rafmacalaba/datause-encoder-data huggingface dataset:
number of supply interruptions per operating period (7000 hours) that Eskom achieved especially between 1990 and 2000 (figure 9). After 1995, its generating units have experienced fewer interruptions than even the international best quartile indicating worldclass plant management and maintenance. In 2000, Eskom peaking stations set new records by claiming only 0.58 separations per 7000 hours well below the international best quartile for that year.

# **_Distribution System Performance_**

Distribution problems can be divided into two main categories: (i) those affecting distribution lines--the potential causes of faults on distribution lines include storm / wind damage, lightning strikes, fallen trees or branches, bushfires, and animal incidents (large birds causing short circuits); those affecting distribution equipment such as transformers and related plant—unexpected load growth in particular locations can cause overloading of distribution transformers leading to overheating and failure in extreme weather conditions.

## **Box 2  Distribution Network Reliability Indices**

## **System Average Interruption Frequency Index (SAIFI)**

The SAIFI of a network is the average frequency of sustained interruptions per customer. SAIFI = (Total number of customer interruptions p.a.) /(Total number of customers served)

## **System Average Interruption Duration Index (SAIDI)**

The SAIDI of a network, commonly referred to as customer minutes of interruption or customer hours, indicates the average duration of a sustained interruption the customer would experience per annum. SAIDI = (Sum of customer interruption durations p.a. ) / (Total number of customers served)

## **Customer Average Interruption Duration Index (CAIDI)**

The CAIDI of a network is the average time needed to restore service to the average customer per sustained interruption.

CAIDI = (Sum of customer interruption durations p.a.) / (Total number of customer interruptions p.a.)

## **Momentary Average Interruption Frequency Index (MAIFI)**

The MAIFI of a network indicates how often on average (frequency) the customers served would experience a momentary interruption (MI) per annum. MAIFI = (Total number of customer MI p.a.) / (Total number of customers served) _Source_ : Kueck et al (2004).<sup>34</sup> Frequency of sustained interruptions is perhaps the most important cause of consumer dissatisfaction with utility performance. The measure most commonly used to record interruption data is the System Average Interruption Frequency Index (SAIFI). The duration of sustained interruptions is another strong factor in customer dissatisfaction

> 34 Kueck, J, Overholt, Kirby, B., Overholt, P. and L. Markel. 2004. _Measurement Practices for Reliability and Power Quality._ ORNL/TM-2004/91, Oak Ridge National Laboratory, Oak Ridge, Tennessee.

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# **Fiscal Competition in Developing Countries — A Survey of the Theoretical and Empirical Literature**

## **Thierry Madiès and Jean-Jacques Dethier**

### **May 15, 2010**

## **Introduction**

The last two decades have witnessed a sharp increase in foreign direct investment (FDI) flows to developing countries in the context of globalization (see figure 1). This has been accompanied by an increase in competition among the developing countries to attract FDI, resulting in many investment incentives offered by host governments and reductions in the restrictions on foreign enterprise operations in these countries.<sup>2</sup>

**Figure 1.**

<!-- Start of picture text --> Long‐term capital flows to developing countries<br>(US$, billions at constant 2002 prices)<br>Grants<br>Private loans<br>FDI<br>Portfolio equity investment<br>Public loans<br>450 3,000<br>400<br>350 2,500<br>300<br>2,000<br>250<br>200 loans<br>1,500<br>150 Public<br>100<br>1,000<br>Grants, Private loans, FDI & Portfolio equity investment 50<br>0 500<br>‐50<br>0<br>‐100<br><!-- End of picture text --> source: World Bank For instance, the ten member countries of the Association of South-East Asian Nations seem to be engaged in a perpetual “incentive war” to attract FDI, especially since the financial crisis in 1997 that has severely affected the region. This competition becomes even fiercer as the ASEAN Free Trade Agreement (AFTA) comes into force. The AFTA would make it easier for multinational firms (MUTLINATIONAL FIRMs) from outside the region to locate

> 2 The border effect literature, in line with the seminal paper by Mc Callum (1995), shows that regional integration should not be over-estimated and that national borders still matter. There is also empirical evidence that Chinese provinces’ greater involvement in international trade went hand in hand with a decrease in domestic trade flow intensity between the mid 1980s and 2000.

