rafmacalaba/datause-encoder-data huggingface dataset:
# **3.2 Brazilian Experience with Antidumping and Safeguard Measures**

# **3.2.1 An Overview**

According to the notifications submitted to the WTO Committee on Antidumping Practices of the WTO, Brazil<sup>19</sup> was ranked eighth among the countries which had resorted the most to antidumping measures between 1995 and 2002, following India, the United States, the European Union, Argentina, South Africa, Australia and Canada (Table 2). Brazil, together with India, Argentina, South Africa, China, South Korea and Turkey, is one of the countries that began seriously adopting antidumping measures in the 90s. The case of India is worth mentioning, since it largely outperformed other countries with a longer tradition in the use of such legal instruments.

Table 2 Number of Antidumping Cases Initiated between 1995-2002 – Main Countries

|Country/Bloc|1995|1996|1997|1998|1999|2000|2001|2002|Total|
|---|---|---|---|---|---|---|---|---|---|
|India|6|21|13|27|65|41|79|80|332|
|USA|14|22|15|36|47|47|76|35|292|
|EU|33|25|41|22|65|32|29|20|267|
|Argentina|27|22|14|8|24|45|26|14|180|
|South Africa|16|33|23|41|16|21|6|4|160|
|Australia|5|17|42|13|24|15|23|16|155|
|Canada|11|5|14|8|18|21|25|5|107|
|Brazil|5|18|11|18|16|11|17|9|105|
|Mexico|4|4|6|12|11|7|5|10|59|
|China|na|na|na|na|0|6|14|30|50|
|South Korea|4|13|15|3|6|2|4|9|56|
|Turkey|0|0|4|1|8|7|15|17|52|
|Subtotal|125|180|198|189|300|255|319|249|1.815|
|Total|157|224|243|256|356|294|366|309|2.205|

Source: WTO, AD Initiations by Reporting Member, 2003. Author's calculations Na = not available China is the main target in antidumping cases (with 232 actions against it), followed by South Korea (92), Taiwan (75), the USA (69), Japan (68), Russia (66), Thailand (58), Brazil (54), and

> 19 For comparison purposes, the number of investigations quoted is that reported by Brazil to the WTO Antidumping Committee.

17
Kilic, Talip. 2018. “Focus on Women’s Employment: The Value of Time.” Presentation made at the Workshop on Improving Data Collection on Women and Youth Employment, World Bank and IFAD, November 2018, Rome.

Kilic, Talip, Amparo Palacios-Lopez and Markus Goldstein. 2015. “Caught in a Productivity Trap: A Distributional Perspective on Gender Differences in Malawian Agriculture.” _World Development_ 70: 416– 463.

Kochhar, Kalpana, Sonali Jain-Chandra, and Monique Newiak (eds). 2017. _Women, Work and Economic Growth: Leveling the Playing Field._ International Monetary Fund, Washington DC.

Koolwal, Gayatri. 2018. “Counting Rural Women’s Work Under 19th ICLS: New Insights from ILO’s Country Pilots.” Working Paper, Data2X.

McKenzie, David, and Christopher Woodruff. 2014. “What are we learning from business training evaluations around the developing world?” _World Bank Research Observer_ 29(1): 48-82.

Mammen, Kristin, and Christina Paxson. 2000. “Women’s Work and Economic Development.” _Journal of Economic Perspectives_ 14(4): 141-164.

Malapit, Hazel J., Kathryn Sproule, and Chiara Kovarik. 2017. “Using cognitive interviewing to improve the Women’s Empowerment in Agriculture Index survey instruments: Evidence from Bangladesh and Uganda.” _Journal of Gender, Agriculture and Food Security_ 2(2): 1-22.

Malapit, Hazel J. L., and Agnes R. Quisumbing. 2015. “What dimensions of women’s empowerment in agriculture matter for nutrition in Ghana?” _Food Policy_ 52: 54-63.

Mellor, John W. 2017. _Agricultural Development and Economic Transformation: Promoting Growth with Poverty Reduction._ Palgrave Studies in Agricultural Economics and Food Policy (Christopher Barrett, ed.), Palgrave Macmillan.

Moser, Christine M., Barrett, Chris B. 2006. “The complex dynamics of smallholder technology adoption: The case of SRI in Madagascar.” _Agricultural Economics_ 35 (3): 373–388.

