rafmacalaba/datause-encoder-data huggingface dataset:
Policy Research Working Paper 10692

# **Abstract**

Gender norms are often emphasized to help explain gender gaps in the labor market. This paper examines global patterns of gender attitudes and norms toward the stereotypical gender roles of the _male breadwinner_ and _female caregiver_ , and broad support for _gender equality in opportunities_ , and studies their relationship with economic behavior. Using data collected via Facebook from 150,000 individuals across 111 countries the paper explores how gender beliefs and norms are related to labor supply, household production, and intrahousehold decision-making power within a country. The paper provides descriptive evidence that the more gender equitable or counter-stereotypical are beliefs and norms, the more likely women are to work, the more time men spend on household chores, and the higher the likelihood of joint decision-making among couples. The findings suggest an underestimation of the support for gender equality globally and the extent of underestimation varies by gender and region. The paper concludes with a discussion of potential entry points for policy to help address gender norms.

This paper is a product of the Gender Innovation Lab, Africa Region and the Development Data Group, Development Economics. I. 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 spapineni@worldbank.org and tkilic@worldbank.org. A verified reproducibility package for this paper is available at http://reproducibility.worldbank.org, click **here** for direct access.

<!-- Start of picture text --> TRANSPARENT<br>ANALYSIS<br>G<br>R E<br>N<br>S<br>I P<br>Y E<br>K A<br>C A<br>R P<br>LI R<br>O E<br>O C<br>W R<br>H<br>S<br>P<br><!-- End of picture text --> _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
It is intuitive that the effects should in general be small because for both methods the net entry term depends critically on the difference between average productivity of entering and exiting businesses. Put differently, both the entry and the exit term subtract off whatever base is used, so at first glance it might appear that the base is irrelevant (the base term in each component cancels out in the net). Consistent with this perspective, computing the difference between the FHK and BG net entry terms yields: where Pt-k is the average productivity of incumbents and P<sup>X</sup> t-k<sup>is the average productivity of</sup> exiting businesses in the base year. Thus, if the share of activity (in this case employment) accounted for by entering and exiting businesses is the same then the difference is zero. As seen in section 4, for most countries the share of activity accounted for by entry is about the same as that for exit with the latter typically slightly larger since exiting businesses tend to be larger than entering businesses. Thus, this difference in weights does not matter for most countries. However, for Korea and to a lesser extent Portugal and Taiwan (China), the share of employment accounted for by exit is substantially less than the share of employment accounted for by entry -- hence the sensitivity for these countries.

|**Table 10:**<br>**FHK-BG**||||
|---|---|---|---|
|**Difference in**||||
|**Net**||||
|**entry**Country|Net Entry|Exit/ Entry Share|Incumbent/Exit<br>Productivity|
||Difference|Difference|Difference|
|**Argentina**|0.006|-0.012|0.098|
|**Chile**|-0.015|-0.022|0.432|
|**Colombia**|0.005|0.008|0.627|
|**Estonia**|-0.008|-0.031|0.28|
|**Finland**|-0.003|-0.013|0.251|
|**France**|0.004|0.034|0.107|
|**Korea, Rep.**|-0.06|-0.122|0.495|
|**Latvia**|0.01|-0.001|-0.037|
|**Netherlands**|0.003|0.028|0.025|
|**Portugal**|-0.016|-0.039|0.394|
|**Slovenia**|0.014|0.059|0.252|
|**Taiwan**||||
|**(China)**|-0.019|-0.077|0.264|
|**UK**|0.008|0.148|0.051|
|**USA**|0.004|0.012|0.299|
|**West**||||
|**Germany**|0|0.001|0.274|

Notes: The reported figures are the time series averages. The first column is the product of the second and third column. However, since the reported figures are averages over time, the identity may appear not to hold (the product of the averages is not the same as the average of the product).

39
# Where:

Fuel Price variation is the difference between actual and reference fuel cost component.

Actual fuel cost component is the fuel cost component in the pool price on which the DISCOs are being charged by CPPA in a particular month; and Reference fuel cost component is the fuel cost component for the corresponding month projected for the purpose of tariff determination.

