rafmacalaba/datause-encoder-data huggingface dataset:
|Financial Education and Financial Literacy|Soren Nellemann|
|---|---|
|Financial Education and Financial Literacy|Soren Nellemann|
|Financial Education and Financial Literacy|Soren Nellemann|
|Financial Education and Financial Literacy|Soren Nellemann|
|Financial Education and Financial Literacy|Soren Nellemann|
|Financial Education and Financial Literacy|Soren Nellemann|
|Financial Education and Financial Literacy|Soren Nellemann|
|Financial Education and Financial Literacy|Soren Nellemann|
|Financial Education and Financial Literacy|Soren Nellemann|
|Financial Education and Financial Literacy|Soren Nellemann|
|Financial Education and Financial Literacy|Soren Nellemann|
|Financial Education and Financial Literacy|Soren Nellemann|
|Financial Education and Financial Literacy|Soren Nellemann|
|Pacific Regional Audit Initiative(PRIA)|John Nyaga|
|Pacific Regional Audit Initiative(PRIA)|John Nyaga|
|Pacific Regional Audit Initiative(PRIA)|John Nyaga|
|BZ JSDF ImprovingChildren's Health|Carmen Carpio|
|BZ JSDF ImprovingChildren's Health|Carmen Carpio|
|BZ JSDF ImprovingChildren's Health|Carmen Carpio|
|BZ JSDF ImprovingChildren's Health|Carmen Carpio|
|BZ JSDF ImprovingChildren's Health|Carmen Carpio|
|BZ JSDF ImprovingChildren's Health|Carmen Carpio|
|BZ JSDF ImprovingChildren's Health|Carmen Carpio|
|BZ JSDF ImprovingChildren's Health|Carmen Carpio|
|BZ JSDF ImprovingChildren's Health|Carmen Carpio|
|Southern Africa Trade and Transport Facilitation Project|Gylfi Palsson|
|Southern Africa Trade and Transport Facilitation Project|Gylfi Palsson|
|Southern Africa Trade and Transport Facilitation Project|Gylfi Palsson|
|Southern Africa Trade and Transport Facilitation Project|Gylfi Palsson|
|Southern Africa Trade and Transport Facilitation Project|Gylfi Palsson|
|Southern Africa Trade and Transport Facilitation Project|Gylfi Palsson|
|Southern Africa Trade and Transport Facilitation Project|Gylfi Palsson|
|Southern Africa Trade and Transport Facilitation Project|Gylfi Palsson|
|Southern Africa Trade and Transport Facilitation Project|Gylfi Palsson|
|Southern Africa Trade and Transport Facilitation Project|Gylfi Palsson|
|Southern Africa Trade and Transport Facilitation Project|Gylfi Palsson|
|Southern Africa Trade and Transport Facilitation Project|Gylfi Palsson|
|Southern Africa Trade and Transport Facilitation Project|Gylfi Palsson|
|Southern Africa Trade and Transport Facilitation Project|Gylfi Palsson|
|Southern Africa Trade and Transport Facilitation Project|Gylfi Palsson|
|StrengtheningTCU Financial Audit of the Consolidated Financial S|tatJoseph Kizito Mubiru|
|StrengtheningTCU Financial Audit of the Consolidated Financial S|tatJoseph Kizito Mubiru|
|StrengtheningTCU Financial Audit of the Consolidated Financial S|tatJoseph Kizito Mubiru|
|TH: Institutional CapacityBuildingon ICT Policies in Thailand|Seda Pahlavooni|
|TH: Institutional CapacityBuildingon ICT Policies in Thailand|Seda Pahlavooni|
|TH: Institutional CapacityBuildingon ICT Policies in Thailand|Seda Pahlavooni|
|Strengtheningthe Capacityof Public Sector Internal Audit|Agnes Albert-Loth|
# **References**

- Agostinelli, F. and G. Sorrenti (2018, January). Money vs. time: family income, maternal labor supply, and child development. ECON - Working Papers 273, Department of Economics - University of Zurich.

