rafmacalaba/datause-encoder-data huggingface dataset:
# B.1 Baseline Construction

The model baseline extends for a period of 200 years, with an emphasis on the rst century, and the starting point being the world economy in 2004. It is consistent with the IPCC (2000) A1B climate change scenario's storyline that describes a future world of strong economic growth, global population that grows quickly until mid-century and slows thereafter, the rapid introduction of new and more ecient technologies, and balanced energy use across all sources. It also foresees that, as the economy grows, its economic structure changes toward a service economy, including the expansion of ecosystem services sector. The majority of model's baseline parameters are based on the Global Trade Analysis Project (GTAP) v.7 data base (Hertel 1997, Narayanan and Walmsley 2008) and its satellite data for land use and global climate change policy (Hertel et al. 2009). The values of other model parameters come from variety of the international or government agencies (United Nations, FAO, U.S. Energy Information Administration) and economic, agronomic and environmental science literatures. The values of baseline parameters are summarized in Table A.3 and Figure B.1.

## B.1.1 Primary Resources

### B.1.1.1 Land

The data for land endowments come from the GTAP Integrated Global Land Use Data Base (Lee et al. 2009) and GTAP Global Forestry Data Base (Sohngen et al. 2009b). We dene the managed lands as the sum of crop land and accessible forest land areas. The natural land is dened as inaccessible forest land. Other land areas, such as built-up lands, pastures, grasslands, savannah, shrublands, desserts, and barren lands, are not included in the current version of the model. The data for initial allocation of unmanaged natural lands come from Antoine et al. (2008, p.8, Table 3).

We assume that marginal access cost per hectare, _c_<sup>_N_</sup> _t_<sup>, is a quadratic function</sup> of converted natural land: In equation (B.1), the parameter _ξ_ 0<sup>_n_determinesthelong-runnaturalland</sup> access costs with respect to accessed hectares. The parameter _ξ_ 2<sup>_n_governs the size</sup> 9
_Figuire_ 3. Wages asmd _iForeigim_ MDirect RmvestmneiDnt <!-- Start of picture text --> 2.5 -<br>ZR 2 - ..-.........<br>a) 1.5-<br>0.5 "".... .<br>0<br>-0.5<br>After 1 year After 2 years After 3 years After 4 years After 5 years<br>Effect on an exra 1% of GDP in FDI...<br><!-- End of picture text --> _Source:_ Based on work in progress by Freeman, Oostendorp and Rama (2001). This figure reports the results of a regression explaining the log of wages, in PPP dollars, as a function of the ratio of foreign direct investment to GDP (based in dollar figures). There is one wage observation per country, occupation and year, over period 1983-1998. The regression includes fixed effects for all countries, occupations and years. It also controls for the trade-to-GDP ratio. Data on wages are from Freeman and Oostendorp (2000) and data on other variables from the World Bank. The solid line reflects the point estimate of the coefficient of interest. The dotted lines indicate the 95 percent confidence interval.

The evidence reviewed so far refers to the effects of international trade and foreign direct investment on wages. However, financial crises can have a substantial impact too. Using time series data from 67 crisis episodes, Diwan (2001) shows that the labor share of income falls, often dramatically. In most cases the subsequent recovery is not total, implying that episodes of crisis bring a net loss to labor. To the extent that international capital mobility makes financial crises more likely, the short-term losses from globalization could be much larger than suggested by the evidence on the effects of international trade. For Diwan, the sheer magnitude of the losses is due to the fact that capital has become highly mobile, whereas labor is not. Because the returns to capital cannot be adjusted downwards in times of crises, the entire burden of the adjustment falls on labor.

