rafmacalaba/datause-encoder-data huggingface dataset:
variety externality, welfare losses from preferential reduction of services barriers are possible due to a loss of varieties from excluded countries. Gains from a free trade agreement with the EU that includes a preferential 50 percent reduction in the ad valorem equivalents of the barriers in the services sectors will produce significant gains for Kenya, deriving primarily from services commitments. Kenyan gains from liberalization with the EU region are considerably larger than the gains from an agreement with the African region because a small increase in the price to EU producers can be expected to induce a relatively large increase in the number of varieties. In addition, when rents are captured initially, the terms of trade loss on partner services and the lost rents on services from the Rest of the World are larger when partners have low supply elasticities. We have conducted extensive sensitivity analysis to determine confidence intervals for the results and found plausible ranges of key parameters that lead to estimated losses for preferential liberalization of services with the Africa region.

# **References**

- Arnold, Jens, Aaditya Matoo and Beata Javorcik (2007), ―Does Services Liberalization Benefit Manufacturing Firms: Evidence from the Czech Republic,‖ World Bank Policy and Research Working Paper Number 4109.

- Baldwin, Richard E., Forslid, Rikard and Haaland, Jan (1999), "Investment Creation and Investment Diversion: Simulation Analysis of the Single Market Programme," in R. Baldwin and J. Francois (eds.), _Dynamic Issues in Applied Commercial Policy Analysis_ , Cambridge: Cambridge University Press.

- Balistreri, Edward J. and James Markusen (2009),  ―Sub-national differentiation and the role of the firm in optimal international pricing,‖ _Economic Modellling,_ 26(1), 47 - 62.

- Balistreri, Edward J., Thomas F. Rutherford and David G. Tarr (2009) ―Modeling Services Liberalization: The Case of Kenya,‖ _Economic Modeling_ , Vol. 26 (3),May,  668-679.

- Broda, Christian and David Weinstein (2004), ―Variety, Growth and World Welfare,‖ _American Economic Review_ , 94 (2), May, 139-144.

- Broda, Christian, Josh Greenfield and David Weinstein (2006), ―From Groundnuts to Globalization: A Structural Estimate of Trade and Growth,‖ National Bureau of Economic Research Working Paper No. 12512.  Available at:

- http://faculty.chicagobooth.edu/christian.broda/website/research/unrestricted/BrodaGroundnuts.pd f

.

36
Association, at the end of 2002, individual investors accounted for only 7.2% of the amount of US government securities, excluding savings bonds, outstanding.<sup>93</sup> The majority of trades in government securities are performed by telephone, although broker-dealers use the inter-dealer brokerage system and computer technology to indicated interest to one another, and the use of electronic systems has increased substantially.<sup>94</sup> Generally, each inter-dealer broker maintains a proprietary system between itself and its primary dealer customers. The primary dealers have access to the inter-dealer broker screens, which they use to trade among themselves. Inter-dealer brokers thus facilitate information flows between primary dealers while providing anonymity to individual trading dealers.

The Federal Reserve manages the National Book-Entry System (NBES), which is an electronic system for recording ownership and legal interests of government securities by depositary institutions. Depository institutions then maintain the records for their customers. Depository institutions can carry out settlement functions for themselves and their customers through NBES. An alternative recording system is maintained by the US Treasury Department. Treasury-Direct is a book-entry securities recording system which can be accessed directly by investors. Securities held through Treasury-Direct are typically held to maturity.

2. _GSEs._ GSE securities are traded in the over-the-counter market, also largely through inter-dealer brokers. The volume of secondary market trading in GSEs is significantly lower than that in Treasury securities, but still reasonably high compared to the rest of the debt market. As with the Treasury “when issued” market, there is an active “to-be-announced” market in GSE securities, which is essentially a forward or delayed delivery market. Traditionally, trading has been based on telephone discussions. Electronic trading has begun to take hold, particularly in the inter-dealer market. According to the Bond Market Association, there were 12 electronic systems that covered the GSE market in 2000. As a result of the use of electronic platforms, transparency, efficiency and accessibility have improved, at least for the dealer community.

