rafmacalaba/data-use-mentions-extended huggingface dataset:
# **Trade Elasticities in Aggregate Models: Estimates for 191 Countries**

by Shantayanan Devarajan, Delfin S. Go, and Sherman Robinson<sup>1</sup> Keywords – trade elasticities, Armington, econometric estimates JEL codes – F14, C13, C68

> 1 Devarajan: Georgetown University, sd294@georgetown.edu; Go: The World Bank, dgo@worldbank.org; Robinson: Peterson Institute of International Economics, srobinson@piie.com. We thank Jongrim Ha and Ergys Islamaj for comments and suggestions and Hiroko Maeda and Eric Roland Metreau for help in downloading and interpreting the data from the World Bank World Development Indicators and the country classifications used for various country groups. We also thank all collaborators of the 1-2-3 model for their previous insights.
in the survey. Source: Own calculations based on ENIGH96 survey Tables 6 and 7 show real mean hourly salaries and mean hourly labor earnings, respectively, for total hours in main occupation, and total hours of main occupation plus secondary occupation. The hourly salary difference between teachers and other groups is quite high due to wide variance of the non-teaching group, and because teachers work relatively few hours.

**Table 6. Mean Hourly Salary** **<u>(Constant 1994 pesos)</u>**

|**Type of Occupation**|**Mean**<br>**Sa**<br>**(Main O**|**Hourly**<br>**lary**<br>**ccup Hrs) **|**Mean A**<br>**Hourly**<br> **(Main Oc**|**djusted**<sup>**1/**</sup><br>**Salary**<br>**cup Hrs)**|**Mean**<br>**Sal**<br>**(Tota**|**Hourly**<br>**ary**<br>**l Hrs)**|**Mean A**<br>**Hourly**<br>**(Total**|**djusted**<sup>**1/**</sup><br>**Salary**<br>**Hrs)**|
|---|---|---|---|---|---|---|---|---|
||**Urban**|**Rural**|**Urban**|
A Metadata Schema for Data from Experiments in the Social Sciences<sup>∗</sup> Jack Cavanagh<sup>†</sup> Jasmin Claire Fliegner<sup>‡</sup> Sarah Kopper<sup>†</sup> Anja Sautmann<sup>§</sup> Keywords: Randomized control trials, metadata, data publication, secondary research, trial registration JEL codes: C10, C81, C90

> ∗We would like to thank David Rhys Bernard, Merc`e Crosas, Maya Duru, Benjamin Morse, Julian Gautier, Steven Glazerman, Jakob Hennig, Maria Ruth Jones, Jessaca Spybrook, Wendy Thomas, Gabriel Tourek, James Turitto, Keesler Welch, Lars Vilhuber for providing detailed feedback on the metadata schema, and Caitlin Brown and Rachel Griffith for providing helpful comments on the paper. We would also like to thank Davi Bhering, Simon Cooper, Michael Gibson, Sabhya Gupta, Katharina Kaeppel, Daniela Muhaj, Isabela Salgado, Sheral Shah, and Selva Swetha for helping us test the schema with data from 29 RCTs. We also thank Mehmood Asghar, Barbara Bierer, Olivier Dupriez, Julie Goldman, Rebecca LI, Katherine McNeill, Amy Nurnberger, Limor Peer, Matthew Welch, and Julie Wood, for support and feedback at various stages of the project. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.

> Supplementary materials, including proposed controlled vocabularies, can be found in the associated GitHub repository https://github.com/sakopper/rct ~~m~~ etadata ~~s~~ chema.

> †J-PAL/MIT, email: jcavanagh@povertyactionlab.org and skopper@povertyactionlab.org

> ‡The University of Manchester, email: jasmin.fliegner@manchester.ac.uk

> §Development Economics Research Group,
We demonstrate a number of important patterns using the WFP survey. First, we demonstrate that households that became displaced tended to leave districts where violence was high and escalating before and after displacement. In our baseline specification, monthly fatalities in origin districts increased following displacement by 1.43, which represents approximately 36 percent of the average monthly fatalities in regions where non-displaced households lived in the months prior to their most recent survey.<sup>3</sup> Importantly, displaced households tended to flee to districts where violence did not escalate as rapidly as in origin districts, illustrating that that displacement decisions were strongly affected by violence.

