rafmacalaba/datause-encoder-data huggingface dataset:
# **References**

Albania (2003), National Water Association Report Belarus Ministry of Environment (2004), Environment Status Report (in Russian) ENERDATA, http://www.enerdata.fr ERRANET, http://www.erranet.org/Products/Tariff/Database/TariffIndex

- IBNET, http://ib <u>net.org</u>

ISPA (1995 – 2005), Press releases on water and sanitation grants

- ISPA (2000 – 2004), Increasing Involvement of the Local Authorities of the Baltic States in the EU Enlargement Process

ISPA (2001), National ISPA Strategy for Slovenia Environmental Sector OECD www.oecd.org/document/49/0,2340,en_2649_34343_1839281_1_1_1_1,00.html

- OECD (2003a), EAP Task Force, Data sets and performance indicators for Armenia, Azerbaijan, Georgia, Moldova, Kazakhstan, Kyrgyz Republic, Russian Federation, Tajikistan and Ukraine

- OECD (2003b), Urban Water Reform in Eastern Europe, Caucasus and Central Asia, Progress since the Almaty Ministerial Conference

Tapio Saavalainen and Joy Ten Berge (2004), Energy Conditionality in Poor CIS Countries, IMF

- Tapio Saavalainen and Joy Ten Berge (2006), Quasi Fiscal Deficits and Energy Conditionality in Selected CIS Countries, IMF Working Paper, WP/06/43

- UNDP/GEF (2004), Assessment and Development of Municipal Water and Wastewater Tariffs and Effluent Charges in the Danube Basin, Danube Regional Project, Hungarian Environmental Centre

- Vivien Foster and Tito Yepes (2005), Is Cost Recovery Feasible for Water and Electricity, The World Bank

World Bank (1999), Benchmarking Water and Sanitation Utilities: A Start-Up Kit, <u>http://www.worldbank.org/html/fpd/water/topics/bench/bench_network_indicatordef.html</u> World Bank (2004), World Development Indicators Database World Bank (2006), Measuring Financial Performance in Infrastructure, Report No.: 36058-ECA

- World Bank (2006), ECADATA Indicators Database, http://ecadata <u>worldbank.org</u>

WRc Group, http://www.wrcplc.co.uk 11
18 The top-left panel of Figure 8 imposes a single frontier but colors the low-poverty school districts lighter (in green) and the high-poverty ones darker (in red). The high-poverty districts have worse test scores in absolute terms and at a given level of spending. The top-right panel shows that a single frontier produces higher inefficiency estimates for the high-poverty districts than for the low-poverty districts. There is a prima facie case therefore that separate frontiers might be warranted. The bottom-left panel allows for different frontiers, with the frontier for the high-poverty districts being (mostly) similarly shaped but somewhat to the left of that of the lowpoverty districts. The bottom-right panel shows that separate frontiers yield a much more similar inefficiency distribution between the poor and less poor school districts than a single frontier.

Comparing the top and bottom maps of each pair in Figure 9 shows graphically the effect on the inefficiency distribution of allowing poor and less poor districts to have different frontiers: both the one-output and multiple-output maps become lighter in shade as we move from the top where no allowance is made for poverty to the bottom where poor and less poor school districts are allowed to have separate frontiers. It is noteworthy, however, that the lightening of shade is less than is the case when one moves rightwards in Figure 9 from the single-output model to the multi-output model. The implication is allowing for different frontiers across different outputs is already sufficient to take into account the differing circumstances of the poorer and less poor school districts.

# VI. **CONCLUSIONS**

In this paper, we propose a hybrid approach to measuring efficiency that blends the themes from the Data Envelopment Analysis (DEA) and Stochastic Frontier Analysis (SFA) and illustrate different aspects of it with four empirical applications. We conclude the paper by recapping the strengths of our hybrid approach, summarizing the steps to be followed in implementing it, and highlighting the key aspects of the methods demonstrated in our empirical illustrations.

