rafmacalaba/datause-encoder-data huggingface dataset:
( _c_<sup>_P_</sup> _T t_<sup>_, c_</sup> _Nt_<sup>_P, c_</sup> _t_<sup>_P_)</sup><sup>_,_(</sup><sup>_cC_</sup> _T t_<sup>_, c_</sup> _Nt_<sup>_C, c_</sup> _t_<sup>_C_)and(</sup><sup>_cO_</sup> _T t_<sup>_, c_</sup> _Nt_<sup>_O, c_</sup> _t_<sup>_O_)arethetradable,non-tradable,and</sup><sup>_aggregate_con-</sup> sumption of Periphery, Core and Outsider countries, respectively. _PNt_<sup>_P, P_</sup> _Nt_<sup>_C, P_</sup> _Nt_<sup>_O_are the nominal non-tradable good prices of Periphery, Core and Outsider countries,</sup> respectively.

_PT t_<sup>_E, P_</sup> _T t_<sup>_O_are the nominal tradable good prices of the Eurozone and Outsider countries, respectively.</sup> 8 _Pt_<sup>_P, P_</sup> _t_<sup>_C, P_</sup> _t_<sup>_O_</sup> are the nominal _aggregate_ prices of Periphery, Core and Outsider countries, respectively.

_b_<sup>_j_</sup> _it_<sup>iscountry</sup><sup>_j_’sholdingofcountry</sup><sup>_i_’sbondinunitsofthetradablegood.Werefertothemas</sup> the “real” bond holdings.

# _3.2. Periphery Country_

The expected life-time utility of a representative Periphery household is given by: _<u>t</u>_ Households derive utility from aggregate consumption ( _c_<sup>_P_</sup> _t_<sup>), real money holdings (</sup><sup>_M_</sup> _Pt_<sup>_P P_) and experience</sup> disutility from working. The utility takes the form of GHH utility function to remove wealth effects and hence generate a decline in employment in bad times.<sup>9</sup> Aggregate consumption consists of tradable and non-tradable consumption: Hence the aggregate price takes the form: _ψt_<sup>_P_</sup> _≡_ ( _φP β_ )<sup>_t_</sup> Π<sup>_t_</sup> _k_ =0<sup>_cP_</sup> _T k_<sup>_−μ_</sup> is country P’s discount factor. It has two components. ( _φP β_ )<sup>_t_</sup> is the standard discount factor component. _φP_ is the coefficient reflecting country P’s patience.<sup>10</sup> The second component Π<sup>_t_</sup> _k_ =0<sup>_cP_</sup> _T k_<sup>_−μ_</sup> is the (uninternalized) endogenous discount factor that depends on the aggregate (economy-wide) level of households’ tradable consumption. As in Schmitt-Groh ́e and Uribe (2003), this is a simple technical device to induce uniqueness of the deterministic steady state and stationary responses to temporary shocks. Specifically, the endogenous discount factor decreases with the aggregate tradable consumption, which the representative household takes as given. In addition, _μ_ = 0 _._ 001 is set very small so that in the short run, the deviations of the endogenous discount factor from the standard discount factor _β_ are negligible.

> 8Note that Periphery and Core share the same nominal tradable price, since they have the same currency.

> 9See Greenwood et al. (1988) for further details.

> 10Similarly, _φC_ and _φO_ are the patience coefficients for countries C and O. To ensure Outsider lends to Periphery and Core, we assume that O is more patient (i.e. _φO > φC_ and _φO > φP_ ).

7
Dollar, David, Tatjana Kleineberg, and Aart Kraay (2013). “Growth Still Is Good for the Poor”. World Bank Policy Research Department Working Paper No. 6568.

Feldkircher, M., & Zeugner, S. (2009). Benchmark Priors Revisited: On Adaptive Shrinkage and the Supermodel Effect in Bayesian Model Averaging. IMF Working Paper No. 09/202, International Monetary Fund.

Fernandez, C., Ley, E., & Steel, M.F.J. (2001a). Model Uncertainty in Cross-Country Growth Regressions. _Journal of Applied Econometrics_ , 16(5), 563-576.

Fernandez, C., Ley, E., & Steel, M.F.J. (2001b). Benchmark prior for Bayesian model averaging. _Journal of Econometrics_ , 100(2), 381-427.

Hauk, William and Romain Wacziarg (2009). “A Monte Carlo Study of Growth Regressions”. Journal of Economic Growth. 14:103-147.

