rafmacalaba/data-use-mentions-extended huggingface dataset:
# **IV Empirical Strategy: Intent to Treat**

We estimate an Intent to Treat (ITT) model by comparing RAMV and non-RAMV miand thus the individual for the PEP. To isolate se- grants exploit eligibility requirement lection into the RAMV, we control for a broad set of pre-migration and pre-program covariates, including some that potentially drove selection into the RAMV. We estimate the following model: (1) _Yijkt_ = _α_ 0 + _α_ 11[ _RAMVijk_ = 1] + _θ_<sup>_′_</sup> _Xijk × ζt_ + _φjk_ + _δk_ + _ζt_ + _εijkt_ where _Yijkt_ is the outcome of interest for household _i_ located in city _j_ of state _k_ in year _t_ , 1[ _RAMVijk_ = 1] is an indicator variable for migrants who were registered in the RAMV census, _Xijk_ is a vector of household controls that we interact with wave fixed effects ( _ζt_ ), which captures the migrant’s economic and social characteristics before migration and the program roll-out. Appendix Table A.5 reports the descriptive statistics for the control variables. We control for the time of residence in Colombia to attenuate the assimilation bias as well as for city and state fixed effects ( _φjk_ , _δk_ and wave fixed effect _ζt_ ). We also leverage the time variation across both waves on the outcomes of interest. Even though we do not have data on pre-Covid-19 baseline conditions for the outcomes, we exploit the variation of the outcome variables waves RAMV through by interacting eligibility with the wave fixed effect. We report our main results with and without the interaction. Standard errors are clustered at the family level.

The outcomes of interests focus on four dimensions that measure the resilience of forced migrants to cope with the pandemic: (i) capacity to adhere to NPIs, (ii) access to medical services when required, (iii) protection and detection against COVID-19, and (iv) resilience to the economic impacts of the pandemic (see Appendix A for the description of each index). The ITT provides
Policy Research Working Paper 8653

# **Abstract**

What goes on inside the classroom is central to student learning. Despite its importance, low and middle-income countries rarely measure teaching practices, in part due to a lack of access to adequate classroom observation tools and the high transaction costs associated with administering them. _Teach_ , a new, open-source classroom observation tool for primary classrooms, was developed to capture the quantity and quality of teaching practices in these settings with a simple, easy-to-administer tool. This paper validates the use of _Teach_ scores for system diagnostics by providing four types of evidence. First, it provides evidence that the practices included in the tool have a clear conceptual underpinning. Second, almost 90 percent of local observers in Mozambique, Pakistan, the Philippines, and Uruguay were highly accurate using _Teach_ after a four-day training. Third, using data from 845 classrooms in Pakistan, the paper shows that Teach scores are internally consistent, present moderate to high inter-rater reliability in the field (.75 intraclass correlation coefficient), and provide substantial information that allows to differentiate teachers, even those with similar but not equal scores. Finally, teachers who display effective practices, as measured by Teach, are associated with students who achieve higher learning outcomes.

This paper is a product of the Education. 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/research. The authors may be contacted at molina@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
Moldova BOOST data.

3 This section was prepared by Irina Capita.

> 4 The analysis relies on two BOOST data sets for Moldova – the core BOOST database available on the Ministry’s of Finance website, available at <u>https://mf.gov.md/en/buget/transparen%C8%9Ba-bugetar%C4%83/baza-de-date-a-cheltuielilor-publice, and the Health</u> Moldova BOOST data set developed as part of the 2018 Health Public Expenditure Review for Moldova.

11
In the _prosperity_ scenario, even though prices increase by 2% in the high impact scenario and agriculture revenue decreases by 1.17%, the impact on poverty is nearly inexistent, because households are less vulnerable to food price increases and there are fewer unskilled farmers.

# Natural disasters

To account for climate change impacts on natural disasters by 2030, we assume that the fraction of the population that will be annually affected<sup>3</sup> by a disaster (about 10% of the population in Vietnam today) increases by 40 percent in the low impact case and by 120 percent in the high impact case, which is in the range reported by (Bouwer, 2013; IPCC, 2014, 2012) for the expected rise in economic losses. Ultimately, these numbers will depend on the effectiveness and timeliness of adaptation to new climate conditions.

