rafmacalaba/datause-encoder-data huggingface dataset:
clear rules on how to measure and verify carbon emissions and the implications for import restrictions that the WTO would then enforce. Otherwise, the imposition of carbon tariffs have the real possibility of leading the world to a debilitating escalation of trade disputes with the WTO Appellate Body determining legal standards with little “legislative” input from Member governments. This research makes clear that the current policies under consideration are fraught with serious problems without further guidance from Members.

# **References**

AB (2001) Appellate Body Report, European Communities—Measures Affecting Asbestos and Asbestos-Contain Products, WT/DS135/AB/R.

AB (1998) Appellate Body Report, United States—Import Prohibitions of Certain Shrimp and Shrimp Products, WT/DS58/AB/R.

AB (1996) Appellate Body Report, United States—Standards for Reformulated and Conventional Gasoline, WT/DS2/AB/R.

Bernard, A.B. and J. B. Jensen (2004). "Exporting and Productivity in the USA", _Oxford Review of Economic Policy_ . 20(3): 343-357.

Bhaghwati and Mavrodis (2007). “Is action against US exports for failure to sign Kyoto Protocol WTO-legal?” _World Trade Review._ 6(2): 299–310.

Bown, Chad P. (2005). "Global Antidumping Database, Version 1.0” World Bank Policy Research Paper No. 3737, October 2005 Bordoff, Jason (2009). “International Trade Law and the Economics of Climate Policy: Evaluating the Legality and Effectiveness of Proposals to Address Competitiveness and Leakage Concerns” in Brainard, Lael and Isaac Sorkin, Eds. Climate Change, Trade and Competitiveness: <u>Is A Collision Inevitable?</u> Washington DC: Brookings Trade Forum.

Brainard, Lael and Isaac Sorkin, Eds. (2009). Climate Change, Trade and Competitiveness: Is A <u>Collision Inevitable?</u> Washington DC: Brookings Trade Forum Frankel, Jeffrey (2009). “Address the Leakage/Competitiveness Issue in Climate Change Policy Proposals,” in Brainard, Lael and Isaac Sorkin, Eds. Climate Change, Trade and Competitiveness: <u>Is A Collision Inevitable?</u> Washington DC: Brookings Trade Forum.

Gros, Daniel and Christian Egenhofer (2009) “Climate Change and Trade: Taxing Carbon at the Border” Center for European Policy Studies.

Horn, Henrik and Petros Mavroidis (2010). “Border Carbon Adjustments and the WTO.” Mimeo.

Houser, Trevor, Rob Bradley, Britt Childs, Jacob Werksman, and Robert Heilmay (2009). <u>Leveling the Carbon Playing Field</u> Washington DC: Peterson Institute for International Economic Policy.

Hufbauer, Gary Clyde, Steve Charnovitz and Jisun Kim (2009). Global Warming and the World <u>Trading System, Washington DC: Peterson Institute for International Economic Policy.</u> Ismer, Roland and Karsten Neuhoff (2007). “Border tax adjustment: a feasible way to support stringent emission trading” _European Journal of Law and Economics_ . 24(2): 137-164.

Jensen, Michael (2009). “Leveling or Mining the Playing Field? Implementation Problems of Carbon-Motivated Border Adjustment Taxes,” World Bank PREM Notes, December 2009, No. 146.

