rafmacalaba/datause-encoder-data huggingface dataset:
The WGI draw exclusively on perceptions data on the quality of governance. Perceptions of governance matter because individuals and firms are likely to act on those perceptions. In addition, for hard-to-measure dimensions of governance such as corruption (which by definition leaves no “paper trail”) there often are few alternatives to relying on perceptions when comparing outcomes across countries. This is not to say that perceptions are the only thing that matters when measuring governance across countries. However, they can provide a valuable “reality check” relative to more objective or factual measures of specifics of the legal, regulatory and institutional environment, particularly in environments when there is likely to be a substantial gap between de jure rules and their de facto implementation.

In this paper we have also updated and expanded our earlier analysis on three key methodological issues relevant to the WGI: the possibility of correlated perceptions errors, the robustness of the WGI to alternative weights, and the possibility of global trends in governance that are not captured by our baseline methodology. Our updated analysis confirms our earlier findings that these issues are not major concerns for the WGI. Admittedly imperfect indirect evidence suggests that correlated measurement error across expert assessments may not be significant. While such correlations might indicate a different weighting scheme, we also find that the baseline WGI estimates do not change very much when compared with an alternative approach that imposes equal weights. Finally, our updated analysis continues not to show any evidence of substantial trends in global averages of governance. This in turn implies that our baseline WGI estimates which are rescaled to have zero mean in every year are informative about both relative and absolute movements in governance over time.

We conclude with a final caveat worth reiterating. While the WGI can be a useful resource for broad cross-country and over-time comparisons of various dimensions of governance, they are not a panacea for measurement – particularly when evaluating specific governance reforms in specific countries. In these cases, the WGI must be supplemented with more granular country-specific data that can shed light on the likely impacts of reforms to improve governance and institutional quality.

30
16

# Annex 1: General criteria for Inclusion In **the** IFC Indexes'

IFC selects stocks for inclusion in the indexes on the basis of three criteria: size, liquidity, and industry. The indexes include the largest and most actively traded stocks in each market, with a target index total representing the top 60% of total market capitalization at the end of each year and, as a second step, the top 60% of total trading value during each year. Size is measured by market capitalization; liquidity is the total value of shares traded during the year.

Only stocks that are listed on one of the major exchanges in the emerging markets are included in the index. The index will not include stocks whose issuing company is headquartered in an emerging market but listed only on foreign markets.

If several stocks meet the liquidity and size criteria, but only one or two are needed, IFC selects the stocks that represent industries that are not yet well represented in the IFC index.

In a few instances, particularly where multiple classes of stocks are common (e.g., Brazil and Mexico), IFC may include in the IFC index more than one class of stock for the same company even though they are not necessarily actively traded. The purpose is to give a balanced view of the capitalization of companies that have other classes of stock that are actively traded.

Stock market "float' (i.e., the amount of issued stock held by the general public and generally assumed to be available for trading) is not a consideration in weighing the indexes, due to the difficulty of obtaining accurate information in a timely manner.

'This annex and annex 2 is copied from the IFC methodology notes.
Figure 30. AME, age <!-- Start of picture text -->
1.0<br>0.9<br>0.8<br>0.7<br>0.6<br>20 30 40 50 60<br>Age<br>Men 2019 Men  2021 Women 2019 Women 2021<br>Pr(labour force=1)<br><!-- End of picture text -->

Figure 32. AME, Marital status, ages 15-59 <!-- Start of picture text -->
1.0<br>0.9<br>0.8<br>0.7<br>0.6<br>Single In union<br>Marital status<br>Men 2019 Men  2021 Women 2019 Women 2021<br>Pr(Labor force=1)<br><!-- End of picture text -->

Figure 31. AME, household zone <!-- Start of picture text -->
1.0<br>0.9<br>0.8<br>0.7<br>Urban Rural<br>Housing zone<br>Men 2019 Men  2021 Women 2019 Women 2021<br>Pr(Labor force=1)<br><!-- End of picture text -->

