rafmacalaba/datause-encoder-data huggingface dataset:
**Table 1:** Technology sets considered in the numerical model

|Set<br>|Abbreviation|Composition|
|---|---|---|
|High carbon technology|Legacy|Average<br>thermal<br>production<br>mix<br>in<br>2008: 40% gas , 50% coal , 10% oil (EN-|
|||ERDATA, 2012)|
|Low carbon technology|Gas|Efcient gas|
|Zero carbon technology|Wind|Onshore wind|

**Table 2:** Fuel price trajectories of the fossil technology sets in $ /MWh (EU, 2011)

||2008|2025|2035|2050|
|---|---|---|---|---|
|Legacy|42.5|46|48|39|
|Gas|65|76|75|60|

_Proof._ Let _x_ ̃ _z,t_ and _x_ ̃ _l,t_ be two investment schedules that equalize the operational LCOE accross technologies, such that: Comparing the allocation implied by (20) with the optimal rule stated in Corollary 1 shows that equalizing the operational LCOE across technologies misses the future increase for carbon and fossil resources prices, and misses the term correcting for future changes in the value of capacities, leading to relatively too much investment in gas power and not enough investment in renewable power.

In the next section we use numerical simulations calibrated on the European power sector and find that during the optimal transition to clean power, renewable power plants should be built at an operational LCOE several times higher than new gas power plants.

# **4. Numerical application to the European electricity sector**

# _4.1. Functional forms, data and calibration_

We calibrate the model with data from the European power sector as described in the European 2050 Energy Roadmap (EU, 2011). In this numerical application, efficient gas power plants (the low-carbon technology) and onshore wind (the zero-carbon technology) are used to phase out the existing polluting capacities represented as the average legacy thermal production mix (Table 1).

To better fit the data, we express installed capacity _ki,t_ in peak capacity (GW), and production _qi,t_ in GWh/yr. Production is constrained by a maximum number of operating hours _Hi_ . For instance, a given windmill will produce 13
**Table 2: Baseline difference between schools in treatment and control districts**

||(1)<br>Control|(2)<br> Treatment|(3)<br>Difference|(4)<br> p-value|
|---|---|---|---|---|
|Number of Girls Class 5|7|4|3|0.0000|
|Number of Girls Class 6|26|16|10|0.0000|
|Number of Girls Class 7|24|14|10|0.0000|
|Number of Girls Class 8|21|12|10|0.0000|
|Number of Boys Class 5|9|7|2|0.0000|
|Number of Boys Class 6|36|30|6|0.0000|
|Number of Boys Class 7|31|25|6|0.0000|
|Number of Boys Class 8|29|22|7|0.0000|
|Ratio of Filled to Sanctioned Teaching Posts|0.850|0.868|-0.018|0.0000|
|Student-Teacher Ratio (All Classes/Sanctioned)|27.670|28.667|-0.997|0.0000|
|Student-Teacher Ratio (All Classes/Filled)|33.848|34.158|-0.310|0.0591|
|Rural (1 = Yes)|0.878|0.912|-0.034|0.0000|
|Toilets|0.492|0.480|0.011|0.0070|
|Drinking Water|0.819|0.811|0.008|0.0106|
|Electricity|0.466|0.304|0.162|0.0000|
|Boundary Wall|0.607|0.519|0.088|0.0000|
|Main Gate|0.678|0.581|0.097|0.0000|
|Play Ground|0.365|0.385|-0.020|0.0000|
|Library|0.178|0.207|-0.029|0.0000|
|Biology Lab|0.190|0.139|0.051|0.0000|
|Chemistry Lab|0.205|0.155|0.050|0.0000|
|Physics Lab|0.270|0.225|0.045|0.0000|
|Home Economics Lab|0.033|0.027|0.006|0.1278|
|Computer Lab (Morning)|0.042|0.030|0.012|0.0037|
|Computer Lab(Evening)|0.016|0.007|0.009|0.0003|
|Observations|30,283|26,620|||

Notes: Treatment districts have literacy rates < 40 percent. Control districts have literacy rates > 40 percent. Column 3 is calculated by subtracting column 2 from column 1. Enrollment figures are school level averages. The indicator variables describing school infrastructure and facilities are representing the fraction of all public schools with the facility/infrastructure available. For instance, 49 percent of all schools in all control districts have toilets and 48 percent of all schools in all treatment districts have toilets. In contrast 61 percent of control district schools have a boundary wall while only 52 percent of treatment district schools do.

