rafmacalaba/data-use-mentions-extended huggingface dataset:
due to variation in facilities such as hours of electricity an organization typically receives, the quality of the phone network or internet access. An aggregate index of the quality of facilities at the organizations we study also explains less than 1% of the variation in the likelihood of relying on tacit knowledge.<sup>19</sup> Rather, the culture of information use leans strongly towards a reliance on tacit knowledge.

# **3.3 Basic Descriptives on the Determinants of Information**

Given the importance of the tacit beliefs of bureaucrats measured by our survey, we now turn to an investigation of their determinants. Our data allows us to provide broad descriptive evidence on the

> 17The reliance on tacit knowledge is in contrast to the fact that many public officials believe relevant information is available from other sources. We asked officials how long it would take to receive statistical data as an input to their work from the relevant directorate, and the median response was 1 day (the mean was 1.8 days and the 90th percentile was 2 days). Averaging bureaucrat responses to the organization level, the 90th percentile slowest organization delivered the data within 3 days.

> 18Many public officials believe that the tacit information they have is sufficient for the successful implementation of their daily work. Our respondents stated that in 70.9% of the projects they worked on, “I have necessary information regarding the current state of service delivery in my jurisdiction/my area of work”. This was consistent across tiers (federal, 73.6%; regional, 72.9%; district, 69.4%), sectors (Agriculture, 71.1%; Education, 75.3%; Health, 74.3%; Revenue, 66.1%; Trade, 67.6%) and grades (Grades 1-5, 71.1%; Grades 6+, 70.7%).

> 19The index of facilities is an organization-level average of z-scores of the facilities questions asked
|Share 35-49|0.17|0.19|0.19|0.20|
|Share 50-64|0.10|0.11|0.11|0.11|
|District avg HH age structure|||||
|Share 15-24|0.16|0.17|0.17|0.17|
|Share 25-34|0.15|0.15|0.15|0.16|
|Share 35-49|0.18|0.19|0.19|0.20|
|Share 50-64|0.10|0.11|0.11|0.11|
|Religion and social group|||||
|Hindu|0.84|0.84|0.83|0.83|
|Share Hindu in district|0.82|0.82|0.82|0.81|
|Share sched caste in district|0.71|0.73|0.74|0.76|
|Household type|||||
|Self-employed|0.56|0.52|0.55|0.58|
|Share self-employed in district|0.54|0.51|0.53|0.55|
|Share casual labor in district|0.03|0.03|0.03|0.04|
|Share reg wage worker in distric|0.07|0.07|0.07|0.08|
|Principal industry|||||
|Agriculture|0.65|0.62|0.58|0.62|
|Industry|0.15|0.18|0.20|0.19|
|Share industry in district|0.17|0.20|0.22|0.21|
|HH expenditure|||||
|Avg misc services in district|
polynomial regression of the dummy for the extensive margin with respect to the distance between OD pairs for the alternative cut-offs. Consistent with a priori expectation, the probability of being isolated increases with an increase in distance between the OD pair. We consider an OD pair isolated if the predicted probability is more than 0.5 (blue dashed horizontal line in Figure 4). The higher cut-off implies an extensive spatial margin at around 3,900 km which coincides with the maximum distance we observe in our trip data. We use the higher cut-off for the rest of the analysis. When the probability of being isolated is averaged by states, the estimates indicate that three Indian states (Mizoram, Nagaland, and Sikkim) and two territories (Puducherry, Daman and Diu) can be considered isolated in the sense that 50 percent of the potential trade links in these states and territories see very few trips between them (Figure 7).

# 5 **Short-term Impact of the Pandemic**

## 5.1 **Impact of transport cost shock on the intensive margin**

In this subsection, we use the estimates from Tables 3 and 6 to simulate the impacts of the freight rate increase on trade flows focusing on the post-lockdown period. This exercise limits its scope to look at the immediate impacts of a freight rate increase in the very short-run and thus does not attempt to utilize the general equilibrium framework. The rationale behind the narrow focus is that the COVID-19 shock has affected nearly all facets of the economy, and a broader evaluation of its impacts on spatial development would have to wait in light of the more devastating wave that started after March 2021.

