rafmacalaba/datause-encoder-data huggingface dataset:
# **-** **<u>Table 3. Effective tariff rates by sector and country if virtual C is taxed at $50/ton CO2</u>**

|**Sector **|**BRA**|**CAN**|**CHN**|**E15**|**eit**|**IND**|**JPN**|**liy**|**MEX**|**RUS**|**USA **|**xhy**|**xmy**|**xx1**|**ZAF**|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Agriculture|2%|3%|6%|1%|4%|5%|2%|2%|2%|7%|3%|3%|3%|2%|6%|
|Construction|1%|1%|11%|1%|2%|6%|1%|4%|1%|7%|1%|2%|4%|1%|6%|
|Coal|1%|4%|18%|2%|8%|4%|0%|5%|0%|14%|3%|3%|3%|2%|6%|
|Chemicalsrubberand plastics|4%|5%|17%|2%|7%|12%|3%|14%|4%|27%|5%|5%|10%|2%|12%|
|Electricity|5%|23%|126%|18%|33%|68%|14%|34%|39%|86%|44%|39%|50%|30%|159%|
|Food processing|2%|2%|8%|1%|3%|5%|1%|2%|2%|8%|3%|2%|3%|2%|4%|
|Finalservices|0%|1%|5%|0%|2%|2%|1%|3%|0%|5%|1%|1%|2%|1%|2%|
|Naturalgas|7%|8%|81%|1%|6%|7%|0%|4%|3%|10%|2%|0%|4%|3%|2%|
|Gas distribution|8%|1%|156%|3%|8%|0%|0%|1%|5%|10%|4%|0%|5%|9%|322%|
|Ferrousmetals|9%|6%|23%|3%|12%|18%|4%|19%|7%|23%|6%|6%|15%|6%|29%|
|Non ferrousmetals|7%|7%|24%|2%|6%|21%|2%|10%|2%|21%|6%|2%|8%|7%|15%|
|Mineralproducts etc|7%|4%|35%|3%|8%|28%|3%|17%|4%|23%|7%|8%|15%|6%|22%|
|Crude oil|3%|4%|13%|2%|6%|3%|0%|1%|2%|5%|3%|1%|2%|1%|1%|
|Other mining|5%|13%|12%|2%|6%|9%|3%|4%|33%|4%|4%|2%|5%|3%|20%|
|Refined oil|2%|8%|12%|2%|3%|2%|1%|3%|9%|10%|5%|0%|10%|4%|13%|
|Paperproducts etc|3%|4%|13%|1%|3%|20%|2%|7%|2%|5%|4%|3%|7%|2%|5%|
|Services|1%|1%|5%|0%|2%|4%|1%|3%|2%|5%|1%|1%|3%|1%|3%|
|Transport services|17%|12%|11%|4%|7%|8%|3%|11%|17%|22%|11%|5%|14%|4%|12%|
|Other manufacturing|2%|1%|8%|1%|2%|7%|1%|3%|1%|13%|2%|2%|3%|1%|5%|

Note: Virtual carbon intensities have been estimated using the BTIO approach. Source: authors.

28
|“Coronavirus<br>could cost the<br>global economy<br>$2.7 trillion.”<br>(Orlik et al.<br>2020)|NiGEM —a<br>large scale<br>model of<br>the global<br>economy;<br>Static<br>assessment|Model allows:<br>-monetary<br>policy to<br>respond to<br>weaker<br>growth;<br>- a degree of<br>import<br>substitution|Scenario 1|Major blow to China, and<br>spillover to rest of world;<br>shock to China’s GDP in<br>1Q - China’s GDP growth<br>to slow 4.7 ppt below our<br>baseline forecast|GDP loss (2020):<br>World (1<sup>st</sup>Quarter) = -1.2%<br>China = -0.7%<br>Japan = -0.1%<br>Indonesia = -0.3<br>Canada = -0.1%<br>Germany = -0.1%<br> <br>|
|---|---|---|---|---|---|
|||Model does<br>not allow:<br>-supply chain<br>impacts to<br>propagate<br>beyond the<br>countries in<br>which they are|Scenario 2|Outbreaks cause localized<br>disruption, countries with<br>reported >100 cases in<br>early March suffer half of<br>the shock to China|GDP loss (2020):<br>World = -0.8%<br>China = -1.5%<br>Japan = 01%<br>US = -0.2%<br>France = -1.3%<br>Germany = -1.2%<br>Russia = -0.9%|
|||first felt|Scenario 3|Widespread contagion,<br>countries with current<br>reported cases >100<br>suffer the same shock as<br>China, and countries with<br>any reported cases suffer<br>half of the shock suffered<br>by China|GDP loss (2020):<br>World = -1.9%<br>China = -2%<br>Japan = -2.2%<br>Indonesia = -2.8<br>USA = -1.3%<br>Germany = -2.8%<br>Brazil = -1.7%|
||||Scenario 4|Global pandemic, all<br>countries suffer a severe<br>shock|<br>GDP loss (2020):<br>World = -3.1%|

- 23 -
one corresponding to Equatorial Guinea.

