rafmacalaba/data-use-mentions-extended huggingface dataset:
Figure A2. The Progression of Severity: Medium Cases <!-- Start of picture text --> Brazil Canada Chile Germany<br>50<br>40<br>30<br>20 1<br>1<br>1<br>10 1<br>0<br>Dominican Republic Iran, Islamic Rep. Mexico Peru<br>50<br>40<br>30<br>1<br>20 1<br>1<br>1<br>10<br>0<br>Mar 1 Apr 1 May 1<br>Portugal Turkey United States<br>50<br>40<br>30<br>20 1<br>1<br>10 1<br>0<br>Mar 1 Apr 1 May 1 Mar 1 Apr 1 May 1 Mar 1 Apr 1 May 1<br>Daily severity ratio (percent)<br><!-- End of picture text --> Source: Johns Hopkins University; World Health Organization. Note: The daily severity ratio is the daily equivalent of the cumulative severity ratio. Horizontal line shows nth cause of death above or around the maximum daily severity ratio. Cut-off date for all data is May 23.

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# **4.1.1** **_Entry Model_**

The hypothesis of the entry model is whether the household head or member decides to start nonfarm enterprises given the rural investment climate in the community level. In other words, what are determinant factors for a household head to start a nonfarm business? These factors are further decomposed into household characteristics and community RIC variables. The entrydecision data are obtained from the household head’s decision to enter or not to enter nonfarm business during the last three years before the survey time in 2010. The Probit model is applied to assess which household characteristics and community level RIC influence the decision of entry (Deininger _et al._ 2007):

**_Z_** _i= 0+ 1 (_ **_H_** _i) + 2 (_ **_C_** _j) + 3 (_ **_D_** _j) + εi_ (1)

where _i_ denotes a household, _j_ denotes a community, **_Z_** denotes the household head’s entry decision (1: yes; 0: no), **_H_** denotes a series of household characteristics, **_C_** denotes a series of community-level RIC variables, **_D_** denotes governorate (province) and rural dummies, and _ε_ denotes an _iid_ error term. Variables included in **_H_** are household size, the head’s years of education, the head’s age, household agriculture income and other income. Community RIC variables included in **_C_** are hours to district market (access to market), percentage of electrified household (electricity), percentage of secure drinkable water available (water), percentage of high school educated (education), dummy for availability of physician (health), dummy for availability of management service (management), dummy for availability of formal financial institutions (access to finance), dummy for security (safety), dummy for
The strong reliance of African countries on their natural resources and market size has been well evidenced by many studies.<sup>4</sup> It might be more pertinent to look at which countries have been most successful in attracting FDI over the past few years, when they could not rely on the natural resources and the size of their domestic market. To do so, we propose to normalize the value of total FDI inflows by GDP and the total value of natural resources in each country. For simplicity, we label this indicator as the business climate for FDI (FDIBC):

# FDIBCj= FDIJ /(GDPj * NRj)a

where FDI is defined as the FDI inflows in country i, GDP as the gross domestic product and NR the value of natural resources (all of them expressed in dollars). Equation (1) assumes that the elasticities of FDI inflows to changes in GDP and natural resources are both equal to unity (a = 1), which seem consistent with the estimated elasticities that will be reported later in the paper for the group of African countries surveyed in this note.<sup>5</sup>

> 4 See for example, Pigato (2000) for a review.

> 5 The assumption that both elasticities equal unity is valid for the group of African countries covered in this note. However, if the sample is widened to include industrial countries for example, this assumption does not hold because of the large differences in GDP level between countries (for example, United States and Burundi).

