rafmacalaba/data-use-mentions-extended huggingface dataset:
employed  in  exporting  firms.  This  finding  stands  in  sharp  contrast  to  results  obtained  using average  firm  level  wages  instead.  One  reason  for  this  difference  is  simply  that  the  use  of detailed  worker  level  data  allows  us  to  take  into  full  account  any  changes  in  the  composition of  the  workforce  (by  controlling  for  both  observable  and  time-­‐invariant  unobservable worker  characteristics)  following  trade  policy.  Furthermore,  by  taking  worker-­‐firm  match effects  into  account,  we  are  able  to  control  for  changes  in  the  composition  of  firms’  match quality  following  trade  policy  changes.  If,  following  trade  liberalization,  exporting  firms improve  their  average  match  quality  by  hiring  workers  with  better  match  quality,  the estimates  of  the  coefficient !!" without  controlling  for  match  effects  would  mistakenly  be estimated  as  significant  even  in  the  absence  of  any  true  effect.

Table  4.3,  which  compares  the  changes  in  the  distribution  of  estimated  match  effects  in exporting  firms  relative  to  non-­‐exporting  firms,  confirms  this  point.<sup>38</sup> Comparing  the  period 1998  to  1990,  we  see  an  increase  in  both  the  mean  and  the  median  match  effect  in  exporting firms,  while  the  mean  and  median  match  effect  both  fall  in  non-­‐exporting  firms.  As  we  have noted  earlier,  match  fixed  effects  in  equation  (3)  absorb  both  worker  and  firm  fixed  effects in  addition  to  time-­‐invariant  match  quality  of  a  given  employment  spell.  Consequently,  our finding  of  an  improvement  in  average  match  effect  in  exporting  firms  between  1990  and 1998  summarizes  the  combined  effect  of  a  change  in  workforce  composition  in  these  firms in  terms  of  improvement  in  worker  quality  in  time  invariant  worker  specific  characteristics, such  as  innate  ability  (captured  by  worker  fixed  effects)  and  improvement  in  the  quality  of the  worker-­‐firm  matches  (captured  by  match-­‐fixed  effects).<sup>39</sup> The  improvement  in  the distribution  of  match-­‐specific  ability  in  exporting  firms  is  consistent  with  the  Helpman, Itskhoki,  Redding
# **Summary of Findings**

This paper reviews sociological data on privatization and the development of a housing market in Russia through 1996. In doing so, it also considers demand for housing and household mobility in Russia, and people's attitudes to the use of narket methods in making decisions about their housing. The data have been collected by means of surveys of the urban population over the last several years that are largely unknown outside of Russia.

Since early 1997, housing reform has increasingly moved into the center of the focus of the Government of Russia's reform efforts. Present policy calls for a reduction, in stages, of housing subsidies (for which both tenants and owners of privatized apartments are eligible) so that cost-recovery will be 100% by 2000. But household incomes are not expected to rise commensurably, and it is likely that housing's share of the household budget will grow for most Russians.

By the end of 1996, some 55% of the Russian housing stock was privately owned. Rates of privatization in Moscow indicate that the process peaked in 1993 and since then has abated considerably, essentially coming to a halt in 1996; the same pattern, with a latter start date, was manifested in smaller cities. Not surprisingly, high-quality apartments in city centers have much higher rates of privatization than lowerquality housing at a distance from the center. Other factors influencing the decision to privatize are the demographic characteristics of the occupants, the households' value orientations and level of education, and the household income.

On the whole, privatization has thus far produced a class of owners that is far from unifonn. Two disparate demographic groups emerge as most likely to have privatized their apartments, and these two groups have essentially opposite effects on the development of the Russian housing market. Pensioners, constituting by far the largest group of privatizers, are generally not inclined to move and, thus, exert a negative effect on household mobility which, in turn, retards the advancement of the housing market in Russia
public funds.** The repetition rate is the highest of all the benchmark countries for primary and the third highest for secondary schooling, and is part of the reason for ‐ as well as an implication of ‐ the high pupil teacher ratios (Figure 42).

**Figure 42: Repetition Rate and Pupil‐Teacher Ratio for Primary and Secondary Schooling, 2007.**

<!-- Start of picture text --> 60<br>40 Pupil‐teacher ratio, secondary<br>35 Primary Secondary 50 Pupil‐teacher ratio, primary<br>30 40<br>25<br>20 30<br>15 20<br>10<br>5 10<br>0 0<br>Source: DDP data, World Bank.<br><!-- End of picture text -->

**Despite some problems, Table 10 below suggests that skills are not a current constraint to economic activity as both unemployment and underemployment are high for all levels of education.** However, there is an important difference across education levels. Unemployment increases with the education level, indicating that the better educated, and probably better off, prefer to be unemployed looking for employment than to work and be underemployed. Conversely, the lower educated and poorer segments of the population have to accept whatever employment they can find and, therefore, are more often under‐employed. So it seems that Togo still has potential to use more of the educated workers and to use the ones already employed in a more productive fashion.

