26% 35%<br>26|2728233031|32, | | |?[18[19[20|216 VOL-COA-CoalOL-GilGAS-GasOMN-MineralsCMT-OMT -Meat Bovine Vegetable productsmeat oilsnec.pradsandnec. fats 5.0%73%5%2%78%39%0% 00140%04%59%43%65% % 0600%15%73%68%639% % 35%am23%74%70%85% 117%78%436%97%6%69.0% 10.1%5.0%73%4.5%27%78%39% 0.0%a.0%0%0.0%o.0%o0%0% 0.0%14%0%4%5.9%43%65% 0 00%00%0.0%00%00%.0%0% 006100%15%73%68%69% % 00% 00%00%00%00%00% 35%1%23%74%70%85% 0%oxVALUE!ax0%0%0% 12%78%16.8%36%87%16.3%69.0% 00% 00%0%0%0%00%<br>3334 |( 2 23 MIL-DaiPCR-P r yocessed productsrice 18.0%34% 2%63% 28%839% 23%90% 614%05% 18.0%9.4% 0%0.0% 2.1%6.3% 0.0% 28%89% 00% 29%30% 00%0 14%0.5% 0%<br>35 (24 SGR-Suger 186% 55% 78% 84% 513% 198.6% o.0% 5.5% 78% 84% 13%<br>36, |25 OFD-Food products ne.c. B3a% 78% 86% 86% 513% 133% 0% 7.8% 0.0% 86% 00% 86% 00% 513% 00%<br>a7, |28 B_T-Beverages and tobacco products 22% 129% 64% WI% 672% 27.2% a.0% 12.9% 0.0% 76.4% 0.0% 17.2% a0% 672% 0%<br>38 |?|28 WAP-TEX- Textiles Wearing apparel 193.0%08% 42%3% 48%42% 66%93% 517%647% 5.4%85% 3.4%25% 39%33% 54%73% 15.7%28.7%<br><!-- End of picture text -->
<!-- Start of picture text --> 4A B Leloje| F_ ja ! J kK M N ° R s<br>1 4<br>3]<br>45 |4<br>SIMULATION Scenario:;<br>6 | mre<br>89}| "0101.2100"“0101.2900" 7793.00<br>10) "0101.3000" 0.00 SIM_IIl Menu x<br>11] '0101.9000" 472<br>12) "0102.2110" 0.00<br>15|1314 |} "0102.2120""0102.2130""0102.2190" 5.000.000.00 SelectPanne Data cr——“<br>16| "0102.2910" 0.00 1 Tamework: ~— @ Homoger C Differentiation<br>17} "0102.2920" 5.00 MODENA,<br>18)19) "0102.2930""0102.2990" 0.000.00 Scenarios:PES | =<br>o21| Bisietted"0102.3900" abe0.00 2019 Tariff Reform.<br>22 “0102.9000" 0.00<br>2324)| "0103.9100""0103.1000" 0.000.00 Apply<br>25| "0103.9200" 0.00<br>26) "0104.1000" 5.00<br>27) "0104.2000" 0.00<br>28 | "0105.1100" 5.43<br>29 "0105.1200" 0.00<br>30 } "0105.1300" 15.00<br>31) “0105.1400" 0.00<br>32 "0105.1500" 0.00<br>3334| "0105.9400"“0105.9900" 15.0015.00<br>35| "0106.1100" 0.00<br>36 "0106.1200" 0.00<br>37) “0106.1300" 0.00<br>38| "0106.1400" 0.00<br>39 "0106.1900" 7.28<br>| UTAS | README | I SIM_I| SOY SIM_III_OUT } TARIFF_RANKING | OUTPUT_TARIFFS_CALCULATIONS | @<br><!-- End of picture text -->