4
# **4.2.3 Social Protection**

Table 4 shows the summary statistics of those enrolled in _Cadastro Unico_<sup>_ ́_</sup> in December 2019 by their nationality. The di↵erences in household characteristics between Venezuelans and Brazilians are posted in column (3) and (6) and the standards errors are clustered at the province level and seems to be stark and statistically significant. Compared to 23 million Brazilian, about 18,000 Venezuelans are registered in the single registry. Venezuelans registering in _Cadastro Unico_<sup>_ ́_</sup> are on average poorer than their Brazilian peers. Average income of registered Venezuelan is R$85, while that of Brazilian is R$307. 72.3% of Venezuelan live in extreme poverty, with an income less than R$89 while 48% of Brazilians registered have an income less than R$89 (Figure 6a). Venezuelans registered are also more educated with 27% having some tertiary education compared to 3% of Brazilians _Unico ́_ haveregisteredhigh schoolhavingdegreestertiarycomparededucation.to 42%Figureof Venezuelans6b shows thatin20% _Cadastro_ of Brazilians _Unico ́_ havingin _Cadastro_ high school degrees. Except in Roraima and Amazonas, registered Venezuelans’ family sizes (2.8) are also slightly bigger than the registered Brazilian family size (2.7). According to Table 4, Venezuelans seem to have more younger children in the age cohort 0 to 5 years than Brazilians. Registered Venezuelan households are also less likely to have female heads and average age of heads are lower than the registered Brazilian households. Conditions of living seems to be almost similar for both Brazilians and Venezuelans, with about 54% reporting that they have simultaneous access to water supply, garbage collection, adequate sanitation and electricity. However, Venezuelan heads of the households are more likely to be employed and be self-employed than Brazilian heads, although the months worked is substantially lower. Overall, the summary statistics reveal that Venezuelans who register for social assistance are poverty-stricken
# **Table 2.  Cross-Country Comparison of Key Vulnerability Ratios**

|**Country**|**Jordan**|**Egypt**|**Israel**|**Morocco**|**Turkey**|
|---|---|---|---|---|---|
|Return on Assets|6.1%|12.4%|1.8%|10.7%|4.2%|
|Total Debt (estimated) to Assets|24%|40%|45%|27%|35%|
|Total Liabilities to Total Assets|30.1%|47.6%|49.0%|40.3%|45.9%|
|Current Liabilities to Total Liabilities|81.5%|76.4%|67.9%|79.5%|73.4%|
|Short-term Debt to Total Debt|67.9%|43.6%|47.2%|69.9%|65.4%|
|Quick Ratio|1.92|1.13|2.80|.|1.71|
|Interest Coverage|8.96|7.03|3.60|73.15|3.39|
|Interest Coverage Less than 0.75|0.0%|0.0%|23.4%|0.0%|23.6%|
|Altman Z-score|5.46|4.61|3.02|.|3.62|
|PercentFloat|52.7%|35.6%|45.8%|28.7%|36.4%|
|N obs(based on total debt)|18|39|149|14|168|

Source: Worldscope Data Quick Ratio is a
As previously mentioned, unemployed youths are not included in our analysis. As shown in table 3, this group of youths compares similarly to the employed youth in average age and schooling, as well as in the proportion of indigenous population, of people who are illiterate and of those who still attend school. The main difference between both groups is the high percentage of females and lower percentage of people who are single in the unemployed group. Although the data of ENCEL 2017 did not include information on the activities carried out by these youths, due to the low school attendance rate in this group, it is reasonable to think that females are committed to household activities and it is not possible to know if they will later be incorporated into the labor market or not.

**<u>Table 3. Characteristics of occupied and unemployed youths of ENCEL 2017</u>**

||ENCEL 2017Youths|questionnaire (n=5,101)*|
|---|---|---|
|Variables of interest|Occupied (n=3,310)|Unemployed (n=1,449)|
|Average age|25.98|25.87|
|Females (%)|38.55|89.99|
|Indigenous (%)|26.13|25.53|
|Single (%)|38.88|22.22|
|Illiterate (%)|3.08|2.42|
|Attend school|3.66|4.89|
|Average years of schooling|9.27|9.34|

* There are youths for whom it is not possible to know their condition of activity, because they did not answer the relevant questions. Source: Own elaboration from ENCEL 2017.

# C. _Design for estimating the effects of PROSPERA_

Defining a design to estimate the impacts of PROSPERA represents a methodological challenge at the present time. One possibility would be to use the experimental design
2018/19 that started a new panel. Unlike in Ethiopia, in Nigeria the refresh was partial, with the survey continuing to track 1,425 of the original households. Because there is not yet a second round of data for the refreshed households, we exclude them from the analysis Data cleaning and removal of non-agricultural households yields 9,145 observations from 3,412 distinct households across four survey waves.

