rafmacalaba/data-use-mentions-extended huggingface dataset:
1.00)|975|

**_Notes_** :

- The table shows the regression results on pre-specified COVID-19-related outcomes using the phone survey midline data. Each row is its own dependent variable.

- The independent variable of interest is the TOT treatment indicator, which is the predicted value from a first-stage regression of treatment implementation on treatment assignment.

- The outcomes that require definitions are: The COVID symptoms index is a mean effect index of an indicator for any household member testing positive for COVID and the summation of total symptoms experienced by the respondent. The COVID Riskiness index is a mean effect index of indicators that increase the likelihood of COVID contraction by the respondent including leaving the house, attending social gatherings, not keeping distance from others, going to mosque or other religious institutions, going to grocery store or market, and leaving village/neighborhood. The change in consumption is obtained from asking “Please tick below the changes in your family’s consumption in the past 30 days compared to before the COVID-19 outbreak began: Increase, No change, Decrease.” Then, increase is coded as +1, no change as 0, and decrease as -1. Learning activities are done in the last 24 hours and include homework, e-learning, educational programs and videos, and reading. Child alertness is the respondent’s answer to “On a scale of 1-7, with 1 being tired and 7 being alert, how does (child name) feel right now? ” and is normalized by deducting the control group’s mean and then dividing the result by the standard deviation of the control group.

- The regressions also have assessment month-by-year fixed effects, enumerator fixed effects, community-level controls (Irbid/Mafraq governorate and population quartile), and household-level controls (vulnerability-assessment quartile, shelter program, baseline number of children, baseline number of children plus adults, respondent gender, and respondent age).

- In the parentheses are robust standard errors clustered at the locality level. Regressions are weighted by the number
schooling on factors such as political knowledge (Weinschenk and Dawes 2019), wages (Bingley et al. 2009), and health (Fujiwara and Kawachi 2009). Heckman et al. (2016) and Card (1999) further present theoretical models that build on variations of the simple static models presented by Becker (1964) to estimate the private returns to education. However, estimating the social returns using these models remains a</mark> limitation.

<mark>Thus, while numerous methods have been advanced to causally estimate the ex-post returns for education, the lack of large-scale panel data limits generalizability of these results. Using compulsory schooling laws as a source of exogenous variation is thus one possible way to overcome this limitation especially because many countries have historically introduced or made changes to their compulsory schooling laws at different times. The next sub-section presents an overview of some studies that make use of these laws to establish causality.</mark>

# **<mark>3.3. Compulsory Schooling as an Instrument and Its Use in Climate Research</mark>**

<mark>Numerous studies have used changes in compulsory schooling laws, where some individuals are randomly forced to stay in school longer than their peers, as a natural experiment to research the effect of educational attainment on various aspects of human development. For example, Angrist and Krueger (1991), using longitudinal data from the U.S. Censuses of 1960, 1970, and 1980 for men show that compulsory school attendance laws can have a positive effect on schooling and earnings (see also Domnisoru 2021). The authors further suggest that compulsory schooling laws</mark> <mark>9</mark>
# **1 Introduction**

Each year, hundreds of billions of dollars are spent on targeted social protection programs. The importance of these programs increased dramatically in the past 18 months: In 2020, global extreme poverty increased for the first time in two decades, and most countries expanded their social protection programs, with more than 1.1 billion new recipients receiving government-led social assistance payments (Gentilini et al., 2020).

— — Determining who should be eligible for program benefits _targeting_ is a central challenge in the design of these programs (Hanna & Olken, 2018; Lindert et al., 2020). In high-income countries, targeting frequently relies on tax records or other administrative data on income. In low- and middle-income countries (LMICs), where a large fraction of the workforce is informal, programs often require primary data collection. The difficulty and cost of collecting data, and the variable quality of what gets collected, can introduce significant errors in the targeting process (Deaton, 2016; Jerven, 2013; Grosh et al., in press). These issues are exacerbated in fragile and conflict-affected countries, where two-thirds of the world’s poor are expected to reside by 2030 (Corral et al., 2020).