<!-- Start of picture text --> a4 # B— Sector c OutputD Tariff UpstreamE UpstreamF UpstreamG _Rate ofH 1 J ChangeK<br>Tariff (% Tariff (% total Tariff (% Effective in pp.<br>0 gross output) input cost) input goods Protection<br>m|12) |1|2 — POR-Paddyrice WHT- Wheat jie.2%4.2% 143%2.3% 87%85% 87%8.6% 03%218% 06%18.2% 3.6%0.0%<br>13, |3 GRO-Cereal grains nec. 7.1% 3.2% 5.1% 87% 10.8% 7% 0.0%<br>14.) |4 — V_F- Vegetables, fruit, nuts 10.5% 2.0% 4.2% 7.9% 69% 110%<br>1816)1) |5|8|? OSD-CilC_B-SugarPFB-Plant-based seedscane, sugarfibers beet [37%(0.0%|13% 2.2%2.0%35% 4.9%84%85% 8.6%85%85% 2.8%47%37% 37%0.0%08% 0.5%0.0%0.0%<br>18) |8 OCR-Cropsnecc. 6.0% 0.9% 5.0% 83% 6.3% 65%<br>13 |9 CTL - Bovine cattle, sheep and goats, horses 26% 4.9% 3.0% 110% 4.8% 28%<br>20) |10 QAP- Animal products ne.c. 34% 28% 57% 85% 14% 36%<br>21) |! RMK-Raw Milk 0.0% 4.7% 97% 115% 31% 0.0% 0.0%<br>22) |12 WOL- Wool, silk-worm cocoons 24% 5.0% 6.3% 7.0% “12.7% 24% 0.0%<br>23) |13 FRS-Forestry 5.2% 11% 47% 9.6% 54% 51% 0.2%<br>24)25) |14|18 FSH-FishingCOA-Coal (40%2.6% 13%28% 2.8%30% 5.6%4.0% 24%8.2% 2.8%4.0% 0.0%0.0%<br>26) |16 = OIL-Cil |15% 2.2% 2.6% 4.3% “37% 15% 0.0%<br>27) | GAS-Gas 25% 2.2% 2.8% 4.2% 14% 10% 15%<br>28) |18 OMN-Minerals nec. 38% 08% 14% 7.2% 6.9% 27% -0.2%<br>23)30 |19.|20 CMTOMT -Bovine- Meat productsmeat prods ne.c. 2.2%59% 03%03% 07%07% 4.8%4.9% 4.9%10.3% 3.2%7.2% 0.0%<br>31 [21 VOL- Vegetable oils and Fats 51% 73% 8.0% 12.8% 287% 5.3%<br>32,33) |22|23 MIL- PCR-ProcessedDairy productsrice 3378% 3% 0.4%16% 09%24% 5.0%59% 66.5%5.0% 43.4%3.3%<br>34) |24 SGR- Sugar 18.1% 05% 07% 15% 82.7% 20.7%<br>35 |25 OFD-Food products ne.c. 215% 53% 2% 13.6% 52.8% 24.2%<br>3637, |26|27 B_T- TEX- BeveragesTextiles and tobacco products 29.4%135% 21%2.4% 2.9%3.2% 74%5.3% 98.4%44.3% 318%16.3%<br>38)33/2940) |28|30 WAPLEA-LUM- - WoodLeather Wearing productsproducts apparel 16.6%20.6%20.8% 35%20.8%10% 37%209%5.3% 135%212%5.8% 397.1%6.2%17.9% 26.1%23.4%19.2%<br>41)42)43 [31|32|33 PPP P_C-Petroleum,CRP -- Chemical, Paper products, rubber,coal products publishing plastic products 5jar55% 9% 0.9%17%3.0% 13%12%34% 7.3%18%10.0% 52.0%106.9%48.5% 18.7%5.8%39%<br>44.) |34 NMM- Mineral products n.e.c 19.6% 27% 30% 4.2% 164.6% 24.4%<br>45) |35 |_S-Ferrous metals 113.8% 2% 23% 3.0% 129.8% 18.0%<br>48)47) [36 NFM-Metalsne.c. lan 24% 26% 35% 1.2% 34% 07%|<br>48)43 [37|3839 © MVH-MotorFMP-OTN- MetalTransport products vehiculesequipment and partsn.e.c. (88%|7.4%26.8% 07%12%0.8% 09%16%11% 64%a1%9.2% 60.0%18%24.2% 20.5%36.4%95%<br>50/40 ELE - Electronic equipment |9.3% 07% ag% 6.8% 36.5% 10.5%<br>51/41$2)53, |42|43 OME OMF-Manufactures ELY-Electricity- Machinery andnec. equipment n.e.c. |7.9%31.9% 0% 0.8%24%3.0% 12%2.8%36% 7.8%75%5.0% 23.4%106.5%03% 88%20.9%30% 0.0%<br>4) [44 GDT-Gas manufacture, distribution 10.0% 06% 07% 45% -25% 0.0% 0.0%<br><!-- End of picture text -->
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# — Goodsupstream Sectorstariff ranked(as % total by largestinput reductioncost) in BaselineUpstream(%input — SimulatedUpstream(% input Changeinp.p. | |# Service Sectorsin upstream tariff ranked(as % bytotal largestinput reductioncost) BaselineUpstream[as % total SimulatedUpstream(as % total Changep.p. in<br>720 PCR-RMK-Raw Processed Milk rice 24%97% 17%93% 05%OI% 12 OBS:WTR-Other Water: business collection,servicespurification and distribution ai%0.9% 01%10%<br>34 CTL-BovineWOL - Wool, silk-wormcattle, sheep cocoons and goats, horses 30%63% 86%61% 04%0.2% 3.4 ROSDWE - Recreation- Dwellings & Dither Services 19%1% 2%20%<br>56 QAP COA-CoalAnimal products n.e.c. 57%30% 55%29% aye01% 56 — CMN-WTP - Communications Water transport 27%28% 3.0%30%<br>7 GAS-Gas 28% 27% 01% 7 OF! - Other Financial intermediation 29% 34%<br>8 OIL-Gil 28% 28% 0.0% 8 ISR-Insurance 29% 35%<br>370.1112 NFM-Metalsne.cNMM-S-FerrousPC-Petroleum,Mineral metalsproductscoal productsne.c. 268%30%23%17% 263023%17% % ae0.03%001%00 910112 OSGATPTRD-OTP- -- Trade Other Transport OtherAir transport Services (Government) 4.0%68%72%7.0% 47%8%81%79%<br>13. GDT-Gas manufacture, distribution 07% 07% | ]13 CNS- Construction: building houses Factories offices a 13.1% 65%<br>14 CMT- Bovine meat prods 07% 07% 4<br>15 OMT -Meat products ne.c. 07% 07% 6<br>16 ELY -Electricity 38% 38% 6<br>1? MIL- Dairy products 03% 09% v<br>38 LUM- Wood products 5.3% 53% 8<br>39 FMP - Metal products 09% 10% 9<br>20 ELE - Electronic equipment 03% 10% 20<br>21 SGR- Sugar 07% 08% 21<br>22. OMN-Mineralsnec. 14% 15% 2<br>23 OME -Machinery and equipment ne.c. 12% 13% 2B<br>24 OTN- Transport equipment n.e.c. 1% 12% 24<br>25 PPP - Paper products, publishing 13% 15% 5<br>26 MVH-Mator vehicules and parts 18% 18% %<br>27 B_T- Beverages and tobacco products 29% 3% 2<br>28 FSH-Fishing 28% 3% 2<br>29 TEX- Textiles 32% 38% 23<br>30 OFD - Food products ne.c. 27% 7.9% 30<br>31 OMF - Manufactures ne.c. 28% 31% 3<br>32 V_F - Vegetables, fruit, nuts 42% 4.8% 32<br>33 O8D-Oil seeds 49% 54% 3<br>34 OCR-Crops ne. 50% 55% 4<br>35 GRO-Cereal grains nec. 51% 55% 5<br>36 FRS-Forestry 47% 5.2% 8<br>37 CRP- Chemical, rubber, plastic products 34% 39% 7<br>38 C_B- Sugar cane, sugar beet 84% ai% 8<br>39 WAP - Wearing apparel 37% 44% 39<br>40 WHT - Wheat 85% 93% 40<br>41 PDR- Paddy rice 87% 9.4% 41<br>4243.44 PFB-Plant-basedVOL-LEA-Leather Vegetableproductsoilsfibers and Fats 85%8.0%20.9% 93%93%23.9% 42844<br><!-- End of picture text -->
