rafmacalaba/data-use-mentions-extended huggingface dataset:
statuses in February 2020, right before the pandemic. We deemed the SYPJ data to be more suited to our question since it allows to look at the trend of employment outcomes before the pandemic as well as right after the shock in the summer of 2020 and therefore allows investigating the effect of the pandemic on the two populations. The CMM data would be better suited to capture the recovery from the pandemic (Krafft et al., Forthcoming). The overlapping portion of the CMM data with the SYPJ data (February 2020 to February 2021) did not allow for a comparable analysis to the one we did in this paper. The overlap is not perfect. CMM data was collected a few months after the bulk of the SYPJ data only allowing for a comparison between the first half of 2021 (CMM) to the second half of 2020 rates (SYPJ). This is not a reasonable comparison especially in a period with rapidly changing events like the pandemic time. In addition, the time intervals are different as CMM data allows for examining annual change (February 2020 to February 2021) as opposed to semi-annual, and that is only one period which does not allow to compare the trend.

In our analysis we focus on young males aged 15 and above who ever worked. Since those who never worked do not have job finding and job separation outcomes we believe including them in the sample will not add much information. Moreover, given low participation rates among women for both populations, the number of women who ever worked in the SYPJ sample is only 229 (75 of whom are Syrians). It is therefore difficult to conduct the analysis by sex. Given the difference in the experiences of women and men in the labor market, we limit the sample to male respondents for whom we have information on their first job.<sup>8</sup> Furthermore, since our analysis spans the period from 2016 to 2020, we limit the sample to those who were 15 or above in 2016. This results in
country. The devaluation of the yuan and its attachment to the dollar have improved over the years the country's competitiveness and strengthened its attractiveness to foreign investors.

Huyen (2015) conducted a survey of several foreign companies established in provinces in Vietnam to identify the determinants of their FDI decisions. The results show the importance of the quality of infrastructure, access to finance and the availability of raw materials in the process of setting up foreign companies in Vietnam. Bartels et al, (2014) highlighted the importance given by multinationals to the quality of living in the host regions, the existence of a favorable environment for innovation and the emergence of industrial hubs allowing investors to benefit from positive externalities. This finding is however nuanced by certain research which found that FDI attractiveness is a dynamic process that is evolving over time with certain categories of projects requiring specific criteria that may not be necessary in the case of other projects. Thus, certain territories may be considered attractive for a specific sector or production activities and not attractive for others within the same location (Hatem, 2004).

# **3. FDI inflows to Tunisia: Where do we stand now?**

The FDI inflows to Tunisia have experienced a decline since the revolution in January 2011. In recent years, a major part of those inflows was mainly channeled by investors already established in the country. In the recent 2020 edition of the “Doing of Business” index of the World Bank, Tunisia has ranked 78th compared to the 88th position in the 2018 edition. The country was able to gain 10 places in two years. However this is still short of the highest ranking the country ever reached in 2012 when it made it to the 46th position (World Bank, 2020).

Table 1 presents a summary of the FDI data to the country as released by the Agency for the Promotion of Foreign Investments (FIPA, 2019, 2017, 2016, 2015, 2014). Foreign inflows have substantially declined since 2008. The trend has slightly reversed from
column 5 shows that the results for both sales and total costs remain consistent also when using the ACLED dataset to measure conflict exposure, with the effect on profits being negative and marginally significant. These findings are reassuring, given that ACLED covers a substantially different—and smaller—set of countries, as well as a distinct set of conflict-related events compared to ICEWS. While all our results are consistent across these alternative definitions of conflict exposure, they also suggest that firms respond differently depending on the type of event, with the negative effects of conflict being more pronounced when the events are more violent.

Finally, Table A18 shows that our results remain unchanged when accounting for spatial correlation in the error terms, using the arbitrary clustering correction for standard errors proposed by Colella et al. (2023) to re-estimate our baseline model.

