rafmacalaba/datause-encoder-data huggingface dataset:
90

# _Acroeonounlcmpacu_

Durig 198648 GDP growth was relatively high, around 7 percent. Growth mintined through fia policies during ts period resulted in growing deficits and inflation. Prior to elections in September 1987, rismg prices were kept in check by delaying the adjustment of admistered pnces and relying more heavily on domestic borrowing instead of monetzg ihe deficits. However, after the Setember parametay election **s** everal increases in taxes and admiseed prices were implemened to improve govemment finnces Durg this peniod the energ pance index increased by over 41 percnt, whas dh genera the consumer price index increased by over 75 percent Table 7.4 summaz the chage in selected macroeconomic indicators.

m However, oer fctors contnbuted to rsing ination. During 1980-87 real wages Turkey dodIn by 30 percent By 1985 real wages in the manuftuing sector had retuned to their 1965 level. **As** ar=% wage payments as a share of non-agricultuld income had declined from nearly 52 percen in 1977 to 22 pect by 1985. liM fall n r 1 wages was an outcome of political measures (restrictions on tade union activities during this peiod) and fiscal constraint **an** the public **sector tha** were an integral part a(dt stbization and adjument progrn **implnd** in Turky during **ihe** early 1980s. By 1987 and erly 1938, **le** gradud erosion of real wages began to surface in the form of limited strikes and boycotts.

_Table 7.4 Turkey: SelectedAMacroeconomic ndicators, 1986-89 (ercentchangesp.a.)_

||1986|1987|198B|1989|1990|
|---|---|---|---|---|---|
|GDP (growth)|7.4|6.4|4.6|0.6|8.2|
|Governmcntreenuese|18.6|19.1|18.5|20.1|22|
|_clang. in Mice Index 6p.a)_<br>Wholesale<br>price (MIning)<br>|13.6<br>|35.7<br>|69.9<br>|84.1<br>|48.7<br>|
|<br>Wolsale pice (Agr;i)<br>|27.1<br>|29.7<br>|50.9<br>|81.4<br>|70.6<br>|
|Wholeal price|37.7<br>|20.5|41.1|63.8|56.5|
|<br>**Wbolslepric**<br>**n**<br>|32.6<br>|33.6<br>|77.7<br>|64.7<br>|46.9<br>|
|QPI<br>|34.6<br>|38.9<br>|75.4<br>|69.6<br>|60.3<br>|
|Food|30.4|39.8|71.2|66.4|64.3|
|H<br>_uin|-|-|58.7|70.2|**Ia.**|

**StOa(W.**
<!-- Start of picture text --> AFR EAP ECA LCR MNA SAR APD EC NAM<br>40 Total irrigation<br>— 2010<br>— 2050<br>o<br>fad<br>= System change (2010-2050)<br>on || Irrigation net expansion<br>BE System upgrade<br>|| Irrigated area not upgraded<br>|_|| | oe<br>Bo DB B @ OB OD GB DB GB GD B DB Go DB BB OG B OB<br>oO oO oO ® oO oO o oO Oo oO oO oO oO oO oO oO o oO<br>>= >c >ij >= >=} Ee> 5 omy> ix> >= Cc> >— >= >= >i >3 i=> oy— >c<br>= =e Se a — Se ~~ 3 = Be — “Sy ~~ =<br>ao [0 oO oO o oO a oJ oO<br>= = = = = = = = =<br><!-- End of picture text -->
# **4.2 MPPs and APPs of applied nitrogen in Nigeria**

The MPPs of applied nitrogen are estimated as the derivative of the production function with respect to our applied nitrogen variable. The MPPs were estimated using the ‘‘margins’’ command in Stata and represent the average partial effects of nitrogen on maize yields (See Table A1 in the appendix for the APEs of all variables). We also calculate the APP as the change in output due to the use of applied nitrogen. This captures the gain in yield per unit of nitrogen compared to not applying any nitrogen. We manually calculate the APPs at the field level using the coefficient estimates of our production function.