# **3.2** Regression Results

To implement **(1)** we introduce a random error and country-specific effects. The countryspecific effects are needed due to the diverse set of countries in our sample. We also perform our regressions using the share of foreign direct investment on domestic **GDP** rather than the logarithm of foreign investment to control for size. As discussed above, we present estimates for two models. In the first model we restrict the coefficients on all global and local variables to be the same across all countries. Letting **Zt** = **[ZG** Z]', the corresponding regression equation is where the indices t and f represent time and country, respectively.

In the second model, we allow both country-specific intercepts and country-specific slope coefficients on the global factor, but restrict the coefficients on local variables to be the same for all countries. In this case, the regression equation becomes, Given that the values of global variables are the same for all countries at a given point in time, the first model may understate the importance of global factors in explaining the variance of foreign direct investment **by** restricting the variability of the global component across countries. The second model allows for heterogeneous responses to the same global conditions, which increases their predictive power on foreign direct investment at the potential cost of reduced estimation efficiency.

## **3.2.1** Model **1:** Homogeneous Slope Coefficients

The results from estimating Model **1** are presented in Thbles 2 and **3.** In the first of these we include each of the six global variables independently in the regression. In the 12
28 Figure 1. Nominal agricultural protection in Japan, Korea and Taiwan, 1955 to 2002 (percent) <!-- Start of picture text --> 250<br>200<br>150<br>Korea<br>100<br>Japan<br>50 Taiwan<br>0<br>-50<br>-100<br>1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2002<br>i n a l r a te s<br>e i g h te d a v e r a g e n o m<br>W<br><!-- End of picture text --> Source: Anderson and Hayami (1986, Table 2.5), updated by Honma and Hayami (2006).
and Rao Gupta (1997) for a review of the literature; also see Quisumbing et al., (2001). Both the latter studies note that whether and how disadvantaged FHH are is inextricably linked to why they are female headed. Detailed studies for Uganda and Zimbabwe find that households headed by widows are especially impoverished relative to male headed and other female headed households (Appleton, 1996, for Uganda; Horrell and Krishnan, 2007, for Zimbabwe).

Given the discussion in Section 2, one might expect that FHH are not particularly common in Mali. Out of 4,494 households in the 2006 ELIM sample, 179 and 131 in urban and rural areas, respectively, are headed by women. They account for 11.3 percent of the population of urban heads and 4.5 percent of that of rural heads. A vast majority of these female heads are widows ─ 75 percent in urban and 64 percent in rural areas versus less than 1 percent of male heads in both areas. The marital status of the rest of the FHH breaks down as follows: monogamous (11 and 9% in urban and rural areas respectively), polygamous (4 and 22%), divorced or separated (7 and 4%). The first may be left-behind by migrant husbands who may or may not send remittances.<sup>11</sup> The second group is often comprised of women whose husbands live with a co-wife and shares his time between wives living in different locations.

A previous study that examines Mali‘s poverty profile using the 2006 ELIM finds that the poverty rate for FHH is 20 percent lower than for male headed households (MHH) (BackinyYetna et al. 2007). The analysis that follows questions these results. In particular, it argues that an investigation of MHH and FHH in Mali using data from the 2006 ELIM reveals that having a female head per se is not decisive in creating poverty but rather that it is the combination of household and individual characteristics, including female headship, widowhood and divorce, that determine living standards.

Table 1 provides descriptive statistics on the key characteristics of MHH and FHH, with the latter also broken down into widowed and non-widowed female heads separately. For some attributes ─ namely, years of education, number of livestock owned, and whether the outside material of the house is of cement or tile ─ the differences in means appear to be larger between households headed by widowed women and all other households. For others ─ including household size and land owned ─ the key disparities are found to be between MHH and FHH. A distinctive attribute of widowed female heads is their age. They are on average older than other

> 11  The data confirms that husbands are typically not present when women are identified as household heads.  A husband is present in only 2 out of the 310 female headed ELIM 2006 households.

