rafmacalaba/data-use-mentions-extended huggingface dataset:
that of individuals with only secondary schooling. As an example, 2.6 percent of Cambodians aged 25 and above with primary education live abroad, compared to 5.9 percent of those with secondary education and 18.3 percent of those with tertiary education.

Table 1 uses data from the 2008 American Community Survey to show the top 10 occupations for tertiary-educated developing country migrants in the United States according to their level of tertiary education.<sup>1</sup> In order to focus only on migrants who received at least their undergraduate tertiary education in their home country, we consider only individuals who migrated at age 25 or older, with the sample size in the table showing the number of developing country migrants in this survey who meet these criteria. The majority of skilled migrants come from a variety of professions, with computer specialists, accountants, managers, and among higher education levels, scientists and academics, the most common.

Much of the popular discussion and debate about brain drain concerns the migration of doctors and nurses, but as Table 1 shows, while medical professionals are indeed among the most common occupations, they account for only 12-15 percent of educated immigrants. Health professionals are also the minority of tertiary-educated immigrants in other countries. In the 2001 Canadian Census, health professionals constitute 5.7 percent of working individuals who arrived in Canada at age 25 or older and have bachelor‘s degrees or higher, 3 percent of those with a masters degree or higher, and 3.9 percent of those with Ph.D.s. In Canada, scientists, teachers and professors are the most common occupations, accounting for 45 percent of those with masters degrees or above, and 65 percent of those with Ph.D.s. Similarly, according to the 2001 United Kingdom Census, health professionals are also only 6.9 percent of the tertiary-educated migrants from developing countries who came to the United Kingdom after age 24, with this number increasing to 10 percent if one also includes non-professional positions in
illustrates the contribution that bilateral data can make to the analysis of international financial integration.

The South has become more integrated into global finance across all investment types, resulting in significant changes in the composition of international investment. In 2001, the South’s participation in international markets occurred mainly through FDI and loans and deposits, in roughly equal parts, and to a lesser extent through international reserve holdings in the North (table 1). By 2018, the composition of South investment had changed along several dimensions. First, FDI remained the largest investment type for the South, more than doubling in value as a share of world GDP (from 9 to 20 percent). Whereas the bulk of FDI remained North-to-North, growth was strongest for the South as a source of FDI. South-to-North FDI came to rival North-to-South FDI.

Second, portfolio investment expanded rapidly over the sample period. By 2018, portfolio investment was larger than loans and deposits and international reserves. The South’s loans and deposits were only slightly higher (as a share of world GDP) in 2018 than in 2001, whereas portfolio investment grew rapidly, from a much lower level in 2001. In particular, holdings of debt and equity more than tripled for North-to-South, South-to-North, and South-to-South investments as ratios to world GDP (table 1).

Third, international reserves of South countries also gained relative importance during the sample period. Reserve holdings in the North became the largest of all investment types involving the South, amounting to 10 percent of world GDP in 2018. This fact is not surprising as many South countries have run large current account surpluses and accumulated reserve assets issued by the North since the early 2000s. But the South has also become a destination for reserve holdings, in particular, with increases in reserves denominated in Australian dollars and Chinese renminbi, among others (appendix, section A.4). Between 2001 and 2018, the combined value of South-to-South and Northto-South international reserves, as a share of world GDP, expanded at an even
An emerging literature has begun to examine some of the economic implications of robot diffusion, with much of the focus thus far on developed economies. Much of this work has focused on the productivity or labor-saving effects of robotics and their impact on wages or employment.<sup>3</sup> Some studies have begun to assess how robotics may influence the global organization of production, for instance through the potential reshoring back to developed economies of previously imported intermediate inputs (Artuc et al., 2018; De Backer and DeStefano 2021; Dachs et al, 2019). This existing research largely reflects research on the United States, Canada and European countries, with few studies examining impacts on emerging markets (notable exceptions being Artuc et al., 2018, Maloney and Molina, 2019; and de Vries et al., 2020).

