rafmacalaba/datause-encoder-data huggingface dataset:
approach. This estimated elasticity is slightly higher than in the agriculture sector but is much below the coefficient for the manufacturing sector. As in the case of agriculture, the coefficient becomes insignificant and substantially decreases in value once the high-income countries are excluded from the estimations, as shown in Table 3, column 6.

Table 3. Regression Results for Services Value Added.

||<br>|All Countries<br>||All Non<br>|-high-income<br>|Countries<br>|
|---|---|---|---|---|---|---|
||(1)<br>FE|(2)<br>Diff GMM|(3)<br>Sys GMM|(4)<br>FE|(5)<br>Diff GMM|(6)<br>Sys GMM|
|First Lag of Log|0.935***|0.620***|0.975***|0.887***|0.498***|1.009***|
|Services GVA|(90.03)|(4.06)|(41.03)|(61.31)|(2.92)|(67.17)|
|Log Services Electricity|0.044***|0.148*|0.029***|0.045***|0.085|0.004|
|Consumption|(7.95)|(1.87)|(2.66)|(6.42)|(0.97)|(0.58)|
|Observations|1802|1676|1802|1057|979|1057|
|Countries|121|117|121|73|69|73|
|Hansen-p||0.045|0.199||0.125|0.993|
|AR2-p||0.784|0.913||0.256|0.898|
|AR3-p||0.653|0.603||0.965|0.288|

This table shows the regression results of six regressions: The fixed-effects, difference GMM, and system GMM for all countries and non-high-income countries. The dependent variable is the log of contemporary Services GVA. The independent variable of interest is the log of contemporary sector electricity consumption. t statistics are in parentheses and stars indicate significance: * p < 0.10, ** p < 0.05, *** p < 0.01. We exclude observations (years) with over 100% absolute change in electricity consumption or GVA growth year-on-year. The bottom of the table shows the number of observations (years times number of countries), the number of countries in the panel, the Hansen-p value, and the p-values of the tests of second- and third-order autocorrelation. We instrument for the lag of sector value added using the third and fourth internal lag, and for contemporary electricity consumption using the third and fourth internal lag in the Difference GMM and System GMM regressions. Included control variables are share of imports and exports in GVA, share of services in GVA, and the log of capital and labor.

In the case of services, the data clearly show substantial differences in sectoral production, as the composition of services varies across different income groups. Figure 4 demonstrates shares of various sub-industries in services for high- and non-high-income countries. For example, trade that generally requires low-skill manual labor, especially in developing countries, accounts for 45 percent of total services’ value added in non-high-income countries. By contrast, the trade share comprises less than half of that value in high-income countries at the expense of other more capital- and electricity-intensive subsectors such as business services. Thus, structural differences across various income groups can play an important role in production technologies in different sectors, altering the impact of electricity consumption on value added.

17
<!-- Start of picture text --> “><br>le Pa 1.0<br>: 08<br>=<br>B 06<br>(Ul ge ©<br>> a 2 04<br>. y at<br>J ” ie => 0.2<br>00<br>eeit ae -3.2-22-120208 18 28<br>fF ag ltl s<br>. , » SD<br><!-- End of picture text --> <!-- Start of picture text --> ‘i<br>‘<br>06<br>7 Fo<br>303<br>:<br>Jaan<br>10 20 30 40<br>: TH:Cyclones<br><!-- End of picture text -->
# **Table of Contents**

