rafmacalaba/datause-encoder-data huggingface dataset:
Newman, Carol, John Page, John Rand, Abebe Shimeles, Måns Söderbom, and Finn Tarp. 2016. _Made in Africa: Learning to Compete in Industry_ . Washington, DC: Brookings Institution Press.

- Page, John. 2012. “Can Africa Industrialize?” _Journal of African Economies_ , Vol. 21. AERC Supplement 2, pp. ii86-ii125.

- Page, John. 2016. “Are Certain Countries Doomed to Remain Emerging? Globalization, Structural Change, and the Uneven Geography of Convergence.” _Report #6 in 11 Global Debates_ . Brookings Institute.

- Page, John. 2018. “Rethinking Africa’s Structural Transformation: The Rise of New Industries.” In Brahima S. Couliby (Ed.) _Foresight Africa: Top Priorities for the Continent in 2018_ (pp. 64-80). Washington, DC: Brookings Institute.

- Olley, G. Steven and Ariel Pakes. 1996. “The Dynamics of Productivity in the Telecommunications Equipment Industry.” _Econometrica_ 64: 1263–1297.

- Pavcnik, Nina. 2002. “Trade Liberalization, Exit, and Productivity Improvements: Evidence from Chilean Plants,” _Review of Economic Studies_ 69(1): 245-276.

- Melitz, Marc and Sašo Polanec. 2015. “Dynamic Olley-Pakes Productivity Decomposition with Entry and Exit.” _Rand Journal of Economics_ 46(2): 362-375.

- Restuccia, Diego and Richard Rogerson. 2008. “Policy Distortions and Aggregate Productivity with Heterogenous Establishments,” Review of Economic Dynamics 11(4): 707-720.

- Restuccia, Diego and Richard Rogerson. 2017. “The Causes and Costs of Misallocation _,” Journal of Economic Perspectives_ 31(3): 151-174.

Rodrik, Dani. 2015. “Premature Deindustrization.” NBER Working Paper 20935.

Rodrik, Dani. 2017. “Growth without Industrialization?” _Project Syndicate_ (October 10, 2017).

- Syverson, C. 2004a. “Market Structure and Productivity: A Concrete Example,” _Journal of Political Economy_ 112(6) : 1181-1222.

- Syverson, C. 2004b. “Product Substitutability and Productivity Dispersion,” _Review of Economics and Statistics_ 86(2): 534-550.

- Syverson, C. 2011. “What Determines Productivity?” _Journal of Economic Literatur_ **_e_** 49(2): 326365.

- United Nationas Industrial Development Organization (UNIDO). 2009. _Industrial Development Report. Breaking In and Moving Up: New Industrial Challenges for the Bottom Billion and the Middle Income Countries_ . Vienna, Austria.

22
77 and in columns 3 and 4 based on official enrollment rates accumulated across 27 school levels **(see** Table A-1). The estimates of educational attainment are for persons age 20 to 24 in a simple of censuses in about 1970, and approximate the distribut 3n of educational investmnts **made** around 1960. The 1960 accumu'ated enrollment rates are estimated by UNESCO to represent averages for all countries in each region. The figures in column 3 **are** slightly higher than those in column 1 for Asia and Africa. From 1960 to 1980 enrollment ratas increased, slowly converging between **women and men.** But differences remain large. Are these differences in fmale and **male** educational investments explained by **the** diverse economic conditions in these countries? An analysis of educational systems from 80 countries for the period between 1960 and 1980 sought to account for differences in public expenditures on education and enrollment rates on **the** basis of household demand and production theory. More than 90 percent of **the** intercountry variation in public school expenditures per school aged child and 40 to 70 percent of the variation in primary and secondary school enrollment rates are explained by real income per adult, the relative price of teachers, the school-aged share of the population, and urbanization. The proportionate increase in girl's enrollment rates associated with a proportionate increase in income is significantly larger than the analogous incom elasticity for boy's enrollment. Price elasticities **are** also larger in absolute value (negative) for female enrollment rates than for male. Eponomic growth and long run expansion of the educational system that contributes to **the** decline in **the** relative price of teachers appear to increase women's schooling in comparison to men,s. Witain countries over time this' gap between the educational attainment of wonmn and men has also narrowed, but more slowly than forecasted from this relationship
households (Stephens and Barrett, 2011). They can push up the seasonal price gap well beyond the levels expected in settings with well‐functioning markets.

