rafmacalaba/data-use-mentions-extended huggingface dataset:
DIAN data.

29. **While the system of VAT exemptions reduces income inequality by 0.6 Gini points it does so at a cost of 1.54 percent of GDP.** To reach this conclusion, a household data simulation exercise was undertaken of the following possible reform package: (1) Elimination of most VAT exemptions except education, health, transport and financial services. (2) Conversion of VAT exclusions to VAT exemptions to insure that producers are reimbursed for VAT of the inputs that they use. (3) Taxation of real estate transactions. (4) Conversion of reduced VAT rates (0, 1.6 and 10 percent) to the general rate of 16 percent.

30. **Modifications of the VAT system could thus yield substantial revenue gains, part of which could be used to compensate the poor.**<sup>**18**</sup> Given that the VAT exemptions help reduce inequality by 0.45 Gini points, dismantling them would obviously increase inequality. However, the additional tax revenue of 1.54 percent of GDP could be used to compensate the poorest. To illustrate, the fiscal cost (in terms of higher public monetary transfers) of ensuring a neutral impact for the poorest 30 percent is relatively modest at 0.33 percent of GDP, yielding a net revenue gain of 1.21 percent of GDP. Similarly, keeping the poorest half of the population is also comparatively inexpensive. There are several ways in which the poor could be compensated, such as through an expansion of the conditional cash transfer program, which is particularly well targeted to the poor, as discussed in Section 4.

# **3.3 Other Taxes**

31. **The distributional impact of other taxes is largely unknown.** The discussion thus far, has covered about 50 percent of all central government tax revenues (VAT and personal income tax). The remaining taxes consist largely of income taxes for firms (about 30
0.0680]<br>|[0.0641]<br>|
|Constant|-2.206<br>[0.540]***|-0.104<br>[0.436]|-0.369<br>[0.307]|-2.131<br>[0.521]***|-0.394<br>[0.433]|-0.337<br>[0.349]|
|Observations<br>Rd|1,453,762<br>0116|1,453,762<br>0088|1,453,762<br>0089|1,453,762<br>0118|1,453,762<br>0091|1,453,762<br>0095|
|-square<br>Department fixed effects|.<br>Yes|.<br>No|.<br>No|.<br>Yes|.<br>No|.<br>No|
|Clustered std. errors|Municipality|Department|Department|Department|Department|Department|

Sources: 2018 National Census of Guatemala, 2017 and 2018 municipality statistics (FUNDESA and INE), 2017 Latinobarometro survey, 2017 social spending data (ICEFI). Notes: Men ages 25 to 49 who either have spouses that are household heads or that are household heads themselves. Robust standard errors clustered at the municipality/department level.

*** p<0.01, ** p<0.05, * p<0.1.

37
option, since it avoids introducing output subsidies that distort incentives to switch between energy sources as a means to reduce emissions. CAT also encourages efficient pass-through of emissions costs, meaning additional taxation of electricity is not needed. Thus, overlapping the stated RPS and embodied emissions pricing increases by about 10% the costs of a CAT system that would achieve the same emissions reductions as China’s actual TPS. Together, China’s stated policy overlaps reduce the cost differential between China’s TPS and an equivalently stringent CAT system by about two thirds.

These insights are relevant not only to China, but also to other countries considering or implementing a rate-based ETS, such as Indonesia, Kazakhstan, and India, as well as Canada.

In future work, we plan to consider how other pre-existing distortions or market failures might influence the outcomes from overlaps and suggest useful policy responses. For example, output-based allocations can have some benefits in the presence of distortionary taxes on labor, capital, or other factors of production, or investment dynamics under macroeconomic volatility. Clean energy policies are often needed to address technology market failures. Another area for extension is attention to the effects of a range of flexibility mechanisms often incorporated into ETSs to address uncertainty, including banking and borrowing, price corridors, or quantity adjustment mechanisms that make mass-based emissions targets more malleable. Incorporating more real-world policy settings and constraints would provide needed nuance to the recommendations for optimal ETS design and supplemental policy recommendations.

