rafmacalaba/data-use-mentions-extended huggingface dataset:
<!-- Start of picture text --> period period<br>upper/lower coef upper/lower coef<br>(a) Average distance (b) Share contiguous<br>period period<br>upper/lower coef upper/lower coef<br>(c) Average Herfindahl (d) Share wealthy OECD<br>.8 0<br>.6<br>−.1<br>.4<br>−.2<br>.2<br>0 −.3<br>1987−1991 1992−1996 1997−2001 2002−2006 2007−2011 2012−2017 1987−1991 1992−1996 1997−2001 2002−2006 2007−2011 2012−2017<br>0<br>.2<br>−.1<br>.15<br>−.2 .1<br>−.3 .05<br>−.4 0<br>1987−1991 1992−1996 1997−2001 2002−2006 2007−2011 2012−2017 1987−1991 1992−1996 1997−2001 2002−2006 2007−2011 2012−2017<br><!-- End of picture text --> Figure 4: Trends in refugee reach, time effects controlling for source effects, 1987-2017 Note: This figure plots the time effects on the average distance traveled by a refugee, the share of refugees finding themselves in a contiguous country, the average Herfindahl index of refugee shares by source country, and the share of refugees finding themselves in a wealthy OECD country. Throughout, source country effects are netted out.

– where _Outcomest_ is one of the four outcomes reported above log average distance traveled by a refugee, share of refugees going to a contiguous country, the Herfindahl – index of destinations, or share in a high-income OECD country from country _s_ in time period _t_ , and _δt_ and _δs_ are time and source country effects.

Source country effects imply
We also consider alternative scores that could be used for this purpose, in particular the abilitybased scores developed by Felten et al. (2021, 2023a, 2023b, 2018), based on the US O’NET classifications, recently linked to ISCO-08 4-digit occupations by (Cazzaniga et al., 2024), as well as the patent-based scores of exposure to digital technologies by Prytkova et al. (2024), from which a set of technological groups relevant to GenAI could be isolated. Having compared these alternatives, we find that Felten et al. scores are quite aligned with GBB in broad terms, except for catching a much wider group of Managers and Professionals as highly exposed to AI technologies.<sup>11</sup> Since such broad coverage seems somewhat unrealistic in the context of many developing countries, we opt for the scores of GBB, which provide a direct link to ISCO-08 documentation and focus exclusively on GenAI.

This choice is further reinforced by the arguments recently advanced by (Nurski and Ruer, 2024), who find that task- (GBB) and ability-based (Felten, 2023) scores render similar general results in the European context. The authors suggest the task-based analysis using GBB scores is particularly advantageous for evaluating employment impacts, since task bundles are better at representing the daily reality of occupations (see Autor, 2015), and considering the share of affected tasks offers more scope to separate the potential for job transformation or displacement due to technological advancements. Indeed, task-based approaches have been widely used in the literature for this type of analysis (Restrepo, 2023, for concrete examples see Acemoglu and Restrepo, 2022, 2018; Frey and Osborne, 2017;), including the recently increasing use of AI-generated task-level scores (Acemoglu, 2024; Eloundou et al., 2023), used as a blueprint for the GBB scoring method.

In step 1, we tag occupations at 4-digit level in ISCO-08 into three categories established by GBB: “automation potential”, “augmentation potential” and
33

# **Tables and Figures**

**Figure 1: Infant Mortality Trends in Kenya**

<!-- Start of picture text --> 80<br>60<br>40<br>20<br>1993 1998 2003 2008<br>Year<br>Mortality per 1000 live births<br>Infant mortality<br>Postneonatal mortality<br>Neonatal mortality<br><!-- End of picture text --> Source: Authors ̳ calculations using Demographic and Health Surveys microdata. Notes: Figures shown are deaths per 1000 live births. Infant mortality refers to deaths before the age of 12 months. Neonatal mortality refers to deaths before the age of 28 days. Postneonatal mortality refers to deaths at the age of 28 days or later and before reaching 12 months. Neonatal and postneonatal mortality rates sum to the infant mortality rate. These figures are calculated using the actual live births that took place during the period 12-35 months before the survey date.
measured by the SDI vignettes. With increased task shifting, lower-level staff are asked to play a larger role in patient care. Nurses are also more likely to prescribe antibiotics when they are not required (though this varies by country), which may be due to uncertainty about proper prescription or patient pressure for access to antibiotics. The implications for patient health and for contributing to antimicrobial resistance cannot be overlooked. The evidence

> 33 Chaudhury et al. (2006). 34 Goldstein (2013). 35 Macarayan et al. (2019).

