rafmacalaba/datause-encoder-data huggingface dataset:
highlights the fundamental role of digital infrastructure availability and quality as the key supplyside barriers to generative AI usage. Column (3) shows that trade openness, service sector share of GDP, and digitally delivered service exports in total exports are all positively and significantly associated with ChatGPT usage. Column (4) reports positive and significant coefficients of human capital variables: dummy for English speaking country, literacy rate, percentage of the population with intermediate digital skill.<sup>7</sup> We choose intermediate digital skills as ChatGPT is most relevant for workers with such skills.

Column (5) includes all three sets of variables together and shows similar and robust results as the previous columns. The coefficient of log GDP per capita becomes weaker as we include more factors into regressions. It turns insignificant and close to zero in Column (5). Together with the increased adjusted R squared from Column (1) to Column (5), this pattern indicates that the correlation between income level and ChatGPT usage can be broken down into our identified three areas.

The comparison with Google yields additional insights. Specialization in digitally deliverable services, white collar workers, English fluency and digital skills are highly correlated with ChatGPT traffic but not Google traffic. This corroborates our previous findings that ChatGPT is by and large used by white collar workers in tradable, knowledge-intensive industries.

The strong correlation between country-level English fluency and ChatGPT usage can be attributed to several interconnected factors. Primarily, ChatGPT’s training on predominantly English-language data results in better performance and user experience in English. This is compounded by the internet’s English-centric content and the tech industry’s use of English as a lingua franca. Countries with higher English fluency often have education systems emphasizing technology and digital literacy, fostering a more tech-savvy population primed for AI adoption. Early adoption patterns, initially driven by English-language marketing and outsourcing demand emanating from advanced English-speaking countries, may have set a precedent for ongoing usage trends. Additionally, many professional fields that benefit from ChatGPT, such as programming and research, predominantly operate in English, further skewing usage towards English-proficient populations.

> 7This measure is based on the question “% creating electronic presentations with presentation software” in ITU ICT SDG indicator 4.4.1. Digital skills are classified into three levels: basic, intermediate, and advanced, referring to foundational digital abilities, goal-specific technological uses, and specialized ICT professional expertise, respectively (ITU 2020).

32
Table 9: Household and community open defecation practices

|VARIABLES|HH members usually<br>defecate in<br>|HH members pr<br>defecati|actice open<br>on...|HH is aware of community<br>members practicing OD|Child feces are<br>safely removed<br>|
|---|---|---|---|---|---|
||fields/bushes/rivers|Always/regularly|Sometimes|||
||(1)|(2)|(3)|(4)|(5)|
|HWWS (β1)|-0.032|-0.059|-0.057|-0.052*|0.043|
||(0.040)|(0.041)|(0.044)|(0.029)|(0.036)|
|TSSM ( β2)|-0.120***|-0.098**|-0.062|-0.055*|0.117***|
||(0.039)|(0.041)|(0.043)|(0.029)|(0.034)|
|HWWS + TSSM (β3)|-0.074*|-0.085**|-0.042|-0.066**|0.084**|
||(0.038)|(0.039)|(0.046)|(0.032)|(0.033)|
|p-values β1= β2|0.020|0.342|0.884|0.925|0.036|
|for F-test β1= β3|0.264|0.506|0.735|0.649|0.332|
|β2=β3|0.202|0.746|0.632|0.719|0.332|
|_Observations_|3,616|3,610|3,610|3,592|3,619|
|_R-squared_|0.229|0.135|0.108|0.106|0.186|
|_Control Mean_|0.231|0.299|0.513|0.839|0.716|

Notes: Robust standard errors in parentheses clustered at the ward level.*** p<0.01, ** p<0.05, * p<0.1. Coefficients estimated using a linear probability model including block fixed effects.

45
harvest. An even higher percentage of harvested raw nuts – 86 percent in Inhambane and 71 percent in Gaza – are retained for home consumption. Then the question becomes why cashew producers in Mozambique do not sell all the raw nuts they harvest? Several factors contribute to this pattern, but at their core is the fact that even though they have increased in recent years, producer prices were still too low to make it worthwhile for farmers to sell all their raw nuts (see Annex VI).

