rafmacalaba/datause-encoder-data huggingface dataset:
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1.............

# Table of Contents

|Executive Summary ...........................................<br>vi|
|---|
|Introduction ..<br>1|
|I. Methodology and Data ...............<br>3|
|<br>A. Methodology ...............<br>3|
|<br>B.Data ...............<br>3|
|H. Trends in the Bank's Lending to Higher Education.<br>_5_|
|<br>A. 1972-1981<br>_.5_|
|B. 1982-1992 .7|
|C. Regional Distribution<br>.7|
|**M.**Constraints and Interventions .12|
|A. Identified Constraints in the Bank Projects .12|
|<br>B. Analysis of Interventions .17|
|IV. Quality of Interventions .29|
|V. Lessons From Bank Experience.<br>30|
|<br>A. Project Results .32|
|<br>B. Approaches in the Ongoing Projects for Which<br>Results are not Available.<br>3.|
|C. Effective Approaches Under Diverse Socio-Economic Setting .40|
|VI. Concluding Remarks .45|
|References .47|
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1.200<br>1.100<br>1.000<br>0.900<br>0.800<br>0.700<br>0.600<br>0.500<br>1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005<br>Labor Energy Materials<br><!-- End of picture text -->

Figure A.3: Capital Stock Efficiency Indexes, Food Products, Beverages, and Tobacco, United States, 1991-2005 <!-- Start of picture text -->
1.200<br>1.100<br>1.000<br>0.900<br>0.800<br>0.700<br>0.600<br>0.500<br>1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005<br>Labor Energy Materials<br><!-- End of picture text -->

Figure A.4: Capital Stock Efficiency Indexes, Basic Metals and Fabricated Metal Products, United States, 1991-2005 54
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21
**Figure 1:** Estimated time effects for weekday travel

<!-- Start of picture text --> Estimated time effect<br>0<br>-0.1<br>-0.2<br>-0.3<br>154 Cities<br>-0.4<br>20 Largest cities<br>-0.5 Delhi<br>Delhi (< 5km)<br>Hour of day<br>-0.6<br>0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24<br><!-- End of picture text --> The plain black line represents the time effects estimated in column 5 of table 4 for all 154 cities. The dashed black line represents the hour effects from the same estimation but restricts observations to the 20 largest cities. The plain gray line duplicates the same exercise for Delhi only. The dotted gray line only uses observations for which the distance to the center of the origin and destination is on average less than 5 kilometers in Delhi. All 3 - 3.30 a.m. effects are normalized to zero. All the plotted coefficients for 7am to midnight are significant at 1%.

As expected, we also observe fluctuations in travel speed across times of day. In figure 1, the dark continuous line plots the fixed effect of each thirty-minute period estimated in column 5 of table 4. For all cities, the gap between the fastest time in the middle of the night and the slowest at 6.30 p.m. is just 13%. We also note that morning peak hours are more muted than the evening peak hours.<sup>28</sup> The figure also plots the same coefficients estimated only on the twenty largest cities. The patterns are much more marked. The slowest periods in the evening are now more than 25% slower than the fastest in middle of the night. In addition, travel speed starts declining earlier in the morning and recovers later in the evening.

While larger, this difference remains less important than that estimated by Akbar and Duranton (2018) for Bogotá where the slowest period is about half as fast as the fastest. These mild within-day fluctuations may mask a lot of heterogeneity across Indian cities. To investigate this, we repeat the same exercise using only observations from the city of Delhi. Although Delhi is slow, we purposefully do not take the slowest city or a pathological case.

28Although we do not report the results here, we can also estimate time of day effects more accurately using trip fixed effects. The resulting estimates for time of day effects are virtually indistinguishable.

18
-2-

Third, and beyond the above efficiency considerations, the earnings premium associated with different levels of education leads to equity assessments in a given society, e.g. how does the provision of education, and at what level, contributes to poverty alleviation or a more equal income distribution?

The purpose of this paper is to present evidence on the relationship between earnings and education in Latin America for the latest year possible, and to discuss the implications of the findings for school investment priorities in the region. The analysis is necessarily macro, in the sense that a large number of countries has been covered, using a consistent methodology, although at the expense of performing more detailed within-country analyses. Thus the findings presented here should be considered as indicative regarding educational investment priorities in the region, pending more detailed country-specific work.

This study improves upon the previous cross-country studies by applying a consisten. mrethodology of estimation!'. Moreover, the database comes from representative national data sets of the countries in question. The data also allow for the study of time trends of rates of return to cducation, as well as assessing the profitability of different types of the secondary school curriculum.