## **Multi-Year Tariff adjustments**

As stated in the Guidelines for Determination of Consumer-end-Tariff (Methodology and Process) issued by NEPRA in January 2015, under a Multi-Year Tariff, the following adjustment mechanisms would be relevant:

# **_O&M Expense Adjustment_**

The O&M part of Distribution Margin shall be indexed with CPI subject to adjustment for efficiency gains (X factor). Accordingly, the O&M will be indexed every year according to the following formula: Where: O&M(Rev) = Revised O&M Expense for the Current Year.

O&M(Ref) = Reference O&M Expense for the Reference Year.

∆ CPI = Change in Consumer Price Index published by Pakistan Bureau of Statistics latest available on 1<sup>st</sup> July against the CPI as on 1<sup>st</sup> July of the Reference Year in terms of percentage.

X = Efficiency factor

# **_RORB Adjustment_**

RORB adjustment will be made in accordance with the following formula/mechanism: Where: RORB(Rev): Revised Return on Rate Base for the Current Year.

RORB (Ref): Reference Return on Rate Base for the Reference Year.

RAB(Rev): Revised Rate Base for the Current Year.

66
to that in the next poorest one, and so on, until the budget is exhausted.<sup>13</sup> Note that although our analysis is undertaken on the basis of the FGT(2) poverty measure, a similar approach could also have used some more conventional measure of social welfare; one that does allow incomes above the poverty line to also receive some positive social weight. We have found that the broad conclusions from our analysis are not affected by such a change in set-up.

The second targeting scheme that we compare against our benchmark case assumes some knowledge of the spatial distribution of poverty, but does not make use of this knowledge in any particularly scientific or systematic way. This “naïve” targeting scheme was selected in order to contrast with the “optimal” scheme described above. Implementation of an “optimal” scheme might be difficult in practice. Governments often need to be able to communicate in a very clear and simple way how resources will be targeted, and this may preclude the finetuning needed for an optimal scheme. Our naïve scheme attempts to assess how detailed geographic targeting improves efficiency conditional on the types of constraints that governments may typically face in practice.

Of course, countless alternative “naïve” schemes could be implemented. We explore here one, particularly straightforward, example. Experimentation with alternative schemes has not yielded any that is obviously more effective.<sup>14</sup> Indeed, the specific scheme implemented here has the virtue of not only being simple but also surprisingly effective at times.

Our “naïve” scheme takes the following form. We first rank geographic areas by estimated poverty. If our interest was to gauge the impact of our scheme on the headcount rate, we would rank areas by the headcount. But as we wish, in this paper, to assess the impact on the squared poverty gap we rank by those estimates. We have an assessment of overall poverty

> 13 Elbers et al (2004) provide a formal statement of this result. 14 See Elbers et al (2004) for more detail.

9
Policy Research Working Paper 9008

# **Abstract**

This paper discusses the evolution of the Egyptian banking sector and the main trends in financial development in the Arab Republic of Egypt. The paper examines empirically the relationship between the development of the financial sector and economic growth in Egypt between 1980 and 2016. It draws comparisons based on critical financial indicators between Egypt and selected emerging markets and developing economies, using a new data set of financial development indexes released by the International Monetary Fund. Econometric time-series modeling of bivariate regressions for real growth per capita and measures of financial development, to assess the relationship between financial development and economic growth in Egypt, yields three specific findings. First; there is a strong association between real growth per capita and financial development measured. Second; access to and the efficiency of banking services are not associated with real per capita income. Third, the Financial Markets Access Index—which compiles data on market capitalization outside the top 10 largest companies and the number of corporate issuers of debt—indicates that there is a robust association with real per capita gross domestic product. The main policy implications suggest that there should be a stronger focus on promoting a more proactive role for the financial services industry in Egypt. There is an especially critical role for bank financing to support the private sector to maintain an inclusive growth momentum. Further development of the capital market will promote the sustainability of such economic growth.