# **4.2 Mechanisms**

Our results show that conflict exposure reduces a firm’s sales and total costs. These effects are likely driven by a combination of production-side and market-level channels. One mechanism relates to the availability of production inputs. Higher conflict exposure may constrain access to domestic and foreign inputs—such as raw materials, intermediate goods, labor, or electricity—forcing firms to adopt a sub-optimal input mix. This reduction in input quantity and quality lowers overall output and, in turn, depresses sales. At the same time, conflict may reduce sales by changing the level and type of competition faced by firms. In particular, it is possible that conflict increases competition from informal firms, a phenomenon that is well-known to have negative consequences for formal ones. Finally, conflict may lower sales by decreasing demand or increasing the severity of the obstacles to a firm’s activity. In what follows, we take these hypotheses to the data to examine the mechanisms driving the negative effect of conflict on firms’ sales and production input expenditures.

## **4.2.1 Raw materials and intermediate inputs**

Table 2, column 1, shows that conflict exposure
# **5. Conclusions**

In this paper, we have documented a new database of govemance indicators compiled from a variety of existing sources. These indicators report the subjective perceptions of respondents regarding the quality of various aspects of govemance. We summarize a subset of these indicators using six aggregate govemance indicators capturing six core concepts of governance. Govemance, as measured by these indicators, matters a great deal for economic outcomes. We have provided new evidence of a strong positive causal relationship from govemance to better development outcomes.

18
where _p_ and _h_ denote parcel and household, respectively; _y_ represents a specific intra-household reporting discrepancy on ownership/rights based on the spousal cross-reports; and _α_ and _ɛ_ represent constant and error terms, respectively. _D_ is a vector of household and parcel attributes, subsuming those included in the vector _C_ from equation (1), and augmenting those with additional variables to better understand whether certain features of the parcel, household economic activity and spouses’ decision-making in agriculture that could affect reporting discrepancies pertaining to reported and, separately, economic ownership. In the case of parcels where different individuals may be involved in agricultural production or decision making on different plots within, there may also be variation in responses among household members over who is responsible for, and/or owns, that parcel (Doss et. al, 2019). Additional variables in _D_ , therefore, include the number of plots in the garden, whether the household sells some of its crop, and whether the wife is listed as the main decision-maker for cropping activities on any plot.<sup>26</sup> Table 3 presents the distribution of parcel-level responses regarding ownership and rights outcomes, as reported by the principal couples in the IHPS sample.<sup>27</sup> There is spousal agreement regarding individuals with reported ownership, economic ownership, right to sell and right to bequeath concerning 71 percent, 58 percent, 88 percent and 86 percent of the parcels, respectively.<sup>28</sup> The extent of spousal agreement is, however, higher than the levels reported in MEXA T5, where the comparable estimates for parcel-level agreement on reported owners and economic owners stood at 45 percent and 48 percent, respectively.

Country/regional context may contribute to these differences. In studies from Uganda and South Africa, for example, Jacobs and Kes (2015) find that around 10 to 16 percent of women in Uganda own land in their own right, and that the majority of couples disagree specifically on joint ownership.
rants in Kinshasa [The World Bank Reproducible Research Repository]. https://doi.org/ 10.60572/g9ks-a898

- Coad, L., Fa, J. E., Abernethy, K., Van Vliet, N., Santamaria, C., Wilkie, D., El Bizri, H. R., Ingram, D. J., Cawthorn, D.-M., & Nasi, R. (2019). _Towards a sustainable, participatory and inclusive wild meat sector_ . CIFOR.

- Crowne, D. P., & Marlowe, D. (1960). A new scale of social desirability independent of psychopathology. _Journal of Consulting Psychology_ , _24_ (4), 349.

- Dhar, D., Jain, T., & Jayachandran, S. (2022). Reshaping adolescents’ gender attitudes: Evidence from a school-based experiment in India. _American Economic Review_ , _112_ (3), 899–927.

- Edderai, D., & Dame, M. (2006). A census of the commercial bushmeat market in Yaound ́e, Cameroon. _Oryx_ , _40_ (4), 472–475.

- Fa, J. E., Wright, J. H., Funk, S. M., M ́arquez, A. L., Olivero, J., Farf ́an, M. A., Guio, F., Mayet,<sup> ́</sup> L., Malekani, D., Louzolo, C. H., et al. (2019). Mapping the availability of bushmeat for consumption in Central African cities. _Environmental Research Letters_ , _14_ (9), 094002.

