rafmacalaba/datause-encoder-data huggingface dataset:
market more flexible by encouraging the use of fixed-term contracts, the median job tenure (although not the mean) is shorter than in Lithuania.<sup>2 5</sup>

**Table 19 Distribution of Employment** _a)_ **by Job Tenure: Lithuania against Selected Countries**

||Under1<br>year|Iand<br>under2<br>years|2and<br>under5<br>years|5and<br>underIO<br>years|10and<br>under20<br>years|20years<br>orover|Average<br>tenure<br>(years)|Median<br>tenure<br>(years)|
|---|---|---|---|---|---|---|---|---|
|**Lithuania**(2001)|15.4|8.9|21.6|25.4|16.8|11.9|8.3|5.0|
|_Sector_|||||||||
|Private sector|22.2|11.6|25.3|21.9|11.0|8.0|6.1|3.2|
|Public sector|6.1|5.3|16.6|30.1|24.8|17.1|10.7|8.0|
|_Firmnsize_|||||||||
|-10|30.3|12.3|27.3|17.5|8.2|4.4|4.6|3.0|
|11- 19|20.2|12.3|25.3|25.6|11.4|5.3|5.5|4.0|
|20-49|12.9|9.0|24.6|27.3|14.9|11.3|8.0|5.0|
|50+|9.9|7.1|17.6|27.5|21.9|16.1|9.8|7.0|
|Czech**R.**|19.2|36.6||12.0|14.8|17.4|9.0|2.0|
|**Denmark**|25.1|11.4|16.2|18.2|17.7|11.4|7.9|4.4|
|France|_15.0_|8.0|17.7|17.4|23.3|18.7|10.7|7.7|
|**Germany**|16.1|9.4|22.0|17.2|18.4|17.0|9.7|10.7|
|**Poland(1999)**|14.5|11.7|19.0|17.7|20.3|16.7|9.6|6.2|
|**Spain**|35.5|4.9|11.1|14.4|17.7|16.5|8.9|4.6|
|**UnitedKingdom**|19.6|10.7|19.5|23.5|17.3|9.4|7.8|50|
|**United States**|26.0|8.5|20.0|19.8|16.8|9.0|7.4|4.2|

a) Wage and salary workers Note. data refer to 1995, unless stated otherwise.

_Source: Lithuania -LFSMay 2001, Bankstaffcalculations_ _Poland -LFS February 1999, Bank staff calculations_ _OECD countnes - OECD Employment Outlook 1997_ As expected, Lithuania fares worse on another measure of labor market dynamics, which is the percentage of workers in new jobs. There are less new hires in Lithuania than in most of the OECD countries. For example, 15 percent the proportion of new hires in Lithuania some 20 points lower than in Spain and 10 points lower than in Denmark.

> 25 High mean and low median tenure in Spain reflect the dual character of **its** labor market: a flexible segment consisting of workers on fixed-term contracts and thereby short employer tenure, and a ngid segment, consistmg of workers with permanent contracts and thus long employer tenure.

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Having specified the factors determining rebel activity at the comraunity level, we shift to households' reaction to such taxation. As indicated earlier, householLds take their endowment of human capital as given and receive a full income of _Y,_ = _/RWR (A, E) + Ipwp (A, E) + [r-t(S)] Al_ where S is a 0/1 dumnmy indicating rebel presence in the community and t(O) = _0_ while t(l) t, i.e. a tax will have to be paid only if there is rebel activity. Based on the income received at point t, the household chooses c, and I,, consumption and investment, to maximize the value function _V, (Y, >= c,_ - _I, +_ P V,+] (Y,+,). There are thus two ways in which rebel activity will affect the equilibrium level of investment. On the one hand, households living in areas with rebel activity obtain lower total income since they have to pay taxes on their asset endowment. In addition, under the assumption of persistence over time, i.e. that rebel activity will continue in the future, the return to assets received will also be lower due to the expectation of future taxation. Both imply that - < 0, i.e. that rebel activity will reduce investment. Substituting back the factors that have been shown to affect rebel activity earlier provides the basis for a second reduced form equation for household level investment.

where Ii is the level of investment at the household level and the remnainder of the variables are as defined earlier. The intuition is clear - we expect that civil strife would decrease households' incentive to invest, thus reducing capital accumulation, establishment of new enterprises, diversification of the economy, and overall economic growth.

