rafmacalaba/datause-encoder-data huggingface dataset:
**Table 6: Share of Overall  Poverty in the Economy and  Deviation of Counterfactual Cases from Baseline (In Percent)**

|Share|||||Deviation from Ba|seline||||
|---|---|---|---|---|---|---|---|---|---|
|Year|Baseline|Sales tax|Corporate tax|Tariff|Year|Baseline|Sales tax|Corporate tax|Tariff|
|2013|29.5|29.4|30.0|29.4|2013|0.0|-0.3|1.6|-0.3|
|2014|33.3|33.5|34.2|33.3|2014|0.0|0.7|2.8|0.0|
|2015|40.8|40.8|42.5|40.8|2015|0.0|0.1|4.3|0.2|
|2016|39.3|39.0|40.1|39.0|2016|0.0|-0.9|1.8|-0.8|
|2017|32.3|32.3|33.4|32.5|2017|0.0|-0.2|3.4|0.7|
|2018|27.9|28.4|28.1|28.0|2018|0.0|1.7|0.4|0.4|
|2019|26.9|26.6|26.9|27.0|2019|0.0|-1.2|0.0|0.3|
|2020|19.6|18.4|17.5|18.6|2020|0.0|-6.4|-10.8|-5.3|
|Average|31.2|31.0|31.6|31.1|Average|0.0|-0.5|1.2|-0.4|

**Table 7:  Inequality as Measured by the Gini Coefficient**

|Gini Coeffic|ient||||
|---|---|---|---|---|
|Year|Baseline|Sales Tax|Corp Tax|Tariff|
|2013|29.73|29.73|29.84|29.72|
|2014|30.27|30.30|30.38|30.27|
|2015|30.52|30.53|30.72|30.52|
|2016|30.25|30.22|30.33|30.22|
|2017|29.63|29.61|29.68|29.64|
|2018|29.18|29.20|29.13|29.19|
|2019|29.53|29.48|29.43|29.52|
|2020|28.81|28.69|28.51|28.72|
|Average|29.73|29.73|29.84|29.72|

|Change<br>baseline|relative<br>(percent)|to<br>|||
|---|---|---|---|---|
|Year|Baseline|Sales tax|Corp tax|Tariff|
|2013|0.0%|0.0%|0.3%|-0.1%|
|2014|0.0%|0.1%|0.3%|0.0%|
|2015|0.0%|0.0%|0.7%|0.0%|
|2016|0.0%|-0.1%|0.2%|-0.1%|
|2017|0.0%|-0.1%|0.2%|0.0%|
|2018|0.0%|0.1%|-0.2%|0.0%|
|2019|0.0%|-0.2%|-0.3%|0.0%|
|2020|0.0%|-0.4%|-1.0%|-0.3%|
|Average|0.0%|0.0%|0.3%|-0.1%|

