rafmacalaba/datause-encoder-data huggingface dataset:
|P146830|2015|2020|12/20/2019|9 Active|Western Africa|
|---|---|---|---|---|---|
|P146830|2015|2020|6/8/2020|10 Active|Western Africa|
|P146831|2016|2016|1/27/2016|1 Active|Dominican Republic|
|P146831|2016|2016|2/25/2016|2 Active|Dominican Republic|
|P146831|2016|2017|7/7/2016|3 Active|Dominican Republic|
|P146831|2016|2017|12/20/2016|4 Active|Dominican Republic|
|P146831|2016|2017|6/28/2017|5 Active|Dominican Republic|
|P146831|2016|2018|12/30/2017|6 Active|Dominican Republic|
|P146831|2016|2018|6/21/2018|7 Active|Dominican Republic|
|P146831|2016|2019|9/18/2018|8 Active|Dominican Republic|
|P146831|2016|2019|5/2/2019|9 Active|Dominican Republic|
|P146831|2016|2020|12/25/2019|10 Active|Dominican Republic|
|P146831|2016|2020|6/30/2020|11 Active|Dominican Republic|
|P146876|2015|2016|11/16/2015|1 Active|Uganda|
|P146876|2015|2016|6/30/2016|2 Active|Uganda|
|P146876|2015|2017|1/5/2017|3 Active|Uganda|
|P146876|2015|2018|7/19/2017|4 Active|Uganda|
|P146876|2015|2018|2/1/2018|5 Active|Uganda|
|P146876|2015|2018|6/22/2018|6 Active|Uganda|
|P146876|2015|2019|1/21/2019|7 Active|Uganda|
|P146876|2015|2019|6/14/2019|8 Active|Uganda|
|P146876|2015|2020|12/13/2019|9 Active|Uganda|
|P146876|2015|2020|6/2/2020|10 Active|Uganda|
|P146877|2014|2014|5/27/2014|1 Closed|Sao Tome and Principe|
|P146877|2014|2015|12/15/2014|2 Closed|Sao Tome and Principe|
|P146877|2014|2015|6/23/2015|3 Closed|Sao Tome and Principe|
|P146877|2014|2016|12/31/2015|4 Closed|Sao Tome and Principe|
|P146877|2014|2016|6/27/2016|5 Closed|Sao Tome and Principe|
|P146877|2014|2017|12/24/2016|6 Closed|Sao Tome and Principe|
|P146877|2014|2017|6/30/2017|7 Closed|Sao Tome and Principe|
|P146877|2014|2018|12/29/2017<br>|8 Closed|Sao Tome and Principe|
|P146877|2014|2018|6/29/2018|9 Closed|Sao Tome and Principe|
|P146877|2014|2019|12/27/2018|10 Closed|Sao Tome and Principe|
|P146877|2014|2019|6/28/2019|11 Closed|Sao Tome and Principe|
|P146883|2015|2015|2/10/2015|1 Active|Ethiopia|
|P146883|2015|2016|9/13/2015|2 Active|Ethiopia|
|P146883|2015|2016|3/9/2016|3 Active|Ethiopia|
|P146883|2015|2017|9/22/2016|4 Active|Ethiopia|
|P146883|2015|2017|2/9/2017|5 Active|Ethiopia|
|P146883|2015|2018|9/18/2017|6 Active|Ethiopia|
|P146883|2015|2018|4/18/2018|7 Active|Ethiopia|
|P146883|2015|2019|10/29/2018|8 Active|Ethiopia|
|P146883|2015|2019|4/30/2019|9 Active|Ethiopia|
|P146883|2015|2020|11/11/2019|10 Active|Ethiopia|
|P146883|2015|2020|5/21/2020|11 Active|Ethiopia|
|P146890|2014|2020|7/26/2019|1 Closed|Georgia|
|P146930|2015|2020|11/4/2019|1 Closed|Mozambique|
75

