rafmacalaba/datause-encoder-data huggingface dataset:
Northeast average of around 14 percentage points, which is around twice the reduction in the national rate. Since 1996, poverty has remained largely stable only being slightly reduced to around 41 percent in 2001 (Figure 4.1).

The fall in Bahia’s social indicators such as infant mortality and adult illiteracy during 1980–99 corroborates the improvement in measured income poverty. The infant mortality rate dropped dramatically from 95.4 per 1,000 live births in 1980 to 45.4 per 1,000 live births in 1999 (Table 4.1). Today the infant mortality rate in Bahia is among the smallest in the Northeast region and mainly a rural phenomenon. The large advances can be attributed to an improved health care system, increased access to treated water (see Section 6), economic growth, urbanization, and past investments in education (see Section 6), and other social programs. Hence, to further reduce the infant mortality rate in order to reach the national average of 35, especially in the rural areas, further actions are called for. These include general livelihood improvements such as access to clean water and sanitation, high quality education and health care, and a daily caloric intake sufficient to cover the basic needs. Moreover, Filmer and Pritchett (1997) find that a 10 percent increase in income is associated with a 6 percent lower infant mortality rate.

|**Table 4.1: Illiteracy and In**<br>**1980 and 1**<br>**Illitera**<br>**People O**<br>**1**<br>**Per**|**fant M**<br>**999**<br>**cy Rate**<br>**ver Age**<br>**5**<br>**cent**|**ortality**<br> <br>**Infant M**<br>**Per 1,0**<br>**Bir**|**,**<br>**ortality**<br>**00 Live**<br>**ths**|
|---|---|---|---|
|**1980**|**2001**|**1980**|**1999**|
|Brazil<br>25.3|12.4|85.2|34.6|
|Northeast<br>45.9|24.3|131.3|53|
|Alagoas<br>54.0|30.6|159.5|66.1|
|**Bahia**<br>**43.1**|**22.9**|**95.4**|**45.4**|
|Ceará<br>45.5|24.8|155.2|52.4|
|Maranhão<br>51.0|23.4|126.3|54.2|
|Paraíba<br>49.3|27.2|170.6|60.3|
|Pernambuco<br>42.2|22.0|149.8|58.2|
|Piauí<br>49.6|29.4|105.6|45.3|
|Rio Grande do Norte<br>44.4|24.2|169.3|48.7|
|Sergipe<br>46.5|21.4|110.9|45.4|

Adult illiteracy took the same declining path as the headcount poverty rate and infant mortality. During 1980–2001 the illiteracy rate for people over age 15 fell 20.2 percentage points, to 22.9 in 2001. Bahia managed to reduce the illiteracy rate by 55 percent, the largest improvement in Brazil, during 1970-2000. However, Bahia is among the 9 states with the highest illiteracy rate in Brazil. Efforts to lower 13
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identified with an asterisk constitute a somewhat arbitrarily selected set of 'predictions' made one and two years ahead. Subtracting the actual gt from these gives the column called 'errors' for which there is a mean for the time series of 1.9 percent and standard deviation 1.3 percent (cv = .7). Thus short-term forecasting does produce a seemingly less uncertain series. However, for long-term forecasting, the relevant dispersion is that of the series itself.

Attention is now turned to the impact of such uncertain exogenous variables in derived forecasts.

TABLE 1: REAL ANNUAL GDP GROWTH RATES IN OECD

|----------------|g<br>Actual<br>Reported by<br>(latest<br>Ghose (1978)<br>estimates)<br>--------------------------------------------------|P<br> <br>1978<br>Report 8|g<br>redicte<br>1980<br>14|d<br>1982|g-g<br>Errors<br>using*****<br>__---------|g_g<br> <br>g<br>_|
|---|---|---|---|---|---|---|
|1975|-0.7<br>3.0*||||+3.7|-5.30|
|1976|5.2<br>5.4*||||+0.2|0.04|
|1977|3.6<br>5.0*||||+1.4|0.39|
|1978|3.9<br>4.6*|3.8|||+0.7|0.18|
|1979|3.4<br>3.3|4.2*|||+0.8|0.23|
|1980|1.4|4.2*|1.7||+2.8|2.00|
|1981|1.2|4.2|3.5*||+2.3|1.92|
|1982|0.2 (est.)|4.2|3.5*|0.2|+3.3|16.5|

* Selected as 'the' short-term forecast.

