rafmacalaba/datause-encoder-data huggingface dataset:
_ANDR EE_<sup>_ ́_</sup> _AND PAPE_ _SEPTEMBER 5, 2023_ _16_ long-run averages estimated here) and over 20% of the official 5.33% long-run average. The lowest imputation _R_<sup> ̃2</sup> of 0 _._ 71 introduces only an additional 12% measurement accuracy compared to such a golden standard, which is not negligible but suggests that the real-time imputation of price surveys produces a viable alternative to official statistics, particularly when the timeliness and costs of both approaches are pitted against one another.

Another result that is worth commenting on is that the average correlation between the market-level food price indexes is low. At a monthly level the correlation is only around 0.18-0.26, and 0.33-0.57 when the price data is annualized. This means that there is on average little price transmission over short time spans. In this setting of weakly integrated markets, a single CPI index may not be representative of actual location-specific price dynamics.

The table provides some indication that imputation accuracy goes down when covering longer time periods. This may on the one hand be driven by increased price heterogeneity in the past, as indicated by the lower cross-market correlations, or by lower data availability as indicated by table 2. It is hard to gauge from the basic results whether there is erosion in performance taking place, which is part a reason why the trust score from section II.C is useful.

# _B. Prediction results_

The graph in figure 1 shows the modeled food price index using the 2017-2022 data selection, along with the obtained inflation rates and the trust scores. Graphs for the 2014 and 2009 starting points are available in the appendix.

The results show that for each data selection, a similar inflation rate of approximately 17% is obtained for the final period. A 1% difference is sensible since the food baskets are different. All three results also highlight a sharp price rise, with inflation reaching well above 20%, during 2019. In 2017-2018, there is a hiatus in the data collection process, and the trust scores in the three charts clearly highlight this. In the long-run chart (2009-2022), the trust score remains in the acceptable 6+ range. It is also clear that when zooming out, the data gap occurs in a period of reasonable price stability.

Even though the 2009 version has the largest share of imputations, it is worth pointing out that the basket result paints a similar picture for the overall trend as the less modeled 2014 version and more accurate 2017 version. In all cases, the prices move largely sideways from 2014 through 2018, before surging into 2019 and falling rapidly into 2020, and resuming on an upward path. This suggests that for broad monitoring purposes, the procedure produces consistent results across all data dimensions. In conclusion, the system operates well and is able to generate useful country-level monitoring results that are robust to changes in the data collection process.
<!-- Start of picture text --> 2000<br>= 1500 Sy a<br>=<br>= 1000<br>o<br>gs<br>S 500 —<br>0<br>Dec 2009* Aug 2010* Aug 2010<br><!-- End of picture text -->

- @ Started operations on time Delayed up to 1 year

- @ Delayed more than 1 year - construction problems

- B Delayed more than 1 year - connection problems

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been extensively used in the growth, and international trade literature (Levine, 2005). Data come from Beck et al. (2009). In order to reduce idiosyncratic values and avoid a potential simultaneity bias between outward FDI and financial development, we use the average values over the pre-sample 2000-2002 period. The private credit to GDP ratio varies a lot across countries (standard deviation of 49%).

development (FIN_INST ). This measure corresponds to the sum of the values of two _World Bank Doing Business_ indexes measuring the quality of financial institutions: the strength of legal rights index, which indicates “the degree to which collateral and bankruptcy laws protect the rights of borrowers and lenders,” and the depth of credit information index, which assesses “the rules and practices affecting the coverage, scope and accessibility of credit information available through either a public credit registry or a private credit bureau.”<sup>23</sup> Development of these two financial institutions should encourage lending by facilitating the use of a broad range of movable assets as collateral, increasing the rights of creditors in case of bankruptcy, and reducing informational asymmetries. The coefficient of correlation between the private credit to GDP ratio and this measure of the quality of financial institutions suggests that it is indeed the case: it is equal to 0.60, statistically significant at the 1% level.

# **3.4 Banking crises**

BCRISi) which takes the value of one if a source country has been formally identified as having experienced a banking crisis during the period 2008-2010. We rely on the classification of Laeven and Valencia (2013) to identify banking crises episodes. A

> 23Data, definitions, and more information can be found at http://www.doingbusiness.org/data/exploretopics/gettingcredit. The measure ranges from 1 to 15 (worst to best). We use the average value of each index over the 2005-2006 period to construct our institution-based measure.

