rafmacalaba/datause-encoder-data huggingface dataset:
## C.1.2.2 Fossil Fuels

The primary fossil fuels linked to the economic analysis of land use are petroleum products and natural gas. Biofuels substitute for petroleum products and, to lesser extent, natural gas, in energy demand for transportation services. The natural gas is also the key input in nitrogen fertilizers' production. As petroleum products' and natural gas prices are closely related in the long run (Hartley et al. 2008), we use the crude oil price as a reference cost of fossil fuels. Following the U.S. Energy Information Administration (EIA) projections (EIA 2010a)we assume that the cost of fossil fuels is logistic function with declining growth over time: where the parameters _c_<sup>_f_</sup> 0<sup>,and</sup><sup>_cf_</sup> _T_<sup>,reecttheoilpricesinthebeginningand</sup> in the end of this century, and _κf_ is the growth rate in costs of liquid fossil fuels. We obtain the value of the oil price in 2004 from the EIA, and calibrate the values of terminal oil price and the growth rate to match projections of the U.S. Energy Information Administration reference case scenario for 2035 (EIA 2010a, p. 86, Table 10).

# C.1.3 Agrochemical Sector

There are three types of fertilizer used in agricultural production: nitrogen fertilizers, phosphate fertilizers, and potash fertilizers. In our model we focus on nitrogen fertilizers. These fertilizers are particularly important in the climate policy debate, because their production is the most energy- and GHG- intensive. They are also critical to boosting yields in response to scarcity of land.<sup>1</sup> We use the FAOSTAT database<sup>2</sup> to obtain the global production of nitrogen fertilizers in 2004. For fertilizers' production costs and conversion rates we consider anhydrous ammonia (NH 3 ), which is one of the most common nitrogen fertilizers. We use USDA ERS fertilizer use and price dataset<sup>3</sup> to obtain the fertilizers' price. We then subtract the fossil fuels' price from the fertilizers'

> 1Note that by von Liebig's Law of the Minimum (yield is proportional to the amount of the most limiting nutrient, whichever nutrient it may be) the production of other two types of fertilizers will follow the path of nitrogen fertilizers.

> 2Thorough description of the FAOSTAT database is available from the following website: http://faostat.fao.org/.

> 3Thorough description of the dataset is available from the following website: http://www. ers.usda.gov/Data/FertilizerUse/.

15
<u>Figure 9: Institutions under trade as a Function of Parameter Values</u> <!-- Start of picture text --> f 192<br>144<br>96<br>fA 48<br>1.92<br>0 1.45<br>2.00 1.61 0.97 nS<br>LS 1.21 0.82 0.50 nN<br>L<br>N<br><!-- End of picture text --> 48
**Table 4. Household Expenditure on Education by Poverty Status "i,** 1996

|**Type of school**|**Expend**<br>**Fees/Unforeseen**<br>**expenses**|**itures per st**<br>**Services**|**udent**<br>**Materials**t,|**Total**|**Educational**<br>**Expenditures**(%)2/|**Sample**<br>**size3/**|
|---|---|---|---|---|---|---|
|**Private schools**|||||||
|Extreme|2063.7|29.8|842.4|2935.9|14.6|12|
|Moderate|1959.9|111.7|539.4|2611.0|11.1|50|
|Non poor|8216.8|632.7|2750.4|11599.9|16.7|499|
|Total|7662.3|584.6|2560.1|10807.0|16.6|561|
|Public schools|||||||
|Extreme|147.0|5.3|304.4|114.2|6.3|2825|
|Moderate|365.9|24.9|648.0|259.7|7.0|2511|
|Non poor|876.6|113.8|1105.3|523.9|6.7|2544|
|Total|432.9|43.5|652.6|282.3|6.7|7880|
|Urban schools|||||||
|Extreme|253.3|8.9|422.4|171.1|8.2|1186|
|Moderate|432.1|29.8|702.2|291.0|7.6|1867|
|Non poor|2454.3|227.6|1486.6|1042.1|10.5|2561|
|Total|1245.4|108.6|970.2|581.0|9.7|5614|
|**Rural**schools|||||||
|Extreme|69.9|2.4|206.0|69.6|4.4|1651|
|Moderate|213.2|10.8|412.9|159.2|4.9|694|
|Non poor|417.7|58.2|724.9|300.2|4.5|482|
|Total|139.2|10.3|307.9|114.3|4.6|2827|

Source: ENIGH 96 1/ Textbooks, stationery, etc.

