rafmacalaba/data-use-mentions-extended huggingface dataset:
widespread use of English as an international language **.”**<sup>**12**</sup> Globalization has most often been analyzed at the country level. Sources of data and analysis judging country level globalization as a dynamic concept are rare. Some authors use rankings of countries on a number of dimensions of globalization, such as the work of A.T. Kearney (Foreign Policy, 2002) who use a composite index of globalization. This index builds on indicators of trade, finance, personal contact, and information exchange. Using the Kearney data base, we investigate the issue of country level globalization and its impact on city performance, with cities judged on the ability to sustain a track record of services to their citizens.

For globalization at the city level, we utilize data from the GaWC research (Taylor, Walker, 2000), which has city level data constructed from a superposition of networks of accountancy, advertising, banking/finance, insurance, law, and management consultancy firms—the business services indicators of Friedman (1986). This data base demonstrates the “unevenness of globalization” when one looks at the concentration of cities by region for example. We refer to this as the Taylor database. With such data, we can pursue the question of why some cities are left behind in globalization.

# **4 Determinants of City Performance: Key Hypothesis Tests and Simple Model Specification**

Because of the paucity of comparable data on cities worldwide, we need to be nimble on the model specification we use. We search for a specification that allows us to capture the dynamics of globalization, governance and performance of cities, as well as define specific hypotheses to be tested.

## **4.1 A Simple Framework**

We specifically consider the role that governance plays at different levels of globalization of cities. The schematic chart below allows us to pursue this analysis empirically. We distinguish between three dimensions; two of which are ‘inputs’ (
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
without the inclusion of this variable and see no major effect on the results. For these purposes, we will focus results from Specifications I and II when discussing results.

Our results compare outcomes for the three ‘behaviorally enhanced’ arms to outcomes for the cash-only group, in order to test for additionality from the enhancements. Standard errors are clustered at the village (fokontany) level. We judged that no correction for multiple-hypothesis testing was required, despite the number of hypotheses tested, given the heterogeneity of constructs measured and our pre-specification of these analyses.<sup>20</sup> All standard errors are clustered at the level of the unit of randomization (‘fokontany’ or village).

# **Section 5: Baseline Results and Randomization Check**

# **5.1: Key Metrics at Baseline**

The baseline survey provided important insights into the living conditions and nutrition, dietary diversity, and parenting practices of the beneficiaries and control group before the HDCT launch. Food accounts for approximately 74% of monthly expenditure (of which rice accounted for, on average, 50% of expenditure).<sup>2122</sup> Grains and starches combined comprise approximately two thirds of average household consumption of food. Less than 7% is spent on fresh fruits and vegetables. Households on average spend approximately 2,361 ariary (~0.75 USD) a day on food expenditures, or approximately 70840 ariary (~22.65 USD) a month. 41.2% responded that they were never able to “properly” diversify their food intake, while only 7.9% reported always doing so. The data on parenting practices display a great deal of variation. Only 4.5% of the sample reported telling stories to children during the past week at baseline, and less than half took children out for a walk, though other activities such as singing (63.6%) and playing (73.1%) were more common. Overall,
with the community driven schemes possibly explain the sense of satisfaction with the water tariffs even when they are considerably higher than the traditional supply driven schemes.

# **Table 5: Household Cost of Service Index**

|**Indicators**|**Jala**<br>**Sch**<br>**Mean**|**nidhi**<br>**emes**<br>**SD**|**KWA B**<br>**Managed**<br>**Mean**|**uilt &**<br>**Schemes**<br>**SD**|**Other Com**<br>**Managed S**<br>**Mean**|**munity**<br>**chemes**<br>**SD**|
|---|---|---|---|---|---|---|
|HH Opinion on Contribution<br>(High=1,Fair=2,Low=3)|2.08|0.26|2.53|0.72|2.25|0.53|
|HH Opinion on Tariffs<br>(High=1,Fair=2,Low=3)|2.02|0.28|2.19|0.57|1.95|0.2|
|Monthly Water Charges (₹)|62.88|24.11|41.25|13.15|65.00|24.18|

The O&M index was constructed completely based on data collected through technical audits and aims to capture the operational sustainability of the schemes. Unlike the community managed schemes, the majority of the KWA schemes carried out yearly water quality testing. 64 percent
of electricity, a cell phone, television, bicycle, car, and motorcycle; and distance to

> respondent about the type of latrine that her family “has,” while the NFHS-3 asks the respondent about the type of latrine that her household “uses.” Note that Geruso and Spears (2018) define “open defecation,” using NFHS data, as when the household reports not using a latrine.