In this paper we explore a new channel – whether automation leads to export quality upgrading – in both developing and developed economies. Robots are used to undertake a range of repetitive tasks that require a consistent high-level of accuracy, such as handling and assembly of small electronic components, precision welding of car parts and cutting of metal products within strict tolerances (Agapakis et al., 1990; Gunasekaran, 1996; Tilley 2017). In contrast, tasks that are either less repetitive or have larger error tolerances (such as packing boxes) are often still performed by humans, even in high-income economies. Many robots are explicitly designed to achieve greater accuracy and can include sensors that allow the machines themselves to identify product defects (Herakovic, 2010; Herakovic et al., 2011). The widespread diffusion of robotics in manufacturing production may lead to increases in the quality of exported products.

To assess the impact of robots on quality upgrading, we rely on several data sets that span a broad sample of countries (28 developed and 31 developing economies) over a 15 year the period from 2000 to 2015.<sup>4</sup> Export quality is derived using detailed HS
group could also underestimate the wage premium. This is because the public sector wage premium appears to be smallest for the types of workers overrepresented in formal employment, such as better educated workers. Further analysis, based on richer panel data containing information on worker characteristics, skills, and work transitions, is needed to better assess the relative magnitude of these biases and evaluate whether excluding informal sector workers from the control group improves or worsens the accuracy of the estimated wage premia.

Where public sector workers come from, and where they go if they leave, could shed light on the appropriate private sector comparator. A few studies have used panel data to examine transitions between public and private sector jobs, such as tracer studies of separated public sector workers. These studies suggest that for many workers, particularly unskilled workers, the relevant alternative employment includes informal self‐employment and informal sector salaried employment, while the appropriate comparison for skilled workers may be the private formal sector or (formal) self‐employed professionals (Alderman et al., 1996, Rama and MacIsaac, 1999, Girsberger and Meango, 2017, Yassin and Langot, 2017). Girsberger and Meango (2017) present data on transitions between the public, private formal and private informal sectors from West Africa. Most less‐educated workers who leave jobs in the public sector go to the informal sector, and once in the informal sector very few transition into a private formal sector job or back to the public sector. On the other hand, more‐educated public sector workers who leave the public sector tend to go to self‐employment or formal salaried employment. For all workers, the biggest source of transitions into the public sector is from unemployment. Yassin and Langot (2017), which presents transition probabilities for Egypt, also find that most workers who enter the public sector come from unemployment. For workers with a tertiary education, a significant proportion of public sector workers also come from formal self‐employment, most likely as professionals. For more educated workers
- the unavailability of inputs and adequate human resources;

- abrupt declines in domestic demand, leading to lasting impoverishment that persists beyond the end of hostilities.

For investors in CAF states, past losses appear directly related to divestment: 22 percent of MNEs surveyed say that political risk in CAF countries has resulted in scaling back, canceling or delaying their investments.

Perhaps more surprisingly political risk and insecurity also appear to be important constraints for local firms. The enterprise survey conducted by the World Bank in a large set of countries asks whether political instability is an obstacle to the operations of the respective establishment. Only 35 percent of all establishments report that political instability is no constraint. Ten percent report that political instability is the worst constraint they face, i.e. more important than about 10 other factors including finance, corruption or regulation. Furthermore, the tendency of enterprises to report that political instability is an obstacle to their operations is positively associated with 43
used to determine the relationship between the two over the distribution of consumption, meaning consumption can then be used to impute incomes where only consumption data is available from Povcalnet.

Chancel et al. (2023) then use tax data from South Africa and Côte d’Ivoire, the only African two countries where such data was available to the authors, to determine the extent of under capturing of top incomes and then to use this to correct the income distributions for the rest of the countries, themselves almost all imputed from consumption decile share data derived from surveys. Chancel et al. (2023) then parameterize the under-capturing of incomes by percentile using the tax data. They use their results to assume that the bottom 80% of the distribution is unaffected while the top 1% is adjusted upwards by between 50 and 100%.

