rafmacalaba/datause-encoder-data huggingface dataset:
and additionally include a measure of country-level ruggedness to account for higher input costs required on more rugged terrain. Given that unit costs might be higher in countries with high levels of rainfall, we include the three year average of lagged precipitation. We further include the log of GDP per capita to proxy for the price of labor and capital. We decided that contemporaneous GDP would lead to too severe endogeneity problems and therefore chose to use GDP from 1985. We use our measures of corruption and conflict to proxy for _A_ , and include two dummy variables indicating that a country is above the median level of political instability or corruption of the sample.<sup>21</sup> Appreciating that road work contracts require a substantial amount of time to negotiate, we lag time varying country level controls by one year.

To account for differences in the source of unit costs, procurement, financing body, and contractor type, _κapit_ is a vector of dummy variables capturing whether the source of costs is estimated or contracted costs, with the base category being actual costs, the financing body (World Bank, bilateral donor, government, or other donor), and if the work was carried out by an international firm or joint venture. All models include work activity fixed effects to control for systematic differences in costs across work activities, year fixed effects to account for worldwide construction industry trends, interaction terms between work type, and five-year dummies to allow for differences in the evolution of costs for different work types, region fixed effects, and an error term.<sup>22</sup> We have missing values for certain countries for some of the explanatory variables. In this case, we follow a procedure known as modified zero-order regression outlined by (Greene 2003, p.60), in which we include a dummy variable that is equal to one if the variable is missing, and replace the missing observations with zero. We are not interested in the coefficients of the missing dummy variables, so we do not report them when discussing the results.<sup>23</sup> In order to interpret the coefficient estimates on the included variables as causal relationships, we would require that _E_ ( _εapit|X apit_ ) = 0, where _X apit_ denotes a vector of all

> 21We take the median of the distinct country-year observations we have in the sample.

> 22Table S.4 in the appendix shows the coefficients of the work type dummy variables including and excluding country level controls. In the discussion of the results in the next section, we always control for work activity fixed effects, but do not discuss the differences in unit costs across these categories as this is not the main focus of this paper. 23Appendix S.2 discusses the robustness of our results to alternative ways of dealing with missing variables.

14
poor. Policies like the JDY, with its relatively easy documentation process, shorter travel time and lack of minimum balance requirements, could prove to be key in achieving the government’s goal of increasing account ownership. The Reserve Bank of India’s New Payment Banks, more common in remote areas than traditional banks, also help to cut travel costs and improve access for rural residents. The overall increase in account ownership in the last few years, particularly for women and poor adults, is an important step in the right direction. That said, the take-up of any government scheme, including the success of the JDY program, depends on a host of factors, such as demographic distribution, cultural norms, physical infrastructure, and effectiveness of outreach.

This survey reveals several opportunities for India to build on its financial inclusion successes. Clearly communicating documentation requirements for JDY accounts, and encouraging the use of the existing eKYC electronic authentication system, are potential steps. The survey suggests that the vast majority of adults who cite lack of documentation as their reason for being unbanked actually do have what is needed: 94 percent report having an _Aadhaar_ number. If unbanked adults were better informed about application requirements, they might choose to open an account.

Better marketing of the policy is also essential for addressing the concerns of the 35 percent of adults who said they did not apply for an account as the process is too expensive. Adults need to be informed that JDY accounts are free and there are no minimum deposit requirements—a person could easily open a zero-balance account, which does not require any initial deposit. However, the survey shows that about a fifth of JDY applicants were asked to offer some form of unofficial payment, and about half were required to make a minimum deposit. One of the potential ways to increase accountability may be better governance of the agents. Since agents are seen as the face of the bank, particularly in rural areas where bank branches are not widely present, better agents could help anchor trust for financial institutions, which would further the goal of financial inclusion.

