rafmacalaba/data-use-mentions-extended huggingface dataset:
# 3.2 Individual Data

I also use the 2016 Nepal Demographic Health Survey (NDHS 2016) in the analysis. The survey was implemented by New ERA under the aegis of the Ministry of Health of Nepal and was funded by the United States Agency for International Development (USAID). The data collection took place between June 19, 2016 and January 31, 2017.

The NDHS 2016 is a nationally representative survey of the female population ages 15 to 49. The sampling frame for the survey was based on the updated version of the 2011 Nepal Population Census. After the implementation of the 2015 constitution, based on the population several VDCs and Municipalities within districts were merged to form rural development areas and urban areas. The old Village Development Committees (VDC) in the rural and enumeration areas (EAs) in urban places essentially form primary sampling units (PSU) for the 2016 NDHS. In the nal sample, 383 clusters or PSUs were selected with probability proportional to their population size (see Ministry of Health, 2017). Figure A.3 illustrates the coverage of the survey. All 75 districts except Manang and Mustang were sampled; however, these two districts also had zero casualties during the conict and are excluded from the baseline analysis. As illustrated in Figure 3 (months of war) and Figure A.4 (casualty count), conict intensity varied signicantly across the 383 NDHS villages (clusters).

Within the selected DHS clusters, 30 randomly selected households were interviewed, and all women aged 15 to 49 who were permanent residents or visitors who stayed in the household the night before were eligible for the interview. A sub-sample of about half of the households were selected for biomarker information. All children aged 0 to 59 months and women 15 to 49 years in these households were administered the anthropometry, hemoglobin, and blood pressure measurements. I limit the analytical sample to those that are born before or were in utero during the start of the conict in February 1996 so that they are
governments to be responsive. Djankov et al. (2003) find that government ownership in media undermines political and economic freedom. Besley and Prat (2006) develop a model of democratic politics in which media capture is endogenous. The model offers insights into the features of the media market that determine the ability of the government to exercise such capture and hence to influence political outcomes. Our paper is the first to use media data to account for the intensity of and attitudes in reform chatter.

Lastly, our paper is linked to the literature on economic reforms and the political business cycle. Fernandez and Rodrik (1991) provide a theory to explain the “status quo bias” against efficiency enhancing reforms. In their model, individual-specific uncertainty about distributional gains creates a bias against reform. Early work on the political business cycle dating back to Nordhaus (1975) shows that macroeconomic cycles could be induced by electoral cycles. Brender and Drazen (2005) provide evidence that political budget cycles are stronger in weaker democracies (citizens in more established democracies do not get fooled by promises made by politicians). Our paper presents evidence on the importance of polity in mediating reform chatter over the business cycle including during crises.

The remainder of the paper is organized as follows. Section 2 presents the data. Section 3 lays out the empirical strategy and main results around the evolution of the intensity of reform chatter. Section 4 presents several extensions around sentiment analysis and the nexus between reform chatter and actual reforms. Section 5 concludes.

# **2. Data**

This section describes the main sources of data used in this paper. The discussion focuses first and foremost on reform chatter given the novel nature of the data, while other, more established, sources are discussed briefly.

# **2.1 Reform chatter**

We make use of text in the form of newspaper articles as our main data source. The data are extracted from Dow Jones FACTIVA via the Dow Jones Data, News and Analytics Platform. Dow Jones FACTIVA
# **The Data Source**

For the analysis of private transfers, we use data from household and village surveys conducted by the Research Center for Rural Economy (RCRE) at the Ministry of Agriculture and a supplementary survey conducted in 55 villages of four provinces from August to October 2004 in collaboration between Michigan State University and RCRE.<sup>11</sup> All 3999 households in the 2003 wave of the RCRE panel for these four provinces were enumerated, allowing us to match villages and households from the 2004 supplemental survey with a historical panel of villages and households that RCRE surveyed annually from 1986 to 2003.<sup>12</sup> One unique feature of the supplemental survey is that it enumerated the educational attainment, birth year, current occupation, work and migration history, and residence location for all children and other current and former residents (including deceased former residents) of survey households. Using a calendar-based history to collect this information, the supplemental survey allows one to better characterize the individual characteristics of both household members and non-resident family members in all survey rounds from 1995 to 2003. This information is crucial for reducing biases that may be introduced when lacking information on the size and characteristics of the network of non-resident family members, which affect the quality of the transfer network.

Of the 3999 households that were resurveyed, 494 had at least one resident over age 60 in 1995 and this rose to 816 by 2003. These households comprise our analysis sample, which we split into an early period (1995 to 1998) and late period (2000 to 2003) in order to pick up

> 11The Supplemental Rural Household Social Network, Labor Allocation and Land Use Survey was carried out in collaboration with Michigan State University in 2004. Details on the supplemental survey protocol and survey instruments can be found at www.msu.edu/~gilesj/.