The simulation exercise takes the heterogeneous pattern of freight rate changes during the postlockdown period from Table 3 (column 2) and predicts freight rates along existing pre-pandemic routes. These predicted rates are then used in conjunction with trade elasticity to predict the change in trade flow along each route. The estimated decrease in trade flows under two alternative trade elasticity estimates (2.8
impression of the state’s role in development and give credit to the state for helping to leverage external resources. Citizens are also likely to give the state credit where mechanisms to voice complaints about non-state actors exist and where bureaucrats are able to effectively respond to complaints. Under these conditions, non-state service provision is likely to strengthen the contract.

# **5 Data and Methods**

I explore the relationship between external service provision and deference to government using Afrobarometer survey data from 19 Sub-Saharan African countries (see Table 1). Africa is an especially good place to examine these issues because of the large amount of variation both within and across African countries in the extent to which non-state actors, donors and other states are active in service provision and the extent to which governments are relatively effective and fair. Government responsiveness, corruption and reliance on non-public resources vary considerably among localities with consequences for citizen understanding of and relationship to government (Gibson and Hoffman, 2005).

This project relies on the fourth round of Afrobarometer data that surveys Africans’ views towards democracy, economics, and civil society with random, stratified, nationally representative samples. In 2008, trained enumerators conducted face-to-face interviews in local languages with 26,513 respondents across 19 countries.<sup>5</sup> The sample is designed as a representative cross-section of all citizens of voting age in a given country.

The dataset used for this paper has a multilevel structure; individuals are nested within primary sampling units (PSUs), which are nested within countries. The PSUs are the smallest, well-defined geographic units for which reliable population data are available and they tend to be socially homogenous, thereby producing highly clustered data. In most countries, these will be Census Enumeration Areas (Afrobarometer, 2005, 37-38). Although respondents were not sampled based on their ethnic affiliation, there is likely to be a high level of clustering in the dataset around ethnicity. In other work, I discuss the advantages of multilevel modeling (Levi and Sacks, 2009)
are just vehicle importers. Therefore, nationally-owned economic recovery strategies have to be designed and implemented in each country.

In view of these developments, this paper presents an attempt to identify, propose and evaluate interventions that could have promising economic, employment and environmental effects. Although the focus is on the case of Cyprus, an EU member state of about one million inhabitants in the Eastern Mediterranean, the proposed framework has general application, and the underlying tools and processes are selected in such a way as to allow adoption of the approach in other national or regional contexts. We consider short- and long-term impacts of these interventions using a variety of sustainability criteria. This work started in March 2020, a few weeks after the World Health Organization officially declared COVID-19 a pandemic and most countries introduced strong coronavirus-containment measures including widespread lockdowns. To enable a fruitful sciencepolicy interaction that can lead to meaningful recommendations to decision makers (DMs), our main purpose was twofold:

- To enrich the public debate with model-based evidence about the short- and long-term impacts of specific interventions as regards economic output, employment, energy savings and emission reductions; the models used are open-source and available to stakeholders, which increases the transparency of our approach.

- To encourage ownership of the measures by national decision makers in two ways: by extensively receiving feedback from them at different stages of this study; and by building on policies and measures that are largely based on existing national plans so that stakeholders are familiar with

> 1 See webpages of ‘Energy Policy Tracker’ and ‘Greenness of Stimulus Index’.

2
domain. The second most important factor for which this set of countries underperform is _Business Environment_ , which comprises starting a business, resolving insolvency and paying taxes.

**<u>Table 2. How developing East Asian countries perform in terms of institutional factors, 2017</u>**

|**country**|**Political environment**|**Regulatory environment**|**Business environment**|
|---|---|---|---|
|Cambodia|0|1|**1**|
|China|1|1|**1**|
|Indonesia|1|1|**1**|
|Malaysia|0|1|**0**|
|Philippines|1|1|**1**|
|Thailand|1|1|**0**|
|Vietnam|0|1|**1**|

Source: Own elaboration based on GII dataset and WDI dataset. Note: a value of 1 indicates a variable where the country is below the median country AND below the level predicted by its GDP per capita. The median value (for each variable) is calculated within the 127 countries covered under by the GII dataset.