The Bond Market Association has published guidelines governing the mechanics of trading and settling mortgage-backed securities, including GSE pass-through securities. These guidelines, known as the “Good Delivery Guidelines” were developed to facilitate the trading of the to-be-announced market, essentially permitting the securities to trade fungibly by pools according to certain key characteristics enumerated in the Good Delivery Guidelines. The Guidelines have thus contributed to the liquidity of the market, and added to efficiencies in pricing, execution, delivery and settlement.

> 93 <u>www.bondmarkes.com/research.</u>

> 94 _BondMarkets Online,_ October-November 2002, www.bondmarkets.com.  Overall, there were approximately 46 electronic fixed income trading systems based principally in the United States in late 2003.  www.bondmarkets.com/research/ecommerce.

26
red; respectively.<sup>10</sup> As stated beforehand, we set the warning index equal to zero in pre-COVID19 years and consider that municipalities were all in the green category before the crisis began.

Our strategy assumes that municipalities with a higher warning index or greater exposure to COVID-19 infections had parallel trends in dropout, effective promotion rates, and school switching before the pandemic. We formally test whether this assumption holds in the next section, conducting placebo experiments given data availability and other empirical constraints.

Our first estimation strategy involves running the following regression: where yismt represents the outcome for individual _i_ in school _s_ living in municipality _m_ at time _t_ . Warningmt is the municipality-specific warning index that varies over time and therefore β estimates how an increase of one unit in the warning index affects the selected outcome. Xismt is a matrix that includes student- and school-level controls such as gender, urban residence, level of schooling, grade, and whether the school provides bilingual instruction. We also control for secular time trends with year fixed effects. We include fixed effects at two levels of aggregation: municipality ( _m_ ) and schools ( _s_ ). We do not include a specification with student-level fixed effects because it may result in sample selection that may exacerbate biases due to sample selection.<sup>11</sup>

> 10 Figure A.1 in the Appendix shows a municipal-level map of time spent in each color category during 2020 and 2021.

> 11 For instance, some students appear only once in the data because they drop out and the system does not follow them after they exit the school system. In this case, including student-level fixed effects in a regression would remove these observations from the data, only keeping students who are observed multiple times throughout the period. Given that observing an individual multiple times means lower dropout, we would be biasing our estimates by including only “survivors” across the education system over time. Hence, the gains from controlling for student-level fixed effects do not outweigh the costs in our application to the Guatemalan data.

15
# WPS3591

# **Anti-Dumping Policies and Safeguard Measures in the Context of Costa Rica’s Economic Liberalization**

**_Ricardo Monge-González*_** **_<u>rmonge@caatec.org</u>_**

**_Francisco Monge-Ariño**_** **_<u>monge-arino.1@osu.edu</u>_**

## World Bank Policy Research Working Paper 3591, May 2005

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._

* Executive Director of Costa Rican High Technology Advisory Committee Foundation (CAATEC) and Professor of International Trade at Universidad Latina de Costa Rica.

** Associate Member of CAATEC and Teaching Assistant at Ohio State University, U.S.A.
8 <!-- Start of picture text --> Gini Coefficient<br>against GDP per capita<br>0.,<br>CCU<br>0.6 NN0 G0}<br>SEN JRmm T 5 0EX<br>e0.5 NIL M%BMOWt CQIYS CHLtQ<br>W0°'s~~~~60 YZA H9enacyr .u rO'.<br>0.2 IND SA<br>0.2<br>lO to 1000 10000 IO000<br>GDP per Capita<br><!-- End of picture text --> Figure 2 Table 4: Regression of Inequality variables on Log of GDP per Capita and Squared Log of GDP per Capita

|Dep. Variable|olNI|COEFFVAR|THEIL|_RTP40_|
|---|---|---|---|---|
|No of Obs|72|72|72|77|
|Consant|-1.6683<br>(-2.68)|-4.8235<br>(-2.67)|-3.3680<br>(-2.96)|-47.346<br>(-3.02)|
|LGDPPCIY<br>.___________________|0.5780<br>(3.54)|1.6178<br>(3.43)|1.0299<br>(3.45)|13.924<br>(3.41)|
|LUDPCIY2|-0.0390<br>(-3.69)|-0.1112<br>(-3.64)|-0.0699<br>r-3.62)|-0.927<br>(-3.52)|
||0.21|0.23|0.21|0.17|
Table B1: Dynamic Panel & Panel with Interactions Regressions - Fractional Index 2 [Dependent Variable: Food Insecurity]

