rafmacalaba/data-use-mentions-extended huggingface dataset:
**Table 2. Impact of Changes in International Food Prices on the Import Bill, 2010**

||% of 2010 GDP|% of international reserves|
|---|---|---|
|MENA|||
||Food<br>0.62|1.44|
||Grains<br>0.27|0.63|
||Oils<br>0.17|0.40|
||Meat<br>0.04|0.09|
||Sugar<br>0.14|0.32|
|GCC economies|||
||Food<br>0.25|0.45|
||Grains<br>0.07|0.12|
||Oils<br>0.06|0.12|
||Meat<br>0.02|0.04|
||Sugar<br>0.09|0.17|
|Developing oil|||
|exporters|||
||Food<br>0.78|2.28|
||Grains<br>0.33|0.98|
||Oils<br>0.22|0.61|
||Meat<br>0.05|0.14|
||Sugar<br>0.19|0.55|
|Oil importers|||
||Food<br>1.15|4.39|
||Grains<br>0.59|2.32|
||Oils<br>0.35|1.23|
||Meat<br>0.05|0.24|
||Sugar<br>0.15|0.59|

_Source_ : World Bank for international commodity prices and 2010 GDP, IMF for international reserves and USDA for import demand.

Dependency ratios give important insights into MENA‟s
to make sure we obtained the correct numbers.

The focus of this section is on foreign investors, but we also compare their characteristics with domestic producers. The foreign investors’ surveys cover 87 firms in Chile (5), Ghana (16), Kenya (20), Lesotho (15), Mozambique (10), Swaziland (11) and Vietnam (10). Table 1 shows that the majority of foreign investors are in apparel (43), followed by agribusiness (30) and mining (14). Domestic producers’ surveys cover 64 firms in Chile (5), Ghana (10), Kenya (26), Mozambique (6) and Vietnam (17). The majority of these firms are in agribusiness (46), followed by apparel (13) and mining (5).

10
Figure C12: Comparison of urban and rural poverty rates <!-- Start of picture text --> (A) Official definition (B) DOU (C) DB<br>80 80 80<br>60 60 60 MWI<br>MWI<br>NER<br>40 40 TZA UGA 40 TZA UGA<br>NER<br>20 UGATZA MWI 20 ETH 20 ETHTCD LSO<br>VNMGABEGYCOLMRTSENCIVBGDGIN GNBGHAETHBFATCDAGOLSO NER VNMEGYGABCOLMRTGINCIVSENGHABGD GNBTCDBFA LSO AGO VNMEGYGABMRTBGDCOLSENCIVGINGNBBFAGHA AGO<br>20 40 60 80 20 40 60 80 20 40 60 80<br>Poverty rate in rural areas (%) Poverty rate in rural areas (%) Poverty rate in rural areas (%)<br>Poverty rate in urban areas (%) Poverty rate in urban areas (%) Poverty rate in urban areas (%)<br><!-- End of picture text --> Source: International Urban Poverty Database. Note: WorldPop 250m is used for the DOU and DB methods. Dashed lines are 45-degree lines. Urban areas include the categories “Urban center” and “Urban cluster” for the DOU method and the categories “Core” and “Suburb” for the DB method. Dashed lines are 45-degree lines. Poverty is measured using the $2.15 poverty line.

77
# 3.3 Limitations

While applying a web scraping approach to obtain price data in EMDEs is a very cost-effective and efficient way to collect data, particularly compared to conducting expensive surveys, there are several limitations that pertain particularly in the context of EMDEs.

To start with, the web scraping approach does not necessarily yield data that are representative of all properties in the market as we are only able to capture properties of sellers with access to internet and who are able and willing to post their property online. By the same token, accessing house prices on real estate listing websites in emerging economies requires buyers to have access to these online listings. This might not always be the case, especially in lower-income segments of a given market. Particularly in developing areas, information density is low and might lead to data blind zones (Li et al. 2019). Recent research shows that online platforms used for home sales, even in developed markets, may reproduce and even intensify existing forms of inequality within cities (Boeing et al. 2021; Angelo & Vormann 2018).

