rafmacalaba/datause-encoder-data huggingface dataset:
**Figure 6. Cross-border flows and extensive margin of M&A**

<!-- Start of picture text --> Panel A: LAC to North Panel B: LAC to South<br>30 4.0% 18 0.18%<br>3.5% 16 0.16%<br>25<br>14 0.14%<br>3.0%<br>20 12 0.12%<br>2.5%<br>10 0.10%<br>15 2.0%<br>8 0.08%<br>1.5%<br>10 6 0.06%<br>1.0%<br>4 0.04%<br>5<br>0.5% 2 0.02%<br>0 0.0% 0 0.00%<br>Panel C: North to LAC Panel D: South to LAC<br>80 12% 25 1.4%<br>70 1.2%<br>10%<br>20<br>60<br>1.0%<br>8%<br>50<br>15<br>0.8%<br>40 6%<br>0.6%<br>10<br>30<br>4%<br>0.4%<br>20<br>5<br>10 2% 0.2%<br>0 0% 0 0.0%<br>Panel E: LAC to LAC<br>10 5.0%<br>9 4.5%<br>8 4.0%<br>7 3.5%<br>6 3.0%<br>5 2.5%<br>4 2.0%<br>3 1.5%<br>2 1.0%<br>1 0.5%<br>0 0.0%<br>% of Active Connections Total Flows<br>% of Active Connections % of Active Connections<br>Value (2011 Constant U.S. Dollars Billions) Value (2011 Constant U.S. Dollars Billions)<br>1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011<br>% of Active Connections % of Active Connections<br>Value (2011 Constant U.S. Dollars Billions) Value (2011 Constant U.S. Dollars Billions)<br>1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011<br>% of Active Connections<br>Value (2011 Constant U.S. Dollars Billions)<br>1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011<br><!-- End of picture text --> This figure shows the evolution of the extensive margin for M&A and the evolution of the total value of these connections. The extensive margin is represented by the percentage of active connections, that is, the number of country-pairs that have a positive flow in each year over the number of country-pairs with a positive or zero flow in the last year of the sample. North countries are the G7 and Western Europe. South countries are all the others (excluding LAC countries). Offshore centers are excluded from the sample. Flows are expressed in 2011 constant U.S. dollars.
**Does the** picture change when IND and TEEN training program variables **are added as** in the regressions shown in Table 4? A similar **pattern** emerges with regard to the other independent variables, and the regressions again have low overall explanatory power. With the other variables held constant, no significant earning, differentials are reported between VOC and between APP (again, except for 1969) and IND, respectively. However, for **a** number of tho regression runs, the coefficient on TEEN is negative and significant. Although not in line with those of Borus, this particular result is not altogether unexpecteds most of **the** studonts enrolled in TEEN came from low socio-economic backgrounds and many were dropouts from **secondary** schooling. Nore important, tho courses though full-time, lasted for up to a year only (compared with three or four years for other training modes, notably VOC).

Overall, **we** do not see evidence of **an** earnings gap emerging over the longer term **between the** different training modes and particularly between VOC and APPg tho **earlier** results of Borus, relating only to two years posttraining labor market experience, **are** largely confirmed.

13
where _pk_ is the percentage of total pixels in each location with color _k_ , with _k_ equal to 0 for green, 1 for orange, 2 for red, and 3 for dark red, respectively. To obtain the daily congestion index, we compute the index for every hour and take the average over 24 hours. By construction, the figure varies between 0 and 300, where 0 indicates a free-flow traffic condition and 300 a highly congested condition. We interpret the traffic congestion index (TCI) as a measure of aggregate delay from free-flow speed.<sup>6</sup> We use and construct the daily congestion index over the period from March 20, 2019 to May 19, 2020 for several major cities with varying degrees of Covid-19 lockdowns. These include Paris and New York City, subject to strict lockdown measures starting on March 17 and March 20, 2020, respectively. We also include Bogotá and New Delhi, both of which started to enforce strict Covid-19 lockdown policies on March 25, 2020. We use the traffic conditions in Hong Kong SAR, China, a city that did not implement any Covid-19 lockdown measure, as our main control series for modeling the counterfactual scenarios for Bogotá, New Delhi, New York City, and Paris. Alternatively, in addition to using the TCI of Hong Kong SAR, we also use the TCI from cities such as Cairo, Sofia, Stockholm, and Tokyo that opted for relatively softer lockdown measures. Finally, we enrich our control index time series with information on traffic congestion city-level precipitation and temperature from Menne et al. (2012a,b). In what follows, we focus on core cities only. The methodology is, of course, replicable to measures of congestion at the greater metropolitan area level.

