rafmacalaba/data-use-mentions-extended huggingface dataset:
<!-- Start of picture text --> Variables Sector of Accounting financial Revenues, Financial data, 22 indicators on Share of SOEs in Number of firms, Financial<br>operation, data, SOE ownership employment, Ownership data financial total rail total of labor, performance<br>structure and Return on performance turnover vs and total of and netted fiscal<br>Assets. OECD Public revenues transfers.<br>Age of company, Ownership<br>income (netted fiscal Index in Rail;<br>Em Profit/loss, Revenues, ployment, transfers) (WGI), and productivity Corruption performance of Financial SOEs<br>State participation,<br>Level of<br>government<br>(central,<br>subnational), audit<br>status,<br>Ownership<br>structure<br>(participation and<br>shareholders)<br>Source Orbis; Government sources Orbis; S&P Orbis, external Orbis, Orbis, Amadeus SOE Surveys to SOE Surveys to<br>government’s and company Capital IQ; databases government databases; authorities authorities<br>sources, EMIS websites; EMIS; Surveys to public sources, government (2012, 2015 and<br>intelligence, stock exchanges authorities; external sources; 2017)<br>Factiva, external sources databases external<br>government databases<br>sources (full list in<br>annex)<br><!-- End of picture text --> Source: Authors’ compilation based on listed sources.

Preliminary results underscore the power of the methodological approach developed in this paper to capture an unprecedented number of firms with state participation, in comparison to other existing databases. Figure A.1 shows a comparison between the number of firms captured by the World Bank’s Global BOS database and the OECD database. We found that in almost all cases, the BOS database captures significantly more firms with state participation and in
expertise that raises productivity in other firms (World Bank 2012). While jobs with positive spillovers have the highest value for society, the focus of this paper is individual job quality.

The measure of job quality presented in this study is practical without compromising the intended purpose. The selection of dimensions and indicators to determine the quality of employment considers availability and comparability across developing countries. The proposed approach includes four dimensions of job quality that can be found in most countries: sufficient income to overcome poverty, access to job benefits, employment stability, and adequate working conditions. These dimensions are broadly consistent with previous multicountry approaches (Brummund, Mann, and Rodríguez-Castelán 2018; Sehnbruch et al. 2020). Departing from these four dimensions, the paper adopts the Alkire-Foster method—originally conceived to measure multidimensional poverty—for aggregating the characteristics of good jobs into a single index. Limited data availability on the self-employed and casual workers in most developing countries (generally in agriculture) prompted a focus on wage employment. Because the analysis focuses on wage employment, the proposed measure could then be interpreted as a lower bound of job quality, whereby the job quality score for other employment categories is equal to zero. Given all these factors and based on a robust effort of collection and the ex-post harmonization of household and labor force surveys, the proposed job quality measure (JQM) for wage employment in this paper includes a common set of indicators across 40 developing countries and six geographic regions.<sup>3</sup> The main results of the analysis show that the quality of wage employment displays strong heterogeneity among economic sectors. In the public administration, finance and business services, and utilities sectors, most workers have stable jobs and earn a wage above the poverty line, and about two-thirds receive jobrelated benefits and are linked to higher job satisfaction. The quality of wage employment also varies by education, sex, age, and location. Older workers (ages 25 or more) have higher job quality than workers ages 16–24
Policy Research Working Paper 10179

# **Abstract**

As conflicts across the globe escalate and data collection in these settings becomes more sensitive, policy makers and researchers are forced to turn to alternative methods for accurately collecting vital information. This paper assesses the ability of novel and anonymous internet-based surveys to elicit sensitive information in the Republic of Yemen’s conflict by comparing identical sensitive and non-sensitive questions in an internet survey to a concurrent mobile phone survey. There were significant differences between the modalities in all the sensitive questions, with a greater share of respondents expressing sensitive views in the internet survey. The differences between modalities was larger for sensitive questions than for non-sensitive questions, and all the differences were qualitatively identical for subsets of the sample that are underrepresented in internet surveys. Overall, the results suggest that internet surveys can be an effective tool to use in conjunction with other techniques to acquire information that would otherwise be difficult to collect.

