rafmacalaba/datause-encoder-data huggingface dataset:
<u>Table 3d: HRS on Student Engagement</u>

||<br>_Agree/Strongly_<br>_Agree_<br>No Controls<br>(1)|<br>_Agree/Strongly_<br>_Agree_<br>Including Control<br>(2)|_Strongly_<br>_Agree_<br>Including<br>Control<br>(3)|_Including_<br>_Neutral_<br>Including<br>Control<br>(4)|
|---|---|---|---|---|
|Teachers have a good<br>relationship with<br>students|0.030|0.03|0.00|0.01|
||(0.022)|(0.02)|(0.06)|(0.01)|
|Teachers can do more|0.007|0.01|-0.11**|0.00|
|if parents take interest<br>in children|(0.023)|(0.02)|(0.05)|(0.02)|
|Cannot discipline<br>students if they are<br>not disciplined at|0.120***|0.11***|0.05**|0.14***|
|home|||||
||(0.031)|(0.03)|(0.02)|(0.03)|
|Teachers would like|-0.025|-0.02|0.06|-0.02|
|more involvement in|||||
|setting learning goals|||||
||(0.029)|(0.03)|(0.05)|(0.01)|
|Hardest challenge for<br>me is to motivate<br>students|0.032**|0.025*|N/A<sup>25</sup>|N/A|
||(0.015)|(0.013)|||
|Domain 4 Index|0.030|0.05|-0.15|0.13|
||(0.101)|(0.10)|(0.12)|(0.13)|
|Controls|No|Yes|Yes|Yes|

* _p_ <0.1; ** _p_ <0.05; *** _p_ <0.01

> 25 Question not posed on a Likert scale

21
To explore another potential source of farmer over-estimation yield, namely the persistence of rounding of farmer-reported production estimates, Figure 8 presents a histogram of reported quantities for the primary production units, kilograms and 100 kilogram sacks for MAPS I and MAPS II, as well as the maize-producing UNPS 2015/16 sample residing the Eastern region and reporting for the first season of 2015 (overlapping with the MAPS I reference period). There is clear evidence of heaping at common intervals, such as 50 kg, 100 kg, 200 kg, and 300 kg, and 1 sack, 2 sacks, and 10 sacks, which may explain, at least in part, the trend in greater over-estimation on smaller plots. Rounding of self-reported production quantity on plots with lower production may have a more severe impact in terms of percent of total production, therefore resulting in greater bias.

The results of the RIF estimations, as described in section 4, are presented in Table 6. Plot area is indeed negatively correlated with farmer over-estimation in MAPS I across the entire distribution of over-estimation, while area is only significantly different from zero (and negative) in the upper tail for MAPS II and the household panel. Supporting the theory that subjective estimates of production are complicated by multiple harvest periods and crop conditions, the number of conditions in which the farmer reports overall harvest is positively correlated with over-estimation in MAPS I and the household panel (insignificant in MAPS II except for the least productive decile). Also exacerbating yield over-estimation are the farmer-reported seed, household labor and hired labor input quantities, which may, therefore, suffer from the same subjective sources of measurement error. This theory is supported by the correlation of measurement error observed between self-reported plot area and crop production discussed above, and signals potential concern for production function estimations more broadly.

Finally, the enumerator-assessed percent damage in the crop cut sub-plot, which could be taken as a proxy to the damage on the plot as a whole, is significantly and positively correlated with farmer over-estimation, suggesting farmers may not be taking into account crop damage when estimating plot-level production. While some may theorize that farmer over-estimation originates in the inability of farmers to distinguish between the harvest on multiple plots (that is, potentially reporting the production of multiple plots on a single plot), the results in Table 6 suggest otherwise. Whether the household cultivates more than one maize plot or not has no bearing on the degree of farmer over-estimation.

# **7. Conclusions**

<mark>Based on a two-round household panel survey conducted in Eastern Uganda to test the relative accuracy of subjective approaches to data collection vis-à-vis objective survey methods for maize yield measurement, soil fertility assessment, and maize variety identification, we provide unambiguous support for the sensitivity of the plot-level inverse scale-productivity relationship to</mark> 24
new evidence can be used to update the old priors about the parameters. Mindful of the data noise and limitations however, we implement the following practical steps:

- a. First, deflate all cells in the historical SAMs by a single overall GDP deflator so that they are broadly expressed in the same base year prices as a first step.  For models with regional aggregation of countries, this may be the only means possible if deriving weighted aggregation of country specific prices is not feasible.

