rafmacalaba/data-use-mentions-extended huggingface dataset:
# **C. Methodology and estimates**

To test our hypotheses, we use a standard probit regression model with robust standard errors clustered at the regional level and the functional form: Yit = α + βXt + γRt+ δFit + εit, where Y is a dummy variable that takes the value 1 if firm _i_ in region _t_ made a capital investment between 2011 and 2013 and 0 otherwise drawn from the 2014 RUFIGE survey. X is one of the regional-level variables of interest (i.e. measures of regulatory burden or uncertainty) described above. Our primary regional control (R) is the log GRP per capita in 2008 and is meant to control for initial conditions in the regions. We use only this variable in order to present a more parsimonious model, although in additional models we check the robustness of our results by introducing other regional level controls.

Finally, F is a matrix of firm-level controls drawn from the RUFIGE survey. Firm-level controls (F) include log employment at the firm, dummy variables indicating whether the firm received state support or state orders, indicators of whether the firm has foreign or state ownership, an indicator of the type of locality the firm is located in (i.e. city, village, etc.), and a set of dummy variables indicating the firms primary sector of economic activity.

We present descriptive statistics for all variables used in our analysis in Table A3 in the Annex. Similarly, Table A4 provides information on the correlation for our regional-level variables of interest.

Before proceeding, we note again that in this particular paper we do not seek to make strong causal claims about the relationship between uncertainty and investment. This paper is instead meant to be exploratory and seeks to establish the presence of a correlation between these variables to serve as a stepping-stone for future work. We believe that identifying a strong correlation between variation in levels of corruption or inconsistent property rights enforcement, on the one hand, and investment behavior, on the other, is a valuable contribution
the geographical dispersion of informal activity as per the employment-based measure derived from the labor force module of the 2019 census. The measure correlates strongly ( _ρ_ = 0.83, Table A3) with a measure of regional formal sector shares that relies on the administrative data to capture the size of the formal sector (see Figure A9).

Finally, we exclude agricultural and non-market service sectors from our informality measures where possible, as their tax records only cover a small and very specific sub-population of firms and employees. In the case of agricultural firms, the administrative data only capture large-scale commercial agriculture, which is often primarily export-oriented (see Figure A1 and Chacha et al. (2024)). Non-market services are dominated by non-profit organizations and the government with only a few for-profit VAT firms.

**Data on linking patterns of informal firms:** In addition to the above, we use data from a survey with small and medium size enterprises (KNBS, 2016) to derive some insights into the sales and purchase patterns of the informal sector. The survey data only record the main type of buyer and supplier of a firm and hence cannot be directly used to reconstruct a network with informal firms. However, we will use these data to inform the assumptions of our model.

# **2.3 Size of the Informal Sector**

To assess how much of economic activity is generated by VAT-reporting firms, we first compare their value added with Kenya’s national accounts. We find that VAT-reporting firms account

> 14To estimate the Gross County Product, KNBS (2022) relies on a series of data sets including the 2016 MSME survey and the 2019 population census. Most data sets covering informal activity are only collected intermittently.

9
|Burundi|3 months,1year|X|X|
|Cameroon|3 months,6 months|X|X|
|Colombia|Not available|X||
|Gambia|1 month,1year|X|X|
|Ghana|1 year|||
|Kenya|1 month,1year||X|
|Kyrgyz Republic|1 month,3 months|||
|Lesotho|3 months,1year|X|X|
|Mauritius|1 month|||
|Mongolia|1year|X||
|Nicaragua|7 days, 6 months,<br>1year|X||
|Pakistan|1 month,1year|X||
|Sierra Leone|1year|X|X|
|South Africa|1year|X|X|

Household surveys contain richer information on transport in other modules than the expenditure and ownership modules. The following paragraphs provide an overview of the presence of transport-related questions in various modules. We then identify a set of recurrent questions for each thematic across surveys. Table 4 lists the presence of such questions per module per country-survey. More details per country can be found in Annex 1.

9
partially fill the gap in research on Morocco and provide policy makers with critical, useful information. By highlighting the main constraints to equality among young children, this information can help in identifying vulnerable children and in gaining a better understanding of the phenomenon as a whole.