**Table 4: MPPs and APPs of applied nitrogen across farming systems in Nigeria**

||**MPP of**<br>**applied**<br>**Nitrogen**|**APP of**<br>**Applied**<br>**Nitrogen**|
|---|---|---|
|Cereal-root crop farming system|7.98**|7.79|
|Tree crop farming system|12.38+|8.76|
|Root crop farming system|11.15|19.37|

_*, ** and *** are significant at 10,5and 1 percent respectively. + is significant at 15% or less._ The marginal physical product (MPP) for applied nitrogen in Nigeria appears to be quite low. The average MPP of nitrogen across farming systems ranges between about 8 and 13kg. Though usually focused on a very specific location, many studies in Nigeria indicate relatively wide variation in fertilizer yield responses from about 0.2 to 2 (Onuk et al., 2010; Gani and Omonona, 2009) with cases of negative APP (Kehinde et al., 2012). Studies on fertilizer yield response in Mfantseman Municipality in the Central Region of Ghana yielded an MPP value of 0.12. This is quite different from what has been found in East and Southern Africa. Sheahan et al. (2013) estimate an overall MPP of nitrogen for maize production to be about 17 (though this varies across space and time). Matsumoto and Yamano (2011) found marginal products ranging between 11 and 20 across the western and higher potential regions of Kenya while Marenya and Barrett (2009) found the marginal product of nitrogen to be 17.6 for Vihiga district (Western Province) of Kenya. The low MPPs of applied nitrogen in maize production indicate that increasing fertilizer use alone might not be sufficient to increase maize yields to desired levels in Nigeria. Furthermore, given high fertilizer 19
# **Figure 1. Developments in commodity markets**

**B. Cumulative growth in GDP, population, energy and metals consumption, 1996-2016**

# **A. Real commodity prices**

<!-- Start of picture text --> US$ 2010=100<br>160<br>Energy<br>140 Metals<br>120 Agriculture<br>100<br>80<br>60<br>40<br>20<br>0<br>1967 1972 1977 1982 1987 1992 1997 2002 2007 2012 2017<br><!-- End of picture text --> <!-- Start of picture text --> Percent<br>180<br>150<br>120<br>90<br>60<br>30<br>0<br>Aluminum Metals Lead Nickel Zinc Copper GDP Tin Coal Natural gas Energy Crude oil Population<br><!-- End of picture text --> <!-- Start of picture text --> C. Contribution to average annual growth in D. Contribution to average annual growth in<br>metals consumption energy consumption<br>Percentage points China India Percentage points China<br>Other EM7 Other EMDEs India<br>5 3<br>G7 Other AEs Other EM7<br>Other EMDEs<br>4 G7<br>2 Other AEs<br>3<br>2 1<br>1<br>0<br>0<br>-1 -1<br>1996-2008 2008-2016 1996-2008 2008-16<br>E. EM7 share of commodity consumption F. Commodity intensity of output<br>Percent of world total Index, 1965 = 100<br>60 2016 1996 120<br>110<br>40<br>100<br>90<br>20<br>80<br>0 70<br>60 Energy Metals<br>Energy ex China Metals ex China<br>50<br>China Other EM7<br>Oil Natural Gas Coal Aluminum Copper Zinc Maize Rice Wheat Oil Natural Gas Coal Aluminum Copper Zinc Maize Rice Wheat<br>1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015<br><!-- End of picture text -->

## **C. Contribution to average annual growth in metals consumption**

<!-- Start of picture text --> Percentage points China India<br>Other EM7 Other EMDEs<br>5<br>G7 Other AEs<br>4<br>3<br>2<br>1<br>0<br>-1<br>1996-2008 2008-2016<br><!-- End of picture text -->

## **E. EM7 share of commodity consumption**

Sources: BP Statistical Review, U.S. Department of Agriculture, World Bank, World Bureau of Metals Statistics.

A. Deflated using the manufacturing unit value index from the January 2018 edition of the Global Economic Prospects report.

B.-D. Metals aggregate includes aluminum, copper, lead, nickel, tin, and zinc. Energy aggregate includes coal, crude oil, natural gas, nuclear, and renewables.

- C.D. “AEs” stands for advanced economies. Other EM7 includes Brazil, Indonesia, Mexico, Russia, and Turkey.

- C. “Other AEs” contains 18 advanced economies. Other EMDEs contains 32 countries.

- D. “Other AEs” contains 17 advanced economies. Other EMDEs contains 31 countries.

- E. Other EM7 includes Brazil, India, Indonesia, Mexico, Russia, and Turkey.

