rafmacalaba/data-use-mentions-extended huggingface dataset:
Also, in considerations of the development implications of international migration, the substantial movements within the global South are often either understated or ignored. About half of the global stock of migrants from developing countries are living in other developing countries (United Nations 2013a, 2013b; Ratha and Shaw 2007; Bakewell 2009; and World Bank 2015a). Wage differentials across regions within the global South suggest non-trivial potential gains from further South-South migration. Given the considerable variety of demographic dynamics and labor productivity levels across developing regions, there is considerable scope for mutual gains from further South-South migration. Indeed, Artuc et al. (2015) find that non-OECD destinations account for a third of skilled-migration while OECD destinations are becoming relatively less popular as destinations, based on bilateral migrant stock data from 1990 and 2000.

This paper thus re-examines two issues related to the development implications of higher future migration flows that have not been raised in current policy discussions and literature.<sup>5</sup> First is the issue of how the economic costs and benefits of greater migration change over time. Much of the scenario analysis literature has ignored how the impact on a migrant receiving or sending country is dynamic, and this paper will provide a more nuanced perspective on this. In particular, it will examine the evolution of relative factor prices (wages and the return to capital) in response to migration in both regions, and how this dynamic change affects economic consequences of migration over time. Second is the issue of the significance of South-South migration for development, which has yet to be addressed in scenario analyses of global migration.<sup>6</sup> Using a consistent methodology that is ideal for both issues, this paper will provide a sense of the magnitude of the impact of current South-South migration trends on future growth through the a simple negative experiment or stress test – what are the economic consequences if there was some substantially lower South-South migration relative to the baseline scenario.

The following section describes the global dynamic economic simulation framework used to examine
,386|1,063|Consumption|
|Romania|2002|Household Budget Survey|32,285|11,053|Consumption|
|Russia|2003|NOBUS|44,493|25,223|Expenditure|
|Poland|2000|Household Budget Survey|31,847|20,297|Expenditure|
|Lithuania|2002|Lithuania Household Survey|3,000|n/a|Expenditure|

In this study, the focus was on the responses of 59,374 households living in urban apartments in five countries of the ECA region. Almost 50 percent of all households live in urban apartments. Special attention has been paid to ensuring comparability of definitions. For each country, the quintiles were calculated on the basis of expenditure or consumption data. For Armenia, Poland, Russia, and Lithuania, per capita expenditure was used as an income proxy while for Serbia and Romania, per capita consumption was used. The age of the household was coded in four distinct

> 18  The surveys used here are described in greater detail in the associated poverty assessments, available at http://web.worldbank.org/WBSITE/EXTERNAL/TOPICS/EXTPOVERTY/EXTPA/0,,contentMDK:20204084 ~menuPK:443282~pagePK:148956~piPK:216618~theSitePK:430367,00.html (see Armenia 2004; Serbia 2003; Romania 2003; and Poland 2004). Information about the NOBUS in Russia is available at http://www.worldbank.org.ru/ECA/Russia.nsf/0/385AA5BB7DB68980C3256E53004B6F14. The Lithuanian survey is described in Lithuanian Free Market Institute (2002).

> 19  For example, second homes and vacancies.

> 20 The study was performed by the Lithuanian Free Market Institute within the scope of the National Housing Strategy project: http://www.am.lt/EN/VI/rubric.php3?rubric_id=134.

