rafmacalaba/data-use-mentions-extended huggingface dataset:
# **7 Access to Power and Institutions**

Inequality across ethnic groups has been documented in the literature as one of the main potential sources of conflict. Besley and Persson (2011) present a theoretical framework in which fighting for control of the state is an important reason for conflict. Cederman et al. (2010) provide quantitative evidence suggesting that outbreaks of armed conflicts are the result of competing ethnonationalist claims to state power. Cederman et al. (2013) find also that disadvantaged and advantaged ethnic groups have higher propensity of entering into conflict but only where there is also political exclusion. Buhaug et al. (2014) show that countries with very poor (compared to the national average) ethnic groups and those with large discriminated groups from national politics are more likely to experience an armed conflict.

What are the sources of this inequality across ethnic groups? Burgess et al. (2015) provide an answer to this question by exploring the link between access to power, political institutions and inequality in the allocation of public investments. They show evidence for strong ethnic favoritism using variation in political leadership and data on road building in Kenyan districts across the 1963-2011 period. Most importantly, they show that this favoritism in road investments vanishes during periods of "democracy". The authors argue that in the African context where presidential power is based on ethnicity, even weak democratic institutions translate into a decrease in favoritism towards groups in power as political leaders are forced to share public goods across the wider population. Hodler and Raschky (2014) also find that sub-national regions have more intense night light when being the birth region of the current political leader and this favoritism is most prevalent in countries with weak political institutions and poorly educated citizens.<sup>45</sup> At the country level, institutions have often been linked to differences in mean economic performance. However, the fact that strong institutions keep conflict from escalating will obviously also have an impact on volatility. In this aspect our work relates to recent research that
statically significant at 0.1 percent). This suggests that while economic complexity likely plays a role in shaping AI RR<sup>2</sup> oo preparedness for these countries, policy decisions appear to have a more substantial independent impact on AIPI performance. A likely explanation, explored further in the next section, is that some of these countries, especially among UMICs, have greater macro-fiscal capacity than LICs (Choudhary, Ruch, and Skrok, 2024), enabling them to implement ambitious digital and AI-specific policy agendas. However, these countries often lack a fully developed infrastructure, and specifically digital infrastructure (pillar i of AIPI) and a sufficiently skilled workforce (pillar ii of AIPI), which are still in the process of being built, especially in comparison to HICs (World Bank, 2021; 2024c).

> 19 With an impressive AIPI score of 0.76 in 2023 and an ambitious digital agenda, Estonia has the potential to be considered among the AI preparedness overperformers. However, a lack of data on its multidimensional economic complexity limits such classification. Estonia's third national AI Strategy (2024–26) prioritizes advancing AI skills and competencies through education and training reforms, including the introduction of new Master's and PhD programs, elective AI courses, and vocational education. The strategy also emphasizes society-wide upskilling initiatives, such as a digital academy and public sector training programs that aim to train 10 percent of employees annually. Additionally, the Ministry of Education and Research offers specialized AI training for teachers, students, and public sector managers, while the Ministry of Economic Affairs and Communications targets 80 percent AI literacy across society and widespread AI adoption— 100 percent in the public sector and 75 percent in the private sector—by 2030 (Digital FOSS, 2024).

9
complete list of public and private secondary schools in the three regions.<sup>14</sup> All wards with private schools were automatically included in the sample. After this step was completed, remaining wards were selected randomly, with the goal of reaching a sample size of over 400

> 11 The announcement for the awards included a note that students who were absent on the day of the endline test would get a score of zero. Students who were absent at baseline but present at endline would receive the average baseline score for the school.

> 12 Analysis of Tanzania’s 2014 Labor Force Survey suggests that median monthly earnings of secondary school teachers are on the order of $376. This is reported as PPP$934 in Evans, Yuan, and Filmer (2020) based on a survey median of Tanzanian shillings 622,434.  $376 is calculated on the basis of an exchange rate of 1653 shillings per dollar as reported in World Development Indicators for 2014.

> 13 Intervention costs increase by approximately US$ 500 per school, per year if the cost of designing, administering, invigilating, marking, and data-entry of student assessments is factored-in.

> 14 The sample for the study was constructed using complete secondary school lists for three regions in Tanzania – Lindi, Shinyanga, and Morgoro. The school list, disaggregated at the ward level, is derived by triangulating two administrative databases - EMIS 2011 and Inspectorate 2011 data. These data were augmented by a school mapping database created in 2013 as a part of the fieldwork for this research. We included all the schools for which we could find school performance data (Form 2 pass rates) for 2012. The final sample included 61 percent of all public secondary schools and 100 percent of all private secondary schools in the three regions.