- F. Commodity intensity calculated as global energy and metals use (in volumes) relative to global GDP (in 2010 U.S. dollars), including and excluding China.

3
Sixty-one percent of migrants currently residing in cities and towns came from other urban areas, while 47.8 percent of migrants living in rural areas came from the countryside (see Table 2).

Table 2 – Direction of Migration - Urban/Rural (classified according to destination)

|Previous Location|Current l<br>Urban||ocation<br>Rural|Total|
|---|---|---|---|---|
|Urban|61.0||52.2|59.6|
|Rural|39.0||47.8|40.4|
|Total|100||100|100|

Source: Authors’ calculations using Labor Force Survey

# **Migration by Region**

We considered seven regions in Egypt: 1) Cairo governorate; 2) urban Lower Egypt, including three metropolitan governorates (Alexandria, Port Said and Suez); 3) rural Lower Egypt; 4) urban Upper Egypt including urban Giza; 5) rural Upper Egypt (including rural Giza); 6) urban Frontier governorates; and 7) rural Frontier governorates.

Lower Egypt is the preferred destination for migrants (64 percent), followed by Cairo (17 percent), as shown in Table 3. The majority of migrants from Cairo (70 percent) chose Lower Egypt as a destination, as did 46.5 percent of people migrating from Upper Egypt. Additionally 74.9 percent of migrants from Lower Egypt moved to different localities in the same region.

# **Direction of Migration**

Table 3 – Direction of Migration- Region and Urban/Rural (classified according to origin)

|Previous Region|Cairo|Lower<br>Urban|Lower<br>Rural|Curre<br>Upper<br>Urban|nt Region<br>Upper<br>Rural|Frontier<br>Urban|Frontier<br>Rural|Total|
|---|---|---|---|---|---|---|---|---|
|Cairo|3.3|54.8|15.5|20.5|4.5|1.3|0.1|100|
|Lower Urban|26.5|45.1|15.7|6.3|1.4|4.2|0.7|100|
|Lower Rural|9.5|60.7|24.9|2.9|0.5|1.2|0.3|100|
|Upper Urban|30.7|34.9|6.7|18.9|4.1|4.6|0.1|100|
|Upper Rural|20.6|41.9|10.3|15.2|6.4|4.6|1.1|100|
|Frontier Urban|16.9|12.7|4.2|7.0|5.6|49.3|4.2|100|
|Frontier Rural|10.5|26.3|31.6|0.0|0.0|5.3|26.3|100|
|Total|17.0|48.4|15.6|11.8|3.1|3.5|0.6|100|

Source: Authors’ calculations using Labor Force Survey 5
23 Figure 2: Rice NRAs and international rice price, South and Southeast Asia, 1970 to 2008 (left axis is int’l price in current US dollars, right axis is weighted average NRA in percent)

# (a) South Asia

<!-- Start of picture text --> 600 30<br>20<br>500<br>10<br>0<br>400<br>-10<br>300 -20<br>-30<br>200<br>-40<br>-50<br>100 Intern. Price in USD<br>NRA S Asia -60<br>0 -70<br>NRA (%)<br>Intern. price (USD)<br>1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008<br><!-- End of picture text --> Note: Correlation coefficient is -0.70. Countries included are Bangladesh (except for 197073), India, Pakistan and Sri Lanka.

# (b) Southeast Asia

<!-- Start of picture text --> 600 30<br>20<br>500<br>10<br>0<br>400<br>-10<br>300 -20<br>-30<br>200<br>-40<br>-50<br>100 Intern. Price in USD<br>NRA SE Asia -60<br>0 -70<br>NRA (%)<br>Intern. price (USD)<br>1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008<br><!-- End of picture text --> Note: Correlation coefficient is -0.57. Countries included are Indonesia (except for 1970-74), Malaysia, Philippines, Thailand and Vietnam (except for 1970-85 and 2005-08). Source: Authors’ compilation based on their update of data in Anderson and Valenzuela (2008).
28 Table 2. Elasticity of Poverty, Estimated Using International Remittance Data

