rafmacalaba/data-use-mentions-extended huggingface dataset:
that are operating in markets with state participation with the support of complementary sources and government counterparts, but are not captured in ORBIS; (vi) includes non-corporatized companies that are aligned to our proposed definition, but for which the shareholder information in ORBIS is incomplete, and (vii) includes countries with fewer than 30 firms and does not restrict the companies to any threshold in assets. As a result, the Global BOS database includes data on more than 48,000 firms across 80 countries, whereas the second largest database by the number of SOEs, the EBRD database (Borkovic & Tabak, 2020), covers 17,600 firms in 25 countries.

To construct the database, we use the ORBIS data set provided by Bureau van Dijk. The ORBIS database provides the ownership structures between shareholders and their direct subsidiaries and firms’ financial data. However, low data coverage and outdated ownership structures found in the ORBIS database restrict researchers’ ability to use it directly. Moreover, the lack of a uniform SOE definition restricts the ability to identify SOEs across countries to perform cross-country comparisons. Given the data caveats of ORBIS, we complement the information of this database with different data sources including WBG project Databases, external BOS databases, and country-specific firm-level data sets.

Our methodological approach allowed us to identify four times more companies with state participation across 80 countries. It also improved the coverage of financial information by 4.4 times in employment reaching more than 11 million workers, and 3.7 times more revenues surpassing USD 1.5 billion, to provide a more comprehensive measure of the real state footprint in the markets. The Global BOS database sheds light on the very diverse set of sectors in which the state participates as a market player, in many cases without a clear rationale for state’s direct intervention in the form of an SOE. In many countries, SOEs are 33
***|<br>0.85***|
|,|[0.09]|[0.04]|[0.09]|[0.20]|[0.06]|[0.05]|
|# work / # non work|<br>4422***|<br>3494***|<br>382***|<br>2099***|<br>2204***|<br>205***|
||.<br>[3.93]|.<br>[2.86]|.<br>[0.57]|.<br>[2.46]|.<br>[1.97]|.<br>[0.17]|
|Urban|0.78***|0.70***|0.53***|0.55***|0.44***|0.38***|
|Number of Observations|22392|22392|22392|21262|21262|21262|

Note: 1 Variables included but not reported: age squared, province dummies; 2 standard errors are in brackets; 3. *** p<0.01, ** p<0.05, * p<0.1 23
- [50] Ferraro, P. J., Sanchirico, J. N. & Smith, M. D. Causal inference in coupled human and natural systems. _Proceedings of the National Academy of Sciences_ **116** , 5311–5318 (2019).

- [51] African Parks. A Chartered Course: African Parks Annual Report 2020 (2021). URL https://www.africanparks.org/sites/default/files/uploads/resources/ 2021-06/AFRICAN%20PARKS%20-%202020%20Annual%20Report%20-% 20Web%20Ready%20-%20LowRes ~~3~~ .pdf.

- [52] Denny, S., Englander, G. & Hunnicutt, P. v1.0.1 Data and Code for “Private management of African protected areas improves wildlife and tourism outcomes but with security concerns in conflict regions”’. figshare. Dataset. (2024). URL https://doi.org/10.6084/m9.figshare.25560351.

- [53] Geldmann, J., Manica, A., Burgess, N. D., Coad, L. & Balmford, A. A globallevel assessment of the effectiveness of protected areas at resisting anthropogenic pressures. _Proceedings of the National Academy of Sciences_ **116** , 23209–23215 (2019).

- [54] Meng, Z. _et al._ Post-2020 biodiversity framework challenged by cropland expansion in protected areas. _Nature Sustainability_ 1–11 (2023).

- [55] Reynaert, M., Souza-Rodrigues, E. & van Benthem, A. A. The environmental impacts of protected area policy. _Regional Science and Urban Economics_ 103968 (2023).

- [56] Arkhangelsky, D., Athey, S., Hirshberg, D. A., Imbens, G. W. & Wager, S. Synthetic difference-in-differences. _American Economic Review_ **111** , 4088
penalize wives and create disincentives to work for pay if the tax rate for the second earner, typically a woman, is set at a higher rate than it would be if she was a single filer (Apps and Rees, 2005; Grown and Valodia, 2010).

