rafmacalaba/datause-encoder-data huggingface dataset:
in German manufacturing industry over the 1995-2007 period than the change in firm energy intensity does.

Although there has been no attempt so far to decompose energy consumption/CO2 emissions in the Turkish economy by using firm level data in the literature, there are several papers that covered the topic for Turkey by employing data at more aggregated levels. Following the LMDI methodology, Turut Asik et al. (2008) reveal that the main cause of the change in the energy intensity in Turkish manufacturing intensity over the 1992-2001 period is change in the energy intensity at the sub-sector (industries at 4 digits) level instead of a structural effect, i.e. production shift, across sub-sectors. In another study, Tunc et al. (2009) document that over the 1970-2006 period, economic activity was the main component that determined the changes in CO2 emissions in Turkey. Moreover, the sectoral intensity effect had a significant role in the CO2 change, unlike the sectoral structural effect.

Employing a refined Laspeyres methodology, Kumbaroglu (2011) show for the 1990-2007 period that the energy intensity effect has a dominant impact on CO2 emissions in Turkish manufacturing industry. Applying the LMDI methodology on a Turkish manufacturing industry data set at the ISIC 4-digit level, Yilmaz et al. (2016) document that the activity effect was by far the most contributing effect in the increase in energy consumption in Turkish manufacturing industry over the 1981-2011 period. The structural effect had a slightly negative effect and the intensity effect had a slightly positive impact on energy consumption during the period.

# **III. DATA SET AND PRELIMINARY ANALYSIS**

This study makes use of Annual Industry and Services Surveys (AISS) of the Turkstat for the 2005-2012 period. AISS is divided into two parts, based on firm employment size: (i) a census of firms with at least 20 employees in that year and (ii) a large sample of firms with fewer than 20 employees with sampling based on region and NACE 4-digit sector.

The variables used for this study are:

- Firm random codes to identify firms (codes change across firms in a year but are the same over time for the same firm),

- Year and 2-digit NACE sector codes,

- Value of energy consumption,

- Value of production (gross output).

6
# _Developing an Alternative to the Current LIC DSF_

We consider an alternative approach to assessing debt sustainability using the same data as in IMF (2016) based on giving nominal public debt-to-GDP and its dynamics primacy over the PV of PPG external debt-to-GDP. We do five exercises:

- First, we demonstrate that current account deficits are driven mainly by the public sector or fiscal deficit, depending upon the coverage. If this is the case, then it is reasonable to assume that public debt dynamics, which are driven by fiscal deficits, exchange rates and GDP growth, spill over into external debt dynamics. This would be particularly true if the bulk of external debt is accounted for by PPG debt.

- Second, we go back to the basics on the PV of debt and what this means conceptually as public borrowing sources expand to include commercial borrowing from sources such as domestic Treasury Bills (T-bills) and Eurobonds, with the share of official, concessional financing diminishing. We look at the PV of public debt versus nominal public debt.

- Third, we examine nominal public debt and its dynamics in conjunction with market signals, drawing sharply different conclusions about sustainability from those in IMF (2016) _using the same data_ .

- Fourth, we stress the need for an adequate analysis of international liquidity based on the measures used for EMs.

- Finally, we look at the early warning in IMF (2016) versus the alternative presented here.

# _Links between Fiscal and Current Account Deficits_

Table 1 presents data on fiscal and current account deficits as well as nominal external debt and its PPG share for the years 2013-15.

**Table 1: Ghana’s Fiscal and Current Account Deficits 2013-15**

(% of GDP unless otherwise noted)

||**2013**|**2014**(Est.)|**2015**(Prog.)|
|---|---|---|---|
|1. Fiscal deficit|10.5|10.2|7.2|
|2. Current account deficit|11.9|9.6|8.2|
|3. Nominal external debt|35.2|48.5|56.0|
|_4. PPG share(% of 3.)_|_87.2_|_88.5_|_92.9_|

**_Source:_** Lines 1 and 2 are from Table 1, p.25, of IMF (2016), while 3 and 4 are from the external DSA table.

The table shows that the current account deficit (CAD)—which equals the fiscal deficit plus the gap between private investment and saving—is substantially accounted for by the fiscal deficit. Hence, it is not a surprise that PPG external debt accounts for the bulk of total nominal external debt. For LICs, the idea that the CAD is largely explained by the fiscal deficit is not surprising because it is usually difficult for the domestic private sector to borrow from abroad, either because lenders consider it too risky or because of legal restrictions (once again, Zambia is an exception). In this case, the private sector is restricted to borrowing at home and could be “crowded out” even in this space by the government if large deficits push up real interest rates or if banks are statutorily required to hold government securities.