Second, the average patterns mask significant heterogeneity in the ways that different types of violence might have forced displacement. Only for ground battles do we observe an increase in the numbers of fatalities and violent incidents leading up to and following displacement. For terrorism and violence against civilians, violence tends to peak in the months before displacement; and violence from airstrikes does not appear to be strongly associated with displacement. This heterogeneity likely reflects the ways each type of violence affects the local security situation, with ground battles being more persistent, deadly, predictable, and more often associated with capture of territory than other forms of violence in the country (e.g., Al Jazeera 2019; OCHA 2019; etc.). Thus, pooling all violence together likely obscures the timing in which sudden surges of specific types of violence are strongly associated with displacement.

And lastly, we find significant heterogeneity in the amount of violence in origin districts of displaced households both before and after they fled. Approximately one-third of displaced households fled regions with no violent incidents occurring in the month of displacement or the prior six months, and nearly half of households fled with no fatalities during that time period. Furthermore, violence increased in origin districts following displacement of these households, but the average level of post-displacement violence in displacement.

> 3The largest share of non-displaced households only responded to a single survey. And for the households
, the land use data described in section 3.1 is incorporated into the analysis. Specifically, the area surrounding the transmission lines (within 100 meters on either side) is probed on whether it is forested or not. Then, each 1 kilometer section of the grid is classified as either located in a dense forest (at least 75 percent tree cover), located in a light forest (tree cover between 25 and 75 percent), or located in a non-forested area (figure 4.6).

According to this approach, 36 percent of the Vietnamese transmission grid is located in forests, and 15 percent is located in dense forests. The exposure to typhoon wind speeds is then determined individually for the sections of the transmission network located in heavy, light, and non-forested areas.

As mentioned in section 3.7, the digitization process of the power lines could have resulted in errors in the locations of certain sections of the grid. Given the resolution of the wind speed data of about 30km, this should not have a major effect on the overlay with the wind speed data. However, the classification of transmission line segments according to the forest cover in the surrounding area is more sensitive to such errors. Here, the vertical displacement of a power line section could lead to it being classified as nonforested when it is located in a forest or vice versa. Although this inaccuracy is somewhat reduced by looking not only at the area directly underneath a power line but also the area surrounding it, this caveat should be kept in mind when interpreting the results.

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Brazil. The 262 sampled firms who reported getting a construction permit reported on average a time to get a construction permit of 85 days. For the 80 countries with both DB and ES results the average _excess_ of the DB days to get a construction permit versus the ES average is 177 days, almost three times larger than the firm reported average time of 64 days.

Third, we examine the associated between DB and the firms who report very quick times (the ―favored‖ firms) and those who report very slow times (the ―disfavored‖ firms). Using the 10<sup>th</sup> or 25<sup>th</sup> percentile of firm reported times we find that for these firms the DB numbers mean nothing at all. In every country the ―favored‖ firms report very little delay in any of the three indicators (clearing customs, getting construction permits, or operating licenses)—and hence are _massively_ under-water. The median DB time to get a construction permit is 242 days, about eight _months._ The median 10<sup>th</sup> and 25<sup>th</sup> percentiles are one _week_ and two _weeks_ respectively. For time to import goods the results are, if anything, more dramatic, the median DB time is 23 days, the 10<sup>th</sup> percentile is 1 day, 25<sup>th</sup> percentile is 2 days. Moreover, there is almost no variation across countries in the ES ―favored firm‖ responses. The standard deviation across countries in ―favored firm‖ (25<sup>th</sup> percentile) time to get a permit was 12 days (compared to a standard deviation of 119 days in the DB indicator).

