rafmacalaba/datause-encoder-data huggingface dataset:
This project addresses these gaps and sheds light on these questions by gathering original data on Moroccans’ attitudes, experiences, and perceptions around unpaid labor, care work, and paid work outside the home. How do Moroccans’ attitudes and perceived norms around various aspects of unpaid labor and care work within the household relate to one another and to their actual behavior? To what extent do attitudes and perceived norms around household roles hinder the emergence of more gender-equal distributions of labor?

# **3 Data**

## **3.1 Survey Sample**

The original data discussed in this paper come from a survey of 1,038 Moroccan respondents recruited through Qualtrics from January 25 to March 3, 2023. Qualtrics maintains access to a pre-recruited pool of Moroccan respondents who complete surveys in exchange for a small financial incentive.<sup>2</sup> Demographic quotas based on gender, age, and geographic location were employed to ensure a baseline level of respondent diversity. Appendix Table 1 summarizes the demographics of the sample and compares it to that of a national probability face to face survey (F2F) conducted in 2022 by the Arab Barometer. The online sample disproportionately surveys individuals who are currently employed (58% versus 38% in the F2F survey), while under-representing students, the unemployed, housewives, and retired individuals. Online respondents are also more likely to self-identify as only somewhat religious or not religious, rather than religious. Despite higher employment rates, socio-economically, the online sample respondents identify themselves as struggling a bit more, on average, than respondents in the F2F survey, in response to a question about whether their household makes enough money to meet

> 2. In one sense, all respondents in this survey are therefore “employed.” However, completing surveys for compensation likely takes up no more than a small portion of respondents’ time and can be completed from home. While housewives and the unemployed are underrepresented in the sample, around 19% of female respondents self-identified as primarily housewives, and 12.5% of all respondents self-identified as unemployed.

6
the positive dowries is 39,355 rupees with a standard deviation of 82,993 rupees. The dowry information is reasonably accurate since dowries are the single largest lifetime expense and respondents tended to have a good memory of how much was spent. Note that 23% of women said that their husbands had beaten them at some time during the marriage. This proportion is considerably higher than the incidence in the US where one in six women report having been struck by their husbands during the course of a marriage (Gelles and Straus, 1989). The mean year of marriage is 1974, though marriages range from 1950 through 1992. The marriage squeeze ratio averages 1.06 in this sample indicating that there is a slight surplus of women in the marriage market due to younger cohorts being of larger size than older cohorts because of increases in population growth rates. The ratio averages 1.08 for the tenth year of the marriages suggesting that the surplus has been increasing with time.

Table 2(a) presents OLS estimates of the dowry equation. The theoretical model predicted ambiguous effects for all the exogenous variables here with the exception of the income of the wife's family which was negative and the marriage market parameter which was positive. Most of the estimated coefficients in the dowry regression are not significant with the exception of the marriage squeeze ratio which has a positive impact on the dowry and is significant at the 10 per cent level. A 0.01 increase in the ratio raises the dowry by 3000 rupees indicating a rather large effect. The income of the wife's family has a negative coefficient as hypothesized by the theoretical model, but it is not significantly different from zero.

Tables 2(b) presents estimates from the Probit-IV specification of the violence equation. Here a number of effects are consistent with the theoretical predictions. A 10,000 rupee increase over the mean of the wife's parents' annual income increases violence by 3.3 per cent. The observed and positively valued characteristics of the marriage z, that we measure by the number of male children, also has the predicted effect with an additional make child reducing violence by 10 per cent at the mean. This is consistent with the notion that more male children reduce the chances that the husband is dissatisfied. A 10,000 rupee increase in predicted net dowries measured at the mean reduces the probability of violence by 5 per cent, while the husband's family's income has a negative effect which is not significant. This is consistent with the prediction of the theoretical model that the direction of the impact of dowries and husband's income on violence should be the same, but that the dowry effect should be greater than.the effect of the husband's income.

mortality and fertility in the region, it should serve as a good proxy for spouse availability among the potters.