2/ As percentage of household's expenditures 3/ Number of households

**4/** Annual pesos per student

**Table 5. Household Expenditure on Education** by **Pove Ly Status, 196**

|**Poverty Status**|<br>**Expenditu**|**res per stu**|**dent (%)**||**Educational**|**Services, Material**|**s Number of**|
|---|---|---|---|---|---|---|---|
||**Fees/Unforese**<br>**nexpenses**|**e Services**|**Materials**1|**Total**|**Expenditure**<br>(%)2/|**s**<br>**Expenditures**<br>()|**Households**|
|**Urban schools**||||||||
|Extreme|37.0|1.3|61.7|100.0|8.2|5.1|1186|
|Moderate|37.1|2.6|60.3|100.0|7.6|4.8|1867|
|Non poor|58.9|5.5|35.7|100.0|10.5|4.3|2561|
|Total|53.6|4.7|41.7|100.0|9.7|4.5|5614|
|**Rural schools**||||||||
|Extreme|25.1|0.8|74.0|100.0|4.4|3.3|1651|
|Moderate|33.5|1.7|64.8|100.0|4.9|3.2|694|
|Non poor|34.8|4.8|60.4|100.0|4.5|2.9|482|
|Total|30.4|2.3|67.3|100.0|4.6|3.2|2827|

Source: ENIGH 96 1/Textbooks, stationary, etc 2/ As percentage of household's expenditures 13
**Figure  5.2: Simplification of Documentation for Frequent Issuers**

<!-- Start of picture text --> Program U.K. Issuance Euro MTN EU Prospectus<br>Type Programme Directive<br>Programme<br>Basic Documentation Master Prospectus Registration<br>(Periodically (Periodically<br>Document (Periodically<br>updated) updated)<br>updated)<br>Additional<br>document<br>for a Pricing Supplement Pricing Supplement Securities Note<br>particular<br>issue<br>Securities Summary<br><!-- End of picture text --> _<mark>Source:</mark>_ <mark>Author.</mark> Euro medium-term note (MTN) programs are another popular example of institutional offerings. They are the most predominant and speediest way to issue fixed income securities in the Euro market. MTNs are said to account for approximately 60 percent to 90 percent of Euro fixed income issues.<sup>48</sup> An issue can be closed in less than a week of the issuer’s issuance notification. MTNs are issued on a syndicated or non-syndicated basis, continuously in series, through agents or dealers designated by the issuer. The issuer produces and maintains standardized documents, including the offering circular, for all its issues up to a certain aggregate amount of notes (master prospectus). Each time an issue is launched, its specific terms are set forth in a pricing supplement to the offering circular (pricing supplement) (Figure 5.2). Each issue of notes may vary in maturity (ranging from 2 to 30 years), currency, coupon rate, size, denomination, and other features.

# **_5.3 Integrated Disclosure and Shelf Registration_**

Integrated disclosure allows the issuer to incorporate periodic disclosure documents by reference in its public offering documents, thereby easing the disclosure burden of public

> 48 Petr (2007b) and a telephone interview with Mr. Ondrej Petr of International Capital Market Association, London.

37
<u>Table 1a. Average household statistics in 2008 by household migration status</u>

