rafmacalaba/data-use-mentions-extended huggingface dataset:
are available through Our World in Data (https://ourworldindata.org/). The results are presented in Annex Table A. With the consumption-based emissions, China’s payments decline significantly – from USD 35 billion to USD 20 billion. This adjustment does not materially impact other countries.

# _Modification 2: Stock of Carbon Emitted_

The proposal has ignored the stock of emissions thus far. Some countries would argue that they should not be held responsible for emissions when there was little knowledge that emissions were damaging. Others would argue that regardless of foreknowledge, countries that have used more of the collective carbon budget have benefited from the associated development and should pay for it. A middle ground may be sensible here, backing out the stock of carbon emitted thus far, with 1990 as the start year. This is well into a timeframe where high- and low-income countries alike were aware of climate change; the Rio Earth Summit was held in 1992.

One could follow the same principle as above and argue that every individual today has the right to have emitted the average stock of carbon between 1990 and today (again, the fund can consider either carbon production emissions or carbon consumption emissions). So excess emission countries will be those whose per capita average is more than the global per capita average stock emitted between 1990 and today.<sup>5</sup> The last question is what rate the excess stock of emissions should attract. Given that this one-off payment will not change any incentive to act (since the emissions have already occurred), its value can be debated. Arguably, the cost of additional emissions should grow as the carbon budget shrinks, so emissions in the past ought to be costed at a lower rate than current emissions. What payments are required with a “moderate” rate of $0.3 /ton and how does this compare to a GCI of $10/ton? Once again, this would be a one-off payment rather than a recurring payment, though it is
. The average GDP per capita growth rate in small states in Africa, compared to other Sub-Saharan African countries, was twice as high and their median GDP per capita, US$ 2,217 in 2005, four times higher.

Researchers differ in their perspective of the pace and level of public debt accumulation in small states but find no evidence to correlate being a small state with levels of indebtedness or a “debt problem” specific to small states. Dodhia (2005)<sup>18</sup> documents the movement of external debt indicators in 26 small states from 1990 to 2003 and finds the stock of external debt increased by about 1.6 per cent per annum in the 1990s but the rate of growth accelerated to 5.6 per cent per annum between 1999 and 2003. This increase in indebtedness is attributed to three factors: low and volatile GDP growth; high and increasing public sector deficits resulting from a combination of lower revenues and higher expenditures; and higher borrowing costs as concessional

> 13 Favaro, E., Dömeland, D, O’Boyle,W.  and Stucka T. (2010) Small States, the Financial Crisis, and the Aftermath. Chapter of “Sovereign Debt and the Financial Crisis: Will This Time Be Different?” Edited by Braga, C. and Vincelette G.. Washington DC: The World Bank

> 14 World Bank _Statistics for Small States: A Supplement to the World Development Indicators 2011_ The World Bank

> 15 Favaro,E,  Dömeland, D, O’Boyle,W.  and Stucka T. (2010) Small States, the Financial Crisis, and the Aftermath. Chapter of “Sovereign Debt and the Financial Crisis: Will This Time Be Different?” Edited by Braga, C. and Vincelette G.. Washington DC: The World Bank

> 16 Easterly, W., and Kraay, A. (2000) “Small States, Small Problems? Income, Growth, and Volatility in Small States.” World Development 28
As the provincial capital, the city of Gorontalo is bigger and more densely populated compared to West Manggarai, and with such different characteristics we may expect better technology sophistication among bigger businesses. With more avid use of electronic payment technology even before the acquisition of TMD devices, there could be two possible outcomes: the businesses were just equally compliant following the use of TMD or they found a way around installed TMD to hold back their increased taxable transactions. Either way, the final tax payment did not change substantially. It is important to note, however, that our estimates might stem from the lack of statistical power given the small-sized treatment group.

