rafmacalaba/datause-encoder-data huggingface dataset:
change their declared choice toward such higher scoring schools, leading to higher test scores). Our paper builds on this work by showing the role of information in markets where private providers change prices to optimally adjust to an improvement in the quality signal.<sup>9</sup> Three examples of non-experimental but related studies in similar market settings are Figlio and Lucas (2004), Mizala and Urquiola (2013), and Jin and Leslie (2003). Figlio and Lucas (2004) show that house prices are sensitive to school rankings, suggesting that parents are willing to pay more to send their child to schools with higher test scores. However, Mizala and Urquiola (2013), using a regression discontinuity design, find little impact of school rank on enrollments or prices. They rightly caution that they cannot capture the effect of new information in markets since their unit of comparison is not _across_ markets that differ in the extent of third-party information, but rather across providers _within_ a market, which faces the same informational environment. A closer analogue to our paper is Jin and Leslie’s (2003) study on the introduction of restaurant hygiene cards in a single market, Los Angeles, and its impact on food safety and restaurant revenues. They document positive effects, especially among initially poor performers. While our results use an experimental design from a different setting that is able to exploit variation across multiple markets, it is nevertheless noteworthy that the pattern of heterogeneity they uncover is similar to ours.

In short, we show that prices are indeed a key component of how markets react when information improves, and that price changes are differential across initial school quality in a way that is consistent with the predictions of canonical models of asymmetric information. These insights are important in helping

> 9 Hastings and Weinstein's empirical strategy—using choice models to look at demand-side responses to information provision—also follows from their specific context, where prices are administratively determined and school-level responses are unlikely in the short-term. In contrast, our setting allows for supply-side responses both in prices and quality and among both public and private schools, leading us to focus more on the overall structure of the market and how it changes with the provision of information.

6
evaluation came from a baseline survey administered in 2000 and from follow-up surveys in 2001 and 2002. The sample was representative of a poorer part of the Central Region in Nicaragua. Within the evaluation sample, 21 of the 42 _comarcas_ were identified as being areas where coffee was cultivated, 10 in the treatment group and 11 in the control group. The methodology used for the evaluation was double difference estimators, applied to the experimental data samples. The impact analysis finds that household and food expenditures were significantly higher, by around 20%, in treatment localities than in control localities. During the economic downturn, control households increased their number of hours worked as a way of coping with the downturn, whereas treatment households worked about the same numbers of hours. School enrollment rates also rose significantly more in treatment areas than control areas, with a simultaneous decline in child labor. Maluccio (2005) concludes that the RPS program played an important role in helping poor, rural Nicaraguans cope with the economic crisis created by the falling coffee prices.

# **6.3  Summary**

Affecting adult female employment outcomes has not been a primary goal of most CCT programs, which instead have been oriented toward providing incentives for increasing school attendance of children, improving the health of both children and adults, and transferring income to poor families. Nevertheless, the incentives created by these programs have indirectly influenced adult women‟s time use. The rural evaluation of the Mexican _Oportunidades_ program was based on a rigorous experimental design and collected extensive data. The studies we reviewed indicate that the program did not significantly affect mothers‟ labor force participation status but did affect mothers‟ time use in several ways. First, mothers spent substantial amounts of time meeting program requirements, such as bringing children to school and to health clinic appointments and attending the required health talks. Second, in families with girls age 12-17, there is evidence that mothers‟ time engaged in childcare activities increased and that the older girls‟ time in childcare activities decreased, suggesting a substitution of mothers‟ time. Focus group interviews also supported the notion that mothers increased time devoted to childcare while their older girls attended school.

52
Table A1.8: Chinese Taipei; Numbers of tariff headings subject to SSG Actions, - <u>2002 2007</u>

|Year|Volume-<br>based|Price-<br>based|
|---|---|---|
|2002|11|13|
|2003|54|5|
|2004|34|2|
|2005|42|6|
|2006|31|5|
|2007|21|15|

Source: Tabulated from Chinese Taipei notifications, WTO documents G/AG/N/TPKM/8, 20, 27, 36, 46, and 58.

