rafmacalaba/datause-encoder-data huggingface dataset:
|Age|0.6<sup>∗∗∗</sup>|(0.2)|0.8<sup>∗∗∗</sup>|(0.1)|1.5<sup>∗∗∗</sup>|(0.2)|
|---|---|---|---|---|---|---|
|Age, squared|-0.0<sup>∗∗</sup>|(0.0)|-0.0<sup>∗∗∗</sup>|(0.0)|-0.0<sup>∗∗∗</sup>|(0.0)|
|Female|-2.1<sup>∗</sup>|(1.2)|-0.7|(1.4)|-5.6<sup>∗∗∗</sup>|(1.7)|
|Years of education<br>(0.2)<br>**4. Household**|3.4<sup>∗∗∗</sup>|(0.2)|3.9<sup>∗∗∗</sup>|(0.2)|3.5<sup>∗∗∗</sup>||
|% its members in services<br>|0.3<sup>∗∗∗</sup><br>(0.0)|(0.0)<br>|0.4<sup>∗∗∗</sup>|(0.0)<br>|0.4<sup>∗∗∗</sup>||
|% its members in agriculture|-0.0|(0.0)|0.0|(0.0)|0.0<sup>∗</sup>|(0.0)|
|% its members in industry|0.2<sup>∗∗∗</sup>|(0.0)|0.3<sup>∗∗∗</sup>|(0.0)|0.4<sup>∗∗∗</sup>|(0.0)|
|No one employed|22.6<sup>∗∗∗</sup>|(1.7)|25.4<sup>∗∗∗</sup>|(2.5)|11.8<sup>∗∗∗</sup>|(4.0)|
|Household size|-11.8<sup>∗∗∗</sup>|(0.3)|-11.6<sup>∗∗∗</sup>|(0.4)|-9.6<sup>∗∗∗</sup>|(0.4)|
|Dependency ratio|-10.4<sup>∗∗∗</sup>|(0.5)|-10.4<sup>∗∗∗</sup>|(0.7)|-9.2<sup>∗∗∗</sup>|(0.7)|
|Has electricity|54.7<sup>∗∗∗</sup>|(2.6)|51.5<sup>∗∗∗</sup>|(2.7)|41.6<sup>∗∗∗</sup>|(2.9)|
|Owns toilets|44.3<sup>∗∗∗</sup>|(5.7)|45.5<sup>∗∗∗</sup>|(5.3)|24.6<sup>∗∗∗</sup>|(2.9)|
|Own more than 2 plots|10.7<sup>∗∗∗</sup>|(1.7)|7.9<sup>∗∗∗</sup>|(1.9)|11.4<sup>∗∗∗</sup>|(1.8)|
|**5. Aid received in the last year**<br>MASAF or Education aid|4.0<sup>∗∗</sup>|(1.5)|4.3<sup>∗∗</sup>|(1.6)|1.9|(2.0)|
|**6. Geography**|||||||
|Rural household|-9.4<sup>∗∗∗</sup>|(3.4)|-13.2<sup>∗∗∗</sup>|(3.2)|-14.4<sup>∗∗∗</sup>|(3.0)|
|Distance to town (km)|0.0|(0.0)|-0.0|(0.0)|0.0|(0.1)|
|Year and season FE|Yes||Yes||Yes||
|District FE|Yes||Yes||Yes||
|Observations|35627||24643||24643||

Note: FE stands for fixed effects. The dependent variables are per capita consumption without aid (food and cash) in columns 1 and 2 and per capita food consumption (without transfers) in column 3. In Column 2, we restrict the sample to households included IHS 3 and 4 so that we could measure food consumption without transfers in a similar way. Due to lack of access to Stata dofile used to construct consumption aggregates in IHS2, we could not reconstruct food consumption without transfers. Standard errors are clustered at the district level. Standard errors in parentheses: ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01 In conclusion, the welfare analyses conducted in this study show that drought in the growing season reduces consumption per capita (without aid) in the months following harvest by 5 to 12 percentage points. The amount of welfare loss depends on the intensity of the drought. Excess rainfall in the first two months of the growing season (November– December) decreases food consumption per capita by 1.8 percent for each day of excess rainfall in that period, while excess rain in later months of the growing season has less impact on consumption.

21
# **_Quantile Decompositions Across Regions (Southeast and Northeast)_**

The next set of decompositions explores differences between similar areas of the leading Southeast and the lagging Northeast regions. The contribution of the returns and covariate effects in accounting for differences in the distributions of welfare are shown in Figure 3.5.

