rafmacalaba/datause-encoder-data huggingface dataset:
Table 2 Log price and FIR

|Dependent variable: log price<br>Symmetric window:|[-5:+5]||Excl. h=-1|
|---|---|---|---|
|FIR<br>-4.014 ***<br>-0.170<br>1.252 **<br>(0.583)<br>(0.740)<br>(0.572)||||
|FIR X Post<br>0.286 ***<br>0.286 ***<br>0.286 ***|0.287 ***|0.286 ***|0.330 ***|
|(0.091)<br>(0.091)<br>(0.092)|(0.092)|(0.090)|(0.098)|
|Post<br>0.001 **<br>0.001 **<br>0.001 **|0.001 **|0.001 **|0.001 **|
|(0.000)<br>(0.000)<br>(0.000)|(0.000)|(0.000)|(0.000)|
|Bond FE<br>Yes<br>Yes<br>Yes|Yes|No|No|
|Month FE<br>No<br>Yes<br>No|No|No|No|
|Bond char.-month FE<br>No<br>No<br>Yes|Yes|No|No|
|Country-month FE<br>No<br>No<br>No|Yes|No|No|
|Bond-month FE<br>No<br>No<br>No|No|Yes|Yes|
|Bond controls<br>No<br>No<br>No|Yes|No|No|
|Observations<br>107,138<br>107,138<br>107,138|107,098<br>|107,138|96,424|
|N. of bonds<br>738<br>738<br>738|738|738|738|
|N. of countries<br>68<br>68<br>68|68|68|68|
|N. of clusters<br>1,618<br>1,618<br>1,618|1,617|1,618|1,618|
|F (frst stage)<br>1,000<br>693<br>1,008|1,958|2,017|2,023|
|Note:<br>This table presents 2SLS estimates of log bond prices<br>instrumented by _Z_ (Equation (9)), around rebalancing dates.|on the FIR<br> The frst- an|measure (E<br>d second-sta|quation (7)),<br>ge equations|
|are described in Equation (10). The estimations use a symmetri<br>an indicator variable (equal to 1 for the fve trading days after reb<br>efects are dummy variables equal to 1 for each rebalancing mont<br>are fxed efects that interact maturity, ratings, and bond type <br>constructed by dividing a bond’s time to maturity into four difer<br>medium (5–10 years), long (10–20 years), and very long (20+ y<br>Moody’s. Bond type diferentiates sovereign from quasi-sovereign<br>the estimation includes the log face amount and log stripped sprea<br>drops the trading day before rebalancing. Standard errors are cl<br>the sample period is 2016–2018. *, **, and *** denote statistic<br>level, respectively.|c fve-trading-<br>alancing, and <br>h (0 otherwise<br> fxed efects. <br>ent categories<br>ears). Ratings<br> bonds. Bond<br>d of the bond. <br>ustered at the<br>ally signifcan|day window, <br> 0 otherwise)<br>), and bond c<br> Maturity fx<br>: short (less <br> from each b<br> controls ind<br> The last colu<br> country-mo<br>t at the 10%|with _Post_ as<br>. Month fxed<br>haracteristics<br>ed efects are<br>than 5 years),<br>ond are from<br>icate whether<br>mn’s analysis<br>nth level, and<br>, 5%, and 1%|

anticipating the index rebalancing, we would expect to observe pre-trends in bond prices before the actual event. However, our analysis reveals no correlation between the FIR and bond returns in the week leading up to the rebalancing (with the only exception being the day before the event). Finally, if part of the rebalancing-driven inflows were to occur before the event, our FIR measure would overestimate them at the index rebalancing date. This, in 18
2 related to the tax and wage variables. Apparently for these affiliates bankruptcy is not an effective device for firms to moderate tax and wage pressures.

The multinational firm's internationa **l** everage decision is related to its choices regarding the extent and form of repatriated earnings. Earnings generally can be retained, or repatriated in the form of dividends, interest, royalties, etc. U.S. multinationals' earnings repatriation decisions have been a focus of previous empirical research. Kopits (1972) offers an early study that estimates dividend payout equations for subsidiaries to their U.S. parents using U.S. tax data aggregated at the country level. Hines and Hubbard (1990) and Altschuler and Newlon (1991) recently exaniined the impact of tax price effects on repatriation behavior using micro data, which makes it possible to examine the role of parent company characteristics in the determination of repatriation behavior.

