rafmacalaba/datause-encoder-data huggingface dataset:
exposure to development policy lending, we use two measures—the number of development policy loans and the number of prior actions issued by the organization—that are categorized by the World Bank “policy cluster.” We then follow the same cross-walk procedure (see Table A1 in the Appendix) that we used to construct _n_ _~~e~~ sw_ _~~m 1~~ p_ to measure these variables at the country-policy-cluster-year level. These variables— _dpl_ _~~p~~ roj_ _~~n m 1~~ p_ and _pa_ _~~m 1~~ p_ —measure whether a respondent worked in a specific country and policy cluster that received one or more DPLs or PAs, respectively, from the World Bank during a year in which he or she evaluated the policy influence of the World Bank.

We also rely on measures of annual per capita disbursements through development policy loans ( _lnaid_ _~~d~~ pl_ _~~d~~ isb_ _~~p~~ c_ _~~0 m 1~~ p_ ) and annual per capita disbursements through investment loans ( _lnaid_ _~~i~~ nv_ _~~d~~ isb_ _~~p~~ c_ _~~0 m 1~~ p_ ). However, the World Bank does not categorize its financial disbursement data by policy cluster, so these variables are measured at the country-year level. Also, given that _lnaid_ _~~d~~ pl_ _~~d~~ isb_ _~~p~~ c_ _~~0 m 1~~ p_ , _dpl_ _~~p~~ roj_ _~~n m 1~~ p_ , and _pa_ _~~m 1~~ p_ are strongly correlated, we never introduce these variables simultaneously to minimize the risk of multicollinearity.

For ease of exposition, we refer to these five measures of exposure to World Bank assistance as “treatment” variables (see Table A2). We set all of these variables to the mean values that they assumed between a respondent’s start year and the prior year since the receipt of World Bank assistance in the year prior to a respondent’s start year could affect the World Bank’s reported level of policy influence in the very next year.<sup>20</sup> Additionally, in our baseline model, we include six country-level control variables to account for political and economic attributes of countries that may correlate with the extent to which the World Bank influences the policy priorities of its client countries. First, to account for the possibility that a government’s propensity to recalibrate its policy priorities is a function of its bargaining power vis-`a-vis donors and creditors (Girod and Tobin 2016; Parks and Masaki 2019), we control for net official development assistance as a percentage

> 20Due to their highly skewed distributions, we log the two treatment variables with monetary values and replace their missing values with minimum positive values.

16
The CEQ assessment aims to incorporate the largest number of policies for which there are data of a certain quality and level of disaggregation. Any fiscal components for which the expenditure categories are too broad, or for which the benefits (or payments) cannot be clearly mapped into the household survey data, are not deemed as informative and are not included in the exercise. Table 1 provides a snapshot of the fiscal expenditures covered by this assessment. Defense spending and infrastructure are not covered, nor are the subsidies destined to farmers or water subsidies. Subsidies on food items and energy products are captured by the exercise. Crucially, a large share of the social protection portfolio is also incorporated, with the exception of the budget line on housing and public utilities. Government expenditures on education are integrated into the analysis, although the level of disaggregation to assign benefits is restricted by the availability of two budget lines only: primary, preparatory, and secondary, on one hand, and tertiary education, on the other. The public service pension fund is included, but, as will be explained, the study treats these expenditures as part of the public sector or civil service wage bill rather than as a tax and transfer program.

It is important to highlight that the CEQ exercise should not be understood as an estimate of all expenditure categories that may have an effect on households’ welfare. Some public expenditure elements have private analogues. In certain countries, the existence of private pension funds may allow individuals who do not belong to the public contributory pension system to contribute and receive income from these. Such goods and services are included in measures of income but are not part of the fiscal system and we do not attempt to determine their impact on welfare or inequality. Households may also spend on private education and tutoring for their children. As long as these are not payments or benefits from the public system, they are not part of the exercise.

In what follows, we provide an overview of each of the main fiscal tools and illustrate the policies that are part of the CEQ analysis. Given data limitations, the analysis is based on a snapshot of the Egyptian policy landscape as of 2015. This is because the latest available HIECS is from calendar year 2015, and the crux of the exercise is to be able to map the information from administrative data (government accounts) to the distribution of Egyptian households observed in the survey and estimate the benefits (payments) they receive (make).

