rafmacalaba/datause-encoder-data huggingface dataset:
be hurt in the future. This has led to some surprising findings in the past. An RCA analysis for the euro area by di Mauro and Forster (2008) found that in contrast to other advanced economies, euro area specialization patterns overall have not changed much over last one and a half decades. They found neither a decline in the specialization in labor-intensive products, nor the expected shift towards more skill-intensive production.

This surprising finding might be due to the fact that the RCA analysis is performed on the basis of gross export values which do not fully reflect the effects of international production fragmentation as discussed above. As an alternative, RCA can be performed on the basis of GVC incomes in the production of final goods. Thus the usefulness of RCA analysis is retained, albeit with a different interpretation. Based on GVC incomes, an RCA larger than one for a product indicates that the country derives a higher share of its overall GVC income in the GVC production of this product, relative to other countries. Thus the country specializes in activities in the GVC production of this product. It does not necessarily follow that the country is also a major exporter of the product as it might carry out valuable activities upstream in the production process, or alternatively it may produce for a large domestic market.

In Figure 4 we provide the results of an RCA analysis for the EU27 based on GVC incomes in six groups of final manufacturing products. RCA is calculated as the EU27 share in world GVC income for a product group divided by the EU27 share in world GVC income for all groups. We find that the EU27 has a strong and increasing RCA in activities related to the production of machinery and transport equipment. RCAs in non-durables and in chemical products are on a declining trend. The latter is rebounding since the crisis, but the former continues its secular decline. Participation of the EU27 in the production of electrical equipment is traditionally low, notwithstanding the presence of some very successful European firms in particular product niches. It has declined further since 2007.

**Figure 4 Revealed comparative advantage of EU27, by group of final manufactures (%)**

<!-- Start of picture text -->
1.3<br>1.2<br>1.1 mach<br>1.0 trans<br>0.9 chem<br>0.8 non-<br>dur<br>0.7<br>1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011<br><!-- End of picture text -->

Note: Revealed comparative advantage calculated as EU27 share in world GVC income for a group of manufactures divided by same ratio for all manufactures. Final food manufacturing products (Food: produced in ISIC rev.3 industries 15 & 16), Other non-durable products (Tex: 17 to 20, 36, 37); Chemical products (Chem: 23 to 26), Machinery & metal products (Mach: 27 to 29); Electrical machinery products 25
Table A1.6: Republic of Korea; Numbers of tariff headings subject to SSG - <u>Actions, 1995 2004</u>

||Tariff Lines subject|to actions|
|---|---|---|
|Year|Volume-based|Price-based|
|1995|0|3|
|1996|0|5|
|1997|2|7|
|1998|2|7|
|1999|2|6|
|2000|13|8|
|2001|8|8|
|2002|3|7|
|2003|10|8|
|2004|4|8|
|Sums|44|67|
|Memo it<br>tariff lin<br>trigger<br>notified|em: Number of<br>es for which<br>prices were<br> ‘up-front’|99|

Source: Tabulated from national notifications of SSG actions, available on the WTO documents website.

Note: Product coverage; principally Groundnuts; Wheat, buckwheat along with their starches, Other vegetable starches and meals; Peas and beans; Ginseng and products.

56
city level for India. However, the National Sample Survey Organization (NSSO) routinely conducts representative surveys of household expenditures which are the closest proxy measure to household income levels. These data are available at the district level which is bigger than the city.<sup>11</sup> Our _Expenditure_ variable is taken from the 50<sup>th</sup> (1991-92) NSSO round.<sup>12</sup> _Population_ equals the total adult city population in 1991 and _Metro_ is a dummy variable equal to 1 if a store is located in the metropolitan cities of Delhi, Mumbai, Chennai, Hyderabad, Kolkatta and Bangalore and 0 otherwise. Larger cities (in terms of population) and the metropolitan cities are known to be more developed and richer and also the main beneficiaries of the ongoing retail boom.

