rafmacalaba/datause-encoder-data huggingface dataset:
# **Leadership and the Independent Regulator**

Mark A. Jamison<sup>1</sup> Public Utility Research Center University of Florida

# **Introduction**

When the Governor of Iowa appointed Dennis Nagel to be Chairperson of the Iowa Utilities Board, the only thing the Governor requested was that Nagel <u>not</u> do anything that would cost the Governor the next election. The Governor didn’t mention protecting consumers, protecting shareholders, or even obeying the law – he only asked that Nagel not cost him the next election.

It is likely that politicians appoint independent utility regulators<sup>2</sup> with an expectation, even if unspoken, that the regulators will provide a certain amount of stability. Controversies over utility regulation can cause politicians to lose elections, particularly in view of the well-accepted proposition that regulation should protect consumers (Kahn, 1988). John Carlin was able to defeat incumbent Governor Robert Bennett to become Governor of Kansas in 1978 in part because Carlin convinced voters that Governor Bennett was not tough enough on utility companies. Regulatory controversies can cause unrest in other ways. Ugandans took to the streets of Kampala in June 2003 to protest a price increase allowed by the electricity regulator. News writers in Trinidad and Tobago accused the Regulated Industries Commission (RIC) of putting the profits of the incumbent telephone company ahead of consumer interests in April 2004 when the RIC closed down unlicensed telecommunications providers ( _Trinidad & Tobago Express_ , 2004).

What happens when a regulator whose function is, in part, to keep things calm lands in the middle of controversy? The regulator finds him- or herself in peril. In

> 1 Director, Public Utility Research Center, University of Florida, Gainesville, Florida, USA, http://www.purc.org; and Associate, Cambridge Leadership Associates, http://www.cambridge-leadership.com/.  I would like to thank Sanford Berg, Lynne Holt, Toni Sotkiewicz, Paul Noumba Um, Janice Hauge, Patricia Mason, and an anonymous referee for their helpful comments.  Any errors are my own.  An earlier version of this paper was titled “Survival Guide for the Independent Regulator.”

> 2 A regulator is generally considered independent when he or she has arm’s-length relationships with industry, consumers, private interests, and politicians.  This paper focuses on independent regulators, but the leadership concepts described are general and could apply to many roles that require exercising leadership.

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result of early childbearing. To account for this, we use Anderson, et. al. (2003) to adjust for a ―motherhood tax,‖ which reduces lifetime wages by 5% (for those with one child) and 10% (for those with two children). This is done primarily for discussion purposes as it draws upon research done in the United States and thus has conclusions that would not apply in a developing country.

Again, it should be noted that these costs are underestimates of the true social costs of adolescent pregnancy since they only measure the losses attributed to lower productivity due to truncated human capital accumulation. The many other implications of early motherhood are summarized in various sources (World Bank 2007, Cunningham et. al. 2008b).

# **3. Out of school: The  cost of cutting her education short**

Among the developing countries in our study, India and China have the lowest dropout rates in both primary and secondary levels, with only 1% of girls not completing primary school and 6% and 25%, respectively, not completing secondary school (Figure 1). Tanzania and Senegal tell a different story, though, with less than 60% of girls completing primary education. Low secondary completion rates are also observed in the African countries in the sample, with less than 20% of girls in the age range of 15 to 24 completing their secondary education in Burundi, Ethiopia, Senegal, and Uganda, for example. Secondary dropout rates are higher than primary dropout rates in all countries. School dropout rates for girls are higher than those for boys in most developing countries, partly due to reasons outside the girl’s control, such as early marriage or cultural norms that prioritize investments in boys (Levine et al. 2009).

