rafmacalaba/datause-encoder-data huggingface dataset:
In assembling the World Bank EU Standards Database), we follow the approach of Swann et al. (1996) and Moenius (2000, 2004, 2006). We rely primarily on Perinorm (www.perinorm.com), an extremely rich bibliographic database maintained by the British, French, and German standard-setting bodies. It contains over 1.1 million records from 22 (mostly OECD) countries. Each record corresponds to a single national, regional, or international standard. It provides a short verbal description, from which it is usually possible to identify the product or sector to which the standard applies. Perinorm also indicates when links exist to equivalent standards in other jurisdictions, including at the regional (CEN) and international (ISO) levels. It is therefore possible to identify with precision both the stock of EU standards, and the subset of them that translate ISO norms into local practice. We refer to this second category of European standards as being “harmonized with ISO standards” or “internationally harmonized”.<sup>8</sup> It is important to highlight that Perinorm is not primarily intended as a tool for research. On the one hand, this is a strength: Perinorm is designed to facilitate industry access to—and purchase of—product standards, which suggests that there is a commercial incentive to ensure completeness.<sup>9</sup> This end-user focus makes Perinorm somewhat unwieldy for doing applied international trade work. In particular, Perinorm classifies standards according to the International Classification for Standards (ICS), for which there is no concordance to the product

> 8 We are using equivalency with ISO standards as a proxy for harmonization with de facto international standards. Due to lack of data, our analysis excludes private standards, or norms issued by other bodies, which might also fulfill a similar role. However, we are confident that we are capturing an important part of international standardization activity in this area, since ISO has issued over 300 standards affecting the textiles and clothing industry. These standards focus on test methods and specifications that help promote product consistency and conformity to expectations (ISO, 2004). ISO’s work in this area involves 25 participating countries and 52 observers (http://www.iso.org/iso/en/stdsdevelopment/tc/tclist/TechnicalCommitteeDetailPage.TechnicalCommitteeDetail?CO <u>MMID=1479, consulted on 26 February 2007).</u>

> 9 It can be argued that the incentive structure in place for TBT notifications works in the opposite direction, since Members do not want to “invite” WTO disputes by providing information on changes in their technical regulations.

- 9 -
**-38-**

Figure 15. Real effective exchange rates Comparing countries grouped by favorable and unfavorable policy environments Index 1970-75 100 <!-- Start of picture text --> 200<br>180<br>160<br>140-<br>120 -<br>100<br>80<br>,o II I<br>1970 1972 1974 1976 1978 1980 1982 1984 1986 1988<br>- FPE -UPE<br><!-- End of picture text --> Figure 16. Weather patterns in Africa Comparing countries grouped by favorable and unfavorable policy environments <!-- Start of picture text --> 0.15 -<br>0. 1<br>0.05 F<br>-0.05 -<br>-0. 1<br>-0.15-<br>-0.2<br>-0.25- . I . .<br>1980 1981 1982 1983 1984 1985 1986 1987<br>deviations from trend in cereal yield<br>FPE 3UPE<br><!-- End of picture text -->
<!-- Start of picture text --> i coYo eR-<br>— Predicted exports ———Actua_exoorts f |<br>i ae<br>—_ = 4]<br>bees<br>is) af i siszY<br>1990 1961 1992 1953 2994 1995 1996 1997 1998 1995 2000 2001 20C2 2003 2304 2C05 2006<br><!-- End of picture text -->
# 4. Description of the Data and Variables

The theoretical model is applied to data from the Peru Living Standards Survey (PLSS). The survey results provide informiiati **o** r. a variety of topics:n household composition, demographics, housing conditions, health, education, migration, labor force activities, housework, farm and nonfarm busines-es, and household expenditures (see Grootaert and Arrlagada 1986; INE 1988).