36
- Krueger, A. O. 1974,  ̳The Political Economy of Rent-seeking Society‘. _American Economic Review_ , 64, (3), 291–303.

- Krueger, A. O., 1983, ―The Role of the World Bank as an International Institution,‖ _Carnegie-Rochester Conference Series on Public Policy_ , Vol. 18, pp. 281-312.

- Krueger, A. O., Maurice Schiff and Alberto Valdés, 1992, The Political Economy of Agricultural Pricing Policy, Five Volume World Bank Comparative Study, Johns Hopkins University Press: Baltimore.

- Landes, D. 1998. _The Wealth and Poverty of Nations: Why Some Are So Rich and Some So Poor_ . New York and London: Norton.

- Lau, L. J., Qian, Y. and G. Roland, 2000,  ̳Reform without Losers: An Interpretation of China‘s DualTrack Approach to Transition‘, _Journal of Political Economy_ , 108, (1), 120–43.

- Lavigne, M. (1995). _The Economics of Transition: From Socialist Economy to Market Economy_ . New York: St Martin‘s Press.

- Lin, Justin Yifu, 1995, ―The Needham Puzzle: Why the Industrial Revolution Did Not Originate in China _?" Economic  Development and Cultural Change_ 41 (January 1995): 269-92.

- -------, 2009, _Economic Development and Transition:  Thought, Strategy and Viability_ (Marshall Lectures), Cambridge University Press.

- -------, 2011a, ―A Global Economy with Multiple Growth Poles.‖ In _Post-Crisis Growth and Development: A Development Agenda for the G-20_ , eds. Shahrokh Fardoust, Yongbeom Kim, and Claudia Sepulveda, 77-105. Washington, DC: The World Bank.

- --------, 2011b, ―From Flying Geese to Leading Dragons:  New Opportunities and Strategies for Structural Transformation in Developing Countries,‖ UNU-WIDER annual lecture, Maputo:  Mozambique.

- ---------, 2011c, ―New Structural Economics: A Framework for Rethinking Economic Development.‖ _World Bank Research Observer,_ no. 2,   Vol. 26, pp. 193-221.

- ---------, 2012a, _Demystifying the Chinese Economy_ , Cambridge University Press: Cambridge.

- ---------,2012b, _Benti and Changwu_ : _Dialogues on Methodology in Economics._ New York: Cengage. Original Chinese version published by Peking University Press.

- ---------, 2012c, New Structural Economics, World Bank: Washington D.C.

- Lin, Justin Yifu an Zhiyun Li, 2008, ―Policy Burden, Privatization and Soft Budget Constraint,‖ Journal <u>of Comparative Economics, 36 (2008), pp. 90-102.</u>

- Lin, Justin Yifu and Yan Wang, 2008, ―China‘s Integration into the World: Development as a Process of Learning and Industrial Upgrading,‖ World Bank Policy Research Working Paper Number 4799.

- Lin, Justin Yifu, F. Cai, and Z. Li, 1996,  ̳The Lessons of China‘s Transition to a Market Economy‘. _Cato Journal_ , 16, (2), 201–31.

- Lin, Justin Yifu, Xifang Sun and Ye Jiang, 2009, ―Toward a theory of optimal financial structure, ― World Bank Policy Research Working Paper Number WPS 5038.

50
and in country-years where and when political institutions are weak. In a theoretical contribution, Chang, Hevia and Loayza (2010) develop a model in which expropriations and privatizations reflect a tradeoff between more redistribution and better incentives for effort, and argue that this helps to explain why expropriations are more likely in resource-rich economies when commodity prices are high.

The only other study we are aware of that looks at sovereign default and expropriation at the same time and the interrelationship between them is by Tomz and Wright (2010). They find that, for the world as a whole, defaults and expropriations have occurred in alternating, rather than coincident waves. They suggest that one possible reason is that punishment spillover for failure to honor contracts across these two types of foreign investment is small. If -- to the contrary -- foreign investors and creditors cooperated fully in punishing countries for either form of sovereign theft, countries that defaulted would also have incentives to expropriate as well, since they have already incurred the full punishment for expropriation (and vice versa). This would imply a coincidence of default and expropriation events which is not observed in the data. We expand on their analysis by documenting in more detail the patterns of association between the two types of events, and their correlates, at the country level.