Table A1.9: Philippines; Numbers of tariff headings subject to SSG Actions, 1995 - <u>2004</u>

|Year|Volume-<br>based|Price-<br>based|
|---|---|---|
|1995|0|0|
|1996|0|0|
|1997|0|0|
|1998|0|0|
|1999|0|0|
|2000|0|0|
|2001|0|0|
|2002|1|6|
|2003|0|0|
|2004|0|2|

Source: Tabulated from Philippines notification, WTO documents G/AG/N/PHL/33. (Philippines notifications are not available for later years.) 58
# **VI. References**

Andres, Luis, Jose Luis Guasch and Sebastian Lopez Azumendi. 2008. "Regulatory Governance and Sector Performance: Methodology and Evaluation for Electricity Distribution in Latin America. " Policy Research Working Paper 4494. World Bank, Washington, DC.

Calderón, César, and Luis Servén. 2003. “The Output Cost of Latin America’s Infrastructure Gap.” In William R. Easterly and Luis Servén, eds., The Limits of Stabilization: Infrastructure, Public Deficits, and Growth in Latin America. Washington, D.C.: World Bank.

CEER (Council of European Energy Regulators). 2008. 4th Benchmarking Report on Quality of Electricity Supply 2008. Brussels. http://www.energy-regulators.eu/.

Cubbin, John and Jon Stern. 2006. “The Impact of Regulatory Governance and Privatization on Electricity Industry Generation Capacity in Developing Economies.” The World Bank Economic Review Advance Access, World Bank, Washington, DC.

Djankov, Simeon, Rafael La Porta, Florencio Lopez-de-Silanes, and Andrei Shleifer. 2002. “The Regulation of Entry”, Quarterly Journal of Economics 117(1):1-37.

Dollar, David, Mary Hallward-Driemeier, and Taye Mengistae. 2003a. “Investment Climate and Firm Performance in Developing Countries.” In Economic Development and Cultural Change, 54:1–31, October 2005 Eifert, Benjamin. 2007. " Infrastructure and Market Structure In Least-Developed Countries", http://beifert.googlepages.com/ Estache, Antonio and Martin Rossi. 2004. “Have Consumers Benefited from the Reforms in the Electricity Distribution Sector in Latin America?” Policy Research Working Paper 3420. World Bank, Washington, DC.

Estache, Antonio and Martin Rossi. 2005. Do regulation and ownership drive the efficiency of electricity distribution? Evidence from Latin America. Economic Letters. Volume 86, Issue 2, February 2005, Pages 253–257 Foster, Vivien, and Steinbuks Jevgenijs. 2009. “Paying the Price for Unreliable Power Supplies: In-House Generation of Electricity by Firms in Africa.” Policy Research Working Paper 4913, World Bank, Washington, DC.

Gassner, Katharina, Alexander Popov and Nataliya Pushak. 2009. “Does Private Sector Participation Improve Performance in Electricity and Water Distribution? Trends and Policy Options No. 6, World Bank, PPIAF, Washington, DC.

21
by the Penn State Commuting Zones/labor Markets Data Repository.<sup>8</sup> This crosswalk uses the commuting zone methodology devised by the Economist Research Service (ERS) at the US Department of Agriculture, updated to 2010 zones and modified to correct for some discrepancies by Fowler et al. (2016).<sup>9</sup>

# **3.3 Local labor market exposure**

We proxy for local labor market exposure using the pre-trade war sectoral employment composition of commuting zones. The employment data are originally produced by the US Census Bureau in the form of the County Business Pattern (CBP) database, and consists of employment per county and sector at the North American Industry Classification (NAICS) 6- digit level, representing the most detailed view of the United States’ industrial structure available to the public. The CBP data are extracted from the US Census Bureau’s Business Register, consisting of administrative tax records of all private US non-farm employer establishments, supplemented by additional information from sources such as the Economic Censuses, Annual Surveys, Current Business Surveys, and Company Organization Surveys, as well as the Bureau of Labor Statistics and the Social Security Administration.<sup>10</sup> We then aggregate the county-level variables up to the commuting zone level using the crosswalk discussed in Section 3.2. This leads to 625 commuting zones, with total postings over the period ranging from 98 to 277,0274 per commuting zone, and total employment (excluding farm employment) ranging from 279 to 6,830,871.