> 6 Dasgupta et al. (2020) use the same underlying information from Google Maps to calculate the average speed within a city. Their approach, however, requires estimating a city-specific correspondence between speeds and colors and assuming that the correspondence is stable over time and over different locations in the city. It overlooks the fact that free flow may occur at low speeds in the presence of speed limitations, which are common in urban contexts. In contrast, the TCI focuses on the severity of delays while making it possible to aggregate the information over the whole network even when free-flow conditions might involve different speeds in different parts of the network.

16
Table 6b: Care and Domestic Responsibilities - Correlation with Female Caregiver Norm: Attitudes and Norms

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
||Main<br>Caregiver:Presch<br>ool or School<br>Children|Hours spend on<br>care activities<br>(Main Caregiver<br>Children)|<br>Hours spend on<br>household chores<br>(Main Caregiver<br>Children)|<br>Main<br>Caregiver:Presch<br>ool or School<br>Children|Hours spend on<br>care activities<br>(Main Caregiver<br>Children)|<br>Hours spend on<br>household chores<br>(Main Caregiver<br>Children)|
||**_Female Ca_**|**_regiver Norm: W_**|**_oman's most imp_**|**_ortant role is to ta_**|**_ke care of home_**|**_& children_**|
|Female|0.07***|2.40***|1.12***|0.05***|2.27***|1.16***|
||[0.01]|[0.18]|[0.06]|[0.01]|[0.33]|[0.11]|
|Personal Belief:Agree(No=0/Yes=1)|0.03***|0.41**|0.14**||||
||[0.01]|[0.16]|[0.06]||||
|Personal Belief:Agree*Female|0.03**|0.64**|0.22**||||
||[0.01]|[0.25]|[0.09]||||
|Community Norms (0/1)||||0.00|1.06***|0.43***|
|||||[0.02]|[0.29]|[0.10]|
|Community Norms (0/1)*Female||||0.04**|0.63|0.07|
|||||[0.02]|[0.47]|[0.16]|
|Constant|0.29***|4.59***|3.79***|0.28***|3.41***|3.54***|
||[0.05]|[0.94]|[0.34]|[0.05]|[0.84]|[0.35]|
|Observations|22381|9592|10165|21211|9180|9670|
|Adjusted R-squared|0.22|0.15|0.17|0.22|0.16|0.17|
|Mean Female|0.47|9.40|4.26|0.47|9.42|4.27|
|Mean Male|0.41|6.22|2.94|0.42|6.20|2.94|
|Norms+Fem(Coeff)|0.06|1.05|0.36|0.05|1.69|0.51|
|Norms+Fem(SE)|0.01|0.20|0.07|0.02|0.37|0.12|
|Norms+Fem(P-value)|0.00|0.00|0.00|0.00|0.00|0.00|
|Sample|ALL|ALL|ALL|ALL|ALL|ALL|
|CountryFE|YES|YES|YES|YES|YES|YES|
# **3.** **<u>Summary of the Literature</u>**

We conducted a large, narrative literature review of the various interventions that either sought to or resulted in increased educational attainment, delayed childbearing, and/or delayed marriage for adolescent girls in low- and middle-income countries.<sup>45</sup> To begin, we started by including relevant studies that we already knew about. We then used references from these studies to expand the search, along with suggestions of studies recommended to us by colleagues, peers, and reviewers. In the end, we included a total of 104 interventions from 70 studies (of which 11 interventions from 7 studies are in HIC settings) - see <u>Table A1</u> for the list of the interventions and associated studies.<sup>6</sup> To summarize this review, we divided interventions into 16 categories:

# <u>Intervention Categories:</u>

- (i) improving quality of schools

- (ii) construction of schools

- (iii) mandatory schooling laws and length of school days

- (iv) removing bans on pregnant girls attending school

- (v) raising age of marriage laws

- (vi) job opportunities and information on job opportunities

- (vii) vocational training

- (viii) life skills training, mentoring, and empowerment programs via girls’ clubs

- (ix) other life skills training, mentoring, and empowerment programs

- (x) provision of sexual and reproductive health services

- (xi) vouchers and subsidies for FP services and modern contraceptives

- (xii) information on SRH and FP

- (xiii) cash transfers

- (xiv) in-kind transfers

- (xv) information on returns to education and academic performance

- (xvi) other interventions that seek to improve social norms, aspirations, and empowerment

We then allocated each intervention to a category and, _based on our reading of the literature_ , summarized the overall effectiveness of each category on our three outcomes of interest. Table 1 below presents our summary, while <u>Table A1</u> contains a list of all studies used to inform our review, which includes detailed information on each study, such as country, intervention(s), assigned intervention categories, direction and statistical significance of effect size for each outcome variable, and publication status. Moreover, Appendix A provides a more descriptive

> 4 For a definition of a narrative review and how it differs from other review methodologies, such as meta-analysis or vote counting, see Evans and Popova (2016).

