rafmacalaba/data-use-mentions-extended huggingface dataset:
**Figure 5 – Total Income Effect: Direct and Indirect Effects of Tobacco Taxes (Tobacco price increase, medical expenditure, and working years gained) , by Decile**

<!-- Start of picture text --> 3.0<br>2.5<br>2.0<br>1.5<br>1.0<br>0.5<br>0.0<br>1 2 3 4 5 6 7 8 9 10<br>Deciles<br>Low-bound elasticity Medium elasticity Upper-bound elasticity<br>Income Gains (%)<br><!-- End of picture text --> _Source:_ Estimates based on Russia Longitudinal Monitoring Survey–HSE 2016. _Note:_ Estimates assume a price shock of 25 percent.

# **6. Discussion**

Despite the wealth of research on the negative effects of tobacco consumption and on the benefits of various public policy mechanisms aimed at reducing tobacco use, questions remain about the progressivity or regressivity that these entail. The implementation of tobacco taxes is considered one of the most effective ways to discourage tobacco use. Nonetheless, this policy has a direct impact on household incomes, especially among low-income households that allocate a larger proportion of their budget to buying smokes. Moreover, the net effect of an increase in tobacco taxes depends on the price elasticity of this product across different segments of the population. The price elasticity determines the magnitude of the income shock and the benefits gained from a decline in tobacco consumption.

To assess the net welfare gains from this policy, one must look beyond the direct impact on household income and consider other benefits of lower tobacco consumption, including a reduction in medical costs and an increase in the potential working years associated with good health. Thus, it is critical to justify the maintenance or intensification of the use of tobacco taxes by means of a demonstration of the aggregate monetary gains or losses generated. Moreover, the policy should focus on low-income
br>-0.3 GDP per capita PPP (log)<br>WFH index - adjusted by home internet access WFH index - adjusted by home internet access<br>WFH index - adjusted by home internet access<br><!-- End of picture text --> Note: The vertical axis measures the corresponding task index, in standard deviations from the mean of the (A) PIAAC, (B) STEP and (C) LMPS samples. A higher value indicates that jobs are more amenable to WFH.

# **3.2 Within-country findings**

There are large disparities in terms of jobs’ amenability to working from home within countries. Figure 6 shows differences with respect to the average worker for the whole PIAAC, STEP and LMPS data sets.<sup>7</sup> Across most countries, women are more likely to have jobs more amenable to WFH. This is because they are less likely to have jobs intensive in physical/manual work than men. Educational attainment is strongly linked

> 7 Tables showing country-level findings are available in the Online Appendix

> (http://www.hernanwinkler.com/uploads/5/5/1/1/5511764/appendix_jobs_amenability_to_wfh_v13.xlsx).

14
a large-scale housing project in Angola, financed with a $2.5bn oil-backed loan from ICBC. What counts as “big-ticket items” varies by country [#5]. South Sudan’s Nadapal-Torit-Juba Road cost around $169mn and was financed through oil-backed China Eximbank loans [#27]. This amount may sound relatively small compared to those in Ghana and Angola, but is nonetheless the largest loan that South Sudan had contracted with China as of 2020.25 There are several possible drivers as to why RBLs tend to flow into infrastructure projects. These are often low internal return rate projects that would not

> 25 CARI data set. The second largest is the New Juba Airport loan, $158mn from Eximbank in 2013.

13
Institute).

The Freedom House measures political rights (PR) and civil liberties (CL) using, respectively, an index which ranges from 1 to 7, with a higher score indicating more freedom. The ratings are determined by a list of questions. For the political rights index, for example, the questions are grouped into three sub-categories: electoral processes; political pluralism and participation; and functioning of the government. The civil liberties questions are grouped into four subcategories: freedom of expression and belief; association and organization rights; rule of law and personal autonomy; and individual rights. The sum of each country’s sub-category scores translates to a rating from 1 to 7. Following Acemoglu et al. (2005) we transform the indexes so that they lie between 0 and 1, with 1 corresponding to the most-democratic set of institutions.

