rafmacalaba/data-use-mentions-extended huggingface dataset:
2013; Pape, Petrini, & Iqbal, 2018), there is no empirical study that examines gender differences and livelihoods in a forced displacement situation. We build on these studies to investigate the gender differences among refugees and hosts and compare the two groups, where displacement is increasing, and the literature thus far has focused on the impacts on hosts. The results both contribute to the empirical knowledge on the gender dimensions of forced displacement and inform governments and humanitarian agencies on supporting appropriate livelihood strategies for refugees, across gender lines and refugee groups.

The remainder of the paper is organized as follows. Section 2 provides background information on the four main refugee groups in Ethiopia. Section 3 describes the data used in the analysis

> 2 While the paper used data from the Skills Profile Survey 2017, the situation for refugees in Tigray region is completely changed since first drafting the paper. The conflict in Norther Ethiopia is creating a full-scale humanitarian crisis with many internally displaced individuals as well as having a direct impact on Eritrean refugees. According to the UNHCR, two refugee camps (Shimelba and Hitsats) were destroyed following the conflict, leading Eritrean refugees who lived in the two camps prior to the crisis to flee and leaving them in urgent need of support and safety. Thus, any findings related to Eritrean refugees should be interpreted with caution as they do not reflect their current situation.

2
a plausible range for each of these parameters.

# **Some empirical values of** _τ_

Beegle et al. (2012) consider the cost implications for various types of questionnaire in Tanzania. For example, Beegle et al. (2012, Table 10) report that four households can be interviewed by an interviewer per day. Assuming that each interviewer interviews for eight hours a day, it takes 8 _·_ 60 _/_ 4 = 120 minutes to complete a survey with recall consumption model.

The same study also reports that it takes on average 41 minutes to complete a short consumption module with 17 most important items (Beegle et al., 2012, Figure 1). Therefore, the proportion of time that is spent to collect consumption data is 41 _/_ 120 _≈_ 0 _._ 34. If we assume that the data entry cost is roughly proportionate to the time needed for data collection and that there is no other cost items, we have _τ ≈_ 1 _−_ 0 _._ 34 = 0 _._ 66 in this case.

This estimate may be close to the upper bound of _τ_ . The average time to complete a recall consumption module is longer when a long questionnaire (with 58 items) is used and when a longer recollection period is adopted, even though the difference is modest. When the personal diary format is used, the cost of collecting consumption data can be much higher because it involves frequent visits to the household, As reported in Beegle et al. (2012, Table 10), the number of interviews that can be performed per day may be as low as 0.35. Assuming that the time required to complete the non-consumption component remains the same, the value of _τ_ when personal diary format is used is _τ ≈_ 0 _._ 66 _·_ 0 _._ 35 _/_ 4 = 0 _._ 06.

It should be noted that Beegle et al. (
# _Public Investment Drive_

The government, reelected in February for a second term, has changed the infrastructure planning process to address some of these weaknesses. The Prime Minister’s office lays out the development priorities, while the planning agencies are charged with fleshing out design details.<sup>11</sup> The government’s plans to support investments of up to 1.7 trillion Baht (US$43 billion), raising public investment by an average of 70 percent over the next five years (Figure 14.A and Figure 14.B). In real terms, public investment would exceed the 1997 peak from 2007 onwards. First, the government has identified high level business, professional and hospitality services as new growth areas, including fashion, advertising, health care, tourism, and conventions. Such services are primarily located in Bangkok and require dense, high transaction business environments with easy accessibility. The first priority implies a focus on urban mass transit infrastructure and communication, accounting for close to half of the planned spending. Second, the government views the industrial base in peri-urban areas centering on a few dominant clusters such as automotives and furniture. Improvements in the logistics system are to boost competitiveness and reduce the wedge between prices paid by consumers and received by producers.<sup>12</sup> The third priority is to improve energy efficiency. Thailand spends a large share of GDP on power, and energy imports amount for around 5 percent of GDP. Indeed, one in four Thai firms consider electricity costs as among the three most urgent investment climate concerns. Indeed, in the 2004/5 Thailand Investment Climate Survey, one in four firms view energy costs as a serious business constraint (World Bank 2005a).

