rafmacalaba/data-use-mentions-extended huggingface dataset:
NIGER’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE Niger’s capacity to efficiently move goods and connect manufacturers and consumers with international markets is 3 above the regional average. When asked to 2.5 give feedback on Niger’s logistics 2 ―friendliness,‖ operators (global freight forwarders and express carriers) gave it an 1.5 LPI ranking of 2.54, above the West 1 African average of 2.42 (figure 5). 0.5 Senegal, Benin, Guinea, and Togo—all 0 coastal countries—received a higher LPI. But Niger’s LPI is higher than Mali’s and Burkina’s, despite the fact that it is farther from the coast than these countries. _Source:_ Zooming in on the components of the LPI _Note:_ in Niger, operators gave the lowest scores to (i) the efficiency of the clearance process (that is, the speed, simplicity, and predictability of formalities) a border control agencies, and (ii) the quality of trade and transport-related infrastructure (for example, ports, railroads, roads, information technology).

**Figure 5. Niger’s Logistics Performance Index ranking  and that of other West African landlocked countries**

<!-- Start of picture text --> 3<br>2.5<br>2<br>1.5<br>1<br>0.5<br>0<br>Logistics Performance Index<br>Senegal Benin Guinea Togo Nigeria Niger Cote d'Ivoire Gambia, The Ghana Liberia Mali Burkina Faso Guinea-Bissau Sierra Leone SSA<br><!-- End of picture text --> _Source:_ World Bank 2010d.

_Note:_ The Logistics Performance Index is based on a worldwide survey of operators on the ground (global freight forwarders and express carriers), providing feedback on the logistics ―friendliness‖ of the countries in which they operate and those with which they trade. Such operators combine in-depth knowledge
million housing units will have to be added to provide adequate housing to the growing population by 2030. Regarding the deficit at the dimension level, we estimate that at least 603 million HHs (close to 2.8 billion people) in emerging economies lack access to at least one of the seven dimensions of adequate housing. Our prediction indicates that not only HHs residing in slums are lacking access to adequate housing, as the UN estimation suggests (UNSD 2019). Instead, the housing deficit is much more wide-ranging, extending to HHs in formal settlements.

In constructing the AHI, we present a transparent framework for harmonizing seven dimensions of housing adequacy recorded in countries’ most recent HH surveys. To the best of our knowledge, this is the first broad, cross-country study applying a uniform methodological framework on housing adequacy and related housing deficit estimations. This cross-country assessment of existing adequate housing deficits will facilitate consistent projections of how the adequate housing deficit will rise within the context of urbanization and population growth.

In our analysis, we highlight important within-country variation in housing needs across different segments of the population. Further, in separately estimating the current housing deficit and the forward-looking deficit likely to manifest over the next decade, we draw a fundamental distinction between the stock and flow housing deficits. This differentiation is often not appropriately stated in existing estimations of housing deficits. Instead, stock and flow deficits are frequently used interchangeably, partly because only one type of estimation is available for the country.

The remainder of this paper proceeds as follows: in the next section, we briefly discuss how our approach relates to existing literature on estimating housing deficits and outline how literature assesses the relationship of housing adequacy and socio-economic outcomes. We discuss how recent estimations of housing deficit largely abstain from differentiating between the stock deficit of currently inadequate housing units and the flow deficit – often referred to as the quantitative housing deficit denoting how many additional housing units will have to be built. Next, we outline our methodology in constructing the _AHI,_ which is the basis
.

> 4 The Wealth index from DHS is defined after accounting for water, sanitation, and hygiene (WASH) variables. We eliminate the WASH variables to create our own wealth index using the Principal Component Analysis.

> 5 The highest incidence of DD (in percent) was experienced by Bolivia (25.3), Haiti (23.0), Burundi 2.0), Uganda (22.0), Senegal (21.5), Burkina Faso (20.0), The Gambia (20.0), Cambodia (19.8), and Malawi (19.6).

7
premia, see section 4.2). This suggests that adjustments applied to both the REER and CPI-PPI ratio are justified.

