rafmacalaba/data-use-mentions-extended huggingface dataset:
<mark>conducted (Table 1).</mark><sup>5</sup> <mark>Hereafter any reference to “small states” should be understood as excluding those classified as high income, absent any indication to the contrary.</mark> Based on 2011 data the average per capita income of small states was US$4,759 and only 9 percent were classified as low income. By comparison, as a group low and middle income countries had an average per capita income of US$3,631 and 27 percent were classified as low income. Small states often stand out as the richest country in a region, for example in South Asia the per capita income of Maldives (US$6,530) is more than four times that of India, the largest economy in region. There is however, a marked disparity between the per capita income levels of small states. They range from The Gambia, the poorest, with a GNI per capita of US$610 to Antigua and Barbuda with a per capita income of $12,060.

Examined separately, small states exhibit disparate public debt levels. Several including Antigua and Barbuda, Grenada, Seychelles and St. Kitts and Nevis built up very sizeable public debt on account of expansionary fiscal policies and fixed exchange rate regimes. St. Kitts and Nevis has the highest nominal public debt-to-GDP ratio of any small state, 154 percent at end 2011, and the comparable ratio for Antigua and Barbuda and Grenada is in excess of 90 percent, after substantial debt restructuring with official and private creditors<sup>6</sup> . However, almost half of the 35 small states classified as low and middle income have ratios of public debt to GNI below 50 percent and Timor-Leste has no public debt whatsoever. The composition of public debt portfolios also differs. In general small states rely more heavily on external sources rather than domestic markets for their public financing needs but there are some important exceptions. For example in Antigua
. The <u>Addendum gives the results. There was little</u> improvement in the targeting performance of the PMT.

So far we have focused solely on the inclusion and exclusion rates as the measures of targeting performance. These appear to be the most popular measures in the literature, though others have been proposed and used in some studies. Probably the most promising example of the latter when the policy objective is poverty reduction is the targeting differential (Ravallion, 2000, 2009). Recall that the normalized _TD_ is in the range [-1, 1], with zero corresponding to a uniform (un-targeted) transfer.

Table 10 gives summary statistics on the normalized targeting differential using both the Basic and Extended PMTs. The mean _NTD_ for Basic PMT is 0.21, meaning that if program participation was based on the PMT scores the participation rate for the poor would be 21% points higher than that for the non-poor. Using Basic PMT, in four of the five countries with panel data, the _NTD_ is higher using timemean consumption; this rises to five out of five using the Extended PMT. Returning to the cross-section surveys, the poverty-quantile method yields the highest _NTD_ , at around 0.49 on average for Basic PMT, 21
country differences in the quality of governance, our focus here is limited to measuring the perceptions of interested parties -- residents of a country, entrepreneurs, foreign investors, and civil society at large -- regarding the quality of govemance in a country. Although this kind of data is inherently subjective, there are several reasons why it is useful in measuring governance. First, for many issues such as the prevalence of corruption, objective data is almost by definition rather difficult to obtain. There are few alternatives to subjective indicators if one wishes to measure these aspects of govemance. Second, perceptions of the quality of governance may often be as important as objective differences in institutions across countries. While a country may nominally enjoy a set of sound institutions according to certain standards, the confidence of residents of a country in these institutions is also required if they are to contribute to good govemance. Third, we have found in other work that subjective perceptions can have significant explanatory power for future economic outcomes.<sup>4</sup> We use our definition of govemance to organize a subset of the govemance indicators into six clusters. We summarize two key aspects of the process by which those in authority are selected and replaced with clusters labelled "Voice and Accountability", and "Political Instability and Violence". We capture the capacity of the state to implement sound policies with two clusters we refer to as "Government Effectiveness" and 'Regulatory Burden". Finally, two clusters labelled "Rule of Law" and "Graft" capture the respect of citizens and the state for the rules which govem their interactions. For each of these six clusters, we use the procedure laid out in Kaufmann, Kraay and Zoido-Lobat6n (1999) to construct aggregate governance indicators. The advantage of these aggregate indicators is that they span a much larger set of countries than most individual indicators, permitting cross-country comparisons of between 155 and 173 countries, depending on the aggregate. More important, the aggregation method we use provides quantitative measures of the
Our first aim was extending the analysis to as many countries as possible, subject to the feasibility of imputation. Hence, we ended up analyzing 15 countries of the region. In cases where imputation was not feasible for certain periods, we used data from at least two survey rounds in those countries. Table 1 presents the corresponding countries, the rounds used in each country, and levels of aggregate data imputation.