13
Policy Research Working Paper 10818

# **Abstract**

Energy poverty has gained attention in the context of increasing energy prices and the recent energy crisis in Europe. However, measuring energy poverty and characterizing the energy poor are challenging, given that expenditure surveys (household budget surveys) often need more information to characterize the energy poor. Additionally, there is no consensus on how to measure and monitor energy poverty. It is also unknown how and why it differs from income poverty. While income poverty relies on a well-defined poverty line, energy poverty does not have a clearly defined energy poverty line that indicates the minimum energy necessary for satisfying basic needs. In addition, monetary poverty and other welfare measures are measured with income in EU countries using the European Survey of Income and Living Conditions. Therefore, it is not straightforward to characterize energy affordability among the monetary income poor or to estimate the overlap between official income poverty and energy poverty. This paper explores statistical matching as a potential strategy to overcome these data challenges in the context of Bulgaria. Via data fusion, a unique dataset is generated that contains information on energy spending shares, income-based indicators of poverty and inequality, and additional variables on households’ living conditions and welfare. For this purpose, the paper first generates a harmonized dataset, which consists of the European Survey of Income and Living Conditions and household budget survey data. It then employs different imputation models and chooses the best-performing one to impute energy spending shares into data. Based on the resulting dataset, it overlays energy poverty with monetary poverty. The findings show that a large share of the energy poor is not income poor, calling for differentiated policy measures to tackle energy poverty. Importantly, these findings depend on the underlying definition of energy poverty. This paper contributes to a growing body of literature exploring the potential of statistical matching to improve the current data environment in the European Union.

This paper is a product of the Poverty and Equity Global Practice. It is part of a larger effort by the World Bank to
mechanism through which risk aversion manifests, one plausible conclusion is the increased perception of background risk.

In Vietnam, (Reynaud and Nguyen, 2012) find experience of floods to have a significant positive effect on demand for insurance, which may reflect higher levels of risk-aversion. Also in Vietnam, (Dang, 2012), combining historical and contemporary survey data in Vietnam, finds that individuals living in villages that frequently experience a disaster and those who recently experienced a shock show higher levels of riskaversion. (Abreha, 2007) finds similar results of drought experience and risk-aversion among farmers in Ethiopia.

However, some studies find the opposite – that exposure to natural disasters can make people more riskloving. In Louisiana, Eckel et al. (2009) conducted an experimental test on individuals exposed to Hurricane Katrina in September 2005, a month after the storm. He finds evacuees to be more risk-loving (less risk averse) after the storm, although this effect is not observed 10 months later. Various other analyses provide evidence on such change in perception: (Page et al., 2014) provide evidence of a similar risk-loving effect with households who suffered loss as a result of the 2011 Australian floods in Brisbane;<sup>13</sup> (Hanaoka et al., 2015) exhibits similar findings using panel data from Japanese households after the 2011 earthquake and tsunami, but only for men, who gamble and drink more after the event.<sup>14</sup> Andrabi and Das (2010) find the 2005 earthquake in Pakistan increased risk aversion; Said et al. (2014) find a similar result for the 2010 flood in Pakistan.

But not all studies confirm the finding that risk preferences change. (Bchir and Willinger, 2013), in a field experiment of lahars risk in Peru, find no significant difference of risk aversion between exposed and nonexposed households. (Becchetti et al., 2012) finds a similar result of no significant difference in a sample of 380 Sri Lankan microfinance
<!-- Start of picture text --> UAE: Spactral Density Function of Real Credit Algeria: Spactral Density Function of Real Credit<br>0.00 0.10 0.20 0.30 0.40 0.50 0.00 0.10 0.20 0.30 0.40 0.50<br>Frequency Frequency<br>Evaluated at the natural frequencies Evaluated at the natural frequencies<br>Qatar: Spactral Density Function of Real Credit<br>0.00 0.10 0.20 0.30 0.40 0.50<br>Frequency<br>Evaluated at the natural frequencies<br>B- Oil importers<br>Jordon: Spactral Density Function of Real Credit Morocco: Spactral Density Function of Real Credit<br>0.00 0.10 0.20 0.30 0.40 0.50 0.00 0.10 0.20 0.30 0.40 0.50<br>Frequency Frequency<br>Evaluated at the natural frequencies Evaluated at the natural frequencies<br>6 6<br>6.00 6.00<br>4 4<br>4.00 4.00<br>2 2<br>2.00 2.00<br>0 0<br>0.00 0.00<br>log Periodogram -2 log Periodogram -2<br>-2.00 -2.00<br>-4 -4<br>-4.00 -4.00<br>-6 -6<br>-6.00 -6.00<br>6<br>6.00<br>4<br>4.00<br>2<br>2.00<br>0<br>0.00<br>log Periodogram -2<br>-2.00<br>-4
term), and b) productivity (damage) losses (the second term).