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 authors may be contacted at standon3@worldbank.org or tvishwanath@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, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
text --> <!-- Start of picture text --> 90%<br><!-- End of picture text --> Source: Authors’ elaboration based on ORBIS data and BOS database We find that omission error varies significantly across regions and countries. For instance, in Bosnia and Pakistan, at least 40% of the final SOE registry corresponded to firms that were classified as private firms in ORBIS. Supplementary data sources to ORBIS also allowed to reduce the omission error from firms that were not covered in ORBIS even in ECA countries where ORBIS tends to have a better coverage according to the literature (Kalemli-Ozcan, Sorensen, Villegas-Sanchez, Volosovych, & Yesiltas, 2015).

15
0.326|0.092|0.294|

_Notes_ : The dependent variable is a dummy for firm _i_ having positive imports of capital goods in year _t_ . The imported input share is the ratio of imported inputs over total inputs. Firm size is measured by the logarithm of wage-bill and it is included in columns (2) and (4). Heteroskedasticity-robust standards errors clustered at the firm level are reported in parentheses.<sup>_∗∗∗_</sup> ,<sup>_∗∗_</sup> , and<sup>_∗_</sup> indicate significance at the 1, 5 and 10 percent levels respectively.

_Empirical fact 3: The decision of sourcing capital goods from abroad is positively correlated with imports of intermediate goods._

# As a final step, we explore if this complementarity between foreign technology and imported interme-

diate goods translates into a higher global efficiency of the firm in the production process. We investigate 5We rely on wage-bill as a measure of firms’ size since total employment is not reported in the Prowess dataset.

9
the amount of inputs they receive, and how the inputs are utilized. The eventual impacts of the program are therefore closely linked to the quality of the targeting process. Using two-wave panel data from Malawi, the goal of this study is to help improve the targeting of TFISPs by providing guidance for deciding on whether such programs should be targeted at poor or non-poor farmers.<sup>1</sup> The specific research question that this study seeks to answer is: _Do non-poor farmers use inorganic fertilizer more efficiently than poor farmers?_ Efficiency is measured as kilograms of maize obtained per kilogram of nitrogen applied, otherwise known as nitrogen use efficiency (NUE). The steps involved in the empirical approach used in answering this question will help clarify issues such as whether the poverty and food security goals of TFISPs can be achieved together by estimating if poor farmers obtain the same NUE as non-poor farmers, and estimating whether crowding out varies significantly across poor and non-poor farmers. By answering these questions, the study provides an empirical standpoint for the targeting discourse that has in the past been based mostly on anecdotal evidence.

The decision of whether a subsidy should be targeted at poor or non-poor farmers depends on three main factors. The first factor is the objective of the programs. Officially, TFISPs in SSA have two main objectives: 1) increasing smallholder agricultural productivity and food production, to improve household food security and national food sufficiency; and 2) reducing poverty by increasing the income levels of beneficiaries. Though both are admirable, these objectives potentially have different implications for targeting. The objective of ensuring household food security and national food sufficiency suggests that the programs should be targeted at productive 1 Targeting is made up of two processes: 1) identification of the targets, i.e., the category of people to be targeted, and 2) identification of the most appropriate method used in delivering the inputs to beneficiaries. The effectiveness of both processes are equally important in ensuring successful targeting. This study focusses on the first process
Komives, Whittington, and Wu "Infrastructure Coverage and the Poor: A Global Perspective"

# 7. **Conclusions**

Coverage statistics are widely used to paint a picture of infrastructure conditions in developing countries, and they are often the only global, cross-country data available for infrastructure services. It is thus important to utilize coverage statistics to their fullest advantage while at the same time being careful not to read more into the data than they can in fact reveal. In this paper we have utilized a new data source, the World Bank's LSMS surveys, to construct infrastructure coverage statistics for a pooled sample of households from fifteen countries.

Several of the results from our analyses using of these LSMS data sets are worth recapping. First, electricity coverage was higher than coverage of other infrastructure services at all income levels; 65 percent of the households in the sample had electricity in their homes. By contrast, only 38 percent of households had in-house water taps (the infrastructure service with the next highest level of coverage). The relative ranking of coverage rates among the four infrastructure services (electricity

* water 0 sewer P telephone) held across all income levels.

Second, infrastructure coverage for electricity, water connections, and sewer connections all rise but at different rates as household income (as measured by a consumption aggregate) increases from about US$100 to US$250 per month. We want to emphasize again that the 55,500 households in this pooled data set are not representative of the global population in developing countries. We believe however, that our findings regarding these relationships between infrastructure coverage and household income art relatively robust with respect to the countries in the pooled sample and the sampling procedures used within countries.