|1<br>Introduction.......................................................................................|1|
|---|---|
|2<br>Countries and Data Included in the Global Antidumping Database......................|7|
|2.1 The “Master” Spreadsheet for Each Country – AD-CTY-Master..........|9|
|2.2 The “Product” Spreadsheets – AD-CTY-Products...........................|11|
|2.3 The “Domestic Firms” Spreadsheets – ADT-CTY-Domestic-Firms......|12|
|2.4 The “Foreign Firms” Spreadsheets – ADT-CTY-Foreign-Firms...........|12|
|3<br>More Country-Specific Information on AD Sources and Data...........................|13|
|3.1 Argentina (ARG).................................................................|15|
|3.2 Australia (AUS)..................................................................|16|
|3.3 Brazil (BRA)......................................................................|18|
|3.4 Canada (CAN)....................................................................|19|
|3.5 Colombia (COL).................................................................|21|
|3.6 European Union (EUN).........................................................|23|
|3.7 India (IND).......................................................................|25|
|3.8 South Korea (KOR).............................................................|26|
|3.9 Mexico (MEX)...................................................................|27|
|3.10 New Zealand (NZL)............................................................|29|
|3.11 Peru (PER).......................................................................|30|
|3.12 Turkey (TUR)...................................................................|31|
|3.13 United States (USA)............................................................|32|
|3.14 Venezuela (VEN)...............................................................|34|
|3.15 South Africa (ZAF).............................................................|35|
|4<br>AD-Using Countries with Data on Initiations Only........................................|36|
|5<br>Countervailing Duty-Using Countries in the Database.....................................|38|
|5.1 The “Master” Spreadsheet for Each Country – CVD-CTY-Master.......|39|
|5.2 The “Product” Spreadsheets – CVD-CTY-Products........................|41|
|5.3 Canada (CAN).....................................................................|41|
|5.4 Mexico (MEX)....................................................................|42|
|5.5 United States (USA)..............................................................|43|
|6<br>Caveats for Data Use, Discovery of Errors, and Conclusions.............................|45|
|Appendix..........................................................................................|47|
|References........................................................................................|52|

ii
One could be concerned that our estimates may be biased towards zero due to missing peer data, an issue highlighted by Ammermueller and Pischke (2009) and Sojourner (2013) in peer effect estimation. Contrary to more detailed parental information collected through the parental questionnaire, however, information on child migration status and duration of stay in the Netherlands was collected from the school administration at the beginning of the school year. As a result, the variable on years spent in the Netherlands used to identify immigrant status and construct our treatment variable - the share of immigrants in the grade - is missing for less than 4% of students in the sample.

To illustrate the limited consequences of missing peer data in our sample, we use the method suggested by Ammermueller and Pischke (2009) which corrects peer effect estimates for missing peer data. As commonly done in the peer effect literature, our treatment variable was constructed using all students in the grade for which immigrant status is not missing. This estimator is referred to as Individual Deletion Procedure (IDP) by Ammermueller and Pischke (2009), who propose a correction method to the IDP estimator which tends to be biased towards zero. Approximately, their correction scales up the IDP point estimate and standard errors by the inverse of the fraction of data observed.<sup>23</sup> Given that peer immigrant status is observed for 96% of observations in our sample, our point estimate adjusted for missing data is -0.032, very similar to our IDP estimate reported in Table 4 (-0.031). The effect of missing peer values on the magnitude of our estimates is therefore minimal.

The evidence reported in section 5.3 suggests that an upward bias in our estimates due to the repeaters in the grade (proxying for the share of low-ability students) by one standard deviation decreases the mean test score of other students by 0.03 of a standard deviation.

> 23See Ammermueller and Pischke (2009) for more details on the correction method using the IDP estimator.

31
where The estimation starts by assuming _α_ = 1. After the first iteration, a second regression is estimated to obtain values of _α_ for each importer, based on the hypothesis that quality preference is positively related to income. These new values are used to determine _θ_ and _σ_ , to start the process again. This estimation is run iteratively until the change of _α_ is not significant.

# **A.2 Details on the model for estimating the effects of tariff liberalization**

In this appendix, we offer details on the implementation of the framework proposed by Hoekman, Ng and Olarreaga (2002), which we employ for simulating the effects of AfCFTA and a full tariff liberalization towards all countries in the imports of digital goods.

## **A.2.1 The model**

The import demand for each HS product at the 6-digit level of country i is given by: where _Ti_ is the MFN (Most Favored Nation) tariff in country _i_ ; _Ai_ is a demand parameter in country _i_ ; and _E_ is the import demand elasticity. Import demand elasticities are specific at the product and country level, and at the 6-digit HS classification level. These elasticities were obtained from the Feenstra and Romalis (2014) methodology for digital goods, and from GTAP for the remainder goods.

The export supply from country _j_ to country _i_ for each year _n_ is given by: where Θ is the elasticity of export supply, which is calibrated equal to 1.5, in line with the standard values used by Francois and Hall (2006). Π _i→j_ is the level of tariff preferences granted by country _i_ to the exports of _j_ in year _n_ . Thus, Π _i→j_ if _i_ ’s imports from _j_ must pay the MFN tariff. Conversely, if Π _i→j_ = 1, then _j_ ’s exports enter duty-free in _i_ . On the other hand, _Bj_ it is a supply scale parameter.

