rafmacalaba/data-use-mentions-extended huggingface dataset:
positive relation between happiness and income inequality and Schwarze and Harpfer (2003) argue that Germans are only weakly inequality averse because a reduction in inequality does not increase well-being. In this paper we follow this last approach by interpreting a positive sign of the happiness-inequality relation as an indication that higher inequality is appreciated and provides a sense of satisfaction to individuals (the Hirschman/Rothtschild mechanism), and a negative sign as an indication that higher inequality is not appreciated and provides a sense of dissatisfaction (the Runciman/Yitzhaki mechanism).

Empirical evidence on the sign and significance of the happiness-inequality relation is controversial and heterogeneous. As described below, one can find positive, negative or non significant relations depending on the particular study considered.

Morawetz et al. (1977) have shown how two communities in Israel with different levels of income inequality differed in average happiness, where income inequality was found to be higher, average happiness was found to be lower. Schwarze and Harpfer (2003) find life satisfaction to be negatively correlated with inequality using the German socioeconomic panel over 14 waves and Hagerty (2000) using aggregated data for eight countries finds that average happiness levels are lower where income distributions are wider. On the contrary, 3
to G20 High Income economies<br><!-- End of picture text --> Notes: _Exports hijk_ defined here as country _j_ ’s share of total world exports of product _h_ to policy-imposing country _i_ at year _k_ , for years _k_ around the imposition (year _t_ ) and removal (year _T_ ) of country _i_ ’s antidumping import restriction. Constructed by the author with policy data from Bown (2012) and HS-06 import data from UN Comtrade via WITS.

25
# Appendix A – Detailed Data Description

## Street Networks

Data for the street networks for pedestrians for both Rio de Janeiro and Kinshasa were extracted from OpenStreetMap (OSM). A global collaborative community of mappers who all contribute and maintain publicly available data related to roads, points of interest and administrative boundaries (OpenStreetMap, 2022). Researchers estimate that approximately 80% of the street grid is accounted for on OSM and it is widely accepted as a reliable source of road network data (Barrington-Leigh & Millard-Ball, 2017). A base walking speed of 3.6km/h was used across the road network. Digital Elevation Models (DEM) for both cities were also incorporated into the street networks to account for the impact that slopes might have on walking speed with these being adjusted through Conveyal’s R5 engine using Tobler’s hiking function.<sup>17</sup>

## Transport Networks

Transit networks in both cities are described in the General Transit Feed Specification (GTFS) format. GTFS is a standardized framework for transit service schedules and geographic information. It was created with the intention of standardizing the way public transit data is published and used (particularly for software applications) globally and utilizes a series of relational .txt files to efficiently store this data (Google, 2021). It includes data related to the stops, schedule (frequency-based or specific schedules), and routing related to different services within a transit system and can often include other supplementary information such as fares or details regarding informal modes of transit. It is generally used to support navigation for familiar software applications such as Google Maps, Waze, CityMapper, etc. and has also been used extensively in the study of transit networks (Barbeau & Antrim, 2013).

Rio’s feeds were provided by transport operators (Fetranspor and SuperVia) and represent a typical day of operations in November 2018 for the metro, rail, light rail, BRT, municipal bus, and ferry services operating within the municipal boundaries. The municipal buses
Policy Research Working Paper 9639

# **Abstract**

Drawing on various macro- and micro-data sources, the authors present robust evidence of an inverted U-shaped relationship between female labor force participation and inequality. Overall, female labor force participation is found to have a strong and significant dis-equalizing impact in at least three groups of developing countries with relatively low initial levels of participation. A decile-level analysis shows that female labor force participation has higher levels of returns among top deciles compared with the lower deciles in the developing countries analyzed. This evidence focuses attention on the importance of developing policies specifically targeting women in lower deciles of the income distribution.

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 vmolini@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
.

To estimate how these measures of budget rigidities affect the probability of countries getting into fiscal distress and the likelihood of doing a fiscal adjustment, we use the following strategy. First, we identify periods of adjustment needs based on two different methodologies: one based on consecutive years of primary gaps above certain threshold, and the other based on the level of debt. Second, we identify periods of fiscal adjustment as years where there was at least a 0.1% of GDP improvement in the cyclically adjusted primary balance for two consecutive years (Escolano et al., 2014). Then, we estimate a linear probability model using a panel of 182 advanced and developing countries controlling for economic and political factors that previous literature has identified as potential determinants of governments having fiscal adjustment needs and the ability to do so.