The survey modules on nonfarm family businesses, labor force behavior, and the personal characteristics of household members are particularly useful. For each family enterprise the PLSS gives the labor inputs of family workers, the value of output sold or consumed. by the household, expenditures on purchased inputs, and the value of assets. The survey does not contain information on physical units and prices of output and nonlabor inputs -- only total values. The survey shows how long each business has been open, the type of output, how many months it operated in the past year, and whether the business is in the home, at other fixed premises, or has no fixed premises. The information covers 3,360 businesses: 1,178 in metro Lima, 1,480 in other urban areas, and 702 in rural areas. The most popular activity is retail trade: 425 firms in Lima, 714 in other urban areas, and 298 in rural areas. In the analysis of the retail sector we excluded 51 observations urban because i) only children under 15 were employed (five in Lima, four in other urban areas, seven in rural areas), ii) no sales revenue was reported (nine in Lima, eight in other urban areas, three in rural areas), and iii) values of capital or expenses were more than 100,000 intis (six in Lima and nine in other urban areas). This reduced the sample to 1,386 firms: 405 in Lima, 693 in other urban areas, and 288 in rural areas.

A novel feature of this study is that it explicitly addresses the predominate role of women in the retail sector by distinguishing among three 22
# **Lessons for developing electronic distribution systems for government securities**

This study provides interesting lessons for other countries interested in designing and implementing electronic distribution systems for government securities. These lessons fall under three categories: the main goals in creating such systems, the various design elements that help achieve those goals, and the preconditions required for such systems to succeed.

# _Main goals_

The first step in creating an electronic distribution system for government securities is to clearly identify the system’s goals—because these goals will have a profound effect on how the system is designed. The two main goals in creating the U.S. TreasuryDirect system were to make it as easy as possible for the public to purchase and hold securities directly with Treasury (especially in the form of risk-free assets) and to empower them to directly participate in their government’s borrowing program.

Achieving the first objective depends on four criteria: what percentage of the general public the system is able to reach, how convenient and easy it is to use the system, whether the system is perceived as being trustworthy, and whether the returns from direct purchases of securities compare favorably with those from other savings products. The first three criteria are significantly affected by the scheme’s overall design. For the fourth, one of the main benefits from the direct distribution of securities is that it eliminates intermediation costs. As a result investors achieve higher returns using legacy _TreasuryDirect,_ and will continue to do so when marketable securities are offered in the new TreasuryDirect, than they would if they invested in the same securities using a broker. Historically, returns on deposits and money market mutual funds have also been lower than those on Treasury securities, in the latter case due to fund fees (table 2).

**Table 2 Average returns on various U.S. savings products, 2002**

<u>(percent)</u>

|_Product_|**Return**|
|---|---|
|Money market accounts|0.71|
|1-year certificate of deposit|0.83|
|5-year certificate of deposit|2.94|
|Moneymarket mutual funds|0.88|
|182-dayTreasury bill|1.02|
|5-year Treasurynote|3.30|
|10-year Treasurynote|3.97|

_Sources:_ Bureau of Public Debt; Banxquote.com; Investment Company Institute, _Mutual Fund Year Book 2002._ Although reducing costs to the issuer—the Bureau of Public Debt—was not been a primary objective of the legacy _TreasuryDirect_ system, the scheme has lowered the costs of issuance in addition to eliminating intermediary costs for investors. With the new TreasuryDirect and the sale of electronic savings bonds, it is expected that Public Debt will gradually reduce the costs of the savings bond program as well. And with recent advances in technology, the costs of establishing electronic 13
Appendix Table 7: RDD estimates of gaps in teacher behaviors and teacher absence associated with teachers hired pre- and post-FPE <u>(bandwidths of 5, 7, and 10 years)</u>

