rafmacalaba/datause-encoder-data huggingface dataset:
When solar PV prices dropped below $30/MWh 2-3 years ago in competitive bidding, some studies attempted to investigate the reasons. Examining the PV prices of around $30/MWh in Saudi Arabia and the United Arab Emirates by 2017, Apostoleris et al. (2018) find that only special conditions allow that price level. These conditions include zero land costs, low labor costs, low financial costs, and preferred access to grids. These countries also have access to low-wage migrant workers. Lower labor costs reduce fixed O&M costs. Similarly, Dobrotkova et al. (2018) point out that only in exceptional conditions, the LCOE of solar PV could go below $30/MWh. These conditions include a combination of high capacity factors, low equipment prices, low-risk investment environments, and smart project development strategy. Considering the lowest capital costs of solar available today and other favorable conditions, LCOE of solar PV could drop below $30/MWh. However, as we discussed above, we could not come up with LCOE below $20/MWh even with the most favorable conditions.

# **Figure 3. LCOE with favorable conditions and recently observed auction prices of solar-PV ($/MWh)**

<!-- Start of picture text --> 32.00<br>28.00<br>24.00<br>20.00<br>16.00<br>12.00<br>8.00<br>4.00<br>0.00<br>150 MW -- 200 MW -- 1200 MW 900 MW - 300 MW --116 MW -- 375 MW --163 MW -- 800 MW --<br>Jordan Tunisia -- Abu - Dubai Saudi Chile Mexico Brazil Qatar<br>Dhabi Arabia<br>Auction price Discount rate 5% Discount rate 6%<br>Discount rate 7% Discount rate 10%<br>24.89 24.40 24.20 23.40<br>21.48<br>18.93<br>16.90 16.95 15.69<br><!-- End of picture text --> Note: Columns represent auction prices, lines represent LCOEs from this study. LCOEs are calculated based on the lowest capital costs reported to date ($618/kW in India), highest capacity factor recorded (27.5% in MENA countries) and optimistic value for economic life (25 years).

Sources for auction prices: Kelly-Detwiler (2020) for Qatar and Deign (2019) for the rest.

19
**Figure 8: Growth Accounting and Labor Productivity**

<!-- Start of picture text --> A. Sources of Growth, Total Economy and Major B. Employment by Sector, 1991 to 2004 (Person)<br>Sectors, 1977 to 1996<br>11 36,000,000<br>10 33,000,000<br>9 30,000,000<br>27,000,000<br>8<br>24,000,000<br>7<br>21,000,000<br>6<br>18,000,000<br>5 15,000,000<br>4 12,000,000<br>3 9,000,000<br>2 6,000,000<br>1 3,000,000<br>0<br>0 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004<br>Overall Economy Agriculture Industry Manufacturing Services<br>Labor Quantity Labor Quality Land TFP Agriculture Manufacturing Other Industry Services<br>C. Net Capital Stock by Sector, 1984 to 2004 (1988 D. Labor Productivity Overall and by Sector, 1991 to<br>Constant Prices, Million Baht) 2004, Agriculture 1991=100<br>10,000,000 900<br>850<br>9,000,000<br>800<br>8,000,000 750<br>700<br>7,000,000<br>650<br>6,000,000 600<br>550<br>5,000,000<br>500<br>4,000,000 450<br>400<br>3,000,000<br>350<br>2,000,000 300<br>250<br>1,000,000<br>200<br>0 150<br>1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004p 100<br>1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004<br>Agriculture Manufacturing Other Industry Services<br>Economy Agriculture Manufacturing Other Industry Services<br>E. Capital Output Ratio by Sector, 1984 to 2004 (1988 F. Capital Labor Ratio by Sector, 1991 to 2004 (GDP<br>Constant Prices, Million Baht) 1991=100)<br>5 450<br>400<br>4 350<br>300<br>3<br>250<br>200<br>2<br>150<br>1 100<br>50<br>0 0<br>1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004p 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004p<br>Economy Agriculture Manufacturing Other Industry Services<br>Economy Agriculture Manufacturing Other Industry Services<br><!-- End of picture text --> 20
28 export growth can be obtained using import growth or trade growth<sup>[Levine and</sup> Renelt (1990)], arid (4) the empirical results obtained by these cross-country studies of growth and export growth break-down when government<sup>spending growth</sup> is included [Levine and Renelt (1990)), we should not<sup>base our support or</sup> opposition to export promotion policies on existing cross-country<sup>growth</sup> regressions involving exports.