11
Policy Research Working Paper 7823

# **Abstract**

Major resource discoveries have transformed growth prospects for many low-income countries. However, the sharp downturn in commodity prices in recent years is affecting resource investment in these countries, and may delay the development of recent discoveries into production. This study investigates lead times from discovery to production for a unique data set of gold and copper discoveries worldwide during 1950–2014. The study employs standard parametric and nonparametric duration analysis. The results suggest an important role for copper prices; for instance, an upswing at the time of discovery can hasten the development of the mine by two to three years in low-income countries. There appears to be a similarly beneficial impact on lead times of sounder macroeconomic policies and quality of governance.

This paper is a product of the Development Prospects Group, Development Economics. 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 fohnsorge@worldbank.org and tkhan@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
**Figure 1: Distribution of COVID-19 Deaths by Age Group for Each Country**

<!-- Start of picture text --> Afghanistan<br>Pakistan<br>Philippines<br>South Africa<br>Colombia<br>Peru<br>Brazil<br>Mexico<br>Malaysia<br>Argentina<br>Chile<br>Hungary<br>Portugal<br>Spain<br>Korea, Rep.<br>Italy<br>Japan<br>France<br>Canada<br>Germany<br>Netherlands<br>Sweden<br>Australia<br>Denmark<br>Norway<br>Switzerland<br>0 10% 20% 30% 40% 50% 60% 70% 80% 90% 100<br>Under 50 50-59 60-69 70-79 80+<br><!-- End of picture text --> Note: See Appendix for data sources. Countries are shown ranked by 2018 GDP pc in current USD from WDI.

**Figure 2: Hypothetical COVID-19 Deaths by Age Group, Using Country Age-Specific COVID-19 Mortality Rates and the Mean High-Income Population Age Distribution**

<!-- Start of picture text --> Afghanistan<br>Pakistan<br>Philippines<br>South Africa<br>Colombia<br>Peru<br>Brazil<br>Mexico<br>Malaysia<br>Argentina<br>Chile<br>Hungary<br>Portugal<br>Spain<br>Republic of Korea<br>Italy<br>Japan<br>France<br>Canada<br>Germany<br>Netherlands<br>Sweden<br>Australia<br>Denmark<br>Norway<br>Switzerland<br>0 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%<br>Under 50 50-59 60-69 70-79 80+<br><!-- End of picture text --> Note: See Appendix for data sources. Countries are shown ranked by 2018 GDP pc in current USD from WDI.

4
|P050439|2001|2012|12/4/2011|23 Closed|Uganda|
|---|---|---|---|---|---|
|P050439|2001|2012|6/30/2012|24 Closed|Uganda|
|P050439|2001|2013|3/17/2013|25 Closed|Uganda|
|P050439|2001|2014|9/17/2013|26 Closed|Uganda|
|P050439|2001|2014|4/1/2014|27 Closed|Uganda|
|P050440|2006|2006||0 Closed|Uganda|
|P050440|2006|2007|9/18/2006|1 Closed|Uganda|
|P050440|2006|2007|10/4/2006|2 Closed|Uganda|
|P050440|2006|2007|6/7/2007|3 Closed|Uganda|
|P050440|2006|2008|12/18/2007|4 Closed|Uganda|
|P050440|2006|2008|6/30/2008|5 Closed|Uganda|
|P050440|2006|2009|12/31/2008|6 Closed|Uganda|
|P050440|2006|2009|6/30/2009|7 Closed|Uganda|
|P050440|2006|2010|12/31/2009|8 Closed|Uganda|
|P050440|2006|2010|6/30/2010|9 Closed|Uganda|
|P050440|2006|2011|4/20/2011|10 Closed|Uganda|
|P050440|2006|2012|1/4/2012|11 Closed|Uganda|
|P050440|2006|2013|11/14/2012|12 Closed|Uganda|
|P050440|2006|2014|7/8/2013|13 Closed|Uganda|
|P050440|2006|2014|1/23/2014|14 Closed|Uganda|
|P050440|2006|2014|6/22/2014|15 Closed|Uganda|
|P050441|2000|2000|6/15/2000|3 Closed|Tanzania|
|P050441|2000|2001|5/17/2001|5 Closed|Tanzania|
|P050441|2000|2002|4/30/2002|8 Closed|Tanzania|
|P050441|2000|2003|3/28/2003|10 Closed|Tanzania|
|P050441|2000|2004|6/29/2004|16 Closed|Tanzania|
|P050441|2000|2005|12/29/2004|18 Closed|Tanzania|
|P050473|1999|2019|5/7/2019|1 Dropped|Russian Federation|
|P050474|2001|2001|6/25/2001|1 Closed|Russian Federation|
|P050474|2001|2002|3/18/2002|3 Closed|Russian Federation|
|P050474|2001|2003|5/7/2003|6 Closed|Russian Federation|
|P050474|2001|2004|6/9/2004|8 Closed|Russian Federation|
|P050474|2001|2005|4/27/2005|10 Closed|Russian Federation|
|P050474|2001|2005|6/14/2005|11 Closed|Russian Federation|
|P050474|2001|2006|1/10/2006|12 Closed|Russian Federation|
|P050474|2001|2006|6/7/2006|13 Closed|Russian Federation|
|P050474|2001|2007|12/8/2006|14 Closed|Russian Federation|
|P050483|2000|2000|6/26/2000|1 Closed|Yemen,Republic of|
|P050483|2000|2001|6/20/2001|3 Closed|Yemen,Republic of|
|P050483|2000|2002|4/2/2002|5 Closed|Yemen,Republic of|
|P050483|2000|2003|1/16/2003|7 Closed|Yemen,Republic of|
|P050483|2000|2004|1/16/2004|9 Closed|Yemen,Republic of|
|P050483|2000|2005|4/29/2005|11 Closed|Yemen,Republic of|
|P050483|2000|2006|9/26/2005|12 Closed|Yemen,Republic of|
|P050483|2000|2019|5/7/2019|13 Closed|Yemen,Republic of|
|P050484|1999|1999||3 Closed|Egypt,Arab Republic o|
|P050484|1999|2000|6/21/2000|5 Closed|Egypt,Arab Republic o|
mixed model) all show goodness-of-fit measures of about .90. This means that all the models are about the same in terms of goodness of fit, and all are about equally good.

