rafmacalaba/data-use-mentions-extended huggingface dataset:
because of nationalization.<sup>19</sup> To guarantee that the results were not confounded by access to the labor market and other services by these Colombians, the sample was stratified to exclude families with a Colombian citizen 10 years of age or older, and with a member who held PEP from a different wave.

The structure of the survey is explained below:

1. **Screening Module:** the screening was designed to be done in a first call to determine the family’s eligibility for the survey. The screening and the survey were to be answered by any adult in the nuclear family of the person who was originally contacted. The person who answered the survey became the main respondent and would be the only one to provide information for themselves. In turn, they had to answer the survey, from a third-person perspective, for all other family members.

In the screening, the main respondent was asked for their age, place of birth, Venezuelan ID number, current city of residence, whether they had a Venezuelan passport, and if they had registered in the RAMV census in 2018. If the contact came from the sampling frame of irregular migrants, they were asked if they had PEP and for the date of their arrival in Colombia. For the census sample, this information was available. Only families whose main respondent arrived in Colombia from January 2017 to December 2018 were eligible.

2. **Household Roster Module:** the main had to answer respondent sociodemographic, educational, and PEP-related questions for every member of the nuclear family:

- _Sociodemographic_ : age, relation to household head, citizenship (Colombian and/or Venezuelan) and proof of citizenship, gender, civil status, date of arrival in Colombia, date when they became part of the family, and cities of birth and residence in Venezuela.

- _Education_ : maximum level of education before migration, current level of education and enrollment, degree validation in Colombia, reasons why their degree is invalid, and
Poor based on wealth data<br>NON-POOR<br>Consumption Poverty line<br>POOR A : Poor based on consumption<br>data/Non-poor based on wealth data<br><!-- End of picture text -->

> 9 The first-best approach to identifying consumption measurement errors would be to administer a small consumption-based survey questionnaire in the areas where underreporting and over-reporting are deemed most serious, revisiting the same households sampled in the IOF 2008/09. A comparison with the original data would indicate the extent to which questionnaire design and fieldwork operations may have impacted consumption data quality, diet-composition and caloric intake indicators. This information could then be used to adjust the consumption data in the IOF 2008/9.

> 10 With this asset index, suggested by Filmer and Pritchett (2001), principal-components analysis is used to calculate the weights of the index. The first principal component, the linear combination capturing the greatest variation among the set of variables, can be converted into factor scores, which serve as weights. The rationale for using this index is that it captures the household’s permanent welfare dimension better than simple consumption data, and can provide more reliable rankings between households.

> 11 The use of the 30th and 70th percentiles is not arbitrary: choosing extreme and symmetrical values (+/- 20 percentiles from the median) of the wealth distribution allows us to screen consumption observations that were reasonably inconsistent with their corresponding wealth-index value.

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labor within the government, in practice the system appears to encourage the worst elements of individual rent-seeking and the further separation of pay from job performance criteria.

# B. EMPLOYMENT

Documenting the size, growth and structure of government employment would appear to be a relatively straight-forward matter. In many developing economies, however, it is not. In some cases it has even proven necessary to conduct a civi. service census in order to determine who is actually working for the government. For purposes of cross-country comparisons, available data are limited because no standardized system for collecting public employment statistics exists. Neither the ILO nor any UN agency regularly requests estimates of public employment. What is available are individual country sources which ofter. differ widely according to whether: (1) national, local and/or public enterprise workers are included; (2) actual or budgeted positions are reported; and (3) casual or daily workers are counted. Needless to say, information is even scarcer on how the st-ucture of government employment is changing, that is, on the relative expansion of managerial, professional, skilled and unskilled labor.

According to the data assembled by Ozgediz (1983), reproduced as Table 5, public employment growth in developing nations in the late 1970s, in most cases, well exceeded comparabl **e** mployment growth in OECD countries. Furthermore, more recent data, Table 6, complemen **t** he earlier estimates of high rates of government employment growth. Tha data suggest that rapid expansion of government employment has beon maintained for many years in a significant number of low and lower-middle economies.

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and compensation for work accidents or occupational diseases. However, in many countries and also in Bhutan the enforcement of labor laws is relatively lax and some employers are able to avoid compliance (cf. Fandl, Kugler, Vodopivec and Weber, 2011, for details on compliance with Bhutan’s Labor and Employment Act). Against this backdrop, the BLFS 2014 collects data for regular paid employees, casual paid employees and contract / piece paid workers in state-owned or private enterprises, and data on their _de facto_ coverage by fringe benefits.