While internet access is less of a concern in the five countries we outline here (cf. Annex 1), expanding the methodology to other countries might become problematic. In Burundi, for instance, only 5 percent of the overall population use the internet either via computer, mobile phone or other digital devices (World Bank 2022). In comparison, in Brazil, close to 74 percent of the population use the internet (World Bank 2022). In countries with relatively low internet penetration rates, HHs might adhere to alternative pathways to buy properties: personal interactions with real estate agents, classified ads in newspapers or through informal, personal interaction. Hence, the web scraping approach might not be suitable to capture local property markets where online advertisements are not frequently used and might, therefore, weaken the generalization of the results to localized markets.

Second, the price data collected are concentrated in countries’ biggest business cities and urban centers. This is not
The Measuring Early Learning Quality Outcomes (MELQO) tools (UNESCO, 2017) were the foundation of data collection, locally adapted to the Egyptian context. The MELQO tools have two main components, the Measure of Early Development and Learning (MODEL) for measuring the development of children aged 3-6, and the Measure of Early Learning Environments (MELE). The MODEL collects data through a child direct assessment, parent report of child development (including home and family background), and teacher report of child development. The MELE collects data via classroom observation, a teacher interview, parent interview, and school director interview. The tools were designed specifically to measure child development and quality of early childhood education in low- and middle-income countries (Raikes et al., 2019).

The MELQO tools were developed initially and piloted in 2015 in non-representative samples, and then the pilot-tested tools were used in national studies starting in 2016. The tools were finalized and publicized in 2017 (Raikes et al., 2019). The tools were selected for a nationally representative study of kindergarten teaching and learning in Egypt. The primary goal of the Egyptian data collection was to identify quality issues and inform subsequent teacher training efforts for Education 2.0, as well as to validate a new quality assurance system.

The MELQO tools were translated into Arabic and adapted to the Egyptian context and curriculum in collaboration with the MOETE, kindergarten teachers, and kindergarten supervisors. An adaptation workshop occurred in May 2019 that included a careful review of items by a group of stakeholders along with addition or modification of items to align with national standards and cultural priorities (for example, items specifically focused on the implementation of Egypt’s new standards for pre-primary classrooms). The tools were programmed into Android tablets using ODK-X software (Brunette et al., 2017). Pre-piloting of the instruments subsequently took place in Egypt in two governorates, ten schools, ten classrooms, with ten teachers and 30 children. Training of the master trainers, a mix of MOETE officials
**A1.12 Institute for Management Development (WCY)**

The Institute for Management Development is an research and educational organization based in Lausanne, Switzerland. As of May 1999, it can be reached at http:Ilwww.imd.ch.

The Institute for Management Development has published the World Competitiveness Yearbook since 1987. Until 1996, this was a joint effort with the World Economic Forum. We use data from the 1998 edition of the World Competitiveness Yearbook.

The World Competitiveness Yearbook analyzes the competitive environment in 47 countries. It is based on both objective data and surveys of perceptions. The survey questions over 4,000 local and foreign enterprises operating in the countries under analysis. Mean scores on the survey questions are reported in the yearbook for all countries. In Table A1.12 we list the questions included in the governance database.

<!-- Start of picture text --> Table A1.12 World Competitiveness Survey<br>Number Question<br>Domestic economy<br>1 y108 The parallel economy as an obstacle to business development<br>Internationalization<br>2 y230 Protectionism as an obstacle to imports from abroad<br>3 y231 Controls on foreign investors ownership of companies<br>4 y232 Obstacles to foreign bidders on public contracts<br>Government<br>5 y316 Real personal taxes as a burden to work initiative<br>6 y320 Improper practices in the public sphere<br>7 y321 Real corporate taxes as a burden to entrepreneurial activity<br>8 y324 Extent of tax evasion<br>9 y326 Legal framework as an obstacle to compettiveness<br>10 y328 Transparency: The government communicateit s intention s uccessfully<br>11 y329 Political system as an obstacle to development<br>12 y330 Exposure of public service to political interference<br>13 y331 Bureaucracy as an obstacle to business development<br>14 y332 Effective implementati o n f govemment decisions<br>15 y333 Custom's administration as a burden to
other leaders in the same country, and higher growth than other leaders in the same region at the same time. If we instead compared leaders to an absolute standard of ≈2% global trend growth, the fraction of significant leaders is very similar (7.7%), as is the fraction of significant leaders who are democratic (42%).<sup>43</sup> Moreover, the overall highest- and lowest-growth leaders are the same as in Table 3. However, there is an overall shift towards leaders with positive significant contributions, driven by a greater fraction of leaders of European countries during the postwar boom and leaders of Asian countries, as these are no longer held against a higher regional growth standard.<sup>44</sup>

# **5. Conclusions**

In this paper, we start with a growth model where leaders matter for growth in general. We combine growth data and an optimal signal extraction methodology to estimate the size and significance of the growth contribution of each individual national leader since 1950. Many of these significant leaders are little known or long forgotten. We show that our optimal methodology strongly dominates the naïve method of attributing individual leader growth averages to leaders.