Jaumotte, Florence, Subir Lall, and Chris Papageorgiou. (2013). “Rising Income Inequality: Technology, or Trade and Financial Globalization,” _IMF Economic Review, v. 61, no. 2, pp. 271-309._ Jones, Chad and Peter Klenow (2011). "Beyond GDP: Welfare Across Countries and Time". Working Paper, Stanford University.

Klenow, Peter and Andres Rodriguez-Clare (1997). “The Neoclassical Revival in Macroeconomics – Has It Gone Too Far?”, in Ben Bernanke and Julio Rotemberg, eds. NBER Macroeconomics Annual. Cambridge, MIT Press, pp. 72-103.

Kraay, Aart (2006). “When Is Growth Pro-Poor? Evidence from a Panel of Countries”. Journal of Development Economics. 80:198-227.

Lopez, J. Humberto and Luis Serven (2006). “A Normal Relationship? Growth, Inequality, and Poverty”. World Bank Policy Research Working Paper No. 3814.

Milanovic, Branko. (2000). “Determinants of Cross-Country Income Inequality: An 'Augmented' Kuznets Hypothesis”, _Essays in honour of Branko Horvat, pp. 48-79._ Perri, Fabrizio and Dirk Krueger (2003). "On the Welfare Consequences of the Increase in Inequality in the US". NBER Macroeconomics Annual.

Pew Research Center (2013). “Economies of Emerging Markets Better Rated During Difficult Times”. <u>http://www.pewglobal.org/2013/05/23/economies-of-emerging-markets-better-rated-during-difficulttimes/.</u> Sala-i-Martin, Xavier, Gernot Doppelhofer, and Roland I. Miller. (2004). “Determinants of Long-Term Growth: A Bayesian Averaging of Classical Estimates (BACE) Approach.” _American Economic Review, 94(4): 813-35._ Sen, Amartya (1976). “Real National Income”. Review of Economic Studies.

29
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# **Comparative Analysis of AI-Predicted and Crowdsourced Food Prices in an Economically Volatile Region**

Julius Adewopo,<sup>1,2</sup> Bo Pieter Johannes Andrée,<sup>1</sup> Helen Peter,<sup>2</sup> Gloria Solano-Hermosilla,<sup>3</sup> Fabio Micale<sup>4</sup>

## **Affiliations**

1. Data and Development Group, World Bank, Washington, DC. USA

2. International Institute of Tropical Agriculture – IITA, Ibadan, Nigeria 3. Universidad Pablo de Olavide, Sevilla, Spain 4. European Commission Joint Research Center (EC-JRC), Ispra, Italy corresponding author(s): Julius Adewopo (jadewopo@worldbank.org) JEL Classification: Q11 – Aggregate Supply and Demand Analysis: Prices Keywords: Food Price, Crowdsourcing, Artificial Intelligence, Ground truth, Data
10. Chiquiar, Daniel and Gordon H. Hanson, “International Migration, Self-Selection, and the Distribution of Wages: Evidence from Mexico and the United States,” Journal of Political Economy, 113(2), 239-81, April 2005 11. Dahl, Gordon B., “Mobility and the Return to Education: Testing a Roy Model with Multiple Markets,” Econometrica, 70(6), 2367-2420, November 2002 12. de Brauw, Alan and John Giles, “Migrant Opportunity and the Educational Attainment of Youth in Rural China,” IFPRI and Department of Economics, Michigan State University, Washington DC, September 2006 (mimeograph) 13. de la Briere, Benedicte, “Household Behavior toward Soil Conservation and Remittances in the Dominican Republic,” University of California, Berkeley, 1996 (unpublished PhD thesis) 14. Fafchamps, Marcel and Forhad Shilpi, “The Spatial Division of Labor in Nepal,” Journal of Development Studies, 39(6), 23-66, 2003.

15. Fafchamps, Marcel and Forhad Shilpi, “Cities and Specialization: Evidence from South Asia,” Economic Journal, 115(503), 477-504, April 2005.

16. Fafchamps, Marcel and Forhad Shilpi, “Subjective Welfare, Isolation, and Relative Consumption,” Journal of Development Economics, 2008 (forthcoming) 17. Fafchamps, Marcel, and Forhad Shilpi, “Isolation and Subjective Welfare: Evidence from South Asia,” Economic Development and Cultural Change, 2009 (forthcoming) 18. Fujita, Masahisa, Paul Krugman and Anthony J. Venables, The Spatial Economy: Cities, Regions, and International Trade, MIT Press, Cambridge and London, 1999 32
14 most likely due to the large share of oil in its imports (35%)Y If the oil price estimate is too low, the gains are even larger. On the other hand, if revenues from the services rendered by the Baltics in the transit trade are included the "gains" from trade maintenance are reduced.

