rafmacalaba/data-use-mentions-extended huggingface dataset:
24</sup> The sample is limited to low and middle-income countries according to the World Bank’s classification in 2008 and spans about a quarter of a century, from 1980-2004. The complete list of available poverty spell observations in _Povcal_ at that time consists of 52 countries and 219 country-spell observations. As poverty measures fluctuate substantially in some

> 21 <u>http://esa.un.org/unup/. April, 2008.  In the urban economics literature, urban concentration is often</u> measured by urban primacy—the share of the urban population living in the largest city. This is adequate when most data points are small countries. The approach is too limited when the spells cover many large countries, which have more than one metro area, as is the case here (Henderson, 2010).

> 22 By using the one million or more as cut-off to define a metropolis, measurement challenges in consistently defining rural and urban areas across countries are also circumvented (Ravallion, Chen, Sangraula, 2007). Nonetheless, noise in the data cannot be denied, also when categorizing the population in agricultural and nonagricultural categories, instead of rural and urban. In depth analysis by Christiaensen and Todo (2013) shows that the measurement error is neither systematic nor systematically correlated with changes in poverty.

> 23 http://www.emdat.be/, June 2009.

> 24 http://www.systemicpeace.org/inscr/p4manualv2010.pdf

18
barriers to information about labor market opportunities, particularly for women who face mobility restrictions as in some of the countries in the region, where women are still legally required to ask permission to their husbands to travel outside the home (World Bank (2018 _b_ )). Second, it could provide more flexible forms of employment –such

> 1World Development Indicators, https://data.worldbank.org/indicator/SL.TLF.CACT.FE.ZS?locations=ZQ1W-ZJ-7E, accessed on November 7, 2018.

1
<br><br>|0.302|0.302|0.302|0.303|0.400|0.400|
|R(x)<br>|0.112|0.070|0.111|0.071|0.089|0.053|
|S(x)<br>*) The simulate<br>mean 30, Gini|0.032<br>d distributions ha<br>0.3; Correlation: p|0.032<br>ve the following<br>eriod 1: 0; perio|0.021<br>parameters: First<br>d 2: ≈ 0.3.|0.021<br>outcome: mean 1|0.000<br>00, Gini 0.5; S|0.000<br>econd outcome:|

Table 2 presents results for the three inequality measures when the outcome variables in period 2 are based on a simple transformation of the period 1 data: namely, the second variable is unchanged from period 1 while the first is obtained from its period 1 value by an additive or a proportional increase. A uniform increment of 20 units lowers multidimensional inequality, as is seen in the top panel of Table 2: declines from _0.160_ to _0.130_ , declines from _0.173_ to _0.144_ , and from _0.312_ to _0.284_ MMLLMM . That increment also results in a drop in the TTheeiiee MM MM rearrangement component for all three indexes, and the structural component declines for MLD GGiinnii MM and Theil, and remains at 0 for Gini. In contrast, a proportional increase in the first outcome by 20% increases multidimensional inequality as seen in the bottom panel of Table 2. grows from _0.158_ to _0.171_ , grows from _0.170_ to _0.185_ , and grows from _0.311_ to _0.323_ MMLLMM ,
can be estimated fairly precisely from the observed exchange rate data, while this is not the case in later periods.<sup>2 5</sup>

> 2' The scatter diagram, along with Figure **1,** also provides some clues for the relatively poorer verifiability of the third period vis-h-vis the first two. In the early part of the third period (approximately **_50_**

> observations), the exchange rate was practically pegged to the upper part of the band, but starting in early

> **1990** the peso started appreciating until it finally reached the lower band. This marked break in the

**_25_**
**Figure 1.2. Income per capita, Gini Coefficients, and Percentile Ratios for Urban and Rural Areas, RLMS 1994-2015**

