rafmacalaba/datause-encoder-data huggingface dataset:
communities, including the Scheduled Castes (SC) and Scheduled Tribes (ST). Additionally, the DPEP sought to strengthen state capacity and encouraged the formation of Village Education Committees and other local bodies to mobilize communities and raise awareness of the importance of primary education for all children, among other things. Decentralization and local empowerment were much emphasized components of program delivery, alongside the prioritization of student learning (Pandey, 2000).

Districts with female literacy rates below the national average of 39.3 percent in the 1991 Census of India were designated as program eligible. The DPEP was rolled out in a phased manner, with 42 districts receiving the program in Phase-1 (1994-2001), 80 districts receiving the program in Phase-2 (1996-2002), 27 districts receiving the program in Phase3 (1998-2003), and the remaining 70 districts receiving the program in subsequent phases that began in 1999-2000 (Azam and Saing, 2017). The program was implemented at a large scale with total funding of USD 1,317 million. The central Government of India bore 85 percent of program expenditure, aided by international donors such as the World Bank and the UK Department for International Development; state governments bore the remaining 15 percent. To ensure that resources at the state level were not substituted away from existing education budgets, the central government stipulated that these budgets were to remain unchanged, thus creating a large DPEP-induced increase in government education expenditure (Sunder, 2020).

The DPEP facilitated the construction of more than 160,000 schools and the training of 1.1 million teachers, and increased funds for primary school education by between 17 percent and 20 percent (Azam and Saing, 2017). Approximately 51.3 million children benefited from the program’s infrastructural and administrative expansions (Jalan and Glinksya, 2013).

# **2.2 State Elections in India**

Elections to state legislatures in India are held every five years for each state. Candidates contest in first-past-the-post elections in constituencies (or seats) across each state, and winners are appointed as MLAs in the state. The political party, or a coalition of parties, with a simple majority of MLAs in the assembly forms the government in the state with the assent of the Governor of the state. The state government then serves a five-year term, unless the assembly is dissolved by the Governor in exceptional circumstances. A new party or coalition can govern for the remainder of the five-year term in such cases, but an election is again held once the term ends. Ruling MLAs from the governing party or coalition wield substantial power; they can hold ministerial positions, spend budgetary resources in their constituencies, and pass state-level legislation. The years in which state elections are held are decided according to rules set out in the Constitution of India, and cannot be changed 6
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# 3. VOLATILITY OF CRUDE OIL AND PRODUCTS PRICES. BASIS RISK. AND LIQUIDITY

# A. Volatility of Crude Oil vis-a-vis Products

During the past 15 years PDVSA has been transformed from an exporter of mainly crude oil to an exporter of mainly refined products. In 1976, out of a total production of 2.3 million barrels per day, PDVSA refined domestically 43.4% and exported about two-thirds of the refined output. The remaining 56.6% of production was exported as crude oil. In 1991, out of a total production of 2.5 million barrels per day, PDVSA refined domestically 43.1 % and again exported about two-thirds of the refied output. Of the remaining production, 38.7% was refined and sold abroad by PDVSA's subsidiaries. Thus only 18.2% of total production was sold as crude oil.

One of the reasons given for PDVSA's shift to refined product exports was to achieve a reduction in the price volatility faced by PDVSA. This would be so if product prices are less volatile than crude oil prices and/or have a low correlation with oil prices. However, based on the estimated coefficients of variation, the price volatility of crude oil prices is not significantly higher than the price volatility of diesel and gasoline for the period 1970-90 as well as the subperiod 1980-9011 (see Table 1).

"The coefficients of variation were calculated on annual data. Monthly data of product prices tend to show seasonality, which could raise the estimated volatility. Seasonal adjustments would be necessary if monthly data were used.
population were spread over more but smaller cities, each unable to support the economies of scale inherent in rail mass transit. Although it is a widely held perception that sprawl in land use causes more aggregate car miles to be driven, recent results from modern theoretical models of the urban economy in which both jobs and residences decentralize with sprawl (e.g. Anas and Rhee, 2006) demonstrate that the total miles driven can actually decrease with sprawl as jobs can move closer to workers during the decentralization process. Anas and Pines (2008) have shown that pricing congestion can cause population to spread from larger to smaller cities reducing total congestion, while increasing the developed land area which corresponds to more sprawl.

