rafmacalaba/data-use-mentions-extended huggingface dataset:
# **1. Introduction**

Global trade has grown slowly after the Great Recession, and 2019 is projected to continue the trend as one of the years with the weakest trade performance since the 2008-2009 global financial crisis. A growing body of work analyzes the determinants of the current slowdown in global trade (Constantinescu et al. 2018; Hoekman 2015; Haugh et al. 2016; IMF 2016). Recent analyses attribute the trade slowdown, in varying degrees, to such factors as changes in the composition of economic activity away from import-intensive investment, a slowing pace of global value chain growth and trade liberalization, and an increase in trade protectionism.

This paper draws upon recent pioneering work on the measurement and impact of economic policy uncertainty (Baker et al. 2016) to examine whether policy uncertainty can help explain the slowdown in world trade growth. To motivate the analysis, Figure 1 juxtaposes the growth in volume of world merchandise imports against an index of global economic policy uncertainty. The figure does suggest that there is a negative association between the two, and the particularly weak trade performance in 2019 has coincided with unusually high levels of economic policy uncertainty in that year.

**Figure 1.** **_World import growth and policy uncertainty, January 2012 to June 2019_**

<!-- Start of picture text --> 350 6<br>300 Monthly global economic policy uncertainty 5<br>index<br>4<br>250<br>3<br>200<br>2<br>150<br>1<br>100<br>0<br>World merchandise trade volume (year‐<br>50<br>over‐year percent change of 3 month ‐1<br>moving average, right axis)<br>0 ‐2<br>2012 2013 2014 2015 2016 2017 2018 2019<br>index<br>percent<br>Mar Jun Sep Dec Mar Jun Sep Dec Mar Jun Sep
# FIGURE 13 Foreign-currency import invoicing and pass-through

A. Share of imports invoiced in foreign currency <!-- Start of picture text --> Percent Interquartile range Median<br>100<br>80<br>60<br>40<br>20<br>0<br>All Advanced EMDEs<br>economies<br><!-- End of picture text --> <!-- Start of picture text --> B. Share of foreign-currency invoicing and pass-<br>through from monetary policy shocks<br>Ratio<br>0.8<br>Interquartile range Median<br>0.6<br>0.4<br>0.2<br>0<br>Low High Low High<br>Advanced economies EMDEs<br><!-- End of picture text --> <!-- Start of picture text --> C. Share of foreign-currency invoicing and pass- D. Share of foreign-currency invoicing and<br>through from monetary policy shocks in EMDEs average exchange rate pass-through in EMDEs<br>100 100<br>90 90<br>80 80<br>0 0.2 0.4 0.6 0.8 -0.2 -0.1 0.0 0.1 0.2<br>Pass-through, ratio Pass-through, ratio<br>percent<br>Foreign currency invoicing,<br>percent<br>Foreign currency invoicing,<br><!-- End of picture text --> Note: Share of imports invoiced in foreign currency based on data for 50 countries calculated by Gopinath (2015). Pass-throughs are defined as the ratio between the one-year cumulative impulse response of consumer price inflation and the one-year cumulative impulse response of the exchange rate change estimated from factor-augmented vector autoregression models for 29 advanced economies and 26 EMDEs over 1998-2017. A positive pass-through means that a currency depreciation is associated
implications for a country’s trade patterns, which are more important than the total number of firms in a sector.

# **4. Export Characteristics and Dynamics of Superstars**

We have identified superstars—the top 1 percent of the firms in each country—as a group of firms remarkably different from the others in the same country. They explain a disproportional share of the differences we observe in export/production structures across countries. In this section, we examine the characteristics of superstars, including size and sectoral distribution and their contribution to export growth and diversification.

# _Export Characteristics_

Table 5 records summary statistics on superstars using the 2006-2008 averaged data. Depending on the size of the country and therefore the size of its export base, the number of superstars varies from a handful of firms, as it is the case of many African countries, to 324 in Mexico, the largest exporter in our sample. These firms are remarkably larger than the non-superstars. In fact, the median firm is on average, nearly 40 times larger. And despite being a relatively small number of firms (28 if we consider the median number of superstars), they represent, on average, over 50 percent of exports, whether in all trade or only manufacturing.

