rafmacalaba/data-use-mentions-extended huggingface dataset:
therefore part of our country fixed effects and are not included as controls. In the following paragraph, we discuss the control variables that capture aspects of the grievance, resourcemobilization and political-opportunity theories.

Grievance-based approaches to uprisings underscore the role of injustices resulting from for example the distribution of power and resources in a society (Cederman, Weidmann, & Gleditsch, 2011; Gurr 1970). Poverty is often regarded as the most important source of economic grievances; yet, poverty data are insufficiently available for most low-income countries. Therefore, we follow Chenoweth and Ulfelder (2017) and use _infant mortality_ per 1,000 live births (World Bank) as a proxy. In line with most grievance-based models of resistance, our regression model also includes financial control variables that capture the _income level_ ( _GDP_ (ln)), _economic growth_ (GDP growth), _oil rents_ (% of GDP) and _inflation._<sup>9</sup> The effects of _GDP_ and _GDP growth_ are theoretically ambiguous. On the one hand, an increase in _GDP_ tends to imply an increase in living standards and hence, a decrease in grievances and accordingly anti-government sentiments. On the other hand, increases in _GDP_ might also set off modernization to the extent that citizens who are no longer preoccupied with addressing their basic needs take to the streets to demand more civil liberties. Periods of slow economic growth may create anger among citizens affected directly through reduced employment and investment opportunities and indirectly through eroded capacity to finance social safety nets. However, periods of fast economic growth may leave some segments of the population behind, creating anger about lack of opportunities as illustrated by the ‘tunnel effect’.

The effect of oil rents is also theoretically uncertain, as rents not only increase the government’s capacity to provide public services, but also increase citizens’ expectations of the

> 9 The inflation variable contains several outliers. Therefore, we winsorized it at the 1st and 99th percentile. We also estimated regressions with a non-winsorized inflation coefficient, which shows
of equal exposure among all countries within a given ISCO-08 2-digit category. In this section, we apply the calculations that predict the use of a computer at work and therefore introduce variation at the level of individual occupations within countries.<sup>31</sup> Since access to a computer is a crucial starting point for the use of GenAI technologies, by applying this additional criterion, we separate the theoretical exposure of occupations to GenAI from the practical feasibility of such a transformation in a given country context, as a function of available digital infrastructure. We focus this part of analysis on three principal dimensions: country-specific digital characteristics, exposure type and gender. This worker-level approach suggests that the workers’ skills are crucial to determine the digital divide and the effective exposure to GenAI. But it is worth noting this gap could also be determined by firm characteristics or AI investment decisions. Although there is no clear agreement on the effects

> 30 These general patterns also hold when conducting a conditional analysis. In particular, we estimate OLS regressions to simultaneously account for the different drivers of exposure to GenAI by pooling the microdata for all 16 countries (Table A3 in appendix). To consider the sampling frame of the household surveys while at the same time giving the same weight to every country, the population weights are normalized so that they add up to 1 within each country. We do not use the population weights in their raw version because the results would be driven mostly by large countries such as Mexico and Brazil. The main differences from the unconditional figures analyzed above are that women are less exposed to augmentation than men, and that rural and urban workers are equally exposed when controlling for other factors.

> 31 See Section 3.2 for technical details.

22
of the sample design – producing accurate quarterly poverty estimates at the national level and producing accurate annual estimates at the division level for urban and rural areas. First, we round up numbers calculated using the modified version of the Neyman equation. Second, although the modified Neyman allocation is supposed to minimize standard errors of poverty rates at the district level given the original allocation of the sample across districts, this would fail to assign sub-samples of sufficient size to some CCs. Since producing reliable poverty statistics at the stratum level (urban, rural and CC areas of each division) is also a goal of the HIES 2016/17, we deviate from the original modified Neyman allocation and move some of the PSUs from urban and rural areas to CCs. This deviation will slightly increase the standard errors of poverty statistics for the district level including the CCs, but it will overall reduce poverty statistics at the stratum level.<sup>19</sup> Third, we allocate a minimum of four PSUs to each stratum of a district so that at least one PSU is allocated to each quarter. Fourth, the HIES 2010 sample did not assign any PSU to the urban Khulna district, but the minimum of four PSUs are assigned to this stratum in the HIES 2016/17.<sup>20</sup> Lastly, to produce nationally representative poverty rates for each quarter, the sample needs to be allocated to the four quarters of the data collection uniformly. Munoz (2004) suggested that to avoid seasonality, the entire sample should be randomly split into four quarters. However, randomly allocating the sample across quarters does not guarantee a full removal of seasonality. In particular, if the number of PSUs allocated to a stratum in a specific district is a multiple of four, then we can allocate the same number of PSUs to all quarters. Yet, if the number of PSUs allocated to a stratum in a specific district is not a multiple of four, there is