The ultimate goal of the JDY scheme is to expand financial inclusion. While the first step involves opening a bank account, it is important to ensure that adults use their account in beneficial ways. The 2014 Global Findex found that 43 percent of accounts in India were dormant. Although the survey does not ask about account usage, official JDY account figures 24
Somali regions.<sup>10</sup> Several terrorist attacks occurred during fieldwork of the SHFS.<sup>11</sup> The analysis exploits detailed household data with dates of interviews and GPS positions to evaluate the immediate impact of attacks against civilians in Somalia. The types of incidents considered correspond to attacks from Al-Shabaab against civilians.<sup>12</sup> We concentrate on measuring the effect within a week due to data availability given that i) the questionnaires used a recall period of 7 days for food items, which is the main component of the consumption aggregate; and ii) only one household was interviewed 8 days or more after an incident.

# 3.1 Data sources

The main sources of data correspond to detailed household information on sociodemographic characteristics, perceptions and poverty conditions from Waves 1 and 2 of the Somali High Frequency Survey, as well as location and time of attacks from The Armed Conflict Location & Event Data Project (ACLED). In this way, household data from the SHFS is combined with geo-tagged information on attacks to identify households exposed to terrorist incidents.

Wave 1 includes 4,117 households interviewed between February and March 2016, ‐ which are representative of 9 of the 18 Somali pre war regions, as the remaining areas were inaccessible for security reasons at the time of fieldwork. Wave 2 expanded the ‐ coverage to 17 pre war regions, interviewing 6,092 households between December 2017 and January 2018.<sup>13</sup> ACLED data records locations and intensity of armed conflict coded by researchers who collect information from secondary sources, NGO reports, local and international news reports and research publications (Raleigh and Dowd 2015). The database is unique due to its geographical level of precision when reporting the latitude and longitude of the attacks, indicating the location of an incident (Figure 2). All the violent incidents reported in ACLED that occurred a week before the start and end of data collection of Wave 2 of the SHFS were considered as consumption data is recorded for the week preceding the interview. Attacks from Al-Shabaab against civilians were manually identified.

> 10 Interviews were conducted between the February 10 and March 17, 2016 for Wave 1, while between the December 4, 2017 and January 16, 2018 for Wave 2.

> 11 The incidents did not affect data collection plans of Wave 2. The share of completed interviews increases across weeks during fieldwork, as it would be expected.

> 12 Al-Shabaab perpetrated attacks outside Somalia for the first time in 2010 in Kampala, and a few years later on the Westgate mall in Nairobi, suggesting that the organization is part of the global network with strong connections to Al-Qaeda (Varin and Dauda 2017).

> 13 The SHFS was not designed as a panel survey. Waves 1 and 2 correspond to repeated cross-sections, representative of Somali regions.

7
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**Table 7 The Determinants of Evasion Gaps -Allowing for More Flexible Functional Form**

|**The Detem**|**inants of Evas**|**ion Gaps – Allo**|**wing for Mor**|**e Flexible Fun**|**ctional Form**||
|---|---|---|---|---|---|---|
|_Dependent Variable:_|_Log Va_|_lue Gap_|_Log Qua_|_ntity Gap_|_Log Pr_|_ice Gap_|
|||V<br>||Q<br>||P<br>|
||(1)<br>**coef/se**|(2)<br>**coef/se**|(3)<br>**coef/se**|(4)<br>**coef/se**|(5)<br>**coef/se**|(6)<br>**coef/se**|
|(T≥40)|0.169***|0.246***|0.163**|0.227***|0.000|0.014|
||(0.058)|(0.065)|(0.070)|(0.079)|(0.026)|(0.032)|
|(20≤T<40)|-0.047<br>|-0.068<br>|-0.034<br>|-0.065<br>|-0.016<br>|-0.010<br>|
||(0.054)|(0.058)|(0.066)|(0.073)|(0.025)|(0.030)|
|Ben Ali %|0.426***||0.111||0.298***||
||(0.132)||(0.134)||(0.08)||
|Offshore %|-0.076||-0.004||-0.071***||
||(0.061)||(0.067)||(0.021)||
|Public %|<br>-0.286***||<br>0.117||<br>-0.417***||
||(0.076)||(0.091)||(0.050)||
|Ben Ali % * (T≥40)||0.881***||0.138||0.705***|
|||(0.209)||(0.200)||(0.138)|
|Ben Ali % * (20≤T<40)||0.234||0.209||0.034|
|||(0.162)||(0.200)||(0.103)|
|Ben Ali % * (T<20)||-0.339||-0.265||-0.077|
|||(0.343)||(0.361)||(0.132)|
|Offshore % * (T≥40)||-0.206**||-0.112||-0.098***|
|||(0.086)||(0.090)||(0.027)|
|Offshore % * (20≤T<40)||<br>0.110||<br>0.147||<br>-0.024|
|||(0.097)||(0.112)||(0.037)|
|Offshore%* (T<20)||<br>0.038||<br>0.085||<br>-0.044|
|||(0.119)||(0.144)||(0.050)|
|Public% * (T≥40)||<br>-0.446***||<br>0.045||<br>-0.482***|
|||(0.138)||(0.170)||(0.097)|
|Public% * (20≤T<40)||<br>-0.223**||<br>0.208*||<br>-0.441***|
|||(0.102)||(0.116)||(0.075)|
|Public%* (T<20)||-0.194||0.089||-0.318***|
|||(0.158)||(0.199)||(0.089)|
|Log (NTB+1)|0.007|0.003|0.018|0.013|-0.013|-0.013|
||(0.022)|(0.022)|(0.025)|(0.025)|(0.011)|(0.011)|
|Country*Year FE|<br>Yes|<br>Yes|<br>Yes|<br>Yes|<br>Yes|<br>Yes|
|<br>N|49347|49347|48724|48724|48724|48724|
|Number of products|1386|1386|1386|1386|1386|1386|
|R2|0.038|0.040|0.034|0.034|0.034|0.036|
|Adjusted R2<br>Note: Standard errors are|0.036<br>clustered bypro|0.037<br>duct. *** p=<0|0.031<br>.01, ** p<0.05|0.032<br>, * p=0.10. The|0.032<br>sample is conf|0.034<br>ined to product|

