rafmacalaba/data-use-mentions-extended huggingface dataset:
In line with worldwide AD activities, the chemical and iron-steel industries dominate the filings in terms of the number of cases.<sup>5</sup> The remainder of the paper is organized as follows. The second section describes the data, the third section presents the empirical methodology and results, and the fourth section concludes.

# **_<<A>> DATA_**

We employed detailed export data from the Brazilian customs office, SECEX _(Secretaria de Comércio Exterior_ ), which gathers export reports using product codes at the plant, month, and NCM ( _Nomenclatura Comum do Mercosul_ ) levels. The NCM codes are eight-digit numbers, the first six digits of which coincide with the first six digits in the HS. The destination information is transformed from the Brazilian country codes into the international ISO system. The product codes at the six-digit level in the Brazilian data, for which no corresponding HS entries exist, were removed from the data. All export values in the SECEX data are reported in current U.S. dollars (USD), free on board (fob). We used observations on exporting plants, declared export values, export destinations and export quantities, and we aggregated monthly plant-level export information to years and firms. We focused solely on manufactured products. The main variable of interest in our analysis is the unit price of the exported good _p_ , shipped by firm _f_ to destination _d,_ which is modeled as follows: ufpd =<sup>Export valuefpd</sup> . Quantityfpd 6
modifies local rainfall and temperature averages. Hence, the resulting projections from the various global climate models were tailored to different regions in Mexico by Climate Change Knowledge Portal at a resolution of 50 x 50 km approx. Baseline data on daily temperature and precipitation employed for the projections cover the period from 1950 to 2000, and projections for average daily temperature and precipitation are for 2030 âĂŞ 2039.

To determine how greenhouse emission will affect changes in precipitation and temperature, climatic models need to be combined with emission models that predict the amount of manmade greenhouse emissions<sup>8</sup> . The Third IPCC Assessment Report accepts several emission models. We choose A2 which assumes a business as usual scenario (i.e., same trend changes in economic growth, use of fossil fuels and population growth).

Annual temperatures are expected to rise between 0.49 and 2.46 degrees Celsius in 2030-2039, compared to the historic average from 1950-2000. The largest increases in temperature are expected in coastal areas, including the Yucatan Peninsula and the southern region of the Gulf of Mexico, comprising the states of Quintana Roo, Yucatan, Tabasco, Campeche and Veracruz. In the Pacific, the states of Sonora, Sinaloa, Nayarit, Guerrero and central Jalisco exhibit the greater increases in temperature. Central Mexico appears less warm than coastal regions; although some desertic areas in Hidalgo and Queretaro display temperature increases (see Fig. 1).

Annual rainfall will change between -169 mm and +57mm. However, as Fig. 2 shows, almost the entire country will be facing a drier world by 2030-39. Desertic areas in states like Chihuahua, Nuevo Leon, Zacatecas and San Luis will become drier, as will the states of Chiapas, Tabasco, Veracruz and Puebla.

# **2.4 Poverty**

The integration of income and consumption data from household surveys with census data has enabled the creation of municipal poverty maps in Mexico<sup>9</sup> . We rely on such information for 2000
a comprehensive picture of financial flows to the electricity sector.

By integrating off-budget data sources, a more complete picture can be provided. Over the period 20102018, public expenditure for the reporting 48 countries averages 0.35 percent of the GDP with a clear rising trend (Figure 23a). The private sector contributed 0.21 percent of GDP towards expenditure, while OECD donors contributed just 0.05 percent of GDP in the power sector during the same period (Figure 23b, c).

25
|

_Note: Results are based on repeated cross-sectional labor force survey data that are merged with industry level trade data. Industry fixed effects are at two-digit ISIC Rev 4 level. Worker’s control includes age, age squared, gender, marital status and 8 groups of educations in column (1). In column (2) and (3) gender is excluded while in column (4) and (5) education is not included as these regression are gender and skill based, respectively. Standard errors clustered at industry level. t-statistics in parentheses. p < .10, ** p < .05, *** p < .01_ 29
**Table 8. The share of minimum wage earners in survey data, imputed survey data, and tax data (only full-time employees).**

||**EU-SILC**|**Imputed EU-SILC**|**Tax data (July 2020)**|
|---|---|---|---|
|**Less than 95% of MW**|5.11%|5.44%|2.7%|
|**95 – 105% of MW**|6.84%|17.6%|24.7%|
|**More than 105% of**||||
|**MW**|88.06%|76.96%|72.6%|

