rafmacalaba/data-use-mentions-extended huggingface dataset:
# 1. Introduction

Across countries, women’s total (paid along with unpaid) work burdens are often substantially greater than men’s due to disproportionately greater time allocated to unpaid activities (United Nations, 2015) that can in turn hamper women’s ability to search for better economic opportunities, earn higher incomes, and seek health and educational investments for themselves and their children (Bauman et al., 2019; Del Boca et al., 2014; Menon et al., 2014). Several factors affect the extent to which men and women allocate time across paid and unpaid work, self-care and leisure. These include gender norms related to time allocation (Heintz et al., 2017; Fafchamps and Quisumbing, 2003; Bittman and Wajcman, 2000); availability of and returns to employment activities (Folbre et al., 2014; Olivetti, 2006); socioeconomic attributes of households and individuals (Goldin and Katz, 2018; Ilahi, 2000); and individual preferences (Donald et al., 2020; Guryan et al., 2008). Our paper aims to zero in on the relatively understudied role of individual asset ownership, as an indicator of individual socioeconomic status, in time allocation decisions of men and women. We examine this question within and across households using new, nationally representative survey data from Cambodia, where individual-level, self-reported data on ownership of a diverse set of assets was collected alongside data on individuals’ time use.

Ownership and control over assets, such as residential and non-residential land, financial accounts, mobile phones, vehicles and other durables, is one important dimension of socioeconomic status for which individual-level data have typically been limited, particularly in low- and middle-income countries. Asset ownership and control can affect time use through different channels associated with individual socioeconomic status. Assets can raise productivity and incomes, for example, by widening access to resources such as financial services markets and networks (Doss, 2013; Doss et al., 2020), and by potentially improving individuals
<br>**nsfers**|
|---|---|---|---|---|---|---|---|---|
||Special<br>treatment|Consent|Performance<br>of a contract|Legal<br>obligation|Legitimate<br>interests|Access/<br>deletion|Rules on<br>transfers|No data<br>localization|
|**Algeria**|**●**|**●**|**●**|**●**|**●**|**●**|**●**|**●**|
|**Bahrain**|**●**|**●**|**●**|**●**|**●**|**●**|**●**|**●**|
|**Iran, Islamic**<br>**Rep.**|**●**|**●**||||**●**||**●**|
|**Israel**|**●**|**●**||||**●**|**●**|**●**|
|**Kuwait**||**●**||||**●**||**●**|
|**Lebanon**|**●**|||||**●**||**●**|
|**Morocco**|**●**|**●**|**●**|**●**|
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LIBERIA’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

|Operating cost recovery|% total costs|55.6|**100**|110|
|---|---|---|---|---|
|Connections per employee|number|95.7|**195.5**|158.6|
|Total hidden costs as % of revenue|%|286.7|**102.7 **|121.4|
|U.S. cents per m3|**Liberia**|Scarce wat|er resources|Other developing<br>regions|
|Residential tariff (at 10 m3)<br>Non-residential tariff (at 100 m3)|**44.5**<br>**221.3**||60.3<br>120.7|3.0 – 60.0|

*Access data for Liberia is taken from the Demographic and Health Survey 2007 to ensure comparability with the benchmarks. More recent data and comprehensive data exist from the 2008 National Census.

** Utility data are taken from the AICD utility database for 2005. The year 2005 is used to ensure consistency across countries benchmarked. Source: Banerjee and others 2009; Morella and others 2009, derived from AICD water and sanitation utilities database downloadable from http://www.infrastructureafrica.org/aicd/tools/data **.**

According to the 2010 Joint Monitoring Program (JMP), access to improved water sources increased from 58 percent in 1990 to 68 percent in 2008 (table 6). Over the same period, however, access to piped water fell from 11 percent to 2 percent. The resulting shortfall was met by an expansion in intermediate options, such as stand posts and boreholes, although the exact mix of this expansion is hard to determine from the survey evidence.

# **Challenges**

The JMP reports that access to
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the firm level. The average is almost normally distributed with a very high (unweighted) mean around 45%. When firms export, they export a lot. We expect exporters to perform a lot better on most performance dimensions, for example achieve higher productivity (Van Biesebroeck, 2005).