# 3. The Distrnbutii of W@ gef

On the surface, it may seem that globalization does not affect the wage distribution. Figure 4 reports a measure of the dispersion of wages by occupation, across different levels of openness to trade. Each point in this figure corresponds to a country and year, over period 1983-1998. The data on wages by occupation are, once more, from Freeman and Oostendorp (2000). The dispersion measure used to construct this figure is the standard deviation of the log of wages, which measures the typical gap, in relative terms, (
In contrast, highly persistent or permanent income shocks have a substantial effect on the present value of future earnings, and therefore lead to significant changes in consumption even if workers can borrow or have own savings (Constantinides and Duffie (1996) and Krebs (2003a and 2004)). Thus, from a welfare point of view, persistent income shocks matter the most, and we therefore focus on the relationship between trade policy and the persistent component of income risk.<sup>7</sup> More specifically, after obtaining the estimates of the persistent component of income risk for each year and industry, we use these estimates in conjunction with tariff data (as a proxy for trade policy) to study empirically the effect of trade policy on income risk.

In addition to the empirical analysis of the relationship between trade policy and income risk, this paper also provides a quantitative evaluation of the welfare consequences of any changes in income risk that are brought about by changes in trade policy. If insurance markets and other institutional arrangements for sharing individual income risk are missing (incomplete markets), then changes in income risk will alter consumption volatility and therefore workers’ welfare. To find out how income risk is linked to consumption volatility and welfare, we use a dynamic general equilibrium model with incomplete markets in which the consumption/saving choice of workers in the presence of idiosyncratic income risk is explicitly modeled. As is well known, general versions of such models are difficult to solve, and most work in the literature has therefore been computationally intensive (Aiyagari (1994), Huggett (1993), Krusell and Smith (1998), Rios-Rull (1996)). In contrast to this literature, we rely upon an extended version of the incomplete-markets model recently developed and analyzed by Constantinides and Duffie (1996) and Krebs (2004) that is highly tractable,

> 7To see the importance of this distinction more clearly, consider the example of a worker who loses his job due to plant closure or any other “exogenous” event. If the worker quickly finds a new job that pays him as well as the previous job, then the worker’s consumption level is not likely to drop by too much either during or after the period of unemployment. If, on the other hand, the worker is forced to accept a job that pays him a permanently lower wage because, for example, firm- or occupation-specific human capital has been lost, then the worker’s likely response is to reduce consumption.

3
<!-- Start of picture text --> Counting vector CCDF by scenario Difference between counting vector CDF curves<br>First-order stochastic dominance (α=0)<br>0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1<br>Counting vector (relative scale) Counting vector (relative scale)<br>Baseline Alternative Confidence interval (95 %) Estimated difference<br>B4.3. SOSD: Social Sustainability Index B4.4. Rank correlations<br>Social gaps Rank correlation Cutoff values<br>Social Sustainability Index by cutoff and scenario<br>Second-order stochastic dominance Metrics coefficient k = 33% k = 50% k = 75%<br>Spearman 0.95*** 1*** 0.98***<br>H<br>Kendall tau-b 0.83*** 1*** 0.94***<br>Spearman 0.93*** 0.97*** 0.65*<br>A<br>Kendall tau-b 0.83*** 0.89*** 0.5*<br>Spearman 0.98*** 0.98*** 0.98***<br>M0<br>Kendall tau-b 0.94*** 0.94*** 0.94***<br>0 20 40 60 80 100<br> Cutoff k (relative scale) ***p<0.01, **p<0.05, *p<0.1<br>Baseline Alternative<br>.1<br>1<br>.8 0<br>.6<br>-.1<br>CCDF<br>.4 Difference<br>.2 -.2<br>0<br>-.3<br>.5<br>.4<br>.3<br>.2<br>Social Sustainability Index .1<br>0<br><!-- End of picture text -->

# **C. Sensitivity Analysis: Number of indicators per dimension, 3 instead of 4.**

_C1. Change in the indicators included for each dimension, one indicator removed at a time. Baseline scenario: All indicators included. Alternative scenario: Quality of employment indicator removed (weights within the Social Inclusion dimension were scaled from 6.25% to 8.33% so all weights add up to 1)._ Peru

# C1.1. FOSD: Counting Vector

<!-- Start of picture text --> C1.2. Dominance Analysis<br><!-- End of picture text --> <!-- Start of picture text --> Counting vector CCDF by scenario Difference between counting vector CDF curves<br>First-order stochastic dominance (α=0)<br>0 .2 .4 .6 .8 0 .2 .4 .6 .8 1<br>Counting vector (relative scale) Counting vector (relative scale)<br>Baseline Alternative Confidence interval (95 %) Estimated difference<br>.2<br>1<br>.8 .15<br>.6<br>CCDF .1<br>.4 Difference<br>.05<br>.2<br>0<br>0<br><!-- End of picture text --> C1.3. SOSD: Social Sustainability Index C1.4. Rank correlations 46
change potential of black carbon. They note in particular that China and India alone account for 25-35% of global black carbon emissions. Providing energy efficient and smoke-free cookers, along with reducing soot emissions from coal combustion in small industries, could reduce warming from black carbon in East Asia by 70-80%.

