rafmacalaba/datause-encoder-data huggingface dataset:
# 1. Introduction

The COVID-19 pandemic has put a lot of pressure on already strained health care systems across the Latin America and the Caribbean (LAC) region (World Bank, 2020). Among others, the explosion of costs, such as prices paid for pharmaceuticals (Chernew & May, 2011), has contributed to failings in equitable and quality health care provision in the region. COVID-19 emergency states and the accompanying pressures to deliver critical goods and services fast have further contributed to price pressures in already fragmented markets.

Responding to these policy challenges, this study aims to

- document hitherto understudied variations in prices paid for pharmaceuticals, equipment and medical supplies within countries and markets; and

- identify effective procurement strategies for lowering prices within existing regulatory frameworks with the help of purchase-level explanatory models.

These ambitions are made possible by the unique data set compiled from public sources enabling price analysis at the very detailed, purchase level (i.e. within countries and markets over time). This represents a major departure from most prior studies which had to rely on market-level average prices across countries (e.g. Danzon & Chao, 2000). We make use of public procurement data as gathered by public authorities in 9 countries and territories across the LAC region through their electronic procurement systems. This data is 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.

Our sample of 7 countries (Ecuador, Brazil (federal), Paraguay, Panama, Uruguay, Peru, and Costa Rica) and 2 territories (Amazonas (Brazil) and Santa Catarina (Brazil)) is exceptional in its breadth; still, it misses out on important countries in the region and hence may represent a skewed sample. Nevertheless, the wide country coverage of our sample allows for making cross-country claims in the region, by exploiting the variance in terms of institutional and regulatory set-up. We make use of traditional regression analysis as well as machine learning (random forests) methods to explain prices paid for pharmaceuticals, equipment and medical supplies. Based on in-depth discussions with policy makers, we devise realistic policy interventions which in turn can be used to estimate savings scenarios.

First, we find that prices paid even for highly standardized pharmaceuticals vary greatly across as well as within countries. The latter is surprising given that regulatory and institutional frameworks are largely fixed within each country. Second, a high proportion of within-country and within-market variation can be explained by standard features of procurement policy implementation such as the length of advertising tenders. Third, our explanatory models point at the potential for lowering prices across the region by about 14% by implementing low-level, yet impactful changes to how purchasing is done.

2
**Figure 11:  Learning value added (% difference between posttests and pretests) per completed VR training course [Socio-emotional Skills]**

<!-- Start of picture text --> Buttussi et al.(2018) Self-efficacy assessing an emergency<br>(VR narrow view)<br>Confidence on ability to prioritize<br>Vincent et al. (2008) resources<br>Self-efficacy addressing an emergency<br>Buttussi et al.(2018)<br>(VR Wide view)<br>Vincent et al. (2008) Confidence on ability to identify risks<br>Confidence on ability to prioritize<br>Vincent et al. (2008) treatment<br>Confidence that others will consider<br>Vincent et al. (2008) me effective<br>Vincent et al. (2008) Confidence on being effective<br>Akpan & Strayer (2010) Attitude towards use of PC<br>Akpan & Strayer (2010) Attitude towards science<br>Akpan & Strayer (2010) Attitude towards frog dissection<br>-30.0 -10.0 10.0 30.0 50.0 70.0 90.0 110.0<br><!-- End of picture text --> _Note_ : studies in bold are in the field of engineering, science, and technical education. Underlined studies are in the field of health and safely. Other studies are in the field of general education. The bold vertical dotted line represents the average observed effects. Gray vertical dotted lines represent the standard deviation of the observed effects.

Finally, results from Akpan and Strayer (2010), which use VR training to simulate a frog dissection, do not find a significant effect of the use of VR training in students change in attitude towards learning anatomy and/or using PC assisted instruction.