<!-- Start of picture text --> 4jA| B a e | T<br>Rate of Change in<br>Effective p.p.<br>a Protection<br>ti] |1 — PDR-Paddyrice 8.2%<br>| [2 WHT-Wheat 215% 03<br>|4) |3[4 — V_F- GRO-Cereal Vegetables,grainsne.c.fruit, nuts 70.0%16.8% 08%<br>| |5 OSD-Oil seeds 24% 03%<br>16)1|13,20/10 | |6|?|8|3 — CTL-Bovine C_B-SugarPFB-Plant-basedOCR-Cropsnec.QAP- Animalcane,cattle,products fiberssugar sheepne.c.beet and goats, horses 1%5%87%4.3%18% 0.4%“1<br>21)22) ||12 — WOLAMK-Raw- Wool,Milk silk-worm cocoons 8.8%“118%<br>23| |13 FRS-Forestry 50% 0.4%<br>24) |i4 FSH-Fishing 22% 0.2%<br>25| | COA-Coal 18.7%<br>268) | OL-Cil -35%<br>27| |? GAS-Gas 5.8% 7a:<br>28) |18 OMN-Minerals nec. 63% 05%<br>23) |19 CMT -Bovine meat prods 49% 0,<br>30) |20 OMT -Meat products ne.c. 12.8%<br>31 |21 VOL- Vegetable oils and Fats 418% “B10<br>32) |22 MIL-Dairy products 76.4%<br>33} |23 PCR-Processedrice 64%<br>34} |24 SGR-Suger 89.5%<br>35) |25 OFD-Food products ne.c. 613%<br>36, [25 B_T- Beverages and tobacco products 105.8%<br>37 |27 TEX- Textiles 54.7%<br>38) |28 WAP - Wearing apparel 510.8%<br>33} |23 LEA-Leather products 45.9% B21<br>40) |30 LUM- Wood products 225%<br>41) |31 PPP-Paper products, publishing 64.8%<br>42| |32 P_C-Petraleum, coal products 119.4%<br>43) |33 CRP-Chemical, rubber, plastic products 510%<br>44| [34 NMM- Mineral products ne.c. 217%<br>45| |35 |_S-Ferrous metals 3.2%<br>46) |35 NFM-Metalsne.c. 10.2% 0%<br>47)48|43) [37|38[39 | MVH-MotorFMPOTN- -MetalTransport vehiculesproductsequipment and partsnee.c. 738%27%B17%<br>50| |40 ELE -Electronic equipment ay<br>51) |41 OME - Machinery and equipment ne.c. 26.0%<br>2| |42 OMF-Manufactures nec. 128.0%<br>53| |43 ELY-Electricity 0.4% 02%<br>54) |44 GDT-Gas manufacture, distribution 25%<br><!-- End of picture text -->
<!-- Start of picture text --> ~& B. c o E F on G H I J K<br># = == Sector Output Tariff Upstream Upstream Upstream Rate of Change<br>Tariff (% Tariff (% total Tariff (% Effective in p.p.<br>0 gross output) input cost) input goods Protection<br>ni | [1 PDR-Paddyrice 01% 25% 50% 51% 4.8% 0.0%<br>2) |2 WHT-Wheat NZ4 2.5% 93% 10.5% 0.8% 31% 0.0%<br>13)14.) |3|4 — V_F- GRO-Cereal Vegetables,grainsnec. fruit, nuts 12%23% 2.0%18% 3%39% 37%4.8% 2.2%09% 24%12% 0.0%<br>18 |5 OSD-Oil seeds 0.9% 2.0% 45% 5.3% “19% 09% 0.0%<br>16/6 —C_B- Sugar cane, sugar beet 0.0% 25% 7.0% 7.9% -3.9% 0.0% 0.0%<br>7 |? PFB-Plant-based fibers 0.2% 25% 6.0% 67% -38% 01% o1%<br>8 |8 OCR-Cropsnec. 3.6% 2.0% 12% B% 2.0% 39%<br>139) |9 CTL Bovine cattle, sheep and goats, horses 00% 20% 37% 41% 4.3% 00%<br>20) |10 QAP- Animal products ne.c. 07% 17% 35% 4.0% -2.0% 07%<br>Zi)22) |1l|12 — WOL-MK - Raw Wool,Milk silk-worm cocoons 0.0%2.2% 21%17% 4.3%2.2% 4.7%24% 4.0%2% 0.0%2.2% 0.0%0.0%<br>23) |12 FRS-Forestry 0.6% 19% 7.9% 95% “17% 06% jo.0%<br>24)25) |14|15 FSH-FishingCOA-Coal 0.0%3.6% 07%10% 2%0.8% 25%0.9% “19%43.8% 0.0%3.6% 0.0%0.0%<br>26) /16 OIL-Gil 13% 08% 09% 1% am% 13% 0.0%<br>27) | GAS-Gas 01% 0.8% 10% 12% “37% 0.0% 0.0%<br>28) |18 OMN- Minerals nec. 0% 11% 2.0% 27% 2.3% a1% 0.0%<br>23) |19 CMT -Bovine meat prods 0.0% 11% 27% 34% “17% 0.0% 0.0%<br>30)31/21|20 OMTVOL- Vegetable- Meat productsoils andne.c. Fats 2.2%2.4% 1%2.3% 2.3%25% 29%2.8% 2%19% 2.8%25%<br>32, |22 MIL- Dairy products 12% 1% 2.8% 3.2% am 15%<br>33, [23 PCR-Provessedrice 0.0% 12% 19% 2.2% “35% 00%<br>34) |24 SGR-Suger 0.4% 12% 15% 1% “3.2% 05%<br>35 |25 OFD-Food products ne.c 0.7% 15% 2.2% 25% -25% 0.8%<br>36 |26 B_T-Beverages and tobacco products 0.8% 11% 15% 19% “10% 09%<br>3738) |27|28 TEX-WAP - WearingTextiles apparel 7.9%23% 15%16% 20%1% 23%2.2% 25.5%17.3% 9.8%3.2%<br>3340, |2930 LEA-LeatherLUM- Wood productsproducts 10.3%35% 11%13% 11%7.0% 12%7.8% 1270.5%2.8% 118%45%<br>41) [31 PPP-Paper products, publishing 6.2% ag% 13% 1% 184% 80%<br>42)43 |32[33 P_C-Petroleum,CRP - Chemical, rubber,coal products plastic products 38%5.0% 10%14% 10%16% 1%19% 28.4%730.3% 3.6%54%<br>44. [34 NMM- Mineral products n.e.c. 51% 12% 13% 15% 44.3% 7.4%<br>4546)47 |35|36 |_S-FerrousNFM- Metals ne.c.metals wi%33% ag%10% 11%11% 12%12% 23.4%5.7% 9%27% 06%|<br>48) |37 FMP- Metal products 6.8% 08% 11% 15% 22.3% 86%<br>43S0 |38[39|40 © MVH-MotorOTN-ELE - ElectronicTransport vehicules equipment equipment and partsn.e.c. 16.2%4.0%6.8% 08%08%1% 14%11%10% 18%15%14% 65.8%25.4%7% 23.8%5.3%27%<br>51) |41 OME - Machinery and equipment n.e.c. 4.8% 09% 12% 17% 12.8% 5.3%<br>52, |42 OMF -Manufactures nec. 8.0% 10% 12% 15% 47.5% 95%<br>53, |43 ELY-Electricity 0.0% 15% 17% 19% 9.2% 0.0% 0.0%<br>4 [44 GDT-Gas manufacture, distribution 0.0% 08% 1% 14% -38% 0.0% 0.0%<br><!-- End of picture text -->

