rafmacalaba/data-use-mentions-extended huggingface dataset:
mark>NICHD Early Child Care Research Network. “Child care and children’s peer interaction at 24 and 36 months: The NICHD study of early child care.”</mark> _<mark>Child Development</mark>_ <mark>(2001): 1478-1500.</mark> <mark>NICHD Early Child Care Research Network. “Nonmaternal care and family factors in early development: An overview of the NICHD Study of Early Child Care.”</mark> _<mark>Journal of Applied Developmental Psychology</mark>_ <mark>22, no. 5 (2001): 457-492.</mark> <mark>NICHD Early Child Care Research Network. “Early child care and children’s development prior to school entry: Results from the NICHD Study of Early Child Care.”</mark> _<mark>American educational research journal</mark>_ <mark>39, no. 1 (2002): 133-164.</mark> <mark>NICHD Early Child Care Research Network. “Early child care and children’s development in the primary grades: Follow-up results from the NICHD Study of Early Child Care.”</mark> _<mark>American Educational Research Journal</mark>_ <mark>42, no. 3 (2005): 537-570.</mark> <mark>NICHD Early Child Care Research Network. “Social Competence with Peers in Third Grade: Associations with Earlier Peer Experiences in Childcare 1.”</mark> _<mark>Social Development</mark>_ <mark>17, no. 3 (2008): 419-453.</mark> <mark>Qiu, Xiaoju, Jing Yu, Tingyu Li, Nanhua Cheng, and Liqi Zhu. “Children’s inequity aversion in procedural justice context: A comparison of advantageous and disadvantageous inequity.”</mark> _<mark>Frontiers in Psychology<
# **References**

Allen, Franklin and Douglas Gale (2007) _Understanding Financial Crises_ (New York: Oxford University Press).

Asonuma, Tamon and H. Joo. (2020). Sovereign Debt Restructurings: Delays in Renegotiations and Risk Averse Creditors. _Journal of the European Economic Association_ , 18(5): 2394–2440.

<mark>Asonuma, Tamon and Christoph Trebesch (2016). Sovereign Debt Restructurings: Preemptive or Post-Default,”</mark> _<mark>Journal of European Economic Association</mark>_ <mark>Vol. 1(1), February, 175-216.</mark> Barro, Robert J. and Jose Ursua, (2008). “Macroeconomic Crises Since 1870,” _Brookings Papers on Economic Activity_ Spring, Vol. 39(1) 255-350.

Barron, Matthew, Emil Verner, and Wei Xion (2021). “Banking Crises Without Panics,” _The Quarterly Journal of Economics_ , Volume 136, Issue 1, February 2021, Pages 51–.

Barry, John M. (2009). _The Great Influenza: The Story of the Deadliest Pandemic in History_ . Penguin Books.

Beers, David, Elliot Jones, and John Walsh (2020). BoC-BoE: Sovereign Default Database: What is New in 2020,” Staff Analytical Note.

Benjamin, D. and Wright, M. (2009). “Recovery Before Redemption: A Theory of Delays in Sovereign Debt Renegotiations” April.

Bernanke, Ben S. and James, Harold, (1991). “The Gold Standard, Deflation, and Financial in the Great Depression: An International Comparison.” In G. Hubbard (ed.) _Financial Markets and Financial Crises_ (Chicago: University of Chicago Press for the NBER).

Bordo, Michael, Barry Eichengreen, Daniela Klingebiel, and Maria Soledad Martinez-Peria (2001). “Is the Crisis Problem Growing More Severe?” _Economic Policy_ 16, April, 51–82.

Bordo, Michael
assessment, the project aggregates information to the village level—where villages are coded using three categories: a ‘fully functional’ village produces sufficient water for more than 80 percent of the village beneficiaries;<sup>8</sup> a ‘partially functional’ village produces sufficient water for at least 40 percent of beneficiaries; and a ‘non-functional’ village produces water for less than 40 percent of the beneficiaries.

