rafmacalaba/data-use-mentions-extended huggingface dataset:
# **Figure 1**

Trends in Employment and Wages in the Manufacturing and Service Sectors <!-- Start of picture text --> All Workers Routine Occupations Non-Routine Occupations<br>1980 1985 1990 1995 2000 1980 1985 1990 1995 2000 1980 1985 1990 1995 2000<br>All Workers Routine Occupations Non-Routine Occupations<br>1980 1985 1990 1995 2000 1980 1985 1990 1995 2000 1980 1985 1990 1995 2000<br>Manufacturing Services<br>22 100 18 55 7 50<br>21 90 16 50 6.5 45<br>20 40<br>80 14 45<br>19 6 35<br>18 70 12 40 30<br>17 60 Service Employment (millions) 10 35 5.5 25<br>Manufacturing Employment (millions)<br>19 15 26<br>18 14.5 24<br>17 14 22<br>16 13.5 20<br>Real Hourly Wage 15 13 18<br>14 12.5 16<br><!-- End of picture text --> _Notes_ : Employment and wage calculations are based on the Current Population Survey Merged Outgoing Rotation Groups (MORG). Sample includes all part-time and full-time workers. Wages are in 2005 dollars. Definition of routine workers is based on occupational task content. Details are available in the data appendix.
tionally, certain aspects of forcibly displaced populations—including a disproportionate share of women and children whose access to health care was already precarious before migration—may produce diverse effects from those in the Global North.

We focus on the Permiso Especial de Permanencia (PEP), a regularization program that Colombia offered in 2018 to approximately half a million undocumented Venezuelan migrants there. PEP beneficiaries received work authorization and full access to social services for up to two years.<sup>2</sup> We examine how PEP impacted household fertility by leveraging information from two waves of the Venezuelan Refugee Panel Study (VenRePS), a representative survey of undocumented Venezuelan migrants who were living in main urban centers in Colombia before PEP.<sup>3</sup> Approximately half of the households in the survey were eligible for the PEP program.

Using panel data on 1,346 households, we compare the probability of having young children (conceived after the program launched) among households that were eligible and ineligible for PEP before and after the program began. Specifically, we observe each household at three points in time: at baseline, two, and three years after PEP’s rollout. Our models include household-survey and wave fixed effects to account for unobserved time-varying factors that potentially shaped household fertility. In addition, they incorporate a rich set of municipality baseline covariates interacted with time trends to address non-parametric changes in city-wide characteristics affecting childbearing choices.

We find consistent and robust evidence that the PEP program decreased childbearing likelihood among migrants. Based on our main estimates, migrant households eligible for PEP were 3.9 percentage points (pp) less likely to have children less than one year old, 7 pp less likely to have one-year-olds, and 1.8 pp less likely to have two-year-olds. Falsification tests confirm the lack of changes in the probability of having children conceived

> 2PEP was followed in 2021 by the opportunity, via a separate program, to enjoy the same benefits for an additional 10 years.

> 3Bogot ́a, Medell
<!-- Start of picture text --> 200 109<br>_— 190 108<br>a)S 180 107 =<br>i 170 106 &<br>S 160 105 4<br>S 150 S<br>‘ns 104 6<br>wLan}3 140130 103 &=<br>= 102 9<br>& 120 OL<br>fe 110 100 ><br>© 100 =<br>= 30 98 §<br>= 70 97 A<br>~ 60 96<br>50 95<br>SGCae NmMrtuMNwWrFrAGtKoeANmMyaHNwWrRARS<br>amnananraar~aaaraanroéesdedsdsescs$soeoeodd<br>raArmaoramrmarAanrmri_~aramraomrmrmoeaige°®v oo oo 78 0 OO OC 86 2<br>aod dH nt TF tT TT TH TN NNN NNN ON NN ON ON<br>== Internationalfood price index === Domestic food price index<br><!-- End of picture text -->
What we do have is cross-sectional, ex-post data on recipients and non-recipients of safetynet programs in the greater Rangpur region for both monga and non-monga periods. With this data, we can compare such outcomes as starvation status between recipients and non-recipients; however, we cannot establish causality between receiving the safety-net program and food deprivation. Since a household’s seasonal food deprivation also depends on a host of other factors, it is important to control for all such factors (both observed and unobserved) to determine the impact of safety-net programs on seasonal hardship. This is the challenge of an ex-post program evaluation using cross-sectional survey data.