Upon estimating the cointegrating relation coefficients using the whole sample of data, estimates of misalignment converge (see Table 3, columns 2-5). According to the unadjusted REER, the peso’s overvaluation is estimated at 27% (31% without the HP filter). If instead we use the adjusted-REER measure, the peso becomes overvalued at an estimated 46% (62% without the HP filter).<sup>31</sup> This is an upper bound if we consider that the CPI-to-PPI ratio is underestimated.<sup>32</sup> In fact, when we use our upward-adjusted CPI/PPI ratio, misalignment estimates converge further: overvaluation becomes 39% (49% without the HP filter) for the adjusted-REER. Finally, in a scenario of large CPI/PPI adjustment, estimates of currency misalignment converge to 21% (26% without the HP filter), considered to be a lower bound.

This analysis reveals that, considering our adjusted measures for REER and CPI/PPI, the peso is overvalued by approximately 39%, with a range of 21–46% when we correct the REER and CPI/PPI ratio for underestimated CPI.

<u>Table 3: VEC Cointegration Relation</u>

||<br>Pre-2008<br>RE|<br>Full sample<br>ER||Adj-REER<br>Full sample||
|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|
|Terms of trade|0.642***|0.586***|0.402**|0.537***|0.567***|
|Productivity differentials (CPI/
33 theoretically that the supply-side benefits of co-location should be small for UNT firms. Likewise, Glaeser and Resseger (2010) find for the United States that AEs are strong in cities with higher levels of skill and nonexistent in less skilled metropolitan areas. Lastly, Burger et al. (2022, Panel H of Table 2) use firm-level data for 649 metro areas to estimate AEs for 98 economies. labor on urban developing They regress log productivity log density, finding an average AE of 10%-15%. They then interact density with dummies proxying for whether the firm belongs to the tradable sector, finding AEs that are twice higher than in the non-tradable sector. Congestion forces might also be stronger for the UNT sector than for the UT sector.<sup>51</sup> Finally, as shown by the trade literature, international trade affects endogenous innovation (Melitz and Redding, 2021). As such, having higher shares of firms and workers in UT rather than UNT may also promote growth.

Just like production cities can evolve into consumption cities through deindustrialization, consumption cities could still evolve into production cities in the future. We showed that they do not necessarily lack human capital, but other constraints that we do not observe constrain urban such constraints include may productivity growth; institutions, control of corruption, and investment climate, among others. There are many examples of successful transitions. The United Arab Emirates’ largest cities – Dubai and Abu Dhabi – have uncontestably become production cities by expanding FIRE. Likewise, the growth of Malaysia’s manufacturing reshaped the economic structure of the Kuala Lumpur metro area. However, since understanding what distinguishes dynamic consumption cities, which evolve into production cities, from stagnant ones is beyond the scope of this paper, we leave this question for future research.<sup>52</sup>

# **REFERENCES**

Ades, Alberto F and Edward L Glaeser, “Trade and circuses: explaining urban giants,” _The Quarterly Journal of Economics_ , 1995,
Table 1: Daily Resting Energy Expenditure (REE) and Activity Level (AL) <u>estimates</u>

|Panel A: REE (calories/day) from National Family Health|Survey (NF|HS)|
|---|---|---|
|Ever married women 15-49<br>Women 15-49|Men 15-54|Children 0-3|
|1998 (NFHS-2)<br>1128<br>.|.|502|
|1998 Rural<br>1114<br>.|.|499|
|1998 Urban<br>1169<br>.|.|513|
|2005 (NFHS-3)<br>1145<br>1140|1496|492|
|2005 Rural<br>1126<br>1122|1471|485|
|2005 Urban<br>1189<br>1177|1542|517|
|2015 (NFHS-4)<br>1180<br>1169|1531|514|
|2015 Rural<br>1161<br>1151|1510|507|
|2015 Urban<br>1221<br>1205|1570|532|
|Panel B: AL (rest=1) from Time Use Survey (TUS)|1998-1999||
|Men 15+<br>Women 15+|Boys 6-14|Girls 6-14|
|1998-99 mean<br>1.93<br>1.89|1.49|1.51|
|1998-1999 SD<br>0.47<br>0.34|0.23|0.23|
|1998-99 Rural mean<br>2.03<br>1.96|1.50|1.53|
|1998-99 Urban mean<br>1.69<br>1.71|1.47|1.46|

_Source_ : Authors’ analysis based on 1998-1999 India Time-Use Survey (TUS) and National Family Health Survey (NFHS)
2018; Laczko _et al._ , 2009; Raleigh _et al._ , 2008). While informal coping mechanisms may be good enough for dealing with shorter-term impacts of the weather in subsistence agricultural contexts, those same circumstances are poorly suited to deal with longerterm effects of climate change (Collier _et al._ , 2008). Examples of such coping mechanisms are holding of low-return liquid assets or diversification of crops and economic activities. In addition, households can sell productive assets (Deaton, 1992) or seek to enhance their families’ human capital (Jacoby & Skoufias, 1997). Compared to these remedies however, migration often has superior potential to increase migrants’ welfare gains (Clemens, 2011).

