rafmacalaba/data-use-mentions-extended huggingface dataset:
# _2.2 Trends in Education Spending_

Average government education spending around the world doubled in real terms between 1999 and 2015 but spending in terms of share of GDP increased only slightly (Figure 2). Much of the 4 percent annual growth in public education spending was fueled by economic growth rather than significant increases in the share of GDP devoted to education. The share of GDP devoted to public education spending increased from an average of 4.6 percent in 1999 to an average of 4.7 percent in 2015.

**Figure 2: Real government spending on education has grown significantly since 1999** _Public education spending, constant 2015 PPP dollars and percentage of GDP, 1999-2015_

<!-- Start of picture text --> 40,000 5.0<br>30,000 4.5<br>20,000 4.0<br>10,000 3.5<br>0 3.0<br>Public education spending Public education spending as % of GDP<br>Linear trend (spend) Linear trend (spend as % of GDP)<br>Total public education spending (Milllions, constant 2015 PPP $)<br>Public education spending (% GDP)<br>1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015<br><!-- End of picture text --> _Source:_ World Bank calculations based on UIS and IMF online databases.

_Note:_ The bars show the country average of total public spending on education for all countries with data available in each year.

# **_Low-Income Countries Have Seen Faster Growth of Public Education Spending as Share of GDP_**

Low-income and upper-middle-income countries have registered relatively large increases in the share of GDP devoted to government education spending since 1999 (Figure 3). For example, countries that were classified as low-income in 2017 increased their public education spending from 3.2 percent of GDP in 1998 **–**
from Eurostat. Appendix A provides more detailed description of the content of the video, and example screenshots, as well as more details on the other interventions.

The theory underlying this type of intervention is that potential migrants underestimate the risks of migrating the backway for at least two reasons. The first is that they are likely to receive a selective picture of the journey, with news of failures and other stigmatized events becoming less likely to be shared with others than successes. Second, as discussed above, the context of our intervention is one where there were rapid changes in policies that had increased the risks of the journey. While some substantial policy changes (like a sharp increase in deportation risk) may quickly be transmitted to potential migrants, other migration journey events or outcomes may be less frequent and more stigmatized and hence less well-known (as attested by our own survey knowledge questions). This context will create knowledge gaps and an information set including outdated facts. This intervention may then provide new information, as well as potentially increase the salience of these risks.

**_Treatment 2 (information and Senegal alternative)_** was shown the same videos as the information and deterrence group, given additional information about migration to Dakar, Senegal, and offered financing and support to pay the cost of travel to Dakar. The information about Senegal was also conveyed through a video documentary towards the end of the baseline, including testimonies of Gambian migrants residing in Dakar. These migrants narrate their migration experience to Dakar, conveying information on the cost of migrating, type of job opportunities and earnings, and general advice to prospective Gambian migrants. Moreover, respondents in these settlements were provided with the opportunity to migrate to Dakar for free. They were asked if they were interested in migrating to Dakar for the purpose of looking for jobs. Respondents in this group were then given (i) a labeled cash transfer of 1,200 GMD (about 20 euros) that could be used to pay for the cost of a bus journey to
Figure 20: Part-time employment by age groups. (Early 2000s/Mid-2010s) <!-- Start of picture text --> 100%<br>90%<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>2000s 2010s 2000s 2010s 2000s 2010s 2000s 2010s 2000s 2010s 2000s 2010s 2000s 2010s<br>Russia Georgia Kyrgyzstan Turkey Armenia Albania Moldova<br>>64 years old 50‐64 40‐49 25‐39 15‐24<br><!-- End of picture text --> Source: Own calculations based on Household surveys Figure 21: Temporary employment by age groups. (Early 2000s/Mid-2010s) <!-- Start of picture text --> 100%<br>90%<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>2000s 2010s 2000s 2010s 2000s 2010s 2000s 2010s<br>Georgia Kyrgyzstan Turkey Armenia<br>>64 years old 50‐64 40‐49 25‐39 15‐24<br><!-- End of picture text --> Source: Own calculations based on Household surveys A less clear picture is observed in the case of temporary employment. Indeed, in the four countries where temporary employees are identified, we observe different dynamics in the age profile. On the one hand, Kyrgyzstan and Turkey do not evidence significant changes in the age profile of temporary workers in the last 10/15 years. On the other hand, Georgia and Armenia present changes in the age profile but in opposite directions. Armenia shows today a higher share of the older groups within temporary employees while Georgia evidences a younger profile of
# **Table 6 - Microfinance indicators from CGAP financial access database**

