rafmacalaba/datause-encoder-data huggingface dataset:
A. Sanghi and D. Johnson

|12|Kenya’s apparel exports to the US (2000-2014)||
|---|---|---|
||UN Comtrade 2015||
|13|Kenya’s services exports overall are strong<br>UN Service Trade 2015||
|14|Kenya’s FDI is low. Trend in red (1980-2014)<br>World Development Indictors World Bank 2015 . . . . . . . . . . . . . . . . . . . . . . . .|19|
|15|Kenya underperforms in attracting FDI relative to potential. Trend in red (1980-<br>2014)||
||World Development Indictors World Bank 2015 . . . . . . . . . . . . . . . . . . . . . . . .|19|
|16|Kenya’s Gross domestic savings has fallen sharply since 1993. Trend in red (1980-<br>2014)||
||World Development Indictors World Bank 2015 . . . . . . . . . . . . . . . . . . . . . . . .|20|
|17|Chinese FDI in Kenya is growing quickly since 2009 (2003-2012)<br>UNCTAD FDI/TNC database 2015<br>. . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|21|
|18|China’s FDI represents large share of total FDI (2003-2012)<br>Authors’ own calculation based on World Development Indictors World Bank 2015 . . . . .|21|
|19|Investment from China rising; investment from UK and US falling<br>KenInvest 2015||
|20|China and France top sources of FDI infows for Kenya (2012)<br>UNCTAD FDI/TNC database 2015<br>. . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|22|
|21|China and UK hold the most FDI stock in Kenya (2012)<br>UNCTAD FDI/TNC database 2015<br>. . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|23|
|22|Kenya’s mobile telephone subscriptions grew quickly over last decade<br>International Telecommunications Union 2016||
|23|China invests most in metals and communications in Kenya (2003-2015)<br>fDi Intelligence from The Financial Times Ltd 2015<br>. . . . . . . . . . . . . . . . . . . . . .|25|
|24|Chinese companies invest most in manufacturing (2003-2015)<br>fDi Intelligence from The Financial Times Ltd 2015<br>. . . . . . . . . . . . . . . . . . . . . .|25|
|25|Chinese investment per project highest in headquarters creation and manufacturing<br>(2003-2015)||
||fDi Intelligence from The Financial Times Ltd 2015<br>. . . . . . . . . . . . . . . . . . . . . .|26|
|26|Chinese companies create most jobs in the communications sector (2003-2015)<br>fDi Intelligence from The Financial Times Ltd 2015<br>. . . . . . . . . . . . . . . . . . . . . .|28|
|27|Chinese companies in manufacturing sector create the most jobs (2003-2015)<br>fDi Intelligence from The Financial Times Ltd 2015<br>. . . . . . . . . . . . . . . . . . . . . .|28|
|28|<br>Chinese companies in manufacturing sector create the most jobs per project (2003-<br>2015)||
||fDi Intelligence from The Financial Times Ltd 2015<br>. . . . . . . . . . . . . . . . . . . . . .|28|
|29|<br>China is ffth largest creator of jobs. India creates the most jobs (2003-2015)<br>fDi Intelligence from The Financial Times Ltd 2015<br>. . . . . . . . . . . . . . . . . . . . . .|29|
|30|US is a top donor for Kenya. Chinese fnance does not meet OECD/DAC aid criteria<br>(2013)<br>OECD DAC aid database 2015 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|31|

4
The results of the analysis suggest that teachers’ subject-matter knowledge is important, and that 1.0 but also 2.0 standard deviation increases in teachers’ subject-matter knowledge are realistic medium-term targets for education policy making in Indonesia. A 1.0 and 2.0 standard deviation increase in teachers’ subject-matter knowledge across-the-board, is predicted to increase student-learning outcomes by 0.25 and 0.50 student-level standard deviations respectively by the time students complete the six-year primary cycle. These effects are substantial and would amount to 17.5 to 35.0 points respectively on the scale used by PISA if such impacts would persist until age 15.

The paper is organized as follows. Section (2) presents the teacher test score data and provides an intuitive account of the level and spread of subject-matter knowledge among Indonesia’s primary school teachers. Section (3) presents the statistical value-added model and presents ways of dealing with the difficulties of estimating the parameters of such models, e.g. these difficulties are due to measurement error in test scores and the time persistence of unobserved inputs. Section (4) presents regression results of three different value-added models: the standard level model, the school fixed-effects model, and a student-teacher fixedeffects model. Section (5) concludes and discusses the findings within the broader context of Indonesia’s education system.