Mu, Ren, and Dominique van de Walle. 2011. “Left behind to farm? Women's labor re-allocation in rural China.” _Labour Economics_ 18: issue S1, S83-S97 Mueller, Bernd and Man-Kwun Chan. 2015. _Wage labor, agriculture-based economies, and pathways out of poverty: taking stock of the evidence._ LEO Report No. 15, USAID.

Munshi, Kaivan. 2004. “Social learning in a heterogeneous population: Technology diffusion in the Indian Green Revolution.” _Journal of Development Economics_ 73(1): 185–213.

Palacios-Lopez, Amparo, Luc Christiaensen, and Talip Kilic. 2017. “How much of the labor in African agriculture is provided by women?” _Food Policy_ 67:52–63.

Sarkar, Sudipa, Soham Sahoo, and Stephan Klasen. 2018. “Employment Transitions of Women in India: A Panel Analysis.” Working Paper, IZA Institute of Labor Economics.

27
Countries with good institutions rarely default. Figures 1 and 2 show the negative relationship between the two measures of institutional quality and sovereign default probability. The correlations of sovereign default probability with the ICRG institutional index and regulation of participation are -0.53 and -0.44, respectively. They are significant at 1%. Columns 1 and 2 of Table 3 report the summary statistics of institutional quality and government polarization of nondefaulters, one-time defaulters and serial defaulters. Non-defaulters, countries that have not defaulted during this period, have a substantially higher index in both measures of institutions than defaulters, and the difference is statistically significant at 1%. However there is no statistical difference between one-time defaulters and serial defaulters.

Column 3 of Table 3 shows the average government polarization. Non-defaulters on average have higher polarization than defaulters; and among defaulters, one-time defaulters are less polarized than serial defaulters. However, these differences are not statistically significant. This is also shown in Figure 3. It seems that default probability is not positively correlated with government polarization. In fact, the pairwise correlation between default probability and government polarization is 0.01 and it is not significant. I will argue that polarization does not matter under the presence of good institutions because powerful groups, regardless of how polarized they are, cannot influence government decisions. Therefore countries with more polarized government do not necessarily default more times. To show this, I perform an OLS analysis where the dependent variable is the default probability. I first show that quality of institutions is an important determinant. Second, conditional on having weak institutions, default probability is higher in countries with a higher degree of government polarization.

It could be argued that there might be omitted variables that could explain cross-country differences, in both institutional quality and default frequencies; for example, stage of development. This is a valid argument since most advanced economies enjoy better institutions and rarely default. The correlation between real GDP per capita and default probability is -0.46, significant at 1%. Another issue is that if a country does not have external debt, either because it does not need to borrow abroad or it has no access to foreign funding, its sovereign default probability will be zero by definition. In fact, countries with higher debt as a percent of GNI are also those that default more often. The average debt as a percent of GNI of non-defaulters is 0.11 while the average of defaulters is 0.60. Finally, countries that have defaulted in the past are more 9
**Table 7: Distribution of the poor by Poverty Status of their household**

||Hous|eholds|Ce|lls|Indivi|duals|
|---|---|---|---|---|---|---|
||N|%|N|%|N|%|
||(1)|(2)|(3)|(4)|(5)|(6)|
|_Basic needs threshold_|||||||
|Poor in non-poor households|158|8.97|219|5.10|865|6.18|
|Poor in poor households|446|25.31|1,514|35.27|5,610|40.11|
|Non-poor in poor households|162|9.19|181|4.22|374|2.67|
|Non-poor in non-poor households|996|56.53|2,379|55.42|7,139|51.04|
|_Nutrition threshold_|||||||
|Poor in non-poor households|79|4.48|110|2.56|419|3.00|
|Poor in poor households|136|7.72|556|12.95|2,133|15.25|
|Non-poor in poor households|84|4.77|100|2.33|201|1.44|
|Non-poor in non-poor households|1,463|83.03|3,527|82.16|11,235|80.32|

Source: “Pauvreté et Structure Familiale” (PSF) survey, authors’ calculations. Poverty status based on per adult equivalent consumption. Equivalent scale: 0.5: children 0 to 14 years old; 1: adults.

Note: The line “poor in non-poor households” gives the number (and share) of non-poor households containing at least one poor cell; then, the number of poor cells found in non-poor households; and finally, the number of poor individuals found in non-poor households. Following lines are to be read in a similar way. The second line gives numbers for poor households where everyone is poor, the third line for poor households containing at least one non-poor cell, and the fourth line for non-poor households where all the cells are non-poor.

