rafmacalaba/datause-encoder-data huggingface dataset:
risk issues will also need to be tailored to the nature of the urban built environment. They will be hampered by the concentrated nature and density and the relatively high cost of land within urban environments. The urban environment is also more dynamic requiring consideration of distribution of population at various timescales. Many people living in the peripheral or noncentral areas of cities commute to the city center or other employment nodes during workdays. As a result the affected number of people may rise significantly should an event occur during daytime in workdays. An example from Mumbai confirms this situation: the dynamics of night-time and daytime population density varies between the average normal densities of 27,000 persons per sq km to 114,001 persons per sq km (UNISDR 2009).

Impermeable urban areas add to the flood hazard problem. High levels of urbanization in river flood plains and different parts of its catchment might also change the frequency of occurrence of flooding in a particular area. In the mid-1970s when urbanization was just starting to accelerate, a study by Hollis (1975) showed that small floods might increase up to 10 times with rapid urbanization, and big floods with return periods 100 years or over might double in size if 30% of roads were paved. In addition, because of the complications associated with impermeable surfaces, drainage systems, solid waste, debris, movable assets and the sites of buildings, the predictability of flood flows was much lower once the affected urban settlement was reached.

Changing climate means we can no longer expect to keep water away from urban settlements. This dynamic leads to unpredictability in hazard, exposure and vulnerability. It has often been overlooked by the traditional approach to the defense of urban areas. In this approach, water has been directed away from urban settlements using hard-engineered defenses often early on in the pathway near to the source of flooding, where well-understood hydrological and hydraulic models result in high levels of predictable protection. However, changing flood patterns resulting from climate change, increased intensity of rainfall and rising sea levels are likely to ensure that such an approach will be less feasible in the future. Flash flooding and overtopping mean that the full complexity of flooding within an urban setting will need to be addressed.

# **1.3 Is the number of flood events increasing?**

**Many of the major flood events over the last decade (listed in Annex 1) and in previous decades have occurred in highly concentrated urban areas.** Clearly there are many other incidents which have cost enormous numbers of life and property. The list demonstrates that in the short span of 10 years there are enough events in every region of the world to identify flooding as a global problem and to warrant urgent and concerted international action towards risk reduction. In the different parts of the world, Asia-Pacific is the worst affected in terms of economic impact and size of population involved, followed by Africa. There has been a

6
**Figure 6: Annual growth rates (%) of urban and rural labor supplies (2005-2030)**

<!-- Start of picture text --> Urban workers<br>45 degree line<br>3.5 SSA<br>2.5<br>MNA SAS<br>XEA<br>1.5 PER<br>XLC<br>CAC<br>MEX<br>COL<br>ARG<br>CHL<br>CHN BRA 0.5 USA<br>ECA Rural workers<br>-1.5 EUR XHY -0.5 0.5 1.5 2.5 3.5<br>-0.5<br>-1.5<br><!-- End of picture text --> Source: World Bank staff calculations.

Note: ARG-Argentina, BRA-Brazil, CAC-Central America & Caribbean, CHL-Chile, CHN-China, COL-Colombia, ECA-Europe & Central Asia, EUR-EU27 & EFTA, MEX-Mexico, MNA-Middle East & North Africa, PER-Peru, SAS-South Asia, SSA-Sub-Saharan Africa, USA-United States, XEA-Rest of East Asia, XHY-Rest of high-income, XLC-Rest of Latin America The changes in urban and skill premia can be linked directly to changes in inequality. Inequality will also be determined by other factors such as the rate and structure of economic growth rand the initial inequality level. However wages are likely to be a key determinant of inequality, hence the results in Table 1 should provide a good first take on likely changes in inequality. We can anticipate that countries like Argentina, Chile and Mexico are likely to register decline in inequality as both premia decline at a rate above 1 percentage point a year. China and the rest of East Asia also register a significant decline of skill and urban premia and thus their middle class will likely be expanding at a fastest rate among world regions.

17
The historical scenario, (A1 in table 3) assumes that all variables are set equal to their 10year historical averages<sup>15</sup> : real output growth is now lower, set at 4 percent per annum as compared with the baseline growth of 5 percent; fiscal surplus is also smaller, 6 percent of GDP, compared with 6.5 percent under the base case, while the real interest rate on domestic debt is much higher, 19.3 percent, compared with about 10-11 percent in the baseline scenario. Table 4 lists all the assumptions under the historical scenario. The resulting debt dynamics is much less favorable, as public debt to GDP ratio will decline only marginally, from 60 to 55 percent of GDP by the end of the projection period (See Table 3).

