rafmacalaba/data-use-mentions-extended huggingface dataset:
Policy Research Working Paper 10863

# **Abstract**

Empirical evidence on the potential impacts of generative artificial intelligence (GenAI) is mostly focused on high-income countries. In contrast, little is known about the role of this technology on the future economic pathways of developing economies. This paper contributes to fill this gap by estimating the exposure of the Latin American labor market to GenAI. It provides detailed statistics of GenAI exposure between and within countries by leveraging a rich set of harmonized household and labor force surveys. To account for the slower pace of technology adoption in developing economies, it adjusts the measures of exposure to GenAI by using the likelihood of accessing digital technologies at work. This is then used to assess the extent to which the digital divide across and within countries will be a barrier to maximize the productivity gains among occupations that could otherwise be augmented by GenAI tools. The findings show that certain characteristics are consistently correlated with higher exposure. Specifically, urban-based jobs that require higher education, are situated in the formal sector, and are held by individuals with higher incomes are more likely to come into interaction with this technology. Moreover, there is a pronounced tilt toward younger workers facing greater exposure, including the risk of job automation, particularly in the finance, insurance, and public administration sectors. When adjusting for access to digital technologies, the findings show that the digital divide is a major barrier to realizing the positive effects of GenAI on jobs in the region. In particular, nearly half of the positions that could potentially benefit from augmentation are hampered by lack of use of digital technologies. This negative effect of the digital divide is more pronounced in poorer countries.

This paper is a product of the Poverty and Equity Global Practice. 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
In the Appendix, we provide supplements to the survey, empirical exercises, and results in the main text. We begin with describing the sampling framework and weight construction for the Firm-level Adoption of Technology (FAT) Survey in Appendix A. Then, in Appendix B, we explain details on the construction of some of technology measures used in empirical exercises in the main analysis. It also provides additional figures on the structure of business functions and technologies. Finally, we provide the additional results in Appendix C, Appendix D, Appendix E, and Appendix F.

# **A FAT survey**

The Firm-level Adoption of Technologies (FAT) data are based on multi-country, multisector, and representative firm surveys to measure technologies adopted and used by firms. Table A.1 provides the number of technologies covered in the FAT survey as well as other existing surveys. Compared to existing surveys, the FAT survey covers a larger number of technologies and business functions. It also covers the agriculture sector in addition to the manufacturing and service sectors.

The survey also provides balance sheet information and selected information on business owners. Furthermore, the survey collects information on potential drivers of and barriers to technology adoption. The survey has been conducted by the World Bank in partnership with public or private local agencies across three countries: Brazil (the state of Cear ́a), Senegal, and Vietnam. The data were collected between June 2019 and March 2020.<sup>51</sup> Table A.1: Coverage of firm-level technology surveys

|Surveys|# of<br>Technologies|# of<br>Business Functions|Includes Firms<br>in Agriculture|
|---|---|---|---|
|Firm-level Adoption of Technology Survey|287|59|Yes|
|Survey of Advanced Technology (SAT)|57|3|No|
|Community Survey on ICT Usage and E-Commerce in Enterprises|9|0|No|
|Information & Communication
## **4.9.2 Incorporating labor market regulations**

The model also incorporates some important labor market regulations. For example, the progressive tax structure is taken into account in computing after-tax income. Fees that workers pay for health and disability insurance are also taken into account. Lastly, the model incorporates the fact that informal sector workers typically do not pay these taxes and fees.<sup>35</sup>

# **5 Description of the data**

The estimation and simulations are based on data from three sources: the _Encuesta de Protecci ́on Social_ (EPS) longitudinal survey, linked administrative records of pension balances and contributions to retirement accounts (obtained from the Chilean supervising agency for pension fund administrators (the _Superintendency of Pensions_ or SP) and data on the returns achieved by Chile’s pension fund administrators (the _Administradoras de Fondos de Pensiones_ ).

The EPS survey was first administered in 2002 (originally under the name _Historia Laboral y de Seguridad Social_ ) by the Microdata Center of the University of Chile. Originally, the sampling frame was individuals affiliated with the AFP or the older INP pension systems. The survey data were then linked to the administrative records of the pension accounts of the sampled individuals. In 2004, 2006 and 2009 three follow-up surveys were administered, and the sample was augmented to include individuals that were not affiliated to any pension program, to obtain a total sample of 20,114 individuals, representative of the Chilean population in 2004. We use information on the 16,150 respondents who were interviewed in the 2006 round and use the survey weights to correct for attrition and non response.

