rafmacalaba/data-use-mentions-extended huggingface dataset:
Estimates tend to be more similar between the two surveys for people in Gaza Within the overall pattern of differences, estimates disaggregated by region are more similar between the PPCS and the Facebook survey in Gaza than in the West Bank for six of the seven core indicators calculated for both regions (conflict exposure is excluded here because it is calculated only for Gaza). For these six indicators, the greater degree of similarity is substantial: the relative difference between the two surveys tends to be about half as much in Gaza as it is in the West Bank. (For the one indicator in which estimates are more similar in the West Bank, the differences in estimates are close between the two regions.)

**Figure 2: The relative difference between Facebook and PPCS estimates is smaller in Gaza than in the West Bank**

This may be in part because there is more homogeneity in Gaza around geography, socioeconomic characteristics, and exposure to conflict than there is in the West Bank. For example, the West Bank is more than 15 times the size of Gaza and more varied in the distribution of its population over type of location (urban, rural, or camp). Per capita welfare is also more variable in the West Bank than in Gaza: the sample standard deviation of the most recent per capita welfare aggregate is about 1.5 times larger in the West Bank than in Gaza. Estimated poverty rates vary more within the West Bank, with a difference of about four percentage points between the poorest and wealthiest governorates in Gaza compared to about 10.5 percentage points between the poorest and wealthiest governorates in the West Bank (this difference in variability increases at lower levels of disaggregation).<sup>9</sup> Experience of violent conflict also tends to vary more across the West Bank than it does in Gaza, as suggested by the pattern of events recorded by the Armed Conflict Location and Event Data Project (ACLED) during the May 2021 violence. Over a period of 15 days, ACLED
Table 10: Fertilizer data

||Problems|Solutions/Assumptions|
|---|---|---|
|1|The survey asks if a household received inputs<br>from the government or a cooperative, but it<br>does not ask if they were received through the<br>FISP or FSP program, or if the household<br>participated in these programs.|**Main Assumption:**A household is considered a<br>participant of the FISP or FSP in the period 2008/2009 if<br>it did all of the following:<br><br>Grew maize.<br><br>Used fertilizer and/or purchased seeds.<br><br>Obtained seeds and fertilizer from the<br>government or from a cooperative.|
|2|Related to the previous problem, we cannot<br>differentiate households that received benefits<br>from FSP from those that benefitted from FISP.|**Main Assumption**: Given that households which<br>benefitted from FSP received a smaller package than<br>FISP participants, and that FSP is targeted to household<br>that grow on smaller areas, scenario 2 described below,<br>partiallyaccounts for thisproblem.|
|3|The survey does not allow respondents to record<br>more than one source of fertilizer and seeds.|**Main Assumption:**We assume that the household only<br>received fertilizer and seeds from the government if it<br>reported doing so. All expenditures associated to these<br>inputs were recorded aspaid to thegovernment.|
|4|The survey does not specify the amount of<br>fertilizer and/or seeds that households received<br>from the government or cooperatives.|**Main assumption for Scenario 1:**All households<br>received one full FISP package of seeds and
**Table 1D:** Summary Statistics: Consumer Pyramids Data