A related issue concerns decentralization, and specifically local administrative capacity. Yet, there is a mismatch between capacity and needs at the local level. Local administrations have limited influence in Thailand’s public sector. The central government appoints the chief local officials, determines local salaries
to the MoLSA PMT models, in that they both use per capita consumption as a dependent variable and they both have regional models. The current work compares the potential overlap between MoLSA PMT and both old (VM) and new (SEVAT) MCNA models. The next section describes how this is done.

# 3.3 Pseudo-Proxy Means Test (PPMT) Methodology

The 2016 Vulnerability Assessment data<sup>8</sup> and the 2018 Multi Cluster Needs Assessment (MCNA) survey that formed the basis for CLCI’s VM and the SEVAT models do not have the full set of SSN PMT variables. To fill this assessment gap, we propose a probabilistic pseudo-PMT (PPMT) formula that includes only indicators that are common to the national PMT and the vulnerability models (VM and SEVAT) and provides its performance criteria. Using the data from the nationally representative SWIFT survey, we run an ordinary least squares (OLS) regression of household consumption on the common set of proxy indicators, _X_ s. For each common indicator, we assign a pseudo-weight based on its estimated association to household consumption as below: where, is a per capita monthly expenditure of household in natural log, which is a function of a vector of covariates/proxies, , and a random idiosyncratic error term, . and s are the estimated weights yy ii for a constant term and pseudo-proxy variables respectively. jj jj XX εε αα� ββ̂ As discussed in Box 1, we acknowledge the possibility of alternative approaches than the one presented here. There are, however, genuine concerns over data privacy and sharing, divisions in terms of concepts, and philosophy. Given the circumstances and scope of preliminary focus to be purely technical, we believe the tool could prove useful and is technically robust to allow the initial work to begin and momentum and discussion to ensue.

_Box 1: Possible alternative approaches? – A discussion_ The method employed in the paper may not be the only approach possible in this context. Creating a PPMT that is limited
|0|1|2381|
|Caste=OBC|0.47|(0.50)|0|1|2381|
|Caste=General|0.11|(0.31)|0|1|2381|
|Religion=Hindu|0.68|(0.47)|0|1|2378|
|Religion=Muslim|0.20|(0.40)|0|1|2378|
|Religion=Christian|0.04|(0.18)|0|1|2378|
|Religion=Sarna|0.09|(0.29)|0|1|2378|
|Has BPL card|0.41|(0.49)|0|1|2382|
|Lack of social support (0-9)|1.11|(1.50)|0|9|2388|
|Knows women in business (0/1)|0.51|(0.50)|0|1|2380|
|Adult connectedness (0-4)|2.57|(1.06)|0|4|2384|
|Urban|0.30|(0.46)|0|1|2388|
|Observations|2388|||||

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_y_ estimated on the basis of the growth estimate from month _m_ in 2020 by _Hy_<sup>_m_,</sup><sup>_PYA_is</sup> estimated by the dierence in dierences: While we cannot rule out that GDP estimates might have changed for reasons unrelated to Covid over this time interval (January - June 2020), it is safe to say that most of the changes are due to Covid-19. The dierence in dierences calculation assures that changes in the 2019 growth rates, which cannot have been due to Covid-19, are eliminated. We assume that this additional poverty lasts only for one year, so that the number of poverty-years is directly equal to this additional number of poor people. Our analysis therefore focuses on the short-term eects of the pandemic on poverty.

These are conservative assumptions, in the sense of yielding a small number of povertyyears, for at least two reasons. First, we assume that the additional poverty generated by the pandemic lasts only for one year. This assumption also allows us to avoid using GDP forecasts beyond 2020, the uncertainty around which is extremely large. Second, our baseline scenario assumes that all incomes grow - or shrink - in the same proportion, although there are reasons to believe that the poor could be aected more than proportionally by the recession (Bonavida Foschiatti and Gasparini, 2020).