<!-- Start of picture text --> Panel A: Urban Panel B: Rural<br>9.5 .5 9.5 .5<br>9 9<br>.45 .45<br>8.5 8.5<br>.4 .4<br>8 8<br>.35 .35<br>7.5 7.5<br>Total income per capita<br>7 Gini for income .3 7 .3<br>Panel C: Urban Panel D: Rural<br>9.5 5 9.5 5<br>90/50 ratio<br>9 50/10 ratio 9<br>4 4<br>8.5 8.5<br>8 8<br>3 3<br>7.5 7.5<br>2 2<br>7 7<br>199419951996 1998 2000200120022003200420052006200720082009201020112012201320142015 19941995199619982000200120022003200420052006200720082009201020112012201320142015<br>199419951996 1998 2000200120022003200420052006200720082009201020112012201320142015 199419951996 1998 2000200120022003200420052006200720082009201020112012201320142015<br>Log of income Gini coefficient Log of income Gini coefficient<br>Ratio Ratio<br>Log of income Log of income<br><!-- End of picture text -->

**Note:** Estimation results are obtained based on total household income per capita, using the repeated cross sections for each year. All numbers are deflated with December to December regional CPIs and weighted with population weights.

61
capture the (short or medium-run) network effects. Interestingly, when colonial links are included in such a stock-flow model, they turn out to be insignificant since their effect is absorbed by the existing network. From an economic point of view, the implications of those results is that recent migrants tend to come because they can rely on a network at destination, not because of past colonial links that offer current advantages.

The measurement of migration flows in destination countries is also a tricky issue. In most countries, we can rely on census data to provide the stock of migrants in a given year. For most national census rounds, a ten-year frequency is the rule. Therefore, the only way to measure migration flows over a ten-year period is to take the difference between stocks in two successive census rounds. This in turn raises several additional complications. First, the net migration flows are affected by the mortality rate of migrants present in the initial census. Second, there can be significant return migration which varies across origin and destination countries. For instance, using US data, Rosenzweig (2008) shows that the level of skill premia in the origin country is an important factor for the return migration rate of skilled migrants and students. Third issue arises due to regularization (legalization) programs implemented for undocumented migrants who are not recorded in many censuses (such as in most European countries). If regularization programs are implemented between the two censuses, the stocks of migrants in the second census and hence the size of the migration flows will increase in the data without actual movement of people. Another theoretical argument developed by Br ̈ucker (2006) suggests that using net migration flows instead of stocks might be misleading in the case of heterogeneous agents. This is especially important when it comes to estimating the impact of wage differentials on migration. In models like ours, we do not explicitly include the wage differentials for several reasons. One reason is the absence of reliable wage indices by skill level in most origin countries. A second
methodology in a time-series to estimate changes in housing needs over time to help better project future demand for housing.

Further, since geo-referenced data is only available for a few HCES surveys, we are constrained in performing more nuanced disaggregated analyzes on location or including satellite imagery to estimate changes in urbanization more precisely. In addition, housing deficit calculations like the one suggested by the application of _AHI,_ are limited to homogeneous criteria across countries or rural and urban areas. From a policy perspective, a more informative picture would be the analysis of each city separately with the best available data. However, while it is very informative at the city level, the main purpose of this paper is to provide a framework for comparable assessments of housing deficit numbers. Despite these caveats, the _AHI_ provides the first comparative, microfounded assessment of housing adequacy across 64 emerging economies, and one that can quantify disparities within countries. Large variations across and within countries vividly show that there is no one-size-fits-all solution for adequate housing challenges.

Ultimately, this paper provides one side of the puzzle that encompasses a wide-ranging housing value chain. For example, for the analysis of the supply of housing targeted at different segments, housing prices, housing finance and housing policy are all elements that are needed to put the estimates from this paper in context and implementable use. Bringing these various elements together in the analysis of the country’s housing market is fundamental to more fully understanding housing needs and challenges faced by households in emerging economies.
The results for the Index outcome data are shown in figure 9 for both 2019 and 2020 for the policymaker priors These figures show that the majority of the firms have ITEs centered at zero, with limited potential for heterogeneous effects in both 2019 and 2020. There is a reasonably high probability that at least some firms experienced positive effects, but the same is true of negative effects, and most firms have ITEs estimated to be almost exactly zero. Both the spread of effects and the uncertainty in these tails is more pronounced in 2020 relative to 2019. Overall however it seems the majority of the firms in the sample were unlikely to have seen any results from the intervention, and while some are likely to have benefited, others are likely to have experienced negative effects. This is most likely a result of combining the data with a prior positive correlation on the potential outcomes (so firms who did well in treatment would also have done well in control, and vice versa). We obtain similar results with the academics’ priors, shown in the additional results Appendix F, figure A9.