> 7As a robustness check, the analysis was run on later waves of the data sets (IHDS-II in 2011-12, DLHS-4 in 2012-13, and NFHS-4 in 2015-16) and similar results are found.

5
the relative attractiveness of wage employment and self-employment. Still, the results showing that participants to the entrepreneurship track are more optimistic suggest that graduates feel more confident to be able to benefit from new opportunities opened to them. As such, they point to the broader impacts of employment programs on youth behaviors and attitudes towards the future.

# **<u>References</u>**

- Almeida, R., J. Arbelaez, M. Honorati, A. Kuddo, T. Lohmann, M. Ovadiya, L. Pop, M. Sanchez-Puerta, and M. Weber. (2011). “Improving Access to Jobs and Earnings Opportunities: The Role of Activation and Graduation Policies” Social Protection and Labor Discussion Paper No. 1204. Washington, DC, World Bank.

- Almeida, R., and E. Galasso. (2010). "Jump-Starting Self-employment? Evidence Among Welfare Participants in Argentina." _World Development,_ 38 (5).

- Almlund, M., A. L. Duckworth, J. J. Heckman and T. Kautz (2011). “Personality Psychology and Economics.” NBER Working Paper No. 16822.

- Angel-Urdinola, D; A. Semlali, and S. Brodmann (2010). “Non Public Provision of ALMPs in AMCs: An Inventory of Youth Programs”. Social Protection and Labor Discussion Paper No. 1005. Washington, D.C., World Bank.

- Attanasio, O. P., A. D Kugler., and C. Meghir. (2011) “Subsidizing Vocational Training for Disadvantaged Youth in Colombia: Evidence from a Randomized Trial.” _American Economic Journal: Applied Economics_ , 3 (3).

- Banerjee, A. and E. Duflo. (2008). “What is middle class about the middle classes around the world?” _Journal of Economic Perspectives,_ 22(2).

- Baumol, W. (1968). “Entrepreneurship in Economic Theory
error. However, even with a seemingly generous allowance for the fact that our survey-based incomes need not accord with the targeting criteria used by the program, we find that half the eligible population is not being covered. Also, there are signs that the program is doing better at reaching the chronically poor than the transiently poor, which will impede its ability to act as a safety net.

Performance in both targeting and reducing poverty varies greatly across the municipalities in charge of implementing _Di Bao_ . However, the cities that are better at targeting DB are generally not the ones where the scheme has the most impact on poverty. Our comparison of poverty impacts across China’s cities reveals that only one of the various measures of targeting performance found in the literature has significant power in predicting the program’s impact on poverty. Prevailing measures of targeting performance that put heavy weight on the program’s ability to concentrate benefits on the poor are largely irrelevant to the scheme’s total impact on poverty, though one of these measures has some value as an indicator of cost-effectiveness. In considering future efforts to achieve a greater impact on poverty from this program, policy makers should focus instead on assuring more complete coverage of the poor.

26
Table 3. The effect of food insecurity on vertical and horizontal trust by economic development.

|||Econom<br>|ic development<br>Lower-middle-<br>|rankings (GNI pe<br>Upper-middle-<br>|r capita)<br>|
|---|---|---|---|---|---|
||World sample|Low-income|income|income|High-income|
|Trust in national institutions||||||
|Food insecurity|-0.027***|-0.020***|-0.019***|-0.035***|-0.067***|
||(0.004)|(0.006)|(0.006)|(0.007)|(0.011)|
|Trust in friends and family||||||
|Food insecurity|-0.117***|-0.120***|-0.109***|-0.111***|-0.129***|
||(0.005)|(0.009)|(0.008)|(0.012)|(0.009)|
|Controls|_yes_|_yes_|_yes_|_yes_|_yes_|
|Number of observations|388,602|69,101|106,410|100,728|112,363|

_Notes:_ Models estimated using unweighted individual-level data from the 2014-17 Gallup World Poll. Standard errors are in parentheses and clustered by country and robust to heteroskedasticity. References categories are single or never married, elementary education, employed full-time, poorest quintile, rural area or farm, and the year 2014. Each model includes country and years fixed effects. Low-income countries are defined as those with a gross national income
crisis, has been attributed to the large exchange rate depreciation in Ukraine and a subsequent rise in defaults on FX loans (see De Haas and Knobloch (2010)). The household-level and firm-level survey data presented in sections 4.1 and 4.2 cast doubt on this exchange rate explanation: Only 51% of household loans in Ukraine are denominated in FX, while a mere 8% of the loans extended to firms in Ukraine are unhedged FX loans. At the aggregate level corporate (household) loans account for 60% (40%) of all bank loans in Ukraine, suggesting that in total roughly one-quarter of all bank loans in the country are denominated in FX<sup>12</sup> . Moreover, the LITS data suggest that in Ukraine only 16% of household mortgages denominated in FX were in default in 2010. Income shocks as well as a drop in house prices are thus more likely to have spurred the strong increase in nonperforming loans.

