rafmacalaba/data-use-mentions-extended huggingface dataset:
one-price (LOOP): in the absence of trade costs, prices of identical goods should be equalized across locations. Due to differences in the cost of non-tradables (e.g., rents) that affect retail prices, one would typically not expect the strong form of LOOP (equal price levels) to hold. The weak form of LOOP allows prices levels to differ but contends that prices should co-move. Using a panel of prices, the typical estimating equation for this method is where the dependent variable is the variance (or standard deviation) of log price differences ln( _pot/pdt_ ) over time. In using this specification, the main focus of the literature has been explaining cross-country price differences and real exchange rates (Engel and Rogers, 1996). In these applications, researchers use a panel of prices from various regions/cities in two countries, and include a border dummy to distinguish location pairs within the same country from those that are in separate countries. The same method can also be used to gauge the so-called ‘border effect’ across administrative regions within a country as Borraz et al. (2016) do in the context of Uruguay. In a recent contribution, Before introducing in detail a notable exception that estimates intra-country trade costs from spatial price differentials (Atkin and Donaldson, 2015), let me first discuss methodological issues that researchers have to be mindful of while using this approach. The first challenge is the high data requirement to obtaining prices of _identical goods._ In recent years, prevalence of bar-code level scanner price datasets have enabled researchers to circumvent this issue and use price dataset containing information for a high number of consumer goods. Such data, however, is typically available for a handful of developed markets. Another recent innovation is to scrape online prices, but this also remains limited to countries where online shopping has a non-trivial market share (Cavallo and Rigobon, 2016).

Second, even with a competitive trading sector, the absence of arbitrage or perfect price comovement does not imply
ownership structure can also be a mediating factor. They find that vertical spillovers tend to arise from projects with shared domestic and foreign ownership but not from fully foreign owned plants. Du et al. (2012) examine how industrial policy – specifically tariff liberalization and tax subsidies – affects the magnitude and direction of FDI spillovers in China’s manufacturing sector from 1998 through 2007. The study finds that liberalization measures during the critical 1998–2007 period on balance served to enhance productivity growth in Chinese industry. A study by Farole and Winkler (2014), using World Bank Enterprise Surveys data, shows three types of mediating factors: firms’ absorptive capacity, investors’ spillover potential measured by the share of FDI output sold domestically, and host-country institutional factors. Finally, Espitia et al. (2017) show that distance, the level of development of the host country, and institutions are important mediating factors using panel data for 62 countries from 2005 to 2012.

In addition to firm productivity, employment, wage and export performance, the effects of FDI have also been found on domestic investment and innovation, although these aspects have received far less attention. On domestic investment, Wang (2010) uses data from 50 countries over 1970 to 2004 and finds that FDI had a negative contemporaneous effect on domestic investment but a positive cumulative effect on domestic investment. Miun et al (2002) ask a similar question and find mixed results in the Czech Republic, Hungary, and Poland. In Sub-Saharan Africa, Ndikumana et al (2008) find FDI to crowd out domestic investment. On innovation, Cheung et al (2004) find FDI has increased the number of domestic patent applications in China.

Recently, using a large cross-country firm-level data set, Alfaro and Chen (2017) find factor market reallocation in labor and capital markets from less productive to more productive firms accounts for the majority of aggregate productivity gains from foreign multinational competition. Bao and Chen (forthcoming) show another important source of gains from FDI arises from domestic firms’
Öztunalı (2018), Atesagaoglu _et al._ (2017) and Atiyas & Bakı ̧s (2014). Similar parameter values are used in the Penn World Tables to make cross-country comparisons.

Guner _et al._ (2008) estimate the span of control parameter, _γ =_ 0.802 for the US in which the mean establishment size is 17.09 employees. In addition, Leal (2014) estimates the same parameter for Mexico whose mean establishment size is 5.5 employees and finds _γ =_ 0.76. Since Türkiye’s mean enterprise size is 3.6 employees, I choose _γ =_ 0.7. Given _γ_ , the value of _α_ can be determined as _α =_ 0.34/ _γ =_ 0.486.