21
or consumption from the HFPSs to the 2019 welfare distributions discussed above and (b) estimate the size of the change for each household. We need to make several strong assumptions to use the HFPSs, which in many countries is the only data that were (and will ever be) collected in 2020. It is also important to emphasize that for those countries that use the HFPSs, total growth follows the growth in national accounts (adjusted using a pass-through rate as explained in Section 4.2). The phone surveys are only used to allocate this growth to various groups within the country and thus allow for within-country distributional changes.

The method we employ differs a little based on whether the phone survey collected data on income changes (which was asked for 24 countries) or consumption changes (which was asked for 13) countries, since the consumption question only asks whether consumption did not change or fell (but not whether it increased). The method using income changes is described below. Further details, including the method for consumption changes, are provided in the Appendix.

# _(a) Mapping the income changes from the phone surveys to the 2019 welfare distribution_

In the phone survey, households are asked whether they lost, gained, or experienced no change in their income since the start of the pandemic. Employing a multinomial logit regression for each 11
a major global player in the supply of critical minerals while decarbonizing the mining value chain; improving processing technologies; reducing mining’s footprint on water, energy, and waste. It also requires ensuring human rights and enabling territorial development in mining regions, to create an appropriate political environment for future mining projects.

As above, we use the LTGM-NR to analyze global energy transition growth scenarios in Peru, though now we shock growth drivers in the resource sector. The resource sector dynamics differ from the nonresource sector in three ways. First, production depends on the industry-specific productivity, the stock of physical capital, and the country’s proven reserves of the required natural resource (but not on labor or human capital). Second, the stock of natural resources increases with an exogenous stream of discoveries but depletes via extraction. Third, all production generated in the resource sector is exported at international commodity prices, which fluctuate exogenously and have a direct impact on the income generated by the sector. The LTGM-NR includes a fiscal rule that determines whether extra government revenue from the taxes on copper production are saved abroad, spent on government consumption, or spent on public investment (which has the same effects on growth as private investment). The first two have little effect on growth, though the final “Hartwick” rule can generate substantial extra investment and growth when commodity prices are high.

# **5.1. Supplying the Global Green Transition Scenario**

We construct a Global Green Transition Scenario using the path for future copper prices estimated by Boer et al. (2021). The paper identifies metal-specific demand shocks, estimates supply elasticities, and determines the price impact of a global net zero emissions scenario on metal prices. According to these estimates, in a net-zero emissions scenario, copper prices would increase by more than 60 percent 19
buying and under-investment in a broad, untargeted public service which is particularly valuable for the poor: public health. It provides supporting arguments to interpret this correlation as arising from underlying conditions of clientelist political competition which generates weak incentives to provide broad, pro-poor public services. This evidence has implications for the design of various policy tools of transparency and accountability that are currently in vogue in international development programs.

# **References**

- Acemoglu, Daron. 2001. “Inefficient Redistribution,” American Political Science Review, 95(3): 649-661.

- Aker, Jenny, Paul Collier and Pedro Vicente. 2011. “Is Information Power? Using cell phones during an election in Mozambique,” Working Paper, Trinity College, Dublin

- Banerjee, Abhijit, Rukmini Banerji, Esther Duflo, Rachel Glennerster, and Stuti Khemani, 2010. “Pitfalls of Participatory Programs: Evidence from a randomized evaluation in education in India,” _American Economic Journal: Economic Policy_ , 2(1): 1-30.