10
<!-- Start of picture text --> eo‘ ae WeeOeSa © Returnees Only<br>a - ya as ¥f © IDPs Only .<br>a port \ Ds eaafezal Zn \ 7 © Neither H<br><!-- End of picture text --> <!-- Start of picture text --> é : a |<br>om |<br>vx | e erenee<br>woos yo -SY 7 oma |<br>Seq A . C<br><!-- End of picture text -->
- McLaren, John. 2002. “A theory of insidious regionalism.” Quarterly Journal of Economics 117(2): 571608.

- McMillan, John. 1986. Game Theory in International Economics. New York: Harwood.

- McMillan, John. 1989. “A Game-Theoretic View of International Trade Negotiations: Implications for Developing Countries.”In Developing Countries and the Global Trading System, edited by John Whalley, 1. London, UK: MacMillan.

- Melitz, Marc and Gianmarco Ottaviano. 2008. “Market size, trade, and productivity,” The Review of Economic Studies 75(1): 295-316.

- Mitra, Devashish. 1999. “Endogenous lobby formation and endogenous protection: a long-run model of trade policy determination.” American Economic Review 89(5): 1116-34.

- Mitra, Devashish. 2002. “Endogenous political organization and the value of trade agreements.” Journal of International Economics 57(2): 473-85.

- Mrázová, Monika, David Vines and Ben Zissimos. 2013. “Is the GATT/WTO’s Article XXIV bad?” Journal of International Economics 89: 216-32.

Mrázová, Monika. 2011. “Trade agreements when pro...ts matter.”Mimeogr., London School of Economics.

- Nicita, Alessandro, Marcelo Olarreaga, and Peri Silva. 2014. “Cooperation in WTO’s Tari¤ Waters.” Mimeogr., University of Geneva.

- Olarreaga, Marcelo, Isidro Soloaga, and L. Alan Winters. 1999. “What’s Behind Mercosur’s CET?”CEPR Discussion Paper No. 2310.

- Olson, Mancur. 1965. The Logic of Collective Action: Public Goods and the Theory of Groups. Cambridge, MA: Harvard University Press.

- Ore...ce, Gianluca and Nadia Rocha. 2014. “Deep integration and production networks: an empirical analysis.” The World Economy 37(1): 106-136.

- Ornelas, Emanuel. 2005a. “Trade creating free trade areas and the undermining of multilateralism.” European Economic Review 49(7): 1717-35.

- Ornelas, Emanuel. 2005b. “Rent destruction and the political viability of free trade agreements.”Quarterly Journal of Economics 120(4): 1475-1506.

- Ornelas, Emanuel. 2005c. “Endogenous free trade agreements and the multilateral trading system.”Journal of International Economics 67(2): 471-97.

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115
14 the design of export rebates that typically refund taxes paid only on first round inputs, thereby depriving exporters of the full competitive edge which would otherwise prevail. Nonetheless, it must be expected that the effect of cascading is less for goods produced in industries subject to VAT; this is borne out by the calculations reported below.

Column (4) of Table 2.6 shows the cascading effect0l, i.e., the wedge between producer prices under the existing system of taxation and that prevailing under a VAT covering all sectors at the same statutory rates as at present. To this column (5) adds the present wedge between consumer and producer prices [column (3)] to arrive at the full tax burden. The table shows that there are considerable differences between the statutory tax rate and the full tax burden. Thus, some basic intermediates such as electricity, coal and coke and heavy chemicals suffer taxation that is well in excess of their statutory rates; this is also true of the sectors statutorily subject to Business Tax. It also illustrates the point that sectors subject to VAT suffer less from cascading, although they are affected to some extent.

The extent of cascading is, however, understated by Table 2.6. This is because unavailability of data precluded incorporating the effect of taxes paid on capital goods. In contrast to other intermediate inputs, the effect of cascading is not ameliorated in this case even in sectors under the existing VAT, because the VAT does not allow credit for taxes paid on purchases of fixed assets.

The calculations confirm that the existing system of indirect taxation is less efficient than a single stage sales tax or a value added tax. They also lend support to the notion that a VAT covering all sectors would, by eliminating the cascading effect of indirect taxes, make the incidence of taxation more closely approach that desired by policymakers in formulating the statutory rates of taxation. It would also provide a framework that ensures duty free access to all inputs, direct and indirect, going into export production.

# 2.6 Equity

Indirect taxes are not seen as an equity-enhancing instrument in China, except in the sense of equalizing profitability across enterprises. However, the equity impact of existing taxes is complicated by the fact that taxes set with reference to enterprise profitability, when shifted on to prices of free market sales, have different effects on households at different income levels.

> W The way in which this is calculated is explained in World Bank (1990, p. 221).
passive management.