**Table 1.** National household surveys used and level of imputation.

|**Country**|**Survey**|**Years (rounds)**|**Community-level**<br>**Income Equation**<br>**(Z, C)**|**Community-**<br>**level Risk**<br>**Equation (N)**|
|---|---|---|---|---|
|Argentina|EPHC-S2|2003, 2008, 2011,<br>2017,2021|ADM 1|ADM 1|
|Bolivia (Plurinational<br>State of)|EH|2002, 2008, 2011,<br>2017,2021|ADM 1|ADM 1|
|Brazil|PNAD, PNADC-E5|2008, 2011, 2017,<br>2021|ADM 1|ADM 1|
|Brazil (cities)|PNAD, PNADC-E5|2017, 2021|ADM 1|ADM 2|
|Chile|CASEN|2009, 2011, 2017,<br>2022|ADM 1|ADM 2|
|Colombia|GEIH|2011, 2021|ADM 1|ADM 1|
|Dominican Republic|ENFT, ECNFT-Q03|2002, 2008, 2011,<br>2017, 2021|ADM 1|ADM 2|
|Ecuador|ENEMDU|2008, 2011, 2017,<br>2022|ADM 1|ADM 2|
|El Salvador|EHPM|2002
Figure 3. Plurilateral Regional Trade Agreements of Eurasian Countries <!-- Start of picture text --> Estonia<br>Armenia Iceland, Liechtenstein,<br>Norway, Switzerland,<br>Russian Federation<br>Belarus<br>Ukraine<br>Kazakhstan EU<br>Kyrgyz Republic<br>Moldova Serbia<br>Tajikistan<br>Montenegro<br>Croatia<br>Uzbekistan<br>FYMR Albania Kosovo<br>Turkmenistan Georgia<br>Bosnia and Herzegovina<br>Azerbaijan<br>Pakistan EC-OCT<br>Afghanistan Turkey Iran<br>Romania<br><!-- End of picture text --> Source: Authors’ elaboration using data from the WTO and the Global Preferential Trade Agreements Library accessed through WITS (2012).

# **Trade Policy Barriers in Services**

We start the analysis of trade policy barriers in services with the examination of trade barriers in the Eurasian region, as domestic policy constraints may dampen competition and create anti-export bias. To conduct the analysis, we employ a new dataset constructed by Borchert et al. (2012a and 2012b), which provides trade restrictiveness indexes and detailed information on policy restrictions in the services arena. Restrictions to services trade considered in the dataset include: i) cross-border supply of services (mode 1), ii) supply of services through commercial presence (mode 3), and iii) temporary presence of natural persons (mode 4). The perspective is one of a foreign service provider that wishes to provide services to consumers in a foreign country, and therefore it considers policy measures that affect both domestic and foreign service provides as well as measures that discriminate against foreign service providers.

The database covers 103 countries across all regions and income groups of the world. For each country, five major service sectors are covered that encompass a total of 19 subsectors: i) financial: retail banking (lending and deposit acceptance) and insurance (automobile, life, and reinsurance)
and _H i_ in the reduced form are quadratics in educational attainment and age, measures of health status of the respondent (ADL z-scores, described below), and the average educational attainment of a spouse and adult children of the household. As some older workers may find that their time is best utilized in provision of care to relatives and that this may influence employment decisions, we also include numbers of grandchildren and living parents, respectively, of the household head and spouse.

The three data sources used to examine employment outcomes in China (CHARLS 2008), Indonesia (IFLS 2007) and Korea (KLOSA 2006) provide roughly comparable information on labor supply, health status and education of both the respondent and spouse, and information on parents and children regardless of current residence in the household.<sup>24</sup> We estimate the reduced form labor supply model in each country: where labor supply,<sup>_L_</sup> _Si_<sup>, is a binary indicator of whether individualworked for one hour or</sup> more during the previous week. We expect that higher values of educational attainment of elderly,<sup>_E_</sup> _i_<sup>, will be associated with higher wealth and savings and, as leisure is a normal good,</sup> may be negatively related to elderly labor supply.<sup>25</sup> Similarly, we expect that access to own pension income, _Peni_ , and pension income of a spouse, _Pen_  _i_ , will be negatively related to employment.

The health of older workers and elderly are measured using two z-scores calculated from responses to a set of activities of daily living questions, **ADL** _i_ .<sup>26</sup> For each of the three data

> 24An important benefit of knowing the numbers of grandchildren and living parents independent of whether they are members of the household is that we are able to control for potential demand for a family care-provider without
fidelity or the intensity of these interventions.

In developing country settings, rigorous evaluations of preschool programs have emerged in the last decade. Results have ranged from no effect in some settings (Bouguen, Filmer, Macours and Naudeau, 2017) to positive effects in others (Brinkman et al., 2017; Jung and Hasan, 2016; Burger, 2010; Martinez, Naudeau and Pereira, 2012; Nores and Barnett, 2010). Systematic reviews of preschool interventions in developing countries reinforce the wide range of impacts seen in international settings (Tanner et al., 2015). The contrasting evidence from different settings has led some to question whether early childhood education can even have lasting impacts (Stevens and English, 2016).