- Fjeldsoe, B. S., Marshall, A. L., & Miller, Y. D. (2009). Behavior change interventions delivered by mobile telephone short-message service. _American Journal of Preventive Medicine_ , _36_ (2), 165–
We also include São Paulo as a case where bank privatization happened under the auspices of the federal government.  For the regressions including bank-level performance indicators as explanatory variables, we lose observations since we do not have observations for most of the liquidated banks.  In the cases where we have bank-level data for more than one state bank, we average these data for use in the ordered probit analysis.

> 31 While the observed variable _Transformation_ has only four outcomes, there is a continuous underlying unobserved variable, the political preference of state governments.  Each outcome of _Transformation_ corresponds to a specific range of political preferences.  We use standard maximum likelihood estimation with heteroskedasticity-robust standard errors.  In robustness tests, we also use the multinominal logit model, which yields the same results.

> 32 The political, economic, and financial variables used here, and in subsequent sections, are elaborated in the paper’s appendices.

14
<br>Volume 3C|Economic Analysis of Transfer Programs Targeted on People with<br>Disabilities|283|
|U MI|<br>John Bound;|Journal Article|1999|Labour Economics|The Dynamic Effects of Health on the Labor Force Transitions of<br>Older Workers|250|
|U MI|Hazem Adam Ghobarah; Paul<br>Huth; Bruce Russett;|Journal Article|2003|American Political Science<br>Review|Civil Wars Kill and Maim People--Long after the Shooting Stops|225|
|U MI|<br>Rukmalie Jayakody; Sheldon Danziger;<br>Harold A Pollack;|Journal Article|2000|<br>Journal of Health Politics, Policy<br>and Law|Welfare Reform, Substance Use, and Mental Health|167|
|U MI|.<br>Arline T. Geronimus; John<br>Bound; Lisa J. Neidert;|Journal Article|1996|<br>Journal of the American Statistical<br>Association|On the Validity of Using Census Geocode Characteristics to Proxy<br>Individual Socioeconomic Characteristics|153|
|U MI|<br>Michael Chernew; Dennis P.<br>Scanlon;|Journal Article|1998|Inquiry|Health Plan Report Cards and Insurance Choice|129|
|U MI|<br>Michael Chernew; Kevin Frick;|Journal Article|1997|Health Services Research|The Demand for Health Insurance Coverage by Low-Income|129|
||Catherine G. McLaughlin;||||<br>Workers: Can Reduced Premiums Achieve Full Coverage?||
health infrastructure; (2) modernization; (3) economic structure; (4) food prices; (5) ecological characteristics; and (6) availability of health care facilities and providers.

Covariates related to public health infrastructure in the community include indicators of the prevalence of water connections, sanitation facilities and the presence of infrastructure for garbage disposal. With respect to the first two, we consider as covariates the proportion of households in the community with piped water connections and flush toilets. Most studies have treated water and toilet facilities as household variables. Since we do not have information to test whether these inputs are simultaneously determined with choices about the health of children in the household, we decided to include them first as community-level variables and therefore as exogenous to the attainment of child height.1<sup>6</sup> With respect to the third, a dummy variable indicates whether there is a public dumpsite in the community.'<sup>7</sup> Communities in Suchitepequ&z and Totonicapan are the best and the worst equipped respectively (in the latter department only one per thousand of the surveyed households have tap water, only one percent have flush toilets, and only one of the 15 surveyed communities provides a public dumpsite for garbage disposal).

Measures of modernization include the percentage of households in the community with a television and the proportion of women in the community who have had either formal schooling or adult literacy classes. These variables aim to capture ideational effects on norms and behavior about modern feeding and child care practices.