39
|P105897|2011|2014|12/31/2013|7 Closed|Papua New Guinea|
|---|---|---|---|---|---|
|P105897|2011|2014|6/29/2014|8 Closed|Papua New Guinea|
|P105897|2011|2015|12/11/2014|9 Closed|Papua New Guinea|
|P105897|2011|2015|2/22/2015|10 Closed|Papua New Guinea|
|P105897|2011|2016|7/24/2015|11 Closed|Papua New Guinea|
|P105897|2011|2016|12/29/2015|12 Closed|Papua New Guinea|
|P105910|2008|2008||0 Closed|Cameroon|
|P105910|2008|2009||0 Closed|Cameroon|
|P105910|2008|2010||0 Closed|Cameroon|
|P105910|2008|2011||0 Closed|Cameroon|
|P105910|2008|2020|9/4/2019|1 Closed|Cameroon|
|P105956|2007|2009|8/5/2008|1 Closed|Bhutan|
|P105956|2007|2010|8/11/2009|2 Closed|Bhutan|
|P105956|2007|2010|6/12/2010|3 Closed|Bhutan|
|P105958|2009|2013|12/23/2012|7 Closed|China|
|P105958|2009|2014|10/26/2013|8 Closed|China|
|P105990|2010|2010||0 Closed|India|
|P105990|2010|2011|5/7/2011|1 Closed|India|
|P105990|2010|2012|12/18/2011|2 Closed|India|
|P105990|2010|2012|5/31/2012|3 Closed|India|
|P105990|2010|2013|12/27/2012|4 Closed|India|
|P105990|2010|2013|6/12/2013|5 Closed|India|
|P105990|2010|2014|11/19/2013|6 Closed|India|
|P105990|2010|2014|5/3/2014|7 Closed|India|
|P105990|2010|2015|10/4/2014|8 Closed|India|
|P105990|2010|2015|6/3/2015|9 Closed|India|
|P105990|2010|2016|12/21/2015|10 Closed|India|
|P105990|2010|2016|6/30/2016|11 Closed|India|
|P106013|2015|2016|11/17/2015|1 Closed|Colombia|
|P106013|2015|2018|7/25/2017|2 Closed|Colombia|
|P106017|2008|2008||0 Closed|Yemen,Republic of|
|P106017|2008|2009||0 Closed|Yemen,Republic of|
|P106017|2008|2010||0 Closed|Yemen,Republic of|
|P106017|2008|2011||0 Closed|Yemen,Republic of|
|P106017|2008|2012||0 Closed|Yemen,Republic of|
|P106017|2008|2013||0 Closed|Yemen,Republic of|
|P106038|2008|2008|6/2/2008|1 Closed|Brazil|
|P106038|2008|2009|11/25/2008|2 Closed|Brazil|
|P106038|2008|2009|4/8/2009|3 Closed|Brazil|
|P106038|2008|2010|11/10/2009|4 Closed|Brazil|
|P106038|2008|2010|4/22/2010|5 Closed|Brazil|
|P106038|2008|2011|12/11/2010|6 Closed|Brazil|
|P106038|2008|2011|6/29/2011|7 Closed|Brazil|
|P106038|2008|2012|1/11/2012|8 Closed|Brazil|
|P106038|2008|2013|8/14/2012|9 Closed|Brazil|
|P106038|2008|2013|5/15/2013|10 Closed|Brazil|
|P106038|2008|2014|12/16/2013|11 Closed|Brazil|
|(8)<br>Agriculture<br>0.011<br>(0.014)<br>0.067**<br>(0.027)<br>518,368<br>NO<br>YES<br>3.509<br>0.0611<br>t region, the<br>regional time|
|---|
|(7)<br>Sales<br>-0.004<br>(0.009)<br>-0.022<br>(0.015)<br>518,368<br>NO<br>YES<br>1.112<br>0.292<br>_level_.<br> clustered a<br>ed efects,|
|(6)<br>Service<br>0.012**<br>(0.005)<br>-0.000<br>(0.005)<br>518,368<br>NO<br>YES<br>2.769<br>0.0961<br>_DHS cluster _<br>andard errors<br>nd region fx<br> p<0.1|
|(5)<br>Working<br>0.035***<br>(0.010)<br>0.073***<br>(0.020)<br>518,368<br>NO<br>YES<br>3.038<br>0.0814<br>_ne, as well as _<br>l B: robust st<br>trol for year a<br>, ** p<0.05, *|
|(4)<br>Agriculture<br>-0.001<br>(0.013)<br>0.069***<br>(0.025)<br>478,288<br>YES<br>NO<br>6.609<br>0.0102<br> _and closest mi_<br>-0.009<br>(0.020)<br>(0.018)<br>[0.018]<br>0.065**<br>(0.022)<br>(0.032)<br>[0.032]<br>518,368<br>0.0128<br>0.0205<br>0.0198<br>rentheses. Pane<br> regressions con<br>efs. *** p<0.01|
|(3)<br>Sales<br>-0.002<br>(0.008)<br>-0.017<br>(0.016)<br>478,288<br>YES<br>NO<br>0.829<br>0.362<br> _closest mine,_<br>0.001<br>(0.012)<br>(0.012)<br>[0.012]<br>-0.014<br>(0.025)<br>(0.026)<br>[0.026]<br>518,368<br>0.481<br>0.518<br>0.518<br>er level in pa<br>rackets. All <br> religious beli|
|(2)<br>Service<br>0.019***<br>(0.005)<br>0.001<br>(0.005)<br>478,288<br>YES<br>NO<br>6.399<br>0.0114<br> _regional, the _<br>0.020**<br>(0.010)<br>(0.008)<br>[0.008]<br>0.000<br>(0.007)<br>(0.006)<br>[0.006]<br>518,368<br>0.0618<br>0.0359<br>0.0350<br>the DHS clust<br>ster level in b<br>ducation, and|
|(1)<br>VARIABLES<br>Working<br>_Panel A. Mineral and mine fxed efects._<br>active (20 km)<br>0.029***<br>(0.010)<br>inactive (20 km)<br>0.081***<br>(0.020)<br>Observations<br>478,288<br>Mineral FE<br>YES<br>Mine FE<br>NO<br>F test: active-inactive=0<br>5.627<br>p value<br>0.0177<br>_Panel B. Clustering of standard errors at the_<br>active (20 km)<br>0.026<br>(0.016)<br>(0.015)<br>[0.015]<br>inactive (20 km)<br>0.081**<br>(0.033)<br>(0.034)<br>[0.034]<br>Observations<br>518,368<br>p value region<br>0.0923<br>p value (mine level)<br>0.0973<br>p value [mine and DHS cluster]<br>0.0962<br>Panel A: robust standard errors clustered at <br>mine in parentheses and at the mine and clu<br>trends, living in an urban area, age, years of e|