- Akresh, R., D. De Walque, and H. Kazianga (2013). _Cash transfers and child schooling: evidence from a randomized evaluation of the role of conditionality_ . The World Bank.

- Almond, D., B. Mazumder, and R. van Ewijk (2011, December). Fasting During Pregnancy and Children’s Academic Performance. NBER Working Papers 17713, National Bureau of Economic Research, Inc.

- Angelucci, M. (2015, March). Migration and Financial Constraints: Evidence from Mexico. _The Review of Economics and Statistics 97_ (1), 224–228.

- Angrist, J. D. and V. Lavy (1999). Using maimonides’ rule to estimate the effect of class size on scholastic achievement. _The Quarterly journal of economics 114_ (2), 533–575.

- Araujo, M. C., M. Bosch, and N. Schady (2016, September). Can Cash Transfers Help Households Escape an Inter-Generational Poverty Trap? NBER Working Papers 22670, National Bureau of Economic Research, Inc.

- Attanasio, O., H. Baker-Henningham, R. Bernal, C. Meghir, D. Pineda, and M. RubioCodina (2018, September). Early Stimulation and Nutrition: The Impacts of a Scalable Intervention. NBER Working Papers 25059, National Bureau of Economic Research, Inc.

- Attanasio, O., S. Cattan, E. Fitzsimons, C. Meghir, and M. R. Codina (2018, July). Estimating the production function for human capital: results from a randomized controlled trial in Colombia. IFS Working Papers W18/18, Institute for Fiscal Studies.

- Avitabile, C. and R. de Hoyos (2018). The heterogeneous effect of information on student performance: Evidence from a randomized control trial in Mexico. _Journal of Development Economics 135_ (C), 318–348.

- Baez, J. E. and A. Camacho (2011, May). Assessing the long-term effects of conditional cash transfers on human capital: Evidence from colombia. IZA Discussion Papers 5751, Institute for the Study of Labor (IZA).

- Baird, S., C. McIntosh, and B. Özler (2011). Cash or Condition? Evidence from a Cash Transfer Experiment. _The Quarterly Journal of Economics 126_ (4), 1709–1753.

25
# _REFERENCES_

- <mark>Abdullah, Fizan, Yiyi Zhang, Thomas Lardaro, Marissa Black, Paul M. Colombani, Kristin Chrouser, Peter J. Pronovost, and David C. Chang. “Analysis of 23 Million US Hospitalizations: Uninsured Children Have Higher All-Cause In-hospital Mortality.”</mark> _<mark>Journal of Public Health</mark>_ <mark>32, no. 2 (2010): 236-244.</mark>

- <mark>Adhvaryu, Achyuta, and Anant Nyshadham. “Endowments at Birth and Parents' Investments in Children.”</mark> _<mark>The Economic Journal</mark>_ <mark>(2014).</mark>

- Aizer, Anna and Jeffrey Grogger, “Parental Medicaid Expansions and Child Medicaid Coverage.” NBER Working Paper 9907, (2003). Cambridge, MA.

- Aiyar, Anaka, “The Spillover Effects of Health Insurance on the Household: Evidence from Vietnam.” Unpublished. (2016).

- Banerjee, Abhijit, Esther Duflo, and Richard Hornbeck. “Bundling Health Insurance and Microfinance in India: There Cannot be Adverse Selection if There is No Demand.” _The American Economic Review_ 104 no.5 (2014): 291.

- Barofsky, Jeremy. “Universal Health Coverage for the Poor: A Cost-Benefit Analysis of Mexico’s Seguro Popular.” Brookings Institution Working Paper (2015). Washington, D.C.