While the effect of distance on performance has been extensively studied in the literature, the role that active management plays on the investment strategies of foreign asset managers has received little attention. A growing number of empirical studies use complete transaction records within a country, and compare foreign-managed versus domestic-managed funds. Some of these studies provide support in favor of the superior skills of domestic investors (Hau, 2001; Choe et al., 2005; Dvoˇr ́ak, 2005), others document that foreign investors outperform locals (Grinblatt and Keloharju, 2000; Seasholes, 2004; Barber et al., 2009), and some find no differences in performance (Seasholes and Zhu, 2010). Despite using comprehensive transaction level data, most of these studies do not distinguish within each group (foreign or domestic), whether one investor is making all the poorlyperforming trades or if the trades are performed by different investors.<sup>1</sup> In other words, these studies do not account for within group heterogeneity that might explain portfolio returns, and what they estimate is an average performance gap between locals and foreigners.

We investigate the issue of style and distance using a unique transaction data set from the Colombian Stock Exchange. The advantage and novel aspect of the data is that for all the transactions in the market between January 2, 2006, and January 29, 2016, we have an investor identifier on both sides of the transaction which allows us to track each fund or individual over time. Given the panel structure of the data, we are not only able to compare average performance between foreign and domestic investors, but we can also study the extent to which performance is related to investor/fund characteristics, such as style and fund flows. Despite the vast segment of the literature that focuses on investors who are separated by borders, to the best of our knowledge, we are the first to use information on active management together with complete transaction history to analyze the relative skill and performance of foreign versus local investors.

We find that on average, foreign investors trade at unfavorable prices relative to domestic investors. The average disadvantage relative to domestic institutions is 8 basis points for stock purchases and 7 basis points for sales. These estimates are quantitatively similar to those reported by Agarwal et al. (2009) in Indonesia over an 8 year period, and smaller but in the same order of

> 1This is typically a consequence of data restrictions in order to guarantee the anonymity of each trader. The exceptions are Hau (2001) and Seasholes and Zhu (2010). Hau (2001) uses data on a group of professional traders but does not study the role of active versus passive management. Seasholes and Zhu (2010) on the other hand focuses on individual investors.

2
|Poverty gap|**4.6***|**4.4***|5.3|**4.2**|**4.3***|**4.2**|**4.4***|**4.5***|**4.9***|4.6|
|---|---|---|---|---|---|---|---|---|---|---|
||(0.4)|(0.4)|(0.5)|(0.4)|(0.4)|(0.4)|(0.4)|(0.4)|(0.4)|(0.3)|
|USAID Poverty gap|**26.3***|**25.3***|**27.5**|**24.9***|**25.1***|**25.2***|**25.4***|**25.5***|**26.9***|25.9|
||(1.1)|(1.1)|(1.3)|(1.1)|(1.1)|(1.2)|(1.1)|(1.1)|(1.1)|(1.1)|
|N|4,644|4,644|4,644|4,644|4,644|4,644|4,644|4,644|4,644|4,644|
|**_Panel E: Viet Nam 2014-2016_**|||||||||||
|Headcount poverty rate|15.0|13.3|6.1|8.4|10.6|12.5|13.3|11.4|**9.6***|9.6|
||(0.5)|(0.6)|(0.5)|(0.5)|(0.5)|(0.6)|(0.6)|(0.6)|(0.5)|(0.4)|
|Near-poverty rate|9.3|8.5|4.9|6.0|**7.1***|8.0|8.5|7.7|**6.8***|6.9|
||(0.4)|(0.4)|(0.3)|(0.4)|(0.3)|(0.4)|(0.4)|(0.4)|(0.3)|(0.3)|
|Extreme poverty rate|2.0|2.0|0.7|**1.2***|**1.5**|2.1|2.1|2.0|2.0|1.2|
||(0.2)|(0.3)|(0.1)|(0.2)|(0.2)|(0.3)|(0.3)|(0.3)|(0.3)|(0.2)|
|Poverty gap|4.0|3.7|1.5|**2.3**|2.9|3.5|3.7|3.2|2.9|2.5|
||(0.2)|(0.2)|(0.2)|(0.2)|(0.2)|(0.2)|(0.2)|(0.2)|(0.3)|(0.2)|
|USAID Poverty gap|**26.7**|**27.6**|**24.9**|**27.4**|**27.1**|28.1|**27.8**|28.3|30.1|26.1|
||(0.7)|(0.9)|(1.3)|(1.2)|(1.0)|(1.0)|(0.9)|(1.2)|(1.7)|(0.9)|
|N|9,347|9,347|9,347|9,347|9,347|9,347|9,347|9,347|9,347|9,347|
|**_Control variables_**|||||||||||
|Food expenditures|||Y||||||||
|Non-food expenditures||||Y|||||||
|Furnishings and durable household expenses|||||Y||||||
|Health expenditures||||||Y|||||
|Education expenditures|||||||Y||||
|Utilities expenditures||||||||Y|Y||
|Household assets & house characteristics||Y|Y|Y|Y|Y|Y|Y|||
|Demographics & employment|Y|Y|Y|Y|Y|Y|Y|Y|Y||

