rafmacalaba/data-use-mentions-extended huggingface dataset:
under the age of five. Field data for the 2012 group, the reference year of the panel, were collected between March and July 2012.

# _2.2 Measures of ECD_

ECD is a complex and multidimensional process that starts even before children are born. This paper focuses on ECD in terms of health and survival; nutrition; and children’s social, emotional, and cognitive development from the time when a child is still in the womb until the child is five years old (the age of school entry in Morocco). Twelve different indicators (see Box 1) are used to assess the status of ECD in Morocco, its evolution over time, and the extent of inequality in ECD. These indicators were selected due to their important contributions to ECD while keeping in mind the limitations of the data. For instance, while we would ideally be measuring cognitive

|**Box 1. ECD Indicators Examined**|
|---|
|1. Prenatal care|
|2. Trained attendant at delivery|
|3. Infant mortality (dying in the first year)|
|4. Fully immunized|
|5. Stunting|
|6. Underweight|
|7. Wasting|
|8. Iodized salt|
|9. Early childhood care and education|
|10. Parental development activities|
|11. Violent child discipline|
|12. Work and domestic work|

development directly, such data are not captured in national surveys in Morocco. For this reason, we examine important inputs to cognitive development such as early childhood care and education, and parents’ engagement in development activities.

The measures we use for health and survival are prenatal care, having a trained attendant present during delivery, infant mortality (dying in the first year of life), and whether children are fully immunized at the age of one. Infant mortality represents the ultimate loss of human 4
_Formal employment_ is a binary variable that equals one when the director reports that almost all the program graduates from the previous year are employed or self-employed in the formal sector. Directors were given three possible options regarding how many of the past year’s graduates found employment or self-employment in the formal sector: almost all; some; almost none or none.

_Wages_ is the average salary of the graduates from last year, whether they work in the formal or informal sector. Purchasing power parity (PPP) adjustment of wages was done using the 2019 PPP conversion factor.

# **Other variables based on survey data**

_Student characteristics (PCA Score)_ consist of an index created to reduce the dimensionality of the average student characteristics at the program level available in the survey data. It was built using a Principal Component Analysis approach and includes the following student characteristics (see Panel B in Table 1): percent of full-time students, percent of female students, percent of students that are 25+ years old, academic deficiencies in incoming students. The latter are indicators of whether program directors reported that missing skills among incoming students in math, oral expression, reading, writing or other competencies.

_Program characteristics (PCA Score)_ follows a similar logic and includes the following program characteristics (see Panel B in Table 1): program duration (semesters), number of cities where the HEI offers the program, whether the program has high quality accreditation, total number of students in the program last year, and program age (years).

_HEI characteristics (PCA Score)_ also follows a similar logic and includes the following institution characteristics (see Panel B in Table 1): HEI age, whether the HEI is a university, whether the HEI is for profit, and number of programs in the HEI. Note that whether the HEI is public is not included in this index; it is included as a separate “fixed” control in our estimation strategy.

58
Policy Research Working Paper 5880

# **Abstract**

This paper combines firm-level data from 89 countries with updated country-level data on financial structure, and uses two estimation approaches. It finds that in lowincome countries, labor growth is swifter in countries with a higher level of private credit/gross domestic product; the positive effect of bank credit is especially pronounced in industries that depend heavily on external finance; and banking development is positively associated with more physical and human capital investment. These findings are consistent with predictions from new structural economics. In high-income countries, labor growth rates are increasing in the level of stock market capitalization, which is also consistent with predictions from new structural economics, although the analysis is unable to provide evidence that the association is causal. It finds no evidence that small-scale firms in low-income countries benefit most from private credit market development. Rather, the labor growth rates of larger, capital-intensive firms increase more with the level of private credit market development, a finding consistent with the history-based political economy view that banking systems in low-income countries serve the interests of the elite, rather than providing broad-based access to financial services.

This paper is a product of the Finance and Private Sector Development Team, Development Research Group. 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://econ.worldbank.org. The author may be contacted at rcull@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank
# **5.2 Effects on business practices and performance**

While the effect of the information sheet on use of SEBRAE services was small in absolute magnitude and temporary, firms may have relied on other mechanisms to improve their business practices. First, firms may have sought out business development services from providers other than SEBRAE. Panel A of table 3 show that, during the follow-up survey, 15.4 percent of control firms reported using services from other providers. This number was not statistically significantly different in the treatment group, indicating that the intervention did not cause firms to seek out business development services from other sources.

Second, firms may have improved their business practices without help from SEBRAE or other service providers. Some of the actions listed on the information sheet were concrete and can in principle be implemented without help, such as “keep a register of suppliers” or “conduct a customer satisfaction survey.” However, column 2 in table 3 shows no effect of receiving an information sheet on business practices.

Given that the information sheet had no effect on business practices, we do not expect it to affect business performance. Indeed, columns 3 through 6 in table 3 show no robust effect of the intervention on business performance, measured using follow-up survey variables (sales and profits), as well as administrative data on the number of employees and business closures.

