rafmacalaba/datause-encoder-data huggingface dataset:
The sixth column of Panel A includes all three the institutional variables at the same time.<sup>19</sup> We find that the regulatory cost variable changes sign and loses significance. The other variables, including judicial efficiency and generalized trust, change very little from the regressions which include those variables separately and remain statistically significant at the 5 percent level.

Several of the variables in the regressions reported in Panel A may be subject to endogeneity. For example, there may also be a direct connection between the presence of larger firms and the development of a more effective judicial system, in that larger firms may demand a better judicial system. We tackle these endogeneity issues by instrumenting for judicial effectiveness, the cost of regulation, the measure of generalized trust, financial development, and foreign ownership. For each of these institutional factors, we use two instruments. Following the lead of Acemoglu, Johnson and Robinson (2001) and Rodrik, Subramanian and Trebbi (2002), we use historical data to measure the component of the institutional environment which has deep historical roots. In particular, we use the log of the number of indigenous speaking people in the state in 1900. The _encomienda_ system imported by the Spanish treated indigenous labor as a resource to be used by the immigrant Europeans. Hence, larger numbers of indigenous people might be expected to be associated with a worse institutional environment. The 1900 data is the earliest measure of indigenous population we could find at the state level.<sup>20</sup>

> 19 We exclude the financial development and foreign ownership variables, because of their statistical insignificance in the previous regressions. Adding the financial development and foreign ownership variables does not qualitatively change the results.

> 20 The states of Quintana Roo and Baja California Sur were created after 1900, carved out of Yucatán and Baja California, respectively.  We use the data from Yucatán and Baja California, respectively. Given that there was essentially no population in 1900 in what is now the state of Quintana Roo, we might alternatively assign Quintana Roo a value of zero for indigenous population. Doing so makes most of the results stronger, so to be conservative we use the data for the Yucatán for Quintana Roo.

31
that could explain that event. This would be a problem if oil prices drop because of demand factors but the analysts interpret this as supply driven.

We minimize this possibility by _not_ considering the days that have important demand announcements recorded by Seeking Alpha analysts. We also do not consider announcements about U.S. oil inventories because oil inventories could reflect both supply and demand factors. Furthermore, we also cross check with independent economic calendars to see if there are important surprising demand announcements in the 29 event days. We removed 4/12/2015 as there were numerous Fed speeches (Harker, Dudley, Bullard, Kocherlakota spoke at the “The New Normal for the U.S. Economy” forum hosted by the Philadelphia Fed), as well as the one by ECB President. Thus, we have 28 event dates.

To increase our confidence that these 28 days are primarily supply events, we also use U.S. news coverage to provide a check. We use www.newslibrary.com to count how many articles with the words “economy” or “economic growth” appear in 526 U.S. national news outlets. The number of the articles represents how intensively news about the economy, or “demand news”, is covered. The assumption is that the higher the count for a day, the more significant demand news is for that day. We collect article counts for all the days since 1/1/2014. We check econometrically if the average article count for those 28 event days is higher or lower than that for the non-event days. Table 2.1 shows that the average count is marginally smaller on the event days than the non-event days, indicating that demand factors are marginally smaller in the events days.

10
discussed in this paper (Rew and Horner 2003 for the US, Koller et. al. 2004 for Brazil). However, we cannot conclude that indigeneity is not a valuable characteristic for understanding youth behavior since it is possible that other variables that consistently emerge as key explanatory factors are correlated with being indigenous and thus pick up the variance. For example, there is a high correlation (significant at the 1% level) between indigeneity and household poverty (low parental education level). Or, feelings of being discriminated against, which may be ethnic, racial, spatial, or based on other variables, emerge as a key explanatory factor for earlier age of employment for boys and girls.

Three micro-factors - household poverty, relationship with the family, and use of/relationship with local institutions - repeatedly emerge as key explanatory factors in both countries for all seven behaviors, i.e. significant at the 1% level in the stepwise regressions. Household poverty, proxied by parental education level, explains a significant amount of the variance in all the behaviors considered for both genders in both countries. Young men and women who live in poorer households leave school earlier, are more likely to be inactive, start working earlier, engage in riskier sexual activity, have their first pregnancy (females only) at a younger age, and engage in fewer activities than those in wealthier households. This correlation emerges even though we have controlled for connectedness with parents and others. They are, however, less likely to justify drunkenness compared to youth from wealthier households in Mexico. The strong role of household poverty is confirmed by studies from other countries.<sup>16</sup> The importance of personal connections with peers, parents or other adult figures and positive behaviors found in the US, Latin American, and Caribbean literature is confirmed for the cases of Mexico and Chile. A positive relationship with parents as well as living with both parents explain some of the variance in all seven behaviors and is positively correlated with not being inactive, not dropping out of school early, older age

> 16 For example, positive shocks to household income has been found to affect school attendance in Mexico (Skoufias and Parker 2001), Brazil (Duryea, Edwards, and Ureta 2003), and Colombia (Attanasios, Meghir, and Santiago 2005), negative shocks to household income affect labor force entry (Cunningham and Maloney, forthcoming for Mexico and Argentina), household poverty increases youth violence in Brazil (World Bank 2007), Colombia (Duque, Klevens, and Ramirez 2003) and the US (Grogger 1998, Mocan and Reese 1999).

