rafmacalaba/datause-encoder-data huggingface dataset:
shocks for passenger EVs. While it is possible that some heavy-duty EVs might not share the same charging stations with passenger EVs, the construction and operation cost of charging infrastructure for heavy-duty EVs will spillover to those for passenger EVs, which suggests correlation between heavy-duty EV stock and passenger EV charging infrastructure. Similarly, the construct labor cost reflect supply side cost shocks which directly affects the construction of charging stations but unlikely to be correlated with demand side factors. The interaction with the fast charging model dummy allows for the effect to be potentially different for fast charging models and slow charging models.

It is possible that financial and non-financial incentives that we are interested in are correlated with unobserved demand shocks or other policies. We include time fixed effects to control for common demand shocks around the globe and country fixed effects to control for nation-specific shocks that do not vary over time. However, we acknowledge that these EV incentive policies could be endogenous if they are designed and implemented in response to time-varying and country-specific shocks.

# **Regression Results**

In this section we report the effects of key market drives for global EV diffusion based on the empirical framework described in the previous section.

**Parameter estimates** Table 2 reports the estimation results of the EV demand model from five specifications. We include in our regressions the cost of purchasing a new vehicle measured by price minus financial incentive, the number of available charging ports, driving range, vehicle size and an indicator for non-financial incentives. The first three columns report estimates from OLS, in which we add different sets of fixed effects to control for potential confounding factors. The last two columns are estimates using different instrumental variable strategies to address the price and charging station endogeneity aforementioned.

The regression in column (1) suffers from different sources of confounding factors which bias the price coefficient towards zero, although all the coefficients have intuitive signs. Column (2) adds brand (e.g., Tesla) fixed effects in order to control for brand-specific time-invariant confounders such as brand loyalty and unobserved car attributes (such as product quality) that are correlated with price while affecting sales. It also adds fuel type fixed effects (i.e., BEV or PHEV) to account for different consumer preferences for different fuel types. The price coefficient becomes more than doubled in magnitude suggesting that it is important to control for these 8
Policy Research Working Paper 5426

# **Abstract**

This paper examines the effects of trade on growth among Central America-Dominican Republic Free Trade Agreement countries. To accomplish this task, the authors collected a panel data set of 136 countries over 1960– 2010, and estimated cross-country growth regressions using an econometric methodology that accounts for unobserved effects and the likely endogeneity of the growth determinants. Following recent empirical efforts, they tested whether the impact of trade openness on growth may be more effective after surpassing a “minimum threshold” in specific areas closely related to economic development. The analysis finds not only that there is a robust causal link from trade to growth, but also that the growth benefits from trade are larger in countries with higher levels of education and innovation, deeper financial markets, a stronger institutional framework, more developed infrastructure networks, a high level of integration with world capital markets, and less stringent economic regulations. On average, rising trade has benefited growth in Central America-Dominican Republic Free Trade Agreement countries. However, the lack of progress in structural reforms has not allowed these countries to maximize the potential benefits from trade.

This paper—a product of the Office of the Chief Economist, Latin America and the Caribbean Region—is part of a larger effort in the department to understand the drivers of growth. This paper was prepared for the “DR-CAFTA and the Complementary Agenda” project conducted by the Central America CMU (LCC2C). Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at ccalderon@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
**Figure 10. With a 10 Percent Investment Share of Oil Revenues, Workers Will Gain** **<u>More Than Retirees</u>**

<!-- Start of picture text --> 15%<br>13%<br>11%<br>9.4%<br>9%<br>7% 6.3%<br>5%<br>3%<br>1%<br>-1% 2% 3% 4% 5% 6% 7% 8% 9% 10% 11% 12% 13% 14% 15%<br>Oil revenue as a share of output<br>Change in workers' consumption Change in retirees' consumption<br>Change in consumption<br><!-- End of picture text --> Source: Author’s simulations based on a general equilibrium OLG model calibrated for Brazil.

The direct implications of a policy that is directed at investing a certain share of oil revenue will also be growth-enhancing. Even a small share of oil revenue could lead to sufficient capital accumulation to further promote the capital-labor ratio and, thus, income per capita. Our simulations reveal that slightly more than 1 percent of oil revenue as a share of GDP is necessary to ensure positive income growth. Consequently, if oil revenue increases to 10 percent of GDP as a result of the Pre-Salt discoveries, then a 9.01 percent increase in income per capita can be achieved if 98 percent of oil revenue is invested so the economy reaches its dynamically efficient level. Table 3 illustrates this point and reveals that the higher the increase in oil as a share of GDP the higher income growth can potentially be achieved — given that the efficient investment share is adopted.

