rafmacalaba/data-use-mentions-extended huggingface dataset:
(UNICEF, 2021). Women in the region may be left behind as they are marginalized in the technology-based economy. In all countries of the region, male internet users are higher than female users. In countries such as the Arab Republic of Egypt, Tunisia, and the Republic of Yemen, more than 60% of men use the internet but less than 50% of women do (International Telecommunication Union, 2019; Arab Barometer, Wave V, 2018-2019). This gender digital divide hinders women’s access to information, services, and economic opportunities (Elnaggar, 2007; Ben Moussa and Seraphim, 2017).

Drivers of the gender digital divide may include the cost of ICT, women’s lack of the skills necessary to use it, irrelevance to the tasks and roles society expects them to complete, lack of access, social norms and stereotypes, and safety and security concerns. Therefore, access to the internet is a necessary but not sufficient condition to ensure that women will benefit from internet availability, especially in developing countries. This paper is an attempt to investigate whether bridging the digital divide through an inclusive internet environment might reduce the gender gap in labor force participation in MENA countries. An inclusive internet environment considers the availability and affordability of internet and technology, relevance of internet content, trust and privacy in the internet, and inclusive policies (World Bank, 2016; Singh, 2017; The Mobile Gender Gap Report, 2018, the Economist Intelligence Unit, 2021).

The paper contributes to the extensive literature tackling the gender economic gap in the MENA region in two ways. First, unlike the existing literature, the paper considers the potential role of an inclusive internet environment, measured by the Inclusive Internet Index, in reducing the gender gap in the labor market. The advantage of this index is that it controls for the inclusiveness of the internet environment and not only access to the internet. As found by Valberg (2020), ICT, measured by internet users and mobile subscriptions, is
).

International Monetary Fund. (2008). Regional Economic Outlook: Sub-Saharan Africa. Washington, D.C.: IMF.

- International Monetary Fund. (2015). Article IV consultation 2015. IMF Country Report. October. Washington, D.C.: IMF.

- International Monetary Fund. (2018). Article IV consultation 2015. IMF Country Report. June. Washington, D.C.: IMF.

- Johnson, S., Ostry, J., and Subramanian, A. (2006). Levers for growth. _Finance and Development_ , 43(1): 28-31.

- Johnson, S., Ostry, J., and Subramanian, A. (2010). The prospects for sustained growth in Africa: Benchmarking the constraints. IMF Staff Papers, vol. 57. Washington, D.C.: IMF.

- Jones, B., and Olken, B. (2008). The anatomy of start-stop growth. Review of Economics and Statistics 90(3): 582-587.

- Martins, P. (2014). Structural change in Ethiopia – an employment perspective. World Bank Policy Research Working Paper 6749. Washington, D.C.: World Bank.

- Martins, P. (2015). Structural change in Ethiopia – an update using 2013 LFS data. Background paper prepared for the report ‘Ethiopia’s Great Run: The Growth Acceleration and How to Pace It’.

- McMillan, M., Rodrik, D., and Verduzco-Gallo, I. (2014). Globalization, structural change, and productivity growth. _World Development_ , 63: 11-32.

Moller, L., and Wacker, K. (2015). Ethiopia’s growth acceleration and how to sustain it: Insights from a cross-country regression model. Policy Research Working Paper No. 7292, Washington, D.C.: World Bank.

Nguyen, H. (2014). Real exchange rate and export growth in Ethiopia.
*|0.3%|3.2%|-0.9%|3.2%|-2.1%|-2.8%|-2.3%|-2.1%|

_Source:_ Staff calculations based on GUS data.

# **C. WHAT IS BEHIND THE INCREASE IN CONSUMPTION INEQUALITY?**

Changes in inequalities in consumption can be the result of a number of factors. Four important ones are: (i) changes in the distribution of original income; (ii) changes in the progressivity of the tax and benefit system; (iii) changes in the distribution of savings; and (iv) changes in relative prices of the basket of goods bought by households at different points along the income distribution. Unfortunately, the Polish HBS has very limited information on household savings. Thus, the analysis below focuses on the other three factors identified above.