- <mark>Basiotis, P. Peter, Carol S. Kramer-LeBlanc, and Eileen T. Kennedy. “Maintaining Nutrition Security and Diet Quality: the Role of the Food Stamp Program and WIC.”</mark> _<mark>Family Economics and Nutrition Review</mark>_ <mark>11, no. 1/2 (1998): 4.</mark>

- <mark>Basu, Anirban, and David Meltzer. “Implications of spillover effects within the family for medical cost-effectiveness analysis.”</mark> _<mark>Journal of Health Economics</mark>_ <mark>24, no. 4 (2005): 751-773.</mark>

19
trade, however, our estimate on _WTOij,t_ conrms the ndings of Larch et al. (2019) for positive WTO eects when domestic trade ows are used to estimate gravity equations. In robustness analysis, which is available upon request, we conrm that when the model is estimated without domestic trade ows, the estimate of the eects of WTO is smaller and it is not statistically signicant.

Second, we obtain a positive, sizable, and statistically signicant estimate of the impact of bilateral investment treaties (BITs) on trade ows. A possible explanation for this result is multinational production. Third, we do not obtain a signicant estimate of the impact of economic sanctions on trade. This result is consistent with estimates from Felbermayr et al. (2020), who argue that average estimates of the eects of sanctions may mask signicant heterogeneity across the eects of sanctions by type. In addition, Kirilakha et al. (2021) demonstrate that the relative importance of trade sanctions has fallen signicantly over time. In robustness analysis, we allowed for dierential eects of dierent types of sanctions. This did not aect our main ndings and conclusions.

Finally, the estimates of the time-varying border variables from our specications, which we visualize in Figure 3, reveal signicant globalization eects during the period 1990-2011. Due to the use of pair xed eects, we need to drop one border variable, and we selected the border in 1990 as the baseline. Thus, all other border estimates are obtained as deviations from 1990 and should be interpreted accordingly, i.e., the positive and increasing estimates in Figure 3 capture the positive eects of globalization (smaller impact of borders) on international trade. The dip in 2002 is probably a reection of the economic recession during this period, while, due to the use of three-year intervals, the deep global economic recession of 2009 is not captured in our graph.

In column (2) of Table 1 we allow for heterogeneous eects between shallow vs. deep trade agreements. To this end, we capitalize on the data from World Bank's DCRTA, cf. Hofmann et al. (2019) and Mattoo et al., eds (2020), to dene _DTAij,t_ as an indicator that takes a value of one for deep trade agreements (i.e., we use the variable `pta_mapped' 22
We also create more outwardly gendered categories by looking at whether men or women are the main income earners (earn more than 60 percent of household labour income).<sup>14</sup> As expected given the very low participation rate of Jordanian women in the labour force, almost 70 percent of households are ‘male earner majority’ households and only 10 percent are ‘female earner majority’ households (Figure 4). Again, there is some overlap with the fiscal and care categories, but not a perfect one. In particular, most of the households where men are the main income earners are in the ‘married couple with dependents’ care category (Table 4) or in the ‘worker(s)’ (with and without children in school) fiscal categories (Table 5).

<!-- Start of picture text --> No labour income<br>10<br>19<br>No earner majority<br>6<br>Male earner majority<br>(>60% income)<br>66 Female earner<br>majority (>60%<br>income)<br><!-- End of picture text --> Source: 2017-18 Household Expenditure and Income Survey (HEIS) and World Bank calculations.

**_Table 4. Care and gender earner category comparison (percentage of total households)_**

|||**GE**|**NDER EARNE**|**R CATEGORIE**|**S**|
|---|---|---|---|---|---|
|||Female<br>>= 60%|Male<br>>= 60%|No income|Neither<br>earns 60%|
|**ES**|No dependents|3|15|6|1|
|**RE**<br>**ORI**|Single adult, dependents|2|1|2|0|
|**CA**<br>**ATEG**|Married couple, dependents|3|46|5|4|
|**C**|Other|2|3|5|0|

Source: 2017-18 Household Expenditure and Income Survey (HEIS) and World Bank calculations.

