rafmacalaba/data-use-mentions-extended huggingface dataset:
- East Asia and Pacific: Indonesia, Solomon Islands

- Europe and Central Asia: Albania, Bulgaria, Croatia, Georgia, Kosovo, Romania, Ukraine

- Latin America and Caribbean: Argentina, Brazil, Peru, Uruguay

- South Asia: Bhutan

- Sub-Saharan Africa: Burundi, Ethiopia, Ghana, Kenya, South Africa.

The database classifies an enterprise as an SOE if the state directly or indirectly owns more than 50 percent of its shares or the state is the ultimate controlling entity, through majority ownership of common stock or any other mechanisms of control. This definition is in line with the European Union’s definition of public undertakings in Commission Directive 2006/111/EC.

The database provides panel data on financials of SOEs in the power and transport sectors at the SOE/year level that are consistent over time and comparable across SOEs no matter where they operate. To ensure consistency and reliability, researchers collected the data using a standardized accounting data template that was populated using the information on financial statements. To ensure that quantities like <mark>earnings before interest, taxes, depreciation, and amortization</mark> (EBITDA) or operational subsidies in the database are comparable across SOEs and years, they identified each item as defined by the template using the notes to the financial statements rather than relying on the way such items are presented in the SOEs’ main financial tables. Data reliability was further ensured through quality assurance checks by alternate analysts and accounting experts.

The database provides a standardized representation of the income statement, balance sheet, and cash flow statement of each SOE. It also contains supplementary items, including currency risk, debt/loan analysis, maturity profiles of assets and liabilities, and SOE ownership structure.

We used financial data on 135 SOEs in 19 countries for the 2009–18 period. Some of the regressions are based on fewer SOEs (92–118), because of data availability issues regarding subsidies, retained earnings, and other financial information. Table 2 presents the country and sectoral
on income distribution of trade, financial development or government spending are, however, mixed.

Finally, another strand in the literature highlights the relevance of initial income distribution for the subsequent elasticities between mean income growth and poverty reduction (e.g. Bourguignon, 2003; Ravallion and Chen, 2007; Klasen and Misselhorn, 2008; Ravallion, 2012; Christiaensen et al. 2013; and Crespo-Cuaresma et al., 2016). This work looks at the highly non-linear relationship between these two variables. Foster and Székely (2008), for instance, adopt a “general means” approach that places progressively less weight on incomes higher up the distribution. Their empirical findings show that the more weight is given to lower income households, the smaller the elasticity between growth and their poverty-sensitive income measure, suggesting that lower incomes do not grow in proportion to average incomes.

# **III. Data, Methodological Approach, and Cross-Country Results**

# **_Dependent Variable_**

Following Dollar et al. (2016), this paper draws on household data from two sources: the World Bank’s POVCALNET covering primarily developing countries, and the Luxembourg Income

> 7 See for instance Le Goff and Singh (2014) or Singh and Huang (2015).

-7-
with its AROPE rate decreasing only slightly from 17.8 percent to 16.4 percent, marking a reduction of 1.3 percentage points. Other subregions, such as Northern Europe (NE) and Western Europe (WE), experienced increases in their AROPE rates during the same period (Figure 1 and Table A3.1).

**Some EU member states present strikingly divergent trends in multidimensional poverty compared to the EU average.** Greece stands out with a striking absolute decrease of 3.7 percentage points. Conversely, Sweden, France and Finland experienced a notable increase (2.1, 2.0 and 1.2 percentage points) (Figure 2 and Table A3.2). Despite the heterogeneous changes in anchored AROPE rates, nearly three-quarters of European countries have reduced multidimensional poverty between 2021 and 2022 (standing significantly below the 45-degree line in Figure 2). Bulgaria and Romania experienced changes ranging from an increase of 0.5 percentage points to a decrease of 1.8 percentage points. Despite these changes, both countries continue to have extremely high incidences by European standards.

> 39 In practice, attrition and the incompleteness of data may impede the seamless tracking of individuals or households over extended periods and pose potential limitations on the overall accuracy and reliability of the findings.

> 40 To monitor AROPE over time, we construct an absolute measure of anchored AROPE, or at Risk of Poverty or Social Exclusion with an Anchored Income Threshold. This indicator modifies the traditional AROPE indicator, by adjusting the income threshold used to measure poverty or social exclusion to account for changes in the overall standard of living over time. This adjustment helps to ensure that the poverty threshold remains fixed and consistent across different years, allowing for more accurate comparisons of poverty rates over time.