Our approach has several strengths. First, it focuses on overall efficiency and does not require data on input prices. Second, the efficiency of a unit is assessed against a comparable real-life unit instead of some hypothetical comparators. Third, the approach is nonparametric, imposes few distributional and functional form assumptions, and allows for statistical noise in
<u>Table 3: Specific and multidimensional inequalities in Azerbaijan, 2016-2023.</u>

||**2016**|**2023**|
|---|---|---|
|**Specific Inequalities**<br>|||
|**Income**|||
|Mean|852.46|1384.44|
|Gini|0.253|0.339|
|**Education (years)**|||
|Mean|10.304|11.091|
|Gini|0.094|0.115|
|**Health**|||
|Mean<br>|3.448|3.511|
|Gini<br>|0.166|0.159|
|**Multidimensional Inequality**<br> <br><br>|||
|A(x) = M(x)<br><br>|0.181|0.260|
|<br> M(x)<br>|0.144|0.230|
|<br><br> m(x) = R(x)<br>|0.037<br>|0.029<br>|

Multidimensional inequality as measured by increased significantly between 2016 and 2023, from 0.144 to 0.230. This increase is due to (i) changes in the specific inequalities, (ii) GGiinnii MM changes in the effective weights as dimensional means change, and (iii) changes in the rearrangement term. Holding (ii) and (iii) fixed and altering specific inequalities from the 2016 to the 2023 levels raises the average specific inequality from 0.181 to 0.238. Incorporating the 2023 means alters the effective weights from 0.50, 0.25, and 0.25, respectively, to 0.61, 0.20, and 0.19, thus increasing the average further to 0.260. Finally, the rearrangement term fell slightly from 0.037 to 0.029, reflecting greater alignment of dimensions and ensuring that the increase in multidimensional inequality (namely, from 0.144 in 2016 to 0.230 in 2023) is more pronounced than the increase in average specific inequality.

Figure 4 depicts the associated Lorenz curves for 2016 and 2023. The solid curves represent , the Lorenz curve of the aggregate distribution from the actual data array. The dashed tt curve is ss associated with the aligned aggregate distribution or, equivalently, the LL (pp) ss<sup>tt</sup> tt weighted average of the specific Lorenz curves. The vertical difference between the solid and ss̿ LL (pp) ss̿<sup>tt</sup> dashed curve is the Lorenz rearrangement function for the year, while the average vertical difference is clearly linked to the Gini rearrangement term. Each 2023 curve is below its respective 2016 curve, indicating that multidimensional inequality is unambiguously higher in 2023 than in 2016. Replacing the Gini with any Lorenz-consistent inequality index would preserve the conclusion that multidimensional inequality rose from 2016 to 2023. II 27
This is smoothly increasing in _y_ and _r_ . The fixed poverty line in utility space is: where _z_ is the poverty line in income space. Equation (2) defines implicitly the function: This shows how the poverty line for “type _x_ ” people varies with the _m_ , to keep utility constant. Let _η_ denote the elasticity of the monetary poverty line with respect to the mean, we find that: where _MRS_ is the marginal rate of substitution between _y_ and _r_ (i.e., _MRS_ = ( ∂ _v/_ ∂ _y_ )/( ∂ _v/_ ∂ _r_ )). The value of _η_ will be somewhere between zero and one. This prediction is confirmed by a number of empirical studies of both objective and subjective poverty lines.<sup>17</sup> Thus we can rationalize theoretically the notion of a poverty line that is absolute in the welfare space, but relative in the income space, as discussed in the previous section. But where does the direct interdependence embodied in (1) come from? And why is it necessarily a <u>negative external effect (whereby having a better-off reference group lowers welfare)? And</u> where is the evidence that it exists? The rest of this essay will take up these issues.

# **_3.2 An expository structural model of the sources of interdependence_**

The above formulation is too general to provide any insights into the competing (positive and negative) sources of interdependence, and it only then that we can try to understand why one might find an interaction effect between concerns about RD and one’s own level of living. However, it must be acknowledged that there are many possible sources of interdependence, particularly in a developing country context. The following model pits two stylized sources against each other: interdependence arising from the existence of positional goods and interdependence arising from informal risk-sharing arrangements.

