rafmacalaba/data-use-mentions-extended huggingface dataset:
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
17

# **_Assessing the performance of SAE poverty predictions_**

To assess the performance of the SAE poverty prediction technique (including the empirical validity of the parameter stability assumptions), we assess whether the predicted poverty rates closely match the observed rates in a wide variety of settings. If so, parameter instability may not be a pressing concern, suggesting that SAE techniques and data on consumption subcomponents and/or household assets can indeed be used to approximate the evolution of poverty within a country when comparable consumption data are absent. If, on the other hand, the results cannot capture the observed changes in poverty, caution in using such techniques would be warranted.

In particular, to judge the success of the prediction models, we focus on whether the imputation-based poverty estimate for the second survey round lies within the 95% confidence interval around the ―true‖ (directly-estimated) poverty rate for that year. To be sure, standard errors can also be estimated around the imputation-based poverty rates. An alternative procedure would thus be to test whether the imputation-based poverty rate in the second round is statistically distinguishable from the directly estimated poverty rate for that year. Such a test would be less conservative than the one we apply: it would reduce the likelihood of rejecting equality of the predicted and observed poverty rate. Moreover, it is important to acknowledge that the calculation of standard errors on the imputation-based poverty rates would not capture the uncertainty associated with the underlying assumption of parameter stability.

To shed preliminary light on how characteristics of the consumption models and settings affect the predictive power of the SAE prediction techniques, a meta-analysis of the prediction results from these different surveys and models is further pursued in
harmonized by the World Bank for other regions. These revisions of the welfare aggregate constitute the move of the SEDLAC project from version 02 to version 03.<sup>25</sup> As the revision of this project is still in process, data reported in PovcalNet are based on SEDLAC03 for most recent years and on SEDLAC-02 for previous years, with the cut-off period being generally the year 2000 (except for Argentina, for which data using SEDLAC-03 begins in 2003, and Chile, which uses SEDLAC-02 throughout the whole period). Given this problem of comparability, we recalculate headcount ratios for some countries so that the SEDLAC version used in each country is the same for the whole period under analysis.<sup>26</sup> Because of this, poverty rates for some countries and years may differ from the figures reported in PovcalNet.

Table B.1 summarizes the data used for each country.

**Table B.1**

Data Summary for Poverty Calculations

|**Country**|**Period**|**SEDLAC**<br>**version**|
|---|---|---|
|Argentina|1991-2017|02|
|Bolivia|1997-2017|02|
|Brazil|1992-2017|03|
|Chile|1990-2015|02|
|Colombia|2001-2017|03|
|Costa Rica|1990-2017|03|
|Dominican Republic|2000-2016|03|
|Ecuador|2003-2017|03|
|El Salvador|1995-2017|02|
|Honduras|1991-2017|02|
|Mexico|1992-2014|03|
|Panama|1995-2017|03|
|Paraguay|1995-2017|02|
|Peru|1997-2017|03|
|Uruguay|1995-2017|03|

> 25 For details on the main methodological changes in SEDLAC-03 that directly affect welfare measurement, see Atamanov _et al_ . (2018).

> 26 Notice that even though the SEDLAC project may remain the same, the frequent changes in survey methodologies over time introduce
1.2 The QAS tool_

The starting point for Egypt’s KG QAS tool was the Brief Early Childhood Quality Inventory (BEQI).<sup>6</sup> The BEQI was built on the MELQO tools, but was designed to be a simpler tool than MELQO, integrated into monitoring systems or used for formative assessment (ECD Measure 2022). As an example of simplification, items that were measured on a four-point scale in MELQO were typically transformed to yes/no for the BEQI. Some items, however, were identical across the two tools. The BEQI was adapted to the Egyptian context, including Egypt’s recently adopted KG quality standards (see Appendix 1), as discussed in more detail below. A summary of the features of the diagnostic research tool and QAS tool is provided in Appendix 2.

