rafmacalaba/data-use-mentions-extended 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<
# **3 Home-based work across countries**

Our first exercise is to calculate the share of jobs that can feasibly be performed at home for all the 107 countries for which data are available in the ILO database. On average, 23.9% of all jobs can be done from home based on the standard DN2020 measure. However, once we account for internet access, this share drops to 18.7%. Put differently, failing to account for internet access would cause us, on average, to overestimate the share of jobs that can be performed from home by almost 30 percent.

Measures of the feasibility of home-based work that do not consider internet access are thus upwards biased, and this bias is especially large in low income countries. Figure 1a plots both the DN2020 and our modified home-based work (HBW) measure against GDP per capita. The distance between the two fitted lines is a measure of the magnitude of the bias associated with ignoring ICT and internet access constraints. The bias is largest in the poorest countries. For example, 5.5% of all jobs in Ethiopia can be performed from home according to the DN2020 measure, while accounting for internet access reduces the prevalence to 2.1%. Even more strikingly, in Nepal the number of jobs that can be performed from home drops from 14.7% to 6.3% once internet constraints are accounted for. By contrast, in rich countries such as Switzerland, Sweden, the United Kingdom, the Netherlands, and Luxembourg—where the DN2020 measure would be between 40 and 55 percent—internet access constraints hardly matter.<sup>6</sup> How large is this bias in different parts of the global income distribution? Figure 1a does not provide a direct answer since it does not consider country size. Figure 1b presents the share of telecommutable jobs by level of income of the countries, weighted by the size of their employed population and separating between types of home-based work. The portion of jobs requiring
Amin (2020). “The Labor Productivity Gap between Formal Businesses Run by Women and Men.” _Feminist Economics_ 26(4): 228—258.

<mark>Islam, Asif, Silvia Muzi, and Mohammad Amin (2019). “Unequal Laws and the Disempowerment of Women in the Labour Market: Evidence from Firm-Level Data.”</mark> _<mark>The Journal of Development Studies</mark>_ <mark>55(5): 822-844. DOI: 10.1080/00220388.2018.1487055</mark> Jayachandran, Seema, and Ilyana Kuziemko (2011). “Why do mothers breastfeed girls less than boys? Evidence and implications for child health in India.” _Quarterly Journal of Economics_ 26(3): 1485–1538.

Jayachandran, Seema and Rohini Pande (2017). “Why are Indian children so short? The role of birth order and son preference.” _American Economic Review_ 107 (9): 2600–2629.

<mark>Klasen, S. (2020). “From ‘MeToo’ to Boko Haram: A survey of levels and trends of gender inequality in the World.”</mark> _<mark>World Development</mark>_ <mark>128: 1-10.</mark> <mark>Klasen, Stephan (2019). What explains uneven female labor force participation levels and trends in developing countries?</mark> _<mark>The World Bank Research Observer</mark>_ <mark>34(2): 161–197.</mark> <mark>Klasen, Stephan, TU THI NGOC Le, Janneke Pieters, and Manuel Santos Silva (2021). What drives female labour force participation? Comparable micro-level evidence from eight developing and emerging economies.</mark> _<mark>The Journal of Development Studies</mark>_ <mark>57(3): 417-442.</mark>
relatively stable source of credit. They also analyze the impact of home-country government support for the parent banks on their lending activities in emerging Europe because this support might have come with requirements to focus on lending in their home markets. However, they do not find any impact of these support packages on lending of foreign bank subsidiaries in emerging Europe.

Finally, they study the impact of the so-called Vienna Initiative (VI), a coordination platform for multinational banks, International Financial Institutions, European Institutions as well as home and host country regulatory and fiscal authorities. During this process, multinational banks signed commitments to keep their subsidiaries capitalized in five countries. While this decreased fears of foreign bank withdrawal from these countries, it was not clear whether banks would not instead withdraw funds from other countries to keep up their exposure in the VI countries. They find that lending of foreign banks participating in the VI was somewhat more stable than lending of non-VI foreign banks. Moreover, their results do not hint at negative spillover effects since they do not find VI banks to withdraw from non-VI countries.<sup>14</sup>

> 13 Mihaljek (2009) finds similar results also using country-level data from the BIS.

> 14 Cetorelli and Goldberg (2011) show in a country-level study that in VI countries the contraction in domestic bank lending was attenuated.

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assemble a balanced panel data set on annual gross inflows and outflows, drawing from the International Monetary Fund’s Balance of Payments Statistics (BoP). The panel comprises 85 countries<sup>10</sup> and spans the years 1979-2015. We further group the sample countries into three categories: 19 advanced countries, 28 emerging countries, and 38 developing countries, as shown in Table A.1.

> 10We start by constructing a balanced panel comprising all the countries with complete data from 1979 to 2015. This yields a total of 98 countries. We exclude from this sample 13 very small countries with population fewer than 500,000 in 2005.