# 3.1.2 Conditional forecasting precision

The main paper on conditional forecasting accuracy is by Feldstein (1971). His key simplifying assumption is to introduce stochastic forecast exogenous variables xF but to assume that these are independent of estimates of the regression coefficients. Other important restrictions are serially independent disturbances, no lagged endogenous variables and linearity in the
Lee estimates seem closer to the African official statistics. As the brain drain is particularly important in African countries, the Barro and Lee indicators are preferable. Consequently, data for _N tj_ , _s_ are taken from De La Fuente and Domenech (2002) for OECD countries and from Barro and Lee (2000) for non-OECD countries. For countries where Barro and Lee measures are missing (about 70 countries in 2000), we transpose the skill sharing of the neighboring country with the closest human development index regarding education. This method gives good approximations of the brain drain rate, broadly consistent with anecdotal evidence.

## **2.3. Changes between 1990 and 2000**

The number of skilled migrants has drastically increased in recent decades. This is partly explained by the many “quality-selective” policies that were introduced in OECD countries in the 1980s and 1990s. The stock of high-skilled immigrants residing in the OECD increased by 70% between 1990 and 2000, while that of unskilled immigrants increased by only 30% during the same period (Docquier and Marfouk, 2006). However, this rapid increase in the stock of skilled migrants does not imply that the rate of skilled migration increased at a similar rate because the last decade was also characterized by a sharp rise in the educational attainment in sending countries. Consequently, as shown in the Docquier-Marfouk's database, the brain drain only experienced a minor change between 1990 and 2000 (from 5.0 to 5.4 percent).

# **3. RESULTS FOR SMALL STATES**

Table 2 presents information on skilled and average emigration rates, and on the schooling gap (defined below), for 1990, the period 1990-2000, and 2000, with focus on small states. The data are provided for small states as a whole, and grouped according to population size, region and income, and for small developing island states. The emigration rates are also provided for other country groupings, including those with somewhat larger population, the world, and high-income countries and developing countries as a whole.

Table 2 presents a number of interesting findings. We start with those associated with the skilled emigration rate (first column).

15
4 agencies--in our hypothetical example, evaluations of 100 American universities, drawn from six different education organizations. A set of variables to be tested are then defined--e.g., quality of the faculty, research facilities--and given a simple numerical scale. Two independent coders then read the different evaluations and, based on their subjective analysis of that information, code the level of each variable. The coder generates this numeric score based upon quantitative -nd qualitative analysis in the original report. The final set of data, usually the average of the two coder's evaluations, is then used for statistical analysis.

The statistical data of the 121 rural water supply projects in this study were assembled from project documents using the systematic case review method'. Ex-post project assessments by development agencies typically combine limited quantitative evaluations with subjectve judgements of project performance. Each project document was read by two independent readers. From this information, these two readers coded specific project variables (e.g., oveal success of project) onto a scale from I to 7, creating 144 distinct variables.

Appendix Table 1.1 shows the list of coded variables (along with the basic summary statistcs). These can be divided into four groups:

- project performance indicators (e.g. "overall project effectiveness", "percent of water system in good condition');

- e measures of participation (e.g. 'overall beneficiary participation", "participation in construction");

'Finsterbusch and Van Wicldin m (1987) used the systematic case review approach for their study of USAID projects. While Esman and Uphoff (1979) did not exactly follow this methodology, their basic approach was similar: converting independent project evaluations to a numercal scale.
rates of access to upper secondary education in China. Yunnan Province has a relatively low upper secondary (gross) transition rate at 82 percent in 2015 in comparison with other provinces in China.