# **2.4 Data, definitions, and descriptive statistics**

To test the hypotheses discussed above, we use community- and household-level information from two large household surveys, the 1999/2000 Uganda National Household Survey (UNHS) and the 1992 Uganda Integrated Household Survey (IHS). Both surveys include household as well as community 12
a high level of informality in the countries, because the poorest individuals are more likely to work in informal sectors, evading tax payment and leaving all the burden on the small number of the richest individuals, who are employed in formal sectors.

**Figure 3. Distributional Effects of Direct Taxes in Mali and Niger, 2014**

<!-- Start of picture text --> 3.a Direct taxes as a share (%) of market income per decile<br>6<br>5<br>4<br>3<br>2<br>1<br>0<br>1 2 3 4 5 6 7 8 9 10<br>Mali Niger<br>3.b Share (%) of direct taxes paid by market income deciles<br>100<br>90<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>1 2 3 4 5 6 7 8 9 10<br>Mali Niger<br>Source: Authors.<br><!-- End of picture text --> Direct taxes in Mali and Niger are much more progressive than in other countries (Figure C.1). This is related to the high level of informality of the two countries’ economies, with a large proportion of the population working in informal sectors and therefore not paying the WST while the small proportion working in formal sectors pay almost all the direct taxes. As mentioned in the preceding paragraph, the former are essentially the poorest individuals and the latter the richest ones.

6
dam and Shimantan Reservoir Dam systems that occurred in August 1975 killing 26,108 people. The dam systems built on the Ru River in Henan Province in Central China collapsed due to heavy rainstorm inundating thousands of square kilometers. Besides the 26,108 direct deaths caused by flooding and inundation, the accident was estimated to cause deaths of another 145,000 people through subsequent epidemics and famine.<sup>9</sup> Contrary to general perception, the numbers of accidents and fatalities are much smaller from nuclear energy sector. During the 1970-2008, one nuclear plant accident killed 31 people (Figure 3). The Chernobyl nuclear disaster in current Ukraine occurred on April 26, 1986 where the Chernobyl 4 nuclear reactor exploded dissipating the large amounts of radioactive materials. Of 600 workers present on the site during the accident, 134 received high doses and suffered from radiation sickness. Of these, 28 died in the first three months and another 19 died in 1987-2004 of various causes. 530,000 people who were involved in recovery operations between 1986 and 1990 have got minor radiations and bear a potential risk of late consequences such as cancer and other diseases.<sup>10</sup> The most recent nuclear disaster is the Fukushima Daiichi nuclear reactors in Japan caused by the 2011 (March 11), tsunami triggered by massive earthquake. The tsunami disabled the cooling systems of Fukushima Daiichi nuclear reactors Unit 1, 2, 3 and 4 thereby resulting in meltdowns and explosions and releasing radioactive materials for three days. Despite the scale of the disaster, no immediate deaths were linked to radiation due to the accident reported, however, some studies (Normile, 2012; Hoeve and Jacobson, 2012) estimate, based on the linear nothreshold theory of radiation safety, 130–640 deaths in the long-run due to the radiation. Selected list of power plant accidents is presented in Table A4 in the Appendix to show examples of major accidents in the power generation industry.

# **4. Occupational Risks in Energy Supply Systems**

Occupational impacts here refer to health impacts due to long-term exposure of people working in energy supply chain including coal mines, power plants, oil refinery, gas processing plants and energy transportation business. These people get exposed to harmful substances (e.g., dust in coal

> 9 Yi Si, "The World's Most Catastrophic Dam Failures: The August 1975 Collapse of the Banqiao and Shimantan Dams," in Dai Qing, The River Dragon Has Come!, M.E. Sharpe, New York, 1998. <u>http://www.sjsu.edu/faculty/watkins/aug1975.htm. Retrieved on June 7 2017.</u> 10 Chernobyl: The Chernobyl accident - UNSCEAR's assessments of the radiation effects, <u>http://www.unscear.org/unscear/en/chernobyl.html, retrieved on June 10, 2017.</u>

