rafmacalaba/datause-encoder-data huggingface dataset:
Policy Research Working Paper 11057

# **Abstract**

Research on the labor market implications of artificial intelligence has focused principally on high-income countries. This paper analyzes this issue using microdata from a large set of low- and middle-income countries, applying a measure of potential artificial intelligence occupational exposure to a harmonized set of labor force surveys for 25 countries, covering a population of 3.5 billion people. The approach advances work by using harmonized microdata at the level of individual workers, which allows for a multivariate analysis of factors associated with exposure. Additionally, unlike earlier papers, the paper uses highly detailed (4 digit) occupation codes, which provide a more reliable mapping of artificial intelligence exposure to occupation. Results within countries, show that artificial intelligence exposure is higher for women, urban workers, and those with higher education. Exposure decreases by country income level, with high exposure for just 12 percent of workers in low-income countries and 15 percent of workers in lower-middle-income countries. Furthermore, lack of access to electricity limits effective exposure in low-income countries. These results suggest that for developing countries, and in particular low-income countries, the labor market impacts of artificial intelligence will be more limited than in high-income countries. While greater exposure to artificial intelligence indicates larger potential for future changes in certain occupations, it does not equate to job loss, as it could result in augmentation of worker productivity, automation of some tasks, or both.

This paper is a product of the Human Capital Project, People Vertical. 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 mweber1@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
# **1. Introduction**

Globally, unmarried individuals have been found to have higher mortality and morbidity than married ones. Anderson and Ray (2018) argue that widowhood accounts for some 35% of excess adult female mortality in Africa. The plight of widows is well documented in the legal, human rights, and sociological literatures on Nigeria.<sup>2</sup> In a 2008 public opinion survey, 58 percent of Nigerians said widows faced a great deal (25%) or some (33%) discrimination relative to other women, and far more than divorced women (World Public Opinion.org 2009). The mistreatment of widows is a grave and recognized concern as evidenced by the many Nigerianbased NGOs focusing on the rights of widows and by special modules in the most recent Demographic and Health Surveys (DHS) concerning violence and dispossession following a husband’s passing. Over 42 percent of Nigerian widows are found to be completely dispossessed.

A recent body of research has shown that many African widows face considerable disadvantage, and consequently, large average well-being deficits relative to married women in their first union (Djuikom and van de Walle 2018; Anderson and Ray 2018; van de Walle 2013). The evidence also points to significant heterogeneity across countries, with pronounced hardship in some, and benefits to widows in others (Djuikom and van de Walle 2018). It is clear that their situation depends on the social norms concerning widows that they are subject to. These rules differ between Christian and Muslim widows, suggesting that widowhood may not necessarily have the same consequences for these groups.

For example, the DHS data point to higher incidences of both dispossession among, and violence against, Christian widows compared to their Muslim counterparts. Islamic inheritance law stipulates a better treatment of widows than does customary family law which often applies to Christians (Ostien and Dekker 2010). There is also evidence that the Muslim religion as practiced in West Africa provides a semblance of a safety net, or insurance, to women who have suffered marriage dissolution, through high, and socially expected, remarriage rates facilitated by the continued practice of polygamy (van de Walle 1990; Rehman 2007). One might thus expect widows to fare somewhat better among Muslims.

> 2 See for example, Okoye 1995; United Nations 2001; Ekelukwa 2002; Sossou 2002; Emery 2003; Durojaye 2013; NIALS 2013; Genyi and Genyi, 2013.