We find that the actual levels of rigid components of public spending (wages, pensions and interests) are positively associated with the probability of countries getting into fiscal distress and are negatively associated with the ability to consolidate. This result may appear counterintuitive, as many advanced economies with relatively high shares of rigid spending have had successful fiscal consolidations. However, we find more robust evidence by using the share of non-structural rigid spending (when spending on public wages and pensions is above what the fundamentals of their economy would suggest), which may explain why these apparently rigid economies have been able to pursue fiscal consolidations. Moreover, the effect of rigid expenditure seems to be more relevant for economies with higher inequality, government with lower margin of majority and countries with lower institutional

- 7 -
of this review, it was noted that some of the ERRs estimated and reported in the PADs and ICRs were not always systematically captured in the IEG database.  For example, some PADs/ICRs reported multiple ERRs (sometimes by project component) that were often reflected as an average in the IEG database or not captured at all.  Systematically capturing estimated ERRs would provide more refinement to the data and provide a more robust estimate of the levels of rates of returns for different sectors/projects and sub‐components of a project.  This system could be captured through the Bank’s management information system (or Ops Portal).  This would enable the systematic capture of ERRs (even when this is done only for specific components or involves multiple estimates or for project restructuring).   This could then be downloaded automatically to the IEG ICR Review templates and validated by IEG at the time of their independent review.

- II. **Place more emphasis on the EA as a way to systematically think through project design and how risks will impact design** . This would also include devising an approach to risk analysis that focuses on informing the design of the project or the approach to implementation.  The aim would be to use the EA to devise approaches to increase the project’s expected return and reduce the risk to the PDO. Risk analysis could also be used to prioritize and make explicit in the results framework what variables to monitor more closely during implementation.  This analysis would then be used to discuss risk mitigation in the Risk section of the PAD and inform the project (SORT) ratings.  There could also be more systematic discussions during the PCN and Decision Review meetings by requiring that reviewers comment specifically on whether the EA adequately underpins the design or approach outlined in the project; or whether it provides sufficient evidence to demonstrate that the chosen design or implementation approach is the best to maximize the project’s expected return.

- III. **Strengthen the database on EA in order to facilitate better analysis and identify flags,
” definition that includes visual, hearing, speech and physical disability. But even in this group of six surveys, the definition of each type of impairment varies. For example, in Cambodia 1999 the physical disability category contains a detailed list of potential cases—“amputation of one limb; amputation of more than one limb; unable to use one limb; unable to use more than one limb; paralyzed lower limbs only; paralyzed all four limbs”—whereas in Jamaica 2000 there is simply one category described as “physical disability (legs and arms)”. More generally, in some countries the definition is stricter than in others. In Mongolia and Myanmar sight and hearing are described as “problematic” whereas in other surveys they are characterized as “blind” and “deaf”.<sup>5</sup> The second main data constraint in carrying out this analysis is the fact that surveys do not identify large numbers of individuals as having a disability. Therefore, any subsequent analysis such as the correlation between disability and poverty, or disability and schooling, will suffer from imprecision. Table 3 highlights this point by showing the number of youth identified in each survey and the subset with a disability. For some surveys the small sample problem is especially acute, for example the Jamaica 2000 survey identifies only 14 youth as having a disability, the Sierra Leone survey identifies only 28.

> 5 Note that another non-consistent aspect of the data is the coverage in terms of age: the upper age limit is sometimes 14 in Burundi and Myanmar.

4
On quantification, the analysis of the (quasi-)opportunity costs of conserving natural areas based on wealth accounting data in _The Changing Wealth of Nations_ (World Bank 2011) sheds light on the disproportionate burden of conservation costs as a share of GDP being borne in developing countries in comparison with OECD countries. The reason for this is the much larger share of total wealth (and therefore national income) that is derived from land in developing countries compared with OECD countries.

To put these values of protected areas in context, the annual GEF finance and co-finance for biodiversity conservation in all developing countries was, based on a 20 year average, only 8% of the opportunity cost of conservation in low income countries in 2005. This indicates that the opportunity cost of conservation borne by low income countries is not being substantially offset by international flows of concessional finance.