7
# **Table 7 Productivity Commission template for restrictions in banking services**

|Weight -<br>foreign<br>index|Weight -<br>domestic<br>index|Score|Restriction|
|---|---|---|---|
||||**_Restrictions on commercial presence_**|
|0.1900|0.1900|1.00<br>0.75<br>0.50<br>0.25<br>0.00|**Licensing**<br>Issues no new banking licenses<br>Issues up to 3 new banking licenses with only prudential restrictions<br>Issues up to 6 new banking licenses with only prudential restrictions<br>Issues up to 10 new banking licenses with only prudential restrictions<br>Issues new banking licenses with only prudential restrictions|
|0.1900|0.1900||**Direct Investment**<br>The score will be inversely proportional to maximum equity<br>participation permitted in an existing domestic bank. For example,<br>ownership to a maximum of 49 percent of a bank would receive a<br>score of 0.51.|
|0.0950|0|1.00<br>0.50<br>0.00|**No new licenses and JV arrangements**<br>Issues no new banking licenses and no entry is allowed through a joint<br>venture with a domestic bank<br>Bank entry is only through a joint venture with a domestic bank<br>No requirement for a bank to enter through a joint venture with a<br>domestic bank|
|0.0190|0|1.00<br>0.80<br>0.60<br>0.40<br>0.20<br>0.00|**Movement of People - Permanent**<br>No entry of executives, senior managers or specialists<br>Executives, specialists and/or senior managers can stay a period of up<br>to 1 year<br>Executives, specialists and/or senior managers can stay a period of up<br>to 2 years<br>Executives, specialists and/or senior managers can stay a period of up<br>to 3 years<br>Executives, specialists and/or senior managers can stay a period of up<br>to 4 years<br>Executives, specialists and/or senior managers can stay a period of<br>more than 5 years|
||||**_Other Restrictions_**|
|0.1425|0.1425|1.00<br>0.75<br>0.50<br>0.00|**Raising funds by banks**<br>Banks are unable to raise funds from domestic sources<br>Banks are restricted from raising funds from domestic capital markets<br>Banks are restricted in accepting deposits from the public, or face<br>interest rate controls<br>Banks can raise funds from any source with only prudential<br>restrictions<br>Continued|

