rafmacalaba/data-use-mentions-extended huggingface dataset:
_Figure 27: Saving share for different groups of population_ <!-- Start of picture text --> 10.0% 10.0%<br>9.0%<br>8.0% 8.0%<br>7.0%<br>6.0% 6.0%<br>5.0%<br>4.0% 4.0%<br>3.0%<br>2.0% 2.0%<br>1.0%<br>0.0% 0.0%<br>2014 2015 2016 2017 2014 2015 2016 2017<br>Average Vulnerable Average Well-being improved<br>Non-vulnerable Well-being worsened<br><!-- End of picture text --> Note: Net saving share in disposable income Source: Authors’ calculations based on quarterly HBS.

The vulnerable population had slightly lower saving shares on average, and experienced a somewhat sharper savings decline during the crisis (Figure 27). In 2016, those in the vulnerable group were saving only 3.8 percent of their disposable income, while the non‐vulnerable saved 4.6 percent. Differences are more pronounced if we compare those who reported experiencing either improving or worsening material well‐ being. Those reporting worsened well‐being also have lower saving shares, but the decline in the savings share in this group was not very pronounced during the crisis. Those who experienced job losses had particularly low saving shares, dropping to mere 0.8 percent in 2016. Those who were coping with the worsened well‐being through the decrease in expenditure or through the help from the relatives also had very low or even negative saving shares (1.9 and ‐3.3 percent correspondingly in 2016). Those who report improvements in well‐being have a significantly higher savings share (7.2 percent in 2014), but also experienced a very sharp decline in savings during the
total traffic deaths but the result loses significance as he adds other variables (medical treatment proxies) to the model. Using U.S. county level data for 1970 and 1980, Keeler (1994) also found a positive but insignificant effect of elderly on the total traffic deaths per capita. 24 This result differs somewhat from Noland’s (2003a) finding that increasing population reduces total fatalities.  Noland attributes negative coefficient on total population to increasing congestion.

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However, it **is** important to bear in mind that available measures may bias the African rural linkage estimates downward because of a key feature of the African rural economy--the high share of nonmarketed goods and services ln total consumption. Because they are not marketed, many rural African goods and services are not measured. The Gusau consumption study, for example, does not impute a value for consumption of home produced nonfood cou'odities. And none of the studies values nontraded household services-cooking, cleaning, laundry and child care. Furthermore, major agricultural improvements take the form of labor time invested in tree crop planting, land clearing, leveling or ridging. A recent study in Nigeria estimated that 80 percent of total agricultural investment took this form, while only the remaining 20 percent represented cash expenditure on equipment (AdesimL, 1983). Performed primarily with unremunerated family workers, labor-based investments are normally excluded from calculations of the value of agricultural investment. Because the Asian economies are more highly monetized, more of these services enter the measured consumption accounts. Thus, in the same way GNP comparisons exaggerate income gaps between rich and poor countries--because the development of labor markets monetize what were previously intra-household labor flows and becas e high female workforce participation leads to booming restaurant, laundry, house cleaning and childcare businesses--the Asia-Africa comparisons are likely to overstate the apparent linkage gap between the two regions. The differences may well diminish in the future as rural Africa becomes increasingly monetized.

# VII. PROBABLE MAGNITUDE OF THE AGRICULTURAL GROWTH NULTIPLIERS A. Direct Cross-Regional Estimation

Available regional data from Togo and Sierra Leone (ILO, 1982d, Natlon et al., 1979), enable us to relate regional differences in rural
<br>(0_._134)|0.004|[0_._501]|
|Household Has Any Outstanding Loans|0_._678<br>(0_._467)|0_._687<br>(0_._464)|0.009|[0_._689]|
|_Psychological Well-Being_|||||
|Primary Woman Is Very Happy or Quite Happy|0_._362<br>(0_._481)|0_._360<br>(0_._480)|-0.001|[0_._951]|
|Primary Woman Life Satisfaction Rating (1-10)|5_._010<br>(2_._978)|5_._018<br>(3_._012)|0.008|[0_._951]|
|Primary Woman Is Depressed (7-CESD _≥_8)|0_._691<br>(0_._462)|0_._668<br>(0_._471)|-0.023|[0_._279]|
|Number of Households|1,107|817|||

_Notes._ This table is constructed using baseline data and indicators for UP households. The sample includes households surveyed in at least one of the endline data collection rounds with baseline data available. Baseline data is not available for 6% of households. Baseline total consumption estimate includes food (purchased, produced, and received as a gift), personal and household items, education, health, household repairs, social expenses (weddings, funerals, religious expenses, and other ceremonies), temptation goods, and legal expenses. The national poverty line threshold is AFN 2,064 (US$ 30 nominal) per capita per month from the Afghanistan Living Conditions Survey (2018). This poverty line consumption excludes legal, health, household construction, and repair expenditures, and includes expenses on consumer durables and housing. We use consistent consumption with this figure to classify households with respect to
Bank sought to influence during our period of study (2004-2013). We cannot be certain that all survey respondents provided answers on the basis of policy decisions that they themselves made or directly witnessed, but the types of respondents who participated in the survey are substantially more proximate to the empirical phenomena of interest—World Bank influence on the policy priorities of its client countries—than any other population.<sup>15</sup> A public opinion survey or a survey of experts would have been far less useful, as these populations generally know little about the inner-workings of government (Hoffmann-Lange 2007; Tansey 2007; Parks and Masaki 2019).