**Table 10: Un‐and Underemployed by Education Level for Rural, Urban Areas and Lomé, Togo 2006.**

||**U**|**nemploye**|**d**|**Un**|**deremplo**|**yed**|
|---|---|---|---|--
- Hettige, H., 2006. When do rural roads benefit the poor and how? An in-depth analysis based on case studies. Operations Evaluation Dept., Asian Development Bank, Metro Manila, Philippines. Available from https://www.adb.org/sites/default/files/publication/29406/when-rural-roads-benefit-poor.pdf.

- Heyns, A.M., 2016. A multi-objective approach towards geospatial facility location. PhD Thesis. Stellenbosch University.

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- Heyns, A.M., van Vuuren, J.H., 2018. Multi-type, multi-zone facility location. Geographical Analysis 50, 3–31. doi:10.1111/gean.12131.

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- Huber, S., 2015. Accessibility of peripheral areas in Nepal: the role of infrastructure development and environmental constraints as limiting factors. Research Paper. TU Dresden. Berlin. Available from http://rgdoi.net/10.13140/RG.2.1.3241.0962.

- ICIMOD, 2013. Land cover of Nepal 2010 [Data set]. Available from https://doi.org/10.26066/
Hospitals that undertake high volumes of relevant procedures are likely to be stronger candidates for accreditation, as studies have shown that surgical complications and mortality rates are inversely related to the volume of procedures (see, e.g., Birkmeyer et al., 2002 and Katz et al., 2002). JCI accredited hospitals are responsible for renewing their accreditation every three years, and have to collect and report outcomes data on services rendered as well as quality indicators. Studies have found a positive association between hospital performance reporting and hospital quality improvement efforts (see, e.g., Hibbard et al., 2003).<sup>31</sup> The receipt of JCI accreditation by foreign hospitals could be used as the basis for selection to join the provider network of an industrial country insurer.<sup>32</sup> Foreign hospitals could also signal quality by establishing affiliations with reputed hospitals in industrial countries and mirroring their procedural standards, guidelines and clinical pathways. Foreign investment in the health care sector, sometimes inhibited by developing country restrictions, could also be a powerful vehicle for improvements in technology, managements and overall standards.

# _7.2 Accreditation of foreign medical professionals_

The second proposal is for doctors and nurses in export-oriented health care organizations to credibly signal quality by passing, respectively, the United States Medical Licensing Exam (USMLE) and the US National Council Licensure Examination for Registered Nurses (NCLEX-RN).<sup>33</sup> Since foreign medical graduates that practice medicine in the US are obliged to pass these examinations, a natural extension from a trade in health care perspective would be to institute a similar requirement for foreign physicians in exportoriented health care organizations abroad. It is also possible that as part of the residency program for physicians, foreign medical boards could form partnerships with medical centers in developed countries involving an exchange of medical experts, for example, as in the partnership between the Methodist Health care System of San Antonio and Mexico (see Albro and Norton, 1997)
- 24 -

**Figure 10:  Long-run Fees over Assets for Average, Highest and Lowest Cost MPF**

<!-- Start of picture text --> 14%<br>12%<br>10%<br>8%<br>6%<br>4%<br>1.66%<br>2%<br>1.18%<br>0.49%<br>0%<br>Average Cost MPF Lowest Cost MPF Highest Cost MPF<br>1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030<br><!-- End of picture text --> Note: Real wage growth = 4%, rate of return = 8%. Source: Own calculations on HFSA data.