36
|Household Energyand Universal Access Project|Koffi Ekouevi|
|---|---|
|Household Energyand Universal Access Project|Koffi Ekouevi|
|Household Energyand Universal Access Project|Koffi Ekouevi|
|Household Energyand Universal Access Project|Koffi Ekouevi|
|Household Energyand Universal Access Project|Koffi Ekouevi|
|Household Energyand Universal Access Project|Koffi Ekouevi|
|Household Energyand Universal Access Project|Koffi Ekouevi|
|Multisectoral HIV/AIDS Project|Francois Honore Mkouonga|
|Multisectoral HIV/AIDS Project|Francois Honore Mkouonga|
|Multisectoral HIV/AIDS Project|Francois Honore Mkouonga|
|Multisectoral HIV/AIDS Project|Francois Honore Mkouonga|
|Multisectoral HIV/AIDS Project|Francois Honore Mkouonga|
|Multisectoral HIV/AIDS Project|Francois Honore Mkouonga|
|Multisectoral HIV/AIDS Project|Francois Honore Mkouonga|
|Multisectoral HIV/AIDS Project|Francois Honore Mkouonga|
|Multisectoral HIV/AIDS Project|Francois Honore Mkouonga|
|Second Environmental Management and CapacityBuildingProject|Martin Fodor|
|Second Environmental Management and CapacityBuildingProject|Martin Fodor|
|Second Environmental Management and CapacityBuildingProject|Martin Fodor|
|Second Environmental Management and CapacityBuildingProject|Martin Fodor|
|Second Environmental Management and CapacityBuildingProject|Martin Fodor|
|Second Environmental Management and CapacityBuildingProject|Martin Fodor|
|Second Environmental Management and CapacityBuildingProject|Martin Fodor|
|Second Environmental Management and CapacityBuildingProject|Martin Fodor|
|Second Environmental Management and CapacityBuildingProject|Martin Fodor|
|Second Environmental Management and CapacityBuildingProject|Martin Fodor|
|Second Environmental Management and CapacityBuildingProject|Martin Fodor|
|Second Environmental Management and CapacityBuildingProject|Martin Fodor|
|Second Environmental Management and CapacityBuildingProject|Martin Fodor|
|Second Environmental Management and CapacityBuildingProject|Martin Fodor|
|Second Environmental Management and CapacityBuildingProject|Martin Fodor|
|Second Environmental Management and CapacityBuildingProject|Martin Fodor|
|Second Environmental Management and CapacityBuildingProject|Martin Fodor|
|Second Environmental Management and CapacityBuildingProject|Martin Fodor|
|Second Environmental Management and CapacityBuildingProject|Martin Fodor|
|Second Environmental Management and CapacityBuildingProject|Martin Fodor|
|ANDHRA PRADESH COMMUNITY FOREST MANAGEMENT PROJECT|Grant Milne|
|ANDHRA PRADESH COMMUNITY FOREST MANAGEMENT PROJECT|Grant Milne|
|ANDHRA PRADESH COMMUNITY FOREST MANAGEMENT PROJECT|Grant Milne|
|ANDHRA PRADESH COMMUNITY FOREST MANAGEMENT PROJECT|Grant Milne|
|ANDHRA PRADESH COMMUNITY FOREST MANAGEMENT PROJECT|Grant Milne|
|ANDHRA PRADESH COMMUNITY FOREST MANAGEMENT PROJECT|Grant Milne|
|ANDHRA PRADESH COMMUNITY FOREST MANAGEMENT PROJECT|Grant Milne|
|ANDHRA PRADESH COMMUNITY FOREST MANAGEMENT PROJECT|Grant Milne|
|ANDHRA PRADESH COMMUNITY FOREST MANAGEMENT PROJECT|Grant Milne|
|ANDHRA PRADESH COMMUNITY FOREST MANAGEMENT PROJECT|Grant Milne|
|ANDHRA PRADESH COMMUNITY FOREST MANAGEMENT PROJECT|Grant Milne|
- 14 -

# **Table 7.** The Distribution of Traffic by Vehicle Class and Distance Travelled (1987)

# A. Interurban

# Percent distribution of VKT

||Cars|Pickups|Buses|Mammy<br>wagons||Trucks||
|---|---|---|---|---|---|---|---|
||||||Light|Medium|Heavy|
|Greater Acera|25|16|22|1 1|9|6|11|
|Volta|20|25|22|14|9|3|7|
|Eastern|21|16|22|18|8|6|9|
|Central|20|16|20|_15_|10|8|1 1|
|Western|18|_15_|19|17|10|7|14|
|Ashanti|22|14|24|13|9|7|1 1|
|Brong<br>Ahafo|16|19|_15_|_15_|13|_5_|17|
|Northern|10|_15_|1<br>1|14|12|9|29|
|Upper<br>West|15|28|1<br>1|10|_15_|_5_|16|
|Upper<br>East|19|27|**_15_**|**_5_**|13|6|_15_|
|**National**<br>**average**|<br>**19**|**19**|18|**13**|**11**|**6**|**14**|

# B. Urban

||Total<br>length<br>of<br>roads<br>(kmn)|Average<br>hourly<br>traffic|<br>Pe<br>Cars|rcent<br>distrib<br>Taxis|ufion of VK<br>Mammy<br>wagon|T<br>Buses<br>and<br>trucks|
|---|---|---|---|---|---|---|
|Accra-<br>Inner<br>city|_150_|800|50|25|10|_15_|
|Accra<br>-<br>Outercity|800|440|45|25|12|18|
|Tema|**_155_**|**450**|40|30|10|20|
|Sekondi-Takora|di 195|360|30|30|10|30|