In this context, how to explain the outstanding performance of China and Vietnam in terms of innovation output performance despite their (low) scores of institutional framework? A potential answer might lie within the remaining enabling factors; results displayed in Table 1 suggest that these two countries outperform in other underlying factors which might compensate for the negative result for the _Institutions_ pillar. Precisely, China outperforms in all remaining enabling factors: _Human capital_ , _Infrastructure_ , _Market Sophistication_ and _Business sophistication_ . Vietnam outperforms, at least relative to the predicted value, in _Human capital_ , _Infrastructure_ , and _Market Sophistication_ . Figures 8.a to 8.d display these results.

**Figure 8. Actual, predicted
# II. Taxonomy of New Traffic Data Sources

It is useful to start by presenting a taxonomy of the different data sources used in big data analyses. We distinguish (i) direct physical sensing (in situ or remotely) from (ii) social media sources (so called human and social sensing) and (iii) urban sensing (through information generated by transportation operators). This is presented below.

# - A. In situ and Remote Sensing

The primary sources of data for traffic studies traditionally included standard data collection efforts such as household or com- traffic commodity flow surveys, possibly plemented with first-generation in-situ and remote sensing systems such as inductive loops, overhead radar detectors, and static video cameras, among others. Recent developments in information and communication technology (ICT) have stimulated a proliferation of new traffic data sources. These include new generations of road-side static sensors such as LiDAR (Light Detection and Ranging), microwave radars, and acoustic sensors which measure speed, noise and traffic flow on the road network. The rapid penetration of mobile phone technology has also considerably lowered the costs of collecting travel behavior data. For example, GPS, GSM, and Bluetooth are increasingly used to generate real-time and reliable traffic volume data, travel speed and time, as well as origin-destination flows (see, e.g., Mitchell, 2014). It is also possible to measure mobility patterns from anonymized data extracted from call detail records (CDR).

Recently, the literature has also acknowledged the role of unmanned aerial vehicles (UAV), including drones and unmanned High-Altitude Pseudo-Satellites (HAPS) in providing valuable data for traffic analysis (see, e.g., Barmpounakis et al., 2016; Khan et al., 2017). Unlike static traffic monitoring systems and ground vehicles, UAVs can collect resolution data at and scales. UAVs can high mobility fine grained spatial temporal also perform data collection tasks similar to manned aerial vehicles while ensuring rela3
21 <u>Table 4: Survey ‘families’ used in health equity side of HEFPI database</u>

|Survey<br>|Title in full<br>|<br>Core topics<br>|<br>No. of countries<br>|<br>History<br>|<br>Data collection<br>method<br>|Sample size<br>|Further<br>information on<br>data and access<br>|
|---|---|---|---|---|---|---|---|
|DHS|Demographic<br>and Health<br>Survey|Population, health, and<br>nutrition, with a focus on<br>reproductive, maternal and<br>child health|91 low and middle-income<br>countries (LMIC), 74 in<br>HEFPI (data sets with<br>restricted access and those<br>collected before 1990<br>excluded, several collected<br>from 2016 to be added)|Ongoing, dating<br>back to 1984.<br>First HEFPI<br>data from 1990<br>(Phase 2)|Face-to-face<br>interviews,<br>physical<br>measurements,<br>biochemical<br>measurements|Typically<br>4,000-<br>15,000<br>households|https://dhsprogra<br>m.com/|
|ECHP|European<br>Community<br>Household Panel|Multipurpose panel survey with<br>adult health module|15 European high-income<br>countries, 14 in HEFPI<br>(Germany micro-data not<br>available)|1994-2001|Face-to-face<br>interviews|Typically<br>4,000-<br>12,000<br>
# 3. Methods

## 3.1. Occupational exposure to GenAI

We combine multiple datasets to estimate occupational exposure to AI, leveraging the distinct advantages inherent in each dataset to ensure a comprehensive analysis.