# _4.4. Impacts of VR training on learning efficiency_

In the context of this study, learning efficiency is defined as any savings in the form of inputs, time, or performance errors that VR training could contribute to. One of the promises of using simulators vis-a-vis traditional instruction is their potential to save training costs and minimize the risks and errors faced when novice students intend to master some skills they will use in real life. Ideally, VR training should contribute to more efficient use of inputs, more expedited completion of tasks (or time-in-task), and fewer performance errors. The meta-analysis includes a total of 12 experiments that study the effects of VR training on the utilization of inputs (such as materials and time to complete a task) and performance errors. A total of 11 out of 12 experiments included in the meta-analysis find that VR training is associated with higher learning efficiency levels when compared to traditional training. In fact, experiment results indicate that, on average, students who are exposed to VR training are, on average, up to 30 percent more efficient (using inputs, time, and/or avoiding performance errors) than students exposed to traditional training per additional hour of instruction. Results from available experiments indicate 26
are randomly allocated, we abstract from unobserved heterogeneity in individual, household, or village characteristics.

In a second step, we formally estimate the marginal survey design effects using the following specification: where<sup>_y_</sup> _i_<sup>are the different labor statistics (such as labor force participation, labor</sup> supply, earnings, and occupational choice) for the _i_<sup>th</sup> child, _Ph_ is an indicator variable for the proxy treatment of children in household _h_ , S _h_ is an indicator variable for the short questionnaire treatment of children in household _h, Xi_ is a vector of child and household characteristics for the _i_<sup>th</sup> individual, _D_ captures district indicators, and ɛ is the stochastic error term, which is randomly distributed across households.

Survey data are also used to estimate behavioral equations, for example how the age of the child and other personal and household characteristics impact the probability of the child working. We investigate whether point estimates of key covariates (vector Z) in these equations vary when different survey designs are used, focusing on four important covariates of child work, as identified by seminal papers in the child labor literature: the child’s age (Edmonds, 2009), household size (Edmonds, 2005), household assets (Basu and Van, 1998), and household land size (Bhalotra and Heady, 2003). To do this, we interact the survey assignment variables with each of these variables minus its mean value in the sample ( _Zi_ – mean _Z_ ), while controlling for the survey assignment effects, the covariate of interest, household and individual characteristics ( _Xi_ , which includes _Z_ variables), as well as district indicators. We estimate the following specification:

**3. The data**

9
where _j_ indexes locations (or cities). To simplify notation we will treat both _x_ and _g_ , as scalars, although we actually estimate coefficients of two indicators of business environment and three variables of economic geography. To these we add three error components of ln _AitR_ , two of which we assume to be time invariant. Let one of these,<sup>θ</sup> _i_<sup>,</sup> summarize the effect of unobservable establishment characteristics on productivity. Let the second, μ _j_ , be the effect of unobservable location characteristics on the same. The third, ε _it_ , is a zero mean, iid, random shocks uncorrelated with the other two and the indicators of geography and business environment. Assume also that the relationship between ln _A R_ and all of its determinants is linear such that where _i_ = 1,.., _N_ , _j_ = 1,..., _J_ ; _t_ = 1,..., _T_ ; α , β , γ are constants, and _z_ is a set of observed firm or establishment characteristics. We observe ln _A R_ and z annually for the three year period 2001-2003 so that _T_ = .3 We also observe _xijt_ only for the last of these three years, while our observation of _g ijt_ are obtained from sources other than the World Bank survey and relate to the 1990s. However, we assume that _x_ has been constant for each firm over the three-year period of interest and that _g_ would not have changed significantly since the late 1990s. In other words, we assume _xijt_ = _xij_ and _g ijt_ = _g ij_ so that we can re-write (3) as Assuming further that both _g ij_ and _zijt_ are exogenous to ln _A_<sup>ˆ</sup> _ijtR_ , (4) can be estimated as a random effects model consistently by GLS provided that<sup>_x_</sup> _ij_<sup>is also uncorrelated withθ</sup> _i_ and μ _j_ , or we can fully control for both of the latter variables. Since we have defined location at the fairly aggregate level of the city we can, in principle, control for location 18
# **1. Introduction**

The informal sector accounts for 30 to 40 percent of total economic activity in the poorest countries, and a much higher share of employment (La Porta and Shleifer, 2014, Gollin, 2002). It is particularly pervasive in poor African countries such as Malawi, where 93 percent of firms have not registered with the government.<sup>1</sup> These firms are largely small and unproductive (La Porta and Shleifer, 2014), and the informal status of these firms is often associated with a number of costs to firms, including lack of access to external finance (de Soto, 1989; World Bank, 2013). Governments around the world have attempted to reduce informality by making it easier to formally register a business, with the _Doing Business_ project of the World Bank finding 368 reforms took place in 149 economies between 2003 and 2012 (World Bank, 2013).

