rafmacalaba/datause-encoder-data huggingface dataset:
up the survey-based estimates with common reference years across countries, including 2005. Population-weighted aggregate measures were then formed by region and globally.

Table 1 reproduces the Chen-Ravallion estimates of the poverty rate for the $2 a day line. Some notation will help for brevity. Let _Ft_ ( _z_ ) is the poverty rate (often called the “headcount index”) at date _t_ =(19)90 and (20)05 for the poverty line _z_ in $’s per day at 2005 PPP. So Table 1 gives _F_ 90 (2) and _F_ 05 (2) by region and in the aggregate for the developing world.

On repeating these calculations using the $13 a day line, I find that over 95% of the developing world in 2005 is deemed to be poor by US standards; Table 2 gives my estimates of _F_ 90 (13) and _F_ 05 (13) . It is striking how few people in the developing world are not poor by US standards. Possibly most notable is the count for India, which implies that only three million people are not poor by this standard. Under-reporting of consumptions in India’s National Sample Surveys, or selective compliance in the survey’s random samples, could well be leading to an under-counting of those not poor by US standards. But even if we use a poverty line of $9 a day—equivalent to using the US poverty line but increasing all consumption levels by about 50%, which would seem a very large adjustment for measurement error—the count of those in India who are not poor by US standards rises to only six million in 2005.

It is no less striking how little<sup>_F_</sup> _t_ (13) has fallen very little over time—by only one half of a percentage point (Table 2). So the number of people living in poverty by US standards has risen over the period. In 2005, there were only 250 million people in the developing world who were not poor by US standards, rising from 170 million in 1990. Defining the “Western middle class” as those living above the US poverty line, the developing world added 80 million to the count of the middle class by this definition over 1990-2005.

Notice that, on comparing Tables 1 and 2, China’s poverty ranking changes relative to the rest of the developing world. Using the $2 line, China had a lower incidence of poverty than the average excluding China in 2005, but this reverses using the $13 line; less than 2% (25 million people) of China’s population had attained the US poverty line in 2005.<sup>12</sup> China’s accounted for about one quarter of the increase in the size of the Western middle class over this period.

> 12  The 2005 ICP round was the first time that China had participated officially in the ICP, and the price data imply a higher PPP than past estimates from other sources, implying that China is poorer than we thought. The estimates reported here have attempted to correct for the “urban bias” in the sample frame of the 2005 ICP for China; for details see Chen and Ravallion (2009).

7
**Annex 2 Literature review on the impacts of Covid-19**

|Title & Authors|Model|Assumptions|Scenarios||Results|
|---|---|---|---|---|---|
|“Coronavirus:<br>The world<br>economy at<br>risk” (OECD<br>2020)|NiGEM<br>macro<br>model|Monetary<br>policy is<br>allowed to be<br>endogenous.;<br>The automatic<br>fiscal<br>stabilisers are<br>allowed to<br>operate fully in<br>all<br>countries,<br>implying that<br>governments<br>do not react to<br>the shock by<br>attempting to<br>maintain a<br>previously<br>announced|Contained<br>Outbreak<br>(short lived<br>but severe<br>downturn in<br>China)|-Domestic demand in<br>China and Hong Kong,<br>China is reduced by 4% in<br>the first quarter of 2020,<br>and 2% in the second<br>quarter of 2020.<br>-Global equity prices and<br>non-food commodity<br>prices are lowered by 10%<br>in the first half of 2020.<br>- Higher uncertainty is<br>modelled via a small rise<br>of 10 basis points in<br>investment risk premia in<br>all<br>countries in the first half<br>of 2020. This raises the<br>cost of capital and<br>reduces investment.|China GDP loss = -0.2<br>percentage point in 2020H1;<br>Reduction of China import<br>demand = -6%;<br>World GDP is reduced by to<br>0.5 percentage point in 2020;<br>Global trade declines 0.9% in<br>2020 (and 1.4% in first half of<br>2020)|
|||budget path.|A “domino”<br>scenario:<br>broader<br>contagion|-Domestic demand in<br>most other Asia-Pacific<br>economies, including<br>Japan and Korea, and<br>private<br>consumption in the<br>advanced northern<br>hemisphere economies is<br>reduced by 2% (relative to<br>baseline) in the second<br>and third quarters of<br>2020.<br>- Global equity prices and<br>non-food commodity<br>prices are lowered by 20%<br>in the first nine months of<br>2020.<br>- Heighted uncertainty is<br>modelled via an increase<br>of 50 basis points in<br>investment risk premia in<br>all countries in 2020.|World GDP is reduced by up to<br>1 1⁄2 per cent; World trade is<br>declining by around 3 3⁄4 per<br>cent in 2020|
|“Coronavirus<br>Casts Shadow||Assumption<br>that the supply<br>side of the<br>economy–|Only one<br>scenario|Top Asia-Pacific: Risk<br>Coronavirus restrictions<br>set back China’s growth<br>(risk level: High);|China GDP growth loss of 0.7<br>percentage point in 2020. Full<br>recover in 2021; Global GDP|