31
# **Table 4.** Continued

|Norway|High|State-owned company seeks to install 1 GW wind<br>capacity and produce 3 TWh by 2010; no other targets<br>as Norwayhighlyreliant on hydropower|Feed-in tariff of 8 øre /kWh (approx. €10/MWh) for 15 years;<br>for each øre above 45 øre/kWh, tariffs declines by 0.6 øre (1<br>NOK = 100 øre); capitalsubsidies; tradable energy certificates|
|---|---|---|---|
|Pakistan|No information|5% of power generation from renewables by 2030,<br>1,100MW ofwind power|Limited feed-in tariff at 9.5¢ per kWh|
|Peru|Noinformation|6,200kW of installed wind capacity by2014|Noinformation is available.|
|Philippines|Noinformation|4.7 GWinstalled capacity of renewables by2013|Some taxcredits andincentives; publicinvestment|
|Poland|13.5 GW onshore;<br>possible 2.0 GW<br>offshore, but limited<br>by protected areas|7.5% of total primary energy supply (TPES) from<br>renewables by 2010; 15% by 2020; 7.5% of electricity<br>from renewables by 2010. Estimate: 2.5 GW by 2010, 5<br>GW by2015,12GW by2020|Power purchase obligation requires utilities to obtain 7.5%<br>from renewables by 2010. Capital subsidies, tax incentives and<br>public investment|
|Portugal|700 GWh/year|3,750 MW of electricity generation from wind by 2010,<br>5,100 MW by 2013, 45.6% of electricity output from<br>renewables by2010|More competition to promote wind by linking the grids of<br>Portugal and Spain; feed-in tariffs, capital subsidies, tradable<br>energy certificates|
|Russia|30,000 TWh/year<br>(37% in Europe, 63%<br>inSiberia/Far East)|No targets|No information is available.|
|Singapore|No information|Installation of 50,000 m<sup>2</sup>of solar thermal systems by<br>2012; complete recovery of energy from municipal<br>waste|No information is available.|
|Slovakia|Noinformation|31% ofelectricity outputfrom renewables by2010|Feed-intariffs and taxcredits; publicinvestment|
|Slovenia|Noinformation|33.6% ofelectricity outputfrom renewables by2010|Feed-intariffs;no capitalsubsidies ortax incentivesforwind|
|Spain|40 MW onshore; 5<br>MW offshore|30.3% of electricity consumption from renewables and<br>29.4% from wind, with 20 GW installed capacity, by<br>2010|Wind producers choose: fixed tariff of 7.32¢€/kWh reduced to<br>6.12¢€/kWh after 20 years, or premium of 2.93¢€/kWh<br>combined with cap (8.49¢€/kWh) and floor (7.13¢€/kWh)<br>prices; taxcredits and publicinvestment are also used|
|South Africa|32,228 MWh (5,000<br>MWh national grid,<br>111 MWh rural min-<br>grid, 1,117 MWh off-<br>grid, 26,000 borehill<br>windmills)|10,000 GWh or 0.8 Mtoe renewable energy contribution<br>to the final energy consumption by 2013|No information is available.|
|Sri Lanka|Noinformation|No target; potential is being examined.|USAID funded windmapping survey;feed-intariffs|
|Sweden|No information|60% of electricity output from renewables by 2010, 10<br>TWh of electricity production from wind power by 2015<br>(4 TWhonshore, 6TWhoffshore)|Feed-in tariffs, tax incentives, capital subsidies, tradable<br>energy certificates; production support or environmental bonus<br>that declines eachyear; easiercertificationofdesignated sites|

18
# TAKLE 4 **IRAZIL:** CONPOITION Of THE PUtLIC SECTOR DEFICIT (PER CENT OF **GOP)**

|Years|Operational<br>PSUR<br>(1)|BasicDalance<br>(Surplus)<br>(2)|Do estic<br>Debt<br>Servic<br>**(3)**|Foreign<br>e Debt<br>Servic<br>**(4)**|Gross Fixed<br>e<br>Capital<br>Foreati<br>**(5)**|Domestic<br>on<br>Transfers<br>**(6)**|
|---|---|---|---|---|---|---|
|1991|<br>**5.20**|-16.91|**1.50**|**2.08**|7.64|**10.89**|
|**1982**|<br>**7.0**|-16.85|2.37|2.49|**7.53**|**11.46**|
|**19m**|**3.50**|-19.11|**3.6**|3.22|**5.49**|**10.84**|
|1984|<br>**2.70**|-18.19|**3.30**|3.14|**5.20**|9.25|
|1985|<br>**4.30**|-16.12|3.44|**2.97**|5.43|8.58|
|1986|<br>**3.50**|-15.27|2.23|2.51|**5.0**|9.03|
|1987|<br>**5.50**|-12.17|2.17|**2.10**|**5.0**|**8.40**|

