rafmacalaba/data-use-mentions-extended huggingface dataset:
of WHO’s Model List of Essential Medicines.<sup>14</sup> The metric is calculated as the percentage of those 14 medicines which are available and in-stock on the day of the facility visit. The list of equipment necessary for a fully functional facility also varies, depending on the level and type of care provided. The SDI reports on four basic pieces of equipment (a thermometer, stethoscope, blood pressure cuff and weighing scale) which should be expected in any facility offering preventative and curative health care services. Equipment availability is reported as the percent of facilities with all four pieces of equipment available, while medicine availability is reported as the average percent of the 14 medicines available at facilities.

## 3.2.3 Caseload

Good care depends on having facilities adequately staffed, with the right type and number of health care providers. The SDI measures caseload, defined as the number of outpatient visits per health care provider per day, and this indicator is intended to measure the burden of care placed on each provider and the relative utilization of each facility. The indicator is calculated based on the number of outpatient visits within the last three months, the number of days per week that the facility is open, and the number of health care providers (including all types of providers that regularly offer outpatient consultations). The denominator is adjusted for the presence of providers to reflect the total number of health care providers available to provide care.

## 3.2.4 Provider absence rate

In order to provide quality care, doctors, nurses and other medical staff must be available at the facility. The SDI collects a full roster of staff during the initial survey data collection visit and then measures the presence or absence of up to 10 randomly selected staff during an unannounced second visit. The absence rate is calculated as the percent of randomly selected staff who are absent from the facility on the day of the second visit, out of

> 12 UNICEF (2019). 13 Leslie et al.
- Average number of days for exporter to get goods through customs. This is an inverse measure of the speed with which goods are processed through customs.

- Total number of inspections by officials per year. This is also an inverse measure; a higher number of inspections reduces the flow of services from the relevant public input.

_Firm-level estimates of the shared business environment_

> There is only one indicator of _B j_ in the Enterprise Surveys, namely the firm’s experience of interruptions to the power supply.  We use this in dummy variable form:

- Power cuts (=1 if more than one per month, =0 otherwise)

_How can the answers to the “Access to Finance” and “Tax Rates” questions be interpreted?_ There are a number of reasons why “finance” and “tax rates” cannot be interpreted in the same way as the public input constraints (Carlin et al. 2006).

If finance had the character of a public good like telecoms or customs regulation then we would expect that a high score would mean that better access to finance would boost output. However, because of the agency problems characteristic of the firm, better access to finance may result in more funding of pet projects that do not raise output (on average) but lead to higher default rates.

Where the finance system is working well, we would expect that access to finance is a constraint on at least some managers. This is not the case of a well-functioning electricity system or customs administration.

Finally, if financial institutions are functioning well, the perception of its availability as a constraint should be inversely related to the quality of investment projects the firm has available to fund, so that high scores may indicate poor quality projects rather than the potential for increased output.

11
each simulation. This leads to a given area benefiting from EB prediction of the area effect only in a portion of the simulations, since it is likely that an area is not selected in every single bootstrap sample. This leads to CB-EB point estimates that are far off from the Census EB estimates obtained with the proposed MC procedure of Section 6. Another reason might be that the parameters used to simulate the census vectors in CB-EB approach are not kept fixed across simulations, making difficult the convergence to the conditional expectation defining the estimator. When using the CB-EB approach with PSUs, there is still a clear difference with the Census EB estimates obtained with the new bootstrap procedure of Section 6.

Turning now to the traditional ELL approach, Figure 2 shows ELL estimates for the poverty rates, based on the model with only _x_ 1 and _x_ 2 (labeled “ELL”) and the same, but including the area means of _x_ 1 and _x_ 2 in the model (labeled “ELL with context”), together with true poverty rates and Census EB estimates obtained using the original MC procedure of MR (2010), without appending the survey data. In this plot, one finds that the traditional ELL estimates face the same troubles as the CB-EB ones. This is in line with what MR (2010) find in their simulations. Given the present simulation set-up with a poor model fit, ELL performs much worse than MR’s Census EB method in terms of point estimates. Also note the rather flat nature of ELL, which is mostly due to the model providing very little information and, given the limited explanatory power of the correlates, the ELL estimates fall close to the average poverty rate of 16 for all the areas.<sup>22</sup> What is somewhat concerning is that the addition of contextual variables to the model for ELL, in the form of the area means, does little to improve the point estimates. Thus, even if the survey data are not preserved in the Census
A simple counterfactual to estimate the overall impact of conflict on household expenditures is to consider a hypothetical situation in which the levels of foreign aid, casualties, and troops on the ground, would be the same as in the province with the lowest intensity of conflict. The overall impact can be computed as the joint effect of the increase in the level of these variables relative to the level of the province with the lowest intensity of conflict multiplied by the value of the corresponding � coefficients in the preferred specification. This computation is conducted at the level of each individual household in the NRVA sample, but it is straightforward to aggregate the results at the level of the province, the region, or the country.