||<br>Overall<br>average|<br>Migrant<br>average|Non-migrant<br>average|Difference<br>(migrant -<br>non-migrant)|
|---|---|---|---|---|
|Total expenditures (Quetzales)|35161.01|47143.97|31314.63|15829.34***|
||(30319.71)|(38190.96)|(26250.58)|(3925.61)|
|Per capita expenditures (Quetzales)|5074.35|6000.77|4776.98|1223.79**|
||(4069.83)|(4548.96)|(3864.32)|(495.26)|
|Per capita food expenditures (Quetzales)|2753.62|2641.30|2789.67|-148.37|
||(2695.92)|(1790.15)|(2929.31)|(234.96)|
|Food share (food / total expenditures)|0.57|0.51|0.59|-0.07***|
||(0.19)|(0.20)|(0.18)|(0.02)|
|Poor = 1|0.83|0.76|0.85|-0.10**|
||(0.37)|(0.43)|(0.35)|(0.04)|
|Extremely poor = 1|0.49|0.38|0.52|-0.14**|
||(0.50)|(0.49)|(0.50)|(0.06)|
|Reported their income adequate for necessities = 1|0.41|0.58|0.35|0.22***|
||(0.49)|(0.50)|(0.48)|(0.05)|
|Reported their food expenditures sufficient = 1|0.46|0.63|0.41|0.21***|
||(0.50)|(0.49)|(0.49)|(0.05)|
|Children < 6 years old food secure = 1|0.38|0.47|0.35|0.12*|
||(0.49)|(0.50)|(0.48)|(0.05)|
|Household size|7.47|8.41|7.17|1.24***|
||(2.86)|(3.20)|(2.68)|(0.36)|
|Head is male = 1|0.82|0.67|0.87|-0.20***|
||(0.38)|(0.47)|(0.33)|(0.05)|
|Head age (years)|41.29|49.23|38.75|10.48***|
||(14.16)|(12.56)|(13.70)|(1.22)|
|Head education (grades)|2.12|1.65|2.27|-0.62***|
||(2.25)|(1.77)|(2.36)|(0.22)|
|Head identifies as indigenous = 1|0.55|0.58|0.54|0.03|
||(0.50)|(0.50)|(0.50)|(0.06)|
|Own more than 1 hectare land = 1|0.24|0.40|0.19|0.22|
||(0.43)|(0.49)|(0.39)|(0.05)|
|Receives remittances = 1|0.27|0.84|0.08|0.75***|
||(0.44)|(0.37)|(0.28)|(0.04)|
|Number of household observations(N)|428|104|324|428|

Notes: Sample of households with a child under three years old in 2008. Migrant household is defined as a household with at least one migrant in the U.S. in three of the four years 2005-08. Standard deviations in parentheses under average and standard errors estimated allowing for clustering at the sample cluster level in parentheses under difference. Exchange rate was 7.7 Quetzales to U.S. dollar in 2008. *** indicates significantly different at p<0.01, ** at p<0.05, and * at p<0.10.
|**Table A5: Number of Paternal Orphans Explained by **<br>A|**Spousal Co**<br>ge 15-19|**ntagion**<br>20-24|25-29|30-34|35-39|40-44|45-49|Total|
|---|---|---|---|---|---|---|---|---|
|_Guinea_|||||||||
|Proportion women who are in union|33.2%|67.5%|84.3%|92.2%|92.7%|92.5%|93.7%||
|Women in union: Deaths|44.71|77.35|80.47|72.30|59.28|49.22|74.25||
|Women in union: Mean number of living children|0.7|1.6|2.6|3.7|4.4|4.7|5.1||
|Women in union: Maternal orphans <15 years old|32.6|123.0|206.0|243.5|220.2|190.3|308.7||
|Number of spousal male deaths|13.2|22.8|23.7|21.3|17.5|14.5|21.9|135.0|
|Percentage of male deaths (15-59) explained by Ebola||||||||18.8%|
|Number of paternal orphans created through spousal contagion||||||||255.0|
|_Liberia_|||||||||
|Proportion women who are in union|14.3%|52.4%|72.6%|79.8%|78.4%|76.2%|77.9%||
|Women in union: Deaths|34.1|111.2|135.5|124.8|100.7|80.4|98.5||
|Women in union: Mean number of living children|0.8|1.6|2.5|3.5|4.1|4.9|5.2||
|Women in union: Maternal orphans <15 years old|27.3|175.6|338.8|412.6|363.2|331.4|429.6||
|Number of spousal male deaths|6.6|21.5|26.3|24.2|19.5|15.6|19.1|132.8|
|Percentage of male deaths (15-59) explained by Ebola||||||||9.5%|
|Number of paternal orphans created through spousal contagion||||||||238.4|
|_Sierra Leone_|||||||||
|Proportion women who are in union|18.8%|58.5%|81.7%|88.9%|87.3%|85.9%|82.0%||
|Women in union: Deaths|38.5|107.4|134.2|124.1|99.0|76.8|97.6||
|Women in union: Mean number of living children|0.7|1.5|2.4|3.3|4.0|4.5|4.8||
|Women in union: Maternal orphans <15 years old|27.3|162.1|326.2|383.6|337.2|278.9|382.7||
|Number of spousal male deaths|5.3|14.8|18.5|17.1|13.6|10.6|13.4|93.2|
|Percentage of male deaths (15-59) explained by Ebola||||||||8.3%|
|Number of paternal orphans created through spousal contagion||||||||149.6|
|Totalpaternal orphans explained bymaternal mortality||||||||643.0|