||||**Panel A:**<br>**Rural**<br>**Adults**|||
|---|---|---|---|---|---|
|**Variable**|**Mean**|**Std. Dev.**|**Min.**|**Max.**|**N**|
|Female|0.47|0.5|0|1|388273|
|Literate|0.73|0.44|0|1|388273|
|Age|35.33|13.97|15|65|388273|
|LFP|0.43|0.5|0|1|388273|
|Muslim|0.1|0.3|0|1|388273|
|SC/ST|0.34|0.47|0|1|388273|
|Unhealthy Individual|0.02|0.14|0|1|388273|
|On Medication|0.02|0.12|0|1|388273|
|Has Cell Phone|0.56|0.5|0|1|388273|
|Has Health Insurance|0.03|0.18|0|1|388273|
|Has Bank Account|0.68|0.46|0|1|388273|
|Has Credit Card|0.01|0.08|0|1|388273|
||||**Panel B:**<br>**Rural**<br>**Workers**|||
|**Variable**|**Mean**|**Std. Dev.**|**Min.**
<br>Mental health assistance[=1]|259|0.77|0.42|0.00|1.00|
|_Panel E. Outcomesfor Labor Market Access_||||||
|Labor index (SD)|5,592|0.12|1.10|-0.47|2.72|
|Employed [=1]|5,592|0.22|0.41|0.00|1.00|
|<br>Monthly wage (somoni)|5,592|357|1,086|0.00|40,000|
|<br>Weeklyhours worked(somoni)|5,592|11.75|23.72|0.00|84.00|
|_Panel F. Outcomesfor Income and Consumption_||||||
|Income and consumption index (SD)|5,592|0.01|1.00|-7.03|8.29|
|<br>Monthly house income (somoni)|5,587|5,159|3,412|0.00|30,850|
|Monthlyconsumption(somoni)|5,592|11,151|234,477|0.00|12,000,000|
.” US<br>Geological Survey Data<br>Series 832, ftp://chg-<br>ftpout.geog.ucsb.edu/<br>pub/org/chg/products/<br>CHIRPS-<br>2.0/docs/USGS-<br>DS832.CHIRPS.pdf.|Sergio M. Vicente-<br>Serrano, Instituto<br>Pirenaico de Ecología,<br>Zaragoza, Spain.<br>Santiago Beguería,<br>Estación Experimental<br>de Aula Dei, Zaragoza,<br>Spain.|Verdin, J., and R. Klaver.<br>2002. “Grid-cell-based<br>Crop Water Accounting<br>for the Famine Early<br>Warning System.”<br>_Hydrological Processes_<br>16 (8): 1617–30. DOI:<br>10.1002/hyp.1025.|Copernicus Service<br>information 2018.|Didan, K. 2021.<br>MODIS/Terra<br>Vegetation Indices<br>Monthly L3 Global<br>0.05Deg CMG V061<br>[data set]. NASA<br>EOSDIS Land<br>Processes DAAC.<br>Accessed 2021-06-24<br>from<br>https://doi.org/10.50<br>67/MODIS/MOD13C2.<br>061.|
|Additional<br>information/<br>notes||||Range of WRSI values:<br>< 50: Failure<br>50–60: Poor<br>60–80: Mediocre<br>80–95: Average<br>95–99: Good<br>99–100: Very Good<br>0: N/A<br>The following values<br>are flags (and not<br>specific WRSI values in<
Development of Vocational Training Duell, N. (2021) Greening of the labour market – impacts for the Public Employment Services. Small scale study. European Network of Public Employment Services EcoCanada (2016). Competencies for Environmental Professionals in Canada. National Occupational Standards. Environmental Careers Organization of Canada EcoCanada (2021). A National Sector Workforce Strategy to Address Environmental Talent Needs and Gaps. PPT Environmental Entrepreneurs (2020) CLEAN JOBS, BETTER JOBS An examination of clean energy job wages and benefits Gregg, C.; Strietska-Ilina, O.; Büdke, C. (2015) Anticipating skill needs for green jobs: a practical guide. Skills and Employability Branch Employment Policy Department. ILO. Geneva.

ILO (2011) Study of occupational and skill needs in green building: final report / International Labour Office, ILO Skills and Employability Department (EMP /SKILLS ). – Geneva: ILO , 2011 ILO (2011) Study of occupational and skill needs in renewable energy: final report / International Labour Office, ILO Skills and Employability Department (EMP /SKILLS ). – Geneva: ILO , 2011 ILO (2011). Comparative analysis of methods of identification of skill needs on the labour market in transition to the low carbon economy: final report / International Labour Office, ILO Skills and Employability Department (EMP /SKILLS ). - Geneva: ILO ILO (2013). Meeting skill needs for green jobs: Policy recommendations ILO. (2019) Skills for a greener future: A global view based on 32 country studies. Geneva: ILO. ILO. (2021) Global framework on core skills for life and work in the 21st century. Switzerland: 43
(10 years old|or less)||
|Disruptive|<br>2.172***|<br>1.795***|<br>2.417***|2.029***|
||(0.107)|(0.080)|(0.074)|(0.100)|
|Disruptive_×_Africa|-1.891***|-1.451***|-2.150***|-1.609***|
||(0.173)|(0.126)|(0.144)|(0.145)|
|Disruptive_×_LAC|-1.297***|-1.024***|-1.407***|-1.114***|
||(0.225)|(0.173)|(0.364)|(0.260)|
|_R_<sup>2</sup>|0.229|0.193|0.230|0.195|
|Observations|15634|15634|15634|15634|
|Countries|33|33|33|33|
|Pa|nel c. Older fr|ms (11 years ol|d or more)||
|Disruptive|2.944***|2.161***|3.095***|2.328***|
||(0.397)|(0.135)|(0.411)|(0.151)|
|Disruptive_×_Africa|-2.339***|-1.616***|-2.526***|-1.840***|
||(0.489)|(0.241)|(0.485
# **8. Conclusion**

