rafmacalaba/data-use-mentions-extended huggingface dataset:
8 Republic the share of privatized housing was 68%, in Kalmykia - 54%/, in Altai Krai - 56%, in Omsk Oblast - 63%, and in the Agin Buryat Autonomous Region -66%.

In contrast, less than 25% of housing passed into private ownership in the republics of Mordovia and Sakha (Yakutia), the Chuvash Republic, Arkhangelsk and Ulianovsk ciblasts, Nenets, Komi-Permyak and Taimyr autonomous regions.

The analysis of privatization rates in the Russian Federation regions shows that:

- privatization was supported everywhere in Russia;

- the scope of privatization of housing varies substantially across regions;

- in most regions, privatization reached its peak in 1993, after which rates began to decline rapidly;

- by 1996, privatization ceased to be a mass-scale phenomenon.

In other former Soviet republics housing privatization can be viewed as a completed process. In the Baltic states, especially Lithuania, practically all housing is private. The Russian situation is particular in that even though 55% of the housing stock is now privately owned, a large proportion of housing (39%/6) remained in state or municipal ownership. Ukraine holds a place in-between Russia and the Baltic states.

# 1.2. Social dimensions of housing privatization in Russia

The goal of the work described here is to sum up changes in the distribution of residential property by the end of 1996. The factors and social consequences of housing privatiza1ion are examined depending on the size and category of Russian settlements on the basis of sociological swveys conducted in the past few years in Moscow (population: 8,664,000), Vladimir, an oblast administrative center (population: 340,000), and Gorodets, a small town in Nizhny Novgorod oblast (population: 33,000).

Specifially, we aim to show the component parts of the housing privatization processes that could exert an adverse or, on the contrary, beneficial effect on housing reform in Russia: the transition to market methods, housing maintenance reform, development of private
Policy Research Working Paper 9014

# **Abstract**

This paper analyzes the differential impact of migration on labor supply of the left-behind household members in Nepal, where international migration for employment, predominantly a male phenomenon, increased substantially between 2001 and 2011. Using the Nepal Living Standard Survey data, the paper extends the analysis by incorporating the impacts on the extensive and intensive margins. The study also answer the question: if they are not wage-employed, in what activities are the remaining household members engaging instead? The paper finds that, in response to out-migration of some family members, women realign their priorities and reallocate their time from market employment to self-employment and home production, possibly filling in the roles vacated by the migrants. In contrast, the income effect dominates the impact of migration on the left-behind men; that is, men value their leisure more because of the remittances from abroad and decrease their overall supply of labor. Additionally, the research finds significant heterogeneity in the supply of labor by age, skill, and household head status among the left-behind women, pointing toward intrahousehold bargaining.

This paper is a product of the Poverty and Equity Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The author may be contacted at lphadera@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations
reveals that not only is financing skewed towards specific countries, but towards one specific crisis.

Figure M uses country of origin data for each of the 25 largest refugee hosting countries to create an estimate of financing for refugee education by crisis.<sup>29</sup> This analysis shows that ODA for refugee education in LICs and MICs is heavily skewed towards countries hosting Syrian

> 27 Ibid.

> 28 Cost data on refugee education financing is taken from Silva (2023) and refers to the costs of education for refugee children via the host country’s national education system.

> 29 This calculation assumes that all the refugees in the country receive the same proportion of ODA for education.

26 Ibid.

20
, informal associations, or Rotating Saving and Credit Associations (ROSCA, or _tontine_ in Senegal) and savings held at home.

# _Descriptive Statistics_

Our sample of interest in the first step of our empirical analysis is composed of all coresident monogamous and polygamous unions made of a husband and his only or first-rank wife. We restrict our sample to unions in which women are between 15 and 60 years old and drop the 60 unions with the husbands who are Christian. Our final sample for the first step of the analysis is thus made of 1,388 unions surveyed in the first wave of the PSF survey.

On the subsample of 457 men living in a polygamous union (434 observations, once missing values on years of marriage are taken into account), we observe that the majority of polygamous men first married around 25 years old and took a second wife just before 40. The median interval between first and second marriages is 10 years, and 75 percent of second marriages occurred in the first 16 years of the first union.