# **3.  Data on Sovereign Defaults and Expropriations**

Our main dataset on sovereign defaults and expropriations is taken from Tomz and Wright (2010). This is a dataset of country-year observations spanning 191 countries since 1929, which identifies country-years in which the two types of sovereign theft occurred. In their dataset, sovereign defaults occur when a country fails to make required payments to foreign private creditors within the required period. It therefore does not include defaults against foreign official creditors, such as bilateral official lenders and multilateral aid agencies. The data records each year that a country is in default, since default episodes often last for multiple years. In fact, the typical length of a default episode in their dataset is 8 years during the 19702004 period that we consider in this paper, and 9 years if considering the whole sample from 1929 to 2004. In this paper, we are interested in the onset of sovereign defaults rather than 6
|**(12)**<br>**IES**|28.686<br>(2.241)**<br>2.359<br>(1.981)**|1.786<br>(1.411)<br>-1.08<br>(-1.743)*<br>-1.072<br>(-1.838)*<br>370<br>2.561<br>1.098<br>0.964<br>1.440<br>l. These<br>lation z-|
|---|---|---|
|**(10)**<br>**(11)**<br>**ND HIGH-INCOME COUNTR**|32.289<br>22.159<br>(6.91)***<br>(1.784)*<br>1.984<br>(1.708)*|1.67<br>(1.349)<br>-0.913<br>-0.828<br>(-1.755)*<br>(-1.757)*<br>-1.275<br>-1.213<br>(-1.629)*<br>(-1.713)*<br>379<br>370<br>3.454<br>2.663<br>1.208<br>0.995<br>0.716<br>0.564<br>1.477<br>1.344<br>dicates 1% significance leve<br>ion problem. For serial corre|
|**(9)**<br>**LE-INCOME A**|12.14<br>(1.15)<br>2.254<br>(1.906)*|2.374<br>(1.742)*<br>373<br>3.362<br>1.302<br>0.663<br>1.139<br>vel, and *** in<br>veridentificat|
|**(7)**<br>**(8)**<br>**UPPER MIDD**<br>**ion (Sample 2002-2012)**|24.095<br>5.872<br>(6.595)***<br>(0.583)<br>1.854<br>(1.702)*|2.255<br>(1.771)*<br>*<br>*<br>382<br>373<br>2.857<br>2.637<br>1.010<br>1.299<br>0.861<br>0.963<br>1.435<br>1.339<br>indicates 5% significance le<br>lem where H0: there is no o|
|**(6)**<br>**IES**<br>**f Tax Corrupt**|-22.449<br>(-1.9)*<br>7.729<br>(3.097)***|4.892<br><br>(2.907)***<br>-0.556<br>(-2.163)**<br>-2.295<br>*<br>(-3.196)**<br>490<br>2.897<br>0.882<br>1.073<br>1.416<br>ance level, **<br>tification prob|
|**(5)**<br>**ME COUNTR**<br>**terminants o**|-2.582<br>(-0.258)<br>|6.301<br>(3.854)***<br>-0.444<br>(-1.768)*<br>-2.025<br>*<br>(-2.816)**<br>490<br>3.190<br>0.912<br>0.975<br>1.278<br>es 10% signific<br>s for overiden|
|**_Dependent variable: Tax corruption_**<br>**(1)**<br>**(2)**<br>**(3)**<br>**(4)**<br>**LOW-INCOME AND LOWER MIDDLE-INCO**<br>**Table 11 - Panel Regression with two country groups: De**|**Constant term**<br>-11.728<br>-20.793<br>-40.215<br>-0.516<br>(-1.295)<br>(-2.326)**<br>(-3.6)***<br>(-0.056)<br>**_Tax simplification_**<br>**log(Tax payments)**<br>9.923<br>7.15<br>9.69<br>(4.049)***<br>(2.863)***<br>(4.002)***|**log(Tax time)**<br>7.97<br>6.765<br>(5.122)***<br>(4.226)***<br>**_Political and Political Institution Determinants of Corruption_**<br>**Bureaucracy Quality (higher better quality)**<br>-0.378<br>(-2.098)**<br>**Democratic Accountability (higher better)**<br>-2.826<br>(-4.037)**<br>No. of observations<br>499<br>499<br>499<br>490<br>J-statistics<br>2.912<br>2.706<br>2.014<br>3.010<br>Arellano-Bond serial correlation test AR(1)<br>0.816<br>0.716<br>0.819<br>0.712<br>Arellano-Bond serial correlation test AR(2)<br>0.877<br>0.815<br>0.774<br>0.974<br>Jarque-Bera normality test<br>1.462<br>1.258<br>1.157<br>1.557<br>Note: The estimation method is a panel - GMM. Annual data are used. t-statistics are given in parenthesis. * indicat<br>significance levels are equal to one minus the probability of rejecting the null hypothesis of zero coefficients. J-test i<br>tests, H0 is "there is no serial correlation"; and for normality test, H0 is "normal distribution".|