Figure 33. Oaxaca-Blinder decomposition of real labor income per hour, ages 18-59 <!-- Start of picture text --> 40%<br>37%<br>30%<br>31% 28%<br>20% 25%<br>25%<br>10% 11% 9% 3%<br>3% 2%<br>0%<br>-10% -21% -21% -20% -24% -21%<br>-20%<br>2015 2017 2019 2020 2021<br>Explained Unexplained Estimated gap<br><!-- End of picture text --> Note: gap is estimated as a proportion of women's income. All estimates are significant at 1% (except 2020, which is 10%).
# **Table A.3.2. Conditional correlations between digital occupation level and cognitive and socioemotional skills requirements (replication of Table 6)**

_Ordinary Least Square (OLS) regressions of digital occupation levels on dummies taking the value of one if the skills count of a skills category of an occupation is above the mean_

# _A. Occupational skills profile_

||||Occup|ation digit|al level||
|---|---|---|---|---|---|---|
|||Low vs.<br>Verylow|<br>Medium<br>vs. Low|High vs.<br>Medium|High vs.<br>Low|<br>High vs.<br>Verylow|
|||(1)|(2)|(3)|(4)|(5)|
|**Cognitive**|Thinking|0.24|0.19|0.23|**0.65***|**2.26*****|
|||(0.15)|(0.13)|(0.13)|(0.26)|(0.38)|
||Communication|**0.28***|-0.19|0.15|-0.13|-0.33|
|||(0.13)|(0.14)|(0.24)|(0.24)|(0.25)|
||Organization|0.24|-0.05|0.05|0.06|-0.45|
|||(0.13)|(0.13)|(0.17)|(0.27)|(0.61)|
|**Socioemotional**|Emotions|-0.01|**0.28***|0.06|0.40|0.91|
|||(0.13)|(0.13)|(0.18)|(0.27)|(0.54)|
||Relationships|**-0.28****|-0.26|-0.25|**-0.58***|-0.45|
|||(0.10)|(0.18)|(0.28)|(0.28)|(0.27)|
||Personal growth|**-0.20***|0.02|0.18|0.13|-0.21|
|||(0.09)|(0.17)|(0.18)|(0.28)|(0.26)|
||Constant|0.45***|1.46***|2.05***|1.22***|0.32|
|||(0.09)|(0.09)|(0.09)|(0.17)|(0.29)|
||N|79|76|48|54|51|
||R-sq|0.30|0.15|0.13|0.24|0.61|

47
# Table 11: NIS Efficiency GMM Estimator

|**Observations**|105||||||
|---|---|---|---|---|---|---|
|**Countries**|43||||||
|**Period**|1985-99||||||
|**Lagged Patents**|0.580 ***|0.634 ***|0.583 ***|0.603 ***|0.586 ***|0.534 ***|
||(0.14)|(0.17)|(0.18)|(0.14)|(0.12)|(0.16)|
|**ln R&D**|0.147 *|0.071|0.179|0.057|0.127|0.118|
||(0.08)|(0.06)|(0.13)|(0.05)|(0.11)|(0.10)|
|**ln US trade**|0.229 ***|0.274 ***|0.204 ***|0.322 ***|0.201 ***|0.237 ***|
||(0.06)|(0.08)|(0.07)|(0.08)|(0.07)|(0.07)|
|**Natural Resources**|-0.011|0.001|-0.012|-0.026|0.013|-0.020|
||(0.03)|(0.00)|(0.01)|(0.02)|(0.01)|(0.02)|
|**ln GDPcap**|0.013 ***|0.012 *|0.009 *|0.007 **|0.010 ***|0.010|
||(0.00)|(0.01)|(0.01)|(0.00)|(0.00)|(0.01)|
|**Quality**||0.018<br>(0.01)|||||
|**Colaboration**|||0.025<br>(0.02)||||
|**Education**||||0.047 ***<br>(0.00)||0.037 ***<br>(0.01)|
|**IPR**|||||0.010 ***<br>(0.00)|0.003<br>(0.00)|
|**Sargan Test (p)**|0.88|0.85|0.86|0.90|0.99|1|
|**First Order (p)**|0.51|0.35|0.24|0.15|0.18|0.11|
|**Second Order (p)**|0.22|0.27|0.23|0.30|0.29|0.32|