Another attractive feature of the _2014 Reform Efforts Survey_ is that it provides rich data on the individual characteristics of policymakers. The survey asked each government official a battery of questions about his or her position (rank), primary area of policy expertise, job responsibilities, educational background, length of government service, reform orientation, personal communications and interactions with donor agencies and international organizations, etc. We can therefore control for characteristics of policymakers that may affect the ways in which they perceive or report the influence of the World Bank. The fact that the survey asked respondents to evaluate the influence of the World Bank in a specific country, policy domain, and time period also enables us to model with fixed effects at the level of country-policy cluster pairs.<sup>16</sup> We are thus able to expunge the potentially confounding

> 15A key strength of the survey is the fact that it includes _attributional_ responses from policymakers with firsthand knowledge of the domestic and external factors that influenced the policy priorities and reform efforts of their governments. However, given that individual respondents evaluated the influence of the World Bank based on their own personal observations and experiences, a natural question that arises is whether these evaluations correlate with independently-generated measures of development policy change. Masaki and Parks (2019) provide evidence that suggests the construct validity of our dependent variable is strong. With data from the
category, the data openness element assigns scores to countries based on the classification of microdata data accessibility summarized in table 4 using the scoring criteria outlined in table 7.

**Table 7: Scoring criteria for the data openness element**

|**Data**<br>**category**|**Data sub-**<br>**category**|**Representative indicator/**<br>**data sets**|**Scoring**criteria|
|---|---|---|---|
|**Firm data**|N/A|Enterprise survey or<br>Annual survey of industry<br>or equivalent.|0 if up to date representative microdata accessibility is<br>classified as “No coverage” or “No openness”.|
|**Price data**|N/A|Commodity price survey<br>or equivalent.|50 if up to date representative microdata accessibility is<br>classified as “Satisfactory openness”.|
|**Individual/**<br>**Household**<br>**data**|**Consumption**<br>**data**|Household budget survey,<br>Household income and<br>expenditure survey,<br>Living Standards<br>Measurement Survey<br>(LSMS) or equivalent.|100 if up to date representative microdata accessibility is<br>classified as “Excellent openness”.|
||**Labor Force**<br>**data**|Labor Force survey or<br>equivalent.||
||**Health data**|Demographic and Health<br>Survey (DHS),<br>Multiple Indicator Cluster<br>Survey (MICS) or<br>equivalent.||
|**Census data**|
# **1     Introduction**

The tendency of firms to match the maturity of their assets and liabilities is well established in the literature (Hart and Moore, 1995; Demirgüç-Kunt and Maksimovic, 1999). Firms that operate in environments where the availability of long-term finance is limited due to market failures and policy weaknesses, such as weak information infrastructures, macro and political instability, poor contract enforcement, and weak investor protection, tend to be at a disadvantage when it comes to financing their long-term investments. A firm that can only use short-term debt to finance long-term assets continually needs to roll over its credit, which introduces liquidity risk as creditors may at some point refuse to roll over their financing.

Liquidity risk potentially increases firm-level economic volatility, as firms that cannot refinance their investments may be forced to prematurely sell them at reduced prices possibly inducing bankruptcy. Anticipating future liquidity risk, a firm with access to only short-term finance may be reluctant to invest in long-term assets, with adverse consequences for its growth. This suggests that the availability of long-term finance has potentially important implications for economic volatility as well as for the level and growth rate of economic activity.

In this paper, we examine the relationship between long-term debt finance and economic volatility using firm-level data for a set of 76 countries over the 1995-2013 period. We consider a firm-level growth volatility variable based on accounting data, and an asset return volatility variable based on stock market data. In order to deal with the identification problem that less volatile firms may be attracting more long-term finance, we relate our measures of firm-level volatility to financial and institutional development proxies in combination with an index of a firm’s preference for long-term debt use, following Rajan and Zingales (1998). A firm’s preference for long-term debt is captured by US firms’ use of long-term debt in that 2
people interviewed in previous survey rounds and add new individuals to ensure that new members of the population such as migrants and newborns are included (Glewwe and Jacoby 2000). To this end, panel surveys establish rules to define interview targets in follow-up rounds and create specific protocols to track households and individuals that move away from their original location. Moreover, they adopt refreshed samples (i.e., rotating panel or split panel) and protocols for the inclusion in the survey of individuals not previously involved in the study.

