rafmacalaba/datause-encoder-data huggingface dataset:
## **Table 3: OLS Estimates of Wage Determinants Overall, By Occupational Exposure of a Group to Trade and Offshoring**

# Dependent Variable: Log Wage

|Specification|Lagged Log of<br>Low Income<br>Affiliate Emp|Lagged Log of<br>High Income<br>Affiliate Emp|Lagged<br>Import<br>Penetration|Lagged<br>Export<br>Share|Obser-<br>vations|R-<br>Squared|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|1983-1991|0.004|-0.006|-0.213***|0.051|1,390,331|0.52|
||(0.02)|(0.02)|(0.08)|(0.2)|||
|1992-2002|-0.061***<br>(0.02)|0.049***<br>(0.02)|-0.323**<br>(0.13)|0.489**<br>(0.19)|1,677,764|0.49|
|1983-1996|-0.014<br>(0.02)|0.010<br>(0.02)|-0.259***<br>(0.08)|0.171<br>(0.16)|2,181,111|0.51|
|1997-2002|-0.111***<br>(0.03)|0.098***<br>(0.03)|-0.312**<br>(0.14)|0.485**<br>(0.21)|886,984|0.48|
|Female|-0.054**<br>(0.02)|0.049**<br>(0.02)|-0.167<br>(0.12)|0.340*<br>(0.19)|1,491,459|0.49|
|Union|0.004|-0.011|-0.074|-0.095|549,056|0.37|
||(0.02)|(0.02)|(0.12)|(0.16)|||
|High School|-0.046**|0.037**|-0.203**|0.188|1,475,120|0.44|
|or Less|(0.02)|(0.02)|(0.08)|(0.18)|||
|College or|-0.030*|0.028*|-0.136|0.108|1,592,975|0.44|
|More|(0.02)|(0.02)|(0.18)|(0.14)|||
|Over 40|-0.061***<br>(0.02)|0.053***<br>(0.02)|-0.201***<br>(0.08)|0.093<br>(0.14)|1,262,929|0.48|
|Over 50|-0.067***|0.054***|-0.287***|0.087|550,042|0.48|
||(0.02)|(0.02)|(0.08)|(0.14)|||

_Source_ : See Table 1.

_Note_ : Each row represents a separate regression. The independent variables are listed in the column headings, and the subsample of interest is listed in the row heading. Robust standard errors are clustered at the industry level and reported in parentheses below coefficient estimates. Wage specifications control for a worker's gender, age, race, experience, whether in a union, imputed computer use rate by occupation and include year, education, state, industry, and two-digit occupation fixed effects.
# **Table 3. Trends in horizontal and vertical equity in education and health service delivery**

||Quintile|Hor<br>(Wo|izontal/<br>reda le|spatial e<br>vel data:|quity tre<br>2009-2|nds<br>013)|<br>(hou|Vertical<br>sehold<br>data:|equity tr<br>& individ<br>2000-201|ends<br>ual level<br>4)|
|---|---|---|---|---|---|---|---|---|---|---|
|Indicators|groups, ratios<br>& differences|2009|2010|2011|2012|2013|2000|2005|2011|2014|
|NER|All groups|80.6|79.1|83.1|83.2|86.3|32.3|45.7|64.2|65.0|
||q5 Richest|81.9|80.9|83.4|84.8|86.8|68.1|71.3|86.1|82.7|
||q1 Poorest|80.4|79.6|84.4|84.3|84.2|20.8|28.3|51.9|48.3|
||q1/q5|0.98|0.98|1.01|0.99|0.97|0.31|0.40|0.60|0.58|
||q5-q1|1.5|1.3|-1|0.5|2.6|47.3|43.0|34.2|34.4|
|CAR|All groups|46.2|-|58.7|61.2||6.4|14.9|29.0|44.7|
||q5 Richest|47.7|-|61.5|64.5|-|24.2|33.9|51.6|56.7|
||q1 Poorest|38.7|-|52.5|55.5|-|3.2|4.5|14.7|30.9|
||q1/q5|0.81|-|0.85|0.86|-|0.13|0.13|0.28|0.54|
||q5-q1|9.0|-|9.0|9.0|-|21.0|29.4|36.9|25.8|
|ANC|All groups|57.7|-|69.1|74.2||9.2|10.7|14.2|22.3|
||q5 Richest|59|-|72.2|73.9|-|30.1|34.0|38.5|55.1|
||q1 Poorest|55.8|-|68.2|72.2|-|4.1|3.7|5.4|8.6|
||q1/q5|0.95|-|0.94|0.98|-|0.14|0.11|0.14|0.16|
||q5-q1|3.2||4.0|1.7||26.0|30.3|33.1|46.5|
|SBA|All groups|14.1|-|19.8|20.1||4.7|4.7|8.6|15.5|
||q5 Richest|15.4|-|24.5|23.9|-|20.9|21.2|40.2|55.6|
||q1 Poorest|11.6|-|18.6|18.5|-|0.9|0.7|1.7|4.5|
||q1/q5|0.75|-|0.76|0.77|-|0.04|0.03|0.04|0.08|
||q5-q1|3.8|-|5.9|5.4|-|20.0|20.5|38.5|51.1|