This paper is a product of the Office of the Senior Vice President for The 2030 Development Agenda, United Nations Relations and Partnerships. 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 arostom@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 promote the ME movement in its areas, the education system reverted to its previous elitist form in French-controlled areas. Put differently, the ME program did not exist in French-controlled areas during the war. This bimodal system lasted until the end of the war in 1954, when the DRV gained control of the North of Vietnam and could implement the ME movement for the entire North Vietnam. As a result, more than 90 percent of the adult population in North Vietnam were reported to be literate by the end of 1958 (Ministry of Education, 1995).<sup>10</sup> The ME movement in South Vietnam during the Indochina War, however, was not as strong as in North Vietnam (which is supported by our empirical estimation shown in a later section).<sup>11</sup> Consequently, the Indochina War offers a unique natural experiment for us to study the impacts of education access under two independent and diametrically different education systems.

We can employ two different identification approaches to evaluate the long-term impacts of the ME program. Our first identification approach relies on whether the (average) school-age child had access to school, which depends on whether their residence area was DRV-controlled or French-controlled. Since school-age children residing longer in DRV-controlled areas might also have had more school access, our second identification approach makes use of the differential duration of exposure to school access that children had. Figure 1 plots the number of years that each province in Vietnam was under the DRV’s control up to the end of 1954. The DRV completely controlled three provinces in North Vietnam, which are Thanh Hoa, Nghe An, and Ha Tinh

> 10 Since individuals can catch up on their education (i.e., going back to schools as adults), we would expect to capture the net impacts of the ME program only. We return to more discussion in a later section.

> 11 See, for example, the “History of Quang Ngai” (Provincial Government of Quang Ngai, 2015), a South Vietnamese province occupied by the DRV during the First Indochina War. In South Vietnam, for the entire Indochina War period, the DRV had complete control over three provinces: Quang Ngai, Binh Dinh, and Phu Yen, while the remaining provinces were under French control. South Vietnam followed a different political regime and a different model of education for the subsequent two decades following the end of the Indochina War (i.e., during 1954-1975). See, for example, Herring (2013) for a narrative on the history of Vietnam, and Elliott (2000) for a captivating biographical story of four generations of a Vietnamese family spanning this period.

10
households at the top 60 percent. An increase in unemployment and a reduction of social government programs may hit poorer households harder, given their more intense dependency on transfers and higher risk of becoming unemployed (or lower employability).

_Figure 4: Unequal Distribution of Employment and Assets, such as human capital, and of consumption of food across the Russian Population, 2011_ <!-- Start of picture text --> 0.6 0.6<br>Total Occupation<br>0.5 0.5<br>Share of food in total<br>0.4 consumption (right axis) 0.4<br>Unskilled in<br>0.3 Non Agri 0.3<br>Skilled in No Agri<br>0.2 0.2<br>0.1 0.1<br>Skilled in Agri<br>Unskilled in Agri<br>0 0<br>0 10 20 30 40 50 60 70 80 90<br>Population percentiles<br>Share of food<br>Percent share of total population<br><!-- End of picture text --> Source: HBS data (2011) To get some insights on the implications of these assumption we carried out two additional simulations. In the first one we assume that all those who lose their jobs in the contracting sectors – up to 3.1 percent of total employment – become unemployed. In the second one we analyze the implication of a cut in government transfers.

Abstracting from multiplier effects,<sup>19</sup> what would be the distributional impact of the increase in unemployment? The first step of the simulation was to identify those who are more likely to become unemployed.<sup>20</sup> Not surprisingly, the results of the econometric specification show that less qualified,

> 19 Clearly, if employment goes down (or equivalently if unemployment increases), GDP will also go down and that would trigger a reduction of incomes, then consumption will decrease with another round of reductions. This negative multiplier effect is not accounted here.

> 20 Specifically, workers more likely to be unemployed are selected through a multinomial probit estimation that computes the probability of either being employed in tradable sectors, employed in nontradable sectors, or being unemployed according to individual and household characteristics. These characteristics include gender, education level, age, marital status, household size, living in urban or rural areas, and headship.

