rafmacalaba/datause-encoder-data huggingface dataset:
positively correlated with the population size of cities. A similar correlation is observed for Colombia between 2008 and 2010 (column 1 in Table 4 ), where 4.1 percent of population escaped from poverty.

We then estimated regression models in Equation (1) for Chile and Colombia to examine the heterogeneity in the link between the probability of poverty transition and city population size by flood risks. As shown in Column 4 in Table 3 and Table 4, the interaction term between the log of city population size and the flood risk variables is negative (−0.005 for Chile and −0.003 for Colombia), indicating that the upward mobility in large cities tends to be limited if they face high flood risks. With flat lines for high-risk areas and steep lines for low-risk areas, Figure 4 clearly shows such heterogeneity by flood risks.

**Table 3. First-difference model (dep var: the probability of transition from poor to nonpoor), Chile**

||**(1)**|**(2)**|**(3)**|**(4)**|
|---|---|---|---|---|
|Log population 2015|0.0083<sup>***</sup>||0.0075<sup>***</sup>|0.0089<sup>***</sup>|
||(0.0004)||(0.0004)|(0.0004)|
|Flood risk is high||−0.0174<sup>***</sup>|−0.0136<sup>***</sup>|0.0380<sup>***</sup>|
|||(0.0012)|(0.0011)|(0.0085)|
|Flood risk is high # log population 2015||||−0.005<sup>***</sup><br>(0.0008)|
|Constant|0.641<sup>***</sup>|0.736<sup>***</sup>|0.654<sup>***</sup>|0.638<sup>***</sup>|
||(0.0041)|(0.0006)|(0.0040)|(0.0050)|
|Observations|36,035|36,035|36,035|36,035|
|R-squared|0.012|0.006|0.016|0.016|

_Note:_ The table summarizes the estimation results of synthetic panel models in Equation (1). The dependent variable is the probability of each household’s transition from poor to nonpoor between 2011 and 2015. Standard errors in parentheses are estimated with 1,000 bootstraps.<sup>*</sup> _p_ < 0.1,<sup>**</sup> _p_ < 0.05,<sup>***</sup> _p_ < 0.01.

**Table 4. First-difference model (dep var: the probability of transition from poor to nonpoor), Colombia**

||**(1)**|**(2)**|**(3)**|**(4)**|
|---|---|---|---|---|
|Log of population 2010|0.0029<sup>***</sup>||0.0021<sup>***</sup>|0.0025<sup>***</sup>|
||(0.0002)||(0.0002)|(0.0002)|
|Flood risk is high||−0.0101<sup>***</sup>|−0.009<sup>***</sup>|0.0223<sup>***</sup>|
|||(0.0006)|(0.0006)|(0.0075)|
|Flood risk is high # Log pop. 2010||||-0.0025<sup>***</sup><br>(0.0006)|
|Constant|0.132<sup>***</sup>|0.170<sup>***</sup>|0.143<sup>***</sup>|0.138<sup>***</sup>|
||(0.0029)|(0.0002)|(0.0030)|(0.0032)|
|Observations|119,692|119,692|119,692|119,692|
|R-squared|0.001|0.002|0.003|0.003|

_Note:_ The table summarizes the estimation results of synthetic panel models in Equation (1). The dependent variable is the probability of each household’s transition from poor to nonpoor between 2008 and 2010. Standard errors in parentheses are estimated with 1,000 bootstraps.<sup>*</sup> _p_ < 0.1,<sup>**</sup> _p_ < 0.05,<sup>***</sup> _p_ < 0.01.

11
When there are no barriers to cross-country capital movement between the domestic market and the U.S., transaction costs are zero, and the two markets close at the same time, arbitrage should be instantaneous and costless. If the price of the underlying stock is higher than the price of the DR, investors can make an instant profit by buying the DR, transforming it into the underlying stock and selling this stock. This will drive the price of the underlying stock down and the premium back to zero. The reverse holds when the price of the DR is higher. In principle, the premium will be equal to zero. If a shock occurs too late during the day to be arbitraged away, closing prices will differ, but this difference will disappear quickly the next trading day.<sup>10</sup> In reality, however, instantaneous and costless arbitrage does not exist. Many factors can affect arbitrage, including capital controls and crises, as mentioned above.

