rafmacalaba/datause-encoder-data huggingface dataset:
school (Kazeem and Musalia, 2016). Also studying the education dimension of poverty, Bertoni et al. (2018) measured the impact of Boko Haram violence on school enrollment and attendance rates in Nigeria’s Borno state and found a negative impact of violence. Likewise, Nwokolo (2019) and Rotondi and Rocca (2019) studied the effect of Boko Haram violence on nutrition and birth weight, and found that violence reduces birth weight.

Popoola and Adetola (2016) studied five dimensions of multi-dimensional poverty (safe drinking water, sanitation, housing, health, and nutrition) based on the DHS survey of 2008, but limit the analysis to children under 5. Not distinguishing between adults and children, Ajakaiye et al. (2016a,b) measured multi-dimensional poverty based on a slightly different set of dimensions (including housing, water, sanitation, electricity and education) over four waves of the DHS from 1999-2013. All three studies, however, show irrespective of dimensions included that poverty is concentrated in the North and in rural areas. In a state level analysis, Ajakaiye et al. (2016a) also showed that the most deprived states are, while concentrated, not solely in the North.

To the best of the authors’ knowledge, Adetola and Olufemi (2012) and the World Bank’s 2016 poverty assessment are the only available studies, which analyzed multi-dimensional poverty indicators in relation to monetary poverty with data from 2008 and reaching up until 2013 respectively, whereby latter does not have a specific focus on child poverty. This analysis hence aims at filling the gap in the literature by providing more up to date monetary and multi-dimensional child poverty estimates and insights on how these measures overlap.

# 2.3 Children’s Rights in Nigeria

The Federal Government of Nigeria adopted the Child’s Rights Act in 2003 in line with both the United Nation’s Convention on the Rights of the Child and the African Charter on the Rights and Welfare of the Child to provide and protect the rights of Nigerian children. It is a 24-part Act that factors in different dimensions to ensure that the best interest of a child is safeguarded in all actions concerning a child undertaken by an individual, public or private body including the court of law.

The Child’s Rights Act is put in place by the Federal Government for all children of the federation, but a federal Act does not automatically get applied to all States until State legislature make the national law applicable in its territory. 25 out of 36 States in Nigeria so far have adopted the Child’s Rights Act. 11 5
35 Table 2: Estimation Results of Basic Model

|Depend|able Variabl|e is the Log|(FY1975-<br>of the Rea|FY 1990)<br>l Exchange R|ate (RER) I|ndex||
|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|t7)|
|Measure of|RERT|RPT|RPX|RPM|RPT1|RPX1|RPM1|
|Constant|0.95|1.10|1.13|1.12|1.68|1.71|1.72|
|Capital<br>Exp./Total<br>Govt. Exp.|0.02<br>(0.05)|-1.01***<br>(-4.02)|<br>-1.02***<br>(-3.75)|-1.02***<br>(-4.03)|<br>-1.47*<br>(-2.04)|-1.41*<br>(-1.82)|-1.46*<br>(-2.04)|
|Total Govt.<br>Exp/GDP|-7.01***<br>(-.5.89)|<br>-3.97***<br>(-5.35)|<br>-4.05***<br>(-4.99)|-4.00***<br>(-5.34)|<br>-4.87***<br>(3.51)|-5.16***<br>(-3.63)|-5.07***<br>(3.69)|
|Log Terms of<br>Trade (Price<br>of Exports<br>to imports)|<br>-0.04<br>(0.29)<br>_______|0.16*<br>(1.95)<br>____|-0.60***<br>(-6.64)|<br>0.39***<br>(4.73)|<br>0.05<br>(0.38)|-0.71***<br>(-4.88)|<br>0.28*<br>(1.97)|
|Productivity<br>Growth<br>(export<br>differences)|<br>0.25<br>(1.67)<br>|0.22**<br>(2.41)|0.21*<br>(2.10)|0.21*<br>(2.27)|0.42***<br>(3.18)|<br>0.40**<br>(2.95)|0.40***<br>(3.09)|
|Current<br>Account Def.<br>GDP|2.37**<br> <br>(2.39)|1.57**<br>(2.53)|1.46**<br>(2.17)|1.54**<br>(2.46)|0.43<br>(0.49)|0.35<br>(0.39)|0.39<br>(0.46)<br>I|
|AR (1)||..<br>_|..<br>_________<br>________|..|0.73**<br>(2.47)|0.75**<br>(2.50)|0.75**<br>(2.60)|
|R2|-0.88|O.91|0.93|**O.9-1**|**.86**|**..28**|0.87|
|FStatistic|23.1|31.4|42.5|33.0|15.8|19.2|17.3|
|D. W.<br>Statistic|2.32|2.02|1.85|1.96<br>I|2.22<br> <br>_I|2.25|2.26<br>_<br>_|
|No. of<br>Observations|16|16|16|16|16|16|16|

