rafmacalaba/datause-encoder-data huggingface dataset:
ing the components of equation 8. They show that the percent variation in _H_ can be further decomposed into population changes within groups, changes in the headcount within groups, and a multiplicative effect. Furthermore, the percent variation in _A_ can be decomposed into the weighted sum of percent variations of each of its dimensional components.

However, if the indicator is not cross-sectionally decomposable, this approach fails. This is the case when attempting to decompose the changes of the headcount ratio _H_ into the variation attributed to each of its dimensions. Due the nonlinearity of _H_ , an explicit closed-form solution for this decomposition is not feasible. However, a counterfactual simulation methodology may be used to work around this. That is the topic of the following section.

# **3 Methodology**

The decomposition approaches described so far are not appropriate to look into the dimensions responsible for changes in the headcount ratio _H_ over time. In this section, we analyze the problem of decomposing changes in _H_ , and describe a counterfactual simulation methodology to address the issue. First, we outline the overall problem of decomposing changes in _H_ . Then we summarize the counterfactual simulation methodology based on Barros et al. (2006), Azevedo et al. (2013a) and Azevedo et al. (2013b). The section concludes by addressing the advantages as well as the potential caveats of applying this method to the headcount ratio _H_ .<sup>2</sup> Let us assume that there are two observations of the multidimensional headcount _H_<sup>_t_</sup> , for _t_ = 1 _,_ 2; the first, 1, corresponds to the initial observation while 2 corresponds to the final observation. We also observe the two associated datasets of information on achievements **y** _i_<sup>_t_:</sup><sup>**y**</sup> _i_<sup>1=(</sup><sup>_y_</sup> _i_<sup>1</sup> 1<sup>_, y_</sup> _i_<sup>1</sup> 2<sup>_, . . . , y_</sup> _iD_<sup>1)</sup><sup>_,i_=1</sup><sup>_,_2</sup><sup>_, . . . n_1and</sup> **y** _j_<sup>2=</sup> � _yj_<sup>2</sup> 1<sup>_, y_</sup> _j_<sup>2</sup> 2<sup>_, . . . , y_</sup> _jD_<sup>2</sup> � _, j_ = 1 _,_ 2 _, . . . , n_<sup>2</sup> _._ . Additionally, we observe a set of demographic variables **z**<sup>1</sup> _i_<sup>_,_</sup><sup>**z**2</sup> _j_<sup>.In the case of panel data, individuals can be tracked across</sup> time, in which case variables are indexed by the same index, _i_ , at both periods and _n_<sup>1</sup> = _n_<sup>2</sup> . Otherwise, the datasets refer to repeated cross-sections. The _change in the_ 2The do-file code that implement this decomposition could be provided upon request 8
# **_Statistics on trade in value-added cover services as links, outsourced inputs, or final products in global value chains_**

As long as services are final products or inputs supplied by other firms in the production process, inputoutput tables can help to identify their contribution to value added in output or exports (Francois and Woerz 2008; Nordås 2008).

The starting point for analyzing the contribution of services to global value chains is the decomposition of value added in exports by its origin. Following seminal work by Hummels, Ishii, and Yi (2001), an expanding literature has proposed alternative decompositions of trade in value added and measures of participation in GVCs (Koopman, Wang, and Wei 2014; Foster-McGregor and Stehrer 2013; Los, Timmer, and de Vries 2016). As in Miroudot (2016), the analysis here relies on the calculations published by the Organisation for Co-operation and Development–World Trade Organization Trade in Value Added (OECD–WTO TiVA) project, which feature several indicators that account for services value added in trade (OECD 2013).