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# **REFERENCE**

AFAD (2013). “Syrian Refugees in Turkey, 2013. Field Survey Results”, Republic of Turkey, Prime Ministry, Disaster and Emergency Management Presidency.

Ceritoglu, Evren, H., Burcu Gurcihan Yunculer, Huzeyfe Torun, and Semih Tumen. (2015). “The Impact of Syrian Refugees on Natives’ Labor Market Outcomes in Turkey: Evidence from a Quasi-Experimental Design”, MRPA Paper No. 61503, posted January 23, 2015.

Del Carpio, Ximena V. and Mathis Wagner. (2015). “The Impact of Syrians Refugees on the Turkish Labor Market”, World Bank Working Paper 7402, August 2015.

Del Carpio, Ximena V., Doreen Triebe, and Mathis Wagner. (2015). “Short-term Impacts of Syrians Refugees on the Turkish Labor Market”, Working Paper, May 2015.

Erdogan, M. Murat. (2014). “Syrians in Turkey: Social Acceptance and Integration Research”, Executive Summary and Report, November 2014.

Kirisci, Kemal. (2003). “Turkey: A Transformation from Emigration to Immigration” November 1, 2003. (http://www.migrationpolicy.org/article/turkey-transformation-emigration-immigration) Orhan Oytun, and Sabiha Senyucel Gundogar (2015). “Effects of the Syrian Refugees on Turkey”, ORSAM Report No 195, January 2015.

Ozturkler, Harun, and Turkmen Goksel. (2015). “The Economic Effects of Syrian Refugees on Turkey: A Synthetic Modelling”, ORSAM Report No: 196, January 2015.

UNHCR (2013). “Countries Hosting Syrian Refugees, Solidarity and Burden-Sharing, Background papers for the High Level Segment”. Provisional Release, September 2013.

UNHCR, Turkey Syrian Refugee Daily Sitrep, (22 March 2013) UNHCR, Turkey Syrian Refugee Daily Sitrep, (22 November 2013) UNHCR, Turkey Syrian Refugee Daily Sitrep, (15 September 2014) 18
15 and 20 percent, respectively. In Kyrgyz Republic, the dummy variable for household head performed better (partly because of the lower number of "unemployed" in Kyrgyz Republic, where many family members work on the same private plot and are thus automatically not counted as unemployed) and resulted in reductions of welfare in the order of those in Russia. Additionally, in Russia and in Estonia, households headed by individuals not in the labor force (inactive heads) were associated with declines in household welfare of 15 and 12 percent, respectively.

The final significant factor was whether the household head had university education. In all three countries, welfare gains were approximately 15-20 percent. In Estonia and Russia, primary education of the head was also significant and in the expected (negative) direction, reducing household welfare about 8 percent in both cases. In Kyrgyz Republic, vocational-technical training was also associated with improved welfare-having a household head with it would raise household welfare 10 percent relative to a household head with secondary education. In Russia, however, vocationaltechnical did not have a significantly different return from secondary education.<sup>7</sup> Demographic factors, except for the number of children and female headship, were generally not very important for household welfare in the three FSU countries. In each case, adding children meant reducing household welfare, from a low of a 6 percent reduction in Kyrgyz Republic to a 12 percent reduction in Russia. In Russia, there was a

> **17** For Estonia, data on household heads with vocational-technical education were combined with households with general secondary education. Although the base data set for Estoma did have a very few individual household heads with vocational-education, they were few in number and their welfare level improbably high. It is possible that these households should have been classified elsewhere, but it was not possible to discern the exact definition differences that led to the non-comparability.

74
|Colombia|0.16|0.15|0.37|0.32|
|Georgia|0.05|0.06|0.41|0.48|
|Kenya|0.16|0.21|0.40|0.23|
|Ukraine|0.06|0.01|0.47|0.46|
|Vietnam|0.11|0.23|0.31|0.35|

_Note_ . The sample includes 25- to 64-year-olds in self-employment or wage work. Employers, unpaid workers, and part-time workers are excluded from the sample. _Source_ : STEP Surveys (2014).