Many of these potential "gains" from maintaining existing trade flows have already been reduced by actual policy developments. As terms of trade adjust, the incentive to trade with the East is reduced. Russia, for example, has started to increase prices of many raw materials close to world prices for many export products such as petroleum. On the import side the need for a Table 5: Estimates of **terms** of trade losses from price adjustments in intra-republic trade (1990)

||Trade Balance in<br>foreign prices|Trade Balance in<br>domestic prices|Terms of Trade<br>loss|
|---|---|---|---|
|Country|(1)<br>(exports/imports)|(2)<br>(exports/imports)|(211)-i|
|Estonia|0.92|0.62|-32%|
|Latvia|1.07|0.81|-24%|
|Lithuania|0.89|0.58|-35%|

Source: Calculated from data in World Bank (1992a).

preferential trading area for trade maintenan is questionable, because the Baltics have low duties for many inputs, reducing the need for and the importance of preferences. At the extreme, Estonia has zero duties for most goods. Also the existence of other trade interventions reduces the potential benefits from preferential duties. The price incentive for Eastern trade is reduced by the existence of export taxes in Russia and other FSU countries for many raw materials, which raise their prices closer to world market levels. The status of these taxes in a preferential sThe estimates compare FSU-Baltic prices to estimates of world prices in 1990 given 1990 trade volumes. But the world price estimates used in the table are likely to be subject to large margins of error. The estimated world price for oil, for example in 125 % the domestic price. In other sources (Tarr-Michalopoulos 1992b) domestic oil prices in the FSU have been quoted as being a tenth of world prices. The data also excludes services, which for the Baltic as transit centers would improve the balances (Brown-Belkdndas 1993).
underestimated the positive effects of experienced contract teachers.

The effect that we find may be linked to the greater hiring of female teachers after the regime change. One story consistent with our results is that when salaries go down, the number of male applicants decreases. If female applicants feel that they will not be fairly treated in a recruitment process (or, indeed, if they are not), the teaching pool will not include educated and high TVA female teachers. It could be that the lower salaries induced more women to apply and be hired for these jobs, leaving the quality of new entrants unchanged after the hiring regime change. We cannot directly test this since applicant data are not kept, but it would be consistent with a number of patterns that we find, and in particular, the lack of any effect of teacher salaries on observable characteristics, including education levels and test scores.

Our results also suggest that, at least in low income countries, policies that increase wage levels to attract higher-skilled teachers, like those advocated by Auguste et al. (2010), would be costly and ineffective. Since higher levels of education are not correlated with TVA and only weakly correlated with teacher test scores, the best teachers are not those with the greatest education, and government outlays increase by paying these teachers their outside options in low-income countries. More remarkably, wages are already so high that even a 35% decline has no impact on the education levels of new recruits. This paper suggests that public sector compensation could be significantly redesigned to better account for the realities of low-income countries. Combining lower salaries (or salaries that are more strongly tied to teacher productivity) with greater investment in other school characteristics or student incentives could allow low-income countries to generate considerable fiscal savings.

# **References**

- Altonji, J. G. and R. K. Mansfield (2014). Group-average observables as controls for sorting on unobservables when estimating group treatment effects: The case of school and neighborhood effects. _NBER Working Paper #20781_ . Cambridge, MA.

- Andrabi, T., N. Bau, J. Das, and A. I. Khwaja (2010). Are bad public schools public “bads?” Test scores and civic values in public and private schools. _Working Paper_ . Cambridge, MA.

- Andrabi, T., J. Das, and A. I. Khwaja (2008). A dime a day: The possibilities and limits of private schooling in Pakistan. _Comparative Education Review 52_ (3), 329–355.

- Andrabi, T., J. Das, A. I. Khwaja, and T. Zajonc (2006). Religious school enrollment in Pakistan: A look at the data. _Comparative Education Review 50_ (3), 446–477.