48. An alternative way to examine fees in Hungary entails the examination of the long-run equivalence of fees over contributions and assets, building on the general methodology developed in Diamond (2001). Figure 11 shows the compensating variation in fees of contributions that would be required to maintain the final balance constant if the fees on assets is set to 1 percent for an individual with a wage growth of 5%, a working career of 40 years ( _T_ = 40), and no interruption in service ( _T_<sup>*</sup> = 40). It shows that fees on assets of 1 percent are equivalent to fees of 15 to 30 percent on contributions, depending on the net rate of return (gross rate minus fees). These results are consistent with the simulations shown in Figure 7 and Figure 8, and illustrate the long-run impact of asset fees charged during the whole accumulation phase. In particular, the long-run total fee over assets of 1.18 percent for the average MPF would translate in a long run contribution fee of 17 – 35 percent.<sup
Special Rule from August 2001, whereas Swaziland was eligible from July 2001, the effect may already be reflected in the exports data from 2001 onwards. Column 3 reports the export elasticity estimate at an average preferential margin when the dummy, _RA_<sup>_j,k_</sup> _i,t_ , is equal to one from 2001 onwards for Botswana, Malawi and

> 17  The indicator, available in the form of a ranking for 178 countries, is a simple average of the regulations affecting ten stages of a business’ life: starting a business, dealing with licenses, employing workers, registering property, getting credit, protecting investors, paying taxes, trading across borders, enforcing contracts and closing a business. Being quantitative rather than subjective, the indicator is less subject to bias than the more-widely used indicators of governance.

> 18 We use Doing Business (DB) data for 2008 released on June 1, 2008, as it includes more African countries in the sample than reports in previous years.  Indeed, for instance, DB 2006 coverage is limited to 6 out of the 22 African countries.  Yet, for these six countries, the relative ranking of DB 2008 does not change significantly with respect to DB 2006.

17
integration with the EU. We overcome this challenge by being the first to utilize the new International Trade and Production Database for Estimation (ITPD-E), which was construced by Borchert et al. (2021 _a_ ) for the U.S. International Trade Commission. The two main advantages of ITPD-E for our purposes are that (i) it covers all

> 3The economic integration process between EU and the CEFTA countries has been slowly progressing for the last 15 years. Four CEFTA countries (Albania, Montenegro, North Macedonia and Serbia) are EU candidate countries, while Bosnia and Herzegovina and Kosovo are potential candidate countries, and Moldova recently signed a bilateral free trade agreement with the EU. The recent EU strategy in 2018 provided a target accession date of 2025 for Serbia and Montenegro, with the possibility for the other Western Balkans countries to join them, but this target date may be delayed given the 2020 COVID-19 pandemic. A review of the status of accession process and challenges (Grieveson, Grubler and Holzner, 2018) finds that target date is ambitious and may be established more as an incentive for reforms in the countries. Nonetheless, there is still a possibility that in five years or so, Serbia and Montenegro will be new EU members, with the other countries to follow in the next 10 years.

3
12 months prior to the survey ranges from 0 up to 44, with high numbers concentrated in small areas in Maiduguri. Of the sample, 39% were exposed to any local violence captured in the ACLED dataset, and the average individual was exposed about 7 documented events.

**Household gender and composition. Table 2** reports information on gender and composition at the household level, broken down by IDP status. Of the surveyed households, 34% are headed by a female and there is no significant difference between IDP and non-IDPs. In terms of composition, non-IDP households are slightly larger. While the mean difference with non-IDPs is statistically significant, it is modest in magnitude (6.24 members in non-IDP households versus 5.93 members in IDP households). The slight difference between IDPs and non-IDPs is driven by a slightly higher average number of adults in the household, for both men (1.51 versus 1.29) and women (1.54 versus 1.36). By contrast, IDP and non-IDP households do not differ significantly in the number of boys, girls, and children in total (around 3 on average). Notably, IDP and non-IDPs also do not differ systematically in terms of gender composition. This includes the number of women versus men in the household, the number of girls versus boys, and the number of economically active women versus economically active men.

# **5 Results**

# **5.1 Harmful food coping among households**

**Table 3** presents baseline estimates on the role of gender and displacement for engaging in harmful food-related behaviors (harmful food coping). We find strong evidence that IDP households employ significantly more harmful food coping than non-IDP households. Being an IDP household is robustly associated with a decrease around .25 standard deviation in the rCSI. In absolute terms, this corresponds to a 2.97-point increase, which is sizable and concerning given that the mean absolute score is 11 and scores above 10 are
upstream of gauge stations, and exclude upstream watersheds with large-scale obstructions (e.g. dams) from the analysis

# **Regression analysis variables**

Two models relating flow to current and past BWI values are presented for two basins. We regress the BWI values on the gauging station runoff per month (Equation 2) where _Q_ is discharge at a station for month _m_ , and _n_ is number of previous months or lag months.

