rafmacalaba/data-use-mentions-extended huggingface dataset:
two children, I define the siblings sex composition pairs as _same sex_ if they have the same gender and _mixed sex_ if otherwise. The former is also decomposed to define sibling pairs of _two girls_ and _two boys_ .

Table 1 indicates that the average number of children ever born for Albanian mothers with at least two children is 2,60. Some families have as many as 9 children. Among all mothers with two children, about 41% considered having a third child, an event indicated by the variable _More than 2 children_ . Both the number of children and the proportion of mothers with three or more children are quite similar to other studies in the literature (e.g. Angrist and Evans, 1998). Moreover, just 50% of the households with at least two children had either a girl or a boy as a firstborn and also just 50% of all two child households had children of the same sex. However, the occurrence of two boys in a row is slightly higher (25%) than the occurrence of two girls

> 10In Albania, it is increasingly likely for a child over 18 years old to have moved to a different household. For instance, using the LSMS surveys, which provide information on the sons and daughters who have left the household, I find that the average age at which a child in Albania left his home is 23.66 during the period studied. Moreover, the age at which a child left the household is quite different between males and females. For example, the average age at which a girl left home is 21.92 years old, while for boys it is 25.41. These descriptive statistics indicate that Albanian girls leave their homes quite earlier compared to boys and this is generally due to marriage.

> 11It is important to note that the difference in observations between the married mothers (7,480) and the married fathers (5,496) is due to the number of observations as provided in the official 2008-09 Albanian DHS.
# _3.1 Gridded population data_

In their original AI analysis, Uchida and Nelson (2010) constructed estimates of urban population shares for a global cross-section of countries for the year 2000 using two global gridded population data sources – CIESIN’s _GRUMP_ (Global Rural-Urban Mapping Project) v 1 and Oak Ridge National Laboratories’ _LandScan_ population grids.<sup>25</sup> Uchida and Nelson’s estimates were then subsequently updated to the year 2010 as part of the World Bank and IMF’s Global Monitoring Report 2013 on _Rural-Urban Dynamics and the Millennium Development Goals_ (World Bank and IMF, 2013). In this update, the same two global gridded population data sources were used except that the gridded population data instead now referred to the year 2010. In the case of GRUMP, the 2010 population data are based on the projection of data from earlier years.<sup>26, 27</sup> Meanwhile, for the application of the cluster method, Dijkstra and Poelman (2014) limited their attention to the EU-27 countries, and used a mixture of population grids for the year 2006 based on different methods.<sup>28</sup> Subsequent to this, Deuskar and Stewart (2016) have also applied the cluster method to a further 53 countries using _WorldPop_ gridded population data.<sup>29</sup> In this paper, we use updated estimates of the national share of population living in urban areas based on the application of both the AI and cluster methods. We generate these estimates using three different population grids – _LandScan 2012_ , _GHS-Pop_ and _WorldPop_ . All three of these grids have extensive cross-country coverage and are derived using “top-down” methods which involve the dasymetric distribution of population data for sub-national administrative units across grid cells. Where the three methods differ is in the exact weights they use to distribute the population within administrative areas. Although the exact methods are proprietary, _LandScan_ derives the weights using a modeling process that relies on “ _primary geospatial input or ancillary
s own economy to the production of exports, rather than simply counting the total gross export value. It is obtained by summing the pure forward GVC trade and the traditional trade flows.<sup>10</sup> Data on trade agreements come from the World Bank’s Deep Trade Agreements Database (Mattoo et al., 2020). It maps the coverage of 52 selected policy areas in 279 agreements notified at WTO and signed after 1958. It also includes information about the legal enforceability of each policy area.

> 8Other international input-output tables are also available but have either fewer countries or fewer years. The global Eora MRIO tables with a harmonized 26-sector classification across countries (also known as Eora26) can be accessed from http://worldmrio.com/simplified/. For further description of the data, its sources, and construction, see Lenzen et al. (2012, 2013).

