rafmacalaba/data-use-mentions-extended huggingface dataset:
|(0.00902)|(0.0105)|
|_IHST_( <sup>�</sup><br>_X_<sup>_k_</sup><br>_jt_<sup>)</sup>|0.677***|0.612***|0.606***|0.614***|0.616***|
|_,_|(0.0333)|(0.0239)|(0.0232)|(0.0238)|(0.0242)|
|Observations|1,075,452|1,075,452|1,075,452|1,075,452|1,070,496|
|Year FE|YES|YES|YES|YES|YES|
|Flexible distance controls|NO|NO|NO|YES|YES|
|U.S. trade policy controls|NO|NO|NO|NO|YES|
|Pseudo R2|0.451|0.479|0.489|0.497|0.499|

Note: Robust standard errors in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1. Estimates over the sample of HS6 products with values of the upstreamness index _<_ = 1 _._ 3. The LHS variable on all models is _M_<sup>_k_</sup> _j,t_<sup>, the total value of PR’s product</sup><sup>_k_imports of product</sup><sup>_k_from place of origin</sup><sup>_j_</sup> in year _t_ . All models are estimated using the PPML estimator on PR’s import data pooled across observations at the year, HS6 digit product, and place-of-origin level, with year
Reduction Strategy Paper. IMF Country Report 08/125. April 2008

- World Bank. 2005. Benin Investment Climate Assessment. Africa Region, Private Sector Unit. November.

- ———. 2011. ―Draft Program Document for a Proposed Grant to the Republic of Benin for a Seventh Poverty Reduction Support Grant (PRSG-7).‖ Africa Region, World Bank, Washington, DC. June.

- World Bank and IFC (International Finance Corporation). 2009. Enterprise Survey. Benin Country Profile. Washington, DC.

## Financing

Briceño-Garmendia, Cecilia, Karlis Smits, and Vivien Foster. 2009. ―Financing Public Infrastructure in

- Sub-Saharan Africa: Patterns and Emerging Issues.‖ AICD Background Paper 15, Africa Region, World Bank, Washington, DC.

44
part of this paper are likely to be biased downwards. This will be the case, in particular, to the extent that there are settlements that were below �<sup>�</sup> (i.e. the population threshold used to define a “sizeable” settlement) in 2000, but whose populations have since grown to exceed this threshold. The new agglomerations that will have been created as a result of this will be missed by our updated AI estimates.<sup>35</sup>

# **4. Results**

In implementing the AI and cluster methods, we use the same parameter values as Uchida and Nelson (2010) and Dijkstra and Poelman (2014) respectively. Hence, for the AI, we assume �<sup>�</sup> � 50,000, �<sup>�</sup> �150 people per km<sup>2</sup> , and �<sup>�</sup> �60 minutes, while, for the cluster method, we assume �<sup>�</sup> �� �300 people per km<sup>2</sup> and �<sup>�</sup> �� �5000. For these parameter values, we generate estimates of the share of each country’s population that lives in urban areas using each of the three gridded population data sets described above – i.e. _LandScan 2012_ , _GHS-Pop_ and _WorldPop_ . We also estimate national urban population shares using the cluster method based solely on the share of population living in high density clusters, for which �<sup>�</sup> = 1,500 people per km<sup>2</sup> and �<sup>�</sup> = 50,000 people. Hence, overall, we generate a total of nine different sets of “consistent” estimates of national

> 31 Both _WorldPop_ and _GHS-Pop_ data are available
consideration of the complexity of the linkages between the underlying causes of malnutrition and the economic situation of the family, the analysis is also carried out separately for resource‐rich (top 60%) and resource‐poor (bottom 40%) households based on an asset index constructed for that purpose.