Our dependent variable is a ‘sustainable water system’ at the village level that we identify using the following criteria: (a) _oldest_ water systems from PAMSIMAS Phase I period of 2008−2012; (b) _consistently fully functional villages for the last five consecutive years_ (2014−2018); and (c) never received any _rehabilitation._<sup>9</sup> Figure 1 shows that this subset of villages consisted of around 3,588 schemes or 55 percent of all schemes built in the first project period. In comparison, villages at the opposite end of the distribution, that is, schemes that were effectively abandoned, amounted to 2 percent of PAMSIMAS Phase I schemes.<sup>10</sup> To obtain our independent variables, we supplemented the project’s database with contextual village data from the Indonesia Village Potential Survey (PODES). **Figure 1. Number of Villages Categorized as Fully** _Community participation_ is the **Functional and Not Functional Built in the First Project** recorded number of community **Period** participants during the series of 4000 participatory events held at project inception.<sup>11</sup> _Village_ 3500 _socioeconomic status_ was proxied 3000 by: _village revenue per capita_ ; _tariff collection levels by schemes,_ 2500 constructed as two dummy 2000 variables, ‘more than operational 3588 and maintenance costs’ and ‘less (55%) 1500 than operational and maintenance costs’; and 1000 _proximity to growth centers_ , 121 measured using transport costs 500 2% from the village office to the 0
recorded agricultural commodities from the farm-gate to the nearest market town. For an average distance of 12 km traveled, farmers report ad-valorem transport costs of 11.4% on average, with the median at 6.5%. Kebede then uses responses at the village level to estimate the cost of transport by road types (paved, gravel, cobbled or earth roads). Additional information comes from standard price data collected from farms as well as main market towns, which is informative about the potential share of transportation in overall trade costs estimated from price differentials. These costs are then used to calculate least-cost itineraries and shipping cost measures throughout the entire road network, which are in turn used to impute a model-based market access measure for a quantitative welfare exercise.

In Section 2, I discussed the important question of whether transport costs are per-unit (additive) or ad-valorem (multiplicative). Both anecdotal evidence as well as direct measurement from surveys suggest that the former is a better characterization of transport costs. Imputation based methods are designed to deliver additive per19
71

# Table III.13

# Prices Changed for Contraceptivis at Family Planning Association in Selected Eastern Caribbean Countries (1986 U.S. Dollars)

|Method|Antigua|St. Lu<br>FPProgram|cia<br>Industry|Grenada|
|---|---|---|---|---|
|Orals|$.37/cycle|<br>$.37/cycle|.74/cycle||
|Eugynon|||.56/cycle||
|Microgynen|||1.11/cycle||
|Neogynon|||.74/cycle||
|Condoms|.12@|free|.04@|.05@|
|Injection|3.70|(some charge)|||
|Neo Sampoon|||.93 for 20|.74 for 20|
|other Foam Tabl|ets||.46/vial|.74,/vial|
|Diaphragm|5.56|||7.41|
|Jelly|1.85/tube|||2.85/tube|
|IUD||||7.41|

Source: Data collected by The Urban Institute.
Additionally, at least two of the following three criteria must hold:

- a. The act must be aimed at attaining a political, economic, religious, or social goal;

- b. There must be evidence of an intention to coerce, intimidate, or convey some other message to a larger audience (or audiences) than the immediate victims;

- c. The action must be outside the context of legitimate warfare activities.

The data set contains dierent variables that characterize each attack by type: assassination, bombing, kidnapping, and others; and by weapons used, target, perpetrator, and consequences. Critically for this paper, the availability of the number of killed and people wounded as a result of each attack allows measuring the level of violence and the exact coordinates of the incidents to match them to household location (Districts, States or Zilas, depending on availability for each country).

Between 1972 and 2016, Bangladesh suered 1,605 terrorist attacks, India 10,978, and Sri Lanka 2,981 (GTD, 2016). Businesses have been the target in 10 percent of cases in India and 5 percent in both Sri Lanka and Bangladesh. Government institutions were targeted in 20 percent of cases in Bangladesh, 15 percent in India, and 12 percent in Sri Lanka. Citizens and their property - which includes public areas, markets, commercial streets, busy intersections and pedestrian malls - were targeted in 25 percent of cases in Bangladesh, 26 percent in India, and 19 percent in Sri Lanka. Transportation was targeted in approximately 8 percent of attacks in all three countries. The rest of the target types include tourists, utilities, and educational institutions, among others. Interestingly, religious sites are not particularly targeted, making up less than 4 percent of terrorist cases in all three countries. Lastly, military targets are important only in Sri Lanka (25 percent, versus 1 percent in Bangladesh and 7 percent in India).

