rafmacalaba/data-use-mentions-extended huggingface dataset:
Figure 5: Trends in Students’ average Academic Performance by Year of Study: 2010–2014 <!-- Start of picture text --> 62<br>60<br>58<br>95% CI<br>First Years<br>95% CI<br>Second Years<br>56<br>2010 2011 2012 2013 2014<br>Academic Year<br>Student Overall Weighted Mean (OWM)<br><!-- End of picture text --> _Source_ : Own calculations using the NUL students records data for the academic years 2010, 2011, 2012, 2013, and 2014. _Notes_ : OWM is the overall weighted mean.

aged 16-18 years because they are of the appropriate age for first year of university, and those aged 19-23 years as the control cohort.

Figure 6 presents trends in students’ academic performance between 2010 and 2014 by treatment cohort. We can see from the figure that the trends in academic performance of the 16-18 year olds and the 19-23 year olds are parallel throughout the period. As mentioned earlier, the control group (i.e. the 19-23 year cohort) possibly includes children directly affected by FPE. Hence, it is more than likely that the observed drop in average performance of this cohort in 2012 is largely driven by the decline in performance of fraction of old but FPE-treated children. This further supports the idea that our DID method under this treatment group definition will only identify the lower-bound effect of FPE on academic performance.

# **5.2.3 Compositional differences**

Notwithstanding the above evidence establishing the second years and the 19-23 year old university students as the plausible counterfactuals for the respective treatment groups, compositional differences between the control and treatment groups can cause the violation of the parallel trends assumption. That is, the identifying assumption may be implausible if pre-treatment individual characteristics that are correlated with the dynamics of the outcome variable are not balanced 16
information reduces search costs and facilitates co-ordination among agents, thereby increasing productive and market efficiency. Mobile phones also create employment and increase incomes by spurring demand for mobile-related services. In addition, mobile phones enable social networks to respond more immediately to idiosyncratic shocks, thereby reducing household exposure to risk.

On the basis of a study of 409 small business entrepreneurs in Kenya, Mbogo (2010) finds that for micro-businesses, factors responsible for adoption and continued usage of mobile money transfer include convenience, support, cost, satisfaction and security.

Camner and Sjöblom (2009) compare the adoption of M-PESA in Tanzania to adoption in Kenya. They find much lower adoption rates in Tanzania, which are explained by the lower mobile telephony market share of Vodacom, which is the M-PESA provider in Tanzania, its inability to build a strong agent network, and its lower marketing budget. Other factors are Tanzania's generally lower GDP growth, smaller proportion of unbanked population, higher financial illiteracy, more dispersed population and the absence of a national ID.

# **4 The Basics of Mobile Money**

Mobile money systems consist of electronic money accounts that can be accessed via mobile telephones. They are often likened to simple bank accounts, although a basic mobile money system does not pay interest or provide loans. Each of the mobile service providers in Kenya currently has a mobile money service. Safaricom‘s M-PESA was introduced in March 2007, Zain‘s Zap (now known as Airtel Money) was initiated in February 2009, yuCash started in December 2009, and Orange‘s Orange Money was launched in November 2010. The following description is based on M-PESA, which is by far the largest system, although the other systems are similar.

To have a mobile money account and make a deposit, a customer must own a cell phone SIM card with the mobile operator and register for a mobile money account. The customer then can make cash deposits at the physical offices of one of the operator‘s mobile money agents. These cash
consumption, and they should be matched by individual household, using the IDs available in the ProGres registration data.

Further, the PDM data measure consumption using relatively fewer variables than those in round 4 of the Chadian Household Consumption and Informal Sector Surveys (ECOSIT4). As such, the poverty estimations from the PDM data could be improved. <mark>The poverty estimates used in this paper do not reflect the official poverty estimates monitored by the government and the international community. The interest of this paper was to test a cross-survey methodology, and, for this purpose, we used a subsample of UNHCR refugee data that are not nationally representative of the refugee population in Chad. By contrast, official poverty statistics require national consumption surveys conducted by the national statistical office, with samples that are nationally representative. T</mark> he UNHCR and the World Bank are working closely to improve and increase comparable refugee data to nationals. Thanks to these recent efforts, in 2018/19, refugees were included for the first time in the ECOSIT4. When these data become available, it will be possible to run a similar analysis to assess how cross-survey imputation fares using nationally representative consumption measures for poverty, and to understand how cross-survey imputation can predict household poverty outcomes for refugees and host populations alike with comparable data.

