rafmacalaba/data-use-mentions-extended huggingface dataset:
Figure 28: Relationship between Fertilizer Use and Crop Sales, 2014/15 <!-- Start of picture text --> 100%<br>beans<br>orange banana<br>mango spice<br>80%<br>roselle other crops egypt beans onion cotton<br>arabic gum other fruits dates tomato potato<br>60%<br>sesame groundnut<br>lentils<br>wheat<br>40% maize<br>sorghum<br>20% millet<br>0%<br>0% 20% 40% 60% 80% 100%<br>Proportion of crop produce sold<br>Proportion HHs using fertilizer<br><!-- End of picture text --> _Source:_ Authors’ calculations using NHBPS 2014/15. _Note_ : HHs = Households.

# Irrigation

Less than one-fifth of households engaged in irrigated agriculture in Sudan (Figure 29a). States on the River Nile and its tributaries, the Blue and White Nile, have higher rates of irrigation. For example, the River Nile and Al-Gezira states have cultivated lands that are nearly 100 percent irrigated. States in regions where the poverty is highest, such as Kordofan and Darfur, have nearly no irrigation of cultivated lands. Irrigation use has decreased between 2009 and 2014/15, from 23 percent to 18 percent, with the largest reduction seen in the Darfur region, where irrigation was already at very low levels.

Within states where there are significant levels of irrigation, such as in the Eastern region, Blue and White Nile regions, and Khartoum state, poor agrarian households are generally less likely to have access to irrigation for farming (Figure 29a). In these regions, expanding irrigation to the poor may be more economical than doing so in regions with very little irrigation infrastructure, such as the regions of Kordofan and Darfur. In these regions, there is little difference between irrigation access between the poor and the non-poor, because none
Figure 9: The Lifetime Cost of Adolescent Pregnancy of Current Cohort of 15-19 Year Old Girls, as a Share of Annual GDP <!-- Start of picture text --> 35%<br>30%<br>30% 27%<br>26%<br>25%<br>18%<br>20% 17%<br>15%<br>15% 11% 12% 12%<br>10%<br>10%<br>5% 2%<br>1% 1% 1% 1%<br>0%<br>Lifetime Cost % of GDP<br><!-- End of picture text --> Source: Authors’ computations based on data from ILO KILM, WDI, and World Bank HNPStats 30
set at different rates across sectors within countries. Here we begin by inquiring whether there are some sectors that uniformly receive more import protection than others, and if there are patterns to this variation across countries.

Figure 2 provides detail on the average applied MFN rates and legal bindings by industry for three groups of policy-imposing countries - the G20 high-income, the G20 emerging, and the other developing countries as classified in Table 1. We break up the universe of imported goods in the Harmonized System into sixteen different sectors.<sup>14</sup> The figure illustrates a number of relatively clear patterns to the cross-sectoral variation in applied tariff levels. First, high-income countries have lower average applied MFN tariffs than emerging economies and other developing countries almost universally across sectors; sector-bysector, higher-income economies have lower tariffs than emerging economies which, in turn, have lower tariffs than poorer developing countries. Second, across country groupings, the average applied MFN tariffs are also typically higher in sectors such as agriculture (animals, vegetables, and

> 13As a point of comparison, consider the importance of customs revenue in total fiscal revenue in the United States before the ratification of the 16th Amendment in 1913 which made constitutional the US federal income tax. Data from International Historical Statistics (Palgrave MacMillan, 2013) reports that customs revenue made up an average of 47 percent of total federal tax revenue collections during the decade of 1900-1909. On the other hand, after both the income tax and the beginnings of the GATT tariff liberalization, customs revenue made up an average of less than 1 percent of US total federal tax revenue collections during the decade of 1950-1959.

> 14Industry classification is given in Table Appendix B.

10
Table 4: Frequency Distribution of Countries on Local Government Independence and Their Relative Significance

|Name|N|Min|Max|Distribution|ountries, om|ountries, tena<br><br><br>|
|---|---|---|---|---|---|---|
|LG inde.<br>pendence<br>.<br>(Security<br>of<br>exis:<br>tence)|182|0|1|.<br>.<br>a<br>=|.<br>0 - 89 countries|i<br>-<br>Denmark,<br>Brazil,<br>Austria,<br>Norway,<br>Sweden,<br>Switzerland<br>0.75<br>-<br>Poland,<br>Iceland.<br>Canada.<br>Ethiopia, Germany,<br>Belgium,<br>Estonia,|
|||||=||USA,<br>Finland,<br>Japan, Korea<br>Denmark(59.4)|
|LG||||||Uszbekistan(55)|
|expendi-<br>|15g|0|50.4||j0.02 - 29 countries|China/51.4)<br>|
|tures||||=.<br>ial.<br>=G||Sweden (44.2)<br>Japan(41.4)|

Source: Authors’ calculations based upon data sources reported in Annex Table Al. Note City states are excluded from the rankings
SDD2: This assumes that only the data on outcomes are missing. Thus standard corrections can be made for selection bias based on other observables at the baseline.