lotes: Data in parenthes.s are t-sta tistis. *** Significant at 1% or

****** Significant at between 1 and 5%. * Significant at between 5 and 10%.
Calculation of either the EB or the MEB of stamp duties requires information on the relevant elasticities and on the stamp duty rates, and there are no estimates of these elasticities. Nevertheless, it is possible to calculate the MEB for different combinations of tax rates and elasticities, in order to illustrate the possible magnitude of the MEB of stamp duties.

Suppose, for example, that the stamp duty rate is 10 percent, the elasticity of demand is - 1, and the elasticity of supply is 1. Then the MEB of the stamp duty is 0.05, so that raising 1 additional rupee in tax revenues generates a misallocation valued at Rs. 0.05. The MEB increases with an increase in the stamp duty rate or with increases in the (absolute value) of the elasticities. If the stamp duty rate is 15 percent, the elasticity of demand is -2.5, and the elasticity of supply is 2.5, then the MEB of the stamp duty is 0.1875. The MEB for various combinations of the parameters is given in Table 5.

|**Table 5. Marginal**|**Excess Burd**<br>**Elasticity**|**en of Stamp**<br>**of Demand,**|**Duties: Partia**<br>**Elasticity of Su**|**l Equilibriu**<br>**pply**|**m Model**|
|---|---|---|---|---|---|
|**Stamp Duty Rate**|<br>-0.5, 0.5|<br>-1,1|<br>-2.5,2.5|<br>-5, 5|-10,10|
|0.05|0.0125|0.025|0.0625|0.125|0.25|
|0.10|0.025|0.05|0.125|0.25|0.5|
|0.15|0.0375|0.075|0.1875|0.375|0.75|
|0.20|0.05|0.1|0.25|0.5|1|
|0.25|0.0625|0.125|0.3125|0.625|--|

In order to put these numbers in some perspective, it is useful to compare these magnitudes with those of other taxes in other countries. For example, Ballard, Shoven, and Whalley (1985) calculate the MEB of different U.S. taxes.<sup>25</sup> For labor taxes, they estimate MEBs that vary from 0.121 to 0.230, depending on the relevant elasticities; for sales taxes, the MEB varies from 0.256 to 0.388; and for excises, it varies from 0.035 to 0.115.

While illustrative, however, these largely mechanical calculations are likely to grossly underestimate the true efficiency cost of stamp duties because they fail to capture some dimensions of behavior that stamp duties are likely to distort.

First, the MEB framework assumes that there is a single “partial equilibrium” market in which a distortion exists. The recognition that land markets are only one market among many will substantially increase the distortions from stamp duties. For example, suppose that the market in which stamp duties are imposed is one of only two markets. In this more “general equilibrium” framework, it is straightforward to demonstrate that the elasticity of supply of the taxed commodity can be much larger than assumed in the calculations in Table 5, which implies a significant increase in the MEB of stamp duties.

