rafmacalaba/datause-encoder-data huggingface dataset:
Taking Stock of Wholesale Power Markets in Developing Countries: A Literature Review infrastructure‐privatization‐regulation‐competition

- Kessides, I. N. (2013). Chaos in power: Pakistan’s electricity crisis. _Energy Policy_ , _55_ , 271–285. https://doi.org/10.1016/j.enpol.2012.12.005

- Kim, S., Kim, Y., & Shin, J. S. (2013). The Korean Electricity Market: Stuck in Transition. In F. P. Sioshansi (Ed.), _Evolution of Global Electricity Markets_ (pp. 679–713). Academic Press.

- Krishnaswamy, V. (1999). Non‐payment in the electricity sector in Eastern Europe and the Former Soviet Union, 1–136. Retrieved from http://documents.worldbank.org/curated/en/1999/06/440475/non‐payment‐electricity‐ sector‐eastern‐europe‐former‐soviet‐union

- Krishnaswamy, V., & Stuggins, G. (2003). _Private participation in the power sector in Europe and Central Asia : lessons from the last decade_ (World Bank working paper series No. 8). Washington, DC. Retrieved from

http://documents.worldbank.org/curated/en/2003/07/2477725/private‐participation‐ power‐sector‐europe‐central‐asia‐lessons‐last‐decade

- Littlechild, S. (2000). Privatization, competition and regulation in the British electricity industry, with implications for developing countries, 1–78. Retrieved from http://documents.worldbank.org/curated/en/2000/02/693344/privatization‐competition‐ regulation‐british‐electricity‐industry‐implications‐developing‐countries

- Littlechild, S. (2009). Retail competition in electricity markets — expectations, outcomes and economics. _Energy Policy_ . https://doi.org/10.1016/j.enpol.2008.09.089

- Littlechild, S. C. (2003). Wholesale Spot Price Pass‐Through. _Journal of Regulatory Economics_ , _23_ (1), 61–91. https://doi.org/10.1023/A:1021883431400

- Lovei, L. (2000). The Single‐Buyer Model a Dangerous Path. _Public Policy for the Private Sector_ , (Note number 225).

- Malgas, I., & Eberhard, A. (2011). Hybrid power markets in Africa: Generation planning, procurement and contracting challenges. _Energy Policy_ , _39_ (6), 3191–3198. https://doi.org/10.1016/j.enpol.2011.03.004

- Maurer, L., & Barroso, L. (2011). _Electricity Auctions : An Overview of Efficient Practices_ . The World Bank. https://doi.org/10.1596/978‐0‐8213‐8822‐8

- Maurer, L., Bogetic, Z., & Kessides, I. N. (2007). _Current And Forthcoming Issues In The South African Electricity Sector_ . The World Bank. https://doi.org/10.1596/1813‐9450‐4197

- Millán, J. (2007). _Market or State? Three Decades of Reform in the Latin American Electric Power Industry_ . Washington, D.C.: Inter‐American Development Bank. Retrieved from https://publications.iadb.org/bitstream/handle/11319/430/Market or State%3F.pdf?sequence=1

- Milligan, M., Frew, B. A., Bloom, A., Ela, E., Botterud, A., Townsend, A., & Levin, T. (2016). Wholesale electricity market design with increasing levels of renewable generation: Revenue sufficiency and long‐term reliability. _The Electricity Journal_ , _29_ (2), 26–38. https://doi.org/10.1016/j.tej.2016.02.005

|MIT. (2011)._The Future of the Electric Grid_. Retrieved from|
|---|

41
appear to perform somewhat better than the consumption based social tariffs in Central America. Both schemes have lower errors of inclusion and a less regressive distributional impact. However, the Argentine provincial social tariff schemes perform significantly better than the Colombian cross-subsidy system, albeit with very high errors of exclusion reflecting their modest scale.

Regarding comparisons with other social programs, the Coady statistic shows that the Colombia, Guatemalan and Honduran schemes all perform well below the median level of 1.25 for targeted welfare programs, and even further below the benchmark level of 1.50 for well-functioning programs. Unfortunately, the Coady statistic for the Argentine scheme could not be computed from the available data.