Table S.1 in supplemental appendix provides descriptive statistics of socioeconomic characteristics of our first step sample made of monogamous and polygamous first-rank unions.

14
to indicate both novelty and improvement as elements of innovation (Galindo‐Rueda and Van Cruysen, 2015). Furthermore, self‐reported innovation may be biased by social desirability that, in turn, might have a differential effect on respondents from different cultural backgrounds (Johnson & van de Vijver, 2003). As innovation is commonly associated with increased productivity and growth, it may perceived as a desirable outcome. Therefore, respondents to innovation surveys may be inclined to over‐ report innovations when follow‐up questions aiming at ascertain the accuracy of the answer are not asked and, furthermore, the tendency to over‐report innovation may vary across countries.

Finally, methodological issues can also have an impact on the measurement of innovation across firms of different size, as documented in a recent study conducted in Poland. The study, which compares innovation rates of micro and non‐micro firms, shows higher innovation rates for micro firms as compared 8
_ANDR EE_<sup>_ ́_</sup> _SEPTEMBER 28, 2022_ _16_ Note that the R<sup>2</sup> for South Asia is calculated based on only 22 samples, and hence it should with high probability be lower than in the other regions purely due to the small sample size rather than actual worse prediction performance.<sup>18</sup> The model seems to perform reasonably well still, for instance when the focus is on the MAE of 0 _._ 99 rate points, which is below that of some other regions.

# _B. Prediction Results_

In this section, the final optimized model is used to assess the likely compound effects of the pandemic and Ukraine war and compare the forecast to estimates for the 2008 Global Food Crisis period.<sup>19</sup> The main text presents plots for all countries combined, additional plots for specific regions and incomes are in the appendix (figures A1 and A2).

The results are generated by projecting the covariates forward using the WEO. Outlooks are available for GDP, population sizes, and inflation figures. The WEO tends to have positive bias, documented for instance by Gatti et al. (2022), and may be understood as economic targets that can be met if identified risks are managed. The April WEO is put together during January – March. As such, the data does not yet fully reflect the impacts of recent major events. Risks and associate downside trajectories are however discussed, so counterfactual scenario values of inflation, growth and poverty here are used based on the downside multipliers of the April report to investigate the additional impacts of downside economic risks. Specifically, the downside projections consider a further increase in global food prices of +6% in 2022 and +3% in 2023, and reduced growth of -2% below default projection growth rates in 2022 and 2023, converging to -0.9% by 2027, as a result of increased tightening by central banks to
who seek child support cite "severe physical violence" by the father. Evaluators collected qualitative and quantitative data among 180 clients and 182 matched controls to examine whether or not the services increased the incomes of clients, reduced domestic violence, or improved the quality of children’s education. The evaluation found that 38% of clients obtained some child support; in half of those cases, child support made a substantial contribution to their standard of living (though none of the mothers felt that the amount was sufficient or fair). Levels of domestic violence were similar among clients and nonclients before seeking services, but lower among clients after receiving services. However, researchers acknowledged that women who feared future harm from their former partner may have been less likely to seek services at legal aid clinics in the first place, pointing to the difficulty in assessing evaluation outcomes when the clients accessing the services often represent a more empowered group of women than the control group. From a cost-effectiveness point of view, researchers suggested that the legal aid clinics had missed an opportunity to "capitalize on [a] spillover effect" by publicizing women’s rights and the effectiveness of clinics in representing women's interests. They argued that this type of campaign might make it easer for other women to achieve out-of-court settlements without having to go through the risks and difficulties of formal court proceedings.

# **Community mobilization**

# **_Reforming community-based non-formal systems of justice_**

In certain parts of the world, the formal justice system has a limited presence. For example, Human Rights Watch has written about the role of semi-official traditional councils, _mahallas,_ in Uzbekistan, which arbitrate local disputes and act as gatekeepers to the formal justice system. Unfortunately, they frequently protect perpetrators of gender-based violence, force women to live with ongoing abuse, and block women’s attempts to access the police and the courts, sometimes threatening to testify against women in court (Human Rights Watch, 2003a). Human Rights Watch argues that reforming these traditional
**Figure 7: Drought return periods and losses for Uruguay under different warming scenarios (based on 2023 sector values)**