> 25  See Charles L. Ballard, John B. Shoven, and John Whalley (1985), “General Equilibrium Computations of the Marginal Welfare Costs of Taxes in the United States”, _The American Economic Review_ , 75 (1), 128-138.

15
countries later formed a model for Guinea worm eradication campaigns in other affected countries.

# _Strategies for Dracunculiasis Eradication_

As elaborated in Hopkins and Ruiz-Tiben (1991), key activities of dracunculiasis eradication can be grouped into three operational phases. _Phase I_ includes: the establishment of a national campaign coordinator, office/secretariat; completion of national baseline village-by-village surveys for case-detection and prevalence statistics; and preparation or revision of a national plan of action. _Phase II_ incorporates implementation of interventions and includes activities such as: the identification and training of village-based workers for each endemic village; implementation of villagebased surveillance using case registries; initiation of a comprehensive health education/mobilization strategy; provision of cloth filters (and training in their usage); provision of safe sources of drinking water (such as bore-hole wells) and control of copepod populations using Abate (temephos); and monitoring the coverage as well as quality of surveillance/interventions. _Phase III_ involves implementation of casecontainment strategies: as the expected case load of the village-based health worker becomes one (or below) case per worker per day, increasingly stringent surveillance and control measures are initiated -- shifting the focus from the village or community-level to the individual case-level. In addition, after Phase III, post-eradication activities include the maintenance of adequate surveillance for 3 years after the last case and a request to WHO to evaluate and certify the attainment of eradication.<sup>9</sup>

# _Target Population Coverage_

GDEC eradication activities progressively reached larger sections of the target population. The initiation of control interventions reached _all_ target countries only in **_1995._** Table 2 reports the years in which there were increments in target population coverage, i.e. the years in which the various countries initiated intervention activities associated with the campaign.

**Table 2. Target Population Coverage.**

|Year|Countries Where Control Activities Initiated|
|---|---|
|1983|India|
|1987|Pakistan|
|1989|Ghana, Nigeria|
|1991|Cameroon|
|1992|Burkina Faso, Senegal, Togo, Uganda|
|1993|Benin, Cote d'lvoire, Mali, Mauritania, Niger|
|1994|Chad, Ethiopia, Kenya, Sudan|
|1995|Yemen|

9 See Hopkins and Ruiz-Tiben (1991) for additional details regarding strategies for dracunculiasis eradication.

4
housing expenditures is likely to rise and policies that improve housing finance and provide support for housing access and improvements will become more relevant.

# **6 References**

- African Development Bank. 2012. “A Comparison of Real Household Consumption Expenditures and Price Levels in Africa.” Tunis: African Development Bank.

- African Development Bank. 2013. “African Housing Dynamics: Lessons from the Kenyan Market.” _Africa Economic Brief_ 4(3).

- Arimah, Ben C. 1992. “An Empirical Analysis of the Demand for Housing Attributes in a Third World City.” _Land Economics_ . 68(4): 366-379.

Angel, Shlomo. 2000. _Housing Policy Matters: A Global Analysis_ . New York: Oxford University Press.

- Buckley, Robert M. and Jerry Kalarickal. 2006. _Thirty Years of World Bank Shelter Lending: What Have We Learned_ ?” Washington, D.C.: World Bank.

- Centre for Affordable Housing Finance in Africa. 2013. _Housing Finance in Africa: 2013 Yearbook_ . South Africa: Centre for Affordable Housing Finance in Africa.

- Cohen, Barney. 2004. “Urban Growth in Developing Countries: A Review of Current Trends and a Caution Regarding Existing Forecasts.” _World Development_ 32(1): 23-51.

- Collier, Paul and Anthony J. Venables. 2013. “Housing and Urbanization in Africa: Unleashing a Formal Market Process.” _CSAE Working Paper WPS/2013-01._ University of Oxford Centre for the Study of African Economies.