We find that only a small fraction of leaders (around 7%) have significant positive or negative growth contributions: knowing that leaders matter for growth _in general_ is very different from knowing _which_ leaders matter for growth. Our findings suggest much more caution is needed in policy debates that attribute growth to leaders. We do find significant leader effects for some famous “benevolent autocrats” and “bad emperors.” However, leaders in non-autocratic countries

> 43 Specifically, here we do not adjust for region X year observables in the first stage and we set for all leaders. 44 Using WDI growth data produces a similar fraction of significant leaders (6.6%) and democrats (31%). PWT 7.1 γγ= 0 produces smaller estimates of A , which reduces the
<!-- Start of picture text --> 10<br>South Africa<br>8<br>Vietnam<br>Kenya<br>6<br>Tanzania<br>4<br>Sub‐Saharan<br>Africa<br>2<br>0<br>1990 1995 2000 2005 2010<br>Average years of education<br><!-- End of picture text --> Note: Educational attainment for population aged 25 and over is based on authors’ calculations using harmonized microdata from the International Income Distribution Database-I2D2 version 6 (World Bank 2013). Survey year is rounded to the closest year multiple of five. The estimated average schooling for South Africa is calculated using an older version of I2D2 (December 2011). The number of countries and surveys used to estimate the regional average for Sub-Saharan Africa varies every year: for example, in 1990 it was based on eight country surveys but in 2005 it included at least 20 <u>countries.</u> While workers completing basic education are needed for the vast majority of small, informal businesses, higher-skilled workers are critical to the emergence of a diversified economy. Figure 2 shows the current distribution of the population aged 25 and over by different education levels for Tanzania compared to several other countries in SSA and Asia. For Tanzania, the proportion of the population with at least a secondary education is 11 percent and with a tertiary education, only 5 percent. These are very low figures compared to South Africa, Malaysia and Vietnam.

In fact, Tanzania has one of the lowest gross tertiary enrollment ratios in SSA<sup>14</sup> but this is beginning to change with the rapid growth of higher education, driven largely by the private sector. Progression from secondary to tertiary education is available through two routes: enrollments in universities/colleges administered by the Tanzania Commission of Universities (TCU) and in nonuniversity technical education institutions regulated by the National Council of Technical
<!-- Start of picture text --> 0.6 II<br>I<br>I<br>Ee |<br>g P !<br>=.) I<br>e 04 el<br>ae|<br>=¢<br>3 ’ oY ?<br>5 : !<br>fo) J<br>£ 0.2 Py ¢ 4 r 4<br>> r e<br>Ss td °<br>gsa 0.0 eee ooo oo<br>© I<br>oI<br>I<br>I<br>-0.2 ;<br>600 800 1000 1200 1400<br>Annual Sales (Thousands Inflation-Indexed Unit of Account)<br><!-- End of picture text -->

<!-- Start of picture text --> I<br>0.2<br>|<br>|<br>I<br>0.1<br>a=?rfiole |1 bd °<br>°<br>s ° e | °<br>wo2° I e ° e°<br>3it olf<br>a -0.1 | °<br>4<br>|<br>I<br>-0.2 I<br>I<br>I<br>|<br>03<br>600 800 1000 1200 1400<br>Annual Sales (Thousands Inflation-Indexed Unit of Account)<br><!-- End of picture text -->
Figure 5: Poverty Eradication capacity Index and Poverty Headcount Index <!-- Start of picture text --> EAS<br>ECS<br>LCN<br>MEA<br>SAS<br>SSF<br>0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1<br>Poverty Headcount Index Poverty Headcount Index<br>1 1<br>~p) r)<br>.8 .8<br>.6 .6<br>.4 .4<br>.2 .2<br>Poverty Eradication Capacity Index, I(P Poverty Eradication Capacity Index, I(P<br>0 0<br><!-- End of picture text --> a smaller share of poverty gaps as the index approaches zero. Of the 27 countries in our sample having an _I_ ( _P_<sup>_∼p_</sup> ) below 0.5, 22 are in Sub-Saharan Africa.