The main indicator is the total value added ( _VA_ ) of the services sector embodied in gross exports of industry _i_ and country _c_ as a percentage of total gross exports of _i_ of country _c,_ ( ���� ���,� ). It is calculated as: ���� ���,� = ∑�∈� ��,�(��,�)�� �����,�/�����,� + ∑∑� �∈� ��,�(��,�)�� �����,�,� /�����,� (1) where ��,� is the value-added share of services industry _j_ in country _p_ . B is the global Leontief inverse of the intercountry input-output matrix, �= (�−�)<sup>��</sup> , and therefore its _ji_ -th element (��,�)�� represents the total requirements of _j_ from _p_ to produce a unit of _i_ in country _c_ . �����,�,� is gross exports from country _c_ to country _p_ for any given industry _I_ , and �����,� is total gross exports for country _c_ and industry _i_ . The first term then represents all direct and embodied domestic services value added in the exports of product _i_ from country _c_ , and the second term represents all foreign domestic services value added embodied in the exports of product _i_ from country _c_ .

The services content of gross exports can then be decomposed into a domestic and a foreign part, and the domestic part can be further decomposed into the direct domestic services industry value added content of gross exports (the value added from the exporting services sectors), the indirect domestic services content of gross exports (the domestic services value added embodied in other exporting industries), and the re-imported domestic services value-added content of gross exports (domestic services value added in imported intermediate inputs used in exports; figure 1).<sup>8</sup> Note that this decomposition can also be analyzed by services type. A similar approach can be used in measuring the services value added embodied in foreign final demand.

> 8 See OECD (2013) for details on the algebraic definitions of these components.

6
Effect, Progress Made, and Challenges Ahead.” _Lancet_ 377 (9781): 1962–75. https://doi.org/10.1016/S0140-6736(11)60053-6.

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Reynolds, Sarah. 2020. “Do Health Sector Measures of Violence against Women at Different Levels of Severity Correlate? Evidence from Brazil.” Showalter, Kathryn. 2016. “Women’s Employment and Domestic Violence: A Review of the Literature.” _Aggression and Violent Behavior_ 31 (November–December): 37– 47.

Ulyssea, Gabriel. 2018. "Firms, Informality, and Development: Theory and Evidence from Brazil." _American Economic Review, American Economic Association_ 108 (8): 2015–47.

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Vyas, Seema, and Charlotte Watts. 2009. “How Does Economic Empowerment Affect Women’s Risk of Intimate Partner Violence in Low and Middle Income Countries? A Systematic Review of Published Evidence.” _Journal of International Development_ 21 (5): 577–602. https://doi.org/10.1002/jid.1500.

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25
supply of fuel because of post-election conflict in Kenya that began in late 2007 and extended well into 2008.

Third, the model does not allow for second-round effects as producers adjust their price expectations when making planting decisions for the subsequent season. If producers, including net buyers, believe the price increase will be sustained they would most likely increase production, which would relieve at least some of the upward pressure on staple food prices as well as increase income from staple food production. The multi-market analysis by Ulimwengu and Ramadan (2009) shows that these second-round effects dampen the adverse welfare effects of the price shock, but do not completely counteract them.

Finally, the analysis does not incorporate possible mitigating measures, such as food subsidies that might be established, or increased, to dampen the impact of the food price shock.

The primary data source for the present analysis is the Uganda National Household Survey (UNHS) 2005–06. The survey was conducted by the Uganda Bureau of Statistics, and interviewed over 7,400 households throughout Uganda between May 2005 and April 2006, inclusive. This comprehensive living standards survey is the source of information about household expenditure patterns and total consumption. Information on food prices comes from FoodNet, a multi-agency agricultural research network operating in several east African countries. The price data were collected for numerous food items in ten agricultural markets spread across Uganda, with weekly observations covering the period from January 2003 to October 2008.

The welfare change in equation (1) will vary for each household, depending on the value of net purchases or sales as a fraction of total household consumption (and the share of wage income in total household income, which is not considered here). The impact will also vary by the magnitude of the price increase, which varies by region. To illustrate how the NBR varies by household expenditure (income) level and region we estimate a series of nonparametric regressions, which trace the relationship of NBR against total consumption, showing how the average impact of the price increases varies by income level.