# _4.1.3 Tool adaptation_

The BEQI and MELQO tools were adapted to the Egyptian context and curriculum in collaboration with the MoETE, Kindergarten teachers, and Kindergarten supervisors. Both tools were adapted in a May 2019 workshop. For data collection, tools were programmed into Android

> 5 The MELQO tools have been used and validated in other LMIC contexts (Raikes et al. 2020).

> 6 The BEQI has been used in other country contexts as well (Raikes et al. 2023).

8
|31|
|Textiles|5.62|2.65|425.62|0.70|0.73|0.20|591|132|
|Total|10.63|3.78|376.43|0.58|0.51|0.39|1477|322|

Note: authors’ calculations based on Financial Statements Analysis of Companies (Non-Banking) public at Pakistan Stock Exchange and data on foreign ownership from Bureau van Dijk. Sales are in billion PKR while wages are in million PKR.

Publicly listed firms are among the largest in the economy, accounting for 13 percent of Pakistan’s GDP in 2017. Firms in our sample employ on average 380 employees and are spread across 11 sectors, with a larger presence in the textiles sector. In terms of sales, the median publicly listed firm is more than six times larger than a relatively large medium-sized firm in Pakistan, yet about 16 percent of firms in our sample would classify as medium-sized firms.<sup>9</sup>

> 7 A similar data set is available for 168 listed companies in the financial sector, which are not considered in this analysis. 8 BvD collects ownership data from official registers, stock exchanges, annual reports, company websites, and news outlets.

> 9 Using 2017 data, the median publicly listed firm has annual sales of about 4 billion PKR compared to the upper threshold for a medium firm, as indicated by the State Bank of Pakistan, update to 2017 is 600 million PKR.

8
stock of high-quality human capital may be better able to adapt to future economic conditions and grow regardless of their current economic structure.

We assess how urban human capital correlates with our structural variables of interest by obtaining from IPUMS the average number of years of education of adults (ages 25 to 65) living in the urban areas of 53 out of the 116 sample countries for the year closest to 2000. We use the same cross-sectional regression as in our study of sectoral or informal employment (eq. (10)) and include MFGSERV (2000), NRXGDP (average in 1960-2020) and DEINDU (change in 1980-2000), as well as the urban share (2000). The results, shown in col. (1) of Panel A in Table 9, suggest that NRXGDP and DEINDU countries might have

> 37For each country one by one, and since we know the PCC and population of each city c. 2000, we can estimate the degree of statistical dispersion of the PCC measure – i.e., the Gini index – within the country.
value of zero before 1991 in all industries when FDI was strictly controlled. After that year it shows the percentage of the industry that is opened to FDI. Foreign held companies are usually better at technology adoption and they are more innovative than domestic firms. They can get easier access to technology, capital resources, and R&D facilities of the parent company which facilitate innovation and growth. Seker (2011), Criscuolo et al. (2010), and Almeida and Fernandes (2008) provide empirical evidence on the relationship between FDI and innovation. There are also studies like Haddad and Harrison (1993) and Sinha (1993) that find a positive impact of foreign ownership on firms’ productivity levels.

India is a federal democracy and industrial relations fall under the joint jurisdiction of central and state governments in the constitution. Labor regulations are affected by both jurisdictions. Key central legislation is the Industrial Disputes Act of 1947 which sets out the conciliation, arbitration, and adjudication procedures to be followed in the case of an industrial dispute. Although all states have the same starting labor regulations, they have diverged from one another over time. Besley and Burgess (2004) divide states into three groups: pro-worker, pro-employer, and neutral. A state has a pro-worker labor regulation if the legislations benefitted workers compared to employers. They show that states which had pro-worker labor regulations experienced lower output, employment, investment, and productivity in registered or formal manufacturing sector. In the Prowess database some firms provided information on the geographic location of their headquarters. Following Besley and Burgess (2004) I set a dummy equal to one if the firm is located in a state that has pro-worker labor regulations. I analyze whether the relationship between licensing reform and innovation varied with the labor regulations in the states.