||Dynamic Panel Regressions||Dynamic Panel Regressions with Government Stri|ngency Score|
|---|---|---|---|---|
|Ethiopia|Malawi|Nigeria|Ethiopia<br>Malawi|Nigeria|
|-1<br>-.8<br>-.6<br>-.4<br>-.2<br>0<br>.2<br>.4<br>.6<br>.8<br>1<br>Effect on Food Insecurity|-1<br>-.8<br>-.6<br>-.4<br>-.2<br>0<br>.2<br>.4<br>.6<br>.8<br>1<br>Effect on Food Insecurity<br>|-1<br>-.8<br>-.6<br>-.4<br>-.2<br>0<br>.2<br>.4<br>.6<br>.8<br>1<br>Effect on Food Insecurity|-.4<br>-.3<br>-.2<br>-.1<br>0<br>.1<br>.2<br>.3<br>.4<br>Effect on Food Insecurity<br>-.1<br>-.08<br>-.06<br>-.04<br>-.02<br>0<br>.02<br>.04<br>.06<br>.08<br>.1<br>Effect on Food Insecurity|-.1<br>-.08<br>-.06<br>-.04<br>-.02<br>0<br>.02<br>.04<br>.06<br>.08<br>.1<br>Effect on Food Insecurity|
||Mild<br>Moderate<br>Severe<br>Index||Mild<br>Moderate<br>Severe<br>Index||

_Note:_ The figures plot results using our second fractional index that spans pre- and post-outbreak data. This index differs from our main fractional index in that it is not standardized across countries. Results come from dynamic panel and dynamic panel with interactions regression (see Equation 3.1 and 3.2). Horizontal lines represent 95 percent confidence intervals calculated using standard errors clustered at the household.
levels to month-over-month percentage changes No series has been seasonally adjusted prior to downloading. Therefore, many of the series exhibit some degree of and sometimes a strong degree of seasonality. As discussed below, we will attempt to model the seasonality explicitly when present.

A few of the GIEWS price data series contain missing observations. The missing observations were replaced using simple linear interpolation before applying this transformation.

The Google Trends data are transformed as follows. Some of the search terms are available at weekly frequencies while other series are only available at monthly frequencies. For those that are available at a weekly frequency, we take the maximum value in each month to be the value for that month. This differs from the approach of Vosen and Schmidt [2011] and Carri`ere-Swallow and Labb ́e [2013] who aggregate the weekly data into monthly series by taking the monthly average of the indicators. Since the data are relative, we do not wish to first smooth them in this way. This could mask potentially important, short-lived events. Further transformations to the Trends data are described in the next subsection.

# **3.1 Challenges in Using Google Trends Data**

Several challenges present themselves when working with the Google Trends data in a developing country context. First, as pointed out by Carri`ere-Swallow and Labb ́e [2013], Google Trends historical data are not constant over time. Within the same 24-hour period, the results will be the same. However, from day to day the results can be different. Indeed, not only do the values change, but on one day monthly data may be returned. On another biweekly or weekly data for the same keyword search. It is unclear, what exactly – is driving these differences whether different normalizations, sampling considerations, or something else, but for practical purposes we can treat the data as being recorded with sampling error with the same consequences. For the present study, we collected data on all of the keywords for ten days over a period of one month. Figures 1 and 2 show the sampling error for two representative series collected during this period.

These series are chosen to be representative of all of the series used and show the two most salient features for the purposes of this study. First, the sampling error is evident 6
Disclosure), there is one row that compares those observations with a value of 0 with those observations with a value of 1. For the other regulatory indicators - Accounting Standards, Investor Protection Standards, Dividend Repatriation Restrictions, Capital Repatriation Restrictions, and Capital Inflow Restrictions - there are two rows. The row first compares regimes ranked 2 with regimes ranked 1 and the second compares regimes ranked 1 with regimes ranked 0.

