rafmacalaba/data-use-mentions-extended huggingface dataset:
horizon allows us to uncover more nuanced differences in the dynamics in these different decades. Indeed, to our knowledge, we are the first to attempt such a long-term analysis of mobility for India.

Regarding depth, while Dang and Lanjouw (2018) mostly focus on poverty mobility between the poor and the non-poor, we offer a richer analysis in this paper of mobility patterns for three different income groups consisting of the poor, the vulnerable, and the secure (whom we might also denote as the ‘middle class’). Dividing the population into these groups, rather than the more conventional breakdown into deciles, quintiles, or terciles, offers an additional level of insight into the underlying patterns observed, and allows us to relate our discussion to the large literature on poverty in India. We distinguish the vulnerable from the secure via a method introduced in Dang and Lanjouw (2017), whereby we identify a subset of the non-poor that has a consumption level above the poverty line but faces a heightened risk of falling into poverty in the next period. But for comparison purposes, and to gauge the overall robustness of our findings, we also report findings on mobility based on quintile-based transition matrices. These more refined disaggregations of the population groups are more relevant to policy advice than one might think, especially since attention has been increasingly placed on vulnerable population groups for more sustainable poverty reduction.<sup>2</sup> Moreover, we take advantage of the large sample size of the NSS surveys not just to explore patterns of mobility at the all-India level, but also to consider patterns of welfare dynamics at the state level—recognizing that the forces and policies at work in the Indian economy have often been different and uneven across states. We also examine differences between urban and rural areas and among population groups defined in terms of education levels, occupation sectors, and caste. The combination of a longer time horizon and more disaggregated state-

> 2 For example, the United Nations has called for more rights to economic resources
Table 2: Village-Level Public Health Infrastructure

||Health facility<br>index<br>(1)|Connection<br>index<br>(2)|Public program<br>index<br>(3)|Auxiliary<br>Nurse Midwife<br>(4)|Female<br>doctor<br>(5)|
|---|---|---|---|---|---|
|Fraction of female leaders|1.730*|2.138***|0.973**|0.980***|0.837***|
||(0.919)|(0.528)|(0.455)|(0.246)|(0.263)|
|Observations|7,696|7,700|7,722|7,723|7,722|
|Outcome mean|0.000|0.000|0.000|0.646|0.219|
|State fxed efects|x|x|x|x|x|
|District characteristics|x|x|x|x|x|

_Notes_ : Data comes from the 2012-13 District-level Household and Facility Survey (DLHS) of India. The columns present 2SLS estimates specified in the following equations: where _Yvds_ is an outcome for village _v_ in district _d_ in state _s_ . The other variables in these specifications have the same definitions as in our main specifications described in Section 3. Standard errors are clustered at the district level. The outcomes are as follows: an index for the presence of health facilities in a village (column 1), an index for connection to health facilities with an all-weather road (column 2), an index for public program implementation in a village (column 3), an indicator for the presence of Auxiliary Nurse Midwives in a village (column 4)
update program|-0.0359**<br>(0.0167)||||
|Employment outcomes or employers' requests are very important to|<br>-0.0152||-2.561||
|<br>update program|(0.0295)||(2.007)<br>||
|Ellt td itt t dt|||-1.152||
|nromen rens are very mporan o upae program|||(1153)||
||-0.0366*|0.0320**|.|0.241**|
|Student feedback is very important to update program|(0.0215)|(0.0144)||(0.101)|
|Analyze student performance to solve problems more than once per|<br>0.0919***|<br>0.0476**|5.854**|<br>0.267*|
|year|(0.0298)<br>|(0.0211)|(2.313)<br>|(0.146)|
|Collect student satisfaction data more than once per year|-0.0398**<br>(0.0199)||2.386*<br>(1.261)||

87
This limitation is addressed by applying an approach similar to the difference-in-differences method widely used to identify how the evolution of a given part of a system differs from the rest of the system if “treated” by an intervention (Angrist and Krueger 1999). In this case, the application of the method is aimed at identifying whether the typical shopping-purpose trip attractor zones within the urban area have had a different evolution of mobility demand for the HBO and NHB groups than other parts of the urban area. The origin-destination matrices identify trips by carriers (cab drivers, public transport drivers, urban goods distribution vehicles, etc.) in a grouped manner. Specifically, two groups of zones are defined:

- “Treatment” group, or areas that are considered to be shopping trip attractor zones and that are likely to have been impacted by the potential substitution effect created by the growth of e-commerce (the "treatment", in this context), such as areas in which a major shopping center is located, areas in which a major commercial center and adjacent areas are located, and areas that concentrate one-third of trips destined for shopping activities, according to the 2019 household mobility survey; and

- Comparison group, which includes other areas of the city.