# **VI. References**

Aguiar, A., Chepeliev, M., Corong, E., McDougall, R., & van der Mensbrugghe, D. (2019). The GTAP Data Base: Version 10. Journal of Global Economic Analysis, 4(1), 1-27.

Böhringer, C., & Behrens, M. (2015). Interactions of Emission Caps and Renewable Electricity Support Schemes. _Journal of Regulatory Economics, 48_ , 74-96.

Böhringer, C., & Rosendahl, K. E. (2010
**Table A4** Impact of Treatment on Intimate Partner Violence (IPV), Separating Goal into High and Low Strategies

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
||**Boys**<br>**Treatment**|**2-3 strategy**<br>**Treatment**|**1 strategy**<br>**Treatment**|**Endline**<br>**Control Mean**|**Observations**|
|IPV often index||||||
|treatment x post|-0.229**<br>(0.100)|-0.404***<br>(0.130)|-0.134<br>(0.112)|0.000|5,182|
|treatment x post x Goal|0.121<br>(0.170)|||||
|IPV in last year index<br>treatment x post|-0.150*|-0.242**|-0.129|-0.000|5,182|
||(0.078)|(0.122)|(0.120)|||
|treatment x post x Goal|0.118<br>(0.140)|||||

_Notes._ This table presents estimates of a modified specification of equation 1 that splits the goal treatment indicator into two mutually exclusive categories for females assigned to the _Goal_ arm: set 2 or 3 strategies or set 0 or 1 strategy. For each outcome defined at the start of the rows, the coefficients from a single regression are presented in two sub-rows, with estimates of
## Figure 3: Legal protection in the workplace

<!-- Start of picture text --> Protection against discrimination<br>on ethnic grounds<br>Protection against discrimination<br>on religious grounds<br>Protection against discrimination<br>on sexual orientation<br>Protection against discrimination<br>on gender identity grounds<br>Protection for equal<br>remuneration regardless of ethnicity<br>Protection for equal<br>remuneration regardless of religion<br>Protection for equal remuneration<br>regardless of sexual orientation<br>Protection for equal remuneration<br>regardless of gender identity<br>Bulgaria Mexico Morocco Netherlands Tanzania Vietnam<br><!-- End of picture text --> _Source_ : World Bank Equality Project.

# _4. Access to Property_

Access to property is the cornerstone of economic security. Land ownership is important not only as a means of capital accumulation but also as a source of income. Moreover, property reinforces bargaining power and agency. In addition, the ability to leverage property as collateral is important for obtaining credit and starting a business.

The access to property indicator seeks to capture the minority gap in the ability to own, manage, control, and inherit real property and to register and transfer a private business. The initial findings show that, although many property-related laws are apparently minority neutral, hidden regulations and gaps in the law are at the root of many forms of indirect discrimination regarding access to land and property rights.

In Mexico, “inequality has a particular effect on the most vulnerable sectors of the population, such as indigenous peoples—among whom the poverty rate is four times higher” (InterAmerican Commission on Human Rights 2015, 42). Article 27 of the Mexican constitution recognizes indigenous peoples’ lands, protects their property, and grants them preferential rights over their traditionally occupied territories. Still, the struggle remains in the way the property law translates into inclusion and economic empowerment. Mexico has not implemented a
et al. (2017) approach energy poverty through a vulnerability lens and develop six factors that relate to energy poverty and vulnerability (Figure 2). First, households might face poor access to energy. However, in the context of EU countries, access to energy is usually not the main problem. Second, they might face constraints based on the limited affordability of energy. This constraint may be particularly binding in the current inflationary context because of the Ukraine conflict. Third, they might be subject to poor flexibility, which is the inability to switch to energy sources that are more appropriate to their needs. Fourth, energy efficiency is a potential vulnerability factor because households might experience high energy losses due to poor energy efficiency at home. Fifth, some households might not be able to meet their energy demands from a cultural or socioeconomic perspective. Lastly, households might suffer from a lack of or limited policies addressing energy poverty. With respect to measurement possibilities, Thomson et al. (2017) detail the same categories presented in Figure 1.

