rafmacalaba/datause-encoder-data huggingface dataset:
16 between different output varieties within an industry, σ n , which is not sufficiently identified by the share equations alone. The output shares of the hypothetical country, appearing as s<sup>*</sup> nt , are measured using sample averages in (18), but will be fully absorbed by year fixed effects, δ nt in the share equations (16). Then the equality of σ n can be imposed between (16) and (18) to identify these elasticities.

Our data set covers 34 countries from 1982 to 1997, a total of 342 observations. GDP is measured in constant 1995 U.S. dollars to make cross country and time series comparisons appropriate. We will use (9) to measure export variety from country c to the U.S. in every years 1972 – 1988 (using the TSUSA data), and 1989 – 1997 (using the HS data). This gives a consistent comparison of export variety in each country relative to the hypothetical country producing all varieties.

There are three kind of primary factor endowments: labor, capital and agriculture land. Labor is defined as the number of persons in the labor force of each country. Capital is constructed using perpetual inventory method using real investment of the countries. Real investment is obtained by deflating the gross domestic capital formation of the countries with the respective GDP deflator on domestic capital formation. In addition, we construct the base year capital stock using an infinite sum series of investment prior to the first year, assuming that the growth rate of investment of the first five years are good proxy for investment prior to the first year. All these data, together with price deflators of GDP, exports and imports are available in World Development Indicators (World Bank, 2003).

We aggregate up all export goods into N = 7 sectors: agriculture, textiles & garments, woods & papers, petroleum & plastics, mining & basic metals, machinery & transport equipment, and the electronics. The value added of these sectors are available in the UNIDO
investments will be greatest. This is particularly so for labor in the short-run, before migration has had the opportunity to arbitrage away spatial real wage differentials associated with geographical asymmetries in market access. A better understanding of these dynamics would help policy makers anticipate geographically differentiated impacts from road investments. For instance, if better connectivity widens disparities in the short-run, the additional induced migration to leading urban areas that might be expected to occur in the long-run will accelerate demand for housing and public services in those areas.

The New Economic Geography (NEG) framework pioneered by Krugman and various coauthors (Krugman, 1991a, 1991b; Fujita _et al_ ., 1999) provides a suitable starting point to address these questions. Unlike earlier theoretical paradigms, these models enable explicit microeconomic assumptions to coexist alongside increasing returns to scale within a general equilibrium framework (Ottaviano and Thisse, 2004). This seamless theoretical integration has enhanced the attractiveness of NEG models among the wider economics profession, and provides us with a theoretically and empirically coherent way to explore research questions relating to the spatial impacts of major interregional transport projects. The key idea that is central to the NEG is the notion that transport costs affect economic outcomes. This is not a new idea of course (see, for example, Ottaviano and Thisse, 2004, on this point), but the NEG provides a rigorous framework for empirical modeling and enforces internal consistency. As explained in detail below, we measure transport cost changes (as reflected in reduced travel times) due to China's investment in the NEN using Geographic Information Systems (GIS) methods. This produces changes in economic outcomes under the theoretical and empirical model. We focus on the short-run equilibrium outcomes of the NEG model, rather than longrun outcomes. The latter would require an analysis of migration in response to real wage disparities, which, although undoubtedly important to a complete assessment of the NEN's eventual impacts, is beyond the scope of the current paper.<sup>4</sup> The theoretical relationship between wages and "market access" – the so-called NEG wage equation – lies at the core of our analysis. This builds on the now well-established literature which estimates the wage equation (see, _inter alia_ , Hanson, 1997, 2005; Redding and Venables, 2004; Brakman _et al_ , 2006; Fingleton, 2005a, 2006, 2008; Fischer and Fingleton,

> 4 It may also be argued that, from a spatial perspective, the short-run impacts are the more interesting. In the long-run, it is obvious that labor mobility will help to erode any short-run changes in regional and/or urban-rural disparities induced by a change in transport costs. By contrast, the short-run impacts on spatial and urban-rural inequalities are theoretically ambiguous as to their direction. Furthermore, modelling the short-run impacts is a prerequisite for modelling the long-run impacts.  Finally, the short-run may last many years or even decades.