Naturally the average fraction of output exported is larger if we limit the sample to manufacturing firms, but the difference is not huge. Slightly more than half of the domestic firms in the sample are in the manufacturing sector. On this sample, the average export intensity is 8.6%, while it is 4.2% for other firms. Among manufacturing firms, only 22% export, while exporters represent fewer than 10% of non-manufacturers.

The results for the changes in the average export intensity indicate that there twice as many countries where exporting increased as decreased. The average change, across the 33 countries for which we can calculate it, is 3.1 percentage points. The median change is only half as large. The standard deviation is rather large (at 7.9), but this is driven by a single negative outlier. Only 7 of the 33 countries show a change in export intensity of more than 5% either positive or negative.

In sum, we can conclude that only few firms export, firms that do export a lot, but the fraction of exporters is barely growing over time.

> 8 The question on exported output has missing observations for all firms in the surveys of Liberia and Sierra Leone in 2009.

17
number of other regulatory and system attributes to enable the estimation of equations to predict the numbers of staff required.

The data were of generally high quality although there were some question marks for individual country responses. More importantly, the definition of “professional” in numbers of professional staff may well have been rather different (and less demanding) for many DTEs relative to the North American and EU countries in the sample.

The DPS data confirmed that there were fixed staff costs for countries establishing an electricity regulator. This raises serious problems for small countries like Grenada or Namibia. They also, however, showed that developing country regulators had noticeably _higher_ staff numbers per million customers per sector regulated - a mean number of 59 staff per million for developing countries as opposed to a mean of 15 per million for developed countries. _The difference was that developing countries had a much lower number of connected customers relative to population_ . Plotting average cost curves, developing countries were concentrated in the relatively steep initial portion of the curve, with many above the fitted line<sup>31</sup> .

The data above indicate some of the difficulties for developing countries in establishing an effective regulatory capability. This was demonstrated more directly in the econometric equations used to explain staff numbers and professional staff numbers. The estimates from the regressions suggested that, even for small countries with limited electricity systems, the minimum number of regulatory staff required was around 30-35, including around 15-20 professional staff. Predictions using the relevant equations for three small, developing countries not in the DPS sample – Guatemala, Sri Lanka and Zambia - yielded the following numbers for staff requirements:

||Total Staff|Professional Staff|
|---|---|---|
|Guatemala|58|33|
|Sri Lanka|48|26|
|Zambia|57|45|

Clearly, many developing countries do have staff (and professional staff) numbers at or above the DPS predictions. Latin American regulators generally seemed to be well-staffed and to have
_Sources: Beaudry and Sowa (1994): (1) Ewusi (1984); (2) and (3) Authors' calculations from the GLSS._ Evidence from the pattern of migration flows cannot really provide firm conclusions about the difference in the incidence of poverty. The absence of significant movement of labor from the rural to the urban economy need not necessarily imply that the poverty ratio is no worse in the latter. Migrants may respond to the expectation of employment _a la_ Harris-Todaro, or to the expectation of earnings growth. Alternatively, there might be serious impediments to the movement of labor due to economic or social barriers. On the other hand the existence of significant urban-to-rural migration would suggest that there is prima facie evidence of higher incomes, and hence lower incidence of poverty in the urban sector.

We have seen above that rural-urban migration was an important part of the economic landscape of Ghana in the years before its decline. Beaudrey and Sowa (1994) have put together data on net migration (i.e. inflow minus outflow) for different regions and periods. They are reproduced in Table 6.9.

Column (1) of the table indicates that prior to 1970 46.5 percent of net migration was directed towards Accra. The next two columns show that the pattern of migration was completely reversed in the 1980s. Instead of being a region of net inflow, Accra has become the major source of migrants. Between 1982 and 1987, net migration out of Accra accounted for almost 60 percent of net outward migration in Ghana. The main destination migrants has become the Western region, which is the region of expanding cocoa production. For some purposes information on the volume and pattern of gross migration might be useful. We produced tabulations for such migration as it took place over the last three years prior to the surveys of 1988-89 and 1991-92. These data are reproduced in Table 6.10 below.

