rafmacalaba/data-use-mentions-extended huggingface dataset:
popular in the U.S.

A key issue is the modeling of revenue risk. Joint distributions and accounting for correlation between two random variables must be addressed. Precise measures of yield risk (the yield density) and price risk (the price density) are first needed. However, the measure of these marginal densities is still incomplete for capturing revenue risk, as yields and prices are rarely independent. To the extent that yields are spatially correlated, yield shortfalls on an individual farm are likely to be correlated with higher prices, meaning that yields and prices tend to be negatively correlated. Any consideration of revenue risk that utilizes measures of price and yield risk must also consider the degree of correlation between yields and prices. In the case of a negative correlation between yields and prices, a “natural hedge” exists such that revenue shortfalls associated with low yields are naturally offset by higher prices. This implies lower risk and premiums for revenue coverage than would be obtained from independently insuring price and yield. Measurement of correlation may not be straightforward since annual data are used (since contracts are annual) and thus data may be limited. Correlation is also location-specific. The empirical evidence suggests that price and yield will be much more highly correlated in major growing regions than is the case as one moves outside of such regions. The U.S. Revenue Assurance plan adopted an innovative parametric assumption when modeling revenue risks. Insurance rates for a particular level of coverage from the standard yield crop insurance program are used as a base measure of risks. A beta distribution is assumed to be appropriate for yields. Using assumptions about the minimum and maximum possible yields, the parameters of the beta function are first estimated. This

> 12 In 2003, revenue insurance products represented about 60% of total crop insurance premium volume and about 55% of total acres insureds (RMA 2004).

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, destruction of physical assets diminishes borrowers’ wealth, affecting banks’ ability to fully recover the value of a loan in case of bankruptcy (wealth effect). On the other hand, government intervention, the availability of insurance, financial support by banks or alternative funding sources can keep borrowers afloat, financing the recovery in the aftermath of a natural disaster (Baltas et al., 2021). Therefore, whether natural disasters have a negative impact on banks’ asset quality is an empirical question that we address using aggregate data on bank non-performing loans. We also explore whether the relationship varies depending on economic development, as less developed economies may have fewer resources and shallower financial markets to manage the losses from extreme weather events (Loayza et al., 2012). Finally, we summarize emerging policy recommendations for financial authorities to monitor, assess and manage climate and environmental related physical risks to the banking sector.

Climate and environmentally related financial risks originate mainly from two types of sources. **Physical risks** derive from natural disasters and gradual permanent shifts of the climate that can lead to economic costs and financial losses. These risks directly affect banks, potentially increasing credit, operational, market, liquidity, operational, and reputational risks, threatening the profitability and solvency of banks and the overall stability of the financial system. They can either be gradual in nature, such as rising temperatures and sea levels; or abrupt, as in the case of extreme weather events such as wild fires and storms. **Transition risks** are related to adjustment 2
Economies in Fujian,” which covers the period from 1952 to 1998 for all 59 counties in Fujian. We use this data set to construct the average annual real growth rate of gross value of output for agriculture and industries, separately for 1952-1998 (the whole sample period) and for 1978-1998 (the post-reform period).

For outcome measures related to the Great China Famine (1959-1961), we construct two measures. The first measure, which we refer to as _famine control,_ following Meng, Qian and Yared (2015), is defined as the ratio of the number of surviving births (per year) in the county during the period 1959-1961 relative to the number of surviving births (per year) in the same county during the period 1954-1957, as observed in the 1% sample of the 1990 China Population Census [see formula (24)]. The higher this ratio, the more successful the county was at controlling the famine.

We also examine the county-level data rates (deaths per thousand) during the Great China Famine. The county-level number of deaths are mainly collected from the population statistical books published by the provincial Statistics Bureaus in the 1980s.10

**Scope of Study.** Our analysis is restricted to the 59 _counties_ or _county-level cities_ in Fujian province. In the administrative system in China, there are also _prefecture_ level cities (such as Fuzhou, Xiamen, and Zhangzhou). Prefecture-level cities are often treated differently in the statistical yearbooks, with many of the key economic indicators only collected at the prefecture level (instead of the district level within the prefecture, which would be more comparable to counties or county-level cities).

We also restrict our analysis up to 1998 when the Chinese government initiated extensive redrawing of the county boundaries, especially because the housing reform drastically blurred the rural-urban boundaries.

# **4.2 Descriptive Statistics**

Among the 59 counties (shorthand for counties, or county-level cities) in our study, 11 counties were led by cadres affiliated with the FA3
**The EU-LFS data could not be used for this study as it only has information on the wage distribution in deciles rather than specific point estimates.** This poses a challenge when analyzing the data, as point estimates are often needed to estimate labor income accurately. To overcome this issue, one option is to use imputation techniques to fill in the missing values. Specifically, we could use the EUSILC data, which provides detailed demographic characteristics and income information, to impute the missing point estimates on the LFS data. This can help provide a more accurate representation of the wage distribution and enable a more nuanced analysis. However, it is important to carefully consider the imputation process's potential biases and limitations and evaluate the quality of the resulting estimates, especially when several data sources (including administrative tax data) need to be imputed.