Another measure of democracy from the POLITY IV data set is considered. Indicators of democracy measure the general openness of political institutions and combines several aspects such as: the presence of institutions and procedures through which citizens can express e¤ective preferences about alternative policies and leaders; the existence of institutionalized constraints on the exercise of power by the executive power; and the guarantee of civil liberties to all citizens in their daily lives and in acts of political participation. In our data set we consider a composite index (Polity2), that ranges from -10 to + 10. This index is also normalized from 0 to 1, with 1 corresponding to the most democratic set of institutions.

Finally, we also consider Economic Freedom of the World (EFW), an index which measures the degree to which countries’policies and institutions support economic freedom. Five broad areas are distinguished: (1) size of government; (2) legal structure and security of property rights; (3) access to sound money; (4) freedom to trade internationally; and (5) regulation of credit, labor and business. This index is also normalized between 0-1.

# Migration

For
# **6. Conclusions**

This paper uses data from the Life in Transition Survey to investigate trends in public support for climate change policies in Eastern and Central Europe and Central Asia between 2016 and 2023. Our analysis reveals that despite increasing scientific consensus and media attention, the share of respondents willing to pay higher taxes to combat climate change declined significantly between 2016 and 2023. This decline was heterogeneous across countries and demographic groups, with the most substantial decreases observed among men, people 30 to 49 years old, and people with higher education levels.

Changes in the willingness to pay cannot be explained by changes in the structure of the population during this period, such as aging or urbanization. Our analysis shows that changes in attitudes, not demographic shifts, primarily caused the decline in support.

We formulate and test several hypotheses to explain this trend. Our empirical evidence confirms that the decline is not part of a general decrease in willingness to pay for public policies. Personal experiences with extreme weather also cannot explain the change.

The rise of right-wing populism emerges as the most plausible explanation for the declining support for climate policies. Our analysis reveals a negative correlation between right-wing populist attitudes and willingness to support climate change policies across both survey rounds.

These findings have important implications for policy makers in the region. The growing influence of right-wing populism presents a significant challenge to building and maintaining public support for climate action. Our results suggest that policies to combat climate change may need to be framed and communicated in ways that resonate with a broader spectrum of political ideologies and appeal across political divides.

This study highlights the critical linkages between political ideologies, public opinion, and support for climate policies. As the urgency of climate action grows, understanding and addressing these dynamics are critical for developing effective regional climate strategies. The challenge for policy practitioners is to carefully navigate the evolving political landscape, while building public support for impactful climate action.

15
.74***|-34.94***|
|Constant|(0.191)|(828.6)|(260.0)|(2.094)|(0.00281)|(0.00287)|(4.722)|
|Observations|_A_|633|551|496|_a_|_a_|1,034|
|R-squared||0.119|0.540|0.801|||0.459|
|Numberofcoefficients||172|109|102|||173|

_Notes:_ Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1. All relevant variables are in logarithms. _a_ indicates a large number of observations due to the grouped technique.

Projected levels of infrastructure stocks are valued at the unit costs used in Yepes (2008) and shown in Table 4. Time dummies and country fixed effects are used in order to proxy differences in infrastructure prices. In the case of telecommunications and ports, a market age variable accounts for the speed of technological change across countries. Lagged dependant variables are included to eliminate the structural part of interest. Analysis of spurious regressions have been made as in the literature showing that a structure of lagged variables as in Arellano and Bond estimations can eliminate all variance thus eliminating the structural part of interest (Yepes, Pierce and Foster, 2008).

The models are estimated on a worldwide dataset, although with a partial coverage of regions including MENA. The database used for these estimations is an annual panel data of infrastructure stocks, macroeconomic variables, and demographic characteristics (see for details the data annex). Data for MENA countries covers years up to 2008. The data are taken mainly from the World Bank’s World Development Indicators (WDI) complemented with material from 5
**Figure 35. Barriers to Direct investment Services and Cross‐border Services Trade, 2012**

<!-- Start of picture text --> .5 .5<br>.4 .4<br>.3 .3<br>.2 .2<br>.1 .1<br>IND IND<br>TUR TUR<br>ZAF ZAF<br>BRA IDN CHNRUS IDNCHNBRA RUS<br>0 0<br>0 20 40 60 80 100 0 20 40 60 80 100<br>STRI Telecom (Mode3) STRI Finance (Mode3)<br>Services Exports/GDP Services Exports/GDP<br><!-- End of picture text --> Source: World Bank Governance Indicators; STRI.