# **Changes in the Distribution of Original Income and its Components.**

Original income refers to the income that the household has before paying taxes and receiving benefits (other than old-age pensions). As shown in Table 5, over the period 1994-2002 inequality in original income, as measured by the Gini coefficient, increased by around 7 percent, from 0.38 in 1994 to 0.41 in 2002. The increase was surprisingly moderate considering the degree of transformation the country had experienced, particularly since it occurred entirely after 1998 with inequality in original income fluctuating around a stable value in the high growth years of 199498.

Table 5 also shows the main sources of inequality and the main factors behind the increase. Inequalities in labor income were and remain, a major source of total income inequality. In addition their relative role in overall inequality has increased over time until 2001 (from 70.5 percent to 72.3 percent) although it appears to have declined back to 70 percent in 2002.<sup>11</sup> The combination of increased inequality
River Basin prepared a background paper on the basin prior to the visit. The semi-structured interviews were conducted with a focus on understanding the processes of institutional change and the performance of water management institutions at sub-basin, basin, and national scales, matters that were closely within the knowledge of the interviewees. The semi-structured nature of the research allowed for flexible and consistent investigation of issues as they emerged.

The following analysis of the Brantas basin case is therefore based on a combination of sources—documentary materials on Indonesia and the Brantas basin, the background paper prepared for the visit, and the interviews conducted during the site visit. The findings and conclusions therefore do not represent the point of view of a single individual or organization, but emerge from a composite of data collected and reviewed by the research project team.

7
# **3. Aging and Saving**

How will the saving rate evolve when the population is aging in Brazil? That is the main question of this paper. It is well known that population aging is likely to reduce the saving rate because there will be a larger share of elderly who may not have an incentive to save very much. What if, however, the LCH is not supported in Brazil when the elderly also save? In that case, a larger share of elderly will not reduce the saving rate; in fact, population aging may then increase the saving rate, and since the share of children in expected to fall in Brazil in line with falling fertility that would further increase the average saving rate.

To confirm or reject the LCH for Brazil — and to project the most likely trajectory for the future saving rate — are the two main purposes of this section. The section therefore offers a systematic treatment of the partial equilibrium dynamics of household saving rates over the period 2010 to 2050. The analysis builds on the latest 2008 POF household expenditure survey and the PNADE household survey, and a partial equilibrium model is employed to simulate the implications for household saving rates under various scenarios for income inequality. The analysis assumes that saving rates at the household level do not change as a response to aging.

Based on the following discussion, it is found that age-specific saving rates increase up to the age of about 40, after which they remain virtually unchanged on average. As is evident from Figure 1b it matters a great deal to the measure of age-specific saving rates whether or not public pensions are included as part of income. The literature is quite clear on the subject: private pensions should be deducted from the measure of income, but social security pension benefits should not (Bosworth et al., 1991, and Miles, 1999). Mason and Lee (2010) furthermore argue that pensions paid to public workers are components of labor income and simply a cost of producing public goods. As a result
effectively credit constrained. Additionality also refers also to the developmental impact of the scheme, including the survival rate of firms, investment, growth, and job creation. Additionality also has another dimension, namely, the depth of financing: longer term financing instead of short term, financing of immaterial and intangible assets, financing of project type traditionally excluded by the banking sector.

**Assessing additionality remains technically challenging** . Assessing additionality entails comparing the financial and economic performance of guarantee users (the “treatment group”) to those of non-users (the “control group”). The main challenge is to identify a correct control group, consisting of firms with similar characteristics to those of the guarantee users. As shown in Table 17, there are several methods used to assess additionality (see Annex 2 for more details). Some country studies conclude that their guarantee schemes generate significant additionality (Table 18), but assessing additionality remains technically challenging (see e.g., World Bank 2008).

