Production and Scale Eﬃciency of South African Water Utilities: The Case of Water Boards

South Africa is a water scarce country with deteriorating water resources. Faced with tight fiscal and water resource constraints, water utilities would have to adopt technically efficient water management technologies to meet developmental socio-economic objectives of universal coverage, aligned to the United Nation’s Sustainable Development Goal 6 (SDG 6) . It is important to measure the technical efficiency of utilities as accurately as possible in order to inform policy. We do this by using a non-parametric method known as Data Envelopment Analysis (DEA) to determine, measure, analyse and benchmark the technical efficiency of all water boards in South Africa. Our contribution to the literature is twofold: This is the first paper to model technical efficiency of water boards as utility suppliers and guardians of water services in South Africa, and second, we address the over- and under estimation issues of technical efficiency measurement in the water sector. We do this by modelling one of the most pronounced negative externalities from water provision (water losses) as an undesirable output using the approach developed by You & Yan (2011). We find on average, technical efficiency of water boards is 49 per cent, with only three of the nine water boards technically efficient. Six of the smaller water boards showed high levels of inefficiency. Six water boards operate at increasing returns to scale (IRS) and two are scale efficient. Only Rand and Sedibeng water boards exhibited decreasing returns to scale (DRS). Therefore, redirecting potential efficiency savings to optimal uses could result in technical and scale efficiency for the sector. Scale efficiency results seems to support larger regional water boards as small to medium-sized water boards are scale inefficient with low technical efficiency. The ratio model with undesirable output outperforms previous methods to deal with undesirable (bad) outputs, which either provide an over- or underestimation of technical efficiency.


Introduction
The United Nations 2030 Agenda for Sustainable Development was adopted in September 2015. It established 17 Sustainable Development Goals (SDGs) relating to global development outcomes. The establishment of the SDG 6: ensuring availability and sustainable management of water and sanitation for all, reflects increased attention to water and sanitation on the global development agenda. The pillars of the SDG 6 are: achieving universal access to safe and affordable drinking water and sanitation by 2030, improving water quality, wastewater treatment and safe re-use, increasing water-use efficiency to ensure water security, especially in water stressed areas, and implementing integrated water resources management and adequate financing to meet the SDG 6 targets (United Nations, 2018).   Table 1 shows that South Africa is a water scarce and rainfall deprived country compared to the rest of the world, which makes South Africa a good candidate for analysis. This is also affirmed by Masindi and Duncker (2016). The country only receives 465 mm or 50 per cent of the rainfall received by most countries in the world annually. Given its high reliance on surface water, there is a need to manage existing resources efficiently. According to the Department of Water and Sanitation (DWS) (2019), South Africa is facing a water crisis caused by insufficient water resources, and poor infrastructure maintenance and investment. The DWS alludes to plans to diversify the sources of water where water security is to be derived from ground water, water conservation and water demand management (given the high levels of technical distribution water losses-excluding non-technical losses like billing inefficiencies and non-collection, of more than 1 660 million m 3 /a), water re-use, desalination and effective crossboundary water management. DWS estimates the water infrastructure investment deficit to be R33 billion per annum (about $2.2 billion). The quality of rivers and ground water remains poor, signalling weaknesses in water resources management.
In terms of water use, the DWS (2019) reported that agriculture uses 61 per cent of allocated water while municipalities use 27 per cent. The remainder is consumed by other sectors, such as energy, industries, mining, livestock and forestry. As it relates to universal access to basic water services, South Africa is amongst the countries that are performing relatively well. Table   1 indicates that of the 16.9 million households or total population of 59.9 million, 89.9 per cent or 15.2 million households have access to piped water (also see Figure 1   The water sector value chain is comprised of national water resources, regional bulk and local retail water and sanitation services segments. According to Masindi and Duncker (2016), the DWS is the designated custodian of water resources by the National Water Act. It leads policy development and regulates the water and sanitation sector in South Africa. It is also responsible for planning and implementation of large water resource infrastructure projects, issuing of water-use licenses, allocating water, performing catchment management functions, river systems management, water storage and abstraction and return-flow management. At a regional level, according to Masindi and Duncker (2016), there are nine water boards mainly responsible for bulk water purification and distribution, however, some municipalities and the  Table 2, which also outlines their high-level operational performance.  Masindi and Duncker (2016) and DWS (2019), indicated that some challenges facing the water boards, municipalities and DWS include weak governance, lack of adequate funding coupled with inefficient operations to meet and sustain investment requirements, inappropriate financing and pricing arrangements and lack of accountability. Moreover, water is severely under-priced and cost recovery is not being achieved. This results in ineffective operations and maintenance of water supply infrastructure. Gupta, et al. (2012) advised that if the water and sanitation supply revenue from user charges falls short of expenditure (financing and investment gap), it causes assets to deteriorate and threatens the sustainable supply of water and sanitation services.