To examine how the cross-market premium reacts to capital controls and crises, we conduct event studies. These studies allow us to determine whether the cross-market premium behaves statistically differently after an event. We do this in two ways. First, we construct a portfolio of stocks and study its evolution. Second, at the stock level, we compute the estimated post-event deviations from the pre-event mean and variance values. We then report these mean and variance differences and the number of stocks for which these differences are statistically significant.

We analyze the following events: the imposition of capital controls, the lifting of capital controls, significant relaxations in the intensity of capital controls, and crises. In the case of capital controls, the event date (time zero) is marked as the date capital controls change (i.e., they are introduced, lifted, or relaxed). A six-month window before

> 10 The same should apply to temporary non-zero premia due to differences in trading hours between the domestic and the U.S. stock market.

7
The challenge is compounded by the fact that there is limited documentation of this policyrelevant topic in Sub-Saharan Africa, in part because there are no systematic teacher salary data across African countries. UNESCO occasionally documents teacher salaries in terms of country GDP per capita (e.g., UNESCO 2009),<sup>4</sup> but it does not provide information on relative earnings differentials. Understanding the structure of earnings in countries with similar economic structures may be one input into policymaker discussions about setting salaries.

In this study, we assemble data from representative population or labor force surveys across 15 Sub-Saharan African nations. We characterize the demographics of the teaching workforce, compare earnings from the primary job for teachers to other wage workers of similar age, education, rural/urban sector, and gender. We then explore structural factors that may explain teacher wage differentials.

Our analysis shows that, across countries, teacher earnings rise in absolute terms with GDP per capita but that they fall as a percentage of GDP per capita – i.e., they rise more slowly than GDP per capita. Within countries, we find that in seven out of the 15 countries, teachers’ monthly earnings are statistically significantly lower than that of other comparable wage workers. In five countries (Burkina Faso, Côte d’Ivoire, Namibia, Senegal and Zambia) teachers’ monthly earnings are statistically significantly higher than comparable workers. But simple earnings comparisons are complicated by adjustments for working hours. In four of the eight countries where teachers’ monthly earnings are lower and where teachers report their hours, their hourly wage is higher (albeit not statistically significantly so) than that of other workers. Positive premia tend to be higher for teachers on permanent contracts – who get paid more but work similar hours – than for teachers on fixed-term contracts. Teacher earnings are not more determined by age and education than those of other workers. The fraction of teachers in the workforce has also risen across countries over the last two decades. Contrary to expectations, the teacher wage differential (monthly or hourly) across countries is not associated with GDP per capita, the size of the formal sector, the share of female workers in other wage jobs, the rate of unionization, or female labor force participation. We find a positive association between teacher earnings differentials and student learning outcomes, both

> 4 Specifically, these statistics are provided by Pôle de Dakar, the Africa-based office of UNESCO’s International Institute for Educational Planning.

3
## Policy Research Working Paper 10815

# **Abstract**

Using two decades of granular data on foreign direct investments, this study shows a consistent global rise in the concentration of cross-border investments within fewer multinational firms. This concentration is most prominent in developing economies, reaching record highs in recent years. Structural shifts into services do not stand out as the primary driver of variation in investment concentration across countries and over time. Instead, concentration has grown significantly more in destinations facing high economic uncertainty.

This paper is a product of the International Finance Corporation. It is part of a larger effort by the World Bank Group 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 aragoussis@ifc.org; d.rigo@lse.ac.uk; and gianluca.santoni@cepii.fr. A verified reproducibility package for this paper is available at http://reproducibility.worldbank.org, click **here** for direct access.

<!-- Start of picture text --> TRANSPARENT<br>ANALYSIS<br>G<br>R E<br>N<br>S<br>I P<br>Y E<br>K A<br>C A<br>R P<br>LI R<br>O E<br>O C<br>W R<br>H<br>S<br>P<br><!-- End of picture text --> _The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
18,345, an increase of more than 3.5 times. By 2022, 63% of children aged 3-6 were enrolled in preschool programs, with about one-third attending PPP preschools. However, the expansion varied significantly across districts, both in timing and scale, creating temporal and spatial variation. This variation allows us to estimate the causal impact of the policy on female labor force participation (Sankar, 2021).