3
- Qi, Jiaqi, Xiaoyong Zheng, and Hongdong Guo. 2018. “The formation of Taobao Villages in China.” _China Economic Review_ .

Ravallion, M., and S. Chen. 2001. Measuring Pro-Poor Growth. Policy Research Working Paper, No. 2666, The World Bank: Washington D. C.

- Rosenbaum, P., and D. Rubin. 1983. “The central role of the propensity score in observational studies for causal effects.” _Biometrika_ 70(1): 41–50.

- Rosenzweig, Mark R. 1988. “Risk, implicit contracts and the family in rural areas of low-income countries.” _The Economic Journal_ 98(393): 1148–1170.

- Schmitz, Hubert, and Khalid Nadvi. 1999. “Industrial clusters in developing countries-clustering and industrialization: Introduction.” _World Development_ 27(9): 1503–1514.

- Sinai, Todd, and Joel Waldfogel. 2004. “Geography and the Internet: Is the Internet a Substitute or a Complement for Cities?,” _Journal of Urban Economics_ 56(1): 1–24

- Woetzel, Jonathan, Jeongmin Seong, Kevin Wei Wang, James Manyika, Michael Chui, and Wendy Wong. 2017. “China’s Digital Economy: A Leading Global Force.” New York: McKinsey Global Institute.

World Bank. 2016. _Live Long and Prosper: Aging in East Asia and Pacific._ World Bank East Asia and Pacific Regional Report. Washington, DC: World Bank. https://openknowledge.worldbank.org/handle/10986/23133 License: CC BY 3.0 IGO.

- World Bank. 2016. _World Development Report 2016 : Digital Dividends._ Washington, DC: World Bank. © World Bank. https://openknowledge.worldbank.org/handle/10986/23347

- Word Bank. 2019. _World Development Report 2019: The Changing Nature of Work._ Washington, D.C.: World Bank Group. <u>https://hubs.worldbank.org/docs/imagebank/Pages/</u> docProfile.aspx?nodeid=29604362.

- Zeng, Yiwu, Fu Jia, Li Wan, and Hongdong Guo. 2015. “Study on the formation of Taobao Village: taking Dongfeng village and Junpu village as examples.” _Jingji_ 552(35): 12.

- Zhang, Longmei, and Sally Chen. 2019. “China’s Digital Economy: Opportunities and Risks.” IMF working paper No. 19/16.

- Zhang, Xiaobo, and Wu Zhu. 2015. “The spatial patterns of e-commerce in China.” Paper presented at the annual Allied Social Science Associations January 3: 5.

Zheng, Y. 2007. Principal component cluster analysis of the development of rural e-commerce regional infrastructure. Forum on Science and Technology in China, 1, 119–122 (in Chinese).

27
**Table 1. Infrastructure disruption services have multiple impacts on households**

||**Examples of effects of the lack of services**|**Examples of additional costs to**<br>**replace services**|
|---|---|---|
|**Effect of**<br>**individual**<br>**disruptions**|Student not being able to work at night and<br>food turning bad in a fridge due to a power<br>outage, inability to reach emergency<br>services for health issues during<br>communication outage, diarrhea due to<br>consumption of bad water in association<br>with a water outage, time and income (or<br>evenjob)lost duringa transport disruption|Cost of gasoline or batteries, cost of<br>water bottles or time spent to fetch<br>water during a pipe water outage,<br>taxis to replace public transit, etc.|
|**Effect of**<br>**repeated**<br>**disruptions**|Decision not to invest in a fridge due to<br>unreliable electricity, or decision not to take<br>a job due to uncertainty in ability to<br>commute)|Additional investment in self-<br>generation, water reservoirs, or<br>individual means of transportation.|

Each of these effects are driven by a number of different mechanisms, partially overlapping. This complexity makes it difficult to estimate with precision the impact of infrastructure disruptions on households. This note reviews the literature on the different impacts and the mechanisms through which infrastructure disruptions affect households and cover four main networked infrastructure systems: electricity, potable water, transport, and communication. When possible, it estimates the economic cost of infrastructure disruptions through the willingness-to-pay of households to avoid them. While highly uncertain, these estimates can provide a sense of the magnitude of the problem.

