rafmacalaba/datause-encoder-data huggingface dataset:
# **Figure 7: Economic attractiveness of solar technologies when environmental damages of fossil fuel technologies are accounted**

<!-- Start of picture text --> $350<br>Solar CSP (Max)<br>$300<br>$250<br>$200 Solar CSP (Min)<br>Solar PV (Min)<br>Gas CC (Max)<br>Coal^ (Max)<br>$150<br>Coal^ (Min)<br>Gas CC (Min)<br>$100<br>$50<br>$-<br>0 10 20 30 40 50 60 70 80 90 100<br>US$/tCO2<br>US$/MWh<br><!-- End of picture text --> Note: ^Supercritical coal.

# **4. Estimated future growth of solar energy and barriers to realizing growth**

Advocates of solar energy claim that it will play a crucial role in meeting future energy demand through clean energy resources. Existing projections of long-term growth (e.g., until 2050) of solar energy vary widely based on a large number of assumptions. For example, Arvizu et al. (2011) argue that expansion of solar energy depends on global climate change mitigation scenarios. In the baseline scenario (i.e., in the absence of climate change mitigation policies), the deployment of solar energy in 2050 would vary from 1 to 12 EJ/yr. In the most ambitious scenario for climate change mitigation, where CO2 concentrations remain below 440 ppm by 2100, the contribution of solar energy to primary energy supply could reach 39 EJ/yr by 2050.

EPIA/Greenpeace (2011) produces the most ambitious projections of future P **V** installation. The study argues that if existing market supports are continued and additional market support mechanisms are provided, a dramatic growth of solar PV would be possible, which will lead to worldwide PV installed capacity rising from around 40 GW in 2010 to 1,845 18
Table 2: Descriptive statistics

|||Main sample||R<br>(matched|estricted sam<br> with Structur|ple<br>al Survey)|
|---|---|---|---|---|---|---|
||Refugees|Migrants|Natives|Refugees|Migrants|Natives|
|Cohort size|111382|701371|1293071|12379|93150|390071|
|Observations in sample|711056|4473204|1839523|135655|997217|557302|
|_period 2010–2018_|562221|3355287|1839523|99367|703832|557302|
|**Characteristics**|||||||
|Female|0.402|0.583|0.493|0.436|0.612|0.506|
|Married|0.499|0.716|0.458|0.592|0.775|0.478|
|Age|35.24|35.51|41.32|37.47|36.46|41.93|
||(9.796)|(9.218)|(13.55)|(9.270)|(8.934)|(13.33)|
|Age at arrival|29.17|30.27||29.58|30.17||
||(8.285)|(8.499)||(7.915)|(8.128)||
|Length of stay|10.78|10.25||13.73|12.31||
||(6.115)|(5.698)||(4.816)|(4.661)||
|**Labour market outcomes**|||||||
|Employment rate|0.432|0.687|0.853|0.564|0.726|0.860|
|Earnings|25543.6|57380.2|67456.1|28597.5|61070.4|69792.6|
||(23739.6)|(122044.1)|(74814.9)|(24837.6)|(130137.1)|(77559.5)|
|**Education**|||||||
|Secondary I||||0.633|0.338|0.131|
|Secondary II||||0.219|0.268|0.548|
|Tertiary||||0.148|0.393|0.321|

28
-48-

African Development Strategies. New York: Council on Foreign Relations.