8
Policy Research Working Paper 5681

# **Abstract**

Conditional cash transfers are programs under which poor families get a stipend provided they keep their children in school and take them for health checks. Although there is significant evidence showing that they have positive impacts on school participation, little is known about the long-term impacts of the programs on human capital. This paper investigates whether cohorts of children from poor households that benefited up to nine years from _Familias en Acción_ , a conditional cash transfer program in Colombia, attained more school and performed better on academic tests at the end of high school. Identification of program impacts is derived from two different strategies using matching techniques with household surveys, and regression discontinuity design using a census of the poor and administrative records of the program. The authors show that, on average, participant children are 4 to 8 percentage points more likely than nonparticipant children to finish high school, particularly girls and beneficiaries in rural areas. Regarding long-term impact on tests scores, the analysis shows that program recipients who graduate from high school seem to perform at the same level as equally poor non-recipient graduates, even after correcting for possible selection bias when low-performing students enter school in the treatment group. Although the positive impacts on high school graduation may improve the employment and earning prospects of participants, the lack of positive effects on test scores raises the need to further explore policy actions to couple the program’s objective of increasing human capital with enhanced learning.

This paper is a product of the Public Sector Unit, Independent Evaluation 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 author may be contacted at jbaez@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
44 We estimate 44% of rural Malawians cannot afford the adequate diet in the market _even_ at the lower bound and _if spending all their resources on food_ . At current food spending, 62% of rural Malawian households cannot afford a nutrient-adequate diet, as the lower bound cost exceeds current food expenditure. Recalling that households already spend an average 74% of their resources on food, increasing food budgets without increasing incomes would be nearimpossible for many. At the other extreme the shared diet is feasible and affordable to 20% of households within their current food budgets. That leaves 80% of households who cannot afford to completely share meals, either because it is infeasible or costs more than current food spending, and 69.5% for whom it is infeasible or costs more than all available resources.

As discussed above, there are some limitations to the food price and nutrient composition data and that may be consequential to our results. However, several factors lend confidence to the data quality and our ability to draw meaningful conclusions for Malawi from this demonstration case of the household least-cost diets methodology. First, the list of food items includes all items that accounted for at least 0.02% of household expenditure in 2010 (including own-produced goods), indicating prices are monitored for the vast majority of foods households choose to consume. For comparison, the household survey included 129 items that we could identify, convert to kilograms, and match to food composition data, reflecting a total of 93% of all reported item-source observations. We estimate approximately 90% of household expenditure is spent on items that are present in the food item list. At the nutrient level, 94% of all energy and macronutrients is consumed from items in the market price list and for micronutrients at most 22% of consumption comes from items _not_ in the market price list (see Supplementary Table F). Second, missing data are common in agricultural price series, and we cannot assume that lack of a price observation indicates the item was not available in that
Trade in Services under Mode 3 account for almost 60% of total investor-State Disputes ICSID 1987-2017: cases by economic sector <!-- Start of picture text --> Most common services sub-sectors:<br>6y, 4% 54% . Electricity, Power & Wate<br>7% 2. Transportatio Public/private<br>3% 3. Constructio :<br>9% 4. Financial services partnerships<br>5.: Information and Communic (PPPs)<br>17% 6. Tourism<br>13%<br>4% gy 5% | }<br>= Oil, Gas, &Mining m Electric Power & Other Energy<br>m Water, Sanitation & Flood Protection = Construction All sub-sectors tend to be<br>mTourism m Other industry Domestic-market seeking FDI<br>@ Transportation § Services & Trade<br>Finance = Information & Communication<br>w Agriculture, Fishing, & Forestry<br><!-- End of picture text -->

Source: ICSID Caseload Statistics 2019
**Table A.3.7: LAYS expressed as aggregate earnings loss over life cycle for all students today, expressed as a share of government spending in education undertaken during a country’s expected years of schooling**

|||Post-COVID-19<br>||
|---|---|---|---|
|Aggregate|Optimistic|Intermediate|Pessimistic|
|EAP|13%|28%|44%|
|ECA|5%|13%|22%|
|LAC|6%|13%|20%|
|MNA|5%|12%|19%|
|NAC|3%|9%|16%|
|SAR|7%|13%|19%|
|SSF|12%|22%|33%|
|Grand Total|7%|16%|25%|
|HIC|4%|12%|21%|
|UMC|11%|22%|34%|
|LMC|8%|14%|21%|
|LIC|22%|39%|56%|
|Grand Total|7%|16%|25%|
|Part I|4%|13%|21%|
|IBRD|9%|19%|29%|
|IDA/Blend|13%|24%|35%|
|Grand Total|7%|16%|25%|

Source: Authors’ calculation. Life-cycle effect on earnings at present value, as a share of total spending on basic education (2017 PPP $). Simulation 1 results based on latest available LAYS of 157 countries (unweighted average), with the change in LAYS expressed as aggregate earnings loss over life cycle for all students today, expressed as a share of government spending in education undertaken during a country’s expected years of schooling.

