rafmacalaba/data-use-mentions-extended huggingface dataset:
message from this report, would be the same: youth are still differentially disadvantaged compared to other segments of the population.

The main sources of quantitative data for this study are the Sudan 2009 National Baseline Household Survey (NBHS) and the 2014/15 National Household Budget and Poverty Survey (NHBPS). Both surveys, conducted by Sudan’s Central Bureau of Statistics (CBS), employed a two-stage stratified cluster design in which the enumeration areas were selected in the first stage based on the 2008 Census, and individual households were randomly selected in the second stage following household listing exercises in the selected enumeration areas. The sample was designed to be representative both at the national and state levels, accounting for 8,000 and 11,953 households in 2009 and 2014, respectively. These sources are complemented by additional data sources such as the Multiple Indicator Cluster Survey (MICS 2014); World Development Indicators (WDI) database; Doing Business (2019); the Women, Business and the Law (WBL) database (2021); the Enterprise Survey (2014); Global Financial Inclusion (Global Findex 2011– 2014); the Afrobarometer (2018); and the Arab Barometer (2018). Econometric analysis was used to examine the determinants of some outcome variables relevant for women and youth.

Additional information has been drawn from existing empirical literature and country-specific resources, including a review of the current legal and policy framework. The literature review focuses on the perspectives of anthropological, sociological, and gender studies. Given the limited literature specifically focusing on women and youth in Sudan, before conducting the literature review, the team reached out to a number of leading scholars of Sudan, including Balghis Badri, Nafisa Bedri, Janice Boddy, Ellen Gruenbaum, Sondra Hale, Shadia Mohammed, Nada Mustafa Ali, and Harry Verhoeven, who recommended particular scholars’ work. The literature review includes academic monographs, peer reviewed journal articles, and reports from the World Bank and UN organizations, in combination with descriptive survey data from the
will decline from 17.7 percent in 1998 to 10 percent by the year 2000. Funding will be allocated primarily to infrastructure building (38 percent of the total budget), the provision of credit (27 percent), and resettlement (12 percent).<sup>2</sup> ' HEPR documents also emphasize greater decentralization of implementation and active national participation. Activities include awareness campaigns to motivate solidarity and mobilize charitable contributions and other forms of participation from the population. The government promotes HEPR as a "national endeavor" in the revolutionary tradition of Ho Chi Minh to eliminate hunger and raise the living standards of millions of Vietnamese. Much of this rhetoric reflects the government's political commitment to raising living standards for all, a widespread societal concern with rising inequity, and the reality of tight budget constraints.

The program was approved by the national assembly and an Executive Board established in April 1998. Surveys will now be conducted to establish a registry of poor households at the commune level. Each identified poor household will then be given a book that ceItifies their status. This will be valid for as long as they are poor. Local authorities will check this yearly. Possession of this book then qualifies the household for loans, training, fee exemptions, and so on. Without the book, households can not participate in the programs. Other programs will continue, although it is not clear in what capacity.

# **CHALLENGES FOR IMPROVING VIETNAM'S SAFETY NET**

Reforms of the current poverty alleviation and safety net system as embodied by the national HEPR do not propose a change in policy focus. The identified policy areas have all been emphasized in the past and were addressed by some of the larger programs discussed 22
. Sale deeds carry no guarantee of validity, and therefore offer no conclusive evidence of rights in land. Once mutation has been effected, the government, through the tehsil office, provides the landowner with documentary evidence of rights in land, locally called a patta<sup>2 3</sup> , which amounts to evidence of land title. A legal interest in land is not created or transferred until mutation takes place. However, as we describe below, this process is both lengthy and costly, and many landowners do not bother to embark upon it, preferring instead to use their sale deed as evidence of their land rights, and to wait until the next survey and settlement operation during which they expect to be issued with _apatta_ after the land records have been updated.

Registration of transfer deeds of any property is intimated by the sub-registrar to the concerned tehsildar in a prescribed format (on Form No.3). This information is used by the tehsildar to effect the mutation in the ROR<sup>24</sup> . Mutation refers to the amendment of state records to reflect a change in ownership or other rights in land through sale/purchase, gift, inheritance or mortgage<sup>25</sup> .