- Notes; **col.(2):** Obtained as residual. col.(3): The cost (above aonetary correction) of the Federal debt in securities given **bs** the the central Dank. **The:** cost of the net debt to financial sector estimated onthe basis of average **CD** rates in the earket **(30.9, 27.4, 24.3, 23.8. 23.1,** 14.01. and 15X per Near for the sears 1981-87).

- col(4): Estiaated applying the share of the public sector in the total foreign debt on the annual interest cost, minus the interest earned on international reserves, assumed to be 31 below the average cost of debt.

- col(S): Taken from Verneck (1987), Table **36,** page **47,** complemented by National Income Accounts. Estimates for 1986-87.

- col.(6): National Income Accounts (includes social security and subsidies).

- 27 -
12 merely 24 percent in 2004 over 2000, which was by far the smallest increase among comparators. Since export growth was so much stronger, with the export coverage of imports rising from 58 percent in 2000 to 74 percent in 2004, it would appear that import content of exports has declined as domestically produced inputs replaced imports.

In spite of these developments, Turkey’s aggregate trade openness (goods exports plus imports to GDP) increased from an average of 32 percent in 1990-99 to 48 percent in 2000-04. It was also well above the level indicated by Turkey’s size, GDP per capita and distance from major world’s markets. From this perspective, Turkey has been as successful as Slovenia.

Exports of goods were a bright spot in Turkey’s overall performance in 2000-04. The export performance gap between EU-8 and Turkey, which significantly increased between 1993 and 2000, similarly considerably fell in 2000-03, although it remained higher than in 1990 and 1993. Turkey’s export performance has been strongly reminiscent of that of Romania, a late reformer among transition economies, whose exports dramatically picked up in the late 1990s and early 2000s. Both countries registered a 50 percent increase in respective shares in world exports.

While, as discussed earlier, statistics on Turkey’s performance in trade in services raise doubts as to their quality, Turkey’s overall position in terms of services trade per capita vis-à-vis other EU associates and new members is similar to that in trade in goods. It is above the levels in two former Yugoslav republics—Macedonia and Serbia and Montenegro—and Romania, which swapped respective positions in the ranking with Albania (Figure 4).

**Figure 4:** _Trade in services per capita in Turkey and other ‘EU related’ countries in 2004 (in US dollars)_

<!-- Start of picture text --> 948<br><!-- End of picture text --> <!-- Start of picture text --> 620<br><!-- End of picture text --> <!-- Start of picture text --> 422<br><!-- End of picture text --> <!-- Start of picture text --> 352 343<br><!-- End of picture text --> Source: IMF Balance of Payments database.

Trade in services plays, however, much more important role in Turkey’s trade than per capita statistics would indicate. The share of services in total trade was 18 percent in 2004, which is well above this share in trade of most other “EU-related’ European economies. Albania (41%), Croatia (35%), Estonia (25%) and Latvia (20%) had a higher share of services in total trade.