||**Financial in**<br>**Promoting**<br>**rural areas**|**clusion:**<br>**access in**|**Deposits**<br>**Account**<br>**adults**|**:**<br>**s per tho**|**usand**|**Deposit**<br>**Value**<br>**of GDP**|**s:**<br>**as perc**<br>|**entage**|**Loans:**<br>**Accoun**<br>**adults**|**ts per th**|**ousand**|**Loans:**<br>**Value a**<br>**GDP**|**s percen**|**tage of**|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||Agency is<br>responsible<br>(Yes/No)|Dedicated<br>team<br>or<br>unit<br>(Yes/No)|Coope-<br>ratives|SSFIs|MFIs|Coope-<br>ratives|SSFIs|MFIs|Coope-<br>ratives|SSFIs|MFIs|Coope-<br>ratives|SSFIs|MFIs|
|**Malaysia**|√|√|2.78|1
narrow 95 percent confidence intervals illustrate that the small differences in attrition rates across questions are statistically significant at conventional significance levels, there was not a systematic difference in attrition rates between sensitive questions and the other questions in the survey. Some of the attrition rates of sensitive questions were higher than those of non-sensitive questions, and some of them were lower; the question with the highest attrition rate- the question asking about subjective assessments of income- is nonsensitive; and Figure 2 illustrates that the attrition rate was nearly identical between DFA and IRG-controlled regions at every question despite the potentially higher sensitivity of some of the questions in DFA-controlled regions.

Second, internet users differ from the general population and from the samples of other more commonly used survey modalities in important ways that can limit the representativeness of the results. By quantifying the differences between internet surveys, other survey modalities, and the general population, we can begin to caveat the results and to provide important robustness checks for analysis that relies upon internet-based surveys. Although it is difficult to fully quantify differences between the internet surveys and the entire population given the lack of traditional data in the country, we are able to compare difficult-to-adjust demographic variables in the internet survey to nationally representative data from before the conflict, including age, sex, highest education level achieved, and population by governorate.<sup>15</sup> The summary statistics from each survey are presented in Table 3 and suggest exactly what one might expect of internet surveys- respondents to the internet surveys are younger, less likely to be female, more concentrated in urban areas, and tend to be more educated than the general population. However, mobile phone surveys are biased in the exact same ways, with little difference between the mobile phone surveys and internet surveys in the share of the respondents that are female and the average age of respondents. But the respondents to the internet survey do tend to be even more skewed towards

> 15Despite the onset of a forced displacement crisis beginning in March 2015, official figures suggest
In line with the macroeconomic behavior, moderate poverty decreased from 2008 to 2018. However, in the same period extreme poverty increased slightly. As of 2018, Grenada’s poverty rate had decreased by almost 13 percentage points compared to 2008 (the previous year for which data were available). In contrast, extreme poverty in Grenada increased by 1.2 percentage points (figure 1, panel a). Regarding inequality, we observe that during this 10-year period, the Gini coefficient, which measures the inequality of the income distribution, increased from 0.37 to 0.40 (figure 1, panel b). This increase in inequality from 2008 to 2018 may imply some degree of deterioration of conditions for the lower part of the consumption distribution, which is also shown in the increase of the extreme poverty rate during this period.

**Figure 1 Poverty and Inequality Measures**

<!-- Start of picture text --> (a) Poverty and Indigence (%) (b) Gini Index<br><!-- End of picture text --> <!-- Start of picture text --> 37.7<br>32.1 0.45<br>0.40<br>24.8<br>0.37<br>12.9<br>2.4 3.6<br>1998 2008 2018 1998 2008 2018<br>Extreme poverty Total poverty Gini<br><!-- End of picture text --> _Sources_ : Government of Grenada, Central Statistical Office, Survey of Living Conditions and Household Budget Survey (SLCHBS) 1998, 2007–08, and 2018–19.

Grenada’s fiscal position has varied over the years, partly due to natural and external shocks. Following a modest performance in 2009–13, revenues and grant collection shifted to an upward trajectory in 2014, growing by an average of 11.2 percent annually until 2019. Taxes accounted for
Policy Research Working Paper 10710

# **Abstract**

This paper uses the construction of India’s Golden Quadrilateral (GQ) highway to explore the impact of an exogenous increase in market access and competition across the firm life cycle and generates four findings. First, while exit rates fall for all plants, aggregate gains are driven by expansion of young plants. Older plants stagnate or contract, consistent with the challenges of increased competition for incumbents. Second, the benefits of connectivity to young plants depend on access to complementary factors, such as finance, and business conditions, although older plants respond better in more distorted districts, perhaps reflecting access to inputs while protecting output markets as in de Loecker et al. (2016). Third, expanding young plants correspond to capital intensive value chain embedded activities that do not require close coordination with final producers. Fourth, plant-level panel data confirms plant capabilities as central to both the magnitude of the response, and to the composition of plants driving it. Aggregate expansion among young plants is driven by high skill plants while contraction of old plants is driven by low skill plants, consistent with frontier firms being able to escape competition (Aghion et al. 2014).