It is important to also note that the absence of significant effect of POS among hotel observations in both West Manggarai and Gorontalo might signal the industry-specific characteristic which shapes the way it responds to compliance-monitoring tools like POS. Hotel industries in both districts are generally made up of larger businesses with relatively high nominal average tax payments (see Table 2), which made up a large bulk of our hotel observations. Large hotels are usually part of international franchise or the local sizable management—both tend to be very visible and are subject to public audits. This made them were just as compliant even before the arrival of the POS distribution program. In addition to that, opening hotel businesses alike requires licenses and involves more officiation even for small to medium-sized ones. This is very contrast to F&B industry, which usually are quite loose in terms of license if the business size is small.

29
**Table 3. Estimated Mean Annual Out-of-Pocket Healthcare Expenditures** **<u>by NHIS Membership Status, Cedis</u>**

|Welfare decile|Nonm|embers|Me|mbers|
|---|---|---|---|---|
||Mean<br>(1)|Std. dev.<br>(2)|Mean<br>(3)|Std. dev.<br>(4)|
|Overall|344|525|289|523|
|1<sup>st</sup>(poorest)<br>|162|169|91|108|
|2<sup>nd</sup><br>|209|199|150|153|
|3<sup>rd</sup>|250|273|177|189|
|4<sup>th</sup>|214|241|175|217|
|5<sup>th</sup>|216|232|226|281|
|6<sup>th</sup>|278|294|242|269|
|7<sup>th</sup>|283|320|308|335|
|8<sup>th</sup>|393|467|295|329|
|9<sup>th</sup>|396|495|406|488|
|10<sup>th</sup> (richest)|877|1,084|678|1,202|

_Note_ : Welfare = household consumption per adult equivalent. Std. dev. = standard deviation.

Many other studies estimate the effect of NHIS membership on mean OOP healthcare expenditures, usually based on cross-sectional data such as our sample. Garcia-Mandicó et al. (2021) is unique among these studies as the authors use data from the 2005/
# **_remains competitive, and many overqualified jobseekers compete on jobs that require education that is lower than their qualifications._**

The number of vacancies by education requirements, illustrated in Figure 8, shows that the online job portal at Rozee.pk is primarily for jobs that require undergraduate and postgraduate education. Of the vacancies, 75% require an undergraduate or postgraduate education. Thus, the portal is mostly for undergraduate and graduate-level jobs and may have a potential for improving job matching of postsecondary education graduates, whose unemployment rate is high (Bossavie et al., 2018). On the other hand, the tightness (i.e., vacancies per jobseeker) by education indicates that finding jobs for graduate degree holders is not easy because the tightness is low at 0.05.<sup>16</sup> (That is, 100 jobseekers with graduate degrees compete for five jobs that require graduate degrees.) By the same token, while the total number of job vacancies that require undergraduate degrees is the greatest, the tightness at the undergraduate level is as low as 0.17 because of the large number of jobseekers who have undergraduate degrees. By contrast, the tightness at the secondary or less education level is the highest at 0.27 since jobseekers whose highest education level is secondary or lower are few.

**Figure 8. Labor demand, supply, and market tightness by education**

_Source_ : Authors’ calculation using the data from Rozee.pk. _Note_ : The number of vacancies is the total posted since January 2012. The number of jobseekers is the total registered since January 2012.

The lower tightness at the higher education levels may induce high-educated jobseekers to look for low-education jobs, since high-education jobs are less available. In fact, the data show that for jobs that require secondary education or lower level of education, 85% of the applicants have undergraduate and postgraduate degrees (Figure 9). Similarly, for jobs that require a higher secondary education level, 87
|Observations|13,330|13,330|13,330|13,330|13,330|13,330|
|---|---|---|---|---|---|---|
|R-squared|0.674|0.669|0.683|0.682|0.678|0.678|
|Treatment Obs|5342|5342|5342|5342|5342|5342|
|Control Obs|7988|7988|7988|7988|7988|7988|

Notes: Robust standard errors in parentheses;<sup>***</sup> p<0.01,<sup>**</sup> p<0.05,<sup>*</sup> p<0.1; avg_radBuff05 ranges from 0.14 to 16.64.