The lifetime ―costs‖ of female school dropouts, that can be attributed to lost productive capacity due to under-investment in girls’ education, range from nearly 70% of annual GDP in Burundi to a barely noticeable 0.5% of annual GDP in India and China, figures that are comparable to the developed countries in our sample. The last set of bars in Figure 2 shows the costs to Burundi. If primary school dropouts – equal to 27 percent of girls – had just completed primary school before going to work, they would have generated lifetime income equivalent to nearly 25% of Burundi’s annual GDP. And if the 88 percent of girls who were not able to complete secondary school had just been able to 13
assign a polity score to 167 economies, which as of 2013 have a population of more than 500,000.<sup>18</sup> Although the data go back to 1800 for some economies, for many economies the polity constructs start after their independence. For a few economies, we impute the missing polity information by the polity score of their origin economy prior to the split, for example, all of the Post-Soviet states and states formed after the dissolution of former Yugoslavia and Czechoslovakia.<sup>19</sup> It is also possible that birth year is correlated with unusually good or bad weather that may affect the cohort’s availability of food or income. We add time series data on economy averages of yearly temperature and precipitation produced and maintained by the Climate Research Unit (CRU) at the University of East Anglia, UK.<sup>20</sup> Table 6 reports the results. Specification I incorporates two weather measures while specifications II and III include the quality of governance measure. Since the polity variable is missing for many birth cohorts, specification II includes only the observations with polity scores while specification III includes all the observations with a dummy variable indicating the presence or absence of a polity score. The addition of weather or polity measures does not alter our conclusion that increased life expectancy at birth increases lifetime completed years of schooling.

> 18 The data and documentation are available at <u>http://www.systemicpeace.org/inscrdata.html (</u> _accessed on October 8th, 2014_ ). The polity scale varies from “ _strongly autocratic_ ” coded as -10 to “ _strongly democratic_ ” coded as 10. 19 Belize, though included in our sample does not have any polity data. In some cases, we could not use the available polity data since two economies have been consolidated into one, and the surveys do not identify respondents by the origin.  For example, in surveys from Germany, we could not utilize cohorts born after 1945 since the surveys do not identify individuals born between 1946 and 1987 by place of birth, i.e., whether one was born in West Germany or the Eastern part. We exclude cohorts born before 1976 in Vietnam, and all cohorts born in the Republic of Yemen for similar reason.

20 The original weather data (CRU TS 3.21) reports values for each month and each box on a 0.5 degree latitude/longitude grid. CRU assigned each box to a single country. For each country CRU calculated the weighted mean of the values from its constituent grid boxes for each month in turn. Each grid box was weighted by surface area, using the cosine of the latitude. The seasonal and annual values are the means of their constituent months. The CRU TS data set prioritizes completeness, and has no missing data over land. Where observations are unavailable, the 1961-90 monthly climatic mean is used as a substitute. In data sparse regions of the world, this can lead to repeated values, and this can show up in derived products such as CRU CY.

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# **Appendix Table 4: Intent-to-Treat Effects of Daycare Attendance**

||**Append**<br>_Risk_<br>_Tolerance_|**ix Table 4**<br>_Aversion to_<br>_Disadvan._<br>_Inequality_<br>**Economic**|**: Intent-to**<br>_Aversion to_<br>_Advan._<br>_Inequality_<br>**Preferences**|**-Treat Effe**<br>_Delayed_<br>_Gratification_|**cts of Day**<br>_Violations_<br>_Transitivity_<br>_(Toys)_|**care Atten**<br>_Violations_<br>_Transitivity_<br>_(Sharing)_<br>**Decision-Ma**|**dance**<br>_Dominated_<br>_Risk_<br>_Choices_<br>**king Abilities**|_Transitivity_<br>_or Monoto-_<br>_nicity (Risk)_|
|---|---|---|---|---|---|---|---|---|
|Treatment|-0.122|0.109|0.006|-0.580|-0.194|-0.032|-0.054|0.038|
||(0.092)|(0.040)|(0.032)|(0.400)|(0.128)|(0.052)|(0.086)|(0.098)|
||[0.682]|[0.014]|[0.948]|[0.627]|[0.629]|[0.948]|[0.948]|[0.948]|
|Mean_Y_Cntrl|5.309|1.239|1.522|19.820|2.858|0.744|2.485|1.149|
|<br>_Observations_|<br>2,113|<br>2,113|<br>2,113|<br>1,858|<br>2,113|<br>2,113|<br>2,113|<br>2,113|

_Notes_ : This table estimates intent-to-treat effects of daycare attendance on economic preferences and on decisionmaking abilities. All regressions include dummies specific for each childcare center × age group. Robust standard errors between parentheses. Romano-Wolf p-values which adjust for multiple hypothesis testing between brackets (Romano and Wolf 2005a; 2005b). Estimates are slightly different from Figure 4 in the paper because the measures are not standardized.