17
percent under scenario C, and 1.1 percent under scenario A. At the same time, unless we assume that taxes have no incidence upon employees (scenario A), the distributional outcomes would suffer due to formalization, but to a moderate degree as far as the population in general is concerned. Under scenario B, full formalization would result in a 1.4 percent reduction in mean consumable income of the population and subsequently a slight increase in poverty and inequality. Yet for the target group (the formalized employees) the reduction in income would amount to 17.2 percent and poverty rate would go up by 16.6 p.p. Full formalization under scenario C would have similar consequences, although the negative impact on the population and the target group would be much smaller than in scenario B because the workers are assumed to bear only the burden of PIT.

**Table 5: Simulated changes in indicators of income distribution and fiscal budget, reform 1 scenarios**

|||Formaliza|tion rate:||
|---|---|---|---|---|
||0%|10%|20%|50%|
|||Scenar|io 1A||
|Income of 1st decile, %|0.0|0.0|0.0|0.0|
|Income of the formalized, %|0.0|0.0|0.0|0.0|
|Income total, %|0.0|0.0|0.0|0.0|
|Absolute poverty rate of the formalized, p.p.|0.0|0.0|0.0|0.0|
|Absolute poverty rate, p.p.|0.0|0.0|0.0|0.0|
|Gini index, p.p.|0.0|0.0|0.0|0.0|
|Total budget balance, %ofGDP|0.0|0.2|0.4|1.1|
|||Scenar|io 1B||
|Income of 1st decile, %|0.0|-0.3|-0.6|-1.5|
|Income of the formalized, %|0.0|-16.4|-16.2|-17.2|
|Income total, %|0.0|-0.3|-0.6|-1.4|
|Absolute poverty rate of the formalized, p.p.|0.0|14.9|13.4|16.6|
|Absolute poverty rate, p.p.|0.0|0.3|0.6|1.4|
|Gini index, p.p.|0.0|0.1|0.1|0.4|
|Total budget balance, %ofGDP|0.0|0.2|0.4|0.9|
|||Scenar|io 1C||
|Income of 1st decile, %|0.0|-0.1|-0.2|-0.5|
|Income of the formalized, %|0.0|-5.1|-5.0|-5.3|
|Income total, %|0.0|-0.1|-0.2|-0.4|
|Absolute poverty rate of the formalized, p.p.|0.0|5.1|4.1|3.2|
|<br>Absolute poverty rate, p.p.|0.0|0.1|0.2|0.2|
|Gini index, p.p.|0.0|0.0|0.0|0.1|
|Total budget balance, %ofGDP|0.0|0.2|0.4|1.0|

Source: Authors’ calculations using RUSMOD for 2017 policy year and 2016 RLMS-HSE data.

Formalization is costly for workers if the economic incidence of direct taxes falls on them. Figure 8 illustrates the outcomes of reform 1 for different types of socio-demographic groupings. In case of random formalization of 50 percent of informal workers, the transition to formality is costly for the workers in most cases. There are no losses for workers under scenario A, because SIC is assumed to have no incidence of workers, so changes in it do not affect their take-home pay. In scenario B, where workers are responsible for paying all additional SIC and PIT due to formalization, their new gross and net earnings decrease substantially. The additional social benefits cannot compensate the higher taxes, which is why the net effect is negative and especially so in the middle of the income distribution. Scenario C represents the intermediate case, where workers bear a part of tax burden (PIT, but not SIC), which yields a lower negative effect. The negative effects of scenario B are similar 22
challenge to be able to estimate what the trade transactions cost is for MENA countries. This study uses the results from Zarrouk (2003) survey, which suggest that inefficiencies in trade facilitation amount to some 10.6% of the value of traded goods. This amount is taken as the total trade transactions cost arising from inefficiencies. However, in order to adopt the above methodology we break this into an indirect (iceberg) and a direct TTC (tax component). To obtain the iceberg component we adopt Hummels (2001) estimate that each day saved in shipping time, in part due to faster customs clearance, is equivalent to a 0.5 percentage point reduction in ad valorem tariffs. The results of the Zarrouk (2003) survey suggest that average clearance from ports in MENA countries ranges between some two to ten days, hence the indirect cost component of the TTC is estimated to be 3%. The remaining 7.6% is attributed to the average direct cost component. Given the generally higher documentary requirements needed for agricultural goods it is assumed that the direct trade transactions cost for agricultural goods is twice that of nonagricultural goods. Hence the direct TTC for agricultural goods is about 12%, whereas that for non-agricultural trade is about 4%.<sup>5</sup> As discussed earlier, the indirect TTC are introduced as technical progress in importing.<sup>6</sup>