Superstars are also more diversified. Figure 2 shows the average number of product and destinations by type of exporter–superstars in red dots vs. non-superstars in black dots. We observe that in any given country, the group of superstars always exports more products and 17
These point estimates are converted to gridded estimates at a spatial resolution of 0.1 x 0.1 degrees (approximately 10km x 10km) for the purposes of calculation. The software also provides an option to use NMA station data modified with satellite data on cloud cover to improve the grid of estimated precipitation<sup>9</sup> .

**<mark>Box 2.1: WFP LEAP calculation of yield shortfall</mark>**

<mark>LEAP calculates yield shortfall by combining a model for water balance (the FAO’s Water Requirement Satisfaction Index (WRSI)) with a model describing crop yield response to water stress (Doorenbos and Kassam, 1979):</mark> <mark>100‐((1‐(1‐A/B)*K</mark> y <mark>)*100)</mark> <mark>Where:</mark> <mark>‐ A is the</mark> **<mark>Actual Evapotranspiration</mark>** <mark>;</mark>

- <mark>B is the</mark> **<mark>Total Water Requirement without water stress</mark>** <mark>; and</mark>

<mark>‐ K</mark> y <mark>is a crop specific factor – “</mark> **<mark>Yield Response Factor</mark>** <mark>” ‐ for growing seasons or stages of growing seasons,</mark> derived empirically from actual measured crop yield responses to water under good growing conditions<sup>10</sup> . <mark>The WRSI gives the ratio of</mark> **<mark>Actual Evapotranspiration</mark>** <mark>to</mark> **<mark>Total Water Requirement</mark>** <mark>(i.e
# **2.2 The US steel industry's use of antidumping, countervailing duties, and safeguards over 1989-2003**

The US steel industry has frequently sought government intervention to shield it from imports since the 1960s (Rosegrant, 2002), and it has been among the most frequent US users of TTBs since at least the early 1980s. Over the 1989-2003 period of our sample, the US steel industry and its unions filed hundreds of petitions against firms from dozens of exporting countries over thousands of steel products alleging that foreign steel was dumped into the US, subsidized by the foreign government, or otherwise a threat to injure the domestic industry.

Table 1 describes the dozens of investigations that have resulted in the imposition of duties over hundreds of different 10-digit Harmonized Tariff Schedule (HTS) steel product categories during this period. For a comparison of the frequency of these actions, as we describe below, we have a panel of 1471 unique 10-digit HTS steel product codes from chapters 72 and 73 that are represented in the US trade data over the same 1989-2003 period. The first row of this table, for example, suggests that roughly 1 in 6 of these 10-digit steel products were hit with a safeguard during this time period, 1 in 5 were hit with a countervailing duty (CVD), and 1 in 4 products were hit with antidumping.<sup>9</sup> In many instances antidumping and countervailing duties end up being “redundant” policies – in the sense that they are applied against the same product from the same trading partner at the same time.<sup>10</sup> In our formal investigation below we consider antidumping, countervailing duty, and suspension agreement outcomes _jointly_ ; furthermore, we sometimes use shorthand to refer to them solely as “antidumping” in our comparison of the results for safeguards.

In addition to the 2002 steel safeguard (described below) that affected some 272 unique 10-digit HTS categories, in 2000 the US also imposed much smaller tariff-rate quota safeguards on five
||||||fan|||
|---|---|---|---|---|---|---|---|
|Total|1246|51.9|54.6|2.6|Television,<br>radio|15.8|Poultry,<br>goats,<br>swine|

Source: Own computations using June 2010 (EFSA II) surveys. Notes: The sample is restricted to the households residing in the six strata that cover areas directly affected by the earthquake. *Among households practicing agriculture before the earthquake.