In contrast, there is a set of firms that do find regulations an obstacle, and the extent of delay for those firms does vary widely across countries. The ―disfavored‖ firms—
# **Table 8: Panel Regressions – Robustness of main result to alternative indicators: Volume of small transactions for all crypto-assets**

|<br>Dependent Variable: Log monthly on-chai<br>|<br>n crypto-assets volum<br>|<br>e (transaction size<br>|<br><US$10K), US dollars<br>|<br>|<br>|
|---|---|---|---|---|---|
|Variable|(1)|(2)|(3)<br>**All Countries**|(4)|(5)|
|**Change in broad money (yoy)**|0.663*|0.136|0.070|0.108*|0.066|
|Change exchange rate (mom)|-0.974|-0.045|0.873**|0.734|0.604|
|Change Bitcoin price (mom)||0.648***|0.570***|0.677***|0.527***|
|Change Ether price (mom)||0.276***|-0.070|0.009|-0.004|
|US 10-year Yield (inflation adjusted)|||-0.311***||-0.405***|
|**US 10-year breakeven inflation rate**|||0.990***||1.252***|
|**US BBB Corporate bond spread**||||-0.284***|0.131***|
|Change gold price (mom)||||-1.324***|-1.190*
Bell, M., Zanobetti, A., Dominici, F. (2013) Evidence on vulnerability and susceptibility to health risks associated with short-term exposure to particulate matter: a systematic review and meta-analysis. American Journal of Epidemiology. Vol. 178: 865–876.

- Bright, E., Rose, A., Urban, M. (2016) Landscan 2015 High-Resolution Global Population Data Set. Oak Ridge, TN: Oak Ridge National Laboratory (ORNL).

- Chay, K., Greenstone, M. (2005). Does air quality matter? Evidence from the housing market. _Journal of Political Economy._ Vol. 113(2): 376-424.

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Policy Research Working Paper 10711

# **Abstract**

Women often face more hurdles than men in obtaining finance. This is especially so when credit supply is limited and financial markets are less developed. As a result, owners of firms may prefer men over women as top managers of their firms, widening the gender gap in top manager positions. This paper tests this idea using firm-level survey data for small and medium-size formal manufacturing enterprises in 47 developing countries. The results confirm a positive relationship between credit supply and the likelihood of having a woman versus a man as the top manager.

This positive relationship is much stronger in industries that are more dependent on external sources of finance for technological reasons. It is also stronger in countries with poor coverage by credit bureaus and low competition between banks, which is consistent with “statistical” and “taste-based” discrimination against women borrowers. The main result is robust to several endogeneity checks, sample alterations, and alternative measures of credit supply and financial development.

This paper is a product of the Global Indicators Group, Development Economics. 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://www.worldbank.org/prwp. The authors may be contacted at mamin@worldbank.org.

_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
# FIGURE 2 Comparison of potential growth estimates

A. Correlation of potential growth, 2000-21 B. Correlation of potential growth estimates with actual growth, 2000-20 <!-- Start of picture text --> For. For. Correlation coefficient<br>PF MVF HP BK CF BW (WEO) (CF) UCM 1.0<br>PF 0.8<br>MVF 0.6<br>HP<br>0.4<br>BK<br>CF 0.2<br>BW 0.0<br>For. (WEO) -0.2<br>For. (CF) PF MVF UVF For. UCM<br>UCM<br><!-- End of picture text --> Source: World Bank staff estimates.

Notes: “PF” stands for production function approach; “HP” for Hodrick-Prescott filter; “BK” for BaxterKing filter; “MVF” for multivariate filter; “CF” for Christiano-Fitzgerald filter; “For. (WEO)” or “For.” for five-year-ahead growth forecasts from the IMF World Economic Outlook database; “For. (CF)” for fiveyear-ahead growth forecasts from the Consensus Economics; “UCM” for Unobserved Components Model. A. Figure shows the within-country correlation during 2000-20 between different measures of potential growth. Red represents greater than 80 percent, orange represents 60-80 percent, yellow represents 40-60 percent, and light blue represents 20-40 percent. Unbalanced sample of 37 advanced economies and 63 EMDEs for 2000-21.