Note: VKT = Vehicle-km traveled per year. **Source:** Bhandari (1990).
were included. For the analysis, we selected a random sample of about 6 percent of spells. Second, being of administrative nature, the information is free of problems typically faced by survey data (such as non-response and interviewer bias). Third, by combining information about unemployment and subsequent employment spells, the information at our disposal not only covers the whole unemployment spell, but it also contains accurate information about the timing of transitions from unemployment to employment. In contrast to many studies using administrative data on unemployment spells where information about the job-finding date is based on unreliable reporting of unemployed workers themselves (as they have little incentive to do so), we have independent information about the start of post-unemployment job reported by employers. And fourth, we have the information about wages the unemployed earn in their post-unemployment jobs.

# **3.2 Formation of comparison groups**

One feature worth exploiting in setting up the empirical analysis is the fact that the change in the Slovenian benefit law introduced different rules for different groups of unemployed. This allows us to disentangle the effects of changing the potential duration of benefits from the effects generated by changes in labor market conditions (such as a general increase in demand for labor), other policy changes, or a combination of these factors. We adhere to the “difference in differences” approach: We – compare outcomes before and after the reform exploiting the exogenous variation in potential benefit durations introduced by the reform – and compare outcomes for a natural control group to outcomes for several treatment groups. In short, we exploit specific design features of the changes in UI law. Note that another feature of the – – Slovenian UI system that PBDs vary across groups of workers does not identify unambiguously the relationship between PBD and quality of post-unemployment jobs. According to the law, individuals with longer work experience are entitled to longer PBD. But individuals with a longer work history are typically older, so if they would find better jobs this could be attributed not only to their longer PBD but also to their work experience.

Our basic strategy is to compare the quality of post-unemployment jobs before and after the law changed. The legislative change provides an exogenous variation in PBD. However, to be able to unambiguously identify causality underlying po8
_Figure 8: Interest rate sensitivity of derivatives, 2002 to mid 2006_ <!-- Start of picture text --> bn DKK<br>80<br>2005<br>60<br>2004<br>40<br>2006 (1st half)<br>2003<br>2002<br>20<br>0<br>2003<br>-1.5 -1.0 -0.5 0.0 0.5 1.0 1.5<br>2002<br>2006 (1st half)<br>-20<br>2004<br>-40 2005<br>-60<br>-80<br><!-- End of picture text --> _Table 13: Interest Rate Sensitivity of Derivatives_

|||Interest Ra<br>|teChange<br>||10 Year government bond<br>|
|---|---|---|---|---|---|
||- 1%|-0.7%|+0.7%|+ 1%|yield(end ofperiod)|
|||_DK_|_Kbn_||Percent|
|2002|25.1|13.8|-6.8|-9.9|4.46|
|2003|26.9|16.2|-6.9|-8.4|4.47|
|2004|43.8|28.2|-16.3|-21.7|3.85|
|2005<br>|75.8|49.6|-31.2|-39.7|3.28|
|2006(1<sup>st</sup>half)|43.4|26|-12.1|-14.1|4.12|

Source: Danish FSA. Bloomberg (yields) Figure 8 and Table 13 shows the interest rate sensitivity of the derivatives positions. In aggregate the pension sector has in all years used derivates to increase the duration of its assets. As discussed earlier, pension institutions have exchanged floating rate payments for fixed rate receipts in the Euro-swap markets, or bought derivative structures with floors resulting in receipts if rates go below certain levels. The floors (and other similar derivatives) have resulted in asymmetric interest rate sensitivities on the derivatives side. In effect the institutions bought convexity giving them a larger gain when interest rates fall compared to the losses they would suffer when rates go up. For example, in 2005 a 1 percentage point fall in interest rates would have produced a gain from derivatives of DKK 80 billion, whereas a 1 percentage point increase would have resulted in a DKK 40 billion loss. In addition the interest rate exposure from the use of derivatives increased over time, as shown by the successively higher level of losses from a 1 percentage point reduction in rates.