Before making any change in the ROR, the Tehsildar<sup>26</sup> calls for objections to the transaction within a stipulated period. Any objections to the transaction are dealt with by the tehsil court. Sometimes, the matter may be referred to the next higher court of the Sub-Collector or above. Once the mutation is allowed, the Tehsildar issues a notification to update the RORs of the land transferor(s)! transferee(s). After mutation and correction of RORs, a 45-day appeal period is allowed for challenging the mutation. This is particularly relevant in the case of multiple brothers with potential claims on a parcel of land, one of whom may challenge the transaction. Finally, certified copies reflecting the latest status of land ownership are issued
we show in Section 4, this volume of traffic, scaled to city sizes, is some 175 times smaller than the river-crossing passenger traffic in Kisangani. It is also around five times smaller than the volume of passenger traffic between East and West Berlin in 1988 – well before the dismantling of the Berlin Wall.<sup>12</sup>

# **3. ASSESSING THE POTENTIAL FOR CROSS-BORDER INTEGRATION: NEW EVIDENCE**

**How large is the potential for trade expansion across Malebo Pool?** Compared to the two cities’ size, economic importance and role as gateway to the Lower Congo region, economic exchanges between Kinshasa and Brazzaville are extremely limited. Such limited observed trade could be the result of either substantial barriers to trade or a lack of tradable products. In order to investigate the reasons behind this limited trade, we assess the extent of existing and potential cross-border economic interactions in Kinshasa-Brazzaville using primary data collected for the purpose. We collect and compare retail price data on both sides of the river, testing for symptoms of market segmentation in the form of systematic price differentials that would indicate the existence of trade barriers. In addition, we analyze the results of structured interviews carried out with trading firms in both capitals for a qualitative assessment of barriers to trade, and for an estimation of the latent potential for trade expansion.

> 12 In 1988, there were some 1.9 million passenger crossings between East and West Berlin – about one crossing per three inhabitants of the Berlin metropolitan area. The 700,000 estimated crossings across Malebo pool represent about one crossing per 15 inhabitants of the Kinshasa-Brazzaville conurbation.

15
for 5 out of 30 municipalities for the most recent election of 2007 before the survey). Missing electoral data may not be unrelated to underlying political characteristics of municipalities.<sup>26</sup> Greater poverty has been widely hypothesized as facilitating vote buying (Stokes, 2005), especially in interaction with electoral competition (Weitz-Shapiro, 2012). Small area estimates of poverty are available from the National Statistics Office in the Philippines. These estimates combine information from nationally representative household surveys, with information from the census, to generate what the poverty literature considers to be some of the most reliable estimates at lower levels of disaggregation (Elbers et al, 2003).

There is little robust and significant correlation of poverty incidence with vote buying (columns 4-7). Although the coefficient point estimate in most specifications suggests that greater poverty could be associated with greater vote buying, the estimate is not typically statistically significant. At the very least, this casts doubt on any poverty-based alternate explanation for the correlation between vote buying and health services.<sup>27</sup> There are no robust patterns in the data to show that greater competition (measured through (lower) margins of victory, or as (lack of) entrenchment of clans) and higher poverty together explain greater vote buying. One possibility for why poverty and its interaction with available measures of electoral competition do not show-up significant here is because of the highly disaggregated nature of the sample, relatively small municipalities within one province in the Philippines. Poverty

> 26 Another measure of electoral competition was also included in various specifications—the number of candidates competing for mayoral office—but this was typically insignificant, in the majority of specifications. 27 In the main results presentation in Section 4, the paper uses various specifications and measures of poverty to establish that the correlation of interest—between vote buying and health services—is not driven by poverty.

13
,375|2,421|1,136,716|
|||||||A|verage:|2,391|1,196,278|
||||||S|ample v|ariance:|366,300||

Third, calculate the Empirical Bayes Credibility Factor for probable yields at the Threshold Yield Collective level using the yield data. For cotton in Gujarat the credibility factors for each district are given in Table 5.

