rafmacalaba/data-use-mentions-extended huggingface dataset:
**Table 5: SDR weights (1986‒1990 and 2010‒2014)**

|Currency|1986‒1990|2010‒2014|
|---|---|---|
|Euro<sup>14</sup>|0.310|0.374|
|Pound sterling|0.120|0.113|
|US dollar|0.410|0.419|
|Yen|0.120|0.094|

_Source_ : International Monetary Fund, staff calculations.

One clear feature of the threshold levels is that they have declined relative to average world GNI per capita (current US$, Atlas). This is of course expected, since the thresholds are adjusted for inflation only, and not for economic growth. One question is whether the decline has been at an appropriate pace.

Table 6 provides some additional analysis of the thresholds relative to average world GNI per capita (Figure 3 also illustrates the same trends). The low/middle income threshold to world GNI per capita declined from 16 percent in 1982 to 10 percent in 2014, while the lower middle/upper middle threshold to the world average fell from 65 percent to 38 percent. Another way to illustrate the trend is that if the ratio of the threshold for low income countries to world GNI per capita in 2014 had remained at its 1997 (fiscal year 1999) level (that is, about 14 percent), the threshold for FY16 (2014 data) would have been around US$1,500 rather than US$1,045. As a result, about 12 countries classified in the lower middle income category would have remained in the low income category.

> 14 Prior to the introduction of the Euro on January 1, 1999, the Deutsche mark and French franc were represented in the basket; their relative shares were 0.19 and 0.12, respectively.

24
the inequalities in incomes that will prevail in the future (Checchi and van de Werfhorst, 2014). Table 2 shows the evolution in dispersion in test scores for all three subjects, over the period 2000 to 2012. The dispersion measure presented in Table 2 is the coefficient of variation. Between 2000 and 2006, when learning outcomes in reading—the only subject allowing a comparison between those two years—registered a large decrease, the dispersion also increased substantially. The increase in dispersion in reading test scores was reversed during the period 2006 to 2012. A reduction in inequality of learning outcomes during the period 2006 to 2012 was also observed in math and science test scores. As shown below, the reduction in dispersion of test scores is largely accounted for by improvements in performance by the bottom end of the distribution.

_Table 2: Evolution of Dispersion in Learning Outcomes, by Year and Subject Area_

||**2000**|**2006**|**2009**|**2012**|
|---|---|---|---|---|
|**Reading**|23.6|28.7|25.0|23.8|
|**Math**|28.1|23.2|21.9|19.5|
|**Science**|24.7|23.2|23.3|20.8|

_Source_ : Authors’ calculations based on PISA/OECD. Dispersion is measured as the coefficient of variation defined as the standard deviation over the mean multiplied by 100.

9
on measures imposed by other countries and customs territories.

In principle, transparency through notification and reverse notification supports discussion in the WTO of measures taken. In practice many WTO members have not lived up to their transparency obligations, notwithstanding commitments by G20 Trade Ministers to notify the WTO of trade-related measures taken. As of October 9, 2020, 55 WTO members had submitted 245 notifications related to COVID-19. These span export restrictions and import liberalization/trade facilitation measures as well as changes in product regulation (standards) and economic support programs. Brazil is the leader in having notified 28 measures, followed by Kuwait (16), the United States (14), the European Union (12), the Philippines (11), Thailand (11) and the Republic of Korea (9) (Figure 1). Three-quarters of these COVID-19 related notifications pertain to product standards for medical supplies and PPE.<sup>8</sup> Through October 9, 2020 only 58 COVID-19 related notifications did not pertain to sanitary and phytosanitary (SPS) or technical barriers to trade (TBT). This compares to 645 measures – both export restrictions and import liberalization/facilitation – targeting food and medical products in the GTA data set.<sup>9</sup>

> 5 Note that the data do not include changes in health and safety standards applicable to the products concerned, i.e., sanitary and phytosanitary measures and technical barriers to trade (product standards).

> 6 The ITC considers overseas and autonomous territories separately from the home country. This is not done in the GTA or WTO monitoring exercises. See https://www.macmap.org/covid19.

> 7 The 2013 Agreement on Trade Facilitation similarly has transparency requirements, requiring WTO members to publish promptly information on import, export or transit restrictions or prohibitions.

