rafmacalaba/data-use-mentions-extended huggingface dataset:
6. Lesotho: Desk provided data for 1982–2003. 7. Macedonia, FYR: Desk provided data for 1993–97. 8. Moldova: Desk provided data for 2000. 9. Niger: Desk provided data for 1995–2003. 10. Romania: Desk provided data for 2000–2003. 11. Slovak Republic: Desk provided data for 1999–2003. 12. Tajikistan: Desk provided data for 1997–2001. 13. Ukraine: Desk provided data for 2000. 14. Venezuela: Desk provided data for 1997–2003.

No data on remittances were available for the following countries, and they were therefore excluded from the analysis: Afghanistan, I.S. of; Angola; the Bahamas; Bahrain; Bhutan; Brunei Darussalam; Burundi; Canada; Congo, Dem. Rep. of; Iraq; Kuwait; Liberia; Libya; Qatar; Singapore; Taiwan Province of China; United Arab Emirates; Uzbekistan; Vietnam; and Zambia.

Details of some other key variables are as follows.

- _Domestic Output_ .  This is measured as real GDP per capita. It comes from the Penn World Table Version 6.1.

- _Main Host Output_ .  For each country _i_ , this is measured as real GDP per capita in the country _j_ which contains the largest share of country _i_ ’s migrant workers.  It is from the Penn World Table Version 6.1 and the above-mentioned migration data from the OECD.

31
**Figure 6. NTM coverage: Brazil vs other countries, 2015 (%)**

<!-- Start of picture text --> 100.0 89.0<br>80.0<br>66.4 64.6<br>61.1<br>60.0 47.5<br>40.0<br>25.8 22.6<br>20.0<br>5.9<br>0.0<br>Sanitary and Technical Quantity Price<br>phytosanitary barriers controls controls<br>Brazil Other<br><!-- End of picture text --> Source: World Bank staff elaboration using UNCTAD TRAINS data and UN COMTRADE data Note: coverage ratios capture how much trade is subject to a NTM measure. It does not reflect the level of NTM restrictiveness, just the incidence. It doesn’t include local content requirement.

**Figure 7.  Services trade restrictiveness index, Brazil relative to LAC average**

<!-- Start of picture text --> 70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>Brazil LAC average<br>Source: World Bank staff elaboration using World Bank STRI<br>dataset<br><!-- End of picture text --> Note: higher values mean more restrictive regimes.

Among NTMs, Brazil is imposing local content requirements (LCRs) to an increasing number of products. For instance, in 2012, the Brazilian authorities issued regulations related to the industrial and trade regime for the automotive sector. The use of local content requirements was also prevalent in the oil and gas sectors, a tendency reversed in the more recent licensing rounds. A recent analysis conducted by Stone, Messent and Flaig (2015) maps the use of LCR measures applied between 2008 and April 2014 in several countries
<mark>features of remittances and aid flows imply that they can support activity in the face of economic adversity and help facilitate better risk sharing outcomes.</mark><sup>6</sup> Our paper relates to some other strands of the literature that focus on the behavior of different types of capital flows. For example, it is related to the seminal work by Lucas (1990) on the capital allocation puzzle that considers why so little capital flows from rich to poor countries. Many studies provide explanations for the puzzle: institutional underdevelopment and expropriation risk leading to capital flowing uphill (Alfaro, Kalemli-Ozcan and Volosovych 2008; Bai and Zhang 2010; Benhima 2013); investors overreacting to signs of trouble in EMDEs (Chinn, Dooley and Shrestha 1999; Chinn and Kletzer 2001).<sup>7</sup> Recent research considers the links between remittances and aid flows and illicit capital outflows. While flows such as development aid are found to be positively correlated with illicit outflows in the presence of capital controls, remittances are negatively correlated with illicit outward capital flight (Steinkamp and Westermann, 2021). Aid may be susceptible to being diverted by the recipient governments, but a similar behavior is unlikely to be tolerated by foreign workers supporting their families back home through remittances.

The remainder of the paper is structured as follows. The next section briefly describes the database and empirical framework. Section 3 analyzes the linkages between different types of capital flows and international risk sharing, and undertakes a battery of robustness tests. Section 4 offers a few concluding remarks.