**In this study, we match income data from the 2020 tax administrative dataset with the 2021 EU-SILC (reference income year 2020) using data matching techniques; then, we assess the degree of tax compliance and possible overestimation of the share of minimum wage earners in the tax data.** Following the literature, we assume that the "true"  income is reported in the survey, while "reported" income is reported to the tax authority. After imputing tax income into the EU-SILC, we assess the degree of tax evasion and the impact of tax evasion on the share of minimum wage earners.

**Before matching the administrative tax data and the EU-SILC datasets, it is essential to carefully** We must evaluate the **evaluate the comparability of the datasets before conducting any analysis.** comparability of the two datasets regarding the target population and income. This involves determining whether the datasets are compatible and suitable for comparison. The extent of comparability determines the accuracy of the results and the conclusions drawn from the analysis. Therefore, it is important to ensure that the datasets are matched appropriately and that the analysis considers any differences in the target population or
|Mean|Std. Dev|.<br>p(10)|p(25)|p(50)|p(75)|p(90)|
|---|---|---|---|---|---|---|
|Te<br><br>|chnology <br>|Intensive<br>|||||
|High Technology<br>0.554<br>LowTechnology<br>0.531|0.142<br>0.152|0.389<br>0.326|0.446<br>0.417|0.565<br>0.535|0.670<br>0.649|0.718<br>0.718|
|<br><br>Kn<br><br>|owledge <br>|Intensive<br>|||||
|Knowledge Intensive<br>0.546|0.141|0.364|0.462|0.569|0.651|0.713|
|Not-Knowledge Intensive | 0.507|0.154|0.269|0.424|0.515|0.621|0.694|
|Notes: ‘Management Score’ is calculated follow|ing MOPS|(See Bloom|et al.,|2018). ‘H|igh Techn|ology’ and ‘Low|
|Technology’ sectors are as defined by EUROST<br>*No-KnowledgeIntensive’servicesareas|AT indicat<br>definedby|ors based on <br>EUROSTAT|NACE<br>indica|Rev. 2. <br>torsbase|‘Knowled<br>don<br>NA|ge Intensive’ and<br>CERey.<br>2.|

||Mean|Std. Dev.|p
>0<br>2013 2014 2015 2016 2017 2018<br><!-- End of picture text --> Source: International Monetary Fund, World Bank.

A. GDP-weighted average across 32 low-income countries. “Bilateral” includes public and publicly guaranteed (PPG) loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. “Multilateral” includes PPG loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. It excludes loans from funds administered by an international organization on behalf of a single donor government. “Bonds” include PPG bonds that are either publicly issued or privately placed. “Commercial” includes PPG debt from commercial bank loans from private banks and other private financial institutions, as well as export and supplier credits.

B.C. Dashed blue lines denote the interquartile range, while solid blue line is the median. Includes 30 low-income countries and excludes Somalia, South Sudan, and Syria due to data restrictions.

D. Figure shows the percent of low-income countries eligible to access the IMF’s concessional lending facilities that are either at high risk of, or in, debt distress. A country is considered to be in debt distress if it is experiencing difficulties in servicing its debt, as evidenced, for example, by the existence of arrears, ongoing or impending debt restructuring, or if there are indications that a future debt distress event is probable. The sample includes 30 low-income countries.

5
Table A.13: Innovation Capacity and Post-Sanction Innovation Performance

||(1)<br>**Patent A**<br>Total|(2)<br>**pplication **<br>Intl.|(3)<br> **(Intensive Margin)**<br>High-Tech|
|---|---|---|---|
|Treatment_×_Post|-0.237***|-0.260**|-0.214***|
||(0.042)|(0.107)|(0.064)|
|Treatment_×_Post_×_Top|0.260***|0.033|0.083***|
||(0.039)|(0.070)|(0.027)|
|No. Obs|5,442|2,040|3,690|
|R-squared|0.653|0.515|0.610|
|Firm FE|Yes|Yes|Yes|
|Sector-year FE|Yes|Yes|Yes|

Note: We estimate the coefficients using OLS on a set of patenting firms. Robust standard errors clustered at the 2-digit ISIC sector level are in parentheses. ***, **, and * indicate significance at levels 1 percent, 5 percent, and 10 percent, respectively.