21
22 8). Gabon has an NTM coverage ratio of over 50 percent, largely<sup>due to various environmental</sup> standard certifications required on tropical timber exports, while the<sup>Congo and Mauritania have</sup> coverage ratios that exceed 40 percent.'<sup>9</sup> However, only 7 of the 29 African countries have NTM coverage ratios that exceed the "all developing country" average<sup>(see the memo item).</sup> Are the types of nontariff measures facing African exports generally<sup>among those</sup> considered to be highly restrictive, or are they likely to be merely<sup>nuisances to trade?</sup> Table 8 provides relevant information by tabulating the OECD coverage of<sup>African exports by all</sup> NTMs and by specific types of measures (i.e., licensing requirements,<sup>MFA restrictions, other</sup> quotas and quantitative restrictions, etc.).'2 Although there are exceptions -- like the variable import levies on sugar from Mauritius, and MFA quotas on this<sup>country's textile and</sup> clothing exports -- Table 8 shows that most of the OECD measures facing<sup>African exports are</sup> types not thought to have important trade restrictive effects. Licensing requirements and various non-commercial regulations (largely eco-labeling requirements)<sup>predominate with the</sup> '<sup>9</sup> These certifications are required under the Washington Convention on International Trade<sup>in Species</sup> of Wild Flora and Fauna Threatened by Extinction. They must state that the<sup>exported product has been</sup> produced in accordance with established international environmental standards aimed at preservation<sup>of</sup> the species in question. See Varangis et. al. (1993) for a discussion of the rationale for,<sup>and effects of,</sup> these measures. These requirements, which almost certainly do not have a major restrictive<sup>Le.ct on</sup> trade, largely account for the Congo's relatively high NTB coverage ratio. Mauritania's<sup>exports of fish</sup> to Japan are subject to quotas and this accounts for the OECD coverage ratio of 48<sup>percent.</sup> 'Several points should be noted with regard to these tabulations. First, the UNCTAD<sup>inventory does</sup> not record a single instance where an African country was subject to a<sup>"voluntary" export restraint.</sup> These VERS are often among the most restrictive types of nontariff measures applied to exports<sup>(Laird</sup> and Yeats, 1991). Second, the sum of the trade coverage ratios for individual<sup>types of NTMs may</sup> exceed the combined coverage ratio for all measures. This is due to "stacking" or<sup>the multiple</sup> application of more than one nontariff measure on a single product. For example,<sup>US raw sugar imports</sup> are subject both to quotas and variable import levies.
Table 2 Estimates of Labor and Capital Adjustment Costs

||Estimates|||
|---|---|---|---|
||Estimate|Std. Error|Description|
|A) Capital Adjustment Costs||||
|Fixed Cost _γ_1|0.0379|(0.008)|SMM|
|Quadratic Cost _γ_2|0.178|(0.016)|SMM|
|Irreversibility Cost _γ_3|0.84|(0.057)|SMM|
|B) Labor Mobility Costs||||
|_Ce_|3.72|(0.48)|SMM|
|_Cu_|4.22|(0.26)|SMM|
|Utility parameters||||
|variance _ν_|2.63|(0.395)|CCP within SMM|
|_η_1|0||Normalized|
|_η_2|0.049|(0.233)|CCP within SMM|
|_η_3|0.411|(0.280)|CCP within SMM|
|_η_4|0.543|(0.286)|CCP within SMM|
|_η_5|0.255|(0.202)|CCP within SMM|
|_η_6|0.671|(0.348)|CCP within SMM|
|_η_0|0.232|(0.307)|CCP within SMM|
|C) Wage Process||||
|_a_1|0.027||Simulated|
|_ρw_|0.943||Simulated|
|_a_2|0.410||Simulated|
|_a_3|0.623||Simulated|
|_σ_<sup>_w_</sup>|0.017||Simulated|
||Moments|||
||Empirical|Simulated||
|_Corr_(_I, I−_1)|0.145|0.163||
|<br>_Corr_(_I, A_)|0.046|0.048||
|<br>_Negative spike_|0.014|0.004||
|_Corr_(_w −_<br>_w, N −_<br>_N_)|0.313|0.301||
|_Corr_(_N_0_, N_0_,−_1)|0.887|0.828||
|_Corr_(_N_0 _−N_0_,−_1_,_<br>_w_ _−_<br>_w−_1)|0.101|0.071||

Notes: table shows the capital adjustment cost and labor mobility cost parameters; utility function parameters; wage stochastic process parameters; and the empirical and simulated moments. Standard errors in parenthesis.