This paper evaluates the extent to which non-traditional administrative data, processed with machine learning, can be used for program targeting. Specifically, we match call detail records (CDR) from a large mobile phone operator in Afghanistan to household survey data from the Afghan government’s Targeting the Ultra-Poor (TUP) anti-poverty program. Eligibility for the TUP program was determined through a _hybrid targeting method_ , combining a community wealth ranking (CWR) and a short follow-up survey. Our analysis assesses the accuracy of three counterfactual targeting approaches at identifying the actual beneficiaries of the TUP program: (i) our _CDR-based method_ , which applies machine learning to data from the mobile phone company; (ii) an _asset-based wealth index_ , which uses asset ownership to approximate poverty; and (iii
data covering 120 developing countries, defined as non-high income by the World Bank, i.e. those with an average GDP per capita below 12,055 2010 US dollars, over 1990-2017. This period covers only post-Cold War years, which had a large impact on arms trade patterns (e.g. Akerman and Seim (2014)), and it also covers the oil price boom of the 2000s. More precisely, I estimate the following regression: _Armsit_ = _βDit_ 5 + _αi_ + _σt_ + _εit_ where _Armsit_ is the total imports of arms in country _i_ in year _t_ , and _Dit_ 5 is dummy equal to 1 in the year of the discovery and the five subsequent years. I use a five-year post-discovery period as this is less than the average delay between discovery and extraction (Arezki et al. (2017) puts it at 7 years.). Country fixed effects ( _αi_ ) pick up factors that vary little year-on-year such as governance, year fixed effects ( _σt_ ) pick up global factors such as arms races. As mentioned above, giant discoveries can be thought of as plausibly exogenous due to their unexpected nature (Arezki et al., 2017; Tsui, 2011; Lei and Michaels, 2014; Cavalcanti et al., 2019). This allows me to interpret the _β_ coefficient as a causal effect. I discuss the results in the next section.

# **3 RESULTS**

Baseline results are in Table 1. It shows the effect of giant discoveries on arms imports based on SIPRI data (columns 1-2), COMTRADE data (columns 3-4), and on the arms share of total imports or GDP, based on COMTRADE and World Bank data (columns 5-8). While odd columns give the results of the baseline regression as described in the previous section, even columns show how robust the results are to the inclusion of an additional control variable, i.e. the number of previous giant discoveries. I include this control as a country which experiences a first
sectional surveys.. Moffitt (1993), Collado (1997), McKenzie (2004) and Verbeek and Vella (forthcoming) discuss conditions under which one can consistently estimate linear dynamic models with pseudo-panels. Our aim here is to show that these methods can also deal with the measurement error problems facing panel data models, and to provide details for the estimation of non-linear income dynamics.

Begin by taking cohort averages of equation (7) over the nc individuals observed in cohort c at time t : where Y c(t),t = (1/nc)<sup>Pn</sup> i=1<sup>cYi(t),tdenotesthesamplemeanofYoverthein-</sup> dividuals in cohort c observed at time t. With repeated cross-sections, different individuals are observed each time period. As a result, the lagged mean Y c(t),t−1, representing the mean income in period t − 1 of the individuals in cohort c observed at time t, (denoted here c (t)) is not observed. Likewise the mean of the lagged square and mean of the lagged cubic are also unobserved. Therefore we replace the unobserved terms with the sample means over the individuals who are observed at time t − 1, leading to the following regression for cohorts c = 1, 2, ..., C and time periods t = 2, ..., T : where 10
is indeed 11% below the 2019-21 average but well above the 7.5 mn. ha attained in 2020 when adverse weather conditions led to widespread winter crop failure, especially in the southern and central part of the country. Figures at national level are in line with those reported in the JRC’s MARS bulletin (Ben Aoun _et al._ 2022) who attribute most of the changes to differences in weather, highlighting the importance of controlling for such factors before attributing changes over time to conflict.<sup>15</sup> A first step towards assessing direct conflict impacts is to compare the deviation of 2022 winter crop area from the 2019-21 level between VCs that were affected by the different types of conflict and those that were not. Results from doing so, reported in panel B of table 2 point towards three relevant factors. First, in conflict-affected villages, winter crop area is more than double that of non-affected ones, suggesting that conflict disproportionately affected villages with high winter crop potential.