# 3. Conclusions concerning Trade-Crow.h Literature

In summing-up this discussion of cross-country empirical studies of growth and trade policy three points standout. First, it is difficult<sup>to read</sup> the carefully documented muilticountry studies by Krueger, Bhagwati,<sup>and</sup> Belassa and believe that trade policy and growth are unrelated.<sup>Yet, the</sup> conceptual and statistical problems with these studies discussed by<sup>Edwards</sup> (1989) makes one reluctant to conclude that "trade liberalization<sup>promotes</sup> growth." Second, although attempts at constructing objective, continuous, internationally comparable proxies of trade policy have thus far been plagued by crippling measurement problems, the potential benefits of constructing "good" proxies for trade policy is enormous. It is only with "good"<sup>proxies</sup> for trade policy that we can address the relevant question:<sup>what is the</sup> relationship between specific trade policies and growth.<sup>Finally, studies of</sup> the relationship between exports and growth suffer from a number<sup>of</sup> interpretational problems: (1) one can substitute imports or trade<sup>everywhere</sup> in these papers without changing the results; (2) these studies have no<sup>direct</sup> link to trade policy in general or to export promotion policies<sup>in particular;</sup> (3) the conclusions of these studies are typically not robust to the inclusion
<!-- Start of picture text --> 1 . 5 1 . 0<br>1up RM1 1% 1. 0 1up LMPI 1% 1up DLCPI 1%<br>1. 0<br>0. 5<br>0. 5<br>0. 5<br>0.0 0.0 0.0<br>2000 2005 2000 2005 2000 2005<br>1 . 5<br>1up RP 1% 3 0 1up LEXCH 1% 1 5 1up CHOWs 1%<br>1. 0<br>20 10<br>0. 5 10<br>5<br>0.0 0<br>2000 2005 2000 2005 2000 2005<br>1 . 0 1 . 0 1 . 0<br>Ndn RM1 1% Ndn LMPI 1% Ndn DLCPI 1%<br>0. 5 0. 5 0. 5<br>2000 2005 2000 2005 2000 2005<br>1 . 0<br>Ndn RP 1% Ndn LEXCH 1% Ndn CHOWs 1%<br>20 2<br>0. 5<br>10 1<br>2000 2005 2000 2005 2000 2005<br><!-- End of picture text --> Figure 3: 1-step and breakpoint Chow tests in broken linear trend model To conclude, we did not manage to ...t a well-speci...ed cointegrated VAR model through the crisis period. The bottomline is that our subsample technique is superior in investigating the dynamics in the VAR model and giving some information about how some channels of the Thai monetary transmission mechanism work. The ...ndings support our argument in section 2 that many structural VAR models about Thailand are misspeci...ed especially when ...tting a model through the crisis. Among them are studies by Hirunraengchok (2004), Song (2002) and Disyatat and Vongsinsirikul (2003). In contrast, it supports the underlying reasoning of Patrawimolpon et al (2001) and Fung (2002) of a subsample-based analysis.

# 6 Short- Run Dynamics: Identi...cation of common driving trends

In this section we will informally describe the moving average (MA) representation of the cointegrated VAR model because it helps us to understand the common stochastic trends (permanent shocks) and the transitory shocks in the model (see Juselius, 2004, for a formal analysis). Speci...cally, we impose restrictions on the common trends. Our primary interest is how permanent shocks to the system permeate on the system variables especially the endogenous variables of interest. This exercise gives us some indication of the monetary transmission mechanism in Thailand insofar as we are able to gauge the e¤ects of monetary policy shocks to the economy.