In the low‐impact case, we assume that affected people lose 20 percent of their annual income if they are poor and 10 percent if they are non‐poor; in the high‐impact case, the losses would be 30 percent for the poor and 15 percent for the non‐poor.<sup>4</sup> These numbers are in line with post‐disaster household surveys, even though much higher values are often observed (Patankar 2015; Patankar and Patwardhan 2014; Noy and Patel 2014; Carter et al. 2007). We also assume that natural disasters affect income only during one year, which is a conservative estimate that is valid for small disasters, but not for large scale events like Typhoon Yolanda in the Philippines or Hurricane Katrina in the United States.<sup>5</sup> Our results show that for natural disasters, in the high impact scenario, the number of poor people rises by 130,000 people in the _poverty_ scenario, and the number of people below 4usd per day rises by 83,000 people. Here again, disasters have nearly no impact on poverty in the _prosperity_ scenario
share of individuals in the adult category. As expected, there is very limited variation of household size and composition across rounds within one year. The lactating women outnumbered pregnant women, and these statuses vary across rounds.

**Household landholding, expenditure, and poverty** : Household landholding is a time invariant variable for which information was collected in the first round. The share of landless households is lower in IFPRI than in HES samples. The distribution of landholding is also less unequal in IFPRI sample compared with HES sample with higher share of households with 1-7.5 acres in IFPRI sample. The mean per capita landholding in IFPRI sample is 0.23 acre (which varies slightly across rounds due to limited variation in household size).

Both samples are similar in terms of mean per capita monthly expenditure, which varies significantly across rounds (p-value of t-tests are not reported) in IFPRI sample with the lowest value observed in round 2 and highest value in round 3.

The absolute and hard core poverty lines based on direct calorie intake (DCI) method are 2122 kcal and 1805 kcal per person per day, based on which the poverty incidence is higher in IFPRI compared to HES sample. HES regional upper and lower poverty lines based on cost of

> 14A comprehensive set of village prices were initially included. Subsequently, I have included the food prices that appear to be significant most of the times for the set of nutrients analysed in this paper.

16
and food prices.</mark> _American Journal of Agricultural Economics_ .

- <mark>Donaldson, D., and Storeygard, A. (2016). The view from above: Applications of satellite data in economics.</mark> _<mark>J</mark> ournal of Economic Perspectives_ , _30_ <mark>(4), 171-98.</mark>

- <mark>Dong, E., Du, H., & Gardner, L. (2020). An interactive web-based dashboard to track COVID-19 in real time.</mark> _<mark>The Lancet Infectious Diseases</mark>_ <mark>,</mark> _<mark>20</mark>_ <mark>(5), 533-534.</mark>

- <mark>Elvidge, C. D., Baugh, K., Zhizhin, M., Hsu, F. C., & Ghosh, T. (2017). VIIRS night-time lights.</mark> _<mark>I</mark> nternational Journal of Remote Sensing_ , _38_ <mark>(21), 5860-5879.</mark>

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# VI. The impact of weather, prices and peace on non-agricultural income and welfare

In light of the importance of prices and weather in driving crop income growth and to determine how well households are able to compensate these changes through the diversification of income, the impact of prices and weather on other sources of income is explored further in this section. We also examine whether positive trends in prices, weather and peace contributed to household consumption growth, and also the degree to which consumption growth is vulnerable to price falls or rainfall shortfalls, using the fixed effects specification set out in equation (2). Table 9 presents the results. Column 1 reports the results for crop income that were discussed in section IV. Columns 2 to 5 detail results for livestock income, agricultural wage income, non-agricultural income and non-farm selfemployment income. Column 6 examines the impact on household consumption using consumption data for 2006 and 2010 but not 2011 and 2012 given concerns over the consumption data collected in later years. Table 10 presents results for the bottom 40 percent.