37
interventions that address both the supply of and the demand for data are needed to fully capture the potential of the data revolution to create a world of greater prosperity and sustainable development (Badiee et al. 2017). Therefore, it is critical for development institutions to allocate resources to countries that would benefit from such interventions the most.

The prioritization of interventions requires a transparent and objective metric to compare countries’ statistical capacity. The Statistical Performance Indicator (SPI), originally developed by Cameron et al. (2021), facilitates such comparisons, both over time and across countries.

This paper discusses the use of the SPI to guide development policies to improve and sustain statistical capacity in developing countries. With greater international attention on emphasizing the user responsiveness of national statistical systems, there is pressure to align statistical products and services to the needs of each country. The SPI can be used to evaluate the capacity of statistical systems to supply data and assess how well this supply aligns with local and international demands for data and services.

Analysis of the distribution of the SPI and its components over a sample of 215 countries reveals two distinct clusters of NSSs—low and high performing—separated by a relatively wide gap in SPI scores. Furthermore, poorly performing NSSs also have high variance across SPI components. Such variance hampers the ability of poorly performing NSSs to deliver their services.

Our benchmarking regression analysis identifies three types of NSSs: a group of NSSs that undersupply data to their users; a group that seems to be producing too much data, given the country’s characteristics; and a group that appears to be at the equilibrium point in terms of demand for and supply of data. These three groups motivate our proposed two-step strategy for building and enhancing sustainable statistical capacity in developing countries.

Our main proposal is to align the NSS’s capacity-building activities with the data priorities expressed by local actors and their capability to act on these data. Only when the supply of data corresponds to the demand for them can NSSs be sustainable in the long term. Our findings emphasize the importance of coordination by the donor community and South–South learning and experience exchange initiatives. Such coordination could be especially important in helping countries that reach middle-capacity status but are challenged to graduate to the pool of top NSSs.

3
Colombian children.

Table 5. Assessing Physical Development Gaps (children 5-10 years old)

||_Bod_|_y Mass In_<br>_(Std)_|_dex_|_U_<br>_(B_|_nderweigh_<br>_MI< 1 SD_|_t_<br>_)_|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Forced Migrant|-0.35***<br>(0.05)|-0.35***<br>(0.05)|-0.38***<br>(0.07)|0.06***<br>(0.02)|0.06***<br>(0.02)|0.07**<br>(0.03)|
|R-squared|0.03|0.03||0.01|0.01||
|Observations|1445|1445|1445|1445|1445|1445|
|Mean Dep. Variable(Colombian sample)|0.17|0.17|0.17|0.15|0.15|0.15|
||_(B_|_Overweight_<br>_MI> 1 SD_|_)_|_H_|_ealth Statu_<br>_(Sick=1)_|_s_|
||(7)|(8)|(9)|(10)|(11)|(12)|
|Forced Migrant|-0.15***|-0.15***|-0.16***|0.03|0.03|0.04|
||(0.02)|(0.02)|(0.03)|(0.02)|(0.02)|(0.03)|
|R-squared|0.03|0.03||0.00|0.01||
|Observations|1445|1445|1445|1445|1445|1445|
|Mean Dep. Variable(Colombian sample)|0.29|0.29|0.29|0.22|0.22|0.22|
|Control variables||✓|✓||✓|✓|
|Propensity Score Matching|||✓|||✓|

_Notes:_ The table presents the estimates of equation 1. BMI is a measure that indicates nutritional status. It is defined by the World Health Organization (WHO) as an individual’s weight in kilograms over the squared height in centimeters. The standardized value of the body mass index following the WHO guidelines. A BMI over one standard deviation (SD) implies that the individual is overweight and a value below one SD indicates that the individual is underweight. Control variables include children’s sex and age, the maximum level of education completed by the most educated parent and grandparent, and the wealth index is the pre-migration wealth index for the Venezuelan sample and the current wealth index for the Colombian sample. Health status is measured as a dichotomous variable equal to one if the caregiver reports that in the last 30 days there was a health complication related to disease or chronic pain, accident, tooth pain, surgeries, or pregnancy complications (for females older than 11 y.o.a). Standard errors are clustered at the household level *** * p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.