Figure 4.2 Composition of population by education level, Yunnan versus national average (2015) <!-- Start of picture text --> 45.00%<br>38.70% 38.80%<br>40.00%<br>32.90%<br>35.00%<br>30.00%<br>25.70%<br>25.00%<br>20.00% 16.90%<br>12.90%<br>15.00% 11%<br>8.70% 8.70%<br>10.00%<br>5.70%<br>5.00%<br>0.00%<br>Illiterate Primary Junior Seconior Tertiary<br>Education Secondary Secondary Education<br>Education Education<br>National 2016 Yunnan 2016<br><!-- End of picture text --> _Source_ . National Bureau of Statistics of China 2016b. http://www.stats.gov.cn.

Yunnan has gradually narrowed the education gap, except at post compulsory level. In recent years, Yunnan has been bridging its education gap with the rest of China. Compared with the results of NBS’ household survey between 2007 and 2016, the share of Yunnan’s population attaining at least junior secondary education has largely increased—from 38 percent to 53 percent. Nonetheless, compared with the rest of China, Yunnan is still lagging in its share of population reaching upper secondary education level and above at 20 percent compared with 30 percent nationwide. In light of this gap, the provincial FY13 development plan outlined specific targets— by 2020, the Yunnan aims to achieve 90 percent GER at upper secondary education level (same as national target) and 45 percent GER at tertiary institutions (slightly lower than national target).

Considering the FY13 education targets, Yunnan has meaningfully increased its education financing and narrowed the financing gap relative to the national average. Yunnan spent a gross CNY 2,204 per capita in 2015 (US$350), about 84 percent of the national average (CNY 2,628 or US$417). While it is low, this level of spending is a much higher proportion than Yunnan’s relative per capita income. This is a welcome investment, since, as noted, Yunnan’s large population is one of its most valuable but underdeveloped resources. In 2015, Yunnan’s financing for education reached CNY 76,746,000 (US$12,182,000); it represented an astounding close to four times increase from that in 2007. In contrast, the national average had increased three times. Yunnan’s faster growth in education financing was noticeable across all sources, with the only exception of donations and fundraising. Moreover, its financing gap with the rest of the country has been rapidly shrinking across the last decade. Yunnan’s education financing used to be at 55 percent of the national average in 2008, 65 percent in 2010, 66 percent in 2012, and 81 percent in 2014 (Figure 4.3). At this rate of increase, Yunnan is on track to reach the national level of financing in education by 2019.

47
# DATA LINKS/CITATIONS FOR MAPS

**_All maps were created by Dr. Andrew Jacobson, Duke University, unless otherwise noted._**

**Cities** : Reed, T. and S. Trubetskoy (2018) The Belt and Road Initiative and the Value of Urban Land. World Bank mimeo. **Ports** : Reed, T. and S. Trubetskoy (2018) The Belt and Road Initiative and the Value of Urban Land. World Bank mimeo.

**Roads & rails** : Reed, T. and S. Trubetskoy (2018) The Belt and Road Initiative and the Value of Urban Land. World Bank mimeo. **Administrative boundaries** : GADM v2.8 ‐ <u>https://gadm.org/index.html .</u>

**Forest change** : Global Forest Watch 2015 v1.3 ‐ https://www.globalforestwatch.org/ .

Hansen, Matthew C., et al. "High-resolution global maps of 21st-century forest cover change." _science_ 342.6160 (2013): 850-853.

**Cropland** 2015: ESA CCI land cover V2.0 ‐ <u>https://www.esa‐landcover‐cci.org/?q=node/175</u>

Cite – ESA Climate Change Initiative – Land Cover Project 2017 Copyright notice: © ESA Climate Change Initiative ‐ Land Cover project 2017 Should you write any scientific publication on the results of research activities that use one or several CCI‐LC products as input, you shall acknowledge the ESA CCI Land Cover project in the text of the publication and provide ‐ the project with an electronic copy of the publication (contact@esalandcover <u>cci.org).</u>

**Protected areas** : UNEP & IUCN. 2018. The World Database on Protected Areas (WDPA). Cambridge: UNEP‐ WCMC. <u>https://protectedplanet.net/</u>

**Pangolin hub & trafficking routes** : Chouvy, P‐A. 2013. An Atlas of Trafficking in Southeast Asia. London: I.B. Tauris.

**Intact Forest Landscape 2013** : Potapov, Peter, et al. "The last frontiers of wilderness: Tracking loss of intact forest landscapes from 2000 to 2013." _Science Advances_ 3.1 (2017): e1600821.