37
Gamson, W. A., 1992. Social psychology of collective action, ch2 in _Frontiers in Social Movement Theory,_ (eds) Morris, A.D. and Mueller C. M., New Haven, Yale University Press Ganle, J.K., Afriyie, K. and Segbefia, A.Y., 2015. Microcredit: Empowerment and disempowerment of rural women in Ghana. _World Development_ , 66, pp.335-345.

Garikipati, S., 2008. The impact of lending to women on household vulnerability and women’s empowerment: evidence from India. _World Development_ , 36 (12), pp.26202642.

Graham, C., 2010. Adaptation amidst prosperity and adversity: Insights from happiness studies from around the world. _The World Bank Research Observer_ , 26 (1), pp.105137.

Gries, T. and Naudé, W., 2011. Entrepreneurship and human development: A capability approach. _Journal of Public Economics_ , 95 (3), pp.216-224.

Goodman, R., 2017. Borrowing Money, Exchanging Relationships: Making Microfinance Fit into Local Lives in Kumaon, India. _World Development_ , 93, pp.362373.

Greaney, B.P., Kaboski, J.P. and Van Leemput, E., 2016. Can Self-Help Groups Really Be “Self-Help”? _The Review of Economic Studies_ , 83 (4), pp.1614-1644.

27
some differences (of the order of one to two percentage points) in the 1984-5 and 1993-2000 periods. Armed with average marginal income tax rates for each country in the sample, we were then able to estimate the (wage-) income elasticity of tax revenues. An increase in income that is distributed equally among current wage earners would increase tax revenues by precisely the average marginal tax rate. The wage elasticity of tax revenues is therefore the average marginal tax rate divided by the average tax rate (which is tax revenues deriving from labor income taxes divided by wage income).? The first two columns of Table 1 show our estimates of the earnings elasticity of tax revenues for the countries in our sample in column A for income taxes and in column B for social security taxes. The first two columns of Table 3 compare our estimates with the OECD’s, for overlapping countries; they are virtually identical. VAT rates are flat, while corporate income taxes are flat in most countries, with some exceptions (such as Brazil). A single rate therefore summarizes both the average and marginal rates for these taxes. The elasticity of these taxes with respect to their respective tax bases is therefore 1. Figure 4 shows all three average marginal tax rates for the countries in the sample.® We then followed Giorno et al (1995) directly in using the elasticities of tax revenues with respect to specific tax bases to obtain an overall outputelasticity of tax revenues. Table 1 outlines the calculation of the output elasticity of tax revenues in detail. The employment elasticity of wages is shown in column D; the output elasticity of employment is presented in column E. As shown in detail in Giorno et al (1995) and Van den Noord (2000), these two elacticities can be combined with the earnings elasticities to obtain the output elasticity of income taxes. As the GDP identity, using income <u>~ 2'Phis measure varies, of course,</u> from year to year. We average this measure across years for each country. 3In Brazil and the United States we show the sales tax rather than the VAT. 7
sample it loses statistical significance.

# **4.1.2 Linear effects on height?**

So far, this paper has largely studied linear regression. A non-linear relationship between sanitation and height could be important for two reasons: first, fixed effects regression could be inconsistent; and second, a linear Blinder-Oaxaca decomposition could be inappropriate. Returning to figure 6, the relationship between sanitation and height appears approximately linear in the Indian data, but may not be among openly-defecating households in the African data.

