metadata: authors: 0: author: Coulibaly,Souleymane
1: author: Montfaucon,Angella Faith Lapukeni
2: author: Nigatu,Natnael Simachew
3: author: Seri Epse Atsebi,Regina Stephanie
count: Philippines
docna: 0: docna: Trade Policy, Green Goods and the Labor
            Market: Evidence from the Philippines
docty: Policy Research Working Paper
abstracts: cdata!: Green goods trade will matter for the
            transition to a low-carbon global economy as well as for its
            adaptive capacity to climate events. This study explores
            green goods trade and related trade policies in the
            Philippines and its relationship with the labor markets. The
            paper finds that the country&#x2019;s green goods trade
            is limited due to certain costly non-tariff measures
            affecting energy transition and other types of green goods.
            Of about 90 measures, five are identified as reform
            candidates. Reforming these could enhance green goods trade,
            as there is a positive correlation between imports and
            exports of green goods. However, increased exports could
            reduce the number of high-skilled workers, while imports
            might increase the shares of female workers within
            industries. Green goods imports also correlate with higher
            earnings across industries. The study suggests that trade
            policy reforms may lead to labor shifts, necessitating
            complementary policies for affected workers when making
            trade policies more climate-friendly.
display_title: Trade Policy, Green Goods and the
            Labor Market: Evidence from the Philippines
listing_relative_url: /research/2024/12/34435941/trade-policy-green-goods-labor-market-evidence-philippines
topicv3: Trade and the Environment m1327130 1856,Trade Policy and Integration m1327407 775,Trade in Goods m1504224 2608,Trade and Climate Change m1504260 3020,Trade and Labor Markets m1504261 2553
publishtoextweb_dt: 2024-12-13T00:00:00Z
dois: 10.1596/1813-9450-11001
origu: Off of Sr VP Dev Econ/Chief Econ (DECVP)
no_of_pages: 56
author_keys: 0: author_key: 733706
1: author_key: 1819671
2: author_key: 1858239
3: author_key: 2085119
model_extractions: Policy Research Working Paper 11001 Trade Policy, Green Goods and the Labor Market Evidence from the Philippines _Souleymane Coulibaly Angella Faith Montfaucon Natnael Simachew Nigatu Regina Seri-Atsebi_ Economic Policy Global Department December 2024
Policy Research Working Paper 11001

# **Abstract**

Green goods trade will matter for the transition to a low-carbon global economy as well as for its adaptive capacity to climate events. This study explores green goods trade and related trade policies in the Philippines and its relationship with the labor markets. The paper finds that the country’s green goods trade is limited due to certain costly non-tariff measures affecting energy transition and other types of green goods. Of about 90 measures, five are identified as reform candidates. Reforming these could enhance green goods trade, as there is a positive correlation between imports and exports of green goods. However, increased exports could reduce the number of high-skilled workers, while imports might increase the shares of female workers within industries. Green goods imports also correlate with higher earnings across industries. The study suggests that trade policy reforms may lead to labor shifts, necessitating complementary policies for affected workers when making trade policies more climate-friendly.

This paper is a product of the Economic Policy Global Department. 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 amontfaucon@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
# Trade Policy, Green Goods and the Labor Market: Evidence from the Philippines<sup>∗</sup>

Souleymane Coulibaly, Angella Faith Montfaucon,<sup>†</sup> Natnael Simachew Nigatu, and Regina Seri-Atsebi World Bank _JEL Classification_ : D22, F13, F16, F18, Q56.

_Keywords_ : Green goods, Non-Tariff Measures, Female worker, Skills.

> ∗The findings in this paper are entirely those of the authors and do not necessarily represent the views of the World Bank and its affiliated organizations. The authors are grateful to Gonzalo J. Varela and Kevin Chua for peer reviewing an earlier version of the paper. We also thank Ralph Van Doorn, Jaime Frias, Israel Osorio-Rodarte, Karen Enriquez, Ruijie Cheng for useful discussions. We thank Bayu Agnimaruto for help in putting together the panel data on non-tariff measures for ASEAN countries. This work benefited from the support of the Multi-Donor Trust Fund for Trade and Development 2 financed by DFID (United Kingdom), SECO (Switzerland), SIDA (Sweden), the Netherlands Ministry of Foreign Affairs, the Norwegian Ministry of Foreign Affairs, Japan’s Ministry of Finance, and the National Graduate Institute for Policy Studies. All errors are our own.

- †Corresponding author.
# **1 Introduction**

Trade in green goods can be defined as the international imports/exports of eco-friendly and sustainable products and services. According to Kang (2020) and based on the Green Goods and Services - North American Industry Classification System (GGS-NAICS) 2012 classification, green industry products can be classified into five categories: i) energy from renewable resources; ii) energy efficiency; iii) pollution reduction and removal; greenhouse gas reduction, and recycling; iv) natural resources, and v) environmental compliance, education and training, and public awareness and reuse. In addition, they are products and technologies that do not consume many resources and are environmentally friendly in their production and utilization (Li et al., 2022). In recent decades, trade in green goods has become more important for international competitiveness since countries worldwide recognize the importance of addressing environmental challenges and transitioning to a more sustainable economy.

At the global level, the advantages of being engaged in green trade are numerous. First, it can boost economic growth by generating jobs and increasing the development of green industries (Bowen, 2012). Second, countries can gain a comparative advantage in their exports (Gong et al., 2020), for example, by focusing on one product where they have the advantage and reduce their dependence on non-renewable imports. Third, being involved in green trade could drive innovation, research, and development in the country to increase its competitiveness in the global market (Liu et al., 2022a,b). Overall, the promotion of green goods could contribute to mitigating the impacts of climate change, increase compliance with international commitments on climate change and reinforce a country’s reputation as a sustainable trading partner.