DWS
The DWS is responsible for water sector policy, support and regulation. Wholly funded by the national governement 17 000 000 17 000 000 2 000 000 -

WTE
The WTE deals with the management of water infrastructure and resources, and the sale of raw water. Largely funded from water tariffs and augmented through the national budget for public interest functions. The Department's asset register indicates a total pipe network of 1 070 km and canal systems of 8 100 km. Of the 5 248 registered dams in South Africa, the DWS/WTE only owns 6% (320), but they account for 86.4% of the retained water 16 000 000 14 000 000 98 000 000 19 142 million m³/a sold.

TCTA
The TCTA is responsible for financing and implementing the development of bulk raw water infrastructure, and providing treasury management services to the DWS. The authority plays an important role in providing: financial advisory services such as structuring and raising project finance, managing debt and setting tariffs; project implementation services; and other technical support to the department and water boards. Funded through the WTE from raw water sales revenue. 5 000 000 6 000 000 --

CMAs
The CMA's mandate is the management of water resources. Funded from water resource management charges and losses subsidised by the DWS through the WTE 753 000 753 000 -19 142 million m³/a managed.

Water Boards
Water Boards provide water services (bulk potable and bulk waste water) to other water services institutions. They own large bulk distribution pipelines, reservoirs and water and waste water treatment plant and serve municipalities and industries 29 000 000 24 000 000 75 000 000 2 528 million m³/a sold net losses of 7% .

Municipalities
Authorised municipalities (WSAs) are responsible for bulk and retail water supply. They purchase bulk water from water boards or directly from the DWS. Municipal water reticulation infrastructure includes more than 290 000 km of pipelines supplying water to 89.9% of the population. 76 000 000 69 000 000 33 400 137 4 980 million m³/a produced, 36% or 1 660 million m³/a lost. Total 143 753 000 130 753 000 208 400 137 -According to Table 2, the scale of operations of municipal water businesses in the water and sanitation services supply space performed by approximately 146 authorised municipalities, exceed that of the nine water boards. The consolidated water revenue and expenditure of these municipalities is 3 times that of the nine water boards; however, the carrying value of assets of the water boards is 2 times larger than that of the municipalities. According to the DWS (2018), the DWS, water boards and municipalities and other state departments own about 854 dams, mostly with high storage capacity and the private sector owns about 4 657 small dams. From Table 2, it is further evident that the nine water boards own large bulk distribution pipelines, reservoirs and water and waste water treatment plants used to serve municipalities and industries. Municipal water reticulation infrastructure includes more than 290 000 kilometres of pipelines currently supplying water to 89.9 per cent of the population. In It is clear that the South African water value chain is inextricably linked with the water resources and water and sanitation services components complementing each other. Despite the financial value of the municipal water business being the largest in the value chain, their efficiency in the supply of water and sanitation services has been widely studied. See Murwirapachena, et al. (2019), Brettenny and Sharp (2016), Monkam (2014), Mahabir (2014) and Dollery and Van der Westhuizen (2009). However, we could not find a study on the technical efficiency of water boards in South Africa, except for a paper by Ngobeni and Breitenbach (2020). For this reason, we opted to analyse the efficiency of water boards in South Africa. Our paper differs from the paper by Ngobeni and Breitenbach (2020) in that we use a methodology developed by You and Yan (2011) to adequately include a very significant undesirable (bad) output related to water provision, namely water losses, in our model (ratio model) and we compare the results with traditional models to illustrate the biased efficiency results from the traditional models. You and Yan have shown that the results from their model provide results superior to other methods of dealing with undesirable outputs. This is discussed more fully under the methodology section.
We analyse the technical efficiency of water boards by applying a non-parametric benchmarking tool called Data Envelopment Analysis (DEA). DEA is ideal to measure and compare the technical efficiency of the nine water boards as they operate in similar conditions.
It is easy to compare their production technologies to determine efficiency. Gupta et al. (2012) recommended the use of DEA for determining the technical efficiency of decision-making units (DMUs). They argued that despite other techniques such as the ordinary least square (OLS) and stochastic frontier analysis (SFA) being used in analysing the technical efficiency of the water industry, DEA is the most appropriate. The OLS technique is easy to use and simple to interpret, however, it suffers from the problem of specifying the functional form for the production technology and is unable to provide information on frontier performance. The SFA, although able to solve the latter problem by specifying a composed error term and splitting the error term into two different parts as a data noise term and error due to the inefficiency, it also suffers from the problem of specifying the functional form and requires specification of the distribution patterns of the inherent error terms. DEA is devoid of these deficiencies. The aims of the study are achieved by analysing data related to expenditure used by the nine water boards and the efficiency outcomes they achieve during the study period, 2018/19, in producing the prevailing bulk water volumes at going tariff rates while taking into consideration water losses. We provide policy makers with information on how well a particular water board is performing relative to its peers, to identify good and bad practices, and finally find more efficient approaches to achieve financial sustainability and reliable water and sanitation supply in the pursuance of national and the SDG 6 objectives.
The rest of the paper is organised as follows: Sections 2 and 3 deal with the literature and methodological specification respectively, Section 4 with the data, Section 5 with the results and Section 6 concludes the study.