Non-linearity can be tested by adding a quadratic term. Already, in table 6, a quadratic term was not statistically significant in the first two Indian DHS surveys. Panel A finds again that, in the Indian NFHS-3, there is no evidence for a quadratic term. In contrast, Panel B does find a quadratic term in the African sample. In light of the evidence for the importance of open defecation per square kilometer presented in section 2.2.3, one possible explanation for this negative quadratic term is that population density is relatively low in these African countries, so open defecation is not as important for health until there is more of it; unfortunately, geographic data such as population density is not generally available at the DHS PSU level.

Many sanitation policy-makers claim that there is a discontinuous increase in health when open defecation is fully eliminated. This belief was importantly popularized by Sanan and Moulik (2007), who assert that “public health outcomes can be achieved only when the entire community adopts improved sanitation behavior, the area is is 100 percent open defecation free, and excreta safely and hygienically confined” (4).<sup>22</sup> Whether the relationship between sanitation and health is linear or another shape, it is clear from figure 6 that there is no 22This conclusion was based on a study by an organization called Knowledge Links in three villages of Himachal Pradesh, which found a “prevalence of diarrhea” of 26 percent in a village where 95 percent of households used toilets, and of 7 percent in a village where 100 percent used toilets. Also see Shuval et al. (1981).

21
Our empirical results also indicate that the educational achievement of the mother matters in the enrollment of children. Female children are more likely to be enrolled than male children in primary schools, a result in line with recent UNICEF findings. Household size plays a major role in the enrollment rates of the 8- to 9-year-old age group but not the 6- to 7-year-old group, suggesting that children in larger households are less likely to attend school than those living in smaller households, probably because the resources are spread too thin or because children have to drop out to help at home. The impact of wealth on enrollment varied. Richer households were less likely to send their children to school in earlier years (6–7 years) and more likely to send their children to school in later years (8–9 years). This also may imply that poorer households are more likely to send their children to school in earlier years, but less likely to send their children to school in later years. Poorer households generally tend to have larger families and may not be able to afford to send children to school in later years.

Overall, providing more improved sanitation at the community level in Bangladesh could help increase enrollment in primary schools. Despite making progress toward the eradication of open defecation, Bangladesh still faces severe deficits in terms of the availability of improved sanitation. Construction of improved toilets in public places as well as incentives for households to construct improved toilets need to be emphasized. The challenge of expanding sanitation services and providing toilets will require the adoption of new and improved technologies, particularly in rural areas. Improved sanitation coverage at the household level needs to be intertwined with improved sanitation in public places, such as improvements in the coverage of sewerage systems, particularly in urban areas. Our findings suggest that the provision of good and proper sanitation is not only a public good by itself, but vital to improvements in health and nutrition, as well as education, particularly for increasing enrollment in Bangladesh’s primary schools.

19
_COTE D’IVOIRE: Growth, Specialization and the Terms of Trade_ have worsened. Progress towards achieving five out of seven MDGs––those relating to primary education, gender equality, and health––ran far behind schedule (Table 2).

**Table 2: Côte d’Ivoire: Progress Towards Key Millennium Development Goals, 2001**

|**Selected Goals and indicators**|**1990**|**1995**|**2000-01**|**Assessment**<br>**of Progress**|
|---|---|---|---|---|
|_Goal 1_: Halve the proportion of people suffering from hunger<br>_Indicator:_Undernourished (% of population)|18%|N.A.|15%<sup>2/</sup>|On track<sup>1/</sup>|
|_Goal 2_: Ensure all children complete primary education<br> _Indicators:_|||||
|Net primary enrollment ratio|47%|53%|64%<br>|Far behind<br>|
|Children reaching grade 5|73%|75%|91%<sup>3/</sup>|On track<sup>1/</sup>|
|_Goal 3_: Eliminate gender disparity in all levels of education<br>_Indicators:_|||||
|Female net primary enrollment ratio as % of male ratio|N.A.|N.A.|75%|Far behind|
|Female net secondary enrollment ratio as % of male ratio|N.A.|N.A.|N.A.|Far behind|
|_Goal 4_: Reduce under-5 and infant mortality rates by two thirds<br>_Indicator_:Under-5 mortality rate (per 1,000 live births)|155|165|175|Slipping back|
|_Goal 5_: Improve maternal health<br>_Indicator_: Reduce by three quarters maternal mortality ratio by<br>2015|N.A.|1,200|N.A.|Behind|
|_Goal 6_: Combat HIV/AIDS, malaria and other diseases<br> _Indicator_: Adults (% age 15-49) living with HIV/AIDS|N.A.|9.8<sup>4/</sup>|9.7%|Behind|
|_Goal 7_: Halve the proportion of people without access to better water<br>sources<br>_Indicator:_Population without sustainable access to improved water<br>sources|20%|N.A.|19%|Behind<sup>2/</sup>|