# 1. The impact of electricity outages

## 1.1. Electricity supply is often unreliable with measurable consequences on households

In many emerging countries, power outages are regular occurrences. As shown in Figure 1, many countries experience more than 100 hours of outage per customer and per year<sup>2</sup> , on average. Poor countries tend to experience more outages, even though the variance is very large, especially at low income levels. The figure at the bottom illustrates the number of outages per customer per year on average in relation to electricity access as percent of population. It shows no clear relationship between coverage and reliability – many of the countries with high coverage also have poor reliability. However, countries with low access to electricity more consistently tend to also have poor reliability.

Outages are not the only disruptions of the electric systems. _Brownouts_ refer to a drop in the voltage of an electric system, which constrains the use of high-voltage appliances (such as refrigerators, televisions, and air conditioners) and often result in malfunction of electric appliances. However, we explore here the impact of power _outages_ , i.e. complete interruptions of power supply through the grid.

> 2 Data retrieved from the Doing Business study http://www.doingbusiness.org/en/data

4
|P113099|2009|2015|11/13/2014|10 Closed|Liberia|
|---|---|---|---|---|---|
|P113099|2009|2015|6/2/2015|11 Closed|Liberia|
|P113099|2009|2016|10/15/2015|12 Closed|Liberia|
|P113099|2009|2016|5/24/2016|13 Closed|Liberia|
|P113099|2009|2017|11/9/2016|14 Closed|Liberia|
|P113099|2009|2017|5/26/2017|15 Closed|Liberia|
|P113101|2009|2010|6/15/2010|1 Closed|Bangladesh|
|P113101|2009|2011|6/16/2011|2 Closed|Bangladesh|
|P113101|2009|2012|6/7/2012|3 Closed|Bangladesh|
|P113101|2009|2019|5/7/2019|4 Closed|Bangladesh|
|P113102|2010|2010|5/20/2010|1 Closed|Yemen,Republic of|
|P113102|2010|2011|1/1/2011|2 Closed|Yemen,Republic of|
|P113102|2010|2012|7/27/2011|3 Closed|Yemen,Republic of|
|P113102|2010|2012|11/12/2011|4 Closed|Yemen,Republic of|
|P113102|2010|2012|5/21/2012|5 Closed|Yemen,Republic of|
|P113102|2010|2013|11/25/2012|6 Closed|Yemen,Republic of|
|P113102|2010|2013|6/5/2013|7 Closed|Yemen,Republic of|
|P113102|2010|2014|8/14/2013|8 Closed|Yemen,Republic of|
|P113102|2010|2014|2/24/2014|9 Closed|Yemen,Republic of|
|P113102|2010|2015|8/30/2014|10 Closed|Yemen,Republic of|
|P113102|2010|2015|3/2/2015|11 Closed|Yemen,Republic of|
|P113102|2010|2015|6/30/2015<br>|12 Closed|Yemen,Republic of|
|P113102|2010|2016|12/11/2015|13 Closed|Yemen,Republic of|
|P113102|2010|2016|6/2/2016|14 Closed|Yemen,Republic of|
|P113102|2010|2017|1/11/2017|15 Closed|Yemen,Republic of|
|P113102|2010|2018|9/19/2017|16 Closed|Yemen,Republic of|
|P113134|2009|2009|6/16/2009|1 Closed|Madagascar|
|P113134|2009|2010|9/22/2009|2 Closed|Madagascar|
|P113134|2009|2010|4/23/2010|3 Closed|Madagascar|
|P113134|2009|2011|10/19/2010|4 Closed|Madagascar|
|P113134|2009|2011|3/21/2011|5 Closed|Madagascar|
|P113134|2009|2012|12/24/2011|6 Closed|Madagascar|
|P113134|2009|2012|6/23/2012|7 Closed|Madagascar|
|P113134|2009|2013|12/22/2012|8 Closed|Madagascar|
|P113134|2009|2013|6/22/2013|9 Closed|Madagascar|
|P113141|2009|2011|1/26/2011|19 Closed|Sierra Leone|
|P113145|2011|2011||0 Closed|Benin|
|P113145|2011|2012|9/20/2011|1 Closed|Benin|
|P113145|2011|2012|5/2/2012|2 Closed|Benin|
|P113145|2011|2013|10/29/2012|3 Closed|Benin|
|P113145|2011|2013|6/7/2013|4 Closed|Benin|
|P113145|2011|2014|12/14/2013|5 Closed|Benin|
|P113145|2011|2015|7/6/2014|6 Closed|Benin|
|P113145|2011|2015|1/23/2015|7 Closed|Benin|
|P113145|2011|2016|8/3/2015|8 Closed|Benin|
|P113145|2011|2016|2/5/2016|9 Closed|Benin|
|P113145|2011|2017|8/4/2016|10 Closed|Benin|
**Table 6: Mean hurdle prices with varying electricity price assumptions**