Figure 3: Mixed causal and measurement model <!-- Start of picture text --> PS VA<br> Good<br>Government<br>GE RQ RL CC<br><!-- End of picture text --> Path Analysis of Mixed Model: Like the CFA measurement model, the mixed model can also be tested using path analysis. Since the model is over-identified, it can be tested. That is, it is possible to compare the correlation between variables that are predicted by the model to have no direct relation (that is, a partial regression parameter of zero) with the observed estimate. In practice, this test is conducted using standardized estimates. Figure 4 replicates Figure 3, adding the standardized estimates of the path coefficient for each direct link. Table 7 reports the path equations that follow if the model is true, and Table 8 compares the predicted and actual correlations. The chi-square test once again summarizes the comparison. With χ<sup>2</sup> (13)= .002, there is a very good correspondence between the data and the mixed model.

15
# _+ β_ 4 _(_ **_Channelij_** _*Commitmentj)_

# _+ β_ 5 _(_ **_Channelij_** _*Com_PrivRafj)+ β_ 6 _(_ **_Channelij_** _*Com_PubRafj)_

_+_ γ’ **_X_** ij _+ εij_ where **_Channelij_** is a vector of individual characteristics that proxy for self-control and other-control problems: an indicator for having a hyperbolic discount rate, a measure of net transfers to others in the year before the baseline survey, an indicator for the “patient now, impatient later” preference reversal, the study participant’s years of education, and an index for baseline level of assets. Other right-hand-side variables include, as before, the baseline controls and stratification cell fixed effects.

We present results from this exercise in two sets of regressions in Table 7. In each set of regressions we show effects on total deposits during the pre-planting season (columns 1 and 4), on total value of farm inputs (columns 2 and 5), and on output (columns 3 and 6). The main effects for the baseline variables that are interacted with the treatment indicators are all included in the set of baseline controls.

We focus our discussion on the coefficients on the ordinary (no raffle) and commitment (no raffle) treatments, since in Tables 4, 5 and 6 we found that the effects of the raffle treatments were inconclusive.

The first set of columns shows results from regressions with treatment indicators interacted with net transfers made to the social network over the last 12 months as of the date of the baseline interview and interactions with an indicator for whether the respondent is classified as exhibiting hyperbolic time preferences based on a series of hypothetical questions at baseline. The second set of columns has results from regressions that include additional interactions with an indicator for the “Patient now, impatient later” time preference reversal, an index for asset ownership and years of education.

In our baseline survey, we ask respondents to make hypothetical trade-offs between receiving some money sooner, or more money later. These questions are designed to be analogous to the questions capturing time-preference reversals used by Ashraf, Karlan, and Yin (2006). Survey respondents are asked whether they prefer MK100 now or MK110 one month from now. Respondents who prefer MK100 now are then asked to choose between MK100 now and MK130 one month from now. The questions continue, with the value of the hypothetical payment in one month increasing from MK130 to MK150, MK200, and MK250, thus soliciting bounds on the discount rate for which the respondent is willing to wait one month to receive money. Later in the survey, after completing unrelated modules, we ask the same questions over 20
MALI’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Table 21. Funding gaps by sector**

<u>US$ millions per year</u>

||ICT|Power|Transport|WSS|Total|
|---|---|---|---|---|---|
|Needs|(178)|(302)|(236)|(311)|(1027)|
|Spending traced to needs|171|143|154|68|536|
|Within-sector reallocation|2|12|5|0|19|
|Potential efficiency gains|0|157|0|43|200|
|(GAP) or surplus|(6)||(77)|(200)|(283)|

Source: Derived from Foster and Briceño-Garmendia 2009.