In order to analyse the relationship between job postings and tariff exposure at the local labor market level, we weight national sectoral exposure measures using baseline employment shares within a local labor market for each industry. The regional (commuting zone) tariff exposure measure is defined as:

> 8https://sites.psu.edu/psucz/data/

> 9The term ‘labor-shed’ is used in this data repository as analogous to commuting zones. Fowler et al. (2016) establish a set of metrics for comparing different labor market delineations and provide an overview of these differences.

> 10We use the modified version of the CBP produced by Eckert et al. (2020), which aims to address the fact that employment is suppressed for the majority of county-industry cells in order to protect confidentiality (see http://www.fpeckert.me/cbp/). The authors use ‘a linear programming method that exploits the large set of adding-up constraints implicit in the hierarchical arrangement of the data to impute missing employment’.

15
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namely, a sharp increase in the minimum wage. Specifically, the government raised the minimum wage by nearly 30% in the two years after President Moon’s inauguration, a move that firms did not expect before December 2016. Given that a significant number of Korean firms had foreign affiliates in 2016, we hypothesize that firms with foreign affiliates attempted to mitigate the shock to the domestic labor market by adjusting their workforce (or task) composition between home and foreign countries, thereby resulting in adverse domestic employment effects.

# **2.2 Minimum Wage Increases in Korea**

The minimum wage increase was one of the most prominent pledges that Moon had emphasized during his campaign for the presidency. He promised to raise the hourly minimum wage to 10,000 won by 2020, which could only be achieved if it rose by 15.7% over the next three In of the the Minimum per year years. support president’s pledge, Wage Commission decided to increase the 2018 minimum wage by 16.4% on July 15, 2017.<sup>12</sup> Another steep increase of 10.9% in the following year, a cumulative increase of 29.1% in just two years, raised strong concern about negative effects on employment and hence on the economy. In response, the government later moderated its initial plan and reduced the minimum wage increase to 2.9% and 1.5% in 2019 and 2020, respectively.

Figure 1 shows the minimum wage increases in 2017 and 2018. In panel (a), the yearly increase in the nominal minimum wage is illustrated with annual inflation rates. Each bar denotes the difference between the two – the yearly increase in the real minimum wage. The minimum wage increase in 2018 was the highest since 2001, when Korea was recovering from a deep economic recession associated with the Asian financial crisis. Relatively low inflation rates in recent years confirm that the minimum wage growth in _real_ terms was also the highest in 20 years. Panel (b) compares Korea’s steep increase in the minimum with that in other countries. Korea’s minimum faster than wage wage grew in most advanced countries or in emerging market economies such as the Czech Republic and Hungary. According to the OECD Statistics Database, the ratio of minimum to median wages in Korea rose from 50% in 2016 to 63% in 2019 (see panel (c)), the highest percentage point increase among OECD countries. This suggests that the minimum wage increases indeed exerted strong cost push shocks on Korean firms.

> 12The minimum wage law applies to entire establishments in Korea with a single rate. That is, the minimum wage rate in Korea does not have region, industry, or occupation variations. The minimum wage must be determined by August 5th for the next year according to Article 8, Paragraph 1 of the Minimum Wage Act and is effective from January 1st in the next year.

6
concessions by higher income countries by more than half, to almost eliminate reductions of applied rates by developing countries (Table 8.1).

The ‘flexibilities’ in the agricultural modalities are considerably larger than those in the market access negotiations on non-agricultural merchandise (NAMA). Table 8.2 shows, for example, that while the agricultural flexibilities would allow developing Members to take back 59 percent of the reduction of agricultural bound rates, NAMA flexibilities would allow a take back of only 11 percent of the formula reduction.