months before and 6 months after the July floods, we first inspect the trend in mean nightlight radiance. Figure 15 shows that both conflict-affected (represented by the red line), and non-conflictaffected (represent by the dashed grey line) pixels follow a similar trajectory until the occurrence of the floods in July. Between t-1 (June 2022) and t+1 (August 2022), nightlight radiance and picks up again afterward, suggesting a temporary power outage due to the flood. However, the decline in radiance is sharper for conflicted areas than non-conflict affected areas. Furthermore, nightlight radiance in the conflict affected area rises more slowly than in the non-conflict area until December 2020, suggesting longer term structural differences between the two groups following the flooding event.

# Robustness checks

common trend. The test consists of re-estimating the difference in difference regressions considering months prior to the flood event of July. The results presented in Table 14 show that the _Treated*PostPeriod_ variable alternate between negative and positive values and is not significant and, confirming the common trend assumption is respected.

27
widening the eligibility for noncontributory pensions lowered labor force participation rates by 5 percentage points and increased informal work.<sup>68</sup> The South African social pension program has been shown to reduce employment among the elderly.<sup>69</sup> These experiences appear consistent with historical evidence from more advanced economies. The introduction of a social pension from age 70 in the United Kingdom, some 100 years ago, lowered labor force participation rates by 6 percent, even with a very low adequacy of benefits.<sup>70</sup> As systems expand, it is important to make sure that programs remain incentive-compatible.

**Information gaps due to limited financial literacy can also impact retirement choices for those covered by pension systems.** Understanding the expected level of benefits and how that will impact welfare in older age is a prerequisite for making rational labor market decisions. Surveys in Sweden and Ireland show that knowledge of future pension benefits is at best limited, and that the most economically vulnerable groups know the least about their future benefits.<sup>71</sup>

**In addition to rethinking pension systems, flexible work arrangements may be needed to accommodate work preferences that change with age** . There is evidence that mature workers need

> 61 Butrica et al. 2006.

> 62 Meghir and Phillips 2010.

> 63 Bussolo et al. 2015, World Bank 2016.

> 64 World Bank 2016.

> 65 Ahlqvist and Boren 2017 and Kyyrä and Ollikainen 2008.

> 66 Pension reform during recession in Germany resulted in alternative pathways pushed by employers - unemployment and partial retirement (Lorenz et al. 2020).

> 67 Bussolo et al. 2015, Gragnolati et al. 2011.

68 Bosch and Guajardo 2012.

69 Baird et al. 2018. The paper shows that cash transfers in general do not lower aggregate labor force participation, however.

70 Gisecke and Jäger 2021.

> 71 Elinder et al. 2020, Barrett et al. 2015.

15
economic growth has reshaped long-standing norms, as in Bangladesh, where women’s employment and mobility in cities have risen due to the growth of the garment sector (Heath and Mobarak, 2015). Nevertheless, many norms remain unchanged or have regressed along with this progress; for example, child marriage remains a widespread practice in Bangladesh despite legal frameworks against it (Male and Wodon, 2017). Advancement in some areas and lack of progress in others illustrate the complex links between norms, gender disparities, and economic development.

While studies in the region are relatively small-scale and country-specific, the literature does converge on social norms being one of the key drivers of low FLFP in the region. In India, Deshpande and Kabeer (2021) find that detrimental social norms that reinforce expectations of women being primarily responsible for household chores lower their probability of employment. Analysis of national household survey data in Bangladesh reveals a strong correlation between a conservative outlook (as indicated by wearing a _burqa_

> 4 For a discussion on demand-side factors see for example, for the case of India, Deshpande and Singh (2021), Klasen and Pieters (2015), Chatterjee et al. (2015), Mehrotra and Parida (2017).

> 5 The reverse is also true, female labor force participation and employment has a transformative potential on gender norms (Seguino 2007).

