rafmacalaba/datause-encoder-data huggingface dataset:
# **<mark>References</mark>**

- Akgunduz, Y. E., & Plantenga, J. (2018). Child care prices and maternal employment: A meta‐analysis. _Journal of Economic Surveys_ , 32(1), 118-133.

- Angrist, J. D. (2001). Estimation of limited dependent variable models with dummy endogenous regressors: simple strategies for empirical practice. _Journal of Business & Economic Statistics_ , 29(1): 1-28.

- Angrist, J. D. and Pischke, J. (2009). _Mostly Harmless Econometrics: An Empiricist’s Companion_ .” New Jersey: Princeton University Press.

- <mark>Bauernschuster, S., & Schlotter, M. (2015). Public child care and women' labor supply— Evidence from two quasi-experiments.</mark> _<mark>Journal of Public Economics</mark>_ <mark>,</mark> _<mark>123</mark>_ <mark>, 1-16.</mark>

- Berlinski, S., Galiani, S. and Mc Ewan, P.J., (2011). Preschool and maternal labor market outcomes: Evidence from a regression discontinuity design. _Economic Development and Cultural Change_ , _59_ (2), pp.313-344.

- Blau, D. and J. Currie. (2006). Preschool, daycare, and after-school care: Who is minding the kids? _Handbook of the Economics of Education_ , ed. by E. A. Hanushek, and F. Welch, vol. 2, pp. 1163-1278.

- <mark>Brody, C., Hoop, T.D., Vojtkova, M., Warnock, R., Dunbar, M., Murthy, P. and Dworkin, S.L., (2017). Can self-help group programs improve women’s empowerment? A systematic review.</mark> _<mark>Journal of Development Effectiveness</mark>_ <mark>,</mark> _<mark>9</mark>_ <mark>(1), pp.15-40.</mark>

- <mark>Cascio, E. U. (2009). Maternal labor supply and the introduction of kindergartens into American public schools.</mark> _<mark>Journal of Human Resources</mark>_ <mark>,</mark> _<mark>44</mark>_ <mark>(1), 140-170.</mark>

- <mark>Connelly, R., DeGraff, D.S. and Levison, D., (1996). Women's employment and child care in Brazil.</mark> _<mark>Economic Development and Cultural Change</mark>_ <mark>, 44(3): 619-656.</mark>

- Dang, H.A.H. (2012). Vietnam: A Widening Poverty Gap for Ethnic Minorities. In Gillette Hall, and Harry Patrinos. (Eds.). _Indigenous Peoples, Poverty and Development_ . New York: Cambridge University Press.

- Dang, H.A.H. and Glewwe, P.W., (2018). Well begun, but aiming higher: A review of Vietnam’s education trends in the past 20 years and emerging challenges. _Journal of Development Studies_ , _54_ (7), pp.1171-1195.

- <mark>Dimova, R., & Wolff, F.-C. (2011). Do downward private transfers enhance maternal labor supply? Evidence from around Europe.</mark> _<mark>Journal of Population Economics</mark>_ <mark>, 24(3), 911– 933.</mark>

- Du, F. and Dong, X.Y., (2013). Women’s employment and child care choices in urban China during the economic transition. _Economic Development and Cultural Change_ , 62(1): 131-155.

24
-81 .

# Other Problems

About 20% of projects had substantial delays because of the utility's general Inefficiency, lack of control or poor judgement. Either consultants were terminated prematurb.i, or force account operatlons were poorly organized and supervised, or there may have been shortages of staff to administer constructlon programs. Technical problems were cited as a reason for delays and cost overruns for about one half of the projects but most of the problems were considered not to have been within the control of the utility but were unforeseen design and engineering problems, such as additlonal preparation works for construction projects and faulty Installation of equipment. Poor cost estimates which are ultimately the responsibility of the utility (although the Bank has ta approve such estimates) was one of the reasons for project cost overruns for about 20% of projects. Revisions to Drolects was a reason for cost overruns for about 40% of projects, at least some of which were due to poor preparation of the original project by the u.llity. Of the Investment programs for which there were delays In Implementation, about 25% were the result of Institutional Inability to carry out the program.

One of the most slgnificant correlatlons observed was between Institutional performance and lvel of losses. The higher the losses the noorer the Institutlonal performance. Poor bililng systems, Inability to read meters correctly and In a timely fashion, poor control of Illegal connections and poor operatlon and maintenance of the network were all contributory factors to high loss levels. Admittedly strong government support Is often required to Implement effective Iss control programs, but much can still be done by the utility itself If It has the required determinatlon. Overloaded distrbutlon systems also account for substantial losses but many of the systems are overloaded because of Illegal connectlons and failure of the utility to control the growth of new connectlons.