37
country level (such as used in the DBI) may actually be quite deceptive about _de facto_ policy impacts on the ground. _De jure_ rules may have little relationship with the incentives and constraints actually facing economic agents. Indeed, Hallward-Driewmer et al. find virtually no correlation in Africa between country-level policies and policy actions reported in micro enterprise data; the within-country variation in the latter exceeds the between-country variation in _de jure_ rules. This reflects the potential for idiosyncratic deals by firms to get around rules.

The (domestic and international) policy relevance of any composite index of development data is also questionable in the absence of any ―contextuality‖—the many conditions that define the relevant constraints on country performance. It is not credible that any one of these indices could be considered a sufficient statistic for country performance even with regard to the development outcome being measured. Very poor countries invariably fare poorly in the rankings by the various indices discussed above. However, these indices tell us nothing about how we should judge the performance of these countries, given the constraints they face. We may well rank them very differently if we took account of the country‘s stage of economic development. Such conditional comparisons raise their own concerns that need to be taken seriously, as discussed in Ravallion (2005). However, without greater effort to allow for the circumstances and history of a country it is not clear what we learn from the index. The greater use of benchmarking and time series comparisons will help here, though we also have to be aware of the fact that differing initial conditions at the country level can have lasting effects on a country‘s development path.

Policy applications also call for greater transparency about the tradeoffs built into the index. Consider a simple characterization of the problem of allocating public resources across a set of indicators that have been aggregated into a composite index. The policy maker has a set of policy instruments available for improving the index. Let us also assume that these policy instruments have known costs that can be mapped one-to-one to the underlying indicators. A policy maker deciding how best to improve the composite index by shifting resources between any two components should compare their MRS in the composite index with the relative marginal costs of the corresponding policy instruments. And the optimal allocation of a given 28
**<u>Table 2: Kenya Insurance Sector Ownership Shares, by Region (6 of 7)</u>**

|||<br>**Owner**|<br>**Income**<br>**(million**<br>**KSH**|<br>**Company**<br>**Market**||**Market Sh**|**COME**<br>**are by Region**|**(%)**|
|---|---|---|---|---|---|---|---|---|
|**Insurance Company**|**Shareholder(ISO Country Code)**|**ship %**|**2007)**|**Share**|**KE**|**GB**<br>**EU**|**EAC**<br>**SA**|**US**<br>**ROW**|
|Pacis Insurance CompanyLtd|||162|0.49%|0.49%||||
||Luna Registered Trustees(KE)|35.87|||||||
||Archdiocese Of Nairobi(KE)|32.56|||||||
||Association Of Sisterhoods(KE)|5.42|||||||
||Diocese Of Nakuru(KE)|4.65|||||||
||Religious Superior Confrence(KE)|2.34|||||||
||Diocese Of Muranga(KE)|2.20|||||||
||Diocese Of Ngong (KE)|2.09|||||||
||Diocese Of Kisii(KE)|**1.71**|||||||
||Diocese Of Isiolo(KE)|1.63|||||||
||Diocese Of Machakos(KE)|1.12|||||||
||Diocese Of Nyahururu(KE)|1.00|||||||
||Diocese Of Embu(KE)|0.90|||||||
||Diocese Of Garissa(KE)|1.00|||||||
||Diocese Of Marsabit(KE)|1.00|||||||
||Archiocese Of Kisumu(KE)|1.00|||||||
||Catholic UniversityOf East Africa(KE)|1.63|||||||
||Others(KE)|4.00|||||||
|Pioneer Life Assurance Company|||89|0.27%|0.27%||||
||Rose Waruinge(KE)|9.00|||||||
||Mtalaki Mwashimba(KE)|11.00|||||||
||James Olubayi(KE)|80.00|||||||
|Phoenix of East Africa Assurance|||525|1.60%|1.60%||||
||Transworld Investment Limited(KE)|77.87|||||||
||Kiruma International(KE)|8.93|||||||
||Bawan Limited(KE)|3.40|||||||
||Others(KE)|10.00|||||||
|Real Insurance Company|||746|2.27%|||||
||Mureka Investments(KE)|69.00|||1.57%||||
||Zaniki Holdings Ltd(KE)|15.00|||0.34%||||
||The Globe Insurance Company (UK)|15.00||||0.34%|||
||Kenya Farmers Association(KE)|1.00|||0.02%||||
|Standard Assurance Company|||522|1.59%|||||