44
# **<mark>Difference-in-Differences</mark>**

<mark>Although many of the previous studies mentioned in this review have used multiple years of data, this temporal aspect has not been a key component of the estimation strategy. With the differencein-differences approach, changes over time are contrasted. However, in order to avoid other factors influencing the outcomes over time, there is an additional contrast between treated and untreated groups, where treatment is access to childcare. This strategy has been used in many contexts where government has scaled up childcare programs, resulting in variation in coverage over both time and place. We summarize the policies used in DiD studies in Table A1 rather than providing details on each of them in the text. These studies often examine large-scale government changes using national data, resulting in estimates that are more generalizable than those from RCTs or RDD studies.</mark> <mark>For the change between the outcomes of the treated and untreated groups to reflect causal estimates two assumptions need to hold. First, the changes between the groups prior to increase in availability of childcare need to follow a similar trajectory. Second, there cannot be time-variant factors simultaneously correlated with access to childcare and the outcome variables, which researchers do not observe and cannot control for.</mark> <mark>The first assumption can be tested if at least two data points are available prior to rollout of a childcare program. The second one is not a testable assumption. To increase the likelihood that the second assumption holds, some studies refine control groups by adding another dimension in a triple difference framework, such as age of children eligible for institutional childcare. The additional dimension reduces the likelihood that some unobservable factor, which is correlated with access to preschools affects women’s labor market outcomes. Because access to preschool in a triple-difference framework is defined through a combination of three factors (for example, time of the reform, its geographical coverage and age of children), the likelihood of confounding factors varying similarly
distribution grow at 8 percent, while those in the top 60 percent grow at 4.7 percent. B. Bars represent GDP-weighted aggregates. Diamonds represent GDP aggregates weighted according to each country’s share in total poverty.

# **5. Conclusion**

Growth in low-income countries has benefited from a confluence of favorable cyclical and structural developments since 2001 that have allowed the number of LICs to more than halve over this period, as many countries achieved MIC per capita income levels. These factors have, at various points, included a commodity price boom, cyclical rebounds from the collapse of centrally planned regimes in the early-1990s, debt relief, fewer armed conflicts in Africa, trade integration, and improved business climates and policy frameworks. However, the cyclical factors that contributed to LICs reaching MIC status were either unique events (transition rebounds) or are 13 Mozambique’s deep-water gas fields are estimated to hold more oil-equivalent reserves than are held by Angola or Nigeria—Sub-Saharan Africa’s two largest oil producers—and production is set to start by 2022/23 (World Bank 2015a). Oil reserves in Uganda are estimated to be the fourth-largest in Sub-Saharan Africa, and production could start within the next three years (Alkadiri et al. 2014). Large off-shore gas fields were recently discovered in Tanzania.

14
07|(0.25)|1,321|

**_Notes_** :

- The table shows the regression results on pre-specified main outcomes using the 2022-2023 phone survey data. Each row is its own dependent variable.

- Has savings _≥_ 30 JD is an indicator for whether the household has more than 30 JD saved (in a bank or otherwise). Lent informally last month is an indicator for whether FR lent assistance to someone outside the household in the form of money or goods with the expectation of being paid back in money, goods or favors. Lent informally (JD) is the amount of this informal lenting in JD, including zeros. Borrowed informally in the past month is defined as above for borrowing, not lending. Borrowed or lent formally is an indicator for whether or not FR took out a formal loan (e.g. commercial bank, commercial lender mobile service provider, etc.) in the past 12 months.

- The regressions also have assessment month-by-year fixed effects, enumerator fixed effects, community-level controls (Irbid/Mafraq governorate and population quartile), and household-level controls (vulnerability-assessment quartile, shelter program, baseline number of children, baseline number of children plus adults, respondent gender, and respondent age). In the parentheses are robust standard errors clustered at the locality level. Regressions are weighted by the number of people interviewed in each household. Statistical significance represented by * (10%), ** (5%), and *** (1%). Q-values are calculated per Anderson (2008) and correspond to Family xxx in the preanalysis plan.

Table (F5) Impacts of the Program on Earnings, Labor, and Occupational Choice

|**Outcomes**|**Treatment**|**(se)**|**FDR**<br>**q-values**|**Control**<br>**mean**|**(sd)**|
has identified as vulnerable, including own-account work (self-employed with no employees) or contributing family work.<sup>5</sup> Furthermore, about three-quarters of the population live in rural areas, but activities have diversified rapidly away from agriculture. Since 2013, the share of employed men in agriculture has fallen from about 48 to 30 percent (and the share in wage and salaried employment increased from 45 to 57 percent). For employed women, the share in agriculture has fallen from 51 to 34 percent between 2013-19, and those in wage or salaried work from 39 to 50 percent.<sup>6</sup>

## 2.2  Cambodia Living Standards Measurement Study-Plus (LSMS+) Survey

The analysis uses data from the Cambodia LSMS+ Survey<sup>7</sup> which was implemented in October 2019-January 2020 by the National Institute of Statistics, with support from the World Bank LSMS+ Program<sup>8</sup> . The

> 5 ILOSTAT Database, 2020.