_Figure 2: Energy poverty through a vulnerability lens_

|Access|Affordability|Flexibility|Efficiency|Needs|Practices|
|---|---|---|---|---|---|
|•Poor<br>availability|•Poor<br>affordability|•Inability to<br>switch|•High loss of<br>energy|•Mismatch<br>with needs|•Lack of<br>policies|

_Source: Compilation of findings by_ _<mark>Thomson et al. (2017)</mark>_

**In Table 1, we summarize different indicators measuring energy poverty gathered by Gouveia et al. (2022) and add several indicators identified from our review of the relevant literature.** The table reveals that energy poverty indicators in the EU mainly rely on three different types of data sources: EU-SILC (European Union Statistics on Income and Living Conditions),
Iraq [oil production]. We previously used Al Amarah to illustrate our methodology for identifying sampling areas.

# **5.1 Landfills in Madrid Province, Spain**

Recent technical research has used satellite data from Sentinel-5P and other sources to detect methane flares from large emissions sources (Maasakkers et al. 2022; Ouerghi et al. 2021). Particular attention has focused on emissions flares from two large landfills – Valdemingómez and Pinto -- southeast of Madrid, Spain (Tu et al. 2022; ESA 2021).

Figure 7 provides summary information about these two sites. Figure 7(a) displays the GGU (Madrid Province) identified for this exercise, along with the sampling area for Sentinel-5P data (bordered in blue).<sup>17</sup> Both landfill sites are identified in the figure. Figure 7 (b) displays satellite images of the two sites, along with the results of methane plume detection on August 20, 2021 (ESA 2021). Methane concentrations in the plume are colored from red (highest) to blue (lowest).

Figure 7 (c) draws on our 5-km global database to display mean methane concentration anomalies for the GGU sampling area from January 2019 to November 2022. Our data corroborate the recent studies, identifying the landfill cells and their immediate neighbors as the highest-intensity areas in the Madrid GGU. The data also indicate that prevailing winds typically disperse emissions from the two sites toward the southwest.

Our 5-km database contains 3,300 observations for the GGU sampling area. Figure 8 plots the observations against time, with mean monthly observations superposed. The plot reveals significant annual variation in 2019 – 2021, with peaks in the first quarter of each year. We see no sign of a “covid effect” in these data, since the most notable variation is in 2019 and the annual series for 2020 looks very similar to the series in 2021. Although some monthly fluctuation remains in 2022, the previous first-semester “peaking” is not present.

This
A natural next step would be to use data on house prices to estimate the top tail of the wealth distribution, and extend the analysis to other countries.

# **References**

- Aguiar, M. and Bils, M. (2015). Has consumption inequality mirrored income inequality? _American Economic Review_ , **105,** number 9, 2725–56.

- Alvaredo, Facundo (2010). The rich in argentina over the twentieth century, 1932-2004. In _Top Incomes: A Global Perspective_ (eds Anthony B. Atkinson and Thomas Piketty), pp. 253–298. Oxford University Press.

- Alvaredo, Facundo (2011). A note on the relationship between top income shares and the gini coefficient. _Economics Letters_ , **110,** number 3, 274–277.

25
.391|0.379|0.367|
|Philippines|0.368|0.320|0.314|0.313|0.309|0.305|
|Thailand|0.378|0.369|0.268|0.327|0.348|0.370|
|Venezuela|0.425|0.426|0.351|0.350|0.347|0.345|

* 1995 data is based on projections. See Appendix I for methodology.

# _Policy_ **_Reform and Education:_** _Thte Case of_ **_Chiina_**

The Chinese government started to invest heavily in education and public health since the 1950s. Its social indicators outstripped those of other low-income countries. Primary enrollment in 1980 reached 103 for girls and 121 for boys, and secondary enrollment was 37 and 54. But factors such as a lack of openness, restricted labor mobility, distorted wages and prices, prevented educated workers from finding the most productive use of their talents.