Next, I assume a composite lognormal-Pareto distribution for the distribution of managerial abilities in Türkiye such that the top 1% of the distribution would follow a Pareto distribution. The introduction of distortions requires seven additional parameters to estimate: _λ_ , _τ_ , _φ_ , _z_ ˆ, _α_ 0, _α_ 0 and _b_<sup> ̃</sup> . Finally, there are nine parameters (i.e., two for the distribution of ability and six for size-dependent distortions) to estimate: _σ_ , _s_ , _λ_ , _τ φ_ , _z_ ˆ, _α_ 0, _α_ 1 and _b_<sup> ̃</sup> . I discipline these parameters by matching nine moments from the data: the mean plant size, the fraction of plants with 1–49 employees, the fraction of plants with 50–99 employees, the employment share of plants with 50–99 employees, the employment share of plants 10See Scollnik (2007) for more details.

13
tory status that Colombia had previously granted to Venezuelan migrants. The difference between previous waves of PEP and this one (PEP) was that the latter was explicitly created for Venezuelans registered in the RAMV, and as such, focused on undocumented immigrants. According to the decree, these immigrants could request the PEP migratory status during the third quarter of 2018. Each migrant had a window of time in which to apply based on their RAMV registration number, which they received as their record was entered into the system in real time. This number was exogenously allocated to each immigrant across all the authorized points. By the end of that year, 281,307 of the 442,617 eligible individuals had applied for and received one.

According to the RAMV data described in Table 1, the migrants under consideration were young and had low levels of education: less than 10 percent of RAMV migrants were older than 60 years, and over 50 percent of this group had completed at least secondary education. This group was younger but slightly less educated than the Colombian labor force. According to 2018 population estimates, approximately 13.5 percent of the Colombian population was older than 60 years, and 61.5 percent of the active labor force in 2017 had completed at least basic secondary education. At the time of the RAMV survey, 46.3 percent of working-age migrants had some level of employment in the informal sector.

# **III DATA**

We exploit monthly and city variation in our estimates. The data we use can be grouped as follows:

1. . _Crime reports by migrant victims_ We use individual level reports from victims from the Colombian National Police that include reports on all crimes denounced in Colombia between January 2017 and December 2019 (one year before and after the regularization program). They include information on the type of crime, the municipality of reporting, and the nationality and the gender of the individual who reported it. We construct crime counts on a monthly basis for each main city in

9
Figure 1: Informality vs. financial fragility <!-- Start of picture text --> (a) Probability of falling into arrears vs. Informality index (b) Predicted probability of falling into arrears by type of<br>informality<br><!-- End of picture text --> <!-- Start of picture text --> .6<br>90<br>80<br>.55 71<br>70 67 68 64<br>61<br>60 56 58 53<br>.5<br>50<br>40 33<br>.45 30 29 30 27<br>20<br>.4 10<br>0<br>-.2 0 .2 .4 .6 .8 Fully formal Necessity De Soto Parasite<br>Informality index<br>Round 1 Round 2 Round 3<br>(c) Weeks to continue operating with cash available vs. Infor- (d) Weeks to continue operating with cash available by type<br>mality index of informality<br>25 18<br>14 14<br>20 13<br>12<br>10 9 9<br>15 8<br>5<br>10<br>0<br>5 Fully formal Necessity De Soto Parasite<br>-.2 0 .2 .4 .6 .8<br>Informality index Round 1 Round 2<br>Probability of falling into arrears<br>Probability of falling into arrears<br>Weeks to continue operating with cash available<br>Weeks to continue operating with cash available<br><!-- End of picture text -->

**Notes:** Both panels control for sector, age, and country. Source: Authors’ calculations using data from the World Bank Business Pulse Survey (BPS).