Rainfall shocks do not impact income from livestock; perhaps because for livestock the rainfall over multiple seasons is important. However, wage employment and self-employment out of agriculture are inversely and significantly correlated with rainfall. The results suggest that diversification of productive activities can be an important risk hedging strategy for households in Uganda, particularly the poorest. If agricultural income is affected by climate shocks, households can offset this with increased non-farm income. It is not clear whether household labor is pulled into own-farm agricultural production as a result of the increased demand for agricultural labor when the rainfall is good or whether household labor is pushed out of agriculture as a result of a desperate need to smooth consumption when rainfall is bad.

Although some of the weather shocks can be insured through diversification, households are not able to fully insure their consumption from the impact of weather. A decrease in rainfall of 10 percent results in decline of 4.8 percent in per capita consumption (4.1 percent when considering households in
7</sup> Facility-based surveys were conducted in 108 rural health centers in the seven PBF district

> 4 Spitamen district was selected as a pre-pilot district. The remaining treated districts are Mastcho, J. Rasulov and Devashtich in Sughd region and Yavan, Farkhor, J. Balkhi and Kubodiyon in Khatlon region.

> 5 The control districts in Sughd region are Asht and Konibodom. The selected districts in Khatlon region are Temurmalik, Vakhsh, Pyanj, Jilikul, A. Jomi, Khuroson and Kumsangir.

> 6 We could not identify other interventions that were introduced in the control districts in the duration of the study through other sources.

> 7 The facility-based survey was conducted in November to December 2014 prior to the launch of the PBF program. The household survey was conducted from March to July 2015 because of unforeseen delays related to procurement of tools for anthropometric measures and obtaining of administrative data needed for the sample framework. Respondents reported about care received in the preceding two years, so we expect the timing to have negligible

7
As is well known, in the linear regression model with no individual heterogeneity, if the measurement errors are serially uncorrelated, the OLS estimate of β1 will be biased towards zero, leading one to conclude that there is less persistence in the income process than is truly the case. The Arellano-Bond instrumental variable approach will also be inconsistent with measurement error except in special cases. In the linear model, if there is no autocorrelation in the measurement error, using Yi,t−3 in place of Yi,t−2 as an instrument will give consistency.<sup>6</sup> However, in the quadratic and cubic cases earlier lags will still result in inconsistency, and even in the linear case, autocorrelation in the measurement error will rule out the use of earlier lags as instruments. Data from validation studies on income in the U.S. does indeed suggest positive autocorrelation in the measurement error (Bound and Krueger, 1991). As a result, in the presence of measurement error, these panel data estimates will be inconsistent and therefore not enable one to determine whether there are non-convexities in the income mapping or to detect the presence of poverty traps.

# 2.3 Estimation of non-linear income dynamics with pseudopanels

We propose using pseudo-panel methods to consistently estimate the income mapping. A pseudo-panel tracks cohorts of individuals over repeated cross-

> 6 To see this, note that the error term in the differenced equation contains ∆εi,t−β1∆εi,t−1. As a result, Yi,t−2 will be correlated with the error term through εi,t−2. If the εi,t are serially uncorrelated, then E (Yi,t−3εi,t−2) = 0 and Yi,t−3 will serve as an instrument.

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|Industrialization level|1|2|Social advancement|1|3|
|Infrastructure|1|2|_Urban Management_|2|5|
|_Competitive capacity_|1|2|Efficiency|3|13|
|Innovative capacity|1|2|Place promotion|1|6|
|Learning capacity|2|1|Regional spillover|9|5|
|Resource productivity|1|2|_Sustainable development_|9|6|
|Informationalization|1|2|Ecology|47|6|
|Global linkages|1|2|Environmental quality|29|17|
||||Harmony|4|7|
||||Influence|3|13|
|General ranks|Shanghai||Beijing|||
|Growth potential|2||3|||
|General capacity|1||2|||
|Real capacity*|1||2|||

_Note_ : * composition of up two _Source_ : based on _China Urban Development Report 2001-2002_<sup>_4_</sup> .