The results in Table 8 suggest the following conclusions. First, countries where information about firms, such as price-earnings ratios, is comprehensive and published internationally have larger, more liquid, and more internationally integrated stock markets than countries that do not publish firm information as comprehensively and widely. Second, the data give ambiguous results on the level of accounting standards and investor protection laws. For example, the conglomerate stock market development indexes, INDEX-I AND INDEX-2, indicate that although poor accounting standards and poor investor protection laws are associated with low stock market development, moving from adequate to internationally accepted accounting and investor protection standards is actually associated with a drop in stock market development.<sup>8</sup> Thus, the data do not support the contention that imposing internationally accepted investor protection rules and accounting standards boosts stock market development. Finally, the IFC international capital restriction rankings confirm the time-series findings of section II. There is a strong positive relationship between stock market development and lower restrictions on capital flows. In sum, comprehensive and widely published information about firms along with the unrestricted flow of capital and dividends are positively associated with stock market size, liquidity, and risk pricing efficiency, but reaching some officially defined set of accounting standards and investor protection laws is not strongly correlated with stock market development.

# IV. CONCLUSION

8 There is no statistically significant difference when comparing 0with 2 rankings directly.

19
_Table 4.3 Education_ _<u>and Inequality Variation in Brazil, Argentina, and Peru</u>_

|_Country_|_Study_|_Time period_|_Explanatory power_<br>_(per cent)_<sup>_a_</sup>|_Income effect (per_<br>_cent)_|
|---|---|---|---|---|
|Brazil|Ramos and Trindade (1991)|1977–89|6–20|10–17|
|Argentina|Fiszbein (1991)|1974–88|54–56|38–46|
|Peru|Rodríguez(1991)|1970–84|32–47|34–43|

a The income effect plus the allocation-population effect.

25
**Table 1: Municipality Characteristics for Treated and Control Groups**

||||Control<br>|Municipaliti<br>|es|
|---|---|---|---|---|---|
||||36-m|onth|Planned|
||Treated|All<br> Non-treated|Common<br> Support|Nearest<br> Neighbors|Implementation<br>Candidates|
|_Variables used for level matching_||||||
|Average monthly registrations in 2008|11.9|2.1***|3.3***|10.4|14.7|
|Average monthly registrations in 2009|12.8|2.3***|3.7***|11.2|15.3|
|Average monthly registrations in 2010|12.4|2.2***|3.4***|11.0|15.3|
|Standard deviation of monthly registrations 2008 to 2010|4.1|1.4***|1.8***|3.3|5.0*|
|Population in 2010|56847|11754***|17172***|50423|65632|
|_Variables not used for matching_||||||
|Average monthly registrations in first eight months of 2011|12.8|2.2***|3.5***|12.1|16.0|
|Standard deviation of monthly registrations in first eight months of 2011|4.1|1.3***|1.7***|3.2|4.4|
|Share of Industry in Municipal GDP (%)|26.3|14.9|17.4***|19.4*|35.8*|
|Total GDP per capita growth 2003 to 2008 (%)|182.3|181.7|180.2|166.5*|179.2|
|GDP per capita in 2008|11773|9381|10088|9250|18792**|
|Average monthly Municipal taxes received from firms in 2009|12220|15272|22451|40122|6978|
|Average monthly Municipal taxes received from firms in 2010|40851|5405***|9552***|49521|50077|
|_p-value for testing that:_||||||
|Common linear trend to treatment over 2008 to August 2011||0.030|0.045|0.495|0.612|
|Common non-parametric time trend to treatment over this period||0.000|0.000|0.0004|0.000|
|Common non-parametric time trend to treatment except January 2011||0.000|0.000|0.1078|0.000|
|**Number of Municipalities**|**55**|**767**|**357**|**29**|**18**|

Notes: *, **, and *** denote means statistically different at the 10, 5 and 1 percent levels respectively 17
Department of Census and Statistics (2008), _Household Income and Expenditure Survey2006/07: Final Report_ , Colombo: Sri Lanka.

Department of Census and Statistics (2009), _Demographic and Health Survey 2006/07: Prevalence of Anemia among Children and Women in Sri Lanka_ , Colombo: Sri Lanka.

Emran, M. Shahe and F. Shilpi (2012), “The Extent of the Market and Stages of Agricultural Specialization”, _Canadian Journal of Economics_ , August, 2012.