43
data, and the institutional websites. As such, we recognize the criticism of comparing apple with oranges, and recommends that the findings be treated as indicative. Higher comparability of data is required to reach more reliable results.

We find that: (i) the direct cost of tertiary education is considerably higher in Latin America, 35 percent of GDP per capita, than in high-income countries, 10 percent. Therefore, tertiary education seems less affordable, (ii) the cost of living for accommodation and food is a significant share of costs of tertiary education, 29 percent in Latin America compared to 19 percent in high-income countries, (iii) there is a low level of grants and loans in Latin America. On average, student assistance lowers the costs of tertiary education by only four percent of GDP as compared to 11 percent in high-income countries, and (iv) we confirm the finding of previous studies showing a low level of access to tertiary education as compared to high-income countries.

The paper is divided into four sections and an annex. Section two describes the methodology and data for measuring affordability and accessibility for each country. Section three presents results and findings of the analyses, and section four discusses policy implications. The annex contains more information on data sources and tables with detailed information.

# **2. METHODOLOGY and DATA**

# **2. 1. Methodology for Analysis**

This section describes the methodology developed by Usher and Cervenan, 2005, to estimate affordability and accessibility of tertiary education.<sup>5</sup>

# **2.1.1. Affordability**

We express the estimated costs of attending tertiary education as a function of ability to pay. This study uses GDP per capita at purchasing power parity of each country to express ability to pay<sup>6</sup> .

We measure the costs of attending tertiary education by four indicators of affordability. We only take into account direct costs not indirect costs, such as opportunity costs. The four indicators are the combinations of five cost components: education costs, living costs, grants, student loans, and tax breaks. They are defined below (Table 1).

> 5 An important caveat in all cross-country comparison on tertiary education is the lack of information regarding quality and relevance of tertiary education. These factors are of course important to judge both affordability and access (Hanushek, 2006). As with other cross-country studies, we do not have a reliable indicator of quality or relevance. Therefore, the proposed methodology does not take these important dimensions into consideration.

> 6 Ideally, ability to pay would refer to the amount of after-tax income per household, which can be used for consumption. However, this information is not available due in part to discrepancies both in the definition of household and in the reporting of after tax or disposable income.

4
them as a proxy for future climate scenarios. The return period hazard maps used for each of the future scenarios are outlined in Table 2. Although simplistic, this method allows areas that may be impacted by increasing riverine and extreme rainfall driven flooding to be identified. Clearly there are some significant assumptions and uncertainties arising from this method. However, given the impracticalities of modeling future flood risk in Vietnam, this approach provides a plausible and practical attempt to estimate changing flood risk at the national scale.

For each of the four return periods, four scenarios are modeled (historical, future with low sea level rise, future with medium sea level rise, and future with high sea level rise), combining the coastal and fluvial/pluvial hazard layers (Table 2). Importantly, the flood hazard models do not include flood protection (such as dikes and drainage systems), which can make a large difference in the flood hazard particularly in well‐protected areas. In these well‐protected areas, our flood maps may over‐estimate the flood hazard. For full details on the methodology used to produce these hazard maps, see Appendix 1.

_Table 1. Future scenarios used for Vietnam coastal modeling. RCP stands for Representative Concentration Pathway. We use two RCPs from the recent Intergovernmental Panel on Climate Change (IPCC) report (IPCC 2014) to represent a low climate change and a high climate change scenario. RCP2.6 is a low scenario consistent with temperature increases of 2°C, while RCP8.5 is a high scenario consistent with temperature increases of 4°C. The A1B scenario was taken from a previous IPCC report (IPCC 2007) and represents a medium climate change scenario, in between RCP2.6 and RCP8.5._

|**Simulations**|**Scenario**|**Percentile**|**SLR ‐2100(m)**|
|---|---|---|---|
|Low|RCP 2.6|0.5|0.28|
|Medium|A1B|0.05|0.6|
|High|RCP 8.5|0.95|0.98|