# **5. Conclusions**

During the 1990s, most Latin American countries passed legislation committing themselves to raise water and electricity tariffs towards cost recovery levels. Although nominal water and electricity tariffs increased quite substantially, the combined effects of price inflation and currency depreciation have offset these gains to a considerable degree. Nevertheless, water and electricity tariffs in Latin America remain much closer to cost recovery levels than those found in other developing regions of the world, and are above the average for upper-middle-income countries. However, average residential water tariffs in Latin America at US$0.41 per cubic meter are still barely 40% of OECD levels, while average electricity tariffs at US$0.09 per kilowatt-hour are around 75% of OECD levels.

Overall, the electricity sector is much closer to cost recovery than the water sector. Thus, while 85% of countries (and 69% of low-income countries) achieve some degree of cost recovery for electricity, only 60% of utilities (and 12% of low-income utilities) achieve some degree of cost recovery for water. Indeed, even in OECD countries, only around 50% of water utilities seem to be recovering some degree of capital costs. Moreover, average tariffs for electricity in high-income countries are around twice as high as those in low-income countries, while average tariffs for water in high-income countries are around nine times as high as those in low-income countries.

One of the most commonly cited obstacles to cost recovery tariffs is the problem of affordability of basic utility services by residential households, and low-income families in particular. However, a careful analysis of income distribution in the region suggests that only about 20% of Latin American households would have to pay more than 5% of their income for water or electricity services if tariffs were set at cost recovery levels. However, in the region’s lower-income countries (Bolivia, Honduras, Nicaragua, and Paraguay), reaching cost recovery tariffs would represent a significant affordability problem for around half of the population. Even in cases where tariffs might have to double to reach cost recovery levels, the overall impact on poverty levels in Latin America would be negligible.

34
provides a rather Western-centric view of the world, in which the most central countries are the USA and European hubs, with other regions revolving around them.

# **4 The** **_Air Connectivity Index_ (ACI): Theoretical Basis and Definition**

Connectivity is an intuitive concept, but one without a generally agreed definition. We envisage it as an attribute of individual nodes in a network. Any measure of connectivity should provide a consistent and robust definition of how well the node is connected to the rest of the network. In the case of air transport, we are interested in assessing how well connected each country (node) is to the rest of the world. Intuitively a good definition of connectivity should have the following properties:

1. It should be _realistic_ , in the sense of being supported by an underlying model of transportation flow within the network, where shipments move depending on the size of each node, and the attributes of individual links and nodes. Depending on its degree of realism, the model should take account of the costs associated with moving between nodes on a given link, as well as the finiteness of trips. Ideally, the model should be linked with mainstream models in transportation economics, such as the gravity-type model.<sup>10</sup>

2. It should be _intensive_ , in the sense of being independent of the size of a node. Ideally, two nodes with the same connections to the rest of the world should have the same connectivity even if they have different size. For this reason, measures such as the total number of passengers or amount of traffic do not themselves qualify as indicators of connectivity. This is not to say, however, that size does not matter at all. Indeed, it is widely expected, and frequently observed in practice, that larger nodes are also better connected, but this should not come from the definition of connectivity itself.

> 10 In fact, one of the earliest uses of a gravity-type model includes air transport: see Zipf (1946).

11
(formerly Samasource) and Busara Lab,<sup>5</sup> both based in Nairobi, Kenya.<sup>6</sup> Sama, a San Franciscobased company, has established its presence in Kenya, where it employs people to generate and process data for cutting-edge AI projects for Silicon Valley tech firms.<sup>7</sup> As these tech start-ups (including incumbent firms) grow, they require significant investments in order to survive and scale up. However, given the low levels of capital in many African countries, the importance of FDI in providing the needed capital to African technology firms cannot be overemphasized. In addition, big Silicon Valley firms like Google and Microsoft are investing directly in incubator hubs in Africa to train future tech entrepreneurs. Thus, aside from the direct impact of access to high-speed Internet on socioeconomic outcomes, the infrastructure is also a catalyst for investment in the technology services sector.

