rafmacalaba/datause-encoder-data huggingface dataset:
degree of escalation declined in all countries studied, tariffs for finished manufactured products continued to be higher than those for semi-manufactures in developed countries except for the US, Jaran and Australia (OECD, 1997). Furthermore, various studies of specific product chains in various developed countries show continued tariff escalation in such products as processed foods (wheat flour, orange juice, vegetable oils, dairy products), clothing, leather and wood products (Lindbland, 1997; WTC, 1998, U." `TAD/WTO,1997). These results should be interpreted with caution because of data limitations as well as the continued existence of non-tariff measures in some of these product chains which make calculation of the effective rates of protection difficult. But they all point to the conclusion that tariff escalation - much as tariff peaks - in certain products, though reduced by the URA, continues to be an area of concern related to market access for developing country exports.

# B. Non-Tariff Measures

In the aftermath of the URA, it is probably fair to say that the pervasiveness of core non-tariff measures in developed country trade regimes is at its lowest point in more than 50 years. By core nontariff measures, we mean, as in the case of developing countries, the use of non-automatic I censing, quotas and tariff quotas and voluntary export restraints as well as price control measures such as variable charges, minimunm prices or voluntary export price restraints.

Following the URA, non-tariff measures have been drastically reduced in agriculture, wniere only tariff quotas exist in a number of products in some of the major developed markets. At the samrnl time, as a result of the URA, non-automatic licensing has been reduced in all major developed country markets, and is now focusing primarily on restraints linked to the maintenance of sanitary and phytosanitary standards, technical standards or protection for the environment.<sup>2</sup> ' Finally, as part of the URA, voluntary

> 21 Some concerns however, have been raised as to whether the implementati **o** f WTO agreements in n these areas has been motivated by protection of domestic industries and a number of disputes have arisen regarding the compatibilit) of certain measures implemented by some countries with these agreements.

50
Notes: Data pooled for all four follow-up surveys. Specifications include strata dummies, a variable representing the initial outcome at baseline, a variable indicating missing data at baseline, and dummies for survey round. Panel B includes a dummy for “female”. The Z score index is constructed following Kling, Liebman, and Katz (2007). Adjustments to control false discovery rate (FDR) computed following Benjamin and Hochberg (1995). p-values and q-values are reported below standard errors ( _q-values_ in italics). Clustered standard errors by firms in parentheses. *, ** and *** denote significant at the 10%, 5%, and 1% levels, respectively.

35
-28 -

# Table 3 summarizes the health effects that have been quantfied for SO..

Table 3: Effects **of** 10 pglm<sup>3</sup> Change in SO1 Concentrations

|Sulphur Dioxide|Cental<br>Estimate|High'<br>Estimnate|
|---|---|---|
|Mortality (percent change)|0.48|1.21|
|Respiratury Syinptomss|||
|1,000 child/year|0.18|.26|
|Chest Discomfortfadultlyear|0.10|.15|

#A high (low) esumate is obtained by increaing (decreasing) the coefficient by one estimated derivation-

# 3. Ozone

Ozone is the primary component of photochemical smog. As such, it has been assodated with several adverse respiratory outcomes including incrmeased upper and lower respiratory symptoms, eye irritation (oxidants), restrictions in activity, and exacerbation of asthma. Most of the evidence of the effects of ozone is derived from clinical studies in which subjects are exposed to a known amount of ozone in a controlled setting. For example, healthy individuals may be exposed to moderate levels of ozone in a chamber while engaging in moderate exercise. Unfortunately, these studies usually focus on changes in lung fimction and less so on increases in symptoms. Also, it is difficult to develop doseresponse functions from some of these studies or extrapolate from their findings to the fiee-living population. However, severl epidemiologic studies are available and provide a basis for dose-response estimates.
ECOWAS‘S INFRASTRUCTURE: A REGIONAL PERSPECTIVE

**Figure 2.3  Comparative costs of exporting goods by road and rail where both exist**

<!-- Start of picture text --> 350<br>300<br>250<br>200<br>150<br>100<br>50<br>0<br>road rail road rail<br>Abidjan-Ouagadougou Dakar-Bamako<br>Port Transport Border Administrative<br>landlocked capital(US$ per tonne)<br>Costs of mvoing imports from ports to<br><!-- End of picture text --> Source: Data collected from World Bank 2008, West Africa Trade Hub data, World Bank project documents, and AICD ports and railways database.