A second empirical question the paper addresses is, what direct investment characteristics affect the host country rate of taxation? In this regard, the paper examines two measures of the average tax rate: (i) income and payroll taxes over assets and (ii) all taxes on the foreign establishment over assets. For the developed countries, it is the first tax rate that appears to reflect the characteristics of the foreign investment. In particular, the average tax rate is positively related to the ratio of plant and equipment expenditure to assets, and negatively to wages over assets. Apparently, the immobility of plant and equ pment makes it easier for the tax authority to tax these assets. The latter relationship suggests that tax authorities to some extent undo the claims that organized labor lays on the returns to an affiliate's capital. For the developing countries it is the second measure of taxation, i.e. the ratio of all taxes to assets, that responds to the measures of the plant and equipment and the wage bill. Finally, for developed and developing countries, alike industry-effects are important. In particular, the petroleum industry is taxed the most reladve to other industries.

The remainder of this paper is organized as follows. Section 2 sets out the theoretical model of the relationship between leverage and foreign taxation. Secdon 3 discusses the data and presents some regression results regarding leverage and taxation and Section 4 concludes.
- Studies should achieve at least 45% of the maximum possible score to be ranked as intermediate methodological quality.

- Studies that achieved less than 45% of the maximum possible score should be ranked as limited methodological quality.

This means that studies must have at least two of the three double-weighted criteria in order to be eligible for the high methodological category—because the maximum possible score for studies that are missing two of the double-weighted criteria is 7 points.

# **The basis for the eight criteria: Representativeness and the role of chance**

As mentioned previously, the most frequently used epidemiological design for HIV research is the cross-sectional or survey type. This design is applied to a great variety of themes, most frequently serological and behavior surveys. The following is a review of important methodological concepts that need to be taken into account when interpreting cross-sectional studies.

## **Inclusion and exclusion criteria**

Inclusion and exclusion criteria define the universe of eligible subjects that are established by a study. It is very important to have these criteria in mind when making inferences or

**_Inclusion and exclusion criteria define the universe of eligible subjects, a distinction which controls the applicability of the study results._**

generalizations based on the findings in a study. For example, in a serological survey carried out on female sex workers, the inclusion criteria established that the sex workers belong to city brothels, an approach that excluded streetwalkers. Thus, the findings in that particular study cannot be generalized to all female sex workers of the city, but must be limited to the ones working in brothels.

## **Sample size**

Adequate sample size gives a study the ability to prove its hypothesis; in epidemiology this attribute is called “power of the study”. Adequate size in a sample produces more precision in a study’s findings. The larger the sample

**_The power of the study and accuracy of the results depend on the sample size._**

size, the narrower the resulting confidence interval and, therefore, the more accurate are the results. Sample size calculation allows the researcher to know the minimum number of individuals that will give the study the strength and accuracy desired. In the ASAP data quality assessment tool, this methodological feature was double-weighted because of its importance.

14
13 elasticity of value added in the semitradable sector (equal to yl-y ) .

The thirteen shares for the "middle-income" country are based on 1991 values for Cote d'Ivoire. The economic structure of the "low-income" country is assumed to be relatively more constrained on the production side than the "middle-income" country in terms of imported inputs, local production capacities (infrastructure) and diversification of sectors of production. It could, for example, corresponds to the economic structure of some of the poorer landlocked Franc zone Sahelian countries in Africa. The three productionrelated elasticities, i.e., the supply elasticity in the export sector, the supply elasticity of value-added in the semitradable sector, and the elasticity of substitution between value added and imported intermediate inputs (a) are assumed to be higher in the "middle-income" country than in the "low-income" country. The values used for the short-term elasticity of export supply are respectively 0.11 for the "middle-income" country and 0.053 for the "low-income" country which corresponds to labor shares of value-added in the export sector 0 respectively equal to 0.1 and 0.05 9/. The values used for the supply elasticity in the semitradable sector are respectively 1.5 for the "middle-income" country and 1 for the "low-income" country which corresponds to labor shares of value-added in the semitradable sector y respectively equal to 0.6 and 0.5 10/. The value of the elasticity of substitution between intermediate imported inputs and domestic value added (a) is assumed to be 0.2 in the middle-income country and 0.1 in the low-income country.11/ On the demand side, the substitution elasticity between semitradables and imported final goods (Q) is assumed to be equal to 0.5 in both countries.12/ The level of this elasticity depends essentially on preferences, and the pattern of these preferences does not basically differ in the two typical countries.