The following nicely summarizes the main conclusion of the surveys: ― _The pattern of cashew cultivation that appears to have evolved over a long period of unremunerative prices is one wherein households simply collect raw nuts instead of “producing” them. And even this collection of raw nuts is irregular and incomplete. Almost one quarter of all households do not harvest their cashew trees, and amongst those that do, raw nut harvests, and cashew incomes, exhibit tremendous variation from one year to the next. These patterns are difficult to explain in terms of production variability, suggesting that during the survey years farmers exhibited little interest in cashew cultivation.”_ (World Bank, 2001, p. v).

Paul (2008) analyzing the households in the cashew triangle after the labor intensive factories located in rural areas, also comes to the same conclusions. Most farmers do not treat cashew as a commercial crop. On the other hand, the new labor intensive factories have started to pay higher prices for better quality and by lowering transport costs they can afford to pay better prices. It is not clear whether the average prices reported here are the same where new processing factories are working. Some of these questions can only be answered if there are new comprehensive surveys that analyze similar households.

Recent analyses (Technoserve 2009) show a similar pattern. The vast majority of cashew production is undertaken by small farmers typically owning around 20 trees. Almost 42 percent of farmers in Mozambique own cashew trees which represents about one million producers. Average productivity is low about 2-4 kg per tree while 8-10 kg per tree is attainable. Just like earlier surveys between 10-35 percent of output is consumed at home. The investment in new trees, which most argue are essential to the production growth is undertaken by only 5 percent of farmers over the last 12 months of the survey.

These averages mask large differences among cashew producers. Better off farmers own between 100-300 trees, and constitute between 10-15 percent of all farmers. Middle income farmers own between 40-90 cashew trees and constitute 25-35 percent of all farmers. Poorer farmers on the other hand, own 5-25 trees and constitute 50-65 percent of all farmers. Better off farmers are able to spray the trees and sell later in the season and receive better prices. Spraying increases the yields and improves the quality of cashews. Net income per tree for poorer farmers is estimated to be about $1.20, while the better off farmers earn as much as $3.42 per tree (Technoserve, 2009).

15
the current year, next year, and two-year ahead horizons. For instance, the density forecasts estimated in August 2015 predicts world GDP growth for 2015, 2016, and 2017.

# 3.1 RISK FACTORS AND PRIORS

We select a subset of risk factors, **,** directly affecting world GDP growth. Four random ZZ variables help us to account for substantial risks of world GDP growth. These variables account for absolute deviations from consumer price inflation targets, global energy prices proxied by crude oil prices, global financial conditions quantified by the S&P 500 index price, and world term spread conditions measured by country-individual term spreads.

The density predictions for GDP growth are constructed reflecting upon basic economic mechanisms between output growth and underlying risks. The linear relationship of GDP growth, , and its associated risk factors, , imply a constrained behavior of the variance and YY ZZ skewness as discussed in Proposition 1 and 2. In this application, the assumed responses of global growth to the associated risk factor movements are supported using previous studies.<sup>15</sup> The sign restrictions on the responses of World GDP growth to the changes of its global risk factors are presented in Table 1. The sign of the risk-coefficients in the linear Equation (1) defines the reactions of the skewness to predicted global growth, as seen in Equation (3). In this regard, the global growth skewness is marginally synchronized with the directional change of an associated risk factor.

TABLE 1 − SIGN RESTRICTIONS OF THE RESPONSES OF WORLD GDP TO MOVEMENTS OF GLOBAL CONDITIONS

||World Term|Spreads<br>World (CPI) Infation:<br>Absolute Deviations|World Energy Prices|S&P 500 Index Prices|
|---|---|---|---|---|
|||from Targets|||
|World GDPgrowth|(+)|(-)|(-)|(+)|

> 15 Apart from the literature revision, we also estimated variety of econometric representations for ex-post world GDP growth using OLS and robust White-Huber standard errors. These OLS estimations obtain coefficients of the risk factors with the same signs as the ones presented in Table 1. The OLS estimates are available upon request.

