rafmacalaba/data-use-mentions-extended huggingface dataset:
We further expand our database with recent rounds of PASEC (2014), SACMEQ (2013) as well as PIRLS (2016). The size of our database has a few ramifications. Since the methodology we use to link assessments hinges on overlap in countries which take both an RSAT and an ISAT, the larger the database, the more overlap, and thus all scores are more robust. In addition, we include the largest number of developing countries to date. These countries have the most potential to benefit from educational progress. Thus, a more expansive database enables the inclusion of developing countries as well as enhances the robustness of the methodology used to include them, making each update significant.

In addition to the size of the database, this is the most current database with data as recent as 2017. Given a series of recent global initiatives which focus on education quality highlighted in the 2018 World Development Report, there is significant demand for current, credible and globally comparable measures of learning. This database provides the largest, most comparable, current learning data. Moreover, in this paper we provide a foundation for systematic and continual updates on a periodic basis going forward, enabling tracking of learning outcomes progress and longitudinal analysis.

We introduce a series of methodological improvements. First, we include single years, rather than 5-year intervals. Second, we use all available data to create conversion factors between assessments. Third, we construct a fixed conversion factor for data since 2000. This enables us to deduce changes in scores over time as a function of learning outcomes progress rather than changing conversion factors. Fourth, we provide measures of uncertainty in the form of plausible bounds for our estimates. Fifth, all data are derived directly from test score data. In contrast to black-box and complex imputation, this enables methodological transparency. Moreover, it produces a clear policy lever: when countries perform better on RSATs and ISATs they can be certain the HLO will improve.

# 2. Data

Each assessment included in prior databases is documented in
seminal article in that we elect to focus on a binary classification of construction intensity per district (above or below the aggregated number of schools per 1,000 children planned for the whole country). Our analysis also focuses on the contrast between two cohorts for which primary school construction would have provided a different treatment dose. In the ECED data, the vast majority of mothers are born after 1957-1962 (the comparison cohort used in Duflo (2001)) because the sampling was based on being mothers to young children in 2013. Thus, our analysis contrasts women born between 1966 and 1970 with those born between 1971 and 1975. Since the timing of construction was variable within districts and older children are unlikely to benefit from newly constructed primary schools, the latter cohort (1971-1975) is expected to have accrued most of the benefits of increased access to elementary education generated by INPRES.

We start by estimating models that confirm the effect of planned school construction on the education of mothers interviewed in 2013. For the effect on the mother’s children, we focus on a reduced-form or intent-to-treat analysis because the impact of school construction over the second generation may result from multiple mechanisms. In Figure A1, we reproduce these findings using data from the 2010 Census. The additional data allows us to examine the parallel-trends hypotheses (at least in the dimension of maternal schooling) which sustain our empirical design but, due to aforementioned child-mother age restrictions in our working sample, could not be directly performed.<sup>12</sup> Our full econometric specification based on this reasoning and focused on maternal educational outcomes is as follows: where _Intensed_ is an indicator function for districts where INPRES construction was more intensive than the national level, _Y oungc_ flags mothers in 1971 to 1975 birth-cohorts. Fixed-effects for district of birth ( _μd_ ) and year-of-birth ( _φc_ ) guarantee that the parameter _τ_ is interpreted as a difference-in-differences coefficient. We complement the model with interactions between other

> 12We also rely on the fact that studies of INPRES utilizing alternative data sources
.jad.2010.07.027.

Roder-DeWan, S., Gage, A. D., Hirschhorn, L. R., Twum-Danso, N. A., Liljestrand, J., AsanteShongwe, K., Rodríguez, V., Yahya, T., & Kruk, M. E. (2019). Expectations of healthcare quality: A cross-sectional study of internet users in 12 low-and middle-income countries. PLoS Medicine, 16(8), e1002879. https://doi.org/10.1371/journal.pmed.1002879 Rosenbaum, Paul R. 1987. ‘‘Model-Based Direct Adjustment.’’ Journal of the American Statistical Association 82:387-94.

