rafmacalaba/data-use-mentions-extended huggingface dataset:
**Table 1.1  International Use of Antidumping and the Global Antidumping Database**

||**Number of Antidumping**|**Number of Antidumping**|
|---|---|---|
|**Country**|**Investigations, 1995-2004**|**Measures Imposed, 1995-2004**|
|**User-Countries in the Global Ant**<br>|**idumping Database**<br>||
|Argentina<br>|192|139<br>|
|Australia|172|54|
|Brazil<br>|116<br>|62<br>|
|Canada|133|80|
|Colombia|23|11|
|European Union<br>|303|193|
|India|400|302|
|Mexico|79|69|
|New Zealand|47|14|
|Peru|55|34|
|South Africa|173|113|
|South Korea<br>|77<br>|43<br>|
|Turkey|89|77|
|United States|354|219|
|Venezuela|31|25|
|Subtotal|2244|1435|
|(share of total)|(84.8%)|(86.7%)|
|**User-Countries not yet in the Glo**|**bal Antidumping Database**||
|China (since 2001)|99|52|
|Egypt|38|30|
|Indonesia|60|23|
|Israel|27|15|
|Malaysia|31|18|
|Taiwan (since 2000)|8|2|
|Thailand|34|23|
|Other WTO Members|105|58|
|Subtotal|402|221|
|(share of total)|(15.2%)|(13.3%)|
|Total|2646|1656|
- **Bail, Christopher A., Friedolin Merhout, and Peng Ding** , “Using Internet Search Data to Examine the Relationship between Anti-Muslim and pro-ISIS Sentiment in U.S. Counties,” _Science Advances_ , 2018, _4_ (6).

- **Bandyopadhyay, Subhayu and Javed Younas** , “Poverty, Political Freedom, and the Roots of Terrorism in Developing Countries: An Empirical Assessment,” _Economics Letters_ , 2011, _112_ (2), 171–175.

- **Baum, Matthew A and Philip BK Potter** , “Media, Public Opinion, and Foreign Policy in the Age of Social Media,” _The Journal of Politics_ , 2019, _81_ (2), 747–756.

- **Bellemare, Marc and Casey Wichman** , “Elasticities and the Inverse Hyperbolic Sine Transformation,” _Oxford Bulletin of Economics and Statistics_ , 2020, _82_ (1), 50–61.

- **Benmelech, Efraim and Esteban F. Klor** , “What Explains the Flow of Foreign Fighters to ISIS?,” _Terrorism and Political Violence_ , 2018, _0_ (0), 1–24.

- **Berger, J.M.** , “Tailored Online Interventions: The Islamic state’s Recruitment Strategy,” _CTC Sentinel_ , 2015, _8_ (10), 19–23.

- **and Jonathon Morgan** , “The ISIS Twitter Census: Defining and Describing the Popula-

- tion of ISIS Supporters on Twitter,” _The Brookings Project on US Relations with the Islamic World_ , 2015, _3_ (20), 4–1.

- **Bhuller, Manudeep, Tarjei Havnes, Edwin Leuven, and Magne Mogstad** , “Broadband Internet: An Information Superhighway to Sex Crime?,” _Review of Economic Studies_ , 2013, _80_ (4), 1237–1266.

- **Bloom, Mia, Hicham Tiflati, and John Horgan** , “Navigating
(2020) use panel data from six countries - China, Ghana, Indonesia, Malawi, South Africa and Tanzania – and find an average gain in (spatially price adjusted) consumption of 23 percent from migrating, ranging from 17 percent in Indonesia to 34 percent in Ghana. Gains in income in South Africa and Indonesia are 29 percent and 15 percent respectively.

However, the challenges with these panel data estimates are both that the gains for the types of people who move might be different from what we might expect for those who don’t migrate, and in dealing with the possible selectivity of when individuals decide to move – e.g. they might move because they experience a negative forecast of what their productivity would be at home, and so their prior earnings might overstate what they would have earned had they stayed.

Experimental estimates from efforts to encourage more rural-urban migration address this issue, and do also suggest that, at least in some settings, gains from internal migration are possible for the people who respond to these incentives. For example, Bryan et al. (2014) find Bangladeshis 7
the career mentoring course by 1.6 percentage points (5.2%). Reading time has no effect for respondents in a couple in which the woman already works (Column 3).