# 2. Database and Empirical framework

# 2.1. Database

We examine the relationship between cross-border capital flows and international risk sharing using a database that includes a large sample of EMDEs. Real consumption and real GDP data are drawn from the World Bank’s World Development Indicators (WDI). Population statistics are from the United Nations’ National Accounts Main Aggregates Database. It is widely known that aggregate output and consumption data are nonstationary,
for residence decisions made between November 2002 and October 2004 reveals that out of 98 applications, only 1 was rejected for failure to meet the English requirement, and only 3 others were rejected for failing other requirements of the policy.

> 5 The person who registers is a Principal Applicant. If they are successful, their immediate family (spouse and children under age 18) can also apply to migrate as Secondary Applicants. The quota of 250 applies to the total of Primary and Secondary Applicants, and corresponds to about 70 migrant households.

> 6 Source: GTAP database of Parsons et al. (2005).

- 7 -
s population. This large data set therefore permits cross-country comparison of people‘s tax morale that is based on representative national samples The WVS/EVS is particularly advantageous to this review because it covers a large set of Central Eastern and Former Soviet Union countries. Not only does the WVS/EVS pertain to a wide variety of religions and cultural traditions within each country, but it can assist an analysis of value changes over time. The WVS has been broadly used by social scientists.

To assess the level of tax morale the following question has been used: 7
mark>et al</mark>_ <mark>. (2018b), which c</mark> an be found in the last column of Table 1.

4 One noticeable outlier in Figure 1 is the food products sector, which appears to have an extreme above‐ average data‐intensity. One potential reason is that the US Labor Statistics only records employment data for 10 out of a total of 85 6‐digit NAICS sub‐sectors for this industry that all fall into the 2‐digit ISIC industry of food products as measured by the concordance table. Hence, it is very likely that labor is underreported for this sector which as a result increases the data‐intensity given that our measure is expressed as a ratio in which labor forms the denominator. Therefore, in the regressions we exclude the sector of food products although including this sector does not significantly alter the main results.

5 The authors have contributed to the development of the database at ECIPE. The data set comprises 64 economies and is publicly available on the website of the ECIPE at the link: www.ecipe.org/dte/database.

7
# **I. Introduction**

Vietnam has set up poverty reduction as a major development policy. To achieve this goal, Vietnam has maintained an extensive public safety net and launched a large number of poverty reduction programs. These programs generally benefit from having precise information on where the poor are located, and on how poor they are, see e.g. Bigman and Fofack (2000) and Elbers _et al._ (2007).

The objective of this study is to estimate poverty and inequality for rural Vietnam at different levels of aggregation by combining the Vietnam Household Living Standard Survey (VHLSS) from 2006 and the Rural Agriculture and Fishery Census from the same year. We will produce estimates at the regional, provincial and district level, and will consider both expenditure and income based measures. The estimates are obtained by adopting the small area estimation method put forward by Elbers _et al._ (2003) (henceforward ELL), which has since been used to put poverty on the map in over 40 countries worldwide.

The information on all households provided by the census combined with the detailed information on selected households from the survey makes it possible to estimate poverty at levels of aggregation the survey alone does not allow for. The standard errors of our province level estimates are comparable to the standard errors of the region level estimates based on survey data only. The standard errors of our district level estimates are obviously larger, but still acceptable.

The use of the agricultural census denotes a modest variation on the approach of ELL, which conventionally uses a population census instead. The motivation for appealing to the agricultural census is that the population census is only available once every ten years. In Vietnam, the agricultural census is conducted every five years. This means that by alternating the population census with the agricultural census we are able to triple the frequency of poverty and inequality estimates at the small area level. The latter is important as it makes the small area estimation exercise a more suitable tool to monitor poverty and inequality
.021)|0.562|(0.018)|0.540|(0.022)|
|Central|0.445 (0.015)|0.438|(0.009)|0.434|(0.024)|
|South|0.555 (0.014)|0.537|(0.008)|0.533|(0.026)|

Table 5: Estimates of poverty from the survey, ELL and SEM The small area estimates of poverty for the two approaches are presented in Figure 2, which shows the two poverty maps side by side. A visual inspection of the two maps shows a striking agreement on the spatial distribution of poverty. Both maps suggest low levels of poverty in the urban areas of Mzuzu, Kasungu, Lilongwe, Zomba, and Blantyre, as well as areas of high poverty in the far north and south of the country.