To estimate the short-run impact of higher food prices on absolute poverty, the change in total expenditure in real terms is simulated for each household, using the calculated NBR for each household and the observed price change in the relevant region. The simulated total consumption is then compared to the poverty line calculated from the 8
# **3. Global Aggregation of Disaster Impacts: Data and Methodology**

As seen in the previous section, economic damages of disasters have some tendencies and trends. At the same time, the data for disaster impacts have been still limited and sometimes confusing due to the use of interchangeable terminologies and to the lack of standardized definitions. Moreover, while each disaster is unique, economic impacts of disasters have been analyzed mostly through the case studies of a particular event, rather than in an aggregated context for the generalized understanding of phenomena. In this regard, estimating the global aggregate of disaster impacts has been long-sought in the disaster community. This section presents the data sources for the global aggregate estimation of disaster impacts in this paper. The data used are mostly available for public as secondary data, but the definitions and/or extent of disaster damage data are not standardized to make a direct comparison of the derived impacts difficult.

# _3.1. The Case for a Global Aggregate_

Natural hazards occur around the world with a wide range of intensities. In order to set the cases for global aggregate of impact estimation, economic damage, or loss data of disasters need to be collected. No standardized definitions or frameworks of economic damage and loss are set so far, except the use of ECLAC methodology (UN ECLAC, 2003) for recent disasters. Thus, it is difficult to collect the consistent measurement of economic damage and loss data for past disasters. However, there are a few sources offer the economic damage or loss data of past disasters: EM-DAT database by Centre for Research on the Epidemiology of Disasters (CRED) of Université Catholique de Louvain, NatCat database by Munich Re, and Sigma data base by Swiss Re.<sup>10</sup> In this present study, economic damage data are gathered from EM-DAT and NatCat databases.

The disaster cases are selected from the ones occurred during 1960 to 2007. As mentioned above, there is no standard definition of economic impact; furthermore,

> 10 Some useful comparison of these databases can be found in Guha-Sapir and Below (2002).

10
TABLE A8: Impacts of text messages on socio-emotional outcomes for children aged 3-7 years

||(1)<br>Behavioral Index|(2)<br>Emotional|(3)<br>Conduct|(4)<br>Hyperactive|(5)<br>Peer|(6)<br>Pro Social|(7)<br>Control|(8)<br>Laugh and Smile|
|---|---|---|---|---|---|---|---|---|
|Panel A: Equation 1|||||||||
|ITT|- 0.02|0.08|-0.10<sup>_∗_</sup>|0.10<sup>_∗_</sup>|0.00|-0.01|-0.03|0.02|
||(0.05)|(0.06)|(0.06)|(0.06)|(0.05)|(0.05)|(0.06)|(0.05)|
|Observations|1723|1723|1723|1723|1723|1723|1723|1723|
|Panel B: Equation 2|||||||||
|Nutrition & Stimulation|0.04|0.16<sup>_∗∗_</sup>|-0.03|0.13<sup>_∗_</sup>|-0.02|0.01|0.00|0.06|
||(0.07)|(0.07)|(0.07)|(0.08)|(0.07)|(0.06)|(0.07)|(0.07)|
|Nutrition|-0.03|0.04|-0.11|0.05|0.04|-0.03|-0.09|0.00|
||(0.07)|(0.07)|(0.07)|(0.08)|(0.07)|(0.07)|(0.07)|(0.07)|
|Stimulation|-0.03|0.07|-0.16<sup>_∗∗_</sup>|0.09|0.02|0.00|-0.00|-0.00|
||(0.07)|(0.07)|(0.07)|(0.08)|(0.07)|(0.07)|(0.07)|(0.07)|
|Observations|1723|1723|1723|1723|1723|1723|1723|1723|
|Panel C: Equation 3|||||||||
|ITT Leader|-0.05|-0.00|-0.05|0.02|-0.06|-0.00|-0.03|-0.13<sup>_∗∗_</sup>|
||(0.06)|(0.05)|(0.07)|(0.08)|(0.06)|(0.06)|(0.06)|(0.06)|
|Observations|1438|1438|1438|1438|1438|1438|1438|1438|