Regarding the relationship between trade in environmental goods and labor markets, green trade can indeed imply some trade policies that could affect workers. For instance, shifting towards green trade may require workers in traditional fossil fuel-based industries to re-skill or upgrade their skills to be employed in renewable energy projects, which are stringent regarding the skills and alignment with international reforms. Thus, although 1
green trade can spur the demand for skilled and semi-skilled workers in new green emerging industries, it can also raise concerns about gender inequality (McFarland, 2013; Rocha and Piermartini, 2023) or skills intensity and income inequality (OCDE, 2014), even depending on the types of green goods industries or sectors.

This paper examines the green goods trade landscape in the Philippines to understand the current level, evolution over time, and predominant green industries sectors. We estimate the ad valorem equivalent (AVE) of non-tariff measures (NTMs) on green goods to assess the cost of regulations on green goods imports and identify the most burdensome NTMs and candidates for regulatory climate-friendly trade policy reform. Finally, since reforms of NTMs will increase imports and exports of green goods, we assess the relationship between the imports and exports of green goods in the labor markets. This is done using trade data and labor force survey data at the industry level for the Philippines.

The Philippines makes for a compelling case for this analysis. As this country is vulnerable to climate change impacts like extreme weather events, agricultural disruptions, or rising sea levels, bolding the green trade can support the country’s transition towards a more sustainable area, enhance resilience, and alleviate environmental risks. First, as the Philippines heavily relies on fossil fuel imports, investing in green energy such as hydropower, wind, or solar can reduce the dependence on fossil fuel imports. Second, regarding international commitments, since the Philippines is a member of several agreements in addressing climate change issues like the ASEAN agreement, participating in green trade can help this country align with international obligations and enhance its commitment to addressing climate change. Finally, and more importantly, promoting green goods imports and exports can drive job creation, increase wages for people, and support economic growth (World Bank Group, 2022).

The paper finds that the Philippines’ share of green goods trade is relatively low, partly due to the high incidence of NTMs on imports of green goods. The paper finds five measures out of about 90 as reform candidates, which are costly and have a higher incidence 2
on energy transition and other types of green goods and technologies. These measures are reform candidates, where the underlying regulations are recommended to be reviewed. These reforms would increase the imports and exports of green goods, especially since green goods imports are linked with higher green goods exports. We find that higher green goods exports negatively correlates with the share of high-skilled workers within industries. In contrast, green goods imports are associated with an increase in the share of female workers within industries. Green goods imports are positively correlated with workers’ earnings across industries but not within industries. Overall, NTM reforms likely lead to labor reallocations across industries. Since we do not address endogeneity issues, these results on labor do not necessarily represent causality but provide insight into possible relationships and the need for complementary policies for affected workers when reforming trade policy in line with environmental policies.

We contribute to the literature in two main aspects. First, we provide a recent assessment of the green trade landscape and its possible implications for the labor markets in the Philippines. Second, we investigate the trade costs of NTMs in the context of environmental goods in the Philippines, identify the NTMs that are the most restrictive, and explore their relationship with labor markets (gender, skill intensity, and annual earnings of workers). To the best of our knowledge, we are the first to carry out this kind of study in the Philippines since most of the research papers are focused on European countries or other Asian countries.

The rest of the paper is organized as follows. Section 2 presents the data description on the green goods trade landscape and possible distributional implications. Section 3 estimates the effect of trade policies on green goods and workers. Section 4 investigates the possible reltionship of reforming NTMs and thereby increasing green goods trade, on workers’ gender and skill intensity. Section 5 presents the concluding remarks.

3
# **2 Green Goods Trade Landscape and Labor Markets**

## **2.1 Data**

We rely on data on NTMs, tariffs, and trade values from UNCTAD and WITS from 2014 to 2020 to calculate the AVEs. The data is disaggregated at the 6-digit level of the HS product classification. The NTM data is from the UNCTAD TRAINS (United Nations Conference on Trade and Development - Trade Analysis Information System) dataset, while the tariff and trade data are from the World Integrated Trade Solution (WITS) dataset. The NTM data uses a cut-off query every December 31st to get the annual period from 2008 to 2020, although the data was collected in 2015 and then again in 2018 in collaboration with the Economic Research Institute for ASEAN and East Asia (ERIA). The limit to this data is that this is not a true panel but a “manufactured” panel.

The second database used is the Philippines Labor Force Survey (LFS), which provides insights into the employment status of individuals in the labor force. It includes data on annual earnings, workers’ industrial affiliation, occupation, education level, and other related labor market aspects. The LFS data is collected quarterly, and for this analysis, we focused on the surveys conducted between 2010 and 2020 at the month of October to ensure consistency. It is important to note that the LFS invites different individuals in each round, making it a repeated cross-sectional dataset rather than a proper panel dataset. Although – the data is based on samples it includes weights that allow to make generalizations about the total labor force.

These two databases were merged with additional datasets that identify products classified as green goods. The green goods referred to in this note are based on the list of green products defined by the Green Transition Navigator (GTN)–a compilation of the APEC, OECD, and WTO green goods classifications. To arrive at our final database, we followed the following steps: first, we cleaned the trade data and ensured consistent HS product classification was used across the years. Next, we merged this data with the GTN list of green 4
<!-- Start of picture text --> Product level trade data Labor force survey<br>(2010-2020) (2010-2020)<br>1% wo<br>mo o:<br>Cy es<br>Se £§<br>“ee 8, ge<br>G&S &<br>Industry and individual level<br>merged data (using 2 digit<br>ISIC level)<br>* The industry data is panel from 2010to 2020<br>* The worker data is cross-sectional panel as the LFS don't survey the sample person over time.<br>* We also have firmlevel survey data but were not able to use it<br><!-- End of picture text -->
observe a more significant decline in the share of EG exports from 8.3 percent to 3.9 percent Figure 2 (B).

This decline in recent years has resulted in the share of green goods trade in the Philippines falling below the regional average (Figure 3). Relative to other ASEAN countries, we notice that in 2019, the share of green goods imports in the Philippines estimated at 7.8 percent is around 1 percent behind the regional average (8.6 percent), while countries like Brunei Dar Es Salam outperformed with a share of 13.9 percent (Figure 3, A). Similarly, the share of EGs exports estimated at 3.9percent is below the regional average (4.3 percent) and far behind some countries like China (9.3 percent) (Figure 3, B).