Sargent RH, Laurie S, Weakland LF, Lavery JV, Salmon DA, Orenstein WA, Breiman RF (2022). Use of Random Domain Intercept Technology to Track COVID-19 Vaccination Rates in Real Time Across the United States: Survey Study. J Med Internet Res 2022;24(7):e37920. https://www.jmir.org/2022/7/e37920/ Scherpenzeel, A. C., and J. G. Bethlehem (2011), “How Representative Are Online Panels? Problems of Coverage and Selection and Possible Solutions,” in Social and Behavioral Research and the Internet: Advances in Applied Methods and Research Strategies, eds.M. Das, P. Ester, and L. Kaczmirek, pp. 105–132, New York: Routledge.

Seeman, N., Tang, S., Brown, A. D., & Ing, A. (2016). World survey of mental illness stigma. Journal of affective disorders, 190, 115-121.

Schonlau M, Couper MP. Options for Conducting Web Surveys. Statist Sci. 2017;32(2):27992. doi: 10.1214/16-STS597.

Schonlau, Matthias, Kinga Zapert, Lisa Payne Simon, Katherine Sanstad, Sue Marcus, John Adams, Mark Spranca,
low, and that this was the case not only for newer Rohingya displaced, but for the older cohort of displaced, as well. These figures could still to some extent reflect the displaced population’s mobility, as well as Rohingya’s unwillingness to reveal their identities to surveyors. However, the design of the validation listing questionnaire sought to minimize this effect by asking a series of questions that related to how long the household that been resident in the host community, rather than direct questions on respondents’ selfreported identity as Rohingya. Based on the validation findings, the final decision taken was to use administrative population data available from IOM NPM.

This pattern of refugee prevalence supported having one stratum for the Rohingya displaced living in camps. The sampling strategy for the CBPS therefore focused on generating representative estimates for the camp based Rohingya population in Cox’s Bazar district.<sup>15</sup>

# _Stratification of the host population in Cox’s Bazar district_

For hosts, the sampling strategy was designed to account for the differential implications of a camp-based concentration of close to a million Rohingya displaced for different areas of Cox’s Bazar. As in other displacement settings, the welfare among members of the host population is likely to vary with distance to camps. Hosts living closer to camps may be more likely than those living farther away from camps to compete with the Rohingya in labor and goods markets, as well as markets for natural resources like land and fuelwood. On the other hand, host households closer to camps may gain from camp-based economic opportunities,

> 15 While the original sampling strategy was designed to be representative of all camp-based Rohingya displaced, campsites with older Rohingya displaced refused to participate in the listing due to other political sensitivities. This refusal was maintained despite many attempts. Since the older Rohingya displaced were not a separate stratum, a decision was made to drop these households from the survey.

9
) showed that measured wealth inequality is higher when questions about individual asset ownership assets are asked to each household member individually, rather than to a most knowledgeable household member. This research suggests that whether data was collected from each member or from one member in a household survey will matter when measuring the top tail of the wealth distribution.

> 4 The South African National Income Dynamics Study is one example of a survey which attempted to interview each household member separately as well as obtaining household information from the oldest woman in the household. A shorter proxy respondent questionnaire was completed if the individual could not be interviewed.

8
Rigorous studies on place-based policies in developing countries are much scarcer. Interestingly, their findings tend to support the economic gains of these interventions. This is partly because, by research design, these studies try to overcome the two issues affecting the evaluation outcomes. They either investigate targeted areas that are believed to have the potential to spearhead growth or examine policies that are generous in nature. As a case in point, Lu, Wang and Zhu (2019) look at special economic zones (SEZs) in China, which on average, are areas with geographic and socioeconomic advantages. The interventions lasted decades and were also overarching, including infrastructure investment and institutional reforms. Many zones received subsidies from the central government as well. They find zones have a positive effect locally—on the location of firms, investment, employment, productivity and wages, and create a net benefit nationally.<sup>1</sup> In the case of India, Chaurey (2017) studies the 2003 central tax incentives targeting two states, Himachal Pradesh and Uttarakhand, and Shenoy (2018) investigates a broader set of centrally sponsored interventions targeting Uttarakhand, including both fiscal incentives and infrastructure investment, and over the same period. Himachal Pradesh and Uttarakhand were among the smaller, poorer and less developed states in India. However, these interventions lasted for a decade and were among one of the world’s most generous place-based policies in scale. Chaurey (2017) shows that these tax incentives led to large increases in the number of firms, employment, investment and output in the manufacturing industries in the targeted states relative to other states. Shenoy (2018) finds that nighttime light intensity rose sharply in the targeted state relative to the neighboring state, implying about 28 percent increase in output.