Emissions and fuel use are curbed significantly not only by reductions in the distances traveled and in the number of trips made, but also by improving the speed of travel, which in turn is determined by the amount of road capacity available to accommodate the demand. Thus any policies which can improve the speed of travel in large and highly congested cities could be very beneficial in reducing fuel use and emissions, while raising tax revenues that can be used in a variety of complimentary ways such as adding mass transit capacity or subsidizing high density developments near mass transit lines. Beevers and Carslaw (2005) have studied by means of simulation tests whether the congestion charging scheme implemented in central London in 2003 has resulted in significant speed improvements of about 2.1 km/hour.<sup>3</sup> We report on results from a highly aggregated model of mode choice in commuting representing Sao Paulo in 2002.<sup>4</sup> In that same year, the Brazilian flex-fuel vehicles that run on a mixture of alchohol and gasoline were introduced but these did not have a significant market share until later. In 2003, a year after the date of our data, flex-fuel vehicles made up only 3.7% of the light vehicle market. Therefore, our study is for conditions just before the introduction of these vehicles. In the model, the total number of trips per day is fixed as is also the representative (average) trip distance. The limited data does not allow us to develop a more sophisticated model such as the one for Beijing (see Anas, Timilsina, Zheng, 2009), where the substitutions between commuting and non-commuting (discretionary) travel and between travel and housing consumption were also included and the number of non-work trips was endogenous.

> 3 Ultimately, reductions are also driven by fuel technology, and driving simulations of electric cars and hybrids show reductions in CO2 emissions  as documented by Saitoh et al (2005).

> 4 Source: <u>http://www.metro.sp.gov.br/empresa/pesquisas/afericao_da_pesquisa/afericao_da_pesquisa.shtml</u> and related websites.

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<!-- Start of picture text --> Figure 4.3: Work Sector for Youth Out of School in Haiti, 2001<br>70<br>Agriculture<br>60 Industry<br>Service<br>50 Public<br>40<br>30<br>20<br>10<br>0<br>15 - 19 20 - 24 15 - 24 25 - 34 35 - 44<br>Age<br>Source: Own calculations based on ECVH 2001.<br>Percent<br><!-- End of picture text --> Table 4.7 disaggregates work sectors by gender. The statistics reveal that the labor market in Haiti is dual: one for men and one for women. While 62 percent of employed male youth work in agriculture only 26 percent of employed female youth do. In contrast, 65 percent of employed female youth work in the service sector, but only 17 percent of employed male youth do. This pattern is unchanged over increasing age.

Table 4.7: Work Sector by Gender, 2001 (percent)

|||Mal<br>|e<br>|||Femal<br>|e<br>||
|---|---|---|---|---|---|---|---|---|
|Age|Agriculture|Industry|Service|Public|Agriculture|Industry|Service|Public|
|15–19|76.8|11.7|11.5|0.0|32.1|3.7|61.8|2.4|
|20–24|57.0|16.8|19.3|6.9|23.7|7.1|66.3|3.0|
|15–24|62.1|15.5|17.3|5.1|25.7|6.3|65.2|2.8|
|25–34|43.3|19.3|23.3|14.1|16.8|6.1|68.1|9.1|
|35–44|54.2|13.7|24.4|7.7|21.4|5.2|65.3|8.1|

_Source:_ Own calculations based on ECVH 2001.

To take the labor market analysis one step further, a probit model (see above) is estimated. The marginal impact of characteristics is used to estimate the probability of being unemployed or inactive versus being employed. This regression and the analysis above (Table 4.4) make it possible to identify characteristics of vulnerable youth. Table 4.8 shows findings from the probit regression on unemployment/inactivity (henceforth unemployment).

For youth who are not attending school, age—as an indicator of experience and search time—is negatively associated with the probability of being unemployed. This indicates that as the young people become older they are less likely to be unemployed. In contrast to the education level of youth themselves, the education level of the household 18
Coffee productivity has seemingly not increased substantially in the Dominican Republic despite years of efforts to shift to higher yielding varieties, and a slow shakeout of smaller-scale producers (figure III.1). Average yields in the Dominican Republic are below other coffeeproducing countries in the region and appear to be trending downward.<sup>52</sup> These trends do not portend well for the competitiveness of the Dominican coffee sector. Low productivity is compounded by increasing costs of production, particularly labor costs and high financing costs.

# **Figure III.1 Coffee yields, Dominican Republic and various countries, 1970-2002**

<!-- Start of picture text --> 1800<br>1600<br>1400<br>1200<br>Brazil<br>1000 Colombia<br>Costa Rica<br>Dominican Republic<br>800 Guatemala<br>Mexico<br>600<br>400<br>200<br>0<br>Year<br>Yield (kg/Ha)<br>1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002<br><!-- End of picture text --> Source: FAO Statistical Database Several representative budgets were examined in an effort to measure the costs of coffee production. Estimated costs range from US$45-85 per hundredweight<sup>53</sup> , depending on technology use and yields. As noted above, these estimates are subject to a number of caveats, but compare favorably to estimates from other sources in other countries in the region (table III.11). Low yields, high production costs and poor quality beans are the main constraints to the competitiveness of the Dominican coffee sector.