15
# Industrial Organization and Trade Liberalization: Evidence from Korea

by Jaime de Melo and David Roland-Holst.

# Table of Contents

|1.|Introduction|2|
|---|---|---|
|2.|Trade Policies, Industrial Structure, and Industrial<br>Organization Policies in Korea|3|
|3.|Modelling Imperfectly Competitive Domestic Markets|8|
|4.|Simulation Results|15|
|5.|Conclusions|25|
|Notes||28|
|Refer|ences|30|

This paper draws on joint work with David Tarr. We thank Robert Baldwin, Drusilla Brown, Darni Rodrik, and Marie Thursby for comments on an earlier draft, and Maria Ameal for logistic support. This paper is part of a research project, "Industrial Competition, Productive Efficiency, and Their Relation to Trade Regimes," funded by the World Bank (RPO 674-46).
# **Figure 3: Attitudes towards women are more conservative in the domestic sphere than in the public sphere**

(% of respondents expressing gender-inequitable attitudes) <!-- Start of picture text --> Figure 3 a Public sphere<br>70<br>University degree more<br>60 important for boy<br>(completely / somewhat<br>50<br>agree)<br>40 Completely /somewhat<br>uncomfortable with<br>30 woman as immediate<br>boss<br>20<br>Men should have more<br>10 right to a job, when jobs<br>are scarce (completely /<br>0 somewhat agree)<br>Armenia Azerbaijan Georgia<br><!-- End of picture text --> <!-- Start of picture text --> Figure 3b Domestic sphere<br>100<br>90 Not acceptable for<br>woman to have sex<br>80<br>before marriage, at any<br>70<br>age<br>60<br>Man should normally be<br>50 the breadwinner<br>40<br>30<br>Man should be the main<br>20<br>decision-maker in the<br>10<br>family<br>0<br>Armenia Azerbaijan Georgia<br><!-- End of picture text -->

**_Source:_** Caucasus Barometer Survey (2010)

18
# Strengthening Public Health Systems:

Policy Ideas from a Governance Perspective<sup>∗</sup> Stuti Khemani<sup>†</sup> Sarang Chaudhary<sup>‡</sup> Thiago Scot<sup>§</sup>

**Keywords: politicians, bureaucrats, local government, health services JEL Codes: H1, H4, H75**

∗We are grateful to James Habyarimana and Irfan Nooruddin for undertaking the survey through Georgetown University, and their collaboration in designing it. We thank Shanta Devarajan, Quy-Toan Do, Irfan Nooruddin, Eva Paus, Sergio Schmukler and Adam Wagstaff for comments on this draft. We thank Hemant Pawar and the World Bank’s India Country Team for their insights and suggestions. We thank Dr. Sanjay Kumar in the Government of Bihar, and the ADRI team of Dr. Shaibal Gupta, Dr. Prabhat Ghosh, and Dr. Sudhanshu Kumar for comments during the course of this work. We thank the Cicero team for implementing the survey, and especially Dhananjai Joshi and Ankita Barthwal. We thank Nikhil Utture for excellent assistance with all aspects of the survey. We thank CARE’s team in Bihar, especially Dr. Tanmay Mohapatra, for facilitating preliminary field tests of the survey instruments. We thank the Bill & Melinda Gates Foundation for funding the survey, and Saachi Bhalla, Debarshi Bhattacharya, Santhosh Mathew, Nachiket Mor, Usha Tarigopula and Sandhya Venkateswaran for their previous comments during the survey work. Disclaimer: The views expressed are those of the authors and not necessarily of the World Bank, its Executive Directors, or the governments they represent. Comments are welcome and can be sent to the following email of the corresponding author: skhemani@worldbank.org.