B. Blue bars show the median of within-country correlation during 2000-20 between different measures of potential growth and actual growth. Orange whiskers represent the 25th and 75th percentiles of withincountry correlation during the same period. Unbalanced sample of 37 advanced economies and 95 EMDEs for 2000-20.

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64

|**Country E R**|**Pricing policy and strategy**|**Fuel supply conditions**|
|---|---|---|
||de la Inversión en Energía y Mineria, Supervisory Agency for<br>Investment in Energy and Mining) posts detailed retail prices by<br>location and company and the date of the last price change, but not<br>historical price data.||
|Philip-<br>pines<br>√|<br>**Pricing policy**: The downstream petroleum sector was deregulated<br>in 1998. The import duties on crude oil and petroleum products were<br>reduced from 3% to 0% by an executive order in Jun 2010. In Feb<br>2009, the Department of Energy asked oil companies to explain in<br>writing within 48 hours the justification for price increases, and<br>added that it was checking the dates of the acquisition of the supply,<br>the volume, the value, and the shipment date. On Oct 23, 2009,<br>government imposed price ceilings in response to devastating<br>typhoons, forcing oil companies to lower prices back to the Oct 15<br>level. This led to early closure of some filling stations. The ceilings<br>were lifted on Nov 16, on condition that the oil companies agree to<br>implement discounts and stagger future price increases. Under the<br>label “jump-starting” strategic reserves, the president ordered<br>Philippine National Oil Company-Exploration Corporation, which is<br>99.8% owned by government, to sell diesel at ₱2–3 (US$0.05–<br>0.07)/liter below the market level starting in Jul 2011.<br>**Price negotiation**: Government in 2003 negotiated voluntary diesel<br>
Third, the predominantly macro-derived estimates of the housing deficit miss key dynamics within countries for different segments of the population. Only few studies provide disaggregated estimations of housing deficits for different segments of the population (e.g., income groups). As a result, the needs of these segments compared to other countries, and the relative size of the housing deficit is often not fully understood. Through its micro-foundation, the methodology we propose allows not only for the segmentation within countries, pointing towards often veiled variation of housing adequacy, but also enables comparisons of disaggregated estimations (such as housing adequacy deficits in the biggest business city or deficits among the poorest income groups) across countries.

Fourth, in estimating the _AHI_ using HH expenditure and consumption surveys for the respective countries, we are providing principles of global harmonization across seven dimensions of housing adequacy. Understanding HHs’ access to these adequacy dimensions, such as access to water and sanitation, are key for monitoring progress towards the Agenda 2030. Our proposed harmonization can be considered a micro-founded supplement to existing country level measures of these adequacy dimensions. This might be helpful in providing a more nuanced picture of underlying issues for countries that are lagging behind. Also, applying these globalized standards to harmonize housing variables across HCES, we therewith expand the scope of work pioneered by the World Bank Global Monitoring Database (GMD), which is the World Bank’s repository of multitopic income and expenditure household surveys used to monitor global poverty and shared prosperity.

Lastly, within the existing literature, the two realms on housing deficit and adequate housing conditions remain relatively separate. By introducing the _AHI_ , we combine these two realms, outlining how inadequate housing conditions at the HH level contribute to the overall housing deficit. Hence, we move beyond narrow estimations of housing backlogs by only considering severely dilapidated or overcrowded houses and instead add other key dimensions of housing adequacy which have consistently been found to be a determinant of socio-economic development outcomes and which are a key driver to the realization of the right to
. This is because the number of years is limited in the TiVA data, which would imply standard errors to be adjusted to a very small number of clusters. Furthermore, we believe that the correlation across-countries within time period is potentially less concerning than the correlation across-years within countries.