18
<!-- Start of picture text --> Ge Private Public<br>of * Ne<br>i # 4 ® ., & .<br>ss.<br>.<br>. “e a<br>o *<br>g “5<br>6 ° Customary monetary Customary non-monetary<br>D 8 8<br># *<br>.<br>. 7 &<br>v. ‘al<br>°f<br>*<br>a a i 1<br>9 8.5 8 7.5 7 9 8.5 8 75 7<br>Longitude<br><!-- End of picture text -->
An issue, perhaps most important for Asian experts, is whether expert valuation figures can be interpreted as representing the entire homeland populations or only fractions of them. In several of the Asian countries, most households pay no income taxes, and many do not have formal utility services nor utility bills. Although we asked experts to consider all households in their countries when predicting mean and median WTP, they might have felt it unreasonable to assume that payments could be collected from informal households that do not have formal relationships to taxing authorities nor to utilities. It is unclear how our valuation questions were interpreted by experts from these countries; it represents an area ripe for future exploration on this and other global initiatives.

Finally, even professional environmental economists, who may feel they are “experts” on general issues of the type addressed in our study, do not necessarily have (or feel they have) much insight with respect to the true population values at stake here. As noted, some of the most experienced economists in our sample refused to answer as they felt their basis for doing so was too weak. Such uncertainty is to some degree reflected by the great variety of experts’ answers for any one given country; any claim that an individual expert’s answers “represent” their populations would thus be shaky at best, and so we have more confidence in country averages that are based on larger numbers of experts. Our best hope, however, is that the groups of experts from the different countries have a common set of “anchors” that level their answers and reveal the relative relationships among the answers, if not their differences in absolute terms. If so, one of our main aims with these surveys, to probe average WTP relationships to average national incomes, will have been fulfilled.

32
<!-- Start of picture text --> Yuan/kWh<br>0.40<br>0.35<br>0.30<br>0.25<br>0.20<br>0.15<br>0.10<br>0.05<br>0.00<br><!-- End of picture text -->

**Figure 10: Wind Subsidies per Province by City and Region** (RMB/kWh) _Source:_ Based on the desulfurized coal-fired benchmark price in 2009.

48. According to the subsidy schedule from January to September 2010, the average subsidy in the whole country was RMB 0.224/kWh. The average subsidy for Inner Mongolia and Xinjiang, which are located in resource areas of type I, II, and III, was RMB 0.239/kWh. The average subsidy in other areas was RMB 0.195/kWh. The difference between the two was RMB 0.044/kWh (see Figure 10 and Table 9).

**Table 9: Comparison of Subsidies in Different Regions**

|Wind resource zones|I、II、III|IV|
|---|---|---|
|Price(RMB/kWh)|0.51、0.54、0.58|0.61|
|Areas|Inner Mongolia, Xinjiang,<br>North Hebei, Gansu, western<br>Jilin, part of Heilongjiang,<br>Ningxia|Other areas|
|Installed capacity (MW)|16,090|7,190|
|Generation subsidized(GWh)|21,300|9,700|
|Subsidyline of unitpower(RMB/kWh)|0.239|0.195|
|Overall average subsidy line<br>(RMB/kWh)|0.224||

49. In the Southeast, because the current tariff is RMB 0.61/kWh, developers of wind farms can secure financial internal rates of return (FIRRs) above 8 percent only when they realize equivalent full load operation hours above 1,800 for an investment level at RMB 8,000. The different FIT refers to a different economically exploitable wind resource: For RMB 8,000/kW investment cost: 22
<u>Table 4. Baseline Student Survey: description and balance, only Standard 2 students</u>