_Table 4. Key assumptions for Historical Scenario_

||2006|2007|2008|2009|2010|2011|
|---|---|---|---|---|---|---|
|**Historical**|||||||
|Real GDP growth (in percent)|4.0|4.0|4.0|4.0|4.0|4.0|
|Average real interest rate (nominal<br>rate minus change in domestic CPI,<br>percent)|19.3|19.3|19.3|19.3|19.3|19.3|
|Average real interest rate on foreign<br>debt (nominal rate minus change in|||||||
|U.S. CPI, percent)|6.0|6.0|6.0|6.0|6.0|6.0|
|Change in the real exchange rate<br>(Local currency per US dollar)|-1.0|-0.5|-0.4|-0.8|-0.8|-1.0|
|Inflation rate (GDP deflator, in<br>percent)|6.0|4.5|3.5|3.5|3.5|3.5|
|Primary deficit, percent of GDP|-6.0|-6.0|-6.0|-6.0|-6.0|-6.0|

The country-specific shock scenario, (A3 in table 3), models the impact of country-specific vulnerabilities and risks to the economic outlook. Despite recent improvements, the level and structure of the public debt leave the country vulnerable to a potential loss of market confidence. To model this vulnerability, the country specific alternative scenario was formulated (see table 5 for key macroeconomic assumptions underpinning this scenario). This scenario assumes weakening fiscal efforts leading to lower primary surplus, assumed 3 percent of GDP during 20072011. Weaker fiscal efforts will again trigger a loss of market confidence, exchange rate instability and therefore, a worsening debt dynamics. In this scenario, a loss of market confidence is simulated through a sudden stop in the availability of foreign financing, assuming no new disbursements in 2006. This means that all the interest and principal on external debt falling due

> 15 See the Technical Instructions Manual, section VI.

22
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<mark>Loewenstein, G., Sunstein, C. and Golman, R. (2014). Disclosure: Psychology Changes Everything.</mark> _<mark>Annual Review of Economics</mark>_ <mark>6: 391-419.</mark> Lusardi A, Mitchell O. 2011. “Financial literacy and planning: implications for retirement welling.” <mark>Oxford University Press, 2011, pp. 17-39.</mark> Lusardi A, Mitchell O. 2014. “The Economic Importance of Financial Literacy: Theory and Evidence”, _Journal of Economic Literature_ 52(1): 5–44.

Milgrom PR. 1981. “Good news and bad news: Representation theorems and applications.” _The Bell Journal_ 12(2): 380-91.

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24
# **Figure 2. Emerging Economy Exports Affected by Foreign-Imposed TTBs, 1995-2011**

<!-- Start of picture text --> percent Argentina percent Brazil<br>12 4<br>3.5<br>10<br>3<br>8<br>2.5<br>6 2<br>1.5<br>4<br>1<br>2<br>0.5<br>0 0<br>1995 96 97 98 99 2000 01 02 03 04 05 06 07 08 09 10 11 1995 96 97 98 99 2000 01 02 03 04 05 06 07 08 09 10 11<br>China India<br>percent percent<br>12 7<br>10 6<br>5<br>8<br>4<br>6<br>3<br>4<br>2<br>2 1<br>0 0<br>1995 96 97 98 99 2000 01 02 03 04 05 06 07 08 09 10 11 1995 96 97 98 99 2000 01 02 03 04 05 06 07 08 09 10 11<br>Indonesia Mexico<br>percent percent<br>5 3<br>4.5<br>4 2.5<br>3.5 2<br>3<br>2.5 1.5<br>2<br>1<br>1.5<br>1 0.5<br>0.5<br>0 0<br>1995 96 97 98 99 2000 01 02 03 04 05 06 07 08 09 10 11 1995 96 97 98 99 2000 01 02 03 04 05 06 07 08 09 10 11<br>Russia South Africa<br>percent percent<br>20 3<br>18<br>16 2.5<br>14 2<br>12<br>10 1.5<br>8<br>6 1<br>4 0.5<br>2<br>0 0<br>1995 96 97 98 99 2000 01 02 03 04 05 06 07 08 09 10 11 1995 96 97 98 99 2000 01 02 03 04 05 06 07 08 09 10 11<br>percent Turkey<br>7<br>6 Exports to all economies subject to TTBs in effect<br>5<br>4 Exports to G20 High Income economies subject to TTBs in effect<br>3 Exports to G20 Emerging economies subject to TTBs in effect<br>2<br>1 Exports to all other economies subject to TTBs in effect<br>0<br>1995 96 97 98 99 2000 01 02 03 04 05 06 07 08 09 10 11<br><!-- End of picture text --> Notes: Shares of nonoil exports, constructed by the author with policy data from Bown (2012) and trade-weighting with HS-06 import data from UN Comtrade via WITS, following Appendix equation (A2).