The trade liberalization, FDI reform, and variation in labor regulations across states can affect innovation performances of firms. In the empirical analysis I test whether the licensing reform increased 8
67

# <u>Institutional Quality Only</u>

Lastly, we turn to examine the effects of the quality of institutions, in addition to the rest of the knowledge variables, on economic growth. Tables A5 and A5a present the results of regressions where we included as an additional regressor the institutional quality index from Bosworth and Collins (2003), instead of the Sachs and Warner economic openness variable.

Firstly, we note that all of our ‘new’ regressions exhibit R-squared values that are around 20 percentage points higher than our control regressions. This does strongly suggest that institutional quality does play a large role in determining rates economic growth. Next, we notice that the institutions variable consistently produced estimated coefficients that are positive, which is theoretically consistent, and highly statistically significant. The magnitude of the effect of institutions on economic growth is also considerably large. Take for example Reg A5.2 and A5a.2, which includes patents and phones representing the innovation and ICT pillars, respectively. With the estimated coefficient value of 3.9258 and the regression sample mean of 0.6527 (Table A5b), it can be shown that a 20 percent increase in the quality of institutions tends to increase annual economic growth and output per worker by around 0.5 percentage point.

The next striking observation is that upon the inclusion of the institutional quality variable as a regressor, many of knowledge indicators, which had produce highly statistically significant estimated coefficients before, are now statistically not significant. More specifically, we see that our human capital stock variables, while maintaining their theoretically expected signs, are generally not statistically significant. The situation is similar with our ICT variables, with the exception that some of the ICT coefficients actually return coefficients with negative signs (see Regs A5.2, A5.4, A5.6, A5.7, A5.9, A5.11, A5a.6 and A5a.11). This result, where controlling for institutional quality eliminates prior present independent roles of other
# **[Insert Table 1 about here]**

# **4.1** **_Demographic Information_**

Table 1 provides summary statistics of the pooled sample (2008 and 2009). The percentage of male respondents is 43.9%, consistent with national census averages (Russian National Census, 2002). The average age in the pooled sample is around 45. Most individuals (66%) live in households with three or more individuals, and 28.2% of individuals live in urban regions, defined as settlements with a population greater than 500,000.

With respect to employment, 52.5% are employees (both skilled and unskilled), while 25.5% are retirees. The education level of individuals in our sample is relatively high: only 8.4% of the sample has less than a secondary education, 29.9% completed secondary school, and 61.8% completed a special vocational/ technical school or initiated or completed their higher education, a characteristic that sets Russia apart from other emerging markets and makes it a particularly useful country to study.

The survey asks individuals to report their personal and household monthly income, but these values are missing for almost 30% of the sample, i.e., individuals who refused to answer.<sup>6</sup> For our main regressions in the next section we impute missing income observations and include dummies for brackets of family income rather than using income values.<sup>7</sup>

> 6 In our sample, mean personal monthly income for 2008 is US$ 762 (US$ 2,345 median income), while average family monthly income is US$ 1,494 (US$ 12,500 median). This compares closely with official statistics for 2008 of average per capita monthly income of US$ 1,404. Source: Russian Federation Federal State Statistics Service: http://www.gks.ru/bgd/
Policy Research Working Paper 7783

# **Abstract**

A large national farm panel from India covering a quarter century (1982, 1999, and 2008) is used to show that the inverse farm size-yield relationship weakened significantly over time, despite an increase in the dispersion of farm sizes. Key reasons are substitution of capital for labor in response to nonagricultural labor demand. Family labor was more efficient than hired labor in 1982–99, but not in 1999–2008. In line with labor market imperfections as a key factor, separability of labor supply and demand decisions cannot be rejected in the second period, except in villages with very low nonagricultural labor demand.

This paper is a product of the Agriculture and Rural Development Team, Development Research Group. 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://econ.worldbank.org. The authors may be contacted at kdeininger@worldbank.org; jins@anr.msu.edu; Y.Liu@cgiar.org; and ssingh12@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
local initiatives to reduce the costs imposed by crime and violence, along with local economic development programs to improve access to finance for small and medium sized businesses.