Literature review
As stated above, DEA has been extensively used globally to analyse technical efficiency in the water sector. However, to the best of our knowledge, this is the maiden study to use DEA or any other modelling technique to analyse the efficiency of water boards in South Africa while considering "bad outputs" like water losses which are a central feature of such production processes. In regards to the water sector efficiency literature, Ali, et al. (2018)

Methodology
In this paper, we use the VRS approach reported by Gavurova et al. (2017) and developed in 1984 by Banker, Charnes and Cooper to allow for consideration of scale efficiency analysis. This is called the Banker, Charnes and Cooper (BCC) model. The terminology "envelopment" in DEA refers to the ability of the efficiency production frontier to tightly enclose the production technology (input and output variables). Cooper et al. (2007) and McWilliams et al. (2005) state that DEA was developed in a microeconomic setting and applied to firms to convert inputs into outputs. However, in efficiency determination, the term "firm" is often replaced by the more encompassing DMU. DEA is an appropriate method of computing the efficiency of institutions employing multivariate production technologies. Aristovnik (2012) and Martić, et al. (2009) state that there are input-minimisation and output-maximisation DEA models. The former determines the quantity of inputs that could be curtailed without reducing the prevailing level of outputs. The latter expands the outputs of DMUs to reach the production possibility frontier while holding inputs constant. However, the selection of each orientation is studyspecific. In this paper, we select the input minimisation orientation for the four models. DEA basically erects a production frontier consisting of most relatively technically efficient DMUs in the sample. This process generates technical efficiency measures for each unit in the sample by comparing observed values to optimal values of outputs and inputs. A score of 1, represents the best performing unit in the sample and a score of less than 1 implies that the unit is not performing as well as its efficient peers. DEA determines how much inputs could have been saved and the extent of outputs that could have been improved by inefficient DMUs by emulating the production processes of efficient DMUs.
According to Taylor and Harris (2004), DEA is a comparative efficiency measurement tool that evaluates the efficiency of homogeneous DMUs operating in similar environmental conditions, for example, DMUs dealing with bulk water supply and where the relationship between inputs and outputs is unknown. Wang and Alvi (2011) report that DEA only uses the information used in a particular study to determine efficiency and does not consider exogenous factors. DEA measures the distance of production functions by determining the radial extent of DMUs to the efficiency frontiers. It does so by categorising the DMUs into extremely efficient and inefficient performers. In terms of the DEA methodology, the current study uses the BCC model with the ratio of DMUs complying with the norms of at least being 2 to 3 times the combined number of inputs and outputs.