In this paper, we contribute to the growing literature on the longer-term impacts of preschools in developing countries. We examine the longer-term impacts of early childhood education in a developing country setting using data on two cohorts of children from the Indonesia Early Childhood Education and Development (ECED) Project. The project lasted five years and expanded access to community-based preschools by providing block grants to villages to establish up to two playgroups (targeted for ages 3-4), providing teacher training, and raising community awareness about the importance of early education.<sup>1</sup> The _older cohort_ in our study was age 4 and the _younger cohort_ was age 1 when the project began.

The older and younger cohorts differed in terms of (1) the phase of project implementation when they were exposed to playgroups and (2) the length of exposure to playgroups at the appropriate age. On the first distinction, the older cohort was exposed to playgroups during the project’s first year since they were already age 4 at the start of the project. In contrast, the younger cohort was exposed to playgroups during the project’s third year when they turned 3 years old. As a result, the two cohorts experienced the same project at different phases of implementation. On the second distinction, the older
Fitzpatrick (2012). The vast complexity of food insecurity provides ample directions for more data and rich opportunity to develop a complex model. The intrinsic assumption behind the relatively simple model developed here is that while the exact causal mechanisms that describe how food crisis shocks play out may be vastly complex, much of the impacts are endogenous to the economic state of a country. The corollary is that a good estimate of vulnerability may thus be produced from a few broad-based indicators that capture this internalized fragility.<sup>27</sup> The idea is supported by the good accuracy obtained in the empirical application; the 2019 data was forecast

> 26If food insecurity could accurately be predicted from say an indicator of the quality of governance, but a breakdown in that indicator itself is as equally hard to forecast into the future as an outbreak of food insecurity itself, then little progress has been made in terms of developing a future outlook. Instead, the only thing achieved is to state one unknown in terms of another unknown. Keeping such an important indicator fixed in the outlook in turn bakes in the assumption that food insecurity will remain stable. Essentially, this restricts the exercise from using the wide variety of indicators whose unforeseen drastic deterioration go hand in hand with a drastic deterioration in food insecurity, including protests, political violence, violent conflict, weather impacts, and other indicators that have previously been used to model acute food insecurity situations in previously cited work and related targets such as conflict outbreaks (Celiku and Kraay, 2017).

> 27This is essentially a more elaborate way of summarizing Amartya Sen’s famous statement that no famine ever occurred in a democracy; see a discussion by Rubin (2012), or de Waal’s statement that all modern famines are man-made (De Waal, 2018), both which are ways to say that critical food insecurity outcomes are produced fully endogenously from rising vulnerabilities in a country. It follows that if systems in a country are so broken or corrupt to produce such devastating outcomes,
Figure 1: The Basic Model of Traffic Congestion <!-- Start of picture text --> trip cost<br>msc (marginal social cost)<br>apc (average private cost)<br>b<br>a<br>mecc<br>τ ∗<br>e<br>d (demand)<br>v ∗ v e traffic flow<br><!-- End of picture text --> Measuring the welfare loss associated with congestion is challenging given that standard travel surveys may not be available or have insufficient time and spatial coverage that makes the inverse demand curve or the cost functions difficult to recover (Small et al., 2007). Notable efforts include Walters (1961), Kraus et al. (1976), Keeler and Small (1977), and Dewees (1979) in the US and Canada. The advent of new data sources (in particular: Google Maps, traffic microwave sensors, and GPS) may thus offer promising potential for overcoming these challenges, including in developing countries where data leapfrogging could be occurring.

Akbar and Duranton (2017), for example, provide one of the first empirical attempts that leverage big data to measure the deadweight loss of congestion. Combining a travel survey with counterfactual trip information extracted from Google Maps, the authors successfully recover supply and demand for road travel in Bogotá. In their empirical setting, big data from Google Maps serves two main purposes. First, it provides information on distance and real-time trip duration that overcome extensive data require8
percentage points (the lowest of all significant variables for welfare). More significantly, the ranking of the first five variables remained the same for both poverty and welfare: rural, non-capital city, share of wages in household income, number of unemployed in household, and enterprise ownership.