<!-- Start of picture text --> Crops Livestock<br>1,200 1,800<br>1,600<br>1,000<br>1,400<br>800 1,200<br>1,000<br>600<br>Historical / Baseline Historical / Baseline<br>800<br>RCP 2.6 RCP 2.6<br>400 600<br>RCP 4.5 RCP 4.5<br>400<br>200<br>RCP 8.5 RCP 8.5<br>200<br>0 0<br>0 50 100 150 200 0 50 100 150 200<br>Return period (years) Return period (years)<br>Source: World Bank estimates based on IDB (2019), MEF (2023), and INUMET (2023).<br>Losses (US$mil) Losses (US$mil)<br><!-- End of picture text --> Source: World Bank estimates based on IDB (2019), MEF (2023), and INUMET (2023).

**Figure 8: Expected annual losses under different warming scenarios (based on 2023 sector values)**

<!-- Start of picture text --> Crops Annual Expected Losses ($mil) Livestock Annual Expected Losses<br>($mil)<br>120<br>120<br>100<br>100<br>80<br>80<br>60<br>60<br>40<br>40<br>20 20<br>0 0<br>Baseline RCP 2.6 RCP 4.5 RCP 8.5 Baseline RCP 2.6 RCP 4.5 RCP 8.5
# **Figure 3 Historical and Projected Population Pyramids for Rural China**

# A. Historical Population Pyramids for Rural China

<!-- Start of picture text --> Rural Population Pyramid, 1982 Rural Population Pyramid, 2005<br>90-94 90-94<br>85-89 85-89<br>80-84 80 -84<br>75-79 75-7 9<br>70 -74 70-74<br>65-6 9 65-69<br>60-64 60-64<br>55-59 55-59<br>50-54 50-54<br>45-49 45-49<br>40-44 40-44<br>35-39 35-39<br>30-34 30-34<br>25-29 25-29<br>20-24 20-24<br>15-19 15-19<br>10-14 10-14<br>5-9 5-9<br>Under 5 Under 5<br>-6 -4 -2 0 2 4 6 -6 -4 -2 0 2 4 6<br>Males Females Males Females<br>1982 Population Census 2005 One Percent Population Sample<br>20 20<br>15 15<br>10 10<br>5 5<br>0 0<br><!-- End of picture text -->

- B. Projected Rural Population Pyramids in 2020 and 2030

<!-- Start of picture text --> Rural Population Pyramid, 2020 Rural Population Pyramid, 2030<br>90-94 90-94<br>85-89 85-89<br>80-84 80-84<br>75-79 75-79<br>70-74 70-74<br>65-69 65-69<br>60-64 60-64<br>55-59 55-59<br>50-54 50-54<br>45-49 45-49<br>40-44 40-44<br>35-39 35-39<br>30-34 30-34<br>25-29 25-29<br>20-24 20-24<br>15-19 15-19<br>10-14 10-14<br>5-9 5-9<br>Under 5 Under 5<br>-6 -4 -2 0 2 4 6 -6 -4 -2 0 2 4 6<br>Males
average, households consumed food from 8 food groups and the maximum was 12 food groups. The consumption of 8 food groups compares well with other national-level studies (Jones et al., 2014; Matita et al., 2022). The mean FCS was 49, which indicates that food and nutritional insecurity are not severe but visible. The proportion of households with borderline and poor food security situation were 20.4 and 2.5

> 5 Long-term median rainfall data is sourced from World Bank Group, Climate Change Knowledge Portal (2023). URL: https://climateknowledgeportal.worldbank.org/.

11
**Figure 6. Performance and Poverty and Inequality Impacts of the Heating Allowance under Baseline Scenario (pre-reform)**

**a.Coverage (% of households in each decile) b. Benefit incidence  (% benefits allocated to each decile)**

<!-- Start of picture text --> 71.4<br>11.0<br>1<br>2<br>33.3<br>3<br>31.3<br>8.7 57.2<br>4<br>0.0 0.4<br>5<br> 1 2 3 4 5 6 7 8 9 10<br><!-- End of picture text -->

