rafmacalaba/data-use-mentions-extended huggingface dataset:
)|0.322<sup>_∗∗∗_</sup><br>(0.100)|_−_0.406<br>(0.462)|
|Post_×_ODS_×_DS|_−_0.171<br>(0.501)|0.142<br>(0.255)|_−_0.191<br>(0.144)|_−_0.045<br>(0.269)|0.044<br>(0.272)|_−_0.460<sup>_∗_</sup><br>(0.248)|0.603<br>(0.500)|
|Observations|6,118|6,118|6,118|6,118|6,118|6,118|6,118|
|R<sup>2</sup>|0.527|0.551|0.390|0.484|0.602|0.643|0.528|
|Mean(ODP tons)|-1402|-261|-3|-551|-30|-116|14|

FE triple-difference regressions on a stacked country-level panel with a _±_ 3 year event window around RTA signature. All outcomes are the inverse hyperbolic sine of the difference between ODS emissions and emissions targets under the Montreal Protocol. DS is an indicator equal to 1 if the the ODS provisions are binding with dispute settlement under the RTA. All models include country-event fixed effects. Robust standard errors are (two-way) clustered at the country-event and country-year levels. We report the mean deviation from target across the entire sample in tons of ozone depletion potential (ODP). Statistical significance from two-sided _t_ tests are denoted by<sup>_∗_</sup> p _<_ 0.1;<sup>_∗
Also in line with RZ, we use as proxies for countries’ financial development the following variable: first, the ratio of total capitalization to GDP, with total capitalization defined as the sum of domestic credit to private sector (as provided by the World Development Indicators) and stock market capitalization (as provided by the S&P, formerly IFC, Emerging Markets Database).<sup>7</sup> Although we mostly use total capitalization to GDP, we also study separately the two subcomponents, private credit to GDP and stock market capitalization to GDP. Furthermore, as RZ did, we use the quality of the country’s accounting standards as provided by CIFAR (Centre for International Financial Analysis and Research) as a measure of financial sector development. The financial development measures apply to the beginning of our sample (1980) and the accounting standards measure to 1983, the earliest period for which the index on accounting standards is available (we also used 1990 data and found similar results).

As our measure of the degree of competition in the banking system, we estimate the _H_ - measure using the PR methodology, just as CL did. In CL the _H_ -measure was estimated over the period 1994—2001, a period for which more individual bank data are available from Bankscope. For our purposes, the 1994—2001 period is less attractive because it does not overlap much with the period for which we measure the growth rate of value-added, 1980 to 1990, or the period for which benchmark external financing needs are calculated, 1980 to 1989. Bankscope does not, however, cover a large enough sample of countries before 1987. We also need to estimate the _H_ -statistics over several years (we choose a period of ten years) to have

> 7 In RZ, total domestic credit to GDP is used as a measure of financial development; the authors refer to the sum of total domestic credit to GDP and stock market capitalization to GDP as “total capitalization”. Here,
The other socioeconomic assets described earlier (industrial zones, hotels, health care facilities, schools, powerplants and electrical substations) are in point formats. A hotel, for example, is represented by a latitude-longitude pair located at its center. However, it is not only at this center point at which danger of flooding is relevant, but also at the surrounding hotel buildings and recreational facilities. In order to take this into account, a circular buffer of a fixed length is defined for each asset type described by a point location. This buffer is chosen to approximate the typical size of each analyzed asset class. Flood exposure is then checked not only at the center point, but also at the buffered area around the point. The buffers assigned to assets are 100 meters in radius for hospitals, schools, electrical substations, and industrial areas, and 200 meters in radius for hotels and power plants. Flood exposure is determined as in the previous analyses, with the results computed and protection offsets applied for each province, return period, and set of flood maps.

4.4. Typhoon wind exposure of electricity transmission infrastructure The exposure of electricity transmission infrastructure to cyclones is computed similarly to the flood exposure analysis. For each cyclone return period, an overlay between the cyclone raster and the transmission lines in polygon form determine which parts of the network are affected by which wind speed.