**Figure 7. Conflict precedes aid and the build-up of troops**

<!-- Start of picture text --> Correlation coeffients with USAID flows in year T<br>0.5<br>0.4<br>0.3<br>0.2<br>0.1<br>0.0<br>Casualties ISAF ANA<br>T‐1 T T+1<br><!-- End of picture text --> _Note:_ ISAF stands for international troops (ISAF) and ANA for Afghan troops. Observations are at the provincial level between 2009 and 2014.

The results show that the overall impact of conflict on household expenditures is positive at the national level, but also in each of the provinces (figure 8). The impact is particularly strong among the provinces in the East, South, and the Southwest. Household expenditures are 10 to 18 percent higher than in the counterfactual, despite the large number of casualties. Conversely, the impact is weakest in the North and the Northeast, where the average increase in household expenditure per capita is less than 8 percent. These findings help explain the paradox of limited poverty reduction in the Afghan provinces and regions less affected by conflict.

20
cycle of aquifer replenishment and draw-down throughout AP is central to our analysis of groundwater markets. Although farmers can observe monsoon rainfall along with their own borewell’s discharge prior to _rabi_ planting, they cannot perfectly forecast groundwater availability over the entire season. To measure the degree of uncertainty, as part of the borewell owner’s survey we fielded a well-flow expectations module, which was structured as follows: First, we asked owners to assess the probability distribution of flow on a typical day at the _start_ of (any) _rabi_ season, the metric for discharge being fullness of the outlet pipe (i.e., full,<sup><u>3</u></sup> 4<sup>full,</sup><sup><u>1</u></sup> 2<sup>full,</sup><sup><u>1</u></sup> 4<sup>full,empty).24Next,usingthesameformat,</sup>

> 23Most irrigation water is transfered through unlined field channels with high seepage rates. While our survey also picked up a number of transfers to non-adjacent plots using PVC pipe, usually these cases involved sharing of groundwater between well co-owners or between multiple plots of the same owner.

> 24To appreciate how discharge can be fractional for an extended period, the metaphor for the aquifer to keep in mind is that of a sponge rather than of a bathtub.

14
|
|<br>Brazil|1.5|0.1|-0.0|10|10|58|13|12|
|Mexico|0.8|-0.5|4.0|13|11|42|-128|-136|
|**World**|**1.0**|**0.0**|**0.0**|**-1**|**-2**|**8**|**283**|**0**|

a Removal of those distortions left after the phase-out of the quotas at the end of 2004 b Exports minus imports, both valued at f.o.b. prices as in the GTAP database 6.05
7 about 6.8 years for the full sample of countries, and 6.3 years for the sub-sample of less developed countries. So, if Mexican schooling levels had been below the turning point, it is conceivable that the rapid increase of education attainment in the 1980s may have contributed to greater earnings inequality via an increased inequality of schooling attainment.

The evidence does not support this explanation for the increased earnings inequality in Mexico: using Ram's (1990) method of calculation based on 5 schooling levels, the mean level of schooling in Mexico was 6.45 years in 1984 and 7.65 years in 1994. This means that Mexico started in 1984 with a mean level of schooling that was slightly above the "turning point" according to the LDC subsample and slightly below the turning point for the full sample of countries, while it ended up in 1994 with a mean schooling level substantially above the turning point. If the curvilinear relation is evenly distributed around the turning point, we should clearly expect the inequality of schooling in Mexico to have declined over that decade.

This conclusion is confirmed by decomposing the change of the Gini Index between 1984 and 1994 into the proportion that is attributable to changes in the wage rate, in hours worked and in education attainment. Table 3 presents the results of such an exercise, both for the distribution of Wage and Salary incomes, as well as the broader Monetary income concept. For each income concept, several "synthetic" Gini indexes are calculated:<sup>5</sup> the first two, Gini-1984 and Gini-1994, are based on the distribution of wage rates, hours worked and education attainment that pertain in each year. The next three indexes are derived by recalculating the 1984 Gini index after successively replacing the 1984 distribution of wage rates, hours worked and schooling attainment by its 1994 counterpart. The second column under each income concept reports the value of each Gini index calculated in this manner, while the third column reports the percentage difference of these values
are reported in the upper panel of Table T2.IU.<sup>20</sup> The first two columns contain the evidence from a linear CEF which provides estimates comparable to the existing literature where linearity is a maintained assumption. The IGRC estimate is higher for the daughters, suggesting that the daughters face significantly lower relative mobility in urban India.