# Table 14

Government spending in major Latin American economies (percent of GDP)

||Argentina|<br>Brazil<br>|Chile<br>|Mexico|
|---|---|---|---|---|
||2001|2002<br>1998|2000|1999 (b)|
|Total|35.7|29.1<br>45.5|24.0|22.9|
|Primary|30.4|26.5<br>40.9|23.5|20.3|
|Interest (b)|5.3|2.6<br>4.6|0.4|2.7|
|Central government|18.3|15.1<br>26.8|21.9|15.5|
|Primary|13.9|13.0<br>23.0|21.4|13.0|
|Interest|4.4|2.1<br>3.8|0.4|2.5|
|State/ provincial|14.5|11.6<br>13.6|b|6.3|
|Primary|13.6|11.2<br>12.9|b|6.2|
|Interest|0.9|0.5<br>0.7|b|0.1|
|Local|2.9|2.3<br>5.1|2.1|1.1|
|Primary|2.9|2.3<br>5.0|2.1|1.1|
|Interest|0.1|0.0<br>0.1|0.0|0.0|

a. State and local: 1998.

b. Cash basis. Excludes arrears in Argentina in 2002. Source: IMF (2001b); MECON (2003b) Third, Argentina’s fiscal burden (spending relative to GDP) is intermediate in the region, well below that of Brazil but considerably higher than that in Chile and Mexico. Given the much more favorable fiscal performance in Chile and Mexico than in Brazil over recent years, it would be reasonable to infer that some easing in the fiscal burden through curtailing expenditure would be appropriate in Argentina, rather than a further increase toward Brazilian levels.

Table 15 compares government spending by function in Argentina and Chile. These data by function are totals at the central, provincial, and local government levels. Chile is chosen as the comparator because the international data (IMF, 2001b) do not give the category detail at the provincial and local levels, but for Chile where government spending is organized solely at the central government and local government levels, the central government profile is a close approximation of the total.<sup>71</sup>

> 71 The estimates for Chile in table 13 apply the central government category shares to total spending for central and local governments.  Calculated from IMF (2001).
Faber, B., 2014. “Trade integration, market size, and industrialization: evidence from China's National Trunk Highway System.” Review of Economic Studies. 81(3): 10461070.

Fajgelbaum, P.D. and Schaal, E., 2020. “Optimal transport networks in spatial equilibrium.” Econometrica, 88(4): 1411-1452.

Financial Times. 2018. “China’s Belt and Road projects drive overseas debt fears.” Kynge, James (London). August 7, 2018 Financial Times. 2020. “China faces wave of calls for debt relief on ‘Belt and Road’ projects” Kynge, James (Hong Kong) and Yu, Sun (Beijing). April 30, 2020 Frick, S.A. and Rodríguez-Pose, A., 2016. Average city size and economic growth. Cambridge Journal of Regions, Economy and Society, 9(2), pp.301-318.

Head, K., and Mayer, T. 2014. “Gravity Equations: Workhorse, Toolkit, Cookbook.” Handbook of International Economics, vol. 4, Gita Gopinath, Elhanan Helpman, and Kenneth Rogoff, eds. New York: Elsevier.

Herrera Dappe, M., Suárez-Alemán, A., 2016. “Competitiveness of South Asia’s Container Ports: A Comprehensive Assessment of Performance, Drivers, and Costs. Directions in Development—Infrastructure.” Washington, DC: World Bank.

Hornbeck, R. and Rotemberg, M., 2019. “Railroads, Reallocation, and the Rise of American Manufacturing” National Bureau of Economic Research Working Paper No. 26594.

Hurley, J., Morris, S., and Portelance, G. 2018. “Examining the Debt Implications of the Belt and Road Initiative from a Policy Perspective.” CGD Policy Paper. Washington, DC: Center for Global Development.

International Monetary Fund, 2020. “World Economic Outlook, April 2020: The Great Lockdown” Washington, D.C. Growth Projections Table.