The EPS questionnaire was designed specifically to study Chile’s social protection programs including the pension system. It contains rich longitudinal information on sociodemographic variables, household composition, employment histories, earnings and assets. The data include retrospective employment histories back to 1981 as well as earnings from

> 35 We use information on reported earnings and do not explicitly incorporate minimum wage regulation.
The rate of return is calculated at the moment the investment in education takes place. The estimated returns reflect choices facing individuals at different ages (20, 25, etc) of whether to re-enter the education system. Hence, the returns do not simulate the situation of a 20 year-old facing a choice of continuing education now or re-enter the education system at the age of 25, 30 etc. To illustrate the gains from completing an education degree early in life, we computed the net present value of a tertiary education with a constant discount rate of 5 percent—a level above the current world real interest rate. Figure 5 shows that finishing an education early in life yields a higher value.

,,,

**Figure 5 - NPV across age for tertiary students**

<!-- Start of picture text --> 70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>-10 20 25 30 35 40 45 50 55<br>-20<br>Age of re-entry to the education system<br>Net Present Value of a completed tertiary education (in million Col$)<br><!-- End of picture text --> Source: Authors’ calculation based on Encuesta de Hogares 1995-2000

# **IV-2 HOW INDIVIDUAL  AND EDUCATIONAL FACTORS AFFECT THE RATE OF RETURN; ALTERNATIVE SCENARIOS**

Individual and educational factors significantly affect the rate of return. Below, we outline the main findings from the alternative scenarios. By changing the assumptions behind the baseline case, we can observe the sensitivity of the return to education to: study completion time, part-time work, public subsidies to tertiary education, tuition fees for primary and secondary education, unemployment and tax legislation. Further, we examine the returns by gender. The paragraphs below discuss each factor. The full estimation results are available in Annex table 7.

_<u>Study completion time</u
14

# _Table 1: Surveys Used_

||China Health & Nutrition Survey (CHNS)|Gansu Survey of Children and<br>Families(GSCF)|World Bank China Health VIII project baseline survey|
|---|---|---|---|
|Provinces sampled|Guangxi, Guizhou, Heilongjiang (1997 and 2000<br>only), Henan, Hubei, Hunan, Jiangsu, Liaoning (not<br>1997), Shandong|Gansu (1)|Anhui, Chongqing, Gansu, Guizhou, Henan, Qinghai,<br>Shanxi|
|No. counties sampled|36|20|28|
|Rural or urban or both|Both|Rural|Rural|
|Areas covered|Where possible, the provincial capital and a lower-<br>income city were selected, though in two provinces,<br>other large cities had to be selected. Villages and<br>townships within the counties, and urban and<br>suburban neighborhoods within the cities were<br>sampled.|Villages only|Villages and townships<br>|
|Sampling strategy|A multistage, random cluster process was used to<br>draw the sample surveyed in each of the provinces,<br>which themselves sampled vary substantially in<br>geography, economic development, public resources,<br>and health indicators. Counties in the 9 provinces<br>were stratified by income (low, middle, and high) and<br>a weighted sampling scheme was used to randomly<br>select 4 counties in each province. Villages and<br>townships within the counties and urban and suburban<br>neighborhoods within the cities were selected<br>randomly.<sup>7</sup>|A four-stage stratified random<br>sample was used. First counties<br>were<br>selected<br>(20),<br>then<br>
periencing at least one deprivation.<sup>23</sup> Consistent with the much higher shares of the population that are deprived in non-monetary indicators, the majority of deprivations experienced by the population are due to lack of access to basic services. Figure 3a demonstrates that 63 percent of deprivations on average are contributed by the access to basic services dimension, 21 percent of deprivations are contributed by the education dimension, and only 16 percent of deprivations are contributed by the monetary poverty dimension.

Figure 4 investigates these patterns by reporting the share of the population deprived in each indicator in the World Bank’s MPM by imputed expenditure deciles, where higher deciles are associated with higher expenditure. Further corroborating the validity of imputed expenditure, there is a decline in deprivations for higher levels of expenditure. However, the figures illustrate very significant deprivations even among the richest households in the population. The average deprivation among the highest expenditure decile is 33.1 percent, with the highest deprivations in access to basic services.

To better benchmark these levels of deprivation of the richest, Table 4 compares the average deprivation for the top expenditure decile in PNG to the average of the population in the rest of the world for each indicator in the basic MPM aside from monetary poverty. Strikingly, the deprivations among the top expenditure decile of the population are still often comparable to the average deprivations of the entire population in some of the poorest countries in the world. The education indicators compare the best, where the top income decile in PNG ranks in the 44th and 31st percentiles globally; and the rest of the MPM indicators range between the 21st and the 7th percentiles globally.