|CROUCH, B<br>1981|. R., and S. CHAMALA, eds.<br>Extension Education and Rural Development. (2 vols.) Chichester:<br>John Wiley.|
|---|---|
|DELANCEY<br>nd.|, V.<br>Women and Cooperatives,<br>in USAID study of Cooperatives. Yaounde:<br>U.S Agency for International<br>Development.|
|DEPARTME<br>1984|NT<br>OF COMMERCE/U. S. AGENCY FOR INTERNATIONAL<br>DEVELOPMENT<br>Women of the World: Sub-Saharan<br>Africa. Washington, DC:<br>Government Printing Office.|
|DIXON, R.<br>1982|Women in Agriculture: Counting the Labor Force in Developing<br>Countries, Population and DeveloRment Review 8(3): 539-566.|
|DUE, J. M<br>1987|.,N. MOLLEL ANDV.MALONE<br>Does the T & V System Reach Female-headed<br>Families? Some Evidence<br>from Tanzania. Agricultural<br>Administration<br>and Extension 26(4):<br>2D9-217.|
|EYBEN, R.<br>1982|Report of a Mission to Advise on the Design of a Training and<br>Extension Component for the Proposed Second Phase of the Project<br>PFL/CMR/001.<br>Yaounde, Cameroon: FAO Food Loss Prevention Project.|
|FALLOUX,<br>1983|F.<br>Cameroon: Rural Extension and Training, a draft working paper<br>prepared for the Ministry of Agriculture. Washington, DC: The<br>World Bank.|
|FEDER, G.<br>2986|AND R. SIADE<br>The Impact of Agricultural<br>Extension: The Training and Visit<br>System in India. World Bank Research Observer 1(2):139-160.|
|FOOD AND<br>1983a|AGRICULTURE ORGANIZATION<br>Follow-Up to WCARRD: The Role of Women in Agricultural Production.<br>Rome: Food andAgriculture Organization,<br>Committee on Agriculture.|
|FOOD AND<br>1983b|AGRICULTURE<br>ORGANIZATION<br>The Need for Improved Agricultural<br>Extension<br>for Women Engaged in<br>Agriculture, a paper prepared for the Expert Consultation<br>on Women<br>in Food Production (ESH:WIFP/83/3-Rev.<br>1). Rome: Food and<br>Agriculture Organization.|
|FOODAND|AGRICULTURE ORGANIZATION|
|1987|Camerron National<br>Extension<br>and Training<br>Proiect:<br>Interim|
of firms. A list of variables used and their description is given in Table A.1, and their descriptive statistics are in given in Table A.2 in the appendix.

**Table 1 List of countries in the sample**

|**Economy**|**Incomegroup**|**EU member**|
|---|---|---|
|Croatia|High income||
|Estonia|High income|<br>|
|Slovenia|High income|<br>|
|Czech Republic|High income||
|Hungary|High income|<br>|
|Slovak Republic|High income||
|Belarus|Upper middle income||
|Bosnia and Herzegovina|Upper middle income||
|Bulgaria|Upper middle income||
|Kazakhstan|Upper middle income||
|Latvia|Upper middle income|<br>|
|Lithuania|Upper middle income||
|Macedonia, FYR|Upper middle income||
|Montenegro|Upper middle income||
|Poland|Upper middle income||
|Romania|Upper middle income||
|Russian Federation|Upper middle income||
|Serbia|Upper middle income||
|Turkey|Upper middle income||
|Albania|Lower middle income||
|Armenia|Lower middle income||
|Azerbaijan|Lower middle income||
|Georgia|Lower middle income||
|Moldova|Lower middle income||
|Ukraine|Lower middle income||
|Kyrgyz Republic|Low income||
|Tajikistan|Low income||
|Uzbekistan|Low income||

_Source: World Bank._ A typical concern when using survey data is that of individual perception bias (Kaufman and Wei, 1999). Some firms may, for instance, consistently provide positive or negative answers depending on their overall perception of the business climate. In principle, assuming that the bias is uncorrelated across groups of respondents, individual perception bias contributes only to the standard error of estimates obtained from the survey responses. In cross-country surveys, such as the BEEPS, the group within which the bias is likely to be correlated is the particular country in which respondents operate. Perception bias at the country level could originate from different cultural norms and degrees of political freedom across countries, which may influence the choice 13
# **Appendix B: A User‐Friendly Interface for GEM‐Core**<sup>**21**</sup>

The skills required to make productive use of CGE models in policy analysis are considerable, inter alia including strength in economics, modeling, and a variety of software. The purpose behind the development of a user‐friendly interface for GEM‐Core – and other models in the GEM Suite ‐‐ was to reduce the skills required in terms of modeling and software, permitting the analyst to focus on policy and economics, thereby making GEM‐Core‐based policy analysis more cost‐effective.<sup>22</sup> The analyst who uses the interface, named ISIM, works exclusively in an Excel environment and receives substantial guidance throughout the analytical process. Knowledge of GAMS (the software in which GEM‐Core is coded) and an editor (GAMS‐IDE or other) is no longer needed.

This section briefly describes the structure of ISIM‐MAMS, how users at different skill levels may use and interact with it, and the major steps involved in a typical application with a pre‐defined data set. It is aimed at practitioners interested in applying the MAMS framework or developing similar interfaces for other models.

## **B.1. Overview of the interface**

ISIM was developed using Visual Basic as an add‐in for Excel 2007, later updated to also work with Excel 2010/2013/2016. The user is required to work in a current Windows environment and to have Excel 2007 or newer and GAMS installed on the computer.<sup>23</sup> The reason for using Excel as the front‐end is that the Excel environment is familiar for most analysts, in effect removing the initial barrier for users to start working with ISIM.