# **5.2.2 Heterogeneity Analysis**

We now explore the heterogeneity of treatment effects. One might expect larger treatment effects in households which are more open to attitudinal change, possibly through greater exposure to role models of working women, and who have real prospects of being able to

> significant. We also estimated specifications including controls for whether the respondent is supportive of women working outside the home for pay. The coefficients are similar to the ones reported here as the level of support prior to treatment is balanced across control and treatment groups. Results available upon request.

> 28Weights for (non-)working women were calculated as the number of (non-)working women in the population over the number of (non-)working women in the sample. The population numbers were calculated from the 2020 National Socioeconomic Survey (SUSENAS).

> 29On average people spent 19 seconds reading the information in treatment 1, 26 seconds in treatment 2 and 35 seconds in treatment 3 (as the format of the information is similar in each treatment, the time spent accelerates). No one spent more than 2.5 minutes reading the information. We also estimated a specification with a quadratic term but its coefficient was not significant.

15
# **4. Shift-Share (Bartik) Analysis**

The goal of this section is to assess how export expansion affected local labor market outcomes in Cambodia, exploiting variation in export expansion across provinces and industries between 2010 and 2020 (Bartik, 1991). To this effect, we combine export data from UNCOMTRADE data, and information on local labor market outcomes from CSES data. Details on each dataset and cleaning techniques are described below.

# **_Overview of the Approach_**

We use a shift-share model that assesses the effect of trade on Cambodia’s local labor market outcomes. Following Bartik’s (1991) approach, we estimate how increases in exports per worker (as a measure of exposure to trade) affects informality, wages, and hours worked at the district level. As noted, labor-market outcomes vary significantly across districts. Following many in the literature, we assume that the districts represent relatively independent local labor markets. The identification strategy under this assumption is that labor-market outcomes will vary in response to differential exposure to exports. The differential exposure to exports is calculated using _a priori_ district-level employment in different exporting sectors.

We estimate the following equation: 0 1 , (2) dd dd dd tt+nn tt tt,tt+nn cc cc dd In equation (2), yy −yy is the change in outcome of interest. Examples include the = ββ + ββ xx + XX ′ββ + εε employment rate, informality rate, female participation rate, average annual income average dd dd tt+nn tt annual wage, college premium, or gender wage gap, and so on, identified at district yy −yy _r_ over the period from time to + h. is the change in the export exposure index (change in exports from Cambodia to the United States (US) weighted by sectoral employment). The dd tt,tt+nn export exposure is measured as the growth in Cambodian exports in industry tt tt xx _i_ between multiple time periods captured by the term
**Table 4.3: Outage-Climate correlation**

|**Division**|**Correlation Outages/Lightnings**|**Share of national outage**<br>**events that occurred in**<br>**this division**|
|---|---|---|
|Barisal|-|0|
|Chittagong|**0.49**|**19.4**|
|Dhaka|**0.75**|**46.3**|
|Khulna|0.19|0.9|
|Rajshahi|**0.72**|**20.5**|
|Rangpur|0.26|9.8|
|Sylhet|0.18|3.2|
|**Country**|0.39|100|

# 4.3. System vulnerability to storms

This section aims at determining by how much the probability of observing an outage is increased when wind speed increases. Different definitions of storms are derived from the ERA5 wind data. As highlighted in Section 2.2.2, in Bangladesh these climatic events reflect storms associated with the monsoon season and to a lesser extent to Nor'westers.

For a basic definition of “storms” we count the number of days on which wind speed observations exceed a certain threshold in each state. Starting from 15 km/h, we increase the threshold with constant steps of 5 km/h, in order to identify days with stronger storms. The strongest storms we consider correspond to wind above 35km/h. Table 4.4 summarizes the number of storm days according to the respective thresholds in the period 2000 to 2017 in Bangladesh and the US. Each entry of the climate data corresponds to the average daily wind speed recorded at the centroid of one division or state. In Bangladesh, 0.07% of the days between 2000
Learning from Developing Country Power Market Experiences: The Case of Colombia

|**2016**|167|142|18%|55|100|
|---|---|---|---|---|---|
|**2017**|174|155|12%|59|37|

Source: Analysis based on data collected by the World Bank.