In general, in the Granger-Johansen representation, the C matrix (see Juselius, 2004) contains important information how variables are a¤ected by the stochastic driving trends in the system. For example, the columns of the C matrix indicate how the cumulated shocks to any individual 18
|**Appendix Table 9.3: Imp **|**acts on Meet**<br>Separates<br>Accounts|**ing Specific N**<br>Has revenue<br>projection|**eeds of Investors**<br>Knows customer<br>acquisition cost|Number key<br>metrics tracked|Found out if<br> can protect IP|Has IP or <br> pending|Aggregate<br>Index|
|---|---|---|---|---|---|---|---|
|**Panel A: Impact at Six Mo**|**nths**|||||||
|Assigned to Treatment|0.060|0.066|0.009|‐0.168|0.033|0.054|0.082|
||(0.053)|(0.066)|(0.064)|(0.299)|(0.065)|(0.061)|(0.080)|
|Sample Size|268|268|268|268|269|269|269|
|Control Mean|0.742|0.561|0.409|2.106|0.439|0.364|‐0.059|
|Control S.D.|0.439|0.498|0.494|2.598|0.498|0.483|0.682|
|**Panel B: Impact at Two Y**|**ears**|||||||
|Assigned to Treatment|0.086|0.018|0.061|‐0.092|0.059|0.049|0.084|
||(0.060)|(0.061)|(0.059)|(0.361)|(0.063)|(0.057)|(0.079)|
|Sample Size|291|291|291|269|271|275|298|
|Control Mean|0.577|0.486|0.352|1.667|0.444|0.244|‐0.059|
|Control S.D.|0.496|0.502|0.479|2.854|0.499|0.431|0.692|

69
standard formulation of those estimations. These coefficients are used to calculate the intermediate use part of the regional SAM by multiplying the total supply in the national SAM. The regional tax accounts and value added are introduced afterward by assuming that the residual is the capital value. Output by region and sector is then calculated by summing each activity’s intermediate use, activity taxes, and value added accounts. Investment, government (central and local), household, and export demand for commodities are introduced next. Margins are calculated by disaggregating national margins based on total consumption in the regions. Lastly, when available, commodity taxes are raised using regional tax data (for VAT and tariffs) and total consumption in regions when not (sales and excises).

Next, components of household accounts are regionalized. We start by introducing labor income by compensating employees at the regional level as labor income for the households in that region. We assume all compensation paid to labor in a region goes to the households in that region without transfers to the other areas. We use regional data from household surveys (EU-SILC data<sup>5</sup> ) for debt payment receipts and lending to government, enterprise transfers, and government transfers.

The national accounts are recorded under the region "Italy". National government, debt, capital, investment, savings, and taxes are kept at the national level. We do not have one of those accounts in every region. However, payments or receipts from those accounts can be regional. For instance, direct tax payments by households are regional as households are regional.

Once all blocks are calculated, we estimate the flow of commodities for final and intermediate consumption using a non-linear programming model that minimizes the deviation from supply and demand from the gravity estimations given the supply and demand from each region. The remaining imbalances, which are generally very small, are moved to stock changes if the commodity has stock changes in national SAM. If not, they are moved to household consumption by adjusting household savings. As those are pretty small, they do not cause significant changes.

The estimated flows from commodity accounts of the importing region to commodity accounts of the exporting region are then moved to household demand and intermediate demand accounts of importing region. So, the SAM treats commodity flows as if households and activities of the importing region are buying commodities from the commodity accounts of the exporting region. We assume the share of each importing region in the commodity flows from each exporting region is equal to the share of household or intermediate consumption

> 5EU-SILC is a cross-sectional and longitudinal sample survey, coordinated by Eurostat, based on data from the European Union member states.