Average hourly earnings for men and women are presented in Table 2. There is substantial variation in the earnings distributions across all countries, which is also evident in graphs of the earnings distributions (see Figure A1 in the Appendix). The distributions for most countries skew to the left, indicating that the average earnings in these countries are below the median. Women, on average, earn less than their male counterparts across the board. This

> 13 The proportion of workers reporting zero earnings was within 1 percent of the sample.

12
# **<u>Acronyms and Abbreviations</u>**

|<br>ACD|<br>Armed Conflict Dataset|
|---|---|
|AFD|French Development Agency|
|ARE|United Arab Emirates|
|BHR|Bahrain|
|BP|British Petroleum|
|DJI|Djibouti|
|DZA|Algeria|
|EAP|<br>East Asia and the Pacific|
|ECA|Europe and Central Asia|
|EE/FSU|Eastern Europe/Former Soviet Union|
|EGY|Egypt|
|GDP|Gross Domestic Product|
|GNI|Gross National Income|
|IDB|Inter-American Development<br>Bank|
|IMF|International Monetary Fund|
|IRN|Iran|
|IRQ|Iraq|
|JOR|<br>Jordan|
|KWT|Kuwait|
|LAC|Latin America and the Caribbean|
|LBN|Lebanon|
|LBY|Libya|
|MAR|Morocco|
|MENA|Middle East and North Africa|
|MTBF|Medium-Term Budget Framework|
|MTEF|Medium-Term Expenditure Framework|
|MTFF|Medium-Term Fiscal Framework|
|MTPF|Medium-Term Policy Framework|
|NRM|Natural Resource Management|
|OECD|Organisation for Economic Co-operation and Development|
|OGM|Oil, Gas, and Minerals|
|OMN|Oman|
|PE|Political Economy|
|PEA|Political Economy Analysis|
|PFM|Public Financial Management|
|PPA|Public Policy Attributes|
|PREM|Poverty Reduction and Economic Management Network|
|PRIO|Peace Research Institute Oslo|
|PRMPS|PREM Public Sector Governance Anchor|
|PSD|Private Sector Development|
|PSM|Public Sector Management|
|QAT|Qatar|
|<br>SAU|<br>Saudi Arabia|
|SME|Small or Medium-Sized Enterprise|
|SSA|<br>Sub-soil Assets|
|SYR|Syria|
|TUN|<br>Tunisia|
|UAE|United Arab Emirates|
|WDI|World Development Indicators
and Rubin (1983). The purpose of the paper is to review the logic of this matching method, and illustrate its implementation and the computation of related impact indicators in EViews. PSM is an algorithm that matches treated and nonparticipants on the basis of the conditional probability of participation (the propensity score), given the observable characteristics. If outcomes are independent of participation, conditional on observables, then the use of the matched comparison group would yield an unbiased estimate of the mean impact of treatment.

The paper is organized as follows. Section 2 reviews the structure of two types of matching algorithms. The nearest-neighbor method pairs a given participant with the member of the comparison group with the propensity score that is closest to that of the given participant. Kernel-based methods associate with the outcome of participant **i** a matched outcome computed as a kernel-weighted average of the outcomes of all nonparticipants. This section also provides a brief comparison of PSM with evaluation methods such as double difference (DD), instrumental variable (IV) and Heckman’s method of selection bias correction. A more detailed description of these other methods is presented in Appendix C. Section 3 uses well-known and publicly available data sets to illustrate how to implement in EViews the methods described in section 2. Data on the treated come from the National Supported Work (NSW) Demonstration. The comparison group is drawn from the Population Survey of Income Dynamics (PSID) [Dehejia and Wahba 2002, 1999; Becker and Ichino 2002]. Concluding remarks are made in section 4. Appendix A shows how to estimate the propensity score using the log likelihood object (LOGL) while Appendix B provides the entire computer code for PSM.