9SEM slightly underestimates poverty in areas of the Northern region with comparatively low rates of poverty.

24
phone surveys (HFPS) that covers 80 economies in five out of six World Bank regions, representing a combined population of over 2.5 billion.<sup>2</sup> The HFPS database spans April 2020 through December 2021, with at least four survey rounds available in most economies represented. While this database is unique in its coverage, especially during a period when data collection efforts were halted in many countries at least temporarily, it also comes with a set of caveats. Biases associated with phone surveys were discussed extensively in previous papers. This paper discusses additional caveats related to the way phone survey data measure employment, which are important for interpretation of the evidence.

> 1For comparison, the analysis of initial impacts covered around 30 economies in earlier papers such as Bundervoet et al. (2022).

> 2While phone survey data also exist for countries in South Asia, they are dropped from the analysis because, at the time of writing, data was available only from one survey round and differences in the survey instrument prevented the construction of comparable employment indicators that are representative of the population.

1
employ fewer than five workers, as well as wholly state-owned enterprises. All these factors affect the benchmark for evaluating the differences between foreign multinational and domestic firms.

But how accurate is the WBES when it comes to foreign multinationals? The sampling follows frames provided by national statistical offices or other official sources over three dimensions: firm size, business sector, and geographic region, but does not consider domestic or foreign ownership. However, to the extent that variation in foreign ownership is driven by characteristics the survey is stratified on - such as sector of activity, scale of production, and location, all of which are established drivers of foreign multinational activity – then the surveys should exhibit high alignment in terms of representativeness, subject to a sufficient number of observations on both pools. We restrict the sample to surveys conducted after 2010 where at least 30 foreign multinationals with over 50 percent ownership are present, keeping only 63 surveys of the 139 available (see Table 4 for a description of the sample).
control for food quality at home and other unobserved factors that correlate with body weight and the nutritional quality of FAFH. In exploiting the importance of specific nutrients, we find that _sodium_ in most restaurant menus seems to be responsible for the association between the diet quality of FAFH and excess body weight. This is in line with a number of studies suggesting that dietary sodium intake implicates weight gain (Larsen et al. 2014; Yi and Kansagra 2014).

After considering a number of individual and environmental determinants of obesity,

> 3 Individuals in neighborhoods with low socioeconomic status tend to exhibit a greater prevalence of overweight and obesity relative to individuals in neighborhoods of higher socioeconomic status.

> 4 While data only allow this relationship to be analyzed among women, women are more sensitive to the food environment than men (Hallam et al. 2016; Kuchler et al. 2002).

> 5 The n eighborhood is the district of residence and the direct food environment of the women. Since detailed information on whether women consume FAFH predominately in their neighborhood or socioeconomic group is not available, what we use is an indicator that is expected to correlate with the quality of the most-frequently consumed menus out of home. Qualitative evidence from local experts supports that people eating in restaurants are mostly from the same socioeconomic group of the restaurant’s environment, which is also in line with findings from other urban areas (Cannuscio et al 2014). This is most likely for women from low socioeconomic status (SES) neighborhoods. In neighborhoods of higher socioeconomic status, restaurant clients also tend to be locals who can afford the upper tier eating possibilities, or visitors but from equally wealthy neighborhoods.

> 6 The stratification of districts by socioeconomic group is a quantile measure defined by the average expenditure levels of the households in the respective neighborhoods as reported in the National Survey of Family Budgets (ENAPREF) 2009–2010.

4
probability that the child will subsequently die. Benefo and Schultz (1994) find that households further from a clinic have higher child mortality in Cote d'Ivoire, but in Ghana proximity to clinics appears to increase mortality (statistically insignificant). Lavy, Strauss, Thomas, and de Vreyer (1996) find that the distance to health facilities significantly decreases mortality in rural Ghana (the presence of child services **in** that clinic is also significantly positively related to survival, however). In Malaysia, DaVanzo (1984) finds distance to medical care does not reduce infant mortality conditional on birth weight, but that birth weight is lower the greater the distance to care. Hossein (1989) finds that the presence of a dispensary and presence of a family planning clinic lowers mortality in Bangladesh. Rosenzweig and Schultz (1982) find that in rural Colombia, neither rural health posts, municipal level public and private clinics, dispensaries, or mobile care units are significantly related to child mortality. For urban areas they find that hospitals, clinics, and family planning clinics tend to reduce mortality, however this result is not consistent across all age groups. Rosenzweig and Wolpin (1982) use data from rural India to find that the fraction of villages in a district with a family planning clinic and the fraction with a dispensary, is associated with lower mortality, but that the fraction with an "other health facility (health centers, nursing homes, etc...)" is associated with higher mortality. Sastry (1995) finds that the number of general health facilities are insignificantly associated with higher mortality in Northeast Brazil and insignificantly with lower mortality in South/Southeast Brazil. Overall, the econometric evidence is mixed that there is a health effect of clinics at all, no less that it is large.