5
Table 3.3: Characteristics of migrants and non-migrants **(HH** heads only)

||**Northeas**<br>|**ttoSoutheast**<br>|**SoutheasttoNortheast**|**N**|**Eresidents**|**SEresidents**|
|---|---|---|---|---|---|---|
||**m**|**lrants**|**mirants**||||
||<br>Since19|**g**<br>94<br>Total|**g**<br> <br>Total||||
|**Personal**||**inpercentageo **|**ftotal migrants**||**Inperce**|**ntoftotal**|
|**Data:**|||||**popu**|**lation**|
|Male|77.1|75.0|77.9<br>78.5||73.1|73.2|
|Female|22.9|25.0|22.1<br>21.5||26.9|26.8|
|**Race**|||||||
|White|48.3|54.4|33.17<br>36.4||30.7|64.8|
|Black|6.4|5.7|2.4<br>3.7||6.9|7.6|
|Mulatto|45.0|39.5|63.6<br>59.7||62.2|26.8|
|Location|||||||
|Urban|95.0|96.1|63.8<br>69.6||66.8|89.7|
|Rural|_5.0_|3.9|36.2<br>30.4||33.2|10.3|
|Education:|||Inyears||||
|levelof|5.47|4.87|4.50<br>4.71||3.9|l<br>6.2<br>|
|education|||I|||I|
|Employment:||inpercentage o|ftotalmigrants||**in perce**<br>**popu**|**ntoftotal**<br>**lation**|
|Active|92.9|77.0|84.8<br>83.4||78.9|76.2|
|Inactive|6.9|23.0|15.2<br>16.6||21.1|23.7|
|Employed|93.0|92.2|91.9<br>91.1||95.1|93.9|
|Unemployed|7.0|7.8|8.1<br>8.9||4.9|6.1|
|Formal|70.7|73.1|35.7<br>46.0||45.4|69.4|
|Informal|29.3|26.9|64.3<br>54.0||54.6|30.6|
|Sector|||||||
|Agriculture|6.1|4.5|35.9<br>33.1||37.3|13.1|
|<br>Manufa.|13.0|16.2|7.7<br>7.7||7.5|15.5|
|Construction|19.7|15.0|14.8<br>9.9||8.6|10.2|
|Other|1.2|1.4|1.1<br>1.1||1.4|1.8|
|industries|||||||
|Commerce|11.8|13.8|10.0<br>12.8||12.4|13.2|
|Services|30.7|29.5|13.2<br>14.4||13.8|20.0|
|otherservices|3.2|3.2|1.7<br>2.5||2.2|4.9|
|transport&|5.8|7.3|5.9<br>5.6||4.4|6.7|
|<br>communic.|||||||
|Social|3.5|4.9|3.9<br>6.0||6.0|7.1|
|PublicAdmin.|2.8|3.0|4.1<br>5.0||4.7|5.3|
|Other|2.3|1.3|1.9<br>-<br>2.0||**i.6**|2.3|
|Total|100|100|100<br>100||||
|**Income:6**|||||||
|Income|291.44|304.35<br>|l<br>136.40<br>186.30<br>|l|1.78.72|l<br>389.50|
||1|**~~**|**~~~~povertyheadcount (percent)**||-T|<br>lI|
|**PO**|13.4|<br>10.4|<br>56.2<br>42.5<br>|<br>i|**44.3**|i<br>1.|

_Source:_ Author's own calculations based on PNAD 1999.

> 6 All income figures are in reals and 1997 prices. P0 is the poverty head count based on a poverty line of R$65.

15
just over _5%._ By far the most important natural resource user fees and environmental taxes are the subsoil user fees (see the definitions in Table 4). Land tax revenues generate just under 1% of consolidated revenues2, while forest fees come third with less than 0.15% of consolidated revenues. The share of pollution charges accruing to the budget is even more marginal as it provides less than 0.05% of revenues. Although these revenue categories represent slightly larger shares of the regional budgets, the amounts are low in comparison with the top five revenue categories mentioned above.