Source: Authors’ computation from administrative woreda level data and DHS data. Notes: _Horizontal equity:_ Data trimmed at the 1<sup>st</sup> and 99<sup>th</sup> percentiles; excludes Somali region. Relative inequality (q1/q5) compares the bottom 20% to top 20% of woredas. Absolute inequality (q5-q1) is their difference. _Vertical equity:_ Vertical equity data exclude Addis Ababa. Relative inequality compares the bottom 20% to the top 20% of households. Absolute inequality is their difference.

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# **7.1 The role of undistributed corporate profits**

Here we document the key role played by undistributed corporate profits at the top of the income distribution, and how using microdata on the identity of shareholders matters for our inequality estimates.

We start by documenting the fact that, within the top 1% of earners when considering only distributed income (i.e. excluding undistributed profits), there is a strong positive correlation between income and the probability of being a shareholder. In Figure 6a, we show that around 10% of individuals just within the top 1% of distributed income are large shareholders of a firm filing CIT in 2019 - we define large shareholders as those owning at least 5% of a firm. This probability increases to close to 30% for those in the top 0.1% and reach approximately 60% for those within the top 0.01%. These figures show that individuals that have high distributed incomes are also more likely to be shareholders in corporations - and thus be ”assigned” undistributed profits in our comprehensive income measure.

Nonetheless, when we rerank all individuals considering their total income inclusive of undistributed profits (orange line in Figure 6a), corporate ownership is much more prevalent at the top. The share of corporate owners is 20 p.p. higher, at 40%, for those just within the top 0.1%, and reaches more than 80% for those within the top 0.01%. This is not only driven by individuals already declaring high personal income and being assigned additional undistributed profits. In Figure 6b, we compute for each bin at the top the share of taxpayers with distributed income below the annual minimum wage - these are individuals who would not be classified as top earners otherwise, but only make the cut because we observe their ownership of corporations and assign them profits. Between 10 - 20% of individuals within the top 0.1% have distributed income below the MW. This is one indicator that directly assigning individuals undistributed profits from the corporations they own generates meaningful re-ranking of the identity of top income earners, and also that a meaningful share of these earners do not receive large distributed incomes.

While we have established that observing corporate ownership data matters for the identity of those on the top, an important remaining question is whether it matters for estimates of top income shares using the DINA methodology. Clearly, adding undistributed corporate profits to total realized income leads to large increases in measured income concentration - Alstadsæter _et al._ (2016), for example, show that it more than doubles the share of top 0.1% income in Norway. But the usual DINA approach also adjusts total income to include national account measures of corporate profits – it usually does so by distributing these profits in proportion to _realized dividends_ , instead of directly measuring corporate ownership. To assess whether these matter, we replicate our results ignoring the corporate ownership data and instead distributing 16
percent. This finding is suggestive of increased specialization in advanced nations, which we will return to in section 5.