In Tanzania, the data come from the 2008/09, 2010/11, 2012/13, 2014/15, 2019/20, and 2020/21 rounds of the Tanzania National Panel Survey (TZNPS) (TNBS, 2011; TNBS, 2012; TNBS, 2015; TNBS, 2017; TNBS, 2021; TNBS, 2023). The sample is representative for the nation, and provides estimates of key socioeconomic variables for mainland rural areas, Dar es Salaam, other mainland urban areas, and Zanzibar. As in Nigeria, the fourth wave (2014/15) was a partial refresh, with data being collected from a sub-sample of the original panel households (known as the extended panel) plus the addition of completely new households (known as the refresh panel). The extended panel households were re-interviewed in 2019/20 in what is known as the Sex-Disaggregated Data, which added improved individual-level data. The refresh panel was followed up with in 2020/21. The 2020/21 survey round also included a booster sample, that will continue to be followed in subsequent rounds. However, since at this time there is only one round of observations for the booster sample, we exclude them from the analysis. Focusing on rural, crop producing households we have 9,916 observations from 4,804 distinct households across five survey waves.

In Uganda, we use the data from the 2009/10, 2010/11, 2011/12, 2013/14, 2015/16, and 2019/20 rounds of the Uganda National Panel Survey (UNPS) (UBOS, 2012
LAPOP sample. However, if we just look at ENAHO in December 2018, we see that the share has risen to 1.4%.

The remainder of the table shows the means and standard deviation for the control variables for Venezuelan immigrants in December 2018, as captured in the ENPOVE (Columns 1 and 2), and for the average Peruvian respondents in ENAHO in two periods: December 2018 and 2007-2020 (Columns 3 and 4, and 5 and 6, respectively). Additionally, we also provide descriptive data for the LAPOP and Gallup opinion surveys.

Venezuelan immigrants were slightly less likely to be female and are younger than their local

> 5Centro poblado is the smallest level of geographical disaggregation. In urban areas, they are equivalent to neighborhoods, while in rural areas they correspond to small towns.

> 6There are 198 provinces in Peru which generally correspond to labor market areas. In our regressions, we take the log of share variable. In order to include provinces with no Venezuelans and data prior to 2015, we add 1 to both the number of Venezuelans and the total population of each province. The median province has 55,000 inhabitants and the smallest nearly 3,000 hence this transformation should be immaterial. As the Venezuelan share of the population is very low in most provinces but highly skewed, it is important to measure this variable in logs.

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Policy Research Working Paper 10197

# **Abstract**

Using data from 41,873 individuals across 17 African countries and 13 studies, this paper maps data from various self-reported scales to 10 socio-emotional skills and examine gender differences in these skills and their relationship with education and earnings. Apart from self-control, the findings show a significant male advantage in self-reported skills—men have an aggregate socio-emotional skill level 0.151 standard deviations higher than women, equivalent to the socio-emotional skill gained over 5.6 years of education. This is robust to controlling for positive self-concept. Closing the gender gap in education would close 17percent of this gap. While overall socio-emotional skill and education are positively correlated for both men and women, women do not have a positive correlation with education for some individual socio-emotional skills. The male advantage in socio-emotional skills increases at higher education levels. Socio-emotional skills are associated with higher earnings, especially for women. However, the specific skills associated with higher earnings differ by gender. Interpersonal skills are more strongly correlated with earnings for women than for men, and measures of these skills are often underrepresented, which indicates a key direction for future research. The paper further examines differences in the relationship between socio-emotional skills and earnings by levels of education and occupation. It discusses the implications of these results for interventions seeking to hone women’s socio-emotional skills for labor market success and to address the gender norms that may perpetuate gaps in socio-emotional skills.