We next take this approach to the data.<sup>26</sup>

> 22 One example may be the partial flooding of a plot which destroys the harvest completely in one part but due to plot slopes etc. leaves the rest of the plot unaffected.

> 23 For example, pests or a crop disease may completely wipe out the harvest in parts of the plot but only lead to partial losses in other parts of the plot that could still be harvested.

> 24 In the LSMS-ISA data, farmers are asked for each crop on each plot at the post-harvest visit whether the planted area was greater than the area harvested and (if applicable) what the percent share of the planted area that was harvested was. A value of 0 percent indicates a full loss and values between 1 percent and 99 percent denote partial losses of the planted area. Farmers are also asked to choose up to two reasons for their loss from a list of shocks or specify another reason.

25 This information is only available in the Ethiopia LSMS-ISA data. Farmers are asked for each crop on each plot whether there was any damage on the crop and what the percentage share of and reason for this damage was. In fact, this set of questions is asked _twice_ , once at the post-planting visit and again at the post-harvest visit. Intuitively, one would assume for damage on the crop to only ever increase. However, we find that, depending on the survey wave, in up to 25 percent of cases damage reports at the post-planting visit exceed those at the post-harvest visit. Furthermore, this is not due to trivial differences that could originate from rounding error or the like. We take note of this and trust post-harvest reports for the purpose of loss calculation as these should constitute the most updated farmer estimates of damage.

26 Unless specified otherwise, the analysis excludes plots where the harvest was completely lost (the equivalent of an area loss
6 also evidence that women in some socleties have more children partly to lighten their own burden. Given the heavy demands of pregnancy and lactation on the mother, one would expect that many and/or closely spaced births would result in clear evidence of impaired maternal health. We turn now to the scientific evidence.

Noting the cumulative drain of hard labor, poor diet and frequent, closely-spaced pregnancies, Jelliffe (1986) coined the term "maternal depletion syndrome" to characterize the various multi-nutritional deficiency states, for example, iron-deficiency anemia, thought to be responsible. - While the end stages of this "syndrome" a woman with many closely spaced pregnancies who is chronic.-lly fatigued, anemic and perhaps suffering from osteomalacia as a result of calcium and vitamin D deficiency - are often seen by the clinician, it has proved difficult to document with hard statistical evidence. Bernard et al (1978-79) found in an Indian study that almost 40 percent of women having their fourth or later child were anemic, compared to 21 percent having their first child. On the other hand, there was no evidence for the syndrome in four out of nine countries in the WHO Studies on Family Formation Patterns and Health (Population Reports, 1984).

A major review (Population Reports, 1984) cited three methodological problems that often obscure the relationship between maternal age, family size and birth spacing on maternal (and child) health: confounding variables, especially socioeconomic status, questions about the direction of causality, for example, deliberately shortening the birth interval to replace a lost infant, and multiple, interrelated variables. An especially difficult problem is that of disentangling the effect of poverty, as opposed to childbearing, on maternal nutrition. Regardless of the difficulties of documenting a clear-rat syndrome in population-based studies, we conclude that it is plausible to assume that high parity, especially when they are closely spaced, results in substantial maternal morbidity, other things being equal. This morbidity may range from nutritional deficiency states, to a greater likelihood of some
.4|
|Private rental|3.6|1.8|4.0|4.0|4.0|3.6|4.0|4.2|
|Free orother|1.1|0.7|0.6|2.7|5.1|5.0|5.1|5.0|

_Source_ : Table 1.

*Lithuanian Free Market Institute (2002). _Note_ : For Lithuania‚ “capital” refers to the Vilnius region.

Consequently, there are fundamental differences between the two groups of countries regarding the share of rental segments in the housing sectors. There are also essential differences in the structure of private rental housing. Two countries exhibit substantial private rentals: Lithuania (13.6 percent) and Poland (16.8 percent). In Lithuania most private rental is individual privatized apartments rented informally by their owners. In Poland, a large portion of private rentals are apartment buildings managed by licensed property managers for formal “professional” landlords, who report official incomes and use tax deductions for current and capital repairs.

