rafmacalaba/data-use-mentions-extended huggingface dataset:
20%<br>10%<br>0%<br>1 2 3 4 5 6 7 8 9 10<br>Population distribution by income level<br>Banked Unbanked<br>Source: DANE household study of Bogotá, 2003<br>%<br><!-- End of picture text --> 8. **The unbanked have three times greater unemployment than the banked** (10% are unbanked and unemployed versus 3.6% banked and unemployed). Figure 3.4 shows that the banked population prevails among the government employees<sup>43</sup> , formal sector employees, and employers, most of whom also benefit from social security and health insurance programs. On the other hand, the unbanked tend to work in areas of lower job security and beyond the social safety networks. The unbanked are more likely to be among the self-employed (72% of the total, which includes informal sector workers), domestic workers (81.5%) and unpaid workers in family businesses (78%). The financial 43 Perhaps due to recent policy decisions encouraging payment by direct deposit to bank accounts, 83% of government employees are banked 47
village level.<sup>8</sup> Enumerators first conducted school censuses in each village, from which we sampled up to three schools per village for the absence surveys. During fieldwork, enumerators made three separate visits to each sampled school over a period of 10 months from January - October 2010.<sup>9</sup> Data on school infrastructure and accessibility, finances (income and expenditure), and teacher demographics were collected once for each school (typically during the first visit, but completed in later visits if necessary), while data on time-varying metrics such as teacher and student attendance and dates of the most recent inspections and PTA meetings were collected in each of the three visits. We also assessed student learning with a test administered to a representative sample of fourth grade students in sampled schools. See Appendix A and Appendix Tables 1-3 for further details on sampling and construction of the village-level panel data set.

Teacher absence was measured by direct physical verification of teacher presence within the first fifteen minutes of a survey visit. Data collected during the school census were used to pre-populate teacher rosters for the sampled schools, so that enumerators could look for teachers and record their attendance and activity immediately after their arrival at the school.<sup>10</sup> Once teacher attendance was recorded, all other data were collected using interviews of head teachers and individual teachers.<sup>11</sup> We record teachers as absent on a given visit if they were not found anywhere in the school in the first fifteen minutes after enumerators reached a school. We consider all the teachers in the school to be absent if the school was closed during regular working hours on a school day, and respondents near the school did not know why the school was closed or mentioned that the school was closed because no teacher had arrived or they had all left

> 8This is also why the 2010 wave did not include urban areas. Since school-level identifiers from the 2003 survey were not preserved (for confidentiality
18

- 2) _m - money supply;_

percentage changes of end-of-quarter money stock

- 3) _w - nominal average monthly salary in the enterprise sector;_

- percentage change of a quarter's last month average value vis-a-vis previous quarter's last month average value

- _4) gNT -_ **_;rT_** _= rer - inflation rates of tradable and non-tradable components of CPI index_ difference in percentage changes of end-of-quarter values

- _5) V,- various measures of variance and skewness as well as their products;_

- Definitions of variance and skewness measures used in regressions were slightly modified compared to those given in section 2. They are based on differences in log values of respective price indices instead of individual inflation rates. Using percentage change based measures would give rise to spurious regressions as the explanatory variables (variance, skewness) are based on differences of individual inflation rates and aggregate inflation (explained variable).

Before estimating the model it is worthwhile to take a look at the general correlation table (Table 3). It shows simple correlation coefficients for each pair of variables potentially to be included in the model. The table includes four different monetary aggregates as well as real exchange rate, wages, unweighted, weighted and Theil skewness, variance and standard deviation.

There are several important messages that emerge from the conrelation table:

- Inflation seems to be more closely correlated with standard deviation than with variance.

- Of all the measures of relative price variability, Theil statistics yield highest correlation coefficients with inflation.

- Of three different monetary aggregates: Ml, M2 and Domestic Credit (DC), M2 is the most closely correlated with inflation.

- All monetary aggregates are highly correlated with wages (p between 0.66 and 0.85). As the two variables appear both on the left side of the model equaition, the resulting
literature, we label this as a “personal diary.” The personal diary was carefully designed to avoid double counting. Diary entries are specific to an individual and should leave no scope for double-counting purchases or self-produced goods. It is possible that a “gift” could be given to the household and accidentally recorded by two individuals. However, interviewers were trained to cross-check individual diaries for similar items purchased, produced, or gifted that occur on the same day and to query these during the checks. In many cases, one person will 11 For example, Gibson, Rozelle, and Huang (2003) find the Gini coefficient in urban China is 64 percent higher if respondents kept a diary for one month, with the sample spread evenly across the year, compared with the Gini that results from all respondents keeping the diary for one year. In contrast, the annual mean from extrapolating staggered monthly expenditures almost exactly equals the mean from the annual diaries. Since this experiment was carried out in an urban area, the difference is not primarily due to seasonality but instead to short-term fluctuations within the survey period that are subsequently reversed over the year.