30
HCs in Laos is largely due to the termination of the cooperative farming system in the early 1990s which led to the closure of associated health facilities.<sup>4</sup>

**Table 2: Number of hospitals, hospital beds, and health centers**

||**Thailand**<br>(2001)|**Malaysia**<br>(2001)|**Singapore**<br>(2001)|**Indonesia**<br>(2001)|**Philippines**<br>(2002)|**Vietnam**<br>(2002)|**Cambodia**<br>(2002)|**Laos**<br>(2002)|
|---|---|---|---|---|---|---|---|---|
|Total no. of|1,409|345|29|935|1,738|987|1,073|150|
|hospitals||(g)|||||||
|No. of beds|135,303<br>(a)|44,120|11,840|109,948|85,166|250,000<br>(d)|9,800|6,315<br>(e)|
|Beds per<br>10,000 pop|21.8<br>(b)|18.8|29.0|5.2|10.8|14.8|7.4|9.2|
|Total no. of<br>health<br>centers<br>(HCs)|10,037<br>(f)|...|...|7277 PHC<br>21587<br>SPHC<br>(h)|...|10,000<br>approx.|...|644|
|HCs per|1.6|...|...|1.4|...|1.2|...|...|
|10,000 pop||||incl.<br>SPHC|||||
|Total pop in<br>thousands<br>(c)|61,997|23,467||211,895.4|78,681.2|80,897.8|13,274.8|5,537|

<u>Notes:</u> ...Data not available or not applicable (a) Health Resources by WHO (1999) (b) Assuming that the number of hospital beds remains at the 1999 level.

(c) Calculated from “World Population Prospects: The 2004 Revision Population Database”.

(d) Approximate figure taken from STAT-USA Market Research Report at http://strategis.ic.gc.ca/epic/internet/inimrri.nsf/en/gr108259e.html.

(e) Figure includes 1,241 beds at health centers.

(f) The figure does not include Primary Health Care Units which are made up of trained village volunteers.

(g) Comprising of general hospitals, district hospitals, special medical institutions which are all public and other private hospitals.

(h) PHC and SPHC are Primary Health Centers and Supporting Primary Health Centers respectively.

Looking at countries’ stock of medical personnel, the Philippines and Singapore stand out from the rest of the group in terms of the number of physicians and nurses per 10,000 inhabitants (Table 3).<sup>5</sup> Thailand also enjoys a relatively high number of nurses per population, although 4 According to Leebouapao (2004), more than 700 health centers were closed and the health system almost came to a collapse. The Northern region was the worse hit. As of 2004, 29.1 percent of the villages were at least 16 kilometers away from the nearest health center. To improve access to essential drugs to the poor, so-called Drug Revolving Funds (DRF) were established in the mid-1990s. The system is self-funded through revenues received from the sale of drugs to patients (although poor people are exempted from user charges). The performance of the DRFs has been mixed, as the level of poverty in a district typically dictates the viability of DRFs.

5 One must be careful when interpreting the numbers in Table 3 since the figures available are often exaggerated. Many of the registered health personnel are no longer working or work abroad (see Section III). Moreover, the figures refer to different years. However, given the long training period for physicians, their numbers tend to be relatively stable through time.

5
Policy Research Working Paper 10835

# **Abstract**

Understanding men’s and women’s time use is a key factor in addressing issues and formulating policies related to division of labor, domestic work, and related gender disparities. However, obtaining data on individuals’ time use can be difficult and costly in the context of household surveys. Leveraging unique survey data collected in rural Malawi, this study investigates the possibility of predicting men’s and women’s time allocation to an extensive set of activities, using sensor signal data captured by accelerometers. Using machine learning techniques, the study builds a supervised classification model that is trained on the accelerometer data and a random subset of the time use survey data to predict individuals’ time allocation to 12 broad activity groups.

The model can correctly classify each performed activity in 76 percent of the cases. The analysis shows that with 40 percent of the training data, this method can achieve 90 percent of the maximum level of predictive accuracy reached in the analysis. The findings prove the feasibility of this methodology and offer insights for enhancing both survey and accelerometer data collection processes to build better models. Using the method can improve the quality of costly and difficult to obtain time use surveys with cheaper, yet accurate, modeled estimates, obtained by combining objective data from wearable devices with time use data collected on smaller samples.

This paper is a product of the Development Data 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://www.worldbank.org/prwp. The authors may be contacted at tkilic@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
CONALEP served as an altemative to other programs. The 1997 results are surprising, but as anomalous data this cohort may be treated as an outlier.