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In column (4) we proceed to include a full set of dummies for the geographical regions of Botswana registered by the _Afrobarometer_ to remove as much idiosyncratic geographical variation as possible in how respondents evaluate their living conditions. The association drops marginally to 0.12 and remains negative and highly significant. In columns (5) and (6), we included fixed effects for respondents' tribal affiliation (column [5]), and for each of the 25 occupational categories available in the _Afrobarometer_ survey (column [6]). In both cases, the association between the devaluation treatment and subjective well-being remains substantively and statistically significant.<sup>10</sup> In columns (7) and (8) we zoom in on the discontinuity in the data, i.e. the days immediately surrounding the devaluation. We do so to minimize the likelihood that some unobserved event occurring after—and close to—the treatment is confounding the results. In column (7), we focus on the four days surrounding the devaluation (two days before, two days after); in column (8) we focus on the first day before and the first day after the devaluation. This drastically reduces the sample size, but it does not change the main result: The size of the treatment coefficient is virtually unaffected as is its level of statistical significance. That is, zooming in on narrow bands around the discontinuity generated by the devaluation does not change the negative association between the devaluation treatment and subjective well-being.

In column (9), we control for respondents' assessments of the country's economic conditions, since this could plausibly affect how they perceive their own living conditions by supplying a signal of the existence of an overarching macroeconomic problem. The treatment coefficient barely changes, however, and remains highly significant. In column (10), a control has been added for how respondents perceive their own past personal economic situation. This shows 10 In addition, we have experimented with categorizing particular occupations as export-exposed. However, we cannot know whether individuals within those occupations are indeed engaged in export activities or not. Furthermore, for any clear theoretical implication to hold, we would need to know whether the Marshall-Lerner condition holds in the short run for the particular occupation. As results are as mixed as the theoretical prerequisites, we refrain from showing them.

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- 36 -

Table 3.11b: TRANSFERS, SUBSIDIES AND LENDING INDIFFERENT COUNTRY GROUPI OS (as Percent of Central Government Expenditures and Lending Minus Repayment

|Years|1978|1979|1980|1981|1982|1983|1984|
|---|---|---|---|---|---|---|---|
|**Transfers to**OR**mr**Govt.<br>Level**s**||||||||
|Industrlal<br>Market Economies|14.48|14.17|13.35|12.60|11.63|11.44|11.42|
|Developing<br>Countries|10.28|10.72|10.15|10.38|10.76|10.84|n.a.|
|LAC Countrles|8.16|8.66|7.53|8.26|8.57|9.11|7.95|
|Subsidles<br>&**_Othr_**Curr.<br>Transfers||||||||
|Industrial<br>MarketEconomies|56.28|56.41|55.94|55.71|55.83|56.26|55.60|
|Developing<br>Countries|26.46|27.66|28.69|26.34|28.08|26.90|27.54|
|LAC Countries|33.02|33.47|36.16|32.04|35.98|33.51|33.40|
|**Lending MinusRepayent**||||||||
|Industrial<br>Market Economies|6.40|6.20|5.90|5.20|5.20|4.70|4.50|
|Developing<br>Countries|n.a.|n.a.|10.37|n.a.|n.a.|n.a.|n.a.|
|LAC Countries|12.60|13.10|12.60|15.40|13.60|16.60|14.90|
|**TOTAL IRAtiFERS**||||||||
|Industrial<br>Market Economies|62.68|62.61|61.84|60.91|61.03|60.96|60.10|
|Developing<br>Countries|n.a.|n.a.|39.06|n.a.|n.a.|n.a.|n.a.|
|LAC Countries|45.62|46.57|48.76|47.44|49.58|50.11|48.30|

Source: IMF: (a) 18 Countries, (b) Scattered Sample, (c) Includes Argentina, Bolivia, Brazil, Chile, Colombia, Mexico, Uruguay, & Venezuela Table 3.11c: SOCIAL SECTORS EXPEIOITURES BY COUNIRY GROUPIiGS (as Percent of Total Expenditures of Central Governments)