As noted earlier, our identication strategy exploits
<mark>previous wave. For example, split-off households in Malawi inherit IDs if they are within a certain distance from their previous location (200m). If multiple split offs satisfy this condition, the previous household head is tracked. If no prior head is found, the household with the most tracked individuals is selected to inherit the household ID. If none of these conditions are met, no household inherits the ID. A similar approach was adopted in Tanzania, but without the distance requirement. In general, split-off households inherit the EA and stratum IDs of the household they originated from.</mark> In the following, we discuss in more detail the survey waves included in each country and their respective survey design aspects:

- <mark>In Ethiopia, data from the Ethiopian Social Survey (ESS) were assembled across five survey periods: 2010/2011, 2012/2013, 2014/2015, 2017/2018 and 2021/2022. The panel was fully refreshed in wave 4, and households are therefore not tracked across more than three waves. Split-off households (see Section SI.I) were not tracked in Ethiopia. The first wave of the panel survey (ESS 2010/2011) is only designed to be representative of rural areas and small towns, and the sample was expanded to urban areas from wave 2 onwards (ESS 2012/2013). Furthermore, waves 1 to 3 of the sample were designed to be representative of the most populous regions of the country</mark> (Central Statistical Agency and Living Standards Measurement Study (LSMS), World Bank, 2021) <mark>. In wave 5, the survey was renamed to the Ethiopia Socioeconomic Panel Survey (ESPS).</mark>

- <mark>In Malawi, data from the I</mark> ntegrated Household Panel Survey (IHPS) were assembled across four periods: 2009/2010, 2012/2013, 2015/2016 and 2018/2019. All split-off households were tracked in
>dummy<br>column,<br>treated year<br>as NA (“.”)|Seems like the year column was<br>given a “0” because it was “NA”<br>in the dummy column. But we<br>treat missing in the dummy<br>column as “NA”. So, we favored<br>the dummycolumn.|

_Source: ESMAP RISE dataset and authors’ elaboration based on dataset described in this paper._ 47
Policy Research Working Paper 6225

# **Abstract**

This paper uses firm-level data to study the pricingbehavior of Tanzanian exporters. The important question of how exporting firms make pricing decisions has not received significant attention in the trade literature, which is more focused on factors that determine export flows. The results of the paper show that the free on board price of Tanzanian exports is differentiated both across exporters—within product-destination pairs—and across markets—within firm-product pairs. Moreover, contrasting with existing evidence, price differentiation across destinations seems to be mainly relevant for homogenous goods. This result could indicate either that goods classified as homogeneous can potentially be differentiated by their intrinsic quality (such as coffee), or that firms charge different mark-ups in different markets. Although further work is needed to confirm what leads to price dispersion, allowing for the possibility that food products can be vertically differentiable amplifies the spectrum of existing opportunities for developing countries to exploit product differentiation and market niches. The study also discusses the implications of the empirical findings in light of the predictions of price and quality competition models, but finds that the results cannot be explained by a single trade model of quality or price competition.

This paper is a product of the Trade and Integration Team, Development Research Group. 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://econ.worldbank.org. The author may be contacted at valentina.rollo@graduateinstitute.ch.

_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
mid-level education expenditure is focused on households with children, while the opposite is true about tertiary education.