Although the work presented in this paper remains a valid experiment for cross-survey imputations, the data did not cover the entirety of refugees in Chad, including some refugees who live outside camps. As the latter live in different environments, predicting their welfare may require different sets of variables. And measuring consumption among refugees who rely on a combination of handouts and informal incomes is a relatively new science. Existing survey instruments may need to be adapted, and the meanings of concepts such as utility and capabilities among refugees need to be reconsidered.

# **VII. Conclusion**

UN General Assembly Sustainable Development Goal 1— _End poverty in all its forms_ by 2030— explicitly pledges that “no one will be left behind.
than 1 percent of GDP on social safety-net programs till the end of 1990s, which was less than the 2 percent allocated in Sub-Saharan Africa and far less than the 5 percent average allocation in South Asia (World Bank 2006, 2008). However, the allocation has improved recently – GOB has allocated 2.8 percent of the GDP on food-based safety net programs alone in FY09 budget (World Bank 2008). In addition to the various GOB-supported safety-net programs shown in Table 4, the government also allocates substantial resources each year to natural-disaster relief programs. Over the 12-year period shown in Table 4, safety-net allocations have expanded significantly. That expansion has been mostly due to increase in the VGD, VGF and GR and TR programs, although the budget in FFW has declined.

# **{TABLE 4 HERE}**

Government safety programs usually reach about 4–5 million households according to the World Bank report on the safety net programs in Bangladesh (World Bank 2006). The report finds that approximately 10 percent of the poor are covered by a large number of programs. However, the situation seems to have improved over the last few years. According to HIES data for 2005, national coverage of the social safety-net programs is only 13 percent (with 15.6 percent in the rural areas and only 5.5 percent in urban ones). The same survey in 2010 finds the national coverage to have almost doubled to 25 percent (with the rural and urban coverage being about 30 percent and 9 percent respectively) **.** While coverage is highest for the VGD and VGF programs (just under 7 percent), the average benefit households receive from these programs is less than Tk.1000 (Figure 1). Conversely, the IFS program offers high average benefits, but coverage is quite low.

# **{FIGURE 1 HERE}**

# **{FIGURE 2 HERE}**

10
) or ‘very well’ (71.7%).

In conjunction with the survey, we also collected data on the performance of public officials as assessed in their annual appraisal. Each year, public officials are evaluated by their direct manager on the tasks that they were expected to contribute to. For example, tasks might include ‘Monitor and provide support to the [work] team preparing the budget’ and ‘Support the team to prepare soft and hard copy documents of the budget’. Managers evaluate the quality of contributions bureaucrats make to the tasks they were involved in and produce an overall ‘performance’ score. In addition to this performance-related score, public officials are evaluated on their ‘attitude’ to work, which intends to measure their office behavior and alignment to the organization (Abagisa, 2014; Tereda, 2014). For the year 2016, we collected the performance, attitude, and total scores (which are a weighted average of performance and attitude scores) for each official from a subset of the organizations we visited for which they were available.

9
Standard errors clustered on survey wave survey wave wave a exrors ‘on survey survey wave<br><!-- End of picture text -->