How well banks are able to cope with the credit losses they accumulated during the crisis should depend on their profitability. Figure 4.3.a relates the increase in NPL ratios between 2007 and 2009 to pre-crisis profitability of banks, measured by the 2005-2007 average return on assets (ROA) of banks in that country. The figure shows that, besides Moldova, those countries facing the highest increase NPL ratios did not have particularly profitable banks in the pre-crisis

> 12 See EBRD Transition Report 2010.

35
- [37] Ossa, R. (2015). “Why trade matters after all.” _Journal of International Economics_ 97(2): 266-77.

- [38] Semykina, A., Wooldridge, J. (2010). “Estimating Panel Data Models in the Presence of Endogeneity and Selection,” _Journal of Econometrics_ 157: 375-80.

- [39] Simonovska, Ina and Michael Waugh (2014). “The elasticity of trade: Estimates and evidence,” _Journal of International Economics_ 92(1), 34-50.

- [40] Soderbery, Anson (2021). “Trade restrictiveness indexes and welfare: A structural approach,” _Canadian Journal of Economics_ 54(3), 1018-1045.

- [41] World Bank (2020). _World Development Report 2020: Trading for Development in the Age of Global Value Chains_ , the World Bank.

Table 1: Summary Statistics of Trade Policies of EU for a No-Deal Brexit

|MFN ad valorem tarif|%|
|---|---|
|Maximum|74.9|
|Minimum|0|
|Import weighted average|2.95|
|Import weighted variance|15.17|

Table 2: Summary Statistics of the Estimated Trade Elasticities of the EU

||UK|All|
|---|---|---|
|Mean|-13.90|-47.80|
|Median|-1.76|-20.55|
|Standard Deviation|38.81|70.28|
|Maximum|-0.004|-0.000|
|Minimum|-427.60|-444.38|
|Observations|5,050|417,580|

28
# **1. Introduction**

Maternal depression is a major public health challenge due to its high prevalence and direct and indirect consequences.<sup>4</sup> Globally, depression is experienced by about 10 percent of pregnant women and by 13 percent of women who have just given birth. In developing countries, the prevalence of depression is almost 50 percent higher than in developed contexts: around 15.6 percent of women experience it during pregnancy and 19.8 percent after childbirth (WHO, 2020). Given the limited availability of data on maternal depression in developing countries (Parsons et al 2012) underscores the likelihood that it remains under-diagnosed and undertreated, these figures likely represent a lower bound of the scale of the problem.

Existing studies on maternal mental health warrant concern about the economic and human costs of maternal depression, not only to the women suffering from it, but also to the children in their care (Aizer et al 2016, Wachs et al 2005). Maternal depression may interfere with the consistent, attentive, and responsive caregiving associated with effective parenting (Paulson, Dauber, & Leiferman 2006). Because mother-child interactions during early life shape foundational neural circuits (Shonkoff and Phillips 2000; Knudsen et al. 2006; OECD 2007), neglect or maltreatment associated with maternal depression can undermine children’s brain development and lead to worse health (physical and mental), cognitive, and behavioral outcomes (Stratakis 2006; Gunnar and Quevedo 2007; Center for the Developing Child 2010). Given that it often ‘goes hand in hand with poverty’ (Shonkoff and Phillips 2000), a major concern about maternal depression is that it may increase poverty and contribute to its intergenerational transmission. In particular, maternal depression can intensify the negative effects of material deprivation and exposure to exogenous shocks associated with poverty, and confine children to substandard developmental trajectories and hence worse outcomes later in life. However, despite the potentially far-reaching harmful effects of maternal depression on the welfare of mothers and their children
following the April nadir. There is an uptick in visits to residences in all regions and a drop in visits to retail/recreation locations, again with the biggest changes in April. Interestingly, Latin America and the Caribbean show the largest changes in all four categories, with the biggest drops in visits to workplace, grocery/pharmacy, and retail/recreation locations and the biggest increase in visits to residential locations.

The indicators may even track variability in the severity of the crisis, not just the initial drop. For example, there is an increase in visits to retail and recreation in Europe and Central Asia in July and August, and it coincides with summer in these areas, as well as with a lull in new coronavirus cases.

Across all regions, the pattern is clear: there is a sudden, large decrease in visits to non-residential locations in March and April. Moreover, visits to these locations – especially workplaces and retail/recreation – remain far below their February levels throughout the entirety of 2020.