Note: Potential overspending across sectors is not included in the calculation of the funding gap, because it cannot be assumed that it would be applied toward other infrastructure sectors. Inefficiency estimations based on operational performance data up to 2007 — = data not available.

# What else can be done?

The most obvious way to address Mali’s funding gap is to raise additional finance. At the current level and pattern of spending even if all inefficiencies are corrected, Mali would need to increase its spending by 50 percent what have been seen in recent years. Additional finance might be hard to secure. Even if additional finance is not secured, Mali can reduce the infrastructure funding gap through its own policy choices, and in particular through the technology choices to meet its infrastructure targets. The largest measure that the country could take to reduce its infrastructure spending needs would be to integrate itself more closely within the West Africa Power Pool. If it were to do so, the country would become a net importer of power, importing up to 12.7 terawatt-hours of electricity by 2015, which would reduce annual investment requirements by $124 million (table 22). Another $77 million a year could be saved by adopting lower cost technologies—such as standposts, boreholes, and improved latrines—to meet the targets set in the Millennium Development Goals. Finally, $83 million a year could be saved by adopting appropriate technologies for surfacing of paved roads. If all of these policy measures were adopted, Mali could save $285 million per year, reducing the infrastructure funding gap to $104 million per year.

**Table 22. Potential gain from innovation**

<u>US$ millions</u>

||Before<br>innovation|After innovation|Savings|Savings as % of<br>sector funding<br>gap|Savings as % of<br>total funding gap|
|---|---|---|---|---|---|
|Power trade|302|178|124|No gap|44|
|WSS, appropriate technology|311|234|77|39|27|
|Roads, appropriate technology|236|153|83|108|29|
|Total|849|565|285|101|101|
|Source: Derived from Foster and Briceñ|o-Garmendia 2009.|||||

If all else fails, it may be necessary to extend the time horizon for meeting the infrastructure targets beyond the 10-year period considered here. Simulations suggest that even if Mali is unable to raise additional finance, the identified infrastructure targets could be achieved within a 15-year horizon as long 48
62

# Data Sources

Ghn, Ministy of Mines and Energy, Energy Stadstics Division. Various years.

Green, R., (1987): "Countly Study 1: Ghana, "Stabilization and Adjustment Policies and Programs, WIDER, Helsini.

- World Bank, (1985): GhaL: Towards Structural Adjustment", Report No. _5854-GH,_ Vol. 1&2, West Africa Region, The World Bank, Washington, D.C.

- World Bank, (1986): "Ghana: Issues and Options in the Energy Sector", Report No. 6234GH, ESMAP, The World Bank, Washington, D.C.

- World Bank, (1989): "Ghana: Structural Adjustment for Growthl, Report No. 7515-GI, West Africa Region, The World Banl, Washington, D.C.

- World Ban (1991): 'Ghana: Progress on Adjustment", Report No. 9475-GH, West Africa Region, The World Bank Washington, D.C.

- World Banlk (1992): "Industa Energy Efficiency Acdvity Phase r, VoL 1, Report No. 148A/92, ESMAP, The Wodd Bank, Washington, D.C.

- Wodd Bank, (1992): "Industial Energy Efficiency Activity Phase r, Vol. II, Report No. 148B/92, ESMAP, The World Bank, Washington, D.C.

World Bank, (1992): World Development Report, Washington, D.C.
31 Rajaratnam, J. K, J. R Marcus, A. D Flaxman, H. Wang, A. Levin-Rector, L. Dwyer, M. Costa, A. D Lopez, and Ch. JL Murray. 2010. ―Neonatal, postneonatal, childhood, and under-5 mortality for 187 countries, 1970–2010: a systematic analysis of progress towards Millennium Development Goal 4.‖ _The Lancet_ 375 (9730) (June): 1988-2008.

Rutstein, S.O, and G. Rojas. 2006. _Guide to DHS Statistics_ . Calverton, Maryland: ORC Macro. Sexton, A. R 2011. ―Best practices for an insecticide-treated bed net distribution programme in sub-Saharan eastern Africa.‖ _Malaria Journal_ 10: 157.

