rafmacalaba/datause-encoder-data huggingface dataset:
male labor force that has not completed any degree (54 percent) is employed in the agricultural sector, the majority of female labor with no degree (55 percent) and with an elementary degree (67 percent) is employed in the services sector (Table 6). Forty-five percent of the female labor force with no degree or with an elementary school degree is employed by private households where the daily wage (PHP 129) is far below the average daily wage of an average female worker (PHP 193). More than 80 percent of male labor force who completed college and 90 percent of female labor force work in the services sector. The majority of university graduates have better-paid jobs, working as executives, managers, or professionals.

**Table 6: Industry of Work and Type of Occupation by Highest Degree Completed**

||**No grade **|<sup>**Elementary**</sup><br>**School**<br>**Male**|**High**<br>**School**<br>|**College**|**No grade**|**Elementary**<br>**School**<br>**Female**|<br>**High**<br>**School **<br>|<sup>**College**</sup>|
|---|---|---|---|---|---|---|---|---|
|_Sector_|||||||||
|Agriculture|54.1|33.6|11.4|2.1|48.3|29.2|9.0|1.2|
|Manufacturing|24.1|32.8|32.2|17.2|6.8|14.1|24.1|8.8|
|Services|21.8|33.7|56.5|80.7|55.2|67.3|71.3|90.5|
|_Types of Occupation_|||||||||
|Executives /<br>Managers|0.5|1.0|3.2|16.5|0.3|0.5|2.4|8.7|
|Professionals|0.3|0.7|4.4|40.3|0.3|0.8|5.8|55.1|
|Workers / Laborers|99.3|98.4|92.4|43.2|99.4|98.7|91.8|36.2|

_Source:_ Authors’ calculation based on LFS, 2003-2007

# **3. Estimation and Decomposition Methodology**

In this paper, we estimate the effects on wage of personal attributes (i.e. gender, education, and experience), sectors of employment, and occupations, controlling for region-specific and yearspecific effects, and employ a regression-based inequality decomposition method to examine their respective contribution to wage differential.

Wage is estimated in a standard form of regression as below. The coefficients are used to calculate the relative significance of human capital endowment measured by the highest degree completed.

where i denotes individual, wagei daily wage of individual i; experiencei the number of years of 2 potential experience; experiencei the number of years of potential experience squared (1/10,000); sexi a dummy for sex, which equals 1 if female; elementaryi, an education dummy, which equals 1 if the individual’s highest degree completed is elementary school education; highschooli, which equals 1 if the individual’s highest degree completed is high school education; universityi , which equals 1 if the individual’s highest degree completed is university education; and, _v_ , an error term.

8
28. Guasch, J.L. and J. Kogan, 2006: Inventories and Logistic Costs in Developing Countries: Levels and Determinants – A Red Flag for Competitiveness and Growth. _Revista de la Competencia y de la Propiedad Intelectual_ . Lima, Perú.

29. Hausman, W.H., H.L. Lee and U. Subramanian, 2005: _Global Logistics Indicators, Supply Chain Metrics and Bilateral Trade Patterns_ . Mimeo.

30. Heston, A., R. Summers, and B. Aten, 2002: Penn World Table Version 6.1. _Center for International Comparisons at the University of Pennsylvania_ , October.

31. Holtz-Eakin, D., 1994: Public Sector Capital and the Productivity Puzzle. _The Review of Economics and Statistics_ , 76(1), 12-21.

32. Hulten, C. 1996: Infrastructure Capital and Economic Growth: How Well You Use It May Be More Important than to How Much You Have. _NBER Working Paper 5847_ . National Bureau of Economic Research, Cambridge Mass.

33. Jacoby, H., 2002: “Access to Rural Markets and the Benefit of Rural Roads.” _The Economics Journal_ , 110, pp. 713-37.

34. Micco, A. and T. Serebrisky, 2006: Competition Regimes and Air Transport Costs: The effects of open skies agreements. _Journal of International Economics_ , 20, 25-51 35. Röller, L. and L. Waverman, 2001: Telecommunications Infrastructure and Economic Development: A Simultaneous Approach. _American Economic Review_ , 91, 9009-23.

36. Rodrigues, M.A., Bowersox, D.J. and Calantone, R.J., 2005: Estimation of Global and National Logistics Expenditures: 2002 Data Update. _Journal of Business Logistics_ , 26(2), 1-16.