In our evaluation scenario, we know only that less than one-third of the hard-core poor in the greater Rangpur region received the benefit of the social safety-net program during the 2006 monga season due to supply-side constraints. Household receipt of the safety-net program benefits is not exogenously given. To control for endogeneity of program benefits, we could use an instrumental variable regression technique, which requires one or more suitable instruments. Such instruments would directly affect household receipt of safety-net benefits, but not seasonal food deprivation, which would only benefit indirectly through safety-net program benefits. However, deciding on the appropriate instruments is not an easy task, and we do not have valid instruments to use in the instrumental variable method. Alternately, we propose a two-step procedure that uses the endogenous switching regression proposed by Maddala (1983) to control for endogeneity of safetynet program benefits with induced outcomes of our interest, such as extent of seasonal deprivation.

# **5.1** **<u>Model Description</u>**

We assume that _si_ denotes whether household _i_ receives safety-net benefits during the monga season ( _si_ = 1 when household receives benefits, 0 when it does not), which is determined by the following selection model: 18
# Figure 14: MNA: Infrastructure, health, and education indicators

# A. Infrastructure investment needs

# B. Quality of infrastructure

<!-- Start of picture text --> Percent of GDP Index, 1-7, 7 = best<br>8 7 Advanced economies EMDEs SAR<br>7 6<br>6 5<br>5<br>4<br>4<br>3<br>3<br>2 2<br>1 1<br>0 0<br>Transport Electricity Water and Flood Irrigation<br>sanitation protection<br>C. Universal health coverage D. Health spending below EMDE median<br>Index, 0-100, 100 = best Share of MNA economies<br>100 2019 2010 80<br>80 70<br>60 60<br>40 50<br>40<br>20<br>30<br>0<br>20<br>10<br>0<br>E. Selected human capital indicators F. Infrastructure investment needs<br>Index, 0-1, 1 = best Number of Percent of GDP<br>Human Capital Index years Energy Telecommunication<br>1.0 12 Transport: Ports Transport: Rail<br>EMDE average 11 6 Transport: Road Water<br>0.8 Learning-adjusted years of schooling (RHS)10 5<br>4<br>0.6 9<br>3<br>8<br>2<br>0.4 7<br>1<br>6<br>0.2 0<br>5 Morocco Jordan Egypt, Tunisia Saudi<br>0.0 4 Arab Arabia<br>GCC Other oil Oil importers Rep.<br>exporters<br>SSA SAR MNA LAC EAP ECA High-income<br>countries<br>2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017<br>2000 2002 2004 2006 2008
restructurings, or those involving official creditors such as government-to-government debts (see Reinhart and Trebesch (2016) and Schlegl, Trebesch, and Wright (2019) on official restructurings). Restructurings on domestic-currency sovereign debt are not included.

(iii) Restructurings of medium- and long-term debt. We thus exclude short-term rollovers or bridge financing deals, or other temporary arrangements between debtors and creditors (see Cruces and Trebesch [2013]).

(iv) Finalized deals. We disregard restructurings that were agreed on but were never de facto implemented, for example, when country parliaments reject an agreement.

We rely on a wide variety of sources to compile our restructuring and haircut archive. Importantly, we focus on the annual reports of bondholder organizations that negotiated with defaulting countries in the 19th and early 20th centuries, in particular the British Corporation of Foreign Bondholders (CFB), the US-based Foreign Bondholders Protective Council (FBPC), and the French Association Nationale des Porteurs Franҫais de Valeurs Mobiliéres. The reports provide rich details on past defaults and restructurings and are therefore our most important source. To cross-check the information by the creditor committees and to fill gaps in the data, additional sources were used, in particular annual investor reports such as Fenn’s Compendium of the English and Foreign Funds, Fortune’s Epitome of the Stock and Public Funds, Kimber's Records on Government Debts and other Foreign Securities, Moody's Manuals on Foreign and American Government Securities, and the London Stock Exchange Yearbooks. In addition, we incorporate in our comprehensive database case studies from the literature, communiques of the creditor organizations, official gazettes of the debtor country, and press articles. Our integrative approach compares each data point in the restructuring agreements and the debt instruments involved across available sources.

