rafmacalaba/datause-encoder-data huggingface dataset:
Policy Research Working Paper 9689

# **Abstract**

Containing rapidly growing health care costs in the Latin American and the Caribbean region, especially amid the COVID-19 pandemic, requires an in-depth analysis of prices from a novel perspective. This paper documents hitherto understudied variations in prices paid for pharmaceuticals, equipment, and medical supplies within countries and markets. It also identifies effective procurement strategies for lowering prices within existing regulatory frameworks. The analysis uses public procurement data gathered by governments’ electronic procurement systems in nine countries and territories across the region. The data are uniquely detailed and complete, encompassing the minute detail of purchasing decisions and processes made across all regulated public entities in the study countries and territories. Traditional regression analysis and machine learning (random forests) methods are used to explain prices as a function of procurement decisions and outputs, such as the number of bidders. Based on in-depth discussions with policy makers, the paper also devises realistic policy interventions, which in turn can be used to estimate savings scenarios. First, the findings show that the prices paid vary greatly across and within countries. The latter is surprising given that the regulatory and institutional framework is largely fixed within each country. Second, a high proportion of within-country and -market variation can be explained by standard features of procurement policy implementation, such as the length of advertising tenders. Third, the explanatory models point to the potential for lowering prices across the region by about 14 percent by implementing low-level, yet impactful changes to how purchasing is done.

This paper is a product of Latin America and the Caribbean Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at aoliveira@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
mMean =+1.64*S.E. =—-1.64*S.E.

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Second, we use two indicators of bank efficiency. Overhead cost is our first and primary measure of bank efficiency and is computed as total operating costs divided total costs. Overhead cost varies from less than one percent to 8.3% in our sample, with an average of 3.5%. As alternative efficiency indicator, we use the cost-income ratio, which measures overhead costs relative to gross revenues, with higher ratios indicating lower levels of cost efficiency. This indicator ranges from 33% to 92%, with an average of 62%. In our smaller sample, we find that Islamic banks have significantly higher overhead costs than conventional banks, but only marginally higher cost-income ratios (significant at 10% level).<sup>6</sup> We also note that cost efficiency is significantly higher in our smaller sample than in the large sample.

Third, we use three indicators of asset quality. Specifically, we use (i) loss reserves, (ii) loan loss provisions, and (iii) non-performing loans, all scaled by gross loans. All indicators decrease in asset quality. We note that there might be problems with cross-country comparability, due to different accounting and provisioning standards. Loan loss reserves range from less than one percent to 13.4%, with an average of 4.5%. Loan loss provisions range from less than zero to 4.7%, with a mean of 1.3%. Non-performing loans, finally, range for 0.4% to 20.1%, with an average of 6.2%. In our smaller sample, Islamic banks have significantly lower loan loss reserves and non-performing loans, while there is no significant difference in loan loss provisions.

Fourth, we use several indicators of bank stability. The z-score is a measure of bank stability and indicates the distance from insolvency, combining accounting measures of profitability, leverage and volatility. Specifically, if we define insolvency as a state where losses surmount equity (E<-  ) (where E is equity and  is profits), A as total assets, ROA=  /A as return on assets and CAR = E/A as capital-asset ratio, the probability of insolvency can be expressed as prob(-ROA<CAR). If profits are assumed to follow a normal distribution, it can be shown that z = (ROA+CAR)/SD(ROA) is the inverse of the probability of insolvency. Specifically, z indicates the number of standard deviation that a bank’s return on assets has to drop below its expected value before equity is depleted and the bank is insolvent (see Roy, 1952; Hannan and Henwick, 1988; Boyd, Graham and Hewitt, 1993; and De Nicolo, 2000). Thus, a

> 6 We do not use the net interest margin, a standard indicator of efficiency in the financial intermediation literature (Beck, 2007), as Shariah prohibits the use of interest and Islamic banks should therefore do not show any interest revenue or cost in their financial statements.  Nevertheless, Bankscope reports both for Islamic banks.

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Policy Research Working Paper 9000

# **Abstract**

Many studies have demonstrated that Mexico’s conditional cash transfer program, PROSPERA, has substantial effects on educational attainment. Nevertheless, little evidence exists on whether increases in time spent in school have led to higher learning in the context of the poor areas where PROSPERA principally operates, which tend to have overall low school quality. This study combines data from nationwide achievement tests with administrative data on PROSPERA beneficiaries to estimate impacts on achievement tests. The analysis finds significant effects on learning, as measured by standardized achievement tests, on the order of magnitude of 0.05 to standard deviation, with larger effects for indigenous children. The analysis also confirms large effects on enrollment in secondary and high school, using administrative school enrollment data rather than self-reported household-level data, as generally used in previous studies. Finally, given the existence of several alternative tracks in secondary and high school, the study also examines where PROSPERA beneficiaries enroll. The findings show that most of the increase in enrollment occurs in tele-secondary schools and, at the high school level, in general high schools.