As monetary values of fringe benefits are hard to quantify, they will not be considered in the subsequent analysis of the public/private wage differential. However, Table 2 still lists the prevalence of these benefits separately for public and private sector workers. The table shows that coverage is incomplete both in the public and the private sector. At the same time, public sector workers are more likely to be eligible for each of the eight types of benefits and all differentials are both statistically significant and economically meaningful. In fact, for each of the eight benefits, coverage among public sector workers easily exceeds 90 percent. The benefit with the lowest prevalence among public sector workers is paid annual leave. 94.3 of public sector workers are covered by this benefit. On the other end of the spectrum, 96.5 percent of public sector workers receive compensation for work accidents or occupational diseases. In contrast, only between around 25 percent and 65 percent of private sector workers are covered by each particular benefit. Only 27.0 percent of private sector employees are paid for overtime. Paid sick leave is the most common benefit in the private sector. 64.5 percent of private sector workers receive this benefit. It seems safe to assume that differences between public and private sector would be even more pronounced if data on benefit coverage were also collected for workers other than regular paid employees, casual paid employees and contract / piece paid workers in stateowned or private enterprises.

# **4.
over the period 2012–2062, while a more “optimistic” state could achieve the same results but in a period ending between 2025 and 2030. Lakner, Negre, and Prydz (2014) acknowledge uncertainty in the speed of change for different percentiles of the income distribution. These authors use scenario analysis to test how meaningful the difference of growth rates between the income growth of the bottom 40 percent (B40) and the mean of the income distribution is for future world poverty attainment. Chapter 4 of World Bank (2014) simulates potential outcomes for income growth and its effects on the poverty headcount under a variety of economic growth contexts and distributional considerations highlighting significant uncertainty in future income poverty attainments. More recently, World Bank (2018) discusses poverty projections up to 2030 and tests its premises—of country future growth rates and the nature of this growth—to stress that there is some degree of uncertainty in poverty outcome attainments.

Our paper approximates growth rates in the mean income and the Gini coefficient by combining household—income and expenditure—survey data from 1980 to 2015 with macroeconomic information from 1970 to 2015 and using country typologies. The country classifications denote different sets of interconnected features—institutions—related to production methods, such as government and political systems, regulation, human capital formation, trade structure, and innovation, among others. In this paper, two embedded institutional characteristics—resource-output orientation and GDP per capita growth information—are exploited to derive future poverty and income distribution outcomes by country and country aggregations. Despite the arbitrary selection of the country typologies, these classifications aim to show that there is heterogeneity in poverty and income distribution changes beyond global aggregated outcomes. Our selected country typologies are two among several others that could be used to understand the commonalities of environments in which firms operate. However, to our knowledge, no other study considers these two specific types of country categorizations to explain future movements in poverty rates, inequality, or shared prosperity measures.

We denote resource-output and non-resource-output oriented economies as ROC
growth at very small entities and confirms Bickenbach et al. (2016)’s findings of parameter instability in the SOL approach.

The analysis showed that the strength of such simple models lies in capturing stable features on the ground that correlate with economic activity but that they are unable to capture changes over time. This does not rule out that more sophisticated models and more advanced signal extraction can further improve the value of Landsat or nightlight data in the time series. This is an active research area that is constantly expanding the understanding of remote sensing tools for measuring economic growth.

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_Why Infrastructure Financing Facilities Often Fall Short of Their Objectives_ _S_ hampered by regulatory restrictions (e. i. are institutional funds allowed to invest also in non-investment grade securities).

_9. Adequate Design Features of Facility._ The analysis presented also illustrates that the IFF's success was also adversely affected by micro-level issues relating to its design including inconsistent objectives, an inadequate institutional framework, and inadequate design parameters.

10. _Develop Clear and Consistent Objectives._ The cross country analysis suggests that public-sector counterparts should develop clear and consistent objectives on which to base the design of the facility as some objectives could work at cross purposes. For example, the Government may want to achieve such development objectives via the facility as expanding liquidity, lowering funding cost by offering credit enhancements, better allocating risk between the government and the private sector, developing the domestic capital market, improving the quality of service and increasing competition. However, governments will be unable to pursue all of these objectives at the same time. A better allocation of risk between the private and public sector may lead to higher risk exposure of the private sector, which in turn may reduce private resources flowing into infrastructure.<sup>4</sup> In addition, government should avoid mixing commercial and developmental objectives as instruments for the latter may feature required pricing at levels that may not allow for a market return. Moreover, to provide effective and efficient support to private participation in infrastructure on a transitional basis, the design parameters of the IFF should be consistent with the policy environment.