2
<!-- Start of picture text --> TAL<br>Design: standard THES<br>202021 questionnaire<br>Mortets; 1-4<br>BChOSs-PeMr EnpUlOn :<br>TA?<br>Base surveys: acrass-pear EH pUnLLOn Desig: light household<br>TAPS 2000/21 4 questioniame + reduced<br>. : hat of food bem:<br>for TNPS 2019/20 ) Modets: i, 2,3, 8 amd 9<br>ac05-Fed Mpa<br>Wihel-Year BipUlamced<br>TAS<br>Design: light bouseleokd<br>| Questionnaire +<br>ageregated fooda: nei<br>food items<br>Models: 1, 2.3, 4, 8 amd<br><!-- End of picture text -->
categories: controlling behavior, emotional IPV, physical IPV, and sexual IPV. In conducting this module, we followed WHO’s ethics protocol for IPV research (WHO 2016).<sup>19</sup> The IPV module was administered to all women who had an intimate partner within the 12 months preceding the survey. The sample to which the IPV module was asked is 2,998 women (1,737 in Malawi and 1,261 in Liberia).

# **ACASI Implementation**

In ACASI, respondents listen to questions on headphones and answer questions privately on a tablet. In each country, audio readings of the questions were recorded by an enumerator who was chosen for having clear enunciation. The recorded audio files were uploaded to SurveyCTO, along with an image file containing choice options (i.e. “yes” / “no” / “refuse to answer” / “don’t know”). As shown in Figure A2, the resulting interface on the tablet has a speaker icon (which the respondent could touch to listen to the question) and four images (from which the respondent could choose her answer by touching the screen herself).

In the field, the enumerator explained how to take the module, and then demonstrated how to conduct the module by going through a handful of practice questions with the respondent, and making sure that she could clearly hear the audio and accurately choose the option she intends to. When the respondent felt ready to take the actual module, the enumerator handed the tablet over to the respondent for her to take the module. In order to make sure that she had complete privacy while doing so, the enumerator kept sufficient distance to be unable to see the screen but remained in the same room or vicinity to be available to answer questions. When the respondent hands back the tablet, the screen is blank so that the responses are blinded to the enumerator.

# **Experimental Design**

In each survey round for each country, half of the sample was randomly assigned to ACASI and the other half to FTFI. However, before starting the IPV questions, _every_ respondent was asked

> 19The protocol includes hiring only female enumerators; training enumerators to safely conduct the interviews and to be prepared emotionally for the work; conducting all surveys privately; reiterating consent just before the IPV module; and providing all respondents with an information sheet that listed the services available for women experiencing IPV (including law enforcement and local hospitals).

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14 Figure 11 Money Base as a Share of GDP (seasonally adjusted data <!-- Start of picture text --> 5.0<br>4.5<br>4.0<br>3.5<br>3.0<br>2.5<br>2.0<br>95 96 97 98 99 00 01 02 03<br><!-- End of picture text --> Source: World Bank staff calculations based on Bacen data Figure 12 Monetary Impact of Treasury’s Operations 1999- 2003 (ratio to the monetary base <!-- Start of picture text --> .4<br>.3<br>.2<br>.1<br>.0<br>-.1<br>-.2<br>-.3<br>1999 2000 2001 2002 2003<br><!-- End of picture text --> Source: World Bank calculations based on Bacen data Moreover, base money grew in lockstep with the faster depreciation of the currency. This pressured inflation, which accelerated between June and December, reaching a peak of 5.8 percent per month in November (Figure 13). Monetary growth and rising inflation increased the government’s revenue from money creation up to the equivalent of 2.0 percent of GDP<sup>5</sup> (Figure14). In these circumstances, the authorities’ credibility faltered. It is interesting to note that the seignorage peak occurred in the first quarter of 2003, a few months after the public debt ratio had stabilized and the exchange rate had appreciated. This implies that interest rates could not be lowered as quickly as many would have desired.