- b. We assign greater weight to known data in a limited informational environment. Accordingly, as a second step, we apply the cross-entropy estimation method to target known macroeconomic aggregates in constant prices from the national accounts, which are generally available for most countries; and we rebalance the SAM to preserve, given priors on measurement error, these critical economic aggregates. This approach in effect extends the SAM cross-entropy estimation method to a constant price application.

- c. Alternatively, one could use known relative price indices (relative to the GDP deflator) to target the macroeconomic aggregates. The latter could conceivably be expanded information-wise if auxiliary data such as world prices of exports and imports for various commodities are also available. As mentioned, the more cells in the SAMs that can be deflated to the right base year prices, the better the economic representation that is reflected in the SAM. In any case, to ensure that the SAM adheres to known national account aggregates in constant prices, it should still be estimated and rebalanced appropriately.

- d. In the extreme case, where there are no price and real national accounts data to work with (e.g. in post-conflict or other very data-poor economies), the evolution of the economic structure reflected in the nominal SAMs still adds information to test various prior assumptions about the unobserved parameters. It is still better than using less information from a single SAM.

The method used here follows the earlier approach in Robinson et al. (2001) in that standard additive errors and discrete probability distributions are defined at three levels of information – (1) each cell value in the SAM; (2) the row and column totals (total expenditures and total incomes) in the SAM (SAM balance condition); and (3) the macroeconomic aggregates in the SAM. The formal mathematical statement generally follows the earlier work and is not repeated here. In adjusting the historical SAMs to constant prices under limited information, we assign the smallest standard errors to the macro aggregates since they are known from the national accounts. The end result of the data step is a series of real SAMs that are consistent with the aggregates of national income accounts in constant prices.

6
———. 2005. _Economic Growth in the 1990s: Learning from a Decade of Reform_ . Washington, DC: World Bank

- ———. 2008. _The Growth Report: Strategies for Sustained Growth and Inclusive Development_ . Report by the Commission on Growth and Development. Washington, DC: World Bank

- ———. 2009. _World Development Report 2010: Development and Climate Change_ . Washington, DC: World Bank

- ———. 2011a. “Rebalancing, Growth, and Development: An Interconnected Agenda.” Paper prepared for the meeting of the Group of 20 Finance Ministers and Central Bank Governors, Paris, October 13–15.

- ———. 2011b. _World Development Report 2011: Conflict, Security, and Development_ . Washington, DC: World Bank

- ———. 2012a. “Climate Change and IDA: Managing the Unavoidable, Avoiding the Unmanageable.” October 13, Washington, DC.

http://www.youtube.com/watch?feature=player_embedded&v=SBUUlxehykc.

- ———. 2012b. _Global Economic Prospects January 2012. Uncertainties and Vulnerabilities_ . Washington, DC.

- http://econ.worldbank.org/WBSITE/EXTERNAL/EXTDEC/EXTDECPROSPECTS/0,,contentMDK: 23064688~pagePK:64165401~piPK:64165026~theSitePK:476883,00.html.

- ———. 2012c. _Turn Down the Heat: Why a 4° C Warmer World Must Be Avoided_ . Washington, DC.

http://www.worldbank.org/en/news/feature/2012/11/18/Climate-change-report-warns-dramaticallywarmer-world-this-century.

- ———. 2013a. “Ending Poverty Includes Tackling Climate Change.” Office of the President, July 10, Washington, DC. http://www.worldbank.org/en/news/opinion/2013/07/10/op-ed-ending-povertyincludes-tackling-climate-change.

———. 2013b. _Global Economic Prospects January 2013: Assuring Growth over the Medium Term._ Washington, DC.

http://web.worldbank.org/WBSITE/EXTERNAL/EXTDEC/EXTDECPROSPECTS/EXTGBLPROS PECTS/0,,menuPK:615470~pagePK:64218926~piPK:64218953~theSitePK:612501,00.html.

———. 2013c. _Turn Down the Heat: Climate Extremes, Regional Impacts, and the Case for Resilience_ . Washington, DC: World Bank. http://www.worldbank.org/en/topic/climatechange/publication/turndown-the-heat-climate-extremes-regional-impacts-resilience.