> 6 The statistics come from World Development Indicators (2019), unless otherwise stated.

> 7 The anonymized unit-record data and questionnaires for the Cambodia LSMS+ Survey are publicly available at: <u>https://microdata.worldbank.org/index.php/catalog/4045.</u>

> 8 <mark>The World Bank Living Standards Measurement Study – Plus (LSMS+) program (www.worldbank.org/lsmsplus) works to enhance the availability and quality of intra-household, self-reported, individual-disaggregated survey data collected in low- and middleincome countries on key dimensions of men’s and women’s economic opportunities and welfare.</mark> Since 2016, LSMS+ supported

5
# **The Impacts of COVID-19 on Female Labor Force Participation in the Islamic Republic of**

# **Iran**

Hai-Anh H. Dang, Djavad Salehi-Isfahani, and Minh N. N. Do<sup>*</sup>

# JEL: E24, I30, J21, O12

Keywords: COVID-19, employment, women’s labor force participation, differences-indifferences, triple differences, labor force survey, Islamic Republic of Iran

* Dang (hdang@worldbank.org) is senior economist with the Living Standards Measurement Unit, Development Data Group, World Bank and is also affiliated with IZA, Indiana University, and London School of Economics and Political Science; Salehi-Isfahani (salehi@vt.edu _<u>)</u>_ is professor of economics at Virginia Tech and Research Affiliate at the Middle East Initiative Harvard Kennedy School _;_ Do (minh.nn.do@gmail.com) is a visiting lecturer with University of Economics and Business, Vietnam National University, Hanoi _._ We would like to thank Federico Bennett, Nelly Elmallakh, Haggay Etkes, and participants at workshops at the World Bank for helpful comments on earlier versions and to Arin Shahbazian for research assistance. We would like to thank the Chief Economist’s Office for the Middle East and North Africa (MNACE) for support and helpful comments on an earlier draft.
types of programs was done randomly, they were quite comparable. In our conversations with the director of the SERP program as well as with the SERP facilitators, we found that they were keen to work on their own, but did not always speak the local language fluently.<sup>23</sup> As a result, they were generally given strictly defined and narrow tasks to perform in a specific amount of time, with clear deliverables.

# A Comparison of Facilitators’ Characteristics

We consistently find a distinct difference between the observable characteristics of external and internal facilitators. External facilitators were younger and more educated than local facilitators (Table 3, Panel A). They had fewer children, were less likely to own land and were less reliant on agriculture as a source of income (Table 3, Panel A). Our qualitative observations of these facilitators confirmed these patterns. We observed that the facilitators from Andhra Pradesh spoke up to three languages (Hindi, Telugu and English) and talked, freely and unprompted, about their own personal path out of poverty and their professional experiences. They presented themselves as a group of disciplined anti-poverty professionals whose best qualifications were their own personal journeys of transformation. In rural Rajasthan they stood out for their distinctive way of talking and dressing as well as for their confident demeanor. This was not seen in the case of the local facilitators, who appeared to fit in much more easily with the prevailing norms.

In our survey data, however, we do not find an “experience gap”: both external and internal facilitators seemed to have similar number of years of experience (Table 3, Panel A). There were however, significant differences in reported income (Table 3, Panel A). According to official policy, external facilitators were paid Rs. 1,000 per day, and worked for 30 - 45 days at a stretch, earning more than Rs. 30,000 - Rs. 45,000 in a single rotation with the RRLP. They completed 4
both in magnitude and significance, especially for “log GDP” and “property tax rate”. But the signs are the same for all the coefficients of both models. As discussed above, omitted variables are likely to be correlated with existing variables and based on the results from Hausman Tests, we are confident that the results from the Fixed Effects model provide more appropriate estimates.