**Figure 4.** Population-adjusted Incidence of public programs by geographic region. Uruguay, 2017. **Panel A.** Direct taxes and transfers

<!-- Start of picture text --> 100%<br>80%<br>Direct Taxes<br>60%<br>Direct Transfers<br>40%<br>20% AFAM-PE<br>0%<br>Non-Contributive<br>-20% pensions<br>Food and Other direct<br>-40%<br>transfers<br>-60%<br>-80%<br>Canelones + San Montevideo Artigas + Rivera + Rest<br>José Cerro Largo<br>Panel B. Education<br>120%<br>100%<br>80%<br>60%<br>Child Care<br>40%<br>Preschool and Primary<br>20%<br>Secondary<br>0%<br>Tertiary<br>-20%<br>-40%<br>-60%<br>Canelones + San Montevideo Artigas + Rivera + Rest<br>José Cerro Largo<br><!-- End of picture text --> Source: Authors’ estimates based on ECH (2017), INE and Fiscal Accounts and CEQ Methodology Notes: The shares of the population are as follows: Canelones + San Jose (20%), Montevideo (40%), North (8%), Rest (33%). Total does not add up to 100% due to rounding.

17
we supplement data gathered from Refinitiv with other sources including bond prospectuses, company press releases, available investor relations materials, and second-party opinion reports. Annex A lists the SLB features on which data is collected and their definitions.

**Figure 2: Sustainability-linked bond issuance (US$ Billions)**

<!-- Start of picture text --> 35.00<br> 30.00<br> 25.00<br> 20.00<br> 15.00<br> 10.00<br> 5.00<br> -<br>Q3-2019 Q4-2019 Q1-2020 Q2-2020 Q3-2020 Q4-2020 Q1-2021 Q2-2021 Q3-2021 Q4-2021<br>Year<br>East Asia and Pacific Europe and Central Asia Latin America and Caribbean<br>Middle East and North Africa North America South Asia<br>Sub-Saharan Africa<br>Amount issued (US$ billions)<br><!-- End of picture text --> In addition to the bond level data, we extract firm level financial data from Refinitiv for each issuer for the calendar year of the year of bond issuance, to be used as issuer-level control variables in the regression analysis. These include total issuer assets (in US$ billions) to proxy for issuer size, firm leverage (defined as total outstanding debt as a fraction of total reported assets) and an indicator for public listing status. These controls are intended to capture variation in monitoring or disclosure across issuers. Previous studies show that public shareholders and creditors may engage in greater monitoring of firms and demand more disclosure around its activities (Fung, 2014; Tagesson, Blank, Broberg, and Collin, 2009; Villiers & Staden, 2010). Likewise, larger firms may also be subject to greater scrutiny (Gamble et al., 1996; Neu et al., 1998; Hasseldine, Salama, and Toms, 2005; Villiers, Low, and Samkin, 2014).

# **4.2. Descriptive Statistics**

This section
**Figure 7. Use of a mobile phone or the Internet to access an account varies widely within Europe and Central Asia**

<!-- Start of picture text --> Albania<br>Armenia<br>Azerbaijan<br>Belarus<br>Bosnia and Herzegovina<br>Bulgaria<br>Croatia<br>Georgia<br>Kazakhstan<br>Kosovo<br>Kyrgyz Republic<br>Republic of North Macedonia<br>Moldova<br>Montenegro<br>Romania<br>Russian Federation<br>Serbia<br>Tajikistan<br>Turkey<br>Turkmenistan<br>Ukraine<br>Uzbekistan<br>0% 5% 10% 15% 20% 25% 30% 35% 40% 45% 50%<br>Percent of adults with account using mobile phone or the Internet to access their<br>account<br><!-- End of picture text -->

## _Source_ : Global Findex database.

_Note_ : Length of the bars represents percentage of adults with a financial account who used mobile phone or internet to access their account.

# _Use of mobile phones for financial services_

In some regions of the world – such as Sub‐Saharan Africa – use of mobile money is widespread and a significant share of adults have a mobile money account only rather than a financial institution account. In the ECA region, where ownership of financial institution accounts is higher than in other developing regions, using mobile phones only instead of bank accounts is not common. Account owners are more likely to use online payments from their bank accounts than mobile accounts only.

Mobile phones can be used for financial services in at least two ways (Demirgüç‐Kunt and others 2018), for which China and Kenya serve as examples. In China mobile financial services are provided mainly through third‐party payment service providers, such as Alipay and WeChat, using smartphone apps linked to a bank account or other type of financial
natural endowments, El Salvador faces the most severe environmental degradation of any country in Central America, especially in the areas of forests and water resources. Although the countries in the Region have achieved some progress in the governance of natural resources and environmental protection, more effort has to be made in order to even out the still largely uneven regulatory frameworks, especially with respect to pollution and emissions control. Box 1 reviews the recent environmental developments in the four Central American countries: Costa Rica, El Salvador, Honduras and Nicaragua.