Excess seasonality in prices may further translate into seasonal variation in dietary intake and nutrition, for example, when households are credit constrained or ill‐equipped with other coping strategies, as has been documented in Ethiopia (Dercon and Krishnan, 2000), Bangladesh (Khandker, 2012), and Tanzania (Kaminski, Christiaensen, and Gilbert, 2016).<sup>6</sup> Moderation of seasonal price variation (for example through facilitation of storage or access to credit) could then be a way to increase overall food and nutrition security.

A second reason for refocusing attention to food price seasonality relates to the sharply increased volatility of world food prices in the immediate aftermath of the 2007‐08 world food crisis (Gilbert and Morgan, 2010, 2011), although volatility levels appear to have dropped back since that time (Minot, 2014). This volatility was transmitted to a greater or lesser extent to food prices in developing countries and attracted considerable government attention (Galtier and Vindel, 2012; World Bank, 2012; Ceballos, Hernandez, Minot, and Robles, 2015). Food price volatility arises from both international and domestic shocks to production (harvest shocks) or consumption (changes in purchasing power). However, seasonality (i.e. known fluctuations) also contributes to price volatility (especially domestically) and would require different policy instruments to address it. Little is known on the extent of this possibility.

The third reason relates to the measurement and analysis of poverty (the focus of the first Sustainable Development Goal). Poverty measurement relies heavily on food expenditure information, which is typically collected only once for each household during at a particular point during the year (with a 7 to 30 day recall period). The annual expenditures measures derived from these surveys will be incorrect when food price seasonality is substantial and not corrected for, as is mostly the case in current practice (Muller, 2002; Van Campenhout, Lecoutere and D’Excelle, 2015).

> 6 Few studies explicitly study the link between food price seasonality and seasonality in diets and nutrition. Related studies include Chambers, Longhurst and Pacey (1981), Dostie, Haggblade, and Randriamamonjy (2002), and Stephens and Barrett (2011).

2
27 The checks variable, intended to reflect the number of independent veto players in a country, simultaneously captures some of the other institutional influences that have been the subject of scrutiny. For example, a closed list has the effect of reducing the number of veto players (by increasing control of party leaders over party members in the legislature); at the same time, the literature predicts that the introduction of a closed list should reduce incentives of individual candidates to appeal to special interests. Similarly, the effect of low voting thresholds in proportional representation systems is predicted to be the emergence of more smaller parties appealing to narrower constituencies. This should lead to governing coalitions with more parties, and therefore more veto players. The checks measure therefore allows the net effect of these countervailing institutional influences to be assessed.

The proximity of elections is the final institutional characteristic of countries examined below. A variable tracking the number of years to the next election, legislative or presidential, counting from the first year of the crisis, has been created from information in the DPI.

A key challenge in the analysis of special interest influence is often that special interests line up on both sides of policy debates. When this occurs, analysis of the institutional determinants of special interest influence must take into account the constituencies and supporters of different government veto players. In the case of banking crises, though, the need to do this is diminished by the fact that special interests tend to line up on the same side of regulatory issues. Government policies that permit imprudent banking tend to benefit narrow interests — recipients of sweetheart loans, bank owners and managers - at the expense one for each legislative chamber, and one ifthe fist government party is closer in political orientation (left, right or center} to the first opposition party than to the party of the president. If the legislature is closed list (voters must vote for parties and cannot register candidate preferences) and the president’s party has a majority in parliament, the legislature is not counted as a check. In parliamentary systems, checks is the sum of one (for the prime minister} and the number of parties in the governing coalition; the number of parties is reduced by one if there is a closed list and the prime minister’s party is in the
|2-Other:<br>3-Other:|
|---|

|23. Organigram - Please attached the RBO organigram.|
|---|
|Attach your file here:|

# 24. Share of types of water. Percentage (%) of users that use:

|**Types of Water**|**Percentage (0-100%)**|
|---|---|
|Groundwater Only:||
|Surface Water Only:||
|Both Groundwater and Surface Water:||
|1-Other:||
|2-Other:||
|3-Other:||