Our study confirms that digital skills are common in Southeast Asia. The job postings data from Malaysia in 2016-18 reveals that all occupations require at least some digital skills. Highly digital occupations, however, still represent a very small share of employment in the four countries studied: 5 percent in Malaysia, around 2 percent in Thailand and Vietnam, and 1 percent in Cambodia. Instead, the bulk of employment is in very-low-digital occupations (from 40 percent in Malaysia to 66 percent in Cambodia) and low-digital occupations (from 16 percent in Vietnam to 31 percent in Malaysia). These shares are changing very slowly over time, with the exception of Vietnam’s somewhat rapid shedding of very low digital occupations in favor of low and medium digital occupations We find that occupations from all four digital occupations levels need cognitive and socioemotional skills. While the share of intermediate and advanced digital skills differs across digital occupation levels in Malaysia, all four levels of occupations require roughly the same share of basic digital skills (5-9 percent), cognitive skills (8-12 percent), and socioemotional skills (6-12 percent). Cognitive and socioemotional skills are even more important than digital skills for very-low, low-, and mediumdigital occupations (nearly the entire employed population). The share of cognitive and socioemotional skills combined is greater than digital skills: 23 versus 5 percent for very-low digital occupations, respectively, 20 versus 9 percent for low-digital occupations, respectively, and 16 versus 15 percent for medium-digital occupations, respectively. Conditional correlations estimated through regressions show that medium- and highly digital occupations require more thinking skills (cognitive) than very low digital occupations but require similar levels of the other cognitive and socioemotional subsets as do occupations that are less digitally intensive. Similar patterns emerge for Vietnam, Cambodia, Thailand.

When disaggregating cognitive and socioemotional skills, subsets of these skills categories are found to associate more closely with certain levels of digital skills. Using an exploratory factor analysis for Malaysia, we find three clusters of skills from
capitalization; a measure of the efficiency of stock markets.

- **Bank Z-score:** return on banks’ assets plus the ratio of banks’ equity and

measure of the stability of the banking sector.

The aforementioned dimensional distinction allows us to differentiate and compare the effects of the banking sector and the stock market on economic growth. In addition, unlike the previous literature, we simultaneously examine whether the depth, efficiency and stability of a financial system are important for growth.

The time and cross-country coverage of financial variables varies. Private credit to the real sector is available for the majority of the countries in the data set since 1960. However, the remaining variables are typically available only from the 1980s onward. We average the indicator values corresponding to a selected period (i.e., 1960–2011) and to their data availability. This is a standard procedure in estimating empirical long-term growth models, despite the risk of introducing endogeneity into the model and information loss introduced by averaging over extended time periods. The benefit of averaging is a focus on long-term trends while abstracting from short-term fluctuations. But in our robustness checks, we also use the initial values of financial indicators instead of their average. Given the data availability and the construction of the data set, all the financial variables could be endogenous. We address endogeneity concerns through our BMA approach using lagged variables. Table 1 presents descriptive statistics on the individual financial indicators. Overall, the combined data set of Fernandez et al. (2001) and private credit and new financial indicators leads to 68 and 60 observations, respectively.

12
Besides these static aspects that limit the effectiveness of CRS, the lack of longterm commitment from both the government and the firm further renders CRS contracts ineffective in improving growth. A CRS contract usually lasts for three to five years, not long enough to internalize iong-term investment. The government, moreover, used the CRS as an experiment, not a permanent policy instrument. Finally, SOEs have a tendency to sacrifice their potential long-term benefits for myopic gains: (a) Firm-level pay sensitivity rewards current profits, which further reduces incentives to invest, and (b) Xu (1996) found that better firms were given lower base amounts of transfers including less investmnent subsidies. This may produce "ratchet effects", that is, SOEs under CRS may mimic inefficiency by not working hard so that they get easy performance targets.