A series of outcome metrics are defined and their evolution over time is calculated within the “treatment” and the comparison areas to subsequently compare the two, including, among others: number of trips during the morning (9:00-12:00) and the evening (19:00-20:00) shopping peak hours; and travel from areas inhabited by different income groups<sup>4</sup> to the two types of areas, in particular, focusing on the higher income groups that are known to have higher e-commerce adoption rates.

The CDR-based indicators were combined with complementary data, including, among others:

- For Bogota: E-commerce sales reports for Colombia; a tool developed by the Latin American Center for Logistics Innovation (CLI) that integrates order shipment data from
“ _Of course I want to help Venezuelan migrants, we are all migrants in some way, you know? I am just worried about the The backlash could response from my people. political be difficult for my party._ ” (Elected Official in Latin America, March 15, 2021)

# **I INTRODUCTION**

Robust evidence supports a correlation between migration inflows and voter support for anti-migrant political parties.<sup>1</sup> This might explain the reticence and cautionary actions of political parties regarding actions to facilitate migrant integration. Yet, migration reforms related to economic have different effects than integration might migration inflows per se. For example, voters may lack information about regulations concerning migrant integration, may believe those regulations do not affect them or change overall migration inflows into the country, or may support migrants now in the country as long as more migrants do not come because of those regulations.

We examine the electoral effects of the _Programa Especial de Permanencia_ (PEP) in Colombia. The PEP was a temporary economic integration program for undocumented forced migrants. It was known regionally as a type of regularization program. It was offered to half a million Venezuelans in Colombia for to two The approximately living up years.<sup>2</sup> PEP granted working rights and access to public programs, such as full health and education services, plus access to financial services. It was offered to every Venezuelan in Colombia who registered in a census of irregular migrants between April and June 2018. As documented by Urbina et al. (2023) and Ib ́a ̃nez et al. (2024), the PEP program affected profoundly the well-being of treated Venezuelan migrants through improvements in their

> 1See, for example, Gerdes and Wadensj ̈o (2008); Otto and Steinhardt (2014); Mendez and Cutillas (2014); Barone et al. (2016); Harmon (2017); Halla et al. (2017); Dustmann et al. (2016).
——— Kenya a<br>—S_eTJ _<br>— Cote divoire* =<br>Es .<br>—== Gambia* ———<br>Low incomeoc ee So]asa Zimbabwe; DRC* >=O—_——==<br>as-——FFFJas Rwanda ————rr<br>-—— CAR* =<br>-— —<br>as jonia *<br>— Ethiopia<br>—~[-—— Liberia* ~<br>Middle . Ea 0 1 2 3 4 5<br>income Source: WHO GNursing and midwifery<br>_ * Data for 2008 personnel density (per 1000<br>o.0% ask 1% 1 population)<br><!-- End of picture text -->

<!-- Start of picture text --> population)<br><!-- End of picture text -->
et.al. 2003). There is very little to do to overcome this limitation, except subdivide the sample into different groups. Probably a regional aggregation can be useful, but even at that level there may be extreme heterogeneity.