# **5.3 Reasons for not using SEBRAE services**

During the follow-up survey, we asked firms that had not used a SEBRAE service during the months between the intervention and follow-up survey why they did not use these services. The most given reason was lack of time, with about 60 percent of firms giving this reason (table 4). The second most given reason was that the firm was happy with their business performance: 28.6 percent of control firms gave this reason, dropping to only 15.8 percent of treatment firms. Our interpretation of this effect is that the information sheet made firms realize that they
# **6 Conclusion**

This paper uses a novel survey designed to measure intra-household consumption inequalities. Gaining a thorough understanding of these issues is all the more crucial that a number of new redistributive public policies are developed today, such as the Programme National de Bourses de Sécurité Familiale, PNBSF, a large conditional cash transfer scheme meant as the stepping stone of a broader social safety net. Whether and how the choice of the beneficiary within the household matters is a central question for the design of such program.

This new survey allows us not only to reevaluate the level of inter-personal inequality in Senegal and to reveal the extent of intra-household inequality, but also to analyze how this leads to a reassessment of the poverty diagnostic for the country.

The consumption survey we designed innovates by collecting information at the level of sub-groups within the household, using different respondents for different household cells. A first consequence of this approach is that it allowed us to collect more complete consumption data. Total consumption is measured to be higher than what was obtained with a classical consumption survey at the same period, as well as more unequally distributed. This could have an a priori ambiguous impact on global poverty assessment, since the two effects--one on the mean and one on inequality--should impact poverty estimates in opposite directions. In the context studied here, these two contrary effects seem to compensate each other so that the overall bias is rather small. Now, inequality is shown to be much higher than what was previously thought, with a Gini coefficient reaching 47.1%, while international statistical yearbooks give a Gini of 40.3.

Our results suggest that the more complex the household structure, the bigger the household size, and the more inequality is likely to be underestimated when computed using standard 26
27 _Human Resources_ 32(3): 463-480.

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# Housing Subsidies for Refugees: Experimental Evidence on Life Outcomes and Social Integration in Jordan<sup>*</sup>

_⃝_ r Abdulrazzak Tamim<sup>2</sup> Emma Smith<sup>3</sup> I. Bailey Palmer<sup>4</sup> Edward Miguel<sup>5</sup> Samuel Leone<sup>6</sup> Sandra V. Rozo<sup>7</sup> Sarah Stillman<sup>8</sup>

## **JEL Classification:** D22, J61, O17

**Keywords:** Refugees, Housing, Forced Migration, Social Integration

1 Author order is Certified Random _⃝_ r (AEA Confirmation Code gEv7QgDTbi7f). We are grateful to Hasan Ebussuutoglu, Joaquin Fuenzalida, Jonathan Gorham, Peter Grinde-Hollevik, Mansi Kalra, Meghana Kumar, Charlotte McClelland, Ei Thandar Myint, Jonathan Old, Gufran Pathan, Benjamin Shenouda, and Andy Theocharous for their outstanding support as research assistants. We are also grateful for comments received from seminar participants at University of California Davis, ESOC, World Bank, George Mason University, Georgetown University, American University, and the BITSS Forecasting in the Social Sciences Workshop. University of California Berkeley IRB approval #2019-01-11713. This study is registered in the AEA RCT Registry (AEARCTR-0006141). We gratefully acknowledge financial support from Innovation for Poverty Action’s Peace and Recovery Fund and the World Bank Development Research Group. We thank our Jordan-based implementing partner, and survey firm, Mindset Social & Marketing Research, and in particular, Mohammad Qardan, Rana Samara, and Mohammad Nababteh.

> 2University of California Berkeley, atamim@berkeley.edu

> 3Georgetown University, emma.smith@georgetown.edu

> 4University of California Berkeley, bailey.palmer@berkeley.edu

> 5University of California Berkeley and NBER, emiguel@berkeley.edu

62University of California Berkeley.

> 7World Bank, Development Research Group, sandrarozo@worldbank.org

> 8London School
, medium (20-99 employees; 21.5%) and large (over 100 employees; 13.0%). For most countries, the sampling frame was based on censuses from Statistics Agencies, Ministries of Finance or Economy, or business listings from Business Associations, and typically only included businesses that could be found in some registers or listings. The WBES COVID-19 follow up surveys, by design, cover only formal firms.

We also use data from Google mobility reports around transit stations (Google, 2021) to measure the size of the shock suffered by firms. For countries without available data, we impute data based on the Oxford Government Response Tracker index (Hale et al., 2021). We construct an indicator of the severity of the crisis that is a weighted average of 30-day periods since the start of the pandemic until the date of the survey. Specifically, the 30-day period average just before the survey has a weight of 1, the average from day 31-60 has a weight of 1/2, the average from day 61 to 90 has a weight of 1/3, and so on until the start of the pandemic.

## **3.2 Methodology**

## _Estimation Issues_

To use the harmonized BPS and WBES follow-up data for assessing the impact of COVID-19 on firm performance and recovery, we face two methodological challenges. _First_ , there is heterogeneity related to the differences in country samples, implementation strategy, and the timing of the surveys, also noted in Apedo-Amah et al. (2020). _Second_ , although wave 2 specifically targeted followup surveys of firms from wave 1, some firms were not reachable or declined participation. The

> 6For the BPS wave 1 questionnaire, see Apedo-Amah et al. (2020); for wave 2, see Appendix D. For WBES see http://www.enterprisesurveys.org.