**Note: The dependent variable is log Patents. Robust standard errors were estimated in all the specifications. All specifications include time dummies.The GMM system estimator used two lags of the variables and  first differences to instrument.**

33
Aggregate indicators of poverty are important, but further disaggregation lends some additional insight. Figure 5A shows the poverty headcount rate for urban and rural households for each income concept. Poverty headcount is strikingly higher in rural areas. The measure of consumable income, which includes the effect of all taxes and social transfers, indicates that 24.9 percent of the rural population was poor at the US$5.50 PPP line and 42.8 percent was poor at the relative poverty line. In terms of household type, Figure 5B shows that the impact of indirect taxes on poverty was particularly large among households with multiple children.

<u>Figure 5. Romania. Poverty Headcount Rate, 2016</u> <!-- Start of picture text --> A. Urban and Rural B. By household type<br>(US$5.50 PPP-a-day)<br>45 42.8 70 65.5<br>61.5<br>36.2<br>40 60<br>35.0<br>35 50<br>30 22.8 24.9 27.5 40<br>22.5 27.5<br>25 20.1 21.8 30 21.3 21.7<br>18.1<br>13.9 15.4 20<br>20 12.5<br>15 12.1 9.0 10 1.91.3 2.53.1 7.68.4 0.10.1 0.30.2<br>6.2 9.0 0<br>10<br>5.<br>4.3<br>5<br>0<br>All Urban Rural All Urban Rural<br>US $5.50 PPP 60% of median disposable<br>income<br>Market income + pensions Disposable income Market income + pensions Disposable income<br>Consumable income Consumable income<br><!-- End of picture text --> <u><mark>Source: World Bank estimates based on the Romanian 2016 HBS.</mark></u>

# **V. Progressivity, Marginal Contributions, and Pro‐poorness of Taxes and Transfers**

How did each of the fiscal interventions contribute to the observed changes in poverty and inequality? Figure 6 presents the distributional impact of different components of the tax and benefit system as a share of market income (including pensions) in 2016. Most components of the system are progressive, with the bottom 10 percent of the distribution being net receivers of social benefits. However, in cash terms, households beginning in the second decile were net payers to the treasury in 2016, as the share of taxes paid exceeded the cash benefits received for all but the poorest 10 percent of the population.

16
**Table 1:** Summary statistics for permanent residents and movers

||Mean|Permane<br>Std. dev.|nt residen<br>Median|ts<br>N|Mean|Mo<br>Std. dev.|vers<br>Median|N|
|---|---|---|---|---|---|---|---|---|
|_Family characteristics_|||||||||
|Income percentile|49.72|28.89|49|8409346|52.52|28.50|54|928209|
|Ownership of dwelling|0.76|0.43|1|12843887|0.60|0.49|1|2242290|
|Urban status|0.72|0.45|1|12542856|0.81|0.39|1|2020376|
|Number of siblings|2.23|1.30|2|13257078|2.22|1.38|2|2341902|
|Couple family|0.56|0.50|1|13257078|0.40|0.49|0|2341902|
|_Educational outcomes_|||||||||
|Completed primary|0.63|0.48|1|13234115|0.63|0.48|1|2324712|
|Years of schooling|6.16|3.68|6|8993451|6.34|3.67|6|1763570|

Notes: The samples consider the characteristics of individuals age 14-25. Permanent residents are those individuals living in the same region in which they were born. Movers are those individuals living in a region different than the one where they were born. Average income percentil is computed using percentiles by census-year using the average total income of parents in the previous month or year. The sample size varies across variables due to different availability of data between censuses and also differences in the amount of missing data.

# **III Methods**

As discussed in a recent survey by Chyn and Katz (2021), the potential influence of neighborhoods on individual outcomes can operate through contemporaneous (or situational effects) and past neighborhoods through exposure (or developmental) effects that accumulate during childhood. The econometric approach followed here is focused solely on the latter, which has the key prediction that the gains from moving to places with beneficial characteristics is larger for children who are younger at the time of the move and thus exposed for a longer period.