<!-- Start of picture text --> Correlation: Learning Scores and Net Secondary Enrollment Correlation: Learning Scores and Average Years of School<br>KOR SGP TWN KOR<br>HKG<br>SVK HUNCZESVN SV N HUNSVKCZE<br>MYS BGRLVA POL MYS BGRPOL<br>MEX MDATTO MKDROMCUB LTU THA MEX CUBTTO ROM<br>IDN TUR TUN BRAIRNALB ARG JOR BRAIDNTUNTURIRN J OR ARG<br>DOM PRY COLPER BOL CHL KWT HNDDOMCOLBOL PRYKWT CHLPER<br>VUT SYC<br>TZA MOZMDG KEN MARSWZ ZWE PHL BLZMUS MOZ TZA UGACMR KEN ZWE SWZMUS PHL<br>BWA BWA<br>BFA<br>ZAF ZAF<br>LSO NAM MLI SEN LSO ZMB<br>MWI<br>NE R NER<br>0 20 40 60 80 100 0 2 4 6 8 10<br>Net Secondary Enrollment Average Years of School<br>Data Source: World Bank WDI & Crouch and Fasih (2004) Data Source: World Bank WDI & Crouch and Fasih (2004)<br>600 600<br>500 500<br>400 400<br>Learning Scores Learning Scores<br>300 300<br>200 200<br><!-- End of picture text -->

**Figure 6. Correlation between Learnings Scores and Other Education Indicators**

Other four limitations of the analysis arising from the particular data sources are: First, the level of aggregation. The paper uses aggregate public spending on health and education, while using disaggregate measures of output, such as. primary enrollment or secondary enrollment. Ideally, the input should be use separately public spending in primary and secondary education. Similarly, health care spending could be disaggregated into primary care level care and
declined, as did the severity of poverty, as measured by the poverty gap and squared poverty gap.

Even after making adjustments for the inconsistency of Ugandan labor force data over time, we still find an overwhelming majority of the workforce reporting agriculture as their primary economic activity in the most recent household survey (see Table 1).<sup>8</sup> Indeed, it appears that the Uganda‘s high rate of non-agricultural economic growth allowed only a very small part of the labor force to move into higher value activities. Are these numbers a true reflection of the employment transition? First, Uganda has added 7 million people (i.e. doubled) into the labor force since 1992, and at least 2.7 million (i.e. 40%) of these eventually found primary employment outside the agricultural sector. So the slow demographic transition hides some of the structural shifts. Second, since the first data were collected in 1992 the Ugandan basic multipurpose household survey instrument (UNHS) has been modified, with the improvements designed in part to sweep more people into the labor force. Many of those left out in 1992 worked primarily in agriculture. But the result of the changes makes it impossible to get a consistent series on employment in Uganda. Our estimates attempt to correct for this, but still show a less than expected net flow out of agriculture as a primary source of employment given the extent of the economic transformation suggested by the sectoral GDP numbers, and the high rate of poverty

> 8 The labor module used in Uganda‘s National Household Surveys (UNHS) has been changing since 1992 leading to problems of comparability across surveys. Major changes include the introduction of screening for economic participation in 2002/03 (with short screening) and 2005/06 (with detailed screening) and varying recall periods for main and secondary employment statuses. The level of details on employment statuses also change, with the latest survey (UNHS 2009/10) missing secondary employment in the 12
We consider six dependent variables. The first three economic outcomes are consumption aggregates that are constructed following the guidelines of Deaton and Zaidi (2002) (see appendix B.1 for details). The first indicator measures the total consumption per adult equivalent (AE) of 47 consumption goods. About two thirds of this aggregate is related to the consumption of cereals, fruits and vegetables. The other important food categories are beers and sodas (7.7%), fish, meat, and eggs (7%), condiments (3.3%) and milk (3.3%). The second dependent variable is the part of total consumption that was purchased over the 15 days preceding the survey. This variable is labeled consumption expenditures per adult equivalent. Similarly, the third dependent variable is the share of the total consumption aggregate that was taken from stocks. It is also expressed per adult equivalent. The fourth dependent variable is an indicator of nonfood spending per adult equivalent, which includes spending in terms of clothing, housing, leisure, transport and transfers during the last year. The fifth dependent variable is the tropical livestock units (TLU), which summarizes in one indicator the possession of a wide range of livestock, weighted according to their type and size.<sup>15</sup> In order to improve statistical power, the last variable is a summary index that aggregates information over the five main dependent variables (Kling et al, 2007). The summary index is defined to be the equally weighted average of z-scores of the five components. The z-scores are calculated by subtracting the mean for civilian households and dividing by the standard deviation for civilian households for each component. Descriptive statistics are presented in table 1.