# **2 How much do teachers know?**

The level of teachers’ subject-matter proficiency in Indonesia is said to be particularly low on average, even though rigorous international comparisons do not exist. Muhammad Nuh, for example, the country’s former Minister of Education and Culture reflected on the results of a large scale competency assessment of Indonesian teachers in 2012: “.. on one hand I am happy with the plethora of poor results because it means the data is reliable (The Jakarta Globe 2012).” Low levels of subject-matter proficiency can be a real constraint to effective teaching, even if teachers have a talent for delivering a message to a classroom. This section provides an intuitive account of the level and spread of subject-matter proficiency of Indonesian primary teachers. The data reveals somewhat poor performance overall as the 4
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# **<mark>Table 1 - Summary Statistics (Cont’d)</mark>**

Panel D presents the summary statistics of outcomes at the loan (firm-bank-month) level. Panel E shows the summary statistics of outcomes at the firm-year level. Panel F displays the summary statistics of bank-month-level outcomes. Panel G presents statistics of firm and state characteristics. See Table A1 in the Appendix for variable <u>definitions.</u>

|definitions.|Obs.|Mean|p10|Median|p90|Std. Dev.|
|---|---|---|---|---|---|---|
|**Panel D. Bank Lending to Private**|**Firms**||||||
|Volumef,b,m|1,216,258|13.27|10.64|13.46|15.77|2.41|
|Investmentf,b,m|200,318|12.85|9.55|12.93|16.14|2.64|
|Working Capitalf,b,m|1,100,190|13.27|10.77|13.46|15.66|2.35|
|Interest Ratef,b,m|1,216,258|14.42|8.65|13.43|20.72|5.32|
|Collateralf,b,m|1,216,258|0.32|0.00|0.00|1.00|0.41|
|Maturityf,b,m|845,642|2.89|1.36|3.09|4.22|1.13|
|**Panel E. Firm Real Outcomes**|||||||
|Liabilitiesf,y|1,755|10.06|8.09|9.68|12.96|1.93|
|Assetsf,y|1,818|11.12|9.09|10.54|14.52|2.09|
|Fixed Assetsf,y|1,747|9.44|6.40|8.94|13.89|2.73|
|Salesf,y|1,911|10.68|9.62|10.46|11.99|1.16|
|**Panel F. Bank-Level Indicators**|||||||
|Loansb,m|468|11.92|9.91|12.34|13.33|1.35|
|Loans – Govtb,m|468|8.34|3.85|9.44|10.93|2.94|
|Loans – Non-Govtb,m|468|11.23|9.40|11.57|12.65|1.28|
|IntRate – Govtb,m|468|6.51|4.20|6.23|9.08|2.13|
|IntRate – Non-Govtb,m|468|8.32|5.55|7.78|11.78|2.73|
|BankExposureGovb,Mar16|468|0.32|0.06|0.3|0.75|0.25|
|BankExposureGovs,b,Mar16|468|0.10|0.00|0.05|0.29|0.14|
|**Panel G. Other Variables**|||||||
|Tradable Sectori|83|0.26|0.00|0.00|1.00|0.44|
|GovernmentExposurei|83|0.09|0.01|0.08|0.19|0.08|
|Norths|30|0.46|0.00|0.00|1.00|0.51|

44
Strikingly, when construction is excluded from the Industry sector, the record is worse. Although non-construction output growth was more than 13% a year, annual employment growth was as low as 2.6%. Thus, given a growth elasticity of employment below 0.2, the share of this segment of the industrial sector in total employment has remained virtually unchanged'.

Table **2: Industrial employment growth has been moderate**

||1992/93|1997/98|Absolute<br>increase<br>92/93<br>-<br>97/98|% increase<br>per year<br> 92/93 -<br>97/98|Incremental<br>Employment<br>Distribution<br>92/93-97/98|
|---|---|---|---|---|---|
|Total employment|36,792,448|40,313,856|3,521,408|1.8|100|
|Agriculture|26,182,850|26,770,068|587,218|0.4|16.7|
|Industry|4,343,946|5,296,240|952,294|4.0|27.0|
|Industry less construction|3,804,677|4,325,204|520,527|2.6|14.8|
|Services|6,265,650|8,247,549|1,891,899|5.6|56.3|

_Source:_ VLSS 1 and 2. Employment data is for people between 15 and 65.