- Follain, James R. and Emmanuel Jimenez. 1985a. “The Demand for Housing Characteristics in Developing Countries.” _Urban Studies_ 22(421): 421-432.

- Follain, James R. and Emmanuel Jimenez. 1985b. “Estimating the Demand for Housing Characteristics: a Survey and Critique.” _Regional Science and Urban Economics_ 15: 77-107.

- Giddings, Stephen W. 2007. “Housing Challenges and Opportunities in Sub-Saharan Africa.” Prepared for International Housing Coalition, July.

- Grosh, Margaret E. and Judy L. Baker. 1995 “Proxy Means Tests for Targeting Social Programs: Simulations and Speculation.” Living Standards Measurement Study, Working Paper No. 118. Washington D.C.: World Bank.

- Gulyani, Sumila and Ellen M. Basset. 2010. “The Living Conditions Diamond: An Analytical and Theoretical Framework for Understanding Slums.” _Environment and Planning A_ 42(9): 22012219.

25
where _Si_ is the years of schooling of child _i_ and superscripts _c_ and _p_ stand for child and parents respectively. The focus of the analysis is the parameter _β_ which is known as intergenerational regression coecient (IGRC, for short) in the literature.<sup>10</sup> It is implicitly assumed that the variance of the error term _εi_ does not depend on father's education in any systematic way, and thus _β_ alone adequately captures the inuence of family background. This assumption is valid when the error term captures primarily the variations in children's ability uncorrelated with father's education, and there are no market imperfections. In a model with perfect credit and insurance markets, the optimal investment in a child's education depends only on his/her ability, the family background is irrelevant. Under the plausible assumption that the conditional variance of children's (innate) cognitive ability does not depend on family background, there is no additional information in the conditional variance of schooling attainment that could be useful for understanding the impact of family background on educational opportunities of children.

In a more realistic setting where the credit and insurance markets are imperfect (or missing), we would expect that the conditional variance would reect the interactions of a child's ability with the credit constraint and risk coping strategies of a household. First, consider the implications of credit market imperfections in the absence of income or health shocks. We consider two types of credit market imperfections. In the rst case, the poor (less educated) households pay a higher interest rate but can borrow as much as they want for educational investment (i.e., no quantitative credit rationing).<sup>11</sup> In this case, the poor (less educated) parents invest less, given the ability of a child, because of a higher interest rate, but the investment dierences across children from the same family (or similar family background where fathers have the same education) are determined solely by the ability dierences among the

> 10Among many studies relying on this specication, please see Hertz et al. (2008) and Narayan et al. (2018) for cross country evidence, Azam and Bhatt (2015), Emran and Shilpi (2015) and Asher et al. (2018) on India, Knight et al. (2011), Golley and Kong (2013), and Emran and Sun (2015) on China. For recent surveys of this literature, see Iversen et al. (2019), Torche (2019), and Emran and Shilpi (2021).

> 11This model of credit market imperfections is adopted by Becker et al. (2018) in their recent theoretical analysis of intergenerational mobility.

6
- Schlenker, W., Hanemann, M., and Fisher, A.,2005. Will US Agriculture Really Benefit From Global Warming?  Accounting for Irrigation in the Hedonic Approach, _American Economic Review_ , 95: 395-406

- Seo, S. N., R. Mendelsohn, and M. Munasinghe, 2005, Climate Change and Agriculture in Sri Lanka: A Ricardian Valuation, _Environment and Development Economics_ 10: 581-596.

- Seo, N. and R. Mendelsohn, 2007. _A Ricardian Analysis of Climate Change Impacts on Latin American Farms_ , World Bank Policy Research Working Paper 4163. http://econ.worldbank.org/

- Tao, F., M. Yokozawa, Y. Xu. Y. Hayashi and Z. Zhang, 2006.  Climate Change and Trends in Phenology and Yields of Field Crops in China, 1981-2000. _Agricultural and Forest Meteorology_ , 138:82-92.