The final sample used in the analysis includes 313 external sovereign debt restructurings in 91 countries between 1815 and 2015.<sup>24</sup> This number represents a lower bound, since we summarize multiple restructurings into a single event if they resulted
(1977), is based on ‘an indigenous theory that their participation in these activities would decrease the amount of crops produced’ (Bauer 1977, p. 98).” These attitudes may be changing in Tigray; some female-headed households have had the desire and the courage to challenge such norms, although this challenge can be difficult and these women may be subject to ridicule or intimidation (Abay et al., 2001; Pender and Gebremedhin, p. 138). It is possible that these norms regarding animal traction have extended to the use of machines.

One might also be inclined to explain the mechanization gap as a scale issue. However, a comparison of Albania and Bulgaria, two higher income countries with large gender gaps in mechanization, helps put this argument to rest. The RIGA data indicate that the landoperating size gap between male- and female-headed farming households in Bulgaria is 2.7 hectares, whereas in Albania, it is nearly zero (0.09 hectares).

As with fertilizer, another possible explanation is that women use less mechanical power when markets are thin. However, if we take mechanization use in a country as a proxy for the thickness of markets for mechanized farm tools and equipment, the data do not support this argument because the gap does not decline in countries where a higher proportion of households use mechanization.

Thus, given the large financial outlays associated with mechanization, the most likely explanation for the pattern that we identify is capital constraints. However, we have no concrete evidence for this explanation.

# **_Labor_**

Numerous studies document the lower intensity of labor use in women’s fields. For example, using data from one region of Ethiopia, Pender and Gebremedhin (2006) show 21
alimony.<sup>8</sup> Income is made comparable over time by adjusting for inflation (all incomes are expressed in 2010 prices).

Income growth is evaluated for total household income per capita. Our measures of income inequality and inequality of opportunity are computed using individual income for the highest income earner within their household who are between the age of 30 and 50. The age restriction is included in order to mitigate the life-cycle composition effect on an individual’s income (Roemer et al., 2003; Marrero and Rodríguez, 2012). The motivation for using individual income rather than total family income for the measurement of inequality is that this facilitates the decomposition of total inequality into IO and a residual component.<sup>9</sup> Three different measures of inequality of opportunity are being considered: inequality of opportunity in the acquisition of income; inequality of opportunity in acquiring occupational prestige; and social immobility in occupational prestige.<sup>10</sup> The latter is measured as the correlation between the “occupational prestige” for a given individual and the average “occupational prestige” for individuals from an earlier generation belonging to the same race and the same state where the given individual was born. For the first two measures of inequality of opportunity we assume race and gender as circumstances. For race the following four groups are considered: non-Hispanic whites, Hispanic whites, blacks and others. Unfortunately, the IPUMS USA does not include information on parental education or income.

Race has been shown to be an important factor in determining an individual’s success in life as measured by income or occupational prestige. Initial inequality in wealth between individuals from different racial backgrounds combined with barriers to accessing credit can generate significant degrees of inequality of opportunity (Ferreira and Gignoux, 2011; Marrero and Rodríguez, 2011). There is also evidence of continued differential treatment by race in the U.S. labor market, i.e. racial discrimination (see e.g. Bertrand and
information about the partner across treatments, while Banuri and Eckel (2012c) are interested in the level of nepotism across cultures. However, since the basis of the groups differs in both studies, they cannot be perfect comparisons. Nevertheless, we can draw a few insights from each of these studies. First, Fershtman et al. (2005) demonstrate the importance of groups at the micro-level when studying nepotism. Since both Belgium and Israeli societies are fairly individualistic at the aggregate level, group size or religiosity may be the driving factors behind their results. Furthermore, the US and Pakistan are farther apart on Hofstede‟s individualist-collectivist rankings, and on rule of law indices. The results (greater levels of nepotism in Pakistan driven by risk aversion) in light of this makes sense. Future

> 21 Selecting a non-in-group member as partner in this game means that the trust amount sent is multiplied by 3. Selecting an in-group member as partner means that the trust amount sent is multiplied by 2.5.  Thus, there is a 0.5X cost of selecting an in-group member as partner.