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so

|COUNTRY|PROJECT|**LEGAL**COMPONENT|FINANCIERS|
|---|---|---|---|
|Ecuador|Second Social Development Project -<br>Health and Nutrition<br>(Loan No. 3510 EC), February 8,<br>1993|Legal technical assistance in connection with the design of<br>legal reforms required to achieve a decentralized provision of<br>health services and the development and use of administrative<br>and operations instruments, procedures, norms and manuals<br>in support of such decentralization.|IBRD $70 m.<br>|
||Mining Development and<br>Environmental Control Technical<br>Assistance Project<br>(Loan No. 3655 EC), March 8, 1994|Technical assistance in (a) the application and revision of<br>mining legislation and regulations; and (b) the review of legal<br>and procedural issues with respect to the issuance,<br>registration and termination of mining concessions and<br>processing of licenses.|IBRD $14 m.|
||Sector assessment (August 19, 1994,<br>Report No. 12777-EC)|A diagnostic study of Ecuador's judicial system is carried out<br>as part of a private sector assessment of the country,<br>including issues related to the judicial institutions, operations<br>of the courts, the procedural codes, alternative dispute<br>resolution methods, legal education and the role of the bar<br>association.||
||Public Enterprise Reform Technical<br>Assistance Project (Loan, proposed)|Legal technical assistance in connection with the preparation<br>of the necessary legal and regulatory reforms in the energy<br>and telecommunication**s**ectors.|IBRD $12 m.|
**Table 3: Number of workers in in presumed comparative advantage industries (single digit ISIC)‐ Uganda Business Registry 2001 and 2010**

|**T Dii Id ISIC R 31**|**Numb**|**er of worker**|**s**<br>**Avg.**|**% share**|**of total**|
|---|---|---|---|---|---|
|**wo gt nustry ( ev. .)**|**2001**|**2010**|<br>**Annual %**<br>|**2001**|**2010**|
||**Primary se**<br>|**ctor: Agricul**<br>|**Growth**<br>**ture, Forestry, **<br>|**Fishing and**<br>|**Mining**<br>|
|Crop and animal production|25,023|30,849|2.6|5.1|2.9|
|Fishing and aquaculture|1,358|13,887|102.5|0.3|1.3|
|Extraction of crude petroleum and natural gas||125|||0.0|
|Mining of metal ores|761|||0.2||
|_Primary industries total_|27,142|44,861||||
||**Manufacturing**|||||
|Food and beverages|46,316|47,044|0.2|9.4|4.4|
|Tobacco products|1,374|605|‐6.2|0.3|0.1|
|Textiles|2,898|1,333|‐6.0|0.6|0.1|
|Garments|5,742|20,180|27.9|1.2|1.9|
|Leather and leather products|482|2,198|39.6|0.1|0.2|
|Coke, refined petroleum products and nuclear fuel|280|151|‐5.1|0.1|0.0|
|Basic metals|897|2,448|19.2|0.2|0.2|
|_Manufacturing sub‐total_|57,989|73,959||||
||**Other industry**|||||
|Electricity, gas,steam and hot water supply|1,843|749|‐6.6|0.4|0.1|
||**Services**|||||
|Land transport; transport via pipelines|4,708|4,712|0.0|1.0|0.4|
|Water transport|115|617|48.5|0.0|0.1|
|_Services sub‐total_|4,823|5,329||||
|**Grand Total**|91,797|124,898||||

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conservative simulation (Panel A with 25 percent price shock and short-term or lower-bound elasticity), the bottom 20 percent of the income distribution would observe positive income gains.<sup>19</sup> Those gains naturally increase with the magnitude of the price shock. Panel C shows that under the most optimistic simulations, increasing taxes on tobacco can have high long-term income benefits for the poor and vulnerable households in Georgia. In the medium- to long-term, the simulated magnitude lies between 5 and 6 percent of net income gains for the poorest decile, assuming a 100 percent price increase in cigarettes. This suggests that taxes on tobacco should be considered seriously in Georgia, as a cost-effective policy in both the public health agenda, and the poverty reduction efforts.