- ———. 2013d. “World Bank Group Sets Direction for Energy Sector Investments.” Washington, DC. http://www.worldbank.org/en/news/feature/2013/07/16/world-bank-group-direction-for-energysector.

- World Bank, and United Nations. 2010. _Natural Hazards, UnNatural Disasters: The Economics of Effective Prevention_ . Washington, DC.

- World Development Indicators (database). World Bank, Washington, DC. http://data.worldbank.org/datacatalog/world-development-indicators.

- Zellner, Arnold. 2004. “Statistics, Econometrics and Forecasting.” Stone Lectures in Economics. Cambridge: Cambridge University Press.

74
Figure 9: Case parties’ log revenues around the start of the pre-trial, overall and by speed of pre-trial.

<!-- Start of picture text --> A. Overall B. By pre-trial speed<br>Fast_pretrial<br>Slow_pretrial<br>-7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6<br>month relative to start of pre-trial month relative to start of pre-trial<br>.2 .2<br>.1 .1<br>0 0<br>-.1 -.1<br>-.2 -.2<br>-.3 -.3<br><!-- End of picture text --> Notes: For all x-axes, month is indexed in relation to the case-level pre-trial start month (zero-centered). In panel B, Fast-pretrial and Slow-pretrial log revenue time series are each displayed relative to their reference period (period -1). The gray lines represent the 95% confidence interval for the Fast-pretrial time series and the dashed lines represent the 95% confidence interval for the Slow-pretrial time series.

Figure 10: Firm’s willingness to pay for lawyer fees to achieve pre- vs. post-decree speed (densities) <!-- Start of picture text --> Mean difference: 0.294 million FCFA<br>P-value = 0.000<br>0 1 2 3 4 5<br>mio FCFA<br>Pre-reform avg. duration (5 months)<br>Post-reform avg. duration (3.5 months)<br>1.5<br>1<br>.5<br>0<br><!-- End of picture text --> Notes: kernel = epanechnikov, bandwidth= 0.0990 39
ECCAS’S INFRASTRUCTURE: A REGIONAL PERSPECTIVE

**Table 2.2** . **Road conditions along major transit corridors in ECCAS***

|||Condi|tion (%)|||Type (%)||Tra|ffic volume|s (AADT)|(%)|
|---|---|---|---|---|---|---|---|---|---|---|---|
||||||||||300–|||
|Corridors|Good|Fair|Poor|Unknown|Paved|Unpaved|Unknown|>300|1,000|>1,000|Unknown|
|Douala to Bangui|53.9|23.4|22.7|0|68.6|31.4|0.0|64.6|20.4|15.0|0.0|
|Cameroon|29.6|35.7|34.7|0|52.1|47.9|0.0|53.1|24.0|22.9|0.0|
|Central African<br>Republic|100.0|0.0|0.0|0|100.0|0.0|0.0|86.5|13.5|0.0|0.0|
|_Douala to Ndjamena_|18.9|24.5|56.6|0|67.3|32.7|0.0|25.9|49.4|24.7|0.0|
|Cameroon|18.9|24.5|56.6|0|67.3|32.7|0.0|25.9|49.4|24.7|0.0|
|_Pointe-Noire to_<br>_Brazzaville to Bangui_|29.1|18.9|45.2|7|68.8|25.2|6.0|26.6|11.3|0.0|62.1|
|Cameroon|55.6|38.8|0.0|6|0.0|100.0|0.0|70.2|24.1|0.0|5.6|
|Central African<br>Republic|99.2|0.0|0.0|1|99.2|0.8|0.0|79.5|19.3|0.0|1.2|
|Republic of Congo|0.0|21.3|69.4|9|27.9|62.8|9.2|0.0|6.1|0.0|93.9|
|_Matadi to Kisangani_|30.5|10.0|59.4|0|99.7|0.0|0.3|0.0|0.0|0.0|100.0|
|Congo, Dem. Rep.<br>of|30.5|10.0|59.4|0|30.4|69.3|0.3|0.0|0.0|0.0|100.0|
|_Lubumbashi to_<br>_Lobito_|38.5|29.7|31.0|1|68.1|31.9|0.0|47.0|15.0|0.0|38.0|
|Angola|50.1|0.0|49.9|0|50.1|49.9|0.0|75.8|24.2|0.0|0.0|
|Congo, Dem. Rep.<br>of|19.5|78.1|0.0|2|97.6|2.4|0.0|0.0|0.0|0.0|100.0|

_Source:_ AICD calculations. _Note:_ * Another important corridor between Matadi and Lubumbashi has not been included due to data limitations; **Data for only the Cameroon portion of the corridor is available. Aggregates represent the information only for Cameroon. AADT = average annual daily traffic.