<!-- Start of picture text --> BAe Ww x [Y¥ 2 Aa AB — aC aD cE AF<br># Goods Sectors ranked by largest reduction in Baseline Simulated Change in | |# Service Sectors ranked by largest reduction Baseline | Simulated Change in<br>upstream tariff (as % total input cost) Upstream Upstream pp. in upstream tariff (as % total input cost) Upstream Upstream pp.<br>(%input —(% input {as % total (as % total<br>1 P_C-Petroleum, coal products 10% 1% 1 WTR- Water: collection, purification and distribution 15% 17%<br>2 _S-Ferrous metals 1% 1% 2 ROS - Recreation & Dither Services 13% 18%<br>3. NFM-Metalsne.c. 1% 1% 3 OBS: Dither business services 15% 18%<br>4 ELY-Electricity 17% 18% 4 OSG - Other Services (Government) 27% 3%<br>5 NMM- Mineral products n.e.c. 13% 14% 5 ATP- Air transport 4.9% 55%<br>6 COA-Coal 08% 09% & —CMN- Communications 4.1% 48%<br>789 — OIL-OilELEGAS-Gas-Electronic equipment 09%10%10% 1%11%1% 897 CNSWTPTRD-Trade -- Construction: Water transportbuilding houses factories offices a 4.0%9.0%54% 4.7%10.0%64%<br>10 FMP- Metal products 1% 13% 10 OTP - Other Transport 6.2% 7.2%<br>Tl LEA-Leather products 1% 12% Tl DWE - Dwellings 71% 83%<br>12138.4 OMFOTN-B_T- -BeveragesTransportManufactures equipmentand ne.c.tobacco n.e.c.products 12%1%18% 13%14%17% 13.124 ISR-OF! - OtherInsurance financial intermediation 24.2%278% 29.0%wa%<br>1B16 OMEGDT -Gas- Machinery manufacture,and equipmentdistributionne.c. 1%12% 14%13% 166<br>1?18 — WOL-PPP - Wool,Paper silk-wormproducts, cacaonspublishing 2.2%13% 24%15% v8<br>18 MVH- Motor vehicules and parts 14% 16% 9<br>20 OFD - Food products ne.c. 2.2% 24% 20<br>21 SGR- Sugar 18% 11% 21<br>22 FSH-Fishing 2% 24% 22<br>23 PCR - Processed rice 19% 2% 23<br>24 TEX- Textiles 20% 23% 24<br>25 CRP- Chemical, rubber, plastic products 16% 18% 25<br>26 CTL-Bovine cattle, sheep and goats, horses 37% 4.0% 26<br>27 OMN-Minerals nec. 2.0% 23% 27<br>28 MK - Raw Milk 4.3% 4.8% 28<br>29 WAP - Wearing apparel 17% 2.0% 23<br>30 QAP - Animal products n.e.c. 35% 39% 30<br>31 OMT - Meat products ne.c. 2.3% 27% a<br>32 MIL- Dairy products 28% 29% 32<br>33 GRO - Cereal grains nec. 3% 35% 3<br>34 CMT - Bovine meat prods 27% 3% 34<br>35 VOL- Vegetable oils and Fats 25% 29% 35<br>36 V_F- Vegetables, Fruit, nuts 39% 44% 36<br>37 OSD-Oil seeds 45% 51% 37<br>38 PDR- Paddy rice 5.0% 51% 38<br>38 LUM- Wood products 7.0% 11% 39<br>40 PFB -Plant-based fibers 6.0% 68% 40<br>41 C_B-Sugar cane, suger beet 7.0% 7.9% 41<br>42 WHT - Wheat 93% 10.4% 42<br>43° FRS -Forestry 73% a% 43<br>44 OCR-Crops nec. 112% 12.6% 44<br><!-- End of picture text -->
# _Effects when there is product differentiation_