**Table 3: The share of industry in total employment is increasing slowly**

||Share in total labor<br>force in 92/93 (%)|Share in total labor<br>force in 97/98 (%)|Change<br>92/93-<br>97/98|
|---|---|---|---|
|Agriculture|71.2|66.4|-4.7|
|<br>Industry|11.8|13.1|+1.3|
|<br>Industry less construction|10.3|10.7|+0.4|
|<br>Services|17.0|20.5|+3.4|

_Source:_ VLSS I and 2 Table 4 shows that over the period 1993-97 economic growth has raised the country's real value-added per worker by 39.8%. In Industry, strong growth coupled with weak employment growth has been accompanied by a large 54% increase in average labor productivity. In agriculture and services, value-added per worker has increased by respectively 20.8% and 19.2%. Thus, the productivity gap between industry and other sectors has sharply widened. In only five years, agricultural labor productivity has declined from about 27% of industrial levels to less than 21%.

I Note however that the VLSS employment data used in this section provides a less somber picture than employment data from Vietnam's General Statistical Office (GSO, Statistical Yearbooks). According to GSO data, the average annual growth of industrial employment was 3.1% instead of 4.0% as indicated by VLSS data. This would represent only 23% of the rate of industrial growth. In addition, when construction isomitted GSO indicates employment growth of only 1.7% or 13% of this sector's GDP growth. Official data from GSO thus paints an excessively negative picture.

_5_
# Figure 5. Debt composition

# A. Composition of government debt, 2020

<!-- Start of picture text --> Percent of government debt<br>60<br>Weighted averages Median<br>40<br>20<br>0<br>Held by In foreign Held by In foreign<br>non- currency non- currency<br>residents residents<br>Advanced economies EMDEs<br><!-- End of picture text -->

- C. Composition of LIC external debt, by creditors

<!-- Start of picture text --> Percent of GDP<br>15 2020 2007<br>10<br>5<br>0<br>Multilateral Paris Non-Paris Other<br>Club Club lenders<br>bilateral bilateral<br><!-- End of picture text --> B. Non-concessional external debt in LICs <!-- Start of picture text --> Percent of government debt<br>90<br>70<br>50<br>30 Median Interquartile range<br>10<br>2005 2008 2011 2014 2017 2020<br>1970<br>Percent Multilateral Bilateral<br>Private banks Bonds<br>100<br>75<br>50<br>25<br>0<br>1970 1980 1990 2000 2010 2020<br><!-- End of picture text -->

- D. Composition of LIC external debt since

Sources: Kose et al. (2017); World Bank.

A. Data are available for 29 advanced economies and 44 EMDEs for government debt held by nonresidents and 16 advanced economies and 32 EMDEs for government debt denominated in foreign currency. Weighted averages are computed with government debt in nominal U.S. dollars as weights.

B. Data are available for up to 24 LICs.

C. Public and publicly guaranteed external debt is considered. Other lenders include bondholders and multiple lenders. GDP-weighted average across 23 LICs.

D. Public and publicly guaranteed external debt is considered. Bonds include other private sector creditors. Data are available for up to 25 LICs.

25
**_Energy demand models for policy_** **_<u>formulation</u>_**

Dutton, A.G. and M. Page, 2007, The THESIS Model: An assessment tool for transport and energy provision in the hydrogen economy, International Journal of Hydrogen Economy, 32(12), pp. 1638-54.

- E. A. Hudson, and D. W. Jorgenson, 1974, US Energy Policy and Economic Growth: 1975-2000, Bell J. of Economics, 5, pp. 461-514.

- EIA, 1978, An evaluation of Future World Oil Prices, EIA Analysis Memo EIA010121/4, Energy Information Administration, Washington, D.C.

- EIA, 2000, Integrating Module of the National Energy Modeling System: Model Documentation 2001, Energy Information Administration, US Department of Energy, Washington, D.C.