4
IE-LFS 2019-20 corresponding to the same months of 2020. That is, we used data from the spring quarter of the IE-LFS 2019-20 to calibrate a model that predicts household expenditure and used this model to impute poverty in the AWMS R3. The SWIFT-plus methodology includes fast-changing consumption variables to better capture welfare changes in a context where large economic shocks have occurred. We implemented additional estimation steps to minimize model overfitting, account for estimation error through multiple imputation techniques, and improve out-of-sample performance. In line with previous SWIFT-plus tests done on Afghanistan, we trained separate models for urban and rural samples.

Our results show that poverty in Afghanistan has remained roughly the same as that observed in the spring of 2020 before the regime change. Overall, about 48.3 percent of the Afghan population was poor as of April-June 2023, representing a four-percentage point decline compared to poverty levels observed over the same months of 2020. Poverty trends differ between urban and rural areas. In rural areas, monetary poverty is estimated to have declined from 51 to 44.2 percent while marginally increasing in urban areas from 55.2 to 58 percent, although not statistically significant. These projected poverty rates are robust to in-sample and out-of-sample tests and potential biases from the AWMS R3 sampling.

2
- Income tax data: this includes many variables, including a description of the firm’s activity, revenue, profits, taxes, tax credits, inventory, cost of goods sold (basically all variables from a standard income statement). Because most variables are not reported very well, except for the larger firms, we will focus on sales as a measure of total firm size. This dataset is the universe of registered firms in Rwanda and includes both firms that do or do not have any employees, such as sole entrepreneurships.

These three datasets are merged using the unique taxpayer identification number assigned to the firm. The trade information contains the largest number of unique observations, but not all of them are formally registered firms.

We observe the information in all three data sources for a panel of firms over time, which allows for both a cross-sectional and growth analysis. The sample period starts in 2008 and goes to 2016. The coverage of the payroll and tax data tends to grow over time, but the overall number of firms surveyed is quite limited. This is not unexpected as the administrative information only covers firms in the formal sector. Aggregating over the three goods producing sectors that we focus on—agriculture, mining, and manufacturing—we observe employment for an average 321 firms in each year (of which 42% are from manufacturing). Firm sales is much more widely reported, and across the three sectors we observe an average of 2,926 firms in each year (30% from manufacturing).

The aggregate statistics already showed that engagement in GVCs is rather limited for this small, landlocked country. The administrative data illustrates directly that even the total number of registered manufacturing firms is remarkably small. But that is simply the reality in many economies dominated by the informal sector. It also presents an unavoidable challenge for policy as formal registration is likely to be a first step before firms can start integrating in GVCs.

By construction, our data will miss a limited volume of cross-border economic activity by smaller
helpful in tracking the responses to and impacts of the pandemic, particularly if they are combined with traditional household surveys. Table 1 lists the countries and waves of HFPS that were used for this analysis.

**Table 1: Sample size of HFPS waves and the corresponding multi-topic survey from GMD**

|Country<br>|||Hig <br>|h Frequency<br>|Phone Su<br>|rvey<br>|||||G<br>s<br>|MD<br>urvey<br>|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Wave|1|2|3|4|5|6|7|8|9|10|11|Survey|
|Burkina Faso|1,968|2,210|2,125|2,104||2,071|2,032|2,011|1,998|||2018 EHCVM|
|Ethiopia|3,249|3,107|3,058|2,878|2,770|2,753|2,536|2,222|2,077|2,178|1,982|2015 HICES|
|Gabon|1,656|||||||||||2017 EGEP|
|Ghana|3,265|||||||||||2016 GLSS-VII|
|Gambia|1,436|1,385|1,335|1,332|1,297|||||||2018 EHCVM|
|Mali|1,766|1,935|1,901|1,901|1,766|1,884|1,760|
for cohort-specific measures of high school quality in the analysis.