80
33 TABLE 5 WITHIN ESTIMATES OF EMPLOYMENT SIZE and PRODUCTIVITY DISTRIBUTIONa (Absolute values of t-statistics in parenthesis)

|||**Emgloymen**<br>|**t Size Dist**<br>**Percentile**|**ribution**||
|---|---|---|---|---|---|
||10th|25th|50th|75th|90th|
|ln(M/Q)|-.048|.010|-.168*|-.115|-.120|
||(.812)|(.144)|<br>(2.06)|(1.03)|(1.09)|
|ln(X/Q)|.035*|.007|-.042*|.071*|.061*|
||(2.35)|(.417)|<br>(2.04)|(2.57)|(2.19)|
|ln(Q)|.163|.270*|<br>.165|.349|.624*|
||(1.58)|<br>(2.27)|<br>(1.17)|<br>(1.82)|<br>(3.26)|
|TUR*ln(M/Q)|.226|-.047|.463|.195|.178|
||(1.19)|<br>(.215)|<br>(1.77)|<br>(.551)|<br>(.502)|
|TUR*ln(X/Q)|-.091|-.026|.146*|-.243*|-.214|
||(1.83)|<br>(.452)|<br>(2.12)|<br>(2.61)|<br>(2.30)|
|TUR*ln(Q)|-.577|-.776*|-.306|-.784|-1.25*|
||(1.76)|<br>(2.05)|<br>(.679)|<br>(1.29)|<br>(2.07)|
|**R2**|.805|.902|.932|.925|.936|
|||**Labor Pr**|**oductivity**<br>Percentil|**Distributi**<br>e|**on**|
||10th|25th|50th|75th|90th|
|ln(M/Q)|.007|-.066|-.011|-.046|.044|
||(.066)|<br>(.712)|<br>(.120)|<br>(.520)|<br>(.432)|
|ln(X/Q)|.053|.076*|<br>.069*|<br>-.056*|.055*|
||(1.89)|<br>(3.25)|<br>(2.95)|<br>(2.55)|<br>(2.12)|
|ln(Q)|.714*|<br>.686*|<br>.982*|.896*|<br>1.29*|
||(3.68)|<br>(4.26)|<br>(6.07)|<br>(5.89)|<br>(7.25)|
|TUR*ln(M/Q)|- .003|.182|.067|.280|-.017|
||(.008)|<br>(.612)|<br>(.225)|(.995)|<br>(.052)|
|TUR*ln(X/Q)|-.101|-.187*|-.187*|.258*|.149|
||(1.07)|<br>(2.39)|<br>(2.38)|(3.50)|(1.73)|
|TUR*lrL(Q)|-.890|-.837|-1.85*|-1.55*|-2.93*|
||(1.44)|<br>(1.63)|<br>(3.59)|(3.21)|(5.18)|
|**R2**|.986|.991|.992|.993|.990|

'Separate industry duzmies **and** time dummies for each country were included in the regressions but are not reported.

*Significantly different from zero and the .05 level using a two-tail test.
country and good. Given that the impact of tariffs on imports depends exclusively on the import demand elasticities and that these have been estimated earlier for each HS 6 digit tariff line in 117 countries (see Kee, Nicita and Olarreaga, 2004), we can substitute these estimates into (1) instead of trying to estimate them again. This does not only allow us to avoid imposing any structure on the estimation of those parameters (to avoid running out of degrees of freedom), but also solves for the endogeneity of tariffs (and import demand elasticities) to tariffs as discussed in Tre fl er (1993) or Lee and Swagel (1997). This is achieved by sending the tariff variable (and the now “known” parameters) to the left-hand-side. This constrained speci fi cation may introduce heteroscedasticity in the error term κn,c given that import demand elasticities have been estimated with error and this error in the measurement of the elasticities can be associated with measurement error in NTBs. A White correction could be undertaken to solve for this.<sup>6</sup> After substituting the estimated elasticities and sending the tariff term to the left-hand-side, Equation (1) becomes: According to equation (2), the impact of NTBs (core and domestic support) varies across countries and tariff lines (i.e., βs have subscripts n and c). Given that the international data on NTBs does not have adequate time variation, our sample is essentially a cross section of HS 6 digit tariff lines and products. Thus some structure would have to be imposed on the β parameters to allow them to vary across tariff lines and countries without running out of degrees of freedom. We will therefore allow them to have a product speci fi c impact and the country speci fi c impact will depend on the variables that capture country characteristics; more precisely on endowments as in Leamer’s (1990) comparative advantage approach:

> 6 Alternatively, one could try to correct parametrically using the information on standard errors, but it is not clear that this would solve the heteroscedasticity problem. It will not if as asumed here, μn,c is independently and identically distributed.