_Note:_ The dependent variable in the first column is the first principal component of the 5 subscales of the "Strengths and difficulties" questionnaire, and modules measuring inhibitory control scale and positive demeanor (Laugh and Smile). Controls in panel A and B as in Table 3, controls in panel C as in Table 4. The standard errors (in parentheses).<sup>_∗_</sup> p<0.1,<sup>_∗∗_</sup> p<0.05,<sup>_∗∗∗_</sup> p<0.01 56
# Intensity-Based Rebating of Emission Pricing Revenues<sup>_∗_</sup>

Christoph B ̈ohringer<sup>_†_</sup> Carolyn Fischer<sup>_‡_</sup> Nicholas Rivers<sup>_§_</sup>

**JEL Codes:** Q50, Q54, Q58, C68

**Keywords:** Climate change, policy, carbon pricing

> _∗_ This research was undertaken, in part, thanks to funding from the Canada 150 Research Chairs Program. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.

> _†_ University of Oldenburg, e-mail: boehringer@uol.de

> _‡_ World Bank Group, email: cfischer2@worldbank.org

> _§_ University of Ottawa, email: nrivers@uottawa.ca
||**Pooled**<br>**base**|**Pooled**|**2012-2018**<br>**Base**|**2012-2018**<br>**Base**|
|---|---|---|---|---|
||**informal**|**base formal**|**informal**|**formal**|
|**Age group (<30 omit.)**|||||
|30-49|1.120|1.140|0.985|1.963|
||(0.368)|(0.446)|(0.505)|(1.756)|
|50+|1.106|0.775|2.063|2.646|
||(0.434)|(0.360)|(1.480)|(2.703)|
|**N**|788|675|295|190|
|**Pseudo R-squared**|0.193|0.153|0.279|0.192|

# Source: Author’s calculations based on ELMPS 1998-2018

Notes: *p<0.05; **p<0.01; ***p<0.001. Blank cells indicate dropped perfect predictors

# **4.5.4. Formality and growth**

We turn now to our last dynamic outcome, growth (an increase in the number of workers) among surviving firms (Figure 20). Formal firms are slightly more likely to grow than informal firms, although the gap in growth has narrowed over time. In 1998-2006, while 29% of informal firms grew, 35% of formal firms did so. However, in 2012-2018, while 19% of informal firms grew 21% of formal firms did so. Growth among surviving firms was clearly highest in 1998-2006 and lowest in 2006-2012; the increase in growth must be tempered by rising rates of exit (Figure 16).

65
1 and 3.3 percentage points higher in year 2 compared to the figures estimated under the no-crisis scenario. As a consequence, the average family income in the crisis scenario in year 1 and year 2 is 6.4 and 8.1 percent lower vis-à-vis the benchmark scenarios (Figure 15). At the same time, when compared with 2011, in the absence of a crisis, the average family per capita income in year 1 and year 2, respectively, would be 4.3 and 6.7 percent higher.

Disaggregating family income into its components, under the crisis scenario, the largest falls are observed in capital income, international transfers and private domestic transfers. Yet, since only a relatively small proportion of households have these income sources,<sup>9</sup> the main source of the change in per capita family income is the reduction in labor market income. For year 2, under the crisis scenario, labor income would be 9.7 percent lower than predicted under the absence of a crisis and 0.1 percent lower when compared to the 2011 mean labor income. This means that the main effect of the crisis is in terms of labor incomes lost because of the unfavorable macroeconomic shock that would have hit the economy<sup>10</sup> .

