rafmacalaba/data-use-mentions-extended huggingface dataset:
ness, fatigue, or reduced consciousness. Before the doctors’ consultation, enumerators recorded patients’ basic demographic details, symptoms, and information on any prior treatment/diagnosis.<sup>22</sup> After the consultation was complete, the enumerator recorded details of all prescribed and purchased medications, blood tests taken, and costs paid to the clinic. All told, we completed 2,055 interviews.

**Home Follow-Up Survey.** We randomly selected 1,735 patients for a more detailed follow-up survey conducted in the patient’s home the day after the clinic visit, of whom 1,495 (86 percent) were successfully interviewed.<sup>23</sup> The home survey collected information on changes in the illness, any treatments or tests obtained after the clinic visit, and recorded whether patients were taking the medications that were purchased at the clinic. During this survey, 1,126 patients (75 percent of interviewed patients/65 percent of patients targeted for the home survey) gave separate consent to take an RDT and obtained a valid test result.<sup>24</sup> Appendix Table B5 shows that there are no significant differences in the probabilities of being selected for home survey, taking the home survey, or having a valid home-based RDT by treatment arm.

**Administrative Voucher Data.** Finally, we use administrative data on voucher redemption to quantify how informing patients about vouchers impacted redemption rates.

# **3.3 Background Characteristics and Randomization Ver-**

Table 2 presents summary statistics on the study population and verifies randomization balance. The first column of the table displays means and standard deviations of baseline characteristics in the Control group (patients

> 22We interviewed the patient whenever s/he was aged 15 or older and well enough to take the interview; otherwise a guardian answered the survey.

> 23From November 14-17 2016 we randomly selected up to six patients per clinic per study day to interview at home. Between November 18 and
**Appendix 4.  Number of Respondents to WFP Survey by District, August 2015-July 2018**

<!-- Start of picture text --> (100,10000]<br>(50,100]<br>(10,50]<br>[0,10]<br>No data<br>Source: WFP mobile Vulnerability Analysis and Mapping Survey. Therre are only two districts with zero respondents (those with black<br>borders).<br><!-- End of picture text --> 35
Agency (JICA), the German Development Bank (KfW) and the United Kingdom’s Department for International Development (DFID) - similarly summarize their self-evaluations in a single ‘outcome’ rating. Precisely due to their widespread availability and ease of harmonization, the use of such project outcome ratings is prevalent in the aid effectiveness literature.

However, there are several grounds for doubting whether these outcome ratings capture either donor contribution or the likelihood of sustained improvements in development outcomes. First, they are an aggregation of several subratings and therefore mask variance in the contribution of individual components or interventions. Second, by assessing primarily whether a project has achieved its stated objectives, they may encourage project designers to limit their ambition to what can be easily, and sometimes already has been, achieved.<sup>7</sup> Third, they can reorient time horizons to short-term outputs, which are easier to measure within the project life-cycle, over longer-term efforts to resolve core problems (Andrews 2021).

# **3. Methods**

# _3.1. Research Questions_

We seek to advance the existing literature by considering four questions. First, can any general statements be made about the impact of development aid on sector outcomes? Second, does consideration of aggregate project outcome ratings within each sector mediate the terms of that relationship? In other words, do outcome ratings provide information on the relationship between aid and outcomes? Third, can the application of novel econometric and machine learning techniques better detect associations between project characteristics and development outcomes? And finally, what can such methods illuminate about the characteristics of development projects associated with positive sector outcomes?