**Table 17: Alternative Methods to Assess Additionality**

|**Methodologies**|**Description**|
|---|---|
|Interviews|The basic way of measuring additionality consists of asking guarantee users and bankers:<br>“would you have obtained (extended) the loan without using the guarantee scheme?”|
|Descriptive statistics|<br>The share of guarantees extended to riskier types of borrowers (e.g. small firms, start-ups,<br>firms seeking long-term finance, or SMEs with low risk scoring results) can be used as a<br>proxy to assess additionality.|
|Propensity score<br>matching|This method is based on a survey of enterprises, and employs econometric techniques to<br>make the “control group” more comparable to the group of guarantee users, controlling for<br>firms’ characteristics.|
|Regression<br>discontinuity|Many guarantee schemes use credit
This suggests that LIC central banks have not been able to secure low and stable medium-term inflation rates on their own, and their improved inflation performance may therefore have been largely imported. Therefore, if global inflation were to rise, LICs would face the risk of their own inflation rising in tandem, unless steps can be taken to improve their homegrown anti-inflation credibility.

The next chapter extends the examination of the sensitivity of LIC inflation to global shocks by considering the effects of global food prices on domestic food prices and poverty.

# 7. Poverty Impacts of Food Price Shocks and Policies

In August 2011, international food prices hit an all-time high. This followed the 2007-08 food price spike, which pushed an estimated 105 million people into extreme poverty and prompted widespread concerns about the food security of the poorest. Although food prices have been lower in recent years, they are still significantly above their lows in 2000 (Figure 8). In Chapter 7, Laborde, Lakatos and Martin ask three questions:

- How do food price shocks affect EMDEs and LICs?

- How do countries intervene to reduce the impact of food price shocks?

- What was the impact of the 2010-11 food price shock on poverty?

The study adds to the literature by quantifying the degree to which countries intervened during this episode. The chapter also estimates the impact of the 2010-11 food price spike and associated trade policy interventions on poverty using a general equilibrium model complemented with data from household surveys (Laborde, Robichaud, and Tokgoz 2013).

In some respects, the last two food price spikes (2007-08 and 2010-11) resembled earlier, similar episodes: world prices rose rapidly, whereas domestic prices increased only gradually. In other respects, however, the 2010-11 spike was different from previous episodes. The 2007-08 increase in food prices came after a long period of stability in food prices (Ivanic and Martin 2008). In 2007-08, world prices of all staple foods increased steeply, led by a strong increase in the world price of rice. Most countries reacted strongly
plants and labor force surveys, which our analysis can leverage along with data on imports of robots.

Our key contribution is to document a positive impact of automation on employment in Indonesia (at least until 2015). Consistently with the predictions of a task-based model (Acemoglu and Restrepo, 2018b), the plant level results suggest that the positive impact on employment is driven by the productivity gains of automating plants. Our result differs

> 1A commonly tested hypothesis, for instance, is that adoption of robots in advanced economies induced re-shoring.

2
G. Salmo III, Jonnell C. Sanciangco, Sukristijono Sukardjo, Yamin Wang and Jean Wan Hong Yong. 2010. “The Loss of Species: Mangrove Extinction Risk and Geographic Areas of Global Concern.” _PLOS ONE_ 5 (4): e10095. https://doi.org/10.1371/JOURNAL.PONE.0010095.

- Quartel, S., A. Kroon, P. G.E.F. Augustinus, P. Van Santen and N. H. Tri. 2007. “Wave Attenuation in Coastal Mangroves in the Red River Delta, Vietnam.” _Journal of Asian Earth Sciences_ 29 (4): 576–84. https://doi.org/10.1016/J.JSEAES.2006.05.008.

- Rasmussen, Tobias N. 2004. _Macroeconomic Implications of Natural Disasters in the Caribbean._ IMF Working Paper WP/04/224.

- Sanoja, M. 1992. “Wetland Ecosystems and the Management of Cultural Heritage.” In _Wetlands Management in the Caribbean and the Role of Forestry and Wetlands in the Economy_ , edited by A.E. Lugo and B. Bayle, 66–73. New Orleans: USDA Forest Service.

- Satapathy, D. R., R. J. Krupadam, L. Pawan Kumar and S. R. Wate. 2007. “The Application of Satellite Data for the Quantification of Mangrove Loss and Coastal Management in the Godavari Estuary, East Coast of India.” _Environmental Monitoring and Assessment_ 134 (1): 453–69. https://doi.org/10.1007/S10661-007-9636-Z.