Figure 5, right panel, shows a fuzzier relationship between level of headcount poverty and the Poverty Eradication Capacity Index computed following distribution rule (ii), when the rich are considered responsible for poverty eradication, _I_ ( _P_<sup>_r_</sup> ). Our results show that for poverty levels higher than 30 percent (62 countries in our sample), only four countries (Cape Verde, Namibia, Nicaragua and South Africa) have an _I_ ( _P_<sup>_r_</sup> ) greater than 0.5 (respectively, 0.64, 0.72, 0.78 and 0.96), which means that no marginal tax rate levied on the excess-affluence gaps of the rich generates enough revenue to close the poverty gaps.

Figure 6 shows the relation between the two different _Poverty Eradication Capacity Indices_ obtained by applying the underlying two different redistributive rules: _I_ ( _P_<sup>_∼p_</sup>
for factors readily apparent to those trading at the CBOT to be fully discounted into the Polish wheat market. Lagging Polish wheat prices against LIFFE prices did not improve the correlation results.

# **3.1.3. Cointegration analysis of Polish and CBOT and LIFFE prices**

Cointegration analysis indicates whether there is a stable long-term relationship between two (or more) price series. Thus, prices can show short-run deviations, but in the long-run they move together. We tested for pairwise cointegration between the Polish and CBOT prices and between the Polish and LIFFE prices. The test used is the ADF test and its results are shown below."

||**Polish-CBOT**|**Polish-LIFFE**|
|---|---|---|
|**Nominal**Prices|-2.71|-2.23|
|**Deseasonalized Prices**|-2.65|-2.21|

The ADF statistics are calculated for the period 1990 to 1995 using monthly data, for a total of 72 observations. The ADF statistics are below the 90% critical level (-2.84) thus the hypothesis of cointegration is rejected. This means that according to the ADF test and for the period tested,

**"' ADF is theAugmnented** Dickey Fuller **test** (see Dickey and Fuller, 1979).

_9_
10 20 +95.3%<br>0.5 5 +23.4% 10<br>0.0 0 0<br>1995 2000 2005 2010 2015 1995 2000 2005 2010 2015 1995 2000 2005 2010 2015<br>US$ trillion, constant 2018 US$<br>US$ trillion, constant 2018 US$<br><!-- End of picture text --> 2014 from the World Bank’s Open Data platform (Lange et al., 2018). See Figure 3 for a visual depiction of how individual wealth components evolved since 1995.7 We then linearly interpolate the annual values to obtain monthly figures. Given the slow-moving and persistent process behind wealth, the linear interpolation is reasonable. Throughout this paper, wealth variables are transformed with the base-10 logarithm.

# **2.2 Bond data**

Sovereign bonds of developing markets and middle income countries are sometimes issued in both foreign and local currencies. This study focuses on local currency bonds. This choice is primarily motivated by the larger size of sovereign bond markets in local versus foreign currencies. According to the debt securities statistics of the Bank for International Settlements, the average amount outstanding between 2009–2018 for central government debt is US$ 19.7 trillion for domestic currencies and US$ 78.3 billion for foreign currencies. If we exclude the United States and Eurozone countries, the numbers reduce to US$ 6.6 trillion for domestic and US$ 49.0 billion for foreign currencies. This is also reflected by the broader bond data coverage across countries and time for local currencies.

One concern for this choice might be the “original sin” (Eichengreen et al., 1999; Eichengreen et al., 2002), describing the inability of developing countries to adequately borrow domestically or internationally when issuing debt in their own currency. Instead, countries issue debt denominated in USD or EUR, which insulates the creditor from domestic inflation, liquidity or solvency concerns of the
Kindergarten benefits|0.8%|0.0890|0.2869|0.0016|||
|Primaryschool benefits|4.0%|-0.1891|0.5650|0.0168|||
|Gymnasium benefits|2.0%|-0.2429|0.6189|0.0088|||
|High school benefits|0.6%|-0.0854|0.4613|0.0016|||
|Vocational school benefits|0.9%|-0.3245|0.7005|0.0048|||
|Tertiaryschool benefits|2.3%|0.1564|0.2195|-0.0003|||
|Health<br>Source: own estimates based on 2014 HBS.|7.6%|-0.0056|0.3816|0.0294|||

1. Size equals the ratio of the amount collected or spent divided by total market income.

2. Redistributive effect equals the difference in the Gini before and after the intervention.

3. By definition, the sum of the marginal contributions does not fulfill the adding-up principle so it will not be equal to the redistributive/overall poverty effect unless by coincidence.