We use the AI exposure scores at the 4-digit ISCO-08 level from AI scores from GBB (2023) as the principal indicator of occupational exposure to GenAI. GBB scores were developed based on the technical documentation of ISCO-08, which contains a list of typical tasks for each of the 436 detailed occupational groups at the most detailed, 4-digit level, and which forms the basis on which national statistical labor survey reports are linked to the internationally comparably ISCO-08 standard at the ILO. GBB build on the findings of Eloundou et al. (2023), who demonstrate a close alignment of GPT-4 predictions with a survey of 70 AI experts on the potential of automating occupational tasks with LLMs, and more broadly on Bubeck et al. (2023), who provide extensive tests of the model’s capabilities and demonstrate its capacity for elaborating logical links between items, resolving complex tasks and providing justifications for its decisions. Using the Application Programming Interface (API) of GPT-4, the authors designed a sequential call that loops over each of 3,123 tasks in that documentation and requested the model to assess the technical feasibility of performing a given tasks with GPT-4 or LLM technology of similar capabilities. The model is asked to rate tasks on a scale of 0 to 1, with 1 representing the possibility of performing a given task by the LLM in full autonomy form a human operator, and to elaborate a written justification of each score (no task received a score of 1). The scores and justifications are then reviewed for consistency and stability of predictions over time, with the written justifications reviewed by humans. Tasks with scores above 0.8 (high possibility of automation) are transformed into embeddings, with a semantic clustering algorithm applied to identify the major groups of such tasks, which are subsequently reviewed by humans.

Task-level scores for each
of this extension is not the main focus of this paper. The FSSP is still active in Punjab, and as of 2007, it covered 245,000 beneficiaries in middle school, which correspond to a take-up rate of 51 percent.<sup>10</sup> According to actual quarterly stipend data, the program spent around US$7.3 million in 2007 on stipends for grades 6 through 8.

A previous impact evaluation of the FSSP investigated the short-term impacts of the program on female enrollment. The evaluation uses difference-in-differences (DD) and Regression Discontinuity Design (RDD) models on provincial school censuses to estimate net growth in female enrollments in grades 6-8 (middle school) from 2003 (before the program started) to 2005 (two years into the program) and in stipend public schools relative to nonstipend public schools. Other models estimated compare the differences in female growth enrollment relative to male growth enrollment in a triple-difference framework. Even though the magnitude of program impact estimates varies across different models, this evaluation found that the FSSP increased the number of females enrolled in grades 6-8, on average, by 9-23 percentage points (Chaudhury and Parajuli, 2008).

# **4. Data**

This paper estimates the impact of the program with information at the school and household levels. The main data source for the first approach is the Punjab public school census collected annually in October from 2003 to 2009 by the provincial education department, including data from before and during the program implementation. It covers all public schools in Punjab and focuses on school information (including village name and date of establishment), infrastructure, teaching staff, enrollment, and some organizational indicators.<sup>11</sup> The enrollment data are reported by gender of the school and separated into different grade levels. Schools are also disaggregated by urban and rural areas. The total number of schools is 60,000-62,000 from 2003-04 to 200910, however the sub-sample for this analysis is a pool of cross-sectional data that
AfroBarometer Network (2008).

McKinney, James, Stephane Guidoin, and Paulina Marczak. “OGD Standards Inventory: Open Government Partnership OGD Working Group Standards Stream,” Open North (2015).

Myles, J., and J. Quagagno. “Political Theories of the Welfare State,” _Social Service Review_ 76 (1), 75<sup>th</sup> Anniversary Issue (2009: 34–57).

O’Meally, Simon. _Mapping Context for Social Accountability,_ World Bank Group: Washington, DC (2013).

Pampel, F., and J. Weiss, “Economic Development, Pension Policies, and the Labor Force Participation of Aged Males: A Cross-National Longitudinal Study,” _American Journal of Sociology_ 89 (September 1983): 350-72.