_Table 2. Hazard map scenarios for which the modeling was conducted for Vietnam_

|**Scenario**|**Coastal**|**Fluvial/Pluvial**|
|---|---|---|
|1 in 25|1 in 25|1 in 25|
|1 in 25 Future – Low|1 in 25 + 28cm|1 in 50|
|1 in 25 Future – Medium|1 in 25 + 6cm|1 in 75|
|1 in 25 Future – High|1 in 25 + 98cm|1 in 100|
|1 in 50|1 in 50|1 in 50|
|1 in 50 Future – Low|1 in 50 + 28cm|1 in 75|
|1 in 50 Future – Medium|1 in 50 + 6cm|1 in 100|
|1 in 50 Future – High|1 in 50 + 98cm|1 in 200|
|1 in 100|1 in 100|1 in 100|
|1 in 100 Future – Low|1 in 100 + 28cm|1 in 200|
|1 in 100 Future – Medium|1 in 100 + 6cm|1 in 250|
|1 in 100 Future – High|1 in 100 + 98cm|1 in 500|
|1 in 200|1 in 200|1 in 200|
|1 in 200 Future – Low|1 in 200 + 28cm|1 in 250|
|1 in 200 Future – Medium|1 in 200 + 6cm|1 in 500|
|1 in 200 Future – High|1 in 200 + 98cm|1 in 1000|

5
Table 2: Summary statistics

||Number of<br>observations|Mean|Standard<br>deviation|Minimum|25th<br>percentile|Median|75th<br>percentile|Maximum|
|---|---|---|---|---|---|---|---|---|
|Panel A. Total employment and female labor-market o|utcomes||||||||
|Total number of workers|493|700|815|1|291|450|780|8200|
|Total number of female workers|456|384|541|0|128|252|443|7259|
|Number of female administrative workers|409|3|7|0|0|2|4|102|
|Number of female non-administrative workers|388|399|571|-3|127|250|480|7253|
|Panel B. FDI variables|||||||||
|FDI dummy|494|11%|31%|0%|0%|0%|0%|100%|
|Supplier to FDI firm dummy|494|5%|21%|0%|0%|0%|0%|100%|
|Customer of FDI firm dummy|494|15%|35%|0%|0%|0%|0%|100%|
|FDI sibling dummy|494|37%|48%|0%|0%|0%|100%|100%|
|FDIpartner dummy|494|4%|20%|0%|0%|0%|0%|100%|
|Panel C. Firm controls|||||||||
|Firm size (log of total sales)|489|18.6|1.3|12.6|17.9|18.6|19.5|22.0|
|Exporter dummy|494|77%|42%|0%|100%|100%|100%|100%|
|EPZ dummy|494|12%|33%|0%|0%|0%|0%|100%|

# **3. Empirical specifications**

## _3.1 Impact of FDI_

To examine the impact of foreign ownership per se on firm-level gender labor market outcomes, the following regression is estimated on the full sample of apparel and textiles firms: i F F i **(1)** FF ii XX ii ii where i is one of the outcome variables for firm YY = ββ FF + ββ XX + μμ + εε _i_ in sector _j,_ F is the dummy variable for ii FDI firms, YY is the vector of firm controls (size, exporter status, and EPZ location), FF is an ii ii industry fixed effect, and XX i is an independent and identically distributed error term. Standard μμ errors are clustered by industry and location.εε<sup>6</sup> _3.2 Impact of being suppliers or customers of FDI firms_

> 6 The industries considered are woven, knitwear/sweater, other apparel and textiles.

11
# **Table 1a. Determinants of the stock of migrants**

||(1)|(|2)|(|3)|(4)|
|---|---|---|---|---|---|---|
|Dep. Variable:|ln migrants|ln mi|grants|ln mi|grants|ln migrants|
|ln real wage|0.986<br>[0.00]***|0.9<br>[0.00|23<br>]***|0.<br>[0.0|749<br>0]***|0.608<br>[0.008]***|
|||urban|rural|urban|rural|no split Geo|
|ln ruggedness|-|-0.137|-0.151|-0.109|-0.138|-0.139|
||-|[0.023]**|[0.001]***|[0.044]**|[0.003]***|[0.003]***|
|ln cooling days|-|0.336|0.165|0.156|0.022|0.200|
||-|[0.254]|[0.537]|[0.622]|[0.933]|[0.403]|
|ln heating days|-|0.365|0.392|0.194|0.291|0.172|
||-|[0.056]*|[0.001]***|[0.486]|[0.045]**|[0.394]|
|ln rainfall|-|-0.117|0.679|-0.072|0.679|0.237|
||-|[0.564]|[0.003]***|[0.735]|[0.004]***|[0.23]|
|D yangtze|-|0.235|-0.196|0.051|-0.323|-0.135|
||-|[0.104]|[0.273]|[0.768]|[0.12]|[0.489]|
|nat.res.index|-|-0.014|0.005|-0.009|-0.006|-0.006|
||-|[0.37]|[0.871]|[0.55]|[0.839]|[0.71]|
|% hh water|-|-||0.|117|0.584|
||-|-||[0.|702]|[0.098]*|
|% hh toilet|-|-||1.|197|1.401|
||-|-||[0.00|9]***|[0.004]***|
|% pop gas|-|-||0.|515|0.548|
||-|-||[0.0|22]**|[0.031]**|
|nr.obs|662|662||6|62|662|
|R2|0.589|0.630||0.|670|0.620|