12 While the literature has findings about errors in memory and counting (more error the longer the period and the more transactions to count), almost nothing is known about errors in estimation strategies. For example, if respondents apply current market prices to usual month quantities, it reintroduces seasonality into the “usual” month estimate and so will tend to raise inequality toward the level found with short reference periods. In the Vietnam Living Standards Survey, the unit value for rice, formed from the ratio of “usual” month expenditure to “usual” month quantity, has considerable seasonality, with the same pattern across months that is observed in community market price surveys (Gibson, 2007). The community market price surveys reflect current seasonal conditions because they are carried out only once in each cluster but the unit values should not because they are meant to refer to purchases made in a
in Vocational Training**<br>**organized through the club?**||.337|||.319|||.358||
|**Did you take a microfinance loan from BRAC?**||.127|||.088|||.169||
|**Observations**|1,197|3,592||590|1,796||608|1,796||

**Notes:** Data on potential ELA club members in each village is obtained from the village census administered prior to the intervention, while data on club membership was collected during the ELA Monitoring Survey in 2015. Equality of means is tested by OLS regression of the variable of interest on treatment assignment and district fixed effects, with standard errors clustered at the village level. Data on participation in financial literacy training, livelihood skills training and microfinance is conditional on being assigned to the treatment arm that offered those specific programs.
of the Paper**

The paper has three related objectives. First, following previous studies of infrastructure investment needs, we use a panel of 52 countries to estimate robust functions of “demand” for electricity production and telephone lines (both fixed and cellular). We innovate by employing dynamic, heterogeneous panel estimation techniques that have not previously been employed in this context. Second, using these estimates, we forecast future infrastructure demand in both electricity generation and telecommunications for two alternative growth scenarios (“current” and “accelerated” scenarios). This serves to help assess and build on the current South African public policy effort to create conditions for accelerated and shared growth, inter alia, by eliminating infrastructure 6
# Appendix

## Appendix A1: A Novel Database of Laws and Decrees in the GCC

Our first contribution is a novel database of past and current economic reforms from all the GCC countries. We obtain these data from each country’s national legislative archives by web-scraping from the following sources:

- **<u>Bahrain:</u>** The Legislation & Legal Opinion Commission (link)

- **<u>Kuwait:</u>** The General Secretariat of the National Assembly (link)

- **<u>Oman:</u>** Oman Law (link)

- **<u>Qatar:</u>** Al-Meezan (Qatar Legal Portal) (link)

- **<u>Saudi Arabia:</u>** National Center for Archives and Record (link)

- **<u>UAE:</u>** The Ministry of Justice (link)

After extracting laws and decrees from the online archives, we tagged them according to a classification system corresponding to the WEF’s Global Competitive Index (GCI) pillars and an additional diversification category corresponding to UNCTAD’s Export Concentration Ratio. We conducted the tagging by creating keywords for each category and searching for those terms in each title. For example, the “innovation” category’s key terms include research, intellectual property, artificial intelligence, technology, inventions, etc. If at least one word in a category’s keywords is found, we apply that category’s tag. We allow for a law to fall under multiple categories. We do not include laws for which we found no category matches in our analysis. The WEF’s GCI pillars and their definitions are in Table A1.1.

### **Table A1.1 Definitions of policy pillars**

|**Pillar**|**Definition**|
|---|---|
||
than 10 employees increases the number of workers and enters the survey sample, it will be considered as an entry. Figures for entry and exit rates from the earlier survey sample (that includes plants with five and more employees) are available upon request.

8 The trend is the fitted line from the regression of the variable against the fourth-order polynomial function. We prefer using this methodology to the HP (Hodrick-Prescott) filtering for samples with a shorter time period.

8
are commonly located on the household premises at a distance of 10 meters or less from the house (personal communication DPHE, 2022), while tube wells are generally co-located with housing within a relatively small radius ( _e.g.,_ tube wells serve a surface area of an around 150meter radius according to the Bangladesh National Strategy for Water Supply and Sanitation). While pit latrines often serve a single household, tube wells serve on average ten households (personal communication DPHE, 2022).

To model damages, both vulnerability curves (per hazard) and reconstruction costs are required. In Tables D2 and D3, we present an overview of the vulnerability curves and reconstruction costs per asset type, which were obtained from various reports, personal communication with responsible government agencies, and from the literature.