_Micro level: Project Evaluation._ There are a few evaluations of systematic 17
**<mark>Table A7.2: Baseline Association of Business Outcomes with  Management Measures</mark>**

|||LogEmp|loyment|||Labor Pro|ductivity||
|---|---|---|---|---|---|---|---|---|
|Anexo K Score|0.035***|||0.017***|0.672***|||0.877***|
||(0.006)|||(0.006)|(0.140)|||(0.186)|
|WMS Management Score||0.250*||0.086||4.914||‐0.652|
|||(0.134)||(0.153)||(4.070)||(5.310)|
|MOPS Management Score|||0.869*|‐0.554|||8.994|‐2.894|
||||(0.465)|(0.459)|||(8.650)|(12.164)|
|Sample Size|156|77|95|46|156|77|95|46|
|R‐squared|0.19|0.05|0.03|0.14|0.14|0.01|0.01|0.25|
|Notes:|||||||||
|Anexo K management pract|ices are 141|managem|ent pract|ices divided in|to five sub‐ar|eas.|||
|WMS is World Managemen|t Survey, tak|en for sub|sample of|firms in 2013|. MOPS is Ma|
We use reporting by wives and husbands on household decision making over large purchases in 12 nationally representative surveys in Sub-Saharan Africa to examine the empirical relationship between the mode of decision making and perpetration of IPV by the husband. Sole decision making by the wife is associated with the highest levels of IPV, followed by sole decision making by the husband.

Our main contribution is to demonstrate the importance of including the responses of both husbands and wives in analyses of household decision-making. They do not necessarily provide the same answer about who decides, and the patterns of agreement and disagreement provide important insights about the household more generally. In particular, we find that IPV is the lowest in households where both the husband and wife reports that they make decisions jointly. Every other combination of responses is associated with higher levels of IPV.

We include a number of additional variables that are associated with other theories regarding IPV: attitudes towards violence, similarity of preferences, marital capital, and women’s bargaining power. While some of these variables are themselves significantly related to violence, they do not change our main findings on the importance of agreement that decisions are made jointly for lower levels of IPV.

These results do not causally identify the impacts of agreement that decisions are made jointly on the incidence of IPV. However, the descriptive patterns hold across a wide range of African countries. Shared reports of joint decision making may indicate an underlying level of respect and mutuality within a marriage. Interventions to encourage couples to make decisions jointly may not necessarily impact IPV if they do not help create these deeper relationships. Interventions, however, that build respect and encourage shared responsibility may have a more significant impact. Recognizing that women who live in 27
Historically, mangroves have shown considerable resilience to fluctuations in sea level rise (Alongi, 2009; Erwin 2009; Gilman et al. 2006). However, their adaptation to future sea level rise (SLR) depends on their success in landward progression and is conditioned by the availability of adequate and suitable space for expansion/migration, continued supply of sediment and nutrients from fresh‐water inflows, and a rate of sea level rise that is not greater than the rate at which mangroves can migrate (Ellison and Stoddart 1991; Semeniuk 1994; UNEP 1994; McLeod and Salm 2006; Lange et al. 2010).<sup>12</sup> The ability of mangroves to migrate landward, in turn, is determined by local conditions, such as topography (e.g., steep slopes) and, perhaps more importantly, infrastructure (e.g., roads, agricultural fields, dikes, urbanization, seawalls and shipping channels). If inland migration or growth cannot occur fast enough to compensate for the rise in sea level, then mangrove areas will become progressively smaller with each successive generation and may perish.