The next section presents the methodology of estimation, followed by a section on the evidence. The implications of the findings are discussed in the concluding section.

_I/_ Previous studies on the returns to education in Latin America are listed in the Peferences.
Much of the observed decline in labor market participation in Tigray was primarily driven by the sharp falls in employment in urban areas (see Figure A1 in the Appendix). The share of households who reported participation in any economic activity in urban areas of Tigray declined by 48 percentage points from 72 percent in October 2020 to 38 percent in January 2021. In rural areas, on the other hand, employment quickly recovered after a brief dip in November 2021 to their pre-war levels by January 2021. The latter is related to the return of farm families to cultivate their land albeit at a high security risk.

# **4. Empirical Strategy**

The monthly nature of our data allows us to use alternative empirical strategies to identify the impact of the conflict on affected households. To evaluate the impact of conflict on food security and labor market outcomes, we employ three approaches. The first approach uses a standard Difference-in-Differences (DID) strategy to identify the aggregate impacts of the war by collapsing the months preceding and following the war into pre-war and post-war-onset periods. The second approach estimates disaggregated DID model where survey round dummies are interacted with region dummy variables. Third, we use the ACLED database to generate household-level and time-varying exposure to violent conflict and estimate two-way fixed effects model. We start with the following simple DID specification: _h_ r h 1W W W 2 × W W W 3 _h_ r _h_ r (1) rr rr rr YY = αα + αα WW + αα TTWWTTW TT WW + αα XX + εε where hr stands for a measure of food insecurity and labor market outcomes associated with household _h_ living in region _r_ observed in time _t_ (pre- or post-war-onset). h stands for householdYY specific fixed effects. W W W stands for an indicator variable equal to 1 for the post-war-onset αα period, the period after November 2020, zero otherwise. We note that because the November 2020 rr WW round interviews were completed before the outbreak of the war, this round appears as pre-war. 1 capture aggregate changes in labor market and food security outcomes including in the absence of the war. represents an indicator variable for households living in Tigray region and αα hence those affected by the violent conflict. The first stage of the war, which lasted between rr TTWWTTW TT November 2020 to June 2021 took place in Tigray and later expanded to neighboring regions of 21
(dealers), Smetrix (software developer), and the Securities Exchange Commission. The program’s business plan aims to ensure that all stakeholders receive benefits that outweigh their costs.

# **Treasury**

The Treasury has the strongest incentives of any stakeholder, ranging from achieving lower costs to promulgating best practices through debt management policy.

# _Incentives_

_Increased access and distribution._ With an electronic direct retailing platform, the Treasury can provide direct retail participation in the government securities marketplace. Previously the Treasury relied on dealers to develop a deep, liquid retail market for government securities.

_Cheaper operations._ Dealers are not required to conduct backroom operations when distributing securities under the Expanded Small Investors Program. Between the program application and the Registry of Scripless Securities, backroom functions such as investor accounting and custody, statement rendition, and maturities processing are performed for each dealer. The Gatekeeper software is especially useful because it allows dealers to enter their demand deposit systems with little or no modification. With little upfront costs for banks, it is easier—as well as faster—for the Treasury to bring them into the program.

In addition, the Treasury can fully leverage the capabilities of the Registry of Scripless Securities and pass the savings on to investors, because the registry’s efficiencies—resulting from its real-time processing environment—are directly linked to the program’s retailing system. And because the program was designed to be as paperless as regulations allow, the delivery cost is lower than what private financial institutions are able to deliver.

_Better dealer management and discipline._ The Expanded Small Investors Program, by providing an alternative channel for distributing government securities, is expected to increase the Treasury’s ability to impose higher standards on dealers. First, the program can serve as an important check on the auction process. In 2002, the Treasury has rejected several primary bids in the belief that they were not in line with prevailing market conditions. Because the program is essentially a noncompetitive window for retail investors, the Treasury hypothesizes that with enough participants, dealers will avoid having too wide a spread between their bid rates for T-Bills and their yields on bank liability products—because doing so would encourage customers to switch from deposits to the Expanded Small Investors Program.

Second, because the Treasury has acquired the capacity to provide an almost painless environment for dealers to participate in the program, it theoretically has also acquired leverage to pressure dealers to build bona fide retail channels. Moreover, the Treasury can now quantify the efforts of securities dealers because it knows exactly how many retail investors they have enrolled. This capability makes it reasonable for the Treasury to impose investor quotas and targets as part of its criteria for rating dealers.