Unsurprisingly, migrants believe that these welfare gains can often be realized by moving to rapidly growing cities because of their capacity to offer alternative sources of income such as wage labor (Porter, 2012). That this belief may be reasonable is evidenced by Henderson _et al._ (2017) who find a link between climate and urbanization in a large panel of districts and cities across Sub-Saharan Africa. Crucially, the key dimension of heterogeneity is whether cities are likely to have manufacturing for export. In regions where cities presumably offer manufacturing jobs, drier conditions lead to an increase in both urbanization and total urban incomes. This research builds on earlier studies such as Barrios _et al._ (2006), who estimate an increase in national urban share of 0.45 percent for a 1-percent reduction in national rainfall. Specifically, they find that shortages in rainfall have increased rates of urbanization in Sub-Saharan Africa but not elsewhere in the developing world. They report that this association has strengthened since colonial independence, convincingly arguing that the end of the colonial era coincided with lifting of movement restrictions previously imposed on native populations.

# **3 Data and Empirical Strategy**

To investigate how adverse climatic conditions affect urban extent over the long term, we build a novel panel dataset that covers the entire globe between 1985 and
# A short-term cash transfer program had sustained effects on investment in children

Poverty affects the qualities that others believe a person has, and the experience of poverty affects how an individual perceives himself (Fell and Hewstone 2015). Many Americans hold a stereotype that the poor are low in competence and warmth (Fiske et al. 2002), and it can lead them to treat poor people with contempt (Cuddy, Fiske, and Glick 2007). The self-schemas of the poor can make them feel stigma and shame. In a global survey of people living in poverty, respondents emphasized the humiliations: One deeply felt deprivation is not being able to do what is customary in the society ... not being able to entertain visitors or enjoy social life ... shame from not having toilets for visitors, or money to buy a coffin for burying a relative ... In Etropole, Bulgaria ‘people who cannot afford warm clothes for the winter go to work. Then they come back and stay at home under a pile of blankets, shivering with cold. They don't go out. They are ashamed to meet other people. If they run into a friend and are invited for a drink, they must refuse. So they would rather not go out at all’ (Narayan et al. 2000, 38).

Shame is associated with depleted self-efficacy (Baldwin, Baldwin, and Ewald 2006). The psychological dimensions of poverty make it more challenging for the poor to improve their lot.

Exposure to high-achieving role models can sometimes help people develop self-efficacy (Bandura 1997) and increase their productivity (Henrich and Broesch 2011). But poverty tends to put such role models out of reach, since many residential neighborhoods and social networks are income-segregated. As a result, poor people often naturalize the situation they are in (Dalton et al., 2016; Macours and Vakis, 2014; Tanguy et al., 2014; Duflo, 2012; Ray, 2006; Appadurai, 2004). Then they
about the value of an activity. Our paper examines how investment choices are affected by biases in information transmission.

Our analysis relies on a natural experiment involving exogenously assigned peer groups. Starting in 2008, the Colombian Stock Exchange (CSE) launched a series of professional courses on financial topics (discussed in detail in Section 3),<sup>5</sup> with the majority focusing on equity strategies. Registered individuals were assigned to small sections that studied stock trading in a classroom setting with 16 students per class, on average. Each group was formed based on availability, and the CSE did not use nor it verified past trading experience as a prerequisite to enroll in the program. Hence, the setting resembles a random assignment. We combine class records with administrative microdata of stock transactions to distinguish students with trading experience from those with no such background. In other words, we observe the trades of students who were active in the stock market before participating in one of the CSE’s financial courses. We also observe the trades and performance of students who began trading only after completing a course; that is, after interacting with experienced classmates in their artificially formed group. Overall, our novel data set combines the official class records and trading activity of 13,730 students from over 1,100 courses between 2008 and 2016.