20
Figure 4.3 <!-- Start of picture text --> China Import Shares<br>30<br>25<br>15 -S1⁄2 * '4., EU<br>1980 1990 2000 ~~j* Ct -- m- Korea<br><!-- End of picture text -->

**China's Export Shares**

<!-- Start of picture text --> 10 ;'<br>25 _-d<br>020 s t - tS - . .,, 1<br>:15. =EU_<br>1980~~~~~~-- 1990200<br><!-- End of picture text --> Source: Authors' calculations using IMF, Direction _of_ Trade Statistics 10
<!-- Start of picture text --> == Pre—fisc<br>— Post-fisc<br>=== Poverty line<br>Hl Fiscal impoverishment<br>Fiscal gains of the poor<br><!-- End of picture text --> Population ordered by pre-fisc income <!-- Start of picture text --> == Pre-fisc<br>— Post-fisc<br>=== Poverty line<br>Hl Fiscal impoverishment<br>Fiscal gains of the poor<br>| iS<br><!-- End of picture text -->

Population ordered by pre-fisc income (2,3)
6

**PHN** 1992 **Sector Review**

befits the important role of these projects in poverty alleviation.

_Figure 2: Regional Distribution of PHN Lending,_ **_FMrV2_** <!-- Start of picture text --> Lending Amounts<br>(In US $ millions)<br>Africa $100.3 MENA S28.8<br>10%<br>E. Asria SI19 6, 1 /_<br>13X $280 : : _<br>29%<br>S. Asia v $47.5<br>397 t5<br><!-- End of picture text --> <!-- Start of picture text --> Number of Projects<br>Africa 6<br>38% ENA 1<br>6%<br>ECA 2<br>1 L | 2X<br>E. Asia I<br>6% X . 1 h . . : /<br>S. Asias 3 /LAC 3<br>19% 19%<br><!-- End of picture text -->

# 3. _Cofinancing_

2.10 During FY92, the number of cofinanced projects remained proportionally the same as in FY91. Every region except East Asia had at least one cofinanced project. All of the projects in the LAC and MENA regions were cofinanced, while half of the projects in the Africa rcgion were cofinanced (Table 5). The Bank leveraged fewer additional funds for its PHIN and social sector projects in FY92 than in FY91 accordinig to both measures of additionality--the amcunt donor cofinancing (not recipient governments) adds to the Bank lending for all PHN projects (overall

**ECA**

additionality), and the amount cofinancing adds only to those projects that are cofinanced.

2.11 Social sector projects received more donor contributions than "pure" PHN projects representing 54 percent of overall additionality, compared to 13 percent for PHN-only projects. In the cofinanced social sector projects, additionality was 60 percent; in the PHN-only projects it was 22 percent. Social sector projects offer an attractive means of combining assistance from donors, since they support a broad range of social investments. Most social sector projects support a portion of a larger country social program.

2.12 In-country cofinancers (excluding government counterpart funds) continue to remain a feature of PHN lending. In the East Asia and the ECA regions, one project included cofinancing from state governments and public agencies. Beneficiaries are contributing in one Africa project and two LAC projects. Amounts committed by in-country cofinancers tend to be small, since they are, in most cases, the municipalities, NGOs, and local groups that will be presenting proposals for project funding. In two projects, the beneficiaries' contribution matched at least half of the government's contribution. In the Guyana SIMAP/Health, Nutrition, Water and Sanitation project, the beneficiaries fully matched the amount committed by the government.

2.13 The Bank financed on average 62 percent of total project costs in the PHN sector during FY92. Average total projects costs in the "pure" PHN projects were higher than in the social sector projects--$117 million versus $34 million. Bank loans/credits on average were lower in social sector projects than in PHN-only projects--$13 million versus $79 million--due to the additional leverage gained in cofinancing from external sources in social sector projects.

4. _Project Funds by Expenditure Category_
<!-- Start of picture text --> Figure 1. Frictions, Failures and Paradigms<br>Cantract stage Frictions Undernying problem Paradizm Failures<br>: Agpremetric<br>4cymmaetic ,<br>Uneertai idebnal<br>forts (before, and learning -<br>auh = Cogent<br>| confusion fe Sociol] Agency<br>foilures fodures<br>2 ) |<br>contract costs foilures cA<br>t<br>contrnct costs commitment ce<br>end liability<br>i<br><!-- End of picture text -->
**Table S6. Results from missing data analysis for sustained attention.** Effect of the IST intervention at 9 and 24 months follow-up on sustained attention outcomes for younger (class 1) and older (class 5) children combined using a longitudinal, random effects regression modeling approach. Results presented (i) for all children with either 9 or 24 months follow-up measurements of the outcome (unadjusted), (ii) for those with baseline measurements of the outcome and accounting for age, sex and stratification effects as the primary pre-specified analysis, and (iii) for those additionally with baseline measures of parental education, SES and baseline educational level (measured by baseline spelling) as further predictors of missingness.