This landscape reveals that efforts may need to be made to enhance green goods trade in the Philippines to return to the decade average.

Green goods trade is spread across various industries, but the Industrial-ManufacturingTransportation (IMT) cluster industries dominate value-wise in the Philippines. The top three industries with the highest shares of green goods imports are the Manufacturing of rubber and plastic products; Other Manufacturing; and Manufacturing of fabricated metal products, as depicted in Figure 4, A. Regarding the exports, the Manufacturing of paper and paper products, Manufacturing of rubber and plastic products, and Manufacturing of other non-metallic mineral products have a higher share of green goods (Figure 4, B). However, the Manufacturing of computers, electronics, and optics products, Manufacturing of electrical equipment, and Manufacturing of machinery and equipment (IMT cluster) take the top spots for volume.

6
<!-- Start of picture text --> 16.0<br>14.0<br>120<br>10.0 10a°°<br>8.0<br>6.0<br>4.00.02.00.02.02.0<br>2014 2015 2016 2017 2018 2019 2020<br>mmm Share of Imports% === Average<br><!-- End of picture text --> <!-- Start of picture text --> 20.00<br>18.00<br>16.00<br>14.00<br>12.00<br>10.00<br>8.00 ewww wwwowwowwwnwne --- wwwwwoewen=] 932<br>6.00<br>4.002.000.002.000.000.00 | [|<br>2014 2015 2016 2017 2018 2019 2020<br>mmm Share of Exports% === Average<br><!-- End of picture text -->

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<!-- Start of picture text --> 16.0<br>14.0<br>12.0<br>10.0..<br>8.0 ory meee eee —-—- 86<br>6.0<br>4.0<br>0.02.02.0<br>ss & & Ss & & Re we<br>s e & s Og Ss & S 3<br>Ss & N RX w ¢RSRS ww ww<br>Wms Share of EG Imports (%) === Average<br><!-- End of picture text --> <!-- Start of picture text --> 9.0<br>8.0<br>7.0<br>6.0<br>58<br>--— meee eee eee eee eee eee were eenenne-= e@-=— 43<br>4.0<br>3.0<br>2.0<br>0.01.01.0 a |<br>s & & Ss & & a we x<br>s xs s Ss x RS & x }<br>Ss & v RX w Q RS we w<br>Wm Share of EG Exports (%) === Average<br><!-- End of picture text -->
.

<!-- Start of picture text --> Manufacture of rubber and plastics products * Manufactureof paper and paper products<br>Other manufacturing e Manufactureof rubber and plastics products *<br>Manufacture of fabricated metal products © Manufactureof other non-metallic mineral products. .<br>Manufactureof computer, electronic and optical produ... Manufacture of beverages<br>Manufactureof paper and paper products ‘ Services te buildings and landscape activities<br>Manufacture<br>of electrical equipment O Manufacture ofcomputer, electronic and optical products<br>Manufacture of chemicalsand chemical products hd Manufactureof textiles<br>Manufactureof machinery<br>and equipment n.e.c. @ Manufacture of elactrical equipment<br>Manufacture ofmotor vehicles, trailers and semi-traile.. Manufacture of fabricated metal products.<br>Manufactureof other transport equipment . Manufactureof chemicals and chemical products. .<br>Manufacture of textiles . Manufactureof wood and of products of woodand cork, .<br>Manufacture of other non-metallic mineral products . Manufacture of moter vehicles, trailers and semi-trailers<br>Manufactureof basicmatals = * (Manufacture<br>of machinery and equipment n.e.c. e@<br>Manufactureof wood and of products of wood and cork, Manufacture of other transport equipment<br>Manufactureof beverages Other manufacturing *<br>Waste collection; materials recovery Waste collection; materials recovery -<br>Sarvices to buildings and landscape activities Manufacture of basic metals.<br>0 5 10 15 20 25 30 0 5 10 1s 20 2. 30 35<br>% Share of green import (2018-2020) = % Share of green export (2019-2020) =<br><!-- End of picture text -->
<!-- Start of picture text --> 7s<br>70 e<br>H oo“ Manufacture of textiles<br>4 iia meen<br>gS 2) atta<br>so Manufacture of computer, electronic and optical products:<br>“a<br>40 @ maretectire of crectricat equipment<br>we«®@ () Masutacture of@ moter Mssutsctur vehici s, e trailersof paper and papersemi-trailers products<br>i 5 ooO Manufacture of rubber and plastics products (())<br>> Manufacture of machinery and equipment nec.<br>(_) Manufacture of beverages<br>F} 20<br>75<br>*<br>én eMaratecture@ Marufoctura of other nan-matalinc mineral productse<br>. cfbask matele =o, are cf fabricated metal products<br>o<br>o2 46 ¢ © RBM HB WH RM HM BH RNR<br>& Share of Green Goods import (2028-2020) +<br><!-- End of picture text --> <!-- Start of picture text --> = Manufactureof textiles<br>r)65<br>ss . &<br>j Manufacture of computer, electrons: and opbeal products<br>2 Lad<br>; as<br>; PT] Manufactureof electricaloe. equipment—<br>i 35% Manufacture of wood and of products of woodM an ufactured cork, Manufactureof rubber and of plasticsaa and productsaac<br>3 2s @ Manufacture of machinery and equipment n.¢.c.<br>3<br>‘Waste collection, material recovery Manufacture<br>ee :<br>of beverages<br>isBS Manufacture of other traniport* equipment<br>: Manufacture of fabricated metal products<br>a<br>o2 46 SC HMRMBBARAMHwBHRM BM<br>‘% Share of Green Good Export (2018-2020)<br><!-- End of picture text -->
<!-- Start of picture text --> 38<br>aa qh wortocture of machinery and equipment nec.<br>s ©<br>4 Marafacture of motor weticles, traders and seme-tradkers<br>“ @ Manufacture of computer, slectromc and optical products<br>sa @ Manutacture of basic metals liGnafactere of rubber end plastica products<br>gx o Ld S<br>i Fe ManafactareManufactureof other tranaportof fabricated metal equipment:. products‘Other @) manufacturing<br>3 " 8 .<br>35 Services to bucldengs and landscape actites<br>j = @ Manutecture of wood and of products of wood and cork,<br>s Waste cobection. materials recovery<br>o2: #4 @ tt wowrmreuvwnebeuwenexrteinwnwentnawnw nn<br>‘% Share of Green Goods import (2018-2020)<br><!-- End of picture text --> <!-- Start of picture text --> so Manufacture of machinery and equipment noc.<br>- eo - a “a<br>ie °&<br>wo Manufscture of computer, clectronic and optical products @<br>; 35 @ manutactureof basic metals Manufacture of beverages<br>Z 2 Qorrer manwtocturing Manutectura of rubber and plastics procucts (U9)<br>i as: @:Manutocture ot tabricated metal products e .<br>51si Services bo buildings and landscape activities<br>Fd ” (@ Manufacture of wood and of products of wooed and cork,<br>s Waste collection; materials recovery<br>or @ #@t pvonreouvuebwpenanururmMmaenetnmnbhbkM Mw<br>‘ share of Green Goods Export (2018-2020)<br><!-- End of picture text -->
instruments that are non-trade related.