All the household welfare indicators have decreased on average between 2006 and 2010. This trend shows that living conditions have deteriorated in these three provinces of Burundi during that period. Meanwhile, household size increased during the same period. Table 1 suggests that attriting households purchased more,
private. On average, they find a 9% nominal return, comparable to that of US Treasury bonds at that time (zero premia), but there is substantial time variation. Appendix B3.2 shows that we get very similar results in those subsamples of their dataset for which pricing data exist. Furthermore, in Appendix B4, we add evidence on the 1970s, 1980s, and early 1990s, partly based on secondary market price data for syndicated loans 1985-1993. The results confirm those in Klingen, Weder, and Zettelmeyer (2004): sovereign debt returns were very low in the late 1980s and very high in the early 1990s.

Following large-scale debt restructurings in the early 1990s under the Brady Plan, fixed income markets reemerged as a dominant source of credit to emerging markets. The recent literature mostly explores this post-1990 sovereign bond era with an approach closer in spirit to the one developed in this paper, typically using JP Morgan’s Emerging Markets Bond Index (EMBI) country series (e.g., Broner, Lorenzoni, and Schmukler 2013; Borri and Verdelhan 2015; Andritzky and Schumacher 2018).<sup>18</sup> The results highlight excess returns by country in the 3%-15% range, which is significantly higher than those reported by papers studying the pre-1990s and in line with our results.

A broad takeaway from this previous literature on bond returns is that, historically, emerging market debt delivered returns that were only slightly above the risk-free rate. This made it all the more puzzling why investors continue to flock to this asset class in their search for high yield. Compared with that body of research, we provide a more representative and encompassing picture of the external sovereign bond market. Our bigger sample gives less weight to individual defaulters (like Russia) or episodes that were particularly turbulent (like the Great Depression). We also disregard the (lower) returns of sub-sovereign bonds. Instead, we focus on external sovereign bonds and include all episodes for which price quotations
populations in 2012 (for the UEUS) and 2013 (for the LFS) as the weighted average of the 2007 Census population and the 2014 projected populations by the intervals from the survey years. Below is how the town population is calculated in each year _i_ (=2012, 2013): Figure 3 illustrates the population distributions of the UEUS/LFS towns. As is clearly shown, Addis Ababa has by far the largest population. Since the capital city is influential on any regression estimation, we estimate specifications with and without Addis Ababa fixed e↵ects in every analysis.

Figure 3: Population Distributions in the UEUS/LFS Data Sets <!-- Start of picture text --> Addis Ababa<br>6 8 10 12 14 16<br>Ln Pop<br>UEUS LFS<br>23<br>17.25<br>11.5<br>Frequency in 100,000s<br>5.75<br>0<br><!-- End of picture text --> We conduct analysis with several other population variables to check the robustness of the results. As an alternative town population measure, we draw on 100 m by 100 m grid-level population data from the World Pop 2015. We identify urban agglomerations following the European Union’s definition of high density urban clusters (contiguous grid cells of 1 km<sup>2</sup> with a density of at least 1,500 inhabitants per km<sup>2</sup> and a minimum population of 50,000).<sup>9</sup> The result using the World Population 2015 is presented in Section 5.5.

> 9See measurement details at Eurostat Statistical Explained, Degree of urbanisation classification - 2011 revision.

9
can be taken to be the costs and benefits as perceived by public decision makers charged with this task.) It is a plausible assumption that the marginal cost (MC) to a country’s government of collecting, processing and disbursing such data rises with the amount of data made available. The marginal benefit (MB), on the other hand, can be expected to fall if anything, as diminishing returns set in to collecting and processing extra <mark>data. It is also plausible that the MC of providing data tends to be higher for low-income</mark> countries than for high-income ones. This reflects the lower levels of state capacity generally in poorer countries, stemming from weaknesses in both governance and ICT. The government chooses a level of data availability equating MC and MB, with the implication that low-income counties tend to make less data available as the MC function shifts upwards.

Then we can expect the control function for selection, g(p), to be positively correlated with GDP per capita. However, if one ignores the non-random selection then the control function

> 19 See Vella’s (1998) survey.