Given the focus on exposure, my goal is to determine how much a child’s potential outcomes would improve on average if he were to grow up in a region where the permanent residents’ outcomes are 1 percentage point higher. Following Chetty and Hendren (2018b), I define exposure effect at age _m_ as the impact of spending year _m_ of one’s childhood in a region where the outcomes of permanent residents are 1 percentage point higher.

and non-movers.

8
## Fiscal Issues in Macreeconomic Stabilization

<!-- Start of picture text --> by<br>Lance Taylor<br><!-- End of picture text -->

# TABLE **OF CONTENTS**

<!-- Start of picture text --> Intoucon..... 1<br>1. Macroeconomic Background ..................... 1.<br>2. The iscal Deficit ..................... 8<br>3. Effects of Deficit Reduction ..................... 16<br>4. Effects of Specific Policy Moves ..................... 21<br>References ........................ 26<br>illustrations<br>Figure 1 ..................... 4<br>Figure 2 ................... 6<br><!-- End of picture text -->
Finally, COVID-19 changes the health conditions _H_ . The risk of COVID-19 lowers health conditions at both home and abroad, but is likely to have done so most in Europe, where infection rates are highest.

Taking these pieces together, COVID-19 has lowered earnings opportunities at home, but this is offset by lower earnings opportunities abroad, higher costs of migrating, less ability to pay these costs, and worse health conditions abroad. This leads us to predict that the intention to migrate will fall for migration to both Europe and Senegal. This theory also offers suggestions of which youth will have their intentions most affected. Youth from poorer households are more likely to find the increase in costs and reduction in wealth to make the budget constraint binding. Youth who were never planning on migrating in the first place should only have this intention reinforced, and youth for whom the gain in utility from migrating was extremely high may still find their migration decision unaffected. This appears more likely to occur for Europe as a destination, given the gains from Senegal migration are more modest. The largest change in migration intention should therefore be for youth for whom the initial migration decision is close - e.g., those who are at the margin of wanting to migrate to Europe, or who are considering migrating to Senegal, where the gains are not as large.

# **3. Data and Experimental Methodology**

# **3.1 Setting and Sample**

The Gambia provides a useful setting for studying how migration intentions have changed with the COVID-19 pandemic. Prior to the pandemic, it was the African country with the highest rate of irregular migration to Europe according to Frontex data. This migration typically takes place through what is referred to as “the backway” – an overland journey through West Africa, across the Sahara Desert, into Libya, and then across the Mediterranean to Italy and other European destinations. This is a costly and dangerous journey, but, as noted above, the potential earnings gains if successful are very high. An alternative migration destination for Gambians is neighboring Senegal. Prior to COVID-19, many Gambians would seasonally migrate to nearby rural areas of Senegal for agricultural work, while migrating to the capital city of Dakar would be a more major move, that would yield the possibility of access to better paying urban jobs. Gambians were allowed to migrate to Senegal without a passport, and could work in most jobs without work permits.

8
# **Table 2. SUR Estimates** for Taxes and Functional Expenditure (GDPHS)