The choice of the modeling framework determines how the imposition of import tariffs in a particular sector affects its output prices. When users decide to simulate effects with the product homogeneity framework, the average import tariff across all the products in a particular sector represents the percentage increase in the sector’s output prices. A full pass‐through on domestic prices is therefore assumed under this framework. By contrast, if the product differentiation framework is chosen, UTAS calculates the following three effects: (i) the increase of import prices on account of the tariff, (ii) the pass‐through of the tariff on imports on the goods’ domestic equivalent, and (iii) an overall price effect that combines (i) and (ii). While (i) is calculated simply as the average import tariff on products of a particular sector, (ii) and (iii) depend on user‐defined substitution and supply elasticities as well as a sector’s domestic consumption share (i.e. how much of a sectors’ total consumption is sourced domestically).

UTAS considers first by how much the demand for the imported variety of the sector’s output would increase, if all tariffs were to be abolished. This depends on the sector’s elasticity of substitution. It then calculates by how much the hypothesized increase in the import demand would lead to lower demand for the sector’s domestic output variety. This is done by assuming a Leontief production framework, where the increase in the demand for the imported variety at zero tariffs is exactly offset by an equivalent reduction in the demand for the domestic variety. Next, UTAS calculates how the demand shift from the domestic to the imported variety would translate into a lower output price for the domestic variety. The size of this effect is mediated by the sector’s elasticity of supply. Last, the overall effect of the tariffs in the sector is calculated as a weighted average of the change in the output price for the imported and the domestic variety, where the hypothesized consumption shares of domestic versus imported sector produce at zero‐tariffs are used as the respective weights.

Figure 20 and Figure 21 below illustrate these calculations for a few selected sectors based on Pakistan’s pre‐2019 reform duty rates. Imports of vegetables, fruit and nuts, for example, accrue a tariff of 10.5 percent. The pass‐through of the tariff on domestic prices in this sector is relatively small, amounting to only 2.3 percent. This is because UTAS predicts only a small difference in the domestic consumption share on account of the tariff. By contrast, the pass‐through effect on domestic prices is much larger in the textiles and motor vehicles sectors, where UTAS attributes a much larger increase in domestic consumption shares due to the prevalence of tariffs.

_Figure 20: Price pass‐through of tariffs in selected sectors under product differenciation_ _Figure 21: Domestic consumption share in selected sectors_ <!-- Start of picture text --> 30% 90%<br>80%<br>25% 70%<br>60%<br>20% 50%<br>40%<br>15% 30%<br>20%<br>10% 10%<br>0%<br>5%<br>0%<br>Vegetables, Vegetable Textiles Motor<br>fruit, nuts oils and vehicules<br>fats and parts<br>Imported prdct. Domestic prdct. Overall with tariff without tariff<br><!-- End of picture text --> Source: Authors’ calculations using UTAS 20
# 5.3) Additional considerations

We highlight a series of additional practical considerations that UTAS users may find useful when planning their simulation exercises.

## Choice of modeling framework

The choice of modeling framework should be guided by two main considerations. First, for users predominantly interested in modeling the effects of a tariff reform on a few selected sectors, the choice of framework should consider the homogeneity and the tradability of these sectors. For tradable sectors that produce relatively homogeneous goods, the perfect pass‐through assumption of the product homogeneity is more suitable than for less homogeneous good that are more difficult to trade across borders (e.g. services). Second, data requirements are considerably higher for the product differentiation framework. Users are recommended to at least obtain domestic consumption shares for each sector before embarking on simulations under the product differentiation framework. This should also be combined with a careful sensitivity analysis based on alternative substitution and supply elasticities.

## Duty exemptions and duty drawback schemes

In many developing countries, duty on imported intermediate products is exempt to encourage exports. To ensure that the estimations in UTAS are accurate, the baseline tariff schedule should thus ideally reflect effective (i.e. actually collected) tariffs and not statutory (i.e. de jure) tariffs. An ‘effective’ tariff schedule can normally be constructed using customs transactions data, which report the amount actually collected for each transaction at the import stage. In some countries, however, exemptions are not granted at the import stage but in the form of duty drawback schemes for exporters. Data on such schemes are much more challenging to collect, making it more difficult to factor in drawback schemes. When comprehensive duty drawback schemes are in place, UTAS will most likely overestimate the size of upstream tariffs on downstream costs for exporting industries. Note, however, that in most developing countries duty drawback schemes tend to work imperfectly. There are also large implicit costs associated with using them. This diminishes the possibility of estimation bias in UTAS even when drawback schemes are in place pro‐forma.

## Preferential trade agreements

Preferential trade agreements (PTA) with one or various trading partners are among the most frequent trade reforms. UTAS does not explicitly allow for differential tariff treatment across countries. Instead it relies on the specification of an average effective tariff collected across all countries. To model the impact of a PTA, users will therefore have to establish the impact of the PTA on the average effective tariff for each product outside the UTAS environment. This can be done by assuming a constant import share from PTA‐affected trade partners and computing a corresponding pro‐rata reduction in the average tariff for each product on account of the PTA.<sup>16</sup>

## Restrictions of services trade

Trade restrictions of services trade are not explicitly considered in UTAS. Yet, if trade restrictions can be translated into ad‐valorem equivalents, they can, in principle, be considered in UTAS, but will require some prior manipulation of the pre‐set parameters of the tool. If this is done, UTAS will also consider services trade restrictions in calculating all trade‐related upstream costs on downstream sectors.