We draw on the concept of inequality of opportunity and methods for quantifying unequal opportunities (de Barros, Ferreira, Vega, & Chanduvi, 2009; de Barros, Vega, & Saavedra, 2008; Deutsch & Silber, 2008; Roemer, 1998, 2014; Shorrocks, 2013) to assess inequality in ECD. We demonstrate a number of challenges in terms of unequal early health and survival, nutrition, and social-emotional and cognitive development. Overall, we find that while there have been some improvements in ECD, particularly in health, substantial inequalities and deficits persist. These inequalities and deficits in ECD will affect children into their adult lives and present a substantial barrier to ensuring shared prosperity and adult equality in Morocco. More must be done to ensure children have equitable opportunities to develop and thrive.

This paper proceeds as follows. Section 2 discusses the data we use to measure ECD and inequality over time in Morocco. Section 3 describes the methods we use to assess ECD, quantify inequality, and decompose its origins. Section 4 presents our findings in terms of ECD trends, risk factors, inequality, and the drivers of inequality. Section 5 concludes with a discussion on how to promote ECD and equal opportunities for ECD in Morocco in light of our findings.

# **_2 Data for Assessing Inequality_**

In discussing the data, we first describe the surveys used to measure ECD and then how we measure ECD and circumstances.

# _2.1 Surveys of ECD in Morocco_

The paper relies on four different surveys conducted in Morocco:

- Demographic and Health Survey (DHS) of 2003/04

- Multiple Indicator Cluster Survey (MICS) of 2006/07

- National Population and Family Health Survey (ENPSF) of 2011

- National Human Development Observatory (ONDH
¤er training.

Classes run for only a few hours per day, usually in the morning between 8.30 am and 12.00 am. An average class has an enrollment of 85 pupils but only 35 pupils attend on a typical day. The average pupil-teacher ratio is 27. Absenteeism rates are very high. Enrolled children are absent over half of the time, while teachers are absent approximately 30 percent of the time. Both the range of classroom activities and the quality of the teacher vary a lot by school. In better preschools, the teacher is dedicated to her class and classroom activities include learning the alphabet and numbers, singing, dance, and sports. At the lower end of the quality spectrum, teachers are not very motivated or trained, and the preschool class is a place where children are provided with a relatively safe environment and supervision, but few activities that promote their development. Those schools o¤er little more than baby-sitting services.

We evaluate a school meals program that was implemented from 2000 to 2002 by a Dutch

> 15 The 50 schools from the school meals program are included in this sample.

> 16 The Kenyan GDP per capita was approx. 350 dollars in 1997.

> 17 Kenya Central Bureau of Statistics, Welfare Monitoring Survey II (1994)

> 18 The national institute is the National Center for Early Childhood Education (NACECE). The curriculum for the training was developed jointly by the Kenya Institute of Education, the Van Leer Foundation, and the Aga Khan Foundation. The training is part of a World Bank project.

6
prevalence rate, we compared this to a similar indicator from 47 countries for which a Demographic and Health Survey was administered in the last decade, and included a GBV module based on the WHO methodology. Lifetime exposure to physical and/or sexual violence from intimate partners in these 47 countries ranges from 6.4% in the Comoros to 57.1% in Papua New Guinea. Lao PDR ranks in the 15% of countries with the lowest prevalence of IPV, with a prevalence rate similar to neighboring Philippines (12.2%), Myanmar (16.3%) and Cambodia (18.2%).

The same national survey estimates that 5.1% and 5.3% of Laotian women experience physical and sexual violence, respectively, from a non-partner. For approximately 60% of respondents who reported this violence, the violence was committed by family members. Notably, close to two-thirds of these family members were women, especially mothers and stepmothers. This suggests women in Lao PDR experience relatively low levels of violence by non-partnered men.

Qualitative work (from the Laotian National Commission for Advancement of Women, 2015) outlines four key triggers of IPV: (i) failure of a woman to meet the expectations of gender roles, which assert superiority of a husband; (ii) jealousy, mistrust or infidelity by the husband; (iii) alcohol or drug use; and (iv) financial stress. Notably, women’s participation in a PWP program could affect at least two of these triggers: it may violate gender roles and is likely to affect the financial situation of the household.