- EIA, 2003, The National Energy Modeling System: An Overview 2003, Energy Information Administration, Department of Energy, Report No. DOE/EIA-0581, Washington, D.C. (see http://www.eia.doe.gov/oiaf/aeo/overview/overview.html).

- EIA, 2007, Annual Energy Outlook Retrospective Review: Evaluation of projections in past editions (1982-2006), Energy Information Administration, Department of Energy, DOE/ EIA -0640 (2006), Washington. D.C. (see http://www.eia.doe.gov/oiaf/analysispaper/retrospective/).

- Eltony, M. and N. Mutairi, 1995, Demand for gasoline in Kuwait: an empirical analysis using cointegration techniques, Energy Economics, 17, pp. 249-53.

- Eltony, M. N, 2004, A model for forecasting and planning: The case for energy demand in Kuwait, The Journal of Energy and Development, 30(1), pp. 91-108.

Eltony, M. N. and M. A. Al-Awadhi, 2007, The commercial sector demand for energy in Kuwait, OPEC Review, 31(1), pp. 17-26.

105
diverse set of countries and locations, and including a longer time series of more disaggregated indicators of the quality of institutions.

# **References**

Amemiya, T. 1978. “The estimation of a simultaneous equation generalized probit model,” Econometrica, 46, pp. 1193-1205.

Bigsten, A. P. Collier, S. Dercon, M. Fafchamps, B. Gauthier, J. W. Gunning, J. Habarurema, A. Isaksson, A. Oduro, R. Oostendorp, C. Pattillo, M. Soderbom, F. Teal and A. Zeufack. 1999.“Export of African Manufactures: Macro Policy and Firm Behaviour,” 1999, Journal of International Trade and Economic Development, 8(1), pp. 53-71.

Bloom, D. and J. Sachs. 1998. “Geography, Demography, and Economic Growth in Africa,” in Brooking Papers on Economic Activity, 2.

Clarke, G. 2005. “Beyond Tariffs and Quotas: Why Don’t African Manufacturers Export More?” World Bank Policy Research Working Paper # 3617, June.

Collier, P. .1997. "Globalization: What Should be the African Policy Response? “ mimeo, Centre for the Study of African Economies, University of Oxford.

________, P. .1998. "Globalization: Implications for African Economic Management”, Mimeo, World Bank, Washington, D.C.

Cragg, J. and S. Donald. 1993. “Testing Identifiability and Specification in Instrumental Variable Models,” Econometric Theory, 9, pp. 222-240.

Dollar, D., M. Hallward-Driemeier, and T. Mengistae. 2005. “Investment Climate and International Integration,” World Bank Policy Research Working Paper # 3323. June.

Eifert, B. A. Gelb, and V. Ramachandran. 2005. “Business Environment and Comparative Advantage in Africa: Evidence from the Investment Climate Data,” Processed, World Bank Group, January.

Elbadawi, I.1994. "Estimating Long-Run Equilibrium Real Exchange Rates", in John Williamson (editor). Estimating Equilibrium Exchange Rates. Institute for International Economics, Washington, DC, USA.

_________. 1999: Real Exchange Rate Policy and Non- Traditional Exports in Developing Countries. WIDER Research for Action 46.

Elbadawi, I.A. and C.C. Soludo, 1999. “Our Destiny in Our Hands: Achieving manufactures competitiveness despite the constraints of geography in Africa”. Mimeo.

24
being female, binary variables for each grade excluding grade 10, and a binary variable for whether the school is located in a rural area. These coefficients and their standard errors were estimated using the Monte-Carlo method described in the previous section and detailed in OECD (2002).

Wealth associates strongest with reading competency in Chile where a unit change, or approximately one standard deviation, in a student’s household’s wealth index corresponds to a 30 point increase in competency. From Figure 1, the difference between the top and bottom 10% of wealth is 2.4 points which translates to a 71.2 point difference between the top and bottom 10% or approximately 0.7 of a standard deviation in PISA. This is approximately equivalent to a grade of schooling in Chile.

Uruguay and Mexico exhibit the lowest association of wealth with competency: 17.4 and 14.9 points respectively and nearly half that of Chile. This means that a student in Uruguay and Mexico from a poorer household will only have half the disadvantage than that of a student in a poorer household in Chile. In the high income countries, the association with wealth is much smaller, and in Finland, that it is zero or negative can not be statistically ruled out.