_Source: Own estimation based on the imputed EU-SILC data._

# **IV. Conclusion**

**This paper focuses on assessing tax compliance and underreporting of income in Romania, with a specific emphasis on minimum wage workers.** The study aims to understand the distributional implications of tax compliance and its links with minimum wage policy and design. The paper highlights that tax evasion has significant redistributive impacts, affecting the incidence and distribution of taxes. Reducing tax evasion is crucial for ensuring the effectiveness of the tax system and promoting greater income equality. The study also emphasizes the links between tax evasion and social policy, particularly regarding the distribution of social benefits. Tax evasion can undermine the progressivity of the tax system, leading to fiscal losses and reduced social welfare for eligible recipients. The research also explores the interaction between labor tax evasion and minimum wage policy, emphasizing that payroll tax evaders may be overrepresented among minimum wage earners. This raises concerns about the effectiveness of minimum wage policy and the need to address tax compliance issues among this demographic. The study underscores the importance of addressing tax compliance, evasion, and transparency issues to enhance fiscal position and generate
public awareness of the value of daughters. Progress under this project is a key government priority, as highlighted by the fact that it is part of the local family planning officials’ work evaluations. Another example of an education-centered benefits scheme is the Spring Buds program that allocates money for girls in rural families to continue their education.

There are no rigorous evaluations of the causal impact of these benefits programs on SRB, female child survival, and son preference. It is difficult to isolate the cash transfers’ effect from the other programs bundled into the “Care for Girls” effort. However, there are some criticisms of the program that need to be noted. The criticisms of the program draw on observational data, key informant interviews at various administrative levels, government policy documents, and other policy reviews.

First, while the funding for most “Care for Girls” efforts come from central, provincial, and local authorities, the funding for the benefits program comes solely from local authorities. This makes the liquidity of the program contingent on local economic development; poorer counties are less likely to implement such schemes. The design of the programs is also determined at the county level. The effectiveness of these schemes in changing child sex preferences is contingent on how local authorities prioritize and manage the issue (Li 2007). For example, in some counties, local businesses are obliged to donate to the “Care for Girls” foundation fund, and this fund is used to cover the benefits provided to girl-only families (Murphy 2014). Whether local businesses are required to participate in these efforts, and if so, how much they donate and how they are made to comply with the requirements for donations, will vary across counties.

Second, the program is focused on girl-only families. The program effectively leaves out girls with a male sibling, who are arguably vulnerable in their own way, given that they must face tough competition for parental resources. Take the case of the Spring Buds program. This program, initially managed by the
using both SIPRI and COMTRADE data. The lower bound estimate suggests that in the five years after a giant discovery, arms imports are 25% larger (asinh(0.258)). The arms share of GDP increases by 30%. The arms share of imports also increases, by 17%, though the effect is not statistically significant here. This may be because in some countries total imports increase as well, as the demand for say manufacturing goods increases.<sup>4</sup> Yet the large coefficient suggests that the increase in arms imports is larger than that of total imports on average.

In Table 2 I show the results of estimating the same regressions but on a restricted sample of countries, i.e. those countries which have experienced at least one giant discovery over the period of study. The idea here is that discovery countries in non-post-discovery years provide a more conservative counterfactual than countries without giant discoveries or even without oil exploration, which may be different from discovery countries in terms of arms imports behavior in general. This robustness check confirms the baseline results and even suggests slightly larger effects. The lower bound estimate here suggests that arms imports increase by 31% in the five years following a giant discovery. The coefficient on the arms share of imports is now statistically significant, and suggests that the arms share of total imports increases by around 21%. The effect on arms imports as a share of GDP remains stable around 30%.

In Figure 6 I run the baseline regression of the odd columns in Table 1 but using different post-discovery treatment periods. More precisely, I estimate six different regressions where _Dit_ is a dummy equal to 1 in the year of the discovery only or also in the one, two, three, four or five subsequent years. While I find positive and significant effects across these different specifications, the 5-year period captures the largest effect. This is in line with arms imports occurring not necessarily right after but in any of the post-discovery years, before extraction
We identify structural shocks following broadly the identification approach proposed by Blanchard and Perotti (2002). This methodology relies on the assumption that, due to the institutional and economic constraints, governments cannot react to changes in economic activity within the same period. Originally, this approach was applied to advanced economies with quarterly data. Due to limited data availability, we have to implement it with annual data. While stronger assumptions are needed for the results to be valid in this case (Beetsma et al. 2014), there are also advantages from using annual data. First, shocks from annual data are more likely to capture actual fiscal shocks, since government spending decisions usually follow an annual cycle and new fiscal impulses typically do not appear at quarterly frequency (Beetsma et al. 2008, Beetsma and Giuliodori 2011). This is especially true for many developing countries with larger lags in implementing fiscal policy (Diop and Ben Abdallah 2009). In addition, anticipation effects are usually present in VAR models estimated with quarterly data (Ramey 2011). With annual data, the role of anticipation effects is less relevant as a given shock is less likely to be anticipated one year before. Third, with annual data there are no seasonality effects, which can be important in many developing countries. Beetsma et al. (2006) and Beetsma et al. (2009) compare the results from quarterly and annual data for several countries for which non-interpolated quarterly fiscal data are available and show that recursive identification restrictions for spending shocks in a VAR with annual data delivers plausible results. Born and Müller (2012) provide further evidence that the identification assumptions are not too restrictive for annual data.