Given the somewhat negative effects on average in 2020, it may be surprising that there is still some potential for positive tail effects for other firms. This result may be an artefact of the likelihood model chosen: the Gaussian enforces symmetric tails in the outcomes, which propagates to a Gaussian and thus symmetric distribution of individual treatment effects. The Gaussian may be a particularly constraining assumption in our case especially because the outcome data is not symmetric – it has a large discrete spike at zero and then a positive continuous mass situated away from zero. While we could build a likelihood model to accommodate this, in our case the priors on treatment effects will have a oneto-many mapping to priors on parameters of such a model (because a treatment effect could be achieved by moving firms out of the spike, or from changing the distribution of outcomes for firms already out of the spike in the control state). Indeed, this is a general issue for
)|(0.163)|
|<br>**Observations**|327|327|327|327|
|**R2**|0.279|0.282|0.284|0.293|
|***** p<0.01, ** p<0.05, * p<0.1**|||||

Note: Marginal effects reported; standard errors reported in parenthesis. Source: Authors from World Bank (2010) APER database.

In general, the direction and compositional structure of the association in 2007 is the same as in 2003, but the magnitude is much lower (Table 3). The link between spending in agriculture and vulnerability is found to be statistically significant and positively, but with a markedly lower magnitude than in 2003 (Column 1). The estimated association is being driven predominantly by restricted spending, as it was in 2003 (Column 2). As in 2003, investment spending appears to have a positive relationship with vulnerability to food insecurity (Column 3), suggesting that more investment goes where higher vulnerability exists; however, the magnitude of the effect is much lower in 2007. A similar pattern emerges for infrastructure spending in 2007, but now research and extension spending is positively associated with food insecurity (Column 4). As in 2003, Tarija, Santa Cruz, Beni and Pando are less likely to belong to high/very high vulnerable categories than La Paz, but in 2007 Chuquisaca and Potosí are more likely than La Paz to pertain to high vulnerability categories (Columns 1 through 4). Chuquisaca and Potosí are the two departments with the highest vulnerability to food insecurity.

**Table 3. Effects of Per Capita Agricultural Spending on Vulnerability to Food Insecurity 2007**

||(1)|(2)||(3)||(4)|
|---|---|---|---
This version: 15 Feb 2024 18 <mark>expanding.</mark><sup>8</sup> <mark>Our own analysis of recent data (</mark> **<mark>Figure 4)</mark>** <mark>confirms these trends, showing that since 1970 absolute income inequality has increased fourfold despite the fall in relative income inequality. Rapidly increasing absolute income inequality is likely a source of discontent for many Malaysians.</mark><sup>9</sup> <mark>Furthermore, according to official statistics, in urban areas, non-monetary deprivations in living standards -- crowded living spaces (proxied by the average number of people per bedroom) and access to health care facilities -- remain serious challenges. However, consistent with the official data on absolute income poverty, the overall</mark> incidence of multidimensional poverty is low at 2.6 percent in 2019 (DOSM 2020).

Following Ravallion and Chen (2003) and Ravallion (2019b), we examine the overall changes in income distribution by estimating the growth incidence curve (GIC) for the period 1984-2019 ( **Figure 5** ). This helps us understand the evolution of propoor growth. Reassuringly, the poor benefited the most -- the growth rate in average household income per capita was as high as 3.30% at the 10th percentile while the lowest value was at the very top. Income growth was positive at all quantiles which is consistent with the notion of broad-based reductions in poverty regardless of the poverty line chosen. Higher growth rates for poorer percentiles also helped reduce relative income inequality during the 2000s ( **Figure 4** ).