<!-- Start of picture text --> Figure 13 Figure 14<br>Monthly Inflation Rate in Brazil 2000-2003 Seignorage from Money Creation<br>(seasonally adjusted General Price Level 2000-2003<br>IGP-DI) (% of GDP)<br>6 2.5<br>5 2.0<br>4<br>1.5<br>3<br>1.0<br>2<br>0.5<br>1<br>0.0<br>0<br>-0.5<br>-1 2000 2001 2002 2003<br>2001 2002 2003<br>Source: FGV Source: World Bank calculations described in text.<br><!-- End of picture text --> 5 The figures reported in the text and in the graph are estimated by multiplying the base money as a share of GDP times the growth rate on base money. Eliana Cardoso (1998) estimates the average inflation tax revenue in Brazil during the 50 years ending 1995 at 2 percent of GDP.
Jamal, Arafat 2000. Minimum standards and essential needs in a protracted refugee situation: a review of the UNHCR programme in Kakhuma, Kenya. Policy Development and Evaluation Service, United Nations High Commissioner for Refugees, Geneva.

Jansen, B. 2011. The Accidental City. Violence, Economy and Humanitarianism in Kakuma Refugee Camp. Thesis. Wageningen University, Wageningen, NL, 288 pages.

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Kaiser, T. 2006. Between a camp and a hard place: rights, livelihood and experiences of the local settlement system for longterm refugees in Uganda. _Journal of Modern African Studies_ , 44(4): 597‐621.

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Maystadt, J.‐F., Ecker, O. (2014) “Extreme weather and civil war: does drought fuel conflict in Somalia through livestock price shocks?” _American Journal of Agricultural Economics_ , 96: 1157–1181.

Maystadt, J‐F and Ph.Verwimp, 2014, “Winners and Losers among a refugee‐hosting population,” _Economic Development and Cultural Change_ , _62(4): 769‐809._ Maystadt, J.‐F., M. Calderone and L. You. 2015. “Local Warming and Violent Conflict in North and South Sudan.” _Journal of Economic Geography_ 15: 649‐671.

36
**Figure 1: Commodity price indices—energy and metals**

<!-- Start of picture text --> Index, constant (2020 = 100)<br>320<br> Energy Base metals Precious metals<br>280<br>240<br>200<br>160<br>120<br>80<br>40<br>0<br>1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020<br><!-- End of picture text -->

**Source** : World Bank

**Notes** : The indices (annual averages) have been deflated by the U.S. CPI. Last observation is 2020.

**Figure 2: Commodity price indices—fertilizers and agriculture**

<!-- Start of picture text --> Index, constant (2020 = 100)<br>700<br>Fertilizers Annual agriculture Perennial agriculture<br>600<br>500<br>400<br>300<br>200<br>100<br>0<br>1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020<br><!-- End of picture text -->

**Source** : World Bank

**Notes** : The indices (annual averages) have been deflated by the U.S. CPI. Last observation is 2020.

_— 23 —_
in deprivation indicators. That is, deprivation indicators that fell the most, e.g. those related to assets and electricity, have relatively higher SW weights, while the deprivation indicator related to food consumption which showed little change has a much lower SW weight.

How do these results compare with Balisacan’s (2015) APIS-based estimates of multidimensional poverty (also reproduced in the last column of Table 6)? Among our estimates, the ones most comparable with Balisacan (2015) are those based on 10 indicators, using NU weights and �(�= 1⁄3) as the multidimensional poverty measure for the period 2004-11. We find a fall in this measure by 26% between 2004 and 2013; by comparison, Balisacan reports a decline of 19% over the same period. Thus, the estimates in this paper corroborate the results in Balisacan, though we find evidence of a larger decline in multidimensional poverty. The decline is steeper if we use the subjective welfare weights, and as noted above, steeper still if the measures are based on 9 indicators.

# 3.3 Dimensional contributions to multidimensional poverty

It is also instructive to look at how the contributions of different dimensions and indicators to aggregate multidimensional poverty vary by the weighting scheme and the aggregate measure used. Dimensional contributions can be evaluated using Shapley decomposition methods (Shorrocks, 2013); see Datt (2017) for how these methods can be applied to dimensional decomposition of multidimensional poverty measures. The decomposition evaluates the contribution of a particular dimension � as the expected reduction in the multidimensional poverty measure �(. . ) due to the elimination of deprivation � when the expectation is taken over all possible elimination paths (ranging from � being the first deprivation to be removed to its being the last, and for all possible combinations of the presence or absence of other deprivations). The results of this dimensional decomposition (for the 9 indicator case) are shown in Table 7 for alternative weighting schemes and aggregate multidimensional poverty measures.