This summary of differing methods using survey and administrative data shows that attempts to solve the under-capturing of the rich in household surveys have common elements, although the (non-) 20
Our empirical strategy relies on the parallel trend assumption of the difference-indifference approach. It assumes that the difference in the outcome among exposed and control households would be similar had the attacks not occurred. Any difference between treatment and control is unlikely to have changed over 22 months –between Waves 1 and 2– since the identification strategy compares households within districts of Mogadishu and any other shock is likely to have affected in a similar way both exposed and control households. We also provide evidence to support the conditional independence assumption. Table 8 in the Appendix presents an OLS regression for the exposure of households to attacks in Mogadishu. The consumption level of households, their location and socio-economic characteristics are not associated with the propensity from being exposed to an incident. This suggests that terrorist incidents are likely to be exogenous, conditioned on location fixed effects and household characteristics.

In addition, we employ an instrumental variables (IV) identification strategy to further validate the results from the difference-in-difference approach. For this, we obtain a shift-share type of instrument based on Bartik (1991), exploiting the spatial variation of incidents against civilians and changes in the number of US air/drone attacks against Al-Shabaab in Somalia.<sup>21</sup> US air/drone attacks against Al-Shabaab were manually identified from ACLED data among all events recorded between Waves 1 and 2 of the SHFS. The US air/drone activity against Al-Shabaab is mainly concentrated on South West Somalia, with the number of attacks increasing from 4 in the first semester of 2015 to 21 in the last semester of 2017 (Figure 7 in the Appendix).

To support the validity of the instrument, we examine the location of both, US air/drone strikes against Al-Shabaab, and attacks from Al-Shabaab against civilians in Somalia, with an emphasis on Southern Somalia (Figure 8 in the Appendix). The locations of these two types of incidents are not spatially correlated. US air/drone attacks occurred in locations which are different to those of incidents against civilians. This could be
- 2013; Bangladesh - 2010; Burkina Faso - 2014; Cameroon - 2014; Ecuador - 2014; Ethiopia - 2016; Georgia - 2015; Ghana - 2013; Guatemala - 2014; India - 2012; Iraq - 2012; Kyrgyzstan - 2013; Malawi - 2017; Mali - 2017; Mexico - 2014; Mongolia - 2014; Nepal - 2011; Nicaragua - 2014; Niger - 2014; Nigeria -2019; Peru - 2019; Rwanda - 2014; Senegal - 2011; Sierra Leone - 2011; South Africa - 2015; Tanzania - 2015; Uganda - 2016; Vietnam - 2010. For more details on the country surveys and the RuLIS methodology, visit: <u>https://www.fao.org/in-action/rural-livelihoods-dataset-rulis/en/.</u>

6
18-35, and a set of community surveys, in a sample of 60 rural<sup>3</sup> communities distributed evenly _among_ 4 departments. The rural population of these departments is highly heterogeneous with regard to ethnicity, language group, economic and social structure, climate and topography.

The individual questionnaire collected information on household composition, background data, birth history, prenatal care and assistance at delivery, infants' and children's health status, contraceptive knowledge and use, marital history, social support, health beliefs, community structure, and economic status.

An extensive community questionnaire obtained information on the presence of various types of health care providers in or near the community, as well as on food prices, infrastfucture, social and religious organizations, and social and political structure of the community. In each community three knowledgeable village informants, one staff/member in the health post or clinic, a private physician, a midwife, and one or two other health providers (including traditional/folk practitioners) were interviewed in Spanish.

Height and weight of children under 5 years of age and the height and weight of the mothers were also collected using standard anthropometric procedures.

# **IV.DESCRIPTIVE ANALYSIS OF ANTHROPOMETRIC DATA: LEVELS AND DIFFERENTIALS**

# **4.1 Prevalence of Stunting, Wasting and Underweight**

Following usual practice in nutritional and epidemiological research, we classify a child as wasted, stunted, or underweight when his or her weight-for-height, height-for-age, or weight-for-age respectively are two or more standard deviations below the WHO/NCHS/CDC reference median.