**Figure 15: Impact of the crisis on family income: Proportional change of income components relative to 2011**

<!-- Start of picture text --> 20%<br>10%<br>0%<br>-10%<br>-20%<br>-30%<br>-40%<br>No-crisis Crisis No-crisis Crisis<br>baseline year year 1 year 2<br>(2012)<br>Family income Labor income Capital Income<br>International Transfers Private domestic transfers<br><!-- End of picture text --> Source: WB staff calculations based on data from BCU and ECH 2011 In terms of the impact of the crisis on employment by sector, relative to the absence of crisis, the simulation predicts lower shares of employment in the commerce and services sector. The flipside of this is that the share of the active population employed by industry is higher than in the no-crisis scenario, even though this sector would be largely affected by the crisis in terms of GDP (see Figure 16). Meanwhile, the primary sector experiences no major changes under the crisis scenario relative to the benchmark.

> 9 According to 2011 _Encuesta Continua de Hogares_ , capital transfers are received by 8.4 percent of Uruguayan households, international transfers by 1.8 percent and private domestic transfers by 26 percent of households. In the case of public transfers, by 2011 they represented on average 18 percent of households per capita income.

> 10 It is important to note that the model keeps constant at the pre-shock level income coming from social benefits, pensions and other transfers as explained in section 2. Since these components are very important in the total income aggregate in Uruguay, the ability of the model to explain the total income change may be limited. Please see Annex 2 for further explanation on the validation exercise.

15
5

# ∆ V = consumption terms – production terms ≡ ∆ C – ∆ P =

Oil consumption Energy consumption ∆ [Oil price in current US$ × × × Energy consumption GDP in US$ at 2005 PPP values GDP in US$ at 2005 PPP values GDP in constant local currency GDP in current local currency × × ] – GDP in constant local currency GDP in current local currency GDP in current US$ ∆ [Oil price in current US$ × oil production ÷ GDP in current US$] ≡ (oil price effect through consumption + oil share in energy effect + energy intensity effect + real GDP converter effect + GDP price deflator effect + exchange rate effect) – (oil price effect through output + oil production effect + inverse of current GDP in US$ effect), where PPP is purchasing power parity and ∆ V is expressed in percentage points. Of the nine terms, the real GDP converter effect is always zero, because the ratio of GDP in constant dollars at PPP to GDP in constant local currency remains the same. For countries that do not produce crude oil, the production effects are zero, whereas consumption effects are non-zero for all countries.

With the possible exception of very large oil exporters or importers, governments have virtually no control over the world price of oil, but can influence the share of oil in energy use and the energy intensity of GDP through policy. The sum of these two effects is the oil intensity of the economy, and declining oil intensity can offset the price effect through consumption. This paper defines an offsetting coefficient as <u>(Oil share in energy effect+energy intensity effect)</u> Offset = – (Oil price effect through consumption) This paper selects calendar 1999, 2008, and 2009 for decomposition analysis. 2009 is the last year for which all the data necessary for decomposition are available. For oil prices, this study uses the annual average of the prices of three marker crudes: Brent, West Texas Intermediate, and Dubai Fatah. In 2011 U.S. dollars, the prices were US$23.59 a barrel in 1999, US$101.25 in 2008, and US$63.80 in 2009. The price in 1999 was within US$2 a barrel of the decadal average in the 1990, both in 2011 U.S. dollars. The 2008 average price was the closest of all historical prices to the 2011 average of US$104. Therefore, the level of vulnerability in 2008 is likely to be fairly representative of that in 2011, for which data are not yet available. Decomposition analysis is carried out for the change in vulnerability between 1999 and 2008 (first period) and between 1999 and 2009 (second period). During these two periods, oil prices increased by a factor of 4.3 and 2.7 in real terms, respectively; the price in 2009 was lower than that in 2008 by more than one-third.

Vulnerability calculations in this paper use two different data sources for oil production, consumption, and trade:

1. For vulnerability levels and changes without decomposition (figures 3–6), for all countries for which the International Energy Agency (IEA) has data, net imports are computed from the IEA’s oil import and export statistics. For the remaining countries, net