<u>http://www.intactforests.org/</u>

**Centers of Endemism** : Li BV, Hughes AC, Jenkins CN, Ocampo-Peñuela N, Pimm SL (2016) Remotely Sensed Data Informs Red List Evaluations and Conservation Priorities in Southeast Asia. PLoS ONE 11(8): e0160566. <u>https://doi.org/10.1371/journal.pone.0160566</u>

**Species distributions** (clouded leopard & tiger): Goodrich, J., Lynam, A., Miquelle, D., Wibisono, H., Kawanishi, K., Pattanavibool, A., Htun, S., Tempa, T., Karki, J., Jhala, Y. & Karanth, U. 2015. _Panthera tigris_ . The IUCN Red List of Threatened Species 2015: e.T15955A50659951.

<u>http://dx.doi.org/10.2305/IUCN.UK.2015-2.RLTS.T15955A50659951.en.</u> Downloaded on 10 September 2018.

Grassman, L., Lynam, A., Mohamad, S., Duckworth, J.W., Bora, J., Wilcox, D., Ghimirey, Y., Reza, A. & Rahman, H. 2016. _Neofelis nebulosa_ . The IUCN Red List of Threatened Species 2016: e.T14519A97215090.

<u>http://dx.doi.org/10.2305/IUCN.UK.2016-1.RLTS.T14519A97215090.en.</u> Downloaded on **10 September 2018** .

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- Corbett, Jane. 1988. Famine and Household Coping Strategies. _World Development_ , **16** (9), 1099– 1112.

- Dardanoni, Valentino. 1991. Precautionary Savings Under Income Uncertainty: A Cross-Sectional Analysis. _Applied Economics_ , **23** (1), 153–160.

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Several studies have estimated the impact of the European Union (EU) Emissions Trading Scheme (ETS), which restricts emissions of CO2 across a wide range of firms. Martin et al. (2016) provide an excellent review of the policy in general and summarize the evidence on employment effects, finding that some studies determined the EU ETS had no impact on employment, whereas others find statistically significant negative effects, with the effects varying based on the industries studied, time period covered, and source of data on employment. In general, all the studies suffer from similar limitations as those mentioned previously: most notably, they look only at the effects on regulated industries, and cannot capture spillover effects (positive or negative) elsewhere in the economy.

# _ii. Effects on Wages_

Walker (2013) is the only study that investigates the effect of climate change policies, specifically the 1990 CAAA, on wages. He uses the confidential Longitudinal Employer Household Dynamics data set from the U.S. Census Bureau to follow workers across their jobs over time. This allows him to study two costs associated with environmental policy that have typically been ignored in the literature: long-run earnings loss for workers who leave a sector affected by climate change policy, and wage costs borne by workers who choose to remain in that sector.

Walker uses a difference-in-difference-in-difference technique to make withinsector comparisons before and after the implementation of regulatory policy. He finds that workers in sectors that were affected by the policy suffered persistent losses of more than 5 percent of their pre-regulated earnings in the three years after implementation of the legislation. These losses were borne mostly by workers who were displaced from regulated sectors, rather than those who remained in their jobs. He also calculates that a worker in a cohort experienced a present discounted wage loss of 20.2 percent of their pre-regulated wages (calculated at a 4 percent discount rate). This paper is particularly important not only because it looks into an economic outcome mostly overlooked by the literature, but also because it suggests the presence of long-term negative effects from climate change policies.

19
Table 6.4 also gives separate figures for unskilled and skilled labor, as the respective wage shares of total activity generated. Interestingly, for some of the water-sector projects in Honduras the skilled share appears to be quite high (note also that for hydropower projects in Brazil, basically all engaged workers are high-skilled).<sup>30</sup> These numbers indicate that the “growth dimension” of some of these projects may be substantial; although this of course remains to be scrutinized further. Note again that these numbers are very conservative in the sense of only registering employment that is generated directly by the respective projects.