|Technology|Lcoe<br>$/MWh|Emission factor<br>tons/MWh||Hurdlep<br>$/ ton of|rice<br>CO2||
|---|---|---|---|---|---|---|
||||a*|b|c|d|
|Pulverized coal|55|0,82|67|248|73|-10|
|IGCC|57|0,70|75|287|82|-15|
|Gas|64|0,46|101|427|111|-38|

<mark>*a: OECD mean electricity price (110); b: OECD max (258); c: non-OECD mean (115); d: non-OECD min (47), in $/MWh. All prices are post-tax. Authors' calculations based on data from EC (2008) & IEA (2005).</mark> Using the 2010 mean OECD electricity price results in hurdle prices between $67/t CO2 for pulverized coal and $101/t CO2 for gas. These hurdle prices are well above the fitted median social cost of carbon estimates from Tol (2009), as shown in Table 1 above, as well as the Interagency Working Group‟s (2010) mean estimates, for all assumed pure rates of time preference and discount rates (Table 2 above). Suppose instead we view Tol‟s (2009) 95<sup>th</sup> percentile estimates of the social cost of carbon in Table 1 as „precautionary‟ values – values that, if used in project analysis, would reduce the risk of catastrophic climate change. Comparing this figure with a 1% PRTP, $153/ton of CO2, to the carbon hurdle prices above, we see that pulverized coal at the mean OECD electricity price would not be socially profitable using the 95<sup>th</sup> percentile social cost of carbon. The 95<sup>th</sup> percentile from the Interagency Working Group‟s (2010) mean estimates, $64.90/ton of CO2, can be viewed as essentially the same as the hurdle price above.

Turning to the switching price estimates, we adapt expression (5) to calculate switching prices as 21
128

# Expenditure 6: Other Welfare Expenditure as Percent of Total Government Expenditure Sumary Table

||Nunber|1975|1980|1985|
|---|---|---|---|---|
|ALL DEVELOPING COUNTRIES|48|14.89<br>|13.33<br>|13.08<br>|
|Low Income|15|6.97|6.27|7.15|
|Middle Income|33|18.48|16.54|15.77|
|AFRICA|11|9.78|8.41|7.62|
|Low Income|8|6.05|6.71|6.23|
|Middle Income|3|19.73|12.95|11.31|
|EAST ASIA|6|6.66|6.33|5.19|
|Low Income|1|1.93|2.82|2.78|
|Niddle Income|5|7.60|7.04|5.67|
|SOUTH ASIA|6|9.04|6.26|9.11|
|Low Income|6|9.04|6.26|9.11|
|EMENA|12|19.20|19.59|17.61<br>|
|Middle Income|12|19.20|19.59|17.61|
|EASTERN EUROPE|2|23.20|11.75|14.56|
|Middle Income|2|23.20|11.75|14.56|
|_LAC_|11|21.45|19.39|19.78|
|Middle Income|11|21.45|19.39|19.78|

Note: The summary table includes only the countries<sup>with complete data for the</sup> periods in the table.