> 9While the domestic value-added of trade is our main variable of interest, we study the impact of CAFTADR on all the GVC variables available from Borin et al. (2021). 10See the appendix for more details.

7
have similar poverty and income levels to those of non-tobacco-producing households.<sup>19</sup> As with all empirical research, future studies should test whether these findings hold in other contexts, such as in other countries and with other types of farmers who have differing payment arrangements for their crops.

We do not study the effect of either ordinary or commitment savings accounts in the absence of direct deposit. Direct deposits both reduce transaction costs and operate as a “channel factor” to increase savings. OIBM administrative data reveal that, aside from the direct deposits, other cash deposits into accounts were very low. It is by no means certain that simply setting up

> 19 Based on authors’ calculations from the 2004 Malawi Integrated Household Survey (IHS), individuals in tobacco farming rural households in central Malawi live on PPP$1.48/day on average, while the average for central Malawian rural households overall is PPP$1.51/day.

24
number of observations per demand equation fluctuates between 1,555 and 3,235. Data on per capita income were obtained from the World Bank’s World Development Indica‐ tors (WDI); data on population were taken from the United Nations; data on commodity consumption were taken from the BP Statistical Review (energy) and the World Bureau of Metal Statistics (metals); world commodity prices were taken from the World Bank’s Commodity Price Data and converted into real terms by using country‐specific GDP de‐ flators. Exchange rates were taken from both the WDI and the St. Louis Federal Reserve Bank’s database. Appendix B gives a detailed description of data, sources, and summary statistics.

# **4. Results**

This section presents results from the ARDL model, which include long‐run parameter estimates, adjustment coefficients, Hausman test statistics, and log‐likelihood values (short‐run parameter estimates are not reported). The BIC suggests an optimal lag of one in all specifications; hence the use of ARDL (1,1,1,1). In most cases the Hausman test sug‐ gests that the pool mean group specification is a more suitable choice than the mean group approach, as the _p‐_ values exceed the 5 percent level in nearly all model specifica‐ tions.

Results for commodity‐specific demand equations are reported in Table 1. Of the nine equations, only four (oil, aluminum, copper, and zinc) yielded parameter estimates for the two income and price variables that were both significantly different from zero at the 1 percent level as well as consistent with _a priori_ expectations—positive y� and nega‐ tive y�� and p�. Income elasticities, which were calculated at the median income of our sample according to (4), differ considerably among the four commodities, from a low of 0.3 for zinc to a high of 0.8 for aluminum. Indeed, the estimates exhibited a high
- Dube, A. (2019, October). Minimum wages and the distribution of family incomes. _American Economic Journal: Applied Economics 11_ (4), 268–304.

- Dustmann, C., T. Frattini, and I. P. Preston (2013). The effect of immigration along the distribution of wages. _The Review of Economic Studies 80_ (1), 145–173.

- Fazel-Zarandi, M. M., J. S. Feinstein, and E. H. Kaplan (2018, 09). The number of undocumented immigrants in the united states: Estimates based on demographic modeling with data from 1990 to 2016. _PLOS ONE 13_ (9), 1–11.

- INEGI (2021). National survey of occupation and employment (enoe), population aged 15 years and older. Technical report, Instituto National de Estatistica y Geographia.

- Lokshin, M. and M. Ravallion (2022). A market for work permits. _Economic Policy 37_ (111), 471–499.

- McKenzie, D., S. Stillman, and J. Gibson (2010, 06). How Important is Selection? Experimental vs. Non-Experimental Measures of the Income Gains from Migration. _Journal of the European Economic Association 8_ (4), 913–945.

- National Academies of Sciences, Engineering, and Medicine (2017). _The Economic and Fiscal Consequences of Immigration_ . Washington, DC: The National Academies Press.

- Neumark, D., M. Schweitzer, and W. Wascher (2005). The effects of minimum wages on the distribution of family incomes. _Journal of Human Resources XL_ (4), 867–894.