# **Data**

The model used for this study draws data from the GTAP 6 Data base. Although this version of the GTAP database allows for 87 regions and 57 commodities, its coverage of MENA is rather limited. The database has separate data for Morocco and Tunisia, while the rest of MENA is aggregated into “the rest of North Africa” (RONAF), and “the rest of the Middle East” (ROMIDE). The current study uses a 13 region by 16 commodity aggregation, which captures all the MENA sub-regions, key trading partners and key commodities. Details of the aggregation are provided in appendix 3 table A1.

Trade (imports plus exports) is important for the various MENA sub-regions, with its

> 5 The direct TTC are incorporated into the analysis by using the “Altertax” option within GTAP to adjust the benchmark trade duties accordingly.

> 6 Changes are made to the “ams” variable in the GTAP model.

10
Table 5: Impact of Alternative Conflict Variables on Primary Schooling Attainment

||(1)<br>School<br>Destructions<br>Female|(2)<br>School<br>Destructions<br>Male|(3)<br>Maoist<br>Abductions<br>Female|(4)<br>Maoist<br>Abductions<br>Male|(5)<br>Maoist Control<br>Female –<br>Definition 1|(6)<br>Maoist Control<br>Male –<br>Definition 1|(7)<br>Maoist Control<br>Female –<br>Definition 2|(8)<br>Maoist Control<br>Male –<br>Definition 2|
|---|---|---|---|---|---|---|---|---|
|Explained Variable|Primary|Primary|Primary|Primary|Primary|Primary|Primary|Primary|
|Specification|Eq. (1)|Eq. (1)|Eq. (1)|Eq. (1)|Eq. (1)|Eq. (1)|Eq. (1)|Eq. (1)|
|=1 if 5-9 in 1996 x total|0.0539*|0.0301|0.0638**|0.0231|||||
|district casualties|(0.0302)|(0.0291)|(0.0275)|(0.0259)|||||
|=1 if 5-9 in 1996 x district<br>|0.4176|-1.7848|||||||
|schools destroyed 2002-<br>2006|(3.1298)|(2.3395)|||||||
|=1 if 5-9 in 1996 x total|||-0.0022***|0.0002|||||
|Maoist Abductions|||(0.0005)|(0.0010)|||||
|=1 if 5-9 in 1996 x District<br>controlled by Maoist<br>(Definition 1)|||||0.0916<br>(0.0590)|0.2009***<br>(0.0529)|||
|=1 if 5-9 in 1996 x District<br>|||||||0.1456***|0.0923*|
|controlled by Maoist<br>(Definition 2)|||||||(0.0457)|(0.0506)|
|Constant|0.3271***|0.6697***|0.3261***|0.6701***|0.3255***|0.6648***|0.3240***|0.6681***|
||(0.0260)|(0.0245)|(0.0256)|(0.0246)|(0.0264)|(0.0245)|(0.0265)|(0.0246)|
|Panel Variable|District|District|District|District|District|District|District|District|
|Region x Year of Birth<br>dummies|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Observations|3823|3055|3823|3055|3823|3055|3823|3055|
|No. of Clusters|75|75|75|75|75|75|75|75|
|R-Squared|0.1106|0.0370|0.1118|0.0368|0.1105|0.0415|0.1130|0.0382|

All specifications are estimated using the panel fixed-effects estimator. School destructions and abductions by Maoists are expressed per 1000 inhabitants. Sample only includes individuals surveyed in the Nepal DHS 2006 and aged 5-9 (treatment group) or 16-19 years old (control group) at the start of conflict in 1996. Definition of district controlled by Maoist based either on matches between People’s Army and government classifications as of 2003 (Definition 1) or based on government classification (Definition 2). Standard errors clustered at the district level are in parentheses. *p<0.10, **p<0.05, ***p<0.01.

41
utilities and the regulatory commissions to arrive at an accurate estimate of network losses, and to separate out non-technical from technical losses (Singh, 2006).