However, despite these efforts to make it easier for firms to formalize, a recent review of the effects of these reforms by Bruhn and McKenzie (2014) finds that the reforms have had limited effects, with the majority of existing informal firms not formalizing after it became easier to do so. This is seen in the results of four recent randomized experiments to encourage formalization. In Sri Lanka, de Mel et al. (2012) find no impact of information and free registration costs on registration with the tax authority, but they do find that a significant number of firms are willing to register when offered money to register. In Brazil, Andrade et al. (2014) find no impact of either information or of free registration costs on registration under a one-stop shop for municipal, state, and federal taxes, although they do find that increased municipal enforcement does result in more municipal registration. In Bangladesh, de Giorgi and Rahman (2013) find no impact of an information campaign on business registration (separate from tax registration). Finally, in Lima, Peru, Alcázar et al. (2010) and Jaramillo (2009) find that information and the reimbursement of direct costs leads about one quarter of those treated to register at the municipal level. These existing studies suggest that the majority of informal firms do not find the benefits of registering for taxes at the national level to be enough to outweigh the costs that formality brings in the form of taxes. At the municipal level tax enforcement tends to be much higher than at the national level. However, annual municipal licensing fees tend to be relatively modest, so

> 1 Source: 2004-05 Integrated Household Survey, which shows 93 percent of firms are not registered with the Department of the Registrar’s General (DRG).

2
(night lights increased more than six-fold between 1992 and 1993). Focusing on a more recent period, growth in Somalia is estimated (based on night lights) at 4 percent per annum between 2000 and 2012.

# **3.3 The role of agriculture**

In many countries in SSA, a large part of the population is engaged in small-scale subsistence agriculture. Since this activity is largely unlit, it will not show up in night lights. This means that estimating GDP _levels_ in SSA based on night lights would likely result in an underestimation of “true” GDP. In Liberia for instance, agriculture accounted on average for 67 percent of GDP over the 1992-2012 period. Estimating Liberia’s GDP based on night lights would fail to capture twothirds of Liberia’s economic activity. If growth in agriculture accounts for a sizable part of aggregate GDP _growth_ , growth in night lights would also tend to underestimate GDP growth. In Rwanda for instance, agriculture accounts for about 25 percent of total GDP growth. Estimating growth based on nightlights would hence underestimate true growth.

To what extent does the importance of agriculture underestimate GDP growth? Linking the residuals of specification (1) to the share of agriculture in GDP, we find a positive but weak correlation. Countries where GDP growth is underestimated by nightlights have on average a higher share of agriculture in GDP (29.7 percent) than countries where GDP is overestimated (26.1 percent), though the difference is relatively small. In Table 4, we divide our sample into two based on whether the country has an above or below-average share of agriculture in GDP (average for the countries in the sample).

Table 4: <u>Night lights and growth for more and less agrarian countries</u>

||ln(GDP)|ln(GDP)|
|---|---|---|
||Above average share<br>of agriculture|Below average share<br>of agriculture|
|ln(lights/area)|0.445***|0.656***|
||[0.078]|[0.046]|
|Observations|525|441|
|Countries|25|21|
|R sq.(within)|0.851|0.781|

Notes: Dependent variable is the natural log of GDP in constant LCU. All specifications include country and year fixed effects. Robust standard errors clustered by countries in brackets. ***: significant at 1%-level; **: significant at 5%-level; *: significant at 10%-level. Data source: NGDC (2014) and WDI (2014).

As expected, we find that the association between nightlights and GDP is stronger in countries where unlit agriculture accounts for a smaller part of overall economic activity (column (2) in table 4). In these countries, manufacturing and services account for the bulk of GDP, and growth in these sectors is more likely to turn up in lights. The association between nightlights and GDP remains however strongly significant for the agrarian countries as well.