# **_2.  Matching Methods_**

# **_The Principle of Matching_**

Matching methods can be framed within the context of _nonparametric estimation_ of the relation between an outcome variable for unit **i** ( **yi** ), a dummy variable indicating participation in the program ( **
0.939|0.007|0.640|1,496|
|<br>Improved attitude of health workers|0.963|0.039**|0.015|1,656|
|<br>Improved quality of health services|0.963|0.009|0.406|<br>1,650|
|<br>Improved collaboration between community|0974|0007|0270|1632|
|and health facility<br>Note: data from household surveys. The PBF impact is estim<br>data were only collected during the follow-up. Standard error|.<br>ated using a pr<br>s are clustered|.<br>opensity score wei<br>at the district leve|.<br>ghting spe<br>l. All regre|,<br>cification as these<br>ssions include|

<mark>Note: data from household surveys. The PBF impact is estimated using a propensity score weighting specification as these data were only collected during the follow-up. Standard errors are clustered at the district level. All regressions include controls for age, gender, education level, employment status, household wealth quintile, province, number of health houses in the catchment area, an indicator for having any health houses in the catchment area, catchment population terciles, and an indicator for the catchment area’s RHC being more than 10km away from the district hospital.</mark> Level of statistical significance: *p<0.10; **p<0.05; ***p<0.01 30
not recorded in the FUT (Local Fiscal Account). Likewise, the budget of technical and technological institutions can fall heavily on the contributions of departments and municipalities, making identification of the budget very difficult. Due to these difficulties, and as a temporary alternative, the per capita value per public university student was allocated to higher education students identified in the ENPH. This cost was obtained by dividing the total budget of public universities by those enrolled by the semester.<sup>40</sup> Finally, health care transfers were considered for people enrolled in the subsidized health care system using a cost of insurance approach. Although the survey identifies who a beneficiary of the subsidized health care system is, there are no questions to determine the use of health services. Moreover, following Inchauste and Militaru (2018), this approach keeps individuals who use the services more or who get sick more from receiving a larger transfer, and therefore have a much higher final income just because they are sick. However, instead of a per capita cost, the value of the Capitation Payment Unit (UPC) of the subsidized health care system was assigned, which is the value recognized annually by the Ministry of Health to the EPS for each of the subscribers of the subsidized health care system. Associated risk factors adjust this value regarding age and gender and by the residential zone. A higher value is also delivered to indigenous EPS since, by their nature, they must provide services in addition to the POS.

Additionally, 68<sup>41</sup> percent of the UPC from the contributory health system was included for employees with incomes below 10 smmlv, given that, from the entry into force of Law 1607 of 2012, the portion of the contribution that corresponded to employers was eliminated and replaced by the CREE. Subsequently, Law 1819 of 2016 eliminated the CREE tax (tax with specific destination), and since then, the national government uses resources from the National General Budget to make the missing money transfers to
one of the largest randomized survey experiments in middle-income countries in this literature, and it incorporates best practices for the design of randomized survey experiments from cross-country research in high-income countries.

The paper is structured as follows. Section 2 provides background information about the related literature, hypotheses for this study, and the countries that are included. Section 3 describes the survey methodology in detail, including survey design, data collection, determination of sample sizes, and empirical analysis. Section 4 presents the descriptive findings 7
# A Data appendix

Migration data presented in Section 3 follow Docquier et al. (2010) who construct comprehensive 195x195 matrices of bilateral migration stocks. These matrices cover the two skill groups (college graduates and less educated individuals), and two years (1990 and 2000). Migration is de...ned on the basis of country of birth which is time invariant (contrary to the concept of citizenship, which changes with naturalization) and independent of the changes in policies regarding naturalization. However, this de...nition does not account for whether education has been acquired in the home or in the host country. This induces a potential over-estimation of the intensity of the brain drain migration of children and students can represent an important fraction of total immigration for certain countries.<sup>17</sup> The methodology is described in detail in Docquier et al. (2010) and it consists of three main steps. The starting point (step 1) is the database described in Docquier, Lowell and Marfouk (2009) documenting bilateral migration stocks in OECD host countries. It is based on a collection of census and register immigration data by country of birth and educational level in the 30 OECD countries in 1990 and 2000. This OECD set of destinations does not include the three member states which joined the OECD in 2010 (Chile, Israel and Slovenia) and future members (Estonia and Russia). This database characterizes the education level, origin and destination of about 57.4 million migrants in 2000 and 40.8 million migrants in 1990. High-skill migration represents 35.4 percent of the total in 2000 (30.1 percent in 1990). The second step consists of a collection of similar immigration data from 46 nonOECD destinations in 2000 and 31 destinations in 1990. In the third step, data collected in steps 1 and 2 are used to estimate the determinants of the size of bilateral migration (Mijt<sup>s)from countryitocountryjintheeducationgroupsatyeart.Then,basedonthese</sup>
mechanisms for implementing rules and laws, and access to finance. If informal competition experienced by the formal firms varies systematically with the business environment in which the firms operate, our main results could suffer from the omitted variable bias problem. Thus, we control for the following measures of the business environment all defined at the firm-level: total hours of power outages experienced by the firm in a typical month over the last year ( _Power Outages_ ), a dummy variable equal to 1 if the firm suffered losses due to crime during the last year and 0 otherwise ( _Crime Losses_ ), a dummy variable equal to 1 if the firm reported the functioning of courts as a moderate, major or very severe obstacle for its day to day operations and 0 if it reported it is a minor obstacle or no obstacle ( _Courts Obstacle_ ); and two measures of access to finance which include a dummy variable equal to 1 if the firm has overdraft facility and 0 otherwise ( _Overdraft_ ), and how severe is (lack of) access to finance as an obstacle to the firms’ day to day operations ( _Finance Obstacle_ ) as reported by the firms. The finance obstacle is defined on a 0-4 scale with higher values implying a more severe obstacle.