Table 5. Cotton in Gujarat: Empirical Bayes credibility factors for probable yields

|**District**|**Number of insurance**<br>**units in district**|**Empirical Bayes Credibility**<br>**Factor for probable yields,**<br>|
|---|---|---|
|GJ1|10|93%|
|GJ2|11|18%|
|GJ3|4|93%|
|GJ4|13|79%|
|GJ5|11|91%|
|GJ6|12|50%|
|GJ7|2|0%|
|GJ8|9|53%|
|GJ9|8|72%|
|GJ10|8|94%|
|GJ11|14|91%|
|GJ12|14|24%|
|GJ13|11|0%|
|GJ14|10|87%|

A detailed breakdown of the credibility factor for district GJ8 is as follows: 12
expected to be heavier as compared to a snowstorm in terms of recovery time required. The national highway is the major corridor in this area, though a detour route is still available. The proposed bypass tunnel would run through the landslide-prone area and allow for this road section to remain free from the risk of landslides.

# Indicator Data

Table 3 shows the necessary data for the calculation of resilience indicators for each dimension in Case 3. As with the above cases, the traffic volume in the ordinary state is taken to be the design traffic volume. The travel time is calculated by a route search system like Google Maps. Since alternate routes to National Highway No. 9 exist, a disruption to the road section causes only a 4-minute increase in travel time between Toyooka and Tottori. Using a route search system, areas accessible to the major hospital can be determined and, hence, the population with access can be calculated by drawing on demographic statistics. Because the disrupted section is just a single point, none of the population is isolated, even in the disrupted state.

The expected time for recovery depends on the type of hazard event. A landslide event requires 2 days for recovery, on average.

Table 3. Case 3 Resilience Indicator Data

|Indicators of<br>LoF|Relevant Data|Data source|Content|
|---|---|---|---|
||Traffic volume per min under theordinarystate (x�)|Design traffic<br>volume|6.11 pcu/min<br>(8800 pcu/day)|
|Ei|Traffic volume per min under thedisruptedstate (x�)|Simulation|3.06 pcu/min|
|conomc<br>Loss|Travel time between representative OD (Asago – Tottori)<br>withoutthe project under theordinarystate (t<sup>�����</sup>)|Route search<br>system|109 mins|
||Travel time between representative
school-going children were displaced from one department to another, then it is likely that it would produce an upward bias on the average education impact of war. It is also possible that the household control variables for the comparison group might have changed over time, especially if they migrate. As a result, we use an alternative identification strategy to minimize the potential bias that might arise due to these factors.

# **4.2 Identification using victimization indicators from ENV-2008**

As a next step, we use 11 victimization indicators as potential identifiers of true war victims. The victimization indicators are dummy variables, which takes the value of one for a household or individual being a victim, zero otherwise. It is possible that the self-reported victimization indicators may produce subjective bias related to a particular ethnic group or other identities. The simplest way to detect the extent of this bias is to estimate each victimization indicator as a function of the observable characteristics. The estimated outcome does not conform to any subjective bias generated by any particular variable (for reasons of space we do not show the outcome in the paper; it is available from the authors if requested).

We first estimate the standard linear OLS regression outcomes of years of education as a function of the victimization dummy and household and individual controls on a sample restricted to individuals aged between 10 and 22 (who are likely to be in the primary school during the conflict). In Table 4.3.1 we report the estimated coefficients for the eleven victimization categories (columns M1 through M11). The coefficients of all the victimization dummy variables are negative. The coefficients are statistically significant for victimized individuals or households when they registered deaths or injuries due to conflict, income dropped, lost job, lost livestock and experienced violence due to war. Overall, the estimated war outcomes on education are in line with previous findings, despite the fact that the impact of war is now identified by a set of victimization indicators based on the subjective evaluation of war impact by the survey respondents
states. In most cases, these lockdown measures were in force for about 5-8 weeks, and ‘restricted the movement of residents and led to the closure of business operations, and the closure of regional borders linking lockdown areas with the rest of the country.’ (Amare et al. (2021), pp. 6-7). As shown in Amare et al. (2021), in addition to the effects caused by the nationwide restrictions, the above-mentioned state-level lockdown measures also had an adverse impact on affected households’ well-being. Therefore, school-age children living in states affected by these additional measures are expected to show a higher risk of school drop-out. To test for this hypothesis, we followed Amare et al. (2021) by constructing an indicator for such augmented lockdown measures (labeled as _AugmLockdown_ in our specifications below), which takes value one if the person lives in the states reported above, and zero otherwise.