Table 3 illustrates that, for oil revenue share of GDP lower than 10 percent the efficient investment share of 100 percent. The income growth that is potentially achievable is shown to be of significantly size. If all oil revenue currently obtained in Brazil is invested, GDP per capita will increase by 2.1 percent — and this is without any assumed increase in oil revenue originating from the Pre-Salt discoveries. Depending, then, upon the achievable extraction of oil and its associated (net) revenue, income per capita could increase from around 3 to 10 percent when the range of oil revenue to GDP constitutes 3 to 16 percent of GDP.

When comparing the results in Table 3 with the trajectory for the efficient investment share in Figure 3 it becomes clear that the Pre-Salt oil revenue will not be able to drive Brazil all the way towards its optimal capital stock. The reason is that if oil constitutes less than approx. 10 percent of GDP then it would take more than 100 percent of the oil revenue to reach the efficient capital stock. Figure 12 illustrates this point by comparing the investment share that is actually attainable — i.e. an investment share that is 100 percent at the most — with the efficient investment share.

23
2 approach one would analyze the outcome of IPFs' activity, namely<sup>the performance of the</sup> 2 enterprises themselves This study will use an alternative approach in which investment<sup>funds as new institutions</sup> in emerging capital markets are central. This gives rise to several<sup>questions:</sup>

- How can the behavior of funds be explained?

- Why do certain funds do what they do?

- What is the economic and legal environment in which investment<sup>funds act?</sup>

- What is the role of funds as institutional players in restructuring<sup>firms after</sup> privatization?

To answer these questions, this study will analyze the top ten investment<sup>privatization</sup> funds that emerged in the first wave of privatization in the Czech<sup>Republic and discusses</sup> the main results of the fund survey3.

# **2. An overview of the process of voucher privatization in the Czech**<sup>**Republic**</sup>

A total of 1,491 companies with a book value of CSK277.7 billion<sup>($10 billion) have been</sup> privatized in the first privatization wave, which started in 1991 when<sup>Czechoslovakia was</sup> 4 still united4. While privatization slowed down in Slovakia after<sup>the split, the second wave</sup> has been carried out in the Czech Republic (see Table 1).

2 The idea behind this reasoning is that if enterprises perform well<sup>then the owners must have done</sup> a good job, too. Although this kind of reasoning might be true in some cases,<sup>it would be difficult</sup> to separate the influence of owners on enterprise performance<sup>from other factors such as</sup> competition in product markets. In addition, this kind of approach<sup>would require a considerable</sup> data set on enterprises which is still difficult to obtain at this time.

3 Fund interviews have been carried out in July and August 1994.

4 For a detailed analysis of the privatization process in former Czechoslovakia<sup>see Svejnar and</sup> Singer (1994) and Triska (1994).
page 32

**Unofficial Translation of Main Articles of Draft Law**

**Appendix A:**

# **Main articles of the draft law on livestock insurance**

# **1. Purpose of the law**

- 1.1 Purpose of this law is to regulate the relationships between insurance companies, citizens and legal bodies on compulsory insurance of livestock

# **4. Scope and forms of livestock insurance**

4.1 Livestock insurance can be compulsory and voluntary

- 4.2 Citizens and legal bodies shall insure breeding animals and young<sup>animalstoreplace</sup> aged breeding animals on a compulsory basis

/There were proposals from Mongol Daatgal company and Tushig Daatgal company that livestock insurance should cover all animals not only breeding but this was not<sup>accepted</sup> in the final draft/

# **5. Livestock insurance**

5.1 Livestock shall be insured on a compulsory basis against the following<sup>risks:</sup> 5.1.1 Drought and _dzud_ 5.1.2 Sudden disasters such as flood, strong storns, cold showers, fires 5.1.3 A class contagious diseases

- 5.1.4 some serious diseases in B and C classes

# **6. Insurance agreement and guarantee**

6.1 Livestock insurance agreement and guarantees shall be regulated by the articles<sup>9.1</sup> and 9.2 of the Law on Insurance.

6.3 Livestock insurance agreement shall be based on the livestock census<sup>dataattheend</sup> of year and shall be established by 1 April.

