rafmacalaba/datause-encoder-data huggingface dataset:
# **1. Introduction**

Globalization has led to much faster movements of goods, technology, money, and ideas across borders, but large barriers remain to the movement of people. Despite these barriers, many people move, and by 2005 an estimated 190 million of the world’s population lived outside their country of birth.<sup>1</sup> Large income gaps coupled with diverging demographics between developed and developing countries may amplify the pressures for migration. Holzmann (2005) estimates that without further migration the labor force in Europe, Russia, and high-income East Asia and the Pacific is projected to fall by 43 million between 2005 and 2025, while it is projected to rise by 19 million in China, 77 million in Latin America, 82 million in the Middle East, North Africa and Turkey, 93 million in low and middle income East Asian and Pacific countries, 211 million in Sub-Saharan Africa and 292 million in South and Central Asia.

It has long been observed that people are more likely to migrate while young, so if more migration is to occur, it is likely to be youth doing it. Indeed, in the 1930s Dorothy Thomas concluded that perhaps the only generalization that could be made in regard to differentials in internal migration was that migrants tended to be young adults or people in their late teens (quoted in Lee, 1966). Despite this general observation, very little is known about the characteristics of young migrants. National statistical agencies typically only publish very aggregated information on the age structure of migration, seldom providing cross-tabulations of age with other characteristics of interest such as country of origin. Furthermore, aggregate statistics do not provide information on whether youth are

> 1 Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat, Trends in Total Migrant Stock: The 2005 Revision.

- 2 -
||itle|(10)<br>Net women|0.00201***<br>(0.000767)<br>0.00327***<br>(0.000759)<br>0.0675<br>(0.0882)<br>-0.00121<br>(0.00173)<br>-0.00133<br>(0.00158)<br>0.296***<br>(0.0464)<br>Yes|0.120<br>0.432<br>0.228<br>597<br>is<br>a title,|
|---|---|---|---|---|
||pothetically cot|(9)<br>Co-titling|0.000986<br>(0.00227)<br>0.00654***<br>(0.00180)<br>0.589***<br>(0.167)<br>-0.00870**<br>(0.00403)<br>-0.00879***<br>(0.00277)<br>0.572***<br>(0.123)<br>Yes|0.000345<br>0.0359<br>0.218<br>163<br>roup heading.<br>= 1 if a woman <br>ehold listed on|
||Hy|(8)<br>Take up|0.00374***<br>(0.000920)<br>0.00412***<br>(0.000872)<br>-0.0377<br>(0.0991)<br>0.000182<br>(0.00205)<br>-0.000679<br>(0.00174)<br>0.436***<br>(0.0545)<br>Yes|0.680<br>0.773<br>0.281<br>597<br> the column g<br>ent variable <br>n in the hous|
|useholds|income|(7)<br>Net women|0.00180**<br>(0.000888)<br>0.00331***<br>(0.000820)<br>0.0253<br>(0.116)<br>-0.00123<br>(0.00217)<br>-0.0000545<br>(0.00245)<br>0.295***<br>(0.0506)<br>Yes|0.0818<br>0.161<br>0.244<br>519<br>_X_ is defned by <br>ns, the depend<br>umber of wome|
|al-headed ho|en’s share of|(6)<br>Co-titling|0.000522<br>(0.00213)<br>0.00724***<br>(0.00194)<br>0.820**<br>(0.335)<br>-0.0156**<br>(0.00626)<br>-0.0116**<br>(0.00498)<br>0.545***<br>(0.128)<br>Yes|0.0000212<br>0.00295<br>0.275<br>143<br>4, 5-7, 8-10), <br>-titling” colum<br>es the total n|
|fects - du|Wom|(5)<br>Take up|0.00386***<br>(0.00106)<br>0.00334***<br>(0.000953)<br>-0.136<br>(0.138)<br>0.00171<br>(0.00333)<br>0.00449<br>(0.00274)<br>0.431***<br>(0.0613)<br>Yes|0.596<br>0.449<br>0.275<br>519<br> (columns 2-<br>CRO. In “co<br>riable measur<br>_<_0_._01|
|interaction e|ner|(4)<br>Net women|0.00138*<br>(0.000745)<br>0.00289***<br>(0.000719)<br>-0.0637<br>(0.0932)<br>0.00171<br>(0.00214)<br>-0.000116<br>(0.00171)<br>0.333***<br>(0.0454)<br>Yes|0.0451<br>0.878<br>0.233<br>603<br>on of the table<br>lly paid for a <br> dependent va<br>_∗p <_0_._05_,∗∗∗p _|
|co-titling,|ault female ow|(3)<br>Co-titling|-0.000964<br>(0.00202)<br>0.00488***<br>(0.00156)<br>0.475***<br>(0.176)<br>-0.00378<br>(0.00352)<br>-0.00938**<br>(0.00418)<br>0.680***<br>(0.108)<br>Yes|0.0000439<br>0.936<br>0.190<br>166<br> For each secti<br>usehold has fu<br>en” columns, <br>s. _∗p <_0_._10_,∗_|
|doption and|Def|(2)<br>Take up|0.00325***<br>(0.000898)<br>0.00349***<br>(0.000815)<br>-0.217**<br>(0.0988)<br>0.00288<br>(0.00230)<br>0.00267<br>(0.00189)<br>0.466***<br>(0.0526)<br>Yes|0.782<br>0.992<br>0.283<br>603<br>bility model. <br>able = 1 if ho<br>. In “net wom<br>standard error|
|Table 6: CRO a|X =|(1)<br>Take up|General voucher<br>0.00363***<br>(0.000827)<br>Conditional voucher<br>0.00386***<br>(0.000742)<br>_X_<br>General _×X_<br>Conditional _×X_<br>Constant<br>0.436***<br>(0.0493)<br>Baseline controls<br>Yes|Test 1: _βG_ =_βC_<br>0.780<br>Test 2: _βG_+_βG×X_ =_βC_ +_βC×X_<br>_R_2<br>0.279<br>Obs<br>603<br>**Notes:** All columns show a separate linear proba<br>In columns labeled “take-up”, the dependent vari<br>listed as owner or co-owner on a CRO application<br>combining take-up and co-titling efects. Robust|