# **Figure 1 Value added and workers involved in production of German transport equipment**

# **(a) GVC income shares (in %)**

<!-- Start of picture text --> 100%<br>90%<br>80%<br>70%<br>60% FOR<br>50% GER REST<br>40% GER OMA<br>30%<br>GER TR<br>20%<br>10%<br>0%<br>1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009<br><!-- End of picture text -->

# **(b) Number of GVC workers (in millions)**

<!-- Start of picture text --> 5.0<br>4.5<br>4.0<br>3.5<br>3.0 FOR<br>2.5 GER HS<br>2.0 GER MS<br>1.5<br>GER LS<br>1.0<br>0.5<br>0.0<br>1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009<br><!-- End of picture text --> Note: Panel (a) provides a decomposition of the value of final products from the German transport equipment industry (NACE rev. 1 industries 34 and 35) into the value added in German transport equipment industry itself (GER TR), other German manufacturing industries (GER OMA), all German non-manufacturing industries (GER REST) and in foreign industries. Panel (b) shows the number of workers directly and indirectly involved in production of these products, decomposed into foreign (FOR) and domestic (GER) workers, including low-skilled (LS), medium-skilled (MS) and high-skilled (HS). The skill level of workers is defined by level of educational attainment. _Source:_ Author’s calculations based on World Input-Output Database, April 2012.

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Figures 3 and 4, for both 2010 and t+30 respectively, help to sketch out a more broad-brush view of the trends. To show out these trends we first used a regular linear regression line (in solid red) along with the regression equation (shown next to the graph legend) and in a second step we added an interpolation (the thin dashed black line) by a robust Tukey smoother.<sup>7</sup> This smoother is robust in the sense that it is insensitive to outliers, unlike the common linear regression line. In addition, the Tukey smoother provides insights about the existence of any trend in the data, as well as the trend’s nature (linear or not), while allowing a quick assessment of the degree of linearity present in the data. John W. Tukey designed it as one of the many easily computed statistics of the Exploratory Data Analysis (EDA) methodology aimed at revealing patterns in data (Tukey, 1977).

The increase in the fossil energy share is shown by both trend lines. Concerning the linear regression, the fit is moderate (R2 = 0.51) but significant. Meanwhile the Tukey smoother is not far from the regression line. However, contrary to the linear regression line whose slope is by construction constant, the Tukey smoother tells a two-period story: a gradual increase of the fossil fuel shares up to the early 1990s, followed by a peak around 1995, and then followed by a gradual decrease: the peak share dropped from 90% to 8 % in the most recent studies.

The upward (i.e., increasingly pessimistic) linear trend is still visible but far less pronounced than in figure 3 (R2 = 0.25) because of the more scattered set of forecasted fossil fuel shares in t+30, as compared to the 2010 forecasts. The same remark about the regular regression and Tukey smoother trends apply here.

> 7 It very clearly shows a trend towards pessimism that accelerates in the middle of the 1980’s and then reverses in the post Kyoto years.
- 12-

- The incremental cost of _switching purchases of inputs_ (yam/fabric) away from the cheapest source (when the rule of origin does not apply) to the AGOAdesignated source (Africa or the United States.);

- 'The incremental _transport_ (which could be positive or negative) cost of switching purchases of inputs (yam/fabric) away from the cheapest source (when the rule of origin does not apply) to the AGOA-designated source (Africa or the United States);

- The cost function which translates these input and transport cost differentials into aggregate cost shifts.

* Finally, to translate all these cost and price effects into quantity effects, assumptions are made in the paper about the elasticity of export supply.