Note that the difference is in whether an allowance is made for selection on observables. If the recall of changes since the introduction of the project works well then both SDD1 and SDD2 will be able to address selection based on (time-invariant) unobserved factors.<sup>10</sup> Importantly, the shoestring evaluations were ―tacked onto‖ a full scale evaluation. This was for a large antipoverty program in poor areas of rural China, and the results are reported in Ravallion and Chen (2005) and Chen, Mu and Ravallion (2009). The paper is thus able to compare SDD1 and SDD2 to the ―actual‖ DD, as estimated from high-quality, comprehensive and contemporaneous baseline and follow-up surveys. The implications for the structure of recall errors are also examined.

The findings from this case study suggest that SDD methods are vulnerable to biases that confound identification. Respondents’ perceptions of how their living standards have changed provide a weak and biased signal of consumption changes measured from contemporaneous surveys. There are also signs of ―false positives‖ stemming from the weak ability of retrospective recall of welfare changes to neutralize selection bias based on unobserved initial conditions influencing program placement.

The following section describes the project and data. Section 3 presents the impact estimates, while section 4 explores the responses on recall further to help understand the results in section 3. Section 5 concludes.

# **2. Setting, data and methods**

The project being evaluated is the World Bank’s Southwest China Poverty Reduction Project—the Southwest Program (SWP) for short. This comprised a package of multi-sectoral interventions targeted to poor villages using community-based participant and activity selection. The aim was to achieve a large and sustainable reduction in poverty. The project was implemented in selected poor villages in the designated poor counties of Guangxi, Guizhou and

> 10 On the distinction between
<!-- Start of picture text --> Lithology Rainfall Inventory of locations with past landslides<br>Distance to road Inventory of locations with past non-landslides<br>'<br>1<br>'<br>: Extraction of factor values at sampled<br>i landslide and non-landslide locations<br>;<br>1<br>1<br>; Random sampling to select a fraction of<br>' landslide and non-landslide locations<br>;<br>!<br>'<br>: TrainingForest andClassification testing a Randommodel<br>1<br>|1 S2222222<br>1<br>1<br>1<br>!<br>1<br>1<br>’: odelsatisfactory?performance No<br>!<br>1<br>!<br>1<br>1<br>1 Yes<br>1 [6 |<br>; Compute and store the relative<br>' importance of features<br>;<br>1<br>1<br>Landslide susceptibility and Ves target number of N<br>risk given weight uncertainty iterations reached? °<br><!-- End of picture text -->
to the well’s pump-head. Placards displayed both in text and color whether the arsenic concentration was up to 10 ppb (blue), 25 or 50 ppb (green), or above 50 ppb (red). As shown in Figure 1, the placards displayed two hands holding a drinking cups, one hand holding a drinking cup, or a large cross over a hand holding a drinking cup, depending on the arsenic concentration.

The split of the test fee between the tester and supervisor in groups B and C was the same as in group A, but in B the project gave an additional bonus of BDT12 per sale to testers, to compensate for the additional effort necessary to coordinate group formation.<sup>7</sup>

# **2.2 Power Calculation and Study Design**

The trial was not registered, and was not based on a pre-analysis plan. Comparison of demand for and responses to tests between arms A and B was the primary objective of the study. Data from earlier work in neighboring Araihazar subdistrict (see Bennear et al. 2013), were used to estimate an intravillage correlation of _ρ_ = 0 _._ 09 in switching decisions among owners of unsafe wells and a switching rate of about 30%, a rate at the lower end of what observed in previous studies. It was determined that 50 clusters (villages) would be sufficient to detect meaningful impacts with high probability. In particular, assuming a 30% switching rate from unsafe wells with individual sales (arm A), and even using a conservative 0.12 intra-village correlation in switching rates, and with only five unsafe wells identified through the test sales per village, 49 clusters per arm would have been necessary to have 80% power to detect a 15 percentage points difference in switching rates between arms for a two-sided test of equality at the 5% significance level (effect size = 0 _._ 15 _/_<sup>_√_</sup> 0 _
**Figure 7. Product market regulation in Romania and comparator countries**

