rafmacalaba/data-use-mentions-extended huggingface dataset:
to the poverty gap is more significant than the baseline scenario, the overall income inequality level remains unchanged (Figure 10, panel d).

**Figure 10. Simulated performance and poverty and inequality impacts of the heating allowance scheme under policy scenario 1**

**a. Simulated Coverage (% of households in each decile) b. Simulated Benefit incidence (% benefits allocated to each decile)**

<!-- Start of picture text --> 83.4 4.2 0.2 1<br>2<br>52.3<br>3<br>40.4<br>54.8 4<br>4.1 0.2 0.4<br>5<br> 1 2 3 4 5 6 7 8 9 10 6<br><!-- End of picture text -->

- **c. Simulated generosity (benefits as % of MYPP)**

**d. Simulated Inequality, poverty, and poverty gap impact**

<!-- Start of picture text --> 12.9<br>Marginal Contributions to<br>0.00<br>Inequality<br>Change in Poverty Gap 0.54<br>3.4 Marginal Contributions to<br>0.58<br>Poverty<br>0.3 0.0 0.0<br>1 2 3 4 5 6 7 8 9 10 Disposable<br><!-- End of picture text --> _Source: Own estimates using CEQ and Bulgaria SILC 2021_ _Note: MYPP refers to Market Income Plus Pensions. Deciles are income deciles. The marginal contribution is calculated by comparing the inequality (or poverty) indicator without the transfer and with it. Marginal contributions to poverty are measured using at-risk of poverty lines (AROP), fixed at the baseline scenario before the reform was implemented._

**The adjustment in the Guaranteed Minimum Income (
FAI was about 50 percent of FDI, broadly consistent with recent findings: as mentioned above, ‘phantom’ investments seem to account for around 40 percent of FDI.

> 27These can be accessed at, respectively: https://www150.statcan.gc.ca/n1/pub/13-607-x/2016001/ 145-eng.htm, https://www.bea.gov/data/intl-trade-investment/activities-us-affiliates-foreign-mnes, and https://ec.europa.eu/eurostat/web/structural-business-statistics/data/database.

> 28Meaning entities that are majority owned by ultimate parents that reside in a different country and can determine those affiliates’ general policies.

> 29The host countries covered (with the * indicating non-EU members) are: Austria, Belgium, Bosnia and Herzegovina (*), Bulgaria, Canada (*), Croatia, Cyprus, Czechia, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Ireland, Italy, Latvia, Lithuania, Luxembourg, Malta, Netherlands, Norway(*), Poland, Portugal, Romania, Slovak Republic, Slovenia, Spain, Sweden, the United Kingdom and the United States (*). All those listed as EU members were members throughout the sample period used for estimation.

> 30Here and in results reported below we combine FDI data from the IMF Coordinated Direct Investment Survey (CDIS) and UNCTAD. Bilateral CDIS data are available starting in 2009; these are stock data, from which we back-out flows using the perpetual inventory method, as _It_ = _Kt −δKt−_ 1, with _δ_ = 0 _._ 195. Bilateral UNCTAD data are publicly available only to 2012.

15
The use committees of enquiry, of interpellations and of the ombudsman offices follows a different pattern. The survey data suggest that the use of interpellations as an oversight tool is very common in high-income countries, less common in low-income countries and least common in middle-income countries : they are used in about 81 % of the high-income cases, in about 77% of the low-income cases, and in less than 70% in the middle-income cases. Data are presented in Table 11.

**Table 11. Use of Interpellation by Income Level**

|Do the countries in these<br>|income level|use interpellati<br>|ons?<br>|
|---|---|---|---|
|Incomelevel<br>no|Yes|Total|% yes|
|Low<br>5|17|22|77.3|
|Middle<br>8|18|26|69.2|
|High<br>3|13|16|81.3|
|Total<br>16|48|64||

Finally, the presence of committees of enquiry and of Ombuds offices as oversight tools is most common in middle-income countries, less common in highincome countries and least common in low-income countries. Data concerning the presence of committees of enquiry and of the Ombuds offices by are presented in Table 12 and 13.