The deprivation extends to the entire country and the least deprived provinces in Papua New Guinea have deprivation levels similar to many of the most deprived countries in the world. Figure 5 reports the global ranking of each province in the share of the population that is multidimensionally poor. Even the National Capital District (NCD), with a substantially lower multidimensional poverty rate, still ranks only
APPENDIX <u>Sample: Our original historical database contains 329,637 monthly bond price quotes of 1,712 bonds</u> traded in London and New York and listed in the original sources under categories such as “Foreign Government”. Upon closer inspection, we drop roughly one third of these observations, so as to get to a homogenous sample of foreign-currency sovereign bonds issued by independent states in US dollars or British pounds.

Specifically, we drop:

- 388 bonds that were issued by a state under colonial status. These are colonial bonds by Antigua and Barbuda, Barbados, Fiji, Ghana, Grenada, Guyana, Hawaii, Hong Kong, Iceland, India, Indonesia, Iran, Jamaica, Kenya, Liberia, Mauritius, Morocco, Newfoundland, Nigeria, Palestine, Philippines, Saint Luca, Sierra Leone, Singapore, Southern Rhodesia, Sri Lanka, Straits Settlement, Sudan, Tanganyika, Trinidad and Tobago, and Zanzibar. This reduces our dataset to 292,000 monthly bond prices.

- 256 sub-national bonds, issued by provinces and municipalities but not the central government, reducing the sample to 233,968 monthly bond prices.

- 80 local currency bonds as well as 7 bonds denominated in other foreign currencies (e.g., German Mark or French Franc).

- 12 bonds that were explicitly guaranteed by other governments, via a respective clause in the bond contract. These include the Greek 1833 bonds whose interest and principal payments were fully guaranteed by Great Britain, France and Russia, or the Austrian 6% guaranteed bond of 1923 which had been issued under the auspices of the League of Nations with guarantees by several European governments. Investor reports of the time regard these bonds not as debt owed by the issuing sovereign but as a liability of the guarantor sovereign. For example, the IMM lists guaranteed bonds in the section of the guaranteeing nation, for example, Great Britain.

- Finally, we exclude three sets of bonds: 10 bonds with erratic and infrequent price quotes (this
levels. However, most European countries have a higher than expected GDP growth than suggested by their service value added growth. Perhaps there is a higher share of services being exported or the sophistication is higher, resulting in a larger growth impact of their service value added share?

**Figure 1. Share of service value added as a percentage of GDP vs GDP per capita, 2009.**

<!-- Start of picture text --> 6 7 8 9 10 11<br>Log Per capita GDP PPP $2009<br>Note: black - all countries, red - EU-15, blue - NMS, green - Accession<br>80<br>60<br>40<br>20<br><!-- End of picture text --> _Source_ : World Development Indicators, World Bank, 2010 and authors’ calculations.

4
I investigate whether the enhanced HIPC Initiative has an impact on child health by applying micro data on infant mortality from country-specific Demographic and Health Surveys (DHS) from 56 lowand middle-income countries of which 31 are HIPCs. The retrospective fertility nature of the data from the DHS allows for panel data analysis using the within-mother variation in the probability of survival of babies born before and after the countries reach the Decision and Completion Points of the debt relief program. Within-mother estimation accounts for unobservable characteristics of the family and controls for effects that arise due to changes in the demographic composition. This reduces problems with crosscountry confounding factors – say, if households in HIPCs, on average, are different from households in non-HIPCs – and facilitates a causal interpretation of the results. In addition, this approach allows for distributional analysis of the debt relief effects on child health and the detailed DHS data give indications of potential health mechanisms through which HIPC affects infant mortality.

To the best of my knowledge, this is the first paper which uses cross-country micro data to analyze the potential impacts of debt relief on child health. It aims to contribute to the vast empirical literature on the effects of development assistance in general and debt relief in particular on child health. My findings suggest that when a country qualifies for Decision Point, the probability of infant mortality goes down by approximately 0.5 percentage points or five infant deaths per 1,000 live births. This represents 7 percent of the sample mean and translates into approximately 3,000 fewer infant deaths in an average HIPC in the year of Decision Point. Results are stronger for infants born to mothers who are poor and mothers living in rural areas. Neonatal mortality is largely unaffected by debt relief, which indicates that the impacts of Decision Point on infant mortality go through interventions or policies which affect infant survival after the first month of life. Moreover, analysis of potential health mechanisms suggest that the improvements in infant mortality are driven by improved access to vaccines early in life and during mothers’ pregnancies. Immunization rates against
clusters received at least one agent (46 percent in high priority clusters and 24 percent in low priority clusters).