Other welfare includes social security and welfare,<sup>housing and community</sup> development, and recreational, cultural and religious<sup>affairs and services as</sup> classified by the IMF Government Financial Statistics.

Source: IMF Government Financial Statistics: Consolidated<sup>Central Government</sup> Expenditure.
and employment were also estimated in the second round of work.? In addition, intensities were calculated for manufacturing sectors defined according to the 2-, 3- and 4-digit International Standard Industrial Classification (ISIC).

# 3.2.4 Alternative Estimates of Sectoral Pollution Intensities

The EPA data used in the study only cover facilities releasing pollutants in quantities over a threshold level of emissions. Consequently, pollution intensity estimates based on these data (as in Table 4.3) may be upwardly biased, by exclusion of cleaner facilities. To correct for this, alternative intensities were estimated, by grouping data from manufacturing facilities into three classes. Facilities reporting emissions to the EPA were classified as group (1) if they could be matched to the LRD, and group (2) if this was not possible. Those facilities which did not report emissions to EPA, but were in the LRD, were defined to be group (3).

The pollution intensities derived from group (1) data were presumed to give an "upper bound" estimate for each industrial sector because of their inherent upward bias. For the matched group an intensity estimate defined as the Upper Bound Weighted Mean (known as Upper-Bound (UB) hereafter) was calculated by weighting each plant's pollution intensity by its

> "We have noted in the Executive Summary, it is possible that employment-based intensities are more stable across countries than the value-based measures.

16
|Appen<br>Questions|dix Table 1: Sum|mary Statisti|cs and|Codin|g of Tea|cher M|otivati|on Question|
|---|---|---|---|---|---|---|---|---|
|_Panel A: Teaching_|||||||||
|_Practice/Student_||||||||=1 if answer|
|_Interaction_|Observations|Category†|||Answer|Type†||is|
|_a. Ability_|||I|II|III|IV|V|VI|
|Able to teach all|||||||||
|topics, to even the||||||||Agree or|
|most problematic<br>students|350|A|2%|9%|13%|51%|25%|Strongly<br>Agree|
|Confidence in|||||||||
|teaching all topics||||||||Very|
|in my subject|350|C|1%|3%|50%|46%||Confident|
|Maintain|||||||||
|composure when|||||||||
|student becomes||||||||Most of the|
|disruptive|349|B|13%|10%|23%|24%|30%|time/Always|
|_b. Effort_|||||||||
|Acceptability of|||||||||
|Absence from||||||||Most of the|
|School|350|B|0%|37%|35%|25%|3%|time/Always|
|Frequency of new<br>ways of teaching|349|F|7%|5%|26%|23%|40%|At least<br>Monthly|
|||||||||Agree or|
|Allowed to work<br>independently|350|A|5%|23%|13%|47%|12%|Strongly<br>Agree|
|Cannot discipline|||||||||
|students if they||||||||Agree or|
|are not disciplined||||||||Strongly|
|at home|349|A|35%|40%|8%|12%|6%|Agree|
|Can do more if|||||||||
|parents take<br>interest|349|A|0%|2%|4%|38%|56%|Strongly<br>Agree|
|Like more|||||||||
|feedback from||||||||Strongly|
|Head Teacher|349|A|0%|1%|9%|57%|33%|<br>Agree|