In some cases it appears likely that the household survey data underestimate rental sector size. One problem is that the number of households that rent privately is so small that even small sampling errors result in large changes in the observed mean. For this reason, it is important not to focus on whether the observed level is exactly 2 or 3 percent, but rather to keep in the mind the relative significance of the observed levels (that is, that there are very few private renters). Some under-reporting is also possible owing to private renters misstating their status. Reasons could include:

- high levels of rental informality (and sometimes illegality), caused by tax or rent control evasion, which might induce respondents to deny the existence of rent payments or make false claims about ownership;

- confusion about the character of the relationship caused by unclear ownership title, title that conveys usufruct rather than ownership
# interval into the savings purse and many account holders use this as a simplified contractual savings scheme.

- New clients can use their account facility immediately. The application process is automated and it takes less than five minutes to open an account and receive an ATM card. In addition the client is given a Stop Card that freezes the account when inserted in any ATM to protect the account in case the ATM card is lost or stolen.

# _Transaction Record_

- The receipt provides a good transaction record showing the last six transactions and the total amount in the account as well as the amount in each purse, but no account statement.

# _Performance Premiums and Bonuses_

- With a fully automated system, E Bank can monitor accounts and reward savings performance.' E Plan pays an additional three percent interest premium over the base deposit rate of two percent to clients who maintain balances above the minimum of R 250 for over six months.

- Depositors who' exhibit either regular savings behavior or who maintain a specified

- balance above the minimum, become eligible for drawings and prizes.

- Funeral expenses are a significant burden for families in South Africa, increasingly so because of the spread of AIDS. As an incentives to use E Plan, customers under the age of 61 who maintain a minimum balance of R 250 (US$56) or who do a minimum of three withdrawals per month and therefore generate transaction fees, automatically receive a modest amount, R 1500 (US$333), of life insurance coverage.

7
|
|Suitable forest land (mn. ha)|0.0154<br>[0.092]|||||||
|Max. possible output value non-||||||||
|<br>forest area||0.7000***|0.6124***|0.7121***|0.6769***|0.6872***|0.6218***|
|||[0.126]|[0.149]|[0.129]|[0.138]|[0.132]|[0.151]|
|Max. possible output value forest<br>area||-0.0251|-0.0415|-0.0441|-0.0186|-0.0251|-0.0432|
|||[0.072]|[0.077]|[0.074]|[0.072]|[0.073]|[0.078]|
|Yield gap|0.5036|1.1073|0.4565|1.1729|.7851|0.9373|0.5484|
||[0.681]|[0.754]|[1.002]|[0.817]|[0.886]|[0.811]|[0.983]|
|Land governance index|||-0.1930**||||-0.2296**|
||||[0.095]||||[0.117]|
|Weak investor protection||||-0.0015||||
|||||[0.003]||||
|Control of corruption (%)|||||-0.0057<br>[
# **5. Concluding remarks**

This paper provides new evidence on the positive impacts of mobile broadband coverage on household consumption and poverty status in Nigeria. Unlike previous studies, it uses a data set that is nationally representative with detailed data on consumption and living standards, which tracks households over time and matches them with precise mobile broadband coverage data from operators. It finds that mobile broadband coverage had positive and significant impacts on consumption and poverty reduction, particularly among initially poorer and rural households. These results suggest that the economic benefits of mobile broadband coverage are not limited to households who already participate in more developed economic sectors outside subsistence agriculture or formal wage employment. The results are driven by improved labor market outcomes and are robust to several specifications.

The study also represents an important contribution to the policy discussion about the costs and benefits of expanding internet access. First, by showing that mobile broadband coverage can in fact reduce poverty and improve consumer welfare, the results provide important evidence that policy makers can use when considering the cost-benefit ratio of public policies that seek to achieve universal internet access. Second, the fact that the increase in consumption occurs mostly through improvements in labor market outcomes—particularly among women—highlights the importance of complementary policies to remove barriers to job creation, particularly for more vulnerable groups. Finally, the study highlights the importance of data collection efforts to better understand the potential benefits of digital infrastructure investments in developing countries.