As seen in Table 7, dimensional contributions are highly sensitive to both the choice of weighting scheme as well as the aggregate poverty measure, especially the former. For instance, using nested uniform weights and �(�= 1⁄3) , the health dimension (based on the food consumption indicator) accounts for 82% of total multidimensional poverty in 2013. This is due, in part, to the relatively high incidence of food consumption deprivation (Table 3), but more importantly it reflects the large weight of 1/3 for this indicator (see Table 4). With the cross-dimensional cut-off also set at �= 1⁄3 , this large weight ensures that everyone deprived in food consumption is necessarily multidimensionally poor too, 14
Notes: All regressions add birth governorate fixed-effects, household size and gender. Standard errors are clustered at the birth governorate level. *** p<0.01, ** p<0.05, * p<0.1.

# **5. Conclusions**

A large body of literature examines the long-lasting impacts of early exposure to the war on educational attainment and labor market outcomes in adulthood. Nonetheless, there is little known about the long-term impact on children, who were exposed to the war at the ages of 6 to 12 in Iraq. The Islamic Republic of Iran–Iraq war in 1980-1988 provides an excellent setting to study the longterm impact of war. Apart from all other—human, financial, and emotional—costs, the Islamic Republic of Iran–Iraq war led to a significant drop in educational attainment for both men and women who were of elementary school age during the conflict. The Islamic Republic of Iran–Iraq war had a large impact on men’s education level and women’s education level.

In addition to this significant educational loss, the Islamic Republic of Iran–Iraq war appears to have caused an adverse impact on social welfare of men, such as accessing social security, pension plan, health care, paid leave and job permanence. This impact is not statistically significant for women. We also find that men who were exposed to the war during elementary school age have higher probability of working in hazardous jobs and without air conditioning; however, we find no evidence for women. Similarly, we find evidence of the impact of war intensity on educational attainment and labor market outcomes. We conduct a variety of robustness checks and falsification tests, and our estimation results are still robust.

We believe that the most likely channel through which the Islamic Republic of Iran–Iraq war affects the labor market outcomes is the educational loss induced by the war. The educational loss of men is larger than the educational loss of women, and the Islamic Republic of Iran–Iraq war has more adverse impacts on labor market outcomes for men than for women.

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share of GDP in the region. Excluding these countries yields more representative estimates for the region. China dominates the East Asia and Pacific figures, while India dominates South Asia.

Because Nigeria and South Africa were excluded from the Sub-Saharan Africa estimates in Figure 6 (owing to their dominant GDP share), Figures 7 and 8 plot the two measures of TFP growth for these countries separately. In the case of Nigeria there is a clear anomaly in the GDP growth data in 2004, which boosts the TFP growth rate excluding natural resources in particular – further work on Nigerian data will clearly be required. South Africa exhibits high volatility in the TPF estimates, with modest average rates of TFP growth over the period.

**Figure 7. Total factor productivity growth in Nigeria, 1996-2014**

<!-- Start of picture text --> Nigeria: Total Factor Productivity<br>35.00%<br>30.00%<br>25.00%<br>20.00%<br>15.00%<br>10.00%<br>5.00%<br>0.00%<br>1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014<br>‐5.00%<br>TFP, with natural resources TFP, excluding natural resources<br><!-- End of picture text --> Note: Average TFP growth with natural resources was 3.11%, compared with 2.23% excluding natural resources

**Figure 8. Total factor productivity growth in South Africa, 1996-2014**

South Africa: Total Factor Productivity <!-- Start of picture text --> 6.00%<br>5.00%<br>4.00%<br>3.00%<br>2.00%<br>1.00%<br>0.00%<br>1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014<br>‐1.00%<br>‐2.00%<br>‐3.00%<br>TFP, with natural resources TFP, excluding natural resources<br><!-- End of picture text --> Note: Average TFP growth with natural resources was 0.94%, compared with 0.82% excluding natural resources.