This paper is a product of the International Finance Corporation and the Office of the Chief Economist, Latin America and the Caribbean Region. It is part of a larger effort by the World Bank Group 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 agrover1@worldbank.org and wmaloney@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
studies have used randomized sampling (Newton et al 2009).<sup>14</sup> For recommendations on how to objectively and systematically assess the prevalence of counterfeit medicines, see Newton et al 2009.

Until recently, the high cost of chemical analysis has inhibited large-scale analyses in developing countries. However, new technologies are sharply reducing these costs. For example, Africa Fighting Malaria recently conducted a survey of anti-malarial drugs in six major African cities to determine whether effective drugs were available. Their experience suggests that reliable representative results can be obtained in a given country for as little as US$5,000, and would rise in line with the number of medicines tested and the complexity of the sampling required to obtain a representative sample (author‟s estimate based on Bate et al 2008).

> 13 According to the International Pharmaceutical Federation (FIP – www.fip.org), substandard drugs are “genuine products which do not meet quality specifications set for them.” Expired drugs are those for which the manufacturer can no longer guarantee full potency and effectiveness.

> 14 USAID, in collaboration with the Pharmaceutical Security Institute (PSI) has compiled a list of all published studies and data on counterfeit and substandard medicines in USAID-assisted countries (USP DQI 2009). The accuracy of the data differs from study to study, yet the list itself demonstrates that counterfeit drugs are a serious problem.

______________________________________________________________________________________

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Turning to our own results, data from the Enterprise Surveys reveal notable differences between the management practices of establishments within the LAC region. According to our survey data, establishments in Colombia have the most structured management practices in place, while establishments in Bolivia have the least structured practices (Figure 2). The poor management of establishments that we see, on average, in Bolivia appears to be driven, at least in part, by a number of very badly‐managed firms, shown by the longer left tail of the distribution for Bolivia (Figure 3); this echoes the results highlighted by Bloom and Van Reenen in their 2010 paper showing that much of the variation between countries in terms of management practices is due to a tail of poorly‐managed firms present in some countries. Our results also show that certain establishment characteristics are important predictors of better management; larger establishments, as well as those that export, have higher management scores. Establishments that recruit their manager from outside the establishment (as opposed to those who promote internally), and establishments with formal workforce training programs in place, also have higher management scores. Furthermore, we confirm what was shown in the seminal Bloom et al. (2007) paper—that better management practices are associated with better outcomes. We show that more structured management practices are associated with higher labor productivity and, for a subset of larger manufacturing establishments, higher total factor productivity also. When we break down the results by each individual country, we find notably different impacts across the seven countries studied—for three of the seven countries analyzed (Argentina, Paraguay and Peru), we find no evidence of a positive relationship between management and labor productivity based on the average across all establishments. However, in Argentina and Paraguay, there does appear to be a positive link for establishments of 50 of more employees, while in Peru the data show that establishments must employ at least 100 workers before a significant relationship between management practices and labor productivity can be detected.

We make several
# **2 Causes of increased impacts**

**The situation described above has emphasized the need to analyze the causes of flooding and to understand what can be done to reduce impacts.** This calls for an investigation in the field of physical and anthropogenic issues related to urban flooding. Impact from flooding in the urban environment is caused by the action of hazard on exposed and vulnerable receptors. Increases in impacts from flooding can result from increases in hazard, in the exposure of populations and their assets, or in the vulnerability of these exposed populations and assets to flooding. Recent increases in impacts observed by sources such as the CRED database are seen to be fuelled by changes in all three underlying factors. Hazard, exposure and vulnerability all appear to be increasing and are likely to continue to do so in the future unless active steps are taken to prevent this. Table 2 shows the major factors underlying increases in hazard, exposure and vulnerability of urban populations to flooding. Some of these factors affect more than one underlying driver, but in subtly different ways. For example, urban expansion affects the flood hazard by widening the area of impermeable surface and increasing runoff while simultaneously increasing the exposure by increasing the development outside existing defenses. Increases in urban density, however, often expose more receptors to overtopping of existing defenses while also making areas more vulnerable to damage by higher volumes of flood debris.