Note: Standard errors are clustered by product. *** p=<0.01, ** p<0.05, * p=0.10. The sample is confined to productsource-year combinations (i) in which imports reported in Tunisia and exports reported in partner countries are strictly positive, (ii) from countries which are among the top 15 source countries in terms of cumulative import value (either as declared by partners or as declared by Tunisian customs) over the period 2002-2009, and (iii) products which account for at least 0.01 percentage points of import value (either as declared by partners or as declared by Tunisian customs) over this period.

44
prevailing official block tariff schedule.<sup>23</sup> The solid line in Figure 4 shows monthly predicted margins (and confidence intervals) from a regression of _actual_ log monthly consumption on month since connection dummies, which also controls for preferences _θ_<sup>ˆ</sup> _i_ and for calendar month _×_ year fixed effects to absorb aggregate shocks.<sup>24</sup> Water usage data cover the first 40 months of the utility’s operation in the commune prior to our experiment and includes all (and only) the 1462 households in the experimental sample. The dashed curve in Figure 4 is derived from exactly the same regression procedure but with the dependent variable now being model-generated log monthly consumption; i.e., an unbalanced panel of household specific time-series beginning at initial connection. With the exception of the first and last few months of connection, our model’s out-of-sample fit is quite good. Most encouraging is that model and data seem to agree on how long it takes, on average, for consumption to reach its steady state level starting from the initial month of connection; a static model of demand, by contrast, would trivially, but counter-factually, predict that steady state consumption is achieved at _t_ = 1.

# **5 Optimal pricing of a new utility service**

We are now ready to investigate the optimal pricing of a new utility service. Returning to the model of Section 3, the _ex-ante_ expected indirect utility, i.e., prior to consumption of the service, may be defined as _Ex-post_ utility, i.e., once consumption has reached its long-run steady state level, is similarly given by In defining these utilities, we abstract from transition dynamics. Thus, equation (15) assumes that _C_<sup> ̄</sup> 0 = 0 and remains so over time, whereas equation (16) assumes that _C_<sup> ̄</sup> _t−_ 1 reaches _C_<sup> ̄</sup> _∞_ in

> 23While time-varying demographics, _Nit_ , were not collected prior to the experiment, the estimated household fixed effect _θ_<sup>ˆ</sup> _i_ incorporates the household average demographics _N_<sup> ̄</sup> _i_ over the course of the experiment.

> 24Note that, in an alternative household fixed specification, we could not distinguish cohort (month of connection) and time (aggregate shock) effects.