**Table 12. Committees of Enquiry by Income Level**

|Do the countries in these income lev|els have Com|mittees of En|quiry?|
|---|---|---|---|
|Incomelevel<br>no|Yes|Total|% yes|
|Low<br>2|20|22|90.9|
|Middle|30|30|100|
|High<br>1|19|20|95|
|Total<br>3|69|72||
CEFTA countries (except Kosovo) and all EU members as well, and (ii) that it includes consistently constructed domestic trade flows, which are crucial for the implementation of our methods. We offer further details on the dataset in Section 3.2.

Several main findings stand out from our partial equilibrium estimates. Without going into details, we note that we obtain estimates of the effects of all standard gravity variables that are readily comparable to corresponding estimates from the existing literature. This establishes the representativeness of our sample. In addition to the standard gravity covariates, we use border variables to allow for differential impact of borders on trade within EU vs. trade between EU and CEFTA. We find that, without any exception, the additional barriers to trade between CEFTA and EU are larger as compared to the barriers to trade within the EU in each of the four main sectors in our sample, including Agriculture, Mining, Manufacturing and Services. We also document significant variation in the border differential across sectors. The differences between the CEFTAEU border and the EU-EU border are the largest in Agriculture, followed by Services, Mining, and Manufacturing.<sup>4</sup> Capitalizing on the structural properties of the gravity model, we transform our border estimates into tariff-equivalent effects to find that the border tariff-equivalents that we obtain are significantly larger as compared to the existing differences in applied tariffs for each of the three goods sectors in our sample. Specifically we obtain a border tariff equivalent of 19.65% vs. 11.58% actual weighted average tariff difference in Agriculture, 10.62% border tariff equivalent vs. 1.68 actual weighted average tariff difference in Mining, and 7.92% border tariff equivalent vs. 5.22 actual weighted average tariff difference in Manufacturing. This result supports our assumption that, in addition to tariffs, there are other barriers to trade between CEFTA and EU. Comparison between the tariff data and our border differential estimates suggests that Mining and Agriculture are subject to more significant
Table 3: People management and intermediate outcomes, public and private schools in Latin America

||(1)<br>z-teacher<br>shortage|(2)<br>z-teacher<br>motivation|(3)<br>z-teacher<br>efort|(4)<br>z-teacher<br>shortage|(5)<br>z-teacher<br>motivation|(6)<br>z-teacher<br>efort|
|---|---|---|---|---|---|---|
|Private School|-0.535<br>(0.122)<br>[0.000]|0.591<br>(0.139)<br>[0.000]|0.792<br>(0.128)<br>[0.000]||||
|People Index||||-0.062|0.238|0.074|
|||||(0.035)<br>[0.077]|(0.040)<br>[0.000]|(0.033)<br>[0.026]|
|_R-squared_|_0.152_|_0.142_|_0.154_|_0.139_|_0.169_|_0.123_|
|Observations|3035|3043|3043|3035|3043|3043|
|School controls|Y|Y|Y|Y|Y|Y|
|Country FE|Y|Y|Y|Y|Y|Y|

Notes: The first row reports the coefficients from regressions of a binary indicator (coded to 1 if the school is a private school, 0 otherwise) on the standardized index of three intermediate school outcomes: teacher shortage, teacher motivation and teacher effort. The second row reports coefficients from regressions of
patterns. Even before the 2014 _Hukou_ reform, a growing number of young migrant workers were migrating with their children (Wang et al. 2019). This new pattern was supported by the emergence of private schools for migrant children in some large cities (Pong, 2014), the new generation’s adaptability to urban life and desire to settle in cities (He and Wang 2016) and migrant parents’ view of urban education as a path to social mobility for their children (Kwong, 2004).

> 2Many rural migrants continue to work in cities without temporary residence permits. Evidence from the 2001 China Urban Labor Survey (CULS) suggests that nearly half of the rural migrant population in 2000 were working without temporary residence permits (appendix materials for de Brauw and Giles 2017). Migrants with legal guest worker status have more secure employment and tend to be more established in cities.

> 3Since 2016 based on _The Interim Regulations on Residence Permits_ issued by the State Council, migrants with stable employment or residential place and residing in a city for more than 6 months are encouraged to apply for residence permits ( _Ju Zhu Zheng)_ , which replaced temporary residence permits issued in earlier years. Like the previous regulatory measure of temporary residence permits, this regulation is not enforced.