37. Smith, D., Gordon, A., Meadows, K., and K. Zwick, 2001:”Livelihood Diversification in Uganda: Patterns and Determinants of Change Accross Two Rural Districts.” _Food Policy_ 26, pp. 421435.

38. Soloaga, I., J.S. Wilson and A. Mejia, 2007: Moving Forward Faster: Trade Facilitation Reforms and Mexican Competitiveness. _Policy Research Working Paper 3953_ . The World Bank. Washington, D.C.

39. Wilson, J.S., C.L. Mann and T. Otsuki, 2003: Trade Facilitation and Economic Development: Measuring the Impact. _Policy Research Working Paper 2988_ . The World Bank. Washington, D.C.

40. Wilson, J.S., C.L. Mann and T. Otsuki, 2004: Assessing the Potential Benefit of Trade Facilitation: A Global Perspective. _Policy Research Working Paper 3224_ . The World Bank. Washington, D.C.

41. The World Bank

- _World Development Report 1994: Infrastructure for Development_ . New York: Oxford University Press.

45
16 and Poors of the extent of disclosure practiced by firms in their annual reports in selected countries in the region<sup>6</sup> .

Figure 8. Extent of disclosure practiced by listed firms <!-- Start of picture text --> Indonesia Number of companies<br>Malaysia<br>Number of companies<br>12 18<br>16<br>10<br>Mean 23.79 14<br>Standard deviation 13.35<br>8 12<br>Mean 64.92<br>6 10 Standard deviation 11.85<br>8<br>4<br>6<br>2 4<br>0 2<br>0- 11- 21- 31- 41- 51- 61- 71- 81- 91- 101- 111- 121- 131-<br>10 20 30 40 50 60 70 80 90 100 110 120 130 140 0<br>0- 11- 21- 31- 41- 51- 61- 71- 81- 91- 101- 111- 121- 131-<br>Scores 10 20 30 40 50 60 70 80 90 100 110 120 130 140<br>Scores<br>Thailand Hong Kong<br>Number of companies Number of companies<br>16 10<br>14<br>8<br>12<br>10 Mean 42.57 Mean 42.75<br>Standard deviation 19.85 6 Standard deviation 19.85<br>8<br>6 4<br>4<br>2<br>2<br>0 0-10 11-20 21-30 31-40 41-50 51-60 61-70 71-80 81-90 91-100 101-110 111-120 121-130 131-140 0 0-10 11-20 21-30 31-40 41-50 51-60 61-70 71-80 81-90 91-100 101-110 111-120 121-130 131-140<br>Scores<br>Scores<br>Number of companies<br>Singapore<br>16<br>14<br>12<br>10 Mean 80.58<br>Standard deviation 28.57<br>8<br>6<br>4<br>2<br>0<br>0- 11- 21- 31- 41- 51- 61- 71- 81- 91- 101- 111- 121- 131-<br>10 20 30 40 50 60 70 80 90 100 110 120 130 140<br>Scores<br>Source: S&P (2005)<br><!-- End of picture text --> 6 The analysis looks at disclosure of several elements including the disclosure of related party transactions that is covered in the disclosure requirements index in Table 4.
-15-

production equation as a proxy for a range of inputs which a household may acquire using its family resources (Model IV below). As income is not an input per se, it may be argued that such a form of a nutrition production equation is more accurately a conditional production equation (Strauss 1990). Since, however, income is also included in the input demand equations, no causal pathway is obscured by its being included in this stage as well. Income is also instrumented. Given that the concern is with children's nutrition this is less for the possibility of family heterogeneity (Senauer and Garcia 1991) or simultaneously, than with the conventional errors in variables bias towards zero. The measure for long run income was predicted expenditures per capita, using total dry and irrigated land holdings, land in tree crops, value of vehicles as well as livestock and other physical capital as identifying instruments. In addition, education and household composition are used to explain current household expenditures.