_Hypothesis 5._ Except the effects of firm-level pay sensitivity, the adoption of CRS will, on average, improve neither productivity level nor the growth rate. The adoption of CRS may affect growth rate negatively.

# IV. EMPIRICAL **IMPLEMENTATION**

All variables used are constructed from _A Survey of State Enterprises:_ 1980-1989. Some variables have many missing observations, and each of them has a different set of missing observations. In the empirical analysis, we delete observations where: (1) the dependent variable is missing; (2) capital intensity, the key conventional explanatory variable, is missing; (3) and some key variables are unreasonable outliers.<sup>1 5</sup> The sample, now an unbalanced panel, consists of _716_ firms and a total of 6712 Observations. To avoid losing too much of the sample and to make full use of the information in the data, we impute the value of missing observations of a generic variable xi, by giving them the

> **'**<sup>**5**</sup> **We** consider an observation to be an outlier when its value seems unreasonable,
in the economy. For each firm, ORBIS also provides a large set of indicators including information on the firms’ ownership structure as well as their income statements and balance sheet data.

One of the key obstacles of identifying SOEs in ORBIS is the lack of a specific variable for this identification. The closest proxy variable provided by ORBIS is denoted as the Global Ultimate Owner (GUO), defined as the individual or entity at the top of the corporate ownership structure (majority owner). In theory, one could obtain a list of SOEs by selecting firms whose GUOs are government public authorities. However, the information provided by the GUO is limited to entities of which the state or public authority owns 25% or more (GUO25), or 50% or more (GUO50), and state ownership in SOEs could be lower and still provide the ability of the governments to influence over firms (e.g., golden shares). Moreover, the GUO variable does not include every shareholder at 25% or 50% or more level of ownership; it includes only the largest shareholder at the 25% or 50% threshold level, which may not be a state or a government. As a result, utilizing the GUO variable will yield an incomplete list of SOEs and firms with state participation – and therefore underestimate the businesses of the state. Furthermore, the GUO variable tends to misclassify some firms as private even if they are owned by the government.

The Global BOS database leverages a large set of alternative data sources to compensate for ORBIS’s substantial coverage variation across countries and regions. In general, ORBIS representativeness in ECA countries is better compared to emerging economies in EAP and LAC (Gal, 2013) (Bajgar, Berlingieri, Calligaris, Criscuolo, & Timmis, 2020).<sup>6</sup> Furthermore, the ORBIS raw database suffers from several issues, including duplicates, omission, and measurement error. Several cleaning steps had to be implemented before the raw data in ORBIS
for sectoral FDI presence and imports. In a second step, we estimate the regressions using an instrumental variables two-stage least squares (IV 2SLS) approach.

# **3.2 Data and Instruments**

The World Bank Enterprise Analysis Unit recently published the Enterprise Surveys Indicator Database.<sup>9</sup> This publication covers 215 enterprise surveys for 126 countries over the period 2006 to 2010. Enterprise surveys represent a comprehensive source of firm-level data in emerging markets and developing economies. One major advantage of the enterprise surveys is that the survey questions are the same across all countries. Moreover, the Enterprise Surveys represent a stratified random sample of firms using three levels of stratification: sector, firm size, and region. Sectors are based on the ISIC Rev. 3.1 classification, but in some cases are further aggregated.

The Enterprise Surveys Indicator Database covers a wide range of indicators on firm characteristics, the business environment, innovation and technology, and workforce and skills among others. We merged this dataset with data on firm-level output, value added, and capital stock obtained from the Enterprise Analysis Unit of the World Bank.<sup>10</sup> All local currencies have been converted into US dollars and deflated using a GDP deflator in USD (base year 2000). Exchange rates and GDP deflators have been obtained from the World Development Indicators.

We apply the following rules to the dataset: (i) We include only the most recent Enterprise Surveys for each country; (ii) We include only countries that cover foreign firms in the surveys<sup>11</sup> ; (iii) We drop high-income countries to cover only emerging or developing countries<sup>12</sup> ; and (iv) We drop countries for which we cannot calculate foreign output share due to unavailable output data. We focus only on the effects of productivity spillovers on domestic manufacturing firms, since productivity measures were unavailable for services firms.