3. Road Management Group (RMG) Program and Experiment Design The RMG program emerged from an evaluation of the government’s Poverty Reduction Fund (PRF), a community-driven development program aimed at reducing poverty through infrastructure improvements in education, drinking water, irrigation, health, and transport. An evaluation carried out in 2015 showed that the PRF had been successful in improving infrastructure in rural areas but
14 21. Infrastructure proceeds grew over time as more countries started to tackle these sectors in the late 1990s. They are overwhelmingly concentrated in telecommunications and power, accounting for 50 and 36 percent respectively of total infrastructure proceeds between 1990 and 2003. Nearly 65 developing countries (or 42 percent of developing countries in the PPI database) had generated telecoms proceeds through private participation in one form or another, concentrated largely in Latin America, Europe/Central Asia, East Asia, and more recently the Middle East and North Africa. In electricity and natural gas, 72 countries (or 46 percent of all developing countries) had some form of private participation in existing utilities, with activity concentrated largely in Latin America, Europe/Central Asia, and East Asia. The share of telecom proceeds remained more or less steady over time, while electricity grew slightly mainly due to recent sales or concessions in Europe/Central Asia. Proceeds in the transport sector declined from 14 percent in the 1990s (mainly Latin America) to 8 percent between 2000 and 2003 (in China and Malaysia). And while activity in the water sector grew from 1 to 3 percent, it still continues to account for a very small share of total infrastructure proceeds, again mostly concentrated in Latin America (management contracts, which are excluded from this paper, have been a frequent method of bringing in private sector participation in the water sector).

22. On the whole, privatization of existing infrastructure assets raised $204 billion since 1990, compared to new green field investments which were significantly higher at $350 billion (Figure 9).

**Figure 9:  Total PPI and infrastructure privatization, 1990-2003**

<!-- Start of picture text --> Total PPI and Infrastructure Privatization, 1990-2003<br>Billions of USD<br>120<br>100<br>80<br>60<br>40<br>20<br>0<br>1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000
# **1. Introduction**

Access to basic services and goods in Côte d’Ivoire is limited for children and the overall population. This report presents the findings of an effort to measure and analyze the equality of opportunities for children in Côte d’Ivoire, where ―opportunities‖ are defined in terms of access to basic goods and services in education and critical infrastructure facilities, such as clean water and sanitation. The use of the equality of opportunities framework involves looking beyond measures of _average_ access to basic goods and services. Rather, the measure that underpins the analysis, the Human Opportunity Index (HOI), also takes into account the extent to which _existing_ opportunities in the country are equitably distributed across children of different characteristics, such as gender, economic status, location, and family or parental attributes, which are all circumstances established at birth.

There are a number of compelling reasons why an in-depth country report for Côte d’Ivoire on the equality of opportunities is of interest to the Bank, country policy makers, and other stakeholders in Côte d’Ivoire’s development process. The most important among these reasons is that human development remains a challenge, because the country is still recovering from a series of civil conflicts and political instability. (See Box 1.) The numbers from recent household surveys report that almost half of the children from age 6 to15 years of age do not attend school. Moreover, around one-third of Ivoirian youth younger than age 16 lack access to basic goods, such as sanitation facilities and electric light in the home. Evidence presented in this report also shows that access is unevenly distributed across groups. Because access to basic goods and services represent the minimum level of opportunities a child would need to maximize his or her human and productive potential in life, the low and unequally distributed access severely jeopardizes opportunities for these children.

The following section of this report introduces the underlying concept, properties, and empirical methods for estimating the Human Opportunity Index. Section 3 presents the data
research carried out in the field of entrepreneurship. For a detailed description of the variables used to measure each of those institutions, please refer to Appendix 2.