<!-- Start of picture text --> ge<br>E<br>wo<br>ae<br>&22<br>4<br>B+<br>€<br>B<br>a<br>a<br>FEaa<br>gooS°ooS°<br>nc<br>Apr-2020 May-2020 Jun-2020 Jul-2020 Aug-2020 Sep-2020 Oct-2020 Nov-2020 Jan-2021 Feb-2021_ Mar-2021<br>Months from March 2020 from March 2020 March 2020 2020<br>—*— Female —~®— Male<br>‘Note: Standard errors clustered on survey wave survey wave wave<br><!-- End of picture text --> <!-- Start of picture text --> 2egoegogo<br>.=|=|<br>&<br>Ssieie<br>é<br>a<br>vo33<br>a2<br>3<br>ia<br>ai xt<br>Bofof<br>4<br>oS°°<br>& ny<br>na 7<br>Apr-2020 May-2020 Jum-2020 Jul-2020 Aug-2020MonthsSep-2020from MarchOct-2020MonthsSep-2020from MarchOct-2020Sep-2020from MarchOct-2020from MarchOct-2020 MarchOct-2020Oct-2020 2020Nov-2020Nov-2020 Jam-2021 Feb-2021 Mar-2021<br>—®— Female ——®— Male<br>a exrors ‘on survey survey wave<br><!-- End of picture text -->
1.374|
|Tax administration|How much of an obstacle to firm's operation is tax administration?|1.439|1.319|
|Licensing and permits|How much of an obstacle to firm's operation are business licensing and permits?|1.095|1.238|
|Political instability|How much of an obstacle to firm's operation is political instability?|1.615|1.504|
|Corruption|How much of an obstacle to firm's operation is corruption?|1.780|1.530|

_Source:_ World Bank Enterprise Surveys (2006-10).

21
|(0.059)|(0.097)|(0.061)|(0.786)|(0.924)|(0.781)|
|N|13145|1289|11854|13145|1289|11854|
|ProvinceFE|X|X|X|X|X|X|
|Survey-Wave FE|X|X|X|X|X|X|

_Note_ : Significance level: *0.1 ** 0.05, ***0.01. The dependent variable is level of schooling in years. The coefficient can be interpreted as unit change in years of schooling.

# **5 Results**

We report our estimates of the monetary rate of return for an additional year of schooling in Table 3. Estimates are presented for both OLS and instrumental variable estimators, with the preferred estimates of interest being the 2SLS estimates reported in Columns 4 to 6. Columns 1 and 3 indicate the average rate of return for one more year of schooling among salaried workers, while the remaining columns show genderspecific monetary returns for an additional year of schooling. We find positive return rates for an additional year of schooling on average, as well as by gender, regardless of the estimator used. We find that the rate of return for women is much higher than that for men in Afghanistan.

An additional year of schooling is associated with a 2.5 to 4.0 percent increase in average annualized income among salaried workers, as seen in Columns 1 and 3 in Table 3. While the rates of return are positive, they are much lower than the global average of 10 percent (Psacharopoulos & Patrinos, 2018). The rates of return vary significantly by gender. An additional year of schooling is associated with a 7 to 13 percent increase in the annualized income of women, which is almost four times greater than that for men, which is 2 to 3 percent. Similarly, in Türkiye, a compulsory schooling reform
of respondents report being offered money, ranging from no one reporting such offers, to 75 percent of respondents reporting them.<sup>22</sup> Some features of the vote buying data suggest that it is suited to measuring actual differences across municipalities in the extent to which elections can be won or lost on the basis of vote buying. Among the respondents that answered that they were offered money, 84 percent said they accepted the money, and of these, 51 percent said they voted for the candidate from whom they accepted the offer. This is substantially higher than the evidence from national surveys that is reported by Schaffer (2005). Interpreting the number is difficult however, because of possible social desirability bias in answering that money was accepted without compromising voter rights. Nevertheless, the significant share of respondents that are willing to admit to voting for the candidate from whom they accepted money, suggests that the survey measure is a good proxy for the extent to which elections revolve around the exchange of particularistic benefits.

Table 1 reports the results of estimating equation (1) for various different specifications changing the composition of the correlates contained in the vector X. Some variables exhibited a more robust correlation than others, maintaining their statistical significance across multiple types of specifications. These, more robust correlates represent some of the more important variables in the received literature examining the drivers of vote buying. Each is discussed below in turn.