- 21 -
# **Figures 9c and 9d. Government Revenue and Growth: Egypt**

<!-- Start of picture text --> Fig. 9c. Gov. budget gains as % GDP (Carbon tax =<br>$25)<br>1.8<br>1.6<br>1.4<br>1.2<br>1<br>0.8<br>0.6<br>0.4<br>0.2<br>0<br>2022 2024 2026 2028 2030 2032 2034 2036<br>$25 carbon tax phase out fuel subsidies<br><!-- End of picture text --> <!-- Start of picture text --> Fig. 9d. Growth contribution of carbon tax versus<br>removing fossil fuel subsidies<br>0.07<br>0.06<br>0.05<br>0.04<br>0.03<br>0.02<br>0.01<br>0<br>2020 2022 2024 2026 2028 2030 2032 2034 2036 2038<br>Baseline GDP growth GDP growth with $25 carbon tax<br>GDP growth: remove subsidies<br><!-- End of picture text --> Note: Figure 9a. Results are similar to those for Saudi Arabia (fig. 7a), but with smaller magnitudes. In Figure 9b, at $50 per ton of CO2, although swapping carbon tax and fossil fuel subsidy policies (imposing the tax but keeping the fuel subsidy -as in orange line- versus no tax but removing the subsidy-as in yellow line) leads to largest gain in CO2 reduction (yellow to orange line shift) , the gain in CO2 reduction by removing the subsidies when there is no tax (shift of gray to yellow line) is substantially larger than doing the same at a carbon tax rate of $50 per ton (shift of orange to blue line). While both carbon tax of $25 and removal of fossil fuel subsidies improves the revenue, carbon tax dominates removal of fossil fuel subsidies (Figure 9c), unlike Saudi Arabia and Iraq. As in other cases, both policy instruments cause growth to decline very slightly in the short run, with no long run effect as growth rates converge to the baseline in the long run (Figure 9d).

29
Table 2: Characteristics of different health insurance schemes

|**Characteristics**|**Government Employees**|**Social Security Scheme**|**Universal Health Coverage**|
|---|---|---|---|
||**(CSMBS)**|**(SSS)**|**(UC)**|
|Population Coverage|Civil Servant, pensioners and their<br>dependents (parents, spouse,<br>children)|Formal sector private employee|The rest of Thai population who<br>are not eligible for CSMBS and<br>SSS.|
|**Benefits**||||
|Ambulatory services|Public Only|Public & Private|Public & Private|
|Inpatient services|Public & Private (emergency only for<br>private)|Public & Private|Public & Private|
|Choice of provider|Free choice|Contracted hospital or its<br>network with referral line,<br>registration required|Contracted hospital or its<br>network with referral line,<br>registration required|
|Cash benefit|No|Yes|No|
|Maternity benefits|Yes|Yes|Yes|
|Annual Physical checkup|Yes|No|Yes|
|Prevention Health Promotion|No|Yes|Yes|
|Services not covered|Special nurse|Private bed, special nurse|Private bed, special nurse,<br>eye glasses|
|Copayment|Yes<br>(inpatient at private hospital only)|Maternity,<br>emergency services|30-baht/visit*|
|**Financing**||||
|Source of funds|General tax|Employee & Employer|General tax|
|Financing body|Comptroller General Department,<br>Ministry of Finance|Social Security Office|National Health Security Office|
|Expenditure per capita (in 2006)|8,785|1,738|1,659|
|Per capita tax subsidy (in 2006)|8,785 (plus administrative cost)|579 (plus administrative cost)|1,659 (plus administrative cost)|