||Const|DUMMY<br>_ I|DUMMY<br>2|DUMMY<br>3|_DDO_|GDPHS|R<sup>2</sup>|F|
|---|---|---|---|---|---|---|---|---|
|.SCAL|-4.31<br>(0..6>|14.73<br>(519"*)|1.47<br>(0.86)|7.69|**_-4#2_**<br>**_:(.0.?_**|3.89E4<br>(1.93+.|71.6:|-16.<br>|
|DIRECT|__~<br>-4.87<br>(-0.94)|~~~~~~~~~~~~~~<br>5.56<br>(1.88+)|~~~~~~~~~~~~~~<br>-1.03<br>(-0.56)|~~~~~~<br>5.79<br>(2.76**)|**_0.58_**<br>_(1.83+)_|9.45 E4<br>(4.35**)|58.1|.<br>.... ....._::-_,,-,.I<br>8.89|
|TTAXGDP|13.70<br>(4.44**)|3.72<br>(2.04*)|-0.97<br>(-1.68)|3.94<br>(3.13**)|_-0.26_<br>_(-1L3)_|2.72 E-4<br>(-2.03*)|56.6|9.1|
|EXPGDP|17.28<br>(2.77**)|25.38<br> <br>(6.80**)|-3.27<br>(-1.36)|17.37<br>(6.67*t*)|-0.01<br>_(-O 03)_|11.97 E4<br>(4.44**)|87.1|47.2|
||SUBSIDY|_-0.51_<br>(-014)|2.53<br>(1.21)|3.20<br>(2.54*)|1.37<br>(0.92)|_0.33_<br>_(1.49)_|2.30 E-5<br>_(0.15)_|34.5|3.5|
|BEALT'HGDP|3.95<br>(1.83+)|-0.10<br>(-0.08)|-0.28<br>(-0.36)|0.41<br>(0.48)|_-0.25_<br>(-1-91+)|3.76 E-4<br>(4.06**)|77.5|23.4|
|EDUGDP|2.39<br>(1.49)|1.17<br>(1.29)|-0.50<br>(-0.91)|1.01<br>(I.57)|**_aoos5_**<br>**_(0.05)_**|_1.77_E-4<br>(2.65*)|57.2|8.0|

Legend: highly significant ... ** < 0.01 significant ... * < 0.05 marginally significant ... + **< 0.10** + or -denotes the sign of estimated regression coefficient Method of estimation: SUR i.e. seemingly unrelated regressions (Zellner, 1962). See Appendix III for variable descriptions and rules of presentation.

Source: Fakin and de Crombrugghe (1996), p.9; see also pp.33-34.

13
Finally, the technological transformation in China’s aluminum industry described by Rock and Wang (2011) has resulted in equally large CO2 savings. In the industry as a whole, the CO2 intensity of aluminum production declined by 50% from 25.0 metric tons of CO2 per metric ton of aluminum in 1980 to 12.6 metric tons of CO2 per metric ton of aluminum in 2010 (figure 11). This saved 194 million metric tons of CO2 over business as usual (figure 12). Most Figure 11 China CO2 Intensity of Aluminum Production (Metric Tons of CO2 per Metric Ton of Aluminum) <!-- Start of picture text --> 26<br>24<br>22<br>20<br>18<br>16<br>14<br>12<br>80 82 84 86 88 90 92 94 96 98 00 02 04 06 08 10<br>CO2 Intensity<br><!-- End of picture text --> Source: Rock and Wang (2011) of the savings (118 million metric tons of CO2 or 61% of total CO2 saved) comes from technological upgrading of primary aluminum production, especially the closing of facilities using older Soderberg in-situ cells and shifting to more modern and technologically advanced 160 kA pre-baked and 350 kA pre-baked cells (Rock and Wang 2013). The rest (76 million metric tons of CO2) comes from a shift in technology used to make alumina—a shift from the energy intensive Sinter process to the less energy intensive Bayer-Sinter and Bayer processes.<sup>98</sup>

> 98 In China in 2010 the Sinter process used 28.14 GJ of energy per metric ton of alumina, while the Bayer-Sinter process used 21.95 GJ per metric ton of alumina and the Bayer process used only 10.42 GJ per metric ton of alumina (Rock and Wang 2013).