Medina and Stokes (2002) argue that local economic power is likely to be associated with forms of clientelist exchange other than vote buying, for example, through the leasing of land for cultivation, or the provision of a job. Such models therefore predict lower direct vote buying, offers of money in exchange for votes, at election time, where politicians control economic resources and can use “relational” forms of clientelism (Nichter, 2011).<sup>23</sup> The survey gathered data from key informants at the municipal level (typically consisting of a local business leader,
Figure 8: Assets roughly doubled, and the change is about one-third of their assets (a) Net asset growth between 2015 and 2016 <!-- Start of picture text --> Net YoY Assets Growth (%)<br>175<br>150<br>125<br>100<br>75<br>50<br>25<br>0<br> p98-p99 p99-p99.5 p99.5-p99.9 p99.9-p99.95 p99.95-p99.99 Top 0.01%<br>Top income fractiles<br><!-- End of picture text --> (b) Net asset change as a share of assets in 2016 <!-- Start of picture text --> Net ∆ Assets /<br>Total Assets (%)<br>50<br>40<br>30<br>20<br>10<br>0<br> p98-p99 p99-p99.5 p99.5-p99.9 p99.9-p99.95 p99.95-p99.99 Top 0.01%<br>Top income fractiles<br><!-- End of picture text --> _Notes:_ this figure presents the change in assets reported between 2015 and 2016 by the richest 2% of adults (aged 20 and above) separately by bins of increasing income. Panel (a) compares the change in assets between 2015 and 2016 net of the change in assets between 2014 and 2015. Panel (b) expresses this relative to the amount of assets in 2016. _Source:_ authors’ calculations using data from AFIP statistical yearbooks.

31
share per household (denoted as C), in the HBS.<sup>8</sup> We then follow Rubin (1986) and concatenate the HBS and the EU-SILC.<sup>9</sup> To this end, we take advantage of overlapping variables X, which we harmonize previously and describe later in this paper. We decided to impute energy spending shares into the EUSILC, as the EU-SILC is the richer dataset and contains many variables we are ultimately interested in to describe the energy-poor population in Bulgaria.

_Table 1: Conceptualization of data fusion application in this paper (EU-SILC and HBS in Bulgaria)_

|EU-SILC|**Variables, which form part**|**EU-SILC with information**|
|---|---|---|
|(Imputation for expenditure|**of EU-SILC and HBS**|**on I**|
|C)|||
|**HBS with information on C**|Socio-economic information<br>(X)|HBS<br>(Imputation for income I)|

_Source: Own elaboration based on Bacher and Prander (2018)_

**Before applying multiple imputation techniques to the missing information on energy spending shares in the concatenated dataset, we address the conditional independence assumption.** As we do

not dispose of an auxiliary dataset or auxiliary information on the relationship between I and C, we follow Donatiello et al. (2016b) and identify matching variables that are highly correlated with our variables of interest, the energy expenditure share, later in this paper. We identify one highly 8 While we could also impute income data into the HBS (the approach in Column 3), we decide to do the reverse as the EUSILC gathers more extensive information on individual characteristics, household wellbeing, and social protection programs. EU-SILC data contains a richer set of information and is therefore more attractive as a recipient dataset. Nevertheless, the European Commission (2014) recommends
there is variation in labor market participation across gender. Among working age (18 - 60 years) males, 49.8% of them reported to be paid employees and 70.9% of them reported participating in selfemployment, mostly subsistence farming. Among females, only 23.1% reported to be paid employees while 66.3% of them reported participating in self-employment. This is not surprising given the strong social and traditional family norms in Nepal, which discourage women from participating in paid-work, and where women mostly engage in taking care of children and household chores (CEDAW, 2003). However, with males migrating abroad, the authority over household consumption and investment might shift to female members. Women might become more involved in making decisions on labor market participation as well. Besides continuing to care for children and engaging in household chores, women in Nepal often take up men's roles in family farming and enterprises when male members are abroad (Nandini, 1999). Similarly, women play key roles in deciding the use of remittances and running bazaar economics when husbands are away (Brown and Conneil, 1993). The male-dominated international migration in Nepal may aect non-migrant male and female members in the household dierently.