Economic growth did not take off until the policy reforms started in 1978. China started with easy reforms (such as rural and trade reforms) and only later moved on to more complex and politically more difficult reforms (such as state enterprise and banking reforms). Learning has been incorporated in the process of reforms, as reflected by Mr. Deng Xiaoping's famous saying, "crossing the river by feeling the stones at the bottom." China opened its doors early on to foreign trade and investment by experimentation. It established four special economic zones in 1980; by 1993 it had more than 9,000 economic zones, which played an important role in attracting foreign direct investment, stimulating trade, bringing in advanced technology, and upgrading industries. Similarly China's price reform was gradual. The scope of planning has been systematically reduced. A dual price approach was adopted in 1984 that combined some price setting by the government for products and materials
XPOLICY RESEARCH WORKING PAPER 2101

# Summary findings

To analyze what determInes wages and productivity in Zimbabwe, Verner analyzes an employer/employee dataset from Zimbabwe's manufacturing sector. Verner finds that: f Formal education, training, and experience positively affect wages and productivity positively.

- Women are paid roughly 37 percent less than mern although they are not measurably less productive.

There is no strong indication of ethnic discrimination among employees, but Europeans are being paid more in larger firms, although they are marginally less productive than workers of African origin.

The wage premium for workers who completed secondary school does not necessarily reflect greater productivity but may indicate a shortage of educated workers.

_3_ Workers trained in-house earn more although inhouse training does not instantly affect productivity. Training by outside trainers does improve productivity but is not rewarded with higher wages.

- Apprentices are paid more than non-apprentices. Perhaps an apprentice diploma serves as a screening device, when hiring.

Temporary workers are more productive than permanent workers, perhaps hoping to get a permanent contract.

* Union members earn less than non-union members despite being more productive. Perhaps union members fight more to have skills upgraded than for wage increases.

* Larger exporting firms are marginally less productive and pay marginally less than the average firmn, but are more productive than smaller firms (and their wages match productivity). Workers in larger woods and metals firms are paid less than workers in smaller firms, although they are not less productive.

Exporting firms benefit more than employees do from trade openness and greater productivity.

Foreign-owned firms are more productive than other firms (perhaps because of new technology).

* Firms that employ more expatriates tend to pay more. The more expatriates there are in metals firms, the more productive the employees are, perhaps because the expatriates bring knowledge about new technology to the enterprise.

* Employees in the metal and textile sectors are pald more than those in the food sector, but employees in metals are less productive than employees from other sectors.

- This paper a product of Human
# **Incorrect coding of the consumption of home production data**

In the process of working with the 1993 data, we discovered that the data, as it was originally processed, showed a dramatic decline in consumption of home production. The reason was that in 98 percent of the 25,570 cases, the number of months of consumption of home production were coded as one month, in contrast to 2 percent of the cases in the 1995 survey (table 22). In examining a few questionnaires it was evident that the data for the number of months of consumption of home-produced items had been incorrectly entered on the computer file. Unfortunately, INS was not able to reenter the data for 1993.