Importing freight via rail between Ndjamena and Douala takes longer than via road. On average, it takes around 5 days longer to import goods via rail than road. Much of this is due to the time required to shift freight from rail carriages to trucks for transport to Ndjamena from Ngaoundere via road (figure 2.3).

Despite the lengthy transit times, the cost of importing freight from Ndjamena to Douala provides rail with a competitive edge. The average freight tariffs charged by Camrail ($0.09 per tonne-km) are lower than road transport by $0.05 per tonne-km (figure 2.4). Importers consider the factors of both cost and time when choosing how to deliver their freight.

Among road corridors serving landlocked countries, there are significant cost differences across corridors. Overall transport costs along Central African corridors are very high, while the time taken to move imports is extremely long. The costs and times of moving imports along these corridors are well aligned with observed traffic patterns. An analysis of the time required for imports to reach a landlocked capital from the port ranges between 640 hours to over 1,700 hours (figure 2.5a). Around 50 to 80 percent of the times can be attributed to the rather inefficient operations that characterize Central African ports (figure 2.5b). Additionally, the regulatory processes of customs clearance and technical control in Central Africa are extremely lengthy as well as the time required to import into a landlocked capital.

14
|||||(0.058)|(0.058)||||(0.064)|(0.065)|
|---|---|---|---|---|---|---|---|---|---|---|
|Population (logs)|||||-0.352|||||-1.183|
||||||(0.911)|||||(0.908)|
|Primary Education|||||0.043|||||0.132|
||||||(0.090)|||||(0.088)|
|Freedom from Business|||||-0.281|||||-0.508|
|Regulations|||||(1.044)|||||(0.979)|
|Merchandize Trade|||||0.005|||||-0.012|
|to GDP ratio|||||(0.029)|||||(0.035)|
|Growth rate of GDP per ca|pita (%,||||-0.222|||||0.409|
|annual|||||(0.221)|||||(0.302)|
|Inflation (GDP|||||-0.286**|||||-0.160|
|deflator)|||||(0.118)|||||(0.157)|
|Rule of Law|||||-7.518*|||||-2.965|
||||||(4.400)|||||(3.692)|
|Control of|||||5.588|||||5.453|
|Corruption|||||(4.044)|||||(3.794)|
|Industry fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Year fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Constant|51.342***|24.072*|21.501*|21.615*|11.533|117.807***|86.667***|79.135***|79.023***|94.387**|
||(8.128)|(12.627)|(12.519)|(12.575)|(31.581)|(19.448)|(21.101)|(20.046)|(21.136)|(38.076)|
|Number of observations|18,296|18,296|18,296|18,296|18,296|5,556|5,556|5,556|5,556|5,556|
|R-squared|0.143|0.148|0.152|0.154|0.160|0.290|0.298|0.307|0.327|0.340|

Huber-White robust standard errors clustered on country in brackets. Significance is denoted by *** (1%), ** (5%), * (10%) 55
**-** **<u>Table 1  Characteristics of Migrants and Non Migrants</u>**

