rafmacalaba/datause-encoder-data huggingface dataset:
# **Pessimistic and optimistic trajectories**

In the following analysis, whether one is “optimistic” about future progress in poverty reduction depends on whether one thinks that the new path for the poverty rate in the developing world outside China since 2000 will be extended into the foreseeable future. Current population projections imply that lifting one billion people out of poverty, measured against the $1.25-a-day standard, would require a poverty rate of only 3 percent (to the nearest integer).<sup>10</sup> In describing the two paths to that goal, the discussion will focus on three dates: 2022 (10 years from the time of this writing), 2030, and the date at which the 3 percent target is reached.

The low-case trajectory of poverty reduction implies that the developing world will not reach that 3 percent target by 2030 or even well beyond that date. By this trajectory, the developing world outside China will return to its pre-2000 pace of poverty reduction from 2012 onward, although China will remain on track. Projecting the series forward linearly from 2012 but at the pre-1999 rate of poverty reduction, this trajectory implies a poverty rate of 17.7 percent for the developing world outside China in 2022 and 14.7 percent by 2030.<sup>11</sup> China’s poverty rate would have fallen to zero. Given that current population projections indicate that China’s share of the developing world’s population will be 21 percent by 2022 (and 20 percent by 2030), the overall poverty rate would fall to 14.0 percent by 2022 and to 11.8 percent by 2030. The number of poor would fall from 1.1 billion in 2012 to 0.9 billion in 2022 and 0.8 billion in 2030. In this low-case trajectory of poverty reduction, it would not be until 2060 that one billion people would likely be lifted out of poverty.

The guiding principle for the optimistic benchmark is that the recent success against extreme poverty in the developing world as a whole will be maintained. Two main ways are used here to quantify such a benchmark. The first is based on time-series projections of past experience while the second method (in this section and the next) uses simulations.

> 10 The required poverty rate falls from 3.3 percent in 2012 to 2.8 percent in 2030. The Bank’s current population projections in millions for the developing world, as defined in 2012, are 5,884 in 2012, 6,230 in 2017, 6,561 in 2022 and 7,048 by 2030. Using the 1990 classification of developing countries (to be consistent with _<u>PovcalNet</u>_ ), the numbers are 6,009, 6,355, 6,687 and 7,173, respectively. This difference does not change the poverty counts reported in this paper at the stated number of statistically significant figures.

> 11 The projected poverty rate for the developing world outside China in 2012 (based on the trajectory since 1999) is 21.5 percent (standard error = 0.4 percent), and the rate of poverty reduction based on the pre-1999 trajectory is

> −0.38 percentage points per year.