†World Bank, Development Research Group. Corresponding author: skhemani@worldbank.org ‡World Bank, Development Research Group. schaudhary3@worldbank.org §UC Berkeley Haas School of Business. thiago_scot@berkeley.edu
|**Regressions o**|**Control Gro**<br>**Table VI: Robustness Ch**<br>**f Health Outcomes on Program D**|**ecks**<br>**ummy - Children Age**|**d 0 - 5**||
|---|---|---|---|---|
|**Dependent Variable**|**up**<br>**Mean(Std.Dev.)**|**Model 1**|**Model 2**|**Model 3**|
|||0.021|0.019|0.019|
|_Respiratory diseases_||[0.019]|[0.018]|[0.018]|
||0.355(0.479)|5.812|5.485|5.469|
|||0.000|0.001|0.001|
|_Skin diseases_||[0.012]|[0.012]|[0.011]|
||0.101(0.302)|0.080|1.376|0.764|
|||0.005|0.006|0.006|
|_Other diseases_||[0.009]|[0.009]|[0.009]|
||0.041(0.198)|13.119|15.492|15.861|
|||0.376|0.372|0.372|
|_Installation of cement floor_||[0.028]***|[0.028]***|[0.028]***|
||0.530(0.499)|70.977|70.142|70.250|
|||-0.017|-0.019|-0.018|
|_Construction/expansion of sanitation facilities_||[0.015]|[0.015]|[0.014]|
||0.101(0.302)|-17.021|-18.489|-17.360|
|||-0.001|-0.001|0.000|
|_Restoration of sanitation facilities_||[0.013]|[0.013]|[0.012]|
||0.045(0.206)|-3.156|-1.570|0.663|
|||0.028|0.018|0.018|
|_Construction of ceiling_||[0.024]|[0.024]|[0.023]|
||0.159(0.366)|17.821|11.279|11.290|
|||0.012|0.010|0.011|
|_Restoration of walls_||[0.017]|[0.016]|[0.015]|
||0.111(0.314)|11.199|8.700|9.828|
|||0.035|0.039|0.043|
|_Log of self-reported rental value of house_||[0.040]|[0.031]|[0.030]|
||5.918(0.740)|0.596|0.665|0.732|
|||-0.043|-0.038|-0.030|
|_Log of self-reported sale value of house_||[0.100]|[0.076]|[0.073]|
||10.491(1.168)|-0.413|-0.364|-0.289|
|||-0.042|-0.038|-0.023|
|_Log total income of mothers of children 0 - 5 yrs_||[0.064]|[0.065]|[0.053]|
||7.791(0.665)|-0.534|-0.491|-0.299|
|||-0.015|-0.005|0.015|
|_Log total income of fathers of children 0 - 5 yrs_||[0.028]<br>|[0.027]<br>|[0.025]<br>|
||8.121(0.592)|-0.187|-0.061|0.185|

Notes: (i) Model 1: no controls; Model 2: age, demographic, health-habits and economic controls; Model 3: age, demographic, health-habits, economic and federal-programs controls.

(ii) Reported results: estimated coefficient, clustered standard error at census-block level in brackets and (coefficient/control mean) * 100. * significant at 10%; ** significant at 5%; *** significant at 1%.

- (iii) Missing values in covariates were imputed with zero, and a corresponding dummy variable was then added to the regressions.

33
**Table 9: Impacts of different household members’ migration on yearly hours worked across activities**