29
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29
# **Table 1**

# **Oil Price Summary Statistics**

||**Full**<br>**Sample**<br>_1985‐2015_|**Pre‐**<br>**Boom**<br>_1983‐2003_|**Post‐**<br>**Boom**<br>_2004‐2015_|**Crash 1**<br>_11/25/85‐_<br>_03/31/86_|**Crash 2**<br>_11/08/90‐_<br>_02/21/91_|**Crash 3**<br>_07/14/08‐_<br>_02/19/09_|**Crash 4**<br>_10/01/14‐_<br>_01/29/15_|
|---|---|---|---|---|---|---|---|
|**Nominal price level statis**|**tics**|||||||
|Maximum|145.29|40.42|145.29|31.70|35.53|145.18|91.01|
|Minimum|10.42|10.42|32.48|10.42|18.50|33.87|44.45|
|Max to Min change (%)|—|—|—|‐66.4|‐47.9|‐76.7|‐51.2|
|**Returns statistics**||||||||
|Mean|0.01|0.01|0.01|‐1.33|‐0.35|‐1.29|‐0.86|
|Standard Deviation|2.38|2.42|2.32|4.69|5.18|4.62|2.58|
|Interquartile Range|2.31|2.26|2.41|4.82|6.00|5.54|2.90|
|Skewness|‐0.22|‐0.36|0.06|0.01|0.00|0.41|‐0.39|
|Kurtosis|8.69|9.02|7.97|3.36|3.46|3.80|4.84|
|**Distribution of quartiles**||||||||
|Minimum|‐17.45|‐17.45|‐13.07|‐13.91|‐13.17|‐12.60|‐10.79|
|Median|0.03|0.00|0.06|‐1.37|‐0.27|‐1.27|‐0.89|
|Maximum|16.41|14.03|16.41|11.04|12.68|14.55|5.49|
|25<sup>th</sup>percentile|‐1.12|‐1.07|‐1.19|‐3.84|‐3.32|‐4.54|‐2.21|
|75<sup>th</sup>percentile|1.20|1.19|1.22|0.98|2.68|1.00|0.70|
|**Fraction of days with shoc**|**ks**|||||||
|Greater than +1%|0.28|0.28|0.29|0.24|0.39|0.26|0.18|
|Greater than +2%|0.15|0.15|0.14|0.20|0.32|0.19|0.10|
|Less than ‐1%|0.27|0.26|0.28|0.52|0.45|0.54|0.45|
|Less than ‐2%|0.14|0.14|0.15|0.44|0.30|0.43|0.29|
|Fraction of stable days|0.71|0.72|0.71|0.37|0.38|0.37|0.61|
|_Observations_|_7,575_|_4,759_|_2,816_|_82_|_71_|_113_|_83_|