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usingthe LTGM Human Capital Extension|
|TFP Growth|0.9% 0.6%|Similar to the last 15-year average and the last 30-year<br>median usingPWT 9|
|Investment-to-GDP Ratio|24%|Based on projections of public and private-investment-<br>to-GDP|
|Public Investment-to-GDP Ratio|6.0%|Based on recent trend and IMFprojection upto 2023|
|Private Investment-to-GDP Ratio|18.0%|Based on recent trend and IMFprojection upto 2023|
|Population Growth|1.3% 0.4%|UN Populationprojections(via WB HDN)|
|GDP Growth 2020-50|4.5% 2.0%|4.6% is MTI forecast for 2020 and 4.8% is the IMF Oct.<br>2019 WEOprojections average for 2020-23.|
|Atlas GNI PC Level|US$ 10,460|World Bank WDI for 2018|

Investment-to-GDP is assumed to remain around 24% given recent trends and the IMF Article IV projections for the next few years. For the baseline, we calibrate public investment-to-output and private investment-to-output ratios ( and ) of 6 percent and 18 percent respectively (Figure 2.1). This is based on projections made by IMF (2019), which forecast that declines from 7 percent in 2019 to II<sup>GG</sup> /YY II<sup>PP</sup> /YY II<sup>GG</sup> /YY 8
# **References**

- **Abbott, Pamela and Dixon Malunda** , “The Promise and the Reality: Women’s Rights in Rwanda,” _African Journal of International and Comparative Law_ , 2016, _24_ (4), 561–81.

- **Abramsky, Tanya, Karen Devries, Ligia Kiss, Janet Nakuti, Nambusi Kyegombe, Elizabeth Starmann, Bonnie Cundill, Leilani Francisco, Dan Kaye, and Tina Musuya** , “Findings from the SASA! Study: A Cluster Randomized Controlled Trial to Assess the Impact of a Community Mobilization Intervention to Prevent Violence against Women and Reduce HIV Risk in Kampala, Uganda,” _BMC Medicine_ , 2014, _12_ (1), 122.

- **Adams, Abi, Kristiina Huttunen, Emily Nix, and Ning Zhang** , “The dynamics of abusive relationships,” _The Quarterly Journal of Economics_ , 2024, _139_ (4), 2135–2180.

- **Aizer, Anna** , “Poverty, Violence, and Health the Impact of Domestic Violence during Pregnancy on Newborn Health,” _Journal of Human resources_ , 2011, _46_ (3), 518–538.

- **Akerlof, George A. and Rachel E. Kranton** , “Economics and Identity,” _The Quarterly Journal of Economics_ , 2000, _115_ (3), 715–53.

- **Alik-Lagrange, Arthur, Claire Cullen, Muthoni Ngatia, and Julia Vaillant** , “Preventing Intimate Partner Violence: Impact Evaluation of MIGEPROF Couples Training for IPV Prevention in Eastern Rwanda’. AEA RCT Registry,” 2017.

- **Anderson, Michael L.** , “Multiple Inference and Gender Differences in the Effects of Early Intervention: A Reevaluation of the Abecedarian, Perry Preschool, and Early Training Projects,” _Journal of the American Statistical Association_ , 2008, _103_ (484), 1481–95.

- **Anderson, Siwan and Garance Genicot** , “Suicide and Property
|>500|
|Low|0.5|1||2|3|
|Medium|1|2||3|5|
|High|3|3||5|10|

Note that we are building a counterfactual scenario in which disasters never hit, starting from surveys in which households’ income factors in the impacts of disasters. We therefore apply positive shocks on households, i.e. we _increase_ the income of each household when they are affected by a disaster, to reverse current disasters’ effects on people’s income.

The effect of natural disasters generally lasts more than a year. To simplify, we assume that the economy goes back to normal linearly during a “reconstruction” time that depends on the return period (Table 2). Here again we build three scenarios that account for the uncertainty around reconstruction times. For each event, therefore, we estimate the total impact (over time) by multiplying the yearly impact by N/2 (where N is the reconstruction time).

The impact of each event is represented in Figure 2, with the number of people in poverty because of an event multiplied by the probability of this event.

5
smoothing arguments one would expect less dispersion in the expenditure-based surveys.<sup>15</sup> One might wonder if in our sample the result is driven by the fact that the panel is unbalanced. But even if we give equal weight to each country’s average Gini coefficient we find a similar picture: the resulting overall means are 0.40 for incomebased and 0.42 for expenditure-based observations, respectively. The latter, however, are more concentrated around their mean. In other words, there is a lower frequency of countries with extreme (whether high or low) inequality in the expenditure-based subsample than in the income-based subsample.