**Table 2: Causes of changes in hazard, exposure and vulnerability**

|Hazard|Exposure|Vulnerability|
|---|---|---|
|Climate change and variability<br>Increased rainfall|Larger population in urban areas<br>Higher urban density|Changing wealth/ poverty of population|
|Sea level rise|Development outside|Changing demographics of populations|
|Increased storminess|defenses||
|Changing rainfall patterns|Uncontrolled development|Building design without regard to flood<br>risk|
|Land use change|Lack of operations and<br>maintenance
One of the earlier applications of the use of satellite and geo‐spatial data to predict outcomes was the use of night‐ time lights to predict GDP. Night‐time lights are well‐suited to predicting cross‐country levels of GDP (Henderson et al., 2012; Pinkovskiy and Sala‐i‐Martin, 2016). However, at the within‐country level they are much better suited to predicting population density than welfare, and the correlation of night‐time lights with local wages and local poverty rates has typically been found to be weak (Engstrom et al., 2017a; Mellander et al., 2015). Night‐time lights may therefore not be very well suited to uses akin to small‐area estimation, particularly in a place such as South Sudan where only about 3 percent of households have access to electricity (World Bank, 2018). More recent research has focused on training deep‐learning algorithms to extract a diverse range of features from high resolution satellite imagery, for example counting the number of cars on a street, distinguishing road types, recognizing materials roofs are made of, tree coverage, the contrast and number of jagged edges, etc. (Engstrom et al., 2017). This allows making poverty predictions at a much higher level of disaggregation (Jean et al., 2016; Krizhevsky et al., 2012; Sedda et al., 2015). Engstrom et al. (2017) provide a useful overview of the current state of the literature and show the predictive power of a range of indicators constructed from satellite data in estimating poverty at the village‐level.

In the case of the HFS in South Sudan, predictions from a set of linear models were used to project poverty estimates to inaccessible areas based on already extracted satellite features and geo‐spatial data, given the objective of creating reliable and transparent poverty measures. The poverty imputation follows a process that is relatively similar to small area estimation, though only the point estimates were estimated
DIALOGIC READING AND STORYBOOK EVALUATION IN KENYA 28 PRIMR, and Mango Tree – but these programs are center-based, emphasizing teacher training, and targeting school-aged children (Dowd et al., 2013; Piper, Destefano, Kinyanjui, & Ong’ele, 2018; Piper et al., 2015; Kerwin & Thornton, 2018). Modifications to center-based programs may have logistical advantages in some settings, but they may be less likely to reach younger children. In the present study, only 42 percent of 3-year-olds were enrolled in pre-schools. In a study in Mozambique, even conditional on the availability of high-quality pre-schools in the community, only 55.6 percent of 3- and 4-year-olds were enrolled in pre-schools (Martinez et al., 2012). Scaling up access to high-quality preschool programs in LMICs may be costlier and require more political will than implementing parenting education programs; parenting programs may have broader reach in rural areas, especially for younger children (Daelmans et al., 2017). A comparative study of preschool and parenting education programs in Ethiopia by Save the Children found the parenting program to be as effective at improving children’s early literacy skills and language development as the preschool program (Borisova, Pisani, Dowd, & Lin, 2017). Interventions targeting parents and center-based programs can easily be complements: there is no reason a policy maker could not aim for improvements on both margins. For example, a study of a childcare center in Brazil found that adding parent reading training to their program yielded significant improvements in parent reading behaviors, and higher child receptive vocabulary and working memory scores after 9 months (Weisleder et al., 2017). In reflecting on the policy options, we think it is helpful to be clear on the relative advantages of each approach.

# **Limitations**

There were several limitations of this study. First, the follow-up was conducted five to six weeks after the intervention, giving us the opportunity to see only short-run effects of the intervention. This timing is in line
# _Leakage rates_

Table 2 presents leakage rates by targeting indicator. The leakage rate when there is no targeting (46.50 in 1994, and 51.15 in 1997) corresponds exactly to the fraction of the population which is not in poverty. Table 2 shows that leakage rates would be minimized with geographic targeting by the infant mortality rate (according to the 1994 LSMS) or the FONCODES index (according to the 1997 LSMS). Bootstrapped standard errors (not reported, but available from the author upon request) suggest that geographic targeting with any of the indicators in question is a significant improvement on the no targeting scenario, while the differences in outcomes across indicators are not significant.

# _Concentration curves_

Initial simulations show that the concentration curves for the FONCODES index, the infant mortality rate, and the imputed poverty rate are so close to each other as to be virtually indistinguishable from each other on a graph. For the sake of clarity, I therefore graph the _difference_ between each of the concentration curves and the no targeting baseline case in Figures 1.1 and 1.2. (The "concentration curve" for the no targeting scenario is a straight, forty-five degree line: every individual receives the same transfer, so the cumulative fraction of the transfer equals the cumulative fraction of the population at every point). Figure 1.1 shows that this difference is positive throughout for all three curves: no matter where we take the cut-off between "poor" and "non-poor" to be, the poor receive a larger share of transfers when these are made on the basis of the infant mortality rate, the FONCODES index, or the measure of imputed poverty than when transfers are not targeted. Figures 1 .1 and 1.2 also show that no single concentration curve lies everywhere above all others, although the FONCODES index curve always transfers more resources to poor households than the imputed poverty measure. This is noteworthy given that much recent effort has gone into developing these kinds of measures, in Peru and elsewhere