**Notes** : “—“ indicates not applicable. The observation for January 17, 1991 has been excluded—it dropped 33 percent, from $32.00/bbl on January 16 to $21.22/bbl on January 17 (it was the day coalition forces in‐ vaded Iraq, consequently reducing the likelihood of oil supply disruptions). The ‘fraction of stable days’ denotes the number of days with absolute daily oil price changes that do not exceed 2 percent.

<!-- Start of picture text --> 7 —<br><!-- End of picture text --> _— 7 —_
# Estimating Parameters and Structural Change in CGE Models Using a Bayesian Cross-Entropy Estimation Approach

Delfin S. Go, Hans Lofgren, Fabian Mendez Ramos, and Sherman Robinson<sup>1</sup> JEL codes: C68, C61, C11, C13, E16, J24 Keywords: Computable general equilibrium (CGE) models, Bayesian parameter estimation, cross-entropy method, Kullback-Leibler objective function, model validation and selection, structural change and shifts in trade shares and productivity 1 Delfin S. Go – was a staff of the World Bank when the paper was prepared; Hans Lofgren – The World Bank; Fabian Mendez Ramos- The World Bank; and Sherman Robinson – International Food Policy Research Institute. The study was partially funded by a grant from the Knowledge for Change Program II Trust Fund of the World Bank. We thank Syud Amer Ahmed, Maryla Maliszewska, Karen Thierfelder, Hans Timmer, Dominique van der Mensbrugghe, and Dirk Willenbockel for several discussions, comments and suggestions at various points of the study.
**Figure 1: Simulated net effect of a range of factors shaping subjective wellbeing in Colombia, 2010/2011**

<!-- Start of picture text --> 25.0% 22.8%<br>20.4%<br>19.9% 18.6%<br>20.0%<br>15.9%<br>15.0% 13.3%<br>10.3%<br>10.0% 7.9% 9.0% 6.8% 6.0%<br>5.0% 3.8% 2.0% 4.4% 1.5% 2.7% 1.4% 2.6% 2.7% 0.8% 0.8% 1.1% 4.3% 0.8% 0.2% 0.1% 1.5% 0.7% 4.0% 2.9% 2.8% 0.2% 4.0% 0.5% 0.1% 0.2% 2.9% 0.7% 3.6%<br>0.0%<br>-5.0% -0.9% -0.7% -1.9% -0.9%<br>-10.0% -3.9% -3.7% -8.7% -3.6% -5.3% -7.7% -7.2% -6.8% -4.5% -8.3% -5.3% -3.4% -7.2% -7.5% -5.5%<br>-15.0%<br><!-- End of picture text --> _Source_ : Author’s calculations of predicted probabilities after running probit regression based on LAPOP data. Note: Results here are derived from using the ‘prtab’ command after estimating the complete probit regression model. The same ‘note’ applies as in Table 1.

It is important to stress that part of our subjective wellbeing in life is also influenced by our genes and personality traits, but these are unobserved and so captured in the regression error term as it is not possible to control for such idiosyncratic characteristics. Cross-sectional survey data can, for example, at times overestimate the household income effect on subjective wellbeing as it can include reverse effects of being happy on generating more household income. To help overcome this, the next section assesses the relative importance of perceived changes in subjective wellbeing over time, after having looked above at the correlates of a good life at one point in time. Thus, the econometric methods conducted in the following can help neutralize such latent heterogeneity.<sup>18</sup>

# **Changes in levels of perceived subjective wellbeing between 2008/09 and 2010/11**

It is possible, given the nature of this data set, to draw potential conclusions about what caused shifts in an individual’s perceived wellbeing over the past two years. This is not a panel design with data on the same respondents in 2008/09 and 2010/11 but rather reflects data collected in 2010/11 on respondents’ current levels of subjective wellbeing and their levels two years ago, i.e. they were asked ‘on what step of the ladder were you two years ago, in 2008 (for those asked in 2010) and 2009 (for those asked in 2011)?’ This survey data therefore offers an alternative to panel data and helps to overcome idiosyncratic differences between people (psychological drive, personality etc.) by capturing information on the same respondents over this two year period. Between 2008/09 and 2010/11, 49.4 percent of Colombians reported an increase in their own levels of subjective wellbeing, while 24.9 percent reported a decline and 25.7 percent the same level. While this data provides potentially valuable information on the influences of changes, it is also important to keep in mind that they are perceived changes and that baseline subjective wellbeing levels and/or

> 18 For a discussion on latent heterogeneity, see Ravallion 2012.