To illustrate the role of developing countries for such off-grid technologies, Table 2 presents total patent counts based on inventor country for four types of cooking stoves whose primary applications are in developing country markets.<sup>15</sup> In all four cases, China is the leading source of patents, ranging from 46% to 89% of the total for each technology. Among developing countries, we also include data for India and South Africa, as these countries are important sources of scientific publications, to be discussed below. However, neither is an important source of patenting. This may be because these countries are doing less commercial work on these technologies, or because intellectual property rights are not seen as valuable in India and South Africa.

Because developed country research on indoor cooking stoves may be led by noncommercial entities such as universities or non-profit foundations, which are less likely to patent successful research outcomes, Table 2 also includes counts of scientific publications. Even here, a developing country is the leading source of articles for three of the four stove technologies.

> 15 These patents were identified using a combination of keyword and patent classification searches using the Delphion on-line patent database.  Countries are identified using the first inventor listed on the patent.  If no inventor country is limited, the first priority country (e.g. the country where the application was first filed) is taken as the source of the invention. Scientific publication data are collected using a keyword search of abstracts and titles in the Web of Knowledge database.  Here, the source country comes from author affiliations. For multiple authored papers, affiliations are counted for each country, so that the total number of affiliations may exceed the total number of articles.  Appendix A lists the search terms used for each stove type.  Note that the use of keyword searches may bias downward counts from countries whose patent abstracts may appear in other languages, such as France and Germany.  However, that Chinese patent counts are larger than even the U.S. and Japan, who do the bulk of global R&D, is still notable.

14
other studies and estimate that 44 percent of maize area in eastern and southern Africa (apart from South Africa) and 60 percent in western and central Africa are covered with improved maize seed. To our knowledge, no equivalent studies exist for other major crops in SSA, apart from the preliminary results from the DIIVA project included in Alene et al. (2011). Aggregating evidence using micro-level data found in other studies is similarly challenging due to the slightly different types of seeds (e.g., hybrid, OPV, etc.) and crops included. For more on studies at the country-level, see Appendix 1.

Table 2: Estimated adoption of improved varieties of select crops

||1|998|2|009|
|---|---|---|---|---|
||Total hectares<br>under crop|Percent of land under<br>improved varieties|Total hectares<br>under crop|Percent of land under<br>improved varieties|
|Ethiopia|||||
|Barley|897,360|11.0|913,863|33.8|
|Maize|1,881,000|8.5|1,768,120|27.9|
|Durum wheat|797,998|80.0|1,163,056|77.8|
|Malawi|||||
|Maize|1,243,000|13.8|1,609,000|43.0|
|Groundnuts|170,517|10.0|266,946|58.0|
|Niger|||||
|Millet|-|-|6,513,140|11.5|
|Sorghum|-|-|2,544,740|15.1|
|Cowpea|-|-|5,203,530|17.0|
|Groundnuts|-|-|588,651|11.9|
|Nigeria|||||
|Maize|4,255,000|40.0|3,708,000|95.0|
|Cowpea|-|-|3,768,193|39.0|
|Sorghum|-|-|4,736,730|20.0|
|Millet|-|-|3,749,600|35.0|
|Tanzania|||||
|Maize|1,646,000|4.2|2,961,330|35.4|
|Rice|-|-|627,600|13.0|
|Sorghum|622,400|2.0|874,219|37.7|
|Groundnut|-|-|535,000|32.1|
|Uganda|||||
|Maize|574,000|8.9|887,000|54.0|
|Banana|-|-|915,877|6.2|
|Groundnut<br>Source: CGIAR’s|196,000<br>DIIVA project. More|10.0<br>information can be found at:|253,000<br> http://www.asti.cgiar.|55.0<br>org/diiva|