I assume that an informal risk-sharing arrangement exists amongst a set of self-interested individuals and I consider two homogeneous groups, one of which draws the income _y_ while the

> elasticity of one to _m_ , as assumed by Citro and Michael. But it is unclear why perceptions of RD would exclude certain goods, and particularly “non-basic” goods; this assumption would seem hard to defend. 17 Ravallion (1998) provides evidence consistent with this property based on cross-country comparisons of how the poverty lines used in practice vary with mean consumption.  The analyses of subjective poverty lines for the US by Kilpatrick (1973) and for European countries by Hagenaars and van Praag (1985) are also consistent with this prediction.

9
privately owned companies, since publicly owned companies are likely to face structurally different credit/informality trade offs.

One of the critiques often raised to the IC surveys is that they are not fully representative of the country firms’ size distribution. In particular, since in most countries firms’ distribution is heavily skewed towards micro and small firms, large firms were over-sampled to allow meaningful analysis for all firm categories. This is a potential concern for our analysis: if smaller firms are more informal than large firms, (i.e. they report lower share of sales for tax purposes) we are likely to underestimate aggregate tax compliance. As most surveys do not report weights, we address this concern by weighing tax compliance in each firm category (micro, small, medium, large, and very large) using data from the International Financial Corporation (IFC) SME database as well as other individual census data on the actual distribution of firms by size in each country (see discussion further on).

To proxy for informality, we use the percentage of sales that firms report to declare for tax purpose (FORMALITY). The distinct advantage of using this variable is that, unlike most indirect estimates of informality, FORMALITY captures a well defined concept: tax compliance. Clearly, the complementary drawback is that we measure only a portion of the informal economy. Although it is likely that FORMALITY suffers from measurement error, the lack of alternative audit-based measures of tax compliance that can be compared homogenously across a large number of developing countries makes this variable a particularly useful starting point to address these issues.

Descriptive statistics reported in tables 1-3 indicate that the degree of tax compliance increases linearly with firm size, ranging from an average of 72% of sales declared in micro firms to an average of 86% in very large firms. Tax compliance is higher in high-tech technology industries, particularly in “metals and machinery” and “chemicals and pharmaceuticals”, and lower in more traditional, labor intensive sectors such as “leather” and “wood and furniture”.<sup>8</sup> There is substantial variation across world regions, with lowest average tax compliance in SubSaharan Africa and highest in OECD countries.<sup>9</sup> As for our outcome variables, we measure access to credit with a binary indicator of whether or not the establishment has an overdraft facility or line of credit (CREDIT), and also by

> 8 In the definition of high and low tech firms, we follow Parisi et al. (2006) Low-tech firms are those operating in food, beverages, garments, leather, textiles, wood and furniture, non-metallic and plastic materials, paper, other manufacture, and agro-industry.  High-tech firms are those operating in metal and machinery, electronics, chemicals and pharmaceutics, auto components and other transport equipment. 9 OECD countries in the ICS are: Germany, Greece, Ireland, Portugal, South Korea, and Spain.

7
Ravallion, M. (1990). Rural welfare effects of food price changes under induced wage responses: theory and evidence for Bangladesh. _Oxford Economic Papers_ , 574-585.

Sheales, T., S. Gordon. A. Hafi, and C. Toyne. (1999). “Sugar International Policies Affecting Market Expansion.” ABARE Research Report 99.14. Canberra, Australia.

<mark>Swinnen, J. (2011). The right price of food.</mark> _<mark>Development Policy Review</mark>_ <mark>,</mark> _<mark>29</mark>_ <mark>(6), 667-688</mark> USDA Foreign Agricultural Services (FAS), Nairobi. 2013. “Corn Update Report.” Global Agricultural Information Network Reports.