This paper is a product of the Social Protection and Jobs Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted atswparker@umd.edu and the coordinators of the studies may be contacted at cavilaparra@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
arrangement of their society, by whliah is **meant** that economic **and** educational institutions try to be as self-sufficient as possible, reaching backward into activitles that will protect the quantity and quality of their inputs, And forward to insure outlets for their products. The government has identified as ore of its more general goals the promotion of a greatly enhanced degree of "horizontality", urging enterprises to reduce their autarchic practices, to foster trade with independent supplying enterprises, and to support the extension of independent wholesale and retail trading. In these early stages of reform, the practical results of such exhortation are still quite limited.

The effects of this predilection for self-sufficiency have been very marked in the realm of vocational education and technical training. We describe below the intricate divisions among training institutions, sponsoring enterprises and agencies, and government oversight bureaus. Their complexities are not easy to grasp, but their existence has clearly much to do with the fact that the major industries of China have felt themselves unable to rely on others to supply them with even the minimum quantity and quality of skilled and semi-skilled labor they needed. In that sense, it is difficult to talk of a 'market' for such labor, as that term is used in Western industrialized economies.

China's arrangements for VTE are marked by the exceptionally large share of the total effort that is made by the enterprises themselves. While other countries, for example, Britain, France, and the United States, are trying to find ways to involve employers more closely with secondary education in general, and VTE in particular, China has already concentrated over half of its secondary VTE enrollments in establishments with close ties to enterprises (STS and Skilled Worker Schools), and the others in schools (SVS) that have every incentive to build those ties. However, enterprise-linked schools have been charged more with the provision of pre-service, rather than in-service, technical training of skilled and semi-skilled workers, and this has had severe onsequences for the overall level of technical skills embodied in China's work force.

Over and over again we heard that the quantity and quality of China's skilled and semi-skilled manpower are grossly inadequate, especially for staffing the industrial and commercial development that is being projected: "The work force is characterized by a low percentage of skilled workers, technicians, and trained managers... (World Bank, 1985, p. 24). Present inadequacies may be attributed partly to the political tunmoil affecting all of secondary education during the Cultural Revolution, but this is not the whole story.

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# Panel A: Sorghum

<!-- Start of picture text --> 350<br>300<br>250<br>200<br>150<br>100<br>50<br>0<br>Panel B: Millet<br>350<br>300<br>250<br>200<br>150<br>100<br>50<br>0<br>Burkina Faso Mali Niger Senegal Chad<br>Price (XOF/kilogram)<br>Jan-15 Apr-15 Jul-15 Oct-15 Jan-16 Apr-16 Jul-16 Oct-16 Jan-17 Apr-17 Jul-17 Oct-17 Jan-18 Apr-18 Jul-18 Oct-18 Jan-19 Apr-19 Jul-19 Oct-19 Jan-20 Apr-20<br>Price (XOF/kilogram)<br>Jan-15 Apr-15 Jul-15 Oct-15 Jan-16 Apr-16 Jul-16 Oct-16 Jan-17 Apr-17 Jul-17 Oct-17 Jan-18 Apr-18 Jul-18 Oct-18 Jan-19 Apr-19 Jul-19 Oct-19 Jan-20 Apr-20<br><!-- End of picture text --> Note: All prices are retail prices. Prices shown are national averages. Source: World Food Programme Vulnerability Analysis and Mapping Food Security Analysis tool and World Bank estimates.

). As such, prices of agricultural products are at their highest during the agricultural lean season between June and September. By contrast, livestock prices tend to be lowest in the pastoralist lean season between April and June each year, as households are forced to sell animals at ‘distress prices’ in order to cope (FEWS NET, 2017). Prices therefore provide an early indicator of the seasonal stresses on consumption and welfare that Sahelian households may be facing.

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Pozen, Robert. 2010. ”The US Public debt hits its tipping point”. The Boston Globe, February 23.

Reinhart, Carmen and Kenneth Rogoff. 2009. This Time is Different. Princeton University Press.