On the supply side, it is expected that the share of imported inputs in the production of semitradables in real ( 1I) and in nominal (p ) terms, the share of revenue from the export sector in private sector revenue (O.), and the share of exports in total foreign exchange receipt7 (yx) will be higher 9/ _The choice of these parameters Is_ consitent _with_ the elastlicties _shown in Pritchett (1992)._ 10/ _rhe estimated value for the labor share of value-added (y) is 0.57 in Branson (1986)._ 11/ _Branson uses 0.2 for Kenya._ 12/ _0.5 Is also the value used by Devarajan (1967)._
dure.

# 4 Citizen-centric governance: Preliminary rankings

Based on the estimation procedure described above we report our results in this section. First, we analyze citizen-centric indicators (CGIs) as well as responses on separate questions in all countries in 3 waves of World Values Surveys and Gallup World Poll. Then we compare the indexes by groups of countries, through time (across 3 waves), and with other governance indicators (in particular, Worldwide Governance Indicators). In the last subsection, we give examples of sub-national CGIs in several countries.

## 4.1 Country rankings: Waves 1 to 3

The countries’ citizen-centric governance indicators (CGIs) are presented in Figure 1 and Figure 2. On the first figure we show the estimations based on the data from World Values Survey, for the second figure we use the data from Gallup World Poll (see Section 3.1 for details about data sources). All 3 waves of WVS surveys are shown in Figure 1: (a) Wave 1 - for surveys taken between 1994 and 1998 (53 countries), (b) Wave 2 - for surveys taken between 1999 and 2004 (71 countries), (c) Wave 3 - for surveys taken between 2005 and 2008 (51 countries).

The maps of citizen-centric governance evaluations are, in our opinion, more convenient tool for analysis than the tables with more than 100 records, though those are also available from authors at the request. In Figure 1 we split our sample of countries into 3 broad categories (6 categories in Figure 2): from dark-green highgovernance-quality countries to light-green low-governance-quality countries. While developed countries (especially Scandinavian countries and Switzerland) show stable and high grades, it is rather unexpected that East Asian countries (especially, Vietnam, China) are relatively high rated. In some countries of the Middle East (Jordan, Saudi Arabia) the popular support of the government is also ”unexpectedly” high. At the same time, countries of Central and Eastern Europe are always in the lowest percentiles of the samples.

In Figure 3 we compare citizen-centric governance indicators with corresponding Worldwide Governance Indicators (WBI, 2008), which are considered to be the ”gold standard” of governance assessment by the media. The scale changes from dark-green for countries, which were severely underestimated by WGIs, to darkred for countries, which were greatly overestimated. 27 out of 82 countries in our sample were over- or underestimated at a significance level less than 25% (9 at a level less than 5%) by WGIs in comparison to our assessments. The pattern described in the paragraph above is supported: Middle East and East Asian countries are mostly underestimated (with China, Vietnam, Iran and Saudi Arabia being the leading outliers), while Central and Eastern European countries are too praised by WGI (Latvia, Lithuania, Moldova and Hungary being the leading outliers). Apparently, our indicators reflect last decade’s obvious successes of East Asian and Middle 10
First, most African countries failed to develop manufacturing industries. In 1970, the share of manufacturing in Africa‘s GDP was only 6 percent. In the two decades that followed, African governments employed state-owned enterprises and import substitution policies to expand the manufacturing sector. Although the share of MVA in GDP increased to 15 percent by 1990, it could not be sustained primarily because protectionism favored industries that were ̳comparative advantage defying‘ (CAD). The reforms of the late 1990s fostered liberalization, privatization of public enterprises and competition which led private firms to naturally engage in resourceintensive sectors in which Africa had a comparative advantage. Consequently, by 2008, the share of manufacturing in GDP had shrunk to about 10 percent and was, relative to 1990, a sign of deindustrialization.<sup>3</sup> In comparison to Sub-Saharan Africa, manufacturing was the source of Asian industrialization mirrored earlier in the East Asian miracle and more recently by the emergence of China as the second largest global power. In Asia, the share of MVA in GDP increased from 22 percent in 2000 to 35 percent in 2008.