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# Figure 1. Global output growth and relative per capita incomes

A. Actual output growth <!-- Start of picture text --> Percent 2000-2021 average<br>8<br>4<br>0<br>-4<br>World AEs EMDEs<br>2011 2013 2015 2017 2019 2021 2011 2013 2015 2017 2019 2021 2011 2013 2015 2017 2019 2021<br><!-- End of picture text --> C. Per capita income in EMDEs relative to advanced economies <!-- Start of picture text --> Percent<br>20<br>15<br>10<br>5<br>0<br><!-- End of picture text --> B. Potential output growth <!-- Start of picture text --> Percent<br>Potential growth<br>10<br>Actual growth<br>8<br>2000-2021 potential growth<br>6<br>4<br>2<br>0<br>World AEs EMDEs<br>2000-10 2011-21 2022-30 2000-10 2011-21 2022-30 2000-10 2011-21 2022-30<br><!-- End of picture text --> D. Per capita income relative to advanced economies, 2022 <!-- Start of picture text --> Percent<br>25<br>20<br>15<br>10<br>5<br>0<br>EAP ECA LAC MNA SAR SSA<br><!-- End of picture text --> Sources: Penn World Tables, World Bank.

Note: AEs = advanced economies; EAP = East Asia and Pacific, ECA = Europe and Central Asia, EMDE = emerging market and developing economy, LAC = Latin America and the Caribbean, MNA = Middle East and North Africa, SAR = South Asia, SSA = SubSaharan Africa.

A. Sample of 181 countries.

B.-D Based on production function approach. GDP-weighted averages for a sample of 29 advanced economies and 53 EMDEs.

C. Per capita income differential between EMDEs and advanced economies are defined as the GDPweighted average of percent of GDP per capita of the EMDEs as a ratio of GDP-weighted average of advanced economies.
# **2. Models and Data**

## **2.1 Linking macro results to the village model**

We analyze the village-level impact of two liberalization scenarios: full trade liberalization, as well as a Doha round scenario. Since we do not model the Chinese economy at the national level, the GTAP results _include_ China’s tariff cuts as well as liberalization in the rest of the world.

To link the macro-shocks for China to the village model, we follow the conceptual framework of Winters (2002), and focus on prices and labor demand. Studies of market integration of three main staple crops and fertilizer (Huang _et al._ , 2003; Qiao _et al._ , 2003) show integrated regional and national markets, with village level prices responding to changes at the national level. These integrated commodity markets allow us to directly translate relative price changes derived from the macro level analysis to the village level.

In addition to the transmission of price changes, we analyze the impact of changes in employment opportunities. Studies of trade liberalization find an expansion of labor-intensive sectors in which China has a comparative advantage. We thus need to link aggregate expansion of employment to village level changes in temporary migration to urban areas. Lacking data to quantify the link between national level changes in employment and household decisions, we assume percentage changes in aggregate labor demand to be completely transmitted to the village. Percentage changes in outside village employment are thus set equal to changes in aggregate labor demand. Such a one-on-one relation between national level employment and off-farm activities of the households in the case study village seems justified given the findings in Diao _et al._ (2003). Comparing the change in rural-urban migration across regions following WTO accession, they find the fastest increase in ruralurban migration in the central provinces. The case study village is located in Jiangxi, one of 11
WPS4249 basic characteristics of our cross-country data and summarizes the corresponding empirical results. Section 4 describes the empirical strategy adopted in the micro-econometric section of the paper. Section 5 presents the household survey data used in the paper and the respective empirical results. Finally, section 6 offers concluding remarks.

# **2. CROSS-COUNTRY EMPIRICAL STRATEGY**

_(a) Poverty, growth, and inequality_ In this section we use simple techniques to show that under fairly general conditions the degree of poverty in any given country can be expressed as function of two factors: the average income level of the country and the extent of income inequality, so that to the extent that remittances raise income levels or lower income inequality they will also contribute to lower poverty.

Formally, consider the Foster, Greer and Thorbecke (FGT) (1984) family of poverty measures, given by where α ∈ {2,1,0} is a parameter of inequality aversion, _z_ is the poverty line, _x_ is income, and _f(.)_ is the density function of income. When α = 0, (1) reduces to the familiar headcount ratio, which measures the share of the population below the poverty line _z_ . When α = 1, it measures the poverty gap and when α = 2, it measures the squared poverty gap.