The results for our six countries are summarized in Table 1. We explain how to read Table 1 using the case of Sweden. The rst six rows present basic economic and demographic indicators, such as Sweden's GDP per capita, population, life-expectancy at birth, etc. The second panel presents mortality statistics. Given the age-distribution of the 4,639 Covid-related deaths recorded in Sweden (shown in row 7), each death leads on average to 9.5 lost-years. The total number of years of life lost in Sweden (up to early June 2020) is obtained by multiplying these two numbers
<!-- Start of picture text --> 245<br>579<br>No data<br>FIGURE I<br>Average Learning (2000-2017)<br><!-- End of picture text --> _Notes:_ Learning levels are calculated as an average across subjects and levels over the given period of time. _Source:_ Our HLO learning database.

We present a series of stylized facts in a first application of the database. Figure II contrast years of schooling with learning for the most recent year in which data is available. A few trends emerge from this graph. First, the exponential shape of the line of best fit indicates that there might be a tipping point where countries realize more learning after about ten years of schooling. Second, there is high variance in learning conditional on years of schooling, with some countries reaching around 8 years of expected schooling yet less than 300 on learning, such as Nigeria, while others with similar years of schooling reach almost 400 on learning, such as Tanzania. Moreover, this graph reveals that many developing countries have successfully achieved high schooling, but have not yet realized high rates of learning. A few notable examples with high schooling but low learning include India, Brazil and Ghana. Brazil has 11.7 expected years of schooling, yet a learning score of just 411. India has 10.2 expected years of schooling, yet a learning score of just 366. Ghana has 11.6 years of expected schooling yet only a 229 score on learning. In contrast, some countries outperform the trend, with Vietnam achieving learning on the upper end of the distribution with a score of 514 on learning even without achieving the upper end of expected schooling. These trends reveal that schooling does not translate one-to-one into learning, with significant margins to improve learning both by increasing schooling as well conditional on schooling.

7
Solutions cloud servers daily. The choice of CAPI was guided, on the one hand, by the finding that this technology greatly reduces the number of errors relative to pen‐and‐paper interviewing (e.g. Caeyers's et al., 2012). On the other hand, this technology was essential to the near real‐time monitoring of data collection (section 3.2) and quality control (section 3.3), which were deemed necessary in the Somali context where insecurity and remoteness make close supervision challenging and follow‐up visits costly.

# 3.1. Survey instrument

The consumption modules were the central components of the SHFS wave 2 survey questionnaire. The questionnaire also contained other key components of a multi‐topic household survey, particularly those relevant to the Somali context. These included an individual‐level module with information on education, employment, and health, household characteristics, remittances, displacement, perceptions and subjective welfare, and shocks. The questionnaire was designed in line with best practices (Deaton and Grosh, 2000) and went through several iterations of internal and external expert revision.

The food consumption module consisted of 114 food items drawn from a list of CPI items provided by statistical authorities. To meet the requirements of the Rapid Consumption Methodology (section 4), items were divided in one core and four optional modules, with most commonly consumed items assigned to the core module. The list of items was highly specific (e.g. apples, pears rather than fruits) and selected to cover the basic food categories and adequately reflect the local diet (Smith et al., 2014; Zezza et al., 2017). The list of food items contained various items for food away from home, accounting for both food bought away from home and consumed at home and food consumed outside of the home. Further, to facilitate food quantity reporting for respondents, a list of non‐standard units, along with their conversion to kilograms, was developed for each
household members’ individual incomes is divided by the number of household members to give the income per capita. We use regional poverty lines taking a R$ 80 per capita poverty line (in current 2001 prices) for urban Rio de Janeiro as a basis and adjusting for regional price differences following Paes de Barros (2004). The poverty lines are reported in appendix 7.4.