The report also provided some of the first empirical evidence on the synergies at work in combating malnutrition in the set of countries used in this study when there is simultaneous access to adequate levels in two or more of the three pillars of the underlying causes of malnutrition. While intuitively appealing, the synergies among the three clusters of malnutrition have received little empirical validation. The recent emphasis on sector‐specific nutrition sensitive interventions (World Bank, 2013) rightly emphasizes the synergies that can be exploited _<u>within</u>_ specific sectors such agriculture, water and sanitation, or social protection. The analysis in the report underscores the point that the success of these sector‐specific nutrition‐sensitive initiatives may be constrained by the slow progress in taking advantage of the synergies among the three broad clusters of the underlying determinants of malnutrition: food security, child care, environment and health. Given that the synergies _<u>among</u>_ these three pillars are beyond the scope of any specific sector such as agriculture or social protection, the simultaneous progress towards in all of the three pillars is either taken for granted or underemphasized. As a consequence, nutrition sensitive interventions in specific sectors run the risk of being a sectoral priority in contexts where the chances of significant success may be limited because of no or very low access to improved infrastructure water and sanitation facilities.

44
of the training would be that trained traders cross the border at official hours, with an increase in the non-payment of bribes and a drop in the incidence of gender-based violence. Such a scenario would occur outside the trader-led pathway in Figure 2.

# **4 Experimental design, data, and empirical approach**

## **4.1 Experimental design and baseline data**

We rely on the random assignment of individual traders to examine the impact of the empowerment training program on corruption and gender-based violence (GBV) at the borders between Eastern DRC and Rwanda. The sample frame was generated based on a listing exercise at the Goma/Gisenyi and Bukavu/Cyangugu border crossings between Eastern DRC and Rwanda. Among the 628 crossborder traders included in the sample, 314 were randomly selected to be invited to the training and 314 selected to be control traders.

There were two rounds of survey data collection conducted by an international survey firm with support from the Catholic University of Bukavu. The baseline survey took place prior to the empowerment training workshops in August 2011, and the end-line survey was carried out in August 2013. The survey instrument was a multi-topic questionnaire covering the trader’s demographic characteristics, housing characteristics and assets, expenditure, trade (border crossing, trading activity, corruption, and income), GBV, and access to mobile technology. The questionnaire was piloted prior to the survey, with a particular focus on adapting the survey to the local cultural context. All field staff received training on data collection consisting of instructive lectures and demonstrations followed by practice sessions with non-sampled cross-border traders, and a final briefing about field data collection techniques. A team of researchers from the Catholic University of Bukavu and the World Bank’s Gender Innovation Lab closely monitored all field activities.

As expected, given the fragile context, the field survey team confronted a number of challenges during data collection linked to the mobility of traders and their distrust of outsiders. At baseline, some respondents provided fake names to survey enumerators, making it
Taking these facts into consideration, it is reasonable to assume that if a woman has not carried out the option of aborting a pregnancy during the first trimester, it is most likely that her latent status is _B*_ . Based on this reasonable assumption, we can check the robustness of our findings by limiting the sample of women who miscarried to those who miscarried after the third month of pregnancy.

# **4. Estimation results**

# **4.1. Average impacts**

The estimations based on ENADID data confirm that women who become pregnant as teenagers indeed differ from their peers along a number of characteristics (Table 1). They are more likely to be indigenous and to come from less wealthy families, their parents tend to be younger; they are less likely to live in the same household as their fathers, and they begin sexual activity and forming unions at a significantly earlier ages. Most of these characteristics are “pre-treatment” in the sense that although they are measured after the event of interest occurred, they are unlikely to change as a result of the event (such as ethnicity or parental age). Table 1 clearly illustrates the inevitability of a selection bias in a simple comparison of outcomes among women who experienced a pregnancy as teenagers and those who did not. On the other hand, to validate our empirical strategy we need to demonstrate that among women who experienced pregnancy as teenagers those who miscarried are not significantly different from those who had a live birth. Table 4 presents the corresponding analysis. We fail to reject the hypothesis of equality of the means for all cases except parental ethnicity.