In the sections that follow, we describe the data, harmonization process, and methodology used for the analysis. Then, we draw upon time use data to construct harmonized measures of time allocation across productive and non-productive activities to answer the four research questions. We use data from 19 countries: Albania, Argentina, Bangladesh, Colombia, Ecuador, Ghana, Guatemala, Iraq, Mexico, Moldova, Mongolia, Pakistan, Peru, South Africa, Timor-Leste, Turkey, Uruguay, the United States, and the West Bank and Gaza. We then discuss the potential role of social norms, labor market regulations, and other factors in determining time use decisions. We conclude by summarizing the findings and discussing the main implications for research and policy making.

# **2. Data and methodology**

Depending on the study, the collection of time use data may have the objective of sampling not only across the population, but also across hours of the day, days of the week, and seasons of the year. Some studies collect information on who the respondent was with during the activity, where the respondent was, and whether the respondent was carrying out another activity in addition to the primary task reported. Time use studies often collect information on respondents’ demographic and socioeconomic characteristics.

The choice of method for measuring time use is generally determined by the analytical purpose of the study. Time use data can be collected by direct observation, by self-reporting or by interview. Each instrument and data collection mode has its advantages and disadvantages relative to the reliability of the data obtained, effect on response rate and cost (UNDESA, 2004).

The most widely used method for collecting time use data in large-scale surveys is the _time diary._ This approach enables
365|
|Diabetes only|1.3%|5|1.8%|7|2.5%|10|1.8%|7|0.7%|3|
|Hypertension only|5.5%|22|5.8%|23|5.5%|22|6.3%|25|4.5%|18|
|Both Hypertension<br>and Diabetes|18.5%|74|18%|72|17.7%|71|34.3%|137|3.5%|14|
|Observations|400||400||400||400||400||

Table 6 reports the estimates of the study’s impacts on the uptake of taking both screening tests using OLS regressions. The interventions had a statistically significant impact on the uptake of screening in all intervention groups. Column 1 only controls for facility and provincial fixed effects, column 2 further controls for the level of health facility, column 3 adds controls for age and gender of study participants, and column 4 controls for a broader of socio-economic variables collected during the endline survey. The results are similar across all four specifications and we therefore focus on the specification with the full set of controls. In Column 4, compared to the control group, intervention groups 1 to 3 led to an increase in the screening rate of about 15 percentage points (pp). The highest intervention impact, however, is measured among participants of intervention group 4 (personal invitation with a conditional pharmacy voucher). For them, the uptake of screening on both tests increased by 31.2 pp. Additionally, we conducted a test of equal treatment effects between the different intervention groups. In Column 4, the reported p-values at the bottom of the table indicate that the difference in effect sizes between intervention groups 1 to 3 was not significant, while the effect size in intervention group 4 was significantly different than
<!-- Start of picture text --> 80.0<br>70.0<br>60.0<br>50.0<br>40.0<br>30.0<br>20.0<br>10.0<br>0.0<br>2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017<br><!-- End of picture text --> _Source_ : Philippines Department of Energy.

An important feature of Philippines’ energy policy has been the desire to diversify the fuel source and to reduce the dependence on imported oil products. **Figure** **_7_** shows that the total amount of oil-fired generation has been reduced since 2002, while the use of hydro and geothermal have continued at the levels of the early 1990s, thus seeing their shares in the total reduced. Gas has increased in amount and in its share of the total, and coal has seen an even larger increase in total use and share. This increase in coal use came in two waves. Firstly, during the 1990s when IPPS were encouraged to enter the sector to meet the shortage of capacity, and secondly, post 2010. The initial move away from oil saw the fuel concentration index drop from the high level of 40 percent reached in the mid-1990s to a low value of 21 percent in 2004. The increasing use of gas and coal then led to a rise an of the index to 28 percent by 2015.

**Figure 6: Generation by fuel type (GWh) and fuel concentration index**

27
, for the period in question, but not for all these countries.<sup>22</sup> We intend, in the future, to pursue empirical work where such data are embedded. Secondly, we will seek to correct for possible problems of endogeneity of key variables, which may include public health expenditures, and some of the political variables employed (in particular, “corruption” could be endogenous and simultaneously determined with other features of the political system such as main governance type). These issues are among those we intend, as stated, to address in further work.