11
**Annex table 2: A range of carbon estimates by land use and country**

|Countr|Land use|Area (Km<sup>2</sup>)|Min. Carbon|Max. Carbon|
|---|---|---|---|---|
|y|||(MtC)|(MtC)|
|Albania|Mixed Agro-forestry|4,641.4|4.9|13.2|
|Albania|<br>Moderate to high density crops and livestock|21,714.6|26.1|65.3|
|Albania|Other|1,489.2|1.2|3.9|
|Albania|Unmanaged|319.7|0.4|0.9|
|Armenia|Low density grazing|131.5|0.2|0.5|
|Armenia|Mixed Agro-forestry|1,569.2|2.0|5.2|
|Armenia|Moderate to high density crops and livestock|23,877.8|43.4|95.4|
|Armenia|<br>Other|1,637.0|2.6|5.5|
|Armenia|PAs with forests and wetlands|394.7|0.6|1.3|
|Armenia|Unmanaged|854.4|1.3|2.8|
|Azerbaijan|Low density grazing|783.5|0.6|2.0|
|Azerbaijan|Mixed Agro-forestry|3,646.7|5.0|12.2|
|Azerbaijan|Moderate to high density crops and livestock|68,269.7|71.0|202.7|
|<br>Azerbaijan|<br>Other|7,346.5|4.3|17.5|
|<br>Azerbaijan|PAs with forests and wetlands|2,830.5|3.0|8.6|
|Azerbaijan|Unmanaged|2874.9|2.5|7.6|
|<br>Belarus|<br>Low density grazing|,<br>5,094.6|<br>10.0|<br>20.1|
|Belarus|Mixed Agro-forestry|69,908.9|147.5|298.8|
|Belarus|Moderate to high density crops and livestock|117,042.7|250.9|510.3|
|Belarus|Other|8,080.6|17.9|36.4|
|Belarus|PAs with forests and wetlands|5,509.0|11.3|22.8|
|Belarus|Unmanaged|517.9|1.1|2.3|
|Belarus|Unprotected virgin forest|1,194.2|2.9|6.0|
|Bosnia and Herzegovina|Low density grazing|61.7|0.1|0.2|
|<br>Bosnia and Herzegovina|<br>Mixed Agro-forestry|19,619.2|43.5|92.7|
|Bosnia and Herzegovina|Moderate to high density crops and livestock|30,375.5|51.7|123.6|
|<br>Bosnia and Herzegovina|<br>Other|1,418.5|2.3|5.9|
|Bulgaria|Low density grazing|383.8|1.2|2.4|
|<br>Bulgaria|<br>Mixed Agro-forestry|34,493.0|60.0|127.2|
|Bulgaria|Moderate to high density crops and livestock|68,861.8|90.2|216.9|
|Bulgaria|Other|6005.6|7.1|18.0|
|<br>Bulgaria|PAs with forests and wetlands|,<br>256.7|0.7|1.4|
|<br>Bulgaria|Unmanaged|381.8|0.8|1.6|
|<br>Croatia|<br>Low density grazing|373.5|0.0|0.7|
|Croatia|<br>Mixed Agro-forestry|19,925.8|27.0|72.0|
|Croatia|<br>Moderate to high density crops and livestock|27,944.9|26.2|84.3|
|Croatia|Other|6,707.6|4.6|18.5|
|Croatia|PAs with forests and wetlands|604.2|1.9|4.0|
|Croatia|Unmanaged|803.0|0.3|1.9|
|Cyprus|Mixed Agro-forestry|1,617.6|0.4|3.3|
|<br>Cyprus|<br>Moderate to high density crops and livestock|4,638.3|4.2|12.5|
|Cyprus|Other|2,880.8|2.2|7.3|
|Cyprus|Unmanaged|209.9|0.3|0.6|
|<br>Czech Republic|<br>Mixed Agro-forestry|22,378.0|54.4|114.2|
|Czech Reublic|Moderate to hih densit cros and livestock|375419|716|1549|
|p<br>Czech Republic|g y p<br>Other|,.<br>11162.0|.<br>25.3|.<br>53.5|
|<br>Czech Republic|<br>PAs with forests and wetlands|,<br>7,587.2|<br>23.3|<br>48.7|
|<br>Estonia|Low density grazing|<br>1,130.4|4.0|8.3|
|Estonia|<br>Mixed Agro-forestry|16,054.7|51.0|105.7|
|Estonia|Moderate to high density crops and livestock|18,975.7|64.1|133.3|
|Estonia|Other|5,390.2|16.7|34.5|
|Estonia|PAs with forests and wetlands|2,638.0|7.3|15.0|
|Estonia|Unmanaged|180.9|0.7|1.4|

Source: FAO-Unesco (2007); FAO (2010) and authors‘ calculations 53
phone. While this data collection method was likely to impact the accuracy of the information reported, it helped prevent low response rates or high proxy response rates, as it was logistically difficult to interview all members of a household in person.<sup>10</sup> This protocol was pilot-tested, and from field observations it was concluded that the FAFH module was short and simple enough to make the phone interview a cost-effective option.

Lastly, the final arm (T4 or the “bounding/salience” arm) tested the potential of relying on the traditional single-informant practice for the collection of FAFH. The selection of the household informant followed the same protocols that VHLSS had for implementing the whole consumption module, which asks the interviewer to identify the most knowledgeable person about household expenses. In order to correct for the potentially higher measurement error when collecting from a single household informant, this arm aimed to reduce information asymmetries and improve the memory and reporting of the informant by drawing lessons from survey methodology, psychology and behavioral sciences. More specifically, this arm used salience and bounding to mitigate response error by implementing the following:

- First, the interview was implemented in two visits, with households being administered a “dry-run” of the FAFH module in the first visit by asking about FAFH consumption in the past 24 hours. Informants were also informed that they would be revisited in 7 days’ time and an appointment was agreed when they would be available for the second interview. The households were visited seven days later where the full FAFH information “since the last visit” was collected. The dry-run in the first visit was intended to have two effects: (1) exposing the household informant to the type of information the enumerators were looking for (and thereby making the information salient), and (2) helping to provide a clear starting point for the reference period over which consumption is asked in the second visit (bounding) and thereby combat telescoping.

- Second, the household informant was given a worksheet to help him/her remember to keep track of all household members’ FAFH consumption throughout the week. This sheet had very little information, and its goal was to remind the informant to ask others about their consumption outside the home and help the informant keep track of what he/she and everyone else was eating throughout the week (see Appendix 9.1.3 for more information).