- Tuan, F.,1994. _China Runs Agricultural Trade Surplus with the United States_ .  China Situation and Outlook. International Agricultural and Trade Reports, WRS-94-4. Washington DC: Economic Research Service, United States Department of Agriculture.

- Winters, Alan l., and S. Yusuf, 2007. Dancing with Giant: China, India, and the Global Economy. Washington, DC and Singapore: World Bank and the Institute of Policy Studies.

- Wu, D., Q. Yu, C. Lu, H. Hengsdiijk, 2006.  Quantifying Production Potentials of Winter Wheat in the North China Plain. _European Journal of Agronomy_ , 24:226-235.

- Xiong, W., R. Matthews, I. Holman, E. Lin and Y. Xu, 2007. _Modelling China’s Potential Maize Production at Regional Scale under Climate Change_ , Climate Change, DOI 10.1007/s10584-007-9284-x.

- Yao, F., Y. Xu, E. Lin, M. Yokozawa and J. Zhang, 2007. Assessing the Impacts of Climate Change on Rice Yields in the Main Rice Areas of China, _Climatic Change_ 80:395–409.

21
|TRANSPORT PROJECT|Artan Guxho|
|---|---|
|TRANSPORT PROJECT|Artan Guxho|
|TRANSPORT PROJECT|Artan Guxho|
|TRANSPORT PROJECT|Artan Guxho|
|TRANSPORT PROJECT|Artan Guxho|
|TRANSPORT PROJECT|Artan Guxho|
|Programmatic Social Reform III|Daniel Cotlear|
|ACCOUNTABILITY FOR DECENTRALIZATION IN THE SOCIAL SECTOR|S Alessandra Marini|
|ACCOUNTABILITY FOR DECENTRALIZATION IN THE SOCIAL SECTOR|S Alessandra Marini|
|ACCOUNTABILITY FOR DECENTRALIZATION IN THE SOCIAL SECTOR|S Alessandra Marini|
|ACCOUNTABILITY FOR DECENTRALIZATION IN THE SOCIAL SECTOR|S Alessandra Marini|
|ACCOUNTABILITY FOR DECENTRALIZATION IN THE SOCIAL SECTOR|S Alessandra Marini|
|ACCOUNTABILITY FOR DECENTRALIZATION IN THE SOCIAL SECTOR|S Alessandra Marini|
|ACCOUNTABILITY FOR DECENTRALIZATION IN THE SOCIAL SECTOR|S Alessandra Marini|
|ACCOUNTABILITY FOR DECENTRALIZATION IN THE SOCIAL SECTOR|S Alessandra Marini|
|ACCOUNTABILITY FOR DECENTRALIZATION IN THE SOCIAL SECTOR|S Alessandra Marini|
|ACCOUNTABILITY FOR DECENTRALIZATION IN THE SOCIAL SECTOR|S Alessandra Marini|
|ACCOUNTABILITY FOR DECENTRALIZATION IN THE SOCIAL SECTOR|S Alessandra Marini|
|ACCOUNTABILITY FOR DECENTRALIZATION IN THE SOCIAL SECTOR|S Alessandra Marini|
|Health Sector Reform 2 Project(APL #2)|Richard Florescu|
|Health Sector Reform 2 Project(APL #2)|Richard Florescu|
|Health Sector Reform 2 Project(APL #2)|Richard Florescu|
|Health Sector Reform 2 Project(APL #2)|Richard Florescu|
|Health Sector Reform 2 Project(APL #2)|Richard Florescu|
|Health Sector Reform 2 Project(APL #2)|Richard Florescu|
|Health Sector Reform 2 Project(APL #2)|Richard Florescu|
|Health Sector Reform 2 Project(APL #2)|Richard Florescu|
|Health Sector Reform 2 Project(APL #2)|Richard Florescu|
|Health Sector Reform 2 Project(APL #2)|Richard Florescu|
|Health Sector Reform 2 Project(APL #2)|Richard Florescu|
|Health Sector Reform 2 Project(APL #2)|Richard Florescu|
|Health Sector Reform 2 Project(APL #2)|Richard Florescu|
|Health Sector Reform 2 Project(APL #2)|Richard Florescu|
|Health Sector Reform 2 Project(APL #2)|Richard Florescu|
|Health Sector Reform 2 Project(APL #2)|Richard Florescu|
|Rural Education Project|DingyongHou|
|Rural Education Project|DingyongHou|
|Rural Education Project|DingyongHou|
|Rural Education Project|DingyongHou|
|Rural Education Project|DingyongHou|
|Rural Education Project|DingyongHou|
|Rural Education Project|DingyongHou|
|Rural Education Project|DingyongHou|
|Rural Education Project|DingyongHou|
|Rural Education Project|DingyongHou|
|Rural Education Project|DingyongHou|
|Rural Education Project|DingyongHou|
TABLE 4 QUALITY INDICES (REL. TO US=1.0 IN 1977)