# _3.2. Data_

Data on sector outcomes and country characteristics are obtained from the World Development Indicators (WDI, World Bank 2021), covering 1990 _−_ 2015. For official development assistance (ODA) flows, we used the AidData Core

> 7This may explain the inability of previous researchers to explain more than 30 percent of the variance in outcome ratings.
|0.006***|0.006***|0.002**|
||(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|
|**N (Obs.)**|9468|9334|9330|35404|35406|35404|

Source: Authors’ calculations based on TLMPS 2014, ELMPS 2018 Notes: *p<0.05; **p<0.01; ***p<0.001. Standard errors in parentheses. “Market” denotes an outcome of being reclassified from not 3-month market employed to added market employed (compared to either remaining non-employed or already being detected as 3-month market employed). “Extended” denotes an outcome of being reclassified from not 3-month extended work to added extended work (compared to either remaining non-working or already being detected as 3-month extended working). “Switch” refers to an outcome of being reclassified from 3-month subsistence work to added market employed (compared to any of remaining in 3-month subsistence work, remaining in 3-month market work, or not working).

18
even with a small number of clusters. This may be especially relevant in the context of the first experiment, which had fewer schools per treatment arm than the second experiment. Second, given the large array of hypotheses considered throughout the analysis, we also provide _p_ -values that are adjusted for multiple hypothesis testing across different families of outcomes (List et al., 2019).

# **3.1 Children’s Outcomes**

Table 1 and the first row of Table 2 display the impacts of the _Original_ modality on children’s outcomes, as measured by individual test scores collected two years after the introduction of the mentoring program in each experiment, respectively. For the first experiment, the outcome variables shown in Table 1 are based on administrative records of sixth graders in a national standardized test. For the second experiment, we collect our own measures of cognitive and socio-emotional skills (first to fourth columns of Table 2), as the national standardized test was terminated in 2014.<sup>7</sup> In spite of the differences in measurement of the outcome variable, the separate analyses of the two experiments show consistently inconclusive evidence regarding the effectiveness of the _Original_ modality of the mentoring intervention. Depending on the outcome, the effect of the program in the first experiment ranges from positive to negative and is not statistically different from zero. The effect size of the estimated treatment effect on the overall index for student achievement (column 4 of Table 1)—a Generalized Least Squares (GLS)-weighted average across the three subject tests that increases the power of the analysis (O’Brien, 1984)—is negative, small and imprecise.<sup>8</sup> Effect sizes are consistently positive and slightly more precise in the second experiment, although none of the estimated coefficients gets close to the conventional significance levels. The impact on the GLS-weighted overall index for student achievement across the two cognitive measures and the socio-emotional score is 0.12

> 7Another national standardized test
6 It is necessary to go beyond primary agriculture, however, when evaluating the consequences of reforms under WTO. This is because the WTO negotiations on agriculture involve potential liberalization of a wide range of processed agricultural products as well. In OECD countries domestic subsidies are not paid to processed agricultural products, but the extent of border protection to processing activities is substantial – even after netting out the effect on processors’ costs of having to pay higher than border prices for many primary agricultural product inputs.<sup>7</sup> According to the GTAP database, in 2001 that assistance amounted to $198 billion, which is greater than the estimated $136 billion in total support to primary agriculture for that year (final row of Table 1).<sup>8</sup> The remaining important element to consider is the market price support provided to the agricultural and food sectors of non-OECD countries. At $76 billion for primary agriculture plus $82 billion for food processing, this support is a substantial addition to the support through import barriers of $46 billion provided to OECD agriculture and $198 billion to OECD processed food (row 2 of Table 1). But there are almost no export subsidies in non-OECD countries (row 3 of Table 1), further increasing the prominence of market access.

In summary, the OECD and GTAP databases are very similar in their estimates of the extent of direct support to farmers in OECD member countries (columns 1 and 2 of Table 1); but the GTAP database also includes support via the food processing sector in those countries plus the support to both sets of activities in non-OECD countries. In total, the GTAP database suggests only 19 percent of the dollar value of the transfers to those producers from taxpayers and consumers is in the form of domestic support and only 6 percent comes via export subsidies (see final column of Table 1).<sup>9</sup>

> 7 Again this follows the effective protection concept mentioned above in footnote 5.
Policy Research Working Paper 0