||1978|1979|1980|1981|1982|1983|1984|
|---|---|---|---|---|---|---|---|
|**Industrlal**<br>**MarketEcnaomies (a)**|**56.35**|**56.11**|**56.05**|**56.00**|**55.76**|**55.65**|**54.61**|
|Educatlon|5.12|4.96|4.73|4.56|4.30|4.13|3.97|
|Health|11.18|11.46|11.52|11.50|11.41|11.30|11.53|
|Social<br>Security|37.66|37.35|37.41|37.65|37.77|38.03|36.76|
|Housing&Community|2.39|2.34|2.40|2.29|2.28|2.19|2.35|
|**LAC Coountries(c)**|**45.05**|**45.84**|**43.33**|**43.15**|**39.97**|**40.85**|**41.42**|
|Education|12.17|11.99|11.29|10.87|9.70|9.51|9.88|
|Health|5.51|5.47|4.93|4.84|4.80|4.67|4.92|
|Soclal<br>Security|25.85|27.37|25.52|25.59|24.05|24.83|24.66|
|Housing & Community|1.51|1.01|1.59|1.85|1.42|1.84|1.96|
|**Developing Countrles**<br>**(b)**|**33.09**|**33.57**|**32.73**|**32.79**|**31.19**|**31.92**|**32.22**|
|Education|10.76|10.76|10.55|10.03|9.48|9.60|9.80|
|Health|4.36|4.34|4.14|4.02|4.05|4.07|4.22|
|Social<br>Security|16.06|16.65|15.91|16.31|15.77|16.21|16.07|
|Housing & Coommunity|1.91|1.82|2.13|2.43|1.89|2.04|2.13|

Source IMF: (a) 18 Countries, (b) Scatterd Sample, (c) Includes Argentina, Bolivi., 3razo1, Chli e, Colombla, Mexico, Uruguav, and Venezuela
Second, we have focused on trade in machinery parts and components and final goods as one example of the distinction between networked production and sales of finished products. Production networks also exist in other areas, however. Textiles and apparel is one example. Future work could examine whether our findings can be replicated in other networked production settings.

# **References**

Anderson, J.E., and E. Van Wincoop. 2003. “Gravity with Gravitas: A Solution to the Border Puzzle.” _American Economic Review_ 93(1): 170–192.

Ando, M., and F. Kimura. 2005. “The Formation of International Production and Distribution Networks in East Asia.” In T. Ito and A.K. Rose eds. _International Trade in East Asia_ . Chicago: University of Chicago Press.

Arvis, J.-F., Y. Duval, B. Shepherd, and C. Utoktham. Forthcoming. “Trade Costs in the Developing World: 1995-2010.” Working Paper.

Arvis, J.-F., M. Mustra, L. Ojala, B. Shepherd, and D. Saslavsky. 2010. _Connecting to Compete 2010: Trade Logistics in the Global Economy_ . Washington, D.C.: The World Bank.

Baier, S., and J. Bergstrand. 2009. “Bonus Vetus OLS: A Simple Method for Approximating International Trade Cost Effects Using the Gravity Equation.” _Journal of International Economics_ 77(1): 77-85.

Behar, A., P. Manners, and B. Nelson. 2011. “Exports and International Logistics.” Policy Research Working Paper 5691, World Bank.

Dennis, A., and B. Shepherd. 2011. “Trade Facilitation and Export Diversification.” _World Economy_ 34(1): 101-122.

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functional link with proximate data collection on national and local patterns of local labor mobility.

**Weather data.** Thanks to their panel nature, LSMS-ISA surveys can capture repeated weather shocks, cumulative weather risk, and their potential impact on migration outcomes. From a data collection perspective, to maximize this potential, it would be necessary to incorporate, within multi-topic surveys, a built-in and comprehensive set of (spatially and temporally) high-resolution weather data, ideally collected _in situ_ during the data collection efforts (for instance, through weather sensors), which should: i) not be limited to temperature and precipitation, but also incorporate other key weather variables such as humidity, windspeed, etc.; ii) include _ad hoc_ , agriculture-oriented weather information, such as weather indicators constructed on the basis on crop-specific and local growing season calendars; iii) also include, in addition to weather data, all the equivalent climate variables, based on the long-run averages (e.g., 30-year) of the weather time series. Should such weather data collection _in situ_ be unfeasible due to timing or budget constraints, efforts could be devoted to the integration of existing surveys with crowdsourced weather data (Minet et al., 2017; Muller et al., 2015; Overeem et al., 2013), to obtain the maximum possible spatial and temporal granularity of the climate information. The provision of a set of built-in weather data would prevent risks deriving from independent user integration with external remote sensing weather data, i.e., measurement error due to spatial anonymization and the sensitivity of econometric estimates to the specific weather data product chosen by the researcher (Micher et al., 2022), as well as the use of self-reported weather shocks based on the perceptions of the individuals interviewed – such as in Ocello et al. (2015) – whose accuracy remains questionable. The inclusion of longer-run climatic measures and time series would also allow the implementation of more recent and sophisticated techniques tailored to bridge the external validity gap between weather and climate, such as long differences or the inclusion of several weather lags in the regression models.

