rafmacalaba/data-use-mentions-extended huggingface dataset:
-8.50<sup>_∗∗∗_</sup><br>(0.50)|-13.61<sup>_∗∗∗_</sup><br>(0.78)|
|Average|100.07|129.38|23.08|58.72|
|% of Average|-24.97|-23.58|-36.85|-23.18|

**Note** : Controlling for temperature and humidity, with hourly, weekly, monthly, day-of-week and monitor fixed effects. Contains data from 22nd January to 12th May for 2020 and 2023. The ’Average’ is based off the pre-Covid average in 2020. Regulatory data comes from monitors operated by the CGQA. Satellite estimates are generated from the MERRA-2 assimilation. The satellite value is the MERRA-2 PM2.5 estimate closest to the Bel-air regulatory-grade monitor station. The Low-cost measurements in Column 3 are the unadjusted values from all Purple Air monitors active in 2020 or 2023. The Local calibration is based on calibrating the unadjusted Purple Air data using co-located regulatory data from two local monitors. Winsorizing outliers at 99th percentile.<sup>_∗_</sup> _p <_ 0 _._ 05,<sup>_∗∗_</sup> _p <_ 0 _._ 01,<sup>_∗∗∗_</sup> _p <_ 0 _._ 001.

the coefficient is larger (though still smaller than the coefficient in the regression using regulatory data), and the percent decrease becomes perfectly in line with the regulatory and satellite data.

These findings are encouraging because they demonstrate that it is possible to measure similar impacts of a policy on PM2.5 pollution at the city level using three different data sources. Thus, if there is data available from only one source, it is still possible to measure the impacts of a policy that affects pollution at
share of profitable firms in the population varies from around 60% in Mexico to 85% in Senegal. Most countries feature statutory CIT rates between 25% and 30%, but Albania and Montenegro apply low statutory rates of 15% and 9% respectively.

# **2.2 Methodology**

**Objective** We aim to compute ETRs that are comparable across countries with different tax systems and tax reporting requirements. Thus we select concepts from the CIT returns that are consistently used across countries. To the best of our ability we aim to distinguish variables that measure ‘standard’ deductions—allowed in all countries and for all firms, and reflecting economic costs—and variables measuring tax expenditures, which can be country and firm-specific.

**Accounting concepts** We consider the concepts that can be consistently measured across countries’ tax returns and the accounting relations between them in Figure 1. Total revenue is composed of sales plus other incomes (e.g. non-operating incomes, rents, interests). The deductions that firms typically subtract from their revenue to calculate their profits include the cost of material inputs, labor, and capital costs, as well as financial costs, and depreciation of capital. We call the difference between revenues and costs “net profit” (or loss). This concept is not always directly reported in tax returns as a line item, but can always be reconstructed. We consider that net profit is the best proxy for economic profit that can be constructed from tax return data.

Net profit differs from taxable profit as the latter concept excludes tax-exempt incomes and reintegrates non-tax-deductible costs. After this, investment incentives, capital allowances, and other deductions are applied to obtain the gross tax base, and loss carry-forward is accounted for to obtain the net tax base. Multiplying the net tax base by the statutory tax rate yields the gross tax liability. We validate the data cleaning and variable construction process by ensuring that when we divide the gross tax liability by the net tax base we obtain
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
1|<br>30.4|
|Open defecation|% pop|40.3<br>82.9<br>81.5|14.3|
|||2001<br>2005<br>2007||
|Domestic water consumption|liter/capita/day|50.7<br>37<br>41<br>44|154|
|Revenue collection|% sales|92.7<br>80<br>88<br>90|100.0|
|Distribution losses|% production|34.3<br>22<br>19<br>17|26.8|
|Cost recovery [1]|% total costs|56.0<br>41<br>57<br>67|80.6|
|Operating cost recovery|% operating costs|65<br>161<br>113<br>130|145|
|Labor costs|connections per<br>employee|159<br>116<br>171<br>182|369|
|Total hidden costs as % of revenue|%|163<br>88<br>75<br>59|140|
|||Niger<br>Scarce water|Other<br>|
|||resources<br>2005<br>2006|developing<br>regions|
|Residential tariff|US cents per cubic meter|34<br>34<br>60||
|Nonresidential tariff<br>_Source:_Demographic and Health Sur|US cents per cubic meter<br>veys and AICD water and san|76<br>80<br>120<br>itation utilities database(www.infrastructureafrica.org/aicd/to|3–60<br>ols/data<br>).|

_Source:_ Demographic and Health Surveys and AICD water and sanitation utilities database (www.
# The 1990/91 DHS Firnal Report for Pakistan does not report gender-disaggregated

outcome measures by province. In order to provide comparable information for these provinces, we use the DHS raw data and reconstruct the outcome measures. Because of the small sample sizes in Baluchistan, additional information was used to create accurate measures of gender disparity in that province<sup>7</sup> .