Among the standard measures used to understand the share of products and trade affected by NTMs, the Coverage Ratio and Frequency Ratio are computed. The Frequency Ratio provides the percentage of products affected by one or more NTMs in total imported products. The Coverage Ratio is the share of imports affected by one or more NTMs in total imports (Disdier et al., 2020). The FR is calculated as follows: where _FRjt_ is the frequency ratio of country j in year t; _NTMjkt_ is the presence of a specific NTM for each product _k_ by country _j_ in year _t_ , and _Njt_ is the total number of products at six digits HS by country _j_ at year _t_ .

For the Coverage Ratio (CR), the equation is estimated as follows: where _CRjt_ is the Coverage Ratio of country _j_ in year _t_ , _Vjkt_ is the import value of good k by country j in year t, and _NTMjkt_ is the presence of a specific NTM for each product _k_ by country _j_ in year _t_ that takes a value of 1 if present and 0 if otherwise.

Many NTMs apply to imported products in the Philippines (Table A9), including on capital goods (Table A10), intermediate goods (Table A12), raw materials (Table A13) and consumption goods (Table A11). These span a wide range of products as detailed in Table A14 to Table A21.

NTMs apply to a large share of almost all types of green products in the Philippines in both value and product shares. The products most affected by NTMs are Air Pollution Control, Clean Up or Remediation of Soil and Water; and Efficient Consumption of Energy Technologies and Carbon Capture and Storage.

Figure 7 displays the list of NTMs affecting at least 1 percent of all green goods product 11
categories. It shows that there is heterogeneity in the kinds of measures that affect green goods imports in the Philippines. For example, Other formalities not elsewhere specified (C9) as well as Merchandise-handling or -storing fees (F62) affect 100 percent of all HS-6 products classified as green goods but also affect 100 percent of other goods in the 2014-2020 period. There are differences with other measures, however. For example, Non-automatic import-licensing procedures (E1) affect around 75 percent of import values of green goods but only affect 44 percent of non-green goods. Similarly, testing requirements (B82), and labeling requirements (B31) all apply more to green goods than non-green goods. This is consistent with other findings that suggest that tariffs favor dirty goods Brenton and Chemutai (2021) and it would seem this is the case for NTMs.

Some of the low averages of NTM incidence mask disparities within the types of green goods, as some are subject to more measures than others. For example, while the average for certification requirement for all green goods is just 11 percent of import value, this measure affects 100 percent of air pollution control products and is higher than average for some types of green goods (Figure 8). The relatively lower average is due to the measure not affecting all other products not displayed in Figure 8. Similarly, while the import license fee has a low average, it applies to 30 percent of imports of water supply.

While the above results demonstrate the incidence of green goods is high, these do not necessarily imply that such NTMs are costly. To assess whether these NTMs are increasing the cost of green goods imports, the literature usually embarks on a more systematic analysis, by estimating the ad valorem equivalents of NTMs as demonstrated in the next section.

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<!-- Start of picture text --> Traceability requirements (B85) ff<br>Product registration/approval requirements (B81)<br>Product quality, safety or performance requirements (B7) fy<br>Import licence fees (F65) (===<br>Technical barriers to trade regulations on production processes (B41) [<br>Advance payment requirements not elsewhere specified (G19) J<br>Inspection requirements (B84) [<br>Certification requirements (B83) (IF<br>Traceability requirements not elsewhere specified (B859) (US<br>Testing requirements (882) ry<br>Technical barriers to trade regulations on transport and storage (B42) (.<br>Measure affecting competition (H) _ lls<br>Labelling requirermerts (31) _=<br>Licensing<br>Non-automatic import-licensing other thanfor coveredspecified useurnde (E112)(C1) IIIlll<br>General (multilateral) safeguards (C31) _<br>Additional charges not elsewhere specific (F69) _|lllllll<br>Marking recquirernerts (632) _lll<br>Other formalities not elsewhere specifi (C) _lll<br>Merchandise-handlingor -storing fs (F62)_Tn<br>0.0 10.0 20.0 30.0 40.0 50.0 60.0 70.0 80.0 90.0 100.0<br>m Average of Frequency ratio (%) over sample period m Average of Coverage ratio (%) over sample period<br><!-- End of picture text -->

<!-- Start of picture text --> Waste Water Management and Potable Water Treatment =<br>Cleaner or More ResourceProductsEfficient Technologies and F<br>Efficient ConsumptionCapture of Energy Technologiesand Storage and Carbon |<br>Renewable Energy Ly<br>Heat and Energy Management i —<br>ie) 10 20 30 40 50 60 70 80 90 100<br>@ Avg. Frequency ratio (%) TM Avg. Coverage ratio (%)<br><!-- End of picture text -->
# **2.5 Trade Policies and the Labor Market**

High-skill-intensive industries are strongly associated with lower average duty rates, while there is a weak negative relationship between the duty rate and the share of females working in industries. Looking at the correlation between the share of skill intensity proxied by the share of college graduates and the average duty rate within industries in green trade, it is observed that skill-intensive industries experience a lower average duty rate (Figure 9 A). However, the relationship between the share of female workers and the average duty rate within industries is slightly negative (Figure 9 B). Then, import liberalization in green trade could increase the share of high-skilled workers and, to a lesser extent, increase female labor market participation.