6
<!-- Start of picture text --> Figure 2: Absolute and Relative Word Count for 'equality/inequality’ in IMF Documents<br><!-- End of picture text --> <!-- Start of picture text --> Absolute Number of Keywords<br>6000-<br>4000-<br>7)<br>5 2000-<br>=<br>o<br>>J<br>BO<br>Q<br>e) Relative. Frequency: Number of Keyword per 10000 Words<br>ie)J 8<br>o<br>He)<br>E6<br>5<br>Zz<br>4-<br>2-<br>0- 1DOonwvtrtMDMmnODnoanDDaranDnDDAaAnrBaBrAraDWDBDWDWADMWAADWADADHAHADWEecdsccéscecpceeoecscreoeoecogcredescdcs?dTHoOonwW1 1 NM' 1HOWDOWOoniwrtr1 'OC tOO 'O 1OWOoOoNOC 1ORRRRKR1 ' 'THOwWeoeNniwrwvowmweeoeoenvwervrFrnsoewmweoenrerwtrtomoaonwvrt' ' 'DOAWMHADAAAD ' ‘ D ' 'WDA' 'DWDWSeoesccTseeccrorierrriereN' ' D ‘ 1 1 1 1 ' 'O 1DO 'ON1N<br>a a ©Do © Do © Co «a<br>Year<br><!-- End of picture text --> Note: the top graph refers to the absolute number of times the term ‘equality/inequality' appears in all documents. The bottom graph refers to relative frequency. Both use a 5-year moving average. Data source: IMF E-Library, 48616 documents downloaded between July-September 2021
# **Table A2. Non-migration selection regressions**

||**Selection reg**|**ression corr**|**esponding to**|**subjective**|<br>**adequacy regre**|**ssion of:**|
|---|---|---|---|---|---|---|
|**Characteristics of hhold head**|**Food**|**Clothing**|**Housing**|**Schooling **|**Health care**|**Income**|
|Age of household head|-0.001|-0.002|0.000|0.012|-0.002|-0.001|
||(0.08)|(0.17)|(0.01)|(1.01)|(0.15)|(0.06)|
|Age of household head squared|0.000|0.000|0.000|0.000|0.000|0.000|
||(0.51)|(0.43)|(0.60)|(1.54)|(0.46)|(0.55)|
|Log(education of household head)|-0.110|-0.141|-0.133|-0.098|-0.129|-0.129|
||(3.54)**|(4.74)**|(4.38)**|(3.07)**|(4.21)**|(4.26)**|
|Father employed in non-farm job|-0.055|-0.117|-0.091|-0.065|-0.086|-0.089|
||(0.72)|(1.58)|(1.21)|(0.82)|(1.12)|(1.17)|
|Education of father of head (log)|-0.301|-0.307|-0.319|-0.329|-0.309|-0.316|
||(6.47)**|(6.72)**|(6.98)**|(6.85)**|(6.69)**|(6.87)**|
|Inherited land in ha (log)|0.796|0.693|0.736|0.830|0.750|0.739|
||(10.36)**|(8.54)**|(9.37)**|(10.17)**|(9.59)**|(9.44)**|
|Ethnicity dummies|||included but|not shown|||
|Intercept|1.133|1.184|1.157|0.523|1.179|1.165|
||(4.21)**|(4.43)**|(4.30)**|(1.86)|(4.36)**|(4.33)**|
|Number of observations|2867|2867|2863|2421|2851|2862|

Estimator is Heckman (selection) probit. The second part of the regression is shown in Table 5. The dependent variable is 1 if the head of household resides in birth ward, 0 otherwise. Absolute value of t statistics in parentheses: * significant at 5%; ** significant at 1%
sum of the export supply and import demand elasticities for product _k_ , and _σik_<sup>_m_isameasureofthevarianceofimportsofproduct</sup><sup>_k_fromcountry</sup><sup>_i_.We</sup> augment (7) to include the change in the bilateral real exchange rate between the importing country and country _i_ to control for aggregate relative price changes.