# **Table 6.4: Direct Employment and Economic Activity Generated by “Green” Stimulus Activities in Latin American Countries. Employment in Man Years per US$ Billion. Activity Generation as Share of Total Activity.**

|Sector|Direct<br>employment|Share skilled<br>labor|Share<br>unskilled<br>labor|Share<br>domestic<br>inputs|Share foreign<br>inputs|
|---|---|---|---|---|---|
|Water capitation, Honduras|43,300|28 %|12 %|40 %|20 %|
|Water network<br>rehabilitation, Honduras|58,300|30 %|20 %|40 %|10 %|
|Water network expansion,<br>Honduras|66,700|20 %|30 %|40 %|10 %|
|Water treatment plant,<br>Honduras|25,000|10 %|10 %|80 %|0|
|Rain drainage, Brazil|34,000|8 %|16 %|28 %|48 %|
|Sewerage, Brazil|21,800|4 %|11 %|67 %|17 %|
|Hydro power, Brazil|4,500|5-10 %||9|0-95 %|
|Rural electrification, Peru|23,000|14 %|7 %|26 %|53 %|

Source: Schwartz, Andres and Draboiu (2009).

Which types of “green stimulus” will be most significant for different developing countries requires further investigation, with additional data. The job-creating, and emissions-reducing, effects of such stimulus need to be far more rigorously assessed than has been done to date. One also needs a further analysis of the types of labor engaged by the various projects, by skill category and, ideally, by opportunity cost. Effects are likely to vary widely across countries/regions and among sectors. Most likely, forestry and biodiversity conservation, the water sector, and environmental cleanup (where more remains to be done in many lowincome countries), as well as energy efficiency and some renewable energy production (such as biomass for heat and electricity generation) are even more significant “green” activities in developing countries than in high-income countries; some of these also may be relatively good job generators, at least in the short run.

Among other sectors, the employment-creating and revenue-generating effects of expansion of biofuels production and exports could be substantial in some countries, depending on local conditions and the comparative economics of biofuels versus alternatives. Other renewable energy sectors, including wind power and solar photo-voltaics, are likely to be less significant in developing countries over the short-run than in high-income countries, since developing-

> 30 Precise definitions of “high” and “low” -skilled labor can vary by country and project; in particular the threshold of what is considered high-skilled could be lower in Honduras than e. g. in Brazil.

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quality throughout the US, while the PUC's<sup>mandate essentially requires fair treatment for</sup> consumers. However, in the case of acid rain,<sup>the individuals affected by the pollution (residents of</sup> New England) differ from the consumers of electricity<sup>in the mid-west. A cost effective reduction</sup> in emissions would generally require both a reduction<sup>in output (with consequent increase in price)</sup> and a modification of the production process<sup>(e.g. through the installation of scrubbers). However,</sup> the PUC is unlikely to be willing to raise<sup>the price to the efficient level, since this would mean</sup> mid-westerners were paying for the benefits of lower<sup>emissions accruing to New England residents.37</sup> The EPA, taking this into account, will be forced<sup>to require the adoption of a more stringent</sup> technical standard than otherwise.

This kind of uncoordinated behavior would not be inefficient<sup>if the ministries' mandates</sup> were consistent (i.e. referred to the same individuals).38 The example shows however, that in the case of pollution control, inconsistent mandates<sup>can prevent cost effective emission reductions.</sup> Another example from the US is the ongoing dispute<sup>between the Los Angeles Department of Water</sup> and Power (supplying water and electricity to<sup>3.4 million customers) and the California Department</sup> of Fish and Game over the use of water from the<sup>Owens River.39</sup> One solution to these horizontal coordination problems<sup>is to precisely and exhaustively</sup> define consistent mandates. This, however, would<sup>require the identification of a large number of</sup> contingencies. It would most likely shackle the<sup>central government with red tape, the costs of which</sup> would outweigh the efficiency gains sought in the<sup>original ministerial division.</sup> A more plausible alternative would be to create<sup>a formal or informal institution to</sup> facilitate inter-departmental coordination. Thus,<sup>the efficiency gains of the ministerial structure could</sup> 'Ibis assumes that property rights initially reside<sup>with the generators of electricity.</sup> 3'Note that if two agents' objectives are identical, Nash equilibrium is efficient. 3'New York Times, (1991).