# 11) Gender disparities betweei and within countries in South Asia

We discuss two broad types of indicators: those related to health, which include data on mortality, morbidity, health treatments, and anthropometrics; and educational enrollments at various ages.

# A) Health indicators

_Mortality outcomes._ As an indicator of gender disparity, the ratio of female to male child mortality, defined as the chance of a child dying after turning one but before turning five, is preferable to other mortality indicators. Unlike other studies, we do not normalize mortality rates to a reference population but report actual ratios, a point we return to below. Under-five mortality (5qO) is conventionally divided into neonatal (less than one month), postneonatal infant (between one month and the first birthday), and child (4ql) mortality<sup>8</sup> as deaths at these various ages are typically due to different causes and reflect different disease conditions and health-seeking behaviors by parents. There are two reasons to believe that 4ql

# ' See Appendix 1 for details.

The notation _4ql_ deniotes the probability of a child dying between exact age one and the fifth birthday, and _5qO_ is the probability of dying between birth and the fifth birthday.

6
the most and least impacted groups. Following Chernozhukov et al. (2018), we consider the highest and lowest quintile groups in our GATES analyses. Second, once the GATES have been constructed, Chernozhukov et al. (2018) show how to conduct a classification analysis (CLAN), which gives the average characteristics _Z_ of the most and least affected groups. We follow these steps to ask if there is any detectable heterogeneity in our observed treatment effects as a function of 17 baseline characteristics.<sup>47</sup>

**Results.** We consider heterogeneous treatment effects for our key outcomes. Table 7 presents a GATES analysis, comparing the most affected quintile with the least affected quintile, by baseline characteristics. Panel A includes the main savings and consumption outcomes and pools across the bank and mobile direct deposit treatments relative to the control group. Panel B includes the main “learning-by-doing” outcomes. Because these outcomes mostly involve mobile account usage

> 45These are all listed as potential sources of heterogeneity in the outline of our study in the AEA RCT registry.

> 46All of our estimates employ a Random Forest algorithm in the ML step.

> 47 We use the following set of baseline characteristics: gender, marital status, indicator for parent, education, indicator for literate in the local language, digit span test score, the fraction of income shared with others, control over food expenditures within the household, an indicator for strictly positive savings, an indicator for positive formal savings, an indicator for being able to save BDT 5,000, an indicator for sending remittances in a typical month, an indicator for having prior experience with mobile transactions, and indicator for needing to borrow to pay for basic consumption at the end of the month, number of years in current job, and expected tenure in current job.

23
<!-- Start of picture text --> 15<br>Manufacturing and related trades 196<br>13<br>2<br>Construction fo!<br>3<br>8<br>Wholesale and retail Boe<br>53<br>9<br>Transportation and storage | 8 6<br>6<br>Accommodation and food service ts<br>5<br>12<br>Various professional acts. 19<br>11<br>Z<br>Other service {9<br>10<br>0 10 20 30 40 50 60 70<br>Percent of firms<br>MS ELMPS 2012 inest. MMMM ELMPS 2018 in est.<br>Economic Census Establishment Census<br><!-- End of picture text -->
Value for Money analysis have noted the underutilization of CBA, CEA and other efficiency analyses in the published impact evaluation literature (McEwan 2012; Dhaliwal et. al. 2013). However, no analysis to date has determined the percentage of published impact evaluations that incorporates efficiency analysis, and there has been little systematic investigation of the observed decline in VFM application. A 2008 review found that inconsistent use of language; lack of common measures; lack of quality data on social impacts, outcomes, outputs and program cost; lack of incentives for transparency; and the expense of cost measurement all contribute to low levels of Cost reporting in the social sector (Tuan 2008).

Veteran CBA observers note that the low application of VFM analysis in impact evaluation studies and their often-dubious quality has occurred within the context of waning interest in CBA and Cost-efficiency methods more generally. This downward trend is mirrored in the quality of economic analyses in the World Bank’s project appraisal documents. In 2007 and 2008 just over half of the included economic analyses were of “acceptable” or “good

> 3 Data are from 3ie’s Impact Evaluation Repository. The repository includes published impact evaluations of development interventions carried out in low- and middle-income countries that use experimental or quasi-experimental estimation strategies with a credible counterfactual. 3ie developed the repository using systematic search of over 31 “academic databases in health, economics, public policy and the social sciences provided by platforms such as Ovid, EbscoHost and ProQuest, and libraries and websites from select research organisations and academic institutions.” More than 84,000 potential studies published between 1981 and 2015 were identified and screened, with more than 4,200 (from over 120 countries) selected for inclusion in the repository.