6.4 An insurance company can partly or wholly reinsure livestock<sup>risksby domesticand</sup> international companies.

# **7. Insurance duration**

7.1 The duration of livestock insurance agreement shall be for 365 days since the date of signing.

# **8. Insurance value and premiums**

- 8.1 Value of animals insured shall be established through negotiations<sup>between2parties</sup> 7

8.2 Premium for compulsory livestock insurance shall be 2% of the value7.

8.3 Premium shall be paid by 1 April in cash.

# **9. Insurance indemnities**

9.1 Citizens and legal bodies shall report about animal losses within 72<sup>hoursincasesof</sup> sudden disasters and 2 times per month during droughts and _dzuds._

> 7 There was some indication that the 2% value had been increased to 4%. However, this<sup>is not confirmed.</sup> Even 4% is unlikely adequate to support a totally private market policy with the coverage<sup>outlinedin</sup> Appendix A. Someone inside the Ministry has done some analysis on this issue. However,<sup>wewere ner</sup> able to locate them and learn about this analysis.
# **III. The Performance of the Electricity Industry**

Eskom’s performance appears to be highly efficient, especially when judged by African standards. It has been generating some of the lowest priced electricity in the world and managed to fulfill the government’s ambitious electrification program while maintaining a solid and consistent financial performance. However, there are some questions regarding the efficiency of Eskom’s planning and management of generating capacity and overall investment program.<sup>23</sup>

## **Price, Cost, and Financial Performance Trends**

Average electricity tariffs in South Africa for both domestic and industrial customers are very low by international standards. In 2005, they were the lowest among the

**Figure 4  Average retail electricity prices, 2005 or latest information available US cents / kWh**

<!-- Start of picture text --> 30<br>20<br>10<br>0<br>Industry Households<br>Source: IEA (2005).<br>ItalyJapanTurkeyIrelandHungaryPortugalAustriaSlovak RepublicDenmarkSwitzerlandCzech RepublicUnited KingdomGermanyFinlandPolandMexicoGreeceNew ZelandSpain KoreaChinese TaipeiCanadaUnited StatesFranceNorwaySouth Africa<br><!-- End of picture text --> countries surveyed by the International Energy Agency (figure 4).<sup>24</sup> In terms of world best practice, Eskom has had the lowest observed average price for industrial electricity 23 Eberhard, A. and M. Mtepa. 2003. “Rationale for Restructuring and Regulation of a ‘Low Priced’ Public Utility: a Case Study of Eskom in South Africa.” _International Journal of Regulation and Governance_ 3(2): 77-102

> 24 International Energy Agency (IEA). 2005. _Key World Energy Statistics_ .  .  A recent World Bank study also shows that South African average end-user prices (US cents/kwh) are one of the cheapest in the world at 3 cents for residential and 2 cents for non-residential customers. By comparison, these user prices in upper middle income countries are, on average, about 9 and 7 cents, respectively, while SSA rates are 6 and

15
<!-- Start of picture text --> Youth not employed baseline: in school later<br>|<br>063 064<br>05 ° O72 ° ° 069<br>=<br>—E<br>—<br>=]<br>=)<br>Oo f8]<br>o<br>E<br>c ~.05 mi -.041 ; -.04 e -.027<br>: ~074 @ Pre-covid cohort<br>B Covid cohort<br>4<br>4 months later*** 8 months later*** 12 months later***16 months later***<br><!-- End of picture text -->
_flows in emerging economies: Policies, practices and consequences._

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- Avdjiev, S., McGuire, P., & Wooldridge, P. (2015). Enhanced data to analyse international banking. _BIS Quarterly Review_ , _September 2015_ .

- Bai, Y. (2013). Discussion on “Gross capital flows: Dynamics and crises” by Broner, Didier, Erce, and Schmukler. _Journal of Monetary Economics_ , _60_ , 134–137.

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- Blanchard, O., Ostry, J., Ghosh, A., & Chamon, M. (2015). Are capital inflows expansionary or contractionary? theory, policy implications, and some evidence. _NBER Working Paper_ , _21619_ .

- Bluedorn, J., Duttagupta, R., Guajardo, J., & Topalova, P. (2013). Capital flows are fickle: anytime, anywhere. _IMF Working Paper_ , _13_ (183).

- Borio, C., & Disyatat, P. (2011). Global imbalances and the financial crisis: link or no link. _BIS Working Papers_ , _No. 346_ .