> 12A detailed discussion of a larger nine-province sample from the RCRE panel dataset, including discussions of survey protocol, sampling, attrition, and comparisons with
to a non-Yoruba state. In the phone survey, participants are asked whether they served in the state they were originally posted to or whether they redeployed. I crosscheck their responses with administrative data available on the state they were posted to in that cohort and the state in which they report to have served and I observe that 89 percent of those I observe to have redeployed admit to doing so. In the empirical analysis, I use the treatment status from the original state a student was assigned to as an instrument for the state in which they ultimately served.

Second, there are also reports of students using personal connections or other means to influence their original assignment. The survey asks students whether or not they influenced their posting to their desired state. Ten percent say they did so.<sup>17</sup> While there is no available administrative data to verify their responses, given the high level of accurate responses on the redeployment question, I take their responses as given. To account for these potential endogenous moves, I focus the analysis on those who do not report influencing their posting. I include results on the full sample in the appendix and find that the impacts are qualitatively similar, suggesting that national integration outcomes were not the primary driver of deviations from random assignment.<sup>18</sup> The crucial feature of the randomized allocation is that the characteristics of corps members should not determine their state postings. Analysis of the university administrative data reveals patterns that are consistent with the assignment rules. All the analyses exclude women who are married at the time of posting (3.3 percent of the cohort) as their posting is explicitly non-random. The rule of not posting students to their home state is followed in 99.7% of cases.

To examine whether individual characteristics are correlated with treatment status,

> 17Only 13 percent of those who say they influenced their posting served in a non-Yoruba state.

> 18Fifty percent of those who say they influenced their postings served in Lagos state,
# 1. Introduction

Public attention to Generative AI (GenAI) has been on the rise since the introduction of the conversational models, such as ChatGPT, Bard or Gemini. The impressive abilities of the Large Language Models (LLM), followed by other neural network-based AI systems capable of generating image and even video from simple text prompts have raised a range of important ethical and security questions for national policy makers and international cooperation structures. However, the topic that captures most daily attention of regular citizens is the potential impact of these quickly advancing tools on jobs.

In the United States (US), over half of all adults are more worried than excited about AI in daily life, citing the “loss of human jobs” as their most important concern (Faverio and Tyson, 2022; Pew Research Center, 2023; Rutgers, 2024). In Switzerland, a 2023 survey focused specifically on GenAI revealed that of 1,000 respondents already working with a computer, almost half (43%) were concerned about losing their job in the next five years, with those frequently using GenAI at work being disproportionately (69%) more concerned (Grampp et al., 2023). This suggests a rapid departure from more positive assessments of AI in surveys collected by OECD prior to the arrival of publicly accessible chatbots in late 2022 (Lane et al., 2023; OECD, 2023).<sup>2</sup> Not surprisingly, the potential transformation that might result from the interaction of GenAI with labor markets has also attracted growing attention among scholars. Main research questions have centered around the impact on employment, emerging occupations, productivity and job quality.<sup>3</sup> A recent paper from the IMF provides a comprehensive overview of this literature, at the same time highlighting the scarcity of studies that go beyond high-income countries (HICs) (Comunale and Manera, 2024).

Bridging this research gap, our study provides new evidence on the potential impacts of GenAI across labor markets
sup> The desk review covered 12 major green economic sectors tightly linked to the greening of the economy and selected the occupations commonly employed in these sectors, and hence likely to be subject to change (see appendix B for a list of the 10 steps followed by O*NET to classify occupations and tasks).<sup>34</sup> Using the same desk review, it then categorized the resulting occupations into the following groups: 29 The surveyed measures included Heating and cooling the environment, Increased use of climate-friendly energy plants, Upgrade machinery and equipment, Energy management; Waste reduction, recycling, and waste management; Control of air pollution; Water management; Vehicle upgrades; Improved lighting system; Conservation of natural resources (excluding the use of recycled inputs); and other pollution control efforts. The technologies surveyed included: Equipment that has an Energy Star rating; LEED certified buildings; Energyefficient lighting; Programmable thermostat; Cogeneration (combined heat and power); and Energy-efficient manufacturing equipment.

30 Results from the SILR survey prepared for the Indonesia CCDR.

31 This paper was prepared in 2020 and 2021, with decision review meeting in June 2022. In that month, O*Net released the paper proposing a linguistic approach for green topics (Lewis et al, 2022), which we understand is still in piloting stages, as it is the methodology proposed here.

32 Dierdorff et al. (2009) and Dierdorff et al. (2011).

33 The publications included those “in educational institutions, in academic journals, commissioned reports, industry white papers, and governmental technical reports. Additionally, numerous associated/relevant internet sources on the world of work were reviewed.” Dierdorff et al. (2009). A list of the publications consulted can be found in O*NET (2013).