Second, winter crop area in VCs where crops were damaged directly is significantly lower (by 15.7%) than in VCs that did not experience such direct damage (where the decline was 10.7%), suggesting that interpreting satellite imagery provides additional information. Third, none of the other open-source based

> 13 See Kussul _et. al_ (2016 and 2017) for a detailed description of methodology for crop mapping based on satellite data. As the classification map based on a deep learning model on AWS is more accurate at small fields, it is used below. 14 Threshold segmentation was based on maximum NDVI values and to separate winter cereal and rapeseed, an additional band index was calculated to detect the characteristic yellow flowering of winter rapeseed (d’Andrimont _et al._ 2020).

> 15 The importance of adjusting for time-varying factors is illustrated by Schierhorn _et al._ (2021) who find that weather and climate variables explain 54% of country-level winter wheat yield variability in Ukraine.

9
8 9 Richest<br>Decile of Market income<br>Percent of Market income<br><!-- End of picture text --> Source: Authors' estimates based on PECS 2016/2017; LFS 2017. Notes:

[1] Net cash benefit refers to the system including all direct and indirect taxes, transfers and subsidies, and excluding in-kind health and education transfers. [2] Net total benefit refers to the system including all elements, including in-kind health and education transfers.

40
tenants have registered rights to 1,031 ha of which they cultivated 895 ha.

The differential between legal entities and individuals in median (US$ 1,250 vs. US$ 969) or mean prices (US$ 1,650 vs. US$ 1,777) as well as the mean and median area purchased per buyer (8.5 and 1.5 ha for individuals vs. 37 and 6 ha for legal entities) is smaller in sales than in lease markets although in both cases, the distribution of sold area is heavily skewed with maximum areas bought by the same buyer amounting to 3,195 for individuals and 2,233 for legal entities. With 2,100 ha registered and 1,850 ha cultivated, legal entities that buy land are more than double the size of tenants, a fact that may be suggestive of higher barriers to sale market participation due to ill-functioning financial markets and possibly also speculative elements.

# **3. Descriptive evidence and econometric approach**

To characterize the subjects and objects of market transactions and market structure, we complement price data with information from cadaster, registry, and remote sensing. In addition to highlighting differences between tenants’ and buyers’ farms, this also allows us to identify fields as the basic unit of crop cultivation within a farm, thereby allowing to use within-field variation in formal land rights. We use this as a basis for discussing the model and expected coefficients.

10
for investments in physical capital, but were targeted at small firms with revenue below £22.8 million.<sup>20</sup> To ensure that our results are only capturing the effects of the AIA, and not any residual impacts of the FYA, as a robustness test we exclude firms in our sample with revenue below the threshold necessary to qualify for the FYA.

A second policy of interest was an alteration to the definition of SMEs by the EU in 2008 which in turn affected qualification for the R&D Tax Relief Scheme for UK firms. This definition change shifted the qualifying threshold of assets from €43 million to €86 million, the employment threshold from 249 to 499, and the sales threshold from €50 million to €100 million (Dechezleprˆetre et al., 2016). Again, we explore the robustness of our findings to the exclusion of firms that become eligible for the R&D incentive.<sup>21</sup>

# **3 Data and statistics summary**

The research relies on four types of data: panel data on firm use of cloud, big data and AI technologies; matched employer-employee data; details regarding the introduction and changes to the AIA; and firm capital investment data.

All firm level data are taken from the Office for National Statistics (ONS), which is the UK’s Census Bureau equivalent. Information on cloud, big data analytics and AI adoption is available through the E-commerce Survey. The survey contains questions on firm use of different types of cloud computing, including its use for hardware services such as data, storage, processing, and software services, such as finance software, office software, customer relationship management software and email. We code the firm as an adopter of cloud technologies if it uses any of these different cloud services and zero otherwise.