The identification of the system is achieved by introducing restrictions that can be divided into four segments. First, we assume that there are no contemporary responses of discretionary measures of policy makers in response to each other (a��= a��= 0) and the previous matrix is hence equivalent to the following system of equations: The first equation
# **Appendix 2: The Survey Instrument**

# **Survey of Detailed Skills**

**2021**

# **INTRODUCTION**

**FOR JOB INCUMBENTS** :  The survey is being conducted by the General Statistics Office on behalf of the World Bank. The purpose of this questionnaire is to create an accurate description of your job as a [ **OCCUPATION TITLE** ] for a project that the World Bank is undertaking about working in Vietnam.

Because you know your duties, responsibilities, and skills better than anyone else, we need your help to create this description. The questionnaire only asks what your job requires you to do. The questionnaire does not ask about your job performance.

Please complete this questionnaire as honestly, completely, and accurately as you can. Base your answers on what you normally do in your current job, not on special projects or temporary assignments unless they are a regular part of the job. When answering the questions, imagine you are describing what you do to a neighbor, friend, or someone just hired for your position.

Your responses are anonymous. All responses will be kept confidential. If you have any questions or comments, please contact our survey information center at XXXX. We appreciate your active participation in this important study.

**FOR OCCUPATION EXPERTS** :  The survey is being conducted by the General Statistics Office on behalf of the World Bank. The purpose of this questionnaire is to create an accurate description of work as a [ **OCCUPATION TITLE** ] for a project that the World Bank is undertaking about working in Vietnam.

Because you are familiar with the duties, responsibilities, and skills of a [ **OCCUPATION TITLE** ], we need your help to create this description.

Please complete this questionnaire as honestly, completely, and accurately as you can. Base your answers on what is normally done in this job, not on special projects or temporary assignments unless they are a regular part
|Carrying out the cost data-collection and<br>analysis is costly to the projects||3|5|8|
|---|---|---|---|---|
|Obtaining cost data can be challenging|2|4|1|7|
|Measuring the impact or benefit is the<br>evaluator'sfirst-orderconcern|2|3|1|6|
|Large institutions implement VFM in IEs<br>inconsistently across their programs||3|2|5|
|Institutional conflicts and interests stand in the<br>way of applying VFM to institutional decision-<br>making|2|1|1|4|
|The skills of VFM and IE experts differ and<br>there is not a lot of integration across projects<br>or within institutions|1|2|1|4|
|The importance of VFM will increase as more<br>large-scale randomized evaluations are carried<br>out||2||2|

_Source_ : Analysis of unstructured and semi-structured interview responses to the question: “Thinking about the field, generally, why do you think VFM is so infrequently incorporated into impact evaluations?” Even so, producers suggested that the political calculus differed greatly depending on the amount and on the quality of the evidence provided. Validating this point is the fact that impact evaluations have flourished at current levels of policymaker interest, and that quantitative analysis of the policymaker survey data<sup>7</sup> showed that their willingness to pay for causal information on benefits is not much higher than their willingness to pay for Cost information.

Analysis of the policymaker survey of 407 respondents in the study sample revealed that respondents are willing to pay about 10 percent more for an impact evaluation study as they are for a Cost study, but the observed difference is not statistically significant. Likewise, the average willingness to pay for VFM is also higher than willingness to pay to know
# **Invisible or Mainstream? Disability in Surveys and Censuses in Low- and Middle-Income Countries**

Sophie Mitra, Wei Chen, Justine Hervé, Sophia Pirozzi, Jaclyn Yap

# 1. **Introduction**

The Sustainable Development Goals (SDGs) endorsed by the United Nations member states have the mission to “leave no one behind.” Monitoring the SDGs is critical to achieving this goal. The Inclusive Data Charter signed by the World Bank and other global data partners in 2018 aims to mobilize political support in order to improve the quality, quantity, availability, and financing of disaggregated data to support the 2030 United Nations Agenda’s ambition of leaving no one behind. Article 31 of the United Nations Convention on the Rights of Persons with Disabilities **(** CRPD **)** provides that <mark>“States Parties undertake to collect appropriate information, including statistical and research data, to enable them to formulate and implement policies to give effect to the present Convention”</mark> (United Nations 2006 <mark>). In addition, it stipulates that “the information collected in accordance with this article shall be disaggregated, as appropriate, and used to help assess the implementation of States Parties' obligations under the present Convention and to identify and address the barriers faced by persons with disabilities in exercising their rights.”</mark> Achieving the SDGs and rights for persons with disabilities as per the CRPD requires quality and policy-relevant data on a range of outcomes that are collected and presented by disability status, based on concepts that are in line with a human rights approach to disability, and reflective of the diversity of people with disabilities and various aspects of their lives. This paper assesses the extent to which household surveys and censuses between 2009 and 2018 have included disability questions. It analyzes the types of disability questions implemented during this period, with the particular aim to identify surveys and censuses that can potentially be used to produce SDG or CRPD related indicators disaggregated based on disability
% of seat-km in aircraft of unknown age|40.2|16.4|3.6|0|19.9|13.0|26.7|

_Source:_ Bofinger, 2009. Derived from AICD air transport database downloadable from http://www.infrastructureafrica.org/aicd/tools/data — = data not available.