> 20 More precisely, <mark>p(Z) = Prob(d = 1|Z) is the propensity score for selection, where d = 1 if the country, year combination is observed while d = 0 otherwise, and Z is a vector of covariates for selection.</mark>

12
recognized. While internet access is nearly universal in high-income countries, the share of the total population with internet access in the study countries varies from 25 percent to 86 percent (see Table 1). Thus, it is necessary to be cautious when inferring how the results of an online survey would relate to the sizable share of the population without internet access. At best, online surveys can be broadly representative of the internet-using population, and survey responses can be weighted to match the characteristics of the general population (e.g., older people are less likely to access the internet, and survey weights can be applied to somewhat compensate for this issue). The following subsection provides more information about how and when weights are applied.

A leading global research firm was engaged to collect the sample of respondents for this study by drawing on a panel of survey participants who are broadly representative of the population with internet access in each country. While alternative sampling strategies exist for online surveys (see Appendix A), this approach of drawing respondents from a pre-registered pool was determined to be the most suitable for this study: it is a very reliable and quick way to access a broadly representative sample of respondents with internet access, and it allows incentives to be provided in order to maximize completion rates.<sup>12</sup> Recent examples of seminal studies using an identical sampling strategy for online cross-country surveys include studies testing the impact of information about climate change on public support for policies in high- and middle-income countries (Dechezleprêtre et al. 2022; Dabla-Norris et al. 2023). The survey firm that was used, YouGov, regularly runs online surveys to reach a pre-registered pool of people in these countries who already have experience taking part in surveys on different topics for compensation (e.g., phone credit). YouGov ensures that respondents have a track record of being attentive during the surveys (e.g., they exclude respondents who regularly rush through surveys or only partly complete surveys);
23|90|3|0.3|0.0|118|
|Togo|70|-9|79||0|100|0.3|1.0|54|
|Myanmar|59|0|11||47|19|0.2|0.1|29|
|<br>Colombia|55|21||78|111|0|0.2|0.0|109|
|Azerbaijan|55|5|41||8|82|0.2|0.5|71|
|<br>Kyrgyzstan|38|0|39|3|3|103|0.2|0.5|121|
|<br>Uganda|37|-5|38||4|90|0.1|0.5|52|
|<br>Mozambique|25|-3|26||2|93|0.1|0.3|16|
|<br>Ethiopia|22|0|2||20|9|0.1|0.0|38|
|South Africa|21|0||39|60|0|0.1|0.0|73|

Source: ICAC (2005).

a Cotton, refers to ginned lint or raw cotton. It does not include seed cotton, linters, cotton mill waste, or cotton fibers subjected to any processing other than separation of lint from seed by the gin. Annual data are for the cotton year beginning 1 August.<sup>b</sup> Supply is output plus change in stocks.
American and Canadian Rockies and coastal mountain ranges, and weather and wind patterns that vary from year to year and dictate where smoke is transported. As a result, year-to-year exposure is expected to be driven by weather conditions in locations far from our counties of interest and hence uncorrelated with weather in our county of interest. We find that burned area in California is not correlated with meteorological variables in the primary corn and soybean producing states (Table SI3). Correlations between burned area and extreme degree days for corn, soybeans, and total precipitation are all between _−_ 0 _._ 5 and 0.5 with the majority between _−_ 0 _._ 3 and 0.0.

We control for county-specific determinants of yields that are unchanging over our sample period (e.g. soil quality or elevation) with a county fixed effect. We account for time-varying determinants of yield that are common across counties (e.g. general improvements in agricultural technology) with year-specific fixed effects. We also include a range of annual county-specific meteorological controls to account for correlation between smoke exposure and weather-related determinants of yield. For example, denser smoke plumes may reduce surface-level temperatures in ways that enhance yields.<sup>16</sup> We also include county-specific controls for the fraction of cloud cover to account for the possibly confounding effect that clouds may have on the ability of analysts to accurately draw smoke plumes and the direct impact that clouds can have on yields themselves.<sup>22,5</sup> Summary statistics for all measures included in our regressions are presented in Table SI4. The full estimating equation is: where _Ln_ ( _yit_ ) represents the natural log transformation of the yield of either corn or soybeans in county _i_ in year _t_ . “Low-density smoke” and “high-density smoke” are the number of days during the growing season that each county was under a low-density or high-density smoke plume in the HMS data, averaged across corn or soy pixels. We control for the