Prolonged dwell times at ports, delays in crossing borders, and customs clearance processes add significant amounts of time to moving imports along regional corridors from ports. There is significant variation across corridors in port efficiency, border crossing delays, and time required for customs, clearance, and technical control. The time required to move imports from ports to a landlocked country along the corridors takes anywhere from 400 hours (20 days) to 700 hours (48 days), close to double the times observed in southern Africa (figure 2.4a).

How that time is spent varies. Owing to long distances from landlocked capitals to ports, transport times contribute the lion‘s share to the total for importing freight, followed by expensive administrative processes (figure 2.4b)<sup>1</sup> . Other factors vary as well. For example, the Port of Lomé is far more efficient than the Port of Cotonou or Tema. Abidjan also records shorter delays at ports, giving the port a comparative advantage over its competitor ports (Teme Dakar and Lomé). Crossing West African borders has been described as a time-consuming ordeal. Previous estimates suggested that crossing Ghanaian borders took more than 20 hours (Teravaninthorn and Raballand 2008). Recent estimates for crossing the border between Ghana and Burkina Faso estimate the border-related delays to be between 6 and 8 hours (West Africa Trade Hub 2010a).. Customs processes, depending on final destination, are more expensive and often more time-consuming in West Africa than in southern Africa and parts of eastern Africa. In Niger, customs clearance processes takes up to 11 days.

> 1 The port sector appears to have improved significantly since 2006/07, when the data used for this analysis were collected.

23
# **Table B3.    Average ad valorem equivalent of the specific collected tariff rates by industry tariff rates by industry** <u>a</u>

||**_20_**|**_01_**|**_20_**|**_02_**|**_Ye_**<br>**_20_**|**_ar_**<br>**_03_**|**_20_**|**_04_**|**_20_**|**_05_**|
|---|---|---|---|---|---|---|---|---|---|---|
|**_Industry_**|_Un-_<br>_weighted_|_Trade_<br>_weighted_|_Un-_<br>_weighted_|_Trade_<br>_weighted_|_Un-_<br>_weighted_|_Trade_<br>_weighted_|_Un-_<br>_weighted_|_Trade_<br>_weighted_|_Un-_<br>_weighted_|_Trade_<br>_weighted_|
|_Non-ferrous_|||2.7|2.7|15.9|15.5|5.7|16.0|4.6|15.3|
|_metallurgy_|||_(8.6)_|_(0.0)_|_(5.9)_|_(6.0)_|_(6.7)_|_(4.2)_||_(7.3)_|
|_Chemicals and_<br>_etrochemicals_|20.4|15.0|34.6|24.6|37.2|25.0|32.4|20.2|29.6|21.4|
|_p_|_(26.2)_|_(13.5)_|_(39.2)_|_(29.9)_|_(39.1)_|_(23.3)_|_(43.5)_|_(16.7)_|_(38.8)_|_(11.2)_|
|_Mechanical_<br>_engineering and_|23.5|21.7|43.0|27.5|52.0|24.6|42.3|26.4|38.6|27.9|
|_metal-working_|_(29.6)_|_(18.0)_|_(70.9)_|_(40.9)_|_(111.4)_|_(25.6)_|_(63.4)_|_(16.0)_|_(58.7)_|_(17.5)_|
|_Timber, wood,_|||||||||||
|_pulp_|20.1|19.5|24.7|25.0|29.2|29.1|29.3|28.0|26.4|26.9|
|_and paper_|_(10.5)_|_(5.5)_|_(14.0)_|_(5.8)_|_(15.0)_|_(10.3)_|_(15.3)_|_(10.6)_|_(12.7)_|_(9.8)_|
|_Construction_<br>_materials_|16.4|16.3|20.3|18.8|19.1|22.8|16.1|21.7|17.0|22.5|
||_(9.3)_|_(3.8)_|_(17.0)_|_(3.5)_|_(13.3)_|_(5.8)_|_(6.7)_|_(4.1)_|_(6.7)_|_(3.6)_|
|_Light industry_|15.6|13.0|19.7|20.2|23.8|23.6|17.2|18.5|14.8|16.2|
||_(31.3)_|_(12.4)_|_(22.4)_|_(15.4)_|_(25.5)_|_(13.5)_|_(13.9)_|_(8.9)_|_(8.7)_|_(6.9)_|
|_Food industry_|17.9|19.3|17.6|26.5|21.2|37.7|22.2|33.2|22.1|31.9|
||_(15.2)_|_(9.7)_|_(21.6)_|_(17.8)_|_(24.0)_|_(35.5)_|_(27.3)_|_(39.8)_|_(28.2)_|_(31.6)_|
|_Other industries_|31.4|34.0|46.3|41.4|44.5|46.4|32.5|34.0|31.5|31.9|
||_(9.8)_|_(6.4)_|_(16.0)_|_(10.4)_|_(8.6)_|_(7.3)_|_(20.7)_|_(7.5)_|_(19.0)_|_(7.5)_|
|_Agriculture and_<br>_forestry_|31.2|51.8|20.8|17.2|22.9|19.4|20.3|17.3|18.9|15.4|
||_(28.1)_|_(14.6)_|_(12.9)_|_(12.4)_|_(14.2)_|_(13.8)_|_(12.1)_|_(11.0)_|_(13.2)_|_(10.6)_|
|_Other goods-_|||||||||||
|_producing_|24.4|109.2|23.5|78.8|23.5|65.0|20.7|50.6|15.5|29.8|
|_sectors_|_(48.8)_|_(39.2)_|_(37.3)_|_(37.8)_|_(42.1)_|_(51.4)_|_(27.9)_|_(32.6)_|_(19.1)_|_(23.1)_|