# **V.B Cognitive development: Peabody vocabulary assessment**

Cognitive development is measured using the Peabody Vocabulary Test Score (PVTS) for children and adolescents 5 to 17 years of age in our sample. This is an untimed test of receptive vocabulary for standard Spanish and is intended to provide a quick estimate of the test taker’s receptive vocabulary verbal ability. It includes 125 questions, each containing four picture options with only one correct answer. Items are organized in order 35
La Porta, R., and A. Shleifer. 2014. “Informality and Development.” Journal of Economic Perspectives 28 (3): 109-26.

Lalive, S., and S. Staubli. 2015. “How Does Raising Women’s Full Retirement Age Affect Labor Supply, Income, and Mortality?” NBER Working Paper 18660, National Bureau of Economic Research, Cambridge, MA.

Lian, W., F. Liu, K. Svirydzenka, and B. Zhu, 2021. “A Diversification Strategy for South Asia.” IMF Working 21/202, International Monetary Fund, Washington, DC.

Loayza, N., P. Fajnzylber, and C. Calderón. 2005. Economic Growth in Latin America and the Caribbean: Sylized Facts, Explanations, and Forecasts. Washington, DC: World Bank.

Lopez-Acevedo, G., and R. Robertson. 2016. Stitches to Riches?: Apparel Employment, Trade, and Economic Development in South Asia. Washington, DC: World Bank.

Mabugu, R. E., and W. Simbanegavi. 2015. “Tax and Expenditure Reforms in Africa: An Overview.” Journal of African Economies 24 (AERC Supplement 2): ii3-ii15.

Maliszewski, W., and L. Zhang. 2015. “China’s Growth: Can Goldilocks Outgrow.” IMF Working Paper 15/113, International Monetary Fund, Washington, DC.

Mani, M., S. Bandyopadhyay, S. Chonabayashi, A. Markandya, and T. Mosier. 2018. South Asia’s Hotspots: The Impact of Temperature and Precipitation Changes on Living Standards. Washington, DC: World Bank.

McMillan, M. S., and K. Harttgen. 2014. “What is Driving the African Growth Miracle?” NBER Working Paper 20077, National Bureau of Economic Research. Cambridge, MA.

Mishra, P. 2013. “Has India's Growth Story Withered?” Economic and Political Weekly 48 (15): 51-59.

Montalbano, P., S. Nenci, and C. Pietrobelli. 2016. “International Linkages, ValueAdded Trade, and Firm Productivity in Latin America and the Caribbean.” In Firm Innovation and Productivity in Latin America and the Caribbean: The Engine of Economic Development, edited by M. Grazzi and C. Pietrobelli, 285-316. Washington, DC: Inter-American Development Bank.

Morikawa, M. 2021. “The Productivity of Working from Home: Evidence from Japan.” VoxEU.org, CEPR Policy Portal, March 12, 2021. https://voxeu.org/article/productivity-working-home-evidence-japan.

Moura, R., and R. Forte. 2010. “The Effects of Foreign Direct Investment on the Host Country Economic Growth—Theory and Empirical Evidence.” Singapore Economic Review 58 (3): 1-28.

Nabar, M. S., and P. N’Diaye. 2013. “Enhancing China’s Medium-Term Growth Prospects: The Path to a High-Income Economy.” IMF Working Paper 13/204, International Monetary Fund, Washington, DC.
<!-- Start of picture text --> 12,000,000<br> 10,000,000<br> 8,000,000<br> 6,000,000<br> 4,000,000<br> 2,000,000<br> -<br>Low income Lower middle income Upper middle income High income<br><!-- End of picture text --> _Source: UNHCR (2023)_ Key fact 2: Africa and the Middle East have historically hosted the most refugees, but new and increasing crises are dramatically altering the geography of displacement and the location of refugees Historically, Sub-Saharan Africa (SSA) and the Middle East and North Africa (MENA) have hosted the largest proportions of school-age refugees, as shown in Figure D. In 2017, 70 percent of the global school-age refugee population came from, and were hosted, in these two regions. However, recent conflicts have seen a surge in refugees hosted in countries and regions with previously low refugee populations.