_Source: Human Development Report_ , 2003, UNDP; extracted from the country tables. The table summarizes analysis of progress towards goals for 2015 based on linear interpolation of trends in the 1990s. 1/ Followin <mark>g</mark> the im <mark>p</mark> act of the 2002-03 crisis, the assessment of these two indicators has most likel <mark>y</mark> worsened. 2/ 1998-2000.

3/ 1999-2000. 4/ 1994.

**The 2002-03 crisis worsened the social situation in a very short period of time, resulting in a full-fledged social and humanitarian crisis** . Between 500 and 1,000 persons were killed and about 1.2 million people––about 6 percent of the population–– became internally displaced (IDPs) or sought refuge in neighboring countries. Of this number, about 800,000 were internally displaced, and the remaining 400,000 left for neighboring countries.<sup>6</sup> An estimated 500-700,000 children left school and many schools and medical services effectively stopped operating, in part because of the departure of civil servants and the breakdown of the central administration in the areas controlled by ex-rebels. An estimated 20,000 people were rendered homeless in Abidjan as a result of the government’s destruction of shantytowns ( _quartiers précaires_ ), which were adjacent

> 6 U.N. Report to the Secretary-General, Mission of the Humanitarian Envoy for the crisis in Côte d’Ivoire, 12 January-12 February 2003.

7
**<u>Figure G2: Unconditional quantile regression of height-for-age z-scores on food security measures, expanded controls (Pakistan)</u>**

<!-- Start of picture text --> Per Capita Caloric Availability 1-SSR<br>.05 .15 .25 .35 .45 .55 .65 .75 .85 .95 .05 .15 .25 .35 .45 .55 .65 .75 .85 .95<br>Quintile Quintile<br>95% Confidence Interval OLS 95% Confidence Interval OLS<br>RIF-OLS RIF-OLS<br>1-SSEXR DDS<br>.05 .15 .25 .35 .45 .55 .65 .75 .85 .95 .05 .15 .25 .35 .45 .55 .65 .75 .85 .95<br>Quintile<br>Quintile<br>95% Confidence Interval OLS<br>95% Confidence Interval OLS<br>RIF-OLS<br>RIF-OLS<br>.4 .6<br>.2 .4<br>0 .2<br>-.2 0<br>-.4 -.2<br>-.6 -.4<br>.6 1<br>.4<br>.5<br>.2<br>0<br>0<br>-.2<br>-.4 -.5<br><!-- End of picture text --> 84
# **Water hauling and girls’ school attendance: some new evidence from Ghana**

# Céline Nauges and Jon Strand

_“Women and girls are the “water haulers” of the world. On average, women and girls in developing countries walk 6 kilometers a day, carrying 20 litres of water, greatly reducing the time they have for other productive work or for girls to attend school.”_ UNICEF Children and water, global statistics.<sup>1</sup>

# **1. Introduction**

In large parts of the world, most households have no access to tap water at home. The lack of tap water deprives the household of a number of goods and amenities. First, as focused on in this study, the household is burdened as the water must be brought to the house from outside, at great expense of effort and time. Second, a number of potential uses of water, which are taken for granted by “modern” households, are then excluded, including sanitary and food preparation services, and operation of many appliances (washers, dishwashers, showers, etc.) by households with relatively higher incomes. Third, not having tap water access can be financially costly: convenient alternative services, such as water delivery from trucks, can be far more expensive than taking water from the tap inside the house. Fourth, while tap water is treated and made suitable for human consumption, the same is often not the case for water brought from the outside to the home; its quality is typically much more uncertain.

This study focuses on the first of these four factors. Traditionally in developing countries, water hauling is carried out largely by women and girls. This activity could easily take up a substantial fraction of those household members’ time budget, given that the nearest or more relevant water source is far from the home. This could in turn have a number of undesirable social and economic consequences for the women in these households.