Empirically, changes in the incentive to defect can be interpreted as affecting the probability of a tariff increase or as determining the magnitude of a cooperative tariff increase. Our primary approach is to examine how variation in the data affects the probability of an antidumping or safeguard tariff across time, countries and industrial sectors. We report estimates from both a probit model and a logit model of tariff imposition. As a robustness check, we also use a censored Tobit model to determine the size of antidumping tariffs that are imposed, interpreting _yikt_ as an antidumping

# **2 Data used to estimate US formation**

We estimate the empirical model of US antidumping and safeguard tariff formation on a panel dataset constructed from several primary data sources: (1) trade policy data for the US come from the World Bank’s Temporary Trade Barriers Database (Bown, 2010b), (2) US bilateral imports at the industry level come from the US International Trade Commission’s DataWeb, (3) industry-level foreign export supply elasticities facing the US come from Broda, Lim ̃ao, and Weinstein (2008), (4) industry-level US import demand elasticities come from Broda, Greenfield, and Weinstein (2006), (5) variables describing the characteristics of US domestic industries come from the US Census Bureau, and finally, (6) annual bilateral real exchange rates in foreign currency per US dollar come from the USDA Economic Research Service. Summary statistics for all variables in the dataset are reported in Table 1.<sup>13</sup> The _ikt_ panel includes 49 countries denoted _i_ , 283 North American Industry Classification System (NAICS) 2007 manufacturing industries _k_ at the 5- or 6- digit level of aggregation, depending upon availability, for the years ( _t_ ) 1997 through 2006.<sup>14</sup>

> 13Table 1 includes footnotes which describe how some variables are scaled by factors ranging from 1/100 to 1/10,000 prior to estimation. Our discussion of all quantitative empirical results fully accounts for this scaling.

> 14These 49 countries are: Argentina, Australia, Austria, Bangladesh, Belgium, Brazil, Canada, Chile, China, Colombia, Costa Rica, Denmark, Ecuador, Egypt, El Salvador, Finland, France, Germany, Greece, Hong Kong, Hungary, India, Indonesia, Ireland, Is-

9
23

**Table 12: Growth Rates in Total Trade (X+M) and Output by Income Group and Region**

||Annu|al Trade(g|+s)Growth|(in %)|A|nnual GDP|Growth(in|%)|
|---|---|---|---|---|---|---|---|---|
|CountryGroup|1980-90|1990-00|2000-10|1980-10|1980-90|1990-00|2000-10|1980-10|
|**BY INCOME LEVEL**|||||||||
|Developed economies|7.8|5.2|7.3|6.8|8.4|3.4|5.3|5.7|
|Developing economies|5.4|9.4|12.0|8.9|5.0|6.5|11.6|7.7|
|High income developing economies|6.6|9.6|9.4|8.5|7.1|7.9|7.1|7.3|
|Upper middle income economies|5.4|10.1|13.4|9.6|4.9|7.0|12.7|8.1|
|Lower middle income economies|3.4|7.7|14.0|8.2|3.8|4.4|12.8|6.9|
|Low income economies|3.1|4.1|12.0|6.3|4.1|1.5|9.9|5.1|
|Of which: Least developed countries|2.7|5.8|14.6|7.6|4.2|1.7|12.8|6.1|
|**Memo:**|||||||||
|Developing econ, excl. China & India|5.2|8.7|9.9|7.9|4.8|5.7|9.4|6.6|
|Upper middle income, excl. China|4.8|8.2|9.7|7.6|4.6|5.1|9.7|6.5|
|Lower middle income,excl. India|3.0|7.2|11.5|7.2|2.7|4.8|12.4|6.6|
|**BY REGION**|||||||||
|World|6.5|6.2|8.8|7.2|7.1|3.9|6.9|6.0|
|Developing economies|5.4|9.4|12.0|8.9|5.0|6.5|11.6|7.7|
|Asia|10.1|10.8|12.3|11.1|7.9|7.8|12.8|9.5|
|Latin America & Caribbean|3.9|9.8|9.2|7.6|4.1|6.3|9.3|6.6|
|Europe, Middle East & North Africa|0.1|5.4|13.5|6.2|2.1|4.8|10.9|5.9|
|Sub-Saharan Africa|0.0|3.9|12.9|5.5|1.3|1.3|12.5|4.9|
|**Memo:**|||||||||
|BRICS 5 countries|10.6|8.6|18.2|12.4|9.8|4.3|15.7|9.8|
|OPEC 12 members|-1.9|6.2|14.2|6.0|-0.6|5.0|12.1|5.4|