21
(Wooldridge, 2010). The results are in columns (5) to (8) in Table 7. The first and the second stage results of 2SLS are presented in panels B and A, respectively. In columns (5) and (6), the dependent variable is a dummy variable indicating fraud victimization. In columns (7) and (8), the dependent variable is the total amount of economic losses subject to fraud schemes. The full sample containing all urban households is used in columns (5) and (7), while the restricted sample consisting of approached urban households is used in columns (6) and (8). In all specifications in Table 7, county fixed effects are included and standard errors are clustered at the city level.

Based on the selection model (columns (1) to (4) of panel B in Table 7), the _Ecommerce coverage_ at the community level is a good predictor of fraud approaching. The coverage of community _E-commerce_ is significantly and positively associated with the probability of being approached by fraudsters. More importantly, after considering the potential selection bias, the relationship between credit constraints and fraud victimization still holds. The probability of becoming fraud victims and the total amount of economic losses are both significantly higher for households with credit constraints, and the coefficients are similar to the baseline regression results in Table 5.

When dealing with the endogeneity of credit constraints indicator and sample selection bias jointly (columns (5) to (8) in Table 7), our baseline regression results remain robust. In comparison with Table 5, the coefficients of credit constraints and other explanatory variables are largely the same. Credit-constrained households are more likely to become fraud victims with a larger amount of subsequent economic losses. In all specifications, the F-test statistic for the weak identification test is well above 10, and the Hansen’s J statistic and its P-value suggest that we cannot reject the over-identifying restrictions.

# **7. Alternative Interpretation: Information Acquisition and Financial
welfare. We now divide the UPCs into two groups: products consumed primarily by skilled and primarily by unskilled workers. Using the demographics data and household transactions data from the IRI, we determine which products are primarily purchased by skilled (household head has a bachelor’s degree or greater) households. To define products consumed by skilled and unskilled households, we first compute the median expenditure by skilled households for each UPC.<sup>12</sup> If the expenditure on a UPC is greater than the median UPC-level expenditure by skilled households, we call the UPC “skilled”; otherwise, we call it unskilled. The results are presented in Table 3 and show that the UPC-level prices increased in winner compared to loser counties across

> 12The IRI data only collects demographics for 5-6 FIPS codes. We only use information from consumption expenditure by demographics for households in these counties to construct the skilled and unskilled variables

24
program, which seeks to assist the most food insecure households in the most affected localities and states, by providing unconditional cash transfers to beneficiary families in all 18 states of Sudan.

Sudan has been experiencing multiple macroeconomic shocks, such as low growth, a wide trade deficit, and the emergence of multiple exchange rates since its separation from South Sudan in 2011. The country has implemented an ambitious agenda of reforms, which included the removal of various subsidies, in an effort to reverse years of structural imbalances and high inflation. Parallel to such reforms was the introduction of a quasi-Universal Basic Income cash transfer program (SFSP) that sought to reach the bottom 80% of the population. The SFSP program was eventually discontinued, as, with the onset of COVID19, political discontent ultimately resulted in a military takeover and a renewed effort by the military-led government to achieve fiscal consolidation.

More recently, the Russia-Ukraine crisis that started in March 2022, has further exacerbated the country’s social and economic problems. In particular, Sudan is expected to face significant negative food security implications as food prices reach historic peaks due to the Russian invasion of Ukraine. All of the economic and social problems that have negatively impacted Sudan are expected to have increased the number of poor in the country.

The worsening situation in Sudan has led to renewed efforts to develop a new social safety net program. To support the efforts of Sudan in the targeting of the Sudan Emergency Safety Nets Project (SESNP) to those most in need, we develop and propose a PMT for Sudan based on the latest household survey, the National Baseline Household Budget and Poverty Survey (NHBPS) 2014/2015.

We find that a PMT should be considered for targeting social protection programs in Sudan. The PMT would assist social programs to reach a higher share of the poor and vulnerable, while reducing expenditure going towards households that are less in need, improving both the poverty impact, as well as the sustainability of the program. We explore various specifications of the PMT and find