In the analysis, we calculated the impact under two different assumptions about the cheapest source for importing inputs in order to meet the rule of origin. In the first, the cheapest source was assumed to be South Africa because the ex post data indicated that this was the overwhelming choice by Mauritian and South African exporters facing rules of origin. We also did the calculations on the assumption that the United States was the cheapest source. For this assumption, however, the incremental transport costs of sourcing from the United States had to be calculated which we describe below. Overall, however, results were broadly similar under the two scenarios because the relative efficiency of the United States in producing inputs was offset by the larger transport costs of sourcing from the United States relative to South Africa.

# **Tariff equivalent of export quotas (e)**

These are derived from the estimates for India provided by Kathuria and Bharadwaj (2000). The implicit assumption made is that domestic prices in the Untied States will be higher than the world price by the amount of the export tax equivalents in the exporting countries. SSA exporters would then receive this higher domestic price if they have tariff and quota-free access and the domestic price less the tariff if they do not have such access.<sup>10</sup>

**10** The assumption that the exporter receives the full domestic price in the importing country might not be appropriate because importing country intermediaries may appropriate some of the benefits of protection. Our assumption has the effect of exaggerating the benefits of preferential access as well as the losses from the elimination of protection vis-a-vis the rest of the world under the Uruguay Round agreement on textiles and clothing.
<!-- Start of picture text --> = Non-commodity exporters<br>14 = Commodity exporters<br>40<br>6<br>4<br>0<br>No . .<br>control Controlling for Controlling for<br>terms of trade _ oil prices<br><!-- End of picture text --> <!-- Start of picture text --> a Non-commodity exporters<br>44 = Commodity exporters<br>12<br>No control Controlling for Controlling for<br>terms of trade __ oil prices<br><!-- End of picture text --> <!-- Start of picture text --> = Non-commodity exporters<br>16 = Commodity exporters<br>12<br>10<br>6<br>2<br>0<br>No control Controlling for Controlling for<br>terms of trade oil prices<br><!-- End of picture text --> <!-- Start of picture text -->
| Non-commodity exporters<br>6 = Commodity exporters<br>5<br>2<br>0<br>No control Controlling for Controlling for<br>terms of trade oil prices<br><!-- End of picture text -->
**Table 4. Urban** **<mark>p</mark> overt** **<mark>y</mark> at US$2 a da** **<mark>y</mark> : Countries at risk**

||Initial sit<br>(200|uation<br>5)|2<br>Δin|008 Central Sc|enario<br>Due to|Due to|
|---|---|---|---|---|---|---|
|Country|Poverty<br>Headcount|Poverty<br>Deficit|Poverty<br>Headcount|Estimated<br>Cost|old<br>poor|new<br>poor|
|Nigeria|86.0|42.9|2.9|8.4|8.2|0.1|
|Nicaragua|66.0|24.4|3.9|5.4|5.2|0.2|
|Haiti|61.6|19.7|2.9|3.3|3.3|0.1|
|Madagascar|68.2|13.0|5.0|3.0|2.9|0.1|
|Uganda|76.3|12.8|3.0|2.8|2.7|0.1|
|Benin|56.1|8.3|5.5|2.5|2.4|0.1|
|Cambodia|62.5|8.6|4.8|2.3|2.2|0.1|
|Tanzania|62.2|7.0|5.1|2.0|1.9|0.1|
|Mauritania|39.3|4.3|6.9|1.7|1.5|0.1|
|Pakistan|46.7|3.2|8.4|1.5|1.4|0.1|
|Ghana|37.7|4.3|5.1|1.4|1.3|0.1|
|India|44.2|3.4|6.8|1.4|1.3|0.1|
|Bangladesh|47.4|3.5|5.8|1.3|1.2|0.1|
|Côte d'Ivoire|35.1|3.4|5.4|1.2|1.1|0.1|
|Mali|32.0|2.4|5.7|1.1|1.0|0.1|
|Indonesia|25.8|1.8|7.5|1.0|0.9|0.1|
|Ethiopia|61.0|2.8|4.9|0.9|0.9|0.0|
|El Salvador|25.0|3.2|3.6|0.9|0.8|0.1|
|Guinea|26.2|1.8|7.7|0.9|0.8|0.1|
|Cameroon|25.5|1.7|5.3|0.7|0.7|0.1|