The prevalence rates of wasting, stunting, and underweight for the entire EGSF sample were 0.7%, 61.6%, and 32.2% respectively. We decided to limit the analysis of the EGSF anthropometric data to height-for-age for two reasons: (1) since weight-for-age is a composite of weight-for-height and heightfor-age, deficits in weight-for-age in Guatemalan children almost entirely reflect deficits in height-forage;<sup>4</
J|UNPVSR<br>4/87 (official<br>est.)|--|--|
|Mongolia|Jan 79|<br>1595 F|U.N. 1988 revision<br>(prelim.)|<br>Based on U.N. 1984<br>assessment|Based on U.N. 1988 revision<br>(preLim.)|
|Montserrat|May 80|<br>12 F|U.N. 1988 revision<br>(prelim.)|<br>Bank est.|Bank est.|
|Morocco|Sep 82|20420 F|U.N. 1984 assessment|1987DHS|Bank est.|
|Mozambique|<br>Aug 80|11674 F|Bank projection from census,<br>adjusted for4Xurdercount|Bank est.|Based on official<br>CDR|
|Namibia|May 70|<br>762 F|Bank projection<br>from<br>unpublished 5/81 census|U.N. 1988 revision<br>(prelim.|) U.N. 1988 revision<br>(pretim.)|
|Nauru|Jan 77|<br>7 F|UNPVSR 4/87 (official<br>est.)|||
|Nepal|Jun81|15023J|U.N. 1984 assessment|Based on 1986-87|Based on 1986-87|
|||||Demographic Sample Survey|Demographic Sample Survey|
# **4.  Descriptive Statistics**

In this section, we describe the participants’ characteristics and broadly summarize the results.<sup>7</sup> Table 1 shows that parents of H have much greater education than parents of L. For simplicity, the table groups together Revealed Mixed and Revealed Segregated as the “identity conditions.” The table shows that 45% of all H compared to 12% of all L have a mother with at least six years of schooling. (These are weighted averages across conditions, calculated using Figure 1.) For only 5% of H, compared to 28% of L, both parents are illiterate. Only 8% of H have fathers who are day laborers, compared to 18% in the case of L. These differences highlight the need to examine whether the correlates of caste can explain the differences between H and L in our results. We can do that because the distribution of parents’ characteristics for H shares a common support with that for L. For example, there are not only L who have mothers with no schooling; there are also H whose mothers have no schooling. We collected data on two other variables in the post-play survey: exposure to mazes, and number of participants in a session known that a subject knows.

Table 1 shows that the randomization between the control and identity conditions was largely successful. However, in the identity conditions, participants have parents with a significantly higher level of education and are significantly more likely to have had some exposure to mazes. These differences should, if anything, improve performance in the identity conditions compared to the control. An effect that goes the other way is that the low caste is on average slightly more likely to be in 6<sup>th</sup> than 7<sup>th</sup> grade in the identity conditions.<sup>8</sup> We control for 7 In each time period in which we conducted the experiment (January
_Y es_|_Y es_|_Y es_|_Y es_|
|Demographic|_No_|_Y es_|_Y es_|_Y es_|_No_|_Y es_|_Y es_|_Y es_|
|Education|_No_|_Y es_|_Y es_|_Y es_|_No_|_Y es_|_Y es_|_Y es_|
|Employment|_No_|_No_|_Y es_|_No_|_No_|_No_|_Y es_|_No_|
|Adjusted R2|0_._523|0_._655|0_._667|0_._699|0_._531|0_._658|0_._670|0_._705|
|Nr. of countries|16|16|16|16|16|16|16|16|
|Nr. of hh|134787|134787|134787|88450|134630|134630|134630|88450|

Source: International Urban Poverty Database. Note: *** p < 0.01, ** p < 0.05, *** p < 0.1. Robust standard errors in parentheses. WorldPop 250m is used for the DB method. The dependent variable is the log of per capita consumption expenditures, expressed in PPP and not spatially deflated. Only urban households (“Core” or “Suburb”) are included in specifications (4) and (8). The baseline category is “Other rural” in all specifications, except (4) and (8), where it is “Suburb”. Demographic control variables include household size and household head’s age, sex, and marital status. Education is a categorical control variable that summarizes the education of the household head in seven categories. Employment is a categorical control variable that summarizes the household head’s labor status and employment sector.

significantly higher urban shares and lower rural shares compared to the results obtained with the official urban definitions. In 4 out of 16 SSA countries, more than half of the poor populations reside in urban areas.<sup>27
https://doi.org/10.1017/CBO9781107415324.004

- Flyvbjerg, B. (2009). Survival of the unfittest: why the worst infrastructure gets built—and what we can do about it. _Oxford Review of Economic Policy_ , _25_ (3), 344–367. https://doi.org/10.1093/oxrep/grp024

Government of Fiji & World Bank. (2017). _Climate vulnerability assessment : making Fiji climate resilient_ .