# **I.1. What affects the entry decision of entrepreneurs?**

The two main barriers to firm creation are the administrative burdens on firm creation and the access to finance. Those two are the main obstacles identified by Europeans, according to a periodical empirical study on Entrepreneurship carried out by the European Commission.<sup>4</sup>

# **_Administrative burdens on firm creation_**

Administrative burdens on firm creation might have a large impact on the labor market performance of transition economies. Burdens on firm creation have been proven to prevent job creation in the service sector, where firms are smaller and job creation depends heavily on new firm entry. Hence, in a situation of large job destruction in other economic sectors, high administrative costs on firm creation can result in high unemployment. That was the case in Spain and Italy, where many jobs

> 4 Flash Eurobarometer 134 “Entrepreneurship”.  Done by EOS Gallup Europe upon request of the European Commission (DG XXIII), 2000, 2001 and 2002. In general, around 70% of the people surveyed agree that the existing administrative procedures are complex and 76% claim that the main problem is lack of available finance.

5
Salvador, Guatemala, and Honduras), compared to their counterparts at home. As shown in Table 7, over 90 percent of youth ages 12-17 are in school, meaning that relatively few young migrants of LS and US ages are in America and working. At the same time, young adults ages 18-25 who migrated to the United States at age 18 or later (so were likely to have completed any secondary schooling at home) have higher educational attainment than their counterparts at home. These results suggest that migrants are positively selected on education, and that labor market opportunities in the United States may not be a major factor influencing dropout in Central America. However, population data in the United States undercounts unauthorized migration, and further research would be needed to understand the decision-making processes behind migration in order to provide a more thorough assessment.

# **3.3 Trends in education quality**

An important community or macro level factor not mentioned so far in the empirical analysis is education quality. As discussed in the conceptual framework, the endowments with which children enter school are further expanded depending on the strength of the educational services provided. Low-quality education creates learning deficits in children that build up over time, reducing the benefit of persisting in school. Using a unique dataset from Egypt, Hanushek, Lavy, and Hitomi (2008) provide empirical evidence of this phenomenon – students attending lower quality schools (measured by expected learning gains over time) are more likely to drop out, even when controlling for students’ own abilities. While data on learning outcomes in Central America is not available over a substantial time series, nor at a micro level in recent years, regional exams of the past decade do provide an informative snapshot.

19
percent of the consumption basket.

As Leenhardt and Massuyeau (1995) point out, there was not, however, a large difference in the behavior of prices of consumer tradable and nontradable goods. 1<sup>3</sup> They found a difference of ten percentage points in the tradable (largely imported consumed goods) and nontradable consumer price index between the period December 1993 to December 1994. **14** Based on a similar classification of the CPI, we found that the price index for tradable goods, which account for 46.5 percent of the CPI (table 12), rose 155 percent between December 1993 and April 1995, while the price index for nontradable goods rose 140 percent (table 13).'5 This difference would be even smaller if one were to use the expenditure shares generated by the survey results.'<sup>6</sup> 13 This should not be interpreted to mean that the real exchange rate did not depreciate signi.ficantly. The tradable component of the CPI is comprised of goods that are primarily imported or goods that are domestically produced close substitutes for imported goods. The CPI-based price index for tradable goods in all likelihood does not accurately the index of prices of all imports, including capital and intermediate goods, that would be used in computing the internal real exchange rate for imports. The closest comparison to the calculation based on CPI tradable and nontradable price indices would be the estimate of the internal real exchange rate for imports, valued at domestic prices relative to the price of nontraded goods. This is not the same as the change in the internal real exchange rate based on an index of traded good prices, where traded goods comprise both imports and exports. The change in the real effectiv **e** xchange rate between 1993 and 1995 was 33 percent. For a discussion of the interpretation and calculation of these different measures of the real exchange rate, see Hinkle and Nsengiyumva (1995a and 1995b).

14 Locally grown cereals and
where is the household per capita income in logarithmic scale at the initial time point. The resulting coefficients from equation (2) and the average of the independent variables are used to solve the income equation, and therefore to obtain the amount of income associated to each probability.