> 8 A simple example helps illustrate this. If everyone’s income increases by 10 percent, relative inequality is unchanged, but absolute inequality increases because that 10 percent increase represents a larger income increment for richer households.

> 9 The literature shows that about half of survey respondents perceive inequality
<!-- Start of picture text --> Salary (satisfaction)<br>| am satisfied with my salary. (1-5 Likert scale; % agreeing or strongly agreeing with the statement)<br>Il National averages [| Institutional averages in United Kingdom<br>a 100%<br>oa<br>ao<br>5 80%<br>a<br>ao<br>52 60%<br>oa<br>Dp 40%<br>H<br>2 20%<br>oa<br>o<br>0% 7<br>Yt ¢f a fot if a o “ o<br><!-- End of picture text -->

<!-- Start of picture text --> =n 100%9<br>xo}Cc 80%<br>S 60%<br>7)<br>Fed 40%<br>5 20%<br>oe 0% @ ) @ @ & » @ @ C) @ @ SM eo @ @ &<br>&eC CP SS #& SF S&S SS F& SF EF SEF EE SFX EEES<br>os 3%e * SFa EF FHFx S @ FPF‘a Sy 2 EFFoO o> CFPS > FFTFe EM& EF&<br>ysww fou xe Vv ¥}<br>Ss<br>| Female {fj Male<br><!-- End of picture text -->
Changes in Poverty Headcount - 2011:2016 <!-- Start of picture text --> 3 aea ad<br>TM (1.6,5.9]<br>m (1.1,1.6]<br>(0.5,1.1]<br>(0.0,0.5]<br>CO No data<br>enel<br>Poverty Headcount - 2016 (%)<br>ban pote<br>ria<br>Fi<br>m (52.69]<br>m (40,52)<br>(29,40]<br>[4,29]<br>CONo data<br>enae<br><!-- End of picture text -->
**Figure 11 Fiscal risks from early termination of public-private partnership portfolios of South Asian countries as a percentage of total government revenues**

<!-- Start of picture text --> (a) Low (b) Medium (c) High<br><!-- End of picture text --> <!-- Start of picture text --> 0.30 0.30 0.30<br>0.25 0.25 0.25<br>0.20 0.20 0.20<br>0.15 0.15 0.15<br>0.10 0.10 0.10<br>0.05 0.05 0.05<br>0.00 0.00 0.00<br>2020 2021 2022 2023 2024 2020 2021 2022 2023 2024 2020 2021 2022 2023 2024<br>Afghanistan Bangladesh Bhutan India Nepal Pakistan Sri Lanka<br>Percent<br><!-- End of picture text --> _Note:_ Each data point represents the maximum expected loss at the 99 percent confidence from early termination of the public-private partnerships in the portfolio over the period starting at the end of 2019 and ending at the end of the corresponding year as a percentage of the total government revenue over the same period. The analysis assumes that annual government revenues as percent of GDP during the period 2020– 24 remains the same as in 2019.

_Source:_ Authors’ calculations.

# **_4.4.2. Adverse macro-financial shock_**

Multiple types of macroeconomic shocks have significant effects on the probability of early termination (table 3). The fiscal risks presented so far assume there will be no macro-financial shock such as depreciation of local currency or a stress in the banking sector.

This section simulates the results of a macro-financial shock. The Systematic Banking Crises data set of Laeven and Valencia (2018) identifies 104 banking crisis episodes among the countries included in the
what type of innovation is pursued. It is constructed by summing up 4 questions from the World Bank Enterprise Surveys (WB ES), weighted by the number of questions answered by each firm.<sup>3</sup> It ranges between 0 and 1, with the highest values indicating higher innovation. The second variable ( _innovation dummy_ ) measures whether a firm innovates or not. This innovation dummy takes a value of 1 if a firm innovates in any of the 4 elements composing the innovation score, 0 otherwise. We employ the innovation score in OLS regressions to estimate the association between a firm financial structure and the intensity of firm innovation (see equation 2 below). The innovation dummy is instead used to obtain estimates of a firm

> 3 In detail, if a question is left unanswered, the answer is not considered for the computation of the innovation score. In robustness tests we drop firms for which answers are not available for all the questions composing the innovation score. The results reported in section 3 remain qualitatively the same.