The survey also includes questions on the use of big data analytics and AI. We construct a measure of firm use of big data as a binary variable which
4 finding, Section 6 analyzes the distributional effect of internal and international remittances on different decile groups of households. Section 7 concludes.

# 1.  Data

Data for the study come from a Ghana Living Standards Survey (GLSS 4) done by the Ghana Statistical Service over a 12-month period, April 1998 to March 1999. The survey included 5998 urban and rural households and was designed to be statistically representative both at the national level and for urban and rural areas. The survey was quite comprehensive, collecting detailed information on a wide range of topics, including income, expenditure, education, employment, household enterprises, migration and remittances.<sup>7</sup> It should, however, be emphasized that this survey was never designed as a migration or remittances survey. On the one hand, the survey collected a good deal of information – age, education, occupation – on household members who had once been migrants but have since returned to the household. However, the survey collected very little data either on migrants who are currently working outside of the household or on the remittances that these current migrants send home. For example, the survey collected no information on either the number of current migrants per household or on the characteristics of these migrants. This means that no data are available on the age, education or income earned outside the household by current migrants. With respect to remittances, the survey only asked five questions: (1) During the past 12 months has (your) household received money or goods from a migrant?; (2) What was the total amount of cash (your) household received from this migrant during the last 12 months; (3) What
skills with market needs.

To motivate our data section, it is essential to understand the UN Statistics Division's (ISIC – HS) concordances, which link the International Standard Industrial Classification of All Economic Activities (ISIC) with the Harmonized System (HS). ISIC is a global standard for categorizing economic activities, providing a hierarchical structure that organizes industries based on their primary activities, and is used for various statistical purposes. Meanwhile, the HS, developed by the World Customs Organization, offers a standardized nomenclature for classifying traded goods, facilitating customs, trade, and tariff processes. The ISIC – HS concordances bridge these two systems, enabling us to connect economic activities from Labor Force Survey (LFS) data (ISIC) to specific products in the UN COMTRADE data (HS), thereby enriching our analysis of economic activities and trade flows. Once the ISIC and HS codes are mapped, we can observe the employment allocation of workers across export-oriented industries and evaluate the employment implications of the JC.

The Jordanian LFS is a survey implemented through four rounds on a yearly basis by the Jordanian Department of Statistics (DoS) and provides detailed information on various aspects of 4 | P a g e
# **Figure 1** **<mark>: Cumulative Number Financial Sector Policy Measures Around the World</mark> (up to September 1**<sup>**st**</sup> **, 2020)**

<!-- Start of picture text --> Banking Sector Financial Markets/NBFI<br>Liquidity/funding Payments systems<br>01feb2020 01mar2020 01apr2020 01may2020 01jun2020 01jul2020 01aug2020 01sep2020<br>3000<br>2000<br>1000<br># of measures taken<br>0<br><!-- End of picture text --> Source: World Bank COVID-19 Financial Sector Policy Response Database.

11
<u>http://www.djiboutitelecom.dj/particulier-internet-adsl-particulier.html</u> [accessed November 22, 2018] Notes: * Internet for _particuliers_

**Table 2. Average per capita consumption (in DJF) by quintile**

|**Quintile**|**Average per capita**<br>**consumption(annual)**|
|---|---|
|1|56,182|
|2|108,506|
|3|156,757|
|4|224,548|
|5|496,690|
|National|208,224|

Source: Calculations based on EDAM4.

Note: Quintiles based on the distribution of consumption per capita.

> 8 To put this estimate in context, in 2018, the Broadband Commission for Sustainable Development agreed on a target for 2025 for entry-level broadband services at less than 2 percent of monthly gross national income (GNI) per capita. The current estimate for Djibouti for a fixed-broadband basket is 9.8 percent of GNI per capita (ITU 2018).

> 9 The price drop was accompanied by a reduction in the cap (from 50 GB to 30 GB per month) and the speed (from 3 to 1 Mbit/s).