# **3 Data**

Our main data is project-level data on FDI from fDiMarkets,<sup>8</sup> a subsidiary of the Financial Times. fDiMarkets monitors cross-border investments around the globe and is a major source of data used by the World Bank and UNCTAD<sup>9</sup> to measure cross-border financial flows. It provides detailed information relating to date of announcement, sector, subsector, whether the investment is greenfield (new) or brownfield (expansion),<sup>10</sup> name of investors, source country and city (state), destination country and city, capital investment, and anticipated direct jobs created, among other things. Data on FDI from 2003 to 2018 are used in this study.

We conduct our analysis at the subnational level, specifically at the second administrative level, hereafter referred to as district. For each district and year, we compute the total number of FDI projects and value of investment for each sector. In this paper, we focus on three main sectors: manufacturing,<sup>11</sup> services, energy and real estate (construction).<sup>12</sup> We exclude FDI in telecom and Internet construction, and extractives (mining and petroleum) given that: (i) investments in telecom and Internet construction could be associated with the Internet roll out thereby resulting in reverse causality; and (ii) investments in the extractives are largely influ-

> 5The Busara Lab is used by researchers around the world to conduct surveys, interactive games, and experiments.

> 6Morocco and South Africa now export significant services in the BPO area. The sector generates more than US$1.5 billion of revenues and accounts for 54,000 jobs in South Africa (Manyika et al., 2013).

> 7The company counts Google, Microsoft, Salesforce, and Yahoo among its clients. See: https://www.bbc.com/ news/technology-46055595

> 8https://www.fdimarkets.com/

> 9The United Nations Conference on Trade and Development

> 10About 95% of projects in the database are greenfield.

> 11garments and apparel, machinery, agro-processing, etc.

> 12electricity generation, transmission & generation, real estate construction.

8
# CONTENTS

|World Bank population projections .<br>...............<br>1|
|---|
|Approaches to projecting fertility .<br>................<br>2<br>Previous World Bank projections ....<br>........<br>2<br> <br>|
|U.N. projections ......<br>................<br>2|
|U.S. Bureau of the Census projections ....<br>.....<br>3|
|National projections ......<br>..............<br>3|
|Proximate determinants of fertility approach ...<br>.. 4|
|Socioeconomic determinants approach ....<br>..... 4|
|Analysis of fevtifl y tends .....................<br>s|
|Stages in fertility transition ..................<br>s|
|Pre-nsitional countries .................<br>_s_<br>|
|Transitional countries .......<br>...........<br>8|
|Post-transitional countries .....<br>..........<br>13<br>|
|Movement between stages .......<br>...........<br>15|
|Application to population projections .....<br>.........<br>16<br>|
|Pre :.ansition ............<br>...........<br>16|
|Start and pace of transition .....<br>..........<br>16|
|Late and post-transition .................<br>17|
|Age-specific fertility ........<br>...........<br>18|
|Rapid and slow decline ......<br>...........<br>18|
|Mllustrative<br>projections ............<br>...........<br>20|
|Conclusion .............................<br>20|
|References ..............................<br>28|
12 and in columns (8) and (9) of the table for skilled workers. Own factor-price elasticities are the correct sign and reasonable magnitudes, although given a ratio of unskilled to skilled labor that averages near twvo in the Mexican data, they are not symmetric in estimated cross-price effects. The estimates show that minimum wages have no impact on the employment of either unskilled or skilled Mexican workers within firms--the coefficients are small, often wrong-signed, and statistically insignificant from zero in all cases. This is true in the case of both contemporaneous and lagged minimum wages. 1<sup>9</sup> Columns (5)-(6) and (10)-(1 1) of the table relax the homogeneity condition imposed by looking at factor price ratios and focus instead on In levels of factor prices. Sectoral output prices (at a 4-digit ISIC level) and the predicted value of sectoral output (at a 2-digit ISIC level)<sup>20</sup> are added alternatively as instruments for firn-level output. There is no notable impact of any of these changes on the estimates summarized above.

Table 8 estimates a similar set of employment equations for Colombian firms from 1981-1987. Earlier years were initially excluded from the panel because they did not provide information about factor prices other than labor. Data on the cost of capital were not available in the Colombian data although energy prices were.