35
|**Table 12: Model**|**2 -Single-Shift & I**<br>(1)|**DEB Elementary**<br>(2)|(3)|
|---|---|---|---|
||IDEB Elem(SD)|IDEB Elem(SD)|IDEB Elem(SD)|
|Single-Shift in 2011*Post 2011|0.98|0.784|0.719|
||[0.404]**|[0.431]*|[0.379]*|
|Single-Shift in 2012/13*Post 2012|0.392|0.219|0.282|
||[0.065]***|[0.064]***|[0.130]**|
|School of Tomorrow|-0.021|-0.058|0.057|
||[0.073]|[0.068]|[0.153]|
|More Education|0.029|0.006|0.004|
||[0.038]|[0.036]|[0.094]|
|Mean items in the house||0.078<br>[0.020]***|0.114<br>[0.047]**|
|% Mothers w/ HE||0.35<br>[0.139]**|0.566<br>[0.329]*|
|Participation rate-Prova Brasil Elem||0.042|-0.021|
|||[0.020]**|[0.072]|
|Age-grade distortion||-0.027|-0.015|
|||[0.003]***|[0.007]**|
|Constant|-0.89|-0.825|-1.191|
||[0.021]***|[0.158]***|[0.388]***|
|Observations|3036|2625|368|
|Number of schools|506|525|92|
|R-squared|0.61|0.53|0.54|
|School FE|Yes|Yes|Yes|
|Year FE|Yes|Yes|Yes|
|Restricted Sample<br>Robust standard errors in brackets|No|No|Yes|

<mark>* significant at 10%; ** significant at 5%; *** significant at 1%</mark> Notes: IDEB Elementary Schools 2005, 2007, 2009, 2011, 2013 and 2015. Column (2) refers to years 2007 through 2015. Column (3) refers to only schools that eventually become Single-Shift until 2015, in years 2007 through 2013. There is only one school with low age-grade distortion in 2010 (prior to the program) that became Single-Shift in 2011, so we could not analyze restricting the sample to low-distortion schools. Single-Shift includes CES in this model. By 2013, there were 77 non-certified Single-Shift schools and 2 CES (with complete information). Clustered standard errors in school level.

Tables 13 and 14 present results of model (1) on IDEB and IDERio for middle schools. Columns (1) of these tables present results for certified and non-certified Single-Shift schools as separate variables, controlling for school and time fixed-effect, and for “Schools of Tomorrow” and “More Education” programs. Columns (2) control for student characteristics and participation rate in _Prova Brasil_ . Columns (3) restrict the sample to schools that eventually became Single-Shift until 2015, on the results until 2013. Finally, columns (4) restrict the sample to schools with low agegrade distortion in 2010. We consider schools with less than 37 percent of students with school 2- year delay as having low distortion in high fundamental education level. Thirty-seven percent is 31
|JCE|5.64|23.08|13.76|34.46|0.000|
|---|---|---|---|---|---|
|MSCE|1.96|13.86|19.98|40.01|0.000|
|Non-Univ Diploma and above|0.15|3.86|7.42|26.22|0.000|
|Highest level of formal education acquired in||||||
|the household||||||
|None|68.70|46.38|33.73|47.31|0.000|
|PSLC|15.63|36.32|12.80|33.43|0.926|
|JCE|11.07|31.38|18.66|38.98|0.000|
|MSCE|4.34|20.39|26.32|44.06|0.000|
|Non-Univ Diploma and above|0.26|5.05|8.49|27.89|0.000|
|Size of the household|5.17|2.40|5.47|2.55|0.701|
|Number of infant (<5yo) in HH|0.85|0.85|0.82|0.82|0.027|
|Number of children (5-14yo) in the household|1.62|1.41|1.66|1.41|0.826|
|Number of prime adults (15-60yo) in HH|2.39|1.36|2.84|1.50|0.000|
|Number of elderly (60yo+) in HH|0.32|0.59|0.16|0.45|0.000|
|<br>Household access to loan (0/1)|15.48|36.18|23.09|42.16|0.010|
|Normalized wealth index|0.04|0.06|0.11|0.13|0.000|
|Normalized TLU index|0.01|0.03|0.01|0.02|0.901|
|Total land area owned by HH in Acres|3.55|52.84|2.26|16.94|0.202|
|<br>Household was affected negatively by some|86.71|33.95|84.45|36.26|0.104|
|income Shock During the last 12 months (0/1)||||||
|<br>Household has a migrant network (0/1)|36.52|48.15|30.74|46.17|0.028|
|Rain - EA level CoV of Dec-Jan rainfall from|0.25|0.04|0.26|0.04|0.017|
|1983/84 - 2012/13||||||
|Average 12-month total rainfall (mm)|850.57|87.09|848.91|100.55|0.017|
|Annual Mean Temperature (∞C * 10)|215.97|18.97|218.22|19.84|0.587|
|<br>HH Distance in (KMs) to Nearest Road|10.43|10.05|6.53|7.89|0.000|
|HH Distance in (KMs) to Nearest Population|38.11|17.87|35.17|18.26|0.012|
|<br>Center with +20,000||||||
|Value of daily_ganyu_wage salary in the EA|561.67|599.41|592.09|602.18|0.820|
|<br>(MKW)||||||
|Price of maize grains in the EA (MKW)|201.75|112.90|180.81|109.70|0.001|
|<br>Price of fertilizer in the EA (MKW)|62.12|38.98|63.14|41.31|0.731|
|EA neighbor’s wage employment|13.26|13.52|25.50|17.79|0.000|
|participation||||||
|EA neighbor’s self-employment participation|20.74|14.92|26.32|16.28|0.000|

