rafmacalaba/data-use-mentions-extended huggingface dataset:
We show that our results remain robust to a wide range of values of _δ_ up to at least 0.04 in Appendix Figure O10, which is double our back-of-the-envelope estimate. To illustrate this, we plot the estimated coefficients and their confidence intervals against different levels of _δ_ under various methods in Conley et al. (2012). The three methodologies are the union of confidence interval approach with a support of [0 _,_ 0 _._ 08], the local-to-zero approach with a standard deviation of 0.02, and the local-to-zero approach with a standard deviation of _δ_ .<sup>64</sup>

# **F Auxiliary Data**

Population data are available from the Census Bureau. Data on housing prices are obtained from the Federal Housing Finance Agency (FHFA), which produces housing price indices at the state level from 2006 to 2015. County housing-price indices from 2006 to 2015 are also obtained from CoreLogic through the Fama-Miller Center at the University of Chicago Booth School of Business. Results are presented using state-level housing prices from FHFA, whereas results are robust to using county level housing prices. Labor force and demographic variables are obtained from the BLS and the Census. Tobacco tax data are available from the industry-funded annual report _Tax Burden on Tobacco, 2017_ and are assembled by _Campaign for Tobacco-Free Kids_ . SNAP-recipient characteristics are obtained from SNAP QC data described above. Energy controls, which include annual state-level total energy, electricity, LPG, and natural gas quantities consumed and prices for the residential sector, are obtained from the State Energy Data System (SEDS) maintained by the US Energy Information Administration (EIA). Data on monthly temperature by state are obtained from Berkeley Earth. Data on policy controls refer to state-year measures of amounts paid out by transfer programs available from the BEA. The transfer amounts are logged and these programs include social security, other retirement and disability insurance transfers, Medicare, Medicaid and other vendor payments, military medical insurance, SSI, EITC, other income maintenance programs
br>Energy<br>Consumption<br>Survey (United<br>States)|No|
||Energy efficiency<br>of housing|Collects<br>information<br>about people's housing<br>circumstances and the<br>energy<br>efficiency<br>of<br>housing|English<br>Housing<br>Survey<br>(England)|No|
||Several<br>indicators of<br>energy efficiency<br>and affordability|Several|European<br>Quality of Life<br>Survey|Yes|
|Several|Several<br>indicators of<br>energy efficiency<br>and affordability|Several|Generation<br>and Gender<br>Programme<br>Survey|Yes|
||Multidimensional<br>energy poverty<br>indicators|Combination of several<br>indicators|Several (see<br>main text)|Yes|
|Direct|Energy<br>consumption|Approximation of energy<br>consumption, compared<br>to a norm|Direct meters,<br>Satellite data,<br>and other big<br>data (see main<br>text)|Yes|

_Source: Compilation of findings by Gouveia et al. (2022) and additional literature (as shown in this section)._ 10
# **4. Economic transformation of individuals’ welfare**

The role of ICT adoption in promoting economic development has been studied from a wide range of perspectives including the effects on market outcomes, poverty, income inequality, and human capital formation. In this section, we engage with studies particularly associated with the effects of ICT adoption on income and wealth equity, as well as its effects on social and human capital formation and correction of market failures that could ultimately affect consumer welfare. Moreover, ICT can also foster opportunities to promote human development through the creation and communication of new knowledge, impacts on agricultural production, and increased connection with the rest of the world (Heeks 2010; Prado et al., 2011). As devices that increasingly rely on mobile broadband technologies, such as smartphones, have become more widespread there has been a significant change in consumers’ access to information (Rennhoff and Routon, 2016). For instance, according to Aker and Mbiti (2010), services such as text-messages are helping development in SSA by increasing access to information on a wide range of topics like communication about fair prices, HIV/AIDS medicine provision and reminders, and crime reporting.

Carabregu et al. (2019) study the re-distributional effects of mobile phone and network penetration by looking at the correlation between mobile broadband and income inequality in Western Balkan countries (Albania, Bosnia, Serbia, among others). The analysis uses as dependent variable income inequality, and cell phone diffusion as one of the predictors, and finds that a 1 percent increase in mobile penetration decreases the Gini index between -0.049 and -0.055 points. In addition, Iqbal et al. (2019) study the correlation between ICT adoption and the Human Development Index (HDI) in South Asian countries during 1990 and 2016. The authors find a positive and significant effect of mobile phone usage on human development. Namely, the findings suggest that a 1 percent increase in mobile phone penetration leads to a 0.12
If respondents said yes, they were asked which country they would ideally like to move to. 55 percent said they would like to move to somewhere in Europe, and 7 percent would ideally like to move to Senegal. This “ideal” destination may be different from where they intend to migrate given the costs and difficulty of moving, and we will examine migration intentions and their measurement in the next section.