26
Table 10: Sources of Procyclicality: Including a Political Economy variable

||**Total E**|**xpenditures**||**Curren**|**t Expenditu**|**re**|
|---|---|---|---|---|---|---|
|(1)|(2)|(3)|(4)<br>(5)|(6)|(7)|(8)|
|State GDP / National GDP<br>-1.616|-1.644|*<br>-1.396|-1.866<br>*<br>-1.033|-1.314|**<br>-0.868|*<br>-1.798<br>**|
|_[1.081]_|_[0.931]_|_[0.900]_|_[0.994]_<br>_[0.749]_|_[0.650]_|_[0.482]_|_[0.733]_|
|State Debt / GSP<br>-1.498|**<br>-1.146|*<br>-0.763|-1.239<br>*<br>-0.116|0.389|0.893|0.101|
|_[0.546]_|_[0.665]_|_[0.697]_|_[0.636]_<br>_[0.588]_|_[0.817]_|_[0.704]_|_[0.704]_|
|No. of Years party Pres. = party Gov.<br>0.022|0.007|0.012|0.007<br>0.049|**<br>0.023|0.030|0.031|
|_[0.024]_|_[0.021]_|_[0.025]_|_[0.024]_<br>_[0.020]_|_[0.020]_|_[0.019]_|_[0.026]_|
|State Current Revenues Procyclicality<br>0.418<br>_[0.180]_|**||0.695<br>_[0.117]_|***|||
|State Output Volatility|0.295|||0.071|||
||_[0.693]_|||_[0.606]_|||
|State Current Revenue Volatility||3.120|||3.674||
|||_[2.447]_|||_[2.837]_||
|Intergov. Transfers / State Current Revenue|||0.161<br>_[1.564]_|||1.236<br>_[1.522]_|
|Observations<br>27|27|27|27<br>27|27|27|27|
|R-Squared<br>0.29|0.17|0.22|0.16<br>0.58|0.12|0.24|0.15|
|**_Addendum: Using State Tax Revenues instead_**|||||||
|State Tax Revenues Procyclicality<br>0.264|||0.496|**|||
|_[0.259]_|||_[0.230]_||||
|State Tax Revenue Volatility||3.904|*||3.268|*|
|||_[2.119]_|||_[1.811]_||
|Intergov. Transfers / State Tax Revenues|||0.739|||1.412|
||||_[1.354]_|||_[1.080]_|

***, **, and * denote significance at the 1%, 5%, and 10% levels, respectively.  OLS estimation. Robust standard errors in brackets.

21
The empirics that follow below explore the role of the _I − METR_ , and its two interacting components, the rate differential term ∆ _h_ and the _KF_ - _METRh_ , as the key channels through which taxes affect investment. It also considers one further implication of (4) (and reasons why it might fail): for countries in which some investment occurs, there is, in the absence of any capital constraint on the multinational, no spillover effect on investment in _h_ from the _KF_ - _METR_ in any other country.

# **3 Foreign Affiliate Investment**

This section describes the FAI data used in the empirics, comparing it both conceptually and empirically with the more commonly employed FDI data.

## **3.1 Understanding Foreign Affiliate Investment (and FDI) data**

The two crucial features of FAI data are that they report cross-border investment in physical capital and that in doing so it looks through conduit structures to identify the ultimate parent country. In contrast, as stressed at the outset, much of the empirical work on taxation and cross-border investment has used data not on FAI but on foreign direct investment (FDI),<sup>24</sup> which differ from FAI data in both respects.

First, whereas FAI data capture real investment, FDI data capture funds received by a foreign affiliate that may or may not correspond to tangible investment. These funds comprise both direct net transfers from the immediate parent company (not necessarily the ultimate parent), whether through equity or debt, and earnings retained by the foreign affiliate. And these amounts appear as FDI regardless of their end use. Retentions that are recorded as FDI may, for example, be kept within the foreign affiliate as cash reserves, with

> 24Some studies have used foreign affiliate statistics for other purposes, including Tørsløv, Wier and Zucman (2018) to assess the extent of profit shifting by multinationals (for 2017), and by Fukui and Lakatos (2012) and Ramondo, Rodr ́ıguez-Clare and Tintelnot (2015) to study the pattern of multinational production activities. For US-based multinationals, the Bureau of Economic Analysis (BEA) foreign affiliates data has been widely used (one notable instance being Desai, Foley and Hines Jr (2004)), as it will be below. The OECD’s Activities of Multinational Enterprises (AMNE) Database has also compiled inward and outward foreign affiliate statistics for 31 OECD countries between 2008 and 2016, as described in Cadestin et al. (2018).

12