Figure 8 presents the final set of mobility data, which disaggregates the data by country income. While we do see decreases in visits to workplace, grocery/pharmacy, and retail/recreation locations and an increase in visits to residential locations, one clear pattern is that visits to workplaces decreased the least in low-income countries. Whether this is due to differences in the underlying data – e.g. differences in the people who are contributing to the data – or actual differences in behavior is an open question. One possible explanation is that the classification of residences and workplaces is more difficult in developing economies, where a large proportion of the population is self-employed. In Sub-Saharan Africa as a whole, for example, more than 80 percent of workers may be self-employed (Cho et al., 2016), which means that “residence” and “workplace” may be one and the same.

In all the mobility data, April shows the biggest change from baseline. While visits to many locations increased somewhat
<!-- Start of picture text --> . Overall<br>15 – 24<br>25 – 34<br>Age group 35 – 44<br>45 – 54<br>55 – 65<br>Illiterate<br>Read and write<br>Under intermediate<br>Education level<br>Intermediate<br>Above intermediate<br>Univeristy and above<br>0 20 40 60<br>Unemployment rate (%)<br>Men Women<br><!-- End of picture text --> _Source_ : Tunisian Labor Market Panel Survey (TLMPS), 2014, http://www.erfdataportal.com/index.php/catalog/105.

# 3. Constraints to employment among educated women in Tunisia

This paper focuses on the barriers faced by highly educated women in the Tunisian labor market. This is an important and puzzling group because, as described earlier, although female and male Tunisian university graduates exhibit similar labor participation rates, female unemployment is persistently higher than male unemployment in this highly educated cohort. But any further discussion of the role of gender-based discrimination in producing this gap must be prefaced by a brief description of the other important constraints and challenges facing educated women in the labor market.

In recent years, the Tunisian labor market has been characterized by two trends: an increasing supply of highly skilled labor and yet a stagnant economy that has been unable to increase its demand for labor at the same rate as the increase in supply.

From 1994 to 2004, the number of university graduates from public institutions in Tunisia grew at an annual rate of 12 percent—a rate five times higher than the growth rate of the working-age population (Assaad, Ghazouani, and Krafft 2017). This dramatic shift in the educational composition of new labor market entrants in Tunisia was coupled with the continued growth of 9
the pre-curfew hours.

Since we have data on road traffic crashes from the police, we go further back in time to 2017 and use data from 2017, 2018 and 2019 as a comparison to 2020 and comparing the pre-COVID-19 policies period of Jan 1-March 15 with the post-curfew implementation period of March 27-June 6 for each year. When adding in the historical data, the results remain consistent and significant (Table 6 Panel B).

For the main analysis, the crash data is aggregated daily for each time period, but we also explore running the analysis at the individual hourly level and also aggregating further at the weekly level. In both cases, we find the results to remain consistent and significant (Table 6 Panel C and Panel D).

Given some extreme outliers in the number of crashes in a given daily time interval, we also test removing the top 1% of time intervals with the most crashes (out of all time intervals with at least one crash). This means we remove all time intervals that have 5 crashes or more, and we see that the results remain stable and are not driven by these extreme outliers (Table 6 Panel E).

16
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(forthcoming) in the North Arcot region of South India, also computes an agricultural growth multiplier of 1.83. In this case too, the small threeparameter model generated virtually identical results.

Applying the model to African agriculture, Rogers (1986) computes much lower multipliers. Using a slight variant of the Hazell model along with data from Mauritania, Rogers estimates agricultural growth multipliers under a range of conditions. Most estimates clump around 1.27. eiven the harsh rural economy of Mauritania, this estimate very likely paints an unduly pessimistic picture of agricultural growth multipliers throughout the rest of Sub-Saharan Africa.

Using what are probably more representative African data-consumption parameters from Sierra Leone and Nigeria along with production parameters from farm and nonfarm budget studies across the continent-Haggblade, Hazell and Brown (1987) estimate agricultural growth multipliers on the order of 1.5. This estimate places the African growth multipliers at about 60 percent of the magnitude of those estimated in Asia; a $1 increase in value added from agricultural tradables produces an additional 0.50 of rural income in Africa compared to about $0.83 in the Asian countries for which we have evidence. These Africa multipliers of about 1.5 are also strikingly close to the 1.43 direct cross-regional estimate reported earl'er for Togo (Section VII A).

The model can also shed light on.potential differences in rural linkages generated by alternative agricultural growth strategies. Haggblade, Hazell and Brown (1987) use African farm management data to compare rural income multipliers likely to emanate from small farmer growth strategies with strategies based on promotion of estate agriculture. Under a range of probable production yarameters, the model predicts regional multipliers in the range of 1.45 and 1.55 for both small and large farmer strategies. The surprising similarity between estate and smallholder