6
14)|
|Panel B of Table 8|||||||
|De-jure fnancial openness index|-0.03|-0.00|-0.04|-0.11*|-0.20**|-0.60**|
||(0.33)|(0.04)|(0.06)|(0.06)|(0.09)|(0.26)|
|Private sector credit (% of GDP)|0.41|0.05|0.02|0.04|0.22|0.23|
||(0.42)|(0.05)|(0.07)|(0.09)|(0.18)|(0.35)|
|Country fxed efects|No|No|No|No|No|No|

55
In terms of missing or incomplete data, the Australian spreadsheet lacks details on final antidumping duties imposed such as Australian Customs Dumping Notice (ACDN) documents, dates on which antidumping duties were imposed, and the size of duties imposed for certain cases (especially those initiated between 1996 and 2004). Furthermore, the Australian data does not contain the outcomes of reviews.

# **3.3 Brazil (BRA)**

Brazilian antidumping investigations are carried out solely by the Department of Commercial Defense (DECOM), which is part of the Secretariat for Foreign Trade (SECEX). The Secretariat itself is under the Brazilian Ministry of Development, Trade, and Industry. The outcomes of investigations are published by the Secretariat (SECEX) in circulars, some of which are available online at the Secretariat’s website. The Brazilian data was provided by Honorio Kumé and is based on Kumé and Piani (2005), which includes a history and analysis of the use of antidumping in Brazil. See also Caetano (2005).

The Brazilian spreadsheet contains the standard data items described in section 2, as well as a few additional data items described below.

**Table 3.3.1 : Additional Brazilian Data Included in AD-BRA-v1.0.xls**

|**Column**|**Variable**|**Description**|
|---|---|---|
|**In AD-BR**|**A-Master :**||
|**U.**|**P_AD_DUTY**|The preliminary antidumping duty imposed. If several duties were imposed, this<br>column contains the maximum duty|
|**V.**|**F_AD_DUTY**|The final antidumping duty imposed. If several duties were imposed, this column<br>contains the maximum duty|
|**W.**|**TYPE**|An indicator of review cases (R=review case
# 7. Conclusion

This study uses Random Forest and LASSO Stability Selection to identify the top determinants of interest in migration among Indonesian women and men. Random Forest relies on an importance criterion, choosing variables with a high predictive power, while LASSO Stability Selection uses the rate of appearance criterion based on coefficient sizes association of the independent variables with the outcome variable. We find a large set of common predictors for women and men, such as local labor market characteristics and individual economic characteristics (e.g. income). The models also identify gender-specific predictors. For instance, knowing Indonesian factory workers abroad is a predictor for both women and men whereas knowing Indonesian domestic workers abroad is only a predictor for women. Our models confirm drivers widely documented in previous literature (e.g. social networks) and less explored factors (e.g. access to communication) as predictors for migration aspiration. For policy makers, understanding the gender-specific drivers of migration in a given country and being able to rank them can provide valuable information. Not only does it allow to establish a profile of potential migrants who need to be targeted for specific policies such as information on safe migration, but it also allows to assess whether policy makers should address the “push” factors that lead people to emigrate and encourage local labor market participation, or encourage migration instead. An interesting extension of this research is to be able to use the list of identified predictors with other datasets that may not contain as much information as available in our rich household survey data. This feature would be particularly interesting for policy makers aiming at identifying potential migrants using readily available household surveys. Further research is needed to improve the prediction accuracy of the models. The validity of our identified variables list is likely time- and country-specific, depending on the development level of the region and historical, political and social characteristics of countries. The results presented in this study should therefore be read in light of these considerations.

36