# DATA

The model described earlier was applied to data from Pakistan, collected as part of a larger longitudinal study of rural households. The present study utilizes information from the first year (1986/87) of the three year survey.'" The survey was conducted in the least

**1o** The nutrition literature discusses the use of growth velocity as a measure of nutrition as opposed to achieved levels (Beaton 1990). Use of the panel nature of the data could address issues of velocity and catch up, but would be a different study than that intended here.
**O**

z **o** I m l > x w **9,**

**0**

X)

|Table B1O. Coverage of Health and Social<br>**Insurance**<br>Insurance<br>from Informal Sector Surveys conducted by the author|_<er_<br>~~~~~<br>Taxicabs|245 hour<br>~~~~~~~~~<br>per week<br>Taxicabs|Entire<br>~week<br>Samnple<br>Taxicabs|Entire<br>Samnple<br>Kiosks<br>|Entire<br>Sample<br>Stre<br>Markets<br>|Entire<br>Samnple<br>et Services &e<br>v<br>Vendors<br>|
|---|---|---|---|---|---|---|
|Number of Observations (actual number may vary depending on<sup>the</sup><br>question)|99|101|308|245|137|80|
|Social or Health Insurance contributions from informal activity<br>income|||||||
|Yes|24.2|52.6|37.7|41.6|31.9|42.5|
|Any coverage under Health Insurance|||||||
|Yes|69.4|56.6|62.9|54.7|43.5|45.6|
|Any coverage under Social Insurance|||||||
|Yes|66.4|44.4|55.1|38.2|13.2|16.3|
|Spouse covered by Health or Social Insurance<br>Yes|56.8|53.6|53.5|36.7|24.1|21.3|

_ . _ _ _

**0I** **_C)A_**

**Oi**
and a-half years after the debt relief program. The survey covered 2,897 households in four districts of the western Indian state of Gujarat and included detailed questions on household income, production, consumption and investment, as well as a number of questions on financial decisions and expectations. Households were identified from official beneficiary lists and considerable effort was spent to locate and interview as many borrowers as possible.

# **5.1 Bank Data and Sampling**

The sample frame was drawn from the official beneficiary lists published by banks and credit cooperatives. Bank lists typically included the name, village, pledged land, loan category, date of original disbursal, overdue principal and interest as of December 31, 2007, as well as the total amount eligible for debt relief under the program. This account level data was provided by the six largest commercial banks and the largest credit cooperative operating in the four survey districts. Together these banks account for 91% of all accounts that were eligible for the program in the survey districts and 87% of all debt relief accounts in the state. The sample frame was then restricted to accounts within a narrow band of _±._ 5 hectares around the 100% relief cutoff. This bandwidth was chosen following the cross-validation procedure proposed by Imbens and Kalyanaraman (2012).<sup>14</sup> The initial sample frame consisted of 5,554 accounts. This includes agricultural crop and investment loans, but excludes loans not directly related to agriculture, since these loans were not contingent on landholdings, so that the discontinuity induced by the program does not apply. The sample frame also omits previously restructured loans, since I observe neither the original loan size nor the vintage, or terms of restructuring for these loans. This restricts the set of loans covered to the roughly 70% of accounts for which landholding was determinant of debt relief qualification.<sup>15</sup> Descriptives for the population of qualifying loans in the four sample districts are reported in Table 2. Corresponding figures for accounts in the sample frame appear in Table D.1 in the Supplemental Appendix.

Figure 1 summarizes the distribution of debt relief for treatment and control housholds. The average

> 14The chosen range was the bandwidth that minimized the mean squared error when predicting relief amount with landholding and a 100% debt relief indicator. Because different banks implemented the program cutoff as either two hectares or five acres (2.023 hectares), the bandwidth is calculated at the bank level. 15Bank records were not perfect, and for some accounts no land data was available. Accounts without reported landholding were excluded from the sample frame. Because this was a small number of accounts falling into both the unconditional and 25% relief categories, the resulting attrition was random and unlikely to bias the analysis.