Although almost all of the existing studies on India focus primarily (or exclusively) on relative mobility, it has been emphasized in the recent literature that inter-group analysis of

> 20The table numbers are indexed by country and location. For example, T2.IU stands for Table 2 for the Indian Urban sample, and so on.

17
they occur.<sup>12</sup> In this section we attempt to at least provide modest evidence that the reputation of economic bloggers in the economics profession exceeds that of non-bloggers with similar publication records.

Davis et al. (2011) conducted a survey of academic economists in the U.S., with 299 (15%) responding. The survey asked these academics to list up to three living economists over the age of 60 and up to three under the age of 60 who they “regard with great respect, admiration, or reverence”. Gary Becker, Ken Arrow and Gary Solow were the top choices among the over 60s, and Paul Krugman, Gregory Mankiw and Daron Acemoglu the top choices among the under 60s. The under 60s list of 23 names contains a number of regular bloggers – in addition to Krugman and Mankiw are Steven Levitt, William Easterly, Nancy Folbre, Dani Rodrik, and Tyler Cowen.

We merge this list with a list of the top 500 economists according to the RePEc rankings (based on paper downloads, citations) and also code each of the RePEc top 500 according to whether they blog or not.<sup>13</sup> This data is then used to estimate a probit model to see whether, conditional on RePEc ranking, individuals who blog are more likely to appear on the list of favorite or admired economists. Table 2 shows the results, for the pooled sample in column 1, and separately for under 60 and over 60 economists in columns 2 and 3 respectively. In all three columns we see that, conditional on their RePEc rank, regular blogging is strongly and significantly associated with being more likely to be viewed as a favorite economist. The point estimates are just over 0.4 in all three columns, indicating a 40 percentage point higher likelihood of being on the favorite economist list for economics bloggers. Blogging has the same size impact as being in the top 50 of

> 12 See for example Daniel Drezner (http:
- 55 -

have accordingly increased the number of central government employees by adding 3,000 employees. Local Govemment employment is corrected for the number of local government staff who are not on computerized payroll and are paid directly by local government. Mr. Hooper estimates these to be between 2,000 and 4,000 people. We have added 3,000 people to the number stated in the Government payroll data. Education employment is also from the Uganda Payroll system, September 1996. It includes 68,326 primary school teachers, 15921 Secondary School teachers and 12,419 Tertiary and University faculty. Teaching numbers include under secondary and tertiary approximately 6,000 non teaching staff (e.g.: cooks, cleaners, and office workers working at schools.) Health employment is based on an estimate from Mr. Hooper. Health sector staff was included in both central and non central government data. In order not to double count employment, we have subtracted 4,000 employees to both the estimate of Central Government and Non Central Government employees. Data on military personnel includes 400 Marines and 800 Air Wing personnel.

GDP estimate is from World Tables 1995 and is for 1993. Wages and salaries as percentage of GDP is an estimate taken from SAR of Institutional Capacity Building Project of May 9, 1995 and relates to 1994.

**Zaire**

Data on General Govemment, Education and Employment in State-owned Enterprises is taken from IMF Staff Country Report No. SM/96/45 of February 23,1996 and relates to 1994. Data on military employment do not include enrollment in paramilitary units.

Zambia Unemployment is taken from CIA Factbook and refers to 1993. Paid employment in non-agricultural activities is drawn from the ILO's Yearbook of Labor Statistics 1995 and refers to 1989.

Central Government employment, Education and health employment are from Zambia Public Expenditure Review of April 20, 1995 and relates to 1994. Local Government employment is from the same report but relates to 1993. Education and Health correspond to civil servants in
. We use a pixel size of 100m. We collected data on the NRES version of the BUILT-V product that only include non-residential built up area in the calculation of the built volume. For each city in our data we calculate the standard deviation in the BUILT-V-NRES value across the BUILT-V-NRES cells within that city. We normalize this value by dividing by the sum of the BUILT-V-NRES values within the city.

**Country-level income–** We use data from the World Bank World Development indicators to measure country level GDP per capita (constant 2015 US$) when we examine the correlation between pollution averaged across the entire country and income averaged across the entire country. We also use the Urban population (% of total population) at the country level in the same analysis. Data available here: `https://data.worldbank.org/` .