When discussing tracking in panel surveys, Witoelar (2011) introduced the concept of interview targets and tracking targets. Interview targets are those that need to be interviewed; tracking targets are those individuals or households that should be tracked if they have moved and, if found, should be interviewed. Similarly, the author distinguished between following-up rules and tracking rules in defining the criteria for individuals or households to be interviewed depending on whether they are found in the same location or are movers. To some extent, the LSMS-ISA panel surveys can be seen as a panel of both households and individuals. All households and individuals found and interviewed during the baseline are interview targets across the entire history of the panel sample.

The LSMS-ISA tracking approaches are more heterogeneous and mainly diverge in terms of: (i) targeted tracking units, (ii) characteristics of the tracking targets, and (iii) geographical scope of the tracking. Decisions across these three elements will impact representativeness and cost.

The LSMS-ISA are heterogenous in terms of the unit targeted for tracking and reinterviewing. Usually, these panel surveys have tracked dwellings, households, or individuals, with each option having its own distinct implications. In 2022, the Burkina Faso EHCVM revisited the dwellings previously visited in 2018/19. Nigeria and Ethiopia track and reinterview those households participating in the first wave of data collection but not individuals that split from the household. The Malawi IHPS, Tanzania NPS, and Uganda UNPS track individuals.

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mark>3. Data</mark>**

<mark>We use data from different sources. The data on bond issuances come from the Thomson Reuters Security Data Corporation Platinum database (SDC Platinum). This database contains transaction-level information on new issuances of corporate bonds by public and private firms. From this database, we obtain the date a bond is issued, the face value of the bond, and the yield to maturity at issuance. SDC Platinum also contains additional information that we employ, including the rating of the firm at issuance, the country of the firm, the industry of the firm, the market in which the bond is issued, the type of bond (fixed or flexible coupon), the currency of the bond, whether the issuance is public or private, and the maturity at issuance of the bond.</mark> 14
# **3.2. (Parcel-level) Intra-Household Discrepancies in Reporting**

Self-reported data, with potential cross-individual heterogeneity in the interpretation of and responses to the questions on ownership and rights, can result in diverging intra-household reports regarding the same parcel, particularly in settings where documented land ownership is not prevalent.<sup>25</sup> In the absence of a strategy that unquestionably resolves discrepancies, the fallback option in the prior work, as in ours, has been to accept each person’s response as to whether they are an owner of a given asset.

At the same time, a growing number of studies show that variation in reporting within the household could in fact offer greater insights into intra-household dynamics — including important dimensions of women’s health and economic status. Ambler et al. (2017), for example, use the 2011-12 Bangladesh Integrated Household Survey to show that among women, outcomes related to health, employment, and community group participation are positively associated with cases where both they and their spouse agree on joint decision-making across a range of household economic activities, or joint ownership of different household economic farm and non-farm assets, including agricultural land (as well as, to a lesser extent, on whether the wife is attributed decisionmaking or ownership over a particular area, but the husband is not). Annan et al. (2019) also find, using the Demographic and Health Survey data across several Sub-Saharan African countries, that disagreement is substantial over decision-making and that the direction of disagreement matters. For example, the incidence of women attributing more decision-making power to themselves than their husbands is positively associated with a range of health outcomes for those women and their children. Intra-household discrepancies in reporting can therefore have important implications for understanding development outcomes pertaining to women, both within and outside the household.

In our case, the administration of individual interviews using a common roster of agricultural parcels within the IHPS (T5) sample permits the examination of possible discrepancies in spouses’ reported ownership/rights
# **1 Introduction**

Migration is one of the most politically polarizing issues in advanced countries. This is reflected in the way migration and its effects are described in the news. While some studies look at how migration-related news influences public opinion and votes in destination countries,<sup>1</sup> little is known about the effect of changes in the sentiment of migration-related news in destination countries on _en route_ migrants and their migration choices. Investigating this matter is important to better understand the impact of the increasing use of an aggressive tone by populist leaders trying to discourage migrants from seeking entry into Europe or the US.<sup>2</sup> This paper studies how changes in the sentiment of migration-related news published in migrants’ preferred destination countries affect migrants’ movements within Libya and the timing of their journey to these countries. Libya is the country with the largest number of international migrants in all of North Africa and the major gateway from Africa to Europe.<sup>3</sup> By showing to what extent changes in the news sentiment in destination countries impact the choices of migrants in Libya, we contribute to a better understanding of the determinants of the movements along the most important irregular migration route to Europe.