> 8 This is consistent with the May 14, 2020 G20 Trade Ministerial commitment to: “Reduce sanitary and technical barriers by encouraging greater use of relevant existing international standards
<!-- Start of picture text --> Reading<br>600<br>.<br>500 of Fetend<br>n a<br>oO *..<br>S of<br>B wi?ee<br>g on<br>a 400 * . *»eenited«*Arab6 Emirates<br>*. = °<br>ae Argpia ~jofdan © Qatar<br>ort? Macedonia<br>*<br>%<br>rr<br>300<br>300 400 500 600<br>Girls' Scores<br><!-- End of picture text --> <!-- Start of picture text --> Mathematics<br>600 B-S-J-Zaa (ChipPo<br>*<br>*<br>[RP<br>‘Belgie<br>United) potion<br>500 Ast stc*<br>rae *<br>n aS<br>o */ Malta<br>a8 Ms . A .<br>g ae<br>a Costa Rica\* ewe **Thailand<br>400 cause “a<br>Arsene<br>* Saudi Arabia<br>«<br>Philippines<br>300<br>300 400 500 600<br>Girls' Scores<br><!-- End of picture text --> <!-- Start of picture text --> Science<br>600<br>‘.<br>* +<br>500 4° rinland<br>3e<br>8<br>a 4 batei<br>g Gj<br>ro) riged Arab Emirates<br>a * *<br>400 Jogian *°<br>Qatar<br>@ Saudi Arabia<br>e*%<br>.<br>*<br>300<
# **A Figures and Tables**

Figure A.1: Validation with SICONFI data: commitment phase, by function <!-- Start of picture text --> CE MG<br>20000 100000<br>15000 75000<br>10000 50000<br>5000 25000<br>0 0<br><-25 -20 -15 -10 -5 0 5 10 15 20 25> <-25 -20 -15 -10 -5 0 5 10 15 20 25><br>PB PE<br>15000<br>20000<br>10000<br>10000<br>5000<br>0 0<br><-25 -20 -15 -10 -5 0 5 10 15 20 25> <-25 -20 -15 -10 -5 0 5 10 15 20 25><br>PR RS<br>40000<br>20000<br>30000<br>20000<br>10000<br>10000<br>0 0<br><-25 -20 -15 -10 -5 0 5 10 15 20 25> <-25 -20 -15 -10 -5 0 5 10 15 20 25><br>SP % difference from SICONFI data<br>100000<br>75000<br>50000<br>25000<br>0<br><-25 -20 -15 -10 -5 0 5 10 15 20 25><br>% difference from SICONFI data<br>Frequency<br>Frequency<br>Frequency<br>Frequency<br><!-- End of picture text -->

**Notes:** This figure presents the percentage difference of commitment data from the budget execution dataset in relation to data on committed expenses from SICONFI, as described in Section 3.

36
# **B Results based on GHSPOP**

Figure B1: Cost of living index across urban versus rural areas in 16 SSA countries <!-- Start of picture text --> (A) Official definition (B) DOU (C) DB<br>1.3 1.3 1.3<br>1.2 1.2 1.2<br>1.1 1.1 1.1<br>1 1 1<br>.9 .9 .9<br>Urban Rural Urban center Rural Core Town<br>Urban cluster Suburb Other rural<br><!-- End of picture text --> Source: International Urban Poverty Database. Note: DOU: Degree of urbanization. DB: Dartboard. The cost-of-living index is prepared as a spatial deflator for each country in this study. It is normalized to 1 for each country. GHSPOP 1km is used for both the DOU and DB methods.

Figure B2: Share of household heads working in agriculture across urban versus rural areas <!-- Start of picture text --> (A) Official definition (B) DOU (C) DB<br>80 80 80<br>60 60 60<br>40 40 40<br>20 20 20<br>Urban Rural<br>Urban Rural Urban center Rural Core Town<br>Urban cluster Suburb Other rural<br>Share of household heads working in agriculture (%) Share of household heads working in agriculture (%) Share of household heads working in agriculture (%)<br><!-- End of picture text --> Source: International Urban Poverty Database. Note: Each boxplot shows the share of household heads working in agriculture over different geographic areas in 16 SSA countries. GHSPOP 1km is used for the DOU and DB methods.

47
roads in fair condition (Iimi, et al., 2016). Finally, mobile subscriptions had a penetration close to 100% in 2018, but only 23% of the population in Kenya had access to internet services in 2019 (World Bank, 2021). Given these gaps, improving the access and quality of infrastructure has a potentially important role for improving health service provision in Kenya.