- Carizo, Jay, A. 2009. “A Background Paper on the Local Politics in the Province of Isabela,”, Mimeo, World Bank Country Office, The Philippines. Manila

- Chen, B. and M. Cammett. 2012. “Informal politics and inequity of access to health care in Lebanon,” _International Journal for Equity in Health_ , 11:23

- Cruz, C. 2012. “Social Networks and Electoral Strategies in the Philippines,” Working Paper, University of California, San Diego

Dal Bo, E. (2007), `Bribing voters', American Journal of Political Science 51(4), 789-803.

- Dekel, E., Jackson, M. O. & Wolinsky, A. (2007), `Vote buying: General elections', Journal of Political Economy 116(2), 351{380.

- De Dios, E. 2007. “Local Politics and Local Economy,” in A. M. Balisacan and H. Hill
|re_4_1_2_6|0.333|*|||||
|regulatory|re_4_2_1|1|0.167|||||
|incentives|re_4_2_2|1|0.167|||||
||re_4_2_3|1|0.167|||||
||re_4_2_4|1|0.167|||||
||re_4_2_5|1|0.167|||||
||re_4_2_6|1|0.167|||||

_Source: RISE dataset and authors’ elaboration based on methods described in this paper. Notes: *Not scored; **Do not contain the year, cannot be used in a panel format._ 54
crop, fishery, forest, and livestock production values) that are priors in the cross-entropy model, and the outcome of the cross-entropy model.

# **3.2.1 Regional accounts**

We collect regional accounts by sector from various sources into a global database. The data are not balanced over time nor at the geographic level. The variation in the reference year of the regional accounts data influences the temporal balance of the database. This mismatch can influence the regional distribution of the agricultural GDP that may be different than the target reference year of 2010. Given climate<sup>17</sup> and specifically rainfall is important input to crop and livestock production and may contribute to variation across years (Stanimirova et al., 2019; Y. Zhang et al., 2020), we attempt to reduce this source of error by averaging over multiple years when data are available similar to You, Wood, et al. (2014). However, this does not eliminate this mismatch. The availability of data varies when grouped by World Bank income (low or lower middle, upper middle and high income). The average absolute temporal difference (ATD) defined as the mean difference in years between the reference regional accounts and the target year (2010) is higher in the low and lower middle income group. Likewise, the mean deviation of the share of AgGDP by country over the year(s) is larger in low or lower middle compared to high income.

The global regional account database includes national and subnational units at various administrative levels.<sup>18</sup> Following Robinson et al. (2014) in their assessment of Gridded Livestock Of the World (GLW) 2.0, we summarize the average spatial resolution (ASR) of the input regional data, which is the square root of the land area divided by the number of administrative units. We find that on average the ASR decreases from high to low income groups.

# **3.
0.06 and 0.10, the magnitude of the effect is small, however: only between six and ten cents of an additional dollar earned in profits are invested.

Subsequent research based on RPED data confirms that investment is not particularly sensitive to changes in profits. In an in-depth analysis of the manufacturing sector in Zimbabwe 1992-94, Fafchamps and Oostendorp (1999) show that the cash-flow sensitivity of investment is low, even among small firms. Söderbom (2002) obtains a similar result for Kenya. Mazumdar and Mazaheri (2003) use a sample of six countries (adding Côte d’Ivoire and Zambia to the set of countries considered by Bigsten et al., 1999a), and report an estimated profit coefficient is 0.08, thus very similar to that of Bigsten et al. Mazumdar and Mazaheri also split the sample according to firm size, and obtain a profit coefficient equal to 0.09 in the subsample of small firms. They interpret the larger profit coefficient among small firms as evidence that small firms are more credit constrained than large ones. Reinikka and Svensson (2001) obtain a profit coefficient equal to 0.08 based on a sample from 1996-97 of Ugandan manufacturing firms. These authors too find a larger profit effect among smaller firms, which is consistent with the notion that credit access is more of a problem for small firms. The point estimate of the coefficient on profits among small firms is 0.11, and so quantitatively the effect is not particularly large even for small firms.