9
10.34|10.25|||||
|**_ES16_**|12.23|12.49|||||

Notes: (1) The data used are CFPS 2016 with children aged 18-30. (2) IGMEK is Intergenerational Marginal effect when father has K years of schooling. (3) ESK is expected schooling when father has K years of schooling. K=0,6,9,16. (4) Robust standard errors in parentheses; standard errors clustered at province-level in square brackets; *** p<0.01, ** p<0.05, * p<0.1.
Even though it seems obvious that unsafe water and poor sanitation increase both morbiditiy <u>and</u> mortality associated with waterborne diseases, relatively few studies have directly focused on child mortality as a primary outcome of interest. Those that have analyzed the impact of water and sanitation infrastructure on child mortality generally combine national burden of disease accounts with epidemiological estimates of the impact of water and sanitation on morbidity to compute (disability-adjusted) life-years lost due to inappropriate water and sanitation infrastructure (Clasen et al., 2007; Pruess et al., 2002; Pruess-Uestuen et al., 2008). In the most recent study in this area, Pruess-Uestuen and coauthors (2008) estimate that about 3.5 million (child and adult) deaths could be prevented worldwide per year with investments in water, sanitation and hygiene. The studies that have undertaken cost-benefit analyses of improved water and sanitation infrastructure<sup>2</sup> have mostly focused on the benefits from avoided illness cost (and time savings), and only took these indirect estimates of deaths prevented into account (e.g. Hutton and Haller, 2004; Hutton, Haller, and Bartram, 2007; Rijsberman and Zwane, 2008; Whittington et. al, 2008).<sup>3</sup> While diarrhea-focused studies allow some inference about the mortality effects of water and sanitation, the derived links are indirect, and unlikely to reveal the full mortality benefits of water and sanitation infrastructure investment. An extensive literature suggests that the effects of improved water and sanitation on child mortality go beyond their direct diarrheal effect. By lowering the exposure to fecally-transmitted diseases, access to improved water and sanitation does not only lower diarrhea incidence but also considerably lowers the risk of malnutrition (Pruess-Uestuen et al., 2008) as well as the risk of severe infection with other (not fecallytransmitted) diseases, enhancing the chances of survival for protected children (Caulfield et al, 2004; Cutler and Miller, 2005; Ewbank and Preston, 1990;
might both be poorly capturing food access and instead the FIES might be correctly identifying the population with poor food access. However, this possibility does not seem likely. Specifically, we perform a common check on the validity of monetary poverty and corroborate that the share of expenditure devoted to food, itself a measure of food access, is declining for higher expenditure deciles in the household survey (Engel’s Law); and we verify using sub-national food access estimates from other data sources that the regions with the poorest food access are correctly identified by the FCS and monetary poverty and incorrectly identified by the FIES. Furthermore, other research using the same data used in this analysis has extensively validated the quality of the household survey used in the analysis (e.g., Lain and Vishwanath 2021).<sup>6</sup> Combined, the evidence suggests that the FIES is not working well in the particularly important context of Nigeria. However, we further find that although the overall scale is poorly aligned with monetary poverty and the FCS, the alignment varies by the specific coping strategies and behaviors that make up the scale. First, some of the more subjective questions on food access had significant deviations from the predicted relationship between food access and poverty, which is consistent with previous work illustrating difficulties in interpreting subjective welfare questions (e.g., Ravallion 2013; Tandon 2021; etc.). And second, for the more objective questions, some of the questions aligned well with monetary poverty, while others performed less well. This pattern is consistent with the validation of other indices based on food coping strategies (e.g., the Coping Strategy Index), where many coping strategies are not necessarily universal and it is important to adapt the coping strategies that are captured to specific country contexts (e.g., Maxwell and Caldwell 2008).

Overall, one of the key implications of these findings is that it is important to continue to collect a variety of food access indicators and that
friendly manner. While WELCOM is not meant to be a substitute for more detailed empirical analyses, it is useful for informing policy dialogue. WELCOM aims to simplify the estimation of reform scenarios and become the first building block of a broader project by focusing on the price channel and using data sources that are readily available in developing countries.

The results in the Mexican case study included in this paper could also be a useful illustration to inform policy makers and academics on the potential benefits of reducing concentration in key markets through competition-enhancing policies. The hypothetical case study shows that market concentration in key sectors of the Mexican economy has adverse effects on welfare, especially among poor and vulnerable households. Moving toward competitive markets is among the main objectives of the Mexican government, and this requires removing market imperfections and economic distortions to enhance economic performance. WELCOM illustrates the likely poverty and inequality impacts of reducing concentration in the corn and mobile telecommunication markets. The combined effect of expanding competition from 4 to 12 firms in the mobile telecommunications industry and reducing the market share of the dominant oligopoly in corn products from 31.2 percent to 7.8 percent would achieve an effect on poverty reduction of 0.8 percentage points and a drop in the Gini of 0.32 points.