23
CONALEP group is more profitable. Compared to the 1994-1997 cohort, the rate of further training is slightly higher for the 1991-1994 cohort and much higher for the 1990-1993 cohort, which could mean that individuals in older cohorts need to update their skills in order to work efficiently. The proportion of CONALEP graduates who

**Figure 6.**

undergo training because it is a job prerequisite <!-- Start of picture text --> Earnings per month<br>C p<br>Control<br>1800 2000 2200<br>1998 Pesos<br><!-- End of picture text --> increases over time. This suggests two possible explanations: (a) employers' expectations of CONALEP graduates rise as they become more familiar with them, and (b) the technical complexity of jobs held by CONALEP graduates increases over time.

_Earnings and Hours Worked_<sup>_3_</sup> On average, CONALEP graduates earned 17 percent more than the ENE98 control group and 22 percent more than the ENECE99 control (Tables 15 and 16). Controlling for hours <!-- Start of picture text --> Figure 7.<br>Earnings per hour<br>Conalep<br>Control I<br>10 11 12 13<br>1998 Pesos<br><!-- End of picture text --> worked, CONALEP graduates earn close to 20 percent more than the ENE98 control group and 27.5 more than the ENECE99 control group. Even after the 1994 crisis, CONALEP graduates obtained higher earmings than individuals in the control group did. This might indicate that CONALEP has been a good alternative for low income individuals seeking a lasting job. Furthermore, it seems that in downturns such as in the 1994 crisis, 3 Tables 15 and 16 show average earnings, average earnings per hour, and average hours worked for CONALEP graduates and 35
19

# **Table 10. Shares of Local Governments in Total Consolidated Regional Expenditures**

# **and in Select Consolidated Regional Expenditures, %**

|**Summary**<br>**Statistics /Year**|**1992**|**1993**|**1994**|**1995**|**1996**|
|---|---|---|---|---|---|
|1. Share in Total Consolidated Regional Expenditures*||||||
|1.1. Mean|63.93|N/A|63.51|67.32|68.97|
||(1.31)|N/A|(1.08)|**(1.09)**|(1.17)|
|1.2. Standard deviation|12.12|N/A|9.86|10.03|10.83|
|1.3. Coefflicientofvariation|0.19|N/A|0.16|0.15|0.16|
|1.4. Minimunvalue|14.91|N/A|26.81|33.34|36.59|
|1.5. Maximm value|83.94|N/A|84.27|88.30|90.71|
|2. Share in expenditures on National Econony||||||
|2.1. Mean|61.89|59.29|62.92|64.74|65.79|
||(1.71)|(1.79)|(1.73)|(1.5)|<br>(1.69)|
|2.2. Standard deviation|15.77|16.54|15.94|13.87|15.60|
|2.3. Coefficient ofvariation|0.25|0.28|0.25|0.21|0.24|
|2.4. Mifninmvalue|21.96|18.33|16.25|27.76|28.25|
|2.5. Maximum value|89.98|100|100|90.49|93.72|
|3. Share in expenditures on Education||||||
|3.1. Mean|85.56|85.14|87.26|86.06|87.59|
||(0.77)|(0.8)|(0.76)|(0.81)|<br>(0.71)|
|3.2. Standard deviation|7.11|7.36|6.98|7.45|6.54|
|3.3. Coefficient ofvariation|0.08|0.09|0.08|0.09|0.07|
|3.4. Minimumvalue|55.98|53.56|59.05|50.98|59.24|
|3.5. Maxinmunvalue|95.78|100|100|94.64|96.50|
|4. Share in expenditures on Health and Sports||||||
|4.1. Mean|69.94|69.98|69.41|69.24|68.68|
||(1.41)|(1.44)|<br>_(1.53)_|(1.4)|<br>(1.47)|
|4.2. Standard deviation|13.00|13.30|14.10|12.88|13.53|
|4.3. Coefflicientofvariation|0.19|0.19|0.20|0.19|0.20|
|4.4. Minirnunvalue|17.91|17.26|18.40|18.20|13.33|
|4.5. Maxun umvalue|88.16|100|100|90.54|92.18|
|5. Share in expenditures on Social Protection||||||
|_5.1._Mean|22.34|26.31|30.77|67.46|70.79|
||(2.17)|(2.36)|<br>(2.2)|<br>(1.97)|<br>(2.13)|
|5.2. Standard deviation|<br>20.04|<br>21.72|<br> <br>20.31|<br> <br>18.15|<br><br>19.63|
|5.3. Coefficient ofvariation|0.90|0.83|0.66|0.27|0.28|
|5.4. Minirunvalue|0|2.06|2.41|1.18|0.34|
|5.5. Maximum value|100|100|100|96.30|99.59|