2
|65,089|23,107|23,272|20,685|20,448|
|**Mortgages secured b**|**y agric. lan**|**d**|||||
|Amount (US$ bn)|0.4453|0.117|0.051|0.118|0.0533|0.106|
|No. of mortgages|14,843|2,231|3,210|2,363|3,585|3,454|
|Avg. amount|32,740|52,339|15,894|49,811|14,862|30,795|
|**Mortgages secured b**|**y commerc**|**ial land**|||||
|Amount (US$ bn)|0.7613|0.488|0.0322|0.105|0.0532|0.0829|
|No. of mortgages|2,862|773|272|714|511|592|
|Avg. amount|228,194|630,984|118,542|147,291|104,046|140,108|

Source: Rwanda Natural Resource Authority (RNRA), Land Administration Information System (LAIS) 17
002***|-0.003***|-0.000|0.003***|
||(0.002)|(0.004)|(0.004)|(0.002)|(0.004)|(0.000)|(0.001)|(0.000)|(0.001)|
|Observations|48189|15846|15846|15846|15846|109367|109367|109367|109367|

_Note:_ Robust standard errors in parentheses. Sample is restricted to males aged 20-59. Marginal effects of Logit/Mlogit model are reported. To combine the HIES and the RMS sample in Column 1, sample weights are applied. Data source: Bangladesh HIES 2016 for nonmigrants and RMS 2018-19 for return migrants; Nepal LFS 2017-18; Pakistan LFS 2014-15 and 2017-18. * p _>_ 0.1, ** p _>_ 0.05, *** p _>_ 0.01.
# **4. Conclusion**

This paper sheds light on Syrian refugees’ decision to leave Syria and their living conditions once they arrived in one of the three regional host countries - Jordan, Lebanon, and the Kurdistan Region of Iraq. The analysis shows that refugees left following episodes of violence and had little time to prepare for departure. Once in the host country, they faced precarious lives, living in crowded conditions and exposed to negative shocks, with insecure employment or none at all.

The data used in the analysis stem from varied sources, including UNHCR’s registration database, publicly available casualty data, and primary data collected in the three host countries. Similar efforts to collect representative data on refugees in national household surveys are underway in many countries. Such efforts are critical to generate evidence on the transition from short-term humanitarian support to a long-term development response in countries affected by protracted conflict and displacement crises.

10
# **II. Data on Technological Change**

To begin our review, it is important to know how technology is measured. The knowledge that makes any technology a valuable improvement is an abstract concept. We do not observe knowledge directly. Rather, we observe inputs that create knowledge or outputs that contain knowledge. Similarly, knowledge is rarely given directly to developing countries. Rather, it comes embodied in products bought and sold through international trade, or can be tacit knowledge provided by the education of a worker. It is these indirect inputs and outputs of the knowledge-generation process that are used to measure technological change, both as it pertains to green growth and more generally.

Research and development (R&D) data offer a straightforward measure of innovative activity. R&D is an input into the innovation process. Variations in environmentally-friendly R&D spending tell us the relative importance placed on such innovation. However, as R&D is an input, measures of R&D effort do not reveal information about outcomes of the innovation process. Moreover, detailed information on specific types of R&D is often unavailable. For instance, in a survey of energy R&D activity, Gallagher _et al_ . (2011) note that while government spending on energy R&D is widely available for International Energy Agency (IEA) member nations, data on energy R&D efforts from private firms are scarce. As an example of such public R&D data, Table 1 presents public energy R&D budgets for various IEA member countries for selected years. In general, public support for energy R&D has grown in recent years, with the most notable increase coming from energy efficiency and renewable energy research. Moreover, even publicly available R&D data are noisy. Data collection may vary by country. For instance, one country may include basic R&D on semiconductors as investment in photovoltaic research while another may not (Gallagher _et al_ . 2011).

6
teachers may be willing to accept lower wages in new private schools. However, instead there is a strong preference for public schools (better pay and easier job). In addition, the labor market for public and private schools is quite dierent, with the former being non-local and the latter local. Moreover, within private schools, the market is not stratied so it is unlikely that there would be systematic compensating dierentials across dierent private schools. One may also be concerned about whether private school wages are meaningful if the owners also teach (wages may be confounded with prots). We do not think this is a substantive issue. A detailed examination of prots using the smaller sample in the LEAPS database, suggests that median prots are quite comparable to a teacher's wage. Moreover, most of these schools do employ non-family/paid labor and therefore reported wages indeed reect the opportunity cost of hiring (local) skilled women,

> 16An alternate data source is the Pakistan Integrated Household Survey (PIHS). Unfortunately, given the small number of villages that received a GSS, the available sample sizes are too small in the PIHS. With the sample restrictions in our paper, we nd only three villages in the treatment and thirty-one villages in the control set for these data. Moreover, since the majority of (the few) women who work in non-farm activities are teachers, and the vast majority of private school teachers are women, the private school wage bill is likely to reect the wages of skilled women.