> 5 We also include (a) interventions that did not specifically focus on adolescent girls but still included adolescent girls, and (b) interventions that did not include adolescent girls but where we believe the results would likely carry over to them. When presenting our summary of the success of categories of interventions in Table 1, we note whether a given category of interventions has received limited application to adolescent girls; moreover, in Table A1, we precisely describe the target population of each intervention we reviewed.

> 6 <u>Table A1</u> is a dynamic database: readers are encouraged to use the link provided in Table A1 to submit papers that they believe should be included in this review piece.

5
# **5. Conclusion**

The aim of this paper was to examine the impact of trade facilitation on intra-African trade. Port efficiency, customs environment, regulatory environment and services infrastructure are the measures of trade facilitation used. The results show that besides the traditional determinants of bilateral trade, port efficiency and services infrastructures are the factors that have a positive effect on African trade. On the other hand, customs and regulatory environments are the main obstacles to intra-African trade. Overall a coherent and comprehensive trade facilitation agenda should be an essential element of domestic reform. Negotiations at the WTO and Economic Partnership Agreements (EPA), especially because they explicitly recognize the need for technical assistance and capacity building, offer an opportunity. The further consensus about the relevance of trade facilitation to the aid for trade initiative confirms the centrality of the above conclusion for African countries (see Mugadza, 2008).

First, the customs environment is a key factor for accelerating trade among countries. Improvement of African countries' customs can generate positive spill-overs. Second, a good regulatory environment attracts foreign investment and facilitates the trade.

In our sample, 43.53% of importer-exporter pairings had zero bilateral trade. Thus apart from analyzing the effects of different variables on trade, we must also focus on factors that may explain why trade does not occur at all among countries. Employing a selection model allowed us to take account of this censoring process (Francois and Manchin, 2007).

# **Appendix: Data sources and definitions**

The yearly data are constructed for 100 countries and 11 regional trade agreements spanning from 2003 to 2004. These data are computed from the Global Competitiveness Report (GCR) released. by The World Economic Forum, the World Development Indicators (WDI) published by the World Bank, the International Financial Statistics (IFS) released by the International Monetary Funds, as well as the Commodity Trade (COMTRADE), and the Trade Analysis and Information System (TRAINS) published by the United Nations Conference on Trade and Development (UNCTAD). The individual countries and regional trade agreements are presented in table 8.
<!-- Start of picture text --> Training Data<br>Testing Data<br><!-- End of picture text --> This cross-validation exercise is conducted to determine the optimal threshold of the p-value for the stepwise regressions. For a specific p-value, the cross-validation exercise is done and produces the two testing statistics. The exercise is repeated for different levels of p-value, usually between 0.1% and 10%. The optimal p-value is the value where the absolute value of the difference between the actual and the projected poverty rates is minimized. The mean squared error is also examined to check whether the over-fitting problem occurs. If the mean squared error is minimized at a level of p that is smaller than the value where the absolute difference between the actual and the projected poverty rates is minimized, then the former value is chosen as the optimal number.

Figure 12 shows results of cross validation analysis using the Ghana Living Standard Survey (GLSS) 2012/13 data. The average MSE continues to decline as the threshold of the p-value for the stepwise regression increases. If MSEs are calculated in the same sample as where a model is developed, MSEs tend to decline as the p-value increases because the number of variables in a model tends to increase and the model fitness improves as the p-value increases. However, this is not always the case if we calculate MSEs out of sample because of the over-fitting problem. In the case of a cross-validation analysis for GLSS 2012/13 data, we did not see that, but we did see it in the other data set. This suggests that there is no over-fitting problem in the modeling in GLSS 2012/13 for the range of p-values we investigated.

The average absolute values of the difference between actual and projected poverty rates show a different trend. Although the numbers fluctuate, the difference starts increasing once the p-value reaches 6%. Below 6%, the value fluctuates, but it is never below the value at the p-value of 6%. Therefore, we choose 6% as the optimal threshold of the p-value for the stepwise regression procedure.

35
growth rate of GDP per capita (percent, annual); and the percentage of women in the 35-64 years age group relevant for managers. The data source for all these variables is WDI, World Bank.

We control for several firm characteristics that may be correlated with the gender of the top manager and vary systematically with credit supply. Direct evidence on differences between women- and men-led firms is limited to a few firm characteristics. These include labor productivity and growth rate of sales and employment (see Allison et al. 2023, Islam et al. 2020), firm size (see Fairlie and Robb 2009), age of firm (see Blum et al. 1994, Islam et al. 2020), exposure to international markets (see Marques 2015, Haddoud et al. 2021), gender composition of the owners (Sekkat et al. 2015), and gender composition of the workforce (Hurley and Choudhary 2016). We go beyond and account for additional factors such as, quality of the regulatory and business environment that may have gendered effects on firm functioning (see Vershinina et al. 2022. Hanousek et al. 2019), and therefore may affect the choice of woman vs. man top manager.