39
and just requires adjusting expression (3) so that 1 0 are replaced by their time-demeaned analogues 1 0. XX , XX To better understand the uncertainty in our estimates, in addition to the permutation test proposed XX ̈, XX by Abadie et al. (2010) we also consider the prediction interval procedure proposed in Cattaneo, Feng, and Titiunik (2021).

# **3.3 Estimation Details**

The primary difficulty in applying synthetic control in our context is that Australia is an outlier in terms of emissions per capita. Australia’s electricity generation is mainly coal-based, and it is also a major exporter of coal and natural gas. Coal-based electricity, coal mining and natural gas production are energy-intensive industries. In addition, many natural comparison economies that might help form a plausible counterfactual for Australia’s emissions must be omitted from the sample because they implemented policies that would significantly affect emissions during the sample period. For instance, the implementation of the European Union’s Emissions Trading System (EU ETS) in 2005 disqualifies all EU economies.

We form the donor pool for Australia in three main steps. First, we construct a global panel dataset of emissions, GDP per capita, and energy consumption using the data sources previously described. We only consider economies with no missing values in any year for any of the relevant indicators. Second, we use data provided by the World Bank (primarily from the Carbon Pricing Dashboard)<sup>9</sup> to identify every economy that implemented a national-level carbon price or ETS from 1980-2017 and disqualify them from the donor pool. Third, to address Australia’s position as a large economy with relatively high emissions per capita, we limit our estimation sample to the top 35 largest emitters per capita with a population of at least 1 million (both measured on average from 1980-2000). The resulting donor pool is listed in Appendix B.<sup>10</sup> This third restriction speeds computation and reduces concerns of interpolation bias. Including all economies with complete data does not meaningfully change the chosen weights, and therefore does not affect the results.

> 9 https://carbonpricingdashboard.worldbank.org. The Australian carbon tax, incidentally, does not appear in the World Bank’s carbon tracker data.

> 10 We finalize the donor pool with one further exclusion - we drop the United States due to the adoption of hydraulic fracturing in the mid- to late-2000s, which displaced coal-fired generation and caused a drop in emissions around 2010. 12
Learning from Developing Country Power Market Experiences: The Case of Colombia

- Report regularly to the operation committee (CNO) regarding planned and actual system operation and the risks involved in supplying demand.

- Report violation of operation procedures.

XM also performs the following functions as wholesale market operator (ASIC):

- Keep an updated registry of long-term energy contracts in force.

- To calculate settlement, bill, collect and pay transactions among Gencos and suppliers in the wholesale market (the pool, with respect to energy and Automatic Generation Control transactions); and perform other related tasks such as developing and maintaining the required information systems.