Soine recent statistical analysis In EGY to determine the relationship between technical and financlal performance, yielded mixed results. Institutlonal performance was measured by the overall efficiency
# ACl - WLEDGMW

I gratefully adkowledge hlpfiul dicions with the following people on the subject of congestion pricing: Herbert Mohring, Kiran Piat, JosE Cabajo, Anthony Cbin, Ian Heggie, Davd H sher, Peter J. Hills, Peter Jonas, Odd Larsen, Hennig laidsen, Aurelio Menende, Wflliam Pson, Robert Poole, Farideh Ramjerdi, Gabriel Roth, Richard Scurfildd, Koeeh Small, Rom Toh and William Vclcrey. In addition, I thank the foilowing people for providing me with informaion on road pricing tcchnologies: Arnold Brgode (AT&T Smart Card Dision), Ed DeLazier (DuHll Tol Road), Frank Dorrance (Amtch), Al Dunn (Louisiana Department of Trsortation and Development), Robert Frak (Amtch), Wedey Freise (Harris County Toll Road Authority), BiB Harison (MVA Cosutncy), Chris Hill (Casto Rock Consultns), Robert Lambert (Lake Pontdharuain Causeway), Svend Lars (Oslo Rgen), Aa Ln y(egit of Vehicles, Government of Singapore), Alan LAvmeur (Cresceat City Conecion), La Lind (Odo Ringen), Gopinath Menon (Public Works Department, Govenmont of Singapo), Brian Oldridge (Cridgeshir County Council), Bo Peterson (S ,kholm Transport), Heero PolPitch Ministy of Transport and Public Works), Mardn Richards (MVA Consultancy), Richard Riing (Okhoma Tuhrpip3 Authorhy), John Rourlo (AT/Comm), Frank Schepis (Autostade S.p.A.), Robert Selvi (Autostade S.p.A.), Arvid Strand (Micro Design, Trondheim), Brian Taylor (Internationa Road Dynaics Inc.), Tony Thompson (MVA Consultancy), Terje Tretvik (SINTEF Transport Engineering, Trondheim) and Robert Tuckwdl (CambrdgeslIte County Council). Part of the findings of this reseach was preented at the 71st Amnual Meet of the Traportation Research Boar', Washington, D.C., January 12-16, 1992; the 6th World Confence on Transport Research, Lyon, France, June 29 - July 3, 1992; Seminario de Tarificauion Vial 1992 (Ro Pricng Semnr), sponsored by the Ministry of Transport, Santiago, Chile, July 20-21, 1992; and the Symposm on Road Pricig, sponsored by the Swedish National Road Administon and Dep of Rgional Planing, the Royal Istute of Techology, Siguna, Sweden, November 9-10, 1992. Widely disseminated, this paper has bee distibuted at places such as the Congestion Pricing Symposum, sponsored by the Fdal Hihway Administ and the Federal Transit Administration, Arlington, Virginia, June 10-12, 1992, and the Southwest Congesdon Pricing Conference, sponsored by the Univerity of Houston Cetr for Public Policy and Citins Advocat Responsible Transportation, Houston, Texas, January _4-5,_ 1993. Com-men made by pardcipas at these conerenes are acknowledged.

I retain fill responsiblity for any of the views expressed in this paper.

Timothy D. Hau
upper-secondary on-time graduation and test scores. A one standard-deviation (SD) increase in grade 6 test scores is associated with an increase in the probability of on-time lower secondary graduation by 10 percentage points and (conditional on this) with an increase in learning outcomes by 0.6 SD. They also find a strong association between grade 6 test scores and grade 12 outcomes. Lastly, they find that grade 6 test scores and future outcomes have a strong relationship even when comparing individuals with identical family backgrounds, which is done using a subsample of twins.

# **IV. Data and Methodology**

We are fortunate to have access to a number of administrative data sets that we combine to create a data set with information on achievement tests, characteristics of students, PROSPERA receipt and school quality. We now describe the data sets used and our methodology.