We also experimented with the number of workers per household ( _Workers_ ) and literacy rates as alternative proxy measures of city level incomes. However, both these variables are very poorly correlated with non-workers (Table 3), they showed no significant effect on competition, and controlling for them made no difference to our main results (discussed in the section on robustness).

In the robustness section, we show that our main results are robust to a number of additional controls which include literacy rate, sex ratio, number of workers per household, store-size, duration of power outage faced by stores, number of retailers in the city, etc.

The correlation coefficients between our main variables are as follows: _Non workers_ and _Expenditure_ (-.290), _Non workers_ and _Population_ (-.096), _Children_ and

> 11 The NSSO reports data separately for the rural and urban population of the district. The urban part of the district is perhaps a better approximation of the cities in our sample. Our results do not change much if we use expenditure values for the urban or total district population which are correlated upto .916.

> 12 We use NSSO data from the 50th round because it relates to the year 1991-92, closest to the year for our main variable, _Non workers_ . Similarly, for _Population_ , we 1991 values using Census data. We also experimented with 2001 values of _Expenditure_ (NSSO Round 55) and _Population_ but this did not change our results much. The correlation between 1991 and 2001 values of _Population_ equals .901 and .808 for _Expenditure_ .

10
Middle East that have a survey completed in the last three years and have a large sample size of over 600 firms in order to construct sectoral breakdowns.

The World Bank Enterprise Surveys are establishment-level surveys conducted on a stratified random sample of small (5-19 employees), medium (20-99 employees), and large establishments (over 100 employees). The questionnaire includes a wide range of topics, from infrastructure to management practices, labor, and performance. The survey is administered to businesses with at least 1 percent private ownership, that are not cooperatives, and were in full operation for the entirety of the last completed fiscal year. The sector of coverage includes all manufacturing (ISIC 3.1 Rev 15-37); Wholesale and retail trade, repair of motor vehicles, motorcycles and personal and household goods (50-52); Hotels and restaurants (55); Transport, storage and communications (60-64); and Computer related activities (72). The sample contains a total of 11,759 interviews with top managers or owners; and approximately half of sampled firms (6,897) submitted income statement and balance sheet data (Table 1). Exporters account for 7 percent (Kazakhstan) to 31 percent (Morocco) of the sample.

# **<u>Table 1: Sample Details</u>**

|<br>**Country**|<br>**Survey**<br>**Year**|<br>**Last**<br>**Completed**<br>**Fiscal Year**|**Survey**<br>**Sample**<br>**Size**|**Number of firms**<br>**with full income**<br>**statement data**|**Share of**<br>**Exporters in**<br>**the Sample**|
|---|---|---|---|---|---|
|Colombia|2017|2016|989|535|258|
|Greece|2018|2017|559|538|270|
|Italy|2019|2018|610|498|268|
|Jordan|2019|2018|380|169|166|
|Kazakhstan|2019|2018|1,377|640|139|
|Kenya|2018|2017|961|696|304|
|Morocco|2019|2018|755|458|345|
|Peru|2017|2016|976|415|275|
|Portugal|2019|2018|1,057|630|432|
|Russia|2019|2018|1,284|736|235|
|Turkey|2019|2018|1,506|894|501|
|Ukraine|2019|2018|1,305|688|417|
|**Total**|||11,759|6,897|3,610|