14 We focus on labour incomes but similar results are obtained when including other sources of income. We also conduct some sensitivity analysis using a 50 percent threshold instead of a 60 percent threshold with similar results.
Considering all of the recorded natural catastrophes with human or economic losses between 1980 and 2011 offers a better understanding of how the harmful consequences are distributed across income groups. In this time period, a total of 22,200 events were recorded, of which nearly half were experienced in high-income countries (c.f. Figure 1)<sup>.4</sup> At 9% low-income countries experienced a relatively small number of natural catastrophes in this time period. It remains an open question to which extent this actually reflects better monitoring systems in high-income countries. Nevertheless, the distribution of fatalities offers a completely reversed image: nearly half of the 2,275,000 fatalities caused by natural catastrophes in the same time period were incurred by countries in the lowest income group, while highincome countries incurred far fewer fatalities (5%).

<!-- Start of picture text --> Number of natural catastrophes Fatalities (total 2,275,000) Absolute economic losses<br>(total 222,200) (total US$3530bn)<br>5% 3%<br>9%<br>8%<br>17%<br>18%<br>47% 48%<br>23%<br>30% 66%<br>26%<br>HIC UMIC LMIC LIC<br><!-- End of picture text -->

**Figure 1.** Distribution by income group (1980-2011); _Left:_ Total number of loss events; _Middle:_ Fatalities due to natural catastrophes; _Right:_ Absolute economic losses in 2011 values; _Source: MunichRE, NatCatSERVICE, Wirtz_ (2013)

In terms of economic losses due to natural catastrophes, the picture also relies strongly on the applied measure and the considered income group: For the period from 1980 to 2011, absolute economic losses of US$3,530 billion were incurred globally due to disasters (mainly due to earthquakes, floods, and storms), of which two-thirds were incurred by high-income countries (c.f. Figure 1 right). Low-income and lowermiddle-income countries combined incurred just over 10% of the total absolute losses (while incurring more than 75% of total fatalities).

Relative to GDP however, low-income countries incurred on average the highest economic losses as a consequence of natural catastrophes (c.f. Figure 2). Overall economic losses averaged close to one-quarter of GDP in the lowest-income group, while ranging between 5% and 8% in the other income groups. This demonstrates that even though absolute economic losses were low in low-income countries, the economic burden of natural catastrophes is disproportionately high in these countries, with further implications, e.g., for the speed of economic recovery and means for reconstruction. This is confirmed empirically by Skidmore and Toya (2007), who use a cross-section analysis of disaster loss data from 151 countries over the 1960-2003 period and find that the ratio of economic losses to GDP is decreasing with GDP per capita. Bakkensen (2013) finds similar results, even accounting for country fixed-effects. This result confirms that rich countries suffer from most of the losses while poorer countries have higher losses when compared with their resources.

> 4 Events are recorded at country level, i.e. 2012 Hurricane Sandy is recorded as a separate event for each affected country

5
Lewis and Lockheed Because education development often parallels economic development (Baker and Le Tendre 2005), the analyses also controls for selected economic and development indicators: GDP per capita, road density and share of rural population.

The small number of countries for which data are available for average years of female schooling (73) and learning scores (55) combined with the uneven country coverage of other variables produces substantial differences in the number of observations for each analysis. The strong correlation among factors also poses difficulties. Correlations between the primary completion rate and GDP per capita (0.73) and average years of schooling (0.74) are particularly troublesome. We attempt to address the problem with instrumental variables; we report different models to try to ferret out the effects of the correlated variables.

We first examine the relationship between ELF and our schooling indicators (table 4). For all three estimates, ELF is highly correlated with schooling outcomes. Greater within-country heterogeneity is associated with lower rates of female primary school completion. A 1 percent increase in ethnolinguistic fractionalization leads to a .22 percent decrease in female primary completion (table 4 column 1). Moreover, greater within-country heterogeneity is associated with a larger gender gap in primary school completion. For the difference between male and female primary school completion, the dependent variable is calculated as log(male/female) to avoid the loss of observations where the male-female difference is less than or equal to zero. Column 2 shows that a 1 percent increase in ethnolinguistic fractionalization leads to .09 greater male advantage in primary school completion. Finally, learning is lower in more heterogeneous countries. For every 1 percent increase in ethnolinguistic fractionalization, learning scores are .17 percent lower (table 4 column 3). Thus countries with multiple ethnic and language groups are likely to have lower primary completion rates for girls, a widening gap between male and female completion rates, and lower overall achievement.