For agricultural land, we look at spatial crop maps derived from satellite data (Singha et al., 2019), which includes the location of agricultural fields used for rice production in the different cropping seasons (Aman, Boro, Aus). We merge rice yield per hectare and rice price per district from the 2019 Agricultural Production report to create a map of crop value exposed. We use a district-specific correction factor such that the agricultural land per district corresponds with the amount of rice produced ( _e.g.,_ to take into consideration that not all agricultural land is used for cultivation). We ignore the timing of the cyclone events with respect to the cropping season. In general, cyclones strike during the months March – July and September – December, which corresponds to the sowing of Aus (March-May) and Aman (June-July) rice, the harvest of Boro rice (April – May), and the harvest of Aman (November – December). Hence, it is likely that the occurrence of a cyclone affects one or multiple rice crops. Without adequate information on 15
Adding to these global and multi-country systematic studies are reviews from the nine countries, again showing wide variation in resistance between different drugs (see Annex 2).

In conclusion, the evidence presented above and in Annex 1 indicates significant variation in AMR rates across different bacteria/viruses, across different first line drugs and across different countries and sub-regions. However, given that the analysis is not conducted by different types of HAI, an average rate is needed for the present study. Rates of AMR are seen below 50%, well above 50%, and also around 50%. Hence, in the base case analysis, an average rate of 50% AMR is used. The sensitivity analysis uses alternative AMR rates of 25% and 75%.

# 3. Annual hospital admissions

In most studies reporting HAI, rates are typically expressed as number of HAIs as a proportion of hospital admissions. Hence, this present study estimates the total HAIs in each country by multiplying the average HAI rate by the total number of hospital admissions in the latest year of data. Data from national statistical reports have been provided for Ethiopia, Kenya, Mozambique, Tanzania and Uganda, mainly relating to 2022 data. These data are presented in Table 6. For countries that did not report recent health sector statistics the number of hospital admissions per year is estimated using an average of 4 inpatient admissions per 100 population.

**Table 6. Estimated number of HAIs based on HAI rate and hospital admissions**

|**Variable**|**Angola**|**Congo,**<br>**Dem.**<br>**Rep. **|**Ethiopia**|**Kenya**|**Madagascar**|**Mozambique**|**Sudan**|**Tanzania**|**Uganda**|
|---|---|---|---|---|-
As noted above, two factors might contribute to the excellent predictive performance of the partial registry method relative to the direct predictions from the household survey. The first is that the geospatial model is trained to a measure of village welfare that is far more precisely estimated than the one used in the sample. The welfare measure is more precisely estimated both because it is based on data from a much large number households from the census extract, and because it is a predicted welfare measure that largely eliminates classical measurement error. More precise training data improves the ability of machine learning to construct a predictive model, by reducing the risk that particular predictive variables will be fit to random noise in the training data, and by improving the accuracy of the cross-validation procedure used to select models.

A second potential reason for the better performance of the partial registry method is that it uses predictions derived from the census model, which is also used as the benchmark measure of welfare. While this is defensible on the grounds that it improves the accuracy of the predictions, using the same values for prediction and evaluation will overstate measured performance. To address this, as a robustness check we consider the accuracy of the geospatial model predictions for all villages, instead of using the partial registry predictions where available as well as out-ofsample predictions. In this case, the rank correlation falls from 0.75 to 0.73 and the AUC coefficient falls from 0.89 to 0.78. However, this still greatly exceeds the performance of the RWI, the second-best method, which has a rank correlation of 0.20 and an AUC of 0.60 (see Figure 4).

**Figure 4.** Rank correlations, AUC, and R-squared coefficient for the partial registry method.

<!-- Start of picture text -->
1.00<br>0.89<br> 0.90 0.84<br>0.78<br> 0.80 0.73  0.72  0.
# **5 Estimation of the ACI for 2007**

The previous developments can be applied quite generally to many spatial interaction networks.

However, as stated earlier, this paper focuses on an application to the global air transportation network. We estimate an _Air Connectivity Index_ (ACI) for 2007, using data for the month of June. The flow variables are sourced directly from data in the _SRS Analyzer_ system. The SRS database provides the number of direct flights per week among over 200 origin and destination countries, including passenger and cargo flights.<sup>15</sup> In the remainder of this section, we present results from the empirical implementation of the model using the approach outlined in the previous section, and provide a comparison with alternative metrics.

## **5.1 Cost Function Specification**

As discussed in Section 4, we derive the potential terms ( and ) as fixed effects using a Poisson estimator. In the trade literature, it is standard to include the simple logarithm of distance as a measure of trade costs (impedance). In the context of air transport, it is also natural to posit that the bilateral impedance is a function of distance, or equivalently time of flight. However, we take a more refined approach to the parameterization of this dependence for two reasons.