From the school years 2006-2007 to 2013-2014, the Mexican Secretariat of Public Education (SEP) applied the Evaluación Nacional de Logro Académico en Centros Escolares, ENLACE. The test gathered information at the end of each academic year on student performance in math, Spanish and a rotating subject for all third to ninth graders in private and public schools. Beginning in 2007-2008, ENLACE was also given to students in their final year of high school (grade 12). The test was intended to be a low-stakes assessment that would be informative about learning outcomes to SEP and to parents. The proportion of students who take the test is close to 90% (Hoyos, Estrada and Vargas 2018). The last school year of application of the test was 20132014. ENLACE was eventually replaced by PLANEA (National Plan for Evaluating Learning “Plan Nacional para la Evaluación de los Aprendizajes”) during the Pena Nieto administration, this test however is applied only to a sample of students in each school.

An important issue related to using the ENLACE for this study is the possibility of cheating and, in particular, student copying on the ENLACE exams. When the government stopped applying the ENLACE tests, one of the stated concerns was over an increased incidence of suspected student copying. Unless the level of student copying was extremely high with the majority of students engaging in copying, the main way in which cheating might affect the results presented here is if there were differential rates of cheating between PROSPERA and nonPROSPERA students, e.g. that results on achievement tests would reflect both performance on the test and cheating. We are unaware of publicly available data either on the overall level of cheating detected by the Ministry of Education (SEP)/National Institute of Education Evaluation (INEE). Individual answer sheets would be necessary to directly analyze whether there are differential rates of cheating between PROSPERA and non-PROSPERA students based on standard statistical cheating detection methods developed in the education literature. (e.g. Wollak, 2003).

However, since the ENLACE exams are not used to calculate student grades, it is not obvious that PROSPERA and non-PROSPERA students would have different incentives for cheating on 9
year period 1992 to 2001, slightly above the annual growth rate in global population. No radical changes are anticipated in aggregate demand. Despite relatively flat prospects for global demand growth, an expansion in demand for specialty and “issue” cocoa is expected. Since child labor in cocoa production has become a major issue in West Africa, “child labor free” cocoa might be able to fetch a premium. Environmentally friendly and organic cocoa have small markets relative to coffee, but they are expected to expand.

The future of cocoa production and exports is unpredictable. Many countries are responding to the recent price increases by planting more trees. As these trees take up to six years to reach full production potential, oversupply may result in the future. The situation in Cote d’Ivoire, the supply responses in a liberalized cocoa market with entry of large multi-national companies contribute to the uncertainty. Short-term expectations are that international cocoa prices will remain at high levels for at least the next year.

|**Table II.2 World Cocoa Prices, Production and Grindings 1992/3– 2001/2**|
|---|

||Avg.<br>1992/93 –<br>1996/96|1997/98|1998/99|1999/’00|2000/1<br>(est.)|2001/2<br>(est.)|
|---|---|---|---|---|---|---|
|**_Average Annual Price $USD/MT_**||1,660*|1,140|888|1,089|1,778|
|**_Production_**|2,579|2,690|2,808|3,073|2,850|2,800|
|World Total<br>‘000MT|2,579|2,690|2,808|3,073|2,850|2,800|
|Cote d’Ivoire Share|37.2%|41.4%|41.4%|45.9%|32.8%|42.3%|
|Dominican Republic Share|2.1%|2.6%|0.9%|1.2%|||
|**_Grindings_**|||||||
|World Total<br>‘000MT|2,578|2,764|2,762|2,967|3,100|2,900|
|EuropeanShare|50%|47%|46%|46%|||
|US Share|13%|14%|15%|15%|||
|Cocoa Producing Countries Share|29%|30%|32%|32%|||

Source: FAO website.

# _Dominican Republic’s Position in World Cocoa Markets_

The Dominican Republic is among the ten largest cocoa producers in the world (it was the 8<sup>th</sup> largest producer from 1992/93-1996/97), and the third largest producer in Latin America behind Brazil and Ecuador (see table II.3). However, since 1992, the Dominican Republic has had only 1 to 2.5% of the world share of production and a similar share of world exports. The world’s leading cocoa producer, Cote D’Ivoire had a 37% to 46% share of production, and the major four producers – Cote d’Ivoire, Ghana, Indonesia and Brazil a 65% - 77% share. Although its share in global cocoa markets is small in the aggregate, it is a “major player” in the organic cocoa market – accounting for approximately 60% of trade in organics.

28
manufacturing before the pandemic (compared to 32 percent among the bottom 20), i.e., sectors that have traditionally required physical presence and thus are not very amenable to home-based work. These sectors were also among the hardest hit early on, with 28 percent of workers employed in them prepandemic being out of work by May 2020. Even within sectors, those at the bottom and at the top of the distribution were on average similarly likely to be out of work in Indonesia and the Philippines, despite official data showing higher rates of job security and formality among the wealthier.