On the other hand, there is a noticeable difference between the average Gini coefficients of the subsamples based on gross and net income. Gross income-based Gini coefficients average 0.40, whereas those based on net income average 0.33. Although one should be careful in attaching any particular economic interpretation to these figures, it is tempting to view them as reflecting the effect of government interventions that lead to income redistribution.

# **III Empirical results**

We turn to the empirical implementation of the lognormality tests. We perform them on the full sample as well as different sub-samples defined by type of data -- i.e., according to whether the observations are based on expenditure or income surveys and, in the latter case, whether income is measured on a net or gross basis. Apart from allowing some robustness checks, this differentiation is also of interest for two other reasons. First, if the distribution of income is lognormal, and households engage in consumption smoothing, the distribution of consumption will not be lognormal in general.<sup>16</sup> Hence the common practice of pooling together income- and expenditure-based observations in applied work could yield misleading test results in our case. Second, gross income could be lognormally distributed while net income is not -- for example if taxes and transfers are lump-sum rather than proportional to income.

To be
Ni is a dummy variable that takes the value one if a worker used networks of relatives, friends, or acquaintances, and zero if s/he used any other method. The raw averages show that migrants are significantly more likely than native workers to use networks to find a job; on average, 40 percent of migrants used this network, compared to 30 percent of native-born workers. To see how this fact translates into occupational risk exposure, we estimate the multinomial logit model with the three types of occupations (safe, at health-risk and at income-risk) displayed in equation (1). We add two variables based on the networks for each of the migration categories (migrants and natives) so we can assess whether the impact of networks on job safety differs between those two groups. We show that both natives and migrants who used networks to find a job are about 6 percent less likely to have a safe job and that they are more likely to have jobs with either higher income risks or higher health risks (Tables 1-3, Column 7). The use of such networks is mostly associated with higher health risks among migrant workers; by contrast, among native workers, the use of networks increases the probability of having a job with greater income risks. Furthermore, the average migrant-native gap in the probability of having a safe job is reduced in half for migrants from NMS13 and Latin America, and becomes insignificant for migrants from Sub-Saharan Africa, Asia or MENA. Therefore, while networks (in general of co-nationals) are a key source for finding employment for many migrants, the use of such networks can translate into an occupational concentration in certain jobs; the jobs in which migrants who used these networks tended to concentrate proved to be more vulnerable to both health and income risks related to the COVID19 pandemic.

# 6.4. Migrants’ Responsiveness to Labor Demand

Prior studies show that the effects of local labor-demand conditions on immigrants’ labor market outcomes are stronger than those for natives (Azlor et al., 2020
qualitative data is collected, for instance, to understand patterns of well-being across a large country which would follow the familiar logic of stratified probability sampling (Alexander, 2017).

4) Integrating qualitative and quantitative work can be done in different ways. Following classical inferential logic, small-N qualitative work can be conducted using the case study method to develop hypotheses, which can then be tested for their generalizability, possibly mediated by a theoretical model, with quantitative data collected from a representative sample of respondents (Rao, 1997b). The integrated collection and analysis of qualitative and quantitative information can also be analyzed using Bayesian inference (Humphries and Jacobs 2015).

5) Machine learning and Natural Language Processing are a double-edged sword. They offer tremendous advantages in moving us towards analyzing narrative data at scale. Yet, supervised methods that rely on biased training sets, such as sentiment dictionaries developed for western contexts applied to non-western linguistic cultures, can result in substantial bias. Furthermore, as Woolcock (2021) has argued, when machines are used for analyzing data, narratives have the danger of being analyzed out of context and without nuance, resulting in misinterpretation. In other words, relying on machines without sensitive human intervention has the danger of turning reflexively collected data into non-reflexive analysis.

6) Process, studied carefully with qualitative methods, can be extremely valuable to understand the mechanisms of change and thus complement time-variant quantitative studies, particularly impact evaluations. Understanding process matters not just for research but also for policy, where an exclusive emphasis on policies that can only be assessed using experiments or impact evaluations can sharply limit our capacity to imagine and create a better world (Rao, 2019).