While both studies confirm the effectiveness of the interventions at the local level, they are limited by their respective data and corresponding research designs. Relying on the Annual Survey of Industries, the main results of Chaurey (2017) are based on formal companies of
# **3. Design and Methods**

## **3.1 Sampling**

### **3.1.1 Full Sample**

We worked in four districts (Bombali, Tonkolili, Bo, and Kenema) that bisect Sierra Leone from north to south (see Figure A.1(a)). These districts include 319 government-run clinics (PHUs) that provide primary and maternal care. We sampled 254 of these clinics, such that all sampled clinics were separated by at least 3 kilometers to minimize spillovers. As a result, the average distance to the next nearest clinic is 10 kilometers. Moreover, in Sierra Leone, residents are “assigned” to clinics (i.e., told by government what clinics to use), which discourages use of more distant clinics. At baseline, the average clinic in our sample had just over 2 staff members present, reported being open 6 days per week, and saw roughly 450 outpatients in the month prior to the survey. Over 80 percent of clinics had walls and roofs in good condition, accessed piped or protected water, and had stocks of basic medications (e.g., oral re-hydration salts and antibiotics); yet, only 10 percent had functional electrical lighting.

We then randomly sample two communities from the catchment area of each clinic (defined as a 3.2 kilometer buffer around the clinic). This generates a sample of 508 communities. At baseline, we randomly sampled 5 households in each of these communities for an extensive household survey (2,540 households). We also randomly sampled an additional 15 individuals in each village and administered a shorter userfeedback survey focused on recent health episodes, service provision, and satisfaction. At endline, we re-surveyed the 5 households that took the baseline household survey. We also randomly selected 5 of the 15 individuals who took the user-feedback survey at baseline and administered the endline household survey, which was revised to incorporate modules from the user-feedback survey. This
. Dupriez, and N. Troubat. 2014. “Assessment of the reliability and relevance of the food data collected in national household consumption and expenditure surveys.” _International Household Survey Network Working Paper 8_ .

- St. Jeor, S. T., and P. J. Stumbo. 1999. “Energy needs and weight maintenance in controlled feeding studies.” _Well-Controlled Diet Studies in Humans, A Practical Guide to Design and Management._ .

26
monthly<br>average prices of gasoline and diesel in 18 cities on its Web site.|<br> <br> <br>sector to boost capacity and improve<br>product quality over the next several years<br>for self-sufficiency. Revamping of its three<br>main refineries for modernization through<br>the end of 2015 has led Russia and<br>Kazakhstan to sign a crude-for-fuel<br>agreement in 2012, whereby Russia<br>provides tariff-free petroleum products to<br>Kazakhstan and Kazakhstan supplies crude<br>oil. Disagreement over the formula to<br>calculate the amount of crude oil led<br>Kazakhstan in Jan 2013 to consider China<br>for refined products through a tolling<br>arrangement.<br>**Shortages**: Shortages in Jul–Aug 2009 in<br>southern regions prompted the anti-<br>monopoly agency to accuse oil companies<br>of creating artificial shortages to push up<br>prices and launch an investigation. The<br>agency began investigating into 44<br>companies in July 2011, stating that there<br>was no reason for price increases. Fuel<br>shortages began in Apr 2011 caused by a<br>price freeze imposed in Dec 2010, and<br>continued into the summer as wholesale<br>prices exceeded retail prices. Shortages<br>continued to be reported in Oct 2011.<br>Lower domestic prices than those in Russia<br>also discourage fuel imports from Russia.<br>Shortages of high-octane gasoline, one-<br>third of which is imported from Russia, are<br>common. Southern Kazakhstan experienced<br>severe fuel shortages in Nov 2011. Fuel<br>shortages were again reported in Mar 2012.|
|Kenya<br>√|<br>**Pricing policy**: Kenya deregulated its oil industry in 1994. After
As a robustness test, a more parsimonious model is also estimated, in which ActiveAid _ikt_ and CompletedAid _ikt_ are collapsed together into a single treatment variable that equals 1 in the year of commitment of the first project and in all subsequent years. The estimates of the coefficient on the interaction of interest (not reported) are again small and not significant. To sum up, the analysis in this section does not find any evidence of development outcomes being higher in cells with missions than in cells without missions, either after or during the implementation of aid projects.