101
using 2SLS. The results are reported in table 9. The 2SLS coefficient on tariff changes<sup>is</sup> -. 0014. Because coffee prices likely affect the exchange rate, we have also experimented<sup>with</sup> the interaction of coffee prices rather than exchange rates with presample tariffs as<sup>an</sup> instrument. This yielded the tariff coefficient of -. 002 (column 2 of table<sup>9).Thuswe continue</sup> to find that even after accounting for endogeneity of trade policy changes, tariff<sup>reductionsare</sup> associated with the increases in sector specific skill premiums.

In sum, our evidence suggests that sector specific skill premiums have increased proportionately more in industries that experienced larger tariff reductions. These<sup>sectorspecific</sup> wage increases are potentially associated with skill biased productivity<sup>improvementsin sectors</sup> that face more foreign competition. They provide an additional channel through which trade liberalization might have affected the growing skill premium.

# 5. **Conclusions**

This paper explores three channels through which trade liberalization<sup>might have</sup> contributed to the growing return to educated workers in Brazil during the 1<sup>980sand1 990s: the</sup> increase in skill premium due to Hecksher-Ohlin response to trade reforms, the increase in skill premium due to skill-biased technological change that was potentially associated<sup>withtrade</sup> liberalization, and changes in industry wage premiums.

We do not find much evidence that Hecksher-Ohlin type mechanisms<sup>havecontributedto</sup> the growing skill premium. While our results suggest that an increase<sup>in theindustry'simport</sup> penetration is associated with contraction in the industry's share of total manufacturing employment, we find no general relationship between tariff declines<sup>and contractionsin</sup> employment. Moreover, the structure of employment within manufacturing<sup>sector has not</sup> 27
all of these reform components have been phased in at the same time; certain components are yet to be fully implemented (e.g., enhanced community involvement in school councils).

# _Female Secondary School Stipend Component_

This component was first implemented in February of 2004. Fifteen of Punjab’s 34 districts were selected as program districts on the basis of average literacy rate of population 10 years and older as estimated from Population Census 1998 data. The cut-off literacy rate was 40 percent: 15 selected districts below the cut-off were stipend-eligible and the remaining 19 above the cut-off were not (Table 1 shows district level literacy rates). Randomization of stipend districts was not politically feasible, nor deemed to be equitable. Rather, transparent allocation of this component to the lowest-literacy districts was considered to be the least contentious way to introduce this new stipend program<sup>3</sup> .

Under the CCT program the girl receives a stipend conditional on her being enrolled in grade 6-8 in a government girl’s school<sup>4</sup> in a target district and conditional on her maintaining average class attendance of at least 80 percent. Eligible student receives Rs.200 per month<sup>5</sup> . For households which were going to send their daughters to middle/secondary school anyway, it is essentially an income transfer. Only for households with out-of-school daughters, is it an incentive payment. Funds are transferred directly to the student (student’s household) via postal money order from the Education District Office (EDO), which has a special account at the Provincial Program Monitoring and Implementation Unit (PMIU) of the Education Department. An important difference with the Bangladesh FSSS program is that females are eligible for the stipend _only if they attend public secondary schools_ . Indeed the secondary education sector has a dearth of private schools (unlike the primary education sector), however, linking the stipend program to only public schools highlights the initial public focus of the reform agenda. One of the long-run objectives of the stipend program targeted to females is to increase the supply of female teachers and other female service providers (e.g., health care staff), particularly in low literacy and relatively poorer regions of Punjab (most of the stipend districts fall in southern Punjab). Interestingly, Andrabi, Das, and Khwaja (2006), find that female graduates of public high schools

> 3 Implementation of the stipend program within the 15 low literacy districts could have been randomly phased in, however, there was no variation in program implementation within the target districts either. 4 All public secondary schools in Punjab are segregated by gender.