30
**Table 8. Quantile Analysis of Household Expenditure on Educational Public Service**

||**Quant**|**ile regr**|**ession 0.01**|**Quan**|**tile regre**|**ssion 0.50**|**Quan**|**tile regres**|**sion 0.90**|
|---|---|---|---|---|---|---|---|---|---|
|**Explanatories variables**|**Coef.**|Q**0.10**|**M.W. to P.**|**Coef.**|Q**0.50**|**M.W. to P.**|**Coef.**|**Q 0.90**|**M.W. to P.**|
|LogNat per capitaH income|<br>0.82 *|6.27|5.11|0.72 *|7.22|5.22|0.51 *|8.17|4.14|
|Years of Schooling of HH|0.00|0.00|0.00|0.01 *|6.00|0.08|0.02 *|12.00|0.24|
|Region_(rural)_|0.80 *|0.00|0.00|0.64 *|1.00|0.64|0.37 *|1.00|0.37|
|Age|0.16 *|6.00|0.98|0.13 *|11.00|1.44|0.12 *|16.00|1.92|
|Squared Age|0.00 *|36.00|-0.13|0.00 *|121.00|<br>-0.31|0.00 *|256.00|-0.58|
|Gender_(female)_|0.05|0.00|0.00|-0.06 *|1.00|-0.06|0.02|1.00|0.02|
|Number of rooms|0.06 *|1.00|0.06|0.09 *|3.00|0.28|0.07 *|5.00|0.33|
|_Floor (notfinishedfloor)_|0.43 *|0.00|0.00|0.21 *|0.00|0.00|0.13 *|1.00|0.13|
|Sewage_(not sewage)_|0.42 *|0.00|0.00|0.22 *|1.00|0.22|0.14 *|1.00|0.14|
|Number of Children|-0.02|1.00|-0.02|-0.11 *|2.00|-0.23|-0. 11 *|4.00|-0.44|
|Mill's Ratio|0.59 *|1.30|0.77|0.41 *|2.45|1.01|0.22 *|3.68|0.81|
|Constant|-5.35 *||-5.35|-2.28 *||-2.28|1.00 *||1.00|
|Dependent Variable||||||||||
|<br>Log Nat Expenditure||4.01|1.42||5.79|6.01||6.98|8.08|
|Evaluated in pesos||54.91|4.13||326.44|<br>409.05||1075.65|3224.26|

Source: Own calculations based on ENIGH 96 and DGPyP, SEP

* Significant at 5%, ** Significant at 10%

**Table 9. Quantile Analysis of Household Expenditure on Educational Private Service**

||**Quan**|**tile regr**|**ession 0.01**|**Quant**|**ile regre**|**ssion 0.50**|**Quan**|**tile regre**|**ssion 0.90**|
|---|---|---|---|---|---|---|---|---|---|
|**Explanatories variables**|**Coef.**|Q 0.10|**M.W. to P.**|**Coef.**|Q**0.50**|**M.W. to P.**|**Coef.**|**Q 0.90**|**MLW. to P.**|
|Log Nat per capita H income|<br>0.50 *|7.24|3.64|0.53 *|8.30|4.39|0.72*|9.40|6.79|
|Years of Schooling of HH|0.04 *|3.00|0.13|0.03 *|9.00|0.25|0.00|16.50|0.00|
|Region_(rural)_|-0.27|1.00|-0.27|-0.18|1.00|-0.18|0.35|1.00|0.35|
|Age|0.10 *|6.00|0.58|0.03 *|14.00|0.44|0.00|20.00|0.08|
|Squared Age|0.00 *|36.00|-0.11|0.00 *|196.00|<br>-0.17|0.00|400.00|0.02|
|Gender_(female)_|-0.10|0.00|0.00|0.08|0.00|0.00|0.10|1.00|0.10|
|Number of rooms|-0.04|2.00|-0.08|0.03|4.00|0.10|0.01|6.00|0.06|
|Floor_(notfinishedfloor)_|-0.12|0.00|0.00|0.03|1.00|0.03|0.15|1.00|0.15|
|Sewage_(not sewage)_|0.71 *|<br>1.00|0.71|0.39 *|1.00|0.39|-0.16|1.00|-0.16|
|Number of Children|0.09|1.00|0.09|-0.06|2.00|-0.11|-0.02|3.00|-0.06|
|Mill's Ratio|-0.06|0.17|-0.01|0.06|0.75|0.05|-0.03|1.71|-0.06|
|Constant|0.94||0.94|2.09 *||2.09|1.93*||1.93|
|Dependent Variable||||||||||
|LogNatExpenditure||6.13|5.62||7.31|7.28||8.61|9.20|
|Evaluated in pesos||460.01|<br>275.37||1497.22|1445.25||5490.24|<br>9883.40|