> 4Individuals with rural _Hukou_ status can now purchase non-agricultural _Hukou_ status from urban governments, yet in many cases the system continues to work against more permanent migration flows (Fan, 2008). Further, since implementation of the Labor Contract Law in 2008, it has been feasible for migrants to enroll in urban social insurance programs (including both health insurance and pensions), but confusion over enrollment procedures and lack of portability still contribute to low participation rates (Giles et al., 2021).

5
the final disposition of the case (Finger 1993; OECD, 1997), the main indicator used is the number of anti-dumping investigations that had been initiated as of the time of the TPR. For safeguards and countervailing, the actions were so infrequent , that the data shown refer to the number of specific products, usually narrowly defined, on which there were either affirmative actions in place or **(in** going investigations as of the time of the most recent TPR, using both TPR data and WTO notification ;. Thus, while the two sets of data in Table 11 are not directly comparable, the frequency of safeguard and countervailing actions is likely to be much smaller than those for anti-dumping.

The data verify and provide detail on the well known increasing use of antidumping actions, especially by higher middle income developing countries and the emergence of antidumping as the

**Table 11**

# **Trade Remedies by Developing Countries, 1989-1998**

(in % and Number of Products)

|**COUNTRY**|**Antidu**<br>**(1)**|**mping***<br>|**Counter**<br>**(2**|**vailing**<br>**)**|**Safegu**<br>**(3)**|**ards"**<br>|
|---|---|---|---|---|---|---|
||**1989-94**|**1995-98**|**1989-94**|**1995-98**|**1989-94**|**1995-98**|
|Argentina<br>|0.70%<br> <br>|1.12%<br>|2|3||**2**|
|Bangladesh
<!-- Start of picture text --> 8<br>7.9<br>|<br>B78<br>7.7<br>7.6<br>2 0 2 4<br>Z_ndvi2018<br><!-- End of picture text -->

<!-- Start of picture text --> 8.4<br>8.2<br>Lo}<br>a8<br>2<br>7.8<br>7.6<br>4 -2 0 2 4<br>z_temperature2018<br><!-- End of picture text --> <!-- Start of picture text --> 8.6<br>8.4<br>8.2<br>|<br>D<br>2<br>8<br>7.8<br>7.6<br>0 2 4 6 8 10<br>z_rainfall2018<br><!-- End of picture text -->
Policy Research Working Paper 6108

# **Abstract**

A new Services Trade Restrictions Database collects and makes publicly available information on services trade policy assembled in a comparable manner across 103 countries, five sectors (telecommunications, finance, transportation, retail and professional services) and the key modes of service supply. It contains richly textured policy information as well as a preliminary quantification of policy measures. This paper is a guide to the database, and provides a description of the data, how it was collected, how policy information was quantified, and how the data are presented in the publicly available, interactive Web database. The database is best seen as a first response to the strong demand for better information from policy-makers, negotiators, researchers and the private sector. Even in its present version, the database can play an important role in advancing policy reform by facilitating the analysis of services policies, informing international negotiations by providing data on actual policies, and provoking dialogue and refinements by making information on policies publicly available. Through feedback from various interested parties, the database may evolve into a collectively created public good.

This paper is a product of the Trade and Integration Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at amattoo@worldbank.org, i.borchert@sussex.ac.uk and bgootiiz@worldbank.org.

_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
# Table A.6: Psychological and sexual violence

|Full sample<br>(1)|Rural<br>(2)|Urban<br>(3)|
|---|---|---|
|**Panel A: Psychological violence index**|||
|Fraction of female leaders<br>0.138|0.270|-0.092|
|(0.164)|(0.189)|(0.229)|
|Observations<br>62,122|43,981|18,141|
|Outcome mean<br>0.002|0.011|-0.019|
|**Panel B: Sexual violence index**|||
|Fraction of female leaders<br>0.014|0.098|-0.028|
|(0.182)|(0.208)|(0.262)|
|Observations<br>62,122|43,981|18,141|
|Outcome mean<br>0.003|0.019|-0.037|
|State fxed efects<br>x|x|x|
|District characteristics<br>x|x|x|
|Individual characteristics<br>x|x|x|