> 5 In dollar terms, this is little over $US 3 per month. Household survey data from Punjab province (PIHS 2001-02) estimates that per student out-of-pocket cost of middle school education in a public school averages Rs. 160 per month. Median household consumption expenditure from the same data is Rs. 5,900 per month.

4
**Table 8. Sensitivity analysis with respect to selected parameters.**<sup>**Italy**</sup>

|Root**([t)**|_at/a 4_|8p<br>_al/a_<br>**_5_**|8p/<br>6|
|---|---|---|---|
|-0.1701||-0.8116|-0.1134|
|-0.2559||-0.2131|-0.8756|
|-0.83||||
|-2.2392|-1.0005|||
|Root (t)|a<br>_a5 _|_a@l_<br>_/ a 10_|8p_/aW13_|
|-0.1701|0.077|0.4691||
|-0.2559|-0.0522|-0.3783||
|-0.83<br>-2.2392||-0.1663|0.0342|

**Table 9. Sensitivity analysis with respect to selected parameters. Japan**

|Root_(ii_|_)a1 /aa4_|_a1i/a5_|_4p/aC6_|_apl_**_a8_**|
|---|---|---|---|---|
|-0.2392|-0.3004||-0.5683||
|-0.3088|-0.7386||-0.3955||
|-0.5215||0.4214|||
|-0.5804||-1.4076<br>||0.4313|
|Root ( t)|a_/ a3_|_ai/ a,5 _|_a0/ a ,o_||
|0.2392|0.0658|0.2079|0.7199||
|-0.3088|0.0727|-0.2797|-0.7134||
|-0.5215|-0.5513||-1.3056||
|-0.5804|0.4128||1.2991||

30
47 al. 2007), however if the markets do not have a sufficient mix of items, such cash transfers would not be sufficient to provide access to nutrient-adequate diets. Household least-cost diets could be used to identify nutritionally vulnerable households such as those for whom even the lower cost individualized diet is not feasible or affordable, calculate benefits, or assess benefit adequacy in the context of the social protection scheme and for other public programs such as Malawi’s expanding Social Cash Transfer Program (Brugh et al. 2017). In the long-term, policy objectives could focus on making the shared diet feasible and to address the overall cost and its seasonal fluctuation.

The seasonality findings suggest that least-cost diet methods could be used to develop seasonally specific dietary recommendations for low-income consumers that could help smooth consumption and nutrient intakes throughout the year. Our model does not incorporate additional constraints that would be necessary to develop recommended diets such as palatability and diversity (Cost of Nutritious Diets Consortium 2018; Nykänen et al. 2018; Chastre et al. 2009; Frega et al. 2012; Baldi et al. 2013). That said, these findings suggest that doing so could be a useful approach to develop nutrition education to help consumers access high quality diets yearround.
28 improve and aggregate poverty fall is very little comfort to food farmers and urban workers in the north of the country, who probably compare their fortunes with Accra residents. Important clues as to why Ghanaians in the north did not benefit from growth are found in recent papers by Badiane and Shively (1998) and Abdulai (2000), which conclude that markets (more specifically the maize market) in the remoter Northern Region are not very well integrated with the economy at large. This lack of integration most likely impeded the transmission of the benefits of growth to the region.