Emran, M. Shahe and F. Shilpi (forthcoming), “General Equilibrium Effects of Land Market Restrictions on Labor Market: Evidence from Wages in Sri Lanka”, forthcoming in _American Journal of Agricultural Economics_ . Prepublication working paper version available from SSRN.

Emran, M. Shahe and Zhaoyang Hou, 2013. “Access to Markets and Rural Poverty: Evidence from Household Consumption in China, _The Review of Economics and Statistics_ , MIT Press, vol. 95(2), pages 682-697, May.

Emran, M. Shahe, Virginia Robano, and Stephen C. Smith, 2014. “Assessing the Frontiers of Ultrapoverty Reduction: Evidence from Challenging the Frontiers of Poverty Reduction/Targeting the Ultra-poor, an Innovative Program in Bangladesh, _Economic Development and Cultural Change,_ University of Chicago Press, vol. 62(2), pages 339 - 380.

Field, Erica, (2007), “Entitled to Work: Urban Property Rights and the Labor Supply in Peru,” _Quarterly Journal of Economics_ , Vol. 122(4), p1561-1602.

Goldin, C (1995), The U-Shaped Female Labor Force Function in Economic Development and Economic History, in _Investment in Women’s Human Capital and Economic Development_ , Ed. by T. Paul Schultz, Chicago University Press.

Hayashi, Fumio and Edward C. Prescott (2008), “The Depressing Effect of Agricultural Institutions on the Prewar Japanese Economy, _Journal of Political Economy_ , vol. 116(4), pages 573-632.

Harrison, G (1978), Mosquitos, Malaria and Man: A History of the Hostilities Since 1880s, John Murray, London.

Iyer, Lakshmi, Xin Meng and Nancy Qian (2009), “Unbundling Property Rights: The Impact of Urban Housing Reforms on Labor Mobility and Savings in China, _mimeo_ .

28
Jobs are essential for individual well-being, income, and a sense of dignity. The creation of employment opportunities is critical for the Arab countries, and has become even more so following the recent uprisings and revolutions in several countries. The Arab region has been suffering from a high and growing unemployment rate. In that context, it is useful to understand the employment dynamics in developed and developing economies in order to identify the main sources of job creation. This would help in identifying the main drivers and generators of jobs in the Arab world, as well as the characteristics of these enterprises that are fast growing, productive and are able to expand employment, generate most of the new jobs, and enhance overall economic growth.

Similarly, Klapper and Richmond (2011) show that new firm creation appears to be a potentially important source of job creation. This suggests that expanding opportunities for entrepreneurial potential is important for employment expansion and macroeconomic growth. Their analysis also related job creation to the process of creative destruction that occurs in dynamic economies. Allowing old firms to exit and new more productive firms to enter and grow occurs more easily when regulations are fair and lean. In general, a growing literature on labor dynamics across firms finds that new businesses are a vital source of job growth.

Figure (5): New business density (new registrations per 1,000 people ages 15-64) <!-- Start of picture text --> 2.5<br>2<br>Average 2004-2008<br>1.5 Average 2009-2012<br>1<br>0.5<br>0<br>Iraq Algeria Tunisia Jordan Morocco Oman<br>Republic Emirates income<br>Syrian Arab United Arab Low income MENA levels)<br>Egypt, Arab Rep. Low & middle (developing only) MENA (all income<br><!-- End of picture text --> Source: World Bank’s World Development Indicators Database However, amongst the many challenges that face young firms’ survival in MENA is having an enabling environment in place that enables free entry as well as healthy competition. Recent data on new firm’s density relative to population in MENA countries was severely affected by the Arab Spring as it dropped from an average of 1.9 firm/1000 citizen on average during 2004-2008 to reach 0.9 firms/1000 citizen on average for 2009-2012 (see Figure 5). Moreover, only a small proportion of high performing SMEs have the potential to grow into internationally competitive companies, generating significant economic benefits through raised productivity, employment, and economic stability. Research by Ayyagari, Demirguc-Kunt and Maksimovic (2011) confirms that small firms are important contributors to total employment and job creation, but suggests that their productivity and growth is held back by SME-specific constraints. Hence, only a small number is able to grow fast and create a large number of jobs.

9