*Notes: (1) Authors’ own calculations using data from the GIDD; (2) the poverty deficit is expressed as a proportion of the sum of total household incomes; (3) the estimated cost is defined as the difference between the initial PD and the PD after the price shock; (4) households surveys have been standardize to year 2005 using information from national accounts, see Annex I for methodological details and Annex II for a complete list of countries

Interestingly, 17 of the 20 most affected countries at US$2 a day are also among those most affected at US$1 a day (Table 3). The higher number of poor and near poor at US$2 a day than at US$1 a day explains the larger costs. In turn, the relationship between the change in poverty rates and the change in poverty deficits becomes less pronounced. Nigeria and Haiti register similar increases in poverty rates, 3 percentage points, but the monetary cost is much higher in Nigeria (8.4 against 3.3 percent of GDP).
# **3. Data**

We are able to study the impacts of extreme weather events on rural households by merging household and community level data from the Indonesian Family Life Survey (IFLS) with daily rainfall data covering a 25 year period. The combined data set contains information on rainfall, household expenditures, household level socio-economic characteristics, and community level attributes.

Household and community surveys were fielded from late June to the end of October 2000 for IFLS3 and from August 1997 to January 1998 for IFLS2. The surveys include villagelevel data which allows the determination of the extent to which access to better infrastructure or social programs increases resiliency. The consumption aggregate consists of food and nonfood components. The food component consists of 37 food items (purchases and the value of own production or gifts) consumed within the last week. The nonfood component consists of frequently purchased goods and services (utilities, personal toiletries, household items, domestic services, recreation and entertainment, transport, sweepstakes), less frequent purchases and durables (clothing, furniture, medical, ceremonies, tax), housing, and educational expenditures for children living in the household. Transfers out of the household were excluded. All values are monthly figures and are in real terms. To obtain real values, both temporal and spatial deflators were used, using prices in December 2000 in Jakarta as the base.<sup>6</sup> Using daily rainfall data from 1979 to 2004, we calculated the 25 year mean and standard deviations for monsoon onset and the amount of post-onset rainfall for 32 weather stations. The rainfall data from these weather stations were then matched to communities in IFLS. Weather data were merged with household survey data at the community level based on proximity. Only weather stations with complete data for the 25 year period were used. The matched data contained a total of 267 communities and 32 WMO stations. In rural areas, 106 communities in 9 provinces were matched to 27 stations. In rural Java, 66 communities in 4 provinces were matched to 18 stations. The number of communities per WMO station ranged from 1 to 10 in rural areas. 3,290 households were matched to 27 stations in rural areas, and 2,159 households were matched to 18 stations in rural Java.

> 6 The spatial deflator used is the ratio of the location (province, urban/rural area) poverty line (in December 2000 prices) to the Jakarta poverty line.  Thus the spatial deflator used converts the local December 2000 values into Jakarta December 2000 values.

8
- _Local government benefits._ Most local governments provide additional lump-sum grants for newborn children, cash supplements to low income groups (pensioners, disabled, unemployed), subsidies for transportation costs for vulnerable groups (pensioners, unemployed, people with disability, school children, etc.), lump-sum benefits and food packages for the poor, and benefits for students (grants, subsidies for school books, school meals).

Social spending on in-kind transfers in the form of education and health amounted to 10 percent of GDP in 2014. Kindergartens, primary schools, and secondary schools are financed by central, regional and local governments, however the bulk of spending on wages is funded by the central government (World Bank, 2008). The education system is mostly public in Croatia (private expenditure on education is small at all levels of education).