ISIM is packaged with a set of models – including GEM‐Core ‐‐ and data sets defined by country and year. These data sets have been developed by a core team drawing on the databases of existing GAMS‐GEM applications (i.e., applications for models in the GEM Suite

> 21 This appendix borrows from Lofgren et al. (2013).

> 22 ISIM was originally developed as ISIM‐MAMS, an interface for MAMS (see Lofgren et al. (2013)). Reducing the skills required to develop and apply economic models was also a major reason behind the initial development of GAMS (Bussieck and Meeraus, 2003, p. 138). The RunGTAP software for the GTAP model was developed with the same objective (Pearson and Horridge, 2005).

> 23 One drawback of relying on Excel is that reprogramming of parts of ISIM was required to maintain compatibility across different Excel versions.

‐54‐
according to a social accounting matrix (SAM) by <u>Chen and Timilsina</u> (2012). We have considered 31 production sectors and 27 goods and services. While the oil refinery industry produces three commodities, gasoline, diesel and other petroleum products, electricity is produced from seven different types of technologies (i.e., hydro, wind, biomass, coal, oil, natural gas and nuclear). The disaggregation of the electricity sector to various power generation technologies is crucial, as without this disaggregation, simulations of policies to promote renewable energy-based electricity generation is not feasible.

**Table 1. Production sectors and consumption goods considered in the model**

|**Industry/sectors for production**|**Goods and services for consumption**|
|---|---|
|1. Sugarcane industry|1. Sugarcane|
|2. Soybeans industry|2. Soybeans|
|3. Other agriculture industry<br>|3. Other agricultural products<br>|
|4. Livestock|4. Livestock|
|5. Forestry|5. Forest products|
|6. Crude oil & natural gas|6. Crude oil & natural gas|
|7. Coal mining|7. Coal|
|8. Metals and mineral mining|8. Metals and minerals|
|9. Food & beverages|9. Food & beverages|
|10. Textile & leather|10. Textile & leather|
|11. Wood industry|11. Wood|
|12. Pulp, paper & furniture|12. Pulp, paper & furniture|
|13. Oil refining|13. Gasoline|
||14. Diesel|
||15. Other petroleum products|
|14. Biofuels|16. Biofuels|
|15. Chemicals|17. Chemicals|
|16. Metal industry|18. Metals|
|17. Non-metallic minerals industry|19. Non-metals (e.g., cement, lime, glass)|
|18. Machinery & equipment|20. Machinery & equipment|
|19. Other manufacturing|21. Other manufacturing goods|
|20. Hydropower|22. Electricity|
|21. Coal fired electricity generation||
|22. Natural gas fired electricity generation||
|23. Oil fired electricity generation||
|24. Windpower||
|25. Biomass fired electricity generation||
|26. Nuclear power generation||
|27. Gas processing industry|23. Process gas|
|28. Construction sector|24. Construction goods/services|
|29. Commercial sector|25. Commercial services|
|30. Transport sector|26. Transport services|
|31. Service sector|27. Other services|

4
TFP growth (which picks up the exogenous factors that influence growth in an economy) was assumed to be 2 percent a year. This is somewhat conservative but not out of line with previous studies for India. The NCAER CGE model assumes TFP growth of 3% p.a., as do the Energy and Research Institute (TERI) MOEF Model and the IRADE AA Model. However the same studies cite others that assume figures of between 1 and 3 percent. Given this range an assumed value of 2 percent seems reasonable.

20. The assumed annual growth rate in real GDP from 2010 to 2030 is estimated at 6.7 percent. The economic growth (measured as an index) rises from 100 in 2010 to 367 in 2030. This is the Conventional GDP Growth (BAU) scenario estimate (as per NCAER and recent IMF projections) without correcting for implication of any new policy changes to deal with pollution..

21. The standard GTAP model‘s structure has been modified to allow substitution between capital and energy (by increasing the elasticity of substitution from 0 to 0.5, as in the GTAP-E model). This modified version of the model is close to the energy version of the GTAP model (called GTAP-E) but does not comprise a nested structure in the energy block (which would require more data than was available).