> 22 This data set also has some other limitations, in terms of reliability, and  with regard to the definition of motor vehicle classes.

40
Sincewelackinformationonthetrue</sup> poverty rates, this model cannot be immediately fit.

Instead, direct estimates of poverty rates from survey data _y_ ˆ _i_<sup>_d_areusedastheoutcomevariable.</sup> These estimates are nevertheless subject to sampling error because they are based on the areaspecific sample data. The second part of the model, thus, assumes that the direct estimates are centered around the true poverty rates as follows:

> 13In all cases, we pass every eligible variable to the machine-learning model and let the algorithm conduct variable selection.

> 14For a thorough review of small area estimation methods refer to Rao and Molina (2015), for a review of methods in the context of poverty mapping refer to Molina et al. (2022) as well as reviews by Pfeffermann (2013) on other developments in the area.

11
*5. Estimation Results**

The paper estimates the impact of the share of informal workers on emissions per value added using cross country and industry data in the following regressions: OLS, OLS with control variables, industry fixed effects, country fixed effects, and industry and country fixed effects as shown in Table 1. A problem in applying OLS is that regressors can be correlated with the error term and this correlation between regressors and the errors violates an assumption necessary for the consistency of OLS. For example, 12
We consider estimators based on onefold and twofold nested error models, including unit-context (UC) models with only aggregated covariates, and direct estimators. Concretely, we consider the following:<sup>39</sup>

# **Direct Estimates**

1. Direct estimates from the survey samples for each municipality. These are calculated with weights from the considered design. Specifically, the calculation of FGT indicators under the two-stage design detailed in subsection 4.1 using the inclusion probabilities is obtained as:

- _τ_ ˆ _a_<sup>_direct_</sup> =<sup>�</sup> _h_ _<u>pach</u>_ <u>�</u> _pach_<sup>_Fach_;</sup>

where, _pach_ is the inclusion probability for household _h_ in cluster _c_ and in municipality _a_ , and _Fach_ is the FGT or welfare measure of interest for household _h_ in cluster _c_ and in municipality _a_ .<sup>40</sup>

# **Unit-level models:**

1. _CEBa_ : Model fit is done using Henderson’s Method III (with sampling weights) and estimates are obtained using CensusEB as noted in CMN (2020). The fitted model reads:

- _yach_ = _xachβ_ + _zacα_ + _taω_ + _gsλ_ + _ηa_ + _εach_ ;

- where _xach_ is a vector of household specific characteristics, _zac_ contains cluster level characteristics, _ta_ includes municipality level characteristics and _gs_ is composed of state level characteristics. The random effects, _ηa_ , are specified at the municipal level.

2. _CEBc_ : Model fit is done using Henderson’s Method III (with sampling weights) and estimates are obtained using Census EB as noted in CMN (2020). The difference with model (1) is that random effects are specified at the PSU level. That is, the fitted model is:

- _yach_ = _xachβ_ + _zacα_ + _taω_ + _gsλ_ + _ηac_ + _εach_ ;

where, _ηac_ , is a random effect for cluster _c_
.0|35.0|7.6|21.1|43.7|40.1|
|Europe& Central Asia|11|38.8|34.3|11.4|22.1|58.8|49.0|
|Latin America & Caribbean|10|34.0|34.3|14.9|11.3|57.0|42.3|
|Middle East & North Africa|5|21.9|21.0|6.7|13.4|32.0|27.1|
|South Asia|3|32.2|31.5|7.3|25.3|39.8|38.6|
|Sub-SaharanAfrica|7|30.1|27.0|10.5|16.4|48.1|28.0|

Source Global Financial Development Database, 2008-10 data.

Note: OECD = Organisation for Economic Co-operation and Development a. Weighted average by total population.
countries, Berg, Mattinen and Pattugalan’s (2013, p. 52) report “an overwhelming preference for cash over in‐kind assistance”.<sup>2</sup> But quantitative studies describe more varied responses. Ethiopian data from the late 1980s showed that across three rural localities, 19, 83 and 90 percent would have liked to have received food rather than cash payments (Webb and Kumar, 1995). Predilections for in‐kind transfers are also reported in surveys conducted in India (Khera, 2013), Niger (Hoddinott, Sandström and Upton, 2013) and, to a lesser extent, Afghanistan (UNHCR 2015).