||Dependent<br>Poverty H<br>($1.08/pe|Variable =<br>eadcount<br>rson/day)|Dependent<br>Pover|Variable =<br>ty Gap|Dependent<br>Squared P|Variable =<br>overty Gap|
|---|---|---|---|---|---|---|
|Variable|(1)|(2)|(3)|(4)|(5)|(6)|
|Per capita GDP (constant 1995|-1.197||-1.432||-1.416||
|dollars)|(-9.10)**||(-9.67)**||(-8.77)**||
|Per capita survey mean income||-2.278||-2.652||-2.534|
|(expenditure)||(-16.85)**||(-15.90)**||(-14.05)**|
|Gini coefficient|2.845<br>(5.56)**|3.256<br>(9.38)**|4.167<br>(7.22)**|4.641<br>(10.84)**|4.760<br>(7.76)**|5.053<br>(11.05)**|
|Remittances as share of country<br>GDP|-0.116<br>(-1.98)*|-0.160<br>(-3.64)**|-0.205<br>(-3.11)**|-0.172<br>(-3.18)**|-0.214<br>(-2.88)**|-0.211<br>(-3.68)**|
|Constant|13.144<br>(11.12)**|15.786<br>(19.47)**|14.611<br>(10.99)**|17.396<br>(17.39)**|14.130<br>(9.75)**|16.342<br>(15.21)**|
|N|104|99|104|99|93|92|
|Adj R<sup>2</sup>|0.443|0.746|0.499|0.733|0.517|0.722|
|F-Statistic|28.29|97.23|35.19|90.77|33.84|79.94|

Notes: Estimates obtained using ordinary least squares. All variables expressed in logs. T-ratios shown in parenthesis. Number of observations reduced in table because of missing values. See Annex Table A1 for countries and survey dates. * Significant at the 0.10 level.

** Significant at the 0.05 level.
externalities generated between cross-sectional observations (in this research the unit of observation is province). Failing to address spatial dependence may lead to biased, inefficient, and/or inconsistent coefficient estimates. In order to test for spatial autocorrelation in the data, we conducted diagnostic tests using data for a cross-section of 81 provinces in 2000. For this, we created a spatial weights matrix that shows the presence of potential spatial interaction between neighboring provinces. We ran diagnostic tests for both the spatial error and spatial lag models (Anselin _et al._ , 1996). In a spatial error model there is an autoregressive process in the error term, whereas a spatial lag model assumes a spatially lagged dependent variable. Lagrange multiplier test statistics are used to test the null hypothesis that autoregressive parameters are equal to zero.<sup>7</sup> The null hypothesis is rejected, indicating the presence of spatial autocorrelation, particularly in the case of spatial lag model.

Spatial dependence is caused by the existence of spillover effects between units of observation (provinces) and the presence of a direct influence from activity in one province on neighboring provinces. In this case, it may be that economic activity measured by GDP in one province affect GDP per capita in neighboring provinces. We therefore run separate regressions using the spatial lag model.

# **Results**

Empirical results are provided in Tables 7 and 8. We start in Table 7 with the panel regression results. Results in the first three columns are from regressions where log of GDP per capita is used as the dependent variable. Results in the next three columns

> 7 Spatial data analysis commands developed by Pisati (2001) for STATA are used to conduct the spatial autocorrelation diagnostic tests.  Diagnostic test output presents Moran’s I, Lagrange multiplier and Robust Lagrange Multiplier test statistics for the spatial error model and Lagrange multiplier and Robust Lagrange Multiplier test statistics for the spatial lag model.  See Anselin _et al._ (1996) for a detailed explanation of these tests.

12
that individuals who do worse in the labor market (for a given school attainment), are those individuals who received lower quality schooling (World Bank 2008). This is a difficult proposition to test without adequate information. However, there are several international student assessments that would allow us to look at the question of achievement and inequality. The assessments can be used to rank countries; but they can also be used to estimate equality in learning as well, such as the dispersion in learning outcomes. Is there a relationship between dispersion in learning outcomes and dispersion in returns?

There appears to be an association between dispersion in learning outcomes and dispersion in returns. Figure 11 shows dispersion in returns measured as the difference between the 90<sup>th</sup> and 10<sup>th</sup> quantile and dispersion in learning measured as the difference in 90<sup>th</sup> and 10<sup>th</sup> percentiles. There is an apparent association. The lower the dispersion in test scores, the lower the dispersion in returns. Despite the limited sample of countries, since we are limited to countries that participated in PISA and for which we have quantile return estimates, there is a clear link. In fact, all East Asian countries in our sample exhibit low dispersion in test scores and low dispersion in returns.