Table A.14: Knowledge Distance and Post-Sanction Innovation Performance

||(1)<br>**Patent **|(2)<br>**Application **|(3)<br> **(Intensive **|(4)<br> **Margin)**|
|---|---|---|---|---|
|Treatment_×_Post|-0.851***<br>(0.
Demographic and Health Surveys, which have collected nationally representative data on domestic violence for a variety of countries, state that they follow a modified version of the WHO guidelines (Kishor and Johnson, 2004). However, given the extremely large samples involved (ranging from 2,000 to 90,000), it seems fair to ask if such guidelines can be effectively implemented at reasonable cost.

# _What Does Privacy Mean?_

Beyond the difficulty of conducting private interviews illustrated with the challenging example of studying domestic violence there are more general issues of privacy to consider. For example, once data is collected the researcher often has limited say in regards to the subsequent dissemination of the data. Indeed, while the ethical rationale for privacy is readily apparent, there are also ethical considerations that encourage wide data access. For instance, students, particularly in low income countries, generally have limited access to research grants and other funds that could be used to collect data that will further their research. From this perspective broad access is less extractive than exclusive rights to use a data set granted to a single research team, often from outside the countries where the data has been generated. Moreover, as field research is not as easily replicated as are clinical studies, public posting of data provides a check against inadvertent research errors or excessive data manipulation. Thus, it is common policy that funders of research require that the data collected as part of the project be made publically available within a specified period.<sup>5</sup>

> 5 The research department at the World Bank has such a policy.  Additionally, the World Bank strongly encourages governmental statistical offices to make data collected using instruments it has designed, such as the Living Standards Measurement Survey, publically available.  It often provides a draft letter of data protocol for the country statistical director as part of technical assistance.  However, the ultimate decision on data access to such surveys remains within the statistical office and is governed by local laws and the preference of the government
|
|Logs,Period4|0.320 (0.056)|0.618|(0.043)|0.381 (0.021)|0.285(0.033)|
|Log difference, period 2-1|0.751 (0.098)|0.797|(0.094)|0.682 (0.051)|0.534(0.084)|
|Log difference, period 3-2|0.746 (0.413)|0.869|(0.181)|0.710 (0.039)|0.546(0.073)|
|Log difference, period 4-3|0.779 (0.111)|0.699|(0.190)|0.728 (0.046)|0.42(0.94)|

Note: All measures are for equivalent adults. The equivalence scale is household size to the power of 0.75 (e=0.75). Data consist of balanced panels, 1993-1996 for Poland and 1994-1998 for Russia.

# 4. **Underlying Inequality**

It is common to measure inequality in living standards by inequality in income or expenditure across individuals in a given month. However, income or expenditure in a given month is only an imprecise measure of the living standard of a household. Inco:me or expenditure could be misreported or could reflect transitory events. Whenever measured income (or expenditure) is an imperfect indicator of true living standards, the inequality in measured incomes or expenditures will exceed the inequality in underlyi:ng

> 6 We find the same pattern of measurement in food expenditure, though measurement error in food expenditure tends to be higher than that in total expenditure. See appendix table Al for details.

15
to higher chances that innovation outcomes are successful in the second equation may also (positively) affect productivity outcomes in the third equation (simultaneity).

There have been two main approaches in the literature on how to solve the model. Crepon et al. (1998) suggest solving the model simultaneously using Asymptotic Least Squares.<sup>10</sup> Meanwhile, Griffith et al. (2006) assume that there are no feedback effects and solve the model sequentially, instrumenting knowledge activities in the innovation equation, and innovation outputs in the productivity equation, to avoid endogeneity.

In the original CDM model, the authors solved the model simultaneously using Asymptotic Least Squares (ALS), a minimum distance estimator.<sup>11</sup> This approach is advantageous when the joint distribution of observed variables does not have a closed form and when numerical integration techniques are computationally burdensome. Consistent estimates are first obtained in each equation via maximum likelihood, and then the structural parameters are obtained by minimizing their distance to the reduced form estimates using the empirical covariance matrix weighting function.

Most subsequent papers have followed a sequential approach, where predicted values of one endogenous variable are entered in the estimations of the other equations, correcting for standard errors by bootstrapping methods and using IV in each stage to account for endogeneity, see Griffith, Huergo, Mairesse, and Peters (2006). One benefit to this approach lies in its ability to take into account measurement issues arising in the nature of survey data. For example, firms that do not report undertaking R&D may in fact still possess knowledge outputs. However, one drawback from this approach is that it does not allow to correlation across the different stages that are captured using a structural methodology. Furthermore, standard errors may be underestimated in the model’s last stage.

Recent advances in numerical simulation techniques have made possible solving of the model by full information maximum likelihood with given distributions for the random part of the model. This approach is able to fully exploit
to better understand the characteristics of Haiti’s labor markets from the household and individual perspective, a current gap in the literature.