- Kunieda, M., & Gauthier, A. (2007). Gender and Urban Transport: Smart and Affordable. _GTZ: Eschborn_ . Retrieved from http://www.sutp.org

- Levy, C. (2013a). Transport, Diversity, and the Socially Just City: The Significance of Gender Relations. _DPU,UCL & Universidad Nacional de Colombia (Medellín Campus)_ , _Urban Mobi_ .

- Levy, C. (2013b). Travel choice reframed: “deep distribution” and gender in urban transport. _Environment and Urbanization_ , _25_ (1), 47–63. https://doi.org/10.1177/0956247813477810

- OECD. (2018). International Transport Forum - OECD.Stat. Retrieved February 11, 2018, from https://stats.oecd.org/

- OECD (2019), Infrastructure investment (indicator). doi: 10.1787/b06ce3ad-en (Accessed on 06 June 2019)

- Rajkumar, A. S., & Swaroop, V. (2008). Public spending and outcomes: Does governance matter? _Journal of Development Economics_ , _86_ (1), 96–111.

https://doi.org/https://doi.org/10.1016/j.jdeveco.2007.08.003

- Rentschler, J., Kornejew, M., Hallegatte, S., Obolensky, M., & Braese, J. (2019). Underutilized potential:
Investment 1950-1985, _"_ the coastal region includes Liaoning, Hebei, Beijing, Tianjin,  Shandong, Jiangsu, Shanghai, Zhejiang, Fujian, Guangdong, Hainan; the central region includes Heilongjiang, Jilin, Nei Mongol, Shanxi, Henan, Anhui, Hubei, Hunan, Jiangxi; the western region includes Guangxi, Shaanxi, Gansu, Ningxia, Qinghai, Xinjiang, Chongqing, Sichuan, Yunnan, Guizhou, Xizang. The central region and the western region are considered as the inland regions. See annex 1 for more details.

> 2 Data source: China Statistical Yearbook, 2004.

> 3 See Catin and Van Huffel (2003).

2
associated with an increase in adverse health outcomes for mothers and children (Mahy, 2003; Stover and Ross, 2010). Both within SSA and beyond, policymakers’ attempts to curb high fertility rates through family planning programs have had only modest impacts (Pitt et al., 1993). Miller (2010) finds that family planning explains less than 10% of Colombia’s fertility decline during its demographic transition, while Pritchett and Summers (1994)’s cross-country analysis suggests that desired levels of fertility account for 90 percent of differences across countries in total fertility rates.

In the five countries covered in this study, at the country-level, total fertility rates (TFRs) range from 4.0 in Rwanda to 4.8 in Benin. Within these and most African countries, however, there are marked urban-rural fertility differentials, with differences in fertility rates between the capital cities and rural areas commonly on the order of 2 to 3 children (Lesthaeghe, 2014). In the five countries we study, rural, poorer, and less educated women have higher fertility and higher desired fertility (Ghana Statistical Service and ICF, 2014; Ministry of Planning, Development and Territorial Development, Ministry of Health and ICF, 2015; Ethiopia Central Statistical Agency and ICF, 2016; INSAE and ICF, 2019; Ethiopian Public Health Institute and ICF, 2021; NISR Rwanda, Ministry of Health Rwanda, and ICF, 2021). While we do not have data on the fertility preferences of the women in our study sample, we do have nationally representative data from the most recent rounds of the DHS surveys. When comparing the TFR to women’s ideal family size in these five countries, looking at rural and urban areas separately, the TFR exceeds women’s ideal family size in all cases except for in urban Ethiopia, urban Ghana, and urban Rwanda. Moreover, men’s ideal family size exceeds that of women’s in four of the five countries. Details on fertility and desired fertility rates in