Third, electricity was the only infrastructure service with significant penetration among the poorest 5 percent of the sample households (32 percent had service). Only 6 percent of the poorest households had an in-house water connection; only 3 percent had a sewer connection. Almost 80 percent of the poorest households in
**_Core Indicators for Determinants and Performance of Electricity Sector Reform in Developing Countries_**

impacts of the privatisation program in Gran Buenos Aires area in Argentina using a family expenditure survey of about 5,000 households. Also, firm-level data are far more suitable for comparative efficiency analysis of identifiable units such as distribution utilities within or across countries.<sup>1</sup>

# **_Essentiality and complementarity_**

Collection and analysis of the range of data needed for a thorough study of electricity reform requires an efficient allocation of limited data. Therefore individual core indicators should reflect useful information that is essential to the evaluation of some important aspect of reform. When identifying and defining the core indicators, it is important to view them as part of a wider system with various inter-relationships and feedback loops. While an individual indicator provides insight into some essential aspect of reform, the core indicators should collectively represent the reform system, plus the broader framework within which it resides and operates.

# **4. Reform model and typology of indicators**

# **_Framework for analysis of electricity sector reforms_**

Electricity sector reforms are multi-faceted activities with inte racting factors and a variety of impacts. The process generally involves a set of concrete steps or measures based on a specific model of reform. At one level, these measures involve structural and organisational changes to the industry, and at another level there is a requirement for appropriate institutional arrangements such as legislation and new agencies.

In addition, sector endowments and characteristics such as size, resource mix, historical development, define the initial market structure and starting point and can influence the reform path and outcome. Market structure is then influenced by various measures, such as unbundling of vertically integrated enterprises, as well as institutional factors such as regulatory authorities and legislation.

> 1 See e.g. Jamasb et al. (2003) for a review of the use of efficiency studies of electricity utilities in developing countries.

8
poverty, there is a need for indicators at the disaggregated geographic level. Palma and Gouveira (2022) review the literature on subnational analyses around energy poverty. They found that studies mainly focused on Southern Europe. While they do not mention any studies on Bulgaria, some generated insights could serve as inputs to generate geographically disaggregated analyses in Bulgaria.

**Although there is consensus that the geographical (subnational) distribution and identification of energy poverty is vital for mitigation, most subnational analyses rely on census data, and energy poverty indicators are often not collected as part of the population census.** According to the review by Palma and Gouveira (2022), most subnational studies rely on data collected as part of the national census, although energy poverty indicators are often not included. While other studies take advantage of HBS or EU-SILC for regional analyses, the data is usually only representative at the NUTS 1 or NUTS2 level (depending on the country). Therefore, direct estimates for higher disaggregation levels are unreliable (ibid). Small-area estimates of energy poverty are needed but not necessarily available. Alternatively, many studies incorporating subnational indicators included in the review by Palma and Gouveira (2022) rely on new surveys collected by researchers. Nevertheless, the increasing usage of big data and satellite data generates new opportunities for subnational analyses of energy poverty.<sup>22</sup> National research institutes, for example, might be able to share meteorological data and facilitate the creation of subnational energy poverty indicators based on climate data.

21 The first step is an energy poverty diagnosis. During this first phase, policy makers should try to understand local energy poverty more closely. The second step is the design of a Local Climate Plan, which is a plan of how to best tackle energy poverty by taking an integrated approach at the local level. It involves setting up a detailed strategy and defining short- and long-term goals. The last step is the implementation of the Local Climate Plan. 22 In this context, the World
18

# minister’s or president’s party.

Third, one would expect that stronger parties would rein in party members who happen to have shorter horizons. Again, the DPI provides information that allows this to be taken into account: the number of years that the party of the executive has been in office (independent of the particular executive who has represented the party), and how long the party has existed under its current name. Where the party of the executive has been in power longer than the executive, it is more likely that the party restrains executives with short horizons. Similarly, parties that have been in existence for a long period of time should have a longer time horizon than parties that have not. We count the party’s existence from the date it adopted its name. Parties incur losses when they change their name, not least of which is a loss of brand name. Frequent name changes indicate either that parties have no brand name or are not well-established institutionally (to the extent that these two things are different). Finally, existing investigations of the effects of expected tenure or government . turnover track only one decision maker in a country, the head of government (president, prime minister or cabinet). Most democratic countries, however, have multiple decision makers. A parliamentary government can fall and be replaced by government with an entirely-new set of coalition parties, or by a new government with substantially the same set of coalition partners. A president can be replaced with, or without, the corresponding replacement of the majority parties in the legislature. Theory would suggest that dramatic changes in government policy, or in the potential for expropriatory behavior by governments, are likely only when all decision makers are replaced. Empirical measures of tenure and turnover have not matched this, however.