Table 5 presents the results of the analysis of the impact of early childbearing on the mother‟s outcomes. To illustrate the extent of the selection bias we present two types of estimates. First, we regress the outcomes of interest on an indicator for adolescent childbearing as in equation (1) on the sample of all ENADID respondents aged 19 to 30. The results are shown in columns
Combining equation this with equations 8 and 10 yields: This decomposition breaks changes in the adjusted headcount ratio into changes in the number of poor attributed to each dimension, _Sd_ , changes in the intensity of multidimensional poverty attributed to each dimension, _SA_ , and an interaction effect. The logic behind larger contributions of dimensions in explaining the changes in intensity is very clear: if the majority of poor are no longer deprived in a particular dimension, this dimension will contribute more to the decrease in intensity.

In the case of panel data, larger contributions of a dimension to changes in _H_ are also intuitive. As an illustrative example, consider a case where the change in the headcount ratio occurs over a short period of time so that the demographic variables z remain constant. Assume that many dimensions change, but each individual only experiences changes in one dimension. If all individuals whose deprivation share crossed the multidimensional cut-off experienced change in the same dimension, then this dimension would be the unique contributor to the change. Indeed, Apablaza and Yalonetzky (2013) show that the change in the headcount in the case of panel data can be decomposed into a weighted difference in the transition probabilities of moving in and out poverty. Individuals at the margin of transition may be more prone to changes in particular dimensions, which would influence the transition probabilities and would contribute more to the variation in the headcount.

In the case of repeated cross-section data, changes in the headcount biased towards a particular dimension may arise due to the inclusion of people with different deprivation profiles in the sample, at one of the points of time. Thus, changes would be biased by the inclusion of larger shares of people deprived in one dimension. In applied work, dimensions do not change one at a time for each individual; dimensions may be correlated; and cross-sectional data is the rule and not the exception, especially in the surveys used to measure poverty in developing countries. A general framework to decompose changes in _H_ should thus take into account the notion that dimensions are
classified as “Blend”). All results presented below are simple country averages in which each country carries the same weight (but workers and households within countries are weighted appropriately).

The HFPS data reveal that while falling policy stringency was accompanied by a recovery in both employment and incomes, these were still well below pre-pandemic levels in September 2020. Across 17 countries, employment recovered to only 83 percent of its pre-pandemic average by August-September after falling to 71 percent in May-June. While an average of 63 percent of households in a country reported a fall in income in May-June, 58 percent continued to have lower incomes in August-September compared to pre-COVID levels.

**Figure 15: Decline in employment rates between preFigure 16: Predicted probability of recovering job COVID and May-June (pp) & share of loss lost during the pandemic, by August-September 2020 recovered by August-September**

<!-- Start of picture text --> 40% Unrecovered loss<br>Recovery: May-June to Aug-Sept<br>33%<br>Loss: pre-Covid to May-June<br>29% 80% 67%<br>30% 26% 25% 26% 60% 53% 48%<br>22% 41% 43% 38% 41%<br>20% 40% 34%<br>20%<br>20% 10%<br>12%<br>0%<br>10%<br>0%<br>Gender Age Location Education<br>Note: Results for 14-17 countries. All changes measured in Note: Results for 6 countries from logit models where the<br>percentage points (pp) terms. Loss in employment from pre- dependent variable indicates whether the individual is working<br>pandemic to May-June is split into (i) recovery in employment again in Aug/Sep 2020 (for those who had a job pre-pandemic<br>between May-June and Aug-Sep, and (ii) “unrecovered loss”. but lost
al. 2003; Yoon et al. 2007; Sayre, Raikes, and Devercelli 2018; Wolf 2018; Blimpo et al. 2019). Practice, feedback, and longer (but more focused) training and coaching tend to be more effective (Pallante and Kim 2013; Westbrook et al. 2013; Reinke et al. 2014; Fleisch et al. 2016; Kotze, Fleisch, and Taylor 2019; Popova et al. 2022).