||**Non-migrants**<br>|||**Migrants**<sup>***1**</sup><br>|||
|---|---|---|---|---|---|---|
||Total|Total|Foreign|State|Private|Agriculture|
|Number of Observations|16878|4476|922|560|2008|938|
|Urban (%)|24.3|63.7|72.1|85.0|82.0|4.2|
|Share of female (%)|49.5|55.6|75.4|51.4|49.9|50.7|
|Average age|35.2|28.7|23.5|29.1|29.3|32.3|
|Minority (%)|16.3|9.4|4.2|.9|1.0|37.8|
|Unemployed*<sup>2</sup>(%)<br>|.9|1.1|||||
|Monthly income<sup>*3</sup>(1,000 dong)|888.7|957.8|907.1|1247.5|1112.1|504.2|
|Average education years|7.9|9.2|9.7|12.4|9.3|6.7|
|Education by Stages|||||||
|College (%)|4.8|7.3|2.9|33.2|4.6|1.5|
|Upper secondary (%)|15.8|25.3|31.3|41.6|26.3|8.2|
|Lower secondary (%)|32.7|36.5|45.3|19.8|40.6|30.2|
|Primary (%)|28.4|22.1|17.7|4.5|23.2|35.7|
|No primary (%)|18.4|8.5|2.7|.9|5.2|24.4|
|**Statistics only for migrants**|||||||
|Urban origin (%)||21.7|20.2|41.8|23.9|6.0|
|Unemployed in origin (%)||2.6|1.6|2.0|3.8|.5|
|Distance (km)||563.2|771.4|226.4|419.1|874.4|
|Income change before vs. after mig|ration (%)||||||
|Better||84.7|87.9|81.0|89.2|74.1|
|Worse||3.5|1.4|1.3|1.9|9.7|

Sources: The statistics of migrants and non-migrants are computed from the VMS 2004 and the VHLSS 2004 respectively. Notes:<sup>*1</sup> See the text for the definition of “State”, “Foreign”, “Private” and “Agriculture” categories.<sup>*2</sup> The unemployed category is defined as “those who are not working but sought employment.”<sup>*3</sup> Monthly incomes between non-migrants and migrants are not comparable due to the differences in definition and in sample coverage. Whereas the monthly incomes for non-migrants are based on wage employments in the VHLSS 2004, non-monetary compensation for migrants is converted with equivalent monthly values in the VMS 2004.
capacity to forecast probabilities of non-compliance.

In the initial stages of a reform, a committee of centrally-located experts (often aided by foreign expertise) define the range of potential harms that inspections are meant to avoid and associate likely harms with non-compliant imports of particular products. The central committee also gathers available information from past inspections to understand the frequency and types of non-compliance, and the distribution of non-compliance across a wide range of shipment characteristics. The information on harms and on non-compliant imports is combined to inform a national strategy that generates different _a priori_ inspection probabilities across shipments based on the outcome of a rigorous risk assessment process.

Another central component of reform is the adoption of advanced IT systems.<sup>14</sup> These systems facilitate risk management in a number of ways. First, they allow reliable electronic storage and fast recall of detailed information on past inspection activity and outcomes. Second, IT systems combine detailed information from the import declaration on the shipment’s characteristics with the parameters set by the central committee to make a rapid determination of the risk posed by each shipment. Third, IT can improve compliance forecasts for the less-frequently-observed shipments with statistical analysis of more frequent shipments that share important characteristics with the infrequent shipments. Finally, by facilitating – rapid communication including guidance from the center on news of emerging hazards, or communication to the center of changes in non-compliance rates on the ground – IT helps inspecting agencies to rapidly update the conditional priors that guide the inspection decision in times when compliance rates are changing.

Once it is operational, the risk management system operates as follows. Information from the central committee parameterizes a risk model using information on the severity of harm from non-compliant imports of a given product and past evidence of the likelihood of non-compliance of shipments with different characteristics. When imported goods arrive at a port, border crossing or other inspection location, the trading firm files an import 14A common IT system used in developing country customs agencies is ASYCUDA, a low-cost, off-theshelf risk management system developed by the United Nations Committee for Trade and Development. Even when the purchase price of IT systems is low, agencies must spend considerable resources on retraining personnel to use such systems effectively.

8
<!-- Start of picture text --> Male LFP Female LFP<br>xo"wo £& wo°<br>=}Bw 5Bw<br>=a<br>22<br><° <°<br>2 2<br>Ba BA<br>>r sor<br>®o<br>are) fa) wo<br>Municipalities Municipalities<br>Male unemp. Female unemp.<br>£ wo wo<br>5<br>=}Ew° £Ew°<br>eS eS<br>£ £<br>———) tan =~) TM :<br>S | rt iS) |<br>BA BA<br>>r Sor<br>o ®o<br>Qa fe) fa) wo<br>Municipalities Municipalities<br>SAE Direct<br><!-- End of picture text -->

The lines show the range of deviations within each municipality across simulations. Deviations are topcoded at 0.5 and bottomcoded at -0.5 for the sake of presentation.
# **ONLINE APPENDIX**