7
<!-- Start of picture text --> ey Single output<br>é“SS aeSve><br>a Ye<br>* oar&<br>® ane<br>Inefficiency ($ per pupil) > == ~<br><500 Ga “4 So<br>IH1 2000-25001500-2000 »Rm . a<br>§ 2500-3000 Si oI<br>8 3000-3500 mS<br>— Sian<br>e = Tee o<br>SS<br><!-- End of picture text --> <!-- Start of picture text --> a, q Multiple output<br>é‘a fe‘feaveoSiy<br>Wate 0<br>a Se*ee<br>® aR es<br>Inefficiency ($ per pupil)eat. Lt<—> ~<br><500 fe A So<br>1B1 2000-25001500-2000 SNa Dai & ee<br>2500-3000 \ 5 |<br>3000-3500 Ss a<br>hea<br>e@ 2s eat = |<br>®& Spr<br><!-- End of picture text -->

<!-- Start of picture text --> =, Single output, allowing for poverty<br>w “a<br>i -<br>ae)<br>CT ye ay<br>51 *<br>Inefficiency Re<br>1 ($ per pupil) os HA<br><600 ie oat “is<br>@2000-250081 3000-3500 2500-30001500-2000 xs\ ra: tS 3 |t<br>+ be. eea<br>@ -~<br>» ope A<br><!-- End of picture text -->

<!-- Start of picture text --> =, q Mutliple outputs, allowing for poverty<br>fa i<br>a aleOG<br>em<br>ayBese<br>ne”% 2 dee<br>. Re Se r=<br>Inefficiency ($ per pupil) WO Hi<br>0 <600 i oat Se<br>1500-20002000-25002500-30001 3000-3500 sta\ ——a.: ai. aos : ATa<br>cP=~ SA{oR osOG<br>0 ee<br>@ sea = |<br>- noe<br><!-- End of picture text -->
patterns over the period 2000-2010. For this we decompose export growth for each country under consideration into growth via the intensive margin (more of the same products to the same destinations) and growth via the extensive margin (new products, new markets, or a combination). We perform this decomposition for global export growth, for export growth to a traditional export market— the European Union - 27 (EU-27) —and to new and fast-growing trading partners—countries in the East Asia and Pacific (EAP) region. We then explore whether these countries have gained or lost market share for their most important products and in their most important markets.

**The Role of Diversification in Export Growth: Extensive and Intensive Margin Decomposition** Decomposing export growth into the margins of trade will inform us further on the role played by diversification along the dimension of markets as well as of products by showing the portion of export growth that can be accounted for by the extensive margin.

Figure 13a: Decomposition of Export Growth, 2000Figure 13b: Excluding Oil & Gas, 2000-2010 2010 <!-- Start of picture text --> 2<br>2 RUS UKR<br>RUS UKR KAZ BLR<br>KAZ BLR<br>1.5 UZB AZE<br>UZB AZE 1.5<br>TKM GEO<br>TKM GEO<br>ARM TJK<br>ARM TJK<br>1 KGZ MDA<br>KGZ MDA 1<br>0.5<br>0.5<br>0<br>0<br>Net Old Extinction New New Old<br>products in of products products in products in products in Net old Extinction New New Old<br>old markets new old markets new products of products products products<br>-0.5 markets markets -0.5 in old products in new in old in new<br>markets markets markets markets<br>-1<br>-1<br>-1.5<br>-1.5<br>Portion of Export Growth<br>Portion of Export Growth<br><!-- End of picture text --> Source: Authors’ calculations based on Comtrade and WDI.

Figures 13a & 13b show the decomposition of export growth (for all exports and for non-oil and gas exports, respectively) over the period 2000-2010. The growth rate is decomposed into net growth of old products in old markets, extinction of products, introduction of new products in new markets, introduction of new products in old markets and growth via old products sold in new markets. The figure presents the ECA countries ranked according to their economic size (measured by GDP in 2010, adjusted by PPP).