Based on the discussion above, the baseline firm-level controls include the following (all taken from WBES): firm size proxied by log of number of workers employed at the firm 3 fiscal years ago (lagged) and a dummy variable equal to 1 if the firm is part of a larger establishment and 0 otherwise ( _Multi establishment firm_ ); log of age of the firm; labor productivity equal to log of the ratio of annual sales (in 2009 USD) to the number of workers three fiscal years ago; firm growth proxied by the average annual growth rate of employment (log difference) at the firm over the last 3 fiscal years; dummy variable equal to 1 if the firm purchased fixed assets during the last fiscal year and 0 otherwise; dummy variable equal to 1 if the firm has one or more women owners and 0 otherwise ( _Women owners_ ); share of women workers in the workforce at the firm at the end of the last fiscal year ( _Women workers_ ); exposure to international markets proxied by the share of firms’ sales in the last fiscal year that were made directly abroad ( _Exports_ ), proportion of firms’ 13
**Graph 5 ‐ Liabilities included in the BS**

<!-- Start of picture text --> 30<br>25<br>25<br>20<br>16<br>14<br>15<br>11 11<br>9<br>10 8<br>5<br>0<br>Sovereign debt Currency in Deposits of Legal claims Guarantees Pension Other<br>circulation commercial liabilities<br>banks<br><!-- End of picture text --> Source: Responses to SALM survey

# 5. Main issues related to the production of the sovereign balance sheet

The development of a sovereign balance sheet presents many challenges, ranging from the availability of data, the complexity of the legal structure and governance arrangements between the central government and other public sector entities (sub‐nationals, SOEs, SWF), theoretical considerations pertaining to the valuation methodologies (in particular for illiquid assets and liabilities), differences in accounting principles and standards across the public sector, to list but a few of them. This section examines the challenges reported by the respondents in the production of the sovereign balance sheet.

The respondents cited a number of issues and challenges in producing the government balance sheet. First, balance sheet items are often accounted for at the level of individual department or agency, therefore an efficient central budget management system is needed to consolidate the inputs into the aggregated balance sheet as well as agree on uniform accounting principles and consolidation procedures. Additional challenges concern data collection as well as valuation of some fixed assets for the first time (such as lands, roads, etc.), valuation of assets that have cultural value, contingent liabilities, intra‐group transactions and estimates of the present value of future revenue and expenditure. The issue of valuation of assets and liabilities is particularly complex for the production of the initial balance sheet (initial valuation). Finally, consolidating the balance sheet requires clear understanding of the economic transaction underlying the various components (not only the legal form or accounting category) to ensure consistency and coherence. This seems especially challenging when the assets and liabilities of numerous entities 14
their children, 17 percent of these households currently enroll none of their children, and only 24 percent of these households currently enroll all of their primary school-aged children. Focusing on the 59 percent of households with variation in enrollment outcomes, which is our baseline sample, 51 percent of children in these households are enrolled in the current school year.<sup>8</sup> If we consider whether a child has ever been enrolled in school rather than current enrollment, then 56 percent of children in these households have ever been enrolled. Given these low enrollment rates, on average these children have only completed 1.7 years of school.

Besides enrollment, we also explore two other school-related outcomes (grade progress and starting school at a late age) where sibling ability rivalry might matter. First, we calculate grade progression through school by dividing the child‟s highest grade attended by the number of years since the child started school. The grade progress measure ranges from zero to one, with higher numbers indicating quicker progress towards completing primary school.<sup>9</sup> Second, we define a starting school late variable that equals 1 if the child started school after age 7 or never attended school (late start), takes the value 0 if the child started school at age 7 (on-time start), and takes the value -1 or -2 if the child started school at ages 6 or 5, respectively (early start).

In our analysis of child labor activities, we examine both the extensive (whether a child does a specific activity) and intensive margins (how many hours doing an activity). The survey collected information for each child about whether, during the two days preceding the survey when school was in session, the child engaged in different types of child labor activities in the household or on the farm, and if they participated in an activity, during what period of the day they did so. In this rural setting most people do not have watches, and we found during pre-

> 8 In Section 3.2, we discuss in detail first why we initially restrict the baseline analysis to the sample of households that have within-family variation in enrollment outcomes and second what are the drawbacks of focusing on this selected sample of households that have variation in child schooling. In Section 4.3, we present results incorporating all households and confirm that our main hypotheses about sibling rivalry still hold.

> 9 For children who never attended school, we assign a grade progression measure of zero.

8