> 52 A recent diagnostic carried out for FEDECARES indicates that average yields of its 3,500 members are about 80lbs/tarea. (or 550kg/ha).

> 53 A recent study carried out for COCOCAFE (see Mercedes, 2002), indicates higher costs of production than returns from coffee sales for low, medium and high technified coffee producers, although high technified producers have a lower cost of production per qq and receive a higher price per qq.

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Figure 8: Overall labor market scenario in Roraima.

<!-- Start of picture text --> Working Age Population Employed<br>−20 −10 0 10 −20 −10 0 10<br>Unemployed Underemployed<br>−20 −10 0 10 −20 −10 0 10<br>450 220<br>210<br>400<br>200<br>190<br>350<br>180<br>300 170<br>50<br>20<br>40<br>15<br>30<br>10<br>20<br>10 5<br><!-- End of picture text --> Note: The y-axis is in thousand and the x-axis the distance in quarters from the first quarter of 2018. The data comes from PNADC and solid line shows the local linear fit while the dashed lines show the 95% confidence interval.

Formal employment and informal employment both seem to suggest a declining trend (Figure 9). Among the formally employed, there is a small declining trend for the number of employees, while the number of employer shows an increasing trend till the first quarter of 2020, and then both plummet down in the second quarter of 2020. Own-account workers shows an uptick in the first quarter of 2018 and so does gross income. Both the number of own-account workers and gross income falls during the second quarter of 2020 in response to COVID-19 pandemic.

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comparisons, they found that urban counties saw especially large reductions in NO2 after March 13, 2020, compared to historical data. At the global level, Venter et al. (2020) connected mobility data with environmental indicators to argue that a strong link exists between global vehicle transportation declines and the reduction in ambient NO2 exposure, though they did not assess how differences in lockdown policy measures differentially affected NO2.

Liu _et al._ (2021) correct for temporal trends and accounted for lockdown timing in the Californian context, finding that sharp decreases in air pollutants occurred shortly after the lockdown policies were implemented. This illustrates the important influence of lockdown timing. Another study focusing on Seattle, WA, Cui (2020) find through sophisticated time series analyses that local traffic decreased considerably, while air quality improved during lockdown. He emphasizes the importance of adjusting these analyses to take into account meteorological conditions. Zhang _et al._ (2021) report that, compared with the base period from 2018 to 2019, the COVID-19 lockdown measures significantly reduced air pollutants in China. Winchester _et al._ (2021) find that when cities’ most stringent social distancing policies in the United States were implemented, a significant reduction in average daily congestion and decrease in average daily mobility and average daily NO2 compared to unrestricted days.

In this context, real-time information can play a most valuable role for policy makers in developing countries, particularly to address the questions highlighted in what follows. How has the massive blow in public transportation been affected by the evolution of the pandemic, sector-specific restrictions and the stringency of lockdowns? What were the relative impacts of the disease versus the sectoral restrictions and health policy remedy of lockdown? Has the use of private vehicles increased? What is the evidence that a silver lining story in terms of reduction in congestion can be drawn after the relaxation of lockdown measures?

The focus of these studies has been only on a selected number of countries, mostly the United States and developed countries with the exception of few emerging countries. Using a global sample of 186 cities in 39 countries, this paper assesses how mobility and congestion were affected by the pandemic and lockdown measures, including public transport restrictions.

This paper proceeds as follows. Section 2 describes the data that we will subsequently use to provide a real-time estimation of mobility and congestion. Section 3 provides estimates of the impact of the pandemic and lockdown evolution on mobility and congestion. Section 4 concludes with a discussion on policy implications.

4
online appendix fixed effects, and controls interacted with month fixed effects. As in Table 2, the controls interacted with month fixed effects are Province fixed effects, log population density, and log number of textile manufacturers. Our preferred specification includes both sets of fixed effects along with controls interacted with month fixed effects, as this mirrors our specification with full controls in Table 2.