15
Table 4: Effects of gender quota on male students’ outcomes at the concentration level

||Acc|ess to IHEs|Score relat|ed outcomes|
|---|---|---|---|---|
||Share<br>males|Share males<br>low-SES|High-SES male<br>avg. score|Low-SES male<br>avg. score|
||(1)|(2)|(3)|(4)|
|Estimators|||||
|Panel A: CEELM|||||
|OLS|-0.17***|-0.04|1.20|-0.80|
||(0.03)|(0.03)|(2.91)|(4.08)|
|Borusyak et al. (2021)<sup>_ζ_</sup>|-0.17***|-0.11***|5.64|5.76|
||(0.02)|(0.02)|(4.24)|(4.07)|
|Sun-Abraham (2020)|-0.18***|-0.05|3.82|4.08|
||(0.03)|(0.03)|(2.87)|(4.50)|
|_Control group mean_|0.82|0.39|260|259|
|N|748|748|748|746|
|Panel B: Non-CEELM|||||
|OLS|-0.06***|-0.03*|-1.24|-0.52|
||(0.02)|(0.01)|(2.07)|(2.38)|
|Borusyak et al. (2021)<sup>_ζ_</sup>|-0.05***|-0.04**|-1.18|-0.53|
||(0.02)|(0.02)|(1.78)|(1.93)|
|Sun-Abraham (2020)|-0.046**|-0.02|-0.96|0.19|
||(0.02)|(0.015)|(2.27)|(2.73)|
|_Control group mean_|0.69|0.41|217|216|
|N|2339|2339|2299|2334|
|Concentration FE|✓|✓|✓|✓|
|University*year FE|✓|✓|✓|✓|
|Academic pro*year FE|✓|✓|✓|✓|
|Year FE|✓|✓|✓|✓|

Note: Highly competitive academic programs encompass concentrations within computer science, economics, engineering, law and political science, and medical studies, collectively referred to as CEELM programs. Each coefficient is from a separate regression. The dependent variables in columns 1 and 2 are the share of males and share of males from low socioeconomic backgrounds, which can be interpreted as average percentage point increase in the share of males students in treated concentrations owing to the quota. In columns 3–4, the dependent variables are average score for high- and low-SES male students, respectively; it can be interpreted as points decrease/increase. All the models in Panels A and B include the above stated fixed effects. Standard errors are clustered at concentration level and reported in parentheses. Significance level: *p _<_ 0.1; **p _<_ 0.05; ***p _<_ 0.01 _ζ_ Owing to the imputing nature of the estimator by Borusyak et al. (2021), the actual number of observations used to estimate the result is slightly lower than that with the OLS and Sun-Abraham (2020). The _N_ represents the number of observations used to compute the OLS and Sun-Abraham (2020) estimates.

# **7 Rationale for Quota Impact**

In this section, I use a supply-and-demand framework to explain why the findings correspond with the anticipated impact of a gender quota on education. This allows us to understand the underlying 26
Table 6: Industrial Composition and ICT Intensity: United States & India WDI Value Added Share (%)

||United States|India|ICT share (%)|NICT share (%)|
|---|---|---|---|---|
|Agriculture|1|18|9|91|
|Industry Excluding Manufacturing|8|12|2|98|
|Manufacturing|13|15|5|25|
|Services|78|55|6|30|

_Notes:_ The table reports value added shares and ICT/NICT income shares in the four major WDI sectors, both for India and the United States in 2010. Value added shares are based on WDI data and income shares are approximations based on aggregate estimates of the capital specific marginal product based on Eden and Gaggl (2014) and industry specific ICT and NICT output ratios.

of total capital; _k_ is the aggregate capital stock per-capita, and _p_<sup>_<u>p</u>_</sup> _i_<sup>istheratioofthecapitalprice</sup> and the price of ICT capital. Our model from Section 4 suggests that we can further decompose _<u>ppkiki</u>_<sup>to arrive at the following expression:</sup> where _j_ = 1 _, .., n_ are industries. Note that the ratio<sup>_<u>r</u>_</sup> _r_<sup><u>+</u></sup> +<sup>_<u>δ</u>_</sup> _δ_<sup>_<u>n</u>_</sup> _i_<sup>isanapproximation,usingthefact</sup> that ICT represents a relatively small share of capital (otherwise, the numerator is some weighted average of _δn_ and _δi_ ). Applying these expressions to the specific case of India and the United States then gives rise to the following benchmark ratio: where _us_ and _ind_ are subscripts for the US and India, and _I_ = <u>�</u> _~~n~~ j_ =1�<sup>(</sup> _nj_<sup>_α_</sup> =1<sup>_i,jα_+</sup><sup>_i,jαn,jYj_)</sup><sup>_Yj_.</sup> Using estimates from the Penn World Table 8.1, the dollar value of the capital stock per capita is about 28.5 times higher in the United States than in India, explaining a large part of this disparity ( _k_<sup>_<u>k</u>_</sup> _ind_<sup>_<u>us</u>_=28</sup><sup>_._5).The relative price of aggregate capital relative to the price of ICT capital—roughly</sup> equal to the relative price of NICT and ICT capital, given that ICT constitutes a small share of the capital stock—is about 1.6 times higher in the US than in India, i.e. (( _pi_<sup>_~~<u>p</u>~~_</sup> _<u>pi</u>_<sup>_<u>p</u>_))</sup><sup>_indus_= 1</sup><sup>_._6.</sup> To compute _I_ , we use the values displayed in Table 6, which presents the value added shares 29
# **Table 4: Economic Growth Equation - ARDL Model Dependent Variable: AlogY**