# **Table 4.  Elasticities of primary completion and learning and ethnolinguistic fractionalization**

||(1)|(2)|(3)|
|---|---|---|---|
|Coefficient|log (Female PCR)|log (Male PCR) –<br>log (FemalePCR)|log (Learning score)|
|Ethnolinguistic fractionalization, log|–0.22***|0.09***|–0.17***|
||(–4.27)|(3.99)|(–3.65)|
|Constant|4.06***|0.17***|5.74***|
||62.8)|(6.46)|1)|
|Observations<br>|8<br>8||5|
|_R_<sup>_2_</sup>|0.14|0.12|0.20|

* Significant at the 10 percent level.

** Significant at the 5 percent level.

*** Significant at the 1 percent level. _Note:_ Figures in brackets are _t-_ statistics.

19
**Table 5.4 Handling Charges by Type of Freight, 2012**

|**Country**|**Port**|**20’ Dry**<br>**Container**|**40’ Dry**<br>**Container**|**Transshipment**<br>**Container**|**General**<br>**Cargo**|**Dry**<br>**Bulk**|**Cruise**|
|---|---|---|---|---|---|---|---|
|||**($/con**|**tainer)**|**($/TEU)**|**($/ton**|**ne)**|**($/passenger)**|
|Antigua and<br>Barbuda|St. John’s|237|474|141|12|12|—|
|Dominica|Woodbridge<br>Bay/Roseau|415|933|69|89|7|5|
|Grenada|St. George’s|745|1,192|—|4|4|5|
|St. Kitts and|Basseterre|452|904|20|33|33|5|
|Nevis|LongPoint|322|642|—|36|5|13|
|St Lucia|Castries|480|960|6|4|7|7|
|.|Vieux Fort|480|960|30|4|4|—|
|St. Vincent and<br>the Grenadines|Campden<br>Park/Kingstown|167|333|267|9|9|15|

_Source:_ Authors’ own elaboration on primary data. _Note:_ TEU = twenty-foot equivalent unit. — Not available.

**Per vessel fees, levied on each call, also vary widely across ports (table 5.5).** This may have to do with the spread of costs being levied on each container, versus a one-time charge per vessel. In Dominica, for example, the higher call cost per vessel may be due to lower overall charges per container. Shipping lines are not complaining, however, since the vessel-related charges are all they are responsible for, whereas at other ports the shipping lines are also responsible for per-container handling charges. Overall, the wide variance of charges, per call and per container, and other charges is not unusual in the ports sector, where each port in effect becomes its own market with its own priorities.

**Table 5.5 Port Fees, 2012**

|**Country**|**Port**|**Cost per**|**Vessel ($)**|**Excess Storage C**|**harges ($/tonne/day)**|
|---|---|---|---|---|---|
|||**Cargo **|**Cruise**|**Dry Bulk**|**General Cargo **|
|Antigua and<br>Barbuda|St. John’s|741|333|8|8|
|Dominica|Woodbridge<br>Bay/Roseau|1,317|892|2|2|
|Grenada|St. George’s|413|833|0|2|
|St. Kitts and|Basseterre|516|837|2|2|
|Nevis|LongPoint|100|—|4|4|
|St. Lucia|Castries|89|111|1|1|
||Vieux Fort|89|—|1|1|
|St. Vincent and<br>the Grenadines|Campden<br>Park/Kingstown|—|—|3|3|

_Source:_ Authors’ own elaboration on primary data. — Not available.