In comparing the metro areas of the Southeast and Northeast regions (Figure 3.5a), the returns effect is greater than the covariate effect in accounting for the differences between welfare distributions. Considering that metro Southeast (i.e. São Paolo and Rio De Janeiro) has a high density of economic activity, better infrastructure, and serves as a hub for trade, a large returns effect suggests the presence of agglomeration effects.

Between urban areas (Figure 3.5b) and between rural areas (Figure 3.5c) of these two regions, both the covariate and returns effects contribute to the difference. The upward slope of the returns effects indicates that the returns effect is smaller for the poor relative to those better off.

**Figure 3.5: Quantile decomposition of welfare differences between Southeast and Northeast regions**

- (a) Between SE and NE metro areas (b) Between SE and NE urban areas

<!-- Start of picture text --> Returns and Covariate Effects Returns and Covariate Effects<br>Counterfactual: Metro SE returns and Metro NE attributes Counterfactual: Urban SE returns and Urban NE attributes<br>Brazil 2002/2003 Brazil 2002/2003<br>0 10 20 30 40 50 60 70 80 90 100 0 10 20 30 40 50 60 70 80 90 100<br>percentile percentile<br>returns effect covariate effect returns effect covariate effect<br>.5 .5<br>.4 .4<br>.3 .3<br>.2 .2<br>.1 .1<br>Difference in log welfare ratio Difference in log welfare ratio<br>0 0<br><!-- End of picture text -->

- (c) Between SE and NE rural areas

<!-- Start of picture text --> Returns and Covariate Effects<br>Counterfactual: Rural SE returns and Rural NE attributes<br>Brazil 2002/2003<br>0 10 20 30 40 50 60 70 80 90 100<br>percentile<br>returns effect covariate effect<br>.5<br>.4<br>.3<br>.2<br>.1<br>Difference in log welfare ratio<br>0<br><!-- End of picture text --> 20
**Table I-2 Information on direct, indirect and capital stock losses for selected disaster events**

|**Hurricane Stan in El Salvador, 2005**<br>**(million USD)**||Losses||
|---|---|---|---|
|Sector|Direct|Indirect|Total|
|Social(housing, education, health)|48|102|150|
|Productive(agriculture, industry, commerce, tourism)|22|34|56|
|Infrastructure (water and sanitation, electricity,<br>transport)|106|8|114|
|Environment|21|1|22|
|Emergencyand relief expenditure||11|11|
|Total(ECLAC)|196|145|352|
|Total loss according to EMDAT|||356|
|Capital stock losses (productive sector and<br>infrastructure)as a share of total loss|36%|||
|**Capital stock losses (productive sector and**<br>**infrastructure) as a share of total loss (EMDAT)**|**36%**|||
|**Hurricane Stan in Guatemala, 2005**<br>**(million Quetzales)**||Losses||
|Sector|Direct|Indirect|Total|
|Social(housing,education,health)|630|543|1,173|
|Productive(agriculture,industry,commerce,tourism)|306|1,736|2,042|
|Infrastructure (water and sanitation, electricity,<br>transport)|1,960|1,437|3,396|
|Environment|308|...|308|
|Emergencyand relief expenditure||595|595|
|Total(ECLAC)|3,203|3,716|7,514|
|Total loss accordingto EMDAT|||7,542|
|Capital stock losses (productive sector and<br>infrastructure)as a share of total loss|30%|||
|**Capital stock losses (productive sector and**<br>**infrastructure) as a share of total loss (EMDAT)**|**30%**|||
|**Hurricane Mitch in Honduras 1998**<br>**(million USD)**||Losses||
|Sector|Direct|Indirect|Total|
|Social(housing,education,health)|305|719|1,024|
|Productive(agriculture,industry,commerce,tourism)|1,478|577|2,055|
|Infrastructure (water and sanitation, electricity,<br>transport)|348|164|512|
|Environment|47|0|47|
|Emergencyand relief expenditure||156|156|
|Total(ECLAC)|2,178|1,460|3,794|
|Total loss accordingto EMDAT|||3,794|
|Capital stock losses (productive sector and<br>infrastructure)as a share of total loss|48%|||
|**Capital stock losses (productive sector and**<br>**infrastructure) as a share of total loss (EMDAT)**|**48%**|||

28
# **Do Individual Investors Ignore Transaction Costs?**<sup>*****</sup>

Deniz Anginer, Snow Xue Han, Celim Yildizhan _JEL Classifications:_ G11, G12, G14 _Keywords_ : individual investors; liquidity; transaction costs; investor attention; behavioral bias