Our decompositions of city growth residuals tentatively show that local land use and zoning enforcement is positively associated with city growth, as is the presence of a diverse set of inter industry linkages. One of the major limitations in our efforts to identify the contribution of local characteristics to city growth has been the lack of longitudinal data, which makes it difficult to draw causal relationships. It would be useful to get better data on historic land use and zoning regulations, as well as local public goods, services, and amenities. In further work, we hope to collect additional data on city level characteristics to better identify their impacts on city growth.

26
Policy Research Working Paper 9409

# **Abstract**

Using World Bank PovcalNet data from 1974 –2018 for 135 countries, this paper approximates the identity that links growth in mean incomes and changes in the distribution of relative incomes to reductions in absolute poverty, and, in turn, examines the role of income inequality for poverty reduction. The analysis finds that the assumption that income is log-normally distributed allows one to approximate the identity well. Using this approximation, both the growth and inequality elasticities of poverty reduction are calculated. The inequality elasticity of poverty reduction is larger, on average, compared to the (absolute) growth elasticity of poverty reduction. Moreover, the (absolute) growth elasticity declines steeply with a country’s initial level of inequality. However, despite these results, most of the observed changes in poverty can be explained by changes in mean incomes. This is a consequence of changes in income inequality (as measured by percentage changes in the standard deviation of log-income) being an order of magnitude smaller than changes in mean incomes. Overall, the results highlight the important role income inequality can play in reducing poverty even if prior poverty changes have, in large part, been a consequence of economic growth.

This paper is a product of the Development Research Group, Development Economics. 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 author may be contacted at kbergstrom@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
- 10 -

**Figure 4. Existing road network and districts covered by our sample**

<!-- Start of picture text --> Nepal Roads<br>Districts covered by our sample<br>Nepal DISTRICTS<br>/<br><!-- End of picture text --> Security is a matter of particular concern in Nepal. In particular in the Terai area where our data were collected, more security incidents were reported (Figure 5). According to the UN Nepal Security Database, about 200 security related incidents, such as abduction, attack and murder, happen nationwide every month (Figure 6). For public road procurement, intimidation or other threats could deter bidder participation. Local news has been reporting violence and intimidation against contractors placing bids. In our sample data, a project district is estimated to have experienced 3 incidents every month during the project implementation period. By construction, any measures to avoid conflicts and defend themselves would add to project costs and result in project delays.

The country has been making efforts to improve the justice and security systems to promote rule of law and inclusive and equitable growth (United Nations, 2011). The government enacted the Procurement Act and Right To Information Act to improve procurement environment by legislation. However, it seems that there is still a long way to establish a sound and competitive marketplace for public procurement.
bottom and top 10% observations, WTP range from 0.36 to 19.4% of daily GDP per hour of electricity unserved. We disregard many important dimensions, assuming that the WTP of an individual to prevent power outage is proportional to GDP per capita in the country and disregarding the dependency of this WTP to the frequency and duration of power outages. This leads to underestimating the cost in countries with few and short outages and overestimating it in countries with many and long outages. We then use the data from the annual Doing Business survey<sup>6</sup> to estimate the duration of outages in 139 countries, leading to the estimate between 0.002 and 0.15% of global GDP. For reference, the cost of power outages would be valued at 0.001% of global GDP (USD 1.3 billion per year) if non-served electricity was priced with electricity tariffs, and at 0.007% of global GDP (USD 9 billion per year) if GDP per hour was used to value one hour of power outage.

This cost of power disruption underestimates the real impact on the well-being of households. First, there is a large uncertainty in the estimates of people’s WTP to prevent power outage, and it is unclear whether they include all relevant components, and in particular health effects, impacts through jobs and salaries, cost of generators, etc. In particular, electricity outages have a significant and large impact on firms’ output, what indirectly decrease household’s wellbeing through wage reduction (Rentschler et al., 2019b). However, it is likely that such an indirect impact is disregarded or underestimated in household’s monetization of lost utility. Moreover, it is unclear whether households consider their generator as a sunk cost: once the up-front cost of the generator is paid, there is little incentive for household to pay for service improvement, possibly reducing the WTP. Second, the Doing Business survey is considering outages for the average firm in