But high wind speeds do not have the same effect everywhere. In fact, their disruptive impact on the power grid is mediated by forests. Outages during storms are often caused by trees that fall onto transmission lines and take out a section of the grid (Rentschler et al. 2019b). In order to identify sections of the power grid that are particularly threatened in this way, 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 survey instrument used to collect the logistics data is a detailed questionnaire distributed to experienced logistics practitioners (freight forwarders) in 140 economies.<sup>4</sup> The aim was to focus on the detailed policy and institutional issues reflected in actual operational practices faced day to day in a country by private firms and by the freight forwarders serving as their intermediaries. Freight forwarders are in an excellent position to provide information on logistics, since private firms in most countries use the services of freight forwarding companies to ship their products into and out of the country.<sup>5</sup> A pilot survey of 17 carefully selected economies representing different regions and income levels was used to refine the questionnaire before its use in the larger set of economies. The survey asked separately about imports and exports. In addition, because landlocked economies face a unique set of difficulties, these were separated from coastal economies.

This paper uses the results for 80 of the 140 economies covered by the survey: 17 in SubSaharan Africa, 12 in Central and Eastern Europe, 12 in Latin America and the Caribbean, 11 in East Asia, 8 in the Middle East and North Africa, and 5 in South Asia, as well as 15 major industrial countries (see appendix table A.1)<sup>6</sup> . The economies in the data set range widely across regions, income levels, and extent of economic liberalization, and some face the special challenges of being landlocked and therefore dependent on transit countries’ infrastructure services for access to ports. The sample includes 70 coastal and 10 landlocked economies.

To ensure that the data reflect the conditions that a typical firm or intermediary would encounter, survey participants were asked to base their responses on the institutions and services faced by the typical medium-size firm in a country—not on cases where there might be “special conditions or privileges,” such as for firms located in a free trade zone. In Bangladesh, for example, a garment manufacturer located in
|**Country**|**Hazard**|**Year**|**Vp**|**Vr**|**Source**|
|---|---|---|---|---|---|
|Bangladesh|Flood|2005|42%|17%|Brouwer et al.(2007)|
|Bangladesh|Flood/cyclone|2009|74%|45%|Rabbani et al.(2013)|
|Bangladesh|Flood/cyclone|2009|10%|6%|Akter & Mallick(2013)|
|Honduras|Hurricane|2001|31%|11%|Carter et al.(2007)|

**Table 2: Estimates of the poverty vulnerability bias from prior studies.**

Moreover, these studies only look at the capital owned by households, not at the capital they depend upon to generate their income. This includes also infrastructure (e.g., roads, electricity network) and productive capital (e.g., factories, supply chains). Here, we assume that the vulnerability of housing capital is comparable to the vulnerability of the rest of the capital – for instance, if poor people’s buildings are 20 percent more vulnerable, then we assume that the capital they use to generate an income is also 20 percent more vulnerable.

# 3.2.1. Vulnerability estimates based on housing data

In this national‐level analysis, and consistent with our focus on the socioeconomic drivers of resilience, we use a very simple methodology. We use the Global Building Inventory database from PAGER, by USGS, which provides a distribution of building types (buildings only, not contents) within countries across the world (USGS 2015). This typology has been developed to assess vulnerability to earthquakes but here we use it for all hazards.

There are 106 building types in PAGER, which we aggregate into
a survey of enterprises, and employs econometric techniques to<br>make the “control group” more comparable to the group of guarantee users, controlling for<br>firms’ characteristics.|
|Regression<br>discontinuity|Many guarantee schemes use credit scoring to allocate guarantees to applicants. This<br>method compares firms around the cut-off point. The “control group” is composed of firms<br>just above the line, while the “treatment group” is composed of firms just below.|
|Natural and quasi-<br>natural experiment|This method is based on a naturally occurring event that differently affects different groups<br>of firms (e.g. change of eligibility criteria, use of guarantees by different banks, at different<br>point of time or in different regions)|
|Randomized<br>experiment|A control group is selected using a random process. Some eligible firms applying for the<br>guarantee are randomly rejected. Additionality is measured by comparing these firms to a<br>random sample of guarantee users.|