* Deniz Anginer, World Bank, <u>danigner@worldbank.org. Snow Xue Han, Univesity of San Frnacisco, hanx@sfsu.edu.  Celim Yildizhan, University of Georgia, celim@uga.edu.  The authors wish to thank Jussi Keppo,</u> Jeff Netter, Bradley Paye, Annette Poulsen Tyler Shumway, Nejat Seyhun, Noah Stoffman, Uday Rajan, Paolo Pasquariello, seminar participants at the Ross School of Business at the University of Michigan, Erasmus Liquidity Conference, Tel Aviv University Finance Conference and the Western Finance Association meetings for comments and suggestions.  All errors are our own. This paper is based on an earlier draft titled “Liquidity Clienteles.”
|**Antibiotics**|||||||
|---|---|---|---|---|---|---|
|_Azithromycin tabs/caps/oral available_|0.36|0.27|0.09|0.28|0.22|0.75|
|_Amoxicillin tabs/caps/oral available_|0.88|0.95|0.80|0.78|0.75|0.97|
|_Ampicillin powder available_|0.06|0.11|0.06|0.37|0.28|0.65|
|_Any antibiotic available_|0.90|0.95|0.82|0.78|0.80|0.98|
|_All antibiotics available_|0.05|0.09|0.02|0.26|0.08|0.58|
|**Rehydration**|||||||
|_ORS packet available_|0.82|0.78|0.77|0.80|0.86|0.92|
|_Sodium chloride inj. available_|0.57|0.31|0.68|0.70|0.69|0.72|
|_Has ORS and sodium chloride available_|0.48|0.29|0.58|0.56|0.61|0.64|
|_Neither ORS nor NaCl available_|0.10|0.20|0.14|0.06|0.06|0.00|

± Breakdowns by cadre and education were unavailable for facilities with over 50 medical staff in Tanzania so the counts of doctors, clinical officers, nurses, and other (and counts of staff with different levels of education) are only available for facilities with 50 or fewer medical staff. However, the counts of total medical staff include these larger facilities (Tanzania has 35 facilities with more than 50 medical staff, which increase the averages of total medical staff from 7.59 for public facilities and 14.15 for private facilities to the 10.70 and 17.20 reported in the table above).

> _++_ Clinical Officers were not a medical staff cadre in Sierra Leone

δ Provider education was missing for 1.1% of the sample resulting in the sums of the categories of education equaling less than the total number of medical staff

*Acceptable waste disposal methods include: Open burning in pit or protected ground, placed in covered pit or pit latrine, placed in pit or protected ground, off-site removal (with protected storage) and one or two chamber incinerators.

> _+_ Other arrangements for emergency transport include other official vehicles, private vehicles or another mode of emergency transport.

32
# **Figure 1: Sampling Frame**

<!-- Start of picture text --> Ever Migrate to New Zealand<br>(307)<br>‐ Long survey: 133<br>Short survey: 61<br>Ballot winners (367)<br>Don't migrate (60)<br>Ballot Applicants in 2002‐05<br>‐ Long survey 6<br>(4,696)<br>Short survey 3<br>Ballot Losers (4329)<br>‐ Long survey 143<br>Short survey 39<br><!-- End of picture text --> Notes: numbers in parentheses show population numbers. The survey attempted to interview all individuals who migrated to New Zealand by late 2012 when the sample frame was provided; therefore the interview success rate is 64% (194/307). A random sample of ballot losers and non-compliers (winners who do not migrate) was obtained through village-based sampling. See text for details.

27
15 half of those using LPG used it as their primary cooking fuel. At the opposite end of the spectrum, Angola, Gabon, and Mauritius stood out as those in which the uptake of LPG was not only high but was also essentially synonymous with use of LPG as the primary cooking fuel.

**Figure 8  Comparison of households using LPG in any capacity and those citing LPG as the primary cooking fuel**

_Source_ : World Bank staff calculations using household survey data and RIM LPG Intelligence Daily. _Note_ : LPG prices average Saudi Aramco contract prices for propane and butane in the month that is halfway through the survey period and converted to 2022 U.S. dollars using the U.S. GDP deflators.