**Figure 1.9: GDP growth in Sub-Saharan Africa – protectionist** **<u>policies suppressed growth during 1970 and 2000</u>**

<!-- Start of picture text --> SSA: GDP growth rate (%)<br>13<br>8<br>3<br>-2<br>SSA .. 4 per. Mov. Avg. (SSA ..)<br>Source: WDI December 2011<br>0<br>0<br>1961 1964 1967 1970 1973 1976 1979 1982 1985 1988 1991 1994 1997 20 2003 2006 2009<br>moving average<br>GDP growth rate (%) - 4 year<br><!-- End of picture text --> Second, between 1970 and 2008, Africa‘s mining sector expanded its contribution from 5 to 26 percent of GDP. This was two and a half times the average for developing countries and another signal that the region had regressed in transforming its economic structure from a natural resource based to a modern economy. In recent years, GDP growth has averaged about 5 percent per annum bolstered by a natural resources price boom but it masks the vulnerability of the economy in the absence of a strong manufacturing sector that fosters structural transformation.

> 3 In 1990, 6 of the 52 African countries for which data are available had MVA per capita of at least $200 and in 2010 the number of countries with an MVA per capita of at least $200 was 9. In terms of manufacturing growth, 23 African countries had negative MVA per capita growth over the period 1990–2010 and 5 countries had an MVA per capita growth above 4 percent.

14
calculated from examination of each country’s telecom laws and changes in the laws. In our model for electricity outcomes, we adopt a similar approach and use a ‘snapshot’ 4-element index for 2000. (See Section 4 below for further details of our index and the data.) Gutierrez (2003) finds statistically and positive direct effects of his regulatory index both on tele-density and on efficiency. This result occurs both in static and dynamic models and after testing for the endogeneity of regulation. The estimated effect of a 1-point increase in the index on mainlines per 100 inhabitants varies somewhat depending on the precise model specification but is, in general, of the order of 20%.

The Gutierrez study and its estimates provide a useful benchmark for our modelling of the effects of regulation on developing country electricity industry outcomes<sup>12</sup> .

# **2.4. Results from Studies of Regulation on Developing Country Electricity Industry Outcomes**

As yet, there are only a very few and very preliminary empirical studies e.g. Zhang, Kirkpatrick and Parker (2002) and a part of Pargal (2003). For data availability reasons, the capacity variable for these studies is _generation capacity only_ . This is measured in physical units (ie in Gigawatts) Data on this is available on an annual basis from the US Department of Energy’s EIA website for almost all countries from 1980. Unfortunately, there is nothing similar available for capacity in transmission or distribution<sup>13</sup> .

These papers find only weak effects of regulation, if any, and there are major problems in disentangling the effects of regulation from those of liberalisation. However, the studies are much more preliminary than those for telecoms, particularly in data terms.

In this paper, we have had access to much better data on regulatory governance and its variation across countries. However, our estimation of models for capacity, like those of Zhang et al and Pargal, is also limited to generation capacity.

> 12 See Stern and Cubbin op cit, p. 38-43 for further details of these studies

> 13 Pargal uses the Calderon –Serven infrastructure investment data set for 9 Latin American countries 1980-98.  This divides electricity investment into public and private but appears, again, only to cover generation.  See Calderon and Serven (2002) for a description of these data.

8
# **ANNEX 4: RESULTS Table A.4.1 Results Chile: Innovation Input**

||**Innovation**|**R&D**|
|---|---|---|
|**Constant**|-1.40|-1.68|
|**Firm Characteristics**<br>|||
|Sales gone up|0.16|0.07|
|Oldfirms (Those elderthanthe average ofsample)|-0.09|0.17|
|Firmsize (lnsize)|0.07|0.03|
|Firmage (lnage)|-0.09|**-0.32**|
|Export<br>|**0.49**|**0.79**|
|**Human capital**|||
|Knowledge of managersratedhigh|**0.39**|0.17|
|Knowledge ofsupervisorsratedhigh|0.16|-0.14|
|Knowledge ofworkersratedhigh|-0.01|0.07|
|Managers educationand trainingratedhigh|**0.62**|**0.44**|
|Supervisors educationand trainingratedhigh|-0.10|0.26|
|Workereducationand trainingratedhigh|-0.02|0.23|
|**Areas of education and training within the last 3**<br>**years**|||
|Productionand processratedhigh|0.33|0.15|
|Computersratedhigh|0.11|**_0.32_**|
|Managementratedhigh|**0.46**|0.01|
|Languagesratedhigh|0.07|**0.41**|
|**Innovation obstacles**|||
|Lack of skilledpersonnel|**_0.28_**|-0.14|
|Lack of experiencedpersonnel|-0.03|0.31|
|**Industrial sector **|||
|Food,Beverages andTobacco|Ref.|Ref.|
|Textile, WearingApparelandLeather|**_-0.34_**|-0.17|
|Wood and wood products,furniture|0.32|-0.33|
|Paper, printing and publishing|-0.10|-0.52|
|Chemicals, petroleum, coal,rubberand plastics|-0.02|0.14|
|Non metallic mineral products, except products of<br>petroleumand coal|-0.40|0.00|
|Basicmetal industry|-0.19|0.22|
|Fabricatedmetalproducts,machinery and equipment|-0.12|**0.52**|
|Other manufacturingindustries|0.03|0.10|
|Observations|4570|4570|
|R<sup>2</sup>|0.29|0.34|
|LogLikelihood|-332|-207|

Note: **Bold** is denoting significance in a 5% level. **Bold** + _Italic_ denotes significance in a 10% level.