The estimation of survival times that we discuss is based on several survey questions (Table A1). Available liquidity is calculated using profits and external financing. In particular, the question on sales (question D.2) asks: “In the last completed fiscal year, what were this establishment’s total annual sales for all products and services?” External financing of working 4
**Table 8: Estimates of program effects on, crops grown and composition**

||**No**<br>|**. of crops gro**<br>|**wn**<br>|**Irrigated**||**Area devoted**|**to**|
|---|---|---|---|---|---|---|---|
||**Season I**|**Season II**|**Season III**|**area (%)**|**Paddy**|**Wheat**|**High value**|
||||**P**<br>|**anel A: Overal**|**l impact**|||
|MNREGS|0.166***|0.192***|0.413***|0.057**|-0.035*|0.005|0.035***|
|* post|(0.039)|(0.061)|(0.093)|(0.025)|(0.018)|(0.011)|(0.009)|
|Observations|6,468|6,468|6,468|6,468|6,468|6,468|6,468|
|R-squared|0.679|0.810|0.844|0.849|0.897|0.877|0.839|
||||**Panel B: H**|**eterogeneity b**|**y farm size cla**|**ss**||
|MNREGS *|0.202***|0.302***|0.480***|0.101**|-0.018|0.026|0.031*|
|post*small|(0.054)|(0.082)|(0.119)|(0.040)|(0.031)|(0.020)|(0.016)|
|MNREGS *|0.172**|0.096|0.334**|0.024|-0.050*|-0.008|0.043***|
|post* med.|(0.069)|(0.108)|(0.158)|(0.042)|(0.030)|(0.019)|(0.016)|
|MNREGS *|0.114|-0.026|0.027|0.088*|-0.011|0.010|0.029*|
|post * large|(0.086)|(0.132)|(0.201)|(0.047)|(0.030)|(0.020)|(0.017)|
|Observations|6,468|6,468|6,468|6,468|6,468|6,468|6,468|
|R-squared|0.682|0.820|0.857|0.852|0.898|0.878|0.839|

_Notes:_ High value crops include oilseeds, pulses, and cotton. Household and year fixed effects as well as controls included but not reported. Standard errors, clustered at household level, in parentheses; *** p<0.01, ** p<0.05, * p<0.1 22
**Table 2: Selected Predetermined Conditions, Definitions, and Summary Statistics**

|**Variable**|**Definition**|**Mean**|**SD**|**Min**|**Max**|**Obs**|**Years**|**Source**|
|---|---|---|---|---|---|---|---|---|
|Population|All residents regardless of legal status or<br>citizenship in millions.|32|128|0.01|1360|2,127|2005-<br>2014|Penn World Table|
|Population<br>density|Total population divided by land area in<br>square kilometers.|391|1850|0.14|19073|2,120|2005-<br>2014|World Development<br>Indicators|
|Offshore dummy|1 if a country is an offshore center, where the<br>bulk of financial sector activity is offshore on<br>both sides of the balance sheet, where the<br>transactions are initiated elsewhere, and<br>where the majority of the institutions<br>involved are controlled by non-residents.|0.2|0.4|0|1|2,130|2005-<br>2014|IMF|
|Small states<br>dummy|1 if a country has less than 1,500,000<br>inhabitants.|0.3|0.5|0|1|2,130|2005-<br>2014|World Development<br>Indicators|
|Landlocked<br>countries|1 if the country is entirely enclosed by land,<br>or whose only coastlines lie on closed seas.|0.2|0.4|0|1|2,130|2005-<br>2014|World Development<br>Indicators|
|GDP per capita<br>(level)|GDP per capita is gross domestic product<br>divided population in constant 2010 U.S.<br>dollars.|12|18|0.14|159|1,904|2005-<br>2014|World Development<br>Indicators|

**Table 3: Correlations between Indicators and Predetermined Conditions**

|**Variable**|**GDP per capita**|**Population**|**Density**|**Landlocked**|**Small**|**Offshore**|
|---|---|---|---|---|---|---|
|Trade openness|0.27|-0.16|0.29|-0.02|0.28|0.34|
|Financial depth|0.66|0.12|0.12|-0.22|0.06|0.27|
|Fiscal balance|0.11|-0.04|0.02|-0.03|0.01|0.03|
|Measles immunization|0.26|-0.01|0.09|-0.06|0.05|0.15|
|Sanitation facilities|0.60|-0.06|0.13|0.08|0.17|0.14|
|Life expectancy|0.59|0.01|0.19|-0.29|0.15|0.29|
|Educational quality|0.54|0.14|0.19|-0.13|0.02|0.13|
|Access to finance|0.74|0.03|0.21|-0.24|0.17|0.24|
|Old-age pension|0.38|-0.05|0.07|-0.14|0.21|0.32|
|Income inequality|-0.43|0.11|-0.17|-0.13|-0.13|0.08|