Our analysis combines two main data sources. First, we use data on migrants located in Libya during the period 2017-2020 collected by the International Organization for Migration (IOM). Even though some migrants plan to remain in the country, for most of them Libya is a transit country towards their final destination. While in Libya, migrants often stay for long periods in the same location, live in rented houses, and have an (informal) job (IOM, 2018a). During their stay in the country, migrants often move across locations several times, using different internal routes according to their nationality (Di Maio et al., 2023). To study migrants’ movements within Libya, we use geo-localized monthly data collected by the IOM in 167 Flow Monitoring
Figure A5: Poverty rates across urban versus rural areas by country, $3.65 poverty line <!-- Start of picture text --> (A) Official definition<br>Urban<br>80<br>Rural<br>60 National<br>40<br>20<br>(B) DOU<br>Urban<br>80 center<br>60 Urban<br>cluster<br>40 Rural<br>20 National<br>(B) DB<br>Core<br>80 Suburb<br>60 Town<br>40 Other rural<br>National<br>20<br>Source: International Urban Poverty Database.<br>Note: For the DOU and DB methods, WorldPop 250m is used.<br>Figure A6: Distributions of poor populations across urban versus rural areas by country, $2.15<br>poverty line<br>(A) Official definition (B) DOU (C) DB<br>100 100 100<br>80 80 80<br>60 60 60<br>40 40 40<br>20 20 20<br>Urban Rural Urban center Rural Core Town<br>Urban cluster Suburb Other rural<br>AGO BFA CIV ETH GAB GHA GIN GNB LSO MRT MWI NER SEN TCD TZA UGA<br>AGO BFA CIV ETH GAB GHA GIN GNB LSO MRT MWI NER SEN TCD TZA UGA<br>AGO BFA CIV ETH GAB GHA GIN GNB LSO MRT MWI NER SEN TCD TZA UGA<br>AGOBFACIVETHGABGHAGINGNBLSOMRTMWINERSENTCDTZAUGA AGOBFACIVETHGABGHAGINGNBLSOMRTMWINERSENTCDTZAUGA AGOBFACIVETHGABGHAGINGNBLSOMRTMWINERSENTCDTZAUGA<br>Poverty rate (%)<br>Poverty rate (%)<br>Poverty rate (%)<br>Share of poor population (%)<br><!-- End of picture text --> Source: International Urban Poverty Database. Note: For the DOU and DB methods, WorldPop 250m is used.

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parameter (γ1). Model (2) provides more immediate feedback: The difference between the average CH4 anomaly in the most recent year and the average for all preceding years can be inferred from the sign, size and significance of parameter (δ1).<sup>18</sup> Although the Sentinel-5P platform provides the best available CH4 data, its coverage for our 5- km grid cells is not necessarily complete for all sample months. From an econometric perspective, this implies an unbalanced panel for estimation. Within the Madrid GGU, typical CH4 concentration anomalies can differ substantially across cells (see Figure 7c). To cite one possible consequence, a naive trend analysis could generate spuriously-positive results in cases where early-period observations are more numerous in lower-anomaly cells and later observations are more concentrated in higher-anomaly cells. We control for this possibility with a panel estimator that incorporates cell-specific fixed effects.

Table 7 reports estimates for the Madrid GGU from OLS, Random Effects and Fixed Effects models. In this case the two trend models align with very high significance in all cases. The monthly trend (γ1) is negative and highly significant, yielding an estimated decline of about 0.19 ppb/month (or about 9 ppb over four years). The final-year deviation is also negative and highly significant, yielding an estimated difference of about 3.5 parts per billion between 2022 and the previous three years.

**Table 7:  Trend model results, Madrid GGU**

|Dependent varia|ble: Monthly<br>|Mean CH4 An<br>|omaly<br>||||
|---|---|---|---|---|---|---|
||(1)|(2)<br>Panel|(3)<br>Panel|(4)|(5)<br>Panel|(6)<br>Panel|
||OLS|RE|FE
states, Japan and the United States.

The results reported in Table 7 show that the general findings still hold in the larger country sample. The only exception is the role of remote work in a country and sector which is no longer significant and shows a much smaller coefficient. It appears that in middle-income countries, the possibility of remote work in a sector matters much less in hampering the negative trade effect from shocks, possibly because the type of occupations in a sector cannot be performed remotely. This finding is consistent with the lower role of remote work when we included China to the country sample. Another interesting difference is the size of the upstream shock which becomes smaller when including more middle-income countries. A possible explanation could be the composition of export goods in the larger sample which are likely to be more upstream in the supply chain, implying that a negative shock in an exporter’s source countries has a

> 27 Appendix 4 presents the comparison. We cannot rule out that the aggregation of January and February data is driving some of these differences.

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