**Figure 4: Ghana, incidence of consumption poverty by zone, 1992-1999**

<!-- Start of picture text --> 73<br>7 l<br>62<br>6('<br>*IqqIl9.2 6~2 62<br>o .<br>85X'8 1 = 43<br>A.~~~~~~~~~~~~~~~~~~~~~~3<br>L 26<br>20 i<br>10<br>4<br>A-. U~b. Cotj U.6, Fores V.6, Rink CoAtal Ib-a Forut Rw,al Sa,...h Gha<br>sn,vah<br><!-- End of picture text --> Source: Coulombe and McKay (2001) 'Remoteness' is also important in understanding geographical differences in poverty outcomes in Madagascar. Paternostro, Razafindravonona and Stifel (2001) disaggregate poverty according to an index of remoteness, the latter being a weighted sum of indicators reflecting access to roads, bus stop, agricultural extension services, modem fertilizers, and distance to schools and health facilities (the weights were derived from factor analysis). Their findings (Table 14) indicate an association between the degree of remoteness and the likelihood of being in poverty. They also show that while rural poverty indicators were largely unchanged during 1997 and 1999, households assessed to be the most remote, experienced increased poverty-in contrast to the least remote quintile where poverty indicators actually improved.
Inequality, Economic Growth and Nonrenewable Resources Table 9: Estimated Results from the Non-Recursive Model Specification (income-group heterogeneity)

||**IV Fixe**<br>|**d Effect**<br>|**3S**<br>|**LS**<br>|
|---|---|---|---|---|
||**Gini index**|**GDPc**|**Gini index**|**GDPc**|
|Nonrenewable resource dependence|-0.185***|-0.008|-0.173***|-0.007|
|Lower income|dropped|dropped|5.637**|49.933***|
|Lower income* Nonrenewable resource<br>dependence|0.100**|-0.638***|0.070|-0.640***|
|High-low education ratio|-0.000|-|-0.440*|-|
|High-low education ratio square|0.006|-|0.014**|-|
|Consumer price index (2005)|0.004|-|0.012**|-|
|Services value added share|0.072|-|0.063|-|
|Manufacturing value added share|0.047|-|0.086|-|
|Agriculture value added share|-0.123**|-|-0.123**|-|
|GDPc|-0.046|-|-0.032|-|
|Log Labor Force|-|-15.487***|-|-15.901***|
|Log gross fixed capital formation|-|7.880***|-|7.164***|
|Log exports of goods and services|-|3.043**|-|3.765***|
|Log REER|-|2.477|-|2.754|
|World oil price index|-|0.069***|-|0.064***|
|World metal price index|-|0.004|-|0.009|
|Gini index|-|-0.616|-|-0.616|
|Observations|873|873|873|873|
|R2|0.119|0.445|0.911|0.863|

Note: * ** and *** denote significance at the 10% 5% and 1% levels, respectively.

Accounting for the heterogeneity between country income groups yields a very different picture than that shown in the baseline models. From a methodological perspective, this heterogeneity affirms the importance of considering structural differences between countries at different stages of development. From a macroeconomic perspective, it lends credibility to the idea that the “resource curse” is a function of country-level structural characteristics, such as the management of natural resources rents, the sophistication of public financial management systems and the impartiality of policy processes.

# 4.3 Analyzing a Subsample of Resource-Rich Countries

The model presented below focuses on a subset of countries defined as nonrenewable resource rich according to the IMF’s 2013 Resource Governance Index Report.<sup>16</sup> The full sample included countries in which nonrenewable natural resources played a relatively modest economic role. Excluding these countries can shed light on the possibility that the empirical associations between resource dependence and the Gini index and GDP per capita are affected by the relative economic importance of natural resources. Focusing on a subsample of resource-rich countries also tests the robustness of the estimated relationships by reducing the original sample by one-third<sup>17</sup> . Table 10 summarizes the subsample’s composition by income group of the resulting restricted panel.

> 16 Available at <u>http://www.resourcegovernance.org/resource-governance-index/report</u>

> 17 Based on the IMF classification of resource-rich countries (see note 16), thirteen countries are dropped from our original dataset: Argentina, Bulgaria, France, Greece, Ireland, Italy, Japan, Republic of Korea, New Zealand, Spain, Thailand, Tunisia, and Turkey. The resulting sub-group thus comprises the remaining

> 30 countries.

13