|**Sustained attention**<br>**(score: 0-20)**|||||||||
|---|---|---|---|---|---|---|---|---|
|||**Control**||**Intervention**|**Mean difference**<sup>**d**</sup>|**p-value**<sup>e</sup>|**ICC (**|**95% CI)**|
|||**(50 schools)**||**(51 schools)**|**(95% CI)**||||
||||||||School|Child|
|**CLASS 1**<sup>**~~b~~**</sup>||**Mean (SD)**<sup>**~~a~~**</sup>||**Mean (SD)**<sup>**~~a~~**</sup>|||||
|**Unadjusted**|||||||||
|9-months|1070|8.48 (3.63)|1162|8.43 (3.76)|-0.05 (-0.53,0.44)||||
|24-months|960|13.45 (5.15)|1059|13.20 (4.96)|-0.25 (-0.75,0.24)|0.409|0.04 (0.02,0.06)|0.17 (0.13,0.22)|
|**Adjusted**|||||||||
|9-months|1030|8.52 (3.65)|1144|8.43 (3.77)|-0.16 (-0.61,0.30)||||
|24-months|923|13.49 (5.15)|1041|13.18 (4.96)|-0.41 (-0.88,0.06)|0.311|0.03 (0.02,0.05)|0.13 (0.09,0.18)|
|**Adjusted for predictor**|**s of mis**|**singness**|||||||
|9-months|1013|8.54 (3.67)|1118|8.43 (3.77)|-0.02 (-0.43,0.46)||||
|24-months|908|13.49 (5.16)|1017|13.19 (5.00)|-0.21 (-0.67,0.26)|0.385|0.02 (0.01,0.04)|0.12 (0.08,0.17)|
|**CLASS 5**<sup>**c**</sup>||**Mean (SD)**<sup>**a**</sup>||**Mean (SD)**<sup>**a**</sup>|||||
|**Unadjusted**|||||||||
|9-months|1180|13.38 (5.45)|1231|13.35 (5.13)|-0.07 (-0.65,0.51)||||
|24-months|1007|14.22 (4.90)|1052|14.66 (5.13)|0.31 (-0.29,0.91)|0.083|0.04 (0.03,0.07)|0.52 (0.49,0.55)|

42
However, the removal of tariff exemptions could exacerbate the negative, trade diverting, impact of the preferential trade agreement. It is important that a country also seek to reduce its MFN tariffs to reduce the possible distortion that arise from removing tariffs against particular suppliers and not others. Hence, the removal of tariff exemptions as tariffs against the EU are removed should be integrated into a policy to reduce external tariffs. For example, removing all tariff exemptions would allow Tanzania to cut its MFN tariffs by more than 30% at the same time as implementing the EPA agreement without any adverse revenue impact. Similarly, Ethiopia could cut its MFN tariffs by more than 20% and Kenya by more than 40%.

_Example 3: The Impact of Specific and Complex Duties_ . Another advantage of TRIST is that it can deal more effectively with specific and complex tariffs than can models based on statutory rates. Specific tariffs are those in which the duty is related to the volume or other physical characteristics of the product while complex tariffs combine different types of duty such as ad valorem and specific tariffs. A key feature of specific and complex tariffs is that the level of protection given by the statutory rate is not transparent and in practice will vary across different transactions of the same product. These tariffs are a particularly pernicious source of revenue, with ad-valorem equivalent rates often by far exceeding 100%. More specifically these tariffs tend to be regressive with higher levels of protection on the lower value transactions that are consumed by poor households. While specific taxes avoid the issue of valuation of an import transaction they can allow for discretion and corruption and substantially increase collection costs at customs when all transactions subject to these duties require physical inspection.