34 The 12 green economic sectors include: Renewable energy generation, Transportation, Energy efficiency, Green construction, Energy trading, Energy and carbon capture, Research, design, and consulting services (indirect
have found that foreign firms usually pay higher wages relative to domestic counterparts.<sup>8</sup> For instance, using the cross sectional and individual panel data from the VHLSS 2002 and 2004, Fukase (2013a) finds that foreign firms pay higher wages relative to domestic counterparts after controlling for workers’ personal characteristics and that longer hours of work in foreign firm jobs relative to working in the informal wage sector are an important component of the wage gains. Furthermore, analyzing foreign wage premium separately by gender, she finds that foreign wage differential relative to informal wage sector jobs for the subset of lower skilled workers (without upper-secondary education) is larger for female than male workers, reflecting low earning opportunities for females in the informal wage sector in Vietnam.<sup>9</sup> The existence of foreign wage premium, in particular, relative to the informal sector, is likely to be another reason why foreign firms are drawing Vietnamese workers from rural areas.

Finally, foreign firm employment is concentrated in a few provinces. Figure 4 shows the foreign sector employment levels in seven provinces (out of 64 provinces) in which foreign enterprises generated the most employment during the period 2000-2007. Foreign sector employment was overwhelmingly concentrated in Ho Chi Minh City (HCMC) and two provinces adjacent to HCMC, namely Binh Duong and Dong Nai provinces, as foreign sector employers in these three provinces alone accounted for about 73 percent of total foreign employment in 2004. Although the employment by foreign firms in two big cities in the North (namely Hanoi and Hai Phong) has grown, the number of individuals employed in the foreign sector in these cities was relatively small compared to the South. The high concentration of foreign job opportunities in the Southeast may be among the reasons why the latter region attracted migrants.

> 8 See Lipsey (2004) for a review of the earlier literature.

> 9 The findings in Vietnam are consistent with Glick and Roubaud’s (2006) study which concludes that Zone Franche (the Export
Policy Research Working Paper 9386

# **Abstract**

Senegal has experienced a rapid expansion in fixed and mobile broadband internet infrastructure over the past decade. This paper examines the relationship between access to broadband internet and household welfare between 2011 and 2018 by integrating the latest two rounds of household budget surveys with data on the location of fiber-optic transmission nodes and coverage maps of 3G mobile technology. The results show that 3G coverage is associated with a 14 percent increase in total consumption and a 10 percent decline in extreme poverty. These results are robust to controlling for household demographics and other spatial characteristics, such as region fixed effects, road density, nighttime lights, and elevation above sea level, as well as for access to complementary digital infrastructure, such as 2G coverage or fixed broadband internet. The findings are also robust to an instrumental variable approach that relies on distance to 3G coverage in neighboring areas. These effects are larger among households in urban areas and households headed by men or younger cohorts. Although they are in the same direction, the welfare effects of proximity to fixed broadband infrastructure are not statistically significant.

This paper is a product of the Poverty and Equity 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 tmasaki@worldbank.org and crodriguezc@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
economy factors can affect fiscal procyclicality (Tornell and Lane 1999). As the intensity of such fiscal competition increases in good times, the rise in government spending could be greater than the windfall gains in revenues, resulting in procyclical fiscal expansion. The more claimants there are (that is, the more dispersed the power), the higher government spending may tend to be in good times.

**_Quality of institutions_** _._ The quality of institutions plays an important role in the ability of countries to conduct countercyclical fiscal policy.<sup>10</sup> Indeed, institutional factors, such as law and order, have been found to play a larger role than financial variables, such as financial openness and domestic credit to the private sector, in explaining differences in fiscal cyclicality between advanced and developing economies (Calderón and Schmidt-Hebbel 2008). Government stability and law and order can help reduce fiscal procyclicality by shrinking government discretion and extending the horizon of policy decision-making.

To analyze the linkages between fiscal cyclicality and institutional quality, five country-specific indicators of institutional quality from the PRS Group database are used: political risk, quality of bureaucracy, control of corruption, government stability, and law and order (Table 2.A). The association between procyclicality and the presence of fiscal rules is also examined here because these rules are a significant part of the fiscal institutions in many commodity-exporting countries.

The results indicate that in commodity exporters that rank higher on political risk, fiscal policies have tended to be more procyclical, with a correlation of 0.49 between the cyclical component of real GDP and real government spending, compared with a correlation of 0.28 for countries with lower political risk. The difference between the average correlations for the two country groups is statistically significant (Figure 4.A). Part of the explanation could be that governments in countries with higher political risk may be more focused on short-term outcomes to stay in power.