Why, if given a choice, would many program beneficiaries rather receive an in‐kind transfer instead of cash? Southworth (1945), along with more modern restatements such as Currie and Gahvari (2008) and Cunha (2014) make clear that their models are predicated on an assumption that cash and in‐kind transfers are equal valued. But in practice, this may not hold as equivalent value can be affected by: the purchasing power of the cash transfer (e.g. given prices of purchased goods, how much food can be purchased with a cash transfer); the potential re‐sale value of in‐kind goods (if the in‐kind transfer could be sold, how much cash would be received); the transactions costs associated with receiving a cash or in‐kind payment; and the transactions costs associated with using a cash or in‐kind payment (on this, see Doocy and Tappis, 2017). Beneficiaries might also be concerned about price volatility. Suppose a household can chose between a food transfer and a cash transfer. Food prices might be either

> 2 In standard microeconomic theory, preferences are stable; along with prices and incomes, they determine choices that economic actors make. In papers such as Berg, Mattinen and Pattugalan (2013), the word preference is used in a more colloquial
# **RECENT COHORTS**

A cross-sectional sample may not be very instructive in a rapidly changing economy. For this reason the sample is divided into two groups: recent labor market entrants and workers with more than six years experience. Focusing on recent graduates allows one to reflect the fact that reforms may not have affected all workers by 1993. Younger workers are expected to be more affected by the recent changes as they enter directly into a free market wage economy. In fact, those with less years of labor market experience receive higher returns to schooling: 14 percent versus 4 percent for more experienced workers (Table 9). In China, too, the returns to schooling for younger workers are higher than for more experienced workers, showing that in both Vietnam and China the returns to schooling increase as newer cohorts enter the labor market (World Bank 1996).

Table 9: Vietnam: Earnings Functions for Recent Labor Market Entrants and Experienced Workers

|Variable|Recent Entrants<br>(less than 6 years)|Experienced Workers<br>(more than 5 years)|
|---|---|---|
|Constant|0.217|0.792|
|Years of schooling|0.144<br>(5.9)|0.044<br>(7.5)|
|Years of experience|<br>0.204*<br>(1.3)|0.055<br>(6.5)|
|Experience-squared|0.006*<br>(0.2)|-0.001<br>(5.9)|
|Log hours-worked|0.489<br>(2.6)|0.759<br>(11.7)|
|R-squared|0.138|0.103|
|N|328|1,931|
|Source: VLSS 1992-93|||

Note: t-statistics appear in parentheses
activities, becoming vendors, and traveling outside the village. The _prescriptive norms_ sub-index (4 items, α = .61) assessed perceptions of other men and women believing that women _should_ engage in, i.e., social approval versus censure of, such activities.

_Social cohesion and community closeness_ (9 items, α = .47) assessed social interdependence and collectivism, expectations of social support, feelings of trust and closeness, experienced tension (reversed), and number of enemies (reversed). Interdependence and collectivism items measure how individuals view themselves in relation to others, i.e., as similar, connected, and responsive versus separate, unique, and autonomous; they reflect culturallyspecific forms of motivation that were integrated into the design of the psychosocial interventions. Additionally, the number of enemies, or people who wish to sabotage your success, was included as a locally relevant indicator of social cohesion (Adams, 2005), particularly given that enemies can arise from envy and resentment following inequalities in new economic opportunities or resources.

_Collective action_ (5 items, α = .34) assessed community engagement and support through the number of groups belonged to, the number of leadership positions held, monetary and volunteer contributions to community projects, and self-reported collective initiative. This measure assessed women’s engagement in community leadership as well as the potential of the interventions to create indirect benefits to communities.

_Intra-household dynamics_ (6 items, α = .33) assessed perceived quality of intrahousehold relationships with one’s partner (3 items, α = .09) and one’s household (3 items, α = .25). Items included feelings of trust, closeness, and support as well as experienced conflicts (reversed). Given that many economic and behavioral decisions are made at the household level in the study setting, this measure gauged the extent to which beneficiaries felt aligned with and supported by members of their household in decision-making or experienced tensions (reversed). It also assessed the Inclusion of
- 58 -

larger municipalities such as Yangon and Mandelay which were granted budgetary autonomy. Estimate of employment in these cities is approximately 17,000 people. Military employment data do not include people assigned in paramilitary units, e.g., the People's Police Force (50,000 people), the People's Militia (35,000), and the People's Pearl and Fishery Ministry.