Although this novel simulation method is not free of limitations, including that it does not incorporate into the analysis other relevant welfare-enhancing channels that directly affect consumers, such as the quality and diversity of products, and new consumers that were previously priced out, WELCOM yields reasonable results using readably available household survey data and minimum information on market structure parameters. The value added of WELCOM is thus its ability to estimate, in a timeand cost-efficient manner, the potential distributional effects of variations in product market competition, linking these changes directly to poverty and inequality. Forthcoming extensions of this simulation approach include modules to estimate price and income elasticities empirically, based on household survey data, and exploring additional extensions to include labor market impacts.

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Table 9: Out-of-sample Accuracy of HIES-based Village Population Density Estimates using Public and Proprietary satellite data, 55 Sub-Districts

||Popul|ation D|ensity|Popu|lation|Count|||
|---|---|---|---|---|---|---|---|---|
||_R_<sup>2</sup>|SRC|Mean<br>AE|_R_<sup>2</sup>|SRC|Mean<br>AE|Mean<br>RE|Median<br>RE|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|_A.Model estimates, Public & Propr_|_ietary _|_satellit_|_e image_|_ry_|||||
|HIES-based|0.79|0.91|664|0.60|0.67|607|37%|28%|
|HIES-based (balance correction)|0.78|0.92|665|0.58|0.67|607|35%|29%|
|Census-based|0.81|0.91|679|0.60|0.65|644|50%|28%|
|_B.Model estimates, public satellite _|_image_|_ry_|||||||
|HIES-based|0.75|0.92|663|0.56|0.69|595|34%|28%|
|HIES-based (balance correction)|0.77|0.92|657|0.55|0.69|606|34%|28%|
|Census-based|0.76|0.91|750|0.56|0.63|739|58%|36%|
part of the IV’s construction. Finally, Column 9 shows the results strengthen slightly when using the 1990 city distribution of ancestry stocks for each country. The ancestry variable in the 1990 census is comparable to that in the 1980 census, albeit with slightly less detail. While it is better to focus on the 1980 distributions given that they are fully pre-determined for the sample period, this stability to using 1990 distributions is quite important given the substantial filed over the prior three years. This fixed window uses application years for patents and their citations. A threeyear window focuses on recent knowledge development; a fixed window is also important for having a uniform approach over the sample period, as the ethnicity of inventors can only be determined on patents granted after 1975 when the inventor records become digitized. From this citation set, the procedure next estimates the USbased inventor share of cited work that is not of Anglo-Saxon ethnicity. This is a share-based measure given that patents cite multiple prior patents and most patents have multi-inventor teams. The additional weighting applied to patents is one minus this share. This creates effectively a double weight that multiplies the share of the patent’s own inventor US-based team that is of Anglo-Saxon origin with the added weight based upon the patent’s citations.

12
wages -- as is evident from the coefficients in the estimated wage equation in Table 6 -- which in turn has an additional depressant effect on labor supply.

# **Discussion**

This paper is a study of the impact of OSD on the economic productivity of plantation workers at the Teppi coffee plantation in southwest Ethiopia. Analysis of data from plantation records indicates that the economic impact of OSD is a function of various factors including type of employment, age, and gender. In our sample, relatively older (35+), permanent, male employees have the biggest OSD-related loss in economic productivity in terms of diminished earnings and an adversely-impacted labor supply. In more general terms, OSD causes diminished daily earnings to the order of 10-15%. In other subgroups, such as temporary workers, OSD appears not to have a discernible impact on productivity. This may be due to several factors including confounding effects of alternate income sources, plantation demand driven employment, and small sample size.

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**Table** 2: **Coverage of the Food Stamp Program by Eligibility Category**

||**(Percent)**||||
|---|---|---|---|---|
|_Category_|_1989_|_1990_|_1991_|_1992_|
|Households with children <5 years|20|16.6|26.2|27.3|
|Households with<br>pregnant and|||||
|lactating mothers|n.a.|0.5|7.2|2.5|
|Households with elderly|18.6|22.3|30.1|42.7|

_Source:_ Jamaica Survey of Living Conditions Report, various years -November 1989, 1990, 1991, 1992. Planning Institute of Jamaica.

**Table 3: Percentage of Households in Poorest and Richest Quintiles Receiving Food Stamp Transfers by Eligibility Category**

||_19_|_89_|_19_|_90_|_19_|_91_|
|---|---|---|---|---|---|---|
|_Category_|_Poorest_<br>_Quintile_|_Richest_<br>_Quintile_|_Poorest_<br>_Quintile_|_Richest_<br>_Quintile_|_Poorest_<br>_Quintile_|_Richest_<br>_Quintile_|
|Households with children|<br>34.6|5.3|||||
|<5 years|||||||
|Households with elderly|38.5|5.5|||||
|All Households|31.2|2.2|12.9|1.4|38.3|2.2|

_Source:_ Jamaica Survey of Living Conditions, November 1989, 1990, 1991.