- To organize and hold auctions for the supply of firm energy, whenever it is deemed necessary by XM with the objective of ensuring supply reliability.

- Report to the SSPD and the regulator (CREG) any improper conduct or violation of the procedures and regulations.

As market operator, XM collects and issues payments arising from the pool, and is forbidden of profiting from these funds. Instead, XM’s services as system and market operator are levied to market participants (Gencos, Transcos, Distcos and suppliers) based on the regulated tariff determined by the CREG. Nevertheless, XM is allowed to pursue other non-regulated businesses.

The operator makes publicly available through its website relevant information such as system and market operation procedures and market prices. However, scheduled and dispatched quantities and settlements are only available to selected market participants. Timely and public dissemination of market data (provided that this does not violate confidentiality of commercially sensitive information) has evolved in Colombia from a rather opaque process a decade ago (Wolak, 2009).

The operator is a Legally-unbundled TSO (LTSO) controlled by the major government Transco ISA, and independent from other market participants (Gencos, suppliers and Distcos).<sup>12</sup> System and market operation functions were transferred from ISA to its subsidiary XM in 2005 by CREG’s decree 848. XM’s corporate governance is based on a board of five members elected by the shareholders. Three of the five board members must be independent from XM and other companies to which XM serves. The board of XM has real decision-making authority.

Decisions by the operator cannot be appealed. Operational rule changes must be approved by the regulator. However, the regulator is not allowed to revoke or modify decisions by the board of the operator.

# 5.1.2 Market oversight, rules enforcement and intervention

Colombian regulations provide a general framework for **market intervention** (changes to economic dispatch), when offered prices by hydro plants with reservoirs cause stored energy to fall below a safety level known as Upper Operative Minimum; or when offered prices are inferior to the lower bound set by 12 ISA owns 99% of XM’s shares, and in turn the state owns 62% of ISA’s stakes. In turn, ISA owns and operates a large portion of the Colombian National Transmission System.

30
It should be clear that program evaluation of EPAs on economic welfare grounds is difficult if not impossible. Thus often –if not always– evaluations of EPAs stop short of an assessment based on welfare grounds, and focus on the more modest objective of assessing whether exports have increased or whether new markets have been opened. This paper is no exception. Our goals are twofold: first, to determine whether EPAs are having an impact on exports; and second, to identify the activities and institutional structures of agencies that are positively correlated with exports.

As far as we know, there has been no cross-country statistical analysis of the impact of EPAs on exports. The exception is perhaps Rose (2007), who estimates the impact of embassies or consulates on bilateral trade using a gravity model. Rose argues that as communication costs fell, foreign embassies and consulates have lost much of their role in decision-making and information-gathering, and therefore are increasingly marketing themselves as agents of export promotion. In a sample of twenty-two exporting countries –of which eight are developing countries– and around 200 potential trading partners, he finds that for each additional consulate abroad, exports increase by 6 to 10 percent.

But EPAs are not consulates. In order to assess their efficiency we undertook a world survey of national EPAs to gather information on their objectives, activities and institutional structure. We then econometrically explore the effect of EPA budgets on exports, as well the impact of different institutional structures, objectives and activities of EPAs on exports.

The evidence suggests that on average EPAs have a positive and statistically significant effect on national exports, after correcting for sample-selection and omitted-variable biases, as well as reverse causality. The identification strategies clarify the mechanisms through which EPA services can stimulate national exports. EPAs seem to be be particularly effective when they are most needed, namely, when exporters face onerous trade barriers abroad, and when a large share of the export bundle is composed of heterogeneous goods. Nonetheless, there are notable decreasing returns to scale in resources devoted to export promotion. Thus, as far as EPAs are concerned, small is beautiful.

for multilateral interventions.