This paper focuses on specific dimensions of infrastructure with data availability and for which we found prior suggestive evidence on their link to the quality of health service delivery. In particular, we study access to and quality of electricity (using stability of the connection as a proxy for quality), road quality and accessibility, cellphone, and internet connectivity (2G and 3G), and use of ICT. Likewise, in this paper we are restricted to study only certain aspects related to the quality of health services for which we have data. While we understand that quality of health service delivery is multi-dimensional and complex to measure, we use level of health services’ readiness (i.e., availability of key medicines, vaccines for children, and ANC tests) and health providers’ medical knowledge to treat certain conditions (i.e., diagnostic accuracy) as proxies of quality of health service delivery. For this analysis, we combine geo-referenced survey data collected at the health facility level with geospatial data on infrastructure and location characteristics in order to document some of the cross-sectoral drivers of the quality of health.

We first find that access to electricity and quality of roads are associated with more accurate provider diagnostics for general illnesses and for those affecting primarily the early development of children and threatening the life of pregnant women. We also find that access to electricity is associated with (i) higher availability of vaccines for children, mostly by making it possible to have a working fridge, which is essential to store most vaccines; and (ii) higher availability of essential and priority medicines for children and pregnant women, partially through the use
We also control for national income. Since there are some reports that students in private schools lost less learning than others (Arenas and Gortázar 2022; Jack et al 2022; Wolf et al 2021), we include the proportion of private schools as a control. To gauge to what extent connectivity helped, we include individuals using the internet (percent of the population). To measure pre-existing school quality, we include the score from the World Bank’s Harmonized Learning Outcomes (HLO). To measure the strength of trade unions, we include the trade union density rate. We use the Polity5 dataset as the measure of democracy. See Table 1 for means, standard deviations, and sources.

||**Table 1**<br>|**: Means and**<br>|**Standard Deviations**<br>|
|---|---|---|---|
|Variable|Mean|Std. Dev.|Source|
|Learning loss (SD)|0.23|0.16|see Annex 1 (Learning Loss COVID-19 2020-2022)|
|Weeks closed|20.78|18.21|see Annex 1|
|Death rate/100k|152.36|125.56|Johns Hopkins Coronavirus Resource Center|
|GDP p/c $US|21709|22717|World Bank|
|Private school (%)|14.84|14.10|World Bank|
|Internet (%)|69.44|25.12|International Telecommunication Union|
|School quality|462.08|89.97|Angrist et al. 2021|
|Vaccination rate|59.77|32.17|Oxford Coronavirus Government Response Tracker|
|Stringency index|56.29|8.90|Hale et al 2021|
|Union density|22.38|16.05|International Labour Organization|
|Democracy|7.02|4
# **4. Data**

We use two sets of surveys, the Household Expenditure Surveys (HESs) and the Labor Force Surveys (LFFs). The HESs are the surveys that contain our variable of interest (household expenditure). Hence they are used to construct and estimate the model that we rely on for imputation. The LFSs denote the surveys that are used to estimate poverty, based on imputed data, for time periods that are not covered by the HESs.

Strictly speaking, the HESs in Morocco include two different surveys; the 2000-2001 National Survey on Consumption and Expenditure (NSCE) and the 2006-2007 National Living Standards Survey (NLSS). Both samples measure household expenditure, are nationally and regionally representative as well as representative of urban and rural areas. **The 2000-2001 NSCE** covered 15,000 households and was administered between November 2000 and October 2001 with multiple objectives in mind. It was designed to measure household expenditure and to provide the necessary information to weigh the living standard index constructed for Morocco and other national accounts aggregates. It was also designed to measure household consumption, nutrition, poverty and inequality. The questionnaires included sections on socio-economic characteristics, habitat, energy, economic activities, education, health, transfers, subjective indicators of wellbeing, expenditure, durable goods, anthropometrics, nutrition and also a module administered to the community to measure access to services. **The 20062007 NLSS** covered 7,200 households and was administered between December 2006 and November 2007. The survey focused on household expenditure and revenues and was principally administered to measure poverty, inequality and other dimensions of living standards. The questionnaire included modules on socio-demographic characteristics, social mobility, habitat, expenditures, revenues, credits, transfers, education, health, employment, durable goods and poverty perceptions. The **Labor Force Surveys (LFSs)** of Morocco is a household survey covering all residents on the national territory. The sample size is 60,000 households, 40,000 urban and 20
# 3.1 Improvements to the survey’s expenditure module

In lower income countries, a welfare metric based on consumption is generally preferred to income. As discussed in Mancini, Vecchi (2020), the choice between consumption and income would be inconsequential whenever all income is consumed, and all consumption is financed by income. However, in low-income economies and in contexts characterized by shocks, a metric of welfare based on consumption – which is smoother than income through households’ reliance on savings and borrowing – provides a better measure of living standards.