> 19 Note that we are not changing the
Policy Research Working Paper 10686

# **Abstract**

This paper reviews the main findings from 113 studies produced between 1990 and 2023, focusing on the impact of migration on various child groups affected through the migration path, including left-behind, migrant (voluntary and forced), and native children. The findings reveal that migration influences children’s outcomes in complex and context-dependent ways, and it interacts dramatically with household demographics and public policies. Key results include the following: (i) left-behind children benefit from remittances but experience dramatic declines in their cognitive and non-cognitive development due to parental absence; (ii) immigrant children generally fare better than those in their origin countries but still underperform compared to native children in host countries; and (iii) the impacts of migration on native children largely depend on the adjustment of public service supply to meet increased demand. In cases where education services expand to meet rising demand, the effect on native children can be minimal or even positive. This paper emphasizes the need for more experimental or quasi-experimental research to examine the effectiveness of programs that support migrant and minor host children, and it calls for longitudinal data collection to better understand the challenges and needs of migrant children, particularly in developing countries.

This paper is a product of the Development Research Group, Development Economics. 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 sandrarozo@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
poverty levels.

The randomized assignment also results in each state receiving a diverse cohort of NYSC participants. As such, all corps members, including those in the control group, are exposed to peers from other ethnic groups during their national service. For example, 27 percent of the control group (those who served in a Yoruba majority state) report having a significant amount of daily interactions with Hausas. While substantial, this exposure remains lower than that of those who served in a Hausa majority state as 64 percent of them report a significant amount of daily interactions with Hausas. As such, this paper examines the impact of being immersed in a different ethnic region, relative to being exposed to a cohort of diverse peers.

To measure integration, this paper uses both observable revealed preference outcomes (such as future voluntary migration to other regions of the country outside the context of the program, knowledge of different parts of the country, and close relationships with people from other ethnic groups), as well as stated attitudes towards the country, their ethnic group, and other ethnic groups. To study these questions, I partnered with a public university in southwest Nigeria to administer a phone survey to alumni who participated in NYSC seven years earlier. Collecting outcomes in the context of a broader alumni survey, and not as a study of NYSC, reduces potential concerns of demand bias in participants’ responses.

The results indicate two broad effects of exposure to other regions. The first set of results is that serving outside one’s ethnic region promotes integration, as measured by mobility across regions, knowledge of the country, interethnic romantic relationships and national pride.

Individuals assigned to serve in a different ethnic region are five times as likely to be living outside their ethnic region seven years later (15 percent relative to 3 percent in the 4
are unrelated and serve different markets or consumers (Harris & Todaro, 1970). The second biggest challenge is tax, which suggests that many firms in Indonesia might choose to stay informal to avoid paying taxes or given regulatory issues linked to the perceived complexity of the taxation system. Firm-level features will be updated and analyzed more thoroughly though as soon as the new business enterprise survey is completed in late 2023.

**4.2 Policy challenges associated with pervasive informality**

44. **Fiscal challenges are associated with more pervasive informality.** Over the period 2000-19, government revenues in EMDEs with above-median output informality were, on average, 4-5 percentage points of GDP below those with below-median output informality (figure 17; Ghossein, Yu, and Rana 2022). The composition of tax revenues is also tilted toward trade taxes in economies with more pronounced informality, making the tax system less progressive but facilitating tax collection when income underreporting is widespread. Similarly, in EMDEs with more pervasive output informality, government expenditures were 4-5 percentage points of GDP lower than in those with less output informality.

45. **Constrained government spending in EMDEs with more pervasive informality is reflected in the limited provision of public services, contributing to poorer human development outcomes** . During 2000-19, EMDEs with above-median output informality spent 2.6 percent of GDP on health, which was about 1 percentage point of GDP lower than in EMDEs with below-median output informality (figure 17; Ghossein, Yu, and Rana 2022). Similarly, governments in EMDEs with below-median output informality spent 1 percentage point of GDP more on education than EMDEs with above-median output informality. The lack of spending on education and health led to limited public resources in these sectors.<sup>18</sup> Meanwhile, compensation of government employees amounts to about a third of government expenses in EMDEs with abovemedian output informality, which is above the level in other EMDEs by 6 percentage points.

**Figure 17. Fiscal policies and informality**

<!
.2 Sensitivity Analyses**

The DHS data and the information on HIPC status (which month debt relief at Decision Point and Completion Point is granted, see Table 1) allow for a more detailed analysis of the relationship between debt relief and infant mortality. In a sensitivity test, I thus exploit this monthly variation and the results that are presented in column (2) in Table 9 show that the month of Decision Point is associated with a 0.57 percentage point reduction in infant mortality. This is in line with the baseline findings in Section 4.