> 10 Answering a survey over the phone, which can make reporting harder, is likely to solicit lower-quality responses (and higher item non-response rates) than answering in person (Browning et al, 2014; Safir and Goldenberg, 2008; Tourangeau et al., 2000).

11
# THE GLOBAL FINDEX DATABASE

The findings, interpretations, and conclusions expressed in this volume are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.

The World Bank does not guarantee the accuracy of the data included in this work. The boundaries, colors, denominations, and other information shown on any map in this volume do not imply on the part of the World Bank Group any judgment on the legal status of any territory or the endorsement or acceptance of such boundaries.

The reference citation for the data provided in this volume is as follows: Demirguc-Kunt, Asli, Leora Klapper, Dorothe Singer, and Peter Van Oudheusden. 2015. “The Global Findex Database 2014: Measuring Financial Inclusion around the World.” Policy Research Working Paper 7255, World Bank, Washington, DC.

To download the complete Global Financial Inclusion (Global Findex) database and related reports, visit http://www.worldbank.org/globalfindex.

Design and layout by G. Quinn Information Design, Cabin John, MD.

For questions or comments about this volume, please contact: Leora Klapper The World Bank Group 1818 H Street, NW Washington, DC 20433 USA Email: lklapper@worldbank.org
labor supply, because the higher relative price of carbon-intensive goods and services reduces the real wage of labor, thus encouraging households to work less. They recognize that employment costs are generally a social cost, not a social benefit, an important point often missing in green jobs studies. According to this framework, there can be net job destruction, depending on how the revenues from carbon pricing are used. In the study by Goettle and Fawcett (2009) of the potential implications of a cap-and-trade system for the USA, for example, there are significant reductions in labor input in 29 out of 35 US industries (if there is no revenue recycling). Box 2 gives an informal analysis of the importance of economy-wide effects in a simple neoclassical setting.

Hence knowing how best to model how the aggregate labor market works – and, indeed, how the macro-economy as a whole works – is crucial for a proper assessment of changes in direct and indirect labor demand, and first-round and induced employment effects. There are important differences across types of economy due to different industry structures, labor market institutions and endowments. Probably the most useful concepts to which to attach the label of green jobs are the gross and net numbers of jobs created as a result of green policy implementation. In the case of climate change policies, it would be appropriate to consider the net and gross labor market impacts not only of carbon pricing but also of other efforts to correct market failures contributing to carbon emissions, such as market failures afflicting innovation and the provision of energy infrastructure.

The importance of the implicit or explicit macroeconomic framework is illustrated by Babiker and Eckaus (2007), who show how, in the presence of real wage rigidities or barriers to the sectoral reallocation of labor, climate policy could increase unemployment. Overall labor market impacts can also be influenced by how the revenues from carbon pricing or quota auctions (or other environmental taxes) are used, as illustrated by the literature on the socalled ‘double dividend’ from environmental taxation (see, for example, Fullerton and Metcalf (1997) and Sartzetakis and Tsigaris (2007)). Studies tend to show that if tax revenues are used to reduce payroll tax – a tax on labor supply – employment will fall by less or even increase.<sup>2</sup>

> 2 But they rarely ask why the tax authorities have been levying a payroll tax in the first place.  It may be a second-best way of raising tax revenue and still have a place in the second-best menu of taxes.

10
domestic marlket. The net short run output and employment responses are small. A second important and less surprising result is that the domestic supply curve is much more sensitive to price changes than the export supply function. This implies that any exogenous shocks that lead to an increase in domestic demand, such as an increase in import prices, will have a sizable effect on domestic sales and output, and a contra.tionary effect on exports. Finally, increases in export prices alone were estimated to have a much smaller effect on output and employment than would increases in both export and import prices, as would occur in a devaluation or in the tariff cum subsidy program.

# _2. EMPIRICA L SPECIFICA TION_

As has been observed elsewhere, the evidence from C6te d'Ivoire indicates that firms tend not to specialize in exporting but to sell their products in both domestic and foreign markets. Between 1980 and 198.5 about a third of all manufacturing firms exported to foreign markets. Of those who exported, roughly sixty percent of the value of thei. sales was to the domestic market. We therefore model firm behavio, by allowing firm and industries to produce two different products in a joint production process. The products could be identical or differentiated from each other. Often export products will be similar to the product destined for the domestic market, but distinguished by being of a higher and more uniform quality.