While we accurately observe the stock trades of experienced students, we do not know the specific information that was shared among classmates. To formalize the role of selective communication in our setting, we introduce information-transmission bias in a model of social learning. In the model, potential investors with different trading skills are uncertain about the expected payoffs from trading stocks. These individuals update their beliefs after observing public information in stock prices and signals from experienced investors in their classroom. The theoretical model allows us to identify a set of testable predictions that are unique to the story of selective communication, relative to predictions from alternative or complementary explanations about investment choices under social interactions. For example, we

>
Religion similarity is proxied with an index built as follows: for each country, data on the percentage of population adhering to one of eight major religions is retrieved (data from the CIA World Factbook dataset)—these eight religion groups differ slightly from Melitz and Toubal (2014). Then the following formula for each country pair is computed, which results in a variable ranging from 0 (no believers in common) to 1: _Religion_Sim.ij =_ <u>(</u> _%muslimi * %muslim j_ <u>)+ (</u> _%catholici * %catholic j_ <u>)+</u> ( _%orthodoxi * %orthodox j_ )+ ( _%protestanti * %protestant j_ )+ (3) ( _%hinduismi * %hinduism j_ )+ ( _%buddhisti * %buddhist j_ )+ . ( _%easterni * %eastern j_ )+ ( _%judaismi * %judaism j_ ) I also control for the intensity of economic linkages between countries using the share of bilateral trade (exports plus imports, EXP+IMP) between a given pair over their total trade (COMTRADE data).<sup>16</sup> Trade is a conduit of information that may foster technological partnerships too, while it might be linked to the presence of migrants at the same time. I also account for the common technological specialization of countrypairs introducing an index of technological distance measured as where<sup>_f_</sup> _ih_<sup>stands for the share of patents of one technological class h according to the</sup> IPC classification of country i and _f jh_ for the share of patents of one technological class h of country j. Values of the index close to the unity indicate that a given pair of countries are technologically different, and values close to zero indicate that they are technologically similar (Jaffe 1986). Again, PCT patents are used to compute this index.

16
0063|-0.0270|-0.0542*|-0.0120|
|period|(0.0101)|(0.0438)|(0.0418)|(0.0234)|(0.0552)|(0.0325)|(0.0420)|
|Program * (Poverty rate – 50) *Post-|-0.0107|0.0323|0.0488|-0.0008|-0.0115|0.0403|0.0167|
|program period|(0.0107)|(0.0498)|(0.0453)|(0.0242)|(0.0605)|(0.0384)|(0.0468)|
|District fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Year fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Constant|8.8557***|3.3849***|1.6412***|8.0901***|3.5404***|0.7814***|1.1349***|
||(0.0246)|(0.1341)|(0.1007)|(0.0648)|(0.1239)|(0.0873)|(0.0998)|
|Observations|40,468|40,468|40,468|40,468|40,468|40,468|40,468|
|R-squared|0.287|0.155|0.069|0.087|0.209|0.104|0.075|

Note: This table reports district fixed-effect RDD-DD regressions of income and poverty of households using household-level observations. Robust standard errors in parentheses. Standard errors are clustered by district and village-year levels.
and testimonials about the risks of backway migration from The Gambia to Europe reduces migration intentions in the five years after the information treatment. Similarly, Tjaden and Dunsch (2021) find that peer-to-peer information transmission on the dangers and potential risks associated with irregular migration raised risk awareness and reduced irregular migration intentions in Senegal. Importantly, Bertoli et al. (2020) emphasize that acquiring information about destinations can be costly for migrants. As a consequence, migration flows from countries in which collecting information is more costly or people have stronger priors are less responsive to variations in economic conditions in destination countries.<sup>7</sup> Our paper differs from these previous studies in two main aspects. First, this paper is the first to empirically assess the impact of migration-related news articles in destination countries on the movement decision of migrants located in a (transit) developing country.<sup>8</sup> Second, in our analysis, we study the effect of changes in the _content_ of migration-related news (i.e., their sentiment) on migrants’ behavior, thus improving on previous studies using metrics for media access and exposure. We are also the first to use the GDELT dataset to study migration issues.<sup>9</sup> Finally, our paper contributes to the understanding of the evolution of the Libyan economy (Irhiam et al., 2023; Del Prete et al., 2023; Rahman and Di Maio, 2020; Di Maio et al., 2023) and of the migration phenomenon in the country after the fall of the Gaddafi regime (Di Maio et al., 2023). Moreover, by providing novel evidence on the determinants and the timing of migrants’ movements within Libya, our analysis is also related to studies looking at the dynamics of migration flows to Europe (Battiston, 2022; Deiana et al., 2024) and of international migration flows in Northern and Sub-Saharan African countries (Friebel et al., 2023; Di Maio et al.
father’s education and nonfarm occupation.