- Ottaviano, G. I. P. and G. Peri (2012). Rethinking the effect of immigration on wages. _Journal of the European Economic Association 10_ (1), 152–197.

- Passel, J. S. and D. Cohn (2018). Us unauthorized
# **1 Introduction**

In most of the developing world, wealth is unequally spread geographically and increases with city size (Ferre et al. 2012; Young, 2013; Gollin et al., 2021). This suggests migration as an important avenue for growth and poverty reduction, affecting both those who move (McKenzie et al. 2010, Beegle et al. 2011, Bryan et al. 2014) and those who remain behind (Giles and Yoo 2007, Kinnan et al. 2018, De Weerdt and Hirvonen 2016, Morten 2019). Moves to big cities are, on average, much more lucrative than moves to small towns. But survey data show that few people make it to the cities and many more migrants end up with more modest income gains realized through moves to smaller towns (Christiaensen et al., 2019). A key question is therefore how migrants choose their destinations: why do migrants move to seemingly suboptimal locations?

One consideration is that there are perhaps a number of nonmonetary amenities, such as health, public goods, crime or pollution that serve to compensate for the lower consumption levels (Dustmann and Okatenko, 2014). Gollin et al. (2021) argue against this possibility by showing that in 20 developing countries and across a range of non-monetary amenities the urban gradient remains clearly visible: practically every amenity they can measure seems to improve with population density. The authors interpret this as evidence in favor of a friction model in which people’s mobility is somehow restricted, which allows the spatial disequilibrium to persist. Support for this view from our study setting in Tanzania comes from Ingelaere et al. (2018) who show that there is a widespread desire to move out of the rural areas and into larger urban agglomerations, especially by younger people. But despite their acute awareness of the spatial differences in living standards, for the average young person living in a remote village, moving is also considered a daunting, sometimes impossible, endeavor.

Much of the migration literature
baseline and multiple follow-ups, than by doing a single baseline and single follow-up. For example, when ρ=0.25, power is greater doing one baseline and two follow-ups with _n=333_ , than doing a single baseline and single follow-up with _n=500_ , but also greater than doing one baseline and three follow-ups with _n=250._ As a final illustration, consider the power to detect an impact of microfinance on business profits. Banerjee et al. (2010) report a mean of 550 and standard deviation of 46604 for business profits. With such noisy data, the treatment and control group sample sizes required to detect a 10% increase in profits with 90 percent power using a single baseline and follow-up are over 15 million! Suppose then that they were able to consider a more homogeneous set of firms and measure profits more accurately<sup>8</sup> , reducing the standard deviation to 550. Then assuming an autocorrelation of profits of 0.25, with their sample of approximately 1150 treatment and 1150 control, the power for detecting a 10% increase in profits would be 0.669 with a single crosssection post-treatment, 0.697 with a baseline and single follow-up, 0.892 for a baseline and two follow-ups, and 0.952 with a baseline and three follow-ups.<sup>9</sup> Likewise, taking the example of Karlan and Zinman (2010), who have a control group mean of 17,000, if the standard deviation were the same as the mean, the power to detect a 10% increase in profits would rise from 0.68 with a single post-treatment survey as they have to 0.902 for a baseline and two follow-ups and 0.958 with a baseline and three follow-ups. In both cases then, assuming the standard deviation of profits can be kept or reduced to a reasonable level, the choice of a second or third posttreatment measure would allow them to detect a treatment effect of interest with considerably more power
# 1 Introduction