8
|1404|2.04|2216|1121|1.98|1451|933|1.52|
|1988|2867|1418|2.02|2219|1131|1.97|1433|939|1.50|
|1994|2895|1436|2.02|2248|1144|1.97|1429|946|1.48|
|2000|2901|1454|2.00|2255|1158|1.95|1458|969|1.48|
|2005|2920|1469|1.99|2261|1170|1.94|1462|981|1.47|
|2009|2842|1490|1.91|2243|1188|1.89|1471|1008|1.44|
|2012|2807|1496|1.88|2223|1194|1.87|1475|1016|1.43|

_Source_ : Authors’ calculations using India Time-Use Survey 1998-1999, National Family Health Survey rounds 2, 3, and 4 and National Sample Survey rounds 38, 43, 50, 55, 61, 66 and 68.

_Note_ : Alternative estimates of Total Energy Expenditure (TEE), Rest Energy Expenditure (REE) and Activity Level (AL). For REE we include a linear time-trend in the regression equation and predictions. For AL we consider a specification pooling all men or all women (regardless of age or sector) and including three way interactions of dummies for primary status, 1-digit primary industry, and education level.
Figure 14: Size of formal and informal establishments by age cohort: Manufacturing <!-- Start of picture text --> Burkina Faso (2015) Cameroon (2008)<br>Informal Formal Informal Formal<br>Ghana (2013) Rwanda (2013)<br>Informal Formal Informal Formal<br>Mean employment size relative to entrants<br>Entrant 1−5 years6−10 years 10+ years Entrant 1−5 years6−10 years 10+ years Entrant 1−5 years6−10 years 10+ years Entrant 1−5 years6−10 years 10+ years<br>Entrant 1−5 years6−10 years 10+ years Entrant 1−5 years6−10 years 10+ years Entrant 1−5 years6−10 years 10+ years Entrant 1−5 years6−10 years 10+ years<br>50<br>40<br>30<br>20<br>10<br>0<br>15<br>6<br>4 10<br>2 5<br>0 0<br>25<br>20<br>15<br>10<br>5<br>0<br><!-- End of picture text --> _Source:_ Establishment censuses obtained from the statistical agencies of the selected countries; see section 3. _Note:_ The reference is the total employment for each age cohort and formality status. The figure is constructed based on data for establishments in the manufacturing sector. Establishments with missing employment or age data and state-owned are excluded.

Figures 10–13 illustrate growth differentials between formal and informal establishments by plotting the employment distribution at different age cohorts. Among formal establishments, the employment shares of larger class sizes continuously increase when looking at older age cohorts. These establishments tend to increase in size with additional years of market operation even if they face challenges in the business environment that stifle their growth. As for the informal establishments,
Figure 7: Average Growing Degree Days (GDD) <!-- Start of picture text --> Ethiopia (n = 10,674) Malawi (n = 8,897)<br>154 182<br>153.5 180<br>153<br>178<br>152.5<br>176<br>152<br>151.5 174<br>2010 2015 2020 2008 2010 2012 2014 2016 2018<br>Niger (n = 3,913) Nigeria (n = 9,145)<br>75 145<br>70<br>140<br>65<br>60<br>135<br>55<br>50<br>130<br>2011 2012 2013 2014 2010 2012 2014 2016 2018<br>Tanzania (n = 9,916) Uganda (n = 11,692)<br>184 146<br>182 144<br>180 142<br>178 140<br>176 138<br>2005 2010 2015 2020 2010 2015 2020<br>Year Year<br>CPC ERA5 MERRA-2<br>Growing Degree Days<br>38<br>Growing Degree Days<br>Growing Degree Days<br><!-- End of picture text --> _Note_ : The figure presents the mean growing degree days in a year, disaggregated by Earth observation source. Means are calculated by fitting a fractionalpolynomial line to the data, with 95% confidence interval represented by shaded area. Each line (Earth observation product) in each panel is constructed using all household-year observations ( _n_ ). Variation in lines do not come from variation in the household data that is paired with the Earth observation data. Rather, variation in lines within a panel is solely due to differences in the daily temperatures reported by the Earth observation source.