14
both for sale and for self-consumption. The sewing cooperative has contracts with several enterprises and has become well known in the market.

The longer term prospects for employment are unknown though this will remain a major challenge for residents of Ribeira Azul and other low-income neighborhoods. While many of those living in the _favelas_ migrate to the cities in search of work, unemployment in Salvador is reported at around 20 percent, almost twice the national average.<sup>4</sup> While data are not available for Ribeira Azul, the unemployment rate is thought to be substantially higher.

International evidence on youth training programs has been mixed.<sup>5</sup> Four evaluations of programs in Latin America (Argentina, Chile, Peru and Uruguay) show that intensive investments which combine training and work experiences with other services including psychological development, vocational assessment, etc. can be beneficial. Implementation involves important roles for civil society and the private sector requiring flexible, competitive and decentralized service delivery. While the impacts on employment and earnings in the short run (size months to one year after the training) appear to be quite positive, little or nothing is known on the impact over time.

A recent study in the United States analyzes the impact of investments in human development at different stages in people’s lives on competence and coping skills and concludes that investments in early years of life, even before the formal school system, give the greatest return.<sup>6</sup> From an economic perspective, this would indicate that investing in early childhood education will yield a much higher return. Linking this back to the unit cost data in Section III, annual costs for youth training were almost 60 percent higher than the costs for early childhood programs (R$3500 for training versus R$2200 for the crèche).

The job creation programs in Ribeira Azul are considered to be effective by those interviewed, though given the relatively high cost and mixed international experiences, it would be important to further investigate
include the wage

> 5 Ideally, in order to investigated households’ vulnerability to external shock, one needs panel data to track the same individuals or households, showing how their consumption, investment in health and education, or their labor market behavior change in response to the shock. The analysis sets aside these important dimensions of household welfare due to data availability. This area would be a subject for future research.

> 6 Note that Yemen is one of the poorest countries in the Middle East and North Africa Region with about 1,000 dollars GNI(Gross National Income) per capita.

> 7 This is in line with many other studies that defined informality based on protection through social security. See Portes, Blitzner, and Curtis (1986), Marcoullier et al. (1997), and Saavedra and Chong (1999).

3
take various forms, such as aligning the definition of units, reference period, population, variables, classification, measurement error, missing data, and derivation of variables. For example, in the case of statistical matching between household budget surveys, we harmonize the reference period by annualizing variables whenever possible and appropriate. Additionally, we harmonize the categorization of variables to ensure they are classified in a consistent manner across different surveys. The harmonization process is facilitated by the fact that both the Household Budget Survey (HBS) and the European Union Statistics on Income and Living Conditions (EU-SILC) share a similar definition of a household. This definition, as described by Serafino and Tonkin (2017), states that a household comprises individuals who live together in the same dwelling and share meals or jointly provide living conditions. Essentially, a household refers to a group of people residing in the same place, with a degree of interdependence in their daily living arrangements, such as cooking and sharing meals together.

**We next identify potential matching variables and harmonize them with each other.** Table A.2 in Appendix 1 gives an overview of the variables that are included both in the HBS 2019 and the EU-SILC 2020. We mark those variables that we include in the set of potential matching variables in green. We also depict the respective categorization of these variables. As there are some important differences in the categorization of most of the variables, we harmonize them with each other. Next, we aggregate all indicators at the household level. Based on our assessment of potential matching variables, we harmonize the variables as follows:

- Harmonize HBS regions to EU-SILC regions (BG3 and BG4)

- Create a dummy variable for urbanization, both in HBS and EU-SILC

- Create a categorical income variable of 20 categories, the lowest category representing those households with the lowest income

- Generate household characteristics based on individual-level characteristics, which are as follows:

- Household with at least one child (<15); with at least one pensioner;