This means that much of the bargaining in agriculture will be over which products each country names as its special or sensitive products. Though the modalities suggest that each Member will unilaterally name these products, there has been in fact hard bargaining over which countries will name which products as special or sensitive.<sup>57</sup> Agreed formulas and other modalities state political commitment but not legal obligation. In the end, a Member’s legal obligation will be specified not by these modalities but by the schedule of tariff rates the Member submits. There is no provision in the modalities for an neutral reviewing of schedules for compliance with the tariff-cutting formulas nor with the modalities’ standards for and limits on excepting products from the formula cuts. This screening will be done individually by Members, determinations about whose or which parts of schedules comply with agreed modalities will be part of the negotiations.

It is indeed a possibility that because of their generous flexibilities the modalities do not advance the market access negotiations for agricultural products; they may instead transfer it to a yet-to-come negotiation focused on the schedules that each Member in the end will notify as its legal obligation. In turn, it may not be possible to complete the SSM negotiations until Members have a more precise idea of the tariff schedules from which the SSM will allow exceptions.

# Table 8.1 Summary of the impact of the December 2008 draft agricultural – <u>modalities on applied rates Concessions given by groups of Members</u>

|<br>Group|<br>Applied R<br>Base<br>rates<br>levied|<br>ates<br>Base to<br>formula|<br>Formula to<br>exceptions|<br>Net:<br>Base to<br>Formula +<br>exceptions|
|---|---|---|---|---|
|High income countries|15|-7.5|3.5|4|
|Developingnon-LDC|13.4|-1.9|1.8|0.1|
|LDCs|12.5|-0.3|0.3|0|

Source: Calculated from Martin-Mattoo, 2008, Table 1.

Note: The base rates are ad valorem equivalents of rates applied in 2004, with adjustments for internationally-binding commitments to reform; e.g., Ukraine’s

> 57 See for example the discussion in Bridges Weekly Trade News Digest 10th December 2008.

37
Table 7: Size of Big Island and components (by country)

||Bulgaria|Serbia|Slovak Rep.|Hungary|Romania|Russian Fed.|Spain|
|---|---|---|---|---|---|---|---|
|Network Size|4,399|8,133|4,701|7,791|15,308|61,011|20,721|
|Big Island (BI)|1,934|2,499|2,620|5,106|11,593|22,016|13,888|
|BI (%)|44%|31%|56%|66%|76%|36%|67%|
|Rest|2,465|5,634|2,081|2,685|3,715|38,995|6,833|
|Second BI (in rest)|51|1004|27|18|39|51|98|
|Parties in BI|26|35|19|19|43|24|56|
|%|84%|36%|76%|50%|72%|63%|18%|
|SOEs in BI|58|98|69|296|184|4770|303|
|%|27%|50%|63%|85%|90%|84%|64%|
|Firms in BI|352|250|982|630|2,480|3,693|666|
|%|70%|58%|73%|77%|91%|64%|75%|
|Politicians in BI|997|1,073|748|1,898|4,368|5,270|8,557|
|%|57%|47%|68%|82%|83%|29%|89%|
|Individuals in BI|501|1,043|802|2,263|4,518|8,259|4,306|
|%|26%|20%|38%|53%|64%|26%|46%|