- **c. Generosity (benefits as % of MYPP)**

- **d.Inequality, poverty, and the poverty gap impact**

<!-- Start of picture text --> 10.2<br>Marginal Contributions to<br>0.00<br>Inequality<br>Change in Poverty Gap 0.40<br>Marginal Contributions to<br>2.0 0.17<br>Poverty<br>0.5<br>0.0 0.0<br>1 2 3 4 5 6 7 8 9 10 Disposable<br><!-- End of picture text --> _Source: Own estimates using CEQ and Bulgaria SILC 2021_ _Note: MYPP refers to Market Income Plus Pensions. Deciles are income deciles. The marginal contribution is calculated by taking the difference between the inequality (or poverty) indicator without the transfer and with it. Marginal contributions to poverty are measured using at-risk of poverty lines (AROP), fixed at the baseline scenario before the reform was implemented._

**When considering the combined effect of the Monthly Social Allowance (MSA) and the heating allowance, their aggregate impact on income inequality and poverty
.<sup>33</sup> For the United States, the Chicago-based CRSP Database provides monthly instrument-level data on US Treasuries back to 1925. For England, Ellison and Scott (2020) gathered granular data on prices and issuance patterns of UK government debt 1694-2017.

When combining the bond price dataset with our archive on missed payments and debt restructurings, as described in Section 2, we match at the bond level. However, we do not have prices for all of the 1,134 bonds for which we computed haircuts, because some of the restructured bonds were not regularly traded

> 32 Of these, 70 defaulted on their external debt at some point between 1814 and 2016, namely, Algeria, Angola, Argentina, Austria/Austria-Hungary, Belize, Bolivia, Brazil, Bulgaria, Cameroon, Chile, China, Colombia, Costa Rica, Côte d'Ivoire, Croatia, Cuba, Czech Republic/Czechoslovakia, Dominican Republic, Ecuador, t Egypt, El Salvador, Estonia, Ethiopia, Finland, Gabon, Germany, Ghana, Greece, Guatemala, Haiti, Honduras, Hungary, Indonesia, Iraq, Italy, Jamaica, Japan, Jordan, Kazakhstan, Kenya, Latvia, Lithuania, Mexico, Morocco, Nicaragua, Nigeria, Pakistan, Panama, Paraguay, Peru, the Philippines, Poland, Portugal, Romania, Russia, Senegal, South Africa, Spain, Sri Lanka, Tanzania, Thailand, Trinidad and Tobago, Turkey/Ottoman Empire, Ukraine, Uruguay, Venezuela, Vietnam, Yugoslavia/Serbia, Zambia, and Zimbabwe. The remaining 21 countries never defaulted on privately-held external debt since 1815, namely Armenia, Australia, Azerbaijan, Belarus, Belgium, Canada, Denmark, France, Georgia, Ireland, Lebanon, Malaysia, Mongolia, Namibia, the Netherlands, New Zealand, Norway, the Slovak Republic, South Korea, Sweden, and Switzerland.

> 33 See also Maggiori, Neiman, and Schreger (2020), who use granular data on hundreds of thousands of financial instruments,
net profit margin, while BENECO shows a small negative margin<sup>18</sup> (loss) and the sample average has a large negative margin. The EBITDA (earnings before interest, taxes, depreciation, and amortization) is positive for both MERALCO and BENECO (as well as for the sample average) but may be misleading because it omits expenses related to the cost of capital. Importantly, neither Philippine utility receives government transfers, while the sample average has transfers averaging 5 percent of revenue.

**Table 8: Summary of Financial Indicators against Sample Average**

||**MERALCO**|**BENECO**|**Global**<br>**comparator**|
|---|---|---|---|
|Netprofit (loss) margin (%)|8|-2|-12|
|EBITDA margin (%)|13|2|6|
|Current ratio|0.84|2.00|0.79|
|Debt service coverage ratio|10.33|-1.38|-4.56|
|External financing index ratio<sup>19</sup>|-0.40|-0.35|6.92|
|Investment as apercent of revenues (%)|10|6|18|
|Average borrowingrate (%)|4|4|6|
|Government transfers as a percent of utility<br>revenue (%)|0||6|
|Net capital cost recovery ratio<sup>20</sup>(%)|72||16|

Note: Sample average from the Rethinking Power Sector Reform Project data observatory and includes data from Tanzania, Senegal, Peru, Colombia, Pakistan, the Philippines, Vietnam, Kenya, Uganda, Tajikistan, the Indian states of Rajasthan, Odisha, and Andhra Pradesh, and Egypt

# _3.4.2 Institutions_
values (Go, 2015). In Norway, altruism plays a role at the local government level but mostly by increasing support for education among the middle‐aged who have children, rather than through stronger support for old‐age care among the middle aged who have elderly parents (Rattsø and Sørensen, 2010).