**_Notes:_** _all regressions include province-urban and province-rural fixed effects, as well as controls for a prefecture’s ln(x-coordinate), ln(y-coordinate), ln(area) and four dummy variables denoting whether the dominant language spoken in each prefecture corresponds to one of four of China’s main languages spoken (Mandarin, Yue, Wu and Jinyu (often considered a dialect of Mandarin)). p-values, based on standard errors clustered at the province level, in brackets. ***, **, * denotes significance at the 1%, 5%, 10% respectively. In all columns we allow the coefficients for all geography-related variables to differ between the urban and rural parts of prefectures respectively (effectively this means that we include each geography variable interacted with our rural dummy as well as interacted with our dummy indicating the urban part of each prefecture)._

Table 1b shows results when using different dependent variables instead of migrant stocks when estimating (7). They provide crucial perspective on our choice of identifying the (relative) importance of different migration determinants looking at each location’s migrant population only. Column 1 and 2 show results when considering the share of non-migrants or the share in total Chinese population in each location as the dependent variable respectively.<sup>30</sup> The most striking difference is the fact that we find a much weaker association between real wages and these shares.

> 30 We also performed a regression with the share of Hukou-holders in each location as the dependent variable. The results from this regression were almost identical to those when using non-migrant shares. This is not that surprising as both measures are highly correlated (with a correlation coefficient of 0.996). These results are available upon request.

16
# **Capital Control Liberalization and Stock Market Development**

In light of theory and evidence that the functioning of stock markets may affect national saving rates, the allocation of those savings, firm financing decisions, and economic growth,l this paper addresses two policy questions. First and foremost, what happened to stock market size, liquidity, volatility, and degree of international integration following capital control liberalization in 16 emerging market economies? Second, what is the empirical relationship between stock market size, liquidity, volatility, and international integration and regulations concerning information disclosure, accounting standards, and investor protection? To address these questions, we construct measures of stock market size, liquidity, volatility, and international integration and then examine the empirical relationship between these stock market indicators and both capital control liberalization and national stock market regulations.

To examine the effect of international capital control liberalization on the functioning of stock markets, we test whether indicators of stock market development change following the liberalization of specific capital controls in 16 countries. To do this, we first identify event dates of major policy changes. We then use Perron's (1989) technique and test for a structural break in our stock market development indicators at the event date. As these indicators tend to trend upwards, we distinguish between unit roots and structural changes in the time series properties of the series following capital control liberalization. Although this event study methodology does not control for other factors affecting stock market development, the same event across a wide variety of countries and at different points in time yields similar results.

I For example, Levine (1991) and Bencivenga, Smith, and Starr (1995) argue that enhanced market liquidity can affect resource allocation and economic growth. Devereux and Smith (1994) and Obstfeld (1994) show that the ability to diversify risk internationally can influence national saving, productivity, and long-run growth rates. Atje and Jovanovic (1993), Levine and Zervos (1995), and Demirguc-Kunt and Maksimovic (1996b) provide evidence that the size and liquidity of equity markets is closely associated with economic growth. On the empirical relationship between corporate financing decisions and stock market size and liquidity, see Demirguc-Kunt and Maksimovic (1996a). Considerable disagreement exists, however. Morck, Shleifer, and Vishny (1990) argue that stock markets are a relatively unimportant sideshow, and Shleifer and Summers (1988), DeLong, Shleifer, Summers, and Waldmann (1989) note conditions when stock markets hurt economic activity.