29
31,524,473**|**43,866,696**|**1,593,307**|**76,984,476**|

**Source: Own estimates using rotating panels from ENOE.**

The second transition matrix refers to the recession—that is, between the second quarter of 2008 and the second quarter of 2009. According to this estimate, the fall in total employment was mostly due to the difference between to a gross job destruction (workers who were employed and became unemployed) of 1.2 million workers and a gross job creation (workers who were unemployed and became employed) of 0.833 million (see table 3.6b). The gross flows towards and from inactivity nearly cancel out (both around 8 million people) so most of the job destruction in 2009 can be attributed to firing existing workers in a proportion that more than compensated new hirings. Changes in unemployment can be attributed to the flows between employment and unemployment, already described, and to an additional flow from the inactive population. More than 951,000 initially inactive people became unemployed while only 544,000 formerly unemployed became inactive. This added 400,000 people (about half the total change) to the unemployed during the recession. In other words, the increase in unemployment is associated to the growth of labor supply.

> 6 According to INEGI, the panel follows the interviewers for five quarters and it is representative at the national level.

21
_CHAPTER 6. UNIT LEVEL MODELS: GENERAL INDICATORS_ 41 type of auxiliary information is welcome as long as it is useful to predict welfare, in practice there is no guarantee that area heterogeneity has been completely accounted for by the available covariates, unless a formal statistical test for _σu_<sup>2=0wasconductedineachapplication.In</sup> any case, estimators that do incorporate area effects converge to their synthetic counterparts whenever _σu_<sup>2becomessmall,sothereisnoreason</sup> for excluding area effects.

An additional problem with the traditional ELL bootstrap method is that the noise measures delivered do not correctly estimate the true error measures of ELL estimators. The reason is that, unlike in usual bootstrap methods, the ELL bootstrap procedure does not re-fit the model and reestimate the target indicators with each bootstrap sample (which should be drawn from each bootstrap census). Hence, the real-world estimation process is not adequately replicated in the bootstrap world. As a result, the estimated MSE through this method does not accurately replicate the error incurred in estimation in the real world. Finally, when the sampling clusters are not the areas of interest, the MSE of the ELL estimator for an area indicator _δd_ can be severely underestimated if the area effects are not well explained (i.e., when their variance _σu_<sup>2issignificant).</sup> The ELL method is implemented in the `PovMap` software (Zhao, 2006), which offers a simple point-and-click user interface and is considerably fast and efficient in terms of memory. The `sae` Stata command developed by Nguyen et al. (2018) replicates most of the procedures implemented in the `PovMap` software.

Banerjee et al. (2006) conducted a review of the research at the World Bank and raised several concerns about the ELL method. First of all, they suggested that ELL was not accounting for potential area effects. This diagnosis was correct even if clusters in the ELL model are equal to the areas, because the cluster/area
to have mean zero and a standard deviation of unity in every year.

<mark>We consider two other measures of accountability. The first is the electoral democracy</mark> index (also known as the polyarchy index) produced by the Varieties of Democracy (V-DEM) project. This measures the extent to which a country has free and fair electoral competition, suffrage is extensive, political and civil society organizations operate freely, there is freedom of expression and the media is independent. The index ranges from 0 to 1, where zero indicates a regime furthest away from electoral democracy and 1 is a full electoral democracy. The second

> 23 <mark>We checked against the IMF estimates on the total additional public spending (whether on SP or something else) implemented during the pandemic (IMF 2021); in no case did our estimate exceed the corresponding IMF number.</mark> 24 The ICP provides information of the price level of each category indexed to the world average, which takes a value of 100. The ratio of these price levels provides an indicator of relative prices.

> 25 The number of countries for which social protection and ICP data is available is 81 in 2005, 99 in 2011 and 102 in 2017.