Fear of immigration is once again manifesting itself strongly in Europe and other developed regions of the world.<sup>1</sup> Spurred by the recent crisis and giving voice to a large portion of the public, many politicians argue that immigration’s negative employment and wage e¤ects have become unbearable especially for the less skilled. Do the migration data and economic analysis bear out this pessimistic scenario? Might the populist outcry be ignoring a more important ‡ow of migrants? Does emigration, especially of highly educated workers, a¤ect the wages and employment of non-migrants? The loss of high skilled workers deprives their home countries of the scientists, entrepreneurs, educators and other professionals who drive their economies to higher levels of e¢ ciency and productivity.<sup>2</sup> Immigrants in Rome or Paris are more visible to the European public eye than the Italian or French engineers in Silicon Valley, but are they more harmful to the Italian and French labor markets? This asymmetric view leads to economic misconceptions of the economic e¤ects of migration. The goal of this paper is to assess the impact of recent global labor movements on the wages of those who do not migrate. We focus on the major European countries since they have experienced both emigration and immigration, especially when compared to the United States, Canada and Australia whose experiences have been primarily shaped by in‡ows of migrants. In addition, for comparison, we include several non-OECD countries (Argentina, South Africa and Singapore) with signi...cant migration ‡ows as well as Eastern European (Poland, Czech Republic, Hungary) and other developing countries (Mexico, Turkey) that have established important migration corridors with developed OECD countries. We use a newly available dataset to generate measures of migration ‡ows by education levels for all countries in our sample for the period 1990-2000. We analyze the wage e¤ects on the highly educated (college graduates) non-migrants and less educated (high school
explaining the dynamics of nonperforming loans using pseudo-panel estimation techniques.

# **IV. Econometric Estimation and Prediction Model**

The estimation is based on pseudo-panel (unbalanced panel) models. A panel is said to be unbalanced when observations on different groups do not cover the same time period. Indeed, the time series provided by Bankscope is not uniform across the subset of countries. The series is shorter for a number of countries, and when the length of the data covers the entire reference period for most countries, missing values are observed in a few cases. This configuration has motivated the use of pseudo-panel models, which are especially suitable for econometric analysis in presence of missing values. There are several specifications for these models; this study uses the specification represented by equation (3) below.

Where _wit_ is the dependent variable, representing nonperforming loans expressed as a proportion of loan loss provisions. The variable _Vit_ is a non-constant vector of _i_ regressors, for _i_ = ,2,1 L _N_ . Each cross-section unit is observed for a period _t_ , with _t_ = ,2,1 L , _T_ , and varying across countries. The study focuses on fixed effect estimation, which allows the

> 35 Indeed an extension of operations carried out by troubled banks and financial institutions through access to shortterm financing can only exacerbate the crisis, especially if not accompanied by a systematic operational restructuring of banks coupled with portfolios restructuring.

15
0.07)|(0.06)|(0.10)|(0.09)|
|Observations|6,699|6,699|6,699|6,699|6,699|
|R-squared|0.19|0.23|0.23|0.25|0.25|

_Note_ : Robust standard errors in parentheses (* _p_ < 0.1, ** _p_ < 0.05, *** _p_ < 0.01). The sample includes wage and self-employed workers. _Schooling_ is measured as completed years of education. Reference categories for _Self-employed_ and _Gender_ variables and for computer complexity dummies are those not self-employed, males, and no computer use, respectively. Scores on the reading proficiency assessment and personality and behavior traits have been standardized to a mean of 0 and standard deviation of 1. The model includes country fixed-effects.

32
where O&O stands for overweight and obesity, f and g represent functions and food choice is a measure of how healthy the diet is. This will help to identify the proportion of overweight/obesity attributable to unhealthy diet, and also those NCD risks not mediated by overweight and obesity.