# **2.2.4 Measuring Climate Shocks: Drought and Flood**

Households’ livelihoods and decision to invest in children’s education be affected may by the concomitant occurrence of climate shocks. Given that Nigeria is partially located in the drought-prone western Sahel region, such events are quite regular. As documented by the reports of the International Federation of the Red Cross and Red Crescent Societies (IFRC),<sup>12</sup> some states in Nigeria were severely hit by floods in September 2019 and October 2020. If a drought or flood occurs simultaneously with COVID-19 outbreak and its related lockdown measures, such shocks may confound the true impact of temporary school closure due to COVID-19 on school attendance. Therefore, we control for drought We measure drought in 2019 (before the outbreak of COVID-19 pandemic and school closure) and 2020 (during COVID-19 and before school reopening) using the Standardised Precipitation-Evapotranspiration Index (SPEI), which captures the severity of drought according to its intensity and duration.<sup>13</sup> Since the lean period in Nigeria covers
032]|[0.032]|[0.032]||[0.028]|[0.028]|[0.028]|
|Married|||0.194***|0.194***|||-0.023*|-0.024*|
||||[0.015]|[0.015]|||[0.013]|[0.013]|
|Rural||||0.087||||-0.224|
|||||[0.103]||||[0.170]|
|Observations|23,107|21,900|21,886|21,886|23,383|22,526|22,514|22,514|
|R-squared|0.776|0.804|0.810|0.810|0.756|0.773|0.774|0.774|
|Individual fixed effects|YES|YES|YES|YES|YES|YES|YES|YES|
|Year fixed effect|YES|YES|YES|YES|YES|YES|YES|YES|
|Region dummies|NO|NO|NO|YES|NO|NO|NO|YES|
|Dependent variable mean|0.772|0.770|0.770|0.770|0.171|0.172|0.172|0.172|

Robust standard errors in brackets. *** p<0.01, ** p<0.05, * p<0.1 _Notes._ Each cell represents a coefficient estimate using OLS regression relying on panel data estimation. The analysis is restricted to working age individuals in the years 2012 and 2018. In columns (1) to (4), the analysis is restricted to the sample of
<!-- Start of picture text --> Figure 2.2. The labor market in SDGSIM<br>5<br>4<br>3 Wage<br>curve<br>Demand<br>2<br>1<br>0<br>85 90 95<br>100 - unemployment rate (%)<br>Wage<br><!-- End of picture text --> Source: Authors’ elaboration.

The basic accounting structure and much of the data needed to implement SDGSIM is derived from a social accounting matrix (SAM). The model also relies on complementary stock, elasticity, and base-year employment data.<sup>2</sup> For this study, a new Yemen SAM was built for 2020, primarily drawing on data from the World Bank, the IMF, and the UN. While the quality of data for Yemen is low, the construction of the SAM had the advantage of imposing consistency between data from different sources, as a result producing a coherent database that captures well-known structural features of the Yemeni economy. The disaggregation of the SAM and the model database is presented in Table 2.1.<sup>3</sup> It distinguishes 17 production activities and commodities (or products); seven primary production factors including two labor categories (male and female), private capital, and four natural resources (agricultural land, fish stock, and mineral stocks for oil and gas and other mining); and one household. The government is defined broadly to include donor expenses and receipts.

In 2020, the country’s GDP was 13,999 billion Yemeni riyals; with an exchange rate of 743 riyals per US$ and a population of 29.8 million, this corresponds to a GDP per capita of US$623. Tables 2.2a and 2.2b show a macro version of the 2020 SAM and its accounts, respectively. The SAM, which is expressed as percent
export promotion, an idea that was set forth in Krugman (1984).