<!-- Start of picture text --> 3 3<br>2.5 2.5<br>Avg. of all countries = 1.90<br>2 1.65 1.69 2<br>1.5 1.29 1.33 1.34 1.41 1.48 1.565% 5% 4% 1.5<br>1 1.1311% 14%30% 6%42% 9%40% 10%43% 12%39% 43% 33% 41% 1<br>38%<br>0.5 51% 56% 51% 52% 47% 49% 53% 62% 55% 0.5<br>0 0<br>Top 5 -PMR Slovak Hungary EU-15 Czech OECD Romania Poland Romania<br>Republic average Republic average sim. 2017<br>2013<br>State control (SC) Barriers to entry and rivalry (BER) Barriers to trade and investment (BTI)<br><!-- End of picture text --> _Source:_ Pop and Ore Monago 2020.

_Note:_ The top five performers in the overall PMR indicator are the Netherlands, United Kingdom, Austria, Denmark, and New Zealand. Figure shows 2013 information, unless otherwise indicated. PMR = product market regulation .

**Barriers to entry and rivalry still affect some service sectors.** In fact, there are examples of regulations that hamper competition in retail trade, which concentrates a substantial proportion of high-markup firms in the Romanian economy, as seen previously. Drawing from the OECDWBG PMR data set, Pop and Ore Monago (2020) show that although regulations in the retail trade sector are less restrictive than in the EU-15 and OECD, there is still room for improvement to make the retail trade market more efficient. The authors show that licenses and
organizational issues, such as poor learning conditions and organizational problems, exacerbate the gender gap between girls and boys. That is, boys’ achievement tends to be negatively impacted by challenging learning conditions to a greater extent compared to girls’ achievement. These findings are consistent with previous work on gender gaps in attitudes, showing that girls, in general, tend to report more positive attitudes toward learning (DiPrete and Buchmann 2013). Previous research has also shown that social norms, gender stereotypes, and teacher/school expectations contribute to the performance gap between girls and boys (Stromquist 2007; Page and Jha 2009; Younger and Cobbett 2014; Jha and Pouezevara 2016). In other words, teachers and schools contribute to developing and reinforcing different expectations of appropriate behavior for both boys and girls, which can have the effect of hindering boys’ performance.

Building on the existing research, this paper examines the factors that contribute to the gender gap in student performance in Saudi Arabia. In particular, the paper investigates whether a range of student, family, class, and school variables can explain the achievement gap in mathematics and science between boys and girls at grades 4 and 8. The contribution of these variables in predicting overall Saudi student achievement in these two subjects is also examined. Data from both TIMSS 2019 and Saudi Arabia’s National Assessment of Learning Outcomes (NALO) 2018 were employed. Both TIMSS and NALO assess grades 4 and 8 students’ mathematics and science achievement, as well as collect a wide range of contextual information about students, their families, teachers, and schools.

This paper contributes to the current literature in two main ways. First, it addresses a need for more evidence about the factors associated with the achievement gap between boys and girls in the MENA region, and especially in Saudi Arabia. The paper explores the surprisingly large and consistent performance gap observed between boys and girls in the region. As shown in figure 1, boys in the region tend to achieve noticeably lower scores than girls in reading
--|---|---|---|---|---|
|EFSI|2015|€21 billion|1x|6.7x|6.7x|
|Marguerite|2010|€710 million|1x|11.8x|11.8x|
|GEEREF|2008|€112 million|2x|35.8x|~71x|
|ISIF|2014|€7.6 billion|1x|2.4x|~2.4|
|PAIDF|2007|$625 million|4.2|4|16.7x|
|PINAI|2012|$625 million|25x|_Unknown_|~_25x_|
|MMIF|2008|$408 million|5x|_10.3_|_51.7x_|
|FONSIS<sup>a</sup>|2016|€28 million|1x|9.6x|9.6x|
|PBCE|2012|€230 million|3x|~6x|~19x|

_Source:_ Adapted from Inderst (2016b **)** .

_Note:_ Multipliers in this table are World Bank estimates based on publicly available information on actual invested amounts, often referring to specific projects since detailed information at the portfolio level is generally not available. Furthermore, information on the public versus private share of capital invested at the project level is also not publicly available. As a result, estimates shown in this table are indicative only. Funds that are wholly owned by a government have a fund level multiplier of 1.