20
**Table 10: Poverty and social exclusion impacts of prioritizing selected groups for labor force activation measures**

||More single|Scenario 1:<br>mothers joinin<br>force|g the labor|Increasin|Scenario2:<br>gfamilylabor f|<br>orce supply|Increasing d|Scenario 3:<br>isabled peopl<br>force|<br>e in the labor|
|---|---|---|---|---|---|---|---|---|---|
|country|2008 at-risk-<br>of-poverty<br>rate(%)|Simulated<br>at-risk-of-<br>poverty<br>rate for<br>2020(%)|Simulated<br>anchored<br>poverty<br>rate for<br>2020(%)|2008 at-<br>risk-of-<br>poverty<br>rate(%)|Simulated<br>at-risk-of-<br>poverty<br>rate for<br>2020(%)|Simulated<br>anchored<br>poverty rate<br>for 2020(%)|2008 at-<br>risk-of-<br>poverty<br>rate(%)|Simulated<br>at-risk-of-<br>poverty<br>rate for<br>2020(%)|Simulated<br>anchored<br>poverty<br>rate for<br>2020(%)|
|Bulgaria|20.8|21.6|9.7|20.8|21.1|9.5|20.8|21.4|9.7|
|Czech Rep|9.3|10.6|7.0|9.3|10.6|6.5|9.3|10.5|6.9|
|Estonia|19.8|20.1|11.5|19.8|21.0|11.7|19.8|20.2|11.2|
|Hungary|12.4|11.8|9.7|12.4|10.3|7.7|12.4|11.9|9.8|
|Latvia|25.8|20.7|12.0|25.8|21.2|12.3|25.8|20.7|12.1|
|Lithuania|20.1|16.1|12.0|20.1|16.3|11.5|20.1|16.0|11.7|
|Poland|16.9|13.2|4.9|16.9|11.4|3.7|16.9|12.6|4.1|
|Romania|23.4|19.8|12.1|23.4|18.2|9.2|23.4|20.3|12.1|
|Slovak Rep|11.6|8.8|4.6|11.6|8.4|4.0|11.6|8.6|4.3|

_Source:_ World Bank staff calculations using EU-SILC data.

# **7. Focus on Poland: Increasing pre-school enrollment to achieve Europe 2020 poverty and social exclusion targets**<sup>**17**</sup>

Poland has recently enacted and is debating potential measures focused on improving childcare (wider access to nurseries and kindergartens, support for baby sitters, new ways of financing childcare institutions, etc.). This is evident through recently adopted legislation as well as through specific targets set in the _National Development Strategy 2020_ [ _Strategia Rozwoju Kraju 2020_ ]. These policies set a 2020 pre-school enrollment target of 85 percent of 4-year-olds and assumes an impact on employment rates among youth (15- to 24-year-olds) of 30 percent, and among older workers (55- to 64-year-olds) of 40 percent ( _MRR draft of November 2011, Wskaźniki, p.137_ ). In this section, we present the expected impacts based on applying a variant of our simulation model on poverty and social exclusion that might ensue from increasing employment rates as a result of higher access to pre-school enrollment in Poland. The data employed are the 2010 Polish Household Budget Survey and administrative data from the local data bank.