Higher-skill industries are associated with higher NTMs, while industries with higher shares of females are associated with lower NTMs. The share of college graduates in an industry is positively correlated with the number of NTMs applied to products produced by the industries they work for (Figure 10 A). This means that high-skilled workers tend to work more often in industries that produce heavily regulated products. In contrast, the share of female workers within industries has a weak negative relationship with the number of NTMs applied to products produced by the industries where they work (Figure 10 B). Since the correlations between the NTMs and labor markets are different from those between the tariffs and labor markets, it could be said that lowering NTMs and tariffs in general in green trade across the board could have differentiated effects across gender and education groups.

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<!-- Start of picture text --> Tt : °<br>2 Manufacture of moter vehicles, traders traders 29d pemi-trailers<br>u<br>_ bed Manufacture<br>? adherens Manufacture of rubber and of rubber and rubber and plaatcs products of textiles textiles<br>s ®<br>F ; e Manufacture of woodo woodoo and of products of wood and cori, of products of wood and cori, products of wood and cori, of wood and cori, wood and cori, and cori, cori,<br>+ @ Manufacture of other Manufacture of other of other other nonmetallic mineral procucts mineral procucts procucts<br>g 6<br>. ° e °<br>4 @<br>3 . @, Manufactureof Manufactureofof atectrical equipment@<br>2 ae : ea<br>i ° Manufacture of of computer, electronic and optical products products<br>3 n oP 2 G&G Sh Be He OUMHhCUMOMUMUCUM SSC OOUCUMOCUYUCUMS<br>‘© Average thare thare of females females within industry industry (2018-2020) *<br><!-- End of picture text --> <!-- Start of picture text --> “ Tt : °<br>Pr] * Manufacture of motor wehiches, traders and semi-trailers and semi-trailers semi-trailers 2 Manufacture of moter vehicles, traders traders 29d pemi-trailers<br>a2<br>u<br>u<br>Fr _ bed Manufacture<br>fa) 8 ? adherens Manufacture of rubber and of rubber and rubber and plaatcs products of textiles textiles<br>s ® (@ manutacture of rasder and plastics products rasder and plastics products and plastics products plastics products products s ®<br>j .?? 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# **3 Estimating the Effect of Trade Policies on Green Goods**

In this section, we estimate the ad valorem equivalents of NTMs to assess which NTMs impose significant costs on imports of green goods, then assess the relationship between the NTMs derived from that analysis on workers.

## **3.1 Ad Valorem Equivalents of NTMs**

### **Methods**

To analyze the costs of NTMs, we estimate the tariff ad-valorem equivalent (AVE). AVEs of non-tariff measures (NTMs) involve estimating the uniform tariff that will result in the same trade impacts on the import of a product due to the presence of the NTMs. The AVE of an NTM is often interpreted as measuring the distortion imposed by the NTM on the domestic economy (Montfaucon et al., 2023).

As a methodology, we follow Looi Kee et al. (2009) and Montfaucon et al. (2023) to estimate the ad-valorem tariff equivalents (AVE) of NTMs on green goods in the Philippines over the period 2014-2020. They are estimated by comparing the trade effect of NTMs to the one from tariffs. Specifically, it is theorized that the total effect of NTMs is a product of trade elasticity and AVEs. The following regression specification is then estimated using the Philippines import data as follows: Where: _lnV kt_ is the logarithm of import value of good _k_ in year _t_ ; _τkt_ is the tariff rate on good _k_ in year _t_ ;<sup>1</sup> _NTMnkt_ is the dummy where 1 is the indicator variable denoting the imposition of a specific

> 1 _τkt_ = _ln_ (1 + _tariffkt_ )

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NTM, affecting the good _k_ in year _t_ ; correspond to the types of NTMs. In each case all other NTMs in the data are controlled as well; _δt_ is the time-fixed effect that serves as a control for shocks to a given year; _εkt_ is the error term.

We control for aggregate country-year and product-year-level fixed effects when estimating product groups and cross-country estimations.

Then, we compute the AVE following this ratio: The AVE is defined as the ratio between the estimated coefficient of NTM and the estimated coefficient of ad-valorem tariff, both of which we already derived from Equation (3). This allows turning NTMs into “tariff units” since NTMs are regulatory text which are represented by a dummy variable. The AVE from Equation (4) is only feasible and calculated if the estimated coefficient _βn_ and _σ_ are statistically significant.

# **Discussion**

NTMs have a high cost in the Philippines on green goods imports. Among all the measures, 4 NTMs on Environmental Goods are remarkably high, including Conformity assessment related to technical barriers to trade not elsewhere specified (B89); Product registration/approval requirements (B81); Distribution and location of products after delivery (B853); and Technical barriers to trade regulations on transport and storage (B42). They impose significant costs and, in many cases, are higher than the same measures of non-Green Goods, as shown in Figure 11 (A). For example, while the tariff equivalent of Conformity assessment related to technical barriers to trade not elsewhere specified (B89) is estimated at 99.1% for green goods, it represents only 20% for other non-green goods.

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What share of green goods do these affect? Conformity assessment related to technical barriers to trade not elsewhere specified (B89) affects only a small share of green goods (2.4 percent value and 6.2 percent products), with no category being particularly affected. The highest share is water supply with 3 percent. Therefore, reforms in this measure is unlikely to have any significant impact. This is the case for the rest of the measures, suggesting they may be costly but not widely applied. However, Technical barriers to trade regulations on transport and storage (B42) affect just over 40 percent of both value and product share and is likely to have more impact if reviewed for possible reform.