Understanding the impact of SLR on mangroves must take into account factors that affect the ecological balance of the ecosystem, such as the history of sea levels in regard to development of coastal gradients, relative geomorphic and sedimentologic homogeneity of the coast, coastal processes including tidal range and its stability, density of mangroves, availability of fresh water and sediment, and salinity of soil and groundwater (Belperio 1993; Semeniuk 1994; Blasco et al. 1996; Kumura et al. 2010).

In order to estimate the impact of SLR on mangroves and the potential for adaptation, we use the wetland migratory potential (WMP) characteristic in the DIVA database from the DINAS‐ COAST project (Vafeidis et al. 2008). WMP indicates the potential for wetlands, including mangroves, to migrate landward in response to a 1‐meter rise in sea level. The migratory potential is based on a few
data sources: the IMF’s CDIS data, available from 2009 onward, has a broader coverage of South countries than the UNCTAD data used before 2009. A mitigating factor is that countries with large FDI positions report data for the entire sample period. Moreover, whereas the tables and figures report the raw data, we account for these changes in country reporting coverage in the regression analyses in sections 3 and 4.

A second concern is that bilateral international investment data contain missing observations and it is difficult to distinguish between missing data points and zeros. This distinction is not trivial. Zero values are meaningful for the analysis of international connectedness along the intensive and extensive margins (Helpman, Melitz, and Rubinstein 2008). Incorrectly assuming that a missing observation is zero could lead to an overestimation of the growth in the value of investment between individual country pairs and the number of active links. In the appendix, we explain the methodology we use for each investment type to determine whether missing values are treated as unreported or zero observations.<sup>8</sup> In practice, the treatment of missing values is less consequential for the analysis of the value of investment (section 3) than for that of the number of active links (section 4). Any aggregation of values implicitly treats missing values as zero. Moreover, our regressions at the country-to-country level show that new links explain little of the strong growth in the value of South investment and, thus, the treatment of new links is not of first-order importance (section 3.3). In the analysis of the number of links, the distinction between zeros and missing values can be more consequential. In section 4, we describe how we partially control for changes in bilateral reporting coverage.

> 8 For robustness, we conducted the analyses in the paper by simply assuming that all missing values were actually zerovalued observations. The results were qualitatively similar to the ones reported in the paper.

11
627]|
|LM-stats for<br>under-<br>identification|23.71|66.87|23.71|66.87|23.71|66.87|23.71|66.87|23.71|66.87|
|F-stats for<br>weak<br>identification|6.258|17.49|6.258|17.49|6.258|17.49|6.258|17.49|6.258|17.49|
|Hansen J-stats|90.70|52.03|50.88|52.03|26.80|43.69|112.9|117.9|14.55|30.80|
|Obs.|8946|8870|8946|8870|8946|8870|8946|8870|8946|8870|

Notes: (1) All specifications with GMM standard errors. * p<0.10, ** p<0.05, *** p<0.01; (2) Data source: KMS 2013; (3) Full estimation results with corateis are provided in Appendix B.

25
# **Appendix Table 1: Descriptive Statistics of the Variables Used in Regression Analysis**

|Variable|No. of<br>Observations|Mean|Standard<br>Deviation|Minimum|Maximum|
|---|---|---|---|---|---|
|SME Loans/Total Loans (%)|271|10.1|12.3|0.0|55.0|
|Investment Loans/SME Loans (%)|270|24.1|22.8|0.0|80.0|
|Log Total loans|339|20.8|2.4|13.8|24.7|
|Log Number of Branches|396|3.3|1.4|0.0|6.7|
|Maximum Coverage Registry or Bureau|381|8.0|9.8|0.0|36.0|
|Legal Rights Index|399|3.1|1.2|0.0|4.0|
|Time to Register Property|399|29.4|34.1|2.0|193.0|
|Time to Enforce a Contract|399|662.8|142.8|520.0|1010.0|
|Recovery rate for Closing a Business|348|31.0|15.1|10.0|63.0|
|Time to Close a Business|348|3.6|0.9|1.0|5.0|
|Partial Credit Guarantee % of GDP|399|0.1|0.2|0.0|0.9|
|Lag of Partial Credit Guarantee % of GDP|266|0.1|0.2|0.0|0.9|
|Median Coverage of PCG Scheme|210|68.1|