Assume now that income follows a log normal distribution<sup>9</sup> (i.e. log _x_ is normally distributed). In that case, Lopez and Serven (2006) show that (i) _P_ α<sup>=</sup> _P_ α ( _Y_ , _G_ ) where _Y_ and _G_ _Y_ ∂ _P_ α denote the average per capita income level and the Gini index respectively; (ii) γ = and _P_ α ∂ _Y_

> poverty line is smaller than average income levels) φ> 0 . Moreover, when Lopez and Serven (2006) compare the theoretical quintile shares according to a log-normal distribution with their empirical counterparts using data from 794 household surveys they find that the log-normal approximation fits the empirical data extremely well, to the point that they formally test the null hypothesis that income follows a log normal distribution are unable to reject such null hypothesis.

> 9 Attempts to model the size distribution of income using a log normal distribution have a long tradition in the economics literature, dating back to Gibrat (1931). Gibrat (1931) provided a theoretical justification for this particular functional form and presented empirical evidenced indicating that the log normal distribution offered a good empirical fit to the observed distribution of income. Gibrat’s work was followed by other works extending his basic theoretical framework and offering additional empirical evidence (Kalecki, 1945, Rutherford, 1955).

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household is visited for 5 consecutive quarters, with 20 percent of the sample being replaced each quarter. This allows us to track each individual for a period of 1 year and 3 months.<sup>8</sup> The survey consists of two primary instruments: (i) a socio-demographic survey which collects information from all selected household members on characteristics such as age, gender, and education level at the time of visit, and (ii) an occupation and employment survey which collects information on the labor market status of household members aged 12 years and above, using the week prior to the survey visit as the period of reference. We focus on education and labor variables as our outcome variables, looking specifically at whether individuals work and/or study in a given period, as well as their labor income. The survey also collects information on a rich set of socioeconomic characteristics such as gender, marital status, demographic characteristics, and whether respondents live in rural/urban areas, among others.

Our analysis focuses on the data for individuals aged 15 to 24 years old who completed all 5 interviews in the period between 2005:Q1 and 2013:Q4. This means we have information for 36 cohorts, around 650,000 individuals represented by nearly 3,260,000 observations.

Additionally, we use quarterly state level inflation and industrial production indicators from the Mexican Statistics Office (INEGI). We merge this information with individual information at the state-quarter level to capture the macroeconomic conditions of areas where youths live.

# **2.2 Descriptive Statistics of Nini Incidence**

An individual is generally classified as being a Nini if s/he has not worked for pay _in the labor market_ for at least one hour during the week prior to the survey, and is not in formal education at the time of the survey. According to this definition and using the sample described above, during the period from 2005 to 2013 approximately 21 percent of young people aged 15 to 24 years were Ninis. Of this 21 percent, 15.6 percentage points are accounted for by women and 5.4 percentage points by men. In other words, the majority of Ninis, 76.3 percent, are women and only 23.7 percent are men. These results are in line with those found by Arceo-Gómez and Campos-Vázquez (2012), De Hoyos et al. (2014), Tuirán and Ávila (2012), Negrete-Prieto and Leyva-Parra (2013), and Águila et al. (2013).

> 8 Panel data in the Latin American Region are still rare. In addition to the data for Mexico, there are also panel data for Argentina and Brazil (i.e. rotating panels similar to the one in Mexico, but with different frequencies of data collection). In the results section, we use these other datasets to verify the robustness of our main findings.

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# **2.3 Sample Description**

Our analysis is restricted to households living in the town of _Ecatepec de Morelos_ , which is located in the _Estado de Mexico_ in the outskirts of Mexico City’s Federal District.<sup>10</sup> In 2004, a census household survey (ENCASEH) was administered to 18,593 households to assess their eligibility for the _Oportunidades_ transfer. The roster of the _Oportunidades_ recipients for _Ecatepec de Morelos_ further allows to assess whether and when the households eligible for _Oportunidades_ started receiving any monetary grant.

For all the years between 2005 and 2010 we have information on the universe of COMIPEMS applicants within the entire metropolitan area of Mexico City.<sup>11</sup> The official data collected within the COMIPEMS system feature students’ address of residence, socio-demographic characteristics (gender, age, household income, parental education and occupation, personality traits, among others) and schooling trajectories (GPA in lower secondary school, study habits, among others) as well as the full ranked list of schooling options requested during the application process and related information about students’ placement in the system (including their score in the admission exam).