Our empirical specification of joint production is based on the production theory approach employed by Diewert and Morrison (1988) in their study of aggregate export supply and import demand functions in the U.S. In their approach, one specifies a single profit function and derives supply functions to domestic and foreign markets by partially differentiating the profit function with respect to the output price in the respective market. Our work differs in two main respects. First, we work with sectoral rather than aggregate data. It is not our intention to model the complete trade balance, but rather to model short run responses to price changes among firms in the manufacturing sector. Second, we model the domestic demand for domestically produced goods and imports.

Firms are assumed to operate under perfect competition in factor markets. Thus, all input prices are treated as exogenous. Export prices are also assumed to be exogenously determined. We consider two alternative ways of accounting for the _-4 -_
# **Appendix C. Geographical Representativeness Analysis Sample**

|**Region**|**Number of**<br>**Observations**|**% of**<br>**population**<br>**in sample**<br>**(average**<br>**2010-2020)**|**% of total**<br>**population**<br>**(average**<br>**2010-2020)**|**Difference**<br>**between**<br>**columns 2**<br>**and 3**|
|---|---|---|---|---|
|Roraima|58|0.5%|0.3%|+0.2%|
|Amazonas|244|1.9%|1.9%|0.0%|
|Pará|407|3.2%|4.0%|−0.8%|
|Tocantins|102|0.8%|0.7%|+0.1%|
|Maranhão|392|3.0%|3.4%|−0.4%|
|Piauí|205|1.6%|1.6%|0.0%|
|Ceará|507|3.9%|4.4%|−0.5%|
|Rio Grande do Norte|245|1.9%|1.7%|+0.2%|
|Paraíba|271|2.1%|1.9%|+0.2%|
|Pernambuco|534|4.1%|4.6%|−0.5%|
|Alagoas|245|1.9%|1.6%|+0.3%|
|Sergipe|204|1.6%|1.1%|+0.5%|
|Bahia|877|6.8%|7.2%|−0.4%|
|Minas Gerais|1,141|8.9%|10.2%|−1.3%|
|Rio deJaneiro|1,144|8.9%|8.3%|+0.6%|
|São Paulo|2,824|21.9%|21.7%|+0.2%|
|Paraná|718|5.6%|5.5%|+0.1%|
|Santa Catarina|484|3.8%|3.4%|+0.4%|
|Rio Grande do Sul|718|5.6%|5.5%|+0.1%|
|Mato Grosso do Sul|220|1.7%|1.3%|+0.4%|
|Mato Grosso|150|1.2%|1.6%|−0.4%|
|Goiás|410|3.2%|3.3%|−0.1%|
|Distrito Federal|169|1.3%|1.4%|−0.1%|
|Espirito Santo|428|3.3%|1.9%|+1.4%|
|Amapá|29|0.2%|0.4%|−0.2%|
|Rondonia|114|0.9%|0.8%|+0.1%|
|Acre|33|0.3%|0.2%|+0.1%|

43
# **Figure 7 Investment Ratio to Base: Permanent Increase in Foreign Aid, with Borrowing Constraints**

<!-- Start of picture text -->
1.45<br>1.4 AID-P & I adj AID-P & I adj & trade AID-P & G adj<br>1.35<br>1.3<br>1.25<br>1.2<br>1.15<br>1.1<br>1.05<br>1<br>1 4 7 10 13 16 19 22 25 28 31 34 37 40<br>time periods (years)<br>investment ratio<br><!-- End of picture text -->

_Source: Authors’ calculations._ Sensitivity tests of rising trends in trade shares and trade substitution elasticities result in amplifying somewhat the effect on investment and output while consumption is almost identical or very slightly more (figure 6). The appreciation of the exchange rate is more at every point in time. The eventual reduction of the initial appreciation is, therefore, also relatively less over time (figure 5—as what would be expected from the static case of changing shares and elasticities).

Much concern has also been made regarding the differences in effect between exogenous flows financing pure government consumption rather than investment. Although not directly productive,<sup>13</sup> the expansion of government consumption as an exogenous component of aggregate demand has a Keynesian effect on prices and income. If the same amount in foreign grants all go to government consumption rather than investment, prices will still be bid up in the same way and the consumer will face the same kind of Ramsey saving decision. Hence, the results with regard to the real exchange rate (see figure 5), investment (see figure 7), and output are essentially the same as the investment case. The only difference is that with total absorption or aggregate demand also behaving the same way, the consumption curve over the simulation period will shift

> 13 13Except for public expenditures on social sectors, which potentially may raise human capital in the long run. Because the trade-offs regarding investments for the development of human and physical capital are not the focus of this paper, government consumption is considered to be pure consumption expenditures for goods and services.