35
<!-- Start of picture text --> 25% - 25%<br>20% 20%<br>15% ¢ ~<br><!-- End of picture text --> <!-- Start of picture text --> 5% & 3%<br>ox a. ° fa a<br>Poorest 2 3 4 5 6 7 8 i9 Richest ox +98765432203123456789_<br>2006 #1989<br><!-- End of picture text --> <!-- Start of picture text --> 50% 50%<br>45% 45%<br>40% 40%<br>35% 35%<br>a e<br><!-- End of picture text --> <!-- Start of picture text --> 15% o 15%<br>10% 2 10%<br>* i a 7 a<br>ox = > ox _—__= Hi =<br>Poorest 2 3 4 5 6 7 8 9 Richest 9876543220:123456789<br>2010 » 2006<br><!-- End of picture text -->
Table 16: Program Impact on Child Participation in Other Household Chores

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
|---|---|---|---|---|---|---|---|---|---|
|||6-15 years old|||6-12 years old||1|3-15 years ol|d|
||All|Boys|Girls|All|Boys|Girls|All|Boys|Girls|
|School Meals|0.027|-0.002|0.044|0.028|-0.009|0.056|0.008|0.017|0.012|
||[0.022]|[0.024]|[0.036]|[0.026]|[0.027]|[0.042]|[0.042]|[0.052]|[0.066]|
|Dry Rations|0.065|n.a.|0.116|0.055|n.a.|0.113|0.090|n.a.|0.110|
||[0.030]**|n.a.|[0.036]***|[0.035]|n.a.|[0.042]***|[0.057]|n.a.|[0.068]|
|Follow-up|-0.051|-0.005|-0.092|-0.051|0.007|-0.100|-0.054|-0.042|-0.071|
||[0.016]***|[0.018]|[0.026]***|[0.019]***|[0.021]|[0.030]***|[0.030]*|[0.039]|[0.047]|
|Girl|0.574|||0.528|||0.740|||
||[0.011]***|||[0.013]***|||[0.021]***|||
|Constant|-0.066|0.114|0.373|-0.049|0.106|0.362|0.147|0.176|0.869|
||[0.020]***|[0.020]***|[0.028]***|[0.021]**|[0.021]***|[0.030]***|[0.031]***|[0.043]***|[0.036]***|
|Observations|6047|2462|3585|4757|1931|2826|1290|531|759|
|R-squared|0.40|0.09|0.17|0.37|0.10|0.17|0.55|0.10|0.09|

# Robust standard errors in brackets

* Significant at 10%; ** significant at 5%, *** significant at 1% Dependent variable is whether child participated in other household chores the week before the survey. Regressions also control for child age, orphanhood, parents education, ethnicity, religion, household assets and village fixed effects (not reported).

39
_ANDR EE_<sup>_ ́_</sup> _DECEMBER - 2021_ _12_ bist models have done well on a variety of spatially oriented prediction problems, often reaching accuracy not far below that of deep learning methods while maintaining full model interpetability (Morellos et al., 2016; Ng et al., 2019; Sbahi et al., 2021). While the short-range extrapolation capabilities of cubist models are useful in the current setting, the most beneficial feature for this application here is that it runs much faster than its M5 cousin or other boosting or ensembling methods such as Gradient Boosting Machines and eXtreme Gradient Boosting methods (Hagenauer et al., 2019).

Two regression specifications are considered. Both regression models simply perform a spatio-temporal interpolation, leveraging temporal price trends, geographic proximity, and spatial trends, to make time-varying spatial interpolations between prices of related food items. More precisely, the linear model is of the following form: while the cubist approximates the nonlinear function: In these equations, _P_<sup>_A_</sup> is a vector that stacks all the prices of commodity _A_ observed at all _markets × time_ combinations within a country. Similarly, _P_<sup>_−A_</sup> is a matrix that has the prices of all other food items. In the previous example, it would simply bind columns ( _B, C_ ), and the iterations would simply cycle over specifications according to the scheme _P_<sup>_B_</sup> _∼ P_<sup>_−B_</sup> + _εB_ and _P_<sup>_C_</sup> _∼ P_<sup>_−C_</sup> + _εC_ . In this example, there are just three price vectors, but table A1 shows that the application considers problems with up to 15 price predictors.