# **_Method for estimating PM10 emissions_**

22. The demands for different kinds of energy and the outputs of the different sectors were converted to PM10 emissions using corresponding emission coefficients (α(i,j) and _Βi,_ respectively).<sup>20</sup> The Conventional GDP Growth scenario (BAU) generated PM10 emission estimates for 2010–2030 from fuel use and production activities as described in equation (1) below: (Eq. 5)

- E  = PM10 emissions

- Ci,j  = Demand for fuel products j in Sector i

- i = Sector (firm, household, government)

- _j_ = Energy (coal, crude oil, refined oil and coal products, natural gas, and electricity).

- αi,j  = Emission coefficient associated with the consumption of one unit of energy product j by the Sector i

- XPi = Production activity and process of sector i βi  = Emission coefficient associated with one unit of output in sector i.

23. Both the consumer demand for energy products (C _i,j_ ) and sectoral economic activity (XP _i_ ) up to 2030 were estimated by the CGE model.

24. First, PM10 emission coefficients are taken from the Garbaccio et al. (2000) study for China. This is presently the only source for these coefficients being mapped

> 20 Emission coefficients vary through time to reflect technological change, modernization of power plants, improved energy efficiency and India‘s emission abatement levels (on the basis of  1% annual increase on average in BAU  reported in WDI statistics).

12
# **Introduction**

Fighting poverty is a challenging and complex undertaking faced by policy makers in developing and richer countries alike. This undertaking can include different policy options ranging from crafting short-term safety-net programs that prevent vulnerable households from sliding into poverty during a time of crisis, to designing long-term plans that invest in education and skills formation to promote economic growth. But regardless of the specific poverty reduction strategies, a prerequisite for the success of the whole process is a clear understanding of poverty trends and dynamics (either at any single snapshot in time or over different time periods). Indeed, inaccurate measurement naturally results in an inefficient—and even a wasteful—use of resources if, say, short-term employment programs are employed to address a chronic poverty situation caused by inadequate infrastructure.

Perhaps the greatest challenge with poverty measurement is the fact that household consumption (or income) data—the underlying data source that provides poverty estimates—do not often meet the necessary requirements.<sup>1</sup> For example, such data may simply be unavailable, or may not be comparable from one survey round to the next. As another example, household consumption data are seldom collected on the same households (or individuals) over time, thus making it difficult—if not possible—to track the dynamics of these households’ movements into or out of poverty in different periods.

We offer in this paper a review of alternative poverty estimation methods in contexts where consumption data are unavailable. The economic literature on poverty imputation has grown rapidly in the past 20 years, and various methods have been developed. In fact, methods that have

> 1 We use the terms “income” and “consumption” interchangeably in this review. The latter is often considered to offer better measures of household welfare, especially in developing countries (see, e.g., Deaton (1997)). We focus on monetary poverty in this paper; see Alkire _et al._ (2015) for a comprehensive discussion of the alternative approach of multi-dimensional poverty.

2
the Random Digit Dialing method, using a random number generator from the 2020 Numbering Frame produced by the Kenya Communications Authority. The refugee samples were drawn from the UNHCR’s proGres database, which covers all registered refugees in Kenya.

Two national lockdowns were put in place during the survey periods. The first one started in mid-March 2020 and covered the first months of the first wave of the survey. A second lockdown was introduced in late March 2021, wrapping the fifth wave of the survey. The data was collected by phone interviews with a nationally representative sample of households using cell phones in Kenya, covering both urban and rural areas. The households were called every two months for five survey rounds, to track the impact of the pandemic over time.

The data consists of a wide range of information including the knowledge of COVID19 and mitigation measures, changes in behaviors in response to the COVID-19 pandemic, employment, income, food security, as well as perceptions of the government’s response.

We use the household head gender as the main indicator of gender. Compared to the male-headed households, the female-headed ones are more likely to receive transfers prior to the COVID-19 crisis (see Table 4 and Figure 2). Moreover, they have a higher childcare burden and smaller livestock values. However, these two types of households are not significantly different with regards to their poverty status.

In Kenya, about 37.4 percent of the households are female-headed (The World Bank, 2021 _a_ ). The female household heads are more likely to be separated, divorced or widowed relative to the male household heads. This indicates that it is likely that those female heads became the household head after the male head left the household due to death or divorce.

There are in total three categories of coping strategies by the household. The first was coping using the household internal wealth or resources. These include coping by reducing meals, selling assets or livestock, and taking loans. The second kind of coping would be coping based on help from the social network, including using transfers from family members and assistance from friends. Lastly, very few households in Kenya receive help externally, for example, from governments, NGOs, or politicians.