Source: CGIAR’s DIIVA project. More information can be found at: <u>http://www.asti.cgiar.org/diiva</u>

# **_2.3. Agro-chemicals: Pesticides, insecticides, herbicides, fungicides_**

Pests, diseases, and weeds can considerably suppress crop yields. When weeds are present, a growing plant must compete for sunlight, moisture, and soil nutrients, which reduces its chances of survival. Insects and mites can eat grains or the leaves and roots at any stage of plant growth. Diseases and fungi, too, can ruin an entire field of crops. Actual losses to major crops from all of these types of “pests” are estimated to be about 30 percent of attainable yields as collected from sources around the world (Oerke and Dehne 2004). In an effort to control these unfortunate agricultural realities, farmers can apply agro-chemicals in the form of pesticides, herbicides, fungicides, and insecticides. These agro-chemicals can function as a substitute or complement to increased labor (e.g., manual weeding) or other organic techniques (e.g., the introduction of 5
14 <!-- Start of picture text --> Costs of Electricity Production<br>Small Stand-Alone Systems<br>Electricity Costs (S/kWh)<br>0.7<br>0.7 - POWrER SOURCE<br>I e0 Dise l e 4oc/I<br>0.6 * Diesel 200/1<br>\ & ~~~~~~~~~~Chortas 0 $80/mt<br>0.5 Woodgae/m $20/mt<br>0.4 Wogea$0m<br>0.3><br>0.2<br>10 t00 1000<br>INSTALLED CAPACITY (kW)<br>Source: See Annex<br><!-- End of picture text --> Figure _5_ Generation Costs of Gasifiers vs. Diesel diesel cheaper than either charcoal or wood power gasifiers over the entire range of analysis.

42. With decreasing petroleum prices, it is clear that power gasifiers, like small-scale heat gasifiers, will have a niche only in remote applications where the economic cost of diesel is high due to transport costs, where diesel supply is unreliable, and where there is a surplus of biomass fuels.

# Solar Water Heating

43. The basic principle behind solar water heating (SWH) is simnple: by passing a cool fluid through small pipes imbedded in a black collector plate, exposed to the sun and housed in transparent glass, thermal energy is captured in the heated fluid. SWH designs depend on the end use (industrial process heat, restaurant, hotel, or domestic hot water), load and solar resource profiles (daily and seasonal), and water temperature requirements. The solar collector itself is the most expensbie system component, commonly constituting over _50%_ of installed system cost. As most end uses require additional components, such as circulating pumps, temperature controllers, storage tanks, and heat exchangers, che cost of solar water heating can vary significantly with each application.
# **III Data and empirical model**

## **Sectoral aid data**

Knowledge of the intended purpose of aid is crucial to obtain an accurate estimate of the degree of fungibility. Therefore, the use of sectorally disaggregated aid in this paper constitutes a marked improvement over previous studies that lack complete information on the purposes for which aid is given. Fiscal response models (FRMs) typically focus on the effect of aggregate aid on a recipient’s budget and evaluate aid as being fungible if it is diverted away from public investments or developmental expenditures (e.g., Heller, 1975; Franco-Rodriguez et al., 1998; Feeny, 2007).<sup>7</sup> Early fungibility studies (McGuire, 1982, 1987; Khilji and Zampelli, 1991, 1994) distinguish between military and economic aid and evaluate how these types of aid affect public military and non-military expenditures. Other studies (Feyzioglu et al., 1998; Swaroop, Jha, and Rajkumar, 2000; Devarajan et al., 2007) attempt to investigate aid at the sectoral level but are only able to disaggregate concessionary loans; thus, the omission of sectoral grants may influence their results. In this body of literature, Pack and Pack (1990, 1993, 1999) are the only studies that employ a comprehensive sectoral disaggregation of foreign aid by focusing on countries whose recipient governments report both public expenditures and aid received in a disaggregated form.<sup>8</sup> In addition, several recent studies (Chatterjee, Giuliano, and Kaya, 2007; Pettersson, 2007a,b) have used sectorally disaggregated aid data from the OECD’s Creditor Reporting System (CRS), as described in OECD (2002), to study fungibility.<sup>9</sup> The CRS database disaggregates foreign aid according to a number of dimensions, most importantly the sector or purpose of aid, but has two main disadvantages. First, the CRS data are incomplete. Only some of the total disbursements that flow from each donor to each recipient in any given year are reported. Coverage becomes weaker as one examines earlier periods in time. Second, although information pertaining to commitments is available beginning from 1973, disbursement information is available only for the period after 1990. As a result, many existing papers utilize sectoral commitments even when disbursements are the more relevant quantity.