# **_D._** _Reorientation of foreign trade away from Middle East towards Pan-European markets_

A very significant reorientation of Turkey’s foreign trade towards the EU did not occur after entry into force of the EU-Turkey Customs Union Agreement on 1 January 1996 but much earlier. In fact, 1996 marked historically the highest share of the EU-15 in Turkey’s total trade turnover. The striking feature of Turkey’s dynamics in geographical pattern of foreign trade has been the stability in the share of the EU-25 in total trade turnover since 1996. Interestingly, the
answer for a typical household in a typical village, not to self-assess their own temptations. The survey was conducted online in June-July 2016. Across the 43 responses that we received, we assign each item its average score on the 5-point scale, and classify the top third (6 items) as the temptation goods in the study. These are: sugar, rice, cooking oil, soap, cigarettes, and sweet bananas.<sup>19</sup> For this analysis, household fixed effects are identified, so we can examine a specific behavior associated with not buying in bulk, rather than base tests on variation in losses. The primary outcome of interest is _Khi_ , the number of purchases of item _i_ by household _h_ . We first examine whether households make fewer purchases when bulk discounts are present, a response that indicates at least some attending to bulk discounts. We then test the hypothesis that households ration their stocks of temptation goods by purchasing them more frequently than non-temptation goods. Finally, we estimate the joint effects of temptingness and bulk discounts on _Khi_ , and test for differential responses between high- and low-loss households. Identification in these regressions is from three sources: household fixed effects control for average purchasing behavior; the temptation survey provides an objective, exogenous measure of the temptingness of a good; and the fact that households face some flat and some non-flat unit price schedules in their local markets provides exogenous withinhousehold variation in the presence of bulk discounts.<sup>20</sup> We include item fixed effects, when Regression results are in Table 9. Column 1 shows that after controlling for both household and item fixed effects, the average effect of a bulk discount is to induce fewer purchases. The average household does react to bulk discounts, to some degree. Column 2 shows that this result does not hold without the item fixed effects. Households also react to the temptingess of the good, but in the opposite direction (column 3). Households make 0.65 more purchases on temptation goods than non-temptation goods. This is indicative of consumption rationing for tempting items. When variables for bulk discounts and temptation

> 19The full ranking and average scores from the temptation survey are shown in Appendix S5. There is little correlation between a good having bulk discounts and being a temptation good. The correlation between the temptation dummy and the indicator for bulk discounts is _−_ 0 _._ 07, and it is not statistically different from zero. The relationship is unchanged if we control for district fixed effects.

> 20For the average study household, 56% of observed purchases are of items with bulk discounts, and 44% are of items with flat price schedules.

26
The results in this study are based on general crop agriculture for which data were collected on all crops produced by farmers. Given that different crops have different climate requirements, future studies need to be focused on specific crop responses and adaptations, particularly the staple foods which have long-term implications for food security in the country. This paper does not take into account revenue from livestock production, yet most farmers in Kenya combine livestock and crop production for both subsistence and commercial purposes. Our results show that medium and low potential (mostly semi-arid and arid) areas are expected to be much more adversely affected by global warming. However, these areas are best suited for livestock production by both small scale producers (pastoralists) and large scale ones (ranchers). Analysis of the impact of climate change on livestock production would give a better picture of the impact in arid and semi-arid areas. There is also a need for studies to model the impact of climate change with and without the impact of adaptations that farmers make to counter the impact of climate change. Another shortcoming of this study springs from the nature of the household data used. Though there is data on long-term climate change, the full impact would be better assessed with time series data on crop production. Long-term changes in agricultural production may better reflect the impact of long-term climate change than one-time estimates of production.

25
6 We will pay special attention to the effects of CDP and export growth, as well as tOe choice of parameters, on consumption. Real growth in consumption, percapita consumption in particular, is perhaps the most important criteria in assessing the acceptability of the growth scenario being modeled. Growth which does not increase real per-capita consumption is rarely politically sustainable.

# Income Effects and the Terms of Trade

14. Up to this point, the discussion has omitted the effects of the terms of trade. The present formulation of the accounting framework correctly implies that one must supplement domestic output with net imports (M-X) insofar as expenditures exceed total domestic output (GDP). It says nothing about the gains or losses resulting from changes in the terms of trade. In the social accounting framework used with the RMSM, however, we take these gains or losses into account by making a distinction between gross domestic income (GDY) and gross domestic product (GDP) -- the difference between the two being the income effects of changes in the terms of trade:

# GDY, - GDP, + TTADJt

12) where TTADJ stands for the adjustment to GDP for changes in the terms of trade.

15. In calculating rea'l GDY, we value production for exports in accord with the resulting capacity to import. The capacity to import from exports is calculated by deflating .3xport earnings (exports at current prices) by the price of imports. In this way, we take into account price changes measured against the base year for both exports and imports. The capacity to import, in other words, is equal to exports adjusted for the terms of trade (XTTADJ). It is the equivalent of looking upon exporting as an indirect way of "producing" imports.

XTTADJt - (Xt * XPId)/MPIt 13) Note that XPI and MPI are price indices expressed in local currency terms.<sup>12</sup> These indices should share the same base year as the rest of the national accounts variables.

12. The terms of trade adjustment is independent of exchange rate movements because the formula results are always denominated in the same currency as the national accounts.