29
6. Trade Linkages between China and other B&R Economies

**Source:** Author’s calculations based on GVC indicators extracted using the Wang et Al. (2013) methodology on the Tiva database.

In addition, the Chinese domestic market is more and more important to its B&R partners. The Chinese domestic value-added that returns to China (VS1*), of course, through Chinese imports, has been steadily increasing (Figure 44). The increase has been more important with B&R Economies, suggesting that GVC trade in view of serving Chinese domestic markets is becoming the new paradigm. About 3 % of China’s exports returns from B&R countries. As shown in Figure 29 in Section 4, in terms of the share of VS1*, China is comparable to some developed economies: in 2010 China is among the top with the United States, Japan and Germany.

FIGURE 44: CHINA, DOMESTIC VALUE-ADDED RETURNED HOME (% OF GROSS EXPORTS) <!-- Start of picture text -->
1.6<br>1.1<br>0.7<br>0.5<br>1995 2000 2005 2010<br>1.5<br>1<br>.5<br>0<br><!-- End of picture text -->

**Source:** Author’s calculations based on GVC indicators extracted using the Wang et Al. (2013) methodology on the Tiva database.

FIGURE 45: CHINA, DOMESTIC VALUE-ADDED RETURNED HOME, BY DESTINATION (% OF GROSS EXPORTS) <!-- Start of picture text --> 3.0<br>2.6<br>1.6 1.7<br>1.4<br>0.8<br>0.4<br>0.3 0.3 0.3 0.4<br>0.2<br>1995 2000 2005 2010 1995 2000 2005 2010 1995 2000 2005 2010<br>non-B&R B&R ROW<br>3<br>2<br>1<br>0<br>Domestic Value Added Returned Home (Forward, VS1*)<br><!-- End of picture text -->

**Source:** Author’s calculations based on GVC indicators extracted using the Wang et Al. (2013) methodology on the Tiva database.

35
using a fraction of growth in GDP per capita – as a function of extrapolation time (the time between two household surveys), poverty rates, year of the data, and annualized in real GDP In all of these the of growth per capita. figures, sample countries changes over the x-axis. For example, the countries with an extrapolation time of one year are mostly in Europe and South America, while the countries with an extrapolation of 10 years tend to be in Sub-Saharan Africa. As a result, looking at the trend for a given line is of little interest. However, the gap between the three lines can be used to explore, for example, for countries with a five-year extrapolation time, which method performs better?

It is plausible that using changes from the last survey is a good strategy when the last survey is only a couple of years old, while it is a less sound strategy when the last survey is a decade old or more. Extrapolation time does not matter when predicting poverty rates directly, but the average prediction error might still be a function of extrapolation time for the reason mentioned above – that the sample of countries changes. Panel (a) of Figure 6 shows that even for extrapolation times of five years, predicting changes from the past survey by just using growth in GDP per capita with a passthrough rate outperforms a model using 1,000+ variables trying to predict the poverty rate directly and performs as well as models predicting growth in the mean using 1,000+ variables. For extrapolation times beyond five years, we have too few observations to say anything with sufficient certainty, but we can rule out that just using GDP growth is significantly worse than the other methods.

In panel (b) we look at whether predicting poverty rates directly could be better for either poor or non-poor countries. Predicting poverty rates directly appears to be worse for all poverty levels except for countries with poverty rates around 30%. Particularly for very poor countries and countries with low extreme poverty, modeling changes through GDP is preferred to predicting poverty rates directly.

Deaton and Schreyer (forthcoming) argue that GDP is increasingly becoming detached from national material well-being. This could imply that using growth rates in GDP per capita has become less attractive, relative to predicting poverty rates directly, in recent years. Panel (c) shows that we do not find this to be the case. A possible reconciliation between these two findings is that, while GDP has become increasingly detached from household surveys, household surveys have become more comparable across rounds within the same country, allowing predictions from one survey to the next not to deteriorate.

19