Now consider the effect of uncertainty on investment. There is hardly any second-hand market for used machinery in Africa, which means that investments largely have to be treated as sunk cost (i.e. they are “irreversible”). This is problematic in an environment where uncertainty about future rules of the game as well as market development is a standard feature. Theories of irreversible investment under uncertainty predict that investment will be slower to respond to demand shocks if uncertainty is relatively high. As mentioned in
, Washington, DC.

- Tallapragada, Prasad, Maria Shkaratan, Ada Karin Izaguire, Jaakko Helleranta, Saifur Rahman, and Sten Bergman. 2009. _Monitoring Performance of Electric Utilities_ . Washington, DC: World Bank.

- World Bank. 2010a. “Performance of the Uganda Power Sector (comprising of UEGCL, UETCL, UEDCL and Umeme).” Mimeo, World Bank, Washington, DC.

- ———. 2011a. “Electricity Development Project for Uganda.” Project Appraisal Document, World Bank, Washington, DC, May 31, 2011.

- World Energy Outlook. 2010. The Electricity Database. International Energy Association, World Energy Outlook Database. <u>www.worldenergyoutlook.org/database_electricity10/electricity_database_web_2010.htm.</u>

- World Bank, forthcoming, “Uganda Strategic Review of the Electricity Sector”, Mimeo, Poverty Reduction and Economic Management, Africa Region, World Bank, Washington DC.

# Transport

- Bofinger, Heinrich C. 2009. “An Unsteady Course: Growth and Challenges in Africa’s Air Transport Industry.” AICD Background Paper 16, Africa Region, World Bank, Washington, DC.

53
Policy Research Working Paper 7927

# **Abstract**

Labor market dynamics have played a significant role in the remarkable social gains experienced across Latin America over the recent past. Assessing the quality of employment, beyond the perspective of income, to include other fundamental aspects of jobs—such as whether jobs are secure, provide benefits, or allow human capital accumulation— can shed light on the sustainability of these achievements. This is particularly pertinent given the region’s current economic slowdown. Using harmonized data for 17 countries in Latin America, this paper connects the role of job quality with the recent process of inclusive growth across the region, and particularly with how individuals worked their way out of poverty. The paper first proposes a multi-dimensional measure for job quality, and then uses this measure to compare job quality across countries and over time. The paper also studies some of the correlates of job quality and the relationship between job quality and poverty. The main finding is that job quality across the region began to increase since 2004, coupled with the favorable terms of trade brought to the region by the commodities super cycle of the 2000s. The best predictors of job quality are age, gender, education, formal employment, and union membership. Both health and retirement benefits are the dimensions of job quality that are best correlated with not living in poverty.

This paper is a product of the Poverty and Equity Global Practice Group. 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://econ.worldbank.org. The authors may be contacted at at pbrummund@cba.ua.edu and crodriguezc@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
feels people generally get along with each other, and whether she thinks people would try and take advantage of her if they could. The variable is constructed by summing together the four questions and it takes a value between 0 and 4.

# _Physical capital_

The household physical capital is measured by a wealth index developed by the World Bank and Macro International for UNICEF (2000). The wealth index is the simple average of three factors: housing quality, consumer durables and services. The housing quality component is based on the number of rooms per person in the household and the main materials used for the walls, roof and floor. The consumer durable component is based on the number of assets owned by the household (e.g. radio, refrigerator, bicycle, television, car etc), excluding productive assets. The services component is based on whether or not the dwelling has electricity, whether or not drinking water is piped into the dwelling, the type of toilet facility (private or shared with other households) and the main type of fuel used. The wealth index for each household is a score between 0 and 1.

Furthermore, I use a dummy measuring if the household has suffered at least one shock (such as natural disasters, death in the family or crop failure) which resulted in a reduction in the economic welfare of the household before the mother knew she was pregnant.

> 14 To check the robustness of the empirical results to changes in the definition of social capital, I also carry out some sensitivity analyses. All the results in the empirical analysis are substantially confirmed defining membership as a continuous variable or using as cut point two instead of one membership, i.e. considering members those mothers who are members of at least two community groups.