We use data on about 9,000 households from a 2018 follow-up survey and 2017 administrative data from Airtel Money to measure the effects of the agent rollout on mobile money usage, savings, remittances, self-employment, and food security. In contrast to the studies of mobile money in more connected settings, our results show that the agent rollout did not increase use of mobile money, savings, or remittance transactions. We find weak evidence that the agent rollout decreased transportation costs by 76 percent for those receiving remittances, in high priority areas where more agents were rolled out. Consistent with this result, we also find that the agent rollout increased food security in these areas. Neither of these effects are statistically significant when controlling for multiple hypothesis testing. However, the effects are in line with those in Aker et al (2016), who show that paying government transfers in Niger via mobile money instead of in cash reduced time costs for recipients and led to better household nutrition.

Unlike Sekabira and Qaim (2017), who conclude that mobile money increased nonfarm income in Central Uganda, we find no robust effect on nonfarm self-employment. Overall, our findings suggest that, in poor and remote areas with low mobile phone penetration and low remittances, rolling out mobile money agents has limited effects on the average household.

To our knowledge, our paper is the first to study the effects of rolling out mobile money agents in poor and remote areas, without any additional interventions. In that sense, our results capture the effects of a policy focused only on opening mobile money agents. Our paper is closely related to two other randomized experiments with poor rural households.

First, Lee et al (2021) conducted a field experiment with poor rural households in Bangladesh and their family members who had migrated to the city. Although the study took place in one of the poorest areas of Bangladesh, 99 percent 4
to international staple prices, domestic climate shocks, etc. Using predicted data as a substitute for real data may then offer a valuable alternative.

In recent years, a number of studies have explored the option of predicting household expenditure data into existing secondary surveys in an effort to supplement existing poverty estimates and increase their frequency (Stifel and Christiaensen, 2007; Douidich et al., 2015). Douidich et al. (2015) for example considers the Labour Force Survey as their secondary survey, which is often available at a higher frequency than household expenditure surveys.

There is also a large literature that predicts household expenditure data into the population census, see for example Elbers et al. (2003) and the references therein. The objective here is to

> 15Also, the study does not report the total cost of data collection, but selected cost components instead.

29
inequality of opportunity (Ferreira and Gignoux, 2011; Marrero and Rodríguez, 2011). There is also evidence of continued differential treatment by race in the U.S. labor market, i.e. racial discrimination (see e.g. Bertrand and Mullainathan, 2004; Lang and Manove, 2011). Unequal opportunities between men and women are equally objectionable (Hederos et al., 2014; Calo-Blanco

> 8 Unfortunately, the IPUMS USA database does not provide data on the individual components of income. 9 Note that IO measures the extent to which individual income is driven by individual attributes that are beyond the individual’s control such as race and gender.

> 10 Occupation prestige is derived from the “occscore” variable that is constructed by IPUMS USA which assigns occupational income scores to observed occupations.

7
1986, Goodwin and Piggott, 2001, Rashid, 2004 and Van Campenhout, 2007). For Indonesia, Varela et al (2012), and for Bangladesh, Goletti et al (1995) test the role of transport costs, and distance respectively, explaining a significant portion of the variation of market integration and price differences.

Transport costs, integration and welfare. Going one step further, and assuming a direct link between transport costs and market integration, Donaldson (2010) estimates the welfare benefits of improved connectivity (associated with increased integration). Using colonial data on India, he finds that the benefits of building the railroads in India were threefold. First, because of better infrastructure, price gaps between regions have decreased. Second, trade has been promoted (nationally and internationally). Third, real income has increased because of lower prices. In a recent contribution Allen and Atkin (2016), using data on Indian highways, show that transport improvements result in farmers being able to allocate their land to less volatile crops, avoiding the increased volatility in earnings that they would have otherwise faced, thus amplifying the gains from trade.

Information and the digital dividend. Jensen (2007) argues that, when information is limited or costly, agents are unable to engage in arbitrage, and excess price dispersion arises. The author uses micro‐ level survey data on prices obtained by south Indian fishermen, combined with data on the fishermen and wholesalers’ adoption of mobile phone technologies. He finds that mobile phone adoption is associated with the elimination of waste and near‐perfect adherence to the law of one price. As a result, both consumer and producer welfare increased.

Nominal rigidities. Crucini et al (2010) look at the role of price stickiness in explaining persistent deviations of the law of one price. To that effect, they build a model with nominal rigidities and transport costs that explains how pricing to market, transport costs and price stickiness lead to failures of the law of one price. In their model