In order to make the Demographic and WASH survey better suited for poverty measurement, efforts were made to improve the design of the expenditure module of the survey questionnaire within the limits in terms of overall questionnaire length and complexity deemed feasible by the HNAP team.

In particular, efforts were made to improve the measurement of in-kind assistance and own-production (particularly for food); to use an annual recall period for items consumed infrequently; to expand (to the extent possible) the list of non-food items (Table 4).

_Table 4: Expenditure module of the 2021 and (revised) 2022 Demographic and WASH survey_

|**SUMMER2021 EXPENDITURE MODULE**|**REVISEDSUMMER2022 EXPENDITURE MODULE**|
|---|---|
|_How much did your household spend on the following items_<br>_on a monthly basis?_**_(average of last 3 months)_**|_Please estimate the value of_**_monthly_**_household_<br>_consumptionfor thefollowing items._<br>|
|Food|Food and beverages (_bought or bartered_)<br>Food and beverages(_ownproduced or received_)|
|Electricity|Electricity|
|Cooking|Fuel for cooking|
|Heating/cooling|Heating/cooling|
|Safe water|Water|
|Hygiene items|Hygiene items|
|Transportation(public/private all costs)|Transportation(public/private all costs)|
|Communication|Communication|
|
||||**No. of Villages (HHs**<br>**sampled,**<br>**baseline/midline)**|
|---|---|---|---|
|||**Control Arm: Cash Only**|**77 (963/1204)**|
||**Receiving**<br>**HDCT h**|**Behaviorally Enhanced Arm 1: Cash & Mother**<br>**Leaders (ML)**|**77 (945/1200)**|
|All<br>52<br>communes|**cas**<br>**transfers (38**<br>|**Behaviorally Enhanced Arm 2: Cash & MLs &**<br>**Plan-making**|**77 (970/1197)**|
||**communes)**|**Behaviorally Enhanced Arm 3: Cash & MLs &**<br>**Affirmation**|**78 (1005/1205)**|
||Pure Control (13|communes): No cash|70 (601/1200)|

Note that the results in this paper compare the households in the ‘Cash Only” arm (the ‘control group’) to the three behaviorally enhanced arms. The results of the evaluation of the cash transfer program itself – i.e., the comparison of households in the communes which received cash only to households in the communes randomized to be ‘pure controls’ can be found in the Appendix Tables.

# **4.2: Data Sources**

Key baseline and outcome data come from household surveys. Data were collected in two waves, with the baseline ( _N_ = 3,883, or N=4,484 including the households in the pure control/
**Table 7. Investment to Capital Regressions, Listed Firms data**

||(1)<br>IK|(2)<br>IK|(3)<br>IK|(4)<br>IK|(5)<br>IK|(6)<br>IK|(7)<br>IK|
|---|---|---|---|---|---|---|---|
|Log of net sales|0.04***<br>(0.001)|||||||
|Volatility of Sales||0.02<br>(0.813)||||||
|Sales Growth|||0.17*|||||
||||(0.075)|||||
|Total Debt to||||||||
|Assets||||0.06||||
|||||(0.672)||||
|Interest Rate|||||0.21<br>(0.141)|||
|Gross Margin||||||-0.01<br>(0.930)||
|Net Income|||||||0.44<br>(0.152)|
|year==2002|-0.13|-0.09|-0.21***|-0.16**|-0.13|-0.17**|-0.16*|
||(0.143)|(0.262)|(0.006)|(0.044)|(0.225)|(0.036)|(0.059)|
survey.<sup>11</sup> As expected, all performance measures deteriorate for a smaller number of core items, as well as for a larger number of optional modules (Table 1). Using the minimum number of optional modules (2), we obtain an average bias of 0.021 for FGT0 using no core items, but a considerably smaller bias of only 0.006 (a more than 70 percent reduction) if using 20 core items. An increase of the number of optional modules from 2 to 8 almost doubles the bias for FGT0 from 0.021 to 0.042 using 0 core items. We observe similar trade-offs for FGT1 and FGT2. The trade-offs for the Gini coefficient are less clear as independent of the number of core items and modules the bias is consistently extremely low, almost always below 0.01.