28
**Figure 3. FHH–non-FHH Differences in Headcount Poverty Rate (percentage points)**

Panel A: Self-reported FHH Panel B: Share of females>0.5 <!-- Start of picture text --> 2012201520172020 ****** *** *** *** *** *** 2012201520172020<br>20072012 *** * ** 20072012<br>20102013 * ** *** 20102013<br>2004200820142019 *** *** *** ** ********* 2004200820142019<br>2007 2007<br>200920172011 *** ** ** *** 200920112017<br>2005201020152021 ******** *** * * * ** ****** 2005201020152021<br>-5 0 5 10<br>-15 -10 -5 0 5<br>Panel C: Potential FHH Panel D: Educated females<br>2012201520172020 *** *** *** ** * ****** *** ** * 2012201520172020<br>20072012 * * ** 20072012<br>20102013 ** * * * * ** *** 20102013<br>2004200820142019 **** ** * ********* ** * 2004200820142019<br>2007200920172011 *** ****** *** ** *** *** 2007200920112017<br>2005201020152021 ** * ** * * * *** *** 2005201020152021<br>-40 -30 -20 -10 0 -20 -10 0 10<br>Egypt Egypt<br>Iraq Iraq<br>Jordan Jordan<br>Mauritania Mauritania<br>Palestine Palestine<br>Tunisia Tunisia<br>Egypt Egypt<br>Iraq Iraq<br>Jordan Jordan<br>Mauritania Mauritania<br>Palestine Palestine<br>Tunisia Tunisia<br><!-- End of picture text -->

**Note:** Headcount poverty rates are estimated using per capita household expenditures. Stars indicate significantly higher headcount poverty ratio between FHHs and non-FHHs in each category. ***, **, and * denote statistical significance at the 0.01, 0.05, and 0.10 levels. Negative difference means FHHs are less likely to be poor.

37
complemented by checks designed to catch errors and signal outliers. Among other things, the checks verify that the resulting SAMs are balanced, have values in all obligatory cells, and only have negative values when this is legitimate.

Depending on data availability, the program may deviate from the procedure of Section 3. This may be due to that exogenous data are available for values that are residual under the above procedure and at the same time missing (or deemed to be of relatively low quality) for some information that currently is imposed. For example, if data for government savings are available (which is commonly the case), then this information could be imposed instead of being residual. If so, some other item would typically have to be residual, for example transfers to the household from the government.

Table 4.1 summarizes the country coverage in the database by type of SAM and country income group; data were insufficient for excluded countries. Of a total of 218 countries (across all income groups), sufficient information was available to produce Macro SAMs of all three types for 133 countries. Not surprisingly, the higher the income group, the higher the coverage rate. However, also among low‐income countries coverage seems good (considering general data paucity).

**Table 4.1. Country coverage of macro SAM database**

|Item|Low<br>income|Lower<br>middle<br>income|Upper<br>middle<br>income|High<br>income|Total|
|---|---|---|---|---|---|
|# of countries covered|17|36|34|46|133|
|# of countries ingroup|31|53|56|78|218|
|Share of countries covered(%)|54.8|67.9|60.7|59.0|61.0|

Source: Authors’ elaboration.

# **5. SAM description: An example**

This section demonstrates how selected pieces of information contained in a SAM may be extracted and presented in a set of tables that is in line with how data more often are presented, albeit with the disadvantage of not making links between accounts explicit (which is done within the SAM format). The information, presented in Tables 5.1‐5.5, covers GDP structure (from the spending side), the current and capital accounts of the balance of ‐19‐
Household characteristics are similar across program and comparison schools (Table 2): there are no significant differences in terms of parent education, number of siblings, or the ownership of a latrine, iron roof, or mosquito net (using data from the 2002 student surveys), indicating that the randomization was largely successful in creating comparable groups.<sup>10</sup> Further evidence is provided by comparing the 2000 (baseline) test score distributions, which are nearly identical graphically for cohort 1 girls in Busia (Figure 2). More formally, we cannot reject the hypothesis that average baseline test scores are the same across program and comparison schools for students in the restricted sample, as discussed below.