There are a few caveats in this analysis. First, the exercise does not estimate a precise “zero effect,” so it is not possible to conclude definitively that aid does not at all work better in mission areas. Second, using measures of actual development as outcome variables (instead of project ratings) has some disadvantages: although _access_ to piped water and to electricity may be direct products of specific aid projects, the outcomes themselves measure ownership of private goods. The time span of the analysis may not be long enough for the effects of development aid to materialize into private consumption or investment. Furthermore, proxies based on DHS data may not capture the dimensions of aid that are most affected by the projects. Finally, note that if the “true” differences in aid success between cells with and without missions are small in magnitude, measurement error in several variables could result in too much attenuation bias to be able to estimate them.

# **3.3 Discussion**

The analyses above are derived from a corollary of a potential mechanism, which is that aid is allocated to mission areas because it is thought to be more effective there. However, the results from two different empirical exercises do not lend support to this hypothesis. This conclusion rests on the assumption that the variables considered are actually able to measure effectiveness, as well as on a number of assumptions about the validity of the test. There is also an implicit assumption that the allocating
, Europe (0.81), and South America (0.65). Sub-Saharan Africa realizes only 20 percent of potential production, offering large potential for increasing yields. If it were to attain 80 percent of potential yield, a level usually considered economic, it could quadruple maize output, equivalent to expanding area by of 90 million ha – more than the area suitable for maize expansion close to infrastructure globally – at current yields.

Second, the non-forested non-cultivated area suitable for rainfed cultivation of at least one of the crops considered here amounts to 446, 306, or 198 million ha for population density cut-offs of 25, 10, and 5 persons per km<sup>2</sup> (table 6).

In all scenarios, non-cultivated area suitable for rainfed cultivation is highest in Africa (202, 128, and 68 million ha corresponding to 45, 42, and 34 percent of the total, respectively), followed by Latin America. Within Africa and Latin America, available land is concentrated in a few countries and not always close

> 5 To keep things tractable, we use a 5’ x 5’ resolution that divides the world into 2.2 million grid cells. Computation of output in each cell is based on more disaggregated data. Yields are for 2008. Suitable area is not currently used for crop production, could attain at least 60 percent of the potential yield for this crop, is located in an area with population density less than 10 persons/km<sup>2</sup> , and at 2005 prices will not yield higher gross revenues with any other of the five crops considered here (maize, soybean, sugarcane, oil palm, wheat). Close to infrastructure means a travel distance of less than six hours to the next market based on available transportation.

17
investment accrue to a greater degree for women than for men (DHS sample). Our results also suggest that younger adults and those in middle age benefit more from infrastructure investments.

By 2035 Africa’s working age population is expected to grow by 450 million people, an increase of close to 70 percent. Forecasts indicate that the number of people of working age will grow faster than the number of jobs available for them (World Economic Forum, World Bank, and African Development Bank, 2017). Thus, job creation is a pressing priority for African countries. Our findings suggest that investments in roads and electricity are mutually beneficial drivers of employment and structural transformation in Africa. However, the benefits of infrastructure investment could differ significantly across countries, between rural and urban areas, and across gender and age groups.