The sources of bias discussed above are common to all outcome variables. In estimating the wage equation, we also need to consider the sample selection bias, that is, salaries can be observed only for individuals who participated in the labor force. For example, females who cannot be paid well in the labor market (or who can study more effectively) tend to delay entering the labor force.<sup>21</sup> Furthermore, our sample students are relatively young (especially for the TEEP cohort). Therefore, students attaining higher education may not have participated in the labor force, though it is common that students work and study at the same time in the Philippines. To check robustness of our findings, we also take a two-stage procedure using the inverse Mills ratio (Heckman, 1979) in <u>the appendix.</u>

# **5. Data**

In this section we describe the data we use in our analysis.

# **_Sampling Framework_**

The data come from the survey conducted in eight education divisions (four intervention and four nonintervention divisions) in the Philippines from July 2010 through April 2011. The eight divisions were chosen to represent three major metropolitan areas: Manila (National Capital Region), Cebu, and Baguio. We sampled in such a way as to pair an intervention division with an adjacent nonintervention division in the same area so that the pair shares similar socioeconomic conditions. Our sample was grouped into four areas: (a) Ifugao and Neuva Vizcaya, (b) Antique and Iloilo, (c) Negros Oriental and Cebu, and (d) Leyte and Western Samar. Figure 5.1 maps our sampled provinces.

In each division, relatively poor municipalities (school districts) were chosen first. Municipalities of census-2000 income classes ranked 3 to 5 (the highest income is rank 1 and the poorest is rank 6) were chosen from the adjacent area (near the division
preferences is mediated by electoral turnout; in other words, the preferences of older voters may be different from the preferences of non‐voters.<sup>4</sup> The net impact of ageing on expenditure preferences is therefore an empirical question.

In this paper, we use data from the Life in Transition Survey II (LITS II), which was collected by the European Bank for Reconstruction and Development and the World Bank in late 2010. LITS II covers almost 39,000 households in 34 countries in Europe and the former Soviet Union. The data set provides answers to questions on individuals’ attitudes to policy, gauged in terms of their preferences for allocating government spending to a variety of programs, including health care, education, pensions and assistance to the poor, as well as standard individual characteristics that are known to affect

> 3 See Jaime‐Castillo (2013) for evidence on attitudes and determinants of reform of pension systems in Europe based on Eurobarometer data.

> 4 Although the relationship between turnout and redistribution has been previously studied (Larcinese, 2007), to the best of our knowledge this is the first attempt to link ageing, expenditure preferences and electoral turnout.

3
This simplification does not fit well in a number of situations: for many countries within-survey inflation rates are far from being negligible especially in cases in which data collection spread through different seasons up to covering an entire year. Leaving aside countries where inflation has escalated to hyperinflation, it is not uncommon in present days to see double-digit yearly inflation rates in countries all around the world. In this case the temporal dimension of Ii(r, k, α) cannot be ignored.

National statistical offices often provide temporal consumption price indexes (CPI) that can be relied on to adjust household expenditures for variation over time of the cost of living. The strategy that we propose to deal with spatial-temporal variation takes advantage of the availability of official CPIs, take them as exogenous, and use it to adjust expenditures for within-survey inflation; in particular, we investigate if the _sequence_ of temporal and spatial price adjustment matters.

To illustrate, consider the case of a Paasche price index calculated at the household level (we use here notation introduced in Section 3): where pjh is the market price of commodity j= 1, 2, ... , k for household h, pj0 is the price of h commodity _j_ of a reference group 0, and wj is the budget share of household _h_ for commodity _j_ .

It is common practice to proxy market prices with _unit values_ (Gibson and Rozelle, 2005), so that the estimated version of the Paasche formula above becomes: h 0 where uvj and uvj denote the unit values of commodity j for the _h_ -th and reference group, h respectively. Unit values are defined as the ratio between expenditure xj on commodity j and the corresponding quantity qjh consumed.

In this situation, the key decision is whether to calculate uvjh and ŵjh at _current_ (xjht ) or _inflationadjusted_ values (x̃jht = xjht ⁄CPIt). The analyst has two strategies: 20