Source: Sakunphanit (2006). * The 30-baht copayment was eliminated in 2006.
Please note that, 1) different people have different probabilities to fall ill of cancer. Therefore, the likelihood that different people truly need the medicine is different. If a person will surely not get cancer in the next year, he may not need this preventive measure; and 2) with a given income, a person also needs to buy other goods and services such as food and clothes, and may spend money to prevent from other diseases.

We only want to know the likelihood that you would buy such a medicine, given the following list of prices, to make sure you would not fall ill of cancer in the next year. There is no right or wrong answers; we only want to know how you would react to the different prices. Please select one possibility under each price given below.”

|**Price**|**Definitely not**|**Probably not**|**Not sure**|**Probably yes**|**Definitely yes**|
|---|---|---|---|---|---|
|**Free(0 yuan)**<br>||||||
|**~~)~~**<br>**10 yuan**||||||
|**20 yuan**||||||
|**40 yuan**||||||
|**60 yuan**||||||
|**90 yuan**||||||
|**150 yuan**||||||
|**200 yuan**||||||
|**300 yuan**||||||
|**500 yuan**||||||
|**1000 yuan**||||||
|**2000 yuan**||||||
|**5000 yuan**||||||
|**8000 yuan**||||||
|**10000 yuan**||||||
|**15000 yuan**||||||

# **6. WTP Estimation with MBDC Data**

MBDC format WTP data, offering rich information with extended bid price choices and multiple options of certainty levels, have been used in the past in a number of contingent valuation studies. Several of the studies employing the MBDC data, such as Alberini et al. (2003), implement an extension of the Random Valuation Model proposed by Wang (1997), which views the value that an individual attaches to any amenity (including market traded goods) as a random variable with an unspecified probability distribution (Shaikh et al. 2007). Following this logic, the data obtained from the MBDC format are several observations of an individual WTP distribution that is attached to the amenity in question. Wang and Whittington (2005) and Wang et al. (2004a) further proposed a CV approach called Stochastic Payment Card (SPC). This approach extends the MBDC format WTP question and directly elicits the numerical likelihood information of the respondents beside the verbal likelihood information. With this arrangement, we are able to directly estimate individual valuation distributions from the SPC data provided by the MBDC format questionnaire.

9
countries, statements issued by government agencies (e.g., ministries, legislatures) of individual countries, global news organizations (e.g., Financial Times, Reuters), local news sources in which government officials are quoted, industry journals, and think tank publications. Projects need not be financed by Chinese banks to be included. Given this approach, our database uses a consistent set of criteria to include projects and is comprehensive within those criteria.

Office of the Leading Group for the Belt and Road Initiative (2017, pg. 9) describes the overall geographic scope of the Belt and Road succinctly: “Based on the proposal from President Xi and the need to promote international cooperation and taking into consideration the routes of the ancient land and sea Silk Roads, China has determined five routes for the Belt and Road. The Silk Road Economic Belt has three routes: one from Northwest China and Northeast China to Europe and the Baltic Sea via Central Asia and the Russian Federation; one from Northwest China to the Persian Gulf and the Mediterranean Sea, passing through Central Asia and West Asia; and one - from Southwest China through the Indochina Peninsula to the Indian Ocean. The 21<sup>st</sup> Century Maritime Silk Road has two major routes: one starts from coastal ports of China, crosses the South China Sea, passes through the Malacca Strait, and reaches the Indian Ocean, extending to Europe; the other starts from coastal ports of China, crosses the South China Sea, and extends to the South Pacific.” Note a source of potential confusion is that the “Belt” refers to the overland routes, where many of the projects are road improvements, whereas the “Road” refers to the maritime routes, where projects are primarily ports. Further, note that though Africa may be discussed by some as being part of the Belt and Road initiative given substantial Chinese investment on the continent, it is not included explicitly in these corridors.