**36 Annual Observations (1961-1996) used for Estimation in Column 1, 33 (1964-96) in Column 2 and 29 (1968-96) in Column 3**

|**Variables**|**Column 1**|**Column 2**|**Column 3**|
|---|---|---|---|
|Intercept|1.7191***<br>(2.9756)|1.8816***<br>(2.8476)|0.11886<br>(0.14633)|
|logY,,|-0.13912***<br>|-0.24879***|-0.20547***|
||(-2.7515)|(-4.1521)|(-4.0737)|
|logI|0.12085***<br>(3.0179)|0.14649***<br>(3.0471)|0.15493***<br>(2.8436)|
|logFD|0.060267**|-0.016980|-0.14624**|
||(1.8879)|(-0.25787)|(-1.9245)|
|logT|0.072919**<br>(2.2896)|0.081153**<br>(2.3515)|0.0064441<br>(0.14567)|
|logLRE|---|0.099802***<br>(2.8593)|0.088113***<br>(3.8728)|
|logLRT|---|-0.11418**<br>(-2.1522)|-0.058014**<br>(-2.2898)|
|logLRT14|---|0.13156**<br>(1.8818)|---|
|logLRT,-8|---|---|0.15694***<br>(3.2849)|
|R2|0.41|0.59|0.70|
|DW|1.51|1.94|1.97|
|**Diagnostic Tests**||||
|Serial Correlation'|F (1,29) = 3.1766<br>[0.0851|F (1, 24) = 0.012213<br>[0.913]|F (1, 20) = 0.5234E-4<br>[0.994]|
|Functional Formb<br>|F (1, 29) = 1.1609<br>[0.290]|F (1, 24) = 0.19535<br>[0.662]<br>|F (1, 20) = 0.51799<br>[0.480]|
|Normalityc<br>|X2(2)=1.3812 [0.501]<br>|X<sup>2</sup>(2) =1.2804 [0.527]|X2(2) = 0.18792 [0.910]|
|Heteroscedasticityd|F (1, 34) = 0.67683<br>[0.416]|F (1, 31) = 0.72521<br>[0.401]|F (1, 27) = 0.018070<br>[0.894]|

a Langrange Multiplier test of residual serial correlation b Ramsey's RESET test using the square of the fitted values c Based on a test of skewness and kurtosis of residuals d Based on the regression of squared residuals on squared fitted values

*, ** and *** indicate significance levels of 10, 5 and I percent, respectively. Figures corresponding to parameters are coefficient estimates, with related t-ratios in brackets. Results and footnotes obtained using Microfit 4.0 by Pesaran and Pesaran (1997).

20
The Library of Congress, 2008. Energy Independence and Security Act of 2007. <u>http://www.thomas.gov/cgi-bin/query/F?c110:1:./temp/~c110M5J2LD:e90453:, Downloaded on</u> April 9, 2011.

Timilsina, G.R.,Shrestha, A., 2010. Biofuels: Markets, Targets and Impacts, World bank Policy Research Working Paper, WPS5364, The World Bank, Washington, D.C.

Timilsina, G.R.,Shrestha, R.M., 2007. Alternative tax instruments for CO2 emission reduction and effects of revenue recycling schemes. Energy Studies Review, 15 (1),19-48.

Weber, M., Barth, V., Hasselmann, K., 2005. A multi-actor dynamic integrated assessment model (MADIAM) of induced technological change and sustainable economic growth. Ecological Economics 54(2–3), 306–327.

39
# **Figure 10b: Impact of Education for Northeast**

<!-- Start of picture text --> North East: Education<br>1981 1985 1988 1990 1993 1995 1997 1998 1999<br>0<br>-0.5<br>-1<br>-1.5<br>-2<br>-2.5<br>-3<br>primary1 primary2 secondary tertiary<br><!-- End of picture text --> _Source:_ Authors’ calculations based on PNADs 1981 to 1999. Note: Figures are based on probit coefficients estimates for the education variables.