Reinhart, Carmen and Kenneth Rogoff. 2010. “Growth in a Time of Debt”. American Economic Review, May forthcoming.

Reinhart, Carmen, Kenneth S. Rogoff and Miguel A. Savastano. 2003. "Debt Intolerance". Brookings Papers on Economic Activity, 2003:1,1-74.

Scott, Andrew. 2010. “The Long Wave of Government Debt”. Vox, March 11.

1. Cordella, Ricci, and Ruiz-Arranz (2010) also estimate threshold regressions. These results are not used for the main message of their paper.

2. Reinhart and Rogoff (2010) analyze 20 developed countries; other studies focus exclusively on developing countries. See, for example Pattillo, Poirson, and Ricci (2002, 2004) and Cordella, Ricci, and Ruiz-Arranz (2010), each of which analyzes more than 60 countries.

3 . We use a shorter period of time than Reinhart and Rogoff (2010) and general government debt rather than central government debt.

4. The simple pooled regression does not control for any other economic variables that affect growth or test for the existence of a threshold. Thus, the results should be interpreted with caution.

13
**Table 6: Exports by Product and by Regional Market** (in billions of 2001 rubles)

|Good|Central F|ar East|North|Siberia|South|Urals|Moscow|St.<br>Petersb.|Tumen|North-<br>west|
|---|---|---|---|---|---|---|---|---|---|---|
|MAR|4.9|1.7|1.7|7.1|4.5|13.7|4.3|2.2|6.9|1.2|
|AGR|3.1|0.5|0.2|2.4|3.5|4.4|0.4|0.3|0.2|0.4|
|AIR|5.6|2.4|1.8|7.6|6.8|13.2|18.9|3.0|4.4|1.2|
|CHM|27.5|0.5|2.0|33.2|18.2|92.0|13.7|5.0|0.3|15.2|
|CNM|1.8|0.2|0.1|1.7|1.2|3.4|1.6|0.6|0.2|0.2|
|COL|0.1|4.2|2.6|26.4|1.3|0.9|||||
|CON|4.0|0.4|0.3|2.9|5.6|8.0|7.3|1.6|1.3|0.6|
|CRU||7.1|27.0|15.8|60.3|156.1|||452.8|1.6|
|ELE|1.1|0.3|0.3|1.4|1.0|2.6|1.8|0.4|0.8|0.2|
|FIN|0.7|0.3|0.2|0.9|0.7|1.6|1.8|0.3|0.7|0.1|
|FME|50.5|1.1|3.7|22.4|11.1|83.8|7.1|4.8||28.3|
|FOO|8.7|9.7|2.5|12.6|23.1|10.1|27.0|9.2|0.1|3.6|
|GAS||1.4|3.1|3.6|11.5|21.8|||410.7|0.0|
|HEA|0.3|0.1|0.1|0.4|0.3|0.6|0.8|0.1|0.2|0.1|
|HOU|0.3|0.1|0.1|0.4|0.3|0.6|0.8|0.1|0.2|0.1|
|MWO|22.8|14.2|1.9|43.0|23.3|97.0|95.5|30.4|1.9|5.0|
|NFM|4.6|15.1|18.4|203.1|11.1|101.6|17.2|11.3||0.6|
|OIL|35.5|13.7|4.5|50.9|29.5|114.4|12.8|21.1|11.5||
|OTH|1.7|0.4|0.5|1.9|1.1|4.0|1.4|0.7|1.6|0.4|
|OTI|7.2|0.3|0.4|1.3|2.1|4.9|5.3|1.4|0.0|0.5|
|PST|0.3|0.1|0.1|0.5|0.4|0.8|0.9|0.2|0.4|0.1|
|RLW|0.9|0.3|0.2|1.1|0.8|2.0|1.3|0.3|0.6|0.2|
|SCI|0.6|0.2|0.2|0.7|0.5|1.6|1.1|0.3|0.7|0.1|
|TMS|0.9|0.3|0.3|1.2|1.0|2.1|2.4|0.4|0.9|0.2|
|TPP|3.1|17.1|29.2|35.1|1.7|13.3|2.1|12.7|0.4|8.1|
|TRD|2.0|0.8|0.7|3.0|2.2|5.3|4.5|1.0|3.9|0.5|
|TRK|0.4|0.1|0.1|0.5|0.4|0.9|0.9|0.2|0.5|0.1|
|TRO|0.5|0.2|0.2|0.7|0.6|1.3|1.2|0.2|0.7|0.1|
|CLI|2.6|1.1|0.8|3.4|3.1|6.0|9.0|1.4|1.3|0.5|

See Tables 1 and 2 for sector and region definitions.