Treating Undesirable Outputs
DEA models have found increasing use in efficiency analysis applications where at least one output in the production process is an undesirable output, e.g. pollution or water losses. There is considerable research published on the undesirable aspects of production outputs.
However, You and Yan (2011) have found that the economic implications and the suitability of DEA models incorporating the undesirable outputs should be carefully considered as the results may either under-or overstate efficiency if modelled incorrectly. Breitenbach et al.
(2020) recently used this approach to consider the efficiency of healthcare systems in managing the COVID-19 pandemic and used deaths and infections as undesirable outputs.
The first way that undesirable outputs are dealt with in the traditional DEA model, is to ignore the undesirable output (Nakashima et al, 2006;Hua and Bian, 2007;Lu and Lo, 2007a, b). It is not however, appropriate to ignore the reality of e.g. pollution or water losses during production since undesirable outputs and desirable outputs are generated simultaneously in the production process. Dyckhoff and Allen (2001) dealt with undesirable outputs by modelling them as inputs. However, treating undesirable outputs as inputs fails to reflect the true production process, this is the same approach adopted by Ngobeni and Breitenbach (2020).
There is a specific production technology that links inputs to outputs, and taking an undesirable output as an input in the production process leads to misspecification and misinterpretation, for example, when modelling the pollution as an input using an output-oriented measure, ecological inefficiencies remain undetected. Golany and Roll (1989) suggested a data transformation approach where an undesirable output is converted into a 'normal' output by a monotonic decreasing function. The undesirable outputs (carbon and nitrogen emissions) are treated as normal outputs by taking their reciprocals. Although the pollutant is treated as output, the scale and intervals of the original data get lost and problem with zero values is that it does not have a reciprocal value. The Linear monotonic decreasing transformation was suggested by Seiford and Zhu (2002). A sufficiently large positive scalar βi is added to the reciprocal additive transformation of the undesirable output i so that the final values are positive for each DMUk. This model is criticised for its invariance to data transformation within the DEA model (Lu and Lo, 2007a, b). Fӓre et al., (1989) treats undesirable factors in a nonlinear DEA model based on the weak disposability of undesirable outputs (Zhou et al, 2007).
Weak disposability assumes that to reduce undesirable outputs it is costly because simultaneously, it increases the inputs or decreases desirable outputs (Yang et al, 2008). It tends to increase the desirable output and undesirable output concurrently. Regardless of the form of transformation, as long as the final value of undesirable output included in the DEA calculation remains positive, it increases the efficiency of the DMU. An undesirable output should bring either a negative or positive impact to the performance of DMU; therefore, it is not appropriate for the undesirable output to solely favour the efficiency score.
After comparing the performance of the models discussed above, You and Yan (2011) developed the ratio model, which outperformed all five of these models developed for dealing with undesirable outputs. We therefore opted to adopt the ratio model for the current paper.
The ratio model is different from the previous approaches in that the undesirable output is aggregated in a ratio form with the desirable output. From the conventional BCC DEA model and assuming that there are R DMUr (r = l, 2, . . . , R), that convert m inputs to n outputs, DMUk is one of the R DMUs being evaluated. It is further assumed that DMUk consumes m inputs (i = 1, 2, . . . , m) to produce n outputs (j = 1, 2, . . . , n) and all these outputs are assumed to be desirable. The measure of efficiency of DMUk is then obtained by: In the ratio model, the undesirable output and desirable output are defined as − ( = 1. 2, … , 1 ) and + ( = 1, 2, … , 2 ), respectively ( 1 + 2 = ). For DMUk, the undesirable outputs − ( = 1, 2, … , 1 ) are treated as a new variable , which is called the penalty parameter and is written as: where = penalty parameter for DMUk; = the penalty for individual undesirable output ( = 1, 2, … , 1 ); − = the undesirable output ( = 1, 2, … , 1 ). Since is the penalty charged for producing the outputs, the obtained from problem (4) gives a measure of the total monetary value of undesirable outputs. From the definition of , the greater the amount of undesirable output, the greater is the value of the penalty parameter. Further, the respective value of is associated with the individual undesirable output, therefore has the same value for every DMU. With this model, desirable and undesirable outputs can relate to one another, regardless of disagreement in the units. With the new approach of treating the undesirable outputs in (4), the desirable output p ( = 1, 2, … , 2 ) of DMUk in the ration model is modified as : where + = the desirable output ( = 1, 2, … , 2 ); ′ = the modified output ( = 1, 2, … , 2 ).
The ratio model computes desirable and undesirable outputs as fractions, where undesirable output − is the denominator and desirable output + the numerator. Here the value of the output is interpreted as a ratio of desirable to undesirable output. Using ratios provides a simple and easy way to expose the impact of undesirable outputs in a DEA. The ratio form of the DEA model can satisfy the restrictions of the conventional DEA, which the output variable states must be a positive value. Moreover, the ratio form provides a more distinct way for the desirable and desirable output to describe the presence of an undesirable output on DMU efficiency.
In order to check the stability of our model results, we ran four different model specifications and compared the results. In Model I, we use expenditure as financial input, bulk water tariffs as financial output and water losses (bad output) and bulk water sales volumes as physical outputs (we ignore the effect of bad outputs). In Model II, we use the same variables while excluding the water losses variable from the model (we use only good outputs, ignoring the bad output). In Model III, we use expenditure as the financial input and the ratio of bulk water tariffs to water losses and bulk water sales volumes to water losses as physical outputs (ratio model). Model IV, uses expenditure as financial input and water losses as a physical input, bulk water tariffs as financial output and bulk water sales volumes as physical output (we use the bad output as an input).