Because global LPG prices varied several-fold during the period covered by the surveys, the average Saudi Aramco contract prices for propane and butane in 2022 U.S. dollars per kg are also shown in Figure 8. Because the transportation, storage, and bottling costs also vary several-fold from country to country, compounded by price subsidies in some countries, the free-on-board LPG prices merely give indications of the global LPG price movements and some idea of end-user prices in countries with no subsidies at the time, which include southern African countries except Angola, Kenya, Liberia, Nigeria, Tanzania, and Uganda. As an extreme example of a very large difference between end-user prices and the Arab Gulf prices, the average of maximum retail prices of LPG established by the government of South Africa in 28 regional zones was higher than the Arab Gulf price by nearly US$1.30 per kg,<sup>7</sup> tripling the retail price from the Arab Gulf price and markedly higher than US$0.30 assumed in the calculation of annual household income needed for monthly purchases of 12.5 kg of LPG. By contrast, in Nigeria, where LPG prices have been deregulated for a long time, the difference was US$0.30 per kg.<sup>8</sup> The very high prices of LPG in South Africa and the neighboring countries (except Angola, where LPG has been heavily subsidized) and conversely historically reliable electricity and low electricity tariffs would also explain why few have chosen to cook with it and electricity has remained the most popular source of energy for cooking in Southern Africa.

> 7 http://www.energy.gov.za/files/petroleum_frame.html.

> 8 https://nigerianstat.gov.ng/elibrary.
_Table 6: Spearman rank correlation alternative targeting mechanisms relative to independent PMT estimates_

||Spearman rank<br>correlation|Area under the curve|
|---|---|---|
|100 Poorest Cantons plus machine learning<br>withphone data|0.45|0.73|
|Feasible alternatives|||
|Uniform targeting within poorest<br>prefectures|0.34|0.66|
|Uniform targetingwithinpoorest cantons|0.38|0.68|
|Infeasible alternatives|||
|Asset index|0.51|0.75|
|Progress out of PovertyIndex|0.63|0.81|
|ProxyMeans Test|0.72|0.85|

Source: Aiken et al. (2022)

# 5. Conclusion

Thirty-five years after the publication of Battese, Harter, and Fuller (1988) and seven years after the publication of Jean et al. (2016), the literature on combining survey and geospatial data to predict wealth and poverty is maturing rapidly. It is clear that indicators derived from geospatial data are strongly predictive of wealth and poverty across space in several contexts, although the extent of this correlation depends on many factors. The accuracy of predictions, however, is particularly sensitive to the nature of the household data on welfare or wealth used to train the model. It is also clear that the coverage of the training sample also matters.

Estimates for out-of-sample areas are almost always less accurate than for in-sample areas, because informative sampling introduces bias, and because Bayesian and empirical Bayesian methods do not benefit from sample-based priors. In addition, the evaluation benchmark in out-of-sample areas, if it is a model-based estimate, will also be biased due to informative sampling. Finally, the few studies that have considered the prediction of changes over time have found that this is much harder than prediction across space, even for medium to long-run changes. Seeing short-run changes in welfare from space may prove challenging but is a challenge worth taking on.

The recent literature has offered more discussion than evidence regarding the pros and cons of different methodologies. One dividing line has been the choice of “direct CNNs” trained directly to survey data as opposed to utilizing interpretable geospatial features in a mixed linear or tree-based machine learning model. In Uganda and Sri Lanka, where both approaches have been compared, it seems that the interpretable features approach does at least as well as directly training CNNs, but the evidence on this question remains scant. A second fault line has emerged over the level at which to specify linear models. As a general rule, predictions typically benefit from using the most spatially disaggregated data possible, so if sub-area level data are available, area-level models should be only used as a last resort. This is particularly true when considering an evaluation criterion that combines accuracy and precision, such as mean squared error, since the use of more granular auxiliary data appears to have a larger
hierarchical for all business functions. In the case of sales, for example, firms can use various technologies, and while online sales are more sophisticated technologies that sales on the phone or email, there is no clear sophistication ranking between sales made in the company’s website or using online platforms; both are complementary. A similar example occurs with payment methods; firms may use a variety of them, often depending on the financial infrastructure in the country.

A key advantage of the grid structure is that it allows to accommodate the use of more than one technology by business function. The survey questionnaire is implemented so respondents are asked first about the use of each of the technologies in the grid. Then, for those technologies selected in each business function, the respondent is asked to identify the one that is more intensively used in implementing the tasks of the business function. Finally, when using one of the most advanced technologies, the respondent is also asked to provide the year of adoption. This allows to uncover new facts about technology adoption and use by allowing to build new measures of technology sophistication at the business function level based on extensive measures, the most sophisticated technology, and intensive measures, the technology used more intensively. It, also allows to calculate measures of diffusion lags for advanced technologies.

# **A.1.3 Sector Specific Business Functions**

For the sector-specific technologies, a similar approach was used to identify key business functions and associated technologies in 12 sectors of activity across agriculture, manufacturing, and services (including agriculture-crops; livestock; food processing; wearing apparel; leather and footwear; automotive; pharmaceuticals; wholesale and retail; transportation; financial services; health services; other manufacturing). One business function, fabrication, was also included for all manufacturing sectors. Identifying key business functions and the frontier in each sector required significant interaction with several sector specialists. These functions tend to be associated with sector-specific production processes.