USDA Foreign Agriculture Service (FAS). 2013. “Kenya Annual Sugar Report”. Global Agricultural Information Network Reports.

USDA Foreign Agriculture Service (FAS). 2012. “Kenya Annual Sugar Report”. Global Agricultural Information Network Reports.

USDA Foreign Agriculture Service (FAS). 2012a. “Kenya Bans Genetically Modified Food Imports”. Global Agricultural Information Network Reports.

USDA Foreign Agriculture Service (FAS). 2011. “Kenya Annual Sugar Report”. Global Agricultural Information Network Reports.

USDA Foreign Agriculture Service (FAS). 2010. “Kenya Annual Sugar Report”. Global Agricultural Information Network Reports.

USDA Foreign Agriculture Service (FAS). 2009. “Kenya Annual Sugar Report”. Global Agricultural Information Network Reports.

USDA Foreign Agriculture Service (FAS). 2008. “Kenya Annual Sugar Report”. Global Agricultural Information Network Reports.

<mark>Wodon, Q., & Zaman, H. (2008). Rising food prices in sub-Saharan Africa: Poverty impact and policy responses.</mark> Wohlgenant, M.K. 1999. “Effects of Trade Liberalization on the World Sugar Market.” United Nations Food and Agriculture Organization, Rome.

World Bank. 2009. Eastern Africa - A study of the Regional Maize Market and Marketing Costs. World Bank. © World Bank.

https://openknowledge.worldbank.org/handle/10986/3155 License: Creative Commons Attribution CC BY 3.0.” Fengler, Wolfgang; Kiringai, Jane. 2013. Achieving shared prosperity in Kenya. Washington DC ; World Bank. http://documents.worldbank.org/curated/en/2013/08/18523839/achievingshared-prosperity-kenya 35
classes a rate of 69.5 and 3 classes achieving a rate of 78.8% it was determined that a maximum of 3 classes could be reasonably extracted. With some manual cleanup, all 15 African cities reached an accuracy level above 85%.

Figure 18 below shows Maputo classified into 3 classes (Commercial/industrial, Residential, and Un‐built land) in 2006 and then again in 2012. These land cover maps do not merely show the urban expansion, but they also provide insight into the changes of the land cover composition (i.e. what type of land has been expanding or where has land cover remained constant. In Maputo, a noticeable amount of growth appears to be occurring the north and to the west of the city with new residential land as well as the industrial parks being built. In contrast, the downtown area near the port, remains quite unchanged. It should be noted that when conducting change detection analysis using satellite imagery, there will always be an element of uncertainty introduced by the image as well as the classification process. For example, in Maputo, the scenes were taken during different months and at different angles, the classifier was then tailored to the pixel values in each scene. As a result, buildings in the first scene might appear to have higher values than buildings in the later. The classifier must therefore adjust and identify buildings in the first scene using different criteria than the buildings in the second scene. While this source of error should be recognized, it does not invalidate the value of classifying layers and inspecting their change over time as an exploratory tool.

Figure 18 – Maputo Land cover classification using SPOT5 <!-- Start of picture text --> New growth<br>New growth<br>2012 2006<br>Source: Author’s classification of SPOT5<br><!-- End of picture text --> Although the goal of classifying SPOT5 imagery was to explore how well coarser imagery could be used to analyze urban growth over time, several interesting trends emerged through a cross city comparison. Figure 19 below summarizes the land cover within 5kms of each city’s CBD. The cities have been ordered according to population size, with Kinshasa and Lagos being two of the most populated cities located near the Y‐axis, and Freetown with less than 1 million inhabitants located at the very end.<sup>53</sup> Returning again to Maputo with a population of roughly 1.2 million, the chart shows that within 5 kilometers of its

> 53 Population data taken from <u>http://www.citypopulation.de/, a website that compiles national census data.</u>

29
achieve restructuring, then one would expect to see more immediate effects from sales to concentrated outsiders than from voucher or insider privatization, where it takes time for concentrated blocks to form. In this case, the major differences across countries could arise in the timing of the potential benefits from privatization. A possible hypothesis would be that the speed of the impact of privatization falls alphabetically, as the initial ownership concentration is increasing in the fraction of sales in all privatization transactions. The subsequent dynamics of the privatization effect reflect the possibilities for secondary trading leading to increased concentration, however, and countries with high initial levels of inside and dispersed outside ownership initially may tend to catch up so that the final impact after several years is not very different across countries.