16 More advanced scenarios could also take into account trade diversion due to the PTA. This, however, would require knowledge on the substitution elasticity between the products of different origin.

21
# 6) Conclusion

In modern production processes, the parts and components comprising a product are produced in multiple locations, crossing borders multiple times before they are assembled into the final product. Firms therefore increasingly rely on imported inputs to produce competitively. In fact, several studies have shown that the use of imported intermediates has been associated with improved firms’ productivity and diversification patterns. Trade policy reform aimed at reducing the impact of tariffs in upstream sectors can therefore go a long way in improving firms’ competitiveness and help them play a more active role in global value chains.

This paper presents UTAS: a novel tool developed by the World Bank to help policy makers assess the impact of tariff reforms on production costs across a variety of sectors in a flexible and time‐efficient manner. It also allows users to compute effective protection rates. This is important because high levels of effective protection can result in a significant anti‐export bias and reduce firms’ incentive to export. Based on a simple modeling framework, the tool is intended to allow policy makers to consider how different tariff structures intertwine with firms’ production costs, with the ultimate goal of supporting an informed discussion of the possible consequences of trade reform for the private sector. It allows users to combine country‐specific input‐output data across sectors with information on tariffs by HS code to quantify the impact of different tariff reform scenarios across different sectors, building on a user‐friendly Microsoft Excel environment. The tool can be operated with only basic Excel knowledge and allows users to specify different assumptions to gauge the impact of direct and indirect effects of tariff reform scenarios on the costs of inputs across sectors.

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<!-- Start of picture text --> 12 af |Product Inputs by Sector4 5 © D E F s 4 1 J<br>3ry Sector (Select) Change Data fun Input-Output Calculations<br>5<br>6<br>ss8 (Description =HS6 Code Position<br>3<br>10<br>1<br>2<br>re<br>4<br>5<br>16<br>tT<br>Lz<br>a<br>20<br>a1<br>22<br>23<br>24<br>Ey<br>26<br>at<br>28<br>29<br>30<br>Es]<br>32<br>33<br>34<br>35<br>36<br>ot<br>38<br>39<br>40<br>“1<br>42<br>43<br>44<br>45<br>46<br>4<br>48<br>49<br>50<br>1<br>52<br>53<br>54<br>55<br>56<br>st<br>58<br>53<br>« >» ..| README |S ESET BS Se) TARIFF_RANKING [eOG<br><!-- End of picture text -->
<!-- Start of picture text --> afaje| ic | D | E F S | 4 i J {Ki<br>i<br>2]<br>>|<br>s| | [Competition assumption<br>|<br>os]<br>8|<br>3Gross output compesition: Intermedicte consumption (goods and services as inputs) and Value Added<br>we<br>1) Gaak fingers) HARA<br>12 | Services finguts) hes<br>13 | Vaieas Acted EZ<br>ca<br>5 |<br>16 | Upstream and output tariffs<br>8 Upstream tariff as % of gross output HNA HNA HNA<br>21ee| Upstream tariff as % of total inputs cost HNA HNA HNA<br>23) Upstream tariff as % of total goods inputs cost HNA #NA #NA<br>24<br>25<br>26) Output tariff (average tariff on ) ANA, HMA, H#NA<br>at Rate of Effective Protection HNA #NA HMA<br>28 |<br>23. | Composition of upstream tariff by largest contributing sectors Reset}<br>30=| 5 z ry<br>Baseline , Share in ae<br>st] # Sector in NAICS toupstreamcontribution TariffBaseline structureinput cost ‘SimulatedTariff Change in p.p.<br>32) 1 #Nid, aNd, HN, NIA, #Nis, #NIA,<br>33 | 2 ata aNis, HN, NIA, aN, HINA,<br>34 | 3 aA, #NiA, aN HN, #NtA #NtA<br>35_| 4 BNA, NEA, aN #NEA, #NA, aN<br>36 | 5 HRA, #NEA, aN NEA, #NA, aN<br>at | 6 HRA, HMA, aN NEA, HMA, aN<br>38) 7 eNis, ents, #NA, Na, aNd, aN,<br>33) 8 #Nis, #Nis, HN, Nia, aN, HN,<br>400 9 aNd, ania, HN, aN, aNd, HN,<br>a 10 aN, ats, ana, aN ants, NA,<br>4243|| Rest BNA, aN #NIA,<br>44 1 #Nis, aNd, ahs, Nts, aNis, aN,<br>45 12 #Nis, ents, aN, ants, HN, HN,<br>46 B #Nis, aNd, aN, #NtA, aN, aN,<br>UTAS | README | SIM_| |S | TARIFF_RANKING | OUTPUT_TARIFFS_CALCULATIONS<br><!-- End of picture text -->