With the exception of some outlier performance in Turkmenistan (referring to extinction of products and to variations in the extensive margin categories (new products in new markets and old products in 13
**Table 2.1 ZMVM Emissions Inventory, 1996**

(tons/year)

||**_NO,_**|VOC|PM,0|
|---|---|---|---|
|Industry|28,666|16,279|5,700|
|Services|7,832|234,991|337|
|Transport|84,961|193,100|7,745|
|"Naturalsources"a||134,673|18,072|
|Total|121,459|579,043|31,854|

a. Includes biogenic emissions, forest fires, and open-air refuse burning. _Source:_ INE (1997).

**Table 2.2 ZMVM Emissions Inventory, 1998**

(tons/year)

||_NO,_|_VOC_|_PM10_|
|---|---|---|---|
|Industrya|22,094|17,595|3,173|
|Areasourcesb|8,489|270,190|1,058|
|Transportc|142,603|198,253|8,545|
|<br>Natural sourcesd|11,802|72,670|5,800|
|Total|184,988|558,708|18,576|

a. Excludes heavy industry.

b. Includes lubricant industry, solvent emissions, forest fires, and services sector, and others. c. Includes private vehicles, public transport, taxis, and trucks.

d. Includes biogenic emissions and soil erosion.

_Source:_ Comision Ambiental Metropolitana (http://sma.df.cob.mx/inventario/emisiones 1998.htm on 25 July 2000).

Ozone air pollution is formed from the emissions of NO,, and VOCs. The amount produced depends on the amount and location of emitted pollutants; background pollution levels; atmospheric chemistry; geographical, climatological and meteorological characteristics; and atmospheric transport characteristics. Moreover, the chemistry of ozone formation is quite complicated and nonlinear: under certain conditions an increase in NO, emissions can _reduce_ ozone concentrations. The origins of particulate pollution (PM1o) are less clear. PM1 O may be emitted directly or formed from SO 2 and NO, reacting with other substances in the atmosphere (secondary particle formation). Likely sources of directly emitted particles include building and construction (road construction), diesel trucks and buses, forest fires, open-air refuse burning, some manufacturing industries, and resuspension of road dust. The relationship between emissions and concentrations, however, is not straightforward due to secondary particle formation. The ambient concentration of air pollutants depends on the amount and location of emissions; the source dependent physical characteristics of the emitted PM1 O and PM1O pre_3_
<!-- Start of picture text --> Nigeria<br>Gabon<br>Congo<br>Cote d'ivoire<br>Cape Verde<br>Togo<br>Sao Tome<br>Senegal<br>Cameroon<br>Mozambique<br>Ghana<br>Central African Republic<br>Guinea<br>Burundi<br>Burkina<br>Chad<br>Malawi<br>Uganda<br>Rwanda<br>0 0.2 0.4 0.6 0.8 1<br>measure of distributional incidence<br><!-- End of picture text --> <!-- Start of picture text --> Senegal<br>Gabon<br>Nigeria Kaduna<br>Congo, Dem. Rep. of<br>Cameroon<br>Niger<br>Cape Verde<br>Malawi Blantyre<br>Guinea<br>Malawi Lilongwe<br>Rwanda<br>0 0.2 0.4 0.6 0.8 1<br>measure of distributional incidence<br><!-- End of picture text -->
The quantitative data that we were able to obtain suggest that banks have a significant level of credit exposure to the SME segment. For example, the ratio of SME loans to total outstanding private sector loans (including retail) was 37% in Argentina and 14% in Chile in 2006.<sup>29</sup> The level of credit exposure to SMEs of the _most involved_ and _medium involved_ banks is very high, representing on average 62% and 28% of the banks’ loan portfolios in Argentina and Chile, respectively.<sup>30</sup> In the case of Colombia, the national bankers’ association (Asobancaria) estimates a rapid increase in SME lending from a low base in 2003, with the share of SME lending in the total commercial loan and lease portfolio of credit institutions almost doubling in less than 4 years to reach 25% by 2006. Estimates for Serbia indicate that bank exposure to SMEs reached 34% of the total term credit to entrepreneurs, small, medium, and large firms in 2006. For the developing countries covered by the IFC surveys, the exposure is estimated to be around 16%.