> 19See Egger and Tarlea (2015) and Larch et al. (2019) for a more in-depth explanation of why three-way clustered standard errors should be used in panel-data gravity models.

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WMS countries (Brazil, Canada, India, Sweden, UK and US).<sup>6</sup> They find a strong positive correlation for these countries: moving from the bottom to the top quartile of management is associated with a large increase in student learning outcomes, equivalent to approximately 0.4 standard deviations.

# **2.2 A new approach using existing public datasets**

We now describe our approach, illustrating with the examples of PISA and Prova Brasil.<sup>7</sup> .

**Construction.** In 2012, alongside its famous student proficiency tests, PISA ran school principal surveys across 65 countries which included a wide-range of questions on both operations and people management.<sup>8</sup> As a first step, we classified each of the PISA questions that could fall under one of the WMS topics, identifying 53 PISA questions that fit into 14 of the WMS topics.<sup>9</sup> As a second step, we manually assigned scores for each of these PISA questions following the spirit of the scoring grid of the WMS and the US Census Management and Organizational Practices Survey (MOPS). As a final step, we built the overall management index, and the operations and people management sub-indices, following Anderson [2008]. This methodology weights the impact of the included variables by the sum of their row in the inverse variance-covariance matrix, thereby assigning greater weight to questions that carry more “new information”.<sup>10</sup>

PISA data is excellent for cross-country analysis, but it precludes in-depth analyses within countries as the sample of schools per country is typically small and does not include the necessary identifiers. Many countries, however, conduct their own national detailed surveys with school principals, teachers, and students in addition to administering standardized tests across grades. Latin America is particularly prolific: for example, Brazil’s Prova Brasil, Colombia’s SABER, Chile’s SIMCE
|Nuwara Eliya|1|34|4|
|Matara|–|40|–|
|Jaffna|–|31|–|
|Kilinochchi|–|18|–|
|Total|78,021|65|119,462|

**Table 2:** Risk to assets, risk to wellbeing, and socioeconomic resilience for the entire population of each district of Sri Lanka. Note: though our hazard dataset does not report asset loss exceedance curves for Matara, Jaffna, or Kilinochchi, still we are able to assess their respective levels of socioeconomic resilience using household survey data, and the fact that resilience is the ratio of asset to wellbeing losses.

# 3.1 District-level risk summary

The annual risk to assets, risk to wellbeing, and socioeconomic resilience for each district of Sri Lanka are summarized in Table 2. According to the SSBN model, annual asset losses to precipitation flooding are highly concentrated in Colombo district, totaling over US$40 million per year (1.0% of AHE). Rathnapura district suffers the second highest losses, valued at US$8.2 million per year (0.9% of AHE).
or source of income.

On average, the Daraz sample firms have been selling goods on Daraz for 2.6 years. For most firms in the sample – 69.2 percent - sales through Daraz make up 50 percent or less of their total sales (figure 4).

Figure 4: Percent of total sales on Daraz <!-- Start of picture text --> < 0.25 0.25 to 0.50 0.5 0.5 to 0.75 > 0.75<br>40<br>30<br>20<br>percent<br>10<br>0<br><!-- End of picture text --> Owners of firms in the sample tend to be young, male, and tertiary educated. Some 55.3 percent are younger than 30 years old, while only 3.1 percent are 50 or older (figure A.2). Women comprise 10.7 percent of the sample. Many – 50.1 percent - have completed a vocational program, while 25.2 percent have completed a Bachelor’s degree or higher (figure A.3).

# **3 The Impact of Joining an E-Commerce Platform**

## **3.1 The Impact of Joining Chaldal**

The survey data suggest that firms are joining e-commerce platforms such as Chaldal primarily to access a larger market. More than 90 percent of Chaldal sellers report that they joined the platform to reach more customers (figure 5). Diversifying sales outlets and achieving more steady/reliable sales are also major reasons for joining the platform. Although some sellers also joined Chaldal because it offers better terms than those found on other sales outlets, almost none joined the platform in expectation of higher prices.

10