# _3.2.4 Shortcomings_

The household income generation process is of course much more complex than the proposed micro-simulation model suggests. Many structural features of the Brazilian labor market, such as a high degree of informality in wage-employment and the important role of self-employment, are only accounted for rudimentarily. Additionally, the data used to estimate the mover-stayer model may capture long-term transitions from agricultural into non-agricultural employment, but also some temporary job shift, for example due to seasonality. Doubts have emerged regarding the reliability of the information drawn from the PNAD, in particular on rural, informal sector, and capital income (World Bank 2003).

Finally, it may seem natural to assume that the sectoral movements are somehow related to geographical migration from rural to urban areas, either within or even between the Brazilian regions.<sup>28</sup> Traditionally, migration has acted as an important adjustment mechanism in Brazil. Nearly 40 percent of all Brazilians have migrated at one point in their lives (Fiess and Verner 2003). However, the data does not allow to link intersectoral movements to geographical migration. First, migration-related questions of the PNAD do not cover rural-urban migration. The available information is on whether an individual has moved between municipalities and/or between federal states. Second, using the information on migration in combination with the employment history from the PNAD 2001 suggests that only a minor share, of about 12 percent of those leaving the agriculture, actually migrates to another municipality. Approximately half of these migrants move to another federal state. In light of the importance of migration as an adjustment mechanism and the fact that the decline in
business performance. Among men-owned businesses, personality traits such as power motivation and tenacity are important determinants of business performance. Consistent with what is observed for women-owned businesses, traditionalism tends to either cancel or reverse these effects. The effects of other individual, business and geographical variables are consistent with what was discussed from Table 4.

# **5. Conclusions and implications**

The paper uses multi-topic household survey data from two regions in Ghana to examine the drivers of men- and women-owned business performance, with special focus on personality traits and gender role attitudes. This is achieved by employing both descriptive and econometric analytical procedures. The econometric approach employs linear regression models to examine the determinants of business performance using two indicators – labor productivity and profits.

A taxonomy of men-owned businesses and women-owned businesses showed that although the use of paid employees is correlated with better business performance, the hiring of this category of workers is higher among men-owned businesses, while women-owned businesses tend to use more unpaid workers. The empirical results in general show that the main business owners’ characteristics that are important for enterprise success are age, gender and household poverty status. Enterprise-related factors include the registration status of firms, business tenure, location, the separation of business and domestic expenses, ownership of a business bank account, literacy of employees, use of social media, as well as urban location of the business. The results indicate that characteristics of business performance vary for men and women. Home-based businesses, particularly for women-owned businesses, have negative implications for business performance. Thus, cost restrictions to traditional markets and 19
database construction process are recorded, is included as Table A2 in the Annex.

The HEFPI database can be used to analyze a variety of topics. With the database one can see not just how the population fares but also how different ‘wealth’ or income groups fare on the indicators used in global goals, such as the MDGs and the SDGs: the database allows ‘snapshot’ comparisons as well as comparisons of trends (cf. Wagstaff 2002; Victora et al. 2003; Wagstaff et al. 2014). One can zoom in on a specific topic, such as child malnutrition, and see whether inequalities

> 1 Nor do we replace estimates directly calculated from the survey microdata by modeled estimates. 2 See frontmatter. 3 See frontmatter.
the basis of the raw data, the preferences of older individuals are more likely to be reflected in policy making than those of their younger peers. To shed more light on this possibility, we interacted the age dummy with an indicator of participation in elections. The indicator is defined as a 0‐1 variable to identify the individuals surveyed in LITS II who have voted in the latest election.