This paper adds to a growing body of research examining the impact of various sorts of infrastructure investments on development outcomes. Recent studies have shown that highways and railroads can have substantial impacts on the allocation of economic activity, land use, local economic specialization, and migration (Fajgelbaum and Redding, 2014; Cosar and Fajgelbaum, 2016; Dave Donaldson, 2016; Blankespoor et al., 2017; Berg et al., 2018; Donaldson, 2018; Morten and Oliveira, 2018; Fajgelbaum and Schaal, 2020; Jedwab and Storeygard, 2021). We use the same road panel as used by Berg et al. (2018) and Jedwab and Storeygard (2021). Berg et al. (2018) find a modest impact of improved market accessibility on local cropland expansion, but suggestive evidence of a positive association between improved market accessibility and local GDP growth which could reflect the stimulation of non-agricultural activities. Jedwab and Storeygard (2021) estimates show a positive 30-year elasticity of city population with respect to market access. These are average effects. The importance of heterogeneous effects have been examined by recent papers, examined in the survey of the literature by Berg et al. (2017), but most
# **Annex 1: Measurement of ‘Surges’ and ‘Slumps’**

18 Global sector‐level trend growth rates that were estimated using the INDSTAT 4 2009 Revision 2 and 19 INDSTAT 4 2012 Revision 3 data sets from the United Nations Industrial Development Organization (UNIDO) are used. The two UNIDO data sets were combined to create a database representing 84 sectors 20 (4‐digit NACE) from 134 countries for the time‐period 1993 to 2009. Outlier observations – identified as growth greater than 3 standard deviations above or below the mean for each sector in each country– were removed. This results in dropping about 45 percent of the observations in the data set.

In order to identify surges and slumps, first, a trend output growth rate for each sector is estimated. In order to account for life product cycle effects, only countries in the same GDP per capita quartile as the Russian Federation are included, and an average growth trend of each 4‐digit NACE sector in this group 21 calculated by OLS regression of log output on time. To increase the robustness of the results sectors with fewer than 60 observations from the trend regressions are dropped.

Shocks are defined in terms of “extreme” deviations from this global trend, relative to the distribution of the deviations during 1993‐2009. Since the distribution of deviations could vary by sector, the next step is to calculate the sector‐wise distribution of deviations from trend in the UNIDO data. For each country, sector and year in UNIDO, the percent deviation of actual output from trend output is calculated. For each sector, the 75<sup>th</sup> and 25<sup>th</sup> percentiles of the distribution of this deviation (across countries and during 1993‐ 2009) are calculated. Let D<sup>75</sup> s and D<sup>25</sup> s be the 75<sup>th</sup> and 25<sup>
41.8|15.9|40.2|15.8|
|Formal|42.1|22.6|43.3|20.9|37.0|20.4|
|Informal|58.6|25.6|45.9|21.4|48.0|20.1|

*Non-conditional transition estimates are drawn from the transition matrix; Conditional transition estimates are drawn from the propensity matrix

Table 5: Transitions to self-employment by age and income, conditional on moving, in <u>percent.</u>

|**Arg**|**entina**|**OLF**|**Studying**|**Unemployed**|**Formal**|**Informal**|
|---|---|---|---|---|---|---|
|15-18|Poor|5.6|5.6|9.0|3.7|16.3|
|<br>|Nonpoor|3.8|5.4|5.3|5.3|10.3|
|19-24|Poor|8.8|10.3|13.0|7.4|21.8|
|<br>|Nonpoor|8.0|6.9|3.8|7.2|11.5|
|**B**|**razil**|**OLF**|**Studying**|**Unemployed**|**Formal**|**Informal**|
|15-18|Poor|14.2|15.2|7.9|2.5|12.3|
|<br>|Nonpoor|10.4|11.5|6.8|2.3|8.