Table 2. Natural Resources and the Environment in Russia's 1999 Budget

||Conso|lidated bud|get|Fe|deralbudg|et|
|---|---|---|---|---|---|---|
||Share|mnRUB|mnUS$|Share|mnRUB|mnUS$|
|Totalrevenues|100%|1,197,454|48,284|100%|611,710|24,666|
|Total "greentaxes"|3.72%|44,575|1,797|1.72%|10,496|423|
|Subsoiluserfees|2.51%|30,077|1,213|1.18%|7,190|290|
|Continentalshelf|0.00%|25|1|0.00%|0|0|
|Forestfees|0.14%|1,727|70|0.07%|447|18|
|Waterfees|0.09%|1,035|42|0.03%|198|8|
|Pollutioncharges|0.04%|461|19|0.08%|461|19|
|Land tax|0.91%|10,939|441|0.36%|2,189|88|
|Otheruserfees|0.03%|312|13|0.00%|10|0|
|Otherrevenues|96.28%|1,152,878|46,487|98.28%|601,214|24,242|

Source: Ministry of Finance.

1.4 Of course, natural resources generate more budget revenues than suggested by the single item "green taxes" of Table 1 and Table 2. Natural resources also generate revenues indirectly via profit taxes, excises, VAT, export duties, _etc._ Table 3 reports the contributions of the main natural resource sectors to the revenues collected by the Ministry of Taxation. Out of the tax revenues collected by the Ministry of Taxation in 1999, four of the main natural resources - namely fuels, metals, wood and fish products - contributed around 20%, including over 17% from fuels and metals alone. This proportion is much larger than the 4% of total consolidated revenues due to natural resource user fees. And this is not the whole picture.

_1.5_ Table 3 does not contain the revenues collected by the Customs Committee _(Gostamozhkomitet),_ which is responsible for levying import and export duties, as well as the excises on exported oil (about one-third of the oil produced). For exarnple, _Gostamozhkomitet_ reports collections of RUB 39 bn (around US$ 1.5 bn) in export duties in 1999, _i.e._ over 3% of consolidated budget revenues. Indeed around 80% of export duties

> 2 Land taxes produce relatively large revenues, although large land users (e.g. the firm Gazprom) pay no land taxes for most of their production and transport installations (Voronkov 2000b).

2
|Feb-May 2021<br>|Ago. 2021<br>a|1stSep. 2021|19th Jan - 3rd March<br>2022|
|---|---|---|---|
|Qualitativeworkto|Pilot|Intervention|Post-treatment survey|
|identify barriers||Messages sentto|Phonesurvey|
|Messages design||participants of both<br>|conducted to a sample<br>|
|||experiments|ofparticipants|
# **Nighttime Lights Revisited:**

# **The Use of Nighttime Lights Data as a Proxy for Economic Variables**

Douglas Addison DAddison@worldbank.org Benjamin Stewart BStewart@worldbank.org _Key words:_ earth observation; satellite imagery; DMSP-OLS; NPP-VIIRS; gross domestic product; electric power consumption; capital; population; linear regression; night-time light data; economic monitoring.

_JEL Classification:_ E01, E23, 047, 057 The authors are grateful to Alexei Abrahams, Andrea Coppola, and Stephen Knack for their thoughtful comments, suggestions, and guidance.
(ACM), small scale (AMS), large scale afforestation and reforestation (AR-AM), large scale consolidated afforestation and reforestation (AR-ACM), and small scale afforestation and reforestation (AR-AMS). Finally, the log of abatement cost is regressed on the same set of explanatory variables as in model (III) except that dummy variables for each different methodology and host country are used instead of categorized methodology dummies and region dummies, respectively (model IV). In particular, 86 host country dummies and 206 methodology dummies are used as multiple methodologies are applied for many projects. Estimated elasticities and semi-elasticities for models (II), (III), and (IV) are presented in the third, fourth, and fifth columns of table 4, respectively.<sup>12</sup> The estimates for methodology and host country dummies in model (IV) are suppressed due to space limitation.

As can be seen from Table 4, the coefficient estimate of log of abatement (i.e., elasticity) is positive and highly significant in the log-log model (model I), suggesting that the cost of abatement increases with the volume of abatement. However, a one-tailed test indicates that the elasticity is less than one at 5% significance level, thus implying ‘economies of scale’ whereby output grows proportionately faster than costs.<sup>13</sup> Estimated coefficient of log of project duration is significantly positive, but a similar test suggests the elasticity is not statistically different from one (table 4). This result suggests that, all thing equal, mitigation cost increases with project duration thus implying ‘diseconomies of time.’ Estimated coefficients of all project type dummies are positive and significant except for H/PFCs & N2O reduction projects (table 4). The coefficient estimates for all credit start-year dummies are negative but significant only for years 2007-09, 2012, and later (table 4). Estimated coefficients of the regional dummy variables are negative but significant only for Asia and the Pacific and Europe and Central Asia (table 4), thus implying lower fixed costs in those regions compared to that of Africa, Latin America, and the Middle East.