23
actual regressions I work with two measures of the EMPI. The ...rst one, as discussed above, is constructed by setting �; � and � to the inverse of the median standard deviation of the relevant components across countries. The second, alternative measure is constructed as an unweighted average of the EMPI components outlined in (11), i.e. when setting � = � = � = 1: I use the two di¤erent measures of exchange rate volatility under interventions to ensure that the obtained results are reasonably robust to a simple change in government preferences regarding the applied tools for exchange rate management.

The exchange rate series of MICs’currencies vis-a-vis the U.S. dollar were obtained from the IMF’s International Financial Statistic where I used the end-of-period values. The exchange rates for the EUR, JPY and GBP were calculated using crosscurrency exchange rates where the synthetic USD/EUR exchange rate was taken from the DataStream. The interest rate series used are the money market interest rates from the IMF’s International Financial Statistic where the pre-1999 data for the euro area were obtained from the Fagan et al. (2001) dataset. The data series of countries’o¢ cial foreign exchange (FX) reserves and narrow money (money) were obtained from the IMF’s International Financial Statistic where the series for the euro area was taken from the DataStream and the FX reserves series extended from 1997 back to 1976 using growth rates of German FX reserves.

The series of GDP growth rates and in‡ation were acquired from the GDF & WDI Central database of the World Bank where GDP growth rates and in‡ation for the euro area were taken from the Fagan et al. (2001) dataset and the DataStream, respectively. The money velocity was constructed as the ratio of nominal GDP and money obtained from the GDF & WDI Central database of the World Bank where the euro area equivalents were taken from the Fagan et al. (2001) dataset and the DataStream, respectively. The terms of trade series were taken from the GDF & WDI Central database of the World Bank and the terms of trade series for the euro area was constructed as an average of the euro area 12 countries.<sup>18</sup> The measures of synchronization of movements in yt; �t; vt; it and tt are based on ratios of the national 18I constructed also an average using the four biggest countries Germany, France, Italy and Spain but the series was very similar to that for the euro area 12 countries.

13
12 greater.<sup>2</sup> The overall impact on SSA net farm incomes is a boost of 7 percent, and the real value of the region’s agricultural and food exports would rise by more than 80 percent (Anderson, Martin and van der Mensbrugghe 2006c, table 7).

# **Prospects for policy reform in the next decade**

Since the process of reform is far from complete, pressures to open up markets will continue multilaterally, preferentially and unilaterally, while groups with vested interests in current policies will continue to counter-lobby to retain and increase sectoral assistance. How might these net out?

Multilaterally, the WTO membership has had agricultural tariff and subsidy cuts high on its Doha Development Agenda, but as of July 2006 the political willingness to reform varied too much between the key players for an agreement to be in sight. If no agreement is forthcoming by early 2007, and if the U.S. President is unable to secure a renewal of fast-track authority when it expires in July 2007, it could be several more years before that round of negotiations is concluded. Even then, the extent of reform agreed to could be quite modest. Three components in particular matter. One is the large ‘binding overhang’ in current tariffs and subsidies, which means large cuts in legally bound rates will be required to ensure real cuts in applied rates of subsidies and tariffs. Another is the exceptional treatment for ‘sensitive’ and ‘special’ farm products that subjects them to lesser cuts (and possibly to no tariff cap, should one be introduced for other products). The third is the extent to which developing countries are granted ‘special and differential treatment’ in the form of lesser cuts and longer phase-in periods, with
<!-- Start of picture text --> Figure 7: Expenditures on Computerized Information by industry (% of<br>industry sales)<br>2000<br>4.0%<br>2008<br>3.0%<br>2.0%<br>1.0%<br>0.0%<br>Agriculture Services ICT Services Low tech Medium/High Extractive<br>and related (except ICT) manufacturing tech<br>manufacturing<br><!-- End of picture text --> In the case of innovative property, services in general spend more than manufacturing (as a share of industry sales). This is driven by product development in the financial industry. Low tech manufacturing spends less than medium/high tech manufacturing and extractive industries. Worryingly, R&D expenditures have not increased much in recent years (except in ICT firms, which have a small share in total R&D spending in the economy).

**Figure 8: Expenditures on Innovative Property by industry (% of industry sales)**

<!-- Start of picture text --> 3.5%<br>2000<br>3.0%<br>2008<br>2.5%<br>2.0%<br>1.5%<br>1.0%<br>0.5%<br>0.0%<br>Agriculture and Services (except ICT Services Low tech Medium/High Extractive<br>related ICT) manufacturing tech<br>manufacturing<br><!-- End of picture text --> 24