Health services are provided through the Croatian Health Insurance Fund (HZZO), which is financed from health contributions and the government budget. Although health contributions constitute the budget of HZZO, a large part of total health care expenditures are paid for from the state budget. Dependent family members are insured through other family members who pay contributions, and certain vulnerable groups, such as older pensioners and people with very low or no income, are exempted from paying contributions, yet insured nevertheless. The basic health insurance, which is compulsory, covers about 80 percent of costs of health risks. The basic “basket” of health care services includes: primary care, specialized care, inpatient services, drugs from the HZZO’s list, health care while abroad, dental care, and orthopedic and other appliances. The remaining 20 percent is paid by patients themselves through out-of-pocket outlays, with a certain upper limit. The HZZO offers the ‘supplemental’ health insurance: for a fixed monthly fee, one gets an extension of the basic basket of services which covers a part of the 20 percent of costs not covered by the basic insurance.

# **III. Data sources, method, and assumptions**

## **Data sources**

Data for 2014 were used to conduct this incidence analysis study in line with the availability of survey data. Specifically, we used both the Croatian Household Budget Survey (HBS) for 2014 and the Population Income Survey (PIS) for 2015, which refers to 2014 income.<sup>5</sup> The HBS contains both income and expenditure data, along with demographic and household characteristics, thus enabling the identification of indirect taxes across the distribution. However, the HBS does not have the level of detail on income and benefits received available in the PIS, nor a large enough

> 5 The PIS serves as the basis for the EU-SILC for Croatia, which aggregates some of the categories to arrive at a database that is harmonized with other European countries. We used the 2015 PIS, because the survey asks respondents to report their incomes from the previous year (2014, which is of our interest here).

9
# Figure 7. ECA: Output growth and potential growth

A. GDP growth <!-- Start of picture text --> Percent<br>ECA Median<br>10<br>8<br>6<br>4<br>2<br>0<br>-2<br>2000-21 2000-10 2011-21 2022-23<br>C. Contributions of potential growth and<br>business<br><!-- End of picture text --> cycle to actual growth B. Deviation of output from prepandemic trend <!-- Start of picture text --> Percent ECA<br>2 ECA excl. RUS and UKR<br>ECA excl. RUS, TUR, and UKR<br>0<br>-2<br>-4<br>-6<br>-8<br>2019 2020 2021 2022 2023<br><!-- End of picture text --> D. Contributions to potential growth: EMDEs and ECA <!-- Start of picture text --> Percent Percent<br>8 Growth Potential Cycle 8 TFP Capital Labor Potential growth<br>6<br>6<br>4<br>2 4<br>0 2<br>0<br>EMDEs ECA<br>EMDEs ECA<br>E. Contributions to potential growth: F. Contributions to potential growth:<br>Central Asia Central<br>and South Caucasus Europe and Western Balkans<br>Percentage points Percentage points<br>10 8<br>8 TFP Capital Labor Potential 6 TFP Capital Labor Potential<br>6<br>4<br>4<br>2<br>2<br>0 0<br>-2 -2<br>Central Asia South Caucasus Central Europe Western Balkans<br>2000-10 2011-21 2022-30 2000-10 2011-21 2022-30 2000-10 2011-21 2022-30 2000-10 2011-21 2022-30<br>2000-10 Contribution 2011-21 2000-10 Contribution 2011-21<br>2000-21 2000-10 2011-21 2022-30 2000-21 2000-10 2011-21 2022-30<br><!-- End of picture text --> Sources: Penn World Tables; World Bank Note: ECA = Europe and Central Asia; EMDEs = emerging market and developing economies. RUS = Russian Federation; UKR = Ukraine; TUR = Türkiye. TFP = total factor productivity. Shaded area indicates forecast. GDP weights are calculated using average real U.S. dollar GDP (at average 2010-19 prices and market exchange rates).

A. Bars show period averages of annual GDP-weighted averages. Markers denote the median region, with orange whiskers showing min-max ranges across regions.

B. Figure shows the percent deviation between the Global Economic Prospects report forecasts released in June 2022 (World Bank 2022c) and January 2020 (World Bank 2020c). For 2023, the January 2020 baseline is extended using projected growth for 2022.