4
3 of return uncertainty on the burden a MAT will pose. The approach allows the assessment of the expected tax burden of a minimum tax. It also yields a measure of the value of a minimum tax to a government faced with a high degree of uncertainty about revenue prospects because of the proliferation of tax incentives.

We exploit the similarity between the asymmetries created by minimum taxes and the asymmetry that arises in the link between options and the valuation of the asset on which the options are written. We show how the addition of a minimum tax to a standard corporate tax in effect grants the Government a put option on its share of the business profits it already obtains under the regular tax system. From there on, standard option pricing methodology allows an assessment of the tax burden created by a MAT. We show how the MAT burden is influenced by various carry-ove **r** ules, different depreciation conventions, and, most importantly, by the degree of uncertainty. We finish with an assessment of the minimum asset tax recently considered in Brazil, using sectoral information about the stochasti **c** haracteristics of the link between asset values and income.

The remainder of -'.' paper is organized as follows. Section 2 provides an overview of the desig.. if minimum taxes. Section 3 describes the application of the option pricing approach to the assessment of minimum taxes. Section 4 shows the sensitivity of the. tax burden to rate of return uncertainty and to -various aspects of the tax law. Section 5 applies the methodology to an assessment of a recent Brazilian MAT proposal using sectoral data on corporate income tax revenue and asset value. Section 6 concludes.
||(0.050)|(0.051)|(0.040)|
|---|---|---|---|
|Access to land (1=yes)|-0.008|-0.070|-0.067**|
||(0.029)|(0.048)|(0.033)|
|Access to mechanisms to obtain/replace lost docs (1=yes)|-0.070***|-0.076***|-0.090***|
||(0.026)|(0.027)|(0.022)|
|**Characteristics of the household head**||||
|Male|0.119***|-0.001|0.060***|
||(0.024)|(0.028)|(0.020)|
|Age|-0.005|0.003|-0.003|
||(0.005)|(0.006)|(0.005)|
|Age squared|0.000|-0.000|0.000|
||(0.000)|(0.000)|(0.000)|
|Active in the last 12 months|0.037|-0.021|0.013|
||(0.027)|(0.034)|(0.025)|
|Widow/er|0.178|-0.397***|-0.431***|
||(0.154)|(0.097)|(0.097)|
|Female*Widow|-0.082|0.422***|0.484***|
||(0.162)|(0.113)|(0.106)|
|_Education (base: No education)_||||
|Primary or below|-0.025|-0.000|-0.009|
||(0.026)|(0.034)|(0.028)|
|Secondary and above|-0.130**|-0.011|-0.030|
||(0.054)|(0.029)|(0.026)|
|**Household type**||||
|_Demographic classification (base: Families without children)_||||
|Male Single Caregivers|0.055|0.148|0.135|
||(0.108)|(0.122)|(0.094)|
|Female Single Caregivers|0.203**|0.094|0.139**|
||(0.084)|(0.080)|(0.063)|
|Couples with children|0.183***|0.060|0.093**|
||(0.050)|(0.050)|(0.040)|
|Multi-generation with children|0.170***|0.119**|0.121***|
||(0.049)|(0.050)|(0.040)|

20
|**Table S1.3b Effect of Delhi Metro on Air Quality: Lagged O**<br>**Controls, Siri Fort**<br> <br>|**utcome**<br>|
|---|---|
|**Metro Extension**<br>**NO2**|**CO**|
|(Percentage<br>of pollutant<br> <br>|change in level<br>)<br>|
|**Blue Line Second Extension (Nov 11, 2006)**<br>-2.78|-1.83|
|Std. Error<br>(13.96)|(17.13)|
|First Lag<br>0.60***|0.03***|
|Std. Error<br>(0.09)|(0.004)|
|Second Lag<br>-0.22**|-0.01**|
|Std. Error<br>(0.08)|(0.002)|
|Third Lag<br>-0.01|0.004**|
|Std. Error<br>(0.04)|(0.001)|
|Fourth Lag<br>0.14***|0.001|
|Std. Error<br>(0.01)|(0.001)|
|Cumulative Effect<br>-5.37|-1.87|
|Observations<br>1601|1532|
|Source: Authors' analysis based on data described in the text.||
|For both pollutants results pertain to a nine week window.||
|Std. errors are clustered at one week. * indicates significantly di|fferent|
|from zero at 10 percent level, ** at 5 percent level, and *** at 1|percent level.|

62