Figure D shows the absolute number of school-age refugees by region from 2001 to 2021 (UNHCR, 2023). The position of each region dictates their significance, with the most populace regions positioned at the top of the graph. As this figure shows, SSA hosted the highest number of school-age refugees for 15 of the last 20 years. However, since 2017, there has been a significant jump in the proportion of school-age refugees hosted in both Europe and Central Asia (ECA) and in Latin America and the Caribbean (LAC), with the number of school-age refugees increasing more than fivefold in ECA between 2017 and 2022, and more than 20-fold in LAC over the same period. In fact, school-age refugees in LAC rose from fewer than 100,000 in 2017 to over 1.85 million as of end-2022, driven largely by forced displacement from the República Bolivariana de Venezuela. This shows not only an increase in global displacement – with the number of school-age refugee children tripling in the last 10 years – but an increase in the spread of global displacement, with more countries than ever hosting vast numbers of refugees.

9
- [4] Beck, Nathaniel, and Katz, Jonathan N. [1995], "What to Do (and What Not to Do) With Time-Series Cross-Section Data," _Amerncan Politscal Sczence Revzew_ 89 (September): 634-647.

- [5] Brown, Drusilla K. [1987], "General Equilibrium Effects of the US Generalized System of Preferences," _Southern Economic Journal_ 54: 27-47.

- [6] Brown, Drusilla K. [1989], "Trade and Welfare Effects of the European Schemes of the Generalized System of Preferences," _Economic Development and Cultural Change_ 37 (July): 757-776.

- [7] Clark, Don P., and Zarrilli, Simonetta [1992], "Non-Tariff Measures and Industrial Nation Imports of GSP-Covered Products," _Southern Economic Journal_ 59 (October): 284-293.

- [8] Coates, Daniel E., and Ludema, Rodney D. [2001], "A Theory of Trade Policy Leadership," _Journal of Development Economics_ 65: 1-29.

- [9] Davidson, R. and J. G. MacKinnon [1993], _Estimation and Inference rn Econometrzcs,_ Oxford University Press.

- [10] Devault, James M. [1996], "Political Pressure and the US Generalized System of Preferences," _Eastern Economwc Journal_ 22 (Winter): 35-46.

- [11] Dollar, David, and Aart Kraay [2001], "Trade, Growth, and Poverty," Working Paper 2615, World Bank.

- [12] Dornbusch, Rudiger, and FRankel, Jeffrey A. [1987], "Macroeconomics and Protection," in Robert M. Stem (ed.) _US Trade Polictes mn a Changing World Economy,_ MIT Press, 77-130.

- [13] Federal Register. _Various. Weekly Compilatzon of Preszdential Documents._ Washington, DC: National Archives and Records Administration. http://www.access.gpo.gov/nara/nara003.html??

- [14] Finger, J. Michael, and Winters, L. Alan [1998], "What Can the WTO Do for Developing Countries?", in Anne 0. Krueger (ed.) _The WTO as an Internattonal Organzzatzon,_ University of Chicago Press, 365-392.

- [15] Frasca, Tim [1988], "Chile Cuts Tariffs and Raises Tempers, Trying to Offset US Sanctions," _Christian Science Monitor_ (January 26): 10.

- [16] Gallup, J. L. and J. D. Sachs [1999], "Geography and Economic Development," Center for International Development Working Paper 1, Harvard University.

- [17] General Accounting Office [2001], _Internatzonal Trade: Comparison of US and European Union Preference Programs,_ GAO-01-647, GAO.

23
<!-- Start of picture text --> Figure 1<br><!-- End of picture text --> <!-- Start of picture text --> Coca Bush Cultivation<br> (hectares)<br>250000<br>200000<br>150000<br>100000<br>50000<br>0<br>Bolivia Colombia Peru<br>Source: ONDCP<br>1<br><!-- End of picture text -->

**Figure 2**

# **(a) (b)**

51