Pandey, Priyanka, and Sangeeta Goyal (eds.). “Community Participation in Public Schools: The Impact of Information Campaigns in Three Indian States,” World Bank, Washington, DC (2008).

Pandey, Priyanka, Sangeeta Goyal, and Venkatesh Sundararaman. “Public Participation, Teacher Accountability, and School Outcomes: Findings from Baseline Surveys in Three Indian States,” World Bank Policy Research Working Paper Series (November 1, 2008).

Peisakhin, Leonid and Paul Pinto. "Is transparency an effective anti-corruption strategy? Evidence from a field experiment in India," _Regulation & Governance_ 4(3) (2010): 261–280.

43
worsening of the energy terms of trade in the euro area. This has impacted companies by reducing their purchasing power and increasing operational costs, affecting their profitability and investment decisions. Ari et al. (2022) employed the IMF-World Bank CPAT model and found clear evidence of a regressive impact on European households’ finances. However, the effects on European businesses are less clear-cut, with mixed results regarding the loss of competitiveness. The increase in energy costs for energy-dependent and trade-sensitive industries within the European Union may not be disproportionately higher than for non-EU countries due to variations in natural gas reliance or the use of other energy products across different nations.

When evaluating the effects of fluctuating energy prices, the role of fossil fuel subsidies must be carefully considered. Accurately pricing fossil fuels is essential for the efficient allocation of an economy's scarce resources and investment across sectors and activities. An efficient price incorporates both the supply costs and the environmental externalities of fuel consumption (Coady et al. 2019). However, substantial subsidies or underpricing distort market signals, leading to the overconsumption of fossil fuels, exacerbating global warming, and intensifying local environmental degradation. For instance, Black et al. (2023) report that global fossil fuel subsidies reached an unprecedented $7 trillion in 2023, as governments sought to shield consumers and businesses from energy price spikes triggered by the Russian Federation’s invasion of Ukraine and the post-pandemic economic recovery. These subsidies, while aimed at providing short-term relief, may have inadvertently delayed firms' and households' consideration of environmental costs in consumption and investment decisions. As a result, the adoption of energy-efficient technologies and sustainable practices has likely been postponed, undermining long-term climate goals (Bovenberg & de Mooij 1994; Parry et al. 2021).

Against this backdrop, this paper contributes to the existing body of research by focusing on the impact of country-specific energy price fluctuations during 2019-2023 on firms in emerging economies using the World Bank Business Pulse Survey—a dataset not used yet for this type of analysis in the literature
ways to enable entrepreneurs to better access demand and reduce the frictions that impair their ability to supply products targeted on market opportunities. These include:(i) developing or improving marketplaces with required amenities; (ii) improving access roads and transportation for customers to come to market; (iii) applying marketing knowledge to identify new customers; and (iv) providing market information to customers. Marketplaces exist in all of the places in which community survey data were gathered. These marketplaces may need to be improved with required amenities such as those which were proposed in the Ethiopia RICA proposal (World Bank 2009b). Improving access roads and transportation would provide 18
) and one financial corporation (Finamerica). A more comprehensive supply side analysis is recommended to validate the initial findings presented here.

11. **Access to financial services for low income clients increases transaction costs.** Serving low income clients increases transaction costs, as smaller transaction amounts reduce margins above fixed technological infrastructure costs<sup>30</sup> . On this basis, a comprehensive analysis of cost barriers to financial access should include an analysis from a client affordability perspective and an analysis of banks’ cost structures. In the absence of detailed information on the latter, the following mainly focuses on the aspect of affordability.

12. **Even basic savings accounts are hard to afford for low income clients.** As demand studies in other countries have shown, low income clients are primarily interested in savings accounts as most important financial services. In the absence of any credit risk, they should also be the most affordable financial service for any type of client. In Bogotá, banks which explicitly focus on lower income clients, such as _Banco Caja Social_ or Colmena, require a minimum deposit of COP50,000 to

> 30 Interview with Patrick Tissot, Banco Caja Social, February 2004

35