Access to childcare is treated as an instrument that can be arbitrarily altered so that it reaches the childcare target level in 2020, while keeping all other explanatory variables constant. The estimation model can then be summarized with the following equations:

1. Pr(Childcare = 1) = f(childcare access, H), where H stands for household characteristics

2. Pr(Employed =1) = f(childcare, I, H), where I stands for individual and H household characteristics

> 17 This section is adapted from Bonch-Osmolovskiy and Topińska (2012).
**Table 4. Factors associated with Firm Capabilities in Mozambique**

|Dependent variable|(1)<br>Management<br>practices|(2)<br>Management<br>practices|(3)<br>Management<br>practices|(4)<br>Skills|(5)<br>Skills|(6)<br>Skills|
|---|---|---|---|---|---|---|
||All firms|All firms|All firms|All firms|All firms|All firms|
|Firm 5 years old or less|-0.193||-0.106|0.031||0.050|
||[0.193]||[0.152]|[0.109]||[0.092]|
|10 to 49 workers||0.528***|0.482***||0.218**|0.129|
|||[0.162]|[0.149]||[0.107]|[0.106]|
|50 to 99 workers||1.301***|0.843***||0.500***|0.176|
|||[0.230]|[0.188]||[0.155]|[0.174]|
|100 or more workers||0.958***|0.546*||0.550***|0.305*|
|||[0.289]|[0.290]||[0.161]|[0.174]|
|Manufacturing|0.145|0.128|0.152|-0.119|-0.131|-0.173**|
||[0.128]|[0.123]|[0.132]|[0.097]|[0.096]|[0.079]|
|Extractives|-0.192|-0.456**|-0.332|0.089|-0.021|0.038|
||[0.195]|[0.204]|[0.212]|[0.139]|[0.150]|[0.160]|
|Retail|0.194|0.246|0.125|0.129|0.156|0.014|
||[0.151]|[0.149]|[0.135]|[0.120]|[0.119]|[0.106]|
|Maputo|0.023|0.037|0.110|-0.092|-0.104|-0.027|
||[0.152]|[0.146]|[0.134]|[0.124]|[0.120]|[0.113]|
|Northern Provinces|0.349**|0.389***|0.306**|0.083|0.084|0.019|
||[0.167]|[0.142]|[0.146]|[0.167]|[0.161]|[0.143]|
|Foreign-owned|||-0.071|||-0.197*|
||||[0.154]|||[0.118]|
|Female majority in<br>ownership|-0.416***|-0.368***|-0.301**|-0.149|-0.127|-0.086|
||[0.157]|[0.139]|[0.126]|[0.095]|[0.093]|[0.080]|
|Direct exporter|||0.495***|||-0.019|
||||[0.182]|||[0.151]|
|Indirect exporter|||-0.244|||-0.086|
|Foreign material inputs or|||[0.154]|||[0.136]|
|<br>supplies|||0.259*|||0.363***|
||||[0.142]|||[0.113]|
|Business uses computer|||-0.120|||0.213**|
||||[0.157]|||[0.105]|
|Business uses Internet|||0.180|||-0.057|
||||[0.139]|||[0.124]|
|Business has website|||0.481***|||0.298**|
|Business uses licensed|||[0.153]|||[0.117]|
|<br>technology from abroad|||0.151|||0.473**|
|(excl. office software)|||[0.167]|||[0.229]|
|Controls|Yes|Yes|Yes|Yes|Yes|Yes|
|Observations|636|639|635|636|639|635|
|R-squared|0.108|0.213|0.320|0.329|0.358|0.455|
|Adjusted R^2|0.095|0.200|0.298|0.319|0.347|0.437|

Note: controls include manager or owner age and highest level of education is university. Standard errors in brackets. Management practices and skills are reported as normalized scores (z-indices). *** p<0.01, ** p<0.05, * p<0.1. Excluded categories for nonbinary variables are: firms older than 5 years; firms with 5-10 employees; services sector outside of retail, which is composed of, among others, wholesale, transportation, hotels and restaurants, etc; central provinces.