The DPI records whether parties are in the government coalition or not, and whether
65<br>0<br>60 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 1960s 1970s 1980s 1990s 2000s 2010s 2020-21<br><!-- End of picture text --> Sources: EM-DAT database; Haver; Penn World Tables; UN Population Prospects; World Bank.

Note: EMDEs = emerging market and developing economies; MNA = Middle East and North Africa. GDP weights are calculated using average real U.S. dollar GDP (at average 2010-19 prices and market exchange rates) for the period 2011-21.

A.-D. Period averages of annual GDP-weighted averages. Production function estimates.

A. Sample includes 53 EMDEs, of which 5 countries are from MNA. “Other factors” include trend improvements in human capital, and stable investment growth relative to its long-term average.

- B. Sample includes 53 EMDEs (5 from MNA).

- E. Unweighted averages of seven MNA economies.

- F. GCC = Gulf Cooperation Council. Includes data for 19 MNA economies. Occurrences of natural disasters.
# **4. Discussion**

The results presented in Tables 2 and 3 show the relationship between the level of legal gender discrimination and the probability that firms with and without female owners began operations informally. On aggregate, the results suggest that firms with female owners are less likely to have begun operations informally when the legal framework treats men and women more equally. In contrast, legal equality is associated with an increased probability of beginning informally for firms that are fully owned by men. Similarly, the results presented in Table 4 show that legal equality is associated with a longer period of informality for firms that are fully owned by men. These are unexpected findings, which appear to result from the pooling of data from highly heterogeneous regions. When the results are disaggregated by region, a more nuanced picture emerges.

For firms with female owners in Sub-Saharan Africa, the probability of beginning operations informally is decreasing in the level of legal equality between genders at the time the firm began operations. The marginal effect is approximately -0.002 suggesting that a single point increase in the index is associated with a 0.2 percent reduction in the probability that a firm with female participation in ownership would begin operations informally. Over the past decade, the index has increased by an average of 8 percentage points in Sub-Saharan Africa; according to our model, this would be associated with a 1.6 percentage point decrease in the proportion of female owned firms that began informally. This effect may appear small, but it is meaningful—the enterprise survey data show that, in this region, on average, 13.6 percent of formal firms with a female owner began operating informally. Furthermore, we would not expect the magnitude of the effect to be large given that legal equality in the areas covered by the WBL index is only one of the many factors that may shape a female entrepreneur’s decision to begin operations informally.

For firms in Sub-Saharan Africa that are entirely owned by men, an increase in legal equality is
**Job creation is low in Latgale, Kurzeme and Zemgale** .  For the unemployed in Kurzeme or Zemgale the probability of exiting to employment within a year is lower by 0.10 than in Riga region, while for Latgale differential is even larger: -0.14 (see Appendix Figure 11). This again highlights the serious mismatch problems in the labor market of Latgale. Vidzeme, on the other hand, is not different from the Riga region as far as job search prospects are concerned, but jobseekers in Vidzeme more often end up out of the labor force.  This is an interesting observation because the stock of discouraged individuals in Vidzeme, although higher than in Riga by most definitions, is lower than the national average (see Appendix Table 11). Appendix Table 20 lists districts with highest and lowest estimated job finding probabilities.

**Rural residents are less likely to stay unemployed.** Rural residents have significantly higher probabilities of both finding a job within a year and of leaving the labor force than their urban counterparts (see Appendix Figure 12). Consequently, they are less likely to become long-term (or recurrently) unemployed. It is important to notice that of those who move to employment more than a half find jobs in industries and services rather than agriculture. Many of these jobs are located in cities. As discussed in Section B, the role of commuting in overcoming spatial mismatch of skills in Latvia should not be underestimated.

# **C.  DETERMINANTS OF EXIT FROM EMPLOYMENT**

This section explores factors responsible for labor market transitions in Latvia. The main factors associated with making transitions from jobs to unemployment, and from jobs to nonparticipation, are assessed for individuals who were employed in 2001. Predicted probabilities of each transition type are presented in Figures 20 - 24, and discussed below.

## **Box 2. 1  Estimating Multivariate Models of Labor Force Transitions**

A multinomial logistic model is estimated using data from the Latvian Labor Force Survey 2002. This model