**Table 7: Specific Tariffs in Tanzania**

|Product|Tariff|
|---|---|
|Rice in the husk (paddy or rough)|75% or USD 200/MT whichever is higher|
|Husked (brown) rice|75% or USD 200/MT whichever is higher|
|Semimilled or wholly milled rice ...|75% or USD 200/MT whichever is higher|
|Broken rice|75% or USD 200/MT whichever is higher|
|Cane sugar|100 % or USD 200/MT whichever is higher|
|Of jute or of other textile bast fibres of heading|45% or USD 0.45/bag whichever is higher|
|Worn clothing and other worn articles|45% or USD 0.30/kg whichever is higher|

The data in TRIST is based on actual revenues collected and so TRIST automatically computes ad-valorem equivalents of specific and complex tariffs for each product by country import flow. Other models typically use some average ad valorem equivalent that aggregates away the complexity of these duties. Hence, TRIST can show the revenue and other adjustment implications of removing specific and complex tariffs or of strategies to convert them into ad valorem rates. This may be important information in defining the time period over which such a reform is implemented. Other models are unable to accurately assess the revenue impact of removing these duties.

20
activities to the ENPSF, but much more comprehensive and frequent national survey data would help identify progress and challenges. We particularly recommend adding additional measures of ECD to the ONDH panel for future rounds. ECD is a crucial measure of human development and the regular frequency of the ONDH panel would provide valuable updates. Information can also help galvanize policy and programmatic actions. Data on progress and challenges can help identify what programs and policies are or are not working well and spur change. It is difficult to hold policymakers and organizations accountable for their goals in ECD without frequent measures of progress.

# **_6 Conclusions_**

ECD provides the foundation for subsequent human development. It is a critical period and one during which development is profoundly affected by the early environment. This paper has examined ECD in Morocco across multiple dimensions of development and over time. Morocco has experienced substantial improvement in economic growth over time (Furceri, Mazraani, & Versailles, 2013). However, the persistent inequalities in economic and human development remain major concerns at the highest levels of government (Morocco World News, 2014). The fact that the trends in ECD have been mixed over time and that inequalities have also persisted likely contributes to the unequal human capital and economic outcomes Moroccans experience. The deficits and inequalities we observe in ECD are troubling from the perspective of human rights and issues of equity because children are being affected by circumstances entirely outside of their control. These deficits and inequalities are also limiting the human and economic potential of the country.

As this paper has discussed, there are a number of highly effective ECD investments, often ones with very high returns, which could be implemented to improve human and economic development and promote equity. Targeted ECD investments are critical. As this paper corroborates and other research has found (Vollmer, Harttgen, Subramanyam, et al., 2014), ECD does not necessarily improve simply because of economic growth. Prioritizing ECD and taking policy action is vital for not only improving ECD but also for ensuring greater and more equitable human and economic development.

28
# _(b) Basic results on elections_

In this section, we start by treating the timing of elections as “exogenous,” being driven by differential subdistrict priorities and capacities, rather than village level unobservables affecting volunteerism. As such, election effects are regime-switch effects. Then we turn to evidence that supports this assumption. In the estimation, as noted, we control for a variety of village characteristics relating to size, diversity, survival and destruction from the tsunami, and village social capital, allowing for 2009 differential effects of each relative to 2007. We discuss the results on these characteristics in Section 3d.

The key variable we focus on in this section is whether the village has had an election to date. This information is taken from the 2009 interviews, where we were careful in questioning to distinguish officially sanctioned elections from informal ones, typically held in the first two years after the tsunami, a distinction we will utilize below. For 2009, an election-to-date is whether the village has had an officially sanctioned election from early 2006 up to the date of interview in late 2009 or early 2010. For 2007, an election-to-date means that the village, when interviewed in a specific month in late 2007 or early 2008, had an official election after 2005 but in or before that interview month in late 2007 or early 2008 (as reported in 2009).

Table 4 contains the basic results. Column 1 contains just election-to-date and its differential effect in 2009, with the only control being a time effect. Those having switched regimes by having an election have 40.1% fewer volunteer days. The differential effect of an election-to-date on 2009 volunteer days is negative (suggesting a stronger effect) but insignificant; and that pattern holds in all columns in the table. In column 2 we add in district-year fixed effects, which reduce the effect to a 33.8% 18
# Table 3. Water Price and Water Use by Water Institution