EFSI = European Fund for Strategic Investments; FONSIS = Fonds Souverain d’Investissements Stratégiques; GEEREF = Global Energy Efficiency and Renewable Energy Fund; ISIF = Ireland Strategic Investment Fund; MMIF = Macquarie Mexico Infrastructure Fund; PAIDF = Pan-African Infrastructure Development Fund; PINAI = Philippine Investment Alliance for Infrastructure; PBCE = Europe 2020 Project Bond Initiative.

a FONSIS provides an example of the multiplier effect through SIF investments. In February 2015, FONSIS
1990-1999 period using Compustat data. There are two main reasons for opting for the data in Maskus et al. (2012) instead of the data from Rajan and Zingales (1998). First, the data are more recent and provide a better proxy of external financial dependence for the period for which we have data on exporter-level dynamics. Second, the data are reported in the same classification available for the EDD, ISIC Rev.3, which limits the measurement errors that may be exacerbated when converting the measure from one classification to another using aggregated data.

# **3.3 Other variables**

When trying to disentangle the mechanisms of influence of financial structures on export, we use data from the World Bank Banking Supervision Survey on the supervision, monitoring and regulation of banks in the countries. The database is based on surveys sent to national bank regulatory and supervisory authorities of 107 countries asking comprehensive questions about bank entry requirements, ownership restrictions, capital requirements, accounting and disclosure requirements, and the quality and actions of bank supervisory personnel. We detail these data and variables below.

We also employ measures of the degree of product information complexity and contractual complexity. Data on the degree of relationship-specific fixed costs or information frictions in contracts are from Nunn (2007) and Rauch (1999). Specifically, as in Nunn (2007), for each product the contract intensity index is equal to the proportion of its intermediate inputs that are relationship-specific and is defined as where _θgj_ is the share of input _j_ used to produce one unit of product _g_ based on the 1997 U.S. input-output tables. _Rj_<sup>_neither_</sup> is the share of inputs that is neither sold on an organized exchange nor reference priced.<sup>8</sup> The proxy for industry information complexity is constructed using data from Rauch (1999). First, we convert Rauch’s original classification of products from SITC Rev.2 to ISIC Rev.3. Second, we aggregate the data
abortion services in India if they are not available locally. These health care workers note feeling caught between respecting the law and ensuring that their patients receive safe health care services (Lamichhane et al. 2011).

Strict enforcement could also make medical practitioners reluctant to provide legal reproductive health services that require the use of restricted technology. Access to ultrasound scans for antenatal checks, and access to safe abortion for reasons other than sex-selective purposes could potentially be limited. The case of Korea illustrates how strict enforcements can restrict women’s access to legal health care services altogether. In 2010, the Korean government introduced an action plan to crack down on illegal abortions (arguably in a bid to raise the low birth rate in the country). The prosecution of the arrested doctors following this crackdown was widely publicized, and in response, many obstetricians reportedly became fearful of offering abortion services even for legal reasons (Korean Women’s Association United 2011).

Second, having unwanted girls could affect how the woman is treated in the household and in the community at large. In patrilineal societies, bearing a son matters for women as it allows them to improve their position within the household. For example, Li and Wu (2011) use pooled data from the China Health and Nutrition Survey (CHNS) and multi-variate regression to find that women whose first-borns were sons had more decision-making power, and better nutritional intake. More starkly, Milazzo (2014), using pooled data from the Indian National Family Health Survey (NFHS) and exploiting the randomness in the sex of the first-born child, finds that women whose firstborns were girls were more likely to suffer maternal and adult mortality. These studies are backed by the findings of ethnographic studies as well (DasGupta et al. 2003). In recognition of the lower status and heightened vulnerability of sonless women, the Chinese government in 2002, prohibited ill-treatment of women who give birth to girls or who are infertile (Li 2007).

There is also evidence to suggest that the adoption of ultrasound
1 3 5 7 9 11<br>2011 2012 2013 2014 2015<br>2011 2012 2013 2014 2015 Linear ()<br><!-- End of picture text --> Source: Authors’ analysis using Babajob database Note: The figure shows the estimated real wage trend after controlling for variables of occupation and type of contracts, qualification requirements, and employers’ types. It includes 20 cities in India.