Columns (3), (4), and (5) in Table 9 show that the findings on debt relief at Decision Point are stable to including the two main debt relief variables in the same regression, a variable for the MDRI<sup>7</sup> , and

> 7The MDRI dummy equals one in the year 2006 for non-HIPCs Cambodia and Tajikistan and HIPCs which reached

12
likeliness throughout each metropolitan area by identifying ‘Employment Opportunity Areas’ (EOAs) of different intensity.

These were determined by scoring different indicators on 500-meter cells in a grid covering each city. The scores include the count of employment-related amenities extracted from Google Maps and Open Streets Map, within each cell and its adjacent cells. This count included the number of retail financial establishments, the proximity to major intersections, and the proximity to taxi ranks or bus stops. A full description of this methodology, as well as its validation, is available in a companion paper (Avner et al. forthcoming). The validation performed on Dakar, Kampala and Nairobi shows that, despite widely varying contexts, correlation coefficients linking urban form attributes to the relative distribution of jobs within cities are consistent.

Thus, EOA scores are comparable both within and between cities, and offer an approximation of the distribution of jobs (but not a conversion into a number of jobs, which may differ between cities). This, in combination with the transport networks, generates an analysis of access to the proportion of ‘EOA points’ in the city from any given point, as a proxy for employment opportunities.

For Kigali, the employment data came from the establishment census, finalized in 2017, which gives a number of employees for each city sector. For Bamako, employment data are based on a business registry carried out in 2015, which locates jobs to city neighborhoods.

# **4.3 Population**

The population data for all the cities in the report were taken from WorldPop,<sup>10</sup> an open repository of population density data based at the University of Southampton, using 100x100 meter population density mapping (Linard et al. 2012). The WorldPop population estimates are based on a combination of available census data, land-cover satellite imagery and mathematical modeling. This allows higher resolution estimates than are available from other sources, and on a nation-wide basis, without regard for administrative units, which can be inconsistent,
# 3. Data Sources

**Household-level data:** The analysis utilizes household-level data from the Philippines Family Income and Expenditure Surveys (FIES) for the years 2003, 2006, 2009, 2012, and 2015, collected by the Philippines Statistical Authority. The FIES is used for the official estimation of poverty in the Philippines and it provides detailed information on the components of household food and non-food expenditures (inside and outside the home), household income by source, as a well as a wide variety of household socio-economic characteristics.

FIES data are collected twice for each household, just after the middle of the year (July) and just following the end of the year (the following January), with responses for each survey reflecting economic behaviors over the preceding six months. Responses for each household are then averaged between the two surveys to construct annual estimates. It is important to note that if a household cannot be found in either round of the survey, it is dropped from the sample, which raises some concerns about the use of the annual FIES data made publicly available in any analysis of the welfare impacts of typhoons in general. Typhoon activity in the Philippines is concentrated in the second half of the calendar year, so estimates of typhoon impacts using consumption data averaged over two points, one in June and the other in January of next year, may be somewhat attenuated because the consumption in June is collected prior to the bulk of typhoon events and thus likely to be higher than the consumption expenditures collected in January next year.

We are fortunate to have access to the semester data behind each publicly available annual FIES survey. This offers a number of advantages: (i) it allows closer and better matching of the timing of a typhoon and the date of measurement of consumption and thus an investigation of the short-term impacts of typhoons occurring during the six month interval prior to the collection of household expenditure; and (ii) the possibility of investigating the medium-term impacts of typhons, i.e. the impacts of
7 Fig. 3A <!-- Start of picture text --> Current Account In Industrial Countries<br>(Millions of USS of 1996)<br>200C<br>1500<br>1000<br>500 - A A<br>940 90 912 921 924 933 942 951 9S4 963 972 991 9<br>-5000/V<br>-1000D<br>Average over a 4-quarter period.<br>Fig. 4<br>El Niflo<br>0 Southem Oscillation Index<br>5<br>0<br>5<br>10<br>15 i 2 0 Arc<br>20<br>25<br>Sustained eg wa. I4 h N ode<br>So.une: Australian Commoneeath Bureau of Meteoelogy.<br><!-- End of picture text --> To measure the extent to which external developments accounted for the slowdown, we estimated an equation relating output growth to its own lagged value and a set of external factors<sup>7</sup> , using a dynamic panel data approach, estimated with Seemingly Unrelated Regressions (SUR) methods. This approach has several advantages: first, it allows derivation of general conclusions with respect to the impact of external factors on regional output fluctuations; second, with the SUR method we explicitly take into account "interdependence" among the countries in the group. SUR estimation will 7 Listed in equation (1): Terms of trade, US real interest rate, current account balance of industrialized countries, non-Latin Brady bond sovereign spreads, and El Nino index. Initially we included US GDP but it turned out statistically insignificant. Ahmed (1999) gets similar results.
in FDI attraction in Eastern European countries. However, as _privatization_ and liberalization processes in Turkey are completed in most sectors, this will no longer be a driving force for FDI inflows.