# **Abstract**

The extent to which teachers and school directors are held to account may play a central role in determining education outcomes, particularly in developing and transition countries where institutional deficiencies can distort incentives. This paper investigates the relationship between an expanded set of school inputs, including proxies for the functionality of “top-down” and “bottomup” accountability systems, and education outputs in Albanian primary schools. The authors use data generated by an original survey of 180 nationally representative schools. The analysis shows a strong negative correlation between measures of _top-down_ accountability and students’ rates of grade repetition and failure in final examinations, and a strong positive correlation between measures of top-down accountability and students’ excellence in math. _Bottom-up_ accountability measures are correlated to various education outputs, although they tend lose statistical significance once parent characteristics, school resources and top-down accountability indicators are considered. An in-depth analysis of participatory accountability within the schools focuses on parents’ willingness to hold teachers to account. Here, the survey data are combined with data from lab-type experiments conducted with parents and teachers in the schools. In general, the survey data highlight problems of limited parental involvement and lack of information about participatory accountability structures. The experiments indicate that the lack of parental participation in the school accountability system is owing to information constraints and weak institutions that allow parent class representatives to be appointed by teachers rather than elected by parents.

This paper is a product of the Human Development Economics Unit, Europe and Central Asia Region. 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 tpackard@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
investments by foreign companies. Third, from a practical viewpoint, data are available on these three dimensions at the NUTS-3 level, but data are missing on some other dimensions. European official statistical sources provide information on a large set of variables for NUTS-1 and NUTS-2 regions, but only a reduced amount of information is available at the NUTS-3 level.

**Unfortunately, it was not possible to include data series on firm size.** We would like to estimate the role of micro (fewer than 10 employees), small (10 to 49 employees), medium (50 to 249 employees), and large (250 employees or more) firms in regional economic growth. Such analysis could help reveal the relative importance of initiatives on SMEs in lagging regions, versus initiatives to boost competitiveness of large firms as drivers of growth. Unfortunately, these data series – available from the _Regio_ database provided by Eurostat – present two key shortcomings. First, regional data are based on a too broad classification of firms with respect to their size, as data are available for zero-employment firms, firms with 1 to 9 employees, and firms with 10 or more employees. This represents a key problem, as small, medium, and large firms are grouped together in a unique size class, such that it is impossible to capture the role of SMEs versus large firms. Second, business demography data that are available only from the year 2008 – while our analysis starts in the year 2003 – do not cover nine EU Member States, and present severe missing values or inconsistent estimates over time for the remaining countries.<sup>12</sup> Since the lack of availability is not systematic, we would risk bias in the analysis if we included only the available data.

**We use data for the 2003 to 2017 period, from Eurostat, the Organisation for Economic Co-operation and Development (OECD), and** **_fDi Markets_ (Financial Times).** Given our empirical goal, and considering data
the entire period and 92% of the labor force, are captured for at least two years in the panel.

## **3.3 Policy Variables**

The four policies we consider - final goods tariffs, input tariffs, delicensing, and FDI reform - were discussed in Section 2. Our tariff data are based on the Government of India’s Customs Tariff Working Schedules and the Trade Analysis and Information System (TRAINS) database. Both sources provide rates for approximately 5,000 harmonized system (HS) product codes. Using the concordance of Debroy and Santhanam (1993), we match the product lines with 3-digit NIC-87 codes, and calculate average final goods tariff rates within each of approximately 140 industries.<sup>10</sup> We calculate input tariffs as described in Section 2.

To capture the effects of the delicensing reforms, we use data from Aghion et al. (2008) from 1985 to 1997, supplemented by information from Press Notes from the Ministry of Commerce & Industry from 1998 to 2004. The delicensing variable is a dummy that takes on a value of one if any products in a three-digit industry have been delicensed, zero otherwise. Our measure of FDI liberalization is also based on Press Notes from the Ministry of Commerce & Industry, and takes on a value of one if any products in a three-digit industry have been liberalized, zero otherwise. Table 1 shows changes in these policy variables over time.