51
combustion energy, industrialization, and other infrastructural developments may lead to inferring that air quality may be deteriorating in fast-growing urban centers in Africa. However, actual evidence is limited given the lack of adequate real-time monitoring data sets. Countries in Sub-Saharan Africa particularly have had minimal or oftentimes unsuccessful attempts to implement effective air quality control programs (Mage et al., 1996; Mayer, 1999; Baldasano et al., 2003; Petkova et al., 2013), and for a number of cases, data are nonexistent e.g. the WHO (2016) compilation of urban ambient air pollution in global cities had only 9 SubSaharan African countries with active monitoring in place, a possible reduction from what was reported in Schwela (2012).

> 6 This study referred to WHO guidelines of 1979, but the standards have since been updated and revised. the current standards are the WHO Air Quality Guidelines 2005.

3
the test items in two halves and treating them as separate measures of the underlying true concept. The Cronbach’s � is the mean of all possible split-half coefficients resulting from different splits of the test and the reliability ratio can be estimated as the ratio of true variance to observed variance of a given test. In Table 7 below, we report both the point estimates from estimating specification (2), �<sup>�</sup> , and the unbiased effects �<sup>�∗</sup> ��<sup>�</sup> �<sup>�</sup> , where �<sup>�</sup> is the Cronbach’s � estimate of the reliability ratio with first differenced test score data.<sup>21</sup> The within-student within-teacher results are reported in Table 7. As a benchmark, column (1) reports a simple OLS regression of standardized student test scores (z-scores), on teacher quality and a set of country by district fixed effects as well as survey fixed effects.<sup>22</sup> The point estimates on the four teacher quality dimensions we measure – content knowledge, pedagogical knowledge, pedagogical skills, and effort (absence from class) – are quantitatively large and significant. Students taught by the best possible teachers in the sample, i.e., those who have full scores on the teacher knowledge and pedagogy test and display the full set of skills in the classroom have test scores that are almost 1.5 standard deviations higher than those who are taught by the worst possible teachers. While these results are by no means causal, it should be noted that they exploit only within-district variation in test scores. In column (2) of Table (7), we turn to the estimation of the full model specified in equation (1) that also includes student, teacher and school characteristics. While the coefficients on the teacher variables are reduced somewhat, they are
# **1. Introduction**

Food and fuel commodity prices, which had been rising since 2001 (see Figures 1 and 2), reached record levels by mid-2008 [21, 52, 65]. According to the International Monetary Fund (IMF) primary commodity price database, world food commodity prices increased 100% or more from 2001 to 2008 (in 2005 US $), with prices increasing by almost 300% for rice (see table inset in Figure 2).

The period between 2001 and 2008 was also the period during which production of biofuels such as ethanol and biodiesel produced from food crops grew several fold. During this time, global ethanol production from maize and sugarcane more than doubled from 30 billion liters to 65 billion liters, while biodiesel production from edible oil seeds such as soybean, oil palm, and rapeseed expanded six fold from 2 billion liters to 12 billion liters [46]. The increase in biofuel demand, which was concentrated in the United States and the European Union (EU), was primarily a response to government mandates and subsidies.<sup>1</sup> This has led to the popular opinion that biofuel policies in the high-income countries are one of the principal causes for the inflation in food commodity prices.

Biofuels reduce demand for oil and increase demand for agricultural goods.<sup>2</sup> With crops comprising a small share of the final cost of food in high-income countries, the impact of biofuels on food consumers is small. To low-income countries, where expenditure on raw grains and vegetable oils comprises a much larger share of the household food budget, a given increase in crop prices will have a much larger impact on food consumers.

This paper aims to identify the main factors affecting food commodity prices, and to also quantify the contributions of these factors. A distinguishing feature of our analysis is taking into account adjustments in inventories of agricultural goods in response to these various factors. Although conceptually an important component of food commodity markets, to
impact of the NHIS rollout on the health insurance take-up of households. Our data do not allow us to correctly identify changes in health insurance coverage due to the implementation of the NHIS. We can, however, rely on reported national enrollment rates to get an impression of the extent of NHIS coverage. Figure 2 shows that national enrollment in 2006 was around 20 percent, and increased to almost 40 percent in 2007. These figures, however, are low bounds of the real coverage rate of the NHIS, as households were frequently allowed to enroll under the NHIS after the occurrence of a health shock (Gajate-Garrido, 2013). Our results need to be interpreted keeping in mind that at the time of observation, about 20 percent of Ghanaians, situated higher on the health risk spectrum, were enrolled under the NHIS.