**Note:** Normal linear regression with bootstrapped standard errors is used. The standard errors are calculated using 100 bootstrap replications and are adjusted for complex survey design. All estimates are obtained with population weights. The normal linear regression model with the theoretical distribution of the error terms employs cluster random effects. ’Near poor’ status is defined as living on an income between 100 and 125% of the poverty line. All indicators are expressed in percentage. The true rate is the estimate directly obtained from the survey data. Estimates shown in boldface or with a “*” respectively fall within the 95% confidence interval or one standard error of the true rate.

37
10

- -

properties which the observable variables (X's) should have. First, each variable must be correlated with income, so that the variation in the error term ei in (7) is reduced when the variable is added to the existing set, which in turn implies that reducing predicted poverty will be more highly correlated with reducing actual poverty. Second, each variable should truly be easily observed, so that it cannot be hidden and/or misrepresented by the individuals. For example, if one decides to give transfers to everyone who does not own a certain type of luxury good, such as a car, individuals with cars may be able to hide them and deny that they own a car when screening for eligibility takes place. Third, the variable should not easily be changed by the household. Using the above example, even if it were easy to identify car ownership, some households may actually sell their cars and hire taxis if the added cost were outweighed by the gain from becoming eligible for transfers.

To estimate incomes as in (7) one needs a household survey with the following characteristics. First, the survey must be a random sample from the area under consideration. For example, if orne wants to devise a nationwide targeting scheme the survey must be a sample from the entire country. Two, the income data (or expenditure data, see below) must be relatively accurate, otherwise it will introduce another source of error and in addition will make it more difficult to judge, via equation (10), how accurate the targeting really is. Third, the survey must contain a variety of explanatory variables which can be effectively used as the xi vector.

With such data simple econometric techniques can be used to predict **y** given a matrix X. Since the primary interest is predictive accuracy, rather than estimating any kind of causal structure, the main immediate objective iF
late 1980s, skill-biased technological change has led to job market polarization due to an increased demand for skilled and unskilled workers at expense of middle-class jobs, as new technologies are capable of performing routine tasks traditionally done by middle-wage workers (Goldin and Katz 2007).

Analyzing cross-country evidence, Brueckner, Dabla Norris, and Gradstein (2015) find that national income and inequality are positively related, with education as a possible channel. For a sample of 80 countries, the authors use two instruments for within-country variation of real GDP per capita, including international oil price fluctuations and countries’ trade-weighted world income. The instrumental variables regressions show that, on average, a 1 percent increase in real GDP per capita reduces the Gini coefficient by around 0.08 percentage points. However, the importance of national income in explaining inequality is significantly reduced when education proxies are introduced, making education a probable channel.

# **_Market structure_**

Contrary to the assumptions in the Solow neoclassical growth model, many industries do not have perfectly competitive market structures. Natural monopolies, policy-induced monopolies, or industries supported by rents (particularly in the natural resource sectors) lead to high returns to owners without a commensurate rise in payments to labor. Returns to certain factors— entrepreneurship, capital, land, and resource ownership—rise faster than returns to labor (especially unskilled labor). Scale of market can be important—bigger markets provide higher returns to owners if competition can be avoided. There can also be network effects (such as in high-tech and communications sectors) and tournament effects (for instance, the best sport star earns much more than the second best; singers/actors benefit more from brand in large markets).

Diez, Leigh, and Tambunlertchai (2018) document that a generalized increase in market concentration (associated with higher markups) occurred across advanced economies and across industries. At high levels of markups and profitability, an increase in market concentration leads to lower investment and lower wages, which directly influences the income distribution and growth. De Loecker and Eeckhout (2018) also analyze the global evolution of market power from 1980 to 2016, based on data from Worldscope covering more than 60,000 firms located in 134 countries. They corroborate that the recent trend of rising markups and market power has been predominantly concentrated in advanced economies, while markups in most emerging economies have been either stable or declining.

For the United States, De Loecker, Eeckhout, and Unger (2020) show that markups nearly tripled between 1980 and 2016, increasing from 21 percent above marginal cost to 61 percent. The rise in markups was greatest for firms in the upper tail of the distribution: that is, with markups that 17