_Note:_ The horizontal axis shows the World Bank’s STRI index for direct investment in telecommunications (left panel) and financial services (right panel) since Russia still shows more restrictions on direct investment for these sectors. Both vertical axes depict services trade (in logs) for cross‐border trade and consumption abroad. BRA = Brazil, CHN = China, IND = India, RUS = Russia, WLD = World, ZAF = South Africa, IDN = Indonesia, KOR = Korea.

59
of intergenerational educational mobility:</u> Evidence across countries.” _Pacific Economic Review_ , 26: 70–90.

- <u>Maddison Project Database (2020). Available at:</u>

- https://www.rug.nl/ggdc/historicaldevelopment/maddison/releases/maddison-projectdatabase-2020?lang=en

- <u>Narayan, A., Van der Weide, R., Cojocaru, A., Lakner, C., Redaelli, S., Mahler, D., Ramasubbaiah, R, and S. Thewissen (2018).</u> _Fair Progress? Economic Mobility across Generations around the World_ . World Bank: Washington, DC:

- <u>Neidhofer, G., M. Ciaschi, L. Gasparini, and J. Serrano (2023). “Social mobility and economic</u> growth.” _Journal of Economic Growth_ , https://doi.org/10.1007/s10887-023-09234-8

22
||-0.015<br>(0.188)|-0.008<br>(0.185)|0.112<br>(0.188)|
|_R_<sup>2</sup>|0.288|0.295|0.328|0.288|0.295|0.328|
|Observations|4,393|4,393|4,393|4,393|4,393|4,393|
|Geography controls|Y|Y|Y|Y|Y|Y|
|Population 1991|Y|Y|Y|Y|Y|Y|
|Socio-Economic Development 1991|Y|Y|Y|Y|Y|Y|
|Region FE||Y|Y||Y|Y|
|Province FE|||Y|||Y|

**Notes** The table presents OLS regression estimates measuring the joint effect of the share of refugees in 1928 and the share of Albanian immigrants in 2001 on the presence of sport club in the early 2000s, at the community level . Standard errors are clustered at the province level. *** _p <_ 0.01, ** _p <_ 0.05, * _p <_ 0.1; Source: Author’s elaboration on the 1928 Greek population census and General Secretariat of Sports’ inventory.

37
due to a combination of several factors at the time of crisis, such as preexisting vulnerabilities combined with weak government responses, or with democratic institutions and cultural norms. Otherwise, we should not observe such big differences in the pandemic outcomes, for instance, within the high-income-and-democratic countries sample, such as those between the United States, Germany, and South Korea.

Based on cross-country regressions and when appropriate, the spatial autoregressive models, we find that countries with demographic and environmental vulnerabilities tend to have a greater pandemic spread (as in the case of higher urbanization and air pollution), while the share of elderly is associated with lower pandemic spread. Government delay in restricting international mobility is associated with higher pandemic mortality rate. Increasing the number of days to impose international-travel closedown by one standard deviation (SD) is associated with an increase in pandemic mortality rate by 0.4 percentage points, or 17% of the mean mortality rate. Furthermore, government delay in restricting domestic mobility is associated with higher pandemic mortality when the country has higher obesity prevalence and air pollution (i.e., SO2). Increasing obesity by one standard deviation (SD) would imply a change in the marginal effects of _DelayD_ on mortality (i.e., ∂∂ ∂∂ ∂∂ ∂ ∂ ) from -0.004 to 0.003. Similarly, increasing SO2 by one DD SD from the mean would imply a change in the marginal effect of _DelayD_ on mortality from - 0.008 to 0.003.