<!-- Start of picture text --> 4]15SS SEE SSS EE EE SEEE EE SE EE ee ee ee ee ee ee<br>2s [Competition assumption ee<br>=<br>Reset<br>s ||<br>BASELINE SCENARIO<br>1<br>La<br>x Sector ‘Output Tariff UpstreamTarift (% UpstreamTariff (% total UpstreamTariff (% _ RateEffective of ‘Output Tariff<br>a gross output) input cost) input goods Protection<br>| [t POR-Paddyrice NA NA NRA NA #WALUE! NRA WVALUET<br>2) |2 WHT-Wheat NIA NIA NIA NIA VALUE! NIA VALUE!<br>8 |3 GRO-Cereal grainsnec. NIA NIA NIA NIA ‘VALUE! NEA VALUE!<br>4 | |4 — VF-Vegetables, fruit, nuts NIA NIA NIA NIA VALUE! NIA VALUE!<br>616 | |5|& — C_8-Sugarcane, OSD-Cilseeds sugar beet NIANIA NIANIA NEANIA NIANIA VALUE!VALUE! NEANIA VALUE!VALUE!<br>| |? PFB-Plant-based fibers NIA NIA NIA NIA VALUE! NEA VALUE!<br>| |8 OCR-Cropsnec. NIA NIA NIA NIA VALUE! NIA #VALUE!<br>1320) |3|10 — CTL-Bovine OAP- Animal cattle, products nec. sheep and goats, horses NIANIA NIANIA NIANIA NIANA VALUE!VALUE! NEANIA VALUE!VALUE!<br>a1) | RMK- Raw Milk NIA NIA NIA NIA VALUE! NEA VALUE!<br>22) |12 — WOL- Wool, sik-worm cocoons NIA NIA NIA NIA VALUE! NIA VALUE!<br>23/13 FRS-Forestry NIA NIA NIA NIA VALUE! NEA VALUE!<br>24) |14 — FSH-Fishing NIA NIA NIA NIA VALUE! NIA VALUE!<br>25 [18 COA-Coal NIA NIA NIA NIA VALUE! NEA VALUE!<br>26) (16 OlL-cil NIA NIA NIA NIA VALUE! NIA VALUE!<br>at |? GAS-Gas NIA NIA NIA NIA VALUE! NEA VALUE!<br>28) |18 OMN-Mineralsne.c. NIA NIA NIA NIA VALUE! NIA VALUE!<br>23 [18 CMT-Bovine meat prods NIA NIA NIA NIA VALUE! NEA VALUE!<br>30) |20 OMT-Mest products ne.c. NIA NIA NIA NIA VALUE! NIA VALUE!<br>31/21 VOL- Vegetable oils and fats NIA NIA NIA NIA VALUE! NEA VALUE!<br>32) [22 MiL-Dairy products NIA NIA NIA NIA VALUE! NIA VALUE!<br>33 |23 PCR-Processedrice NIA NIA NIA NIA VALUE! NEA VALUE!<br>34) |24 SGR-Sugar NIA NIA NIA NIA VALUE! NIA VALUE!<br>35 |25 OFD-Food products nec. NIA NIA NIA NIA VALUE! NEA VALUE!<br>36 |28 B_T-Beverages and tobacco products NIA NIA NIA NIA #VALUE! NIA VALUE!<br>st38)33 |27|28 WAP TEX-- Textiles Wearing apparel NIANIA NIANIA NIANIA NIANIA VALUE!VALUE! NEANIA VALUE!VALUE!<br>40) |23 LEA-Leather products NIA NIA NIA NIA VALUE! NEA VALUE!<br>41 [30 LUM- Wood products NIA NIA NIA NIA VALUE! NIA #VALUE!<br>42) [31 PPP-Paper products, publishing NIA NIA NIA NIA VALUE! NEA VALUE!<br>43 |32 P_C-Petroleum, coal products NIA NIA NIA NIA VALUE! NIA VALUE!<br>44) [33 CRP-Chemical, rubber, plastic products NIA NIA NIA NIA VALUE! NEA VALUE!<br>45 [34 NMM- Mineral products ne.c. NIA NIA NIA NIA VALUE! NIA VALUE!<br>46 [35 LS-Ferrous metals NIA NIA NIA NIA VALUE! NEA VALUE!<br>41 [38 NFM-Metalsnec. NIA NIA NIA NIA VALUE! NIA #VALUE!<br>43) [37|38 FMP-Metal MYH-Motor products vehicules and parts NIANIA NIANIA NIANIA NIANIA VALUE!VALUE! NEANIA VALUE!VALUE!<br>43/33 OTN- Transport equipment ne.c. NIA NIA NIA NIA VALUE! NEA VALUE!<br>n_|_tan “LE.Flactronic e n ui nui ry Nao cr 401 LIEL nua LIE<br>| UTAS | README |JRIYM) SIM_1 RUNNIN OU Ne RENO<br><!-- End of picture text -->
<!-- Start of picture text --> jal 8 c o E F G 4 I 4 K t<br>1; [Competitionassumption<br>;<br>6<br>SIMULATION<br>BASELINE SCENARIO INPUT<br>7<br>CJ<br>3<br>* Sector Output Tariff Upstream Upstream Upstream Rate of<br>a Tariffgross output)(% Tariffinput cost)(% total Tariffinput goods(% __ ProtectionEffective<br>| [1 PDR-Paddyrice Nia NIB, a NIA, *WWALUEL NA WALUE<br>2) |2 WHT-Wheat NA NIA Nea NIA, VALUE! Nea #VALUE!<br>13.| |3 GRO-Cerealgrainsnec. NA NIA, Nea NIA, VALUE! Nea #VALUE!<br>16 |4 — VLF- Vegetables, fruit, nuts NA NA, Nea NIA, VALUE! Ne #VALUE!<br>| |5 O8D-Cilseeds NA NIA, NA NIA, #VALUE! Nea #VALUE!