All COMIPEMS applicants have a 16 digits unique student identifier (CURP), that is generated by combining information on student’s name, surname, date of birth and state of birth, plus a 2 digit random generated number. All the relevant demographic information is collected by the ENCASEH, allowing us to construct a “pseudo-CURP”, that only differs from the student identifier in the 2 digit random generated number. After removing all the individuals for which we have missing information or a “pseudo-CURP” that can be potentially matched with 2 or more CURPS, we are left with a sample of 6,173 COMIPEMS applicants in the age group 14-20, for which we have information on the proxy-means score used to assign the _Oportunidades_ transfer.

After excluding those applicants that lack basic sociodemographic information, we are left with a final sample of 5,232 students who applied in the COMIPEMS system during the period com-

> 10The Ecatepec municipality is the most densely populated in the country, with a population of 1,656,107 inhabitants (INEGI 2010). In 2011, Ecatepec was third in the country’s poverty ranking , with 723,559 individuals living under the poverty line (CONEVAL). Ecatepec’s economy is based on industry, commerce and services. Over 1,550 medium and small enterprises are registered in its territory. A large portion of the city’s inhabitants commute to the Federal District for labor or academic activities every day. The city also receives a large number of workers commuting from other municipalities.

> 11In this way we are able to track households that move outside the town of _Ecatepec_ , but stay within the Mexico City area.

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# **4.4.2 Net gains at the regional level**

We then subtract the investment costs from the GDP gains to obtain the net benefits from the corridor. A first cost estimate is provided by the PIDA website, which assess the CAPEX cost of a 2x3 1052km-long highway from Abidjan to Lagos through Accra, Lomé and Cotonou to be 3,000 million USD, hence US$ 3M per kilometer.<sup>6</sup> Given that not all segments of the corridors will be upgraded into 6L roads, we choose a lower estimate as a lower bound, US$ 2M per kilometer. We compare this low bound estimate with other estimates provided from international comparisons. We use the 2018-updated Road Costs Knowledge System (ROCKS) database which was designed to obtain average and range of unit costs based on historical data to improve the reliability of new cost estimates. It relies on all WBG-financed projects with a road component that were completed between the years 2000 and 2017, as well as some roadrelated projects financed by the African and Asian Development Banks. Based on the ROCKS database, the average unit costs per km for a 4L expressway are US$ 5.26M. Unit costs differ across geographic regions and whether we consider estimates or actual numbers which include observed cost overruns. Actual unit costs for a km of 4L expressway range from US$ 3.55 in South Asia to US$8 in Middle-East and North America. There is no estimate for a km of 4L or 6L expressway available for Sub-Saharian Africa. We therefore compare the results using two unit cost estimates: a low-bound of US$ 2M per km and a high-bound of US$ 5.26M per km. Our estimates do not include maintenance costs.

In order to use the investments’ cost estimates in the context of our static model, we cannot simply use the total costs computed above and compare those to a single year of annual gain. Following de Soyres et al. (2020), we use an annualized cost, which allows us to compare one year of income to one yearly equivalent of the investment cost. We assume that the costs are paid overtime with interest rate r, assuming an interest rate r of 5 percent. The equivalent annualized cost A for a total investment cost C is given by: Using Eq. 4.1, the low-bound annualized cost for the whole corridor Dakar-Lagos is estimated at US$400M and the high-bound annualized cost is estimated at US$1052M. We then compute the GDP gains net of investment costs by subtracting the annualized cost to the equilibrium annual GDP gains. The annual GDP gain is computed by using the estimated income gains from Table A3 and the 2019 GDPs (constant 2010 US$ from the World Bank) of all West African countries.

Table 2 provides the estimated net gains using the low and high-bound estimated annualized

> 6https://www.au-pida.org/view-project/2002/

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Source: Authors’ estimates based on results from simulations with MAMS and the microsimulation model.