In 1993, total costs of the program amounted to roughly 0.1 1 percent of GDP.
0.288***|0.296***|0.300**|0.102**|0.070|-0.024|
||(0.134)|(0.067)|(0.049)|(0.072)|(0.123)|(0.051)|(0.051)|(0.044)|
|Hazard ratio|0.008|0.140|-0.035|0.549***|0.136|0.054|0.594***|0.100|
||(0.131)|(0.124)|(0.102)|(0.158)|(0.297)|(0.109)|(0.131)|(0.091)|
|Constant|11.037***|10.017***|10.708***|9.540***|9.639***|10.076***|9.315***|10.335***|
||(0.514)|(0.308)|(0.248)|(0.349)|(0.717)|(0.287)|(0.337)|(0.233)|
|Observations|4,723|3,448|8,358|2,553|1,402|4,488|3,276|3,520|

_Source:_ Calculations based on the Cambodian Socio-Economic Surveys 1996, 1999, 2004, 2007, 2008, 2009, 2010, and 2011.

_Note:_ Standard errors are in parentheses. Standard errors are clustered at the industry level. Statistically significant coefficients at the 10, 5, and 1 percent level are indicated by one
and whether anyone in the households reportedly goes for open defecation. Impact estimates are shown in Table 6. We find that for both indicators, estimated coefficients are closely in line with the impact estimates on toilet uptake, implying that constructed toilets tend to be used by households and hence lead to an accompanied drop in open defecation.

**Sanitation uptake and subsidy uptake in the SBM 2016 snapshot** Finally, in Table 4 we verify the robustness of our results to a different, independently collected measure of toilet uptake – household toilet ownership as recorded by Government officials in the SBM September 2016 snapshot. In addition, we evaluate impacts over time and assess impacts on subsidy uptake. Results on toilet ownership as recorded in the SBM snapshot are presented in columns 3-4 of Table 4. To compare these results to our survey data, in columns 1-2 we present impacts on toilet ownership in September 2016, re-constructed based on the age of toilet construction reported during endline survey. Comparing column 1 to column 3 and column 2 to column 4, we find that results are strikingly similar between the two independently collected datasets.

Interestingly, comparing the longer term impact results in column 3 of Table 4 to the shorter term impact results in column 1 of Table 4 suggests that 84% of the impact on toilet uptake was achieved over the first 19 months of the intervention (February 2015 - September 2016). The more modest additional increase in toilet construction in treatment areas relative to control areas in 2017 could be due to a drought that took place in our study area and to demonetisation in November - December 2016, which greatly reduced MFI lending in the study areas.

Turning to columns 5-6 in Table 4, we look at impacts on subsidy uptake. Interestingly, for subsidy 15
- Potential labor supply is estimated as the population-weighted aggregate of predicted values of age- and gender-specific labor force participation rates from regressions on policy outcomes and cohort characteristics, business cycles, and country effects.

- Potential TFP growth is estimated as the predicted value of a parsimonious panel regression of five-year averages of trend TFP growth on lagged per capita income relative to the advanced-economy average (to proxy convergence-related productivity catchup), education and demographic indicators, and trend investment.

These production function estimates are also contextualized with estimates derived by several other techniques, including univariate and multivariate filters, and long-term growth forecasts from Consensus Economics, and the IMF’s World Economic Outlook database. More detail regarding these estimation approaches and their relative pros and cons can also be found in Kilic Celik, Kose, Ohnsorge, and Ruch (2023).

In addition to analyzing the recent history and prospects for potential growth in EMDEs, the paper estimates the impacts of several illustrative policy reform scenarios, and of more frequent climate-related disasters, on potential growth. The parameters of these scenarios are summarized below, with more detail on their construction available in Kilic Celik, Kose, and Ohnsorge (2023).

An upside scenario is presented wherein investment growth in each economy is assumed to rise over the course of 2022-30 by the most that it increased in any ten-year period during 2000-21. Educational outcome indicators—secondary and tertiary enrollment and completion rates—are also assumed to rise in each country by the largest improvement seen in any ten-year period during 2000-21, except that enrollment rates are capped at 100 percent and completion rates are capped at the highest levels observed in advanced economies in 2019, the latest available data point. Life expectancy is assumed to rise in each country by the largest increase in any ten-year period during 2000-21, but not above the median advanced-economy life expectancy in 2019. In addition, for each age group in each country, the female labor force participation rate is assumed to rise by the largest increase over any ten-year period during 2000-2021, but not to