We investigate whether lag periods improve the statistical correlation of the BWI and gauging station runoff. Lagged runoff values are the average of the concurrent and the previous month(s). We suggest that the size and topography of the catchments strongly influence the duration of the lag. First, snowmelt ―captures‖ the accumulated precipitation and delays the flows until the melting inside the snow pack ―releases‖ the water, which can further increase the lag time. Second, since the BWI integrates one value to represent the total area of accumulation, the size, slope and land use in the area impacts the lag time. For example, this model is much simpler in form than the model in Herold and Mouton (2006).

Like Equation (2), we regress precipitation, _P_ , of the basin on the gauging station runoff per month (Equation 3).

# **Requirements for the model**

Two basic components are fundamental for successful modeling of flow: an accurate measure of the actual surface and subsurface flow on a regular time frame, and a predictive parameter that identifies how the gauge data will fluctuate due to the changes in weather and land use across the basin. The model identifies the probability function for various levels of flow and determines the explanatory accuracy and significance of the model.

The optimal data conditions for the BWI necessitate natural flow upstream of a gauging station. The sensitivity of the SSM/I signal to water restricts our use of the instrument near coasts, since the wetness signal would saturate the results; it is unusable near large bodies of 10
# **1. Introduction**

The Caribbean, a region highly susceptible to losses from natural hazards, is prone to tropical storms and hurricanes formed in the Northern Atlantic Ocean Basin, which includes the North Atlantic Ocean, the Caribbean Sea, and the Gulf of Mexico.<sup>6</sup> The official Caribbean hurricane season runs from June to November, although infrequent storms have formed outside these dates (Johnson 2015). According to the National Oceanic and Atmospheric Administration (NOAA), an average Atlantic hurricane season produces 12 named storms, including six hurricanes, three of which are Category 3, 4, or 5.<sup>7</sup> Between 2017 and 2019, the region experienced three Category 5 hurricanes.

With populations in the Caribbean often subjected to repeated hurricane strikes without respite, a sense of helplessness and hopelessness can set in (Gin and Lubin 1989). Despite overall improvements in living conditions, poverty rates in the region average 30 percent (Bowen 2007). Since 2010, most countries have shown persistently weak economic growth, with annual gross domestic product (GDP) growth rates for 2010–17 averaging only 0.8 percent, compared with 4.7 percent in other small states (OECD et al. 2019). Most Caribbean islands exhibit high levels of growth volatility, which creates uncertainty, hinders economic growth, and negatively affects public finances (Beuermann et al. 2018).

Meteorological damage has long-term consequences at both national and regional levels. According to the Emergency Events Database EM-DAT, 283 disasters caused by hurricanes either made landfall in the region or passed within 69 miles of the Caribbean islands between 1950 and 2014.<sup>8</sup> The database only recorded damages for 148 of these, which amounted to roughly $52 billion (in 2010 constant U.S. dollars). This is equivalent to an average of 1.6 percent of GDP in damages every year in the islands (Acevedo Mejía 2016).

Of
Environment_ 7 (4): 265–84.

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and Banten respectively, lower than the estimation produced by the model.<sup>9</sup>

**<u>Figure 6 Output informality by regions or by provinces</u>**

<!-- Start of picture text --> A. Regional informality B. Provincial informality<br>Percent of total output Percent of total output<br>45 2019 2020 2021 2019 national lvl 50 2019 2020 2021 2019 national lvl<br>40 40<br>35 30<br>30 20<br>25 10<br>Sumatera Java/Bali Kalimantan Sulawesi Nusa Tenggara Maluku Papua Kep Riau DKI Jakarta Banten East Kalimantan Bali DI Yogjakarta Central Kalimantan West Papua West Java Bangka-Belitung East Sumatera South Kalimantan Southeast Sulawesi West Kalimantan North Maluku Maluku South Sulawesi Riau East Nusa Tenggara NAD Gorontalo South Sumatera East Java Central Java Bengkulu Papua Central Sulawesi West Sumatera Jambi North Sulawesi West Sulawesi Lampung West Nusa Tenggara<br><!-- End of picture text --> Source: BPS; World Bank Staff Calculation.

26. **The regional pattern of informal employment seems to mimic the regional pattern of output informality** (figures 7) **.** Informal employment is most prevalent in Nusa Tenggara and Papua regions, which could be explained by the nature of jobs in that province (i.e., agriculture

> 7 World Bank Enterprise Survey, 2015.

> 8 The weighted computation is accounting for the weight of the observation in the survey, while the unweighted computation is excluding the weight. See section 2 for the formula.

> 9 While the model calculated data for 2020 which could potentially show different figures, the result is significantly different where Yogyakarta informal output reached more than 50 percent of the economy and Banten informal output comprised more than 40 percent of the economy using the model.

11