The inclusion of the squared log of abatement as an additional explanatory variable in the model (model II) does not change the results significantly. While the magnitude of the estimates from models (I)

> 12 Estimated coefficient estimates and their standard errors under models (I)-(IV) are reported in annex table 1.

> 13 This test and subsequent ones are one-tailed tests based on a nonlinear combination of estimated parameters and implemented using Stata’s (2012) NLCOM post-estimation procedure.

15
<!-- Start of picture text --> we are trying to learn whether an individual’s (current) (nonmedical) consumption is substantially<br><!-- End of picture text --> interested in computing the percentage reduction in (current) (nonmedical) consumption due to the out-of-pocket medical expenses necessitated by the health shock (cf. Berki 1986). As pointed out in Wagstaff and van Doorslaer (Wagstaff and van Doorslaer 2003), the <!-- Start of picture text --> In the light of this, Flores, Krishnakumar, O'Donnell and van Doorslaer (2008) propose a coping-<br><!-- End of picture text --> <!-- Start of picture text --> FKOV — 0 4 MO? _ By<br><!-- End of picture text --> <!-- Start of picture text --> FKOV = —_<br><!-- End of picture text --> survey (conducted in 2004) did not contain the necessary breakdown. So, it is of interest to know how the theoretically incorrect (but readily computed) measures - the income- and consumptionbased measures - relate to the (more) theoretically correct (but more data-demanding) coping<!-- Start of picture text --> CONS (- —) > CATArxKov (- —), CA<br>FKOV<br>PROV"_ 04M°-By°=Bu _— 9S— Bu<br><!-- End of picture text -->
## Sen, Amartya. "The Political Economy of Targeting"

- van de Walle, Dominique. "The Distribution of Subsidies Through Public Health Services in Indonesia 1978-87"

# Other References

- Anand, Sudhir and Martin Ravallion. 1993. "Human Development in Poor Countries: On the Role of Private Incomes and Public Services." Journal of Economic Perspectives 7:133-50.

- Besley, Timothy. 1990. "Means Testing Versus Universal Provision in Poverty Alleviation." Economica 57:119-29.

- Besley, Timothy and Ravi Kanbur. 1993. "Principles of Targeting." In Michael Lipton and Jacques van der Gaag (eds.), Including the Poor. The World Bank, Washington, D.C.

- Binswanger, Hans, Shahidur Khandker and Mark Rosenzweig. 1993. "How Infrastructure and Financial Institutions Affect Agricultural Output and Investment in India. " Journal of Development Economics 41:337-66.

- Birdsall, Nancy and Estelle James. 1993. "Efficiency and Equity in Social Spending: How and Why Governments Misbehave." In Michael Lipton and Jacques van der Gaag (eds), Including the Poor. The World Bank, Washington, D.C.

- Boadway, Robin and Maurice Marchand. 1995. "The Use of Public Expenditures for Redistributive Purposes." Oxford Economic Papers 47:45-59.

- Browning, Martin. 1992. "Children and Household Behavior. " Journal of Economic Literature 30:143475.

- Cornes, Richard. 1992. Duality and Modern Economics. New York: Cambridge University Press.

- Cox, Donald and Emmanuel Jimenez. 1992. "Social Security and Private Transfers in Peru." The World Bank Economic Review 6(1):155-69.

- Datt, Gaurav and Martin Ravallion. 1993. "Regional Disparities, Targeting, and Poverty in India." In Michael Lipton and Jacques van der Gaag (eds). Including the Poor. The World Bank, Washington, D.C.

- Deaton, Angus and John Muellbauer. 1986. "On Measuring Child Costs: With Application to Poor Countries." Journal of Political Economy 94:720-44.

- Echeverri-Gent, John. 1988. "Guaranteed Employment in an Indian State. The Maharashtra Experience". Asian Survey 28:1294-1310.