This paper is a product of the Gender Innovation Lab, Africa Region. 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 cdelavallade@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
.0|10,731.0|**16,753.0**|3|0|5|7|
|South Sudan_2013|0.0|0.0|0.0|**0.0**|0|0|0|0|
|Sudan_2014|0.0|0.0|467.9|**467.9**|0|0|4|4|
|Swaziland_2014|47.8|0.0|930.2|**978.0**|4|0|77|81|
|Tanzania_2015|2,915.2|344.8|92.4|**3,352.4**|31|4|1|36|
|Togo_2013|0.0|5.5|833.5|**839.0**|0|1|76|77|
|Uganda_2014|3,222.7|0.0|32.7|**3,255.4**|99|0|1|100|
|Zambia_2014|0.0|0.0|50.7|**50.7**|0|0|0|0|
|Zimbabwe_2012|0.0|0.0|1,113.6|**1,113.6**|0|0|11|11|

_Sources_ : Utility annual reports and other documents. _Notes_ : Data are for power purchases from IPPs, EPPs, and imports. Purchases from state-owned generation companies are not included. Imports include purchases from regional SPV generation utilities, even if the generation facility is located within the borders of a given country. The year following each country name is the reference year for countries included in the QFD analysis, or the latest year with available power purchase data for countries not included in the QFD analysis.

92
average starts in 1991 for EMDEs due to lack of earlier data. The EMDE sample includes 126 economies. “ex BRICS” excludes BRICS economies within each group. Large importers refer to the seven EMDE commodity importers ranked in the top 20 EMDEs (ex BRICS) in nominal GDP terms. Other importers include 42 economies. C. Weighted averages. Includes 28 EMDEs with available quarterly data. Long‐term averages start in 1991 for EMDEs and are based on annual data. Last observation is for Q2 2016.

D. Percentage point contribution by each country group to EMDE investment growth.

E.F. Medians across EMDEs of each region to ensure broad‐based representation. Long‐term averages are period averages of annual medians. East Asia and Pacific, Europe and Central Asia, Latin America and Caribbean, Middle East and North Africa, South Asia, and Sub‐Saharan Africa include 8, 12, 18, 10, 5, and 26 economies, respectively.

28
# **2. Literature Review**

There is a large body of work in the survey literature about reporting errors in survey data; Sudman and Bradburn (1974) summarize the extensive work in this area from the 1950s and 1960s. Retrospective data, in particular, introduce various data quality issues linked to recall errors. Over time, respondents may not be able to accurately recall details of events. Accurate remembrance of events will depend not only on the duration of the recall period, but also on the nature of the event being reported. The literature generally categorizes three main types of recall error in household survey data: telescoping, heaping, and recall decay. Telescoping refers to inaccurately identifying the date of events, either forward or backward into the recall reference period. Heaping refers to the use of estimation by respondents that places large numbers of responses at particular points, such as an expenditure of about $100 or dating an event about 6 months ago. Recall decay refers to forgetting details of events. Since the focus of our study is farm input usage and harvest size, rather than the timing of events, we do not focus on telescoping. However, there are areas within agricultural statistics where telescoping would be relevant, such as dates of fertilizer application or input purchase.

It is also difficult to consider the issue of “heaping” because agricultural data are naturally heaped in that both inputs and products are sold in uniform quantities (e.g. 50 kg sacks, oxcarts, etc.), as opposed to variables such as reported age, where one does not expect to find heaping and can then look for it as evidence of reporting errors. Although there is considerable variation in the units reported across the crops and countries we study here, we nonetheless find that most of the quantities are reported in heaped quantities reflecting the normal patterns one would expect for these measures. For example, in the data used in this study from Kenya on maize harvest, more than 90 percent of reports are in terms
of firms that got assistance and a control group of firms that did not, and this for a sample period that ran from before the program to its end.<sup>5</sup> Using this rich data set, we used a wide array of approaches to estimate the effect of the PIP on firm-level exports of treated products (FFV). We ran DID regressions of the value of exports, by product _×_ firm _×_ destination, on control variables as well as a dummy variable marking ‘treatment’ by the PIP. In order to deal with selection issues, we combined the DID approach with propensity-score matching, although the small size of the sample and the systematic differences in size between treatment- and control-group firms considerably reduced the power of

> 4Thanks to Morag Webb for providing us with the data.

> 5Although we had similar customs data from a number of other countries, none covered years _before_ the start of the PIP. After merging, firm IDs have been deleted from the dataset for confidentiality reasons.