Data
Our data was obtained from different sources. The data for the total expenditure, water losses (technical and non-technical) and bulk water tariffs were extracted from the 2018/19 audited annual reports of the water boards and volumes sold were obtained from the National Treasury (2020b). The sample consists of the nine water boards, 1 financial input: expenditure, 1 financial output: bulk water tariffs and 2 physical outputs (water losses and volumes sold).
From Table 3 it is clear that there is substantial variation between the variables with Rand Water an outlier at the higher end and Overberg an outlier at the lower end of the spectrum.

Results
The results of the four model variants are provided in Table 4   That is, with the correct inclusion of water losses as an undesirable output, the average technical efficiency score declines by 30 per cent. Put differently, ignoring the bad output (water losses) by just including or omitting it as an output in the water board production process overstates the average technical efficiency score by 30 per cent. This is a substantial distortion of the efficiency estimates. Model IV is the last of the input minimisation model variants; it shows that using a bad output (water losses) as an input (as in Ngobeni and Breitenbach, 2020) completely alters the production technology by decreasing the average technical efficiency of water boards by 20 per cent to a score of 29 per cent. Therefore, just incorporating a negative output as an input materially underestimates the efficiency scores. As a result, we follow You and Yan (2011) and adopt the best performing Ratio Model III, for the discussion of the results and formulation of the recommendations associated with the input minimisation objective. Using the other three input minimisation models (Models I, II and IV), does not accurately capture a very crucial undesirable output (water losses) and is an inappropriate DEA application that may lead to incorrect conclusions and policy recommendations. The Their results also over-estimated the technical efficiency scores when bad outputs were not considered, as in our Models I and II. Overall, this implies that the general technical efficiency of municipalities may be much lower than previously believed. In regard to water boards, we have shown above that by modelling water losses as an undesirable output, true efficiency is lower than if we choose to omit it in our estimation or incorrectly model it as a positive output.
This has important policy implications when technical efficiency is substantially lower than initially thought and estimated and policy actions also based on these over-or understated efficiency values.
The above technical efficiency results do not mean much if not interpreted together with the inefficiency factors (radials and slacks). Coelli et al. (2005) defined slacks as input excesses and output shortfalls that are required over and above the initial radial movements to push DMUs to efficiency levels. Both the slack and radial movements are associated only with the inefficient DMUs. The radial movements are initial input contractions or output expansions that are required for a firm to become efficient. Therefore, using the Model III results, the average technical inefficiency rate is 51 per cent with the six inefficient water boards needing to reach the optimal efficiency frontier depicted by the Overberg, Rand and Umgeni water boards. Table 5 summarises the efficiency and inefficiency rates as they relate to expenditure. The input minimisation implies using the same or less inputs while maintaining the same levels of outputs. For Model III, The inefficiency rate of 51 per cent is equivalent to wastage in expenditure of R3.7 billion by the six inefficient water boards. The three efficient water boards serve as peers for the inefficient ones. However, given the high levels of water and sanitation coverage backlogs in provinces where the inefficient water boards operate and the financing gap for the water sector, it is proposed that the individual inefficient water boards conduct a detailed review of their expenditure items such as personnel and operational costs through a benchmarking exercise with the efficient peers for improvements. The prospective savings from moving to the efficiency frontier could also be used to extend service coverage given the backlogs in service delivery depicted in Figure 1. Therefore, the study recommends efficiency improvements that could assist to achieve the SDG 6 targets and objectives.
In order to illustrate the relative inefficiency of the smaller water boards below the inefficiency frontier more clearly, one can perform a simple comparison with one of the larger water boards that operate on the efficiency frontier. In Table 6, we chose to compare three of the smaller water boards with Umgeni Waterthe second largest water board that also happens to be scale efficient. We only discuss two of them to illustrate the relative inefficiency. From Table 6, the inefficiency of the smaller water boards become more apparent. Amatola Water with an efficiency score of only 16% for example, has a total expenditure of 18% of that of Umgeni, but sells only 6.7% of the quantity sold by Umgeni. Amatola also has seven times the proportion of water losses compared to Umgeni and charges 1.5 times the tariff of Umgeni.
In the case of Lepelle Water, which has an efficiency score of 34%, total expenditure is 27% Of more importance in the case of the water boards however, is the scale efficiency of the water boards' production technology. The average scale efficiency scores of water boards across the four models range from 62 to 92 per cent. As illustrated in Figure 3, the highest scale efficiency scores are recorded in Model III. The most prevalent form of scale is the increasing returns to scale (IRS) returns to scale (there is potential to improve the extent of operations: an increase in inputs will result in a more than proportional increase in output).