Several studies (e.g., Mavrotas, 2002; Pettersson, 2007a,b) attempt to avoid these problems with the assistance of data from OECD DAC Table 2a, as described in OECD (2000a). DAC2a contains _complete_ aggregate aid disbursements but does not include sectoral disaggregation. These studies estimate sectoral disbursements for each recipient and each year ( _d_<sup>�</sup><sup>_s_</sup> _RY_<sup>) by calculating the share of each sector</sup><sup>_s_in total CRS commitments and</sup> then multiplying these shares by aggregate disbursements from DAC2a ( _DAC_ 2 _a_<sup>_agg_</sup> _RY_<sup>):10</sup> for _s_ = 1 _, . . . , S_ . This strategy yields sectoral aid disbursements even for those years in which only commit6
effects are frozen in each of the baselines, although aggregate GDP is substantially lower in these other cases. SSA’s total GDP in 2030 in the low growth scenarios is USD 1.9 trillion, only two-thirds of the economy in the high growth case. When demographic effects are ignored, the region’s 2030 economy is 6 to 7 percent smaller.

As economies grow, their structures are expected to change as well. For example, Chenery and Syrquin (1973) and Syrquin and Chenery (1989) suggest that as economies grow, the share of agriculture value added in GDP tends to decline, while the shares of manufacturing and services rise, _ceteris paribus_ . In Section 4.1, it was seen how changing demographics can increase domestic savings and thus accelerate growth of the capital stock and capital deepening, in most countries. It can thus be expected that capital intensive sectors, like manufacturing and services, benefit from the increase in capital stock, and can be expected to expand, consistent with the empirical literature. That is indeed the case, as can be seen in the first three bars of Figure 7. In the high growth baseline, services and manufacturing value added as shares of GDP can be seen to rise for Sub-Saharan Africa as a whole. This expansion is somewhat muted when the age structure is frozen (as in the full age structure freeze scenario).

**Figure 7: Value added by sector in Sub-Saharan Africa under various scenarios (percent)**

<!-- Start of picture text --> 100<br>90<br>80<br>50 54 54 50 49<br>70<br>60<br>50<br>40<br>31 30<br>34<br>30 32 31<br>20<br>10 16 14 15 20 20<br>0<br>2011 2030 Baseline - 2030 Age Freeze 2030 Baseline - 2030 Age Freeze<br>High Growth - High Growth Low Growth - Low Growth<br>Agriculture Manufacturing Services<br><!-- End of picture text --> Note: Agriculture also includes fishing and forestry; manufacturing also includes food, natural resources, and infrastructure. Source: Simulation results Perhaps more striking is the comparison of how the region’s economic structure evolves in the low growth scenario. Agriculture’s share of the economy expands from 22 percent in 2011 to 26 percent in 2030, while manufacturing shrinks. Savings and thus investment are affected by aggregate growth, and so there is substantially lower savings as a share of GDP and slower capital formation in the low growth scenario compared to the high growth scenario. Manufacturing is a capital intensive sector, and without sufficient capital, labor moves to the labor intensive agriculture sector, expanding that. This regression of the evolution of economic structure is even more prominent when demographics are frozen, with the agriculture sector becoming an even more prominent part of the economy. While growth in the agricultural sector is critical for poverty reduction (Christiaensen, et al., 2011), agricultureheavy economies can also keep poor rural populations exposed to various vulnerabilities. For example, incomes in agriculture are generally lower and developing country economies with large GDP shares of 20