|YEAR<br>ENDING<br>JUNE|JAPAN|EEC|UNITED<br>KINGDOM|CANADA|USA|AUSTRALIA|REST<br>OF<br>WORLD|
|---|---|---|---|---|---|---|---|
|1975<br>1976<br>1977<br>1978<br>1979<br>1980<br>1981<br>1982<br>1983<br>1984<br>1985<br>1986|0.83<br>0.85<br>0.90<br>0.97<br>0.93<br>0.90<br>0.94<br>0.95<br>0.94<br>1.01<br>1.00<br>0.86|0.84<br>0.83<br>0.87<br>0.98<br>0.92<br>0.94<br>0.89<br>0.83<br>0.94<br>0.93<br>0.92<br>0.93|0.83<br>0.81<br>0.92<br>0.95<br>0.93<br>0.95<br>0.93<br>0.97<br>0.97<br>0.97<br>0.92<br>0.92|0.86<br>0.84<br>0.91<br>0.96<br>0.91<br>0.95<br>0.95<br>0.96<br>1.09<br>0.92<br>0.98<br>1.03|0.94<br>0.96<br>1.00<br>1.06<br>1.02<br>1.02<br>1.00<br>1.10<br>1.21<br>1.22<br>1.29<br>1.36|0.73<br>0.70<br>0.69<br>0.68<br>0.73<br>0.67<br>0.86<br>0.94<br>0.86<br>0.90<br>0.86<br>0.86|0.93<br>0.85<br>0.97<br>0.95<br>0.82<br>0.99<br>1.02<br>0.97<br>0.96<br>0.72<br>0.73<br>0.81|

# AVERAGE GROWTH RATES FOR RESTRICTED PERIOD (1978 TO 1981)

JAPAN EEC UK CANADA USA AUS REST 0.012 0.009 0.003 0.012 0.001 0.066 0.020

# AVERAGE GROWTH RATES FOR 1975-76 AND 1982-86

JAPAN EEC UK CANADA USA AUS REST 0.002 0.012 0.014 0.022 0.053 -.008 -.019 AVERAGE GROWTH RATES FOR 1975-86 JAPAN EEC UK CANADA USA AUS REST 0.006 0.011 0.010 0.018 0.034 0.019 -.004
**Table 1. Loan characteristics in the sample**

|**Source of loans**|**Bank**|**CIF**|**Internal**|**Mixed**|
|---|---|---|---|---|
|Number of loans|255|1086|457|349|
|Amount of loans (Rs million)|8.28|23.72|3.12|4.87|
|Full repayment|0.87|0.63|0.89|0.81|
|% overdue|0.08|0.23|0.08|0.12|
|Interest rate|12.5|13.2|14.8|13.4|
|NumberofSHGs||21|47||