> 22 By “nepotist” we mean subjects choosing to partner with their group members.

20
# I. INTRODUCTION

Polygamy<sup>1</sup> is widespread in many developing countries, and although it has been declining since the 1970s, it remains persistent especially in sub-Saharan Africa. In 2000, the overall prevalence of polygamy was 28 percent in the 34 countries studied by Fenske (2015), based on data from the demographic and health surveys on evermarried women of childbearing age. Although in a polygamous society only a certain proportion of unions actually become polygamous, almost all monogamous women are faced with the risk of polygamy. As noted by Antoine (2002), in Senegal a majority of women will be in a polygamous union at some point in their lives.

Socio-anthropological research suggests that the arrival of a second wife is mostly viewed by women in a monogamous union as a threat (Madhavan 2002; Antoine 2002). This threat may be exploited by the husband to monitor his wife, who has in general no say in this decision (Madhavan and Bledsoe 2001). The extensive literature review provided by Bove and Valeggia (2009) substantiates this fear since it shows a negative correlation between polygamy and women’s health. However, the economic literature is silent on the potential impact of the anticipation of polygamy on the allocation of resources within households. In particular, no paper, to our knowledge, has investigated the strategies that women in monogamous unions may implement in order to avoid the arrival of a cowife or to protect themselves against such an event. Only rare qualitative evidence of such strategic behaviors is provided by demographers: Antoine (2002) mentions one of these strategies of monogamous wives consisting of not sharing their income with their husband and driving him to spend more in order to decrease his saving capacity and impede the arrival of a cowife. Marrying a second wife is indeed costly to the husband: savings are needed to pay for both the wedding and the bride price, which has remained high and remarkably stable over time in Senegal, according to our survey data. As for women, they
to Peru is probably due, among other factors, to the way the surveys collects the information (first three columns). In Chile and Mexico the surveys in 2006 and 2005, respectively, asks for the occurrence of health shocks during the previous 5 years for which household members required hospitalization. The 5-year period probably underestimates the incidence as individuals are more likely to forget the occurrence of shocks. In Peru, however, the surveys in 2002, 2003, 2004, 2005 and 2006 ask for the occurrence of these shocks during the last 12 months, making it more likely to capture all the events.

# **3. Measurement of the middle class**

Existing income or consumption thresholds used to measure and analyze trends for the middle class have been defined using ad-hoc criteria. Milanovic and Yitzhaki (2002) have defined the middle class as those individuals living with a per capita income on $12-50 a day at 2005 PPP (roughly the mean per capita incomes of Brazil and Italy, respectively), while Kharas and Gertz (2010) have used a range of $10 and $100 daily expenditure per person, obtained after adjusting survey distributions to national accounts. These thresholds were defined by excluding those individuals who would be considered rich in Portugal and poor in Luxembourg (the poorest and richest among the industrialized countries, respectively).

Moving to a developing world perspective, Birdsall (2010) defined the middle class to include those with income between $10 day and at or below the 95th percentile of the income distribution. She argues that $10 a day is a high income compared to the global absolute poverty line of $1.25 a day and, although it is still low by OECD standards, it is close to setting a standard for a minimum income threshold for ―economic security‖. In an influential work, B&D have defined middle class as those living with a per capita expenditure on $2-10 a day at PPP, while Ravallion proposed thresholds ranging between $2 a day (
Table 3. Tariff Bound Lines and Total Overhang

||Shar<br>Tariff B<br>Lines|e of<br>ound<br> (%)|Tot<br>Overh<br>(%|al<br>ang<br>)|
|---|---|---|---|---|
|Country|Value|Rank|Value|Rank|
|Armenia|100|1|5.25|49|
|Georgia|100|1|6.12|52|
|Moldova|100|1|2.26|40|
|Ukraine|100|1|1.26|36|
|Kyrgyz Republic|99.9|83|2.87|41|
|Tajikistan|0|152|..|..|
|Uzbekistan|0|152|..|..|

Note: “.”: no data. Number of countries ranked: 164 (TBL) and 150 (TO). Source: World Trade Indicators (2012).