Contract prices in the regulated market have consistently been priced at a premium of about 20% higher with respect to the liberalized market, reaching a 30% premium during 2011 (see Figure 6-3). Price differences in the regulated and liberalized contracts market have raised persisting concerns regarding the possibility of price discrimination by dominant Gencos / Suppliers (CREG, 2006b; SIC, 2016). However, no formal prosecution of market power abuse nor regulatory reform has been held to this date to address price discrimination in the wholesale contracts market. It is worth noting that, partly to address these concerns, the regulator (CREG) has intended to develop an organized futures market for supplying regulated customers since 2006. Such efforts have faced the opposition of existing Gencos, and to this date the organized future market for regulated customers has not been established.

Contract prices are far less volatile than pool prices (see Figure 6-4). Furthermore, pool prices have increased by 158% over the past decade (2005 to 2016) in real terms, that is, pool prices have increased at much faster rates than contract prices, in particular over years 2012-2015, driven mainly by low hydro inflows. Prices fell significantly during 2017 given normal hydro inflows and slow demand growth (see section 6.1.2).

37
angular resolution, lower aliasing effect, and consistent connectivity (Wang and Ai 2018). Annex 2 presents a detailed description of the methodology used to create the hexagon-level data set.

To measure nighttime light before the project, the latest year for which nightlights data are available before the project was appraised is used. To measure nightlights after the project is completed, the longest gestation period possible for WEBs to emerge is used, which runs until 2012.<sup>15</sup> The average sum of light (SOL) value for hexagons within 20 kilometers of the project’s alignment one year prior to project appraisal is 1,337. This value increased to 2,064 in 2012—the last year for which nightlights data are available.

## **3.5. Human Settlements**

We use the Global Human Settlement Layer (GHSL) (Pesaresi et al. 2016) to estimate built-up land cover and urban settlements within the analyzed hexagons. GHSL is based on the European Commission’s Global Human Settlement Grid. This data set utilizes a degree of urbanization model to determine to what degree an area is developed. The data are separated into high-density (cities or large urban areas), low-density (towns and suburbs), and rural grids. Each of these categories is represented by a numerical value ranging from 0 to 3. These numerical values are summed as total values for each year available in the data set. The sum of values represents per-hexagon degrees of urbanization.

# **4. Empirical Strategy**

In addition to understanding whether a large transport corridor investment can yield WEBs on average, it is important to understand the relationship between the realization of WEBs and the environment in which the project is being designed and implemented—that is, the local initial conditions and country characteristics—and the design characteristics of the project itself.

For example, the geographic scope of a project may have a significant impact on the realization of WEBs. On the one hand, projects with
the results in <u>Table 1, for various combinations of parameter values. (We</u> have chosen the succession in pairs of scenarios to help assess the partial effect of the parameters and policy choices.) Column 1 is for no costs of moving (though official taxes on earnings in the U.S. remain). This is obviously implausible, and we only provide it as a benchmark. We then consider a range of values to reflect the potential frictions discussed above. Our parameter combinations in columns 4-6 that give a total cost of 20-30% of U.S. earnings (extra living costs in the U.S. plus the remittance levy) provide a fairly close match to the total migration costs implied by the estimated earnings differentials between the U.S. mainland and Puerto Rico and

> 12 See, for example, Beaudry et al. (2018), who find a wage elasticity of labor demand of -1 based on the variations across cities and industries over 40 years.

> 13 The methods and results using this method are described in <u>Passel (2019).</u>

> 14 <u><mark>Capps et al. (2018) question the Fazel-Zarandi et al. (2018) estimates. Rodilitz and Kaplan (2021)</mark></u> <mark>find similar results to Fazel-Zarandi et al. using data from Mexican surveys of prior illegal migrants to the U.S.</mark>

10
# **1. Introduction**

Women can be disproportionately affected by the negative effects of conflict-induced displacement. Access to essential services such as reproductive health care can be disrupted. Displacement can result in higher levels of gender-based violence (GBV) (Annan & Brier, 2010; Callaway & Martin, 2011; Cohen et al., 2013; Vu et al., 2014; Wirtz et al., 2014). However, it can also provide opportunities to challenge gender norms that limit women’s access to opportunities and ability to make decisions.

Following displacement, women might adopt new roles that would not have been possible before (Justino et al., 2012; Meertens & Stoller, 2001; Pirtskhalava, 2015). For example, in the absence of men, displaced Nuer women in South Sudan took on male responsibilities as income providers and assumed roles perceived as male, including negotiating dowries. To date, however, partly due to data limitations, few quantitative studies take account of the changes in gender relations among women and men in situations of displacement.