Contrary to the notion that authoritarian countries may have an advantage in combating the pandemic,<sup>2</sup> we find that democratic countries have done better in handling the pandemic. Relative to non-democratic countries, democratic countries have responded faster by having significantly lower delay in domestic closedown by about a month. They also have significantly lower pandemic mortality rate, by 0.9 percentage points, or 43 percent of the mean. They seem to be able to reduce the harm to the vulnerable
be over-represented and not the opposite. For example, Smith (2000) uses data from the British Household Panel Survey (BHPS) and finds that self-reported reported wage freezes between two consecutive years are less likely to happen when individuals double check their pay slip before reporting wages. As a consequence, measurement of DNWR in the BHPS which does not take into account rounding errors would tend to over-estimate rigidities. We believe our data are unlikely to suffer from rounding errors since wage information is directly extracted from firms’ 13
available for most countries so results from such studies are questionable. By contrast, we can use data from household surveys and population censuses to compute measures of intra- and inter-regional income differences as well as measures of regional distinctiveness.

There are, however, some limitations to our approach. Since we exploit regional variation, country-wide factors are only captured by fixed effects in the analysis, so we have to bracket some theoretically interesting factors. Another, already-mentioned, issue is that internal borders can be endogenous to demands for sovereignty. This, however, is not a problem for our analysis, since we condition our expectations on a given set of borders. Different complications arise if a group’s homeland extends beyond existing administrative boundaries so the group might seek sovereignty in two or more adjacent regions, none of which individually has characteristics that our theory would associate with sovereignty demand. Alternatively, larger regions may include two competing sovereignty movements. We deal with these complications with appropriate right-hand-side controls in robustness tests.<sup>31</sup>

# **_Proxying Actual Sovereignty_**

We propose an economic measure of sovereignty: the share of regional expenditures that can be financed out of regional revenues. We have these data for 603 regions. The ratio ranges from almost 0 to 1 with a median level of 0.57, pooling all regions and countries. In some cases, the ratio is above 1, but we truncate the index since in those cases we consider that there can be effective autonomy.<sup>32</sup> An unbound version of the index is also used in robustness tests.

31 We code regions with more than one sovereignty movement; adjacent regions with the same sovereignty movement; groups that are not the majority in any single region, but are concentrated in two or more adjacent regions; and we check how these cases influence the results of the analysis.

32 Data for this variable come from national statistical yearbooks and the variable is coded for the year for which we have household survey
Figure 1: Replication of World Bank WDR 2020 Figure 4.9.

**Figure 4.9** The multilateral dimension of the U.S.–China trade war

<!-- Start of picture text --> a. Chinese exports to United States: Share of value added by Chinese trade partner, 2015<br>2.5<br>2.0<br>1.5<br>1.0<br>0.5<br>0<br>b. U.S. exports to China: Share of value added by U.S. trade partner, 2015<br>3.0<br>2.5<br>2.0<br>1.5<br>1.0<br>0.5<br>0<br>JapanKorea, Rep.United StatesGermanyRussian FederationAustralia FranceIndonesiaMalaysiaUnited KingdomHong Kong SAR, ChinaItaly India BrazilSingaporeSwitzerlandThailandCanadaNetherlands Belgium<br>Canada ChinaVenezuela, RB Japan MexicoGermanyUnited Kingdom FranceKorea, Rep.Russian Federation Italy BrazilSwitzerland IndiaTrinidad and TobagoMalaysia NetherlandsAustraliaSaudi Arabia Nigeria<br>Value added (%)<br>Value added (%)<br><!-- End of picture text --> 25
the intervention and of the program; the results show that there is no impact of the program for the schools in the experiment. The results of these specifications suggest that a formal SBM intervention with cash grants did not have an impact on student learning. Since schools in the experimental evaluation sample were equal in expectation before the treatment, also suggests that both groups already had in place formal school autonomy practices and some also had experienced cash grants; then, the intervention just supported these practices but did not change them into better school performance. Finally, schools with more years in PEC had lower and significant school performance (close to a 0.09 σ) at the baseline.