14
The Effect of Immigration on Natives' School Achievement: Does Length of Stay in the Host Country Matter?<sup>*</sup> Laurent Bossavie<sup>†</sup> Keywords: Immigration, education, peer effects JEL classification: I21, J15

> * I am extremely grateful to the Data Archiving and Network Serivces (DANS) in the Netherlands, in particular to Hernan Vierke, for granting me the access to the various waves of the PRIMA dataset. I am also indebted towards Andrea Ichino and Luigo Guiso for constant advice and discussions at the earlier stages of the paper. All remaining errors are mine.

† Current address: The World Bank, 1818 H Street NW, 20433 Washington DC, USA. E-mail: lbossavie@worldbank.org. Tel: 202-751-6478. An earlier version of this paper was written while the author was a PhD student at the Department of Economics of the European University Institute (EUI). The findings, interpretations and conclusions expressed in this paper are entirely those of the author. They do not necessarily reflect the views of the European University Institute of those of the World Bank Group.
# **Table A15: Summary statistics**

|VARIABLES||(1)<br>N|(2)<br>mean|(3)<br>sd|(4)<br>min|(5)<br>max|
|---|---|---|---|---|---|---|
|Absolute poverty|pov_abs|219,792|0.0559|0.230|0|1|
|Child|child|219,792|0.195|0.397|0|1|
|Household with children|hh_ch|219,792|0.499|0.500|0|1|
|Household with 3+ children<br>Household with a single adult|hh_3more_ch|219,792|0.0441|0.205|0|1|
|and children|hh_single|219,792|0.0428|0.202|0|1|
|Retired individual|retiree|219,792|0.270|0.444|0|1|
|Higher education|high_edu|219,792|0.137|0.344|0|1|
|Secondary specialized|ss_edu|219,792|0.179|0.383|0|1|
|Vocational education|voc_edu|219,792|0.0898|0.286|0|1|
|Secondary education|sec_edu|219,792|0.107|0.309|0|1|
|Basic, primary or don't have|||||||
|education|low_edu|219,792|0.0115|0.107|0|1|
|Large city|large_cities|219,792|0.360|0.480|0|1|
|Small city|small_cities|219,792|0.190|0.393|0|1|
|Rural|rural|219,792|0.259|0.438|0|1|
|Brest oblast|1.region|219,792|0.150|0.357|0|1|
|Vitebsk oblast|2.region|219,792|0.128|0.335|0|1|
|Gomel oblast|3.region|219,792|0.153|0.360|0|1|
|Grodno oblast|4.region|219,792|0.114|0.318|0|1|
|Minsk city|5.region|219,792|0.190|0.392|0|1|
|Minsk oblast|6.region|219,792|0.150|0.357|0|1|
|Mogilev oblast|7.region|219,792|0.115|0.319|0|1|
|Employed|empl|219,792|0.451|0.498|0|1|
|Partly employed|part_empl|219,792|0.0222|0.147|0|1|
|Non‐employed|nonempl|219,792|0.121|0.326|0|1|
|Logarithm of income|log_income|219,792|14.91|0.930|3.916|19.60|

42
Table A5. Conflict-induced IDPs settled in Badghis province in 2020.

|Districts|Displaced individuals<br>(or arrival IDPs)|
|---|---|
|Ab Kamari|20|
|Qala-e-Naw|7,862|
|Total|7,882|

# Comparison of IOM and OCHA data

IOM’s CBNA reports arrival IDPs for settlements (in 2020, 1,069 settlements in the Badghis province), which are then aggregated into districts (7 districts in the region). OCHA’s data only contains information for districts; in 2020, conflict-induced IDPs arrived in two districts of the Badghis province.