Source: Computations based on World Bank World Development Indicator database.
-53 -

# APPENDIX TABLE I

# ANGUILLA's TRANSPORT COST PROFILE FOR EXPORTS TO THE UNITED STATES IN 1986

|||||||ANGUILLA||||"OTHERS"|<br>SHAFE I N|
|---|---|---|---|---|---|---|---|---|---|---|---|
|||U.S.|ANGUILLA|NOMINAL FR|EIGHT RATE|TARIFF|"OTHER|S" NOMINAL FR|EIGHT RATE|TARIFF|ANGlUILLA'S|
|osS|<br>TSUSA|<br>IMPORTS(S|) TOTAL|VESSEL|AIR|RATE|TOTAL|VESSEL|AIR|RATE|EXPORTS|
|1|2747040|3,700|4.68|0.00|4.68|0.00|5.36|5.40|5.37|1.68|4.140|
|2|3190500|11,457|4.58|4.58|0.00|7.20|14.69|5.47|31.26|7.41|12.818|
|3|3253954|610|10.00|0.00|10.00|13.61|5.50|3.16|6.05|13.60|0.e82|
|4|3313954|<br>1,172|9.98|0.00|9.98|14.93|6.51|3.22|7.19|14.90|1.511|
|5|3662860|349|11"75|0.00|11.75|11.17|7.68|7.43|29.05|11.54|0.390|
|6|3664700|9,355|11.74|0.00|11.74|7.60|6.39|6.10|12.69|8.12|10.466|
|7|3667930|12,494|11.73|0.00|11.73|8.50|10.4'|8.38|27.24|8.69|13.918|
|8|3668400|1,647|11.72|0.00|11.72|2.49|8.10|7.29|12.13|3.95|1.843|
|9|3721540|1,566|11.75|0.00|11.75|12.77|7.35|1.92|9.35|12.86|1.752|
|10|3843497|5,271|10.40|0.00|10.40|10.21|7.39|3.31|13.27|_10.52_|5.897|
|11|3845299|310|11.61|0.00|11.61|9.35|7.31|5.01|14.88|9.61|0.347|
|_12_|5311140|1,237|36.05|36.05|0.00|3.64|21.75|20.90|43.23|3.65|1.384|
|13|6496500|1,174|0.00|0.00|0.00|0.00|6.35|6.76|5.92|0.00|1.313|
|14|6533740|9,000|1.91|0.00|1.91|6.20|8.37|7.99|21.69|0.57|10.069|
|15|6859059|10,040|3.82|0.00|3.82|5.70|4.90|2.48|5,55|4.58|11.233|
|16|8000035|20,000|0.00|0.00|0.00|0.00|2.53|4.65|1.97|0.00|22.376|

# 5BS DESCRIPTION

I Photographs, engravings, etchings, llthographs, **wood** cuts, stenciled matter nes, bound or not bound 2 Hand loomed fabrics, wholly of cotton, of numbers 35-49 3 Sateens or twoi s, wholly of cotton, 5.9 oz or **less,** fancy or figured, not of different colors, napped, of no. 30-39 4 Sateens or twills, chiefly ot cotton, 5.9 oz or less, fancy or figured, not of different colors, napped, of no. 30-39 5 Cotton dish towels, not jacquard-figured and not ornamented 6 Tablecloths and napkins, of cotton, not hand block printed, except plain woven, nos 7 Cotton furnishings, not *Isewhere specified, other than plain woven, not ornamented 8 Other furnishings of vegetable fiber except cotton, not ornamnted nes 9 Cotton muftiers,scarves,shawls,and vei Is,h_cmd,not knit,not lace,nst,or ornamented 10 Women's, girls', or Infants' wearing apparel, knit, of cotton, not ornamented nspf 11 Women's, girls', or Infants' wearing apparel, not knit, of cotton, not ornamented nspf 12 Refractory and heat-insulating mortars, raming sixes and castables of other than clay 13 Knives, cutting blades for agricultural or horticultural, not elsewhere specified, and for shoe machinery 14 Illuminating articles not elsewhere specified, electric of brass 15 PrInted circuit boards, other than with a plastic Impregnated glass base 16 U.S. products returned after export without being advanced in value or Improved In condition abroad, nas