**Table 22 CMte d'Ivoire: Number of Months of Consumption of Home Production 1993 and 1995**

|_Reported Months_|_Unrevised 1_|_993 survey_|_1993 correc_|_ted survev_|_19_|_95_|
|---|---|---|---|---|---|---|
|_of Consumption_|_Frequency_|_Percent_|_Frequency_|_Percent_|_Frequency_|_Percent_|
|1|25107|98.3|320|1.3|57|1.9|
|2|1|0.0|774|3.0|169|5.6|
|3|||1110|4.3|184|6.1|
|4|||1076|4.2|185|6.1|
|5|4|0.0|752|2.9|123|4.1|
|6|2|0.0|1696|6.6|285|9.5|
|7|||532|2.1|82|2.7|
|8|||886|3.5|128|4.2|
|9|||322|1.3
_Figure 2 Backcasted trend in poverty rates and number of poor at the US$3.20 poverty line, pass-through of 1_ a) Poverty rate, % (US$3.20) <!-- Start of picture text --> b) Number of poor, millions (US$3.20)<br><!-- End of picture text --> <!-- Start of picture text --> 82.0 145.0 142.7<br>80.0<br>79.5<br>140.0<br>78.0<br>76.0 76.5<br>135.0<br>74.0<br>72.0 130.0 125.9<br>71.0<br>70.0<br>125.0<br>68.0<br>66.0 120.0<br>64.0 118.0<br>115.0<br>62.0<br>Backcast, pass-through=1 PovcalNet<br>Backcast, pass-through=1 PovcalNet<br>2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019<br><!-- End of picture text --> Note: the figure shows the backcasted poverty rates series at the US$3.20 poverty line (panel a). Using household consumption data from the 2018/19 NLSS and sectoral GDP growth rates from the World Bank-MFM-Tool, we backcast household consumption over the previous decade by applying the same growth rate to household consumption and mapping the sectoral information to the household’s head sector of employment. Number of poor is estimated using WDI population data (panel b).

# _<u>5.2. Survey-to-survey imputation</u>_

This section presents the results of the survey-to-survey imputation into the 2010/11, 2012/13, 2015/16 and 2018/
difference is driven by the omitted category- a larger share of internet respondents who are not working and are not looking for work (e.g., students, retired, etc.). Although this is difficult to precisely interpret, it could be consistent with individuals who are not working or looking for work having more time to use the internet.

> 17Although there are no comparison questions in the 2014 HBS for the subjective income assessment and the question on employment, it is likely that the mobile phone respondents have higher incomes and are more likely to work than the general population. Although this is difficult to precisely establish, reports derived from the most recent household surveys in the Republic of Yemen suggest that mobile phone respondents have better food access than the general population (e.g., IPC 2020; Favari et al. 2022; IPC 2022). However, the face-to-face survey that was conducted with the least amount of political and conflict interference at the end of 2016 suggest that food access of mobile phone users was similar to that of a survey that was attempting to be nationally representative in a difficult environment (e.g., FAO 2017; WFP 2017; etc.). Only in later years as the political interference in the face-to-face food security assessments likely increased, did the wedge between food security assessments and the food access of mobile phone users become apparent (e.g., Favari et al. 2022).

> 18 Although we are unable to calculate the response rates exactly due to how the RDD survey was

14
Model 4 shows that the observed preference for women result does not change according to clusters.<sup>20</sup> Models 5 and 6 show that employers with vacancies that receive high and low number of applications behave similarly in terms of their gender preferences for longlisting. On the other hand, the tendency to filter males out seems to be somewhat less pronounced in Ankara compared to the European side of Istanbul (Model 7). Finally, the models show occupational clusters (in particular, accounting) and total application size also affect the probability of passing through the filter and making it to the longlist but do not affect gender filtering.

Not all employers use the longlist as the first stage: 11 percent of applicant CVs that are clicked on are CVs that are not in the longlist. This is why we consider both unconditional regressions and regressions conditional on an applicant CV being in the longlist when looking at the determinants of what makes an employer click on a CV. Unconditional regression results are shown in Table 7. A similar result to the case with longlist emerges in this case, where female applicants are significantly more likely to be clicked on compared to their male counterparts. On the other hand, this tendency seems to be more a feature for sales and accounting occupations: Model 4 shows that the effect disappears for marketing and IT clusters. While there seems to be no difference in behavior according to local labor markets, models 3, 5, 6 and 7 show that applicants that are in the same sector with the firm opening the vacancy are more likely to be clicked on by the employers. Finally, as the total application size increases, employers presumably have more CVs

> 18 Separate balance tables for each sector are available upon request from the authors. Balance tables look very similar across sectors, with the exception that for cluster B there is no unbalance by gender on experience.

> 19 As noted, we include a linear age control in all regressions because by construction male candidates are older than female candidates