# **4.3.2 Gender**

The gender of the household head makes a statistically significant difference for poverty. Female-headed households have a much larger likelihood of being poor than male-headed households even, when we include other covariates in the analysis, such as labor market connection, education, etc. This finding shows that one should be careful in putting too much emphasis on unconditional poverty profiles alone. That is, comparing unconditional means (as done in section 3). The parameter estimates and standard deviations for each of the eight years are shown in Figures 11a and 11b.

Since 1990, Rio Grande do Norte and the Northeast show a slightly decreasing trend in the likelihood of a female household head experiencing.

30
# **MACRO-MICRO FEEDBACK LINKS OF IRRIGATION WATER MANAGEMENT IN TURKEY**<sup>*****</sup>

Erol H. Cakmak<sup>∗∗</sup> , Hasan Dudu<sup>**</sup> , Sirin Saracoglu<sup>**</sup> , Xinshen Diao<sup>***</sup> , Terry Roe<sup>****</sup> , Yacov Tsur<sup>*****</sup> JEL classification: C68; O13 ; Q15; Q18 Key words: Computable General Equilibrium; Feedback links; Irrigation Water; Turkey

> *This paper is a product of the project “Macro-Micro Feedback Links of Irrigation Water Management”, funded by the Research Committee of World Bank and managed by DECRG-RU. We are indebted to Ariel Dinar advice and comments. Constructive review comments from Erinc Yeldan are acknowledged with appreciation. Cakmak, Dudu, Saracoglu, Roe and Tsur were consultans to the World Bank on this study.

> ** Department of Economics, Middle East Technical University (METU), Ankara, Turkey.

> *** International Food Policy Research Institute (IFPRI), Washington, DC, USA

> **** Department of Applied Economics, University of Minnesota, St. Paul, MN, USA.

> ***** Department of Agricultural Economics and Management, Hebrew University of Jerusalem, Rehovot, Israel.
4.1.1 Scenario A vs. Baseline: Climate Change Impacts on Agriculture <!-- Start of picture text --> Change in Land Use Relative to Baseline Change in GHG Emissions Relative to Baseline<br>40<br>200 10 Natural Land<br>30 Food Crops 150 8 Conversion<br>20 Livestock Feed 100 6 Livestock<br>10 Biofuels Crops 50 42 BiofuelsOffsets<br>0 Pasture Land 0 0 Use of<br>-10 Protected Forests -50 -2 Fertilizers<br>-20 Managed ForestsUnmanaged Forests -100 -4-6 ForestSequestration<br>-30 -150 -8 Net GHG<br>Accumulation<br>-40 -200 -10<br>2005 2030 2055 2080 2105 2005 2030 2055 2080 2105<br>Change in Consumption of Land Based Good Change in Consumption of Biofuels Relative to<br>and Services Relative to Baseline Baseline<br>0 10 6%<br>8<br>-10 6 4%<br>-20 Processed Livestock 4 2% Consumption of 2gBiofuels<br>-30 Ecosystem Services 2<br>-40 Processed Timber -20 0% Consumption of 1gBiofuels<br>-50 Energy ServicesProcessed Grains -4 -2% Share of Biofuels in<br>-60 -6-8 -4% Liquid FuelConsumption<br>-70 -10 -6%<br>2005 2030 2055 2080 2105 2005 2030 2055 2080 2105<br>Million Ha MtCO2e/yr GtCO2e<br>USD Per Person Million toe<br><!-- End of picture text --> Figure 2: Scenario A vs. Baseline: Climate Change Impacts on Agriculture Figure 2 describes the results of simulations of changes relative to the model baseline for the counterfactual scenario A, corresponding to a reduction in potential agricultural yields as a consequence of climate change. The upper left-hand panel in Figure 2 shows the results for changes in the allocation of land use relative to the baseline scenario. Declining food crop yields result in greater requirements for cropland and fertilizers to produce agricultural output used in the production of processed food and livestock. However, the expansion of cropland is relatively small. Compared to the baseline scenario, the areas dedicated to food crops and animal feed increase, respectively, by 9 million and 19 million hectares (0.75 and 2.5 percent) in 2100. The expansion of cropland comes at the expense of reduction in other land areas. Managed, unmanaged and protected forest areas all lose about 5 million hectares by 2100. Area dedicated to biofuels feed stocks declines by 3 million hectares in 2100. Finally, pasture land area declines by an additional 10 million hectares in 2100. As we show below, the reason for the modest increase in use of cropland is the accompanying decline 26