# **Tariffs in East Asia and the Pacific and the European Union**

Tariffs in EAP seem to be, with the exception of Japan, higher than in Eurasian countries. China’s simple average MFN tariff is 9.289% and Korea’s is 10.284% (Table 4). The highest level of protection in China is observed in products such as rice, barley, wheat, or meslin flour, with tariffs of 65%. Trade barriers in products that are imported from Eurasian countries, i.e., petroleum oils and oils obtained from bituminous minerals, raw hides and skins, and iron ores and concentrates, are minor. In the majority of the cases, these products have no import duty. Korea’s protection is concentrated in buckwheat, millet, and canary seed (tariffs of 800.3%), starches and inulin
To check whether individuals (or their parents) tried to manipulate the forcing variable to being eligible to participate in the formal labor force or to pretend they complied with the law, we perform the McCrary test. We also run some placebo tests to check whether our estimates might be confounded.

# _The McCrary Density Test_

The McCrary density test consists of verifying whether there is evidence of perfect manipulation of the forcing variable around the cut-off point. The test compares the density distributions of the forcing variable around the threshold (McCrary 2008).

Although our birth data comes from household surveys conducted by the Brazilian Census Bureau, which is not related to the public institutions responsible for the surveillance and enforcement of the law, one could still think of the possibility that a family misreports dates of birth to the surveyor, particularly in cases where children are working illegally. If this occurs systematically, one would observe a discontinuity in the density function of the forcing variable around the cut-off point and this would call into question the plausibility of our identification strategy.

We illustrate the results graphically with age defined in weeks and days. The reason for using age in days is to make it as continuous as possible given that the McCrary test is non-parametric (see Lee and Card 2008 for this point).

The figures 2 and 3 show the McCrary density test for age in weeks and age in days respectively. Both figures indicate that there is no discontinuity in the forcing variable around the threshold, that is, there is no evidence of perfect manipulation of the forcing variable.

# _Balance Check_

Another important check of the plausibility of the RDD consists of a balance check comparing observed characteristics of children around the cut-off point. Under the assumption that the law change can be seen as a natural experiment, we should observe ‘treated’ and ‘control’ boys with similar observed characteristics, on average. We perform a test for difference in means and the results are presented in table 3.

In addition to control variable we use in our regressions, we also
**1 Introduction**

Policies for refugees like the right to work and freedom of movement are decided by the governments of host countries. The policies vary widely and change over time (Blair et al. 2021a); for instance, while Tanzania prevents refugees from working even within the strictly-enforced camp boundaries, Colombia has extended the right to work to 1.7 million Venezuelans. Social cohesion is one of the many factors influencing governments’ decisions and often used to publicly justify restrictions and containment. Despite anecdotal evidence to the contrary, governments are often worried that more inclusive policies will lead to crowd out of host citizens, reduced social cohesion, and a backlash against the politicians who facilitated refugees’ access.<sup>2</sup> Restrictive policies remain despite the significant costs to refugees and, in many cases, the host communities through missed opportunities for exchange and growth.<sup>3</sup> In this paper, we examine how attitudes toward refugees are affected by the arrival and presence of refugee populations. We first ask how attitudes respond to a large arrival of refugees on average across low- and middle-income countries (LMICs). We then decompose the findings by the characteristics of hosting environments to investigate whether social tension and negative perceptions are reduced in restrictive environments or whether interactions in a more welcoming environment facilitates trust and mutual gains from exchange. We focus on LMICs, where 86% of refugees reside but where relatively little quantitative research has been conducted to date on the determinants of social cohesion (UNHCR 2021b).

We build and combine three main datasets to address our research question. The resulting dataset covers a large proportion of LMICs between 2005 and 2018. The first is a dataset on attitudes. Our main outcome comes from the Gallup World Poll (GWP), due to its extensive spatial and temporal coverage, and we

> 2See Verme and Schuettler (2019) for a review on the minimal evidence on economic effects on host communities.

> 3See Clemens et al. (2018