Source: Roskomstat unpublished surveys and authors' calculations.

42
the threshold for the gas-fired plant. Nevertheless, in 2009, Turkish electricity’s wholesale prices were approximately 0.10-0.11 US$/kWh and were not likely to decrease for years to come until excess capacity was in the marketplace.

As noted above, policy makers have options to reduce the cost of lignite. In particular, operating the mine at about 13 million tons per year -- roughly two-thirds capacity -- would keep the lignite cost below 6 US$/ton (The World Bank, 2006). Simultaneously, the cost of gas has remained above 220 US$/tcm since 2006. In combination, the conditions that would lead the gas-fired plant to fail the cost effectiveness test seem more plausible than the conditions that would lead rehabilitation to fail the cost-benefit test.

In sum, this analysis leads to several important insights about the merits of our options:

1. Rehabilitating the existing lignite plant fails to meet decision makers’ costminimization goals under plausible future conditions. If gas costs remain low while local lignite costs increase, a new gas-fired plant would be more cost effective.

2. Yet, the cost of local lignite is at least partially under decision makers’ control, suggesting that decision makers can take action to avoid those threatening conditions.

3. Moreover, building a new gas-fired plant fails to pass the cost-benefit test under conditions that are less constraining and more plausible than the conditions in which the rehabilitation fails a cost-benefit test.

These observations could reasonably lead decision makers to conclude that rehabilitating the existing plant is an economically sound choice: it performs well under a wide range of potential future conditions and is less vulnerable than other options. However, other factors may also influence their decision. The relative importance of cost-benefit versus costeffectiveness metrics, decision makers’ risk aversion, and other concerns may shape their choice. Nevertheless, a more complete and more transparent understanding of the merits of decision makers’ options helps inform these deliberations and keeps the decision in the hands of decision makers.

# **5 Key Findings and Recommendations**

Faced with pervasive deep uncertainties about the future, decision makers struggle to choose near-term decisions that make long-term sense. In this paper, we sought to answer three key questions:

1. How are deep uncertainties currently managed in World Bank projects? 2. Can new approaches help projects better manage those uncertainties?

3. Are they practical and applicable, and what challenges do they pose?

To help answer these questions, we reviewed prior project analyses and then applied RDM to the economic analysis of a long-term energy investment in Turkey. Our goal is not to reevaluate the earlier decision, which today would be very different and for example, include climate change concerns. Rather, we show the type of questions decision makers and analysts should ask about the robustness of their investments, and how they can 16
goods may be accessed in different regions and there need not be outlet substitution bias notwithstanding the challenge of finding similar types of outlets in urban and rural areas when surveying prices. Second, comparing Engel curves over time is likely more reliable than comparing them over space, since household survey design is usually stable over the short term and average characteristics of respondents that might affect measurement error also will be fairly stable over time. In contrast, countries might use different methods to survey urban and rural households, and even if the same method is used it may be _de facto_ different (e.g., diary surveys in illiterate rural areas often degrade to unstructured recall, while they may be truer to design in literate urban areas). This matters since key Engel curve parameters are sensitive to differences in how survey questions are posed and answered.<sup>8</sup> Third, Engel curve results on CPI bias are often corroborated by analyses of durables ownership or by comparing subjective reports of well-being over time. In contrast, there is only a diffuse prior about expected patterns of prices over space, except perhaps that prices should be higher in nominally richer areas due to the Balassa-Samuelson effect, although the opposite can be claimed (Muller 2002). Absent corroborating analyses, the burden of proof for relying on the Engel curve deflator for spatial comparisons should be higher than it is for temporal comparisons.<sup>9</sup> The remainder of the paper is structured as follows. Section II describes the context and data, paying particular attention to the spatially disaggregated prices that are rarely available for

> 8. Gibson et al. (2015) randomly assign eight different consumption surveys to households in Tanzania and find the coefficient on real income in the food Engel curve varies by a factor of three between survey designs. This is one of two coefficients that determines the Engel curve deflators, so this fragility suggests that variation over space in survey design or in characteristics such as respondent’s education, wealth, and food acquisition opportunities, which correlate with measurement errors, may spuriously affect the deflators derived from Engel curves.

> 9. An exception is Almås et al. (2013) who attempt to provide corroborating evidence by comparing patterns of calorie sources and self-reported hunger around the poverty lines based on their Engel curve deflator.

8