39
21 _Table 11. Decomposition of Change in Poverty between 1978 and 1987 (in percentage points)_

||Income<br>effect|Distribution<br>effect|<br>Demographic<br>effect|<br>Total<br>effect|
|---|---|---|---|---|
|_Poland_|||||
|Workers|+18.7|-2.4|+2.5|+18.8|
|Mixed|+9.8|-5.9|-0.2|+3.1|
|Farmers|+15.6|-7.7|-0.2|+6.5|
|Pensioners|+19.0|-11.3|+1.2|+6.8|
|Total|+16.7|-4.2|+1.5|+13.5|
|_Yugoslavia_|||||
|Agricultural|+18.3|-15.4|0|+1.0|
|Mixed|+14.4|-3.1|-1.3|+9.4|
|Non-agricultura|l +9.1|+0.2|+1.9|+11.4|
|Total|+11.8|-3.3|-0.7|+7.3|

Notes: Income effect calculated by assuming 1978 level of country-wide real income and everything else a- It Is In 1987. Demographic effect calculated by assuming the 1978 composition of population (between social groups and sizes of households) and everything else as It is in 1987. Distribution effect calculated as residual.

a/ The difference between the total effect and the sum of the three Individual effects Is due to an Interaction term.

_______________________________

# _VII. Conclusions_

Deep economic crisis that characterized Eastern Europe In the 1980's had a strong Impact on average Incomes of the population and on the percentage of people living below the poverty line. The situation deteriorated most sharply In Poland, where the percentage of the poor In total population Increased from less than 10 percent before the crisis to more than 20 percent by the late 1980's. In Yugoslavia, Increase was somewhat less (from 17 to 25 percent). In Hungary overall poverty rate remained constant. Perhaps as important was the change In composition of poverty: while before the crisis most of the poor lived In rural areas, now most of the poor (as many as 70 percent in Poland) live In cities. Social group most affected by the economic decline are urban, socialized sector workers (inclusive of white-collar workers). Their real wages, or real per capita income of their
## 4.2 Concentration Analysis

The evolution of the Herfindahl‐Hirschman concentration index (HHI) associated to each GVC export flow calculated over pooled countries over the years is depicted in Figure 19, revealing that concentration has exhibited a u‐shaped evolution in the case of exports of final electronics, final apparel and final textiles, while it has consistently decreased in final and intermediate exports of motor vehicles and has tended to increase in the case of footwear, especially final goods, until 2010, when concentration in footwear started decreasing. In those chains where we see a U‐shaped pattern, the turning point is located in the early 2000s, from which time point concentration begins to increase. By the end of the analysis period, exports of final apparel, final footwear and final electronics were substantially more concentrated than in the initial year. The concentration levels in the case of intermediate goods belonging to these three GVCs showed trend changes over the years but ended up being mostly unchanged or slightly decreased by the end of the period, whereas they actually decreased in the case of exports of final and intermediate motor vehicles. It is interesting to note that even though there is a U‐shaped pattern for both final and intermediate goods in pooled overall GVC exports, the former tend to have been more concentrated than the latter since the early 1990s and for the rest of the period, and have furthermore become increasingly more concentrated than them over the years (Figure 20).

As Figures 21 and 22 show, intermediate and final apparel products are predominant among the top 20 six‐digit products with the highest values of the HHI index, whereas intermediate motor vehicles are the most frequently found products across the bottom 20 with lowest values of the concentration index. Interestingly, final electronics and final textiles items are found almost equally frequently on both lists.

# 5. Conclusions

The study of GVCs using traditional trade data complements other approaches, such as those based on value‐added trade data, and field studies using principles from managerial economics, industrial organization, sociology, and other fields. It enables a more granular understanding of how countries engage with their trading partners in different stages of the value chain. It is also of use in studying the development of patterns of regional integration. Two recent World Bank flagship studies using the MC‐ GVC method to analyze value chains include _South Asia’s Turn_ (Lopez‐Acevedo, Medvedev and Palmade, eds., 2017) and _Better Neighbors: Toward a Renewal of Economic Integration in Latin America_ (Bown, Lederman, Pienknagura and Robertson 2017).

Future work in this area should include extension of the MC‐GVC approach to other sectors such as chemicals and petrochemicals, food processing, metal ores and manufactures of metal, and potentially services.

13