# **3.2 The COVID-19 Pandemic and the Gambia**

The Gambia registered its first case of COVID-19 on March 17, 2020 when a female traveling back from the UK tested positive for the virus and the first fatality was registered on March 23,

> 9 We collected some information about the youth’s location and occupation from other household members for almost all of the youth that we could not interview in person, but these proxy reports do not include migration intentions, and so are not used in this paper.

9
on the nutritional status of children. The approach adopted closely follows Alderman, Hentschel, and Sabates’ (2002) analysis of the nutritional status of children in Peru.

# _Model_

The nutritional status of a child is assumed to be a function of household inputs, such as nutrients ( _F_ , food), health ( _H_ ), and child care ( _C_ ). The nutritional production function can be written as: where _E_ represents _observed_ neighborhood environmental conditions, _ei_ represents _unobserved_ child specific characteristics, and ε unobserved community effects.

Nutritional production functions, however, are rarely estimated. This is because inputs that go into the nutritional production function are likely to be endogenously determined.<sup>6</sup> Instead, the approach generally adopted is to estimate a nutritional demand equation. To derive this demand equation, we assume households maximize utility over consumption goods ( _G_ ), leisure ( _L_ ), and the nutritional status of members of the household ( _N_ ): subject to the above nutritional production function, time and budget constraints, to yield a nutritional demand function such as: Where _Y_ represents household resources, _E_ environmental conditions, ε unobserved community effects, and μ is a random error term, which includes unobserved individual characteristics _ei_ , as well as measurement error. Nutrient and health inputs are not included in the above equation—only factors which affect the level and efficiency by which these inputs are used are included. Clearly missing in the above demand equation are any relevant prices.

> 6 Some important child specific characteristics, such as length of breastfeeding, preceding birth interval, as well as partial information on food intake and health history are available in the Ethiopia DHS dataset. However, to be able to use these variables, a number of econometric issues must be overcome.

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survey used a weighting procedure to adjust for variable propensity of individuals to have regular access to email and the Internet. The comparisons showed that th <mark>e probability samples were more representative than the nonprobability sample in terms of demographics and electoral participation, even after re-weighting.</mark> <mark>Brüggen et al. (2016) compare the accuracy of results obtained from 18 opt-in online “panels” participating in the Dutch Online Panel Comparison Study (NOPVO) with data from a probability sample collected from the Internet (the LISS panel; Scherpenzeel 2009), and two probability samples collected via computer assisted personal interviewing, and compared all of them with the Dutch government’s registry of all 16 million residents of the country, the Municipal Basic Administration (MBA). The non-probability sample surveys were weighted using General Regression (GREG) and Horvitz-Thompson Estimators. Results indicate that the nonprobability samples yielded less accurate estimates of proportions than the probability samples, and that weighting does not reduce selection bias in the level estimates. Dutwin and Buskirk (2017) compare RDD surveys and nonprobability Internet panel surveys to a highquality in-person survey. The surveys were weighted using propensity weighting, raking, and sample matching. Their results showed that nonprobability samples attained the greatest estimated bias, and the in-person sample, the lowest. The weighting techniques were not able to improve the measures. Maccinnis et al. (2018) compare data across a variety of probability and nonprobability sampling methods in the United States, using a set of 50 measures of 40 benchmark variables. The probability samples interviewed by telephone or the Internet were the most accurate. Internet surveys of a probability sample combined with an opt-in sample were less accurate; least accurate were internet surveys of opt-in panel samples. These results were not altered by implementing poststratification weights provided by survey companies.</mark><sup>12</sup>

> 12 Cornesse et al. 2020 provides an exhaustive literature review of studies comparing non-probability and probability samples.

8
argued by some). Put differently, the issue of increasing women’s participation in the labor market may need to be thought through at the fundamental level of the growth model itself. A key policy question, in that context, is thus how public policy can promote greater flexibility in employment practices.

# **2.3.4 An Improved, but still Insufficient, Innovation Capability**

Morocco’s innovation capability has improved in recent years. However, innovation activities continue to face a number of obstacles (see Djeflat, 2012). In principle, the country has a policy to promote innovation that is inclusive: it includes measures to assist the different phases of the process and the various stakeholders (companies, researchers, etc.), according to proven measures at the international level (support startup of innovative enterprises, the creation of eco‐industrial clusters, and research‐industry collaboration on R&D projects), and through facilitation institutions, such as establishing a single administrative point for grants or financing of innovative projects via the Moroccan Center for Innovation. The number of researchers per thousand individuals in Morocco’s labor force is relatively high, compared to countries such as China, Brazil, Malaysia and Chile (see WIPO, 2011). Equally important, businesses fund almost 30 percent of R&D, and employ about 10 percent of R&D personnel.