On balance, there is considerable scope to include the use values of natural areas in national accounts. This is so because there are flows of user fees and rents associated with the use of natural areas. As a property of natural areas, biodiversity can be implicitly or explicitly valued in the balance sheet accounts, depending on the nature of the benefits it provides (e.g. use values for nature tourists vs. bioprospecting fees), while marginal changes in biodiversity may be valued depending upon the availability of sufficient data to estimate the associated changes in the production of benefits which people value. Non-use values appear to be much less amenable to valuation in the national accounts, owing to measurement issues and the inherently non-marginal nature of some of these values.

17
|
|Any mission|||||||0.15<sup>_∗∗∗_</sup><br>(0.02)|
|Ethnic dummies|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Mean dependent variable|0.25|0.25|0.25|0.25|0.25|0.25|0.25|
|Oster bound|0.11|0.07||0.08|0.09||0.10|
|_R_-squared|0.40|0.40|0.40|0.40|0.39|0.40|0.40|
|Observations|6,876|6,876|6,876|6,876|6,876|6,876|6,876|

_Source_ : Authors’ analysis based on mission locations from Beach (1903) and Roome (1924); aid projects from AidData, World Bank geocoded research release, version 1.3; and control variables from multiple sources, detailed in appendix A. _Note_ : Estimation is by ordinary least squares. Conley (1999) standard errors are given in parentheses, with the cutoff at 2 degrees (220 km). The dependent variable is a dummy for at least one project commitment in the sample period. Control variables include: log distances to coast, explorer route, colonial railway, and Arab trade route; a third-order polynomial in the 18th-century population; presence of a city as of 1800; country dummies; precolonial ethnic dummies; average altitude; Terrain Ruggedness Index; percentage area within 10 km from water source; Caloric Suitability Index; Malaria Ecology Index; and a tropics dummy (which is also interacted with mean altitude). Cells that are more than 90 percent covered by barren land or more than 90 percent covered by forest are excluded from the sample. The lower bound on the coefficient of interest
Policy Research Working Paper 8002

# **Abstract**

This paper studies the trading behavior and performance of foreign investors with different management styles. The analysis uses a comprehensive Colombian data with complete transaction records and unique investor identification, and finds that the aggregate under-performance of foreign investors is attributable to foreign passive funds, that is, those that replicate a benchmark index. These funds pay higher prices to increase the speed of their trades to accommodate daily flows proportionally to their index before market closing. Passive funds face higher transaction costs on days when they trade multiple stocks in the same direction, buy (sell) the same stock multiple times, and make large trades near the daily closing time. Meanwhile, foreign active funds trade at more favorable prices and display higher risk-adjusted returns than any other investor group, including domestic funds with similar active management. The findings highlight the potential costs of index investing in developing countries or in securities with low trading activity (small stocks).

This paper is a product of the Finance and Private Sector 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 apedrazamorales@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
within a household. Consequently, this lack of data can pose a serious challenge for researchers and stakeholders in assessing and monitoring poverty and health for individuals across the world.

Most household consumption and dietary data can be classified as incomplete in one of three ways: (1) incomplete household survey data as either completely missing individual consumption data; (2) partially missing such data, as in a case in which the data is only available for certain administrative areas; or (3) missing such data over time (Dang et al., 2019). The NNS does not exhibit incompleteness in this way and provides an ideal opportunity to investigate questions of individual food consumption and expenditure within a household.

In our analysis, we use data from 2013 (hereafter NNS 2013), which includes the most recent and publicly available household and individual food consumption data. The NNS 2013 includes 10 modules: (1) Anthropometry, (2) Biochemical, (3) Clinical, (4) Dietary – Household, (5) Dietary – Individual, (6) Food Security, (7) Infant and Young Children Feeding, (8) Maternal Health and Nutrition, (9) Socio-Economic – Household, and (10) Socio-Economic – Individual. Note that the Dietary – Household module applied standard food weighting procedures and collected data on all meals (breakfast, lunch, dinner, and snacks) prepared and cooked in the house for all the household members, whereas the Dietary – Individual module conducted face-to-face interviews using a 24hour Food Recall Form to collect food consumption at the individual level. Although more rounds of the NNS (such as the NNS 2015 and NNS 2018) were conducted after 2013, these more recent NNS are not ideal for this study. First, the NNS 2015 did not implement food consumption modules and second, the NNS 2018 was not publicly available at the time at which this work was undertaken.