7) The potential for the direct participation of “respondents,” “beneficiaries,” and “subjects” in research is vastly unexplored. If our purpose as researchers is to assist in the process by which people become better-off, then more can be done to design and share research with people, and even co-create tools so that they can conduct research
**Table S1.8: Robustness: Time of the day**

||DP|FDC|NRM|UPC|
|---|---|---|---|---|
|Enumerator gender (male)|-0.560***|-0.400**|-0.108***|-0.511***|
||(0.150)|(0.159)|(0.034)|(0.116)|
|Enumerator age|-0.021|-0.006|0.002|-0.034**|
||(0.021)|(0.020)|(0.004)|(0.016)|
|Enumerator education|0.108|0.131|-0.075**|0.060|
||(0.109)|(0.121)|(0.035)|(0.110)|
|Enumerator from urban locality|0.454**|0.567***|0.001|0.427***|
||(0.185)|(0.204)|(0.039)|(0.149)|
|Afternoon|-0.005|-0.041|0.035|0.006|
||(0.052)|(0.054)|(0.030)|(0.052)|
|Observations|6,240|6,328|6,547|6,315|
|R-squared|0.485|0.458|0.234|0.478|

_Source_ : Data for the study comes from a survey of farmers in northern Uganda. The survey was conducted by Innovations for Poverty Action.

Note: This table presents OLS regression
three-quarters of the membership had experienced significant banking problems during 1980–96. These studies showed that the extent and nature of the problems varied substantially, including cases of insolvency of one or two large banks and situations in which loss-making government-owned institutions needed chronic recapitalisation. But weaknesses extended to all regions of the world and levels of development. Bank fragility was pervasive and multifaceted, a phenomenon ripe for more systematic empirical investigation.

The surveys provided the raw material to construct a sample, while economic theories and case studies suggested mechanisms and channels through which economic conditions and structural characteristics might impact bank stability. In the rest of the paper we will summarise the main methodological approaches, results, and open questions in cross-country studies of banking crises.

# **3. Two econometric approaches to identifying the determinants of banking crises**

# _The signals approach_

The signals approach, originally developed to identify turning points in business cycles, was first applied to banking crises by Kaminsky and Reinhart (1999). This study focuses on the phenomenon of the ‘twin crises’, namely the simultaneous occurrence of currency and banking crises. To this end, the paper documents the incidence of currency, banking, and twin crises in a sample of twenty industrial and emerging countries 1970–95. Currency crises are identified based on an index of market turbulence developed by Eichengreen _et al_ . (1995), while the onset of a banking crises is assumed to coincide with depositor runs leading to the closure or takeover of one or more banks, or with large-scale government intervention to assist, take over, merge, or close one or more financial institutions, leading to more intervention elsewhere in the financial system.

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Policy Research Working Paper 10825

# **Abstract**

This paper provides the first measures of vulnerability to poverty in Latin America and the Caribbean at the household level looking at natural hazards such as floods, landslides, cyclones, earthquakes, and droughts. It considers seven countries in the region: Brazil, the Dominican Republic, Ecuador, El Salvador, Honduras, Mexico, and Peru. The paper constructs a unique data base that links household surveys from the Socio-Economic Database for Latin America and the Caribbean database to data on natural hazards from the ThinkHazard database and Emergency Events Database, which permits the first estimates of how household exposure to natural events raises the probability of being poor. The results suggest that vulnerability to poverty is related to households’ coping and adaptation strategies, and there is great heterogeneity depending on the natural hazard assumed. The evidence generated by this approach helps in understanding the lack of coping mechanisms and adaptation strategies among vulnerable households. This understanding can be used to mitigate the effects of natural disasters. The evidence-based approach is useful for identifying specific vulnerable communities in disaster-prone areas. Furthermore, the information can be presented in the most disaggregated representative unit that the data allow so that policy makers can build more efficient management policies in their location.

This paper is a product of the Poverty and Equity Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at gcanavire@worldbank.org, solivieri@worldbank.org, mserio@worldbank.org, and aconconi@worldbank.org _The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names