The procedure above results in more than 25
26 Figure 3. Financial System Characteristics, by Income Group, 2010 **Financial Institutions** <!-- Start of picture text --> Depth Access Efficiency Stability<br>High Income Low income<br>Lower middle income Upper middle income<br>80<br>60<br>40<br>20<br>0<br><!-- End of picture text -->

**Financial Markets**

<!-- Start of picture text --> Depth Access Efficiency Stability<br>High Income Lower middle income<br>Upper middle income<br>80<br>60<br>40<br>20<br>0<br><!-- End of picture text --> Source: Authors, based on the Global Financial Development Database.

Notes: The summary statistics refer to the winsorized and rescaled variables (0-100), as described in the text. See also Table 1.
change in the market structure to address the underlying cause of the harm from the unilateral exercise of market power. They simply ask that the firm be a “good citizen” and not maximize profits.

For the case of the U.K., the MALC anticipated punishing those market participants that exercised significant amount unilateral market power. However, one difficulty with this approach is that the major beneficiaries of the unilateral exercise of market power are the firms that exercised little if any unilateral market power. One could therefore imagine some firms finding ways to compensate larger firms for exercising their unilateral market power so that all firms can reap benefits. A second difficulty is distinguishing the exercise of significant market power worthy of punishment from expected profit‐maximizing behavior. In testimony to the United Kingdom Competition Commission, Wolak (2000b) made these and a number of other arguments against the MALC, which the Commission eventually decided not to implement.

Another potential benefit associated with public data release is that it enables third‐ parties to undertake analyses of market performance. The US policies on data release severely limit the benefits from this aspect of a public data release policy. Releasing data with the identities of the market participant masked makes it impossible to definitively match data from other sources to specific market participants. For example, some market performance measures require matching data on unit‐level heat rates or input fuel prices obtained from other sources to specific generation units. Strictly speaking, this is impossible to do if the unit name or market participant name is not matched with the generation unit.

The long time lag between the date the data is produced and the date it is released also greatly limits the range of questions that can be addressed with this data. Taking the example of the California electricity crisis, by January 1, 2001, the date that masked data from June of 2000 was first made available to the public, the exercise of unilateral market power in California had already resulted in more than $5 billion in overpayments to suppliers in the California electricity
<br>the nature of the<br>activity|all registrations<br>associated with vehicles.<br>permission for inter-<br>aimag travel|permission for<br>land use|none|none|none|
|Applicable regulations|many, depending on<br>the nature of the<br>activity|technical inspections|city health and<br>sanitation<br>regulations|none|none|none|
|Governed by provisions of the<br>_Labor Law_|yes|no|no|no|no|no|

_Source:_ Informal sector surveys conducted by the author.

***** excludes moonlighters; items in italics are reasonable<sup>_a priori_assumptions.</sup>
# **<u>Construction of Core Indexes:  Primary outcomes are indicated in bold text.</u>**

1. <u>Core Respondent-level outcomes:  Can be analysis with simple cross-sections comparing</u> UCT, CCT, and control.  No extensive margin issue with any of these variables.

- a. Schooling and Marriage  (replication of QJE results with age-appropriate dependent variables):

- i. **Highest grade completed (S7, Q7)**

- ii. Highest educational qualification achieved (S7, Q9)

- iii. **Achievement, replacing the test scores with the ‘competencies’ (see end of this appendix for further details on construction). We will show the components, as well as the index for the quality index; and show only an index for the quantity.**

- iv. **Ever married (Part II CS, Q2e), ever pregnant (S18, Q1, Q2), number of live births (S18, Q17).**

- **v. Hazard model of age of first marriage (S14, Q1 (and Round 3 data for those already married at Round 3)) and age at first birth (construct using age of respondent and DOB), with ‘uncompleted spells’ for those never married or never first birth.**

- vi. Sexual behavior:  ABC, # partners ever, as in previous papers.  Ever had sex (S12, Q2, Q3, Q4) age at first sex (S12 Q4), total number of partners ever (S12, Q5), sexually active in past 12 months (S12, Q7), condom use last sex with most recent partner (S12, Q23), having a partner with an age difference of more than 5 years (S12Q12)

- b. Health:  Replication of Lancet results (Baird, Garfein, McIntosh and Özler 2012)

- i. **HIV prevalence in R4,** HIV incidence R3-R4