The economic structure of the community is represented by a set of five variables that capture the degree of accessibility to the local economy, of regular employment opportunities in the agricultural sector, and of accessibility to credit institutions. Overall, communities in Chimaltenango and Totonicapan are the least equipped and communities in Suchitep6quez the best equipped. Another indicator of the economic structure of the community is represented by the total number of people living there as of the 1994 Census. The average community
Our findings shine new light on the rapid growth and diverse drivers behind crypto-assets activity and offer support to the notions that crypto-assets are perceived as a risk asset, a potential macro hedge, and a potential tool to support cross-border payments. However, our results should be interpreted with caution. The crypto-assets industry represents an emerging heterogeneous asset class and while our sample spans over 130 countries, it covers just 27 months which include the COVID-19 pandemic and an environment of extraordinary fiscal, monetary, and financial sector conditions which may change when policy support is withdrawn. Moreover, our crypto-assets volume data are based on estimates and rely on important limiting assumptions. Further, our data exclude significant volumes of “off-chain” transactions. Finally, several other factors also inhibit us from drawing strong conclusions: the industry’s track record is relatively short, the technologies are still rapidly evolving, and there are significant uncertainties around future global and national adoption and regulation.

# **References**

Athey, S., Parashkevov, I., Sarukkai, V., and Xia, J. (2016). Bitcoin Pricing, Adoption, and Usage: Theory and Evidence.

Auer, R., and Claessens, S. (2020). Cryptocurrency Market Reactions to Regulatory News. Federal Reserve Bank of Dallas. Globalization Institute Working Papers 381.

Auer, R., and Tercero-Lucas, D. (2021). Distrust or Speculation? The Socioeconomic Drivers of US Cryptocurrency Investments. BIS Working Paper 951.

Basel Committee on Banking Supervision (2021). Consultative Document: Prudential Treatment of Cryptoasset Exposures.

Baur D., Lee A. and Hong, K. (2018). Bitcoin: Medium of Exchange or Speculative Asset?, J Int Financial Mark Inst Money 54:177–189.

Blau, B., T. Griffith, R. Whitby (2021). Inflation and Bitcoin: A Descriptive Time-Series Analysis. Economic Letters 203.

Buterin, V. (2013). Ethereum Whitepaper.

Conlon, T.
variation in outcomes across countries. For a given decline in GDP growth, countries with smaller manufacturing sectors, larger export sectors, and less costly redundancy regulations were better able to protect earnings. Finally, the responsiveness of employment growth to changes in GDP growth increased during the crisis, leading simple projections to understate the extent of employment loss during the crisis.

# **Data and Methodology**

_The paper uses data from published quarterly labor market statistics of 45 middle income countries._ 37 of these countries have information on both GDP growth and employment growth, while 31 have information on GDP growth and earnings growth. Most of the countries lie in Latin America or in the Europe and Central Asia region (ECA). We divide the remaining countries into two regions: East Asia and Sri Lanka, and the Middle East and Africa.<sup>5</sup> The main source of primary data sources on labor market and GDP are country statistics published by the relevant country statistical offices and provided, on a subscription basis, to the World Bank by three private data providers—Haver Analytics, CEIC, and EMED—that collect them daily. These data were supplemented by ILO’s laborsta database.<sup>6</sup> Data on other country indicators--such as per capita GDP, and the size of the manufacturing and export sector-come from the World Development Indicators database.

_Data coverage and sources vary across countries and indicators._ GDP, employment, and unemployment statistics are available quarterly for almost all countries in the sample.<sup>7</sup> Many countries, however, do not report earnings and even fewer provide hours. Moreover, hours and earnings are sometimes reported as changes in indices rather than levels. Since data are often missing for particular quarters, the analysis is limited to year-on-year changes for comparable quarters. Tables A2 and A3 in the appendix list the types of surveys used to generate employment and earnings indicators. In three quarters of the countries analyzed,
each grid cell for the period 2001–2022 using the Google Earth Engine code editor.<sup>5</sup> The measure of forest loss is expressed in hectares per square kilometer (ha/km<sup>2</sup> ).