Towards the end of 2020 and into 2021, the economy in Indonesia and the Philippines started to recover, yet mobility remained well below pre-pandemic levels and below the levels of most other countries in the EAP (Figure 1). At this time, signs of potentially unequal effects emerged, but differences across quintiles in work stoppages were largely not statistically significant across periods (Figure 6). However, evidence suggests that recovery may have been slower for poorer workers in the Philippines and Indonesia. In the Philippines, workers in the top 60 percent of the distribution were more likely to return to work than those in the poorest 20 percent between August and December 2020.<sup>20</sup> In Indonesia, while differences across welfare quintiles were not statistically significant, the top 40 experienced a faster recovery in employment after May 2020, and results from March 2021 also suggest that more educated workers had a faster rebound, particularly relative to those in the bottom quintile.<sup>21</sup> Moreover, as will be shown below, wealthier workers in both countries were less likely to face reductions in labor income compared to poorer ones after the initial impact of the pandemic.

Figure 6. Work stoppages in Indonesia and the Philippines <!-- Start of picture text --> Indonesia<br><!-- End of picture text --> <!-- Start of picture text --> Philippines<br><!-- End of picture text -->

> 20 While work stoppages between August 2020 and May 2021 paint a slightly different picture, data shows that compared to top 60 workers, bottom 40 workers were more likely to stop working due to COVID-related reasons in May 2021, such as being ill/quarantined or having to take care of kids or ill family members.

> 21 See Round 5 report for Indonesia: https://thedocs.worldbank.org/en/doc/0485c24dfb867c75fcf15e98fed9fb130070012021/original/Indonesia-HiFy-COVID-19-Round-5.pdf

15
OECD 2004. _OECD Science, Technology and Industry Outlook_ . Paris: Organisation for Economic Co-operation and Development.

"Pentium's Reign in Jeopardy" 2005. _New Scientist._ February 19.

Pilling, David. 2004. "A Wageless Recovery: Japan's Economic Growth Has Failed to Boost Workers' Pay." _Financial Times_ , Nov. 10 Porter, Michael E. and Mariko Sakakibara. 2004. "Competition in Japan." _Journal of Economic Perspectives_ 18(1): 27-50.

"The Quickening of Science Communication" 2000. _Science._ July 14.

Ramstad, Evan. 2004. "China's Great Hi-Tech Leap." _Far Eastern Economic Review_ , May 13 "Samsung Design" 2004. _BusinessWeek._ December 6.

Saxenian, Anna Lee and Jinh-Yuh Hsu. 2001. "The Silicon Valley-Hsinchu Connection: Technical Communities and Industrial Upgrading." _Industrial and Corporate Change_ 10(4): 893-920.

Sender, Henny. 2003. "A Rude Awakening." _Far Eastern Economic Review_ , Nov. 13 "Small Wonders of the nanotech world" 2004. _Financial Times._ October 11.

"Sony Struggles to Get Back in the Race" 2004. _Financial Times._ October 28. Statistics Bureau. 2003. _Report on Internal Migration in Japan_ Tokyo: Ministry of Internal Affairs and Communications.

Swank, Cynthia Karen. 2004. "The Lean Service Machine." _Harvard Business Review_ , Oct.: 123-128.

Technology Review. 2004a. "5 Killer Patents." _Technology Review_ (May 2004)

-----. 2004b. "The Patent Scorecard 2004." _Technology Review_ (May 2004)

"The 9-in-1 Wonder Chip" 2005. _Fortune._ September 5.

"The Car Company in Front" 2005. _The Economist._ January 27.

"The Incredible Shrinking Country" 2004. _The Economist._ November 11.

Tilin, Andrew and Mariko Mikami. 2004. "Heir to the Throne." _Business 2.0_ , Aug.

Tomiura, Eiichi. 2003. "Changing Economic Geography and Vertical Linkages in Japan." NBER Working Paper 9899.

UNECE. 2004. _Press Release: ECE/STAT/04/P01_ Geneva, Switzerland: 41
# **a. Price-Raising Effect of SPS Measures on Selected Products**

We estimate the price-raising effect of SPS measures by employing the empirical framework explained in Section 3. Specifically, we use interaction terms in our cross-country regression to estimate how different the effects of Guatemalan SPS measures, applied to our selected set of products, are from the average effect across countries and products. We then use Equation 8 to compute ad-valorem equivalents. Table 7 shows the results along with the list of the different NTMs (at the highest level of disaggregation) that importers need to comply with in order to import these products into the Guatemalan market.