Our paper is closer in nature to a second strand of literature that focuses on subjective preferences for the allocation of public spending, rather than on actual outcomes.<sup>5</sup> Busemeyer, Goerres and Weschle (2009) provide a thorough review of the literature and report mixed empirical findings on the effects of ageing on overall welfare spending, probably as a result of aggregation effects. The authors focus on OECD countries and contribute to the literature by analyzing different components of welfare spending and by focusing on the total population, rather than just the labor force. They find significant differences in preferences for redistribution across age groups, particularly for education and pensions, although the strength of these age‐related differences varies across countries.

The literature also highlights the importance of cohort, in addition to time, effects. Fullerton and Dixon (2010) use data for the United States and look at three spending categories (health care, education and social security) over a long time span (1984–2008). They find evidence in support of the “grey peril” hypothesis for education, although their results are mixed for pensions and health care. Our paper is closely related to Sorensen (2013), who uses repeated cross‐sections for 22 countries and finds that when time and cohort effects are taken into account, elderly people prefer lower (higher) spending on education (health care and pensions). These life‐cycle effects vary considerably across countries, but they are generally quite small. One limitation of the data used by Sorensen (2013) is that the alternatives available in the questionnaire for additional government expenditure are not mutually exclusive. In other words, individuals can choose to
in the region, as well as a brief description of hurricane data and our proxy for potential damage from hurricanes. Section 3 explains our empirical estimation approach, and Section 4 presents our results. Section 5 concludes the paper.

# **2. Data and statistics**

To quantify the impact of hurricane strikes on local economic activity in the Caribbean, we used three main data sets: nightlight data as a proxy for local economic activity; hurricane windstorm hazard data from a wind field model; and global mangroves databases.

# **2.1.Nightlight application**

The Defense Meteorological Satellite Program (DSMP) – Operational Linescan System (OLS) nighttime light (NTL) data are primary data on economic activity, available at local, regional and global scales (Bertinellli and Strobl 2013; Henderson, Storeygard and Weil 2012; Chen and Nordhaus 2011). Compared to other remote sensing satellite observations, these data are particularly useful for evaluating policies with highly localized effects, or in countries with poor or non-existent subnational GDP data (Lowe 2014). With their global coverage and long temporal span, DSMP NTL data have also been extensively used in studies on electricity consumption, socioeconomic activities, light pollution, urban ecosystems, and urban extent mapping (Li et al. 2020).

Scientists at the NOAA developed a method to remove cloud-obscured pixels from raw nightlight imageries, as well as sources of transient lights such as the bright half of the local cycle, auroral activity, forest fires, and other events. Since 1992, they have processed raw daily nightlight imageries from around the world, delivering yearly cloud-free nightlight composites that essentially capture nocturnal human activity (Elvidge et al. 2010). NTL data records have digital number (DN) values, ranging from 0 (no light) to 63 (maximum light); the spatial resolution is 30 arc-seconds, and there is near-global coverage of 180oW to 180oE longitude and 65oS to 75o latitude (Li et al. 2020).

For this study, we use
2016. From the city level data we construct real output per worker, our proxy for labor productivity.<sup>3</sup> The data tracks nominal sales of firms within each city. We have estimates of city-level sales values, but not for individual firms. As we do not have access to city-level CPI data, or to sub-sector CPI data, we deflate nominal sales using sector-level national account deflators for Chile as a whole. The pollution data is provided in a coordinate-system. We merge this data using city-level coordinates for both the productivity and the pollution data-set. Table 2 shows that annual pollution concentration for Chile during the period of study exceeds the recommended annual 5 _ug/m_<sup>3</sup> .

> 3The industry classification follows ISIC Rev.4. Furthermore, we did not obtain data on hours worked, which precludes analysis on this measure of productivity.

9