20
Chanda, G. (2011). ”Expert predicts rise in fertiliser prices”. Article published in the Post Newspaper. Tuesday 29<sup>th</sup> March.

http://www.postzambia.com/post-read_article.php?articleId=19369

- Cortez L, Leal MRLV and Sinkala T. (2010). “Why biofuels are important”. In “Food versus Fuel – an informed introduction to biofuels”, Frank Rosillo-Calle and Francis X. Johnsson (Eds.), Zed Books, London and New York, ISBN 978—1-84813-383-9.

- Freim, L-O. (2008). “How will small scale farmers in Zambia benefit from growing of Jathatropha?”. MSc. Thesis. Norwegian University of Life Sciences/Noragric/Development studies. http://www.umb.no/noragric/article/masters-theses-2008/.

- FGV Projetos (2011). “Feasibility study for biofuels and food production in Republic of Zambia”. A progress presentation. http://fgvprojetos.fgv.br/en/publicacoes

Global Renewable Fuels Alliance (GRFA) (2010). “Al Gore’s Flip Flop on Biofuels”. Press Release, November 24. http://www.globalrfa.com/pr_112410.php.

Global Renewable Fuels Alliance (GRFA) (2011). “Qatar's Comments on Biofuels are Self Serving: GRFA”. Press Release, September 27. http://www.globalrfa.com .

International Energy Agency (IEA) (2011). “Technology Roadmap - biofuels for transport”.

www.iea.org/papers/2011/biofuels_roadmap.pdf.

International Energy Agency (IEA) (2011b). “World Energy Outlook 2011 Factsheet”. International Energy Agency. http://www.iea.org/weo/docs/weo2011/factsheets.pdf.

Energy Regulation Board of Zambia (PRB) (2011). “Petroleum Industry Statistics”.

http://www.erb.org.zm/content.php?viewpage=erffs

- Lungu L.H. (2011). “Government Renewable Energy Programmes”. A Presentation at Renewable Energy Forum, Intercontinental Hotel, Lusaka. 24-25 August 2011.

Louw, R. and Andrews, J. (undated). “Exploring CDM Opportunities in Biofuels”.

www.ascension-publishing.com/BIZ/4ABAndrews.ppt

- Matsika, E., Yamba, F., Munyinda, K. (2006). Harnessing sweet sorghum for bio-energy in Zambia. Energy for Renewable Development, 19 (3).

- Menne, W., Gilbertson, T., Anderson, T., Maina, A. and Smolker, R. (2011). “The CDM and Africa: Marketing a new land grab”. Briefing by the African Biodiversity Network, Biofuelwatch, Carbon Trade Watch, the Gaia Foundation and the Timberwatch Coalition. February.

- Ministry of Energy and Water Development (MEWD) (2008a). “National Energy Policy”. MEWD, Government of the Republic of Zambia, Lusaka.

- Ministry of Energy and Water Development (MEWD)  (2008b). “Zambia National Energy Strategy: 2008-2030”. MEWD, Government of the Republic of Zambia, Lusaka.

15
Kommerskollegium (Swedish National Board of Trade), 2009. Climate measures and trade: legal and <u>economic aspects of border carbon adjustment. Stockholm, Sweden.</u> Lockwood, B. and J. Whalley, 2008. <u>Climate change-related border tax adjustments. Mimeo.</u> University of Warwick and University of Western Ontario/CIGI/NBER/CESifo.

Low, P., G. Marceau and J. Reinaud, 2010. <u>The interface between the trade and climate change regimes: Scoping the issue. Second Conference on climate change, trade and competitiveness, The</u> Graduate Institute Geneva, Center for Trade and Economic Integration.

Martin, W. and P. Messerlin, 2007. <u>Why is it so difficult? Trade liberalization under the Doha Agenda. Oxford Review of Economic Policy, Vol 23(3), pp. 347-366.</u> Mattoo, A., A. Subramanian, D. van der Mensbrugghe and J. He., 2010. Reconciling climate change <u>and trade policy. Working paper #09-15. Peterson Institute for International Economics.</u> Washington, DC.