Appendix A describes the 93 improvements to the transportation network included in our database and their status, as well as references to the sources used to include them. Of these improvements, 67 are on the Silk Road Economic Belt and 26 9
livestock, rice and fruits & vegetables sectors are more energy intensive as compared to other crop sectors.

**Table 5: Impacts of oil price increase by 50% on outputs of various crops in 2020 (% change from the baseline)**

|**Country/Region**|**Rice**|**Sugar**<br>**crops**|**Fruits**<br>**&**<br>**vege.**|**Wheat**|**Corn**|**Other**<br>**grains**|**Oil**<br>**Seeds**|**Livestock**|
|---|---|---|---|---|---|---|---|---|
|**World total**|**-2.0**|**3.2**|**-1.7**|**-0.5**|**2.2**|**0.3**|**-1.0**|**-2.1**|
|**High-income**|**-2.7**|**0.1**|**-2.3**|**-0.7**|**3.3**|**0.4**|**1.1**|**-3.4**|
|Australia and New Zealand|-4.2|-0.5|-2.8|3.5|1.7|3.4|1.2|-2.5|
|Japan|-2.2|3.9|-3.4|5.5|9.1|4.3|6.5|-4.3|
|Canada|0.0|0.7|-2.5|2.4|4.4|-2.1|-1.5|-2.0|
|United States|-5.7|-1.2|-1.3|-4.4|5.3|-1.6|-1.4|-3.0|
|France|-2.4|9.5|-3.1|-4.9|-6.2|-0.6|3.2|-4.2|
|Germany|0.0|-0.6|-1.5|-2.4|-3.1|0.0|10.4|-3.7|
|Italy|-8.5|-1.0|-3.1|-2.9|-2.9|-0.3|2.1|-3.7|
|<br>Spain|-0.9|-1.3|-1.5|6.8|2.7|6.5|2.6|-3.0|
|UK|0.0|0.6|-0.9|4.5|0.0|3.8|5.1|-1.9|
|Rest of EU &EFTA|-3.7|-3.0|-3.1|2.3|2.2|-0.5|4.0|-4.3|
|**Middle & Low-income**|**-1.9**|**4.2**|**-1.2**|**-0.3**|**1.4**|**0.2**|**-2.1**|**-0.7**|
|China|-2.6|0.4|-0.7|0.8|2.1|2.4|0.2|-1.2|
|Indonesia|-1.8|9.6|0.3|0.0|0.6|0.0|2.0|-1.2|
|Malaysia|-3.6|-0.7|5.8|0.0|0.0|10.7|6.5|-0.3|
|<br>Thailand|-4.3|9.9|-2.1|0.0|0.0|1.4|3.4|-6.7|
|Rest of EastAsia &Pacific|-1.8|1.5|-0.3|3.8|4.6|-8.1|6.3|-2.0|
|India|-2.8|3.5|0.5|-3.0|-2.3|-2.8|-2.7|-1.3|
|Rest ofSouth Asia|-0.6|0.2|0.5|1.6|2.4|-0.2|4.2|-1.5|
|Argentina|-11.9|-1.5|-5.0|-1.3|-1.3|-2.7|-4.7|-1.6|
|Brazil|1.0|16.9|-2.4|4.5|3.8|1.7|-3.4|-0.5|
|Rest of LAC|0.2|1.8|-2.0|2.2|2.8|1.8|-0.6|-0.3|
|Russia|-0.8|-1.2|-1.1|6.8|0.1|0.1|-1.2|0.0|
|Rest of ECA|-2.0|-2.0|-1.6|-1.2|-2.1|-0.9|-3.2|-2.4|
|MENA|0.9|-2.7|-3.6|1.5|-6.0|-1.0|-2.6|1.9|
|South Africa|0.0|16.4|-6.7|-4.3|-0.6|-0.8|-1.2|-2.4|
|Rest ofSub-Saharan Africa|0.0|2.4|-0.3|2.8|3.1|2.7|2.2|3.2|

Whether or not the production of an agricultural commodity is negatively impacted by an increased oil price also depends on the oil intensity of production. In some countries, such as Argentina and Italy, a 50% rise in oil price from its baseline level (i.e., a 148% increase from the 2009 level) would cause rice production to drop substantially as rice production in these countries is more oil intensive than in other countries. Agricultural production in major biofuel producing countries, like Brazil, Indonesia and Malaysia, is not affected much by the increased 20
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24
these specifications produce results that are similar to the results of the linear weighted aggregation function.