16
with its closest equivalent in the ISIC classification. We then aggregate the HS data to build measures of tariffs at the 3-digit level. To perform the aggregate, we start from the next-to-lowest subheading, calculate the median of the item belonging to it, and iterate on this procedure.<sup>3</sup> We end up with a panel data set of import tariffs for the manufacturing sector across time.

Figure 1 provides some insights into the nature of trade policy and trade reform in different years. It depicts key percentiles of the distribution of import tariffs.

Figure 1 Distribution of Median Tariffs per 3 digit sector <!-- Start of picture text --> 200<br>150<br>100<br>50<br>0<br>1974 1978 1980 1982 1989 1990 1994 2001<br>excludes outside values<br>Median Tariff, 3digit ISIC<br><!-- End of picture text --> Note: Distribution of median tariff within each 3-digit ISIC manufacturing sector for selected years. The ends of the boxes are the 1st and 3rd quartiles, while the line within the boxes corresponds to the median. The ends of the bars show the points which are furthest away from the ends of the box, but at a distance not larger than 3/2 the interquartile range. We do not show outside values (points which are even further away). The quantiles are calculated weighting each sector by its employment level.

The recent historical Argentine trade policy is characterized by at least three different periods. Our starting point in 1974 was one of high protection, with average tariffs in excess of 100 percent, and sectors with median tariffs in excess of 200 percent. Starting in 1976, tariffs were abruptly reduced. The average tariff was cut by two thirds in three years, dropping from slightly above 100 percent in 1976, to 47 percent in 1978 and to 32 percent 3See the Data Appendix for further details on sources of information, the matching of
- Analysis of the GLSS data will enable us to shed further light on the earnings difference between the formal and the informal sector, and between different types of the self-employed.

- A topic of major importance which needs to be investigated from the price and household budget data available in the GLSS is the difference in cost-of-living as it prevails between the different sectors.

- It is quite clear that the private sector employment has been dismal, but very little is known of the dynamics of private sector investments and employment opportunities in Ghana. This is an area which needs further inquiry.

- We noted that growth in non-farm self employment has resulted in increases and diversification of rural incomes, but very little is known of their long term potential for employment and income generation of specific activities. This will facilitate the understanding as to what policies will facilitate towards lifting the millions of poor in rural Ghana.

- It is pertinent that we inquire further and understand the internal dynamics of reform, producer and consumer price behavior and shifts in income sources of rural poor.

- 3 The informal sector has been absorbing large amounts of labor from both urban and rural Ghana. It is not clear whether these employment opportunities are lucrative. Given the worsening trends of poverty in Accra, it is essential that **we** understand the dynamics of this sector in general and those subsectors which are currently providing employment opportunities to the urban poor.

54
br>5<br>2<br>2<br>0<br>0 0<br>All EAP ECA LAC MENA SAR SSA<br>regions min median max<br>host-country's financial sector) home-country's financial sector)<br>Total Assets in host countries (% Total assets Total Assets in host countries (% Total assets<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013<br>No. of home-country banks No. of host countries<br>No. host countries per home country<br>ZAF RUS COL CHN TUR NGA KEN LBN MLI IND MAR MYS BRA LBY ECU<br><!-- End of picture text --> South-Africa, Russia, and Colombia are home to the most EMDE banks which are active in other EMDEs; Lebanon’s banks are active in many host countries 12
when lending to VFs, which averaged 4.13% annually. This may seem low, if the riskiness of lending to some VFs is taken into account; on the other hand, no adjustment is made here for interest paid to VFs on deposits held at the BAAC or GSB, which would reduce net capital costs.

The most difficult capital cost to measure is the proportion of loans that are not repaid and are not used productively. In the 2009 SES, 2.1% of VF borrowers said they had “not yet repaid” their VF loan, and a further 2.4% had repaid “ in part”. If they were truly lost, the unreimbursed funds cannot be deployed elsewhere, and so represent an economic cost. On the other hand, if the funds remain in the hands of the borrower, and continue to be put to productive use, then the fact that they were not repaid to the VF does not represent an economic cost per se; the VF loss is offset by an equivalent gain to the borrower. The relatively high repayment rate suggests, however, that there is a strong “repayment ethic”, and so our working assumption is that non-reimbursed loans were essentially lost, and therefore do represent an economic cost in their entirety.

In this paper we do not measure the impact of VF lending, but for the purposes of our cost-benefit analysis we draw on the findings of Boonperm et al. (2001), who estimate that VF lending in 2004 raised expenditure per household by a statistically significant 3.5%, and income per household by 1.4% (although this number is not statistically significant). These are equivalent to returns, per baht borrowed, of 27-34% (using the consumption estimates for median and mean incomes), or 13-18% (using the income estimates); details are shown in Table 29. Given their standard errors, these estimates are not mutually inconsistent.

When borrowing leads to higher income, the economic benefit is easy