There are existing studies linking dietary risks to burden of NCDs in LMICs, reviewed in the previous section. Most of these studies either look at a single NCD, or a single dietary factor. The Global Burden of Disease project has however a comprehensive study, aiming to link 15 dietary factors with the NCD burden (GBD 2017 Diet Collaborators, 2019). Various concerns have been raised about these estimates. First, the relative ranking of different dietary factors changes somewhat markedly between the analysis using the 2017 data and that using the 2019 data (analyzed by Qiao et al. 2022): Stanton et al. (2022) comment on differences regarding red meat. Second, evidence on dietary intake from representative populations in LMIC are currently inadequate to produce robust and reliable estimates of intake across countries (Beal et al. 2021). A third concern conceptually is how the rest of the diet is adjusted in the model when one individual dietary factor is varied in order to understand its impact on NCD risk. GBD 2017 Diet Collaborators (2019) state that the underlying studies they use are unclear on this adjustment. Finally, GBD 2017 Diet Collaborators (2019) estimates of adverse effects of dietary factors (11m global deaths annually) far exceed the estimates from other studies cited above of adverse effects of overweight and obesity (4m globally); and GBD 2017 Diet Collaborators (2019) imply that the 11m do not include the 4m. One likely explanation for the last point is that of double counting. The diets that individuals consume do not necessarily have only one dietary risk factor, and hence these individuals are at higher risk for more than one NCD. Adding the risks across multiple trials for individual NCDs is therefore likely to overestimate
example, the price of onions increased by around 10 percent in March relative to January before dropping back to January levels in May/June. Likewise, the price of chicken is consistently higher – relative to January – in low-income countries. In fact, chicken does not appear to drop below January prices until the end of the year.

# 4. Which real-time indicators are the best predictors of the 2020 growth rate and the severity of stringency measures?

Our next aim is to understand which real-time indicators best predict 2020 growth rates across countries and the severity of stringency measures.

We begin with regressions of growth rates and the Oxford Stringency Index on the groups of variables described above, as well as our set of macro variables – log of GDP per capita (PPP, 2019), log of 2019 total population, and 2019 primary school enrollment rate. We present results from these country-level regressions in Table 1. The first column uses the actual growth rate as the outcome, while the second column reports the results for deviation from the predicted annual growth rate. The last column of Table 1 uses the Oxford Stringency Index on the left-hand side.

In the first two columns of Table 1, the coefficients’ signs are consistent across the columns. Both price factors have negative signs; Google mobility data is positively associated with growth and its deviation from the 2019 projections. Satellite-derived indicators are also positively correlated with economic activity.

The last column of Table 1 paints a slightly different picture. Both price factors have opposite signs. Higher mobility is associated with the lower value of the stringency index. Of the two remote sensing indicators, only NO2 significantly predicts the stringency index. It is worth noting that when a variable is significant in both columns two and three, it is always of the opposite sign across the two columns.

Column two of Table 2 shows the coefficients of features selected for the growth rate’s deviation from its 2019 projection. Besides three features selected in the previous column–the second price factor and two mobility
7 The 1970s displayed a more complex evolution. Income inequality rose between 1970 ad 1976, reached a peak on that year, and then fell - both for the distribution of total individual incomes in the economically active population (PEA) and for the complete distribution of household per capita incomes - from 1977 to 1981. This decline was almost monotonic, except for an upward blip in 1980. See Bonelli and Sedlacek (1989), Hoffrnan (1989) and Ramos (1993). The recession year of 1981 was a local minimum in the inequality series, whether measured by the Gini or the Theil-T index. From 1981, it rose during the recession years of 1982 and 1983. Some authors report small declines in some indices in 1984, but the increase was resumed in 1985. By that year, the Gini coefficient for the distribution of household incomes per capita had risen from 0.574 in 1981 to 0.589 (see Ferreira and Litchfield, 1996). Hoffman (1989) and Bonelli and Sedlacek (1989) report similar increases.

1986, the year of the Cruzado Plan, saw a break in the series, caused both by a sudden (if shortlived) decline in inflation, and a large increase in reported household incomes. Stability and economic growth led to a decline in measured inequality, according to all authors. Thereafter, with the failure of the Cruzado stabilization attempt and the return to stagflation, inequality resumed its upward trend, with the Gini finishing the decade at 0.606. Table A2.1 in Appendix 2 summarizes the findings of this literature, both for per capita household incomes and for the distribution of total individual incomes in the economically active population.

The general trends identified in the existing literature are mirrored in the statistics for the years with which we concern ourselves in this paper, namely 1976, 1981, 1985 and 1996. The distributions for each of these years come from the Pesquisa Nacional por Amostra de Domicilios (PNAD), run by the Brazilian Geographical and Statistical Institute