_Note:_ _a Ad valorem equivalent of the specific tariff is_ **_(specific taiff rate*100)/(unit value)_** _. Summary statistics is calculated for non-zero specific tariffs only. b Standard deviations in parentheses_ 44
- **_Umbrella groups, networks, and coalitions_** ‐these involve alliances between groups with common interests or activities. Often they are driven by key individuals within community organizations and NGOs or by external actors such as international NGOs, FBOs, or donors. Coalitions tend to focus on advocacy while networks tend to focus on information sharing, coordination, and support to member organizations, although the distinction is not always clear cut.   Additionally, networks are increasingly involved in the disbursement of funds to support community‐level initiatives.

- **_Formal partnerships_** ‐these include formal agreements between community organizations and government or other organizations for the delivery of specific services. Formal service‐delivery agreements are mostly initiated by external actors including government agencies, international NGOs, and donors. There agreements may be the result of proposals from CBOs approved for funding.

- **_Referral relationships_** ‐community organizations may have links with services without any formal partnership agreement, for example, with facilities that provide HIV counseling and testing or with education or social welfare services. Such links are initiated both by community organizations and by external actors.

- **_Coordination mechanisms_** ‐these include local AIDS councils or forums. Coordination mechanisms are usually established by government, although there are examples of mechanisms initiated by NGO umbrella groups, coalitions, and networks.

- **_Informal collaboration_** ‐many community organizations have informal links with other organizations on an _ad hoc_ basis.

# **Box 9: Partnerships and collaboration in South Africa**

A community audit in South Africa (Birdsall and Kelly 2005a) found that 88% of CBOs and NGOs and 50% of FBOs network collaborated with other organizations, but few had formal partnerships. The Departments of Health and Social Development use CBOs and NGOs as service‐delivery partners; service‐level agreements are one of the clearest examples of formal partnerships. Another interface between community organizations and government is local planning and coordination of the AIDS response, although formal coordination is limited in the communities surveyed.