9
is a major driver of land-use change (DeFries et al. 2010). The consequences of climate change will likely disrupt regional agricultural practices and production. Expected temperature increases by 2050 will shorten crop-growing cycles, leading to severe yield reductions and threatening food production systems (Cervigni, Valentini, and Santini 2013; Jones and Thornton 2003; Porter et al. 2014). Coping strategies for the changing conditions may lead to progressive land degradation, further compromising rural livelihoods and increasing their vulnerability to future internal and external shocks (Leichenko and O'Brien 2008). For instance, as productivity decreases due to climate change, farmers may overexploit soil or expand cultivated lands into more marginal lands.

Global remote sensing–based LCLU change products include coarse-resolution land-use information for the Sudan-Sahel region (for example, MODIS MCS12Q1 and GlobCover 2005 and 2009). These products have been integrated with national statistics to generate a more robust outcome (Ramankutty et al. 2008). However, although coarse spatial resolution data meet the observational requirements for the world’s large agricultural regions, they are not well suited for monitoring crops in regions with highly heterogeneous agricultural landscapes dominated by smaller farms, such as those in the Sudan-Sahel region. Furthermore, the static nature of these products fails to capture changes over time (Leroux et al. 2014; Pittman et al. 2010). The opening of the Landsat archive in 2008–09 (Woodcock et al. 2008) and, more recently, the launch of new medium-resolution sensors (Landsat 8, Sentinel-2 Disaster Monitoring Constellation, and so forth) offers unprecedented opportunities to study LCLU change at higher spatial resolution. Several initiatives are already exploiting these new data with increasing processing capabilities to map agricultural landscapes with higher spatial detail at scales more relevant to African agricultural processes. For example, Gong et al. (2013) created a global LCLU map at 30-meter spatial
of host communities to receive and integrate refugees. This is especially important within East Africa, where communal conflict has sometimes taken places between refugees and host communities and exclusionary refugee policies have sometimes been justified with recourse to public opinion. While a range of hypotheses exist relating to the determinants of social cohesion (e.g. labour market competition hypothesis, fiscal burden hypothesis, and group threat hypothesis), contact theory offers a potential avenues for designing policies and programmes at the local level. For example, it may shed light on whether including refugees and host communities alongside one another within assistance programmes or settlement design offers a means to improve social cohesion. Furthermore, applying contact theory to social cohesion within an East African context offers a means to examine whether and to what extent research findings within the ‘North’ apply similarly or differently within the ‘South’.

The underlying research draws upon first-hand quantitative data collected in urban and – camp-like contexts in Uganda, Kenya, and Ethiopia (n=16,608) the Refugee Economies Dataset. Survey questionnaires included modules relating to host community attitudes towards refugees and vice versa, as well as the type and frequency of inter-group interaction. Our analysis primarily focuses on Uganda, because we have survey data on host community interactions with refugees for this country but not for the others. When relevant and feasible, we also present descriptive statistics and regressions using data from the three countries, in order to enable some level of comparison and generalization. We triangulate quantitative results with qualitative data. In addition to describing the patterns and correlates of attitudes towards refugees across all three countries, the research uses ordinary least squares (OLS) and instrumental variable (IV) regressions to explore the complex relationships between individual perceptions, refugee-host interactions, and group perceptions using data from Uganda.<sup>3</sup> Based on this, the paper complements the existing literature on contact theory by being among the first to explore the role of interaction in shaping host community attitudes towards refugees in a developing country
# **Abstract**

This paper offers an assessment of the implications of multilateral trade reforms for poverty in China. We do so by combining results from a global modeling exercise with a national CGE (Computable General Equilibrium) model that features disaggregated households in both the rural and urban sectors. We examine two trade reform scenarios: one involving global trade liberalization, and one involving possible Doha Development Agenda reforms. Using the World Bank’s $2/day poverty line, we find that multilateral trade reforms do in fact reduce poverty in China. The biggest reductions occur in the rural areas – largely as a result of higher prices for farm products.

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