Ordinary least squares estimates of the restricted model are presented in columns (1) and **_Ž)._** Similar to Mexico, the OLS estimates produce wrong signed and significant own-factor price elasticities that contradict conventional wisdom.<sup>21</sup> The fixed effects equations produce more sensible estimates of the relevant parameters. Own-price elasticities are correctly signed and of reasonable magnitude, and given a ratio of unskilled to skilled labor of roughly 2.5, they are roughly symmetric in estimated cross price effects. Consistent with theory, the results yield a significantly higher estimated own-price elasticity for unskilled labor. Energy and raw materials prices are estimated p-substitutes for high skilled labor; energy is an estimated p-substitute for low skilled labor as well and the relationship between raw materials prices and unskilled labor is less stable.

1<sup>9</sup> Minimum wage data in Mexico are revised quarterly, and annual minimum wage are derived as averages of these quarterly figures. For this reason, the lagged minimum wage has greater economic content in an employment equation.

20Predicted values of sectoral output at the two digit level are obtained from an auxiliary regression on sectoral output on output prices and industry dummy variables.

2<sup>1</sup> This may be explained by, among other things, the existence of very sizable firm-size wage effects in both countries over this period.
However the female structural disadvantage does appear to roughly increase with agricultural productivity. The effect ranges from 0.14 at the 10<sup>th</sup> percentile to 0.32 at the 90<sup>th</sup> percentile. The structural effect is always larger than the endowment effect. This suggests for female managers across the productivity distribution that the gender gap is largely a result of differences in returns to factors of production rather than differences in endowments of these factors. The detailed RIF decomposition results will indicate which factors are most important.

# **4.2.2. Detailed RIF Decomposition**

Table 6 contains results from the mean decomposition as well as results from the detailed RIF decomposition for the 10<sup>th</sup> , 50<sup>th</sup> , and 90<sup>th</sup> percentiles. The results for the remaining deciles are suppressed due to space constraints. The variables used in the RIF are identical to those used in the mean OaxacaBlinder decomposition.

The first four columns in panel C contain the detailed endowment effects. Similar to the endowment effect for the mean decomposition, plot size and family labor use are the most important factors. For family labor, female managers have an endowment advantage in the use and intensity of female family labor whereas male managers have an advantage in intensity of male family labor. As mentioned previously, this result is not surprising given the sex of the manager. Female managers have an endowment advantage in child family labor intensity across the three deciles suggesting that female managers consistently benefit from higher levels of child family labor. The relative magnitude of the endowment effect of labor use and intensity appears to be relatively stable across the productivity distribution though the female labor advantage on female managed plots disappears at the 90<sup>th</sup> percentile. In the end, the family labor endowment effects largely cancel each other out. What remains to explain a large portion of the endowment effect is plot size. As mentioned above, this is due to two factors: (i) women cultivate smaller plots on average and (ii) smaller plot size is generally associated with higher agricultural productivity per hectare.

# <<<<<< TABLE 6 AROUND HERE >>>>>>

Overall, the detailed endowment results indicate that differences in household labor as well as plot size play the largest role. Crudely removing the contribution of decreasing returns to scale to plot size, the endowment effect is almost always positive. Further examination that abstracts from differences in plot size may be warranted.

18
# Table 1: Indicators of Trade Regimes Pre and Post Liberalization

|Country<br>(pre-liberalization, post-liberalization)4"|Average U<br>Legal Tariff|nweighted<br>Rates (%)|Tariff R|ange (%)<br>.|Coverage of QR<br>(% of tariff li<br><br>otherwise|s on Imports<br>nes, unless<br>noted)-W|
|---|---|---|---|---|---|---|
||Pre-reform|Post-reform|Pre-reform|Post-reform|Pre-reform|Post-reform|
|Argentina (1 987, 1991)|42 (p)|_15_|15-115 (p)|5-22|62 (of dom. prod.)|A few|
|Chile(1984, 1991)|35|11|35|1|Minimal|0|
|Colombia (1984, 1992)|61|12|0-220|5-20|99||
|Jamaica (1981, 1991)|NA|20|NA|0-45|NA|0(3)|
|Trinidad & Tobago (1989, 1991)|NA|41 (p)|NA|0-103 (p)|NA|A few|
|Uruguay (1987, 1992)|32|18|10-55|12-24|0|0|

Source: Alam and Rajapatirana, 1993.

a/ The pre-liberalization year was chosen to contrast with the post trade liberalization year. It denotes the undertaking of some significant liberalization measures, but not the exact year of trade liberalization itself, e.g., Chile liberalized its trade regime in 1974 and Colombia in 1991.