# **(8) Conclusions**

This paper develops a model of intergenerational educational persistence in a rural economy taking into account the role of parental farm vs. nonfarm occupations, and derives a theoretically-grounded estimating equation which we take to the data from rural India and rural China. We use two unique data sets that include the required information for all the children of the household head irrespective of their residency status at the time of the survey; thus eliminating the truncation bias common in the existing studies based on the standard surveys that use coresidency criteria to define household membership.

The empirical analysis delivers the following conclusions for the sons who went to school in the 1990s or earlier: (i) the intergenerational educational mobility in rural China was significantly higher compared to that in rural India, (ii) the farm/nonfarm occupations

> 42Our main estimation sample consists of 29-65 age cohorts in 2010 to ensure that the sample of children in China refers to the same age groups as in the data for India.

29
global financial crisis on capital structures at both ends, for large, publicly listed firms, and for SMEs as well.

The rest of the paper is organized as follows. Section II describes the data and presents some summary statistics. Section III introduces the empirical approach. Section IV discusses the empirical findings. Section V concludes.

# II. Data and Summary Statistics

Our firm‐level database covers the period 2004‐2011 and comes from Orbis, a worldwide database compiled by Bureau Van Dijk from various national sources.<sup>2,3</sup> Given our focus on the evolution of firms’ capital structures since the global financial crisis, we restrict our main analysis to firms that have at least 6 consecutive years of observations, including 2011. This leaves us with a sample of 277,000 firms established in 79 countries, including 39 high income countries, 25 upper middle income countries, and 15 lower middle and low income countries.

The database is predominantly composed of privately held firms and of SMEs. In the database, 98.7 percent of firms are privately owned and about 1.3 percent of the firms (5,000 firms) are publicly listed on a stock exchange. About 85 percent of firms with employment data

> 2 http://www.bvdinfo.com/en‐gb/our‐products/company‐information/international‐products/orbis

> 3 All firm level variables are winzorised at the bottom 5 percentile and the top 95 percentile. However, in our sample, more than 5 percent of firm observations have zero total debt or zero long‐term debt, so the winsorization has no effect on the bottom percentiles of the distribution.

8
# **2 Ex post quantification of the trade impact of different types of PTAs**

In this section, we estimate the trade impact of the 3 clusters of PTAs identified with the “kmean++” algorithm in a gravity framework in order to recover the partial effect on trade of PTAs associated with the different clusters. Specifically, we estimate the following structural gravity model, using PPML with panel data:<sup>3</sup> Where _Xij,t_ includes both intra-national and international manufacturing trade flows on 5-year intervals from 1982 to 2017.<sup>4</sup> Intra-national sales are needed as the correct benchmark for trade integration is the domestic economy (Yotov 2012). Otherwise the estimated coefficients would suffer from a missing variable bias. The data on inter- and intra-national trade come from, respectively, the CEPII BACI database and TradeProd combined with UNIDO INDSTAT2 2019.<sup>5</sup> Following standard practice, we fill in intra-national flows using linear interpolation between non-missing data and extrapolate remaining missing values using the evolution of country total exports as in Baier et al. (2019). In each year only countries with non-missing intra-national trade flows enter the estimation sample.

Our main variable of interest, _PTAij,t_ , is split across cluster groups: _PTA_<sup>_z_</sup> _ij,t_<sup>=1,</sup><sup>_PTAz_</sup> _ij,t_<sup>=2and</sup><sup>_PTAz_</sup> _ij,t_<sup>=3.The</sup> dummy _INTL BRDRij_ takes the value of one in case of an international trade flow. We interact this dummy with decades indexed by T (leaving the period after 2010 as reference). Exporter-time and importer-time fixed effects control for time-varying Multilateral Resistance Terms, while bilateral fixed effects control for timeinvariant unobserved characteristics of the country pair potentially leading to self-selection into PTAs (Baier & Bergstrand 2007)