Table A6 compares displacements reported by OCHA to those recorded by IOM for two districts that are included in the OCHA’s dataset. For example, OCHA reported the arrival of 7,862 IDPs due to conflict in Qala-e-Naw district. According to IOM, settlements in this district reported 9,350 IDPs that arrived due to conflict and conflict-natural disasters (but 0 IDPs if to look at settlements that reported the arrival of IDPs due to conflict only). This hints that OCHA data may also include displacement due to natural disasters along with those due to conflict, even though the former is not specified.

Table A6. Comparison of OCHA and IOM’s data on displacements in Badghis province in 2020.

||OCHA||IOM||
|---|---|---|---|---|
|Reason for<br>displacement<br>Districts|conflict|conflict;<br>conflict-natural<br>disasters;<br>natural disasters;<br>natural disasters-<br>conflicts|conflict;<br>conflict-natural<br>disasters|conflict|
|Ab Kamari|20|1,881|1,503|154|
|Qala-e-Naw|7,862|12,289|8,510|0|
|Other districts|0|30,396|20,558|9,350|
|Total|7,882|44,566|30,571|9,504|

Correlation analysis of OCHA and IOM’s data on internal displacement was conducted based on a limited number of comparable observations (districts) (Table A7). Pearson’s correlation coefficient is 0.8162 (p<0.01), which, however, may not be interpreted because of too few observations.

Table A7. Arrival IDPs in 2019-2020

|Periods|Districts|IOM|OCHA|
|---|---|---|---|
|Jan-June 2019|Ab Kamari|1071|0|
||Bala Murghab|20445|17335|
||Ghormach|273|0|
||Jawand|1127|0|
||Muqur|1288|0|
||Qadis|1503|0|
||Qala-e-Naw|11432|6778|
|Jan-June 2020|Ab Kamari|972|20|
||Bala Murghab|11011|0|
||Ghormach|0|0|
Table 5: Dependent Variable: Share of imports in total materials

|Sample|All|Dom private|Foreign|Multiple Ind|
|---|---|---|---|---|
|�2001|-0.0237**|0.0538|-0.0241**|-0.0200***|
||(0.010)|(0.047)|(0.011)|(0.006)|
|�2002|-0.0278***|0.137**|-0.0293***|-0.0223***|
||(0.006)|(0.062)|(0.007)|(0.007)|
|�2003|-0.0674***|0.0761|-0.0695***|-0.0678***|
||(0.007)|(0.067)|(0.007)|(0.007)|
|�2004|-0.0837***|0.0813|-0.0852***|-0.0830***|
||(0.008)|(0.061)|(0.008)|(0.006)|
|�2005|-0.114***|0.0483|-0.115***|-0.116***|
||(0.010)|(0.066)|(0.009)|(0.006)|
|�2006|-0.155***|0.0312|-0.157***|-0.144***|
||(0.011)|(0.081)|(0.009)|(0.007)|
|�2007|-0.170***|-0.00236|-0.171***|-0.154***|
||(0.017)|(0.086)|(0.013)|(0.007)|
|�wL<br>PY<br>�<br>it|0.0336|0.417**|0.0328|0.0481|
||(0.042)|(0.190)|(0.039)|(0.042)|
|ln (K=L)it|-0.0035|-0.0252|-0.0040|-0.0040|
||(0.003)|(0.030)|(0.003)|(0.003)|
|N|17831|858|16688|28875|
|R-sq|.0898|.104|.0918|.0896|

Note: Firm and year ...xed e¤ects are always included. Data set: merged NBS and customs data. Column (1) uses the whole sample; columns (2) and (3) include only domestic private and foreign-invested ...rms, respectively. Column (4) includes ...rms that operate in multiple industries as well. Bootstrapped standard errors, clustered at the industry level, are reported in parentheses. * p<0.10; ** p<0.05; *** p<0.01.