Our next set of baseline controls includes macro-level variables. As mentioned above, the ES data we use was collected in different years across countries. Global annual shocks to employment growth could affect our results. Thus, we control for dummy variables indicating the year the ES was conducted in the country (Year fixed effects). Differences in overall economic development and income levels are a cause of concern for our main results. One reason for this is that informality tends to be lower in the richer countries. At the same time, richer countries grow slower than poorer countries (convergence effect). The structure of correlations here implies that failure to control for overall economic development can cause the estimated coefficient value of informal competition to be biased towards zero, weakening our main result (confirmed below)
**Figure 2: Subjective impact of the crisis - by country**

<!-- Start of picture text --> How much has the crisis affected your household in the past two years?<br><!-- End of picture text --> <!-- Start of picture text --> 0% 20% 40% 60% 80% 100%<br>Transition average<br>Albania<br>Armenia<br>Azerbaijan<br>Belarus<br>Bosnia and Herz.<br>Bulgaria<br>Croatia<br>Czech Republic<br>Estonia<br>France<br>Georgia<br>Germany<br>Hungary<br>Italy<br>Kazakhstan<br>Kyrgyz Rep.<br>Latvia<br>Lithuania<br>Macedonia<br>Moldova<br>Montenegro<br>Poland<br>Romania<br>Russia<br>Serbia<br>Slovak Rep.<br>Slovenia<br>Sweden<br>Tajikistan<br>Turkey<br>Ukraine<br>United Kingdom<br>Uzbekistan<br>A great deal A fair amount Just a little Not at all<br><!-- End of picture text -->  Proportion of respondents reporting their household has been affected by at least one specific labour market shock or income loss in the past two years (job loss, wage reduction, reduced remittances, etc.) Source: LiTS II (2010).

6
To undertake our CBA, we use cost data derived from Manning et al. (2006) and outcome data collected from the chief investigator of the project, Professor Ross Homel. Project costs were estimated separately for three distinct stages: development, implementation, and evaluation. The cost analysis method employed and cost estimates are described in the study undertaken by Manning and colleagues (2006). Part of our CBA also requires us to estimate potential costs of a negative outcome – in this case the cost of dealing with behavioral problems – or the avoided costs if the intervention produces outcomes that reduce the probability of a child needing future behavioral management intervention.

Manning and colleagues (2006) estimated the costs of four behavioral management alternatives (grouped into two categories) that may be required if a child presents with behavioral problems in the early years of education. Category 1 consists of programs developed to help improve the behavior of children with borderline or less challenging behavioral problems, while Category 2 consists of programs aimed at helping those children with more severe or extreme behavioral problems (see Table 1). Homel and colleagues (2015) measured difficult and challenging behavior using the Rowe Behavior Rating Inventory (RBRI). The RBRI is a validated teacher checklist used to assess the level of children’s difficult behavior (Rowe and Rowe, 1995). In this paper we classify children who received a score of 20 to 29 as displaying a low level of poor behavior and thus only requiring little assistance (i.e. Alternative 1). We coded children receiving a score greater than 30 but less than 40 as requiring more assistance (i.e. Alternative 2). Children who received a score greater than 40 were identified as having more severe or extreme behavioral problems and fell into Category 2 type programs (RBRI 40 - 49 – Alternative 3; RBRI ≥50 – Alternative 4).

24