_Notes:_ Data are from the 2015-16 National Family Health Survey (NFHS) of India. The table presents 2SLS estimates using the fraction of female leaders in a district in close elections as an instrument. The dependent variables are psychological violence index in Panel A and sexual violence index in Panel B. Column (1) presents estimates for the full sample of women, while columns (2) and (3) restrict the samples to those living in rural and urban areas, respectively. We define close elections as those in which the victory margin is less that 3 percent of votes. All regressions include state fixed effects
that is, their own potential. ıı Clearly both actual and potential values for Korea are higher than those of Vietnam. However, Vietnam yy is much closer to its frontier value (distance AC < BD) and therefore has smaller scope for improvement. Thus, if we rank the countries in terms of their improvement potential (efficiency), Vietnam will be ranked higher than Korea in terms of educational quality. This does not mean, however, that educational quality in Korea is lower than in Vietnam. Our ranking is in terms of efficiency, not in terms of the raw indicator. We discuss this in detail for various indicators in the results section.

# **5. Data** **_5.1 The indicators (_** i **_)_** yy

Following the illustration presented in the conceptual framework, we draw on the theoretical and empirical literatures on economic growth, poverty reduction, and resilience to identify some variables that can be used as indicators of social and economic development (Foa 2013; Loayza, Fajnzylber, and Calderón 2005; World Bank 2013).

Initially, we built a database for 146 indicators covering 207 countries for the period 1950-2014. However, for practical reasons, we narrowed the number of indicators down to 10 indicators and the period of study 11
. That is, we estimate the N-step forecast under these two alternative external conditions, and the difference between the two is the effect of the external factors on growth. For the region as a whole, we calculate that external factors account for 4.6 percentage points of the slowdown (Table 2, Col. 3).

To calculate the total slowdown we use two alternative procedures: one possibility is to use the N-Step forecast using the actual conditions in the external variables (Actual scenario above); or, alternatively, we can use the most likely outcome for the year that can be estimated given that we already have third quarter GDP data for most countries

> 8 Another reason to expect efficiency gains from the SUR methodology arises because El Nino did not affect all countries. Hence the set of explanatory variables in the countries not affected by it is a subset of the explanatory variables included for Peru and Ecuador.

> 9 Initially, the specification did not include the current account in industrial countries. Adding this variable improved the explicatory power of the model and was used as the final specification.. US growth rate was dropped out since it turned out to be not significant. This result coincides with Ahmed (1999). However, it might be the consequence of estimating a panel without allowing for slope heterogeneity. A next revision of this estimation should allow for this flexibility to verify this result.
in the External-Balance submodel also generate a small amount of crowding-in of private investment (15 cents per dollar of fiscal surplus, see Panel F).

Over the long term, the differences in public and private investment accumulate to modest differences in the capital stock and GDP across the different fiscal rules. In the BBR-HRs (both submodel), GDP is about 8 percent above baseline by 2040,

> 51 For example, in 2026, oil revenues increased by just above 2 percent of baseline GDI: a 9% increase in oil production x 35% oil as share of total GDI in 2025 x 70% oil tax rate. 52 Recall that private investment increases in absolute terms because it is a fixed fraction of GDI, which increases relative to baseline after 2025 due to the discovery.

41
comparison of debt management performance in each group finds it does not correlate to income: those classified as low income exhibited the strongest debt management performance (as defined by meeting with the DeMPA scores of at least C) and those classified as lower an upper middle income about the same (Figure 4). On average the first group met the minimum requirement in 36 percent of the dimensions assessed and surpassed it, with a score A or B, in 9 percent: the comparable figures for small states in the upper middle income group were 32 percent and 12 percent, respectively.

**Figure 4: Debt Management Capacity – Comparison by Income Group**

<!-- Start of picture text --> 0 20 40 60 80 30<br>25<br>Low<br>20<br>A<br>Lower-middle 15<br>B<br>10 C<br>Upper-middle<br>5<br>0<br>D C and above Low Lower-middle Upper-middle<br><!-- End of picture text --> Source: DeMPA reports for 17 sample small states<sup>24</sup>

# **How Does the Debt Management Capacity of Small States Measure Up?**

A summary of how small states in the sample perform in each dimension of debt management is discussed below and illustrated by the data in Figure 5.