It is important to note that preschool education in Uzbekistan is not compulsory, and while enrollment has increased significantly, preschool services—especially private ones— remain costly.<sup>3</sup> On average, public preschool fees already represent around 25% of the average monthly earnings of women in regional areas.<sup>4</sup> Despite the high costs, many families are motivated to enroll their children because preschools across all regions follow a standard curriculum set by MOPSE, which focuses on teaching basic language, writing, and math skills as the main aim of the reform was to improve educational outcomes for children by better preparing them for primary school.

Meanwhile, primary education in Uzbekistan is free and compulsory, starting at the age of 6-7, with an enrollment rate of 94%.<sup>5</sup> The overall goal of expanding childcare facilities, particularly through PPP preschools, was to increase access to early childhood education and enhance children’s readiness for primary education, especially in regions where such services were previously limited.

# **3. Data**

_Survey data._ For our main estimation analyses, we use Listening to Citizens of Uzbekistan survey (L2CU) data conducted by the World Bank and Development Strategy Center (2022). The study comprises a 4,000-household nationally representative baseline survey conducted in June/July 2018, and since then a monthly “panel” survey of a subset of 1,500 households from the baseline survey until 2023. This survey is used to measure quar- terly female labor supply, individual and other household-level covariates since June 2018 in each district. Furthermore, Household Budget Survey (HBS) data – yearly pooled crosssectional data from 2015-2022 – is used to do robustness test estimations. The HBS serves as the fundamental data source for examining people’s standard of living and for devising additional measures to enhance the well-being of the population. Furthermore,

> 3Source: https://cabar.asia/ru/uzbekistan-gosudarstvennyh-detskih-sadov-ne-hvataet-chastnye-dorogi 4Source: https://stat.uz/uz/matbuot-markazi/qo-mita-yangiliklar/34143-yuridik-shaxs-maqomiga-egabo-lgan-korxona-va-tashkilotlarda-ishlovchi-xodimlarning-o-rtacha-oylik-nominal-hisoblangan-ish- haqi2022-yil-yanvar-dekabr

> 5Source: https://data.worldbank.org/indicator/SE.PRM.ENRR?locations=UZ

6
**Error! Reference source not found.** depicts the evaluation as it was designed and as it was implemented.

# **The Data: The Surveys, the Measures Collected, and the Children**

# **_The Timing of Survey Data Collection_**

The analysis in this paper is based on two rounds of data – a baseline survey which was conducted from March to June 2009 and a follow-up survey which was conducted approximately 14 months later from July to August 2010. There were discrepancies between the timing of the surveys and project implementation – these are depicted in Figure 4. As a result, villages in batch 1 had already implemented the ECED project for about 6 months by the time the baseline survey was fielded. Likewise, villages in batch 3 (which had not received the ECED project at baseline) started implementation about nine months before the follow-up survey was fielded. Villages without the project had no services provided through the project either at baseline or at follow-up. In each village, data were collected on households, caregivers and the development of two cohorts of children.

# **_The Measures_**

Child development was assessed using an extensive array of internationally-validated and locally-adapted<sup>6</sup> child development measures including:

1. The short version of the Early Development Instrument (EDI)

2. The Strengths and Difficulties Questionnaire (SDQ) 3. Height and weight measurements 4. Demonstrations of child skills (and mother reports of these skills), drawing on a study conducted by the University of San Carlos Office of Population Studies 5. Tests of executive function using the Dimensional Change Card Sorting (DCCS) game 6. Drawing tasks (based on the Draw-a-Man test) 7. Expressive and receptive language tasks Several of the measures above assess multiple domains of child development. A higher score is better for all measures except for the Strengths and Difficulties Questionnaire (SDQ) where lower scores are better and represent fewer difficulties.<sup>7</sup>

> 6 For details on how instruments were adapted and tested, see Pradhan et. al. (2013).

9
## **Figure 8: Shares of explained variance by latent factors**

Each panel depicts the share of variance explained by fitting model (9)-(10) with _퐾_ = 1 _,_ 2 _,_ 3 factors respectively.