19
# **1.1 General Framework**

This study is distinguished from other research on developing issues by the integration of an intertemporal and forward-looking behavior in investment and consumption decisiona in a computable general equilibrium (CGE) kramew3rk app!"table to developing countries. It formulates and uses an infinite-horizon growth model to examine the adjustment, growth and debt problems of a middle-income country using Philippine data for illustrations. This growth model is maltisectoral and intertemporal. It combines the intersectoral efficiency in the allocation of resources resulting from market dearing and endogenous prices of a general equilibrium model with the intertemporal efficiency generated from the dynamic optimization of the firm and the utility of an aggregate consumer.

This quantitative framework allows us to explore several issues about the growth and investment behavior of a middle-income country in a way not possible in models without expectations. While there is growing number of CGE applications to developing issues, for example, Devarajan and Sierra's examination of Thailand (December 1986) and Lewis' study of Turkey (July 1986), these CGE models are static in nature and are ill-suited for analyzing the dynamic processes of growth. I In particular, the effects of external shocks and adjustment policies on investment and growth are inherently dynamic -- an import price shock or a tariff reform changes the expectation of profits permanently and hence the level and pattern of investment. In these previous CGE studies, an economy's movement in time is described by a series of static equilibria strung together by updates in the levels of factor endowments. These updates axe _ad hoc_ and exogenous to the tatonnement. In relation aThe strengths and weaknesses of economy-wide models in analyzing development issues are discused in Beil and Suinivaaan (1984).

2
Table 6. Marginal Contributions to the Change in National Moderate Poverty Head Count Ratio

||Bangladesh,|2000-10|Peru,200|4-2010|Thailand,2|000-2009|
|---|---|---|---|---|---|---|
||Percent|Share of|Percent|Share of|Percent|Share of|
|Contribution to national moderate poverty|point|total|point|total|point|total|
|reduction on account of changes in:|change|change|change|change|change|change|
|**Non-Farm labor income**|**-4.56**|**26%**|**-9.35**|**58%**|**-3.46**|**27%**|
|Returns to characteristics|-3.52|20%|-4.93|31%|-1.25|10%|
|Occupational-choice|-1.61|9%|-3.44|21%|0.08|-1%|
|Economic Sector|-0.48|3%|-0.08|1%|-1.01|8%|
|Education|-0.55|3%|-0.25|2%|-1.34|10%|
|Unobservables factors|1.59|-9%|-0.65|4%|0.06|0%|
|**Farm income**|**-6.02**|**35%**|**-2.74**|**17%**|**-4.91**|**38%**|
|Returns to characteristics|-6.98|40%|-2.04|13%|-4.83|38%|
|Occupational-choice (secondary occupation)|0.56|-3%|-0.25|2%|1.31|-10%|
|Economic Sector 1/|||-0.14|1%|-1.11|9%|
|Education|0.13|-1%|-0.08|1%|-0.56|4%|
|Unobservables factors|0.26|-2%|-0.23|1%|0.28|-2%|
|**Non-labor income**|**1.05**|**-6%**|**-2.28**|**14%**|**-5.80**|**45%**|
|Private|1.05|-6%|-0.85|5%|-3.30|26%|
|Private Donations|||-0.70|4%|||
|International transfers|-1.94|11%|0.19|-1%|-2.19|17%|
|Domestic transfers|1.10|-6%|0.24|-1%|||
|Other transfers|0.58|-3%|0.01|0%|-1.12|9%|
|Capital|1.31|-8%|-0.58|4%|0.02|0%|
|Public|0.00|0%|-1.38|9%|-2.51|20%|
|Public Donations|||-0.45|3%|||
|Public transfers|||-0.90|6%|||
|Pensions|||0.01|0%|-2.51|20%|
|Other non-labor income|||-0.04|0%|||
|**Other**|**-7.50**|**43%**|**-0.78**|**5%**|**2.50**|**-20%**|
|Age/gender|-3.48|20%|-1.17|7%|-1.18|9%|
|Consumption to income ratio|0.93|-5%|-1.73|11%|3.43|-27%|
|Unexplained|-4.95|29%|2.12|-13%|0.26|-2%|
|**Total**|**-17.34**|**100%**|**-16.13**|**100%**|**-12.84**|**100%**|

- Source: Own estimates based on Peru's ENAHO 2004 -2010, Thailand's SES 2000 -2009, and Bangladesh's HIES 2000 -2010.