This paper aims to bridge this knowledge gap by building on the work by Heise and Cislaghi (2020), who propose a definition of gender norms that brings together two streams of theory and practice around gender equality. The first stream is the work on social norms, which emerged from studies in social psychology and evolved with behavioral economics (Bicchieri, 2005; Mackie et al., 2015). The second stream is the study of gender norms advanced by feminist scholars (Badgett & Folbre, 1999; Connell & Pearce, 2014; Connell & Pearse, 2015). The definition is operationalized by measuring behaviors and attitudes (Alesina et al., 2013; Harper et al., 2020). Following previous studies, survey clusters are used as a proxy for reference networks (Blakely, 2000; Kelly et al., 2018; Storey & Kaggwa, 2009; Uthman et al., 2011; Vyas & Heise, 2016).
br>error|(1.779)|(1.278)|(0.777)|(1,407)|(279.8)|(275.7)|
||N|191|191|191|116|135|143|

Note: Robust Standard errors are in parentheses. *,**,*** significance level at 10%, 5% and 1%.

Source: Author's estimates using data from Bangladesh LFS and UN Comtrade. This table shows the first and second stage of the 2SLS regression. In the first stage, we estimate the contribution of US import demand to the increase in Bangladesh exports, and in the second stage we estimate the effect of an increase in exports on real wages. This relationship is estimated for different worker types (male, female, rural, skilled, unskilled, young, and old).

**Table A2. Change in the informality rate after a US$100 increase in exports per worker (Real Bangladeshi Takas of 2017 and percentage) using Two-Stage Least Squares (2SLS) Regression Analysis**

||||1st Stage||2nd|Stage (Informa|lity)|
|---|---|---|---|---|---|---|---|
|Type of worker||2005-2010|2005-2013|2005-2016|2005-2010|2005-2013|2005-2016|
|||(1)|(2)|(3)|(1)|(2)|(3)|
|All|Coefficient|14.14***|10.65***|8.665***|-0.00703*|-0.00369|0.000168|
||Standard<br>error|(1.558)|(1.223)|(0.700)|(0.00361)|
|Sierra Leone|1415|52.3|2011|0.350|58.5|12.7|
|Swaziland|7620|42.0|2009|0.824|72.3|57.0|
|Tanzania|2207|46.6|2011|0.744|55.3|13.6|
|Togo|1255|54.3|2011|0.480|60.5|11.5|
|Uganda|1649|33.2|2012|0.620|74.2|18.3|
|Zambia|3343|64.4|2010|0.518|62.2|43.0|
|Zimbabwe|1524|n.a.|n.a.|0.801|77.7|37.5|
|Mean<br>ote Poverty rates are|2806<br>for $1.90 per perso|42.7<br>n per day at 20|2012<br>11 PPP; estimate|0.472<br>s from PovcalNet|71.1<br>, accessed 8/18/|29.1<br>2016. Mean|

Note Poverty rates are for $1.90 per person per day at 2011 PPP; estimates from <u>PovcalNet, accessed 8/18/2016. Mean</u> poverty rate is for Sub-Saharan Africa as a whole. GDP, literacy, access to improved water and sanitation are all taken from the World Bank’s <u>World Development Indicators. Literacy rate for 2011 or closest available year to 2011 in 2007-15; more</u> recent year for ties. Water and sanitation for 2011.

41
for the level of stock market development (Levine and Zervos **1998)** without changing the conclusion that financial intermediary development exerts a positive impact on long-run growth. We also experimented with two additional measures of financial intermediary development. One measure equals deposit money bank credit to the private sector divided **by GDP.** This is smaller than PRIVATE CREDIT, which also includes other financial intermediaries. The second additional measure equals the

> **9** To get this, recall that the regressors are in logs and note that the ln(25) **-** ln(19.5) **= 0.25.** Then, use the smallest parameter on **PRIVATE CREDIT** from **Table 3,** which equals **2.5,** so that **2.5*(0.25) = 0.63.**
total product export volumes shown in this paper should be considered as an upper bound. The collection of more detailed input, output or supply chain data that is comparable across countries would allow for more accurate depictions of these global value chains. Finally, lags in trade data should also be kept in mind as recent developments and/or interventions are not accounted for.