In Kyrgyz Republic, the picture is less clear. The locational factors which were most significant for household welfare were also for poverty (rural and non-capital city respectively), but the probit found that female headship was in third place (increasing the risk of poverty by 12 percentage points while in the OLS, it was in fifth place (reducing welfare by 19 percent). The correspondence unraveled further for other factors. In the OLS, land access was third most important, raising household welfare by nearly 25 percent, but in the probit, land access was in sixth place and only reduced the chance of poverty by approximately 9 percentage points. The education variables which were found to be significant for Kyrgyz household welfare were insignificant in the poverty regressions, and the probit also did not identify an unemployed head as a risk factor for poverty, although it was found to reduce household welfare by nearly 15 percent.

Aside from some of the rerankings in the Kyrgyz Republic, the overall pattern of poverty determinants is consistent with the pattern demonstrated by the welfare regressions. The findings represent a challenge for social assistance authorities in the FSU countries, since the factors most significant for household welfare and poverty are ones that are extremely difficult to change in the short-run: location and share of wages in household income. Demographic characteristics (number of children, female-headed households) were generally statistically significant, but only increased the risk of poverty 80
program (both pseudo-PMT and full PMT indicate they are eligible). Based on these case types, one can calculate various referral statistics as shown in the figure. Statistic related to the “referral of ineligible cases” and the “non-referral of eligible cases” are comparable to the “inclusion error” and the “exclusion error” under the full-PMT.

Since both the MoLSA PMT and pseudo-PMT are devised on the SWIFT data, one can estimate these statistics using the SWIFT data. However, given the differences between the survey data (nationally representative) and the database, these statistics would be more indicative and “theoretical” in nature. To assess the referral statistics in the humanitarian database, one would have to apply the PPMT and PMT on the database, perhaps by implementing a light survey allowing the implementation of PPMT and PMT formula on a small sample of humanitarian cases.

12
Adding to the findings of the effects of economic instability, Chauvet et al. (2010) explore the extent to which post-civil war situations shape the outcomes of World Bank projects. Unsurprisingly, they find that projects that start in a post-war period have lower chances of success than projects implemented in countries at peace. Caselli and Presbitero (2021) expand on these results using a larger data set of aid projects funded by seven donors over 1980-2012, documenting that a project implemented in a fragile state is eight percentage points less likely to succeed than a similar project in a non-fragile developing country.

There is some suggestive evidence that geopolitical context influences disbursement patterns, project design, and evaluation at the World Bank. Projects in geopolitically important countries have less stringent conditions (Clark and Dolan, 2021), shorter preparation periods (Kilby, 2013), and are disbursed faster (Kilby, 2013; Kersting and Kilby, 2016). Dreher et al. (2013) find no significant average effect of political influence on the likelihood of a successful project evaluation, but some evidence that politics affect project performance in exceptional cases of high political stakes and challenging economic circumstances.

Regarding the overall degree that country factors explain variation in project success, several papers have addressed the relative importance of project and country-level factors in explaining outcomes. Denizer et al. (2013) report that 80% of the variation in World Bank project outcomes occurs within countries, similar to the 75%-90% obtained by Bulman et al. (2017) for both the World Bank and the Asian Development Bank. Using the same method to examine projects by the Australian aid agency (Wood et al., 2020) and seven diverse donors (Briggs, 2020) produced consistent findings that development project success varies more within countries than across countries.

# **3. Data and methods: What is our empirical approach?**

We combine data on World Bank project outcomes from the Bank’s Independent Evaluation Group (IEG) with a new data set
Policy Research Working Paper 8822

# **Abstract**

School safety and classroom disciplinary climate have a direct impact on teachers’ ability to teach and students’ ability to learn. School safety and classroom disciplinary climates have been declining in the Middle East and North Africa region, as is demonstrated in this paper using data from the Trends in Mathematics and Science Study and the Progress in International Reading Literacy Study. The paper then moves on to untangle how disruptive learning environments can have negative impacts on student learning outcomes. Thus, the objective of the paper is to analyze the determinants associated with disrupted learning environments, at the school and classroom levels, in the Middle East and North Africa region and to uncover the impacts these environments have on student learning outcomes. This information will provide policy makers with evidence on disrupted learning environments while offering some recommendations on how these conditions can be improved.

This paper is a product of the Education Global Practice. 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 authors may be contacted at mquota@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 World Bank or the governments they represent._ Produced by the Research Support Team
unbundling of activities. Three types of reforms are particularly important in increasing the efficiency of provision of regulated infrastructure services: (i) allowing entry of new domestic and foreign providers; (ii), where feasible, opening the domestic market to imports of such services; and (iii) the establishment of an independent regulator. The latter is likely to be a key determinant of regulatory effectiveness. Reforms may and often do include privatization, but this variable is captured in the overall investment climate variable, not in the infrastructure services policy reform index. Even if incumbent providers remain state-owned, if regulators permit entry of new providers in the market, such competition can be expected to yield efficiency gains in the industry overall. The EBRD indices suggest that 8