The results, reported in Table 3, confirm the previous findings but also suggest that individual preferences over policy are more similar across age groups for individuals who vote than for those who do not. As before, the estimated coefficients show that older people are less likely to support increasing government spending on education. However, older people who do not vote are even less supportive of raising education expenditure than older people who do vote. The same pattern holds for pensions: the positive relationship between ageing and support for increasing spending on pensions is less 10
- <u>B. Technology Vs. Labor Intensive Industries</u>

- The share of labor-intensive industries in Latvia is larger than the share of technology-driven industries (See Annex 1 for a definition of industrial taxonomies.) This is the reverse of the situation in current EU member states as well as in the most dynamic transition economies (Hungary, Czech Republic, Slovakia and Estonia).<sup>10</sup>

**Figure 4 Technology Driven & Labor Intensive Industries' Share in Manufacturing Production, 1999**

<!-- Start of picture text --> 70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>Bulgaria Czech Estonia Hungary Latvia Lithuania Poland Romania Slovak Slovenia<br>Rep. Rep.<br>Technology-Driven Industries Labor-Intensive Industries<br><!-- End of picture text --> _Source_ : WIIW, 2001

- A recent FIAS analysis<sup>11</sup> suggests that only 2% of the Latvian workforce is employed in high tech sectors, a figure that is far lower than the EU average in general and below the level in the NMS’s in particular.  Similarly, the Latvian Innovation Survey<sup>12</sup> reports that high tech sectors generate only 3%-4% of manufacturing output and 6% of exports, compared with 20%-30% in developing countries.<sup>13</sup>

- 10  Unfortunately, sectoral data at the level of detail required to determine the technology and skill intensity of production and exports is available only until 1999.

- 11  Paragraph 16 of draft FIAS report

- 12  Chapter 3, P. 18

> 13 Data about employment in high tech sectors and high tech exports needs to be interpreted with care. Until recently, for example, Mexico was the world’s largest high tech exporter.  But its exports consisted primarily of
2

# **PRIVATIZATION TRENDS AND RECENT DEVELOPMENTS**

## **I.   INTRODUCTION**

1. This paper takes stock of recent privatization trends, examines the extent to which government ownership is still prevalent in developing countries, and summarizes emerging issues in state enterprise restructuring and privatization going forward.  It shows that:

- Privatization activity for developing countries as a whole dropped off in the late 1990s and, while still at overall low levels, is slowly creeping back up towards the more typical levels prior to the sharp one-time spike in activity in 1997;

- Despite more than 15 years of privatization activity, government ownership is still prevalent in some regions and countries, and in certain sectors in virtually all regions;

- Despite recent evidence of declining public support for privatization, the evidence shows privatization’s resounding success in competitive sectors. In infrastructure the experience is more mixed, with positive outcomes achieved when private participation is combined with proper market structure, regulatory frameworks, and sound contract design;.

- Continued efforts need to be made to improve the performance of state-owned enterprises by: improving privatization policies and institutions; adopting more of a case-by-case approach for complex sectors and countries; and exposing state enterprises to market discipline by allowing new private entry and exit of unviable state-owned firms and improving their corporate governance.

2. The analysis in the paper uses estimated proceeds from privatization transactions that were carried out between 1990 and 2003 as an indicator of privatization activity. Proceeds are defined to include all monetary receipts to the government resulting from partial and full divestitures (via asset sales or sale of shares), concessions, leases, and other arrangements. As such the data do not cover the following: management contracts; new green field investments; investments committed by new private operators as part of concession agreements; and the vast numbers of transfers resulting from mass or “voucher” privatizations across Eastern Europe as these methods did not generate revenues for government.

3. Data were amalgamated from a variety of sources. For the earlier period
. _“Global Information Security Survey”_ , KPMG, UK.

E&Y, (2002). _“Global Information Security Survey”_ , Ernst & Young, UK.

3i, (2002). _“E-security – 2002 and Beyond”_ , 3i white paper in association with the Economist Intelligence Unit.

PWC, (2002). _“Information Security Breaches Survey”_ , Technical Report, Price Waterhouse Coopers.

Glaessner, Thomas et-all. Upcoming Paper, _“Access to Financial Services in South Africa”_ , World Bank, Washington, D.C.

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