||**Total hours**<br>**worked**|**Hours**<br>**worked in**<br>**agr.**|**Hours**<br>**worked in**<br>**non-agr.**|**Hours**<br>**worked in**<br>**wage labor**|**Hours**<br>**worked in**<br>**family**<br>**business**|**Hours**<br>**worked in**<br>**househol**<br>**d chores**<br>**(weekly)**|
|---|---|---|---|---|---|---|
|**Total**|||||||
|Children migrated|54.740|217.113***|-146.556***|-57.960**|-89.217***|0.070|
||(62.992)|(49.866)|(40.821)|(24.358)|(33.205)|(0.696)|
|Husband migrated|-35.929|136.484|-161.052*|-149.779***|-17.996|0.696|
||(188.425)|(107.867)|(96.364)|(51.182)|(85.878)|(2.212)|
|Other migrated|-16.887|-37.483|-63.027|46.560|-96.341**|1.315|
|**Age 16-20**|(122.929)|(73.712)|(71.308)|(60.880)|(42.103)|(1.235)|
|Other migrated|-418.687|-97.926*|-188.047|-77.162|-103.999*|-1.445|
|**Age 21-35**|(408.993)|(50.657)|(225.276)|(203.039)|(58.432)|(3.589)|
|Husband migrated|21.130|213.469*|-187.993|-219.454***|32.642|0.818|
|Other migrated|(282.460)<br>125.201|(115.152)<br>13.964|(155.619)<br>-15.589|(84.302)<br>87.256|(136.851)<br>-152.466*|(3.349)<br>2.193|
|**Age 36-50**|(239.978)|(126.395)|(164.150)|(141.296)|(78.390)|(2.869)|
|<br>Children migrated|-70.198|161.299**|-192.216***|-105.639***|-86.720*|-0.504|
||(74.291)|(63.270)|(58.826)|(36.648)|(45.058)|(0.752)|
|Husband migrated|<br>432.004|<br>498.265|<br>-301.028***|<br>-215.854***|<br>-77.334|<br>5.313|
||(267.183)|(344.061)|(108.830)|(65.288)|(121.100)|(4.898)|
|Other migrated|-42.094|-47.344|-43.488|58.039|-55.075|0.657|
||(163.449)|(108.646)|(105.816)|(94.397)|(75.578)|(1.527)|
|**Age 51-60**|||||||
|<br>Children migrated|213.788*|214.798**|-73.358*|-4.335|-68.931|1.556|
||(111.066)|(84.588)|(44.261)|(3.835)|(44.259)|(1.372)|
|Husband migrated|-105.577|-29.295|7.717|-25.746|33.609|-0.150|
||(479.903)|(181.815)|(175.618)|(18.718)|(171.804)|(3.859)|
|Other migrated|197.761|43.072|34.872|-0.196|35.328|2.365|
||(367.204)|(354.476)|(167.537)|(12.556)|(167.824)|(3.075)|
|**Age 61 and above**|||||||
|<br>Children migrated|447.978*|259.797|-61.971|71.551|-133.522|5.414|
||(253.564)|(163.164)|(120.874)|(73.263)|(94.901)|(3.992)|
|Husband migrated|<br>-623.473|<br>33.147|<br>174.190|<br>-69.453|<br>243.643|<br>-15.102*|
||(1,068.477)|(826.197)|(195.997)|(75.592)|(177.501)|(9.007)|
|Other migrated|<br>-483.022|<br>-350.153**|<br>-357.405|<br>73.729|<br>-431.134|<br>5.476|
||(417.992)|(166.462)|(306.039)|(72.612)|(296.289)|(4.315)|

Notes: See notes for Table 7. Migration variables are valued at one period lagged. Other included but unreported variables are as given in Table 7 along with province/year interactions. Standard errors are clustered at the village level. * denotes significance at 10% level; ** denotes significance at 5% level; and *** denotes significance at 1% level.

41
<!-- Start of picture text --> Fe<br>° \<br>4<br>oO<br>| |<br>|<br>o ‘<br>Y . | ——*®— Grocery/pharmacy<br>Parks<br>° i ——+— Residential<br>y ¥ Retail and rec.<br>. Transit stations<br>} ——+—— Workplaces<br>'<br>Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov’ Dec<br>Month<br><!-- End of picture text -->

Values are deviations from a normal day and are calculated by Google, using Jan. 3rd to Feb. 6th as the baseline.
where Var(<sup>�</sup> _r_ ) is the estimated variance from the residuals of the model in equation (7).

# **3.1.2 Estimating the distribution of** _σ_ ˆ<sup>2</sup>

_η_ ELL (2002) proposes two methods to obtain the variance of _σ_ ˆ _η_<sup>2:</sup>

1. By simulation (ELL, 2002):

- (a) Estimate _ση_<sup>2fromequation(4)whichyields</sup><sup>_σ_ˆ</sup> _η_<sup>2</sup>

- (b) Estimate _σe,ch_<sup>2fromequation(8)whichyields</sup><sup>_σ_ˆ</sup> _e,ch_<sup>2</sup>

- (c) Assuming _ηc_ and _ech_ are independent and normally distributed with mean 0, new values for _uch_ are generated using equation (2)

- (d) Compute a new estimate _ση_<sup>2fromequation(4)</sup>

- ˆ

- (e) Repeat (a) - (d) and keep all simulated values of _σ_<sup>2</sup>

From the simulated values of _ση_<sup>2,thesamplingvarianceof</sup><sup>_σ_ˆ</sup> _η_<sup>2</sup> �Var � _σ_ ˆ _η_<sup>2</sup> ��can be obtained directly.

2. The sampling variance of _σ_ ˆ _η_<sup>2canalsobeobtainedbythefollowingformula(ELL,2002):20</sup>

- (a) This is noted by ELL to be an approximation of the sampling variance of _σ_ ˆ _η_<sup>2.Itassumeswithin</sup> cluster homoskedasticity of the household error component. For a more comprehensive discussion on this matter refer to ELL (2002).