47
Policy Research Working Paper 10870

# **Abstract**

Leveraging unconventional data, including website traffic data and Google Trends, this paper unveils the real-time usage patterns of generative artificial intelligence tools by individuals across countries. The paper also examines country-level factors driving the uptake and early impacts of generative artificial intelligence on online activities. As of March 2024, the top 40 generative artificial intelligence tools attract nearly 3 billion visits per month from hundreds of millions of users. ChatGPT alone commanded 82.5 percent of the traffic, yet reaching only one-eightieth of Google’s monthly visits. Generative artificial intelligence users skew young, highly educated, and male, particularly for video generation tools, with usage patterns strongly indicating productivity-related activities. Generative artificial intelligence has achieved unprecedentedly rapid global diffusion, reaching almost all economies worldwide within 16 months of ChatGPT’s release. Middle-income economies have disproportionately high adoption of generative artificial intelligence relative to their economic scale, now contribute more than 50 percent of global traffic, while low-income economies contribute less than 1 percent. Regression analysis reveals that income level, share of youth population, digital infrastructure, specialization in high-skill tradable services, English proficiency, and human capital are strongly correlated with higher uptake of generative artificial intelligence. The paper also documents disruptions in online traffic patterns and emphasizes the need for targeted investments in digital infrastructure and skills development to harness the full potential of artificial intelligence.

This paper is a product of the Digital Development Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at yanliu@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
temperature.<sup>7</sup> Finally, we conduct a placebo test by using within-sample randomization of temperature. Overall, the results of these exercises remain similar to our main findings.

We also offer further heterogeneity analysis across regions. We employ the temperature bin approach to consider the non-linearity of temperature effects and plot the results in Figure 3. It shows that rising temperature causes higher poverty (Panel A) and inequality (Panel B) in poorer regions such as Sub-Saharan Africa, Middle East and North Africa, and South Asia, but the effects are attenuated in the other richer regions. Furthermore, we also document negative effects of colder temperature on both outcomes, particularly in Europe and Central Asia. We further estimate temperature effects for each country. Plotting the results where each country’s marker is proportional to its real GDP per capital, Figure A5 (Appendix A) shows that countries bearing the largest effect of global warming (e.g., Uganda, Ghana, and Mozambique) also tend to be poorer or located in poorer regions.

Furthermore, we explore different country characteristics that might help mitigate temperature effects. Employing a democracy index that categorizes countries into democracies, authoritarian regimes, and hybrid regimes, we find that countries with democratic regimes appear to be less vulnerable to the impacts of global warming. Additionally, we observe that temperature effects are stronger in countries with higher shares of agriculture and are weaker in countries with higher shares of manufacturing. These findings suggest that institutions could play important roles in mitigating the effects of global warming on different countries and regions.<sup>8</sup>

> 7 These include using (i) log of temperature; (ii) temperature measured in degrees Fahrenheit; (iii) the temperature data from CRU; (iv) the number of days that temperature is above 28<sup>◦</sup> C; (v) dropping regions with temperature being above that level; and (vi) temperature shock, defined as the difference between actual temperature and longterm temperature being greater (less) than 2 (-2) standard deviations (Appendix A, Table A4).

> 8 We also investigate the role of information and communication technologies (ICTs) in poverty reduction by providing access to markets, decreasing transaction costs, and increasing income for a significant proportion of people living in developing countries (World Bank, 2016). Our results show that regions with better access to ICT are less vulnerable to the effects of higher temperature.

17
market, soil depletion and toxicity, saturation of the pool of potential adopters, or caution acquired by learning from the failures of others.

As expected, the epoch dummies, introduced to capture changing market and institutional conditions after 1990, indicate that adoption became less and less likely over time: both are positive, with the latter period (2002-2005) characterized by even slower adoption than the former (1990-2001).

Also as expected, the community fixed-effects (with Santiago Sacatepéquez as the comparison group) also give significant results. Not surprisingly, El Rejón and San Mateo, the most distant and last-incorporated communities into the cooperative, were slower to adopt. There is also indication that households in Pachalí may have also been slower to adopt, while Pacúl and Santa Maria de Cauqué were generally faster.

Finally, the value of the shape parameter ρ deserves some explanation. The estimated value is significantly greater than 1, indicating, somewhat counter-intuitively, a strong positive time dependency in the probability of adoption. The parameter, however, seems to counterbalance the effect of the other time-dependent variables, all showing the opposite relation. In fact, as we run a different specification of the model without the other time variables, the shape parameter assumed a value smaller than 1, thus reflecting the overall negative time drift from the time-varying covariates.

# _Withdrawal_

Table 3 presents the results of the withdrawal estimation. Again, negative coefficients for the AFT Weibull indicate a shorter expected time until withdrawal occurs (shorter duration of snowpeas production). Positive coefficients correspond to households producing the crop for longer periods of time.

27