5
|

a Except for month and district fixed effects which are included in all models in this table. b Joint test that the coefficients are equal across all eight columns. Sample sizes for each module are reported in Table 1. Standard errors in parentheses;<sup>*</sup> ,<sup>**</sup> and<sup>***</sup> denote statistical significance at 10%, 5% and 1% level. The consumption modules that match the numbers in the column headings are:

- 1 Long list (58 items), 14 day

- 2 Long list (58 items), 7 day

- 3 Scaled subset list (17 items), 7 day

- 4 Collapsed list (11 items), 7 day

- 5 Usual month (58 items)

- 6 Household diary, Frequent

- 7 Household diary, Infrequent

- 8 Individually-kept diary, Frequent

11
instance, reduced tariffs will yield welfare gains of 0.2 percent for AfCFTA country members. Cutting NTBs in half would increase welfare gains by 1.6 percent while the full implementation of the WTO’s Trade Facilitation agreement would lead to overall welfare gains of 5 percent by 2035 (World Bank 2019b).<sup>15</sup>

# **6 Conclusions**

The goal of this paper is to provide evidence on the nature of the relationship between international trade integration for Sub-Saharan Africa in a comprehensive and systematic manner. Unlike previous research in this strand of the literature, this paper first examines the impact of trade integration on growth as well as on the sources of growth —that is, physical capital accumulation and TFP growth. Second, it evaluates the impact on growth (and its sources) of the different dimensions of trade integration (trade volume, trade diversification, and natural resource dependence). This implies that the nature of the tradegrowth relationship depends not only on how much you trade but also on what you export and to whom you export (or have trade with). Third, it examines the impact of growth of the composition of trade volumes by product (primary goods vs. manufacturing goods) and the composition of trade volumes by destination (inter- vs. intra-regional trade).

We have collected a comprehensive data set on international trade integration indicators, growth per worker, capital accumulation, and TFP growth for 174 countries across the world, including 45 SubSaharan African countries, from 1970 to 2014. The relationship between international trade integration and growth is estimated using the GMM-IV system estimator (Arellano and Bover 1995, Blundell and Bond 1998). This estimation method accounts for unobserved country and time fixed effects as well as the likely endogeneity (or reverse causality) of explanatory variables, thus providing evidence of a causal relationship between trade integration and growth. The GMM-IV estimation is also corrected for the likely downward-biased estimation of the asymptotic standard errors of the two-step GMM estimator in small samples
The relative polarization indices, shown in Table 3, capture these changes well. The MRP index is always positive and statistically significant ( _p_ -value = 0.00). Decomposing the MRP into the contributions to distributional change made by the segments of the distribution above and below the median, it appears that “downgrading” dominated “upgrading” in the polarization upswing over the course of the first two sub-periods: the value of the lower relative polarization index (LRP) is indeed greater than that of the upper relative polarization index (URP) – 0.26 vs. 0.17 and 0.27 vs. 0.11, respectively – which is consistent with the visual impression from the shape shifts above. The values of the indices in the 2005-12 period denote instead a nearly perfectly symmetric polarization in each tail.

In sum, while often less noticeable in any single period when compared to the large swings in median household consumption expenditure, the growth in polarization was a major contributor to the overall changes in the Ghanaian consumption distribution since the early 1990s. Behind these shape shifts, however, was probably a set of key drivers. The following section relies therefore on GLSS data to examine how the changes above have been associated with consumption growth and, thereby, identifies the main drivers behind the polarization upsurge.

# **3.3 The drivers of growing polarization in Ghanaian household consumption**

The presentation of polarization results over the three sub-periods requires a considerable amount of space. For the sake of brevity, we chose to present only part of the results and made an effort to present the main findings in an abridged format. For example, we decided not to comment on the econometric results of the unconditional quantile regression, and to place the decomposition tables in the appendix, and regarding the polarization decomposition results, to focus our attention only on the top percentiles results (top two and bottom two).

Overall this is not a big limitation since, as shown in panels (a) and (
there. In fact, a UNHCR monitoring survey of voluntary returnees found that 54% returned to Syria to reunite with their families (Hoffman and Makovsky (2021)). The central role that violence plays also underscores the importance of using multiple platforms. As can be seen in Appendix 9, the majority of violence related content is over represented on Telegram, while much of the economic and job related content is over represented on Facebook. By using a variety of data sources we are getting a richer image of the situation on the ground.

> 16Twitter transparency site: `https://transparency.twitter.com/` and Facebook Community Standards: `https://transparency.fb.com/policies/community-standards/`

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