In addition, considering the tariff equivalent on the types of green goods, Figure 11 (B) reveals that the relative cost of green goods types is also significantly higher in the Philippines compared to the other ASEAN countries. This is the case for the case of B42 and B85 (of which B853 is a subset), notably on products such as Cleaner or More Resource Efficient Technologies and Products and Environmentally Preferable Products based on End-Use or Disposal Characteristics. However, these estimates are done on only 12 NTMs with the highest coverage ratios for the PH (Figure 7 shows the top 12, this estimation drops the bottom 12 from this list) and then compared to ASEAN countries, mainly due to computational practicality. Nevertheless, this reveals that B42 is indeed an important measure whose underlying regulations may need to be reviewed when considering climate-friendly trade reform.

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<!-- Start of picture text --> Technical barriers to trade regulations on<br>transport and storage (B42)<br>Distribution and location of products after<br>delivery (8853)<br>Product registration/approval requirements<br>(B81)<br>Conformity assessment related to technical<br>barriers5 to trade not elsewhere speci" (889)<br>0 10 20 30 40 50 60 70 80 30 100<br>mOther mGreen Goods<br><!-- End of picture text --> <!-- Start of picture text --> 32<br>31<br>30<br>29<br>28<br>27<br>26<br>CEfficientJeaner Technologiesor More More RlResource and zEfficientleaner or TechnologiesMore More &Resource and | aProductsnvironmentally basedily onPreferable End-UseProverou<br>Technical. Productsbarriers. to trade | TraceabilityoeProducts requirements. orImport Disposallicence- Characteristics fees (F65)<br>regulationsstorage on transport(B42) and (B85)<br><!-- End of picture text -->
<!-- Start of picture text --> @ Energy Efficiency @ Heat and Energy Management mTM Renewable Energy<br>140<br>118.7<br>120<br>100 85.4<br>80<br>60<br>40<br>Oe 16.4 9<br>20 18. 15.9<br>52 4.5 8.6<br>0<br>Licensing for | Non-automatic Labelling Technical barriers Testing Traceability<br>specified use import-licensing requirements to trade requirements requirements not<br>(E112) procedures other (B31) regulations on (B82) elsewhere<br>than transport and specified (B859)<br>authorizations storage (B42)<br>covered unde<br>(E1)<br><!-- End of picture text -->
<!-- Start of picture text --> wp Air Pollution Control<br>@ Cleaner or More Resource Efficient Technologies and Products<br>w Environmentally Preferable Products based on End-Use or Disposal Characteristics<br>m= Management of Solid and Hazardous Waste and Recycling Systems<br>w Natural Resource Protection<br>33.15<br>21.8<br>48.12 19.68 18.45<br>11.26<br>8.86 9.83<br>Labelling requirements Technical barriers to trade Testing requirements (B82) Non-automaticimport- Traceability requirements<br>(B31) regulations on transport licensing procedures other not elsewhere specified<br>and storage (B42) than authorizations (B859)<br>covered unde (E1)<br><!-- End of picture text -->
<!-- Start of picture text --> 100.0<br>90.0<br>80.0<br>70.0<br>60.0<br>50.0<br>40.0<br>30.0<br>20.0<br>0.0 | —<br>Labelling Non-automatic Technical barriers Testing Traceability<br>requirements import-licensing to trade requirements requirements not<br>(B31) procedures other regulations on (B82) elsewhere<br>than transport and specified (B859)<br>authorizations storage (B42)<br>covered unde (E1)<br>TM Philippines || Average of Comparator Countries<br><!-- End of picture text -->
# **4 Could an Increase in Green Trade Affect Workers?**

There are multiple mechanisms for why green goods trade could affect labor markets differently. Sector-specific skills needs for the production process involving green goods are not evenly distributed across genders and skill groups. Green industries often require specialized skills and knowledge related to sustainability and environmental technologies. The availability of these skills and the preferences of female workers towards these sectors can influence the share of female workers within green industries. If women possess the necessary skills and are more inclined towards environmentally focused sectors, the share of female workers in these industries may be relatively higher. For example, a study by International Renewable Energy Agency shows that renewable energy employs about 32% women, compared to 22% in the energy sector overall (IRENA, 2019).<sup>2</sup> Moreover, the skill level required in the production of green goods could be higher. The 2022 LinkedIn Global Green Skill Report (LinkedInEconomicGraph, 2022) found that in 2021 there were only 62 women for every 100 men considered green talent — a number that has been stagnant since 2015.<sup>3</sup> The sorting pattern of workers into industries with a higher share of green goods exports can be influenced by social norms and stereotypes that shape career choices and the representation of different genders and skill levels. For instance, the perception of green industries as environmentally conscious and socially responsible may attract a specific pool of workers, such as skilled college graduates (OCDE, 2014).<sup>4</sup> The expansion of green goods exports has the potential to increase the demand for labor across all types of industries, and it can be particularly advantageous for previously underrepresented groups. As the demand for labor in the green goods sector grows, the likelihood of employment opportunities for these underrepresented groups also increases like any other

> 2https://www.irena.org/publications/2019/Jan/Renewable-Energy-A-Gender-Perspective

> 3https://economicgraph.linkedin.com/content/dam/me/economicgraph/en-us/global-green-skillsreport/global-green-skills-report-pdf/li-green-economy-report-2022.pdf

> 4https://www.oecd-ilibrary.org/sites/302d43f1-en/index.html?itemId=/content/component/302d43f1-en

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export (Rocha and Piermartini, 2023).<sup>5</sup> This shift in demand may create new job opportunities that were previously scarce for individuals who faced difficulties in finding employment. Furthermore, this shift in demand towards green goods may require more specific skills and tasks, which can open up new opportunities for individuals who may have struggled to find jobs in the past. The expanding green goods market not only affects specific industries, but also has broader implications for the overall labor market.