Table 5 uses data on agriculture tariffs on imports from the European Union for those goods that also benefited export subsidies in the US. These data are from Hoekman, Ng, and Olarreaga (2002).<sup>23</sup> Specifically, Table 5 models the average US tariff (during 1995-1998) on products that also benefited from a US export subsidy. The extra variable that appears in these models is the corresponding European Union average tariff (AVGEU15). That variable is included in order to examine whether US tariffs retaliate against EU tariffs for these goods. The clearest inference from Table 5 is that lobbying spending, whether measured by %PACEXP or RANKPACEXP, is powerfully associated with the imposition of US tariffs on products whose exports are subsidized in the US. This finding is robust for each of the four

> 23 I am grateful to Francis Ng for providing the data and its documentation.

51
29 scale of regional intervention in this sector: on average regional governments do not intend to reduce agricultural support in response to simple cuts in federal transfers. The federal government will need to use a more comprehensive policy to influence regional decision-makers to reform their agriculture policy. One of the components of such a policy should be the redesign of the existing federal transfers for agricultural subsidies. Such subsidies and therefore transfers must be eliminated. If the federal government wants to continue the provision of support for the sector, it should, first, switch such assistance to less distortive types of interventions<sup>2 8</sup> , and, second, set up monitoring mechanisms to ensure that corresponding transfers are actually spent on the designated purposes of agricultural or rural development and can not be reallocated by regional governments.

_Table 16. Determinants of Subsidies to Agriculture, 1993-95_

||1993|1994|1995|
|---|---|---|---|
||(N=78)|(N=78)|(N=77)|
|**Pre-transfer regional revenue**|.045|.042|.065|
||(2.75)|(5.3)|(6.8)|
|**Federal transfers**|||-.028<br>(-3.3)|
|**Unemployment level**|-6.97|-4.28|-4.81|
||(-1.7)|(-2.2)|(-3.7)|
|**Share of rural population,**|1.79|1.51|1.36|
|**1994 data**|(2.8)|(4.2)|(4.8)|
|**Constant**|43.61|33.4|15.3|
|
is more critical of the GCC’s current economic situation or the directions of current economic reforms compared with local media. Sentiment from international media matters because it is correlated with FDI inflows into the GCC.

# II. Taking Stock of Economic Reforms in the GCC

This section takes stock of the implemented economic reforms in the GCC because their content and focus could impact engagement and support from the population and other stakeholders. The objective is to understand whether these reforms are geared towards lagging policy areas. In doing so, a novel database of laws and decrees passed by the GCC countries is constructed. Data are obtained by web-scraping countries’ national legislative archives (see Appendix A1 for the details). All recorded laws and decrees from 1980 to 2019 are collected for each country. Each downloaded legislation has a title, source, and date of the legislation.<sup>4</sup>

## II.1 A Novel Database of Laws and Decrees in the GCC

Since we focus on economic reforms, we map the extracted laws and decrees to ten economic policy

> 3 For example, the Arab Barometer (https://www.arabbarometer.org/) surveys people’s view about current economic conditions among other issues in the MENA region. This is a related, but narrower, approach. GCC countries are not typically covered in the Arab Barometer. For example, only Kuwait was covered in the Fifth Wave (2018-2019).

> 4 Since many laws are in unpassable pdf or image format, we did not obtain the full content of all laws. This is left for future research.

3
lack resources and thus can only apply for a few jobs. Because of high job search costs, poor workers as well as low-skilled workers may use informal job searches through social networks, which is cheap but likely to lead to poor match quality (Matsuda and Nomura, 2018).

As this paper has demonstrated, job portals can provide very useful knowledge for policy makers. Policy makers will be able to understand real-time labor market situations in particular industries and locations. The granularity of job portal data, such as data store time stamps by minutes and seconds, and job locations by GPS code, will be useful for discussing highly precise labor market conditions, and such work can be further explored as a subsequent research to this paper. Real-time text information in job postings and resumes helps identify rapidly changing skill needs and supply, which will lead to providing demand-driven training and job placement services. The paper shows there is a great potential for Pakistan to shift its labor market policy making to a real-time data driven one as enabled by big data and technology.

34