Table 8: Network features (by country)

||Bulgaria|Serbia|Slovak Rep.|Hungary|Romania|Russian Fed.|Spain|
|---|---|---|---|---|---|---|---|
|Nodes|1,934|2,499|2,620|5,106|11,593|22,016|13,888|
|Log(Nodes)|7.6|7.8|7.9|8.5|9.4|10.0|9.5|
|Edges|4,153|6,811|5,077|13,604|32,923|66,246|46,369|
|Average Degree|2.1|2.7|1.9|2.7|2.8|3.0|3.3|
|Density|0.1%|0.1%|0.1%|0.1%|0.0%|0.0%|0.0%|
|Clustering Coefcient|1.3%|8.6%|1.7%|0.8%|1.7%|0.4%|1.4%|
|Average Distance|6.3|7.6|6.6|5.9|5.3|5.2|5.1|
|Random Clust Coef|0.1%|0.1%|0.2%|0.1%|0.0%|0.0%|0.0%|
|Random Ave Dist|8|7.8|8.2|7.9|8.2|8.6|7.8|
|S for small-world|21.65|87.31|13.1|11.3|115.77|47.65|86.26|

Table 9: Betweenness by type and country

||Bulgaria|Serbia|Slovak Rep.|Hungary|Romania|Russian Fed.|Spain|
|---|---|---|---|---|---|---|---|
|Betweenness parties|33%|26%|24%|21%|32%|13%|38%|
|Betweenness SOEs|23%|18%|32%|40%|15%|52%|15%|
|Betweenness frms|2%|8%|2%|1%|1%|4%|5%|
|Betweenness politicians|36%|39%|30%|25%|38%|20%|35%|
|Betweenness Individuals|7%|9%|11%|13%|13%|11%|7%|
||100%|100%|100%|100%|100%|100%|100%|

33
# <mark>4. Existing indicators measuring data accessibility in MENA</mark>

## 4.1 Existing indicators

The evidence in section 3 shows that the availability and accessibility of microdata in the MENA region is very constrained. Yet the existing indicators measuring data openness do not capture this reality. These indicators include the Open Data Inventory (ODIN)<sup>20</sup> published by Open Data Watch; Open Data Barometer (ODB)<sup>21</sup> by World Wide Web Foundation and Global Open Data Index (GODI)<sup>22</sup> by Open Knowledge Foundation<sup>23</sup> (see table 5 for a summary).<sup>24</sup> Of these indicators, the <mark>ODIN ranks highest with regards to country coverage.</mark> <mark>The ODIN covers 178 countries in its 2018/19 version including 17 MENA countries and 187 countries in its 2020/21 version including all 20 MENA countries. Additionally, ODIN also has a substantial proportion of its elements assessing data accessibility or openness - it assesses the coverage and openness of data available on National Statistics Offices (NSOs) websites based on ten elements across two dimensions - coverage and openness. Five of the ten elements measure data coverage i.e., the degree to which data is available and while the others measure access/openness i.e., the degree to which available data is accessible. Each of the five elements in the</mark> coverage dimension is assessed as follows: representative indicators are available and are disaggregated appropriately; data are available for the preceding five years; data are available for the preceding ten years; data are disaggregated at the first administrative level and data are disaggregated at the second administrative level. <mark>Each of the five elements assessed in the ODIN</mark> data accessibility/ openness dimension are assessed as follows: machine readability; nonproprietary; download options; metadata available and terms of use. All th <mark>e elements in the ODIN coverage and openness dimensions are assessed across several data categories and data dimensions (</mark> see annex table A3 and A4 for the full list of data categories, representative indicators in each category and scoring option for the 2018 ODIN). The default ODIN overall score gives equal weights to the three dimensions <mark>. ODIN scores are calculated as a percentage of the maximum score obtainable.</mark>

> 20 Open Data Watch - Open Data Inventory http://www.opendatawatch.com

> 21 World Wide Web Foundation- https://opendatabarometer.org/barometer/

> 22 Global Open Data Index - https://index.okfn.org/

> 23 The Statistical Access Indicator (SAI) by Almeida and Hoogeveen (2015) with 72% (36 out of 50) of its elements assessing data access and covering 49 African countries would have made the fifth indicator. However, it is unpublished, hence excluded from the review.

> 24 The Statistical Access Indicator (SAI) by Almeida and Hoogeveen (2015) with 72% (36 out of 50) of its elements assessing data access and covering 49 African countries would have made the fifth indicator. However, it is unpublished, hence excluded from the review.

16