- 1/ Refers to the secondary occupation of individuals who work as self-employed agricultural workers.

33
Project-Financed PHN Research Table 1. Projects with Research Components by Region and Category, FY'80-FY'91 (Percent) -

|Research<br>Category|Africa|East<br>Asia and<br>Pacific|South<br>Asia|MENA'|LAC|Bank-<br>wide|
|---|---|---|---|---|---|---|
|Medical<br>Demographic/|13|15|13|0|21|13|
|Epidemiolog-<br>ical|43|54|47|60|47|48|
|Human|||||||
|Resources|28|31|47|20|32|30|
|Managerial|74|54|93|53|74|72|
|Financial|_55_|38|27|40|53|47|
|IEC|17|8|40|7|_5_|16|

# a. Includes Turkey.

# _Other 7ypes of Research_

# _Research for Follow-On Projects_

The range of research proposed in the SARs Bank-wide, 21 percent of the SARs included reviewed was extremely broad, even within the some explicit provision for research intended to tabulated categories. In population projects, it was facilitate preparation of a follow-on project common to find provision for family planning (Figure 2). The proportion in Africa was 28 knowledge, attitude, and practice (KAP) research. percent, in East Asia and Pacific 23 percent, in Population projects also included research on South Asia 20 percent, in MENA 7 percent, and materna mortality and on contraceptive use (classified as demographic/epidemiological research). Research into drug prescription practices, distribution, or formulation figured in several projects. 100Pat ...... Structural adjustment loans with PHN activities often included research to look at the social dimens ...... .. sions of adjustment. Nutrition projects, not surprisingly, generally included nutrition-related o ... research, especially the extent of malnutrition (classified as demographic/epidemiological re. ................... ....... search). The importance of malaria in the Amau zon basin led to inclusion of entomological re20 ........_.............. **te.** search in the Brazil Amazon Basin Malaria Control Project. **O MA. At** t MC Book-l Bank-wide, 21 percent of the SARs included some explicit provision for research intended to facilitate preparation of a follow-on project percent, in East Asia and Pacific 23 percent, in South Asia 20 percent, in MENA 7 percent, and Figure 2. Proportion of projects with follow-on project research components by region and Bank-wide.
**<u>Table 8: Farm Size Productivity Relationship: Variation by Farm Size</u>**

||**Yi**|**eld**|**Net profit**<br>**wag**|**(shadow**<br>**es)**|**Net profi**<br>**wag**|**t (village**<br>**es)**|
|---|---|---|---|---|---|---|
||**Holding**|**Plot**|**Holding**|**Plot**|**Holding**|**Plot**|
|Cropped area in ha, (α1)|-0.501***|-0.485***|-0.097***|-0.152***|-0.008|-0.025|
||(11.512)|(15.269)|(3.392)|(4.201)|(0.248)|(0.620)|
|Cropped area in ha * first tercile, (γ1)|0.118***|0.019|-0.050*|-0.007|-0.002|0.025|
||(2.983)|(0.841)|(1.945)|(0.262)|(0.056)|(0.872)|
|Cropped area in ha * second tercile, (γ2)|0.065|0.043**|-0.029|0.023|-0.016|-0.009|
||(1.635)|(2.171)|(1.120)|(1.027)|(0.542)|(0.376)|
|Village fixed effects|Yes||Yes||Yes||
|Household fixed effects||Yes||Yes||Yes|
|**Tests:**|||||||
|α1+ γ1= 0|648.14***|982.65***|222.11***|87.86***|0.75|0|
|α1+γ2= 0|315.54***|457.28***|60.81***|29.94***|1.72|1.72|
|No of observations|3,060|7,308|3,058|7,275|3,060|7,308|
|R-squared|0.335|0.362|0.167|0.077|0.034|0.036|

Note: Coefficients are from the specifications in columns (3) and (7) above for holding and plot levels, respectively with only the relevant coefficients reported. Absolute value of t-statistics in parenthesis: *** significant at 1%; ** significant at 5%; * significant at 1%.

24