This paper has two main sections. First, it provides an overview of labor market development in Haiti during the last decade thanks to an unprecedented harmonization of three different surveys, including the latest 2012 ECVMAS household survey. This first section presents the main trends in employment and participation in Haiti and explores more in-depth the characteristics of non-farm jobs in Haiti. In the paper’s second part, the analysis focuses on the determinants of labor income in the country.

## **Box 1: Of data and surveys in Haiti**

This paper relies on data from three surveys conducted in Haiti between 2001 and 2012: the 2001 _Enquête des Conditions de Vie des Menages (ECVH),_ or Survey on Households’ Living Conditions; the 2007 _Enquête sur L`Emploi et l`Economie Informelle (EEEI),_ or Survey on Employment and Informal Economy; and the most recent _Enquête sur les conditions de Vie des Ménages Aprés Seisme (ECVMAS),_ or Survey on Households’ Living Conditions after the Earthquake.

The 2001 ECVH is a household survey covering 7,186 households and including modules on health, mortality, migration, education, labor, and income. ECVH income data have been previously used to estimate poverty rates in Haiti (Sletten and Egset, 2004) and are, to some degree, comparable to ECVMAS’ income data. The 2007 EEEI is a labor survey comprising 6,620 households with some information at the household level and more detailed data on labor relations, as well as labor and nonlabor income. Finally, the 2012 ECVMAS is a household survey including both income and expenditure information for 4,930 households, detailed data at the household and individual level, and a labor module that encompasses separate sections on agriculture and non-agriculture enterprises for company owners and self-employed workers.

It should be noted that the three surveys do not use an exactly similar questionnaire,
# **Acknowledgments**

This paper draws upon a wide range of contributions from sector specialists on the AICD Team: notably, Dick Bullock on railways, Mike Mundy on ports, Heinrich Bofinger on air transport, Maria Shkaratan on power, Elvira Morella on water and sanitation, Michael Minges on information and communication technologies, Nataliya Pushak on public expenditure, and Alvaro Federico Barra on spatial analysis.

The paper is based on data collected by local consultants, and benefited greatly from feedback provided by colleagues in the relevant World Bank country teams; notably Habib Fetini (country director), Manuela Francisco (country economist), and Kavita Sethi (transport).
|

_Source:_ OPSM, May 2002 3.7 Nonetheless, these data limitations in practice do not materially circumscribe the conclusions of this analysis. In reality, expenditure data were only used to evaluate the efficacy of existing social policies in the Metropolitan Area (for the cases of water, sewerage, electricity, and public transport) and Patagonia (for the case of natural gas). The analysis of social policies for water and electricity in the provinces is based solely on the socioeconomic characteristics of the households, without reference to expenditure levels. Furthermore, expenditure data were not used at all in the simulations of alternative social policies, which are based entirely on normative criteria for subsistence consumption, or depend solely on the socioeconomic characteristics of the household.

# **ANALYTICAL TOOLS**

3.8 Using data from the 2002 survey, it is possible to identify the beneficiaries of current social policy mechanisms in the infrastructure sectors, and evaluate how well targeted they are. This is done by examining the extent to which they reach households that live under the official poverty (and extreme poverty) lines, which currently stand at US$65.7 (and US$27.7) per adult equivalent per month. In a similar fashion, it is possible to identify the impact of applying alternative social policy instruments, and examine their pattern of incidence.

3.9 The evaluation of the targeting properties of current and potential, future social policy instruments are based on a number of standard indicators adapted from the income 14
these, 151 schools with a participation rate above 100% were also excluded, leaving 11,355 schools at the base in both years.

Based on the School Census data, two school-level variables were constructed to characterize teachers’ education for each field or discipline (math and Portuguese): (i) _Percentage of classes taught by teacher with higher education_ , and (ii) _Percentage of classes taught by teacher with higher education in the field_ . The calculation was restricted to the schools with at least three upper secondary school classes for each discipline, so that the indicators reflect at least one class for each series of this teaching stage.<sup>12</sup>

> 10 Harris and Sass (2011) investigate the effects of the specific areas of higher education of teachers ( _Education Major, Math Education Major, English Education Major, Math Major_ e _English Major_ ) so as to take advantage of the greater variability of these variables to try to capture some effects that are statistically significant on student performance.

> 11 ENEM incorporate the Item Response Theory for scores calculation in 2009, when a proficiency scale with an average of 500 points and standard deviation of 100 points was defined, which allowed comparing results of different editions of the exam. Moreover, since this year students’ performance in ENEM has become the main access route to public university, which has significantly enlarged the number participants. The year of 2015 was the last year in which microdata are available. 12 Resulted in 11,336 and 10,857 schools with, respectively, at least three high school math and Portuguese classes in 2009, and in 11,317 and 11,323 schools with, respectively, at least three high school math and Portuguese classes in 2015.

8