One such consequence could be to reduce the ability of children to attend school, depriving them of the education necessary to later obtain gainful employment and raise their economic and social status. The purpose of this paper is to gain understanding of this issue, by investigating further the question of water hauling and its relationship with school attendance for girls in Ghana (Africa). Sub-Saharan Africa is a key region for such a study since, on

> 1 http://www.unicef.org/wash/index_31600.html

2
68

# **A.L Descriptive Statistics**

Appendix Table A3 shows the share of fertilizer imports that are organic, while Appendix Figure A3 shows the use of organic fertilizer in agriculture in Sri Lanka. Appendix Table A4 shows the share of imports subject to import bans from 2017 to 2021.

Table A3: Fertilizer Imports, Total versus Organic

|**Period**|Total Fertilizer Imports(in MM USD)|% Organic|
|---|---|---|
|2016|128|0.078%|
|2017|98|0.128%|
|2018|249|0.148%|
|2019|211|0.072%|
|2020|244|0.111%|
|2021 (no-ban months)|23|0.639%|
|2021(ban months)|11|3.763%|

_Notes_ : for each year, the table shows total fertilizer imports by Sri Lanka in millions (MM) of USD and the percentage of these imports consisting of organic fertilizer. The data comes from the S&P Global Market Intelligence’s Panjiva data platform. A fertilizer is any HS8 product listed in Chapter 31 of the HS system. Organic fertilizers are classified as “animal or vegetable” (as opposed to “mineral or chemical”), and are listed under heading 3101 of the HS system. We break 2021 into two subperiods: the “ban months” (May-November) when the fertilizer import ban was in place, and the “no-ban months” (January-April and December) when the ban was not in place.

Figure A3: Use of Manure in Agriculture <!-- Start of picture text --> 2000 2005 2010 2015 2020 2015 2016 2017 2018 2019 2020 2021<br>(a) Use of Manure (b) Manure’s Share (by weight)<br>7 2.5<br>2<br>6.5<br>1.5<br>6<br>1<br>5.5<br>.5<br>Net manure application (N content, MM kg) 5 Manure’s share of total fertilizer weight (in %)<br>0<br><!-- End of picture text --> _Notes_ : for each year between 2001 and 2021, Figure A3a shows the total nitrogen content (measured in MM kg) of the manure that is applied to the soil as an organic fertilizer in Sri Lanka, net of the nitrogen that is lost through leaching or volatilization. Appendix Figure A3b shows the same quantity as in Appendix Figure A3a, but as a share of the total weight of all fertilizer used in Sri Lankan agriculture, which includes both manure and (23 types of) chemical fertilizer products. The data is from the FAO.
# Figure 16. MNA: Potential output growth

A. Potential output growth B. Potential output growth by different estimates <!-- Start of picture text --> Percent Percent<br>2000-10 2011-21 2000-21 2011-21<br>8 6<br>2022-30 2000-21<br>6<br>4<br>4<br>2<br>2<br>0 0<br>EMDEs MNA PF MVF UVF Exp.<br>C. Investment growth D. Female labor force participation<br>Percent Real fixed investment $/bbl Percent<br>25 Brent crude oil price (RHS) 120 50<br>20 100<br>40<br>15<br>80<br>10 30<br>60<br>5<br>40 20<br>0<br>-5 20 10<br>-10 0<br>0<br>2000 2002 2004 2006 2008 2010 2012 2014 2016 2018 2020 EMDEs MNA<br><!-- End of picture text --> Sources: International Monetary Fund; Penn World Tables; UN Population Prospects; World Bank. Note: EMDEs = emerging market and developing economies; MNA = Middle East and North Africa. GDP weights are calculated using average real U.S. dollar GDP (at average 2010-19 prices and market exchange rates). A.B. Period averages of annual GDP-weighted averages.

A. Estimates based on the production function approach. Sample includes 53 EMDEs, of which 5 countries are from MNA region.

B. MVF = multivariate filter; PF = production function approach; UVF = univariate filter (specifically, the Hodrick-Prescott filter). Expectations-based estimates (“Esp.”) are potential growth proxied by five-year-ahead IMF World Economic Outlook growth forecasts. Sample includes three economies (Jordan, Morocco, and Tunisia). C. Based on growth rate of real fixed investment and Brent crude oil price.

D. Based on female labor force as a percentage as total labor force. Sample includes 155 EMDEs (19 from MNA) from 2012-21.