However, policies to promote innovation are not performing well and the means employed remain relatively modest. R&D expenditure, which averaged 0.7 percent of GDP in recent years, is still modest. According to the Global Competitiveness index of the World Economic Forum (2018), Morocco is ranked 78 out of 140 countries for its innovation capacity. It is ranked higher for R&D expenditures (51) and quality of research institutions (51), but worse for international co‐inventions (90) and patent applications (97). Indeed, while the overall number of patent filings has increased significantly in
the urban/rural divide also figures

> 9 All statistics aggregated above the country level (by income group, region, and the like) use population weights in addition to country-level weights.

> 10 The GWP provides imputed within-country (relative) income quintiles for all observations, but does not publish the imputed absolute income levels. In 2011, income data were imputed for 14 percent of income observations worldwide. Gallup uses published data on individual characteristics as well as proprietary data on each household member to impute income. For additional information see www.gallup.com.

8
. It also serves to probe the robustness of our main results. We estimate the following specifications: _h_ r h 1 _h_ r _h_ r (3) mm YY = αα + αα + φφ BBW WWBBWWBB + ωω where _h_ r stands for the cumulative number battles experienced within 20 km or 30 km radius from households’ residence. All other terms are as defined before. BBW WWBBWWBB Unlike the coarse measure of exposure to conflict in the two DID approaches, which is based on region of residence at the time survey, the granular measures of exposure defined on the basis of distance between location of battle events and households’ residence is less likely to suffer from misclassification of households into affected and comparison. More specifically, it is highly likely that battles that take place close to a household’s residence would affect its food security 23
Most of the existing empirical studies on the link between competition and access to finance use concentration measures as proxies for competition and yield mixed results. Using data from the US, Petersen and Rajan (1994, 1995) find that SMEs are more likely to obtain financing when credit markets are concentrated.<sup>4</sup> Similarly, using a survey dataset of German manufacturing firms, Fischer (2000) finds that more concentration leads to more information acquisition and greater credit availability. On the other hand, using enterprise survey data for 74 countries, Beck, Demirguc-Kunt, and Maksimovic (2004) find that in more concentrated banking sectors firms of all sizes face higher financing obstacles and the impact of concentration decreases with firm size. Chong, Lu, and Ongena (2012) also find a positive association between concentration and credit constraints, using a survey on the financing of Chinese SMEs combined with detailed bank branch information.<sup>5</sup> In contrast to previous studies that equate high concentration with lack of competition, recent papers that use direct measures of banks’ pricing behavior provide less ambiguous findings on the link between competition and access to finance. Using the Panzar and Rosse H- statistic (1982,1987), which captures the elasticity of bank revenues to input prices, Claessens and Laeven (2005) find that competition is positively associated with countries’ industrial growth in a sample of 16 countries over the period 1980-1990.<sup>6</sup> The authors argue that this suggests that more competitive banking sectors are better at providing financing to financially dependent firms. Exploiting a very rich dataset on Spanish SMEs and using the Lerner (1934) index – the difference between banks’ prices and marginal costs relative to prices - as a measure of competition, Carbó-Valverde, Rodriguez Fernandez, and Udell (2009) also find evidence that competition promotes access to finance.<sup>7</sup> At the same time, the authors find that their results for

> 4 A
difference between weighted Internet survey data and the reference survey. Propensity score adjustment seems to work better than post-stratification in settings where they have been compared directly, but other <mark>studies show that the choice of adjustment variables is more important than any reweighting method for the accuracy of estimates. Only one study examines economic indicators (wages) from web surveys. W</mark> hile this study was able to match demography and human capital variables with reference samples, they were unable to match wage distributions. Clearly, all studies are based on high income countries, with little research on developing countries. In this study, we try to address these gaps by studying the accuracy of Internet surveys in a set of six developing countries using the propensity score adjustment method.