Lastly, while standard nationally-representative household surveys such as the LSMS-ISA are clearly disadvantaged with respect to the possibility of investigating the migratory outcomes of localized suddenonset weather shocks, which require _ad hoc_ post-disaster surveys, the use of ‘mixed-mode’ data collections solutions, such as the alternation of standard face-to-face surveys with higher-frequency phone-survey interviews, would allow one to obtain not only more timely and frequent longitudinal information in general but also, in case a sudden shock occurs, the collection of basic migration data in the aftermath of the shock itself, and to do so in an easier and less expensive way compared to the complexity associated with the implementation of a swift _ad hoc_ survey in the area exposed to the disaster.

33
The third category of policy indicators on labor market reform concerns the mechanisms for negotiation and peaceful resolution of labor disputes. Market-oriented reforms in this area focus on confining government involvement in manager-labor relations to the expeditious resolution of labor disputes, for instance through specialized judicial courts and arbitrators. This implies taking government out of the negotiation process and placing it in the role of contract enforcement. Then, some specific policy indicators in this regard would consider whether legislation allows for decentralized and flexible collective bargaining; whether workers are allowed to choose their own unions (which would generate more efficient worker representation through improved competition and contestability); whether labor legislation does not induce labor stoppage by making one party (usually the firm) bear most of its costs; whether labor legislation allows labor strikes only when they are clearly connected to specific contractual claims between workers and their corresponding firms; and whether there are courts and arbitrators specialized on labor disputes.

The last category of policy indicators on labor reform is related to the prevalence of public employment. In many developing countries, public employment has traditionally been used both as a way to solve unemployment problems during economic downturns and as a political tool to reward allegiance to the governing party. Therefore, the process of hiring public employees has often neglected to adequately match workers to the needs of public administration. This overstaffing and excessively high remuneration in the public administration (relative to productivity) has bid up labor costs in the private sector and created little incentive for training and human capital development in the public sector. A policy indicator of reform in this area is the initiation of a public sector downsizing program, one that provides adequate compensation and training to departing staff and prevents the state from losing its comparatively most productive personnel.

On these policy indicators, the main data source consists of various publications by the International Labor Organization, such as the _Labor Statistics Database_ and the _ILO Conventions and Recommendations_ . The drawback of ILO sources is that the relevant information is dispersed and not always comparable across countries. These problems are remedied to a large extent in the database of labor market indicators 22
_groupement_ level data cannot measure the character of the displacement in any meaningful way, but analysis at the individual level can.

# **5.2 Individual Level Relationships**

Two survey waves posed additional questions on local displacement dynamics. While these additional questions restrict comparison with other survey waves, they provide the opportunity to unpack the mixed results found in the aggregate analysis. Poll 14 is a special survey that only samples cities (Ville de Goma, Ville de Beni, Ville de Butembo, Ville de Bukavu, Ville d’Uvira, Ville de Bunia and Irumu in particular) while Poll 15 is a representative sample of all _territoires_ in the three provinces.<sup>11</sup> These survey waves are labeled as “Cities” and “General” samples in the individual analysis. Analyzing these two surveys together enables the comparison of relationships by the local context, which may distort the impact that hosting has on perceptions of social cohesion.

Figure 5 plots the coefficients from series of logistic regressions to account for the binary nature of the dependent variables. The regressions include Province fixed effects and _groupement_ clustered standard errors to account for unmeasured context-specific dynamics. Responses are weighted by the inverse proportion of selection at the _territoire_ in each regression. The relationships between displacement dynamics and perceptions of each manifestation of social cohesion are analyzed separately by running 12 models for each independent variable (6 regressions for each sample).

The regressions are correlations and should not be interpreted causally. Hosting status and displacement flows are likely related to perceptions of social cohesion in indirect ways and the structure of the survey data limit the ability to specify the channels through which these relationships run. Each regression controls for characteristics that may influence respondents’ perceptions of social cohesion outside of the presence of IDPs or refugees in the local community such as province, gender, age, marital status, level of education, employment, and exposure to violence. 11Poll numbers correspond to the number wave in our larger project, as described and shown in Table 2.

28