Standard error of the mean in parentheses.

The valid number of observations in all cases is 85, except for the local governments share in total consolidated regional expenditures in 1994 (83).

The cities of Moscow and St. Petersburg, Chechnya and Ingushetia excluded from the sample.

* Calculated as a ratio of the sum of the total expenditures of all tiers of local government minus the expenditures on transfers from one local government to another and the sum of the total expenditures of local and regional governments minus the expenditures on transfers from regions to local governments and from local governments to regions.
# Rising Incomes, Transport Demand, and Sector Decarbonization

Mathilde Lebrand, Ewane Theophile<sup>*</sup>

**JEL classification** : R21, R41, Q52, Q54

**Keywords** : Transport expenditures, vehicle ownership, household surveys, carbon emissions

> *All authors are with the World Bank. Corresponding author: mlebrand@worldbank.org. We thank Vivien Foster, Theophile Bougna, Jev Steinbucks, Patrick Behrer, Sebastian Vollmer, Somik Lall, and various participants at internal World Bank seminars for their valuable comments on previous versions of the paper. Qiuyan Yin provided excellent research assistance.
underestimate poverty in 2011/12, it appears that this underestimation may in practice be not that large.

# **IV. Constructing Synthetic Panels**

Our findings in the previous section suggest that the sharp decrease in poverty rate between 2009/10 and 2011/12 is reasonably captured by the 66<sup>th</sup> and 68<sup>th</sup> rounds of the NSSs. Put differently, these two survey rounds provide comparable consumption data for most practical poverty measurement purposes, which is a prerequisite for constructing synthetic panel data. We next provide a brief overview of the methods that will be used.

# **IV.1. Overview of the Synthetic Panel and Vulnerability Analysis Methods**<sup>**16**</sup>

Let xij be a vector of household characteristics observed in survey round _j_ (j= 1 or 2) that are also observed in the other survey round for household i, i= 1,..., N. These household characteristics include variables that may be collected in only one survey round, but whose values can be inferred for the other round. These variables may be roughly categorized in three types i) time-invariant variables such as ethnicity, religion, place of birth, or parental education; ii) deterministic variables such as age (which given the value in one survey round can then be determined given the time interval between the two survey rounds),<sup>17</sup> and ii) time-varying household characteristics if retrospective questions about the values of such characteristics in the first survey round are asked in the second round.

> 16 We provide an overview of the methods that construct synthetic panels and vulnerability lines developed by Dang et al. (2014a) and Dang and Lanjouw (2013, 2014) in this section. For more details, interested readers are encouraged to read the original papers.

> 17 To reduce spurious changes due to changes in household composition over time, we usually restrict the estimation samples to household heads age, say 25 to 55 in the first cross section and adjust this age range accordingly in the second cross section. This restriction also helps ensure certain variables such as heads’ education attainment remains relatively stable over time (assuming most heads are finished with their schooling). This age range is usually used in traditional pseudo-panel analysis but can vary depending on the cultural and economic factors in each specific setting.