**<u>Table 7. SPS Measures Influencing Trade in Selected Products in Guatemala</u>**

||**Beef**|**Bread and Pastry**|
|---|---|---|
||**Sanitary and Phytosanitary Measures**|**Sanitary and Phytosanitary Measures**|
||A140 Special Authorization requirement for SPS reasons<br>A150 Registration requirements for importers<br>A310 Labelling requirements<br>A330 Packaging requirements<br>A420 Hygienic practices during production<br>A640 Storage and transport conditions<br>A810 Product registration requirements<br>A820 Testing requirement<br>A830 Certification requirement<br>A840 Inspection requirement|A140 Special Authorization requirement for SPS reasons<br>A150 Registration requirements for importers<br>A310 Labelling requirements<br>A330 Packaging requirements<br>A810 Product registration requirements<br>A830 Certification requirement|
|**AVE**|**68.4%**|**51.4%**|
||**Chicken Meat**|**Dairy Products**|
||**Sanitary and Phytosanitary Measures**|**Sanitary and Phytosanitary Measures**|
||A140 Special Authorization requirement for SPS reasons<br>A150 Registration requirements for importers<br>A310 Labelling requirements<br>A330 Packaging requirements<br>A420 Hygienic practices during production<br>A640 Storage and transport conditions<br>A810 Product registration requirements<br>A820 Testing requirement<br>A830 Certification requirement<br>A840 Inspection requirement|A140 Special Authorization requirement for SPS reasons<br>A150 Registration requirements for importers<br>A310 Labelling requirements<br>A330 Packaging requirements<br>A420 Hygienic practices during production<br>A640 Storage and transport conditions<br>A810 Product registration requirements<br>A820 Testing requirement<br>A830 Certification requirement<br>A840 Inspection requirement|
|**AVE**|**22.0%**|**5.0%**|

Note: This table lists the SPS measures which apply to the selected products. SPS ad-valorem equivalents are statistically significant at least at the 10% level. The HS codes associated with each product are as follows. Beef: 0201, 0202, 0206, 1502. Bread and Pastry: 1905. Chicken meat: 0207. Dairy products: 0401, 0402, 0403, 0404, 0405.

Source: Authors’ computation 23
Five percent of AFD is allocated to universities based on input, process and output criteria. These funds are allocated according to the relative scoring of each university on five weighted indicators: (i) <u>critical mass</u> obtained in each career measured by the number of undergraduate enrollees per program; (ii) <u>internal efficiency</u> measured by student-teacher ratios; (iii) <u>quality</u> measured by the number of faculty holding advanced degrees; (iv) <u>scientific quality</u> measured by the number of research projects per full time equivalent scholar; and (v) <u>scientific production measured by the number of publications per</u> researcher. High weights are placed on quality and scientific activities (84 percent) and low weights are given to efficiency and, particularly, enrollees per program.

Graph 3 reveals that performance funding does not mirror the distribution of AFD. Institutions that receive the least funding per student are all well above the default five percent results-based funding. In absolute terms University of Arturo Prat and Austral University, for example, obtained almost the same amount of performance funding in 2003, while total AFD funding per student was almost three times higher at the Austral University.

Results-based funding provides AFD universities an incentive to perform, especially in regard to the quality of staff and scientific activity. However, due to its structure and size, the mechanism has had limited impact in the sector. Moreover, problems have been encountered in the operation of the mechanism, which reduce its positive implications on transparency in the Chilean university system. Notably, the lack of a national information system with reliable, standardized statistics and unambiguous baselines has given way to errors and inconsistencies in the reporting from universities.

A bill is currently under review in the Chilean Congress that would write national accreditation of tertiary institutions into law and mandate the establishment of a national information system for post-secondary education. As a minimum, institutions would be required to provide statistics related to students, faculty, resources, infrastructure and learning outcomes. Collected information would be validated by the Ministry of Education and published annually (MINUDUC 2003). Consultations have begun between the Ministry of Education and key universities on definitions and criteria, but a final design is yet to be agreed upon.

# **Indirect public support**

All institutions offering tertiary learning opportunities in Chile are eligible for indirect public support. The AFI is distributed to institutions according to their ability to attract the 27,500 highest scoring students in the university admission exam (PAA). In 2003, the distribution of AFI between institutions was: 82 percent for the 25 traditional universities, 17 percent for private universities and 1 percent for IPs and CFTs.

- 5 -