11. _Understand Investor Requirements._ As experience presented in the paper further reveals, to ensure that the design of the facility is offering instruments that addresses the impediments to private participation in infrastructure, the government may want to conduct a thorough demand survey. A demand or broader market survey may also provide guidance to the government on the following issues:

- to identify the nature and magnitude of the financing gap;

- the appropriateness of the fund as an instrument to support private investment
comparable to normal electricity costs.

While Tanzanian firms already face frequent electricity and water disruptions during the dry season, the survey shows that these are amplified in the rainy season. An average firm experiences power outages on about 2.6 days per month in the dry season, with this number nearly doubling to 5.1 days in the rainy season, when outages also last longer. While the mean outage duration is about 5.4 hours in the dry season, these increase to around 11.2 hours in the rainy season. Taking both effects into account and correcting for outliers, an average firm experiences around 17.8 total hours of power outage each month in the dry season, rising to 57 hours in the rainy season.

The lack of reliable and resilient infrastructure systems causes economic efficiency losses. A global study by Rentschler et al (2019) highlights the substantial drag that unreliable infrastructure imposes on firms in developing countries. In Tanzania, firms are incurring estimated utilization losses of nearly $670 million a year (1.8 percent of national GDP) from power and water outages and transport disruptions. The firm survey collected for this study allows us to estimate the share of these utilization losses that are caused by natural shocks. Power disruptions alone are responsible for $216 million in utilization losses a year, of which 47 percent ($101 million, or 0.3 percent of GDP) are solely due to power outages caused by rain and floods. The remaining 53 percent are due to baseline power outages associated with causes other than rain and flooding (such as load shedding or equipment failure). For transport disruptions, about 46 percent of utilization losses ($150 million, or 0.4 percent of GDP) are due to disruption caused by rain and floods. The survey does not find that rain and floods have any significant impact on the incidence of water supply disruptions.

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more likely to spend their money on travel, and when they do, they are more likely to explore new destinations, provided those are safe.

Lack of appropriate data has been an issue to establish causal relationships relating to this, but the recent trend in changing legal environment can help to establish this relationship. Using a data set on the LBGT environment prepared by the World Bank, we explore the effect of changing the legislation with respect to criminalization of LGBT using a difference-in-differences (d-in-d) approach. This paper, in particular, uses a dummy variable that takes the value 1 if the country does not criminalize LGBT and 0 otherwise, and it matches the data in Table 1 of Cortez and Arzinos (2019) to our sample data, resulting in 15 countries that appear in both samples. Since there are some countries that changed their legal attitude towards LGBT (e.g., STP does not criminalize LGBT since 2012), it can explore if the change in legislation is associated with an effect on tourism arrivals. The identification analysis, as it is common in difference-in-difference, is based on the assumption that controlling for observable characteristics, the timing of the implementation of the change in LGBT legislation is not related to other tourism related policy.

Positive attitude towards LGBT population, as measured by not criminalizing LGBT, positively impacts tourist arrivals. Table 6 presents the d-in-d econometric estimators for log of tourism arrivals (columns (1) and (2)) and log of expenditures per arrivals (columns (3) and (4)). Column (1) provides a point estimate of 0.431, statistically significant at 5 percent level, implying that removing the criminalization of LGBT would increase tourism arrivals by 43 percent. Including additional 12
Vignette 4 assesses knowledge on identification and treatment of malnutrition among children. The clinical history included asking the mother and child’s name, the age of the child, chief complaint, assessing history of vomiting, convulsions, difficulty breathing, diarrhea, and inquiring about feeding frequency, food variety, vitamin A supplement, micronutrient supplementation, and deworming. The recommended set of physician examinations for the standardized case included measurement of weight, height and mid-upper arm circumference, comparison against growth charts, breastfeeding/drinking, temperature, lethargy, breathing rate, respiratory distress, wheezing, edema on feet, skin pallor, feeding, and running blood tests and parasite screens.

Vignettes 5 and 6 presented an adult standardized patient which tested provider ability to correctly assess high blood pressure and calculate cardiovascular risk. Clinical history included asking the patient’s age, more detail on the chief complaint, symptom onset period, history of nausea, vomiting, oliguria, visual aura during headache, chest pain; inquire about the patient’s smoking status, alcohol intake, family history of heart disease and stroke, diabetes status, lifestyle and physical activity, food intake, weight change, and whether the patient is currently taking antihypertensives or other medications. The physical examination items included the measurement of vitals (blood pressure, weight and height, pulse), and ordering of urine (specifically creatinine ratio and hematuria) and blood tests (specifically for plasma glucose, electrolytes, creatinine, filtration rate, cholesterol) and an electrocardiogram. The provider was then given the necessary information to calculate the cardiovascular risk. The items for Vignettes 5 and 6 were nearly identical; Vignette 6 did not include a clinical history item asking about weight gain.