20
PPR Working Paper Series

||Ila|Athor|At|Contact<br>for paper|
|---|---|---|---|---|
|WPS266|Policy Changes that Encourage<br>Private Business Investment in<br>Colombia|Mansoor Dailami|August 1989|M. Raggambi<br>61696|
|WPS267|Issues in Income Tax Reform in<br>Developing Countries|Cheryl W. Gray|August 1989|N. Campbell<br>33769|
|WPS268|Shortcomings in the Market for<br>Developing Country Debt|John Wakeman-Linn|September 1989|S. King-Watson<br>33730|
|WPS269|Women in Development: Issues<br>for Economic and Sector Analysis|Women in Development<br>Division|August 1 "89|J. Lai<br>33753|
|WPS270|Fuelwood Stumpage: Financing<br>Renewable Energy for the<br>World's Other Half|Keith Openshaw<br>Charles Feinstein|September 1989|J. Mullan<br>33250|
|WPS271|The Industrial Labor Market and<br>Economic Performance in<br>Senegal: A Study of Enterprise<br>Ownership, Export Orientation, and<br>Govemment Regulation|Katherine Terrell<br>Jan Svejnar|||
|WPS272|Women's Changing Participation<br>in the Labor Force: A World Perspe|T. Paul Schultz<br>ctive|||
|WPS273|Population, Health, and Nutrition:<br>FY88 Annual Sector Review|Population and Human<br>Resources Department|September 1989|S.Ainsworth<br>31091|
|WPS274|The Demography of Zaire:<br>Review of Trends in Mortal-ny and<br>Fertility|Miriam Schneidman|||
|WPS275|Revised Estimates and<br>Projections of International<br>Migration, 1980-2000|Fred Arnold|August 1989|S.Ainsworth<br>31091|
|WPS276|Improving Rural Wages in India|Shahidur R. Khandker|August 1989|B. Smith<br>35108|
|WPS277|The Effect of Formal Credit on<br>Output and Employment in Rural<br>India|Shahidur R. Khandker<br>Hans P. Binswanger|August 1989|B. Smith<br>35108|
|WPS278|Inflation and the Company Tax<br>Base Methods to Minimize<br>Inflation-induced Distortions|Anand Rajaram|September 1989|A. Bhalla<br>60359|
|WPS279|What Determines the Rate of<br>Growth and Technological Change|Paul M. Romer<br>|September 1989|R. Luz<br>61760|
**Table 9: School Construction and Human Capital Investments: Difference-in-differences estimates.**

||1966-1970 Maternal Cohort|Differe|nce in Dif|ferences|
|---|---|---|---|---|
||High INPRES<br>High-Low INPRES||||
||Mean<br>Gap|[1]|[2]|[3]|
|_Panel A: Health_|||||
|Child has been immunized|0.83<br>-0.12|-0.012|-0.011|-0.011|
|||(0.023)|(0.023)|(0.023)|
|||[0.704]|[0.612]|[0.584]|
|Child receives minimum dietary diversity (Yes=1)|0.95<br>-0.01|-0.008|-0.008|-0.008|
|||(0.013)|(0.013)|(0.013)|
|||[0.624]|[0.656]|[0.596]|
|Child receives maximum dietary diversity (Yes=1)|0.32<br>-0.02|0.08|0.082|0.082|
|||(0.062)|(0.063)|(0.062)|
|||[0.408]|[0.464]|[0.512]|
|_Panel B: Education_|||||
|Mother knows closest day-care/pre-school (Yes=1)|0.66<br>-0.30|0.08|0.081|0.081|
|||(0.029)|(0.029)|(0.030)|
|||[0.008]|[0.024]|[0.012]|
|Child ever attended day-care/pre-school (Yes=1)|0.28<br>-0.17|0.041|0.053|0.055|
|||(0.035)|(0.035)|(0.035)|
|||[0.236]|[0.176]|[0.108]|
|Months child attended day-care/pre-school|4.04<br>-2.62|1.009|1.227|1.265|
|||(0.501)|(0.496)|(0.501)|
|||[0.088]|[0.072]|[0.036]|
|_Controls (D-in-D’s)_|||||
|Caregiver district and year of birth FE’s||YES|YES|YES|
|Treated cohort interaction with # school-aged childr|en in 1973 (ln scale)|NO|YES|YES|
|Child gender||YES|YES|YES|
|Child-age FE’s||YES|YES|NO|
|Child’s age(linearly,in months)||NO|NO|YES|