Being female has a positive association with reading competency, but this will be discussed in more detail in the proceeding section.

27
# **Table 13: Cross-country covariates associated with the geographic penetration of branches**

The results of OLS regressions of the natural logarithm of number of commercial bank branches per km<sup>2</sup> on different country characteristics.

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|(13)|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|GDP i l|0.592***|0.599***|0.574***|0.579***|0.426***|0.398***|0.543***|0.608***|0.590***|0.576***|0.558***|0.570***|0.593***|
|per capta (og)|(0.033)|(0.034)|(0.039)|(0.040)|(0.102)|(0.092)|(0.056)|(0.046)|(0.032)|(0.034)|(0.047)|(0.040)|(0.043)|
|Poulation densit (lo)|1.069***|1.072***|1.060***|1.067***|1.060***|1.024***|0.953***|1.067***|1.066***|1.021***|1.058***|1.124***|1.077***|
|p y g|(0.043)|(0.045)|(0.047)|(0.044)|(0.047)|(0.046)|(0.076)|(0.043)|(0.041)|(0.056)|(0.053)|(0.049)|(0.046)|
|Expected real economic growth||0.212<br>(0.255)||||||||||||
|Number of commercial banks<br>(log)|||0.005<br>(0.068)|||||||||||
|Inflation||||-0.005<br>(0.006)||||||||||
|Electricity consumption|||||0.139<br>(0.120)|||||||||
|Land line and cell phone users||||||0.497**<br>(0.194)||||||||
|Road density|||||||0.116<br>(0.094)|||||||
|Absence of violence||||||||-0.046<br>(0.088)||||||
|Offshore financial center<br>Concentration|||||||||0.093<br>(0.220)|-0.347<br>(0.457)||||
|Government bank share|||||||||||-0.226<br>(0.390)|||
|Foreign bank share||||||||||||0.118<br>(0.219)||
|Branch approval|||||||||||||-0.051<br>(0.165)|
|Constant|-<br>14.82***|-<br>15.09***|-<br>14.66***|-<br>14.66***|-<br>14.34***|-<br>15.11***|-<br>14.29***|-<br>14.97***|-<br>14.80***|-<br>14.23***|-<br>14.40***|-<br>14.85***|-<br>14.83***|
||(0.379)|(0.501)|(0.387)|(0.463)|(0.447)|(0.410)|(0.523)|(0.418)|(0.368)|(0.643)|(0.567)|(0.500)|(0.502)|
|N|112|111|101|108|96|84|81|111|112|109|75|75|109|
|Adjusted R<sup>2</sup>|0.873|0.872|0.874|0.872|0.862|0.881|0.871|0.876|0.872|0.860|0.886|0.875|0.866|
|Robust standard errors in parenthese<br>***p<0.01,**p<0.05,*p<0.1|s|||||||||||||