# 2. Results

## 2.1 Mapping global value chains of key decarbonization technologies

To analyze trade patterns in the global value chains for solar PV, wind turbines and EVs, we collated a new dataset of end products, subcomponents, processed and raw materials classified under the 6-digit HS. The 6-digit HS is a standardized classification of traded products used by customs authorities around the world. It is also the most granular classification that is comparable across almost all countries and over time (see the Methods section for more detail). Figure 1 shows an illustration of the solar PV value chain, with example products listed. Due to the challenges of classifying such products under the 6-digit HS (IISD, 2020), our dataset is not exhaustive but intended to focus on the key identifiable elements of each value chain.<sup>2</sup> As EV production includes products that are also used in internal combustion engine (ICE) vehicles, we construct two sets of value chain products: one more broadly defined and one more narrowly defined. The broader set includes HS products associated with the wider vehicle manufacturing value chain, e.g., products used in either ICE vehicles or EVs. The narrower EV value chain only considers products that relate specifically to EVs, e.g., battery end products and components and the assembled EV end product.

> 2 All products included in our dataset were subject to a series of independent evaluations by selected industry specialists (see Methods section 4.1 for further information and Table SI 6 for a list of the included products).

3
# **4 Results**

## **4.1 Conflict exposure, sales, total costs, and profits**

Table 1 presents our main results. Column 1 shows that conflict exposure leads to a large and statistically significant decline in firm sales. This effect remains robust in column 2 after the inclusion of firm-level controls. Based on our estimates, a one-standard-deviation increase in the number of conflict events occurring within a 20 km radius of the firm during the last fiscal year (eight events) leads to a 2.8% reduction in median sales, equivalent to 10,050 USD. Higher conflict exposure also leads to a significant decline in total production costs, measured by expenditures on raw materials, intermediate inputs, and labor (column 3). Results do not change including controls (column 4).

These reductions in both sales and total costs could reflect changes in either prices or quantities. Although the WBES dataset does not allow us to directly test for price changes, it is unlikely that conflict lowers consumer or input prices.<sup>15</sup> This suggests that the observed declines in sales and total production costs are mainly driven by a contraction in output and a reduction in the quantity of inputs and materials used in production.

Finally, our results show that conflict exposure does not affect a firm’s profits (columns 5 and 6).<sup>16</sup> This null effect is consistent with the fact that higher conflict exposure affects both sales and total costs in the same direction, suggesting that firms respond to reduced sales by cutting production costs.<sup>17</sup> As these adjustments result in reduced output, it follows that, while surviving firms’ profitability remains unchanged, overall economic activity is negatively impacted by conflict-related violent events.

**Threats to identification** The firm-level fixed effects in our baseline specification account for all time-invariant firm, sector, and geographical characteristics. However, our estimates may still capture some factors
Given the existing structure of markets in Mexico, the results indicate that the effects of multilateral trade liberalization would concentrate in the northern states, which are more closely connected to international markets. Conversely, households living in the southern states are largely insulated from these effects, not because of the composition of their consumption or income bundle, but rather because of the very limited effect of trade reforms on prices in those areas.

The remainder of this paper is organized as follows. Section 1 describes the extent and distribution of poverty in Mexico. Section 2 analyzes the Doha implications for Mexican households. Section 3 illustrates the empirical framework. Section 4 presents the results. Section 5 concludes. The annex discusses the household data base as well as its reconciliation with, and mapping to, the macro data (GTAP).

# **1. Poverty in Mexico**

Despite Mexico’s status as a middle-income country and a member of the OECD, poverty in Mexico is widespread. Poverty levels have moved substantially during the 1990s decreasing in periods of economic growth and increasing in economic downturns. Extreme poverty<sup>3</sup> was estimated to be about 24 percent in the early 1990s. Economic reforms and growth produced a reduction of about 3 percentage points by 1994. The economic crisis of 1995 and the sharp devaluation of the peso then led to a sharp increase in poverty (to 37 percent in 1996 and 34 percent in 1998). Finally, economic recovery in the late 1990s produced the largest decline in poverty, with extreme poverty falling to pre-crisis levels in 2000 and declining thereafter to about 20 percent in 2002. The

> 3 This corresponds to the food poverty line. The food poverty line is defined as the minimum expenditure necessary to guarantee a 2200 calories intake per day.

3