- Stevens, W., V. Wiseman, J. Ortiz, and D. Chavasse. 2005. ―The costs and effects of a nationwide insecticide-treated net programme: the case of Malawi.‖ _Malaria Journal_ 4 (1): 22.

- Snow, R. W, and K. Marsh. 2002. ―The consequences of reducing transmission of Plasmodium falciparum in Africa.‖ _Advances in parasitology_ 52: 235–264.

- Yukich, J., F. Tediosi, and C. Lengeler. 2007. ―Operations, costs and cost-effectiveness of five insecticide-treated net programs (Eritrea, Malawi, Tanzania, Togo, Senegal) and two indoor residual spraying programs (Kwa-Zulu-Natal, Mozambique).‖ _Swiss Tropical Institute, Basel, Switzerland._

- Yukich, J., C. Lengeler, F. Tediosi, N. Brown, J.A. Mulligan, D. Chavasse, W. Stevens, et al. 2008. ―Costs and consequences of large-scale vector control for malaria.‖ _Malaria journal_ 7 (1): 258.

- Yukich, J. O, M. Zerom, T. Ghebremeskel, F. Tediosi, and Ch. Lengeler. 2009. ―Costs and costeffectiveness of vector control in Eritrea using insecticide-treated bed nets.‖ _Malaria Journal_ 8: 51.

- USAID. 2011. War against Killer Disease. http://kenya.usaid.gov/success-story/993. Accessed on 9<sup>th</sup> June 2011.

- World Health Organization. 2005. _The World Health Report 2005 : make every mother and child count._ Geneva: World Health Organization.

- World Health Organization. 2007. _Long-lasting insecticidal nets for malaria prevention. Trial Edition: A manual for malaria programme managers_ . Geneva: World Health Organization.

World Health Organization. 2008. _World Malaria Report 2008_ . Geneva: World Health Organization.
Education Outcomes, School Governance and Parents‟ Demand for Accountability: Evidence from Albania

**Danila Serra**<sup>1</sup>

**Abigail Barr**<sup>2</sup>

**Truman Packard**<sup>3</sup>

**JEL Classification:** O1, I2, C8, C9

**Keywords** : Accountability, governance, education, lab experiment, Albania

> 1 Department of Economics, Florida State University, dserra@fsu.edu (corresponding author)

> 2 Nuffield College and the Department of International Development, University of Oxford, <u>abarr@economics.ox.ac.uk</u>

> 3 Human Development Economics, Europe and Central Asia Regional Department, World Bank, <u>tpackard@worldbank.org</u>
# **<u>Table C.2 First-stage regressions, Iraq</u>**

||**2007-**<br>**2007**|**2012**<br>**2012**|
|---|---|---|
|Head`s age|-0.001**<br>(0.00)|0.005***<br>(0.00)|
|Head is female|0.012|0.114***|
||(0.02)|(0.02)|
|Highest education level is primary|-0.015|0.069***|
||(0.01)|(0.01)|
|Highest education level is secondary|0.059***|0.305***|
||(0.02)|(0.02)|
|Highest education level is tertiary|0.183***|0.441***|
||(0.02)|(0.01)|
|Urban|0.326***|0.317***|
||(0.01)|(0.01)|
|_cons|13.917***|13.966***|
||(0.03)|(0.03)|
|adjusted R2|0.08|0.13|
|N|12895|18552|

**Note:** ***, **, and * denote statistical significance at the 0.01, 0.05, and 0.10 levels. Standard errors are in parentheses. Regression is estimated using Weighted Ordinary Least Squares. Household heads’ ages are restricted to between 25 and 55 for the first survey round and adjusted accordingly for the second round. The reference groups are household with no primary education and living in rural areas.

80
Figure 2.

Mean Gini for African countries, Latin American countries and world without Africa <!-- Start of picture text --> 58<br>54<br>Latin America<br>50<br>Africa<br>46<br>42<br>World without<br>Africa<br>38<br>34<br>30<br>1960's 1970's 1980's 1990's<br>Decades<br>Gini coefficient (mean)<br><!-- End of picture text --> <u>Table 2. Gini coefficients in Africa and the rest of the world, 1960-2000</u>

|||Africa||Rest|of the Worl|d|
|---|---|---|---|---|---|---|
||Number of|Mean|Median|Number of|Mean|Median|
||observations|||observations|||
|1960’s|10|48.8|55|48|39.3|37.5|
|1970’s|3|47.0|44|42|34.2|31.5|
|1980’s|13|41.0|39.6|132|36.9|32.8|
|1990’s|42|45.9|43.9|182|37.8|36.2|

Source: Calculated from WIDER data set using only observations where inequality measures are calculated across individuals.

10