# **4 Empirical Framework and Results**

## **4.1 Measuring Total Factor Productivity**

We measure TFP for firm _i_ in industry _j_ at time _t_ using a chain-linked, index number method suggested by Aw, Chen and Roberts (2001):

> 10Prior to 1986, tariff data were reported in Brussels Tariff Nomenclature (BTN) rather than HS codes. For these years, we first map BTN codes to HS codes using a concordance table from the TRAINS database.

13
To make the analysis clearer and in line with the descriptive evidence, we sum up bilateral trade values over supplying sectors in a goods (including also primary sectors) and a services aggregates. To allow for adjustments over time in trade flows (Piermartini and Yotov, 2016), we use data from six years in four-year intervals (1995, 1999, 2003, 2007, 2011, 2015). We report robustness checks by (i) leaving the dependent variable at the sector level (see Table A1 in the Appendix) and allowing for sector-specific multilateral resistances; and (ii) using the full 1995-2015 yearly panel of country pairs. For each specification and supplying sector, we report the estimates for the private market next to the ones for the government one to ease comparison. Coefficients in bold are significantly different (at the 10% level) across the two markets.

# **5.1 Gravity results**

Tables 1 and 2 report the PPML gravity estimates for goods and services, respectively. In columns (1) and (2), we use the ‘pooled’ specification – controlling for exporter-year and importer-year fixed effects (see eq (7)). The large and significant coefficient on the ‘same country’ dummy ( _SMCTY_ variable) gives a strong indication of home bias, especially in government procurement. All other trade cost variables (except for distance) are switched off for same-country pairs, so that the omitted category for the ‘same country’ variable includes country-pairs that have, for instance, no contiguous border, a different official language and are no EU members. For trade in goods (Table 1), the estimates in column (1) imply that, relative to the omitted category, government procurement from local suppliers is around 30 times higher. In private markets, local sales are 23 times higher than international sales. As expected, the border effect is much larger in services, and significantly higher in government procurement than in private markets (Table 2). The estimates
# **Dropout**

Table 5.5 and 5.6 show DiD results for dropout rates. **Table 5.5** focuses on overall dropout across grades; **Table 5.6** focuses on lower primary dropout rates. In both cases, we do not observe significant impacts in schools treated in either 2017 or 2018.

**Table 5.5. Impact of PqTR reduction on dropout rates**

||**2017-18**|**2018-19**|
|---|---|---|
|Control|0.031***|0.040***|
||(0.004)|(0.003)|
|Time|0.001|-0.003|
||(0.006)|(0.004)|
|Treatment|0.020***|0.003|
||(0.006)|(0.006)|
|DiD (Treatment and Time)|-0.006|0.005|
||(0.008)|(0.008)|
|Control (N)|118|292|
|Treatment(N)|379|134|

_Data Source: EMIS 2016-2019_ _Note: Robust standard errors in parentheses. *p<0.10, ** p<0.05, *** p<0.01_ _2017/18 compares dropout rates at end 2016/17 and end 2017/18. Treated schools are those where PqTR brought below 90 in 2017._ _2018/19 compares dropout rates at end 2017/18 and end 2018/19. Treated schools are those where PqTR brought below 90 in 2018._