Figure 6. Net income effect Panel A. 25% Price increase Panel B. 50% Price increase <!-- Start of picture text --> 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10<br>Decile Decile<br>Medium-bound Elasticity Lower-bound Elasticity Medium-bound Elasticity Lower-bound Elasticity<br>Upper-bound Elasticity Upper-bound Elasticity<br>Source: Estimation using a price shock of 25% Source: Estimation using a price shock of 50%<br>Panel C. 100% Price increase<br>1 2 3 4 5 6 7 8 9 10<br>Decile<br>Medium-bound Elasticity Lower-bound Elasticity<br>Upper-bound Elasticity<br>Source: Estimation using a price shock of 100%<br>1<br>2<br>.5 1<br>0 0<br>Inconme Gains (%) -.5 Inconme Gains (%) -1<br>-1 -2<br>6<br>4<br>2<br>0<br>Inconme Gains (%)<br>-2<br>-4<br><!-- End of picture text --> _Source:_ Author estimations, based on the IHS 2012-2016 and HIES 2017. Data on smoking-attributable death events is taken from the GBD (2017), and tobacco-related out-of-pocket medical expenses (adapted from Goodchild et al. 2018 and WDI). _Notes:_ Deciles are created based on per capita household total consumption.

> 19 The effect for the third decile is also positive, though close to zero.

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|**Exh**|**ibit 4a: E**|**vide**<br>|**nce from individual cou**|**ntry stu**<br>**Implications**<br>|**dies (I/II)**<br>|
|---|---|---|---|---|---|
|**Study**|**Brief description**|**Time**<br>**period**|**Key findings**|**for market vs.**<br>**intervention**<br>**question**|**Implications for centre vs.**<br>**region question**|
|Barry et. al<br>(2001)|• Explaining<br>impact of SEM<br>and Structural<br>funds on<br>Ireland’s growth||• Both singe market and structural funds have had<br>modest impacts<br>• National economic policies such as the lower tax rate<br>also played an important role|• Mixed, both<br>market and<br>state role<br>important|• Ireland as an autonomous<br>country had several policy<br>instruments at its disposal that<br>regions do not have, e.g., they<br>cannot lower corporate taxes<br>like Ireland did.|
|Farrel<br>(2004)|• Comparative<br>study of impact<br>of structural<br>funds in Ireland<br>and Spain||• Much higher Irish growth was in part supported by EU<br>structural funds<br>• In Spain, while the whole country converged to EU<br>average, internal regional inequality actually increased,<br>with some regions like Madrid and Navarre getting to<br>income levels above EU average<br>• Part of differences in the two cases explained by (1)<br>institutional differences; and (2) policy differences;<br>Spain’s investment was in infrastructure, while Ireland<br>also focused on developing human capital and on<br>industrial policy to attract large MNCs to set up<br>operations in Ireland|• Institutional<br>and market<br>forces<br>differences<br>greater<br>impact than<br>EU<br>redistribution|• Particular types of intervention<br>can work – for example,<br>policies to attract MNCs,<br>however, these instruments<br>are not available to regional<br>governments.|
|Pereira<br>(1997)<br>**Evidence f**|• Examines impact<br>of EU structural<br>funds on the<br>growth path of<br>Greece, Ireland<br>and Portugal<br>**or marketEviden**|1989-<br>93<br>**ce for inte**|• Using simulation results, they show that EU programs<br>had a substantial impact on economic growth and<br>contributed to their convergence to EU standards – 0.3<br>percentage points increase in growth for Greece and<br>Ireland, and 0.5 percentage points for Portugal<br>**rventionist policyEvidence for decentralized appro**|• Shows that<br>increased<br>central<br>investment<br>can<br>accelerate<br>growth<br>**ach**<br>**Evidence**|• Again shows, autonomous<br>and accountable national<br>governments better able to<br>deliver<br>**for centralized approach**|

