rafmacalaba/data-use-mentions-extended huggingface dataset:
models (not reported) were also run with specific types of behavior change as the dependent variable and demographic and other characteristics as the independent variables.

The study was approved by the Institutional Review Boards of the Ifakara Health Institute, the University of California at Berkeley, and by the Tanzania National Institute for Medical Research. All participants provided written informed consent.

# _Outcome Measures_

This analysis is based on self-reported baseline intentions to change sexual behavior and selfreported actual change over the first 4-months of the trial, in addition to baseline self-reported risky sexual behaviors. As with any study that involves self-reported sexual behavior, there is the possibility that unsafe sexual behaviors were under-reported and behavior change was over-reported. Plummer (2004) reports on the validity of the collection of sexual behavior data using five different methods from a study done in Northern Tanzania. While this study was conducted amongst adolescents, and results among adults may be slightly more congruous across the five methods, they find striking inconsistencies in reports of sexual behavior from self-administered surveys, face-to-face surveys, in-depth interviews, participant observation and biological markers. The authors do find that in-depth interviews may be better at eliciting true responses (as measured by biological markers) than face-to-face questionnaires or self-administered questionnaires, but still inconsistencies were common. Social desirability bias is common in these types of studies, and in this case may have been exacerbated by the extended counseling on safer sex practices that participants were receiving throughout the study.

The primary outcomes used in this analysis were as follows: <u>Anticipated behavior change at Baseline: The following question was asked of all study participants at the</u> first study visit at baseline, before participants had received STI or HIV test results and before they had been randomized into one of the three study groups: _Do you anticipate that your enrollment in this study will change your sexual behavior?_ Participants could answer yes, no or don’t know. All those who answered _yes_ to the above question were then asked, _In what way?_ Possible responses
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financial institution with lower capitalization requirements, taking deposits from the public and providing loans primarily to microenterprises and small businesses.

> 33 Julia Paxton and Carlos Cuevas, A Worldwide Inventory of Microfinance Institutions, Sustainable Banking wiith the Poor (SBP) Project. Washington, DC: The World Bank, 1997.

> 34 Philippines - National Summit of the Coalition for Microfinance Standards, August 20, 1998.

32
This survey was conducted in two parts that were designed to collect information on overall program implementation and specific processes, as well as the program knowledge and satisfaction among beneficiaries. Part 1 consisted of observations (including a worker roll call, checks of attendance, and other records) and an interview with a contractor representative on site; part 2 consisted of an interview of one man and one woman randomly selected among the beneficiaries. A total of 279 beneficiaries—3 percent of the 8,883 people working on the 141 subprojects—were interviewed, around half of whom were women.

The endline household survey was administered concurrently among treatment and control households: at the end of implementation for the treatment group and immediately before implementation for the control group (July–August 2012). Thus, although the households in the control subprojects had already been identified for enrollment in the program, they had not yet started working. The endline survey was administered to an average of 20 beneficiary households in each of the 275 subprojects, for a total of 5,506 beneficiary households.<sup>15</sup> The survey covered a range of topics, but focused on measuring the program effects along the following dimensions: (1) labor market outcomes and economic activity; (2) household assets, consumption, and savings; and (3) the utilization of education and health services.

> nonoutliers. For the Winzorization, values above the 99th percentile of the distribution and values below the 1st percentile were replaced with the 99th percentile and with the 1st percentile of the distribution, respectively. Food expenditure and savings were winsorized at the 3rd percentile, considering the volume of outliers.

> 14 Two treatment sites were inactive at the time of the unannounced visits.

> 15 One subproject in the Western Area was canceled during the implementation of the impact evaluation (IE).

7
surveyors were fairly successful at maintaining respondent consistency. In the observational data the primary respondent accounted for 79.8 percent of all reports at the weekly level and the fraction drops somewhat to 77.5 percent in the monthly reports. Despite the qualitatively similar averages, the difference between the two is significant at the 90 percent level of confidence. We therefore estimate recall effects with an additional indicator variable that takes the value 1 if the respondent was the designated primary respondent (for weekly reports aggregated to the monthly level this becomes the fraction of weeks that the primary respondent answered). For the reported number of acute illnesses and total self-medication we find small respondent effects (when the respondent was the primary respondent, reports increase) with significance at the 90 percent level of confidence. In these cases, the estimated recall impact decreases from 0.57 to 0.55 (number of acute illnesses) and from 0.95 to 0.947 (total self-medication) and the declines are not statistically significant. For the other outcome variables considered here, we are unable to detect respondent effects in these specifications (with p-values on respondent identity ranging from 0.32 to 0.76) and there is no impact on the estimated size of the recall effects.