Turning to the business and policy environment, a natural hypothesis is that privatization works best in a business environment that protects the property rights and enforces the contracts that private owners require to ensure a return on their investment and effort (Anderson, Lee, and Murrell, 2000; Black, Kraakman, and Tarrasova, 2000). In this case, countries with better institutional ratings should also have the strongest privatization effects. Conditional on institutional ratings made by international organizations, the size of the privatization effect should decline in alphabetical order among our four countries.<sup>11</sup> An alternative view of the business and policy environment might be that ownership matters least when the environment functions well, as regulation, competition, and hard budget constraints serve to discipline firm behavior. From that point of view, the institutional environment could be a substitute rather than a complement for private ownership.<sup>12</sup> The quality of the institutional environment may also affect the dynamics of the privatization effect. For example, if better institutions result in faster development of financial markets that facilitate ownership reallocation and concentration, then a country with a relatively poor initial ownership structure but good institutions may tend to start off with a low privatization effect but then catch up over time. If we adopt the conventional assumption that concentrated outside ownership is the most likely to deliver productivity improvements, however, then our data do not contain

> 11 These are documented in Brown, Earle, and Telegdy (2004a).

> 12 A related argument is that concentrated ownership is more effective when legal protections are weaker; see LaPorta, Lopez-de-Silanes, and Shleifer (1999).  Brown and Earle (2001) investigate the complementarity or substitutability of private ownership with the competitiveness of product markets and with the privatization of competitors.

14
_Source_ : Gaunt (2005).

Phase 2 of the electrification program was started in 2000. Its main target was to provide 300,000 new households with electricity connections every year.

# **Performance Problems in the Distribution Industry**

While the generation and transmission segments of the industry have been operating reasonably efficiently, the electricity distribution industry (EDI) has been marred by serious structural problems and has been in serious financial trouble for several years now.

## **Structural Inefficiencies**

The total number of local authorities was reduced, through amalgamations, from over 800 to just below 380 in 2001.<sup>74</sup> Thus, approximately 360 municipalities, Eskom, and about 13 private distributors were at one time engaged in the business of distributing electricity in South Africa. The authority of municipalities to engage in the provision of infrastructural services like roads, water and sanitation, health services, and electricity is supported by the Constitution. With respect to electricity services, the Constitution states that “A municipality has executive authority in respect of, and has the right to administer...electricity reticulation.”<sup>75</sup> Only a very few municipalities have been generating their own power. The majority of them have been purchasing power from Eskom for resale to consumers within their boundaries.

Until recently, therefore, the distribution industry was highly fragmented with one very large, several medium-sized, and too many very small, financially non-viable, and poorly-run municipal distributors. These structural arrangements in the EDI were not the result of any rational planning and thus formed no coherent pattern. Not surprisingly, consumers have been facing significantly different tariffs and quality of service standards across the country. In particular:

- the reliability of supply and the ability of municipal distributors to offer secure service to low income households has differed markedly across the country;

- significant disparities have existed in the structure of tariffs and these differences exhibited little or no relationship to the quality of service provided, the cost of supply, or consumers’ ability to pay (table 6);

- unfair discrepancies have existed between Eskom Distribution and municipal distributors purchasing tariffs from Eskom Transmission--to the benefit of some large customers, but the detriment of the domestic and low-income consumers.

> 74 The number of municipal councils was further reduced to 284 by 2005.  Oxford Analytica (2005), _ibid_ . 75 Eberhard, A. 2005. “New Electricity Regulation Bill Proposes Radical Changes”.  AMEU Bulletin September 2005.

45