Partnerships and collaboration can be internally or externally driven. (See boxes 8 and 9.) There is little in the literature concerning factors that enhance or limit partnerships. Birdsall and Kelly (2005b) identified competition between community organizations for funding, clients, territory, and external support for new organizations as factors that work against partnerships and 22
<mark>We apply the synthetic control method methodology on GDP per capita, poverty headcounts and a set of proxy variables for elements of multi-dimensional poverty indicators to measure if and by how much they are affected by sovereign defaults.</mark> <mark>We find that, on average, within three years of a sovereign default, the affected economies have fallen behind by a cumulative 8 percent of GDP p.c. relative to the synthetic counterfactual. Whereas growth slows down the year prior to default, a large part of the effect is driven by GDP p.c. growth of the defaulting country falling 3.6 (2.4) percentage-points behind the counterfactual in the year of default (one year after default). On average, sovereigns recover to the pre-default GDP p.c. peak four years after default.</mark> _<mark>Recovery</mark>_ <mark>defined in this manner, however, does not preclude serious scarring: Over the decade post default, the structural effect on growth leads to a deficit of around 1.5% per year. After one decade, defaulters’ economic output per capita is nearly 17% or one-sixth below the output reached by the non-defaulting counterfactual. We also show that the costs are larger for longer default episodes. To render a comparison with existing findings in the literature easier, we apply our methodology to the same sample used in leading prior works (namely Borenzstein and Panizza 2009). Allowing for time-variant effects leads to significantly larger and more long-lasting effects of default on GDP growth.</mark> <mark>Still, output has been criticized as a measure of welfare because, among other things, it can ignore the distribution of income (See e.g. Fleurbaey 2009; Stiglitz, Durand & Fitoussi 2018;</mark> <u><mark>Aitken 2020). This paper offers initial elucidation on</mark></u> _<mark>the second question</mark>_ <mark>by continuing the evaluation of the costs of default beyond GDP per capita. The first variable we examine in this subsection is thus poverty headcount ratios indexed to 100 in the year prior to default.</mark><sup>8</sup> <mark>We find that these exceed their pre-crisis levels by 30 percent, and the counterfactual’s level by a staggering 70 percent one year after the default. The gap between the defaulter and its control’s indexed poverty headcount level remains wide even a decade after default.</mark> <mark>But poverty headcounts are not without caveats, the literature on multidimensional poverty (e.g. Alkire 2015,</mark> <u><mark>Ravallion 2011), which could be traced to A.K. Sen’s 1976 seminal paper,</mark></u> <mark>highlights multiple issues with them, such as their inability to measure the intensity of poverty</mark>

> 8 <mark>Unfortunately, the best data set available on poverty, PIP data from the World Bank, has a short history and most individual observations are based on imputed values. While these data contain survey observations, these are sporadic and cover a short period. Therefore, for this part of the analysis we are limited to the use of PIP data since 1988 and to rely on many observations which are imputed from growth in that data set.</mark>

5
example, Gerlagh et al. 2009; Baker and Shittu 2006; Gerlagh and Lise 2005) or general equilibrium models (for example, Grimaud and Lafforgue 2008). This extends more general research on induced technological change in the environmental sector (Goulder and Mathai 2000; Nordhaus 2002; Buonanno 2003; Sue Wing 2003; Popp 2004).

The effect of carbon pricing on economic outcomes through technological change induced by carbon pricing and how this technological change interacts with workers’ skills has received little attention in the literature so far, but it is potentially an important pathway for two reasons. First, skills have been shown to be a critical determinant of innovation and a potential enabling factor for technological change in general (see OECD 2011 for a review). A number of studies at the firm level, for example, have found that having higher human capital increases the likelihood that a firm will change production practices to reduce emissions and comply with environmental protection regulation (Blackman and Kildegaard 2010; Dasgupta et al. 2000; Gangadharan 2006; Lan and Munro 2013; Pargal and Wheeler 2006). Second, technological change has been characterized as skill-biased by a large body of literature (for example, Katz and Murphy 1992; Autor, Katz and Kearney 2008), and given this trend it is a reasonable hypothesis that technological change induced by carbon pricing is also skill-biased. Yao et al. (2020), for example, find that higher education is associated with reductions in emissions at the country level; though, the effect of higher education through technological change cannot be distinguished from changes in attitudes or beliefs and subsequently environmental policies.

If carbon pricing induces technological change that increases the productivity of higher skill workers relative to lower skilled workers or, to put it differently, if carbon pricing requires innovation that in turn increases demand for higher skilled workers relative to lower skilled workers, then carbon pricing has an economic impact through increased wage inequality. The elasticity of skill supply in the economy may act as mitigating factor both for the impact of carbon pricing on emissions reduction and on economic outcomes including wage inequality and output. This argument is parallel to skills and automation: if automation complements higher skilled workers’ productivity, then the elasticity of skill supply, for example through the quality of education, enhances the economic benefits and mitigates costs of automation (Bentaouet Kattan, Macdonald and Patrinos 2020).

4
10 years later, it was almost 50 percent. The slow pace of economic recovery, and increasing protectionism in advanced economies, has prompted Asian investors to turn towards investment opportunities within the region. This growing intra-Asian FDI is an encouraging sign for the region as the global economy heads towards a historic slowdown due to the pandemic.