Monjon, S. and P. Quirion, 2010. How to design a border adjustment for the EU Emissions Trading <u>Scheme. CIRED-CNRS. Energy Policy, forthcoming.</u> Moore, Michael O., 2010. Implementing carbon tariffs: A fool’s errand? Mimeo. April. Department of Economics and Elliot School, Institute for International Economic policy, George Washington University. Washington, DC.

OECD, 2005. Trade and structural adjustment: Embracing globalisation. OECD, Paris.

OECD, 2006. The political economy of environmentally related taxes. OECD, Paris.

Ostrom, E. 2009. A polycentric approach for coping with climate change, Policy Research Working Paper WPS5095, The World Bank.

Pauwelyn, J. 2007. U.S. federal climate policy and competitiveness concerns: The limits and options <u>of international trade law. Nicholas Institute for Environmental Policy Solutions. Duke University.</u> Richardson, J.D., 2009. Notes on American adjustment policies for global-integration pressures. In B. Hoekman and G. Porto. Adjustment costs and impact on trade. Forthcoming. World Bank.

Ruffin, R., 1979. Border tax adjustments and countervailing duties. Weltwirschaftliches Archiv, Heft 115-2, pp. 351-355.

Schaus, M. and A. Lendle. 2010. The EU’s renewable energy Directive: Consistent with the GATT- <u>WTO TBT rules?</u> Trade Law Clinic. Mimeo. Hautes Etudes Internationales, Geneva _._ Tumlir, J. International Economic Order and Democratic Constitutionalism, Ordo, vol. 34, 1983, Walzer, R.P. 2009. <u>Europol: $7.4 billion lost from carbon trading fraud in Europe. Green Inc.</u> Available on http://greeninc.blogs.nytimes.com World Trade Organization (WTO). 2007. Trade and environment at the WTO. WTO, Geneva.

World Bank. 2005. Global Economic Prospects 2005: Trade, regionalism and development. World Bank, Washington, DC.

31
Table 7. Implication 3: Effect of Growth Rates on Price Differences

||Dep Variab<br>|le: Log Coo<br>|perative Pric<br>|e<br>|||
|---|---|---|---|---|---|---|
|Variable|(1)|(2)|(3)|(4)|(5)|(6)|
|Abreojos|-0.780***|-0.835***|-0.960***|-1.189***|-1.181***|-1.188***|
||(0.133)|(0.173)|(0.233)|(0.023)|(0.032)|(0.017)|
|Abreojos X large|0.157***|0.139***|0.162***|0.158***|0.162***|0.141***|
|scale|(0.043)|(0.045)|(0.052)|(0.044)|(0.056)|(0.033)|
|Log mkt price|0.395***|1.022|-2.440||||
||(0.117)|(0.845)|(2.406)||||
|Abreojos X log mkt|-0.135***|-0.114*|-0.085||||
|price|(0.050)|(0.060)|(0.075)||||
|Abreojos X ONI|0.056*|0.071**|0.047|0.065**|0.064*|0.064***|
||(0.034)|(0.035)|(0.045)|(0.031)|(0.037)|(0.023)|
|Abreojos X recruit|0.793***|0.722***|0.652***|0.393***|0.274***|0.403***|
|effect|(0.176)|(0.198)|(0.232)|(0.063)|(0.085)|(0.049)|
|Abreojos X ONI X|0.211***|0.193**|0.196**|0.208***|0.097|0.228***|
|recruit effect|(0.074)|(0.075)|(0.086)|(0.074)|(0.090)|(0.055)|
|Estimation method|FE|FE-IV|FE-IV|FE|FE|FE|
|Fixed effects|Sp-mth|Sp-mth|Sp-mth|Sp-mth|Sp-qtr|Sp-week|
|Obs|2871|2600|2538|2871|2871|2871|
|Num. groups|770|575|560|770|340|2152|
|Within-R2|0.905|||0.902|0.876|0.941|

Note: Sample includes observations at the weekly level from Abreojos, Pichilingue, and Sargento. The omitted cooperative category is Pichilingue/Sargento. Columns 2 and 3 treat log market price as endogenous and instrument for it using the one period and two period lag of log market price, respectively. Standard errors are clustered at the level of the fixed effect.

5