8
The main contribution of our paper is proposing a tool that displays these changes but also identifies and quantifies the underlying drivers. We focus our attention on polarization defined as the combination of divergence from global and convergence on mean local incomes. The method developed blends two different frameworks of distributional analysis: relative distribution (Handcock and Morris, 1998 1999) and unconditional quantile regression (Firpo et al., 2009). The advantage over other methodologies is that it allows to single out the different drivers of polarization at different points of the consumption distribution.

Ghana, almost unique among SSA countries, offers the opportunity to analyze the last two decades’ distributional changes, since four comparable household surveys are available. The country also presents interesting specificities. Since 1991, poverty had declined very fast, inequality has not increased dramatically and yet the country has seen a rapid surge in polarization. The results of our analysis suggest that the distributional changes hollowed out the middle of the Ghanaian household consumption distribution and increased the concentration of households around the highest and lowest deciles.

Results on drivers of polarization indicate that although there is some heterogeneity across the various sub-periods in particular in terms of magnitude, household characteristics, educational attainment and access to basic infrastructures all tended to increase over time the size of the upper and lower tails of the consumption distribution and as a consequence the degree of polarization. Urban rural and regional variables started to have a strong impact on polarization only in the last decade; households residing in Greater Accra and the urban areas of Ashanti region performed well and increased their relative economic advantage over the rest of the country.

From a policy perspective, the pro-polarization impact of variables that tend to change slowly over time is of particular concern. It is very unlikely that policy makers can find a quick fix to the problem and any intervention will produce results only in the long run. This implies that the country needs to start now to develop a strategy that, if not able to immediately reverse polarization, at least can mitigate its impact. The
# 6 Proposed way forward

Estimating monetary poverty trends in Syria post 2011 is severely constrained by data quality and availability issues. Evidence presented in this paper suggests that – among possible distribution neutral nowcasting approaches – the one in which baseline poverty estimates based on 2009 grouped data and average consumption growth based on per capita GDP in current prices, deflated by the CPI, would likely lead to the most sensible trend profile of poverty over the conflict period. Moreover, given data constraints, the analysis of consumption data based on the HNAP 2022 Demographic and WASH survey does not evidence any concern regarding the possibility to use these data for the purpose of poverty measurement.

Based on these considerations, the proposed way forward is to use 2022 HNAP-based poverty estimates to anchor the most recent estimates to the best available evidence, and to interpolate the poverty evolution obtained from back-casting 2022 and nowcasting 2009 poverty estimates over the 2009-2022 period using the growth rate of per capita GDP in current prices, deflated by the CPI with a passthrough of 0.7 (Figure 17).<sup>36</sup> <!-- Start of picture text --> Figure 17: Poverty trends 2009-2022<br>$2.15,2017 PPP $3.65,2017 PPP<br>80 80<br>70 70<br>60 60<br>50 50<br>40 40<br>30 30<br>20 20<br>10 10<br>0 0<br>Nowcasting,base 2009 Interpolation 2009-2022<br>Nowcasting,base 2009 Interpolation 2009-2022<br>Backcasting, base 2022 Backcasting, base 2022<br>Source : World Bank staff calculations based on CBS and HNAP 2022<br><!-- End of picture text --> The proposed approach has three main advantages. First, it makes the best use of the best available evidence in a simple and transparent way, therefore substantially improving on current World Bank poverty estimates available in PIP. Second
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37
Conditional expectations of earnings (or consumption as in Cogneau and Mesplé-Somps, 2009) are obtained from the distribution of average income estimated across several categories. These categories are typically determined by parental education and/or parental occupation and, in some cases, geographical location. Some versions of this approach, as in Cogneau and MespléSomps (2009) or Cogneau and Cigneux (2008), use ̳intermediary‘ variables to analyze the link between social origin and income/consumption (such as social position and education). They conclude that a significant part of difference in inequality of opportunity for income/consumption can be attributed to differences in intergenerational mobility linking parental education and occupation with sons‘ education and occupation.

A third analytical approach develops non-parametric statistical tests, in particular, stochastic dominance tests. Leblanc, Pistolesi and Trannoy (2008) define equality of opportunity as the situation where income distribution conditional on social origin cannot be ranked according to stochastic dominance criteria, using non-parametric statistical tests developed by Davidson and Duclos (2000) to compare generalized Lorenz curves. Again, social origin is defined by parental education and/or occupation. Leblanc, Pistolesi and Trannoy (2008) distinguish a risk component and a return component inspired in the original Oaxaca-Blinder decomposition of wage gaps in endowment and return differentials: the former assumes that within-type inequality is fully erased; the latter assumes that between-type inequality is removed. In addition, the paper develops the Gini Opportunity index, which computes the weighted sum of all the differences among areas of opportunity sets and then divided that sum by the mean income of the entire population. That makes the index independent of the wealth of a society, a convenient feature for international country comparison.

6