The data includes 160,888 individuals, from 33,400 households, which were surveyed in NNS 2013
# **I. Introduction**

Egypt achieved rapid growth during 2005-2008. In this period reforms and economic shocks produced gains for some groups, and losses for others. The main objective of this paper is to analyze to what extent the rapid growth experienced in Egypt between 2005 and 2008 has been pro-poor. Pro-poor growth is about changing the distribution of relative incomes through the growth process to favour the poor. This paper will refer to the “relative” definition of pro-poor growth when discussing to what extent growth benefitted the poor in Egypt between 2005 and 2008. There are two definitions for measuring pro-poor growth used in recent literature and policy-oriented discussions. The first and relative definition compares changes in the incomes of the _poor_ with changes in the incomes of the _non-poor_ . Using this definition, growth is pro-poor when the distributional shifts accompanying growth favour the poor. This is the definition that will be used throughout this paper. The second and absolute definition considers growth to be pro-poor if and only if poor people benefit in absolute terms, as reflected in some agreed measure of poverty (Ravallion and Chen, 2003; Kraay, 2003). In this sense, growth in Egypt was indeed benefitting the poor between 2005 and 2008, as the country achieved a substantial reduction in the poverty headcount (from 23.4 to 18.9)<sup>2</sup> .

The paper presents a detailed picture of how different groups above and below the poverty line were affected by this period of positive growth. This analysis is based on the Household Income, Expenditure and Consumption Panel Survey (HIECPS) conducted by CAPMAS (Egypt’s national statistical agency) to trace household consumption and living standards over 2005-2008. The survey is the first large scale data collection in Egypt to monitor the situation of same households over extended period of time.

The study compares Growth Incidence Curves (GIC) based on a cross section of data with GICs based on the panel data. Panel data follows the same households over time (unlike the
follows. First, compared to merchants without the latest digital payment technology, merchants with contactless payment technology experienced an average of 8.3 percent (10.2 percent) more monthly card-sales amount (count) in the 10-month period following the first containment in France in March 2020. Prior to the first shock, there is no difference in the card-sales trend between the treatment group and the control group. Second, the adjustment was rapid, with the effect already very pronounced in the period immediately following the month of the first lockout shock and after the month in which the contactless payment limit was increased. Third, I observe a strong effect of shocks for small businesses, new entrepreneurs, and merchants who make small value per transaction like bakeries. Fourth, using the same difference-in-differences specification, I find consistent results that the number of new

> 2These data were made available thanks to a partnership with Groupement des Cartes Bancaires CB, and I exploit the card payments data in accordance with the EU General Data Protection Regulation, in application of Article 89. I use the abbreviation ‘CB’ to indicate the source of the card payments.

2
0.56|0.24|0.52|0.37|0.34|0.50|0.39|
|2015|0.46|0.55|0.28|0.52|0.37|0.35|0.50|0.50|
|2016|0.47|0.56|0.27|0.53|0.38|0.34|0.51|0.40|
|2017|0.46|0.56|0.26|0.53|0.38|0.33|0.51|0.38|
|2018|0.46|0.56|0.26|0.52|0.38|0.32|0.51|0.41|
|2019|0.46|0.55|0.28|0.53|0.38|0.29|0.51|0.42|
|2020|0.46|0.56|0.25|0.52|0.38|0.29|0.51|0.42|
|2021|0.45|0.52|0.31|0.51|0.38|0.31|0.49|0.42|

_Notes:_ Based on data from EH 2007-2021. Estimation using a rescaled score from the first principal component of the PCA.

Columns (6) and (7) reveal that younger workers not only have lower job quality on average across age groups but have also experienced a decline in job quality over time, decreasing from 0.35 in 2015 to 0.29 in 2020. In contrast, workers between the ages of 25 and 54 have better job quality on average and have not experienced significant declines except during the 2013-2014 period. The oldest population shows a similar trend to that