Temperature is represented as the mean temperature in degrees Celsius, and precipitation is measured as the mean rainfall in millimeters per hour. Both temperature and precipitation data come from NASA’s Global Land Data Assimilation System Project (Beaudoing and Rodell 2020). Air pollution is measured by the concentration of ground-level fine particulate matter (PM2.5), in micrograms per cubic meter, with data obtained from the NASA Socioeconomic Data and Applications Center (Hammer et al. 2022). The Flood Severity Index is based on data from the Global Active Archive of Large Flood Events by the Dartmouth Flood Observatory at the University of Colorado (Brakenridge 2024).

# **4. Documenting the change**

<u>Figure 1</u> shows the changes in the willingness to pay more taxes to combat climate change between 2016 and 2023. The proportion of the population supporting higher taxes for that purpose declined in the 30 countries in our sample, from 36.6 to 30.7. This 5.9 percentage point (16.1 percent) decline resulted from an average drop of almost 12 percentage points for 20 countries in our sample (countries below the 45-degree line in <u>Figure 1) and an average increase of about 6.3 percent in 10</u> countries (countries above the 45-degree line).

The proportion of the population willing to pay part of their income or higher taxes to combat climate change declined among virtually all groups (Table 1) by more than 20 percent for men and only 12 percent for women. By age, the most significant decline was among 40 – 49 (23.9 percent) and 30 – 39 (17.9). Among respondents 70 and older, willingness to pay did not change between the two survey rounds. The differences between the two rounds exceeded 20 percent among people with upper-secondary education, post-secondary education, and bachelor’s degrees. Among respondents with primary or lower levels of education, willingness to pay to combat climate change rose, but the number of respondents in these groups is small. The decline in willingness to pay is most pronounced among married people (a 17.1 percent drop), single respondents (17.0 percent), Christians (20.6 percent), and people with other religious affiliations (21.5 percent). The decline is greater in urban than rural areas.

6
gender matters. Female migration is of interest since its influence on labor supply and child outcomes at home might be very different from that of male migration. World Bank (2008a) argues that men and women show important differences in the determinants of their decision to migrate as well as their opportunity cost of migration. Furthermore, the report finds gender differences in the patterns of remittances, budget allocation of remittance income, and hence gender differences in the impact of migration or remittances on household decisions and welfare.

Predictions from economic theory regarding the differential impact by the migrant’s gender on household decisions and outcomes, such as labor supply and education investment, are ambiguous. With migration and remittances, shifts in income sources may affect intra-household decision making, which suggests potential gender dimensions in the impacts on human capital decisions. When a woman moves abroad to work, increased income from remittances of a female migrant may increase her bargaining power and her influence over investment choices in the household. However, the physical absence from the household is expected to create disconnects and loss of control over the decisions and activities at home. Family disruption can have negative consequences for children’s welfare, and whether the mother or the father goes away may matter. Since the net impact is a priori ambiguous, it becomes an important empirical question.

This study empirically estimates how female and male migration and remittances in Indonesia affect sending households’ child outcomes and labor supply behavior.<sup>1</sup> The analysis uses nationally representative data of the Indonesia Family Life Survey (IFLS) 2000 and 2007. Estimating the causal impact of migration and remittances is usually challenging since the decision to migrate and to remit is likely to be endogenous. To account for endogeneity, this study applies an instrumental variable method using historical migration networks as instruments for migration and remittance receipts.<sup>2</sup>

> 1 The impact on poverty is explored in a parallel paper.  Adams and Cuecuecha (2010) find that receiving remittances reduces the probability of an Indonesian household being poor by 27.8 percent.  Other possible socio-economic effects of migration, such as social impacts, macroeconomic impacts and transfer of knowledge and skills, are beyond the scope of this paper.

> 2 McKenzie and Sasin (2007) provide a detailed discussion on the empirical challenges of estimating the causal impact of migration and possible instruments that have been used in various research questions. Other papers that have used migration networks as instruments to estimate the impact on outcomes in the

3