# **4.1 Methods**

In this study, we investigate the correlation between the share of green goods trade, the share of female workers, the share of high skilled workers, and annual earnings by linking customs-level trade data with the Philippines Labor Force Survey at the 2-digit industry level.

The first analysis focuses on understanding the relationship between green goods trade and the share of female workers and high-skilled workers across different industries as well as within industries (see Equation (5)).

_EmploymentSharejt_ = _μ_ + _σGGExpjt_ + _βGGImpjt_ + _γADjt_ + _δj_ + _εjt_ (5) The second set of regressions focuses on the annual earnings of individual _i_ working in industry _j_ at time _t_ (see Equation (6)).

_AnnualEarningsijt_ = _μ_ + _σGGExpjt_ + _βGGImpjt_ + _ψZit_ + _γADjt_ + _δj_ + _εjt_ (6) _EmploymentSharejt_ measures the proportion of female workers and high-skilled workers

> 5https://www.imf.org/en/Publications/fandd/issues/2023/06/trade-drives-gender-equality-anddevelopment-rocha-piermartini

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within 2-digit industry _j_ at time _t_ . _AnnualEarningsijt_ measures annual earnings of individual _i_ working in industry _j_ ’ at time _t_ . _GGExpjt_ and _GGImpjt_ measure the share of green goods export and import, respectively, in total exports and imports within 2-digit ISIC industries. _σj_ is the industry fixed effect; _μ_ is the constant term; _AD_ is the average duty; and finally, _εjt_ / _εijt_ is the error term.

To facilitate interpretation, these percentages are multiplied by 100. The calculations are based on the Labor Force Survey, incorporating appropriate weights. While incorporating additional control variables would enhance the precision of our estimation, we encountered limitations in doing so due to data constraints. Despite attempts to merge the data with the firm-level survey conducted by the Philippines Statistical Authority, the lack of identical sampling frames between the Labor Force Survey and the firm-level survey posed challenges in the merging and weighting procedures.

The annual earnings data is obtained from a cross-sectional Labor Force Survey. The richness of this database allows us to incorporate various observable characteristics in a Mincer-style regression, including gender, age, marital status, education, and industrial affiliation. However, due to the cross-sectional nature of the data, we are unable to control – for unobservable characteristics that may impact earnings, such as inherent talent which could introduce biases. The annual earnings variable is represented in natural logarithm form, while the green goods trade variables are expressed as shares out of 100. The gender variable assumes a value of zero for male workers and one for female workers. Education level is categorized into eight different groups, ranging from no schooling to incomplete elementary education, elementary graduate, incomplete high school, high school graduate, vocational education, incomplete college, and college graduate and above. The marital status variable is a dummy variable that takes a value of one if an individual is married and zero otherwise. Age is a self-explanatory variable that represents the age of the individual.

Estimating the impact of green goods trade on labor market outcomes is susceptible to endogeneity issues. Firstly, firms make decisions regarding both their trading activities 25
and labor market choices simultaneously, based on their specific needs. Secondly, technological advancements can influence both green goods trade and labor market outcomes. For instance, labor-saving and efficient green technologies may increase exports while simultaneously reducing labor demand and/or lowering wages. Consequently, the estimation process encounters endogeneity challenges. We have attempted to address these issues by exploring potential instrumental variables, but the first-stage F-statistics did not reach statistical significance. Furthermore, due to limitations in data availability, we were unable to control for essential factors such as land, capital, and other relevant variables, which could lead to omitted variable bias. As a result, it is important to interpret all regression results as correlations rather than causal relationships. Given the aforementioned limitations, it is crucial to exercise caution when interpreting the findings and avoid making causal claims. The estimated results should be viewed as indicative of potential associations rather than definitive causal effects.

# **4.2 Discussion**

This paper (Table 3) uncovers important insights regarding the relationship between green goods trade and the composition of the workforce, particularly the share of female workers and high-skilled workers. By accounting for industry fixed effects, we provide a comprehensive analysis of these relationships.

– On average, there is no correlation between gender composition and green goods trade but within-industry analysis conveys a different message. On average, we find no correlation between the share of green goods exports and imports, and the share of female workers as shown in column (1). However, when industry fixed effects are considered, a positive correlation emerges between the import of green goods within an industry and the share of females working in that industry (Column 2). A one unit change in the share of green goods import is associated with 0.28 units change in the share of females (both shares are measured out of 100). This indicates that green goods imports have the potential to enhance the gender 26
composition of specific industries, thereby reducing the gender gap.

Table 3: Relationship between the green goods trade, the share of females, and the share of high skilled workers in the Philippines

||**Share of**|**females**|**Share**|**of**<br>**high**|
|---|---|---|---|---|
||||**skilled**||
||(1)|(2)|(3)|(4)|
|Green Goods Export Share|-0.0018|-0.0976|-0.557***|-0.198*|
||(-0.02)|(-1.12)|(-5.75)|(-1.74)|
|Green Goods Import Share|0.0792|0.276**|-0.0638|0.199|
||(0.57)|(1.99)|(-0.62)|(1.47)|
|Industry FE|No|Yes|No|Yes|
|N|435|435|435|435|
|adj. R-sq|0.003|0.608|0.064|0.723|

_Note: Industry fixed effects are at two-digit ISIC level. T-statistics in parenthesis. Robust standard errors._ There is a negative correlation between High-Skilled Workers and Green Goods Exports. Our analysis reveals a negative correlation between the share of green goods exports and the share of high-skilled workers. A one unit change in the share of green goods export is associated with 0.56 units change in the share of high skilled workers. Importantly, this negative relationship persists even after accounting for industry fixed effects (Column 4) – albeit with a smaller magnitude (-0.20 units). This finding suggests that the lower complexity of green goods exported from the Philippines may explain this association. Surprisingly, there is no statistically significant correlation between green goods imports and the share of high-skilled workers, irrespective of the inclusion of industry fixed effects. This diverges 27
from the negative correlation observed in the descriptive analysis.