Likewise, commodity-exporting countries with lower-quality bureaucracy have often implemented more procyclical fiscal
# **1. Introduction**

Between the late 1990s and 2010, Latin America and the Caribbean (LAC) achieved substantial economic progress: GDP per capita grew by more than 50 percent in real terms (ECLAC), the proportion of poor individuals fell from 27.5 percent in 1992 to 12.6 percent in 2011 (SEDLAC, 2013), and inequality as measured by the Gini index fell by approximately 10 percent during the 2000s alone. Yet even as the region registered these gains, the attention of its public and policymakers has increasingly focused on an economic and social problem faced by countries throughout the region. This problem is the large number of youth who are neither in school nor at work—youth commonly labeled as _ninis,_<sup>_2_</sup> from the Spanish phrase “ _ni_ estudian _ni_ trabajan _”—_ and it has proved persistent and seemingly structural. Unless it is understood better and tackled through policy interventions, the problem of ninis could limit the region’s ability to reap the dividend from its demographic transition, as its large youth cohort moves from dependency into its prime working years.

To address the nini issue, policy makers in the region need to understand how broad the problem is, whether it is growing or subsiding, and who the ninis are. Yet until now, there has been little analysis of the nini phenomenon for the Latin America and Caribbean region as a whole.<sup>3</sup> To provide such a diagnosis of the 15 to 24 year-old ninis, this study draws on micro data from 238 household surveys for 15 Latin American countries spanning the period 1992–2010. For these country-year combinations, we use harmonized data from SEDLAC, the Socio-Economic Database for Latin America and the Caribbean maintained by the Centro de Estudios Distributivos Laborales y Sociales (CEDLAS) and the World Bank, supplemented by surveys assembled in Cárdenas, de Hoyos, and Székely (2015), which include comparable variables for school attendance, economic activity
<u>Figure 11. Growth Rate of the Addressed Demand to Egyptian Exports</u> <!-- Start of picture text --> 30%<br>20%<br>y = ‐0.0102x + 0.1394<br>10% R2 = 0.1259<br>0%<br>‐10%<br>‐20%<br>‐30%<br>2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016<br><!-- End of picture text --> � Note: Addressed demand is calculated using this formula DEMi= ∑��� αij Mj with n the number of trade partners, _j_ partners, _αij_ the weight of Egyptian exports to country _j_ in its total exports αij =Xij/ΣXij and _Mj_ the total imports of country _j_ . _Source: Constructed by the author using International Trade Center data._

# _5.2.3 Trade Policy_

Amalyzing Egypt’s trade policy can also help explain why Egypt is under‐trading in some products and over‐trading in others. Despite significant reforms and liberalization efforts, significant trade barriers prevent the country from fully exploiting its trade potentials and maximizing its gains from trade. Egypt remains among the group of developing countries with the highest frequency index and coverage ratio of NTMs.<sup>9</sup> These non‐ tariff measures vary between technical requirements, conformity assessment, export related barriers and rules of origin. Egypt has signed several preferential trade agreements, but some of these agreements remain shallow and overlapping, leading to a more complex trade regime with a limited impact on trade. This section, looks at Egypt’s trade policy from the tariffs perspective to assess the country’s openness to trade. It also examines several non‐tariff measures that are hindering the fast development of exports. These include administrative and technical barriers to trade, as well as other sanitary and phyto‐sanitary
*<br>-<br>-<br>=<br>-oftheEmperor,in1795<br>&6|t6,220,000——<br><br><br><br><br><br><br><br><br><br><br>|
|<br> THE UNITED KINGDOM AND<br>|<br> <br>D<br>-<br>-<br>-<br>=<br>-=D*<br>=<br>=<br>ins797- -<br>700,060<br>——<br><br><br><br><br> <br><br><br>|
|THE GOVERNMENT OF EGYPT|D*<br>-<br>=<br>=<br>= ofPortugal<br>-<br>in 1797 - -<br>247,205 ——|
|relativetothe<br>|Do<br>-<br>=<br>=<br>=<br>=D<br>~<br>=<br>ins798-<br>-<br>1201313—<br><br><br><br><br><br><br>|
|<br>Ottoman GuaranteedLoan<br>|<br>D*-<br>-<br>=<br>= of Rufia-<br>- in1799--<br>$25,002<br>——<br>Dt.<br>=-=oftheEmperor,Eledoroff<br>Bavaria,<br><br><br><br><br><br><br>|
|<br>of 1855<br>|<br><br>&
area level.

> 6 Dasgupta et al. (2020) use the same underlying information from Google Maps to calculate the average speed within a city. Their approach, however, requires estimating a city-specific correspondence between speeds and colors and assuming that the correspondence is stable over time and over different locations in the city. It overlooks the fact that free flow may occur at low speeds in the presence of speed limitations, which are common in urban contexts. In contrast, the TCI focuses on the severity of delays while making it possible to aggregate the information over the whole network even when free-flow conditions might involve different speeds in different parts of the network.

16