Water Board VRSTE Total Expenditure ( R) Water Losses (%) Bulk Water Tariff ( R) Volumes sold (million m³/a)
Only the Overberg and Umgeni water boards were scale efficient in Model III. The only water board with a scale efficiency of less than 50 per cent is Rand Water. The other water boards surpass the 95 per cent scale efficiency mark.

Figure 3: Scale Efficiency Scores
Sources: DEA efficiency results.
Only the Rand and Sedibeng water boards recorded decreasing returns to scale (an increase in inputs will lead to a less than proportional increase in output: the extent of operations is bigger than is required). Therefore, the water boards operating on the IRS frontier could combine the efficiency savings with private financing or future tariff increases to improve the scale of their operations and expand operational footprint. Those operating at DRS should benchmark with the scale efficient water boards for improvements. In sum, we are not recommending for a reduction in the current expenditure levels of all water boards despite some being inefficient. We are recommending for the efficient use of resources to improve the current operational levels. This implies improved scale efficiency for the seven water boards that are scale inefficient. Policy makers should also consider fast-tracking the reform to merge some of the smaller to medium-sized water boards into large regional water utilities as they operate on less than optimal scale. This is an ideal water board's model for operations, funding and sustainability. However, this reform should be preceded by a detailed benchmarking of operational practices with peer water boards to avoid merging inefficient operations.

Conclusions
The study used four comparative input-oriented DEA models to analyse the technical efficiency of the nine water boards' expenditure efficiency while maintaining the current levels of bulk water tariffs and sales volumes. We used a novel DEA ratio model developed by You and Yan (2011), which treat undesirable outputs by dividing the positive outputs (tariffs and volumes) by water losses to convert them into a ratio that eliminates biased efficiency estimates. Only 3 or 33 per cent of the water boards were efficient. Despite six water boards being inefficient, the inefficiency rate was 51 per cent due to the much larger water boards -Rand and Umgeni Water -accounting for the bulk of the spending and water supply in the sector. Given the results from the scale efficiency, the main policy implication is that the smaller water boards would need to increase the scale of operations to become scale efficient, while the largest and third largest water boards are experiencing decreasing returns to scale, meaning that they need to reduce their scale of operations to attain scale efficiency. The results suggest that holding the outputs fixed, six small to medium-sized water boards could be merged after an efficiency benchmarking exercise to improve scale efficiency. The results from the scale efficiency seems to support larger regional water boards as small to mediumsized water boards are scale inefficient.
In this paper we have demonstrated the importance of modelling bad or undesirable outputs by comparing the results of the ratio model with other model variants as suggested by You and Yan (2011). Our results (in line with You and Yan (2011), confirm the fact that the ratio model more accurately captures the impact of undesirable outputs in the production technology on technical efficiency and eliminates over-and underestimation resulting from incorrect specification of the production technology in traditional models. This method can therefore be recommended for policy applications in other country studies or cross-country studies with confidence.