These findings highlight the potential role of green goods imports in promoting gender equality within specific industries. However, the negative relationship between green goods exports and the share of high-skilled workers necessitates further examination. Understanding the underlying factors driving these relationships is crucial for guiding effective policy decisions. It is important to note that these findings are specific to the context of the Philippines and may not directly apply to other countries. Moreover, the conclusions drawn are based on available data and require cautious interpretation as that correlation does not imply causation. Further research is necessary to deepen our understanding of the intricate dynamics between green goods trade and workforce composition.

On average, green goods imports are positively correlated with the earnings of workers across industries. A comprehensive examination of the relationship between green goods imports and workers’ earnings across industries reveals insightful findings as reported in Table 4. In Column (1), encompassing all workers, the results demonstrate a positive correlation. Specifically, a 10 percentage point increase in the share of green goods imports corresponds to a 0.1 percent increase in annual earnings. Expanding the analysis to focus on gender, Columns (2) and (3) shed light on male and female workers, respectively. The share of green goods imports exhibits a positive correlation with the earnings of both genders. Additionally, Columns (4) and (5) replicate the analysis for high-skilled and low-skilled workers. These groups also display a positive correlation, although the magnitude is relatively lower for high-skilled workers compared to other cohorts. Conversely, the export of green goods does not demonstrate a statistically significant relationship with workers’ annual earnings.

However, when incorporating industry fixed effects (Table 5), the previously observed positive correlations become non-significant. This finding contradicts the descriptive statistics presented, where independent analyses of import-earning and export-earning relationships suggest that green goods importing industries tend to offer better compensation compared to those with lower import levels. Nevertheless, an increase in green goods imports 28
within industries does not appear to be directly associated with earnings. While the results from the Table 5 do indicate some compositional changes favoring females and low-skilled workers, these changes are not strong enough to establish income-level correlations.

Table 4: Relationship between the green goods trade and earning across gender and skill levels (without industry fixed effects)

||All|Male|Female|High skilled|Low skilled|
|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|
|**Annual Earnings**||||||
|Green Goods Export Share|0.00149|0.00399|-0.00233|0.000207|0.00388|
||(0.64)|(1.66)|(-0.79)|(0.07)|(1.24)|
|Green Goods Import Share|0.0105***|0.00875***|0.0121***|0.00484**|0.0166***|
||(4.72)|(4.04)|(3.79)|(2.07)|(5.28)|
|Industry FE|No|No|No|No|No|
|Workers control|Yes|Yes|Yes|Yes|Yes|
|N|77544|52868|24676|15708|61836|
|adj. R-sq|0.365|0.360|0.388|0.036|0.123|

_Note: Results are based on repeated cross-sectional labor force survey data that are merged with industry level trade data. Industry fixed effects are at two-digit ISIC Rev 4 level. Worker’s control includes age, age squared, gender, marital status and 8 groups of educations in column (1). In column (2) and (3) gender is excluded while in column (4) and (5) education is not included as these regression are gender and skill based, respectively. Standard errors clustered at industry level. t-statistics in parentheses. p < .10, ** p < .05, *** p < .01_ 29
Table 5: Relationship between the green goods trade and earning across gender and skill levels (with industry fixed effects)

||All|Male|Female|High skilled|Low skilled|
|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|
||**A**|**nnual Earn**|**ings**|||
|Green Goods Export Share|0.000644|0.00007|0.00128|-0.00014|0.00154|
||(0.44)|(0.06)|(0.69)|(-0.16)|(0.73)|
|Green Goods Import Share|-0.00141|-0.0003|-0.003|0.00019|-0.00259|
||(-0.49)|(-0.19)|(-0.64)|(0.10)|(-0.70)|
|Industry FE|Yes|Yes|Yes|Yes|Yes|
|Workers control|Yes|Yes|Yes|Yes|Yes|
|N|77544|52868|24676|15708|61836|
|Adj. R2|0.441|0.444|0.456|0.267|0.255|

_Note: Results are based on repeated cross-sectional labor force survey data that are merged with industry level trade data. Industry fixed effects are at two-digit ISIC Rev 4 level. Worker’s control includes age, age squared, gender, marital status and 8 groups of educations in column (1). In column (2) and (3) gender is excluded while in column (4) and (5) education is not included as these regression are gender and skill based, respectively. Standard errors clustered at industry level. t-statistics in parentheses. p < .10, ** p < .05, *** p < .01_

# **5 Conclusion**

This paper examines the green goods trade landscape in the Philippines to understand its current level, its evolution over time, its situation compared to peer countries, and the predominant green industry sectors. Second, we estimate the ad valorem equivalent (AVE) of non-tariff measures (NTMs) on green goods to assess the cost of regulations on green goods imports and identify the most burdensome NTMs and candidates for regulatory climatefriendly trade policy reform. Finally, since reforms on NTMs will increase imports and 30
exports of green goods, we assess the relationship between the imports and exports of green goods in the labor markets. This is done using trade data and labor force survey data at the industry level for the Philippines.

The paper finds that the Philippines’ share of green goods trade is relatively low, partly due to the high incidence of NTMs on imports of green goods. The paper finds 5 measures out of about 90 as reform candidates, which are costly and have a higher incidence on energy transition and other types of green goods and technologies. These measures are Labelling requirements (B31), Non-automatic import-licensing procedures other than authorizations covered unde (E1), Technical barriers to trade regulations on transport and storage (B42), Testing requirements (B82) and Traceability requirements not elsewhere specified (B859). These measures are reform candidates, where the underlying regulations are recommended to be reviewed.

As these reforms would increase the imports and exports of green goods, especially since green goods imports are linked with higher green good exports, we also assess the possible effect on workers. We find that higher green goods exports negatively correlates with the share of high-skilled workers within industries. In contrast, green goods imports are associated with an increase in the share of female workers within industries. Green goods imports are positively correlated with workers’ earnings across industries but not within industries. Overall, NTM reforms likely lead to labor reallocations across industries. Since we do not address endogeneity issues, these results on labor do not necessarily reflect causality but provide insight into possible relationships and the need to address potential adverse effects on affected workers when reforming trade policy in line with environmental policies.

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