Table 4: Loss of economic output due to electrical outages and impact unreliable electricity supply on busineess activity <u>Source: World Bank (2013) and Nayar et al. (2012)</u>

|Country|Economic value<br>lost by enterprises<br>due to electrical<br>outages<br>(As a % of sales)|Average time of<br>outages per month<br>(hours)|Percentage of<br>firms citing<br>electricity<br>availability or<br>reliability as a<br>major or severe<br>constraint for<br>growth|Percentage of<br>firms owning<br>generators|
|---|---|---|---|---|
|Afghanistan (2007)|6.4|280|66|71|
|Bangladesh (2007)|10.6|113|78|52|
|Bhutan (2009)|4.3|8|6|18|
|India (2006)|6.6|---|32|41|
|Nepal (2009)|27.0|226|76|16|
|Pakistan (2007)|9.2|69|74|26|
|South Asia overall|10.7|139|53|43|

The non-technical electricity losses due to power theft can involve fraud (meter tampering), stealing (illegal connections), billing irregularities such as non-payment, and corruption (Smith, 2004). In April 2014, power was cut off in 18 government buildings in Pakistan including the Presidential residence because of nonpayment of electricity bills. In Bangladesh, electricity theft was estimated to be at 14%. In India, a third of electricity is lost through non-technical losses each year (Min and Golden, 2014).

_(iii) Weak financial performance_ . Electricity losses translate to lost revenues to the power companies and poor service quality, Revenue inadequacy, due to both nontechnical losses and distorted tariffs, remains one of the grave concerns of the stateowned electric utilities in South-Asia. In Bangladesh, for example, accounts receivable, due to the electricity bills not paid, was equal into 8.7 months of unpaid electricity consumption in 1999- 2000. This improved to about 2 months by 2013-14 in a decade of electricity reform experience (GoB, 2014b). For its part, the Nepal Electricity Authority incurred a financial loss of Nepalese Rupees (NPR) 4 billion in 16
Table 1. Summary Statistics on Insurance Policies Sold in Andhra Pradesh

|**Year**|**Number**<br>**of Policies**|**Number**<br>**of Policy**<br>**Holders**|**Policies**<br>**per Policy**<br>**Holder***|**Premiums**<br>**Collected**<br>**(Rs)**|**Sum**<br>**Insured**<br>**(Rs)**|**Premium**<br>**per phase**<br>**(Rs)**|**Premiu**<br>**m/ Sum**<br>**Insured**|
|---|---|---|---|---|---|---|---|
|2003|792|194|4.08|||||
|2004|1,305|281|4.64|||||
|2005|9,895|3,062|3.23|927,285|9,894,000|93.71|9.37%|
|2006|6,039|4,070|1.48|534,734|6,038,000|88.55|8.86%|
|2007|6,396|2,852|2.24|628,265|6,436,000|98.23|9.76%|
|2008|9,411|3,619|2.60|910,165|9,411,000|96.71|9.67%|
|2009|14,765|7,567|1.95|1,421,190|14,749,000|96.45|9.64%|

Note: In some cases BASIX sold combined policies covering all three monsoon phases. These are counted as three policies (one per phase). Figures are also adjusted for 2003-2004. Source: BASIX administrative data.

1
**Figure 17: Sources of investor financing, all CDM and JI projects**

<!-- Start of picture text --> Share of ECA pipleline offsets by investor country<br><!-- End of picture text --> <!-- Start of picture text --> Sweden<br>3%<br>Other<br>Austria<br>9%<br>5%<br>Unilateral<br>Switzerland<br>27%<br>5%<br>Japan<br>6%<br>Denmark<br>8%<br>Netherlands<br>United<br>24%<br>Kingdom<br>13%<br><!-- End of picture text --> Source: Risoe (2011) and authors‘ calculations.

**Figure 18: Sources of investor financing, CDM and JI projects in ECA**

# **Share of global  pipleline offsets by investor country**

<!-- Start of picture text --> Spain<br>Italy<br>3%<br>Sweden 3% Other<br>3% 8%<br>Germany<br>4% United Kingdom<br>29%<br>Netherlands<br>9%<br>Unilateral<br>Japan<br>15%<br>13%<br>Switzerland<br>13%<br><!-- End of picture text --> Source: Risoe (2011) and authors‘ calculations.

71