- Schuerch, Mark, Tom Spencer, Stijn Temmerman, Matthew L. Kirwan, Claudia Wolff, Daniel Lincke, Chris J. McOwen, Mark D. Pickering, Ruth Reef, Athanasios T. Vafeidis, Jochen Hinkel, Robert J. Nicholls and Sally Brown. 2018. “Future Response of Global Coastal Wetlands to Sea-Level Rise.” _Nature_ 561 (
(0.0283)||||
|1 = Common official or primary language|0.779***<br>(0.0376)||||
|1 = Common colonizer post 1945|0.368***<br>(0.0479)||||
|Distance (Pop weighted)|-1.269***<br>(0.0169)||||
|Constant|-16.81***|-15.13***|16.22***|16.21***|
||(0.226)|(0.454)|(0.0205)|(0.0215)|
|Observations|501,256|501,256|1,088,662|<br>1,088,662|
|Number of CountryPairs|32,554|32,554|32,554|32,554|

**Notes:** Robust standard errors in parentheses (clustered on country pairs). *** p<0.01, ** p<0.05, * p<0.1.  In columns (1) and (2), OLS RE (FE) stands for Ordinary Least Squares Random (Fixed) Effects. The dependent variable in columns (1) and (2) is the natural log of the zero-corrected nominal imports from the CEPII-BACI dataset.  The data cover 1995-2018. In columns (3) and (4), PPML stands for Pseudo Poisson Maximum Likelihood and both (3) and (4) include country-pair fixed effects and importer and exporter time period interaction terms estimated with Stata’s _ppmlhdfe_ command (Correia et al. 2019a, 2019b).  The gravity variables in columns (1) and (2) are from the CEPII Gravity dataset and the regional trade agreement indicator and labor clause indicator are from the Handbook of Deep Trade Agreements
<!-- Start of picture text --> Poly bning, and Re"srch |<br>Poputldon, Health, and Nutrition<br><!-- End of picture text -->

Many developing countries are trying to improve the routine collection of health informiation by strengthening surveys, censuses, and registration systems. At the international level, too, efforts are under way to provide information on health and health interventions, including statistical reporting programs of the U.N. and the World Bank.

In view of the limited financial resources in the developing countries, would a world health survey complement these health infonmation systems and contribute to long-term health care? Is it reasonable to expect that such a survey could identify the pattems and causes of disease and at the same time measure the effectiveness of investments in health?

tradeoffs. Considering the variety of health problems and priorities in developing countries, it is probably more important to develop the expertise to conduct and analyze health studies than to devise a standard questionnaire to collect health &ktta. As for the cost-effectiveness of health programs, a world health survey is not the appropriate vehicle for such evaluations, but it can address such concems as access, coverage, patient costs, and financing systems.

This paper is a product of the Population, Health, and Nutrition Division, Population and Human Resources Department. Copies are available free from the World Bank, 1818 H Street NW, Washington DC 20433. Please contact Anelia Menciano, room S6278, extension 33612.

Although a series of coordinated country health studies could be valuable, there are many The PPR Working Paper Series disseminates the findings of work under way in the Bank's Policy, Planning, and Research Complex. An objective of the series is to get these findings out quickly, even if presentations are less than fully polished. The findings, interpretations, and conclusions in these papers do not necessarily represent official policy of the Bank.

Copyright i 1988 by the International Bank for Reconstruction and Development/The
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**Figure 2: The process of forecasting demand for the medium and long run at AENA**

<!-- Start of picture text --> Indicators reflecting the<br>SoRegional, national and Previous year's<br>inrstfrecasntl nrtn onrt a e passenger traffic ta/<br>\ ~~~traffic I /<br>Indicators reflecting the supply | _ ix model iA/|Impact of transport substitutes l<br>of tourists (number. hotel <<br>* nauionnav nvllrnare ztsv |<br>mt pacOMtofa PBepth regnumber of airlines<br>PAXINTt kPAXINTt t I PlBes PIBinteach nt aiUport<br>SuXDandpplXINT sar norrectea Passenger f Impactofairport characteristics<br>Average plane size /<br>Average occupancy rate<br><!-- End of picture text -->