Wages and Salaries of Union Government (Central Government) are from IMF Report No. SMI95/270 of October 16, 1995 and refer to 1993.

Nepal

# Military employment data do not include paramilitary personnel, e.g., the police force (28,000 people).

Pakistan Unemployment rate is from CIA Factbook 1995 and relates to 1990/91. Data on paid employment in nonagricultural activities are taken from the ILO's Yearbook of Labor Statistics 1995 and are for 1994. Central, Non-Central, Education and Health employment estimates are taken from Public Expenditure Review of October 30, 1992 and relate to 1990. Military employment data do not include paramilitary units, e.g., the National Guard (185,000 people), the Frontier Corps and the Pakistan Rangers, under the control of the Ministry of the Interior (35,000 people for each of these two corps), the Maritime Security Agency and the Coast Guard. Data on wages and salaries for Central Government (in this case, the federal Government) are taken from the Public Expenditure Review of October 30,1992 and relate to 1990. GDP at market price estimate is from the Government of Pakistan's Economic Survey, 1995-1996 and relates to 1990.

# Philippines

Population and Labor Force drawn from Public Expenditure Management for Sustained and Equitable Growth (pg. 56) and refer to 1995. Unemployment figure, for 1994, is from CIA Factbook 1995. Data on paid employment in non-agricultural activities are taken from the ILO's Yearbook of Labor Statistics 1995 and is for 1990. Local Govemment figures are
not provide accurate or up-to-date population estimates for two reasons. First, the accuracy of distributing population relies heavily on the input population data, that is, on the census (Wardrop et al., 2018). However, the census itself may quickly become outdated due to frequent migration patterns, and rapid and uneven population growth.<sup>1</sup> Second, these methods often rely on Night Time Lights (NTL) or other low-spatial resolution or coarse built-up area measures from satellite and other sources to distribute census population, and hence are limited at fine spatial scales (Vogel et al., 2018). This study seeks to overcome these limitations by proposing a “bottom-up” method which

> 1 Wardrop et al. (2018) provide examples of censuses that are outdated in countries such as Lebanon and Somalia due to conflicts, and postponed or cancelled in some others such as Afghanistan, Madagascar and the Republic of Congo. It is especially critical that we are able to obtain local population estimates for conflict ridden and unstable countries not only to track settlements and refugee movements, but also to provide aid and assistance.

1
on corporate bond issuances provided by Dealogic for 67 developing countries over the period 1993-2017. As for loans, we transform the data into a balanced panel at the country-sector-year level.

Our main outcome variables of interest are: 1) the number of syndicated loans in the countrysector, excluding deals with MDB participation; 2) the total size of the syndicated loans (as a share of GDP), excluding both the amount lent by the MDBs and by their partners<sup>9</sup> ; 3) the number of banks per loan, excluding those partnering only in deals with MDB participation; and 4) the average loan maturity.<sup>10</sup> To explore direct mobilization effects we also look at: 5) the number of banks that participate in syndicated lending per loan; and 6) the total size of the syndicated loans (as a share of GDP), excluding the amount lent by MDBs themselves, but including the amount lent by banks co-investing in a syndicated loan with MDB participation.

**Descriptive statistics.** Table 2 reports the summary statistics for the country-sector panel data. On average there are 0.7 syndicated loans in each country-sector-year (Panel A). The average total value of loans in a country-sector-year is 0.11 percent of GDP, which is only slightly higher than the average total value of loans excluding the ones supported by MDBs. The average number of banks (excluding MDBs) involved in syndicated loans is 1.6, similar to the average number of banks without counting the ones partnering in a syndicated loan with MDBs. The average maturity of loans, without counting loans with MDB participation, is 8.7 months. Panel B reports the summary statistics for corporate bond issuances, which refer to a smaller sample than that of syndicated loans. On average, bond issuances in the country-sector pair are more numerous than syndicated loans, but their size (as a percent of GDP) is smaller.

Consistent with the idea that mobilization effects