39
TABLE 20 Mexico's GDP **Composition** (In Percentage)

||1986|1987|1988|1989|1990|1991|1992|1993|1994|1995|1996|1997|1998|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|GDP at market prices|100.00|100.00|100.00|100.00|100.00|100.00|100.00|100.00|100.00|100.00|100.00|100.00|100.00|
|Net indirect taxes|7.98|9.69|8.13|8.30|8.50|8.53|8.56|8.24|8.02|8.61|9.06|9.48|8.54|
|GDP at factor cost|92.02|90.31|91.87|91.70|91.50|91.47|91.44|91.76|91.98|91.39|90.94|90.52|91.46|
|Agriculture, value added|9.48|8.74|7.26|7.11|7.18|6.88|6.11|5.78|5.28|5.00|5.53|5.01|4.82|
|Industry, value added|32.12|34.31|29.50|26.93|26.00|25.64|25.69|24.62|24.70|25.53|25.84|25.87|26.03|
|Construction, value added|4.26|4.06|3.67|3.46|3.59|3.76|4.12|4.40|4.87|3.72|3.78|4.03|4.29|
|Gas, electricity and water, value added|1.45|1.40|1.20|1.22|1.24|1.36|1.46|1.46|1.35|1.16|1.06|1.08|1.07|
|Mining and quarrying, value added|3.61|5.03|2.71|2.17|2.14|1.69|1.60|1.29|1.23|1.58|1.42|1.38|1.22|
|Manufacturing, value added|22.80|23.82|21.92|20.08|19.03|18.83|18.52|17.47|17.25|19.06|19.58|19.38|19.45|
|Services, value added|50.42|47.26|55.11|57.65|58.31|58.95|59.63|61.37|62.00|60.86|59.56|59.63|60.61|
|Transport, storage and communication,||||||||||||||
|value added|7.39|7.24|8.69|8.37|8.32|9.12|8.71|8.54|8.79|9.15|9.26|9.59|9.90|
|Trade, value added|20.51|19.12|23.29|22.88|22.63|21.18|20.86|19.99|19.41|19.15|19.57|19.34|18.30|
|Banking, value added|7.68|7.01|8.91|11.12|12.13|12.53|13.24|14.55|14.89|16.79|13.67|12.10|12.61|
|Public administration and defense,||||||||||||||
|valueadded|16.13|15.19|15.63|15.88|16.29|17.30|18.92|20.97|21.90|20.69|19.31|19.93|20.80|
|Other services, value added|-1.30|-1.31|-1.41|-0.60|-1.05|-1.18|-2.10|-2.68|-2.99|-4.91|-2.25|-1.33|-1.00|

SOURCE: World Bank LDB 51
<!-- Start of picture text --> Post<br>6.0<br>5.0<br>4.0<br>Croatia<br>3.0 EU-15 average<br>Croatia<br>2.0<br>1.0<br>0.0<br>1975 1985 1995 2005<br>Post - Entry<br>6.0<br>5.0<br>4.0<br>Croatia<br>3.0 EU-15 average<br>OECD average<br>2.0<br>1.0<br>0.0<br>1975 1985 1995 2005<br><!-- End of picture text --> <!-- Start of picture text --> Post - Public Ownership<br><!-- End of picture text --> <!-- Start of picture text --> 6.0<br>5.0<br>4.0 Croatia<br>3.0 EU-15 average<br>OECD average<br>2.0<br>1.0<br>0.0<br>1975 1985 1995 2005<br><!-- End of picture text --> 61
**32**

whose demand is reduced by a rise in global interest rates. This effect was also present in the global financial crisis simulation where the risk adjusted discount rise rose sharply (even though real interest rates fell) and the demand for durable goods collapsed. Global trade (see chart 5.6) does not contract in 2009 but falls for several years as growth slows after the fiscal stimulus. By 2014 world trade is above baseline.

# 5.1 **GDP and export effects of fiscal response**

<!-- Start of picture text --> United States Japan<br>4.0 1.0<br>2.0 0.0<br>0.0<br>-1.0<br>-2.0<br>-2.0<br>-4.0<br>-6.0 -3.0<br>-8.0 -4.0<br>GDP Exports GDP Exports<br>United Kingdom Germany<br>2.0 2.0<br>1.0 1.0<br>0.0 0.0<br>-1.0 -1.0<br>-2.0 -2.0<br>-3.0 -3.0<br>-4.0 -4.0<br>-5.0 -5.0<br>GDP Exports GDP Exports<br>China India<br>2.0 14.0<br>1.0 12.0<br>0.0 10.0<br>-1.0 8.0<br>6.0<br>-2.0 4.0<br>-3.0 2.0<br>-4.0 0.0<br>-5.0 -2.0<br>-6.0 -4.0<br>GDP Exports GDP Exports<br>Latin America Other LDC<br>10.0 10.0<br>8.0 8.0<br>6.0 6.0<br>4.0 4.0<br>2.0 2.0<br>0.0 0.0<br>-2.0 -2.0<br>GDP Exports GDP Exports<br>2008200920102011201220132014201520162017201820192020 2008200920102011201220132014201520162017201820192020<br>2008200920102011201220132014201520162017201820192020 2008200920102011201220132014201520162017201820192020<br>2008200920102011201220132014201520162017201820192020 2008200920102011201220132014201520162017201820192020<br>2008200920102011201220132014201520162017201820192020 2008200920102011201220132014201520162017201820192020<br>% Deviation % Deviation<br>% Deviation % Deviation<br>% Deviation % Deviation<br>% Deviation % Deviation<br><!-- End of picture text -->