Source: Aena (I1999 The first forecasts national and interational passenger traffic independently as follows: repectuve nationaland ly _TURt A_ whc efet the~ nm er ftuit,tedge focpnyo oesh _PAXDOMt = kpAXDOMt _ I cc P_IBespt /3 PIBregt_ 9 • InternationalIna eon sae,th eglao cnsrctV model tofrcsJh ipaetafcfrec _PAXINTt = kPAXINTt - I_ **_T_** _PIBespt 0 PIBint " (TURt_ **_Lv_** where, _PAXD and PAXINT_ are national and international passenger traffic respectively; k is a constant factor and cc _8_ d **z, 0,** _2 ,u_ are the elasticitis to the key explanatory variables which include _PIBesp, PIBreg, PIBint_ the variables reflecting national, regional and international GDP repectively and _TUR_ which reflects the number of tourists, the degree of occupancy of hotels, the average stay duration, .......) In a second stage, the regulator constructs a model to forecast the airplane traffic for each destination, relying on the passenger forecasting models just described: _A4ER,_ **_f,=_** _PAXj ,IVTA4j, ,FOi, 1)_
them for the first time.

The main objective of this paper is to diagnose whether public policies are expanding and allocating opportunities equitably, and identify which demographic and regional circumstances should be considered to improve targeting mechanisms. This paper measures the extent to which circumstances beyond the individual’s control feed into inequality of opportunities for the human development of children and youth. _Equality of opportunity_ means that a person’s chances to succeed in life (access to basic services, education, a quality job, and adequate consumption levels) should be unrelated to predetermined circumstances at birth such as gender, location of birth, and socioeconomic and demographic origin.<sup>3</sup> Comprehensive measurement of the levels and changes in inequality of opportunity in Egypt informs the policy debate in the search for a more equitable society. In addition, acting on reducing inequality of opportunity today might prove effective for reducing future inequality of outcomes in the medium and long term–such as income and employment status.

This paper aims to provide answers to the following questions: How unequal is the distribution of opportunities that are essential goods and services for the development of Egyptian children? To what extent has equality of opportunities advanced during the past decade? By how much have the urban-rural and inter-regional gaps in opportunities improved? Which sectors provide better access to opportunities for children in Egypt, and which sectors make it more challenging for children in adverse circumstances? Which demographic and location circumstances are most correlated with deprivation of basic development opportunities in Egypt?

To measure equitable access to opportunities, we use the Human Opportunity Index (HOI) to examine the evolution of 16 basic opportunity indicators grouped in four sectors: four HOIs linked to education, six to basic housing services, four to early childhood development, and three to nutrition and hunger. These 16 indicators cover human development milestones at the three stages

> 2 See Carneiro and Heckman (2003), Schweinhart (2004), Federal Interagency Forum on Child and Family Statistics (2007), Heckman and Masterov (
:<br>Poverty rate|
|CAP|NUTS1/2 level<br>apportioned to<br>NUTS3 level:<br>CAP support per<br>hectare of utilizable<br>agricultural area, CAP<br>support per<br>agricultural work unit|NUTS3 level:<br>CAP support per<br>hectare of utilizable<br>agricultural area, CAP<br>support per<br>agricultural work unit|NUTS3 level:<br>Total CAP<br>support|NUTS1 and<br>NUTS2 level:<br>CAP support per<br>capita|
|CAP data source|FADN database;<br>appointed RD budgets|Clearance Audit<br>Trail System (CATS)<br>database|Clearance Audit<br>Trail System<br>(CATS)database|Clearance Audit<br>Trail System<br>(CATS)database|
|CAP categories|Pillar1: Market price<br>support and direct<br>incomepayments|Pillar1: Market price<br>support and direct<br>incomepayments|Pillar1: coupled<br>and decoupled<br>payments|Pillar1: coupled<br>and decoupled<br>payments|
|Method|Pearson correlation<br>coefficients|Pearson correlation<br>coefficients|OLS regressions|Fixed effects<br>regressions|
|Period|1999|2011|2011|2005-2013|
|Country coverage|EU15|EU28 except<br>Lithuania and Czech<br>Republic|EU28 except<br>Lithuania and<br>Czech Republic|EU28 except<br>Germany|

**The table above shows comparisons of the different methods we adopted in order to study the correlation between poverty and the CAP support.** The Book here refers to “The

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