In terms of incentives, information, and accountability, school report cards or quality ratings (which could potentially report QAS results) can cost-effectively improve education (Andrabi, Das, and Khwaja 2017; Bassok, Dee, and Latham 2019), but if implementation is weak they will have no effects (Aturupane et al. 2014). The global evidence on pay-for-performance for primary teachers is mixed (Glewwe, Ilias, and Kremer 2010; Muralidharan and Sundararaman 2011; Neal 2011; Goodman and Turner 2013). Although there is this rich literature on what works for education, how these policies would play out with a QAS has not previously been investigated.

# **_2.2 Kindergartens in Egypt_**

Children aged 4-6 in Egypt are eligible for KG, which is not compulsory. Children who enroll in KG can do so at KG1 or KG2 grades. At age six, children are eligible for primary school. Kindergartens are overseen by the Ministry of Education and Technical Education (MoETE) in Egypt, which provides public KG classes in public primary schools. Most KG enrollment is in public KGs, but a substantial private KG sector, primarily enrolling wealthier families, exists (El-Kogali and Krafft 2015). In the 2010s, enrollment rates in pre-primary education in Egypt were around 28% (World Bank 2022).

5
# **1. INTRODUCTION**

Cognitive and noncognitive skills are often considered key to understand economic decisionmaking. Empirical work with data from the US and Europe has made important advances in understanding both the causes and the consequences of skill formation (Heckman, 2007). Increasingly, cognitive, noncognitive and technical skills have also become the focus of analysis in development economics, with recent work on the determinants of skill formation (Attanasio et al, 2015), and the importance of skills for later life outcomes (Gertler et al, 2014). Development economists have also long worried about the role of many hard-to-observe skills as potential confounders in empirical analyses.

The low level of skills among farmers in developing countries is thought to be one of the main drivers of productivity differences between the agricultural and other sectors in the economy (Lagakos and Waugh, 2013). Young (2013) argues that sorting on skills explains the urban-rural productivity gaps observed in most developing countries and Gollin, Lagakos, and Waugh (2014) show that productivity differences become smaller when accounting for observed human capital differences. Understanding such potential selection at the micro-level arguably requires measures that go beyond years of schooling attained or literacy and more complex measures of skills or abilities are sometimes included in household surveys in developing countries. Yet the measurement of skills in developing country field conditions poses substantial challenges, and the related measurement error and its potential implications for empirical work have received little attention.

This paper contributes with the results of a skill measurement experiment among farmers in rural Kenya, to document the measurement challenges and discuss potential solutions and implications. We designed and implemented a relatively large survey focused on measuring different types of skills, and use the data to shed light on the reliability and validity of a wide set of commonly used skill measures and on the predictive power of the measured skills. We refer to cognitive skills as those that capture hard skills such as abstract reasoning power, language and math skills; noncognitive skills capture soft or socio-emotional skills, including a wide set
Table 7 shows the findings from another interesting survey targeting executives in foreign companies which compares the investment environment in Beijing and Shanghai. This also favors Shanghai over Beijing with regard to the general business environment and living conditions.

Generally speaking, Shanghai leads Beijing both in overall economic strength measured by various indicators and in terms of living convenience. Our survey results support this conclusion. Shanghai is more attractive to talented people than Beijing. The municipal government invests heavily in the construction and modernization of urban infrastructure. Average payments for employees and stock buyers per million people are higher in Shanghai than in Beijing. The climate of Shanghai is milder compared to that of Beijing. However, although our interviewees in Beijing complained about the deterioration of air quality in the city, the traffic jams, and the bureaucratism of the government officials, they were appreciative of the cultural amenities available in Beijing, which tends to be more tolerant of diversity.

# **_IV. Emerging urban innovative industries_**

We define “innovative industry” to cover three kinds of knowledge-intensive enterprises, the high-tech manufacturers, the knowledge intensive business services, and the creative content providers, which are identified as the key business sectors deemed to have high innovative propensities. These were the subsectors from which we selected the firms for our survey. But this definition is not the standard industrial classification in official statistical yearbooks in China. In order to provide a picture of the development of these industries in Beijing and Shanghai, we, therefore, use alternative indicators, which might be subject to some overlap with each other, to show the number of firms and employees in these sectors in Beijing and Shanghai, as well as their proportions to the national total.