|Bandwidth||Good<br>teacher<br>demeanor|Good<br>pedagogical<br>practices|Classroom<br>environment|Good<br>teaching<br>practices|Share of<br>time<br>teaching|Absent from<br>school|Absent from<br>classroom|
|---|---|---|---|---|---|---|---|---|
|Pooled<br>5|Coef|0.011|0.041|0.025|0.026|-0.033|-0.196|-0.136|
||SE|(0.031)|(0.025)|(0.026)|(0.019)|(0.028)|(0.070)|(0.081)|
||Pval|0.714|0.104|0.327|0.167|0.246|0.005|0.095|
|7|Coef|-0.008|0.046|0.032|0.023|-0.022|-0.173|-0.117|
||SE|(0.027)|(0.023)|(0.022)|(0.016)|(0.026)|(0.060)|(0.072)|
||Pval|0.767|0.046|0.139|0.148|0.388|0.004|0.105|
|10|Coef|-0.010|0.022|0.024|0.012|-0.016|-0.133|-0.080|
||SE|(0.022)|(0.019)|(0.019)|(0.014)|(0.022)|(0.052)|(0.061)|
||Pval|0.654|0.244|0.203|0.377|0.463|0.011|0.189|
|Kenya<br>5|Coef|0.063|0.217|-0.056|0.075|-0.073|-0.247|0.224|
||SE|(0.106)|(0.121)|(0.084)|(0.079)|(0.135)|(0.147)|(0.263)|
||Pval|0.556|0.076|0.510|0.350|0.591|0.098|0.398|
|7|Coef|0.050|0.210|-0.030|0.077|-0.021|-0.295|0.310|
||SE|(0.094)|(0.108)|(0.075)|(0.073)|(0.120)|(0.132)|(0.220)|
||Pval|0.600|0.054|0.690|0.294|0.859|0.028|0.162|
|10|Coef|0.031|0.154|0.003|0.063|-0.034|-0.296|0.255|
||SE|(0.079)|(0.091)|(0.064)|(0.061)|(0.100)|(0.116)|(0.185)|
||Pval|0.698|0.091|0.960|0.307|0.737|0.011|0.169|
|Madagascar<br>5|Coef|-0.024|0.034|0.036|0.015|-0.006|-0.213|-0.266|
||SE|(0.061)|(0.041)|(0.056)|(0.034)|(0.045)|(0.143)|(0.147)|
||Pval|0.699|0.410|0.521|0.650|0.894|0.137|0.072|
|7|Coef|-0.029|0.046|0.036|0.018|-0.005|-0.099|-0.155|
||SE|(0.054)|(0.037)|(0.049)|(0.031)|(0.039)|(0.123)|(0.128)|
||Pval|0.594|0.222|0.460|0.564|0.901|0.425|0.230|
|10|Coef|-0.025|0.029|0.026|0.010|0.016|-0.101|-0.123|
||SE|(0.045)|(0.032)|(0.041)|(0.025)|(0.033)|(0.104)|(0.108)|
||Pval|0.586|0.357|0.520|0.681|0.626|0.335|0.256|

Notes: Table reports coefficient estimates (Coef), Standard Errors (SE), and p-values derived from estimating Equation (1). Each estimate and associated standard error and p-value are the result of a separate regression model. Pooled model includes country fixed effects.

A-14
# **Table 4.1. Real macro indicators in 2012 (constant US$mn) and growth 2012-2030 by simulation (Percent per year)**

||**2012**|**base**|**min+**|**inf+**|**hd+**|**geff+**|
|---|---|---|---|---|---|---|
|**Absorption**|3,403.1|3.5|4.4|4.0|3.6|3.9|
|**Consumption - household**|1,550.8|5.5|6.3|6.1|5.6|5.8|
|**Consumption - UNMIL**|469.9|-40.8|-40.9|-40.8|-40.8|-40.8|
|**Consumption - government**|391.1|6.2|7.5|6.0|6.3|7.0|
|**Fixed investment - private**|717.0|1.4|2.0|1.9|1.5|1.6|
|**Fixed investment - government**|274.4|-3.5|-0.5|-0.1|-3.0|-1.8|
|**Exports**|697.9|9.5|11.9|10.5|9.7|10.2|
|**Imports**|2,253.3|4.1|5.6|4.8|4.2|4.6|
|**GDP at marketprices**|1,847.7|6.0|7.4|6.7|6.1|6.5|
|**GDP at factor cost**|1,638.7|6.2|7.6|6.9|6.3|6.7|
|**Total factor employment (index)**|100.0|3.9|4.9|4.2|4.0|4.1|
|**Total factorproductivity (index)**|100.0|2.2|2.6|2.7|2.4|2.6|
|**GNI**|1,686.0|5.9|7.0|6.7|6.1|6.5|
|**GNDI**|2,807.3|4.2|5.1|4.9|4.4|4.7|
|**GNI per capita (2009 US$)**|423.4|3.6|4.6|4.4|3.7|4.1|
|**GNDI per capita (2009 US$)**|704.9|1.9|2.8|2.6|2.1|2.4|
|**Real exchange rate(index)**|100.0|0.4|0.0|0.4|0.4|0.5|
|**Unemployment rate (%)**|12.8|8.0|6.7|7.2|7.8|7.6|
|**MDG 1 -- poverty rate (%)**|60.2|38.6|33.3|34.7|38.0|37.0|
|**MDG 4 -- U5MR (per 1000)**|90.2|65.5|61.3|62.8|62.6|62.8|
|**MDG 7 -- Water access (%)**|70.8|77.6|85.0|80.7|83.1|81.6|
|**MDG 7 -- Sanitation access (%)**|19.6|25.4|31.7|28.3|29.8|28.7|