11
do enrollment rates depend on factors other than public spending levels? For example, do changes in family incomes have a greater influence than perceived education quality on demand for education? The evidence that increases in public education spending do not automatically raise enrollment rates may have important implications for education policy in other countries.

5.33 Educat.in spending increased its share of GNP significantly between 1980 and 1988. This growth resulted from increasing public spending as a share of GNP combined with an increase in education's share of the public budget. Real education expenditures more than quadrupled between 1975 and 1985. However, primary enrollment rates dropped from 1980 to 1985, even though a pre-existing program of tuition exemption for poor children expanded during the 1979-81 recession. Total primary enrollments declined three percent between 1980-85. Increasing public spending and declining enrollments caused public education spending per primary student to nearly double between 1980 and 1985.

# South Korea: education indicators

||197|5 1980|1981|1982 1983|1984|1985 1986 1987 1988|
|---|---|---|---|---|---|---|
|Finance:<br>|||||||
|% of public budget<br>|12.9|14.6|14.4|17.0 17.9|16.8|16.9|
|% of GDP<br>|2.2|3.7||||4.8|
|real expenditures (index)|41|100||||186|
|Inraectoral finance:<br>|||||||
|recurrent budget share(%)|74|84||||80|
|primary share of budget (%)|62|50||||47|
|3utput (enrollment rates--%):<br>gross primary<br>|107|109|107|109 103|99|96|
|net primary|100|||||94|
|Ouality:|||||||
|per student spending (index)<br>primary repeaters (%)|46<br>0|100<br>0||||192<br>0|
|Equity:|||||||
|% primary students female|48|49||||49|

5.34 The share of recurrent spending in the total budget declined slightly from 1980 to 1985, suggesting that, unlike the pattern found in many countries, investment in education was not neglected during the adjustment period. Female primary enrollment rates exceeded total enrollment rates. One cause for concern is the declining share of sector spending allocated to primary education.

47
Policy Research Working Paper 7204

# **Abstract**

Does online voting mobilize citizens who otherwise would not participate? During the annual participatory budgeting vote in the southern state of Rio Grande do Sul in Brazil—the world’s largest—Internet voters were asked whether they would have participated had there not been an online voting option (i-voting). The study documents an 8.2 percent increase in total turn-out with the introduction of i-voting. In support of the mobilization hypothesis, unique survey data show that i-voting is - mainly used by new participants rather than just for con venience by those who were already mobilized. The study also finds that age, gender, income, education, and social media usage are significant predictors of being online-only voters. Technology appears more likely to engage people who are younger, male, of higher income and educational attainment, and more frequent social media users.

This paper is a product of the Governance Global Practice Group. 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://econ.worldbank.org. The authors may be contacted at tpeixoto@ 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
middlemen, sometimes leaving a harvest to rot. As reported to us by DrumNet, the farmers were outraged but powerless, and subsequently returned to growing what they had been growing before (e.g., local crops such as maize).

Two lessons can be drawn from the DrumNet experience. First, on the positive side, DrumNet succeeded in building trust in the horticultural markets by convincing farmers to make specific investments even when some feared holdup problems with the export buyers, and by convincing buyers to trust farmers and purchase their produce. The second lesson, however, was that because DrumNet’s success depended on their farmers being certified, it should have secured the resources to cover the substantial infrastructure and maintenance costs to achieve it. The eventual collapse of the transactions thus may have generated a _loss_ of trust, the exact problem DrumNet was designed to solve.

24
<!-- Start of picture text --> 250 600<br>@ Coal<br>@ Gas<br>@ Gas/H2<br>200 500 GasTM Geothermal with CCS<br>@ Nuclear<br>@ Hydro<br>2 400 TM Wind<br>2 150 Peak demand 3 TM Solaeel<br>3 # 300<br>o©<br>a S<br>S 100 gS<br>& 200<br>50 100<br>0 0<br>2022 2040 LGT 2040 RNZP 2022 2040 LGT 2040 RNZP<br>Source: World Bank staff estimates<br><!-- End of picture text --> Notes: Gas/H2 = hydrogen gas; CCS = carbon capture and storage.