16
# **References**

- Arayavechkit, Tanida, Atamanov, Aziz, Herrera, Karen Y. Barreto, Belghith, Nadia Belhaj Hassine, Aguilar, R. Andres Castaneda, Fujs, Tony H. M. J., Dewina, Reno, Diaz-Bonilla, Carolina, Edochie, Ifeanyi N., Jolliffe, Dean M., Lakner, Christoph, Mahler, Daniel Gerszon, Montes, Jose, Herrera, Laura Liliana Moreno, Mungai, Rose, Newhouse, David Locke, Nguyen, Minh C., Castro, Diana M. Sanchez, Schoch, Marta, Sharma, Dhiraj, Simler, Kenneth, Swinkel, Rob, Takamatsu, Shinya, Uochi, Ikuko, Mendoza, Martha C. Viveros, Yonzan, Nishant, Yoshida, Nobuo, and Wu, Haoyu. 2021. “March 2021 PovcalNet Update: What’s New,” _Global Poverty Monitoring Technical Note Series_ , no. 15, Global Poverty Monitoring Technical Note Series, Washington, D.C.: The World Bank. <u>http://hdl.handle.net/10986/35340</u>

- Buhmann, Brigitte, Rainwater, Lee, Schmaus, Gunther, and Tim Smeeding. 1988. “Equivalence Scales, Well-Being, Inequality, and Poverty: Sensitivity Estimates Across Ten Countries Using the Luxembourg Income Study (lis) Database.” _Review of Income and Wealth_ , _34_ (2), 115–142. https://doi.org/10.1111/j.1475-4991.1988.tb00564.x

- Deaton, Angus and Salman Zaidi. 2002. _Guidelines for Constructing Consumption Aggregates for Welfare Analysis._ Washington, DC: World Bank. <u>https://openknowledge.worldbank.org/handle/10986/14101</u>

- Desiere, Sam and Dean Jolliffe. 2018. “Land Productivity and Plot Size: Is Measurement Error Driving the Inverse Relationship?” _Journal of Development Economics_ 130 (January): 84– 98. https
A key factor that helps determine both firm productivity, and the ability of firms to export, is the management practices used by firms. Bloom et al. (2016) estimate that differences in management can account for 30 percent of cross-country productivity differences. Better management is also strongly associated with export performance, with Bloom et al. (2021) finding that better managed firms are more likely to export, sell more products to more destinations, and have higher export revenues. However, Colombian firms have low levels of management practices according to global surveys like the World Management Survey, with levels similar to countries like India and Kenya that have much lower per-capita incomes. A prior pilot experiment in Colombia (Iacovone et al., 2022) had worked with a single industry, the autoparts sector, and found that individual and groupbased consulting had improved management practices, but was not focused on exporting or export-oriented firms.

The Colombia Productivity and Export Improvement Program (PEIP)<sup>3</sup> was designed by the Programa de Transformación Productiva (Program of Productive Transformation) (PTP) with the explicit objective of “improving the productivity and export capacity of the participating firms”. It aimed to achieve these goals through providing consulting services to firms to improve their management practices (described in detail below). The program was launched in November 2017. To be eligible for the program, firms had to have existed for at least two years, be formally registered, belong to one of thirteen selected sectors (transport manufacturing, construction, textiles, fruits and fruit products, specialty coffees and coffee products, beef, aquaculture, cocoa products, processed food, cosmetics, pharmaceuticals, plastics and paint products, and basic chemicals), provide financial statements, and complete an online application process. Along with the general call for firms to apply, PTP specifically reached out to a list of firms that had exported at least once in the past five years to see if they were interested in applying. The closing date for
.

In both languages, the moving average incidence of references to poverty peaked at or near the end of the period.<sup>24</sup> This was almost certainly the time when the incidence of absolute poverty in the world as a whole had reached its low point, at least when judged by what “poverty” means in developing countries today.<sup>25</sup> This is consistent with the view that, as poverty falls with overall economic development, the capacity of an economy to address the problem of poverty increases (as argued in Ravallion, 2010), which probably attracts attention to the problem in the literature. Changes in the frequency of public discussion of poverty might be

> 24 As already noted the single year with highest incidence was 1787, but this was a spike, and not the moving average peak.

> 25 As such measurements require survey data, this claim can only be tested for the last couple of decades; see Chen and Ravallion (2010). Bourguignon and Morrisson (2002) provide simulations suggesting that the poverty rate declined from 94% in 1820 to 51% in 1992.