# **Rapid vs. Reduced Estimation**

Traditionally, time savings in administering consumption modules are achieved by reducing the number of consumption items included in the questionnaire. Here we compare the rapid approach with a reduced approach based on the effective time savings achieved. Assuming that only consumed items require substantial interview time, we estimate the average number of items that were administered and consumed by households relative to the total number of items in the full consumption module. The smaller the number of items consumed and administered in the questionnaire, the larger the time savings. This measure takes into account that effective time savings are smaller if fewer but often-consumed items are administered to a household, compared to a larger number of items, which are rarely consumed. The results show simulations with varying numbers of core items and optional modules for the rapid approach, and varying numbers of items included in the reduced module.

> 11 If not noted otherwise, each core and optional module configuration is run 20 times, each using 50 multiple imputations. Note that the same survey data are used to define the core module items (based on highest consumption shares).

11
# Table (D2) Impacts of the Program on Household Consumption and Expenditure

|**Outcomes**|**Treatment**|**(se)**|**FDR**<br>**q-values**|**Control**<br>**mean**|**(sd)**|**N**|
|---|---|---|---|---|---|---|
|Food consumption (aid) USD PPP|-53.05**|(22.40)|[0.02]|218.51|(201.68)|1,569|
|Food consumption (aid) USD PPP (_Per capita_)|-9.73***|(3.44)|[0.01]|35.88|(29.19)|1,569|
|Number of meals consumed the day prior|-0.08|(0.09)|[0.13]|2.05|(0.59)|1,610|

**_Notes_** :

- The table shows the regression results on pre-specified consumption outcomes using the phone survey midline data. Each row is its own dependent variable.

- The independent variable of interest is the TOT treatment indicator, which is the predicted value from a first-stage regression of treatment implementation on treatment assignment.

- The outcomes that require definitions are: Food consumption measures are self-reported and are in USD PPP and are winsorized at the top 1% of values in order to limit the influence of outliers.

- The regressions also have assessment month-by-year fixed effects, enumerator fixed effects, community-level controls (Irbid/Mafraq governorate and population quartile), and household-level controls (vulnerability-assessment quartile, shelter program, baseline number of children, baseline number of children plus adults, respondent gender, and
|-4.45<br>(0.43)||||
|Difference|1.24|1.23|-0.01||||
||(0.65)|(0.45)|(0.72)||||

Notes: This table shows a the difference in cohort births between ages 15 and 19 per 100 (Panel A), and between ages 25 and 29 per 100 (Panel B). Sample includes municipalities with populations >10,000 and <500,000. Estimates weighted by population size. _Treated municipalities_ received at least one new secondary school between 2000 and 2009, _control municipalities_ did not receive a new school between 2000 and 2009.

Source: Authors’ analysis based on school data from the 2002 wave of the Brazilian School Census; official population estimates from the Brazilian Census Bureau; births by age from Brazilian Vital Statistics.

31
Probability > Chi-squared|0.00||0.00||

Notes: 1. Probability derivatives at the mean of each explanatory variable (or for 0 to I change in the case of dummy variables) and z-scores based on robust standard errors. 2. Tobit coefficients and t-statistics.

# **6.3 Collective Action**

In addition to contributing to asset accumulation and access to credit, social capital has also been documented to aid in collective action and collective decision making. This is especially relevant in rural settings where common property resources, such as water, forestry or grazing land, need to be managed by a community (Narayan, 1995; Uphoff, 1992). In Indonesia, there is a strong tradition of mutual help and quite a few of the local associations inventoried by the LLI study were set up for that specific
||||||(0.258)|(0.255)|(0.255)|
|How Much Of An|||||0.034|0.075|0.080|
|Obstacle: Labor||||||||
|Regulations?|||||(0.330)|(0.329)|(0.329)|
|Population (logs)||||||-46.382***|-46.717***|
|||||||(11.098)|(11.036)|
|Urbanization (%)||||||1.378***|1.418***|
|||||||(0.456)|(0.457)|
|WBL Index||||||28.689***|28.240***|
|||||||(8.905)|(8.822)|
|Women can work the||||||1.375|1.508|
|same night hours as||||||||
|men Y:1 N:0||||||||
|||||||(3.206)|(3.190)|
|Women can work in<br>||||||-2.446|-1.892|
|the same industries as<br>men Y:1 N:0||||||(2.184)|(2.189)|
|Ratio of Female to||||||2.956***|3.113***|
|Male Population||||||||
|||||||(