**Note:**

**Unless otherwise noted, the unit for the 2012 column is millions of US$ (at 2009 prices). Unemployment and MDG indicators show values in 2030 in columns other than 2012 (in indicated unit). Other indicators show average annual real growth 2013-2030 in columns other than 2012.**

24
Table 3: Sets move toward closures

||Depe|ndent v|ariable:|Distance|quantile|(km)|
|---|---|---|---|---|---|---|
||0%|25%|50%|Mean|75%|100%|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Treatment fraction|-22.5|-19.5|-18.3|-16.6|1.0|-25.2|
||(8.4)|(11.1)|(11.8)|(11.7)|(13.1)|(11.4)|
|Mean dep. var.|33.1|173.4|286.2|307.1|431.8|692.0|

Notes: All regressions have 33,138 observations. The unit of observation is a potential closuretreatment bin. For each potential closure-treatment bin, I calculate the distance to sets that occur during the period of the potential closure-treatment bin. The dependent variable is a quantile of these distances (in Column 4 it is the mean). All regressions control for the same variables as Equation 1. Standard errors clustered at level of two-week-of-sample by two-degree grid cell.

where _v_ = vessel, _j_ = season, _k_ = set, _d_ = day-of-sample, and _g_ = two-degree grid cell.

The outcome variable is the inverse hyperbolic sine of the tons caught by a given set.<sup>27</sup> The explanatory variable 1 _{Near}vjk_ equals 1 for sets that occurred inside a treatment bin in which there was a statistically significant change in juvenile catch (see Figure 7) and equals 0 otherwise. I define 1 _{Near}vjk_ in this way because I am interested in whether the large spillover effects I estimated in Section IV can be explained by the closures policy communicating information about the value of fishing in those places and times. I include the distance of each set to the coast, vessel by season fixed effects, day-of-sample fixed effects, and two-degree grid cell by season fixed effects in order to partially control for differences in the cost of each set (e.g., sets farther from shore require more fuel, all else equal). Dayof-sample fixed effects also control for the international price of fishmeal, which determines the price fishers receive per ton of anchoveta they land. Therefore, the change in tons per set captured by _β_ 1 represents the change in revenue per set from fishing near closures. To the extent that the fixed effects and the distance of each set to the coast control for cost per set, _β_ 1 can also be interpreted as the change in profit per set from fishing near closures.

When I define the near indicator relative to potential closures, I find that sets that happen 27Vessels report catching 0 tons for 11% of sets in the electronic logbook data.

37
data is from World Bank _(World Development Report,_ various); average years of education is the average number of years of schooling of the adult population (15 and over) and land per worker is the ratio of land area divided by the adult population. For Vietnam export, education and population data are from the Vietnam General Statistical Office (GSO); land data is from World Bank _(World Development Report, 1995)_

# _c) Vietnam's exportpotential: an estimate_

To what extent is Vietnam's comparative advantage in manufactures under-exploited? Using statistical results by Wood and Mayer (1998) can give us an idea of the answer. With a sample of 115 countries and data for 1990, the authors estimated the following regression:

- (1) Ln (Xm/Xp) = -5.11 + 1.47.1n (skill/worker) - 0.6.1n (land/worker) (-14.1) (7.18) (-6.59) R<sup>2</sup> =0.56

where each country's share of manufactures relative to primary exports (Xm/Xp) is related to its "human capital" (skill/worker) and to its relative endowment of land and labor (land/worker). All variables are expressed in natural logs (ln). The coefficients are significant at the 1% level and the explanatory power of the regression is high; the variables account for 56% of cross-country differences in export composition.