Korea is known as a country that excels in ensuring that all students learn. Efforts are made to equip students with the teachers and resources they need to succeed. At the middle school level, a national lottery is held to assign students to private schools. That is part of the Government’s high school equalization policy, in effect since 1973. Initially a response to growing demand for better schools and rampant private tutoring, under this policy the competitive entrance exams were substituted with random assignment of students for all secondary schools (both private and public). In addition, the government subsidizes private schools. Under the equalization policy, all schools, public or private, had to give up their rights to select new students and are required to 29
posterior notation, we draw five plausible values for each child and then estimate sample moment of interest as where _s_ ( _θ_<sup> ̃</sup> _k_ ) may be the variance, 90th percentile, etc, of the _N_ element vector of plausible values ̃ _θk_ .

Unfortunately, no publicly available software can draw plausible values for the model we estimate, making it difficult for other researchers to replicate the TIMSS methodology precisely. We use the Markov Chain Monte Carlo (MCMC) algorithm proposed by Patz & Junker (1999 _a_ , _b_ ) to compute the EAP scores and plausible values. This technique differs from the computational approach used by TIMSS but is highly flexible and relatively straightforward to implement. We provide a fuller explanation of our estimation strategy in Appendix A.

To see whether these concerns are of practical importance, Figure 4 shows the estimated MLE, EAP and PV distributions of ability. The MLE and EAP estimates represent the upper and lower bounds of the variance of the distribution, with the PV estimates somewhere in between. There is a considerable divergence in the shape of the distribution to the left of the mean, while at higher levels of ability, the three distributions look roughly similar. Because individual scores are only weakly informative for poor performers, the MLE and EAP estimates diverge and plausible value methodology is crucial to estimating percentile cutoffs at the bottom of the distribution.

# **3 Data**

We use data collected in 2005 as part of a larger World Bank study designed and led by Kin Bing Wu, and conducted by the Social and Rural Research Institute (SRI) unit of IMRB International.<sup>7</sup> The study includes detailed surveys of principals, teachers, and students in 288

> 7For further details on the survey design and an analysis of the correlates of performance, see Wu et al. (2006, 2007).

11
<!-- Start of picture text --> '<br>co2 —<br>Forests _<br>BlackneseanonCarb: ——<br>Greenhouse Gases _<br>Ozone Exposure _—_ ''<br>Protected Areas |<br>Species Habitat a '<br>Nitrogen Management —_—_|<br>Species Protection !<br>Tree coverage loss —_—_— 2<br>Wetland loss —_ '<br>\ | I<br>-0.4 0.0 0.4<br>$ Positive, sig. Positive, not sig. $ Negative, sig. $ Negative, not sig.<br><!-- End of picture text -->

<!-- Start of picture text --> '<br>'<br>'<br>'<br>'<br>Labor Rights a<br>'<br>''<br>'<br>'<br>'<br>Workersorkers P:Protectioncti ee '<br>'<br>'<br>\:<br>\\'<br>1 I \ | I<br>-0.6 -0.4 -0.2 0.0 0.2<br>$ Positive, sig. Positive, not sig. $ Negative, sig. 4 Negative, not sig.<br><!-- End of picture text -->

<!-- Start of picture text --> '<br>Polyarchy ‘<br>Freedom of Expression<br>Women Political \<br>Participation ‘1<br>1<br>'<br>Political Liberties o_o 1 ooo<br>1<br>1<br>1<br>1<br>1<br>'<br>Freedom ofAssociation 11<br>1<br>\ 1 | l I I<br>-0.2 -0.1 0.0 0.1 0.2 0.3<br>4 Positive, sig. Positive, not sig. 4 Negative, sig. ¢ Negative, not sig.<br><!-- End of picture text -->
that has accompanied the growth in institutional ownership in U.S. stock markets during the 1980s has in fact enhanced stock market liquidity, and thereby lowered the required return on equity and therefore the cost of capital. Also, institutions, with their large professional staffs, are likely to be more efficient than individual investors at collecting, analyzing, and acting on objective, firm-specific fundamental information. Therefore, the increased institutionalization of the U.S. capital markets has actually served to improve the efficiency with which corporations are valued and govemed. Instead of retarding aggregate long-term corporate investment, the growth in institutional equity ownership simply may have redirected capital from firms with less promising investment projects towards firms with more profitable investment projects.