17
Table 5. Main features of global recessions (by region)

|||Glob|al recess|ions||Non-|Global|All|
|---|---|---|---|---|---|---|---|---|
||1975|1982|1991|2009 A|verage|recessions|downturns|years|
|**East Asia and Pacific**|||||||||
|Output|6.6|6.3|8.3|7.5|7.2|7.0|6.7|7.0|
|Output per capita|4.4|4.6|6.7|6.7|5.6|5.4|5.4|5.4|
|Industrial production|...|...|11.1|8.0|9.5|9.9|6.3|9.9|
|Trade|0.2|-2.1|16.6|-6.4|2.1|9.0|4.0|8.6|
|Output (PPP)|6.4|6.0|8.2|7.3|7.0|6.9|6.2|6.9|
|Output per capita (PPP)|4.3|4.3|6.6|6.5|5.4|5.3|5.0|5.3|
|**Europe and Central Asia**|||||||||
|Output|6.2|3.0|-5.8|-5.1|-0.4|3.5|1.5|3.2|
|Output per capita|5.3|2.1|-6.2|-5.4|-1.0|2.9|1.3|2.6|
|Industrial production|...|...|...|-8.7|-8.7|3.8|1.3|3.3|
|Trade|8.5|-1.5|-17.1|-14.3|-6.1|5.8|3.0|5.0|
|Output (PPP)|6.2|3.1|-5.9|-5.4|-0.5|3.4|1.4|3.2|
|Output per capita (PPP)|5.2|2.2|-6.3|-5.6|-1.1|2.8|1.3|2.6|
|**Latin America and the C**|**aribbea**|**n**|||||||
|Output|3.8|-0.6|3.3|-1.8|1.2|4.1|2.6|3.9|
|Output per capita|1.4|-2.8|1.4|-2.9|-0.7|2.0|0.8|1.8|
|<br>Industrial production|...|...|0.3|-6.5|-3.1|2.1|0.4|1.7|
|Trade|-1.7|-10.4|11.2|-10.9|-3.0|6.0|2.5|5.5|
|Output (PPP)|3.7|-0.8|3.6|-2.0|1.2|4.0|2.6|3.8|
|Output per capita (PPP)|1.3|-2.9|1.7|-3.1|-0.8|1.9|0.8|1.8|
|**Middle East and North A**|**frica**||||||||
|Output|-1.3|-6.4|6.9|0.5|-0.1|5.3|5.0|5.0|
|Output per capita|-4.0|-9.5|4.4|-1.6|-2.7|2.7|2.8|2.4|
|Industrial production|...|...|...|...|...|...|...|...|
|Trade|5.0|-7.3|13.4|-7.0|1.0|5.4|7.5|5.1|
|Output (PPP)|-0.5|-5.1|7.2|0.4|0.5|5.2|4.7|4.9|
|Output per capita (PPP)|-3.2|-8.2|4.7|-1.7|-2.1|2.7|2.6|2.4|
|**South Asia**|||||||||
|Output|7.5|3.8|2.3|4.8|4.6|5.0|5.3|5.0|
|Output per capita|5.0|1.3|0.1|3.3|2.4|3.0|3.5|3.0|
|Industrial production|...|...|...|...|...|...|...|...|
|Trade|6.7|5.3|7.4|-6.5|3.2|6.9|4.8|6.6|
|Output (PPP)|7.6|3.9|2.3|4.7|4.6|5.0|5.3|5.0|
|Output per capita (PPP)|5.1|1.4|0.1|3.2|2.5|3.0|3.4|3.0|
|**Sub-Saharan Africa**|||||||||
|Output|0.3|0.3|0.2|3.2|1.0|4.0|3.7|3.8|
|Output per capita|-2.3|-2.6|-2.6|0.4|-1.8|1.2|1.0|1.1|
|<br>Industrial production|...|...|...|...|...|...|...|...|
|<br>Trade|6.4|-10.3|4.5|-9.9|-2.3|4.7|2.7|4.3|
|Output (PPP)|0.3|0.4|0.3|3.6|1.1|4.0|3.8|3.9|
|Output per capita (PPP)|-2.4|-2.5|-2.6|0.8|-1.6|1.3|1.1|1.1|

Note: All variables show percent changes. See footnote of Table 3. Regional aggregates include EMDEs only.

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