# **General comparison**

# _High tech industry_

China has been promoting high-tech industry as part of its national science and technology development policy for over 10 years, and established an authorization system to name high and new tech enterprises ( _Gaoxinjishu Qiye_ ) operating in the high-tech industrial parks in many cities. The authorized “high
agenda, these empirical patterns raise concerns about the future of immigration policies in an aging world, as the elderly will represent an increasingly larger share of the voting population.

Despite these concerns, the relationship between age and attitudes toward immigrants observed in cross‐section surveys – even when controlling for other observable characteristics – cannot directly be interpreted as reflecting a change in attitudes over the life‐cycle. The old not only differ from the young in terms of age, but they were also born and raised in a different time, in a different economic and institutional context. In fact, the observed patterns could reflect different preferences across cohorts or generations. For instance, if older cohorts grew up in a context of lower levels of international immigration than their younger counterparts did, they could have very different perceptions about immigrants that may remain fixed throughout their lives.<sup>3</sup> Hence, the relationship between age and attitudes toward immigrants observed in a cross‐section could be driven by both an age effect – i.e. the fact that attitudes change with age – and by a cohort effect – i.e. the fact that attitudes are different across generations. If the cross‐sectional age patterns were fully explained by a cohort effect, the political support for more open immigration regimes could actually increase in the future as younger cohorts replace those who are more averse toward increased immigration. To our knowledge, this paper provides the first attempt to disentangle these effects for a large group of countries.

> 3 There is empirical evidence that life experiences at certain age levels can explain why some generations have different policy preferences than others. For example, Giuliano and Spilimbergo (2013) find that large macroeconomic shocks experienced during the critical years of adolescence and early adulthood, between the ages of 18 and 25, shape preferences for redistribution.

3
that VE is partially compensated by RE; indeed, horizontal inequity in the form of RE is relatively low. Therefore, most of the redistributive effect of fiscal interventions occurs through a compression of the income distribution (VE).

**Table 4.** Redistributive effect. Uruguay, 2017

||From Pre-Fiscal<br>to Disposable<br>Income|From Disposable<br>to Adjusted<br>Income|From Pre-Fiscal to<br>Adjusted Income|
|---|---|---|---|
|Redistributive effect|0,0326|0,0456|0,0782|
|Vertical equity (VE)|0,0357|0,0469|0,0817|
|Reranking effect (RE)|0,0030|0,0013|0,0035|
|Horizontal inequality (RE/VE)|0,0840|0,0277|0,0428|

Source: Authors’ estimates based on ECH (2017), INE and Fiscal Accounts and CEQ Methodology Notes: Redistributive effect is the change in the Gini coefficient of moving across CEQ income concepts.

In **Table 3** we also report the effect of fiscal interventions on poverty using as threshold the official poverty line as defined by INE.<sup>20</sup> Poverty increases slightly when moving from Pre-fiscal to Net Market Income (as a result of direct taxes and contributions to the health system). But poverty decreases from Pre-fiscal to Disposable Income, that is, after payment of direct taxes and contributions to health system and reception of direct taxes. To isolate the effect of direct transfers we can focus on the passage from Net Market Income to Disposable income: the poverty rate decreases around 3 percentage points.

We calculate four complementary indicators that measure the efficacy and efficiency of _direct transfers_ in reducing poverty ( **Table 5** ). First, the vertical expenditure efficiency measures the percentage of transfers that go to the poor:
123***|
||(0.0318)|(0.0329)|(0.0452)|(0.0420)|(0.0406)|(0.0341)|
|Observations|1843|1843|1843|1843|1843|1843|

Note: This chart is based on answers to question 8, 10, 13, 29 and 30 in Appendix B.***, **, and * indicate significance at the 1, 5, and 10 percent critical level. Standard errors in parentheses.

42