**Data for additional heterogeneity analyses–** In our analysis of heterogeneity by primary source of pollution we use data from the EU EDGAR database on the amount of PM2 _._ 5 emitted from various different sources on a global grid. We assign grid points to countries (or cities) and calculate the total PM2 _._ 5 emissions from each category of source in the EDGAR data (e.g. air transportation). We then aggregate into broad categories (e.g. transportation, industry, etc.). We then determine the primary source of emissions by summing all the categories and choosing the category that accounts for the largest share of the total. Data available here: `https:// edgar.jrc.ec.europa.eu/` . For heterogeneity by history of colonization and equatorial location we assemble indicators for each country based on a review of the country’s history of colonization

14
of the ‘GYM package’ using information from the students’ census evaluation ( _Evaluación Censal de Estudiantes_ , ECE), administered a few months after the intervention for those in the eighth grade, and more than a year later for those in the seventh grade (see **Section 4e** ).

15 Pre-intervention power calculations of the school-clustered randomized control trial suggested a low minimum detectable effective size (MDES) of 12% for the pooled sample, while individual experiments were powered to detect an MDES of at least 17%. To compute MDES in this level-3 cluster RCT, we assumed 2 sections per school with 25 pupils per section, an intra-class correlation at the school-level of 0.25 and within-school correlation across teachers of 0.15. In reality, pre-treatment power calculations were very conservative: not only average schools turned out to be larger, but the intra-class correlation across schools was closer to 0.15. Ex-post calculation based on the 2015 ECE national data suggests that the pooled and individual studies were powered to detect 8% and 12% MDES respectively.

9
# 1. Introduction

Poverty is usually defined as the insufficiency of resources to ensure the basic conditions of subsistence and well-being of individuals in a society. Given its broad importance for policy, monitoring poverty has become a well-established practice around the globe, with the headcount ratio (Foster et al., 1984) being the leading measure for the state of poverty in a country. The measure estimates the share of a population that lives below a monetary threshold deemed to be sufficient to cover the minimum needs of the population – also known as the poverty line.

Despite high levels of inequality and food insecurity (Hoffman, 2021), Brazil has not adopted an official poverty measurement methodology and thus does not have an official poverty line. To identify and track poverty, many researchers rely instead on several administrative lines used to determine eligibility for social assistance programs, such as the eligibility thresholds for the Bolsa Familia Program (Programa Bolsa Família; PBF) and the Cadastro Único eligibility threshold.<sup>1</sup> In addition, Brazil’s national statistical institute, the Instituto Brasileiro de Geografia e Estatística (IBGE), uses the World Bank international poverty lines (US$1.90, US$5.50) in their synthesis report of social indicators (IBGE, 2020).

The use of administrative and international poverty lines, however, is not ideal for monitoring national poverty. While the former may not adequately capture the cost of meeting basic needs, the latter are “reference points” based on poverty line values of comparable countries. The international poverty lines, fixed at US$3.20 and US$5.50 per day (in 2011 purchasing power parity dollars), reflect only typical national poverty thresholds in middle-income countries (World Bank, 2018).

This study contributes to the body of literature aiming to define a poverty line for the Brazilian case. With the exceptions of World Bank (2007) and Rodrigues et al. (2018), in which national poverty line estimates
In summary, the data show a good balance of baseline characteristics across treatment arms and little problem with attrition in either round of data collection – particularly for the primary outcomes at the child level. We now proceed to presenting estimates of ITT effects at the 18-month follow-up. _18-month impacts_ _Primary outcomes (child level)_ At 18-months, we conducted child assessments using the MDAT, described in the Appendix, which evaluates language and fine motor/perception skills in children and was specifically created for use in rural Malawi.<sup>16</sup> Table 3 presents program effects on the overall score, as well as the two subscales (Language and Fine Motor/Perception Skills). Columns (1) and (2) present impacts by treatment arm on the total MDAT score: there were no effects in T2 or T3, but there was a significant effect of T4 compared to the control group (0.13 SD, p-value=0.06; column (2)). In columns (3) and (4), we see that this improvement in T4 is due to the intervention’s effect on language skills, which is 0.19 SD (pvalue=0.010). This improvement is significantly higher than that in T2 (T2-T4=0.22, p-value=0.001), indicating that marginal value of the parenting education is substantial over and above teacher training at the CBCC level. However, it is not significantly larger than the effect in T3, where the teachers were provided small incentives for retention and motivation (T3-T4=0.1; p-value=0.17). Columns (5) and (6) show no effects of T4 on Fine Motor/Perception Skills in comparison to the control group. T4 still performs significantly better than both T2 and T3, but this is due to a negative impact of these two interventions (only significant at the 90% level).

To assess program impacts on positive and negative behaviors among the