In six dimensions of debt management 10 or more small states, two thirds of those assessed, met or surpassed the minimum requirement for:

- coordination with fiscal policies, specifically accurate and timely forecasts on total debt and debt service under alternate scenarios;

- coordination with monetary policy, in particular the imposition of limits on direct access of resources from the Central Bank;

- managerial structure for borrowings and debt-related transactions;

- existence, coverage, and content of the legal framework;

> 24 For analysis purpose, Maldives is classified as lower-middle-income country. Maldives was classified as lowermiddle-income country when the DeMPA was conducted
Female*Years of schooling|0.0275*|(1.75)|0.0209|(1.18)|
|Female*TEEP cohort|0.107|(0.78)|0.189|(1.21)|
|Mother’s years of schooling|0.0105|(1.00)|0.00905|(0.85)|
|Mother’s age|-0.000131|(-0.06)|-0.00277|(-1.27)|
|Log(consumer durables)|0.0488|(1.60)|0.0169|(0.48)|
|Height|0.00557*|(1.79)|0.00698*|(1.97)|
|Number of students per classroom in high school|-0.000624|(-0.70)|0.000799|(0.69)|
|If phone interview|0.232***|(4.60)|0.244***|(3.24)|
|Inverse Mills ratio|-0.0445|(-0.12)|0.376|(0.92)|
|School fixed effects|yes||yes||
|Number of observations<br>|1,716||1,592||

Source: TEEP Tracking Survey. Notes: t values are in parentheses.

* significant at the 10% level; ** significant at the 5% level; *** significant at the 1% level.

44
therefore convergence in the adoption of new technologies [Behrman et al., 2002, Golub and Jackson, 2012, 2010, Alik-Lagrange and Ravallion, 2018]. Especially in traditional societies, social networks may be homogeneous and stratified by income or social hierarchy and thus reinforce social norms in the patterns of intra-household bargaining power [Mukherjee, 2017, Hoff and Pandey, 2006, Mayoux, 2001], which are often skewed to the male in the household. Therefore, understanding how peer effects, whether negative or positive, affect norm-driven household behavior can inform the design of interventions that target women’s education and employment, as well as investments in children.

We use primary data from Uttarakhand, India, to document women’s social networks in the presence of restrictive social norms and virilocal residence. Examining participation in a women’s education program, _Mahila Samakhya_ , we study the impact of having more empowered friends on three sets of outcomes. First, we examine female autonomy: (1) women’s ability to leave the house without permission, (2) women’s access to social safety nets and employment through the National Rural Employment Guarantee Act (NREGA), and (3) women’s likelihood of working outside the household. Second, we consider perceived norms about gender roles: (1) the amount of education girls should receive relative to boys, and (2) whether employment or marriage is the best reason to educate girls compared with boys. Third, we consider a set of outcomes of intra-household bargaining: (1) children’s food consumption and (2) time spent on chores. Our results show that empowered peers significantly improve women’s physical mobility and the likelihood of working outside the household, but they have little effect on perceptions about gender roles. Finally, 4
# **III. Data Sources and Descriptive Statistics**

# **_Survey data on adults in middle-income countries_**

Our study uses a survey database on nine middle-income countries collected over the period 2012-13 under the Skills toward Employment and Productivity (STEP) program of the World Bank.<sup>38</sup> The countries are Armenia, Bolivia, Colombia, Georgia, Ghana, Kenya, Serbia, Ukraine and Vietnam. One adult aged 15-64 years was randomly selected as the respondent from about 3,000 randomly selected households, but we restrict our analysis to the subsample of adults aged 25-54 as our focus is labor market behaviors and outcomes.<sup>39</sup> With a few exceptions, the same household survey instruments were administered in all countries and the data have been harmonized. The survey collected details on skill acquisition (i.e., early childhood education, schooling attainment, training and apprenticeships), measures of skills (cognitive, noncognitive and other job-relevant skills), labor force participation and occupation, family background and socioeconomic status.

Cognitive skills have been measured through a literacy assessment, developed specifically for use in the context of developing countries.<sup>40</sup> These values are not assessment scores of individuals in themselves, but are values imputed from a conditional distribution of assessment scores based on population characteristics. Thus, our estimated returns to cognitive skills indicate

> 38 The STEP project included a total of 14 countries as of 2016, but data on our preferred measure of cognitive skills are not available for five countries (Lao PDR, Macedonia, the Philippines, Sri Lanka, and China (Yunnan Province)), so we drop them in this paper.

> 39 Survey respondents aged 15-24 are more likely to be enrolled in an educational institution or training course or looking for a first job, while respondents 55 and over may have stopped working for pay for a variety of reasons, including voluntary retirement and ill health. To address estimation issues associated