Jones et al. (1990) tested these hypotheses for a sample of 586 U.S. firms for 1982 and 1988. They conclude that institutions have had a positive effect on liquidity; stocks traded heavily by institutions experienced rising turnover, declining volatility and narrowing bid-ask spreads. In other words, institutional investors helped in lowering the required return on equity capital and therefore the cost of capital for the sample firms. Also, the analysis of corporate R and D expenditures showed no strong evidence that institutions induce short-termist (myopic) behavior amongst corporate management. In general, the study found no support for the criticisms often aimed at institutional investors.

These findings are also consistent with those of Jarrel et al. (1985). The study found that, holding industry effects constant, institutional ownership had a positive effect on the R and D behavior of companies, for a sample of 324 companies for the 1980-83 period. i.e., Institutional investors seem to favor firms with high R and D to revenue ratios.

Likewise, using UK data, Nickell and Wadhwani (1987) found that while the stock 4
_Climatic Change_ 81: 71-83.

- Mendelsohn, R. and S.N. Seo, 2007. “An Integrated Farm Model of Crops and Livestock: Modeling Latin American Agricultural Impacts and Adaptations to Climate Change”, _World Bank Policy Research Series Working Paper4161_ , Washington DC, USA.

- Nhemachena, C. and R Hassan, 2007., “Determinants of climate adaptation strategies of African farmers: Multinomial choice analysis, Draft Report, CEEPA, University of Pretoria

- Nordhaus, W. D., 1992. “An Optimal Transition Path for Controlling Greenhouse Gases”, _Science_ 258:1315-1319.

- Nordhaus, W., 2007. “To Tax or Not to Tax: Alternative Approaches to Slow Global Warming”, _Review of Environmental Economics and Policy_ 1 (1):22-44.

- Reilly, J., et al., 1996. “Agriculture in a Changing Climate: Impacts and Adaptations” in IPCC (Intergovernmental Panel on Climate Change), Watson, R., M. Zinyowera, R. Moss, and D. Dokken (eds.) _Climate Change 1995: Impacts, Adaptations, and Mitigation of Climate Change: Scientific-Technical Analyses,_ Cambridge University Press: Cambridge p427468.

- Rosenzweig, C., and D. Hillel, 1998. Climate change and the global harvest: potential impacts of the greenhouse effect on agriculture. Oxford University Press, New York, USA.

- Schenkler, W., Hanemann, M., and Fisher, A., 2005. “Will US Agriculture Really Benefit From Global Warming?  Accounting for Irrigation in the Hedonic Approach” _American Economic Review_ **95:** 395-406

- Seo, S. N.. and R. Mendelsohn, 2007. “The Analysis of Livestock Choice as an  Adaptation to **”**

- Climate Change in Latin America , _World Bank Policy Research Series Working Paper4162_ , Washington DC, USA.

- Seo, S. N. and R. Mendelsohn, 2008a. “Measuring Impacts and Adaptations to Climate Change: A Structural Ricardian Model of African Livestock Management”, _Agricultural Economics_ 38:1-15.

- Seo, S. N. and R. Mendelsohn, 2008b. “An Analysis of Crop Choice: Adapting to Climate Change in South American Farms”, _Ecological Economics_ (forthcoming).

- Seo, S. N. and R. Mendelsohn, 2008c. “A Ricardian Analysis of the Impact of Climate Change on South American Farms”, _Agricultura Technica_ (forthcoming).

- USGS (United States Geological Survey), 2004. Global 30 Arc Second Elevation Data,

USGS National Mapping Division, EROS Data Centre.

- Voortman, R., B. Sonnedfeld, J. Langeweld, G. Fischer, H. Van Veldhuizen, 1999. “Climate Change and Global Agricultural Potential: A Case of Nigeria“ Centre for World Food Studies, Vrije Universiteit, Amsterdam.

- Washington, W., et al., 2000. “Parallel Climate Model (PCM): Control and Transient Scenarios”. _Climate Dynamics_ 16: 755-774.

16