The command, in its present form only allows for estimation using the second method,<sup>21</sup> when estimating the unconditional variance of _ση_<sup>2usingtheELLmethodology.</sup>

# **3.1.3 ELL’s GLS Estimator**

The GLS estimator offered in ELL’s paper is presented in this section. Although not implemented,<sup>22</sup> the estimator is presented for completeness of the original methodology presented by ELL.

Once _σ_ ˆ _e,ch_<sup>2(8)and</sup><sup>_σ_ˆ</sup> _η_<sup>2(4)areestimatedtheseareusedtoconstructamatrix</sup> �ˆΩ� of dimension _N × N_ . The matrix for _Ω_<sup>ˆ</sup> _c_ is a square block matrix corresponding to cluster _c_ , where the rows and columns are the

> 20This is the only option available in PovMap software.

> 21This is also true for the PovMap software.

> 22This is due to the lack of symmetry of the resulting variance covariance matrix when weights differ across observations in a cluster.

6
<!-- Start of picture text --> Partial Risk Guarantees/ Political Risk Insurance by Region,<br>FY2000-07 (US$ million)<br>Sub-Saharan Africa 2,267<br>South Asia 326<br>Middle East and North Africa 325<br>Latin America and the Caribbean 3,484<br>Europe and Central Asia 1,922<br>East Asia and Pacific 633<br><!-- End of picture text --> <!-- Start of picture text --> Partial Credit Guarantees by Region, FY2000-07 (US$ million)<br>Unspecified 60<br>Sub-Saharan Africa 859<br>South Asia 399<br>Middle East and North Africa 320<br>Latin America and the Caribbean 932<br>Europe and Central Asia 96<br>FastiAsia and Pacific 694<br><!-- End of picture text --> <!-- Start of picture text --> Partial Credit Guarantees by Lending Category, FY2000-07<br>(US$ million)<br>1,000<br>300<br>eo<br>400<br>200<br>2000 2001 2002 2003 2004 2005 2006 2007<br><!-- End of picture text --> <!-- Start of picture text --> Partial Risk Guarantees/Political Risk Insurance by Lending Category,<br>FY2000-07 (US$million)<br>1,600<br>1,400<br>7200 m IDA<br>— m IBRD<br>800<br>600 = Blend<br>400<br>200<br>2000 2001 2002 2003 2004 2005 2006 2007<br><!-- End of picture text --> <!-- Start of picture text --> Partial Credit Guarantees by Sector, FY2000-07<br>3%<br>0% | 2% 296<br><!-- End of picture text -->

<!-- Start of picture text --> Partial Risk Guarantees/Political Risk Insurance by Sector, FY2000-07<br>.<br>@ Education<br>2% (0%<br>@ Energy and Mining<br>@ Finance<br>@ Industry and Trade<br>@ Information and<br>Communications<br>@ Transportation<br>= Water, Sanitation, and Flood<br>Protection<br><!-- End of picture text -->
15

# **3. DATA COLLECTION PROCESS**

Policy information for non-OECD countries was collected through questionnaires over the period of 2008-2010 and comparable policy information for OECD countries was obtained from publicly available sources.<sup>5</sup> The questionnaires were developed drawing upon the existing literature on services trade, model questionnaires that had been used in the context of WTO services negotiations, and related work at other institutions such as the Australian Productivity Commission and the OECD. Most importantly, extensive consultations with the private sector representatives and government officials (especially trade negotiators and regulators) helped identify the key policy barriers for each sector.

The questionnaires were completed by local law firms that were familiar with the policy regime in the respective countries and sectors and had experience in working on similar projects. The complete list of law firms that contributed to the questionnaires is contained in Annex Table 6. One law firm was tasked with administering one questionnaire for each country. A series of pilot surveys were conducted in early 2007 for 8 countries for which more than one law firm was used in each country. The results of that pilot indicated that similar responses were provided by the different law firms, for the most part because they were asked to objectively evaluate policy and regulatory documents. Instances in which policies or their implementation was guided by noncodified practices are described in annotations pertaining to the database value fields. The process of data collection and data evaluation was iterative and interactive; once the law firms had completed the surveys, responses were checked for consistency both within and across questionnaires, and follow-up clarifications were sought if necessary.

## _<u>Data confirmation and processing</u>_

Upon receiving the surveys and after processing the information, a summary of applied policies per subsector and mode of supply was submitted to governments for feedback and review. The contact points for the government review were the permanent representatives of the countries to the WTO

> 5  These sources include WTO Trade Policy reviews, countries‟ most recent offers submitted in the WTO‟s Doha negotiations, the AXCO insurance database, OECD reports on “Exceptions to National Treatment for ForeignControlled Enterprises”, and the IMF‟s annual reports on exchange arrangements and exchange restrictions.