The coefficients obtained from this regression allow us to calculate any single country's predicted share of manufactures in total exports on the basis of its human and natural resources. Applying these coefficients to Vietnam, whose resource endowments are shown in table 13, yields the following results:

- (2) Predicted Ln (Xm/Xp) = -5.11 + 1.47.1n (6.28)- 0.6.1n (0.0075) =0.515

- (3) Predicted Xm/Xp = exp.(0.515) = 1.673

- (4) Predicted Xm/X = 1.673/(1 + 1.673) = 0.63

It appears that Vietnam's predicted earnings from manufacturing exports (Xm/Xp in equation 3) amount to 167% of earnings from primary exports instead of its actual 58%. Thus, its predicted share of manufactures in total exports (Xm/X in equation 4) is 63% instead of just 37.6%. In other words, with a more efficient use of its available resources, Vietnam could be expected to increase its share of manufactures in total exports by more than 25 percentage points.

Of course, these estimates must be taken with a grain of salt. First, in the analysis of Wood and Mayer, workers' skills are measured by the average number of years of schooling of the adult (over-15) population, while natural resources are measured by total land area. Both of these measures are imperfect. In particular, average years of schooling may overestimate the skills of Vietnam's workers since it does not take into account the quality of schooling nor skill acquisition through training or on the job experience -areas in which Vietnam are usually considered to fare poorly compared to other countries. In addition, total land area does not account for variation among countries in the quality of their land nor in their oil reserves. Since Vietnam is an oil exporter, total land area may understate Vietnam's 14
# **Figure 11: Timing of Return (%)**

<!-- Start of picture text --> 17<br>13<br>10 10<br>7 7<br>5<br>4 4<br>3 3<br>1 2 2 1 2 1 2 2 2 2<br><!-- End of picture text --> Source: Listening to Displaced People Survey, 2014.

94% of the returnees were displaced inside Mali and 6% outside the country. 67% went to Bamako, 11% in Koulikoro, 9% to Ségou, 5% to Mopti and 3% went elsewhere (Kidal, Gao and Sikasso). The majority returned between June and October 2013 a period that followed the signing of a peace deal between the interim government and rebel factions to allow presidential elections to be held in July (first round) and August (second round) 2013. In October security in the North worsened again and ever since the number of people returning has been very limited.

The main challenges reported by returnees in June 2014 were (i) poverty and food insecurity; (ii) lack of infrastructure (including lack of safe drinking water) and (iii) unemployment. 11% of the returnees stated not to be facing any challenges (Figure 12). Very few reported difficulties living together, that they had to start their life from scratch or that their main challenge was loss of goods and property (as we saw in the previous section, the sub-sample of returnees lost significantly less than the IDPs and refugees).

18
with the incomplete data. First, we grouped the teacher training data into four buckets: none, low, medium, and high, corresponding to the level of detail and completeness provided by the observed teachers. The results are in figure A.7.

**Figure A.7: CLASS Scores Grouped by Reported Training**

<!-- Start of picture text --> 7<br>6<br>5<br>4<br>3<br>2<br>1<br>0<br>None Low Medium High<br><!-- End of picture text --> Source: Guangdong class observations; authors’ calculations.

Overall, the grouping of teachers by reported training does not reveal any consistent patterns in CLASS scores. Indeed, the teachers who gave the most detail on the surveys regarding training history had the lowest average scores for Instructional Dialogue, Content Understanding, and Regard for Student Perspectives, as well as the lowest overall score for Instructional Support. Rather than interpreting this as a correlation between amount of training and teaching quality, it should be understood as a sign that more thorough responses on training history are needed for the next round of observations.

Second, given that teachers are generally required to attend annual training, and due to the wide range in experience by teacher type, we decided to group teachers by years of teaching experience rather than the self-reported training analysis done above. By grouping teachers for every five years of teaching experience (less than five years, six to 10 years, etc.), we created five similarly sized groups (figure A.8). This analysis straddles the line between training history and teacher type.

29
- 39 -

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