By extrapolating from current dynamics, analysts and policy makers can use job-portal data to predict future trends in the labor market. The econometric model developed by Areias et al. (forthcoming) can be used to predict the real wage offers by location and occupation. Such predictions, including both nowcasting and forecasting of the near future, would provide valuable information to policy makers to understand the 22
.06|0.001|
|16-18|0.35|0.35|0.32|0.32|0.26|0.26|0.002|
|19-24|0.44|0.44|0.47|0.48|0.53|0.53|-0.002|
|25-30|0.07|0.07|0.09|0.09|0.14|0.13|-0.001|
|31-49|0.01|0.01|0.01|0.01|0.02|0.02|0.0002|
|**Household wealth**||||||||
|2nd quintile|0.15|0.14|0.16|0.17|0.16|0.16|-0.00003|
|3rd quintile|0.17|0.17|0.17|0.17|0.18|0.18|-0.001|
|4th quintile|0.21|0.21|0.23|0.22|0.23|0.23|0.001|
|Richest quintile|0.23|0.23|0.24|0.23|0.27|0.27|0.003|
|N (1st births)|26,064|23,905|41,430|38,712|22,108|21,074|173,293|

NOTES: This table compares the socioeconomic characteristics of firstborn-boy (FB) and firstborn-girl (FG) families during the pre-ultrasound period and during the two post-ultrasound periods in the NFHS sample. The sample is restricted to first births as only these are quasi-random. SC and ST denote Scheduled Castes and Scheduled Tribes, respectively. Column (7) shows the differences in sample means among FB
- 31 -

Research has shown that wage inequality falls during periods when union density is increasing and rises when union membership is in decline. For example, DiNardo, Fortin, and Lemieux (1996) find that increasing union density contributed to the decline in U.S. wage inequality from 1973-79 and that deunionization was an important factor in the growing wage inequality between 1979 and 1988. In its recent report on inequality, the OECD (2011b) concludes that the decreasing union membership in many OECD countries has made a modest, but significant, contribution to increased wage inequality. The equalizing effect of unions appears to be strongest on the upper parts of the wage distribution, due to a transfer away from their most skilled members.

# _Employment_

It is clear that unions have an effect on employment dynamics. They reduce voluntary turnover and increase job tenure. However layoffs (especially temporary layoffs) are more frequent in unionized firms (Aidt and Tzannatos 2002).

But what is their effect on overall levels of employment and unemployment? Given that unions usually do raise wages, they would be expected to increase unemployment and reduce employment, according to the “monopoly” model. On the other hand, the relationship may not be so straightforward if unions have positive efficiency effects, as suggested by the “voice” model.

On balance, studies are roughly divided between those that find that unions increase unemployment and those that find no significant effect. Almost all the relevant analysis looks at the effect of union density, using aggregate data over time for OECD countries. Examples of studies finding that union density increases unemployment include Bertola, Blau, and Kahn (2002); IMF (2003); Baccaro and Rei (2005); and Nickell, Nunziata, and Ochel (2005). Fewer studies look at the impact of collective bargaining coverage. Nickell (1997) finds that both coverage and density are positively related with unemployment. However, Belot and van Ours (2000) conclude that union density increases unemployment, but collective bargaining coverage
**Figure 4. FDI Inward Stock (% GDP) and Real GDP Growth**

<!-- Start of picture text --> Emerging Europe EU10 countries<br>200 200<br>150 150<br>100 100<br>50<br>50<br>0<br>0<br>-50 -30 -10 10 30 50<br>-50 -15 -10 -5 0 5 10 15<br>Real GDP growth rate Real GDP growth rate<br>EU candidate countries EU neighborhood countries<br>200 200<br>150 150<br>100 100<br>50<br>50<br>0<br>0<br>-50.00 -30.00 -10.00 10.00 30.00 50.00<br>-50 -30 -10 10 30 50<br>Real GDP growth rate Real GDP growth rate<br>Source: Authors’ calculations based on World Bank and IMF data (2011). Note: Period 1997-2008.<br>FDI inward stock (in percent of GDP) FDI inward stock (in percent of GDP)<br>FDI inward stock (in percent of GDP) FDI inward stock (in percent of GDP)<br><!-- End of picture text -->

**Figure 5. Composition of Capital Flows in Emerging Regions (median values within groups)**

<!-- Start of picture text --> 12<br>8<br>4<br>0<br>-4<br>-8<br>-12<br>EU10 EU Candidates EU Neighborhood Developing East Asia LAC<br>FDI Portfolio<br>Banking Official<br>Current Account Balance Foreign Exchange Reserves, change<br><!-- End of picture text --> Source: Authors’ calculations based on World Bank