# **Table 6.10: Migration between Urban and Rural areas in last three years
used as a new metric for the monitoring and evaluation of the efficiency of investment and to better understand the benefits of improving accessibility and resilience of roads.

- **Identifies socioeconomic benefits of rural infrastructure investments.** Economic analysis of rural transport investments has traditionally focused on reduction of road user operating cost by improving road surface conditions. The framework presented here allows to bring social benefits to rural transport investment as gain in accessibility to human capital development centers such as for health and education. At the same time, introducing the gain in accessibility to markets by productive agricultural land, the method identifies a new metric for economic growth that goes one step beyond the more traditional methods focusing on measuring road operating cost.

- **Supports policy dialogue on rural development and resilience.** The data, maps, and results from this model engage decision makers, allowing them to identify areas with lower accessibility and high climate vulnerability. This information can inform strategic goals and action plans for the national and provincial governments.

The analysis has also identified the next avenues for further improvement of this model. The first is to include supply chain resilience, based on enterprise and business census. This will bring an additional dimension to the analysis as it will evaluate economic losses of business interruption. This method has already been developed in the context of Tanzania (Colon, Hallegatte, and Rozenberg 2019), and it is another step toward operationalizing some of the analysis included in the report of Hallegatte, Rentschler, and Rozenberg (2019). There is also potential to assess poverty impacts of road disruptions using the methodology by adding the results of the model to poverty maps. Another avenue is to scale up the analysis for all 27 provinces across Cambodia to produce whole-network analysis which can inform government strategies for closing the infrastructure and accessibility gaps.

# **5. Acknowledgments**

The authors would like to thank the counterparts at the Ministry of Rural Development of the Royal Government of the Kingdom of Cambodia, especially the management and technical staff of
Weber, Michael, 2018. "Burkina Faso Jobs Diagnostic: Overview and Suggestions for a Strategic <u>Framework for Jobs," Jobs Group Papers, Notes, and Guides 30672458, The World Bank.</u> Wiswall, M., & Zafar, B. (2015). How do college students respond to public information about earnings?. Journal of Human Capital, 9(2), 117-169 _._ _“World Development Indicators.” Washington, D.C. :The World Bank., n.d._ World Bank. (2021). _People's Perspectives on ID and Civil Registration in Rwanda_ . World Bank.

World Bank (2013). “Reintegration Opportunities for Vulnerable Households of Ex-Combatants Demobilized through PNDDR : An Overview of the Socioeconomic Situation of Pilot Project Beneficiaries Two Years Following Project Closure - Maniema, Democratic Republic of Congo,” https://openknowledge.worldbank.org/ handle/10986/17859?locale-attribute=en.

WHO 2019. Trends in Maternal Mortality: 2000 to 2017. Geneva, World Health Organization, 2019 30
Policy Research Working Paper 8700

# **Abstract**

The main goal of this paper is to document and analyze the long-term evolution of inequality of opportunity and thus extend the recent empirical literature, which is mainly concerned with its measurement at a specific point in time. Using repeated cross-section surveys for five European countries (France, Germany, Great Britain, Italy, and Switzerland), the evolution of inequality of opportunity is measured for a period of about two decades for the whole populations, as well as for different birth cohorts. Relative inequality of opportunity represents an important portion of total income inequality, with values ranging from 30 to 50 percent according to the standard deviation of logs (and reaching a lower share in case of mean log deviation) and, for all the countries, it shows a stable or declining time trend. When the birth cohorts are followed across time, inequality of opportunity decreases with age: the effect of circumstances seems to weaken over the life cycle. This is a quite different age profile from that of inequality of outcomes (income or consumption), which generally increases with age. A decomposition of the relative inequality of opportunity allows highlighting some key drivers of its time evolution. In all the countries, there has been a clear enhancement of equality of educational opportunity (as captured by a downward trending intergenerational education persistence) and a reduction of the returns to education. However, for some countries, notably Italy, these trends have failed to translate into decreasing inequality of opportunity in the income distribution because of the increasing role of parental networking (an additional channel through which parental background affects the incomes of offspring).