# **A Additional Results**

Table A1: Provider Beliefs in the Control Group: Health Worker Post-Intervention Survey

||(1)<br>N|(2)<br>Mean|(3)<br>SD|
|---|---|---|---|
|_Panel A. Malaria Prevalence and Pressure_||||
|Malaria Prevalence: General Population|43|0.277|0.226|
|Malaria Prevalence: CSCom Patients|43|0.443|0.200|
|Feels Pressure from Patients to Prescribe Unnecessary Medication|43|0.512|0.506|
|Feels Pressure: Antimalarials|22|0.682|0.477|
|_Panel B. Confdence in RDT_||||
|Uses RDT to Diagnose Malaria|43|0.977|0.152|
|Positive out of 100: Simple Malaria|43|55.953|32.969|
|Patients with Positive RDT out of 100 with Severe Malaria|43|88.884|16.449|
|Would Treat Relative with Negative RDT|43|0.465|0.505|
|_Panel C. Confdence in Microscopy Test_||||
|Uses Microscopy to Diagnose Malaria|43|0.698|0.465|
|Patients with Positive Microscopy Test out of 100 with Simple Malaria|43|83.977|21.381|
|Patients with Positive Microscopy Test out of 100 with Severe Malaria|43|95.442|8.525|
|Would Treat Relative with Negative Microscopy Test|43|0.326|0.474|

Notes: Results from post-intervention health worker survey. Sample limited to clinics that received the basic provider training. Malaria prevalence refers to prevalence estimated by the health worker. A health worker is coded as feeling pressure to prescribe if s/he answers yes to the question: Do you ever feel pressure from patients to prescribe certain medicines when you think they are not necessary? Providers answering yes were then asked to specify which medications. Antimalarial also includes quinine.

A0
_ESTIMATING FOOD PRICE INFLATION_ _27_

# Tables and Figures

Table A1—Summary of raw food price data.

|Country|Currency|Markets|Items|Data Coverage|Start date|
|---|---|---|---|---|---|
|Afghanistan|AFN|9/40|4|69.27%|Jan-07|
|Burkina Faso|XOF|63/65|3|47.79%|Jan-07|
|Burundi|BIF|61/68|7|35.93%|Jan-07|
|Cameroon|XAF|12/51|11|20.82%|Jan-07|
|Central Afr. Rep.|XAF|18/40|3|36.89%|Jan-08|
|Chad|XAF|35/56|3|39.17%|Jan-07|
|Congo, Rep.|XAF|5/11|7|44.47%|May-10|
|Congo, Dem. Rep.|CDF|26/83|10|35.43%|Nov-07|
|Gambia, The|GMD|15/28|11|41.91%|Jan-07|
|Guinea-Bissau|XOF|3/45|7|56.63%|Feb-16|
|Haiti|HTG|9/9|6|68.83%|Jan-07|
|Iraq|IQD|18/18|13|48.02%|May-11|
|Lao PDR|LAK|17/17|5|46.04%|Feb-12|
|Lebanon|LBP|26/26|15|61.49%|Mar-12|
|Liberia|LRD|18/24|3|49.44%|Mar-07|
|Mali|XOF|77/126|6|58.37%|Jan-07|
|Mozambique|MZN|25/52|7|63.13%|Jan-07|
|Myanmar|MMK|36/165|3|43.00%|Apr-07|
|Niger|XOF|68/79|4|79.60%|Jan-07|
|Nigeria|NGN|33/35|16|27.59%|May-12|
|Somalia|SOS|18/28|4|55.49%|Jan-07|
|South Sudan|SSP|9/20|8|52.35%|Jan-07|
|Sudan|SDG|14/14|3|58.01%|Jan-07|
|Syrian Arab Rep.|SYP|36/91|15|56.99%|Aug-11|
|Yemen, Rep.|YER|24/24|12|45.48%|Nov-08|
|Average||27/49|7|51.47%||

_Note:_ The first column reports the local currency in which prices are measured, the second column reports the number of markets from which data is used as a fraction of all known market location for which predictions are made, the third columns reports the number of food items for which data is used to construct the food price index, the fourth columns reports the total number of price observations as a share of all _market × time_ combinations where _markets_ is the first number in the third column, the final column reports when the estimated price index starts. _Source:_ The statistics have been prepared by the author for this paper based on end-of-August (2021) food price data from World Food Program.