Table 12. Training Received at Work Matching group: Age **17-65.** Three nearest neighb rs based on propensity

|_Cohort_|_Ctrl. Group_|_Yes_<br>_CONALEP_|_Difference_|_Ctrl. Group_|_No_<br>_CONALEP_|_Difference_|
|---|---|---|---|---|---|---|
|90 - 93|38.8|45.8|7.0|61.2|54.2|-7.0|
|91 - 94|38.2|34.3|-3.9|61.8|65.7|3.9|
|92 - 95|39.9|43.4|3.5|60.1|56.6|-3.5|
|93 - 96|34.4|38.5|4.1|65.6|61.5|-4.1|
|94 - 97|33.3|30.7|-2.6|66.7|69.3|2.6|
|Total'|37.2|38.9|1.7|62.8|61.1|-1.7|
|_ENECE_**_992_**|22.1|||77.9|||
|_ENECE 99, LS'_|12.1|||87.9|||
|_ENECE 99, US'_|39.1|||60.9|||

1. Sample: Workers in the matching group.

2. Sample: All workers.

3. Sample: Workers with lower-secondary complete and 3 years of experience (18 and 19 years old).

4. Sample: Workers with upper-secondary complete and 1-5 years of experience (22-26 years old).

Table 13. Training Related to Work Matching group: Age **17-65.** Three nearest neighbors based on propensity

|_Cohort_|_Ctrl. Group_|_YES_<br>_CONALEP_|_Difference_|_Ctrl. Group_|_NO_<br>_CONALEP_|_Difference_|
|---|---|---|---|---|---|---|
|90 - 93|90.9|95.7|4.7|9.1|4.3|-4.7|
|91 - 94|84.2|88.3|4.1|15.8|11.7|-4.1|
|92 - 95|85.5|85.5|0.0|14.5|14.5|0.0|
|93 - 96|87.3|88.2|1.0|12.7|11.8|-1.0|
|94 - 97|81.4|90.0|8.6|18.6|10.0|-8.6|
|Total'|86.1|89.7|3.6|13.9|10.3|-3.6|
|_ENECE 992_|86.1|||13.9|||
|_ENECE 99,LS'_|<br>74.2|||25.8|||
|_ENECE 99, US'_|77.7|||22.3|||

1. Sample: Workers in the matching group.

2. Sample: All workers.

3. Sample: Workers with lower-secondary complete and 3 years of experience **(18** and 19 years old).

4. Sample: Workers with upper-secondary complete and 1-5 years of experience (22-26 years old).

the ENE98 and ENECE99 control groups respectively.

36
countries j with positive imports on tariff line _h_ ' . That is, the ad valorem equivalent of tariff line h is based on an aggregation defined below of the following: _h h_ ' _h h_ ' ' _k_ ' (7) _aswjt_ ( _h_ ,' _t_ )'  _q jt_ ' _bt_ / _v jt_ '  _h_  _ht_ ( _t_ )' ; _t_ = 2009, 2010, 2011*, t=2011,...2020, h  At , h  AMt To understand equation 7, take the year t= 2011 as an example. Suppose we have a tariff ten digit tariff line h with a specific tariff and the ten digit classification of h does not correspond to any ten digit tariff line in any of the years t’= 2009, 2010 or 2011* where there are positive imports (i.e., h  AMt). Suppose that tariff line h matches at least one tariff line in the base period at the eight digit level. The specific tariff _b_ 2011 _h_ does not depend on any base period data. We look for all ten digit tariff lines _h_ ' in each of our 3 time periods in our base period, where there is a match with tariff lines at the eight digit level and positive imports for some non-CIS country j. In this case, k=8. If there is no match at the eight digit level, we go to six digits or in a some cases to four digits. Equation 7 defines how we calculate the ad valorem equivalent of the specific tariff based on ten-digit tariff line data on imports from country j in year t’.<sup>16</sup>

# **Tariff Rates for Unmatched Tariff Lines in 2011.**

Employing equation 7, in the case of 2011, we calculate the tariff rate for tariff line h  AMt from the following equation: (8) for h  AMt

# **Average Tariffs for 2011.**

# **Unweighted average tariff for 2011.** Combining equations 6 and 8, we have

an estimate of the tariff rate on all tariff lines in 2011. Let n2011 **=** the number of tariff

> 16 For 2011, out of 632 unmatched lines, we were able to match 51 tariff lines at the 8 digit and 278 lines at the 6 digit level and the remaining 303 tariff lines at the 4 digit level. For 2012-2020, out of 343 unmatched lines, we were able to match 221 lines at 8 digit and 100 lines at 6 digit and the remaining 22 tariff lines were matched at the 4 digit level.

12