8
|**Country**|**Type**<br>**of**<br>**firms**|**Power**<br>**supply**<br>**quality variable**|**Method**||**Effect**<br>**of**<br>**low**<br>**electricity**<br>**supplyquality on firms**|**Source**|
|---|---|---|---|---|---|---|
|26 transition<br>economies<br>in<br>Eastern<br>Europe and<br>Central Asia|<br>Business<br>Environmen<br>t<br>and<br>Enterprise<br>Performanc<br>e Survey|<br>Frequency and<br>duration<br>of<br>electricity<br>outages|Estimation<br>firms’<br>function|of<br>cost|<br>The removal of all existing<br>electricity<br>outages<br>would<br>enable cost reductions of 1.3‐<br>1.4% by firms|(Iimi, 2011)|
|Albania,<br>Bosnia and<br>Herzegovina<br>, Macedonia,<br>Kosovo,<br>Montenegro<br>and Serbia|<br>Business<br>Environmen<br>t<br>and<br>Enterprise<br>Performanc<br>e Survey|Percent<br>of<br>annual revenue<br>lost<br>due<br>to<br>power outages|Descriptive<br>statistics<br>firm survey|from|On average, firms
the linear equation (1), the error term is _εi_ = _δ_ ( _Si_<sup>_p_)2+</sup><sup>_ζi_,andtheconditionalvarianceof</sup> _εi_ is a function of father's education simply because of a misspecied functional form. To check this, we estimate the mobility equation (3) and the impact of father's education on the conditional variance dened in terms of _ζi_ . The estimates for various samples are reported in Tables A.4-a.8 in the online appendix section OA.2. The evidence suggests strongly that the relations between family background and conditional variance of children's schooling uncovered in Tables 2-4 are not driven by functional form misspecication of the mobility CEF.

The next question we address is whether the estimated impact of father's education largely reects the omitted cognitive ability heterogeneity of children. For this analysis, we take advantage of the IFLS-2014 survey in Indonesia which collected data on multiple indicators of cognitive ability of a child (measurement taken in 2014 when the children are adult): raven test scores and two memory tests. We construct an index of cognitive ability in two steps. First, we construct the rst principal component of the dierent measures of cognitive ability. In the second step, we regress the rst principal component on age and age squared of a child to take out the Flynn eect. The residual from this regression is our index of cognitive ability of a child. We control for the ability index and its squared in the regression for conditional variance in equation (2) above. The estimates for the full sample are reported in online appendix Table A.4 (see online appendix section OA.3). The main message that comes out is that the estimated eects of father's education on conditional variance of schooling of children are not driven by omitted ability heterogeneity. Even though ability controls reduce the estimated coecient, the inuence of father
One example of how to accomplish this, which has been taken up by some countries, is to rely on a protocol developed by Apple and Google for their contact tracing apps. With this protocol, phones in close proximity of each other will use low energy Bluetooth technology to exchange a temporary key code, which changes every 15 minutes. This information is stored on each phone, rather than in a centralized database. When a user reports a positive test result, the temporary key codes are used to notify other users of their potential exposure. (Zastrow, 2020). This solution serves the intended purpose of notifying users that may have been exposed to the virus, while being relatively privacy-preserving. It cannot be used to enforce quarantine, track, or identify individuals, limiting the scope for surveillance and other inappropriate uses (Privacy International 2020).

In contrast, some countries have taken a more expansive approach to contact tracing, in which more sensitive information was collected and used and, in some instances, shared with law enforcement agencies. In Israel, mobile phone location data was used by the domestic security agency to identify individuals exposed to the virus and by the police to enforce quarantine, a practice which was subsequently challenged as unconstitutional in the Supreme Court (Amit et al. 2020; Bradford et al. 2020). India’s contact tracing app stores location data alongside a set of demographic information, including age, gender, phone number, and travel history, on a centralized server. It was initially mandatory for air and rail travel as well as for government employees to return to work, raising fears this would effectively exclude those without a smartphone from these activities (Arun 2020; Bradford et al. 2020; Privacy International 2020).

# **4. Conclusion**

Around the world, governments are producing more and more data, yet it is highly doubtful that the full potential value of the data is being reaped. In this paper, we have presented a theoretical and empirical argument for why
_Mexico: The 1997 Pension Reform_ _28_ _Inflation_ is especially important for the old PAYG system, since those pensions were computed taking the average of a worker's nominal wages in the previous _5_ years. In a high inflation environment we can expect the fiscal costs arising from the recognition of acquired rights to fall (Table 14).

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Note: These assumptions are for modeling purposes only and can not be taken as O)fficial forecasts on the subject.

IMSS data shows _contribution density_ for its affiliates is on average 0.83 (i.e., for each period of time the effective contribution period is only 83%). Years of service and contributions are affected in the model by this density factor.

Finally, regarding _operational costs,_ the AFORES will be private firms whose only source of income will be commissions charged to their affiliates. Experience in Chile and Peru indicates that commissions (excluding disability insurance premiums) can reach up to 2