This paper is a product of the Office of the Chief Economist, Europe and Central Asia Region. 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/research.
source of energy for lighting (albeit not for cooking). These results are based both on evidence obtained from national household survey data and from the fact that nighttime radiance (as measured by satellite images) increased relatively more in grid cells belonging to areas with project components than in non-project areas. The results are consistent with existing evidence in the economic literature of an (often overlooked) synergy between EE and energy access, according to which improvements in efficiency constitute a channel to also expand access to electricity.

Overall, our results support the view that energy efficiency projects in developing countries can have significant impacts on relevant outcomes that are measurable both from national household survey data and from remote-sensing data from space. At the same time, we stress that the analysis presented in this paper focuses on a specific project where the external validity remains generally unclear. More empirical research to investigate the impacts of energy efficiency projects, and guide policy decisions, is clearly warranted.

14
Anukriti et al. Availability of Girls’ postPooled individual Quasi-experimental study: Decline in female post Reduced family (2016) ultrasound neonatal data from NFHS Triple difference-in-difference neonatal mortality (and size, and increased technology mortality, (1992-93, 1998that combines variation in neonatal mortality). But breastfeeding breastfeedin 99, 2005-06) supply of ultrasound scanners, for every additional girl duration and g, and a family’s incentive to sex that survives until age 5 in immunization immunizatio select (using sex of first-born the post-ultrasound era investments for n status child as an indicator), and sex (relative to pregirls. Families of the child being observed ultrasound), five girls are with higher aborted socioeconomic status (SES) were more likely to abort girls (and state son preference), so the average girl born post ultrasound (versus preultrasound) was more likely to be born into a low <u>SES family</u> 18
in South Asia. Some examples include Chittagong and Khulna (Bangladesh) and Patna and Bokaro (India). They are mostly industrial cities, focusing on a variety of light and heavy industries. Although none of these cities is included in our list of top 10 competitive cities (Table 1) some still show good performance. For example, during this 10‐year period, Patna and Bokaro show an annual average GDP growth that is between 2 and 3 percentage points above India’s average GDP growth. Furthermore, most of these cities are outperforming their nation in terms of employment growth.

> 16 This is a relatively low level of per capita GDP compared to what other studies have found. For example, Grossman and Krueger (1991) find that the turning point comes at $4,119, while Panayotou (1993) finds a turning point of $3,137. However, most of these studies focus on emissions or concentrations of sulfur dioxide. Stern and van Dijk (2016) use PM 2.5 concentration levels and per capita national GDP and still find a relatively high turning point of $3,336. However, the fact that these turning points are higher than our findings is not surprising. Harbaugh et al. (2002) find that the location of the turning point is sensitive to not only the specific pollutant but also the sample used and the specification of the model.

18
whether they work in the formal sector, their labor income, their occupation and the socioeconomic status of their household. ENAHO collects very similar data from Peruvians. Neither LAPOP nor Gallup collect detailed data on employment and occupation.

We first show the information for our main explanatory variable.

In the first analysis, this is the informal employment rate measured in the 2017 census in the centro poblado in which Venezuelans reside which has a mean of 31.2%.<sup>5</sup> This is noticeably below the overall informal employment rate of 59.9% among Peruvians surveyed in ENAHO, indicating the Venezuelans are generally settling in areas in Peru with less formal unemployment. Only 8.0% of Venezuelan immigrants surveyed in ENPOVE are employed in the formal sector (i.e., they have an employment contract with social security benefits), hence the employment rate in the informal sector among Peruvians in a particular location is a good measure of the availability of job opportunities for Venezuelans and the competition with Peruvians for these jobs. We hypothesize that locations with higher informal sector employment rates have more opportunities and less competition with Peruvians for jobs. For this reason, we sometimes describe locations with high informal employment rates as having ’strong’ informal labor markets.

In the second analysis, this is the number of Venezuelan immigrants in a particular month and province (measured in the administrative data) as a share of the total local population measured in the 2017 census.<sup>6</sup> Over the full sample period of our analysis, the mean share of Venezuelans in the population is very low, 0.4% in the ENAHO sample, 0.3% in the LAPOP sample and 0.6% in the LAPOP sample. However, if we just look at ENAHO in December 2018, we see that the share has risen to 1.4%.

The remainder of the table shows the means and standard deviation for the control variables for Venezuelan immigrants in December 2018,