# **DD Approach – To Test for Heterogeneous Effects Such as Intensity of Treatment**

To analyze the change in school average performance that can be attributed to the intervention, two sets of estimations were used. First, it estimated the effect of PEC program on school average test scores from 2005 and 2012; PEC schools are identified from administrative records. Second, it estimated the effect of the intensity of treatment on school performance; PEC schools identified as treatment in the evaluation sample. The second empirical approach assumes that PEC had different intensity among participating schools. This approach considers not only initial years in the program before the treatment but also four years after the program. In this case, the inclusion of dummies defines schools with different years of PEC. Table 6 shows the results of the DD estimation of the impact of PEC on school performance. The first column shows the impact of the PEC on school average; this effect was only significant for 2010 by 0.15 σ, a year after the end of the impact evaluation. The second column shows the results when the program variable is a dummy for the treatment schools; again, there was no impact.

19
28%|33%|36%|28%|
|% Primary or less|49%|51%|46%|41%|42%|38%|32%|34%|30%|
|% Junior High||||||||||
|School|20%|22%|18%|25%|26%|22%|29%|30%|26%|
|% High School|17%|14%|22%|18%|16%|22%|21%|20%|23%|
|% College|14%|13%|15%|16%|15%|18%|18%|16%|21%|

Notes: Authors’ calculations using quarterly Labor Force Surveys. Each year uses information from all available quarters: 2000 uses quarters 2-4, 2007 uses quarters 1-4, 2014 uses quarters 1-2. Full sample uses all individuals ages 23-65, and working sample uses all individuals with positive hours of work ages 23-65. Monthly earnings and hourly wages are restricted to full-time workers (at least 30 weekly hours of work) and are in constant Mexican Pesos of the first quarter of 2014. Married includes cohabitation. Rural refers to individuals living in localities with less than 15,000 inhabitants. Primary or less refers to individuals with less than 9 years of schooling, Junior High School refers to individuals with at least 9 years but less than 12 years of schooling, High School refers to individuals with at least 12 but less than 16 years of schooling, and College refers to individuals with at least 16 years of schooling.

# **3. Results**

Our results can be summarized as follows. The Datt-Ravallion decomposition (Table 2) shows that most of the change in the proportion of workers below the baseline wage is due to a shift in the distribution rather than to a change in its shape. The skewing towards the lower end
All of the components of the HCI are measured with some error, and this uncertainty naturally has implications for the precision of the overall HCI. To capture this imprecision, the HCI estimates for each country are accompanied by upper and lower bounds that reflect the uncertainty in the measurement of the components of the HCI. As described in more detail in Section A4.4, these bounds are constructed by calculating the HCI using lower‐ and upper‐bound estimates of the components of the HCI. The resulting uncertainty intervals are shown in **Figure 8** , as vertical ranges around the value of the HCI for each country. These upper and lower bounds are a tool to highlight to users that the estimated HCI values for all countries are subject to uncertainty, reflecting the corresponding uncertainty in the components. In cases where these intervals overlap for two countries, it indicates that the differences in the HCI estimates for these two countries should not be over‐interpreted since they are small relative to the uncertainty around the value of the index itself. This is intended to help to move the discussion away from small differences in country ranks on the HCI, and towards more useful discussion around the level of the HCI itself and what it implies for the future productivity of children born today.

Another feature of the HCI is that it can be disaggregated by gender, for the 126 countries where gender‐disaggregated data on the components of the index are available. Gender gaps are most pronounced for survival to age 5, adult survival, and stunting, where girls on average do better than boys in nearly all countries. Expected years of school is higher for girls than for boys in about two‐thirds of countries, as are test scores. The gender‐disaggregated overall HCI is shown in **Figure 9** . Overall, HCI scores are higher for girls than for boys in the majority of countries. The gap between boys and girls tends to be smaller and even reversed among poorer countries, where gender‐disaggregated data