**<mark>Table 1:Environmental Regulatory Regime Index</mark>**

|Rank<sup>*</sup>|Country|Index|
|---|---|---|
|1|Finland|2.303|
|14|US|1.184|
|36|Costa Rica|-0.078|
|60|Dominican Republic|-1.014|
|62|Nicaragua|-1.164|
|63|El Salvador|-1.215|
|66|Honduras|-1.300|
|69|Guatemala|-1.532|

<mark>*Out of 71 countries</mark> _<mark>Source</mark>_ <mark>: Esty and Porter (2005)</mark> 5
) higher than kids whose moms are literate. The corresponding numbers for internalizing and externalizing difficulties are 0.08 and -0.02 s.d., respectively. These scores were standardized using the age specific group within the full ECED sample. Appendix Table A5, Panel B, reports the descriptive statistics.

The second instrument we employ is the Early Development Instrument, a comprehensive mea-

> 8As noted in Pradhan et al. (2013), the SDQ is an informant-based assessment of a child used internationally. It has been translated to Indonesian by Wiguna and Hestyanti (2012).

10
771|0.738|Niger|0.767|0.704|0.705|
|||||Nigeria|0.707|0.700|0.693|
|Middle East & North A|frica|||Rwanda|0.727|0.753|0.715|
|Egypt, Arab Rep.|0.781|0.746|0.656|Senegal|0.820|0.810|0.792|
|Jordan|0.769|0.662|0.543|Sierra Leone|n.a|0.764|0.597|
|||||South Africa|0.877|0.836|0.592|
|South Asia||||Tanzania|0.581|0.672|0.711|
|Afghanistan|n.a|0.725|0.561|Togo|0.832|0.839|0.837|
|Bangladesh|0.854|0.861|0.718|Uganda|0.784|0.755|0.704|
|India|0.860|0.759|0.686|Zambia|0.726|0.777|0.741|
|Nepal|0.680|0.684|0.640|Zimbabwe|0.688|0.687|0.721|
|Pakistan|0.823|0.814|0.762|||||

_Notes_ : This table presents the estimated share of the intergenerational component using the Bingley and Cappellari (2019) method for each of the 53 developing countries in the Demographic and Health Surveys (DHS) disaggregated by different decades of birth cohorts (the 1970s, 1980s, and 1990s).

38
Other variables of interest include residence in a rural region, ethnicity dummies and the controls for education of parents. Living in a rural area increases chances that an individual completed at least nine years of schooling, while rural residence is negatively related to a chance of completing 11 grades of schooling. Taken as a group, the estimated coefficients on the dummies for the educational attainment by parents of women are statistically significant at 1% level for the completion of basic or more and secondary or more levels of schooling (Table 1, Col. 6 and 8). For men, the education of their parents has a significant impact on the completion of at least secondary schooling (Table 1, Col. 4). The ethnicity dummies taken as a group have a significant impact on the completion of basic and secondary levels by men (Col. 1 and 3), and secondary level by women (Col. 7). However, the effect of ethnic group is robust to the inclusion of parental education dummies only in the regressions for women (Col. 8).

I also estimated the same regression models for the larger sample, adding to the control group these born in the 1958-1965. The results (not reported) are very similar to those shown in Table 1. The estimated coefficients on the interaction terms are slightly larger in absolute value (significant at the 5% level) in the regressions for women, suggesting that the results reported in Table 1 provide us with a conservative estimate of the effect of this conflict on education.

To test whether the individuals from the affected cohort were able to catch-up on the years of schooling between 2003 and 2007, we should estimate the same base specification for the pooled samples of the 2003 and 2007 data with the same dependent variable and add to the regressions a dummy for a survey year. Note that the 2003 TLSS survey did not include questions on the respondent’s ethnicity or education of his/her parents. Therefore the pooled regressions include only variables that are found in
_Public Administration Review_ 50: 367-73.

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32