# 3 Data and Descriptive Analysis

For this study I use the 2010-2011 round of the Nepal Living Standard Survey (NLSS III) as the primary data source. It is a nationally representative survey of households and communities that is conducted by the Central Bureau of Statistics (CBS) Nepal, with assistance from the World Bank. It was administered between February 2010 and February 2011. It has a panel component of 1,128 households. Half of the households were followed from the rst round and the other half from the second round. The cross-sectional sample has 5,988 households, which were selected in three stages.<sup>4</sup> The survey collected detailed information on multiple topics related to household welfare. The survey provides rich information on household consumption
the methods used to collect them, are discussed in detail below.

## Refugee population, enrollment, and costing data

The costs of refugee education used in this paper and its accompanying dataset refer to the costs of _inclusive_ education provision: i.e. the cost of educating refugees through the national education system of their current host countries. The costs of pre-primary, primary and secondary education are estimated using the methodology outlined in _The Global Cost of Inclusive Refugee Education – 2023 Update_ (World Bank & UNHCR, 2023), which focused on All refugee population numbers used in this paper are taken from the United Nations High Commissioner for Refugees’ (UNHCR) _Refugee Population Statistics Database_ (UNHCR, n.d.). ‘Refugee’ is defined in this paper as those categorized by the UNHCR as either ‘refugees’, ‘asylum seekers’, or ‘other people in need of international protection’. The UNHCR presents demographic data by age categories only. In order to estimate the school-age refugee population in each host country, this paper and its accompanying dataset assume that all refugees aged 5-11 years are uniformly distributed across one year of pre-primary education and six years of primary education, and that refugees aged 12-17 years represent the standard 4
Policy Research Working Paper 11073

# **Abstract**

This paper examines global disparities in artificial intelligence preparedness, using the 2023 Artificial Intelligence Preparedness Index developed by the International Monetary Fund alongside the multidimensional Economic Complexity Index. The proposed methodology identifies both global and local overperformers by comparing actual artificial intelligence readiness scores to predictions based on economic complexity, offering a comprehensive assessment of national artificial intelligence capabilities. The findings highlight the varying significance of regulation and ethics frameworks, digital infrastructure, as well as human capital and labor market development in driving artificial intelligence overperformance across different income levels. Through case studies, including Singapore, Northern Europe, Malaysia, Kazakhstan, Ghana, Rwanda, and emerging demographic giants like China and India, the analysis illustrates how even resource-constrained nations can achieve substantial artificial intelligence advancements through strategic investments and coherent policies. The study underscores the need for offering actionable insights to foster peer learning and knowledge-sharing among countries. It concludes with recommendations for improving artificial intelligence preparedness metrics and calls for future research to incorporate cognitive and cultural dimensions into readiness frameworks.

This paper is a product of the Economic Policy Global Department. 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://www.worldbank.org/prwp. The author may be contacted at pmandon@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the
|x|x|x|
|**Panel B: District Chara**|**cteristics**|||||||||
||Share of|Share of|Share of|Female|Male|||||
||female|urban|SC/ST|literacy|literacy|||||
||population|population|population|rate|rate|||||
||(1)|(2)|(3)|(4)|(5)|||||
|Fraction of female leaders|-0.020|0.202|0.072|-0.103|-0.068|||||
||(0.014)|(0.126)|(0.137)|(0.074)|(0.071)|||||
|Observations|627|627|627|627|627|||||
|Outcome mean|0.486|0.258|0.327|0.550|0.691|||||
|State fxed efects|x|x|x|x|x|||||

_Notes:_ Data are from the 2015-16 National Family Health Survey (NFHS) of India in Panel A and from the 2011 Census of India (obtained from SHRUG) in Panel B. The table presents 2SLS estimates using the fraction of female leaders in a district in close elections as an instrument. We define close elections as those in which the victory margin is less that 3 percent of votes. All regressions include state fixed effects and control for the share of seats in the district that had close elections between women and men. The dependent variables in Panel A include woman’s age, years of schooling, and indicator variables for living in a rural area, religion, and caste. The dependent variables in Panel B include the