First, it is important to allow for a richer set of shapes than the simple logarithm to account for the network structure of air transport. The commercial implications of short- versus long-haul flights are very different. Airplanes can only safely fly a limited distance, thereby making it technically impossible

> developed by Santos Silva and Tenreyro (2006) and Helpman et al. (2008). We prefer Poisson because it is consistent under much weaker assumptions than the Heckman-like estimator used by Helpman et al. (2008). 15 Although from an international trade perspective we are primarily interested in movements of goods, not people, we include passenger traffic in addition to pure cargo flights because a significant proportion,
wages (chasing a moving target) and partly to the better protection of civil servants' real wages compared with that of other groups since the start of the 1990 stabilization.

Table 6: Actual Government Wage Expenditures and its Components (percentage of GDP)

||1988|1989|1990|1991|1992|1993|1994|
|---|---|---|---|---|---|---|---|
|Net Government wages|3.0|4.3|4.1|6.0|5.3|5.3|5.2|
|Gross Government wages|n.a.|n.a.|n.a.|n.a.|6.7|6.7|6.7|
|Government wage cost|4.0|6.0|5.7|8.0|9.1|9.1|9.0|
|**Wedge between wage costs and net wages**|**1.0**|**1.7**|**1.6**|**2.0**|**3.8**|**3.8**|**3.8**|

n.a. not applicable All wage data include central and local govermment expenditures. Wage cost includes contributions to ZUS.

In 1992 net government wages stabilized at around 91 percent of enterprise wages, and rose slightly in 1994. The stabilization of government employees' (net of tax) share of GDP after 1992 still implies a slight improvement of their purchasing power since GDP started to grow again in 1992. There is no clear evidence that there are too many government employees or that they are overpaid. Government employment was apparently (and fortunately) not used as an unemploymentfighting (or hiding) device. Education
# **Introduction**

How much does survey design affect survey results and, therefore, our view of the world? Why is it that the rich report more illnesses than the poor in a large number of surveys? These questions seem largely unrelated but are at the core of this paper. Using the length of the recall period as a lens through which to examine these issues, we show that the number used to fill the blank in the standard screening question, ―Were you sick in the last ___ weeks?‖ can change the results. We compare weekly and monthly recall periods using observational and experimental data and find that not only do different recall periods give very different results, but also that these differences are not uniform across income groups. Reported illnesses, use of health care and the reported labor impacts of sickness are lower in monthly reports, with higher declines for the poor. In the case of doctor visits, the recall period used changes the _sign of the gradient_ between doctor visits and per-capita expenditures (PCE)—in weekly recall surveys, the poor visit doctors more than the rich.

This study stems from a project in Delhi examining morbidity and health-seeking behavior among 300 urban households.<sup>2</sup> At the beginning of the project, in 2000, we noted that the patterns of morbidity and health care-seeking behavior observed in the field among richer households accorded well with theoretical and cognitive models frequently used in the health economics literature. The (relatively) wealthy became sick, selfmedicated as a first resort, and then, sought out treatment from a doctor. For example: ― _I fell sick on Monday with a very bad cold; I took aspirin until Wednesday and then, when I was still not better by Friday, I went to the doctor. The doctor gave me antibiotics, which I took until Wednesday, and now I am fine._ ‖ Yet, among households in poor localities and slums, the underlying models of disease and treatment-seeking seemed to be different
inexperienced potential migrants, ‘low’ death information treatment increased migration by 7 percentage points. This effect, significant at 5 percent level, is almost 30 percent of the migration rate in the control group. For this group, death ‘high’ information also increases migration slightly (9 percent) but the effect is insignificant. Note that since missing data (Attrition-M) is not correlated with death interventions, these points estimates similar to the preferred measure of migration. However, since missing data (Attrition-M) is correlated with wage information treatments, the point estimate for wage information treatments is lower and not significantly different from zero at conventional levels. This is precisely what one would expect if wage interventions led the inexperienced potential migrants to not migrate and therefore more likely to be found during the follow-up survey. Similarly, for the experienced migrants, ‘low’ death information, which increased attrition, has a smaller effect than for other measures. Again, this is what one would expect if ‘low’ death information led the experienced potential migrants to migrate more and therefore less likely to be found during the follow-up survey.

Lee (2009) bounds on effect of the information treatments on the basic measure of migration (MigratedB), also supports that attrition (Attrited-M) captures unobserved migration. As Table 6 shows, the bounds on the effects of the death information treatments for inexperienced potential migrants are tight and similar in magnitude as the effect on the preferred measure of migration (second panel, columns 1 and 2). However, the lower bound on the effect of wage information on migration is similar to the effect on the preferred measure (Migrated-P) whereas the upper bound on the effect is similar to the effect on the basic measure (Migrated-B). That is, selectively dropping a random subset of those who migrated and are from the wage information treatment group, in order to balance attrition, produces an estimate not too different from the effect on the basic measure (Migrated-B). However, selectively dropping a random subset