# Wealth Index

The wealth index estimates the living standard of the household or patients, relative to the living standard of the other households in the sample. In this study, the wealth index for households was calculated by an index using self-reported data including consumer item ownership, amount and value of owned land, the value
# **Table 12. The Short- and Long-Term Stock Market Reactions**

The sample consists of all nonfinancial listed firms in the CSMAR database from 2008 to 2017.This table presents the result of the regression analysis of the stock market reaction in the short and long run. The cumulative abnormal returns in the next two trading days are used to measure the short-term stock market reaction. The increase of Tobin’s Q in the next 5 years after the event net of its 2 years growth before the event was used to measure the long-term stock market reaction. _Titling Firm_ is a dummy variable that is 1 if the firm has allocated land before the land title scheme starting from 2012 and 0 if otherwise. _SOE_ is a dummy variable that is 1 if the firm is affiliated to the government and 0 if otherwise. All variables are winsorised at the upper and lower 1-percentile. Industry FE denotes whether the industry of the listed firms (categorized according to industry categorization from CSMAR database) is considered in the specification. Exchange FE denotes whether the effect of exchange (Shanghai or Shenzhen Exchange) in which the firms are listed is considered in the specification. The _t-statistics_ are computed using standard errors that are White-corrected. The *,**,*** indicates significance at the 1%, 5% and 10% level respectively.

|||Stock Market Re<br>|action [0,2]<br>|||Δ Tobin's Q<br>|[-2 Year, 5 Yea<br>|r]<br>|
|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Titling Firm|0.076***
|156|1.70|118|
|6760|Rails and railway track construction|2,179|146|1.57|92|
|6781|Tubes andpipes,of cast iron|2,032|144|5.18|109|

Source: Based on EU Data from UN COMTRADE Statistics.

Among products with the compound annual growth rate of exports over 2000-03 exceeding 30 percent, electro-engineering industry (SITC. 76 and 77) clearly stands out. Its share in these exports grew from 6 percent in 1997 to 12 percent in 2001 and 14 percent in 2003. More significantly, while in 1996-2000 exports of electro-engineering products had merely kept pace with the growth in EU import demand, with their aggregate share fluctuating between 0.08 percent and 0.09 percent; Bulgarian firms outperformed other exporters each year in 2001-2003. The share of these products in EU external imports increased from 0.08 percent in 2000 to 0.16 percent in 2001, 0.26 percent in 2002 and 0.39 in 2003.<sup>12</sup> The latter value was above Bulgaria’s 12 In consequence, the share of Bulgaria in EU external imports of machinery increased from 0.09 percent in 2001, which equaled the peak level reached in 1996, to 0.15 percent in 2002 and 0.17 percent.
less corruption, etc. (see for example, Acemoglu et al. 2001, Dollar et al. 2005, Hall and Jones 1999, Schmitz Jr. 2005, Syverson 2011). Following the broader literature on institutions and governance quality, we use two separate proxy measures to capture their impact on firm productivity. For the quality of governance, we use the _Corruption_ measure as defined above. The data source for the variable is Transparency International. The variable ranges between 0 and 100, with higher values implying less corruption. For the quality of institutions, we use the _Law and Order_ Index from ICRG. The variable ranges between 1 and 7 with higher values implying better rule of law (from the firm’s point of view).

Regression results in both Table 3 for the pooled sample and Table 4 for UMICs and HICs separately, uncover several factors highly correlated with labor productivity. In the pooled sample and irrespective of the controls in place, labor productivity is lower in UMICs than in the HICs, and the difference is significant at the 1 percent level (columns 1-7, Table 3). This productivity gap is quantitatively large, although it reduces when we include the various determinants of labor productivity described above in the regressions. Specifically, the labor productivity (in USD terms) is approximately 57.5% lower in UMICs when we do not add any additional controls (column 1, Table 3) compared with 32%, our most conservative estimate so far, when all the controls are included in the regressions (column 7, Table 3).

Second, there is a negative and significant relationship between labor productivity and firm-size for the pooled sample, and for the sub-samples of UMICs and HICs. This result holds irrespective of the controls in place (see Table 3 and 4). For instance, a 1 percent increase in the number of workers at the firm is associated with a decrease in labor productivity by 0.29 percent (table 3, column 7). These results support the