Notes: Sample is 2,118 children. Standard-errors in parentheses are clustered at the district of maternal birth. Wildbootstrap significance p-values in brackets.
# **Table A1: Normalized Differences and Comparability Means and standard deviations**

||**(1) Treated**|**(2) Untreated**|**(3) Normalized**<br>**difference**|
|---|---|---|---|
|**Average project budget (US$, thousands)**|148|121|0.04|
||(684)|(320)||
|**Average project complexity (proportion)**|0.27|0.28|-0.05|
||(0.21)|(0.20)||
|**Rehabilitation [yes=1]**|0.21|0.20|0.03|
||(0.41)|(0.40)||
|**Local information requirement index**|-0.04|0.03|-0.05|
||(1.05)|(0.97)||
|**Population (2006) of implementing constituency**|205,000|187,000|0.13|
||(113,000)|(92,900)||
|**Proportion of constituency population in extreme poverty**|0.24|0.25|-0.04|
||(0.14)|(0.15)||
|**Winning vote share (2003 elections) of winning constituency**|0.63|0.61|0.09|
||(0.15)|(0.13)||
|**Observations**|1,137|1,871||

**Notes** : Standard deviations are in parentheses.  Treated projects refer to those for which the relevant representative is on the relevant sectoral committee and untreated to those for which they are not. Normalised Difference is the difference in averages, scaled by the square root of the sum of the variances.  Budget figures originally in Nigerian Naira are converted to US dollars at a rate of US$1:N150.  Population data is from the 2006 Census.  Election data is from the Independent National Electoral Commission official record for the 2003 election.  Figures are rounded to two decimal places where relevant.
Table 9. Livestock's contribution to greenhouse gas emissions (Steinfeld et al., 2006).

|**Parameter**|**Value**|**Comments**|
|---|---|---|
|Livestock's contribution to climate<br>change in CO2equivalent|18 percent|Including pasture degradation and<br>assuming that two-thirds of land-<br>use change is livestock-related|
|Livestock's share in carbon dioxide<br>emissions|9 percent|Not considering respiration|
|Livestock's share in methane emissions|37 percent||
|Livestock's share in nitrous oxide<br>emissions|65 percent|Including production of feed crops|

however. There are substantial regional variations in projected emissions (Smith et al., 2007). For example, there will be substantial increases in greenhouse gas (GHG) emissions in East Asia from animal sources: 153% and 86% in emissions from enteric fermentation and manure management, respectively, from 1990 to 2020 (US EPA, 2006). GHG emissions from agriculture in Western Europe are projected to decrease to 2020 as a result of specific policies (US EPA, 2006). Recent production-system-specific analysis suggests that Africa produced around 7.8 million tonnes of methane per year from domestic ruminants in 2000, and that this figure is likely to increase to 11.1 million tonnes per year by 2030 as a result of increases in livestock numbers (Herrero et al., 2008).

# **_4.2  Climate change's impacts on livestock_**

The impacts of climate change on livestock are considered under the headings feeds and water, livestock diseases and disease vectors, and systems. A more comprehensive review can be found in Thornton et al. (2008a).

# 4.2.1  Feeds and water

As noted above, population growth, economic development and climate change impacts will undoubtedly have a substantial effect on global water availability in the future. The localized impacts of global change on livestock and water resources are starting to receive attention, but 53