Another estimation concern is the possibility of cheating in program schools, but this appears unlikely for a number of reasons. First, district records from external exam invigilators indicate there were no documented instances of cheating in any sample school during either 2001 or 2002 exams. Several findings reported below also argue against the cheating explanation: test score gains among cohort 1 students in scholarship schools persisted a full year after the exam competition, when there was no longer any direct incentive to cheat, and there were substantial, though smaller, gains among program school boys ineligible for the scholarship. There are also program impacts on several objective measures of student and teacher effort – most importantly, school attendance measured during unannounced enumerator school visits.<sup>11</sup>

# **<u>4.2 Sample Attrition</u>**

Teso district primary schools had higher rates of sample attrition than Busia schools in 2001. The gap in attrition across program versus comparison schools was also greater in Teso district, and students who attrited from the sample in Teso appear to be systematically better students than those who participated in the program. These patterns all complicate causal inference in Teso district.

> 10 This comparison in Table 2 relies on the assumption that the household characteristics (i.e., parent education, fertility, and asset ownership) were not directly affected by the scholarship program by mid-2002, which seems reasonable. There is no analogous baseline household survey data.

> 11 Jacob and Levitt (2002) develop an empirical methodology for detecting cheating teachers in Chicago primary schools, which relies on identifying classes where test scores rose sharply in a single year (the year of the cheating) and not in other years, and where many students had suspiciously similar answer patterns. Although we cannot examine the second issue, since we only have total test scores for the district exams, the finding of persistent test score gains in the year following the competition argues against cheating as an explanation for our main results.

15
# **2.  The Global Distribution of Methane Emissions**

The global standard for “bottom-up” methane emissions reporting is the Emissions Database for Global Atmospheric Research (EDGAR (2022)), which combines detailed sectoral activity data with emissions factors that are adjusted for broad regional differences (Crippa et al. 2020; Solazzo et al. 2021). EDGAR reports estimated emissions<sup>4</sup> by sector in raster files with a resolution of 0.10 degrees (approximately 10 km), yielding information for 6,480,000 grid cells. Table 1 reports global totals from these files by sector in 2018, the latest year for which complete estimates are available. Table entries are displayed in descending order of emissions, which total 375.2 million tons.<sup>5</sup> For expositional clarity and simplicity, we confine our analysis to the seven sectors that account for 90% of global methane emissions. Two are in agriculture (Enteric Fermentation, Agricultural Soils), three in energy (Gas, Coal, Oil Exploitation) and two in the waste sector (Waste Water Handling, Solid Waste Landfills).

Priority site selection is most effective when the spatial distribution of emissions is dominated by relatively few large sources. We assess the potential effectiveness of priority-setting in this case by calculating total methane emissions for countries’ Level 1 administrative units (GADM1s). We expand the dataset to include emissions from countries’ marine exclusive economic zones (EEZs), because many oil and gas operations are offshore. We tabulate estimated emissions in descending order, calculate the cumulative percentage of total emissions, and divide the results into deciles. For example, the first decile comprises the largest-emitting areas whose total emissions account for 10% of the global total.

Figure 1 provides a summary table and graph for emissions from 3,812 areas that comprise Level 1 ADM1 and EEZ units. The tabIe reveals a highly-skewed spatial distribution; in the top decile, 7 areas account for 10% of global methane emissions. In the next four deciles, 10% of emissions are accounted for by 14,18, 28 and 40 areas, respectively. In summary, only 2.8% (107) of the 3,812 areas account for 50% of global methane emissions.

Emissions deciles for all areas are mapped in Figure 2, which shows that the 39 areas in the top 3 deciles (accounting for 30% of total methane emissions) are concentrated in relatively few countries. Table 3 displays these areas in descending order of emissions, along with sectoral percent shares. Among the 11 countries represented, China accounts for 17 areas, followed by India and Brazil (6 each), Pakistan and the US (2), and Qatar, the Russian Federation, Saudi Arabia, the Islamic Republic of Iran, Nigeria and Indonesia (1).

> 4 For brevity, we refer to anthropogenic emissions as emissions in this paper.

> 5 As a greenhouse gas, methane is far more potent than CO2.  Applying the standard conversion factor (25), total methane emissions are the equivalent of 9.4 Gt CO2.

4