b/ Even where tariff line coverage is small, domestic production coverage may be significant.
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Table 2-1 Summary Statistics for Spillover Variables

|||Hori|zontal|Bac|kward|For|ward|
|---|---|---|---|---|---|---|---|
||Number of|||||||
|Year|<br>Observations|Mean|Std. Dev.|Mean|Std. Dev.|Mean|Std. Dev.|
|1998|96,135|0.204|0.125|0.059|0.034|0.068|0.103|
|1999|104,253|0.220|0.132|0.066|0.038|0.077|0.120|
|2000|102,745|0.233|0.134|0.071|0.040|0.085|0.136|
|2001|114,735|0.240|0.135|0.071|0.041|0.089|0.142|
|2002|122,464|0.242|0.132|0.073|0.042|0.090|0.143|
|2003|138,377|0.250|0.139|0.075|0.044|0.099|0.166|
|2004|202,735|0.270|0.146|0.082|0.049|0.109|0.180|
|2005|194,274|0.273|0.149|0.083|0.049|0.117|0.199|
|2006|217,062|0.275|0.146|0.085|0.048|0.120|0.201|
|2007|255,042|0.267|0.143|0.083|0.048|0.119|0.199|

34
**Table 4.  U.S. Sugar Import Quotas and Tariff-Rate Quotas (tons of raw sugar)**

|Country|1982/83|1990/91|2001/02|
|---|---|---|---|
|Barbados|17,781|14,239|7,371|
|Belize|27,942|22,376|11,583|
|Dominican Republic|447,065|358,013|185,335|
|Guyana|30,482|24,410|12,636|
|Haiti|14,969|8,030|7,258|
|Jamaica|27,942|22,376|11,583|
|St. Kitts & Nevis|14,969|8,030|7,258|
|Trinidad & Tobago|17,781|14,239|7,371|
|Caribbean Share (%)|22.8|22.5|19.7|
|Total|2,624,150|2,100,083|1,272,283|

Source: USDA 2003.

The North American Free Trade Agreement (NAFTA) introduced a new dimension to the U.S. sugar program by allowing Mexico unrestricted duty-free access to the U.S. sugar market after the phase-in period which ends in fiscal year (FY) 2008. NAFTA became effective on January 1, 1994, and most trade barriers between Canada, Mexico, and the United States were to be eliminated over the subsequent 15 years. The NAFTA sugar provisions were altered by a side-letter agreement prior to the start of the Agreement. According to the side-letter, Mexico’s low-tier tariff sugar exports to the United States were restricted to Mexico’s ‘net surplus production’ of sugar. The net surplus was defined as Mexico’s production less consumption of sugar and high-fructose corn syrup. From FY 2001 through 2007, Mexico was to have duty-free access to the U.S. market for the amount of its surplus, up to a maximum of 250,000 metric tons raw value. Beginning in FY 2008, Mexico was to have duty-free access with no quantitative limits. The validity of the side-letter agreement was disputed by Mexico, while the United States maintained that the side-letter provisions was valid.

The side-letter agreement did not change other NAFTA provisions such as the phased reduction in the U.S. over-quota tariff of 16 cents per pound by a total of 15 percent during the first 6 years, and then in a straight line to zero in calendar year 2008. The raw sugar over-quota tariff was 9.07 cents per pound in 2002 and it drops about 1.5 cents per pound each year. If the world raw sugar prices is in the range of 7 cents per pound and U.S. raw sugar prices are about 18 cents per pound, Mexican producers would benefit from exporting to the U.S. instead of the world market (USDA, 2002). Currently, Mexico does not have a large surplus of sugar to export but this could change due to increased production or reduced sugar consumption because of imports or production of HFCS (which currently has a 20 percent import duty). In future years, the over-quota tariff will continue to decrease and could lead to large imports. Mexico has increased sugar production from about 3.5 million tons during 1989-91 to 5.2 million during 200002, while consumption has increased from 4.0 to 4.5 million tons (USDA). This could lead to various outcomes, and will likely force changes to the U.S. sugar program. For example, Mexico could increase imports of HFCS for use in the soft drink industry and export displaced sugar to the U.S.

8