# **<mark>3. Data and Descriptive Statistics</mark>**

# **<mark>3.1. Data Sources</mark>**

<mark>The study uses several sources of data. The primary data source is the web-survey data collected through the Random Domain Intercept Technology. The survey is offered to the online population at random, provided they incur a browsing data input error. Specifically, if an individual stumbles across a broken link, they will be invited to participate in the survey. After excluding bots, users are invited to participate in a survey in their country’s language. The targeting is based on the IP-address that also reveals information about different regions across the country. Respondents are identified by creating a unique non-personally identifiable respondent code which is a combination of the respondent’s IP address, device details such as device type, internet browser, operating system, and other device details. Using this code, respondents are filtered out from taking the survey more than once, as the various survey waves are designed as repeated cross-sections. While the same person could potentially take the survey from multiple devices, the opportunity cost of time could prevent them from doing so.</mark>
A study by Huang et al. (2011) uses a panel survey, known as the China National Rural Survey (CNRS), collected by the authors to establish a counterfactual ―business-as-usual‖ level of off-farm employment and then analyze the effect of the crisis on off-farm nonagricultural employment. Because the off-farm nonagricultural employment category treats migrants and non-migrant nonagricultural workers as employed in an integrated off-farm labor market, the net effects of the crisis relative to counterfactual levels of both off-farm employment and overall employment can be examined, including work in agriculture.

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all three moments of NSS log consumption using the observed data and adopt our method of moments estimator to obtain estimates of _a_ , _b_ , and _σ_<sup>2</sup> for that year -- and subsequently assume that all three parameters remain constant over time until the next NSS household consumption survey becomes available (which is when the first three moments derived from observed consumption data can be updated). Alternatively, one could continue to adopt the version of Approach 2 we are currently using, namely estimate moments that are found to be comparatively stable over time from observed household (log) consumption data and estimate moments that are found to be less stable from up-to-date imputed consumption data. The latter (and currently adopted) approach may be preferred when the CPHS sample is subjected to notable changes that may significant introduce changes in moments that are not accounted for by reweighting. See Appendix 3.3 for a further discussion on the changes made to the CPHS sample (most notably during the third wave of 2017) and its implication for our method of estimation.

On the choice between Approaches 1 and 2, it should be noted that the two approaches rely on their own set of assumptions. The validity of these assumptions will be context-specific and may vary over time. Approach 1 assumes that the relationship between NSS-consumption and household characteristics such as demographics, education, and employment is stable over time, while Approach 2 assumes that the relationship between NSS-consumption and CPHS-consumption is stable over time. Where possible one should implement both approaches (thereby considering different assumptions) and inspect robustness. Appendix 3.2 compares the relative ranking of households based on their observed CPHS consumption and imputed consumption based on approach 1 of section 4.1 and approach 2 of section 4.2.

# **5 Results**

## **5.1 Main estimates of poverty and inequality**

Both approaches yield qualitatively similar levels and trends in headcount poverty estimated at the $1.90 line: poverty is about 12.3
are visible in the overwhelming majority of the land area. In Figure 3, a closer view of Dar es Salaam, Tanzania’s largest city, shows a contiguously lit area 20–30 km across, extending farther in a few directions along main intercity roads just as the city’s built up area does.

Henderson, Storeygard and Weil (2012) show that light growth is a good proxy for income growth at the national level. Annual changes in gross domestic product (GDP) are correlated with changes in DN, with an elasticity of approximately 0.3 for a global sample as well as a sample of low and middle income countries. In both samples, the lights explain about 20 percent of the variation in log GDP net of country and year The chief strength of the lights lies in their geographic specificity—they are highly local measures. To proceed with lights as a measure of city-level GDP, it must first be shown that the strong national relationship holds for _subnational_ regions. This is problematic because of a mismatch in data availability. Rich countries tend to have good local economic data, but the lights data are heavily topcoded in their cities. Lights topcoding is less of a problem in most poorer countries, and especially in Sub-Saharan Africa, where almost no pixels are topcoded (15 per 100,000, or 3 per 100,000 outside South Africa and Nigeria). However, good local economic data are rarely available. China and South Africa represent good compromises, with relatively little topcoding but relatively high quality income data for a short panel of regions.

China has panel GDP data for two relevant types of subnational regions: cities proper and prefectures.<sup>17</sup> Columns 1 and 2 in Table 1 show, at the city proper and prefecture level, respectively, that the elasticity of GDP with respect to light is significantly positive in a 1990/1992–2005 long difference specification.<sup>18</sup> The point estimate is very similar to
and data is entered into the SAP system. Daily expenditure statement of Pakistan Public Works department is entered into the FMIS ex-post from its 58 formations. Similar steps are being taken to extend this process to capturing daily expenditure data ex-post from Pakistan Post.

However, while this methodology may suffice for getting adequate up to date reports, these transactions would not have been subject to FMIS based ex-ante budget control. This means that for a particularly of expenditure, e.g. debt or even for the subsidies to the State-owned enterprises and the expenditure carried out by the SAEs no FMIS based budget control is being exercised. While it is understood that this is supposed to be done by the various entities that are generating the transactions in an off-line mode, this 17