7
Quantifying war-induced crop losses in Ukraine in near real time to strengthen local and global food security Klaus Deininger<sup>1</sup> Daniel Ayalew Ali<sup>1</sup> Nataliia Kussul<sup>2</sup> Andrii Shelestov<sup>2</sup> Guido Lemoine<sup>3</sup> Hanna Yailimova<sup>2</sup>

**Keywords:** Ukraine, agricultural production, food security, machine learning, conflict/war **JEL codes:** Q10, O13, H56, R14

1 The World Bank, 1818 H Street NW, Washington, DC 2 Igor Sigorsky Kyiv Polytechnic Institute, Kyiv, Ukraine 3 Joint Research Center of the European Commission The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent. Funding support from the European Union (ENI/2017/387-093 and ENI/2020/418-654) is gratefully acknowledged. We thank Heike Bach, Denis Bashlyk, Mariia Bogonos, Martin Claverie, Raphael Dandrimont, Markiyan Dmytrasevych, Baher el Hifnawi, Anthony Gaeta, Ben Hell, Thea Hilhorst, Roman Hrab, Sergyi Kubakh, Andrii Martin, Darina Marchak, Tymofiy Mylovanov, Roman Neyter, Oleg Nivievskyi, Vladimir Popov, Fernando Sedano, Leah Soroka, Viktoria Strakhova, Taras Vysotskyi and Sergyi Zorya for helpful discussions and insightful comments and Daria Manzhura as well as Tania Khorzovskaya for outstanding coordination and administrative support.
**Figure 7. Cross-border flows and extensive margin of greenfield investments**

_Panel A: LAC to North_ <!-- Start of picture text --> Panel B: LAC to South<br><!-- End of picture text --> <!-- Start of picture text --> 6 5.0% 12 0.8%<br>4.5%<br>5 10<br>4.0%<br>0.6%<br>3.5%<br>4 8<br>3.0%<br>3 2.5% 6 0.4%<br>2.0%<br>2 4<br>1.5%<br>0.2%<br>1.0%<br>1 2<br>0.5%<br>0 0.0% 0 0.0%<br>2003 2004 2005 2006 2007 2008 2009 2010 2011 2003 2004 2005 2006 2007 2008 2009 2010 2011<br>Panel C: North to LAC Panel D: South to LAC<br>120 25% 18 2.5%<br>16<br>100<br>20% 14 2.0%<br>80 12<br>15% 1.5%<br>10<br>60<br>8<br>10% 1.0%<br>40 6<br>4<br>5% 0.5%<br>20<br>2<br>0 0% 0 0.0%<br>2003 2004 2005 2006 2007 2008 2009 2010 2011 2003 2004 2005 2006 2007 2008 2009 2010 2011<br>Panel E: LAC to LAC<br>16 8.0%<br>14 7.0%<br>12 6.0%<br>10 5.0%<br>8 4.0%<br>6 3.0%<br>4 2.0%<br>2 1.0%<br>0 0.0%<br>2003 2004 2005 2006 2007 2008 2009 2010 2011<br>% of Active Connections Total Flows<br>% of Active Connections % of Active Connections<br>Value (2011 Constant U.S. Dollars Billions) Value (2011 Constant U.S. Dollars Billions)<br>% of Active Connections % of Active Connections<br>Value (2011 Constant U.S. Dollars Billions) Value (2011 Constant U.S. Dollars Billions)<br>% of Active Connections<br>Value (2011 Constant U.S. Dollars Billions)<br><!-- End of picture text --> This figure shows the evolution of the extensive margin for greenfield investments and the evolution of the total value of these connections. The extensive margin is represented by the percentage of active connections, that is, the number of country-pairs that have a positive flow in each year over the number of country-pairs with a positive or zero flow in the last year of the sample. North countries are the G7 and Western Europe. South countries are all the others (excluding LAC countries). Offshore centers are excluded from the sample. Flows are expressed in 2011 constant U.S. dollars.