4 This research was sponsored by the China Mayor Association, also based on indexes in statistic yearbooks, but with more detailed subentries. The indicate of the general capacity of a city is described from 5 perspectives, i.e. fundamental strength, competitive capacity, social welfare, urban management, and sustainable development, while each perspective is a combination of a set of indexes, for example, the “fundamental strength” was measured by a function with 4 variables, i.e. resources, size of economy, industrialization level, and infrastructure, which can be further calculated with the data in official statistic yearbooks.

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0.11|0.9|0.24|0.8|1.4|
|Mobile phone|subscribers/100 people|12|38.2|21|24.4|33.1|

_Source:_ Data for landline subscribers is from 2005 taken from the Central Bureau of Statistics South Sudan; Mobile phone subscriptions based on World Bank staff estimates for 2011; Internet users is for 2011 taken from Infoasaid (2011). _Note:_ ICT = information and <u>communication technology; GSM = global system for mobile communications.</u> Consumers pay rather high prices for the ICT services that they have access to. Access to international ICT networks is via microwave links to satellite connections in neighboring countries. Gemtel has a microwave link to Uganda from where it connects to the fiber network of Uganda Telecom. International bandwidth is very limited in South Sudan and, as a result, prices are very high. Zain charges about 250 SDG for the modem and 50 SDG per month for a 2 GB connection. Vivacell and MTN also offer Internet services at roughly similar rates. VSATs<sup>6</sup> are far more expensive, costing anywhere between $500 and $4,000 a month depending on specifications.

The quality of existing service is also rather poor. Mobile networks are still embryonic in nature. As a result, mobile phone users often have multiple SIM cards to combat patchy services and poor interconnections. Internet connectivity is also rather poor because modems link to the Internet via the mobile phone network, but connection speeds remain slow (Infoasaid 2011).

Institutional obstacles imposed by the governments of Sudan and South Sudan have stymied the growth of the telecommunications market in the past. Despite a demand for fixed-line telephones, licensing arrangements precluded growth in landline connections. In 2005 total capacity of the telephone exchange was almost 9,400 customers and registered customers were as many as 8,856. The neglect of

> 6 Very small aperture terminal.

36
age profiles of different cohorts are very similar. We estimate equation (1) for three different subperiods (1990, 1995/1996, and 2000/2001; 1995/1996, 2000/2001, and 2007; 2000/2001, 2007 and 2011) and compare the estimated age profiles with those that emerge from using all 5 surveys covering 20 years. As mentioned above, the procedure to model year effects is problematic when the number of cross‐sections is too small. In this case, since we have only 3 cross‐sections for the 10‐year panels, we model the year effects using simple year dummy variables and we introduce age and cohort in a linear way.

Figure 6 shows the estimated marginal effects of age and cohort for the four subsamples of Spain and Turkey, as well as their 95 percent confidence intervals. The effects have the same sign across sub‐periods for each country, and their confidence intervals overlap. Figure 7 shows the estimated effects of age and age squared, even though the effects are slightly different across years, their confidence intervals overlap. Unfortunately, in most cases the effects are not statistically different from zero but, as mentioned at the beginning of this section, the goal of this robustness check was to test that the effects do not dramatically differ by using a 10‐year pseudo panel instead of a longer time span. The results suggest that the assumption that the age profiles of different cohorts are roughly similar seem to hold in this data set.

# **6. Conclusions**

Using household surveys for 24 countries between 2002 and 2012, we find that in most economies, older individuals are more averse toward immigrants because of a cohort or generational effect, not because they become more critical toward open immigration policies over the life‐cycle. Our preferred set of results in Figure 4 shows that in most countries pro‐ 19
|20|

_Source:_ AICD Road Sector Database on 40 Sub-Saharan African countries.

[1] Total network includes the classified and estimates of unclassified and urban networks.

[2] Main network for most countries is defined as result of adding the primary and secondary networks.

[3] Rural network is generally defined as the tertiary network and does not include the unclassified roads.

[4] Source: World Bank—IFC Enterprise Surveys on 32 Sub-Saharan Africa countries. GIS = geographic information system; AADT = average annual daily traffic. — = Not available.