<!-- Start of picture text --> Ft with 1 factors Ft with 2 factors Ft with 3 factors<br>100%<br>80%<br>60% Share of explained<br>variance<br>Share of explained<br>40% variance (cuml.)<br>20%<br>0%<br>1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10<br> 75.6%<br> 66.5% 67.3%<br> 18.9% 16.9%<br> 8.7%<br> 5.2% 3.5% 2.2% 1.2% 0.9% 0.6% 0.5% 0.3% 4.8% 3.3% 2.0% 1.2% 0.9% 0.6% 0.4% 0.2% 6.0% 3.3% 2.1% 1.2% 0.8% 0.6% 0.4% 0.2%<br><!-- End of picture text --> This conclusion is further supported by statistical arguments. The first latent factor accounts for ˆ two-thirds of residual variance and has an AR(1) coefficient of _휌_ = 0 _._ 989<sup>∗∗∗</sup> which is comparable with the average autocorrelation coefficient for bond yields. The second factor accounts for more ˆ than half of the remaining residual variance and has an AR(1) coefficient of _휌_ = 0 _._ 965<sup>∗∗∗</sup> . These characteristics emphasize the necessity of the employed IFE method. The change in coefficients between the FE estimates in (3) and the IFE estimates (6), together with the simulation study in Appendix, Section A.2, give an idea of how strong the bias of a misspecified model is. We proceed with _퐾_ = 2.

# **5 Limitations**

## **5.1 Lack of biodiversity**

For the sake of consistent, global coverage, the wealth data made the deliberate decision to calculate aggregated numbers on the country level. This prevents decompositions beyond what Table 6 presents. One interesting component that affects all renewables is biodiversity. The wealth of species diversity and ecosystem services is currently only accounted for indirectly. These are restricted to terrestrial areas and do not include the blue economy. Loss of biodiversity and collapse of the ecosystem is ranked as top five global risks (WEF, 2020a). Regulators, pension funds and banks are paying more and more attention to the effects on the financial sector (Schellekens et al., 2019; van Toor et al., 2020).

## **5.2 Low data frequency**

A further limitation imposed by the wealth data is its annual frequency. Seasonal effects and other business cycle dynamics that possibly affect bond prices cannot be addressed. The immediate impacts of droughts and floods are not discernible. One promising possibility to address this issue is to include high-frequency and objective geospatial data, such as remote-sensing satellite data or weather station measurements (WWF et al., 2020). This type of data, which has the added benefit of being exogenous by nature, would shed light onto short-term dynamics and enable causal inferences. The statistical framework used in this analysis could be easily extended 24
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24
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44
assumes that the AGE require at least a full school year to be effective. In a second specification, we decompose the AGE _s,t-1_ dummy in a set of dummies that equal one if the school has had AGE for one year and a second if the school has had AGE for two or more years. This addresses the question of whether the AGE impact on outcomes cumulates over time.

# **_4.2. Treatment and Comparison Groups_**

As argued earlier, we exploit the geographic expansion of AGE over time to construct treatment and comparison groups. Our sample of analysis consists of nonindigenous primary schools in rural areas that did not enroll in the Compensatory Program – and hence did not have AGE – before school year 1998-99 for which the targeting index was constructed.<sup>12</sup> We define the set of AGE treatment schools as the set of schools that first received AGE at the beginning of any school year between 1998-99 and 2001-02, and had AGE continuously ever since. Those that had not received AGE before school year 2002-03 constitute the comparison group.<sup>13</sup> Our final sample consists of a balanced panel of 6,027 rural non-indigenous primary schools that we observe continuously between 1995 and 2003. Of these, 42 percent become AGE beneficiaries over the period.<sup>14</sup> Table 1 shows summary statistics for a few school observable characteristics and for the dependent variables in 1997 (baseline) for AGE treatment and comparison

> 12 We limit on the sample to rural non-indigenous primary schools because the vast majority of AGE beneficiary schools are in rural areas and all indigenous schools were automatically incorporated when AGE first started in 1998 in these States.

> 13 Because we only have AGE coverage data until 2003, we do not know whether schools in the comparison group received AGE at later dates.

> 14 To allow comparison across outcomes, we restrict the sample to schools with complete information on outcomes.  Results are robust to the inclusion of schools with missing information for one or more of the outcomes. We also drop from the sample schools with extremely high numbers of students and/or teachers (top 0.5 percent of each distribution and bottom 1 percent of the distribution of students). We have also trimmed schools with values of the dependent variables in the top 0.5% of each distribution.

13
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