||Control|Treatement|Treatment-<br>Control||Control|Treatemen|t<br>Treatment-<br>Control|
|---|---|---|---|---|---|---|---|
|Panel A. Student characteri|stics|||Panel B. Household C|haracteris|tics||
|Age|16.66|15.85|-0.812**|Household Size|9.49|8.93|-0.56|
||(2.02)|(1.55)|(0.34)||(4.43)|(6.15)|(0.57)|
|Gender (male=1)|0.56|0.4|-0.161***|Child to adult|1.67|1.45|-0.216|
||(0.50)|(0.49)|(0.05)||(1.79)|(1.31)|(0.17)|
|Repeting greade|0.08|0.05|-0.03|Parent local|0.63|0.56|-0.063|
||(0.27)|(0.21)|(0.02)||(0.48)|(0.50)|(0.06)|
|Ever repeat|0.34|0.26|-0.081|Mother farm|0.63|0.54|-0.094|
||(0.47)|(0.44)|(0.05)||(0.48)|(0.50)|(0.06)|
|Help with homework|1.47|1.19|-0.271|Father farm|0.47|0.4|-0.071|
||(1.32)|(1.25)|(0.17)||(0.50)|(0.49)|(0.05)|
|Household visit sch|0.75|0.72|-0.023|Mother literate|0.61|0.66|0.047|
||(0.44)|(0.45)|(0.04)||(0.49)|(0.47)|(0.06)|
|Any day absent|0.52|0.4|-0.12|Father literate|0.84|0.81|-0.034|
||(0.50)|(0.49)|(0.08)||(0.37)|(0.39)|(0.03)|
|Absent because of fees|0.54|0.47|-0.072|Mother education|2.97|3.3|0.330*|
||(0.50)|(0.50)|(0.09)||(1.41)|(1.50)|(0.19)|
|Schools is irrelevant|0.34|0.29|-0.05|Father education|3.55|3.83|0.278**|
||(0.50)|(0.50)|(0.05)||(1.39)|(1.52)|(0.16)|
|||||Index of hh services|2.17|2.27|0.104|
||||||(0.73)|(0.71)|(0.12)|
|Sample size|372|546|918||372|546|918|
|Date||||Dec 2010/April 2011||||
|Test for joint significance||||||||
|Chi2|||30.176||||14.033|
|Prob>Chi2|||<0||||0.172|

Note. This table presents the coefficient of the OLS regression of the treatment indicator (equal 1 if treated; 0 otherwise) against each of the listed baseline characteristics, as reported by students.

21
These data constitute an alternative creditor-based source that allows us to discriminate between flows of different maturities. In the TIC dataset, long-term securities refer to securities with an original maturity of more than one year. Short-term capital flows compiled in the TIC system include liabilities and claims arising from deposits due to or from foreign entities, financial instruments with an original maturity of one year or less, borrowings from foreigners and loans and other credits to foreigners<sup>iii</sup> . As we pointed out previously, for Colombia the _balanza cambiaria_ does not provide information on the maturity structure of flows, which renders the analysis of the effect of the controls on the maturity of capital flows particularly difficult.

A crucial question, when assessing the impact of the price-based regulations on inflows, is the appropriate measurement of the tax-equivalent of the reserve requirement. A “naïve” measure of this tax, which is constructed by combining an arbitrage condition and the uncovered interest parity equation, is widely used in the literature. Consider the case where the maturity of the loan (capital inflow) _k_ is larger than the holding period of the deposit requirement ( _h_ ). Assuming that the exchange rate is fixed, the uncovered interest parity would take the form: Where _i_ is the domestic interest rate, _i_ * is the international interest rate and δ is the tax equivalent of the reserve requirement. The following arbitrage equation is also used: 1( − _u_ )(1 + _i_ ) _k_ + _u_ 1( + _E_ [max( _i_ , _i_ * )]) _k_ − _h_ = 1( + _i_ * ) _k_ where _u_ is the unremunerated reserve requirement. The above expression simply states that the return from investing in Chile/Colombia (considering the reserve requirements) has to be equal to the return obtained from investing abroad, i.e. the international interest rate (opportunity cost). Using the UIP condition to substitute for domestic interest rates and using the approximation that 1( + _j_ ) _x_ ≅ 1( + _xj_ ) for a small _j_ , we can derive the usual naïve equation for the tax equivalent: Note that the tax-equivalent is inversely related to the maturity of the capital inflow ( _k_ )<sup>iv</sup> . This measure of the tax equivalent does not take the country risk premium into 8
<!-- Start of picture text --> wy | Lar<br>cri le me<br>yr Sa<br>a ae ro i: |<br>om eo Me<br>ra a c “Oy‘<br>2 y a “|<br>mTM LS ware aa<br>“Th, att eo<br>1940 1970 1980 1400<br>BirthYear Cohort<br>————- ReceivedIDs Before 1988 ——_ ReceivedIDs in 1988<br>vooocce Received[Ds After 1988<br><!-- End of picture text -->
_Science must be understood as a social phenomenon... not the work of robots programmed to collect pure information. Scientific findings should not be elevated to the status of immutable truths._ _—_ Stephen Jay Gould _, The Mismeasure of Man_

# **1 Introduction**

There are 37 weather stations in the World Meteorological Organization’s (WMO) database on the entire continent of Africa. These stations provide weather data for 1.1 billion persons living on the continent (Tzachor et al., 2023). This figure stands in contrast to the 636 weather stations across the European Union and the United States, which provide weather data for 1.2 billion persons in those two regions (Tzachor et al., 2023). Further, because of their uneven dispersion across the African continent, the weather stations that exist cover only about 40 percent of the African population. Weather stations on the continent are often so far apart that the data collected are of limited use, a condition exacerbated by under-investment in their maintenance, which results in a deterioration of the frequency and quality of data reporting. Only one in five weather stations in Africa met the WMO’s reporting standards as of 2019.