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Table 1: Breakdown of hidden costs in Sub‐Saharan Africa (% of GDP)

|**Country**|**Bill collection**|**T&D losses**|**Overstaffing**|**Underpricing**|**Total QFD**|
|---|---|---|---|---|---|
|Benin|0.10|0.24|0.24|-0.30|0.28|
|Botswana|0.05|0.00|0.26|3.04|3.35|
|Burkina Faso|0.06|0.22|0.23|0.48|0.99|
|Burundi|0.32|0.13|0.06|0.44|0.95|
|Cameroon*|0.08|0.39|0.23|-0.03|0.67|
|Cabo Verde|0.53|1.40|0.00|-0.30|1.63|
|Central African Republic|0.09|0.26|0.16|-0.12|0.39|
|Comoros|1.36|1.62|0.32|0.83|4.13|
|Congo, Rep.|0.12|0.39|0.14|-0.10|0.56|
|Côte d’Ivoire*|0.61|0.45|0.17|0.69|1.92|
|Ethiopia|0.23|0.34|0.05|1.07|1.69|
|Gabon|0.02|0.31|0.26|-0.22|0.36|
|Gambia. The|1.84|1.57|1.12|1.29|5.83|
|Ghana|0.10|0.31|0.20|-0.16|0.45|
|Guinea|0.65|0.39|0.13|0.92|2.30|
|Kenya|0.02|0.21|0.16|0.39|0.78|
|Lesotho|0.34|0.00|n/a|0.15|0.49|
|Liberia|0.06|0.21|0.08|0.00|0.36|
|Madagascar|0.89|0.75|0.16|0.42|2.21|
|Malawi|0.30|0.74|0.32|1.11|2.48|
|Mali|0.04|0.46|0.19|0.60|1.29|
|Mauritania|0.48|0.43|0.19|0.42|1.52|
|Mauritius|0.04|0.00|0.41|-0.02|0.43|
|Mozambique|0.17|0.31|0.33|0.11|0.92|
|Niger|0.20|0.16|0.21|-0.09|0.48|
|Nigeria|0.17|0.24|n/a|0.13|0.54|
|Rwanda|0.09|0.35|0.16|0.45|1.04|
|São Tomé and Príncipe|1.94|2.27|0.60|0.77|6.08|
|Senegal|0.35|0.45|0.38|1.00|2.18|
|Seychelles|0.06|0.18|0.35|-0.85|-0.26|
|<br>Sierra Leone|0.21|0.47|0.08|0.10|0.87|
|South Africa|0.15|0.00|0.50|2.78|3.43|
|Sudan|0.00|0.10|n/a|1.32|1.41|
|Swaziland|0.04|0.04|0.43|0.73|1.24|
|Tanzania|0.13|0.14|0.07|-0.06|0.28|
|Togo|0.55|1.15|0.30|-0.40|1.60|
|Uganda*|0.01|0.16|0.08|-0.32|-0.07|
|Zambia|0.12|0.09|0.23|0.79|1.23|
|Zimbabwe|1.26|0.58|0.75|2.59|5.19|
|**_Median_**|**_0.15_**|**_0.31_**|**_0.22_**|**_0.42_**|**_1.04_**|

_Source:_ Trimble at al. 2016. * indicates long-standing private sector involvement.

Table 2 presents the parallel results for Middle East and North African countries as of 2013. In this region the median QFD is much larger than in Sub-Saharan Africa, but this is almost entirely due to the much greater degree of underpricing. The largest QFD of almost 9% of GDP in Lebanon indicates that problems are not confined to very poor economies that find it difficult to manage the utility effectively, or to high-income economies that can afford a large fiscal deficit. Large 8
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