Drawing from the cross-country empirical results presented in the previous section, we try to understand which other factors, apart from the EU crisis and low-spirited prospects for EU accession, may have contributed to the slowdown in FDI inflows to Turkey. We also discuss which policies could be designed to try to correct this trend. This is especially important for the economic development of Turkey, as we have found empirical firm-level evidence pointing to a large and positive contribution of FDI to the productivity of local companies through knowledge spillovers.

Firstly, cross-country regressions identify the existing stock of FDI in a given country as a significant determinant of FDI inflows, since there seem to be some sort of agglomeration and/or 23
7 <!-- Start of picture text --> FIGURE 1: UKRAINE<br>OVERALL ADMINISTRATIVE CONTROL INDEX<br>100<br>90<br>80<br>UKRAINE<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>CHILE<br>Ql Q2 03 04 01 02 Q3 04 01 02 Q3 Q4 Ql Q2 Q3 Q4<br>'92 '93 '94 '95<br>1992 1993 1994 1995<br>Table 1: Data for Overall Admintrative Control Index for Ukraine<br>Ql '92 Q2'92 Q3 '92 Q4'92 Ql'93 Q2 'Q3 Q3'93 Q492 '94 Q3 '94 Q4'94 Ql '95 Q2 '95 Q3 '95 Q4 '95<br>? EXCU GERATEDISTORTIONINDEX. FORE ALOCAIONDISORTON INDEX t00.0 t00.0 100.0 99.0 ....... 99.7 44.4 41.4 40.5 aso 3. 43.4 211 34 2. 60 9.3 9 44. , , 9. .6 .6126.5 76 23.6 00 l. 165 113 O . 58 0 ..... 10. 41<br>.EXPORTRESllCTION INDEX 68.1 79.8 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~.579 74.1 7 _.__._ .. __ ....__ _7 . _ ......_ ........_F__.._I . 75.4 ........ ..- . .......92.0 . _._ . 61.5 ., _ _ ... .... .
Table 6 presents the simulated impact of the matched imputations of height-forage z-scores for all children ages 0–36 month who did not survive till the age of 36 months. The average z-score is higher for children with measured weight at birth than for children with mother-assessed weights at birth reflecting wealth differences.

In 1992, the average height-for-age z-score for boys who survived past age 36 months was –1.913. When the matched z-score estimates are imputed to children who died before age 36 months, the overall average height-for-age z-score becomes –1.936; the difference of –0.023 is not statistically significantly different from 0. Similar differences are observed for other years. In all years, for both boys and girls, the imputations have no significant impact on the overall anthropometric indices. In all cases, the average imputed height-for-age z-score in the simulations based on the survival model is smaller than the imputed z-score that would result in a statistically significant change in the overall height-for-age z-score as shown in first panel of table 2<sup>12</sup> . These results resonate with other studies that use longitudinal data from surviving and nonsurviving children and find a modest amount of selection via child mortality on heightfor-age z-scores i.e., Boerma et al (1992), Moradi (2010)<sup>13</sup> .

The effect of the change in mortality on gender patterns of height-for-age z-scores was also simulated using the results of hazard function estimations. This simulation closes the gender gap in mortality in the age 3–36 month group by artificially increasing the mortality among boys<sup>14</sup> . The simulation assumes that 144 of the boys in the sample with the lowest probabilities of survival did not survive and thus did not contribute to the observed height-for-age z-scores. The simulation of this increase in boys‘ mortality on the data from 1992 of NFHS demonstrates a 0.12 percent increase in the height-for-age z- score. This clearly has a negligible impact on the difference in nutritional status
of Rodriguez (1996), we expect the chances of receiving remittances to be higher if the migrants are members of the nuclear family.

Table A1 presents the results of the first stage regressions using as instruments _Average time abroad, Average time abroad_<sup>_2_</sup> and _Kinship relationship_ . These variables are significant and have the expected signs: _Average time abroad_ shows an inverted U relationship with remittances, and _Kinship relationship_ has a positive coefficient. In order to test the relevance of our

> 12 Rodriguez (1996) analyzes the determinants of remittances flows to Philippines using a survey of overseas workers carried out in 1991. He finds an inverted U relationship between the time spent away and: (i) the decision to remit, and (ii) the remittance amounts. He also shows that remittances tend to be higher when the migrant is part of the nuclear family. Aggarwal and Horowitz (2002) investigate the migrants’ motivations to remit using data from Guyana for 1992 and 1993. They find that households that spent more than one year abroad are more prone to send remittances.

31