# _MDG simulations_

As seen, the MDGs are not achieved under the base. Taking this base scenario as a benchmark, the MDG simulations were generated to assess what would have been required to achieve the MDGs by 2015 if the government had undertaken a gradual expansion in government spending and services starting from 2005, also considering the costs involved, and the economy-wide effects of these actions.<sup>14</sup> These simulations serves the purpose of enhancing our understanding of how an economy like Yemen’s may respond to an ambitious scaling-up of government services, including an assessment of whether, in the first place, these international objectives were feasible for Yemen.

This first set of simulations was run under alternative assumptions about the source of financing for the required additional government spending: foreign transfers (ftr), foreign borrowing (fb), domestic taxes (tax), or domestic borrowing (db). Technically, this means that the rules for balancing the government accounts varied across scenarios, with sufficient increases in the indicated financing source playing the role of clearing the government balance. The ―domestic borrowing‖ scenario is an exception since, in spite of its label, it is a mixed scenario, combining an (exogenous) increase in annual domestic borrowing of around 5 percent of GDP with an endogenous increase in direct taxes (which clears the government balance). This scenario formulation was necessary given that domestic borrowing levels would become unrealistically high in the absence of partial reliance on taxation for marginal financing.

Compared to the base, another change in these scenarios is a modification in the rule for achieving savings-investment balance: private investment adjusts endogenously to maintain balance between total savings (from different sources) and total investment (i.e., investment becomes savings-driven).<sup>15</sup> Consequently, these scenarios capture the

> 14 In addition, scenarios that target one MDG at a time are presented in Al-Batuly and others (2011). 15 The assumption under the base scenario (that private investment was a fixed share of absorption) ensured that private investment would grow at rates consistent with overall economic growth. However, in

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36

||[0.00224]<br>[0.00236]|[0.00260]|[0.00441]|
|---|---|---|---|
|Married|-0.0395<br>-0.0735*|-0.0875**|-0.0684|
||[0.0369]<br>[0.0385]|[0.0426]|[0.0736]|
|Erbil (dummy)|-0.0604**<br>0.0236|0.0382|-0.0578|
||[0.0281]<br>[0.0303]|[0.0328]|[0.0503]|
|Suli (dummy)|-0.105***<br>0.0101|0.0970***|-0.139***|
||[0.0282]<br>[0.0324]|[0.0328]|[0.0515]|
|young_child|0.0310<br>0.0357|0.0196|-0.00732|
||[0.0239]<br>[0.0252]|[0.0278]|[0.0434]|
|HH income|0.0101*<br>0.0104*|0.00287|-0.00904|
||[0.00558]<br>[0.00589]|[0.00648]|[0.0102]|
|Wages higher in private||||
|sector|0.0272<br>0.0168|0.157***||
||[0.0233]<br>[0.0244]|[0.0272]||
|Num cars|-0.0355*<br>-0.0567***|-0.0164||
||[0.0183]<br>[0.0193]|[0.0213]||
|Constant|0.814***<br>0.791***|0.271***|0.876***|
||[0.0658]<br>[0.0693]|[0.0756]|[0.129]|
|Observations|881<br>927|887|598|
|Adjusted R-squared|0.241<br>0.147|0.062|0.132|
|Standard errors in brackets|*** p<0.01, ** p<0.05, * p<0.1|||

**Notes:** Columns 1-3 represents personal beliefs of acceptability of working in the private sector, as well as preference for working in the private sector for both female and male respondents for both working and non-working respondents. Both of these of the dependent variables are re-scaled to between 0-1 for easier interpretation. Column 4 includes both male and female respondent responses on if non-working female (respondent or female counterpart) wants to work. “Social Normative” is a standardized variable of the single question of how many would speak badly of women working in the private sector, whereas “Social Normative Index” is a standardized aggregated variable including all the 4 questions asked about the private sector, including perceptions of risks and discrimination. The other perception variables should be in interpreted in a similar manner.