_Income or Education?_ Finally, heterogeneity in the recall impact by PCE could be driven by covariates correlated with PCE rather than PCE itself. Although most naïve estimates of income-elasticity used in the health literature suffer from similar interpretational issues, it is possible that education (which is highly correlated with PCE) rather than PCE drives the greater recall impacts among the poor. We re-estimate the heterogeneous impact of the recall period with PCE at the household-level with an additional interaction between the education of the household head and the recall period. These regression results are reported in Appendix Table 2. Across the outcomes considered here, we find two consistent patterns. First there is a small drop in the size of the recall impact and in its interaction with PCE, suggesting that part of
_ANDR EE_<sup>_ ́_</sup> _AND PAPE_ _SEPTEMBER 5, 2023_ _12_ The food-specific scores are generated as: where _hd_ is constructed in the same way as _h_ , but on the grids _R_<sup>2</sup> _d_<sup>:=(</sup><sup>_R_</sup> _low_<sup>2</sup><sup>_∗_</sup> _R_<sup>2and</sup><sup>_S_:=(0</sup><sup>_, ..., μS_12</sup> ̄<sup>Notethatduetotheharmonic</sup> _d_<sup>_/R_2</sup><sup>_, ..., R_</sup> _d_<sup>2</sup><sup>_, ...,_1)</sup> _N,d_<sup>_, ...,_1).</sup> averaging of data availability in equation 8, the trust score is always guaranteed to be 10 when the data is fully observed even when no data on the covariate side is available.<sup>6</sup> Values below 6 are dis-satisfactory, indicating that the monitor is dysfunctional. Values of 6 _−_ 8 are in the moderate to good range, and values 8 and above imply that price-tracking is very reliable, and values above 9 imply that the impact of any missing data is unnoticeable.

# Intra-month price ranges

Since there is substantial intra-month volatility that is hidden by static monthly price quotes, the price-level estimates are accompanied by intra-month price range estimates. The aim is to be able to compare price changes to their typical shortrun variations, so to put the significance of change into context. Broadly speaking, a 10% change in basket prices would be a significant rate of inflation if the typical monthly variation in prices is less than a percent, but
.

Self-governance can promote the formation of social capital but also reduce trust in and engagement with the state and related groups. First, if self-governance presents an alternative to absent state institutions, it may reduce engagement with the state and the government even when the state regains control. Moreover, such disengagement can reduce demand for state services and create informational problems that prevent the efficient provision of public goods. Second, disengagement can lead to lasting norms of distrust of the state and associated groups such as large agricultural producers, preventing investment and reinforcing the subsistence economy through the present day. We study the validity of this mechanism by examining contemporary attitudes towards the state, public goods provision, and agricultural outcomes.

Table 4 presents the estimates of equation (1) using data from the Latin American Public Opin28
Douidich et al. (2015) estimate the relationship between consumption poverty and household characteristics using a household consumption survey and then use this model to predict consumption poverty into a series of annual labor force surveys. This approach enables users to leverage existing data sources. If one decides to collect new data for the estimation of poverty, one has the option of only collecting the covariates _x_ , since an estimator for _θ_ can be obtained from the secondary data source. Put differently, the covariate-only sample is typically part of data collection planning when _μ_ ˆ<sup>_DS_</sup> is used but this is not necessarily the case when _μ_ ˆ<sup>_NN,DS_</sup> is used.

ˆ We will not formally derive the precision of _μ_<sup>_NN,DS_</sup> here as this would require us to make assumptions about the dynamics of the model parameters. When a previous survey is used as in Douidich et al. (2015) for example, one would have to make an assumption about how the model has evolved over time (or assume that it is time-invariant), which is not required for _μ_ ˆ<sup>_DS_</sup> . In Section 4.1 we will however provide a brief discussion on the financial costs savings that may be expected when using the non-nested double sampling estimator (under some simplifying assumptions).

# **3 Cost efficient sampling**

To see whether a meaningful reduction in costs is achievable, we examine the trade-offs between financial costs and statistical precision analytically. A stylized yet informative financial cost function is used. For a measure of statistical precision we appeal to the analytic expression for the asymptotic variance. The advantage of studying this trade-off analytically is that it allows us to work out the conditions under which double-sampling may be expected to be most beneficial. And, similarly, under what conditions the benefits will be marginal.