The resulting incomes look quite similar for all three countries; non-poor individuals with a ten percent probability of falling into poverty have income levels at $8.5 a day in Chile, $9.7 in Mexico, and $9.6 a day in Peru, all of them measured using the 2005 PPP (see figure 4).<sup>6</sup>

**Figure 4: Daily income by probabilities of falling into poverty; Chile, Mexico and Peru** _Household per capita income at 2005 PPP_

<!-- Start of picture text --> 20.0<br>18.0<br>16.0<br>14.0<br>12.0<br>10.0 9.7<br>9.6<br>8.0 8.5<br>6.0<br>4.0<br>2.0<br>0.10<br>0.0<br>.00-.02 .04-.06 .08-.10 .10-.12 .14-.16 .18-.20 .22-.24 .26-.28 .30-.40 .50-.60<br>Probabilities of fall into poverty<br>Chile Mexico Peru<br>Per capita income a day (at 2005 PPP)<br><!-- End of picture text --> _Source_ : Author‘s calculations based on data from CASEN, MxFLS and ENAHO panel databases.

These figures result from the complete specifications of the models shown in annex (table A4); however, they are robust to different specifications
2 in the longer version of this paper). Overall, the model projects similar levels of job loss to the surveymeasured projections approach, although there is a large disparity between them in three sectors: _Chemicals_ , _Hotel, restaurant, and transportation;_ and _Wholesale, retail, and other services_ .

_Table 5-12 Georgia: Percentage of Permanent Formal Private Sector Jobs Lost by Sector, Model Predicted and Survey Measured Projections, CFUWBES2 (Winter 2020)_

||Econometric Model<br>Projections|Survey<br>Measured|No. Of<br>observations|
|---|---|---|---|
|1. Chemicals|6.2|-0.2|10|
|2. Food, drink, and tobacco|-2.3|6.9|107|
|3. Garments|22.4|44.3|8|
|4. Hotel, restaurants, and transportation|17.7|21.7|110|
|5. Machinery, electronics, and construction|6.1|8.0|56|
|6. Metals and non-metallic minerals; Plastic and||||
|rubber|-1.5|7.3|47|
|7. Wholesale, retail, and other services|2.2|0.0|188|
|8. Wood, paper, publishing,andprinting|0.6|3.0|13|
|Total|5.9|7.8|539|

Sources: LFS 2019, CFUWBES2, WBES 2013 and 2019. Notes: See notes to Table 5-11.

_Table 5-13 Georgia: Labor Market Impacts, Private Sector Workers, By Sector, Percentage of Pre-COVID Jobs_

||Lost job because of<br>job or business losses<br>due to COVID-19|Stopped<br>working to avoid<br>exposure to the<br>virus|Reduced income<br>because of
The standard global version of the assessment comprises 93 items grouped in 14 subtasks (activities) chosen after analyzing a large cross-country dataset consisting of 24,533 children in 23 countries and identifying items with robust psychometric properties across contexts (Pushparatnam et al., 2021). In addition to the standard AIM-ECD subtasks items, during the contextualization process for Pakistan, a single subtask testing English letters was included so that children could be assessed on both their Urdu and English letter knowledge. **Figure 2** shows the AIM-ECD DA subtasks and the developmental domains to which each subtask belongs (World Bank, 2022).

While the AIM-ECD was not designed for specific use in Pakistan, the constructs it assesses are highly relevant according to both provincial and national sources. _Appendix A: Crosswalk between Student Learning Objectives and AIM-ECD Subtasks_ reviews the alignment of the assessment with the Single National Curriculum and finds good coverage (National Curriculum Council, 2020). _Appendix B: Crosswalk between Punjab Early Learning and Development Standards and AIM-ECD_ reviews the contents of Punjab-specific standards and the AIM-ECD assessment, again finding good matches for many constructs deemed important by the provincial education authorities (Quaid-e-Azam Academy for Educational Development & UNICEF, n.d.).

_Figure 2. AIM-ECD DA Subtasks and Domain Assignments_ <!-- Start of picture text --> AIM-ECD Overall<br>English Letter<br>(Early Learning and Identification<br>Development)<br>Early Early Executive Social English Letter<br>Numeracy Numeracy Functioning Emotional Identification<br>Head, Toes,<br>Listening Receptive Perspective<br>Knees,<br>Comprehensio Spatial Shoulders Taking/Empat<br>n Story Vocabulary Task hy<br>Urdu Letter Producing a Forward Digit<br>Identification Set Span<br>Initial Sound Simple Backwards<br>Addition and<br>Discrimination Subtraction Digit Span<br>Number<br>Name Writing Identification<br>Number<br>Comparison<br>Shape<br>Identification<