# Water supply and sanitation

## **Achievements**

Zambia already has relatively good access to high-end water and sanitation solutions (table 5). About 34 percent of Zambia’s population has access to utility water, whether from private taps or standposts; compared with only 24 percent in other resource-rich countries of Africa. Access to septic tanks, at 18 percent, is far ahead of peer countries.

As for trends in recent years, Zambia – along with other African countries – is moving fastest with intermediate options (figures 5 and 6). The real action in WSS has been in expanding access to wells or boreholes and traditional latrines, for an additional 1 percent of the population each year. By contrast, coverage of high-end solutions such as piped water and flush toilets has declined slightly in Zambia, while growth in standposts and improved latrines has been modest. This pattern is consistent with elsewhere in Sub-Saharan Africa.

12
. South African Journal of Economics, 87(3), 255-282.

- Chetty, R., Friedman, J. N., & Rockoff, J. E. (2014). Measuring the impacts of teachers II: Teacher value-added and student outcomes in adulthood. American Economic Review, 104(9), 2633-2679.

- <mark>Cilliers, J., Kasirye, I., Leaver, C., Serneels, P., & Zeitlin, A. (2018). Pay for locally monitored performance? A welfare analysis for teacher attendance in Ugandan primary schools.</mark> _<mark>Journal of Public Economics</mark>_ <mark>,</mark> _<mark>167</mark>_ <mark>, 69-90.</mark>

- <mark>Chudgar, A. (2015). Association between contract teachers and student learning in five Francophone African countries.</mark> _<mark>Comparative Education Review</mark>_ <mark>,</mark> _<mark>59</mark>_ <mark>(2), 261-288.</mark>

24
05, * p _<_ 0.1). Quadratic model is adopted following Becker et al. (2015). Sample corresponds to children born between 1957 and 1962, or 1968 to 1972. Covariates include birth district FE, year of birth×1971 enrollment, year of birth×1971 number of children, year of birth×water sanitation program, year of birth dummies, following Duflo (2001). Primary completion takes the value of 1 if the child has completed primary and 0, otherwise. Mother’s Edu represents mother’s years of schooling, which was calculated based on the education level completed. Family background is measured by mother’s years of schooling. The variable Inpres measures the number of Inpres schools per 1000 children at the district level divided by the highest number of schools received by one district. For the sake of parsimony, only intercept, linear, and quadratic terms are reported in this table. The tables with all coefficients are available upon request. Data sources: Indonesia’s full count census 2000 and Duflo (2001).

64
Investment in human capital can have little impact on growth unless people can use education in competitive and open markets. The larger and more competitive these markets are, the greater are the prospects for using education and skills. The policy environment could also help to induce quality improvements in education and to calibrate the distribution of education to achieve the greatest welfare improvements.

# **I.** Issues **and Evidence**

Studies have shown the handsome returns to various forms of human capital accumulation: basic education, research, training, learning-by-doing and capacity-building. Education enriches people's understanding of themselves and the world. It improves the quality of their lives and leads to broad social benefits to individuals and society. Education raises peoples' productivity and creativity and promotes entrepreneurship and technological advances, demonstrated in countries from Malaysia to Bolivia to Ghana (World Bank 1991).

We also know that education alone does not guarantee sustainable economic growth. The former Soviet Bloc attained universal literacy decades ago. But because these countries were closed to ideas and products from the outside, their economies stagnated. The Philippines, Sri Lanka, and the Indian states of Kerala and West Bengal have experienced periods of lackluster growth even though their education levels outstripped those of countries and states that matched them in other ways. The real question, then, is when and how education can bring high payoffs.

Using panel data from 12 countries from 1970 to 1994, this paper investigates the relationship of education, policy reforms, and economic growth. We hope to shed light on the iimportance of the distribution of education to economic growth and on how the interaction of education and reforms facilitates economic and productivity growth. We draw on cross-country experience to show how policy reforms bolster returns from education and to highlight the conditions under which investment in both physical and human capital is likely to pay off.

## The Education Puzzle

In the earlier neoclassical models, education was not considered a major input for production and hence was not included in growth models.<sup>