16
**Table 2. Determinants of log TFP in LAC with Interaction Effects**

|**Variables**|**(i)**|**(ii)**|**(iii)**|
|---|---|---|---|
|**ln****_NRD_**|**0.264**<br>**_(6.00)***_**|**0.277**<br>**_(6.32)***_**|**0.272**<br>**_(3.21)***_**|
|**ln****_NRD_*****_DLAC_**|**-0.242**<br>**_(-5.20)***_**|**-0.234**<br>**_(-5.12)***_**|**-0.236**<br>**_(-4.33)***_**|
|**ln****_NRD_*****_DR_**|**0.105**<br>**_(2.38)**_**|**0.098**<br>**_(2.34)**_**|**0.094**<br>**_(1.19)_**|
|**ln****_NRD_*****_DR_*****_DLAC_**|**-0.001**<br>**_(-0.06)_**|**0.026**<br>**_(3.02)***_**|**0.043**<br>**_(2.44)**_**|
|**_E_**|**5.553**|**5.414**|**3.474**|
||**_(3.68)***_**|**_(3.68)***_**|**_(1.56)_**|
|**ln****_NRD_*****_DR_*****_E_**|**-0.060**<br>**_(-1.42)_**|||
|**ln****_NRD_*****_DR_*****_E_*DLAC**|**0.284**<br>**_(2.14)**_**|||
|**_G_**|**0.686**<br>**_(2.61)**_**|**0.796**<br>**_(3.12)***_**|**0.062**<br>**_(0.16)_**|
|**ln****_NRD_*****_DR_*****_G_**||**-0.012**<br>**_(-2.73)***_**||
|**ln****_NRD_*****_DR_*****_G_*****_DLAC_**||**0.038**<br>**_(2.86)***_**||
|**_E_*****_G_**|||**8.692**<br>**(3.60)*****|
|**ln****_NRD_*****_DR_*****_E_*****_G_**|||**-0.073**<br>**_(-1.19)_**|
|**ln****_NRD_*****_DR_*****_E_*****_G_*****_DLAC_**|||**0.314**<br>**_(2.89)***_**|
|**Adjusted R**<sup>**2**</sup>|**0.25**|**0.25**|**0.25**|
|**Observations**|**6948**|**6948**|**6948**|

Note: Figures in parentheses are t-statistics. Significance levels of 1%, 5% and 10% are indicated by ***, ** and * respectively. Regression results on country, year and industry dummies, and the constant, are not reported. _NRD_ is the trade-related North-foreign R&D. _DR_ = 1 for R&Dintensive industries and _DR_ = 0 for low R&D-intensity industries. _DLAC_ =1 for Latin America countries and _DLAC_ =0 for non-Latin America countries. _E_ is the secondary school completion ratio for the population aged 25+. _G_ is the average of six governance indicators described in Section 3.

21
# **Box 3.  The future of water use by crops and water needed for grazing**

In some places water resource limits are already either reached or breached. A growing number of river basins are now "closed": all available water resources are already allocated and used. One third of the world’s population currently lives in basins that have to deal with some form of water scarcity: either physical water scarcity, when there is not enough water to meet all demands, or economic scarcity, which is caused by a lack of investment in water or a lack of human capacity to satisfy the demand for water. Future demands for water for agriculture will grow considerably. Currently, the total amount of water depleted to produce food and feed world-wide is approximately 7,000 cubic km, with 1,300 cubic km of that being used for feed grains (Table B3-1). An additional 840 cubic km is transpired to grow grass that is grazed by animals. Thus the share of water for livestock feeding that comes from grazing is almost 40% and the share of water use in agriculture that is used to produce feed for animals is 18%. There is considerable uncertainty surrounding these numbers: estimating water transpired for grazing is a fairly new exercise and relies on many uncertain factors.

_Table B3-1 Crop water consumption and water needed for grazing in 2000 (km_<sup>_3_</sup> _). From De Fraiture et al._ _<u>(2007).</u>_

||<sup>**a**</sup>|**Cro**<br>|**ps **<br>||**Livestock**<br>|<br>|
|---|---|---|---|---|---|---|
|**Region**|**Cereals**|**Total**|**Share of water**<br>**from rivers &**<br>**aquifers (%)**|**Feed**<br>**crops**|**Crazing**|**Share of**<br>**water for**<br>**grazing (%)**|
|**Sub-Saharan Africa**|557|1,071|6|68|218|76|
|**East Asia**|960|1,661|22|277|96|26|
|**South Asia**|896|1,505|41|16|27|63|
|**Central Asia & E Europe **|525|772|20|277|61|18|
|**Latin America**|326|895|12|190|240|56|
|**Middle East & N Africa**|166|225|61|59|13|18|
|**OECD countries**|640|990|17|426|185|30|
|**World**|4,089|7,130|22|1,312|840|39|

a Includes cereals used for feed Based on demand projections, if improvements in land and water productivity or major shifts in production patterns do not take place, the amount of crop water consumption in 2050 would increase by 70-90%, depending on actual growth in population and income and assumptions regarding the water requirements of livestock and fisheries. If that occurs, crop water consumption may reach 13,500 cubic km, up from the 7000 cubic km being used today.

Sources: de Fraiture et al. (2007); Molden et al. (2007) 28