Given these trends, we look more closely at FDI trends in Asia. The surge in FDI to the region has been linked to the expansion of GVCs mainly in the manufacturing sector. It was driven by multinationals, notably from Japan and the Republic of Korea, relocating downstream parts of the production process in search of lower labor costs mainly through greenfield investments. Greenfield investments which entail building new assets--as opposed to merger and acquisitions ( M&As) which refers to taking over existing assets--has been the dominant mode of entry for FDI in the region especially in the manufacturing sector and for emerging Asian economies. Empirical evidence suggests that greenfield FDI is more GVC-linked compared to M&As which are relatively more market seeking (ADB 2016). Therefore, developing countries tend to encourage and receive more greenfield FDI often through industrial policy instruments such as special economic zones.

Part of the reason that developing countries favor greenfield FDI is also because of its role as a substantial source of job creation. Between 2003 and 2018, greenfield investments created 29.5 million jobs globally, 44 percent of which were in Asia. Manufacturing remains dominant in terms of both number of greenfield jobs and jobs created per project. The share of manufacturing in total jobs created is almost 70 percent, with an average 283 jobs created per project notably in plastic, semiconductors, and automotive OEM. The share of services in total jobs created is just over 28 percent (106 jobs per project) with FDI in the primary sector playing a negligible role in job creation. South Asia, notably India, is a large recipient of services-sector FDI.

As figure 3 illustrates, the manufacturing sector has historically received the largest share of greenfield investments in Asia mainly in real estate; coal, oil, and natural gas; alternative/renewable energy; chemicals and automotive OEMs. However, after a sharp rise in 2018, greenfield FDI to the region fell sharply in 2019 to $230 billion from $323 billion the previous year. The decline was driven by a 50 percent reduction in investments in the manufacturing sectors most pronounced in chemicals and chemical products; computer, electronic, optical products and electrical equipment; and automotive OEM (figure 3). Consequently, the share of manufacturing fell to 44 percent, the lowest since fDi Markets data became available in
quantities reported are not identical for all products. All in all, one of 12 different quantity measures can be applied.<sup>25</sup> Since country specific effects might also influence the result, we further use fixed effects for importing and exporting countries ( _ωi_ and _ιj_ ). Finally, _εij_ denotes a Gaussian white noise error term.<sup>26</sup>

**Table 4:** **_Quality and Trade Costs Effects in Intra-EU Trade_**

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
|G.C. Dist.|0.112***||0.080***||0.075***|
||(0.017)||(0.019)||(0.021)|
|Wei. Dist.||0.136***||0.103***||
|||(0.020)||(0.033)||
|Adjacency|||-0.063***|-0.045|-0.069***|
||||(0.020)|(0.030)|(0.022)|
|Com. Lang.|||||-0.008|
||||||(0.032)|
|Com. Curr.|||||0.029|
||||||(0.026)|
|Obs.|857747|857747|857747|857747|857747|
|R<sup>2</sup>|0.540|0.540|0.540|0.540|0.540|

Notes: All regressions are run with importing and exporting country fixed effects as well as product fixed effects, but for which the coefficients are not reported; G.C. Dist. and Wei. Dist. stand for log of the great-circle distance and the log of weighted distance, respectively; ***,**,* denotes significance at the 1 %, 5 %, and 10 % level, respectively; robust standard errors, which are in parentheses, are corrected for clustering by country pair.

.

Column (1) of Table 4 documents the regression results when all 857747 trade flows in all goods between EU 15. The regression yields a highly significant and positive distance coefficient which means that the unit value of exported products increases with trade costs.<sup>27</sup> This result holds true whether we approximate trade costs with weighted distance or use the unweighted distance measure.

25 Area in square meters, electrical energy in thousands of kilowatt-hours, length in meters, number of items, number of pairs, volume in liters, weight in kilograms, thousands of items, number of packages, dozens of items, volume in cubic meters, weight in carats.

26 Another way to estimate the effect of trade costs on distance is to work with deviations from the mean, like in Hummels and Skiba (2004). If the Alchian-Allen effect holds, then the distance deviation from the mean of each product group should be positive. It would indicate that quality and trade costs are positively correlated. The primary concern of Hummels and Skiba (2004) is to show how freight costs and prices are related. They find that the elasticity of freight rates with respect to price is less then unity which means that when prices double the trade costs less than double. They conclude that the iceberg assumption of shipping costs is therefore not correct. Furthermore, their results indicate that the unit values of trade flows increase with freight costs by 80-141 percent. These findings clearly confirm the Alchian-Allen effect. Hummels and Skiba (2004) also include tariffs in their estimation. Since in intra-European trade no tariffs apply, they cannot be included in our estimation. 27 The R-squared reported do not correspond to ordinary OLS R2, since we estimate a fixed-effect model.

27