3. _Land use type._ The MODIS Land Cover Type dataset offers comprehensive global land cover information with yearly intervals ranging from 2001 to 2020 (Friedl et al. 2010).<sup>6</sup> To classify land use, the MODIS Land Cover Type dataset employs various supervised classification methodologies. These methodologies include the use of the International Geosphere-Biosphere Programme (IGBP), University of Maryland Leaf Area Index (LAI), BIOME-Biogeochemical Cycles (BGC), and Plant Functional Types (PFT). The area in square kilometers and the proportion of different land cover types were computed within each grid cell from 2000 to 2020. The land cover types of interest were urban-built areas, croplands, water bodies, dense-open forests, and grasslands.<sup>7</sup>

# **III.B.2 Remote Sensing Outcomes**

We estimate the effects of the refugee camps on two primary outcomes: night light density and deforestation. To confirm the consistency of the results, we also assess the impact of refugee camps on type of land use, which includes dense-open forest, grassland (typically used for agriculture or pasture), and cropped land. Descriptive statistics for these variables are presented in Table 1. The table also illustrates the years for which each of the outcomes is available. The study employs all the years for which the outcomes were available according to the source.

Moreover, the spatial distribution of the two outcomes we study before and after the Rohingya’s arrival in 2017 is illustrated in Figures 5 to 6. These maps illustrate a dramatic improvement in night light density but mixed changes in deforestation. The maps for the geographical distribution of land use by type are also illustrated in Figures 7, 8, and
than not to experience in her lifetime (Table 1). These thresholds could be adjusted over time to make risks more comparable across hazards.

**Table 3: Thresholds used to define exposure to extreme events**

|Event|Return period<br>(years)|Intensity threshold|
|---|---|---|
|Flood|100|An inundation depth of at least 50cm|
|Agricultural<br>drought|40*|At least 30 percent of cropland or grassland affected in any season, in rural areas|
|Heatwave|100|A three-day running mean maximum Wet Bulb Globe Temperature (WBGT) of > 33°C|
|Cyclone|100|A wind speed of at least the Category2 threshold(37.6 m/s,10 min sustained)|

In Rentschler, Salhab, and Jafino (2022), inundation depths of at least 0.5 meters indicate a high risk that bring disruptions to livelihoods and economic activity, as well as risk to life for select locations and vulnerable groups. In a different work, Huizinga, De Moel, and Szewczyk (2017) show that for a fluvial and marine flood depth of 0.5 meters, the average share of residential assets lost across regions is 0.38 (a range of 0.22-0.49). Cyclone damage functions also indicate direct economic damage in the range of 0.20.5 for category 2 windspeeds for most regions (Eberenz <mark>, Lüthi, and Bresch</mark> , 2021).

Drought results in a loss in income rather than a loss in assets. A moderate (about a 1 in 10 year) drought is predicted to reduce consumption by 15 percent and 9 percent in Uganda and Ethiopia, respectively (World Bank , 2016; Hill and Porter, 2017), and by 10 percent across most agroecological zones in sub-

> 13 The global cyclone hazard maps are available from <u>https:/
that BF did not discourage (formal) labor market participation, although some studies find increases in informal hours worked.<sup>11</sup> Closely related to this is the research looking at labor children reaching adulthood.

> 9Becker (1965) shows that individuals should increase their demand for leisure as non-labor income rises. Following this reasoning cash transfer recipients would be expected to reduce their labor supply as soon as they receive the monetary transfer. Additionally, beneficiaries might be influenced through various other channels – such as the health productivity effect, the self-employed liquidity effect, the insurance effect, and the investment in labor search effect (Baird _et al._ , 2018). Those effects, which may increase or decrease the labor supply of cash transfer beneficiaries, challenge the simplistic theory of the lazy welfare recipient.

> 10In a systematic review, Kabeer and Waddington (2015) finds negative effects on labor force participation in only one program (in Pakistan), though some studies document reduced formality (e.g., Garganta and Gasparini (2015)).

> 11Using the Brazilian household survey and exploiting a change in the program’s eligibility line, Barbosa and Corseuil (2014) do not find evidence of BF distorting occupational choices toward informal jobs. Fruttero _et al._ (2020) use Brazilian data and an unannounced change in the program’s eligibility rules. Restricting their sample to families that had just updated their information before the policy change was announced – hence, those families were unable to update information untruthfully due to the policy change – the authors find that BF beneficiaries are “4.7 percentage points more likely to be found at least once in a formal job in 2010 or 2011 and 3.3 percentage points more likely to be found in a formal job throughout these two years.” The results are especially strong for younger cohorts (between 25 and 35 years old and 35 and 45 years old). De Brauw _et al._ (2015) find no overall impact