Prices on both sides of the equation are modeled in logarithms, omitted from notation here. The vector _β_ 1 captures the linearized relationships between log prices, e.g. the price ratios. The matrix _G_ are group dummies for market-level fixed effects and administrative-level fixed effects, the matrix _S_ are seasonal dummies. Finally, _X_ is a matrix of additional covariates. These include logarithmic coordinates to capture spatial trends, and price trend features engineered from ( _A, B, C, . . ._ ) that capture important temporal variation. The price trend features are engineered by first taking the individual market price trajectories contained in ( _A, B, C, . . ._ ) that have been observed with at least 95% data coverage, the up to 5% missing price points are imputed with a seasonal Kalman filter using a Basic Structural Model. Second, a commodity-specific country price trend is constructed by Kalman interpolating all market trajectories and taking a weighted average based on data coverage. In particular, trajectories that had above 75percentile data coverage are averaged, weighting by normalized data coverage rates. As an example, if there are three markets with above 75-percentile data coverage that respectively have 50%, 75% and 100% data coverage, the weights are (0 _,_ 0 _._ 5 _,_ 1).
**Figure 6: Kernel densities of non‐cognitive measures by gender**

<!-- Start of picture text --> A. Conscientiousness B. Emotional stability<br>Z-Score of Goldberg Big 5 factor - z_gldconsc Z-Score of Goldberg Big 5 factor - z_gldemotstab<br>-4 -2 0 2 -4 -2 0 2<br>Score Score<br>Female Male Female Male<br>. .<br>C. Kindness D. Cooperation<br>Z-Score of Goldberg Big 5 factor - z_gldkindness Z-Score of Goldberg Big 5 factor - z_gldcooperat<br>-6 -4 -2 0 2 -6 -4 -2 0 2 4<br>Score Score<br>Female Male Female Male<br>. .<br>E. Extraversion F. Openness to experience<br>Z-Score of Goldberg Big 5 factor - z_gldextrav Z-Score of Goldberg Big 5 factor - z_gldopeness<br>-4 -2 0 2 -4 -2 0 2<br>Score Score<br>Female Male Female Male<br>. .<br>G. Perseverance of effort (grit) H. Consistency of interest (grit)<br>Z-Score of Duckworth Grit factor - z_grt2persist Z-Score of Duckworth Grit factor - z_grt2consist<br>-4 -2 0 2 -2 -1 0 1 2 3<br>Score Score<br>Female Male Female Male<br>. .<br>.5 .5<br>.4 .4<br>Density .3.2 Density .3.2<br>.1 .1<br>0 0<br>.6<br>.5<br>.4<br>.4<br>Density .3 Density<br>.2<br>.2<br>.1<br>0 0<br>.4<br>.4<br>.3 .3<br>.2 .2<br>Density Density<br>.1 .1<br>0 0<br>.4 .4<br>.3 .3<br>.2 .2<br>Density Density<br>.1 .1<br>0 0<br><!-- End of picture text --> Note: All densities are significantly different except for those of conscientiousness, kindness, and consistency of interest.

**27 |** P a g e
Given these hurdles, our relatively modest goal was to provide both a basic set of minimal information across countries that is essential for economic research while also including additional information, whenever it was available, for countries that report “more” than the basics. Thus, for a substantial number of user countries we have a basic set of information on Harmonized System product codes for the goods that were allegedly dumped and a cause of injury, as well as dates and outcomes of various stages of the investigative process. For most of these countries we were also able to collect information on the names of domestic firms participating in the antidumping investigation, the names of foreign firms being targeted by the investigation, and also the firm-specific outcomes facing those foreign producers at the outcome stage of antidumping investigation that result in the imposition of measures.

Equally important was our intention of transparency for the project. Thus we also attempt to provide information on where and how we were able to collect the data for each country, so that researchers have the ability to go back to original source documentation and both check the accuracy of our data as well as to augment the data for their own use. We make no claim to have fully exploited all of the data available in these government notifications.

Finally, given the fixed cost associated with data collection for each user country added to the database, the data set at this stage is not comprehensive. With limited resources, our strategy in data construction was to first focus on collecting data for the largest and historically active user countries. Thus, as table 1.1 indicates, while we arguably have decent data on roughly 85% of the use of antidumping by WTO members there are a number of relatively new users (e.g., China, Egypt, Indonesia, Thailand) as well as smaller users and even non-members of the WTO for which detailed data have not yet been collected and compiled.

The rest of this paper proceeds as follows. Section 2 describes the structure of the variables and the data contained in the global antidumping database that is common across the 15 user countries. Section 3 then provides more details for each of the 15 user countries on additional, country-specific data included in the database, as well as particular elements of missing data for each user. Section 4 provides a brief discussion of the WTO Member countries that are also users of antidumping but that are not yet part 6
27 Figure 12. Denmark: Ex-post Real Interest Rates (annualized). Source: IMFIFS.

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Figure 13. France: Ex-post Real Interest Rates (annualized). Source: IMFIFS.

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