Despite the increasing importance of SMEs in the lending portfolio, most banks are not yet able to measure the relative importance of the segment in terms of its contribution to income, costs, or risks. In the cases of Argentina and Chile, almost half of the banks do not keep track of the share of total net income generated by SMEs or the revenue share arising from deposits/account management, credit, and other transactions and fee-based services to SMEs. Some estimates indicate that SMEs contribute to 17% of banks’ net income in Argentina and 24% in Chile (but we only received this information

> 29 The banks that provided this information and that are considered in this average account for 64% of total private sector loans in Argentina and 80% of private sector loans in Chile. Future analysis would be useful to explain the differences across these countries.

> 30 These are simple averages of the ratio of SME loans to private sector loans for the banks that belong to each level of involvement. The _most (least) involved_ banks are the top (bottom) third of banks with the highest (lowest) share of SME loans as a percentage of total loans. The _medium involved_ banks are the rest. If the sample were larger, it would be useful to compute statistics for the _most involved large banks_ , because small banks cannot make large loans to large corporations due to diversification and legal lendinglimit restrictions. Thus, because of their size small banks will have a large SME allocation.

18
6 21. In its institutional model, this program includes: a) the contracting of vehicle lots. b) the construction and maintenance of road infrastructure, incorporated to the public domain, by the private sector; c) the provision of services for a period of 8 (eight) years (Municipal<sup>Law) by the</sup> private sector concessionaire; and d) the remuneration of bus companies<sup>for the operation (based</sup> on a standard cost spreadsheet) and for the infrastructure they have built (in 8 years, from the start of operations).

22. The " Corridor and Integration Terminals Program" proposes

- the implementation of 15 new trunk line bus corridors, with a total extension<sup>of 240.1 km;</sup>

- the rehabilitation and modernization of an existing corridor (the famous Nove<sup>de Julho-Santo</sup> Amaro ), with 17.6km;

- the construction of 22 integration terminals for intramodal and intermodal integration;

- the rehabilitation of 8 terminals;

- the construction of 26 transfer stations; and

- the contracting of 1353 articulated buses.

23. Among the benefits expected from this Program, are:

- the improvement in the level of service provided to 70.3 million passengers/month (46% of the total users of the bus system of the Sao Paulo Municipality;

- the guarantee of payment of a single tariff per trip without extra payments;

- D greater intramodal and intermodal integration of the bus system;

- improvement of the level of comfort for the passengers (occupation of 7 passengers/sq.meter);

- the increase in average commercial speeds to 25km./h;

- D the reduction of 400,000 bus km/day;

- the reduction of 2300 buses;

- the reduction of operating costs with energy and fuels;

- the reduction of vehicle emissions;

- the generation of 27,000 new jobs/month, during the implementation of the works (20 months).

24. Among the projects which involve the development of new technologies for the Program it is worth mentioning the 'Electronic Monitoring" and the "Automatic Ticketing".

25. With the implementation of electronic monitoring, the objective is to monitor the circulation of the bus fleet through electronic control by radio waves. The automatic ticketing planned will be implemented through electronic turnstiles and the adoption of magnetic cards for the payment of fares, in all the buses in operation in the municipality (involving about 10,000 turnstiles and 80 million of tickets per month with a total of 180 million monthly trips).

26. With the objective of improving the level of bus services provided by the private companies, a "Transport Quality Program" is being developped since September of 1994. The idea is to promote a competitive environment between the bus companies contracted by the Municipality, aimed at obtaining a significant betterment in the pattern of services provided. With