3
Bonds have always been the main investment asset for the Danish pension sector with heavy emphasis on domestic, callable mortgage bonds. Even though such mortgage bonds have a maturity of up to 30 years their callable nature results in a significantly shorter effective duration especially in an environment of generally falling interest rates<sup>1</sup> . Because the effective duration of Danish non-government bonds is extremely affected by the prevailing interest rate environment, pension institutions have been adding duration by expanding their portfolio of foreign - mainly euro denominated - bonds since the turn of the century<sup>2</sup> . This has of course also increased their exposure to global market fluctuations.

The 1990s saw a deregulation of investment rules and thereby allowed pension institutions to place a larger part of their funds in equities<sup>3</sup> . But since the turn of the century roughly two thirds of equity investments have been foreign. As can be seen in Table 5, this has introduced significant fluctuations in investment returns.

Comparing return on investments to the growth rate of provisions the mismatch between assets and liabilities from 2000 – 2002 becomes evident. Provisions were growing far more than investment returns could cover, leaving the Danish L & P sector with the choice of either absorbing the losses by depleting capital reserves, thereby dangerously lowering solvency, or taking measures to bridge the gap between assets and liabilities.

_Table 5: Asset Allocation, DKK billion and percent, 1999 – 2005_

||1999<br>||200<br>|1<br>|200<br>|3<br>|2005<br>|/1<br>|
|---|---|---|---|---|---|---|---|---|
||DKK|%|DKK|%|DKK|%|DKK|%|
|Land and buildings|59.7|8|60.4|7|72.6|7|92.6|7|
|Subsidiary and associated companies|21.4|3|26.4|3|11.7|1|10.3|1|
|Danish Equity|104.4|14|84|10|51|5|78.1|6|
|Foreign Equity|169.2|22|146.4|17|118.7|12|209.5|16|
|Bonds|409.9|54|534.5|62|706.5|71|794.6|62|
|Other Investments|17|2|30.4|4|34.8|4|25.4|2|
|Derivatives acquired for ALM hedging|||||||67.9|5|
|Total|762.6|100|874.9|100|995.3|100|1,278.4|100|

Notes and Source: Danish FSA.

/1 The numbers before and after 2005 are not comparable. Reporting of investments in bonds, mortgages, cash and term deposits as well as derivatives changes from 2005. Derivatives are before 2005 included in the relevant asset category. From 2005 derivatives acquired for ALM hedging purposes are recorded as a separate category, while derivatives acquired to gain non ALM related exposures still are included in the relevant asset category.

> 1 For a description of the Danish Mortgage system see Ladekarl (1998) and Nykredit (2005).

> 2 The duration of Danish bond holdings in December 2002 was only about 5.4 years, whereas the duration of euro bond holdings was as high as 28.8 years. Source: Nykredit Markets (2003).

> 3 Generally pension institutions can now hold up to 50 percent of their investment assets in equity, whereas companies with high solvency can hold up to 70 percent in equity.

9
- CEC (2004): Developing countries, international trade and sustainable development: the function of the Community’s generalized system of preferences (GSP) for the ten-year period from 2006 to 2015. Communication from the Commission to the Council, the European Parliament and the European Economic and Social Committee, Technical Report COM(2004) 461 final, Commission of the European Communities.

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- Francois, J., B. M. Hoekman and M. Manchin (2005): Preference Erosion and Multilateral Trade Liberalization, CEPR Discussion Paper No. 5153.

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- Hummels, D. (2001): Toward a Geography of Trade Costs. Purdue University, mimeo.

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- Inama, S. (2003): Trade Preferences and the World Trade Organization Negotiations on Market Access, _Journal of World Trade_ , 37(5), pp. 959–976.

- Karacaovali, B. and N. Limão (2008): The clash of liberalizations: Preferential vs. multilateral trade liberalization in the European Union, _Journal of International Economics_ , 74(2), pp. 299–327.

- Klenow, P. J. and A. Rodríguez-Clare (1997): Quantifying Variety Gains from Trade Liberalization. Stanford University (http://www.klenow.com), mimeo.

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- Asia and Pacific Region, Technical Report ST/ESCAP/1146, UNESCAP United Nations Economic and Social Commission for Asia and the Pacific, New York: United Nations.

22