<!-- Start of picture text --> a) EV adoption for passenger cars b) Modal share, public transit (buses and rail)<br>100% 50%<br>~<br>2 80%<br>= 40%<br>5 g<br>°><br>@S 60% %<br>g 5<br>© 2 30%<br>= 40% &©<br>£ £<br>° °<br>5 F 20%<br>S 20% Ls RNZP o= RNZP<br>& == BAU == BAU<br>o% 10%<br>Nv% y S © v % y S % % S © Vv % y S<br>e Fvy FoF Fi) Fx SF¥ F6 F6 eNv FVv FoF Foy F¥ F¥ KF6 FCS<br>Source: World Bank staff estimates<br><!-- End of picture text -->
- Dollar, David, and Aart Kraay. 2001. "Growth is Good for the Poor." World Bank Policy Research Paper 2587. Development Research Group, World Bank, Washington, D.C.

- Easterly, William, and Sergio Rebelo. 1993. "Fiscal Policy and Economic Growth." _Journal of Monetary Economics_ 32: (3): 417-58.

- Elley, W. 1992. _How in the World do Students Read?_ IEA Study of Reading Literacy. Hamburg: The International Association for the Evaluation of Educational Achievement.

- Filmer, Deon, Jeffrey S. Hammer, and Lant Pritchett. 2000. "Weak Links in the Chain: A Diagnosis of Health Policy in Poor Countries." _The World Bank Research Observer_ 15 (2, August): 199-224.

- Filmer, Deon, and Lant Pritchett. 1999. "The Impact of Public Spending on Health: Does Money Matter?" _Social Science and Medicine_ 49(1): 1309-23.

- Gupta, S., M. Verhoeven, and E. Tiongson. 1999. "Does Higher Government Spending Buy Better Results in Education and Health Care?" Working Paper 99/21. International Monetary Fund, Washington, D.C.

- Hanushek, Eric. 1995. "Interpreting Recent Research on Schooling in Developing Countries." _The World Bank Research Observer_ 10(2): 227-46.

- Harbison, R., and Eric Hanushek. 1992. _Educational Performance of the Poor: Lessons from Rural Northeast Brazil._ Oxford: Oxford University Press.

- Kaufmann, Dani, Aart Kraay, and P. Zoido-Lobaton. 1999. "Governance Matters." World Bank, Research Development Group, Washington, D.C. Processed.

- La Porta, R., F. Lopez-de-Silanes, A. Shleifer and R. Vishny. 1999. "The Quality of Government." _Journal of Law, Economics and Organization 15(1)._

- Levine, Ross, and David Renelt. 1992. "A Sensitivity Analysis of Cross-Country Growth Regressions." _American Economic Review_ 82(4): 942-63.

- Maddala, G. S. 1992. _Introduction to Econometrics._ **2nd** edition. New York: Macmillan Publishing Company.

- Mingat, Alain, and J. P. Tan. 1992. "Education in Asia: A Comparative Study of Cost and Financing." World Bank, Human Development Department, Education Group, Washington, D.C. Processed.

- Mingat, Alain, and J. P. Tan. 1998. "The Mechanics of Progress in Education: Evidence from CrossCountry Data." Policy Research Working Paper 2015. Human Development Department, Education Group, World Bank, Washington, D.C.

- Political Risk Services. 2001. _International Country Risk Guide._ East Syracuse, NY.

- Pritchett, L. 1996. "Mind Your P's and Q's: The Cost of Public Investment is Not the Value of Public Capital." Policy Research Working Paper 1660. Development Research Group, World Bank, Washington, D.C.

- Psacharopoulos, G., and M. Woodhull. 1985. _Education for Development: An Analysis of Investment Choices._ Oxford University Press for the World Bank. New York, NY.

- Stevenson, W. H. 1992. "Learning from Asian Schools." _Scientific American_ 267: 70 -76.

- UNESCO, various years. _World Education Report._ Paris: UNESCO.

- World Bank. Various years. _World Development Indicators._ Washington, D.C.: WoTld Bank.

- World Bank. 1994. _Governance: The World Bank's Experience._ Washington, D.C.: World Bank.

- World Bank. 1998. _Public Expenditure Management Handbook._ Washington, D.C.: World Bank.

_28_