- Broner, F., Didier, T., Erce, A., & Schmukler, S. (2013). Gross capital flows: dynamics and crises. _Journal of Monetary Economics_ , _60_ , 113–133.

- Caballero, J. (2016). Do surges in international capital inflows influence the likelihood of banking crises? _Economic Journal_ , _126_ , 281–316.

- Caballero, R., & Simsek, A. (2018). A model of fickle capital flows and retrenchment. _mimeo_ .

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43
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25
<!-- Start of picture text --> Step1:Randomize [x]<br>years j 6 Real exchange rate j “0.731707 735 j 20,97550438<br>start year j 2006 Real domestic rate j 10, 00652846; j 20850371930<br>Number of drawings j 5000 Real foreign int rate j 8.362147406; j 1803390584:<br>Real growth rate 5,000000492% 5,629960193¢<br>Wet | O Use matrix |<br><!-- End of picture text -->

<!-- Start of picture text --> define Covar matrix {x}<br>exchangeh rate domesticinterestrate foreigninterest rate Stowthh rate<br>exchange rate g.0000000 ia] in} 0<br>domestic interest rate o.0000000 0.0000000 oO o<br>fore bon Ierest Lote g.0000000} —_0.0000000 IRNNNTar ener Q<br>CCLth GErat o.coo0000©=8.c000000©~=— 0.000000 err oK<br><!-- End of picture text --> <!-- Start of picture text --> $tep1-Randomize [x]<br>years ( Ez Real exchange rate | -0,731707735 20, 97558438<br>start year 2006 Real domestic rate | 10, 00652846; 20850371931<br>Number of drawings 5000 Real foreign int rate | 8.5621474067 1803300584:<br>O Real growth rate | 5,000000492¢ j 56299601 93t<br>—_ | Use matrix |<br><!-- End of picture text -->
Table 2 Spearman Rank Correlation Coefficients for 7 **Opennese** Measures in Levels: Cross-Section and Anriual Data **i/**

||TR I|TR II|BLACK|DOLLAR|TRADE<br>SHARES|INDIRECT|
|---|---|---|---|---|---|---|
|rrade<br>reform (TRI)<br>(1960-84)|1.0<br>1.0|.10<br>.73w|*44**<br>-.37***|.36<br>.08|.70***<br>.51***|<br>.37<br> <br>.38***|
|Trade reform<br>(1979-88) (TRII)||1.0<br>1.0|-.39***<br>-.34***|.05<br>.14**|.06<br>.26***|.20<br> <br>.52***|
|Black market premium (BLACK)|||1.0<br>1.0|-. 07<br>.00|-.16<br>-. 21***|..61***<br>-.470*|
|ice distortion (DOLLAR)||||1.0<br>1.0|.11<br>.06**|.50**<br>.22*0*|
|-ade shares|||||1.0<br>1.0|.25<br>.36***|
|Disprotection of<br>agriculture (INDIRECT)||||||1.0<br>1.0|

Table 3 Spearman Rank Correlations for Changes in Openness:/

||TR I|TR II|BLACK|DOLLAR|MTP<br><br>|TRADE<br>SHARES|INDIRECT|
|---|---|---|---|---|---|---|---|
|ade reform (TR1)<br>(1960-84)|1.0<br>1.0|-.07<br>.47***|-.60***<br>- . III*"|.01<br>.02|-.11<br>-.14***|.46*<br> .17*0*|.74***<br>.17***|
|ade reform (TR II)<br>(1979-88)||1.0<br>1.0|-. 27*<br>-.11**|.17<br>.12|.07<br>.08|.12<br>.05|-. 23<br>.32**-|
|ack market prefnium<br>(BLACK)|||1.0<br>1.0|.21<br>-. 01|.06<br>-. 01|-. 11<br>.. 15**0|-. 27<br>**..15*5e**|
|ice distortion (DOLLAR)||||1.0<br>1.0|.27<br>.10***|-.385**<br>.14*^*|<br>.16<br> -. 05|
|vement towards<br>International<br>Prices (MTP)|||||1.0<br>1.0|-.10<br>-.03|.27<br>-.09|
|ade shares||||||1.0<br>1.0|.49***<br>.25***|
|sprotection of<br>agriculture (INDIRECT)|||||||1.0<br>1.0|

tes: **1/** Top figure indicates rank correlation for coefficients averaged over entire time period; bottom figure gives annual te rank correlations.

Indicates significant at 15 X level; ** indicates significant at 10 X level; *** indicates significant at 5 X level.

27