**Figure 11. The condition of Angola’s main road network benchmarked against others in southern Africa**

<!-- Start of picture text --> South Africa<br>Mauritius<br>Swaziland<br>Malawi<br>Namibia<br>Botswana<br>Mozambique<br>Madagascar<br>Zimbabwe<br>Zambia<br>Angola<br>Lesotho<br>- 0.25 0.50 0.75 1.00<br>Percentage Road Network Length<br>Good Fair Poor<br><!-- End of picture text --> _Source:_ AICD Road Sector Database on southern Sub-Saharan African countries.

20
a technology, such as 2G, 3G or 4G, by intersecting the estimated site locations with the coverage map polygons from the global Mobile Coverage Explorer (Collins Bartholomew, 2019).

To ensure the analysis is logical, certain areas need to be excluded to avoid overestimation. Firstly, all statistical areas with a name identifier containing ‘lake’ are excluded, to prevent the model overbuilding infrastructure on large water bodies. Secondly, for the vast Saharan desert (covering over 9 million km<sup>2</sup> ), existing statistical boundary products often diverge in their zoning of these regions, with some statistical areas having diameters over 1 million kilometers. Therefore, for the northeastern part of Mali, where statistical boundaries differ considerably in size from other regions being assessed, nine statistical areas are excluded, constituting only 0.4% of the modeled population. If these areas are not excluded, terrestrial investment costs are overestimated. Other analyses exclude the hardestto-reach 10% to 20% of the population, assuming satellite will be the only option to connect in these areas (International Telecommunication Union, 2020), so despite these exclusions this analysis still assesses universal broadband reaching 99.6% of the total population.

To estimate baseline capacity both current and future spectrum bands are used for the assessment period. Average downlink spectrum portfolios for a hypothetical MNO are identified for Côte d’Ivoire (3G: 15MHz@2100MHz and 4G: 10MHz@ 800MHz), Mali (3G: 10MHz@2100MHz and 4G: 10MHz@700MHz), Senegal (3G: 10MHz@1800MHz and 2100MHz, and 4G 10MHz@800MHz and 1800MHz), Kenya (3G: 20MHz@1800MHz and 4G: 10MHz@700MHz and 800MHz), Tanzania (3G: 10MHz@1800 MHz and 2100MHz and 4G: 10MHz@ 700MHz and 1800MHz) and Uganda (3G: 10MHz@1800 MHz and 2100MHz, and 4G: 10MHz@800MHz and 1800 MHz) using available country information (Frequency Check, 2021). To reiterate,
offered a 100% subsidy to lifeline (low-income) customers who consume 0-50 kWh of electricity a month, while non-lifeline customers (consuming above 50 kWh a month) would enjoy a 50% subsidy (Berkouwer et al., 2022). The subsidies were announced on March 29 and took effect from April 1. Eligibility status was determined based on March consumption. The policy was initially announced to last for three months, however, the subsidy for [only] lifeline customers was eventually extended for 12 months.

# **3 Data**

This paper uses administrative data on electricity billing records from Ghana and Rwanda. The Ghana data comes from the Electricity Company of Ghana (ECG), which is the largest distributor in the country with operations in the southern and middle belts. It accounts for nearly 70% of all electricity customers in the country. We use data on billing records of the universe of electricity customers of the ECG from January 2018 to December 2020. The data identifies customer types based on the tariff applicable: residential (households), non-residential, and heavy industries. For each customer and year-month, it records the amount (kWh) of electricity consumed, the monetary value in Ghana Cedis (GHS), meter type (postpaid vs prepaid), and location (district) of the customer. In all, the data contains 42 million customer-year-month observations.

The Rwanda data comes from the Energy Utility Corporation Limited (EUCL), the main distributor, via the Rwanda Utilities Regulatory Authority (RURA). The dataset contains the billing records of the universe of electricity customers in Rwanda from January 2018 to December 2020. The data identifies customer type based on the tariff applicable: residential, non-residential (commercial, hotels, health centers, and public works ( water storage and pump stations, broadcasters)), and small-and-medium industries. Also, all customers in the dataset use prepaid meters: Rwanda has a universal roll-out of prepaid meters, with large and heavy industries the only exception who are allowed to use