Specifically, we consider the problem of minimizing financial costs given a statistical precision constraint and its dual
**Table 2:** Summary data

||**Min**|**Max**|**Mean**|**Observations**|
|---|---|---|---|---|
|**Pollution, annual concentration**(_ug/m_<sup>3</sup>)|0.10|40.40|7.30|39,885|
|**Annual Sales**|6.64|217,146,739|491,574|39,885|
|**Employees**|0|19,480.00|250.00|39,885|
|**Wages**|0|4,724,614|51,613||
|**Number of unique frms**||||1,204|
|**Periods**||||11|
|**Number of cities in 2016 above the average of**5_ug/m_<sup>3</sup>||||82|

# **5.3 Power-sector data**

The power sector in Chile is mostly privately owned, with generation, transmission, and distribution being privately run.<sup>4</sup> A comprehensive effort to make generating companies more competitive in the early 1980s resulted in significant reforms that led to the privatization of the two largest government generators (Serra, 2022). Part of the reform, as stipulated by the 1982 Electricity Law and subsequent amendments, was to ensure that electricity companies in the same area are connected in order to maintain service security and to minimize system operating costs.

The unbundling mechanism resulted in two large interconnected systems; the Central Interconnected System, which accounts for approximately 75% of the country’s power generation and serves mostly households, and the Big North Interconnected System, which serves mostly mining operations.
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, 2024; Eloundou et al., 2023), used as a blueprint for the GBB scoring method.

In step 1, we tag occupations at 4-digit level in ISCO-08 into three categories established by GBB: “automation potential”, “augmentation potential” and “the big unknown”. We then rely on the ILO harmonized microdata collection<sup>12</sup> to obtain the shares of employment at 4-digit level occupations for 18 countries for each of these three AI exposure categories (Figure 7). We also calculate the shares that such exposed occupations make up in the higher, 2-digit level of occupational classification. From this step, we switch to the harmonized household surveys from the SocioEconomic Database for Latin America and the Caribbean (SEDLAC) to calculate AI exposure across and within 16 Latin American countries.<sup>13</sup> Our sample of SEDLAC data consists of about 900,000 individual survey observations, with details by country provided in the Appendix (Table A1).

The advantage of SEDLAC database is that it contains a host of harmonized variables at the individual level, including the income aggregates used to measure poverty, as well as demographic

> 11 See Figures A2 and A3 in the Appendix for a quick visual comparison of GBB scores to Felten et al. (2023) and Prytkova et al. (2024). Felten et al.’s scores cover a wider range of AI than GenAI covered by GBB. Prytkova et al. (2024) focus on tech abilities in patents, which can be far from readiness for market-level deployment. A detailed analysis of these scores at the 4-digit occupational level is available upon request.

> 12 Calculations of 2-digit employment from ILO Micro data repository by David Bescond, ILO STATISTICS.

> 13 SEDLAC is produced by the University of La Plata’s center for Distributional, Labor and Social Studies (CEDLAS) and The World Bank’s Equitable Growth, Finance and Institutions LCR-POV-Poverty and Equity Group (ELCPV). This project aims to
Also, we add community variables that indicate the frequency of events based on historical data. For this purpose, we use the database from EM-DAT (Guha-Sapir et al., 2022), which offers a comprehensive database of natural disasters that took place between 2000 and 2023 at the state and district levels. The database is generated from various sources, including UN agencies, nongovernmental organizations, reinsurance companies, research institutes, and press agencies. The variables obtained from EM-DAT include the number of occurrences of each natural disaster (including floods, landslides, cyclones/hurricanes, drought, wildfires, or extreme temperature events) that took place in a specific year of survey. Additionally, we considered a set of variables that indicate the frequency of the same natural disaster occurring during the lag year of the survey period.

We include drought variables using SPEI data at community level (ADM1, ADM2, or ADM 3). SPEI is a drought index proposed by Vicente-Serrano et al. (2010). The SPEI has a spatial resolution of 0.5°; thus, to aggregate to ADM 3, ADM 2 or ADM 1, we impute each administrative unit with the value of SPEI following the equation i max{a m ); a )} where a is for absolute value and is max or min depending on the result of the conditional ii mm mm mmiimm function. SSIISSII = SSIISSII aa(SSIISSII aa(SSIISSII aa mm Different scientific disciplines use this multi-scalar index to measure drought severity according to its intensity and duration. The advantage of the SPEI regarding other drought indexes, such as the Palmer Drought Severity Index (PDSI) or the Standardized Precipitation Index (SPI), is that it considers the effect of the potential evapotranspiration (PET). It is measured using different time scales to identify different drought types. Short-time scales are mainly related to soil water content and headwater river discharge; medium-time scales involve reservoir storage and river discharge, while long-time scales are associated with variations in groundwater storage. Thus, Acosta et
paying PIT, but do not report doing so. Nonetheless, we are only able to identify about 24 percent of the government revenue figures (a gap of JOD 89 million<sup>22</sup> ), likely due to the richest households not appearing in the survey.<sup>23</sup> We allocate the missing PIT to those households that we had found pay this tax in HEIS and the amount allocated is proportional to their income. We do not have disaggregated administrative records of PIT payers across the distribution to allocate missing taxpayers by decile or income level.