Table A11: Increases in number of paracetamol receipts are associated with increased COVID-19 cases

||Con<br>|_Dependen_<br>frmed CO<br>|_t variable:_<br>VID-19 c<br>|ases<br>|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
||0.496|1.397|0.482|0.552|
|log # paracetamol receipts|(0.175)|(0.792)|(0.169)|(0.738)|
||[0.005]|[0.078]|[0.004]|[0.455]|
|Estimation method|Poisson|Poisson|Poisson|Poisson|
|Controls _×_ Month FE||||X|
|Month FE|||X|X|
|Destination District FE||X||X|
|# observations|158|140|158|124|
|# clusters (Districts)|29|24|29|24|

**Results** We present estimates of Equation A6 in Table A11. Across specifications, we find a 1% increase in the number of paracetamol receipts is associated with between a 0.5% and 1.4% increase in COVID-19 cases; this is relative to the 1% increase we would expect if number of paracetamol receipts were exactly proportional to COVID19 infections. All of our point estimates are positive and of expected magnitude; however, many of these estimates are noisy, and in our preferred specification we can neither reject that a 1% increase in the number of paracetamol receipts is associated

A35
online behavior and website performance. Common applications include optimizing user experience, improving content strategy, measuring marketing campaign effectiveness, and informing business decisions. Large companies often use this data to refine their digital strategies, while smaller businesses and content creators use it to understand their audience better. Investors and analysts also leverage this data to assess the performance and growth potential of online businesses. As digital presence becomes increasingly important across industries, website traffic data has become a crucial tool for understanding and responding to online user behavior.

Website traffic data offers several unique advantages in monitoring generative AI usage compared to other data sources. First, website traffic data provides near-instantaneous information on user engagement. Unlike survey data which tend to lag behind actual usage trends, traffic data reflects real-time user behavior. Traffic data also directly measures actual usage, eliminating the self-reporting biases that can affect survey responses. Traffic data has comprehensive coverage, capturing a wide range of users from casual experimenters to frequent users across countries. The data is also more granular, metrics are at the website level and can be broken down by geographic location, date and time, device type, visit type and other variables. In addition, even periodic surveys have major time gaps in between, while traffic data provides a continuous stream of information allowing trend analysis and quick identification of changes in user patterns.

For our analysis, we obtained traffic data from Semrush, a leading provider of such data. Semrush collects raw data through clickstream analytics, tracking codes, and server log files. It processes approximately 25 billion URLs daily across a global database that contains over 43 trillion links and 500 TB of raw data. By employing proprietary machine learning algorithms, Semrush analyzes this data to generate estimated metrics such as the number of visits, unique visitors, average session duration, and further disaggregates these metrics by countries using users’ IP addresses. For each website, Semrush also infers user profiles based on patterns of online behaviors.

We accessed the monthly traffic data for various tools from January 2022 to March 2024 using the Semrush API. The data includes total visits and unique users per domain from both desktop and mobile devices across geographies. It also encompasses traffic originating from mobile applications. However, it is important to note that this measure of consumer usage does not reflect professional usage through APIs or indirect access via third-party apps. Nonetheless, since most generative AI tools are designed for productivity and work-related tasks for individual usage, our website usage 8
suggests that an abundance of natural resources may encourage countries to assume unsustainable levels of debt.

In Ghana, tightly fought elections in 2008 and 2012, in the context of expected or newly available oil revenues, generated a situation where electoral promises trumped the need to manage expectations, generating spending pressures that ultimately could not be contained. Institutional weakness, which in the resource curse literature is most often referred to by aggregated indexes of institutional quality, took on very precise forms in Ghana. Specifically, nominal fiscal rules and central bank independence were not sufficiently supported by political consensus or by strong legal and other institutional underpinnings, and apparently nor was the integrity of the stabilization and savings funds. Procedures for forecasting oil revenues were not isolated against political upward pressure on the estimates (unlike, for example, in Chile (Solimano and Guajardo 2017)).

Ghana’s story is also not primarily one of reduced growth resulting from externally generated causes such as Dutch disease (if foreign exchange is not sufficiently sterilized) and oil revenue volatility, although the precipitous fall in oil prices that started in 2014 has generated additional problems. But it does correspond well with arguments from the political economy of macroeconomics, surveyed by Persson and Tabellini (2000). A fundamental insight from this literature is that, in economic terms, the implicit discount rate used by politicians may exceed the rate of interest by the probability of the politicians being removed from office. Hence if a political faction expects to be expelled from office in the near future, it will extract oil or minerals much faster than is socially optimal and will also borrow against future oil revenues (van der Ploeg 2011).

Bleaney and Halland (2016) do not find evidence that natural resource wealth in general promotes fiscal indiscipline. Nor do they find evidence that new resource exporters, on average, exhibit less fiscal discipline. In fact, their results indicate that fuel exporters tend to have a better general government fiscal balance. However, their econometric model, based on the most disaggregated fiscal data currently available for econometric analysis, does not explain the performance of some outliers such as Ghana and Mongolia. In that sense, this case study of Ghana complements and extends what can be learned about the resource curse from econometric analysis.

Until around 2012, Ghana was one of the stars of the ‘Africa rising’ story, the toast of the international development community, and a benchmark for other African countries in the areas of democracy and development. In particular: 4