<br>16| 6|? C_B-PFB-Plant-based Sugar cane, sugar fibers beet NIANA NIANIA, NIANA NIANIA, #VALUE!#VALUE! NIANea #VALUE!#VALUE!<br>613 3|3 — OCRCTL-Bovine- Crops n.e.c. cattle, sheep and goats, horses NIANA NIANIA, NIANea NIANIA, VALUE!#VALUE! NIANea #VALUE!#VALUE!<br>20at| |i10 OAPRMK-RawMilk- Animal products ne.c. NIANA NIANA, NIANea NANA, #VALUE!VALUE! NIANea #VALUE!#VALUE!<br>22|a] 13|12 — WOL-FRS-Forestry Wool, sik-worm cocoons NANA NIA,Nea, NANea NIA,NIA, VALUE!VALUE! NANea #VALUE!#VALUE!<br>a4|zi |i45 — FSH-FishingCOA-Coal NANA NIANea, NANea NIA,NIA, VALUE!VALUE! NeNea #VALUE!#VALUE!<br>26)eal |16Ww OlL-oilGAS -Gas NANIA NIA,NIA NANIA NIA,NIA VALUE!VALUE! NeaNIA #VALUE!#VALUE!<br>28|= |1813 OMN-Mineralsne.c.CMT - Bovine meat prods NANIA NIA,NIA NeaNIA NIA,NA #VALUE!#VALUE! NeaNIA #VALUE!#VALUE!<br>30} |20 OMT-Meat products nec, NA NA, Nea NIA, VALUE! Nea #VALUE!<br>ot32) 24|22 VOL—MIL-Dairy- Vegetable products oils and fats NANA NIANIA, NANea NIANIA, VALUE!VALUE! Nia,Nea #VALUE!#VALUE!<br>33| |23 PCR-Processedrice NA NIA, NA NIA, VALUE! Nea #VALUE!<br>34} |24 SGR-Sugar NA NIA Nea NIA, VALUE! Nea #VALUE!<br>35| |25 OFD-Food products nec. NA NIA, NA NIA, VALUE! Nea #VALUE!<br>36| |26 B_T-Beverages and tobacco products NA NIA, Nea NIA, VALUE! Nea #¥ALUE!<br>at| |2?7 TEX. Tentiles NA NIA, Nea NIA, #VALUE! Nea #VALUE!<br>38) |28 WAP- Wearing apparel NIA NIA Nea NIA #VALUE! NIA #¥ALUE!<br>33) |28 LEA-Leather products NA NIA, Nea NIA, VALUE! Nea #VALUE!<br>4041) |3130 = LUM- WoodPPP-Paper products products, publishing NIANA NIANIA, NIANea NIANIA, VALUE!VALUE! NIA,Nea #VALUE!#VALUE!<br>42| |32 P_C-Petroleum, coal products NWA NIA NA NIA, VALUE! NA #VALUE!<br>43.| |33 CRP-Chemical, rubber, plastic products NA Nea, Nea NIA, VALUE! Nea #VALUE!<br>cal44) |3435 MM.LS-FerrousMineral metals products ne NANA NIA,Nea, NANea NIA,NIA, VALUE!VALUE! NeNea #VALUE!#VALUE!<br>46)al [36370 NFM-Metalsnec.FMP - Metal products NANIA NIA,NIA NANIA NIA,NA #VALUE!#VALUE! NeaNIA #VALUE!#VALUE!<br>43.|a |3839° MYH-MotorOTN- Transport vehicules equipment and n.e.c. parts NANIA NIA,NIA NeaNIA NIA,NA #VALUE!#VALUE! NeaNIA #VALUE!#VALUE!<br>so} |40 — ELE-Electronic equipment NA NIA, NA NIA, VALUE! Nea #VALUE!<br>st]se| 4|42 OME OMF-Manufacturesne.c.- Machinery and equipment n.e.c. NIANA NANIA, NIANA NANIA, VALUE!VALUE! NIANea #VALUE!#VALUE!<br>53} |43 ELY-Electricity NA NIA, NA NIA, VALUE! NA #VALUE!<br>s4¢|_|44 _ GOT-Gas manufacture, distribution, NA NIA, Nea. NIA, VALUE! Nea #VALUE!<br>AD bees | README |B | SIM_Il f SIM_ITLIN ESL eSOa) TARIFF_RANKING | OUTPUT_TARIFFS_CALC]<br><!-- End of picture text -->
Imp <u>imp</u> Imp 0 Imp = ~~a~~ Pt xX J(@1/0.5) + 1) x Q, Imp <u>pamP_ pimp</u> nm (a@/0.5) A<0 t>0 1 pimp 1+t OImp ~~_ ~~(=~~ \a+et « A(ar/0-5) 4 1) x QiImp ~~QorP= QP~~ _ ~~at~~ y a(a1/05) Imp 44t Q
Imp Imp 0 = —_ ~~+>)~~ x ~~——~~ 4+ 1] Xx Dom ( <u>Qo gimp= 24</u> ) ~~17a~~ <u>ay</u> ) QyDom Dom Imp Imp 0 ( ~~-—_, >)~~ x ~~—~~ +1) xa =| (c ~~Q~~ <u>gimp</u> ~~-Q,~~ ) <u>1-a@,ay</u> ) 1 o = ~~(at~~ KAM)(a@1/0.5) XK ~~1-a,——~~ +1) XY 1+t ay

t>0 <!-- Start of picture text --> 1<br><!-- End of picture text --> t=0 1 Dom _ <u>(21__Dom — 20Dom 1</u> Dom 1 = ( <u>qpom</u> ~~Ta~~ t DX Po Dom

> pom = (—t x A/S) x ~~1-a—~~ 0 + 1) x QUTM ~~Qi~~ Dom ~~Qo~~ Dom = it yy J(@0/0.5) yy ~~1740~~ epem ao

> Aprom ~~pom~~ —~— ~~pPom_pDom~~ pbom _~ tx ~~A6@o/0.5)~~ itu ye ~~Lao~~ ao

<u>ApimP pDom_pDom</u> ~~———~~ — ~~1+~~ <u>pimp</u> ~~__°_~-~~ ppom {¢
~~>~~ AP= Ao X ~~[a~~ a (a + ApPom ~~oo)~~ _ 1| + (1 — ao) x ~~os~~ 1-a (1 + ApimP ~~oar)~~ - | __ <u>AP/P</u> B= <u>t</u>