19
# **1. Introduction**

The ready‐made garment in sector in Bangladesh showcases how trade has increased female labor participation over the past 30 years. The sector has expanded on average by 17 percent per year since 1980 and represents over three‐quarters of Bangladesh's export earnings. The sector also offered formal job opportunities to women and now employs 4 million workers, an estimated 90 percent of them women, representing a 100‐fold increase in workers within just three decades (Heath and Mobarak 2015). Similarly, recent evidence from Indonesia suggests that women benefit from trade liberalization. Input tariff reduction has been linked to higher female labor participation through a relative expansion of female‐intensive sectors in a region (Kis‐Katos et al. 2018).

Can the positive link between trade and female labor participation in Bangladesh and Indonesia be confirmed for other developing and emerging countries in general? Using a cross‐section of more than 29,000 manufacturing firms in 64 countries from the World Bank’s Enterprise Surveys, this paper assesses whether trading firms have a female labor share premium relative to non‐trading firms. It focuses on four types of trading firms: exporters, importers, global value chain (GVC) participants and foreign firms. We find a female labor share premium for all four types of trading firms, controlling for firm output, capital intensity, total factor productivity and fixed effects. The results also hold controlling for differences in relative wages between men and women, and excluding traditional export sectors (apparel and electronics) from the sample.

Interestingly, the female labor share premium is much higher for production workers compared to non‐production workers (i.e. managers, sales, administration), implying that women in our country sample specialize in low‐skill production. In line with these findings, the female labor share premium for exporters and GVC participants is highest in low‐tech sectors, suggesting the largest benefits for women in low‐skill intensive sectors. The paper also
19.1**|**56.8**|
|Bosnia and Herzegovina|40.1<br>16.4|48.0|
|Croatia|53.7<br>14.4|62.9|
|Macedonia, FYR|40.4<br>31.9|59.8|
|Serbia and Montenegro b)|48.5<br>13.8|56.3|
|**BRU**|**67.9**<br>**9.6**|**72.6**|
|Belarus|-<br>-|-|
|Russia a)|75.8<br>8.0|82.5|
|Ukraine|60.0<br>11.1|62.7|
|**Other CIS**|**56.9**<br>**11.0**|**63.7**|
|Armenia|-<br>-|-|
|Azerbaijan|-<br>-|-|
|Georgia b)|56.8<br>12.3|65|
|Kazakhstan c)|63.6<br>9.3|70.1|
|Kyrgyzstan|-<br>12.5|-|
|Moldova d)|50.4<br>9.8|56.0|
|Tajikistan|-<br>-|-|
|Uzbekistan|-<br>-|-|
|**Memorandum**|||
|OECD|65.1<br>6.9|69.9|
|EU15|64.3<br>7.7|69.7|

- Data not available

Note: Labor Force Survey data Definitions: Working-age population: 15-64 Labor force = employed plus unemployed (aged 15
side|Incorruptible|
|Bribe requested||||
||No|Supply side|Non bribe payers|

More formally, the demand side of corruption is captured by a dummy variable, _Demand_ , which takes the value of 1 if a firm reports spending a positive share of its annual sales to

> 13The question is as follows: "We’ve heard that establishments are sometimes required to make gifts or informal payments to public officials to get things done with regard to customs, taxes, licenses, regulations, services, etc. On average, what percent of total annual sales, or estimated total annual value, do establishments like this one pay in informal payments or gifts to public officials for this purpose?"

> 14Despite that the question is asked indirectly, like most research on bribery, we interpret the answer as if they reflected respondents’ own experience of corruption instead of the average experience of firms in their sector, as the question reads. Asking sensitive questions indirectly is often thought to help decrease under-reporting by allowing managers to admit to paying bribes without incriminating themselves. Using indirect wording in questions about bribery is now standard practice in firm-level surveys. Despite the literal wording of the question which refers to firms like in theirs in their sector, indirect questions about corruption have been interpreted as reflecting respondent’s own experience of corruption. Svensson (2003) for instance looked for correlates between the probability of firms reporting a positive amount of bribes and firm’s own characteristics using an indirect question asking Ugandan firms to estimate how much bribes firms similar to theirs "typically pay each year". Similarly, Alm et al. (2016) and Freund et al. (2015) makes use of these questions.

8