# Allocating indirect taxes: General (GST) and special (SST) sales taxes

To estimate the impact of GST and SST we match the rates to the various expenditure categories in the household survey. If an item is not specifically listed in the GST codes annex, we use a rate that applies to similar items (for instance, guavas and pomegranates are not listed in the GST code but we assign them the tax rate applicable to other fruits). If no similar items appear in the tax code, we apply the general rate of 16 percent. Because the level of disaggregation in HEIS and the GST codes is not the same, there are a few occasions where two GST categories apply to one HEIS item. In this case, we divide household expenditures equally and apply each GST rate to each half. Taxes are only calculated for items that are part of the consumption aggregate except for purchases made outside of the country, which are not taxed nationally, housing rent (actual or imputed), for which we assume a zero GST rate. Because the flow-value of durable goods is included in the consumption aggregate to estimate poverty, we also estimate the value of the tax that would be paid for this value.

The direct household burden of the tax is estimated by multiplying total household expenditures (excluding self-production and gifts<sup>24</sup> ) by the effective tax.<sup>25</
# **3. Modeling the Impacts of Petroleum Subsidy Reform**

Assessing economic and poverty impacts of petroleum subsidy reform requires an economy-wide model that captures the major linkages between subsidy reduction, production, consumption, and households. In addition, given that many of the effects arise from changes in relative prices, social accounting matrix (SAM)–based CGE models are more suitable than SAM-based multiplier models that have previously been used in comparable studies.

The CGE model is used in this paper and is constructed consistently with the neoclassical general equilibrium theory. The theoretical background and the analytical framework of CGE models have been well documented in Dervis, de Melo, and Robinson (1982); the detailed mathematical presentation of a static CGE model is described in Lofgren, Harris, and Robinson (2002). The recursive dynamic version of the CGE model is based on this standard CGE model with the incorporation of a series of dynamic factors. The early version of this dynamic CGE (DCGE) model can be found in Thurlow (2004), and its recent applications include Diao et al. (2007) and Breisinger, Diao, and Thurlow (2009), and Breisinger et al. 2010. A summary of the main equations can be found in Tables A.2 in the Appendix.

To develop the DCGE model for Yemen, we first update a 2007 SAM to represent Yemen’s economy in 2009 as the main database for the model. For the updating process we used national accounts data for 2009 provided by the Ministry of Planning and International Cooperation (MOPIC). Major data sources for the 2007 SAM construction include the latest supply-use table from the Central Statistics Organization (CSO), balance of payments from the Bank of Yemen (BoY), government budget data from the Ministry of Finance, the 2008 Agricultural Yearbook from the Ministry of Agriculture and Irrigation, and the latest Household Budget Survey (HBS 2005/2006). These data sources have been complemented with information from the International Monetary Fund and the World Bank
conventional bricks, iii) providing investment subsidies, tax breaks or arrange carbon credits for alternative bricks, and iv) developing markets for alternative bricks through mandates and regulation and awareness, e.g., governments must use alternative bricks in public buildings and infrastructures, temples, and other public facilities, and local municipalities can also mandate residential buildings to use alternative bricks.

# **7. Concluding Remarks**

The brick industry in Nepal is one of the primary sources of CO2 emissions and local air pollutants. The main fuel used in the brick industry is coal, which is imported, mainly from neighboring India. Coal is responsible for about one-third of the current national CO2 emissions from fossil fuel sources. The reduction of CO2 emissions from the brick industry is crucial for meeting the country’s nationally determined contribution by 2030 and net-zero emission target by 2045. Similarly, brick kilns are one of the main sources of local air pollution, particularly PM2.5, around major cities where concentrations of PM2.5 exceed WHO standards by many folds. This study offers an economic analysis, from both private and social perspectives, of various alternatives to reduce coal consumption and corresponding emission reductions from the brick industry. The main data for the economic analysis is collected from primary sources through a field survey. The paper also offers policy discussion to reduce emissions from the brick industry in Nepal.

The economic analysis finds the costs of brick production vary across the provinces due to variations in input variables (e,g., cost of fuels, labor, land). The production costs of bricks produced from fixed chimney varies between NRs 16 and NRs 20 per brick across the provinces. In the case of the zig-zag chimney, which is more fuel efficient than the fixed chimney, the production costs are between NRs 17 and NRs 19 per brick. If a carbon tax of US$100/tCO2 is introduced, it would increase the current coal price by about 50%, thereby increasing the national average costs of fixed chimney