||||Institution<br>|||
|---|---|---|---|---|---|
||(1)<br>Area-based<br>no change|(2)<br>Volumetric<br>no change|(3)<br>Manager<br>appointment|(4)<br>New<br>smaller<br>group<br>formation|(5)<br>Individual|
|_WUG-level variable_||||||
|Volumetric price of reservoir<br>water (Yuan/m<sup>3</sup>)|—|0.080<br>(0.035)|0.063<br>(0.030)|0.061<br>(0.025)|0.066<br>(0.039)|
|Area-based<br>fixed<br>price<br>of<br>reservoir water (Yuan/ha)|290.5<br>(209.1)|—|—|—|27.9<br>(113.2)|
|_Farm-level variable_||||||
|Total water use (mm)|235|197|163*|114***|175|
||(138)|(85)|(114)|(81)|(133)|
|Reservoir water use (mm)|232|144*|102***|89***|—|
||(140)|(80)|(82)|(66)|—|
|Individual water use (mm)|3|54**|61*|25|175***|
||(9)|(67)|(102)|(67)|(133)|
|No. of obs. (farmers)|11|17|31|25|51|

_Note_ : Standard deviations are in parentheses. Star marks (*) indicate _t_ -test for the mean difference in water use from (1) Area-based no change, *p<0.1, ** p<0.05,***p<0.01.

_Source:_ Authors’ analysis based on data sources discussed in the text.

25
Policy Research Working Paper 8622

# **Abstract**

Inequalities in the opportunity to obtain a good education in low-income countries are widely understood to be related to household resources and schooling quality. Yet, to date, most researchers have investigated the contributions of these two factors separately. This paper considers them jointly, paying special attention to their covariation, which indicates whether schools exacerbate or compensate for existing household-based inequalities. The paper develops a new variance decomposition framework and applies it to data on more than one million children in three low-income East African countries. The empirical results show that although household factors account for a significant share of total test score variation, variation in school quality and positive sorting between households and schools are, together, no less important. The analysis also finds evidence of substantial geographical heterogeneity in schooling quality. The paper concludes that promoting equity in education in East Africa requires policies that go beyond raising average school quality and should attend to the distribution of school quality as well as assortative matching between households and schools.

This paper is a product of the Development Research Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/research. The authors may be contacted at hdang@worldbank.org and jones@wider.unu.edu.

_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
**Table E4: Relationship between food security measures and height-for-age z-scores (Bangladesh): Limited Controls Version**

|VARIABLES|(1)|(2)<br>Height-for-|(3)<br>age z-scores|(4)|
|---|---|---|---|---|
|HFIAS Score|-0.179***||||
||(0.010)||||
|Child DDS||-0.088***|||
|||(0.010)|||
|FCS|||0.192***<br>(0.011)||
|MDDS||||0.128***|
|||||(0.011)|
|Rural|-0.292***|-0.412***|-0.236***|-0.307***|
||(0.038)|(0.039)|(0.038)|(0.041)|
|Rajshahi|0.045|0.086**|0.055|0.060|
||(0.037)|(0.037)|(0.037)|(0.039)|
|Khulna|0.288***|0.318***|0.242***|0.275***|
||(0.043)|(0.043)|(0.043)|(0.046)|
|Barisal|-0.017|-0.024|-0.067|-0.063|
||(0.046)|(0.046)|(0.046)|(0.049)|
|Dhaka|0.057*|0.112***|-0.004|0.061*|
||(0.032)|(0.033)|(0.033)|(0.034)|
|Sylhet|-0.170***|-0.181***|-0.197***|-0.171***|
||(0.040)|(0.040)|(0.040)|(0.043)|
|Chittagong|0.024|0.033|0.022|0.000|
||(0.035)|(0.035)|(0.035)|(0.037)|
|Season 2|0.056**|0.071***|0.015|0.024|
||(0.024)|(0.025)|(0.024)|(0.026)|
|Season 3|-0.009|-0.007|0.002|0.012|
||(0.025)|(0.025)|(0.025)|(0.027)|
|Constant|-1.275***|-1.193***|-1.291***|-1.261***|
||(0.047)|(0.048)|(0.047)|(0.050)|
|Observations|11,556|11,556|11,556|10,144|
|R-squared|0.045|0.026|0.047|0.031|

Source: Authors’ calculations. Note: Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1 HFIAS Score is household food insecurity access scale, child DDC is the dietary diversity score constructed for each child, FCS is the food consumption score and MDDS is the mother’s dietary diversity score.

46