Here, we present all sector-specific business functions and associated technologies covered by the FAT survey in the first and second phases of data collection. These figures complement the information provided in Section 2, particularly Figure 2, which describes the functions and associated technologies for SSBFs in agriculture, among SSBFs. The complementary information is provided for all SSBFs, including Livestock (Figure A.1), Food Processing (Figure A.2), Wearing Apparel (Figure A.3), Leather and Footwear (Figure A.4), Automotive (Figure A.5), Pharmaceutical (Figure A.6), Wholesale and Retail (Figure A.7), Transportation (Figure A.8), Financial Services (Figure A.9), Health Services (Figure A.10), 63
all relevant variables, ensure long-term sustainability and avoid inter- and intragenerational transfers. Data on life tables, investment returns, operating costs and bonus policies need to be regularly published and to be available for public scrutiny.

# **2.6 Regulation of marketing policies**

## **Marketing policies of centralized providers**

As with pricing policies, the regulation of marketing policies is much simpler in a centralized market structure. There is no need for elaborate controls on marketing campaigns and the creation of electronic quotation systems. The irrevocability and nonportability of life annuities do not present the same difficulties and challenges as under a decentralized competitive structure. The centralized institution needs to respond to enquiries from retiring workers by providing appropriate quotations taking into account the choice of product and age of applicants. To be able to do this effectively, it needs to construct life tables by product and age cohort and also apply the appropriate yield curves to calculate the initial annuity payments by type of product.

The main challenge is the creation of a sophisticated delivery system where trained professionals have access to detailed data and are able to respond in a prompt and efficient manner to enquiries from retiring workers. To ensure a high quality of service, this component of the centralized structure can be outsourced through competitive bidding to a small number of private operators, subject to clearly defined standards of accuracy and speed.

## **Marketing in decentralized competitive markets**

In contrast, the regulation of marketing policies presents a major challenge in a decentralized competitive market. Providers of retirement products need to be deterred from engaging in deceptive practices and from undertaking mis-selling and misleading marketing campaigns.<sup>24</sup> The adverse effects of these campaigns are magnified by the involvement of brokers and agents, who are remunerated on a volume basis, receive their commission income upfront and often adopt aggressive tactics in selling the products of particular providers. Special rules need to apply to them, covering training, certification and probity.

The first requirement of an effective regulation of marketing in a decentralized competitive market is compliance with basic conduct rules, such as the ‘know-yourcustomer’ rule and an adequate disclosure of the terms and conditions of different products. However, because annuity products are highly complex, there is also a need for extensive training of agents and brokers. In addition to adequate training, brokers need to pass a certification test as well as the standard ‘fit and proper’ test. Licensed brokers must

> 24 Insurance companies, which are expected to play a central part in most annuity markets, have a global reputation of using unclear practices and promoting aggressive selling campaigns. They also excel in utilizing detailed contracts with innumerable provisions in small print that are incomprehensible to the average consumer but are then used to decline claims.

32
<!-- Start of picture text --> Presence of explicit deposit insurance<br>by predominant ownership type<br>0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80<br>Mainly Govt<br>Mainly Fgn<br>Fgn+Govt<br>Equally shared<br>Mainly local pvt<br><!-- End of picture text --> <!-- Start of picture text --> Share of countries with all top banks rated<br>by predominant ownership type<br>0.00 0.02 0.04 0.06 0.08 0.10 0.12 0.14 0<br>Mainly Govt<br>Mainly Fgn<br>Fgn+Govt<br>Equally shared<br>Mainly local pvt<br><!-- End of picture text -->

**Accountability of bank management** by predominant ownership type

**Share of assets in listed banks** by predominant ownership type

<!-- Start of picture text --> 2.50 2.55 2.60 2.65 2.70 2.75 2.80 2.85<br>Mainly Govt<br>Mainly Fgn<br>Fgn+Govt<br>Equally shared<br>Mainly local pvt<br><!-- End of picture text --> <!-- Start of picture text --> 0.00 0.10 0.20 0.30 0.40 0.50 0.60 0<br>Mainly Govt<br>Mainly Fgn<br>Fgn+Govt<br>Equally shared<br>Mainly local pvt<br><!-- End of picture text --> Figure 4: _Selected indicators of market discipline preconditions by predominant ownership of banks, group means_ (Source: World Bank Regulation and Supervision Survey; for definition of predominant type of ownership see note to Table 2) 25