49
|**Dif**<br>**(std. err.)**|-0.47_∗∗∗_<br>(-0.015)|-0.012_∗∗∗_<br>(-0.001)|-0.0100_∗∗∗_<br>(-0.00096)|52.29***<br>(15.06)|32.49***|0.048_∗∗∗_<br>(-0.0043)|0.11_∗∗∗_<br>(-0.0043)|-0.22_∗∗∗_<br>(-0.0045)|-0.26_∗∗∗_<br>(-0.0052)|-0.0080_∗∗_<br>(-0.0031)|0.81_∗∗∗_<br>(-0.03)|0.19_∗∗∗_<br>(-0.0038)|0.086_∗∗∗_<br>(-0.0022)|0.20_∗∗∗_<br>(-0.0037)|0.12_∗∗∗_<br>(-0.0029)|0.91_∗∗∗_<br>(-0.16)|-0.046_∗∗∗_<br>(-0.012)|-0.12_∗∗∗_<br>(-0.0038)|-0.0085_∗∗∗_<br> on next page|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|**Dalits**|3.38|0.048|0.042|1033.8|1007.6|0.39|0.51|1.24|1.58|0.86|17.3|0.32|0.03|0.5|0.093|3.99|0.57|0.46|0.08<br>Continued|
|**Non-Dalit Hindus**|2.9|0.037|0.032|981.5|972.7<br>(14.1)|0.44|0.62|1.02|1.31|0.85|18.1|0.51|0.12|0.69|0.21|4.9|0.53|0.35|0.072|
|**Dif**<br>**(std. err.)**|-0.76_∗∗∗_<br>(-0.02)|-0.0026_∗_<br>(-0.0014)|-0.0012<br>(-0.0012)|72.56_∗∗∗_<br>(20.12)|57.05_∗∗∗_<br>(18.72)|0.18_∗∗∗_<br>(-0.0058)|0.17_∗∗∗_<br>(-0.0058)|-0.20_∗∗∗_<br>(-0.0054)|-0.28_∗∗∗_<br>(-0.0067)|0.0095_∗∗_<br>(-0.0041)|1.12_∗∗∗_<br>(-0.04)|0.18_∗∗∗_<br>(-0.0052)|0.084_∗∗∗_<br>(-0.0032)|0.21_∗∗∗_<br>(-0.0049)|0.12_∗∗∗_<br>(-0.0041)|0.051<br>(-0.22)|-0.045_∗∗∗_<br>(-0.016)|0.11_∗∗∗_<br>(-0.0049)|0.032_∗∗∗_|
|**Muslims**|3.65|0.039|0.033|1053.4|1029.8|0.26|0.45|1.22|1.6|0.84|17.1|0.34|0.032|0.48|0.085|4.84|0.57|0.24|0.04|
|**Non Dalit Hindus**|2.9|0.036|0.032|980.8|972.7|0.44|0.62|1.02|1.31|0.85|18.2|0.52|0.12|0.69|0.21|4.9|0.53|0.35|0.072|
||**raphic Profle**<br>r of children born|n of ever-born boys who died|n of ever-born girls who died|io of children alive|io of children ever-born|ed|contraceptive use|number of girls|number of boys|x-ratio|frst marriage|**conomic Status**<br> completion of primary school|completion of secondary school|mpletion of primary school|mpletion of secondary school|cres)|old below poverty line|currently working|self-employed|
||**Demog**<br>Numbe|Fractio|Fractio|Sex-rat|Sex-rat|Steriliz|Female|”Ideal”|”Ideal”|Ideal se|Age at|**Socioe**<br>Female|Female|Male co|Male co|Land (a|Househ|Women|Women|

23
- 1.30 -

# A Brif Technical Note

The core concepts of the Ghana Multiperiod Agricultural Sector Model (GMASM) are multinarket equilibrium and forward-looking investmont planning. Markets are linked over thne by the investmont activities of farmers and labor supply and demand changes due to population and Income growth. Demand Is shuply specified owing to a lack of recent demand studies for Ghana. Demand functions are Inoar in own price and per capita consumption expenditure, and parameter estkiatlon utilized base case price and quantity observations alng with elasticity esthiates obtained from demand studies of skuilar countries. Supply functions are defined hDlicitIy using Ihear activity analysis. Multhiarket equlilbrium Is sbuAlated by maxknizing the sum of consumers and producers surplus In each thke poriod, using a nonlinear programmuing algorithm. To avoid arbitrary selection of termhal conditions. the sibulation experients were run for a 50-year horizon without terminal stock levels. Inspection of the temporal trajectories of key variables revealed evidence of some boundary effects In the later tkne periods. However, trajectories through 30-year horizons were free from any observed boundary effects. A complete algebraic specification of the multiperiod model Is given In Section l.D. For readers with some knowledge of the Generalized Algebraic Modeling System (GAMS), a complete specification of the model In GAMS notation, inclAding parameter derivation and calibration, Is given In Annex A of this report.