The lack of station data across Africa means that, for much of the continent, the truth of the weather is unmeasured. The emergence of Earth Observation (EO) and improvements in weather modeling have enabled the development of remotely sensed weather datasets to fill this gap. These products provide an estimate of the on-the-ground conditions of weather, albeit detected from a distance. These EO products can provide myriad measures of weather phenomenon, like precipitation, temperature, wind speed, or humidity. Each EO product uses a different combination of weather sensors and methods for interpolating and interpreting the data from those sensors. And so, while each EO product endeavors to measure the objective truth of weather conditions, each produces its own “truth.” In theory, these “truths” should all be the same. In practice, they are not. Figures 1 and 2 show these different “truths” across six remotely sensed EO precipitation products and three temperature products. One precipitation product reports rainfall of less than five mm while a different product reports rainfall of more than 47 mm for the same location on the same day. Temperature also varies by EO product. One product reports a maximum temperature of 23<sup>_◦_</sup> C while another reports the maximum temperature that day as 27<sup>_◦_</sup> C.

Where does this disparity leave researchers who want to incorporate EO weather data into economic contexts? If using ground-based weather station data, the gold standard for weather measurement, is not possible, then we are left to use EO sources. But, their mismeasurement of weather can introduce two issues. First, if economists are unaware of these measurement issues, we 2
a centrally-scored test; the maximum possible score was 25. In the remaining 51 schools, they were selected based on a “poverty-based” approach. A student’s “poverty score” was determined based on their self-reported (but validated) household and socio-economic characteristics; the poverty index ranges from 0 (richest household) to 292 (poorest household).<sup>10</sup> Under both approaches, half of a given school’s fourth-graders qualified (i.e., the top half of performers, or the poorest half of students).<sup>11</sup> Crucially (for our study), students in all 207 schools completed both types of assessments, independent of their school’s assignment status. Scholarships were offered to beneficiaries for three years (i.e. through the end of primary school), conditional on their continued enrollment, passing grades, and regular attendance. These requirements were moderately enforced.<sup>12</sup> Scholarships were disbursed as a lump-sum payment of approximately USD20 in the first year, and two payments of approximately USD10 in each of the following two years. As reported by Barrera-Osorio and Filmer (2016), these amounts represent about 3.3 percent of the yearly per capita expenditure in the study sample. These transfers are small compared to similar programs in other countries (Fiszbein and Schady 2009); even relatively small impacts may therefore be cost-effective. Our experimental design exploits the randomized roll-out of the program over its two phases. In 2008/09, during phase one, fourth-graders in schools that were selected to disburse the program in the second phase did not receive any scholarship and did not become eligible in the years thereafter.<sup>13</sup> Note that a sub-set of these fourth-grade students would have been eligible under one of the two targeting schemes (merit-based or poverty-based), had their school been selected. In expectation, these 10The aptitude test was based on the 2005/06 Grade 3 National Learning Assessment. The poverty assessment asked respondents about household demographics and possession of a list of assets (as provided in Table 2). See Barrera-Osorio and Filmer (2016) for more details on the student assessment and the poverty score. 11Median students also qualified for the scholarship.Median students also qualified for the scholarship. The number of scholarships was determined the enrolment numbers.

> 11Median students also qualified for the scholarship.Median students also qualified for the scholarship. The number of scholarships was determined using the previous year’s official enrolment numbers.

> 12If a student lost her scholarship, its amount could not be re-allocated within the same school and the same year. Instead, the amount would be used for the subsequent cohort of fourth-graders.

> 13Recall that the program required students to maintain passing grades. Thus, a phase-one fourth-grader who attended a control group school could not become eligible in phase two by repeating the grade.

10