Source: Own calculations based on ENIGH 96 and DGPyP, SEP

* Significant at 5%, ** Significant at 10%

18
be on track to shrink by 4.7 and 2.6 percent respectively. In Table 4, we present some estimates of undernourishment under the original and revised growth scenarios and with different price scenarios. The first price scenario reflects the 17% decline in the World Bank food price index between 2008 and 2009. The second scenario assumes no change in prices between 2007 and 2008. Our objective is to estimate the extent that the economic crisis led to additional numbers of undernourished relative to the absence of the crisis and hence a comparison of these two scenarios provides an indicative range.

|**Table 4: Imp**|**act of the e**|**conomic shock**|**on undernourish**<br>**2009**<br>**Growth Shoc**<br>**Declin**|**ment under**<br>**/a**<br>**k & Price**<br>**e**|**various scenari**<br>**2009**<br>**Growth Shock**<br>**Chan**|**os**<br>**/b**<br>**& No Price**<br>**ge **|
|---|---|---|---|---|---|---|
||**Pre-crisis**<br>**growth**<br>**forecast**<br>**for 2009**|**Latest**<br>**growth**<br>**estimates for**<br>**2009**|Change in<br>Undernourished<br>(in mil)|% Change|Change in<br>Undernourished<br>(in mil)|<br>%<br>Change|
|Sub Saharan|||||||
|Africa|6.3|1.1|2.5|1.1%|5.3|2.4%|
|East Asia|8.5|6.8|2.3|2.0%|3.6|3.1%|
|China|9.2|8.4|1.3|1.3%|1.5|1.5%|
|South Asia|7.2|5.7|16.8|4.3%|22.1|5.7%|
|Latin America<br>Middle|4.3|-2.6|6.1|15.1%|6.4|15.8%|
|East/North|||||||
|Africa<br>Newly|5.3|2.9|1.0|7.6%|1.5|11.1%|
|Independent|||||||
|States|5.4|-4.7|11.3|18.3%|12.7|20.7%|
|Total<br>_Note:_2009/a Refe<br>scenarios in which|..<br>rs to the scenar<br>prices are assu|..<br>ios in which the actual<br>med to have remained|41.3<br>food price fall betwee<br>unchanged from their|4.4%<br>n 2008-09 is take<br>2008 levels.|53.1<br>n into account. 2009/b|5.6%<br>Refers to|

In a scenario where food prices in 2009 remain unchanged, we estimate that there would be 53.1 million people, or 5.6 percent, more undernourished people than if the economic crisis had not occurred. However, we know that food prices actually declined in 2009. The World Bank’s average food price index for 2009 was 17 percent lower than 2008. If we consider this scenario for prices, we estimate that there are 41.3 million people, or 4.4 percent, more undernourished people than if the economic crisis had not occurred.<sup>5</sup> We prefer this latter estimate as it takes into account the actual change in food prices as part of our analysis of the impact of the slowdown in growth.

5 We note that we assume that the two simulated growth scenarios are distribution neutral. This is an important assumption, particularly if one believes that the growth shock experienced due to the financial crisis had heterogeneous impacts on different income groups in different countries.