**Table 5.6. Impact of PqTR reduction on dropout rates in lower primary**

||**2017-18**|**2018-19**|
|---|-
# **1. Introduction**

Measures of per capita income and consumption are among the most frequently cited indicators of economic development. They are widely used in assessments of living standards, economic growth, poverty, and inequality, both within and across countries. Despite their prominence, the two most common data sources for such measures – national accounts systems (NAS) and household surveys (HHS) – often have large gaps between them and offer differing portrayals of living standards and economic growth. For example, for Pakistan in 2015, national accounts data suggest that average household consumption expenditure per capita was $9.3 per day at 2011 PPP, while the household survey indicates it was just a bit more than half of that, $4.9 per day at 2011 PPP.<sup>1</sup> In Botswana, the two recent household surveys suggest that per capita consumption contracted at an annualized rate of -3.3 percent between 2009 and 2015, while the most closely aligned measure from the national accounts system, per capita household final consumption expenditures (HFCE), indicated a robust expansion of household consumption at an annualized rate of 3.7 percent over the same period, as did gross domestic product (GDP).<sup>2</sup> That national accounts data and survey data can lead to such diverging measures of the levels and changes in living standards is a recurring phenomenon across a wide range of countries and statistical systems. A frequently cited case is India, where large discrepancies in measures of household consumption expenditures across the national accounts and the National Sample Survey (NSS) have fueled a vigorous debate about the evolution of poverty and its relationship to economic growth (see for example Deaton and Kozel, 2005; Subramanian and Jayaraj, 2015; Sundaram and Tendulkar, 2003). The issue of diverging estimates from national accounts and household surveys is not limited to less wealthy countries. In the United States, per capita income from the two large national surveys, the
First round: 0.478;Latest round: 0.021<br>P-value of test that exporters differ across groups: First round: 0.003;Latest round: 0.363 P-value of test that exporters differ across groups: First round: 0.679;Latest round: 0.050 P-value of test that exporters differ across groups: First round: 0.983;Latest round: 0.017<br>Change in sales (%) Change in sales (%) Change in sales (%)<br>Change in sales (%) Change in sales (%) Change in sales (%)<br><!-- End of picture text --> _Notes:_ The sample includes 30,807 firms in panel a, 12,161 firms in panel b, 32,145 firms in panel c, 10,306 firms in panels d and e, and 12,161 firms in panel f, in 38 countries.

# **5.4.3 Country Characteristics**

Countries differ in their geographical location, their income - and hence their sophistication in trade and global value chains - but also in how they reacted to the health challenges posed by the COVID19 crisis. Evidence from BPS data supports three findings.<sup>17</sup> _First_ , the decline in sales was smaller and the recovery sharper for exporters in high–income countries relative to their counterparts in non-high–income countries. While exporters in non-high–income countries experienced a drop in sales of 39% initially and 26% later, exporters in high–income countries experienced smaller declines in sales of 26% and 14%, respectively. Qualitatively similar results are found for the probability of falling into arrears (Figure 12). These patterns are mirrored in the country’s specialization and

> 16See Appendix Figure C11 for results on the probability of falling in arrears.

> 17To compute the margins by country characteristics presented in this section, Equation (
minimize the long-term impacts of school closures.

This paper contributes to several strands of the literature. First, it adds to the growing research on the impact of school closures on learning outcomes (Agostinelli et al. 2022; Belot and Webbink 2010; Haelermans et al 2022), especially on the impact of duration on learning loss (Goldhaber et al 2022; Jack et al 2022). It also adds to the literature on the determinants of school closure duration (Kurmann and Lalé 2021; Nitsche and Hudde 2022). This paper uses unique data to estimate the precise impact per week of closures on learning loss across a variety of countries. From a human capital perspective, it makes sense that school closures would lead to learning loss since schooling is a productive endeavor (Becker 1994); therefore, extending the time away from in-person instruction should lead to a reduction in learning outcomes. Programs to support learning loss mitigation have been explored.

# **1. Data**

We supplement the robust data on learning losses compiled by Patrinos et al. (2023) with a few more recent studies (see Annex 1 and https://microdata.worldbank.org/index.php/catalog/5367). Most refer to primary schooling. The number of weeks of school closure is compiled from the same studies. Not all surveys are nationally representative. In the United States, the learning loss surveys are representative, but school closure duration varied state to state, and our figure represents an average. In Nepal, the data come from a study that only includes adolescent girls from one disadvantaged district. Besides school closure and duration, other possible correlates of learning loss are included. One of those controls is the pandemic itself. We include the COVID19 death rate per 100,000 population; the lockdown stringency index; and the vaccination rate.

2