# **VII. Conclusions**

To study the determinants of property tax revenue, we utilize OLS, Fixed Effects Models, and Random Effects Model to research on data from the United States, as well as Australia, Canada, Chile, and all the OECD countries. As expected, the Fixed Effects model fits the data better due to potential endogeneity issues, although they all return estimates as predicted.

The outcomes of our empirical analysis highlight the statistically significant impact on property tax collection of such determinants presenting a country’s state of development and its demographic, fiscal and property tax-specific characteristics. Specifically, the income level, the population density, and property tax rate correlate significantly and positively with property tax revenue, whereas federal transfers and household size have significantly negative impact. In our study, we are able to measure the impact of each of these factors and our research provides coefficients which can be used to make estimates of potential property tax revenues. Our empirical analysis draws in some relevant ‘food for thought’ pertinent to property taxation. First, design of fiscal transfer matters. As noted, property tax reforms typically go in conjunction with deepening fiscal decentralization. Any ‘soft budget constraints’ embedded in fiscal transfer schemes (equalization transfers as a case in point) can influence the fiscal policies of recipient states. Within this frame, lax behavior by states in mobilizing own revenue, most credibly through property tax, tends to be unintendedly compensated by the increased magnitude of equalization or other types of redistributive transfers.<sup>14</sup> The broader policy relevancy that we would venture to highlight here is: The efficiency in
household surveys. In total, we collected data from 3,379 households (92%). These numbers are 814 out of 900 (90%) for those villages that participated in the gender parity lottery. Again **Table A3** suggests no differences by treatment status.

# **Table A3. Attrition by Treatment Status**

|Data Source|Target|Collected<br>Missing|Missing|Beta|(se)|
|---|---|---|---|---|---|
||<br>|<br>control<br>_Tuungane treatment_<br> <br>|<br>treatment<br>||<br>|
|Villages visited|781|735<br>22|24|-0.005|(0.017)|
|Village chief survey|735|714<br>11|10|0.003|(0.012)|
|Household survey|3,675|3,379<br>149|147|0.0004|(0.009)|
|Children’s exam|3,379|1,496<br>966|917|0.024|(0.017)|
|Primary school|735|610<br>67|58|0.0231|(0.027)|
|Health facility|735|504<br>114|117|-0.010|(0.034)|
|||_Genderparity treatment_<br> <br>||||
|Villages visited|190|180<br>6|4|0.0177|(0.032)|
|Village chief survey|180|176<br>2|2|-0.001|(0.022)|
|Household survey|900|814<br>42|44|-
, as a more recent 2019 Glassdoor survey indicates that these figures jumped to 61 percent and 58 percent, respectively, for men and women. A lack of information on how much male counterparts are being paid likely makes it even more difficult to negotiate. Increasing transparency can help women understand and ask for appropriate pay. More broadly, an article in Forbes magazine pointed to studies which showed that keeping salaries secret decreases motivation and performance, while sharing salary information increases performance.

**Lessons learned:** Although sharing information about salaries can seem contrary to social norms in many countries and cultures, evidence suggests that keeping salaries secret is often detrimental to women. Removing barriers to sharing of salary information, as well as requiring transparency in pay bands, is a relatively low‐cost intervention that can level the playing field for women in the workplace.

> 103 David Burkus, “Why Do We Keep Salaries Secret?,” Forbes, February 2, 2016, https://www.forbes.com/sites/davidburkus/2016/02/02/why‐do‐we‐keep‐salaries‐secret/.

> 104 “3 in 5 Employees Did Not Negotiate Salary,” _Glassdoor_ (blog), May 2, 2016, https://www.glassdoor.com/blog/3‐5‐u‐s‐ employees‐negotiate‐salary/.

> 105 Juliet Eilperin, “Obama Takes Executive Action to Lift the Veil of ‘pay Secrecy’ ‐ The Washington Post,” The Washington Post, April 8, 2014, https://www.washingtonpost.com/news/post‐politics/wp/2014/04/08/obama‐takes‐executive‐action‐to‐lift‐the‐ veil‐of‐pay‐secrecy/?utm_term=.2fcae87bbd03.

> 106 Jackson Amy, “New Study: Job Seekers Expect Salary Negotiation & Transparency,” _Glassdoor for Employers_ (blog), April 10, 2019, https: