rafmacalaba/datause-encoder-data huggingface dataset:
farming sector as explained below, characterized by perfect competition and endogenous input prices. Aggregate land supply is fixed but land can move across uses according to relative returns. The yield response to prices is a much needed addition. Keeney and Hertel make the yield response explicit. It depends on the substitution between land, labor, capital, and other factors. Land supply follows a CET structure as described in Figure 2. However, the differentiated bilateral trade response in Keeney and Hertel is a potential drawback. Bilateral export demand assumes differentiation for commodities that are essentially homogeneous across countries (e.g., corn, soybean). The assumed trade substitution elasticities for grains are low (- 2.6). These elasticity estimates are based on econometric work by Hertel et al. (2007). Keeney and Hertel assume that U.S. imports of ethanol from Brazil increase proportionally to the total U.S. ethanol demand, irrespective of price levels, a questionable assumption as explained later in the modeling section and which has important consequences for land allocation.

Birur et al. (2008) use the GTAP database developed by Lee et al. (2005) and (2008) to analyze the impact of biofuel production on global agricultural markets. The latter authors decompose land into 18 agro-ecological zones (AEZs). Birur et al. consider 3 biofuels, ethanol from grains, ethanol from sugar-crops, and biodiesel from vegetable oils. They treat the 2 ethanol productions as imperfect substitutes. The crops considered are coarse grains, oilseeds, sugarcane, other grains, and other agriculture. Crops, grazing, and forestry use the total land endowment. Composite land supply is made of land in the 18 AEZs, which are treated as highly substitutable (CES with an elasticity of substitution of 20). Within any AEZ, land shifts between forest, pasture, and crops with some CET value of -0.11. Within crops, land shifts moving with a CET elasticity of -0.5. Land owners maximize returns on land by choosing an optimum allocation across uses according to relative returns. Figure 3 describes the nested CET tree structure of land supply. This approach essentially parallels Keeney and Hertel within each AEZ.

Banse et al. (2008a) use a modified GTAP-E model which incorporates a 3-level nested approach to land allocation first proposed by Huang et al. (2004), shown in Figures 4 and 5. Banse et al. analyze the impact of the EU biofuel directive on agricultural markets. In addition to the nested CET for land allocation, the authors endogenize aggregate land supply to account for the possibility of new land entering or re-entering agriculture. The reclaimed land comes from non-agricultural uses such as urban areas or forestry.

8
# Policy Research Working Paper Series

|Title|Author|Date|Contact<br>for paper|
|---|---|---|---|
|WPS1<br>033 Latin Americ**a**nd the Caribbean<br>Region (and Northern<br>America)<br>Population<br>Projections,<br>1992-93<br>Edition|My T. Vu<br>Eduard Bos<br>Ann Levin|November<br>1992|0. Nadora<br>31091|
**Table** **<u>5: The impact of price shocks on conflict in Nigeria (2004-11), robustness</u>**

|Method<br>Period<br>Instruments|(1)<br>Nbreg<br>2004-10<br>CINAR<br>oth,PINAR<br>oth|(2)<br>Nbreg<br>2004-10<br>CINAR<br>oth,PINAR<br>oth|(3)<br>Nbreg<br>2004-11<br>CINAR<br>oth,PINAR<br>oth|(4)<br>Nbreg<br>2004-11|(5)<br>Nbreg<br>2004-11|
|---|---|---|---|---|---|
|_CINAR (t)_|0.024*<br>(0.012)|-0.021<br>(0.034)||||
|_PINAR (t)_|-0.010<br>(0.011)|0.035<br>(0.043)||||
|_CINAR (t-1)_||0.079<br>(0.053)|0.066**<br>(0.031)|||
|_PINAR (t-1)_||-0.049<br>(0.070)|-0.032<br>(0.054)|||
|CINAR<br>oth_(t-1)_||||0.051***<br>(0.009)||
|PINAR<br>oth_(t-1)_||||0.015<br>(0.012)||
|CINAR<br>Intl_(t-1)_|||||0.013*<br>(0.008)|
|PINAR<br>Intl_(t-1)_|||||-0.001<br>(0.024)|
|_Oil ind. (t)_|0.003***<br>(0.001)|0.001<br>(0.001)||||
|_Oil ind. (t-1)_||0.002***<br>(0.001)|0.003***<br>(0.001)|0.002***<br>(0.001)|0.002***<br>(0.001)|
|_Oil ind. (t) x post-09_|-0.001<br>(0.001)|||||
|_Oil ind. (t-1) x post-_<br>_09_|||-0.003***<br>(0.001)|-0.004***<br>(0.001)|-0.003***<br>(0.001)|
|Controls|YES|YES|YES|YES|YES|
|Observations|259|259|296|296|296|
|CI+ CI (t-1)||0.058**||||
|PI+ PI (t-1)||-0.014||||
|Oil + Oil(t-1)||0.003***||||

_Dependent variable is the number of any conflict events in the state in year t; all regressions include residuals from the first stage regressions of the endogenous price index on the excluded instruments as a control with appropriate standard errors (clustered at the state level) calculated; *, **, *** indicate statistical significance at the 10, 5, and 1 percent level, respectively. Data are for 37 states for 8 years (2004-11). All regressions include year and region effects as well as region-year effects. The models are estimated through the population-averaged negative binomial estimator for panel data._ 22
71 Malawl 1: PFP - Projections and Actuals <!-- Start of picture text -->
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Malawi 6: PFP Projlcitons and Actuals
While mirroring reduces the extent of missing data in the bilateral datacube, for CGE modeling this is not enough. This is because, to balance trade data, we need to somehow allocate missing destinations if we are to reconcile bilateral data with total values of trade with world (for example based on data from the IMF). On top of this, official statistics can be inconsistent, in that services data can vary widely depending on the reporting countries. In the case of international shipping and transport, reported data may be inconsistent with flows implied by the _cif − fob_ margins for goods. The most comprehensive attempt to confront these challenges in the context of balancing global and bilateral service flows has been through the GTAP project. The efforts to reconcile services data within the project are well documented, and reflect rather creative approaches, based on entropy methods, to flesh out the pattern of trade enough to allow construction of a global dataset for modeling cross-border trade in services (see MacDougall and Hagemejer, 2005; as well as Van Leeuwen and Lejour, 2006). Indeed, for the transport and shipping logistics sector, reconciliation of trade data in services is explicitly linked to the parallel reconciliation of data on trade in goods, as services play a critical role in bridging the gap between reported export flows for goods in one country’s national accounts data, and reported import flows in another country’s data (Gelhar, 1996).

While data on cross-border services trade have a reputation for being crude, they are easy to work with and much more complete in comparison with data on FDI stocks and flows in services. FDI data are plagued by limited coverage, as well as by confidentiality constraints that imply that even if available they cannot legally be reported. As discussed below, FDI data compilation efforts have sometimes been linked to specific modeling projects, like FTAP (Hanslow et al., 2000), the Michigan model (Brown and Stern, 2001) and the Worldscan model (Lejour, Rojas-Romagosa and Verweij, 2008). As an alternative, the French research institute CEPII has endeavored to follow the entropy and reverse gravity methods pioneered for merchandise trade flows to map out a picture of the geographic pattern of FDI refined enough for CGE modeling (Boumellassa, Gouel, and Laborde, 2007). However, such estimates reflect potential FDI (based on what we observe, we guess at what we do not know) and so do not necessarily reflect actual stocks and flows. On top of these concerns with FDI stock and flow data, to actually model establishment trade in services (mode 3), ideally we need – foreign affiliate trade statistics (FATS) data on the value of sales of services by foreign affiliates – as well as value added and employment. As things stand, data on stocks of FDI are often used as a proxy for this more comprehensive, but largely unavailable set of FATS data requirements.

5
Fieldworkers informed participants that stove use is not required to receive a stove, but they were encouraged to use them. Respondents were asked for their formal oral consent and all respondents agreed to participate in the study. There were no refusals.

Households were typically invited to a centralized location, such as a school or _kebele_ office, to receive their stoves and preliminary instructions from field supervisors. Villagers then took the six concrete stove pieces and the clay cooking plate back to their homes. Typically, the following day enumerators came to install the stove in the kitchen area, either in the main home (47%) or in a separate kitchen (53%) by mudding together the six concrete pieces. They also gave the cooks training on how to use the stove and the SUM device was installed on the stove using heat-resistant tape.

The SUM was placed exactly at the back of the stove, because it is the coolest location on the Mirt stove. It was initially believed that the front of the stove was the coolest and several SUM devices were ruined in the first round, because their temperature tolerances were exceeded. Because of such malfunctions, the panel data used in this study are not balanced, with the second monitoring period having the fewest observations. Over the four rounds a maximum of 1,440 observations (360 per round) are possible, but because of stove use monitor failures, observations in each round are about 300, with a total of 1,209 observations across all rounds. There was no attrition in the sample<sup>6</sup>

> 6 A subset of 108 households also participated in three controlled cooking tests.  These tests are not part of this study, but in the analysis we take account of which households participated in this additional research.

**10 |** P a g e
**FIGURE 2: PERCENT OF REPORTED PATIENTS CONFIRMED DURING VERIFICATION**

<!-- Start of picture text --> 100<br>98<br>94<br>89<br>80 83 86<br>81<br>60<br>40<br>Sept. Dec. Feb. May. Sept. Dec. Mar. Jun. Sept. Dec. Mar. Jun.<br>2012 2012 2013 2013 2013 2013 2014 2014 2014 2014 2015 2015<br>North-West Est South-West<br>Percent (%)<br><!-- End of picture text --> 33
21 inequality” years of 1981 to 1993; the “falling inequality” years of 1993 to 2004; and the entire period: 1981 to 2004, over which there is a very small decline in E(0).

**Table 5. A Decomposition of Changes in Inequality by Population Subgroups.**

|||**1981**|**-1993**|||**1993-**|**2004**|||**1981**|**-2004**||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Observed|||||||||||||
|Proportional||0.1|07|||-0.|128|||-0.|035||
|change in E(0)|||||||||||||
||a|b|c|d|a|b|c|d|a|b|c|d|
|Age|0.112|-0.003|0.000|0.002|-0.139|-0.002|0.000|0.017|-0.044|-0.003|0.000|0.019|
|Education|0.110|0.000|0.043|-0.035|-0.089|0.001|0.019|-0.053|0.011|0.001|0.088|-0.136|
|Family Type|0.120|-0.005|0.015|-0.004|-0.138|-0.005|0.022|0.005|-0.039|-0.004|0.040|-0.032|
|Gender|0.116|-0.005|0.000|0.000|-0.120|-0.004|0.000|0.000|-0.018|-0.009|0.000|-0.001|
|Race|n.a.|n.a.|n.a.|n.a.|-0.101|-0.003|0.001|-0.021|n.a.|n.a.|n.a.|n.a.|
|Region|0.141|-0.003|-0.003|-0.024|-0.118|-0.001|-0.001|-0.005|0.012|-0.005|-0.004|-0.028|
|Urban/rural|0.178|0.005|-0.032|-0.040|-0.104|0.002|-0.014|-0.009|0.054|0.017|-0.048|-0.049|

Note: Term a is the pure inequality effect; terms b and c are the allocation effect; term d is the income effect. Source: Authors’ calculations from PNAD 1981, 1993 and 2004.

The first noteworthy feature of Table 5 is the asymmetry in the “explanatory power” of the partitions in the two sub-periods. Between 1981 and 1993, the pure inequality effect (the unexplained term _a_ ) is greater than the observed change (of 0.107) in E(0) for _all_ partitions. This implies that changes in relative means or group compositions across the various population subgroups in all of these partitions can not account for the substantial increase in inequality over this period. Regional convergence, and convergence between urban and rural areas did take place, with substantial negative income effects for both of those partitions. There is also a negative allocation effect in the urban-rural partition, suggesting that the pattern of rural-urban migration in this period was inequality-reducing. But all of these effects go in the opposite direction to the overall increase, so the within-group increase in inequality compensates for those declines.

There were also changes in the educational partition, but they offset each other. Term _c_ indicates a measurable increase in inequality arising from changes in the composition of the education subgroups. We know from Ferreira and Litchfield (2001) that this reflects an expansion in the education of the labor force, with declines in the illiterate and primary population shares, and corresponding increases in the intermediate and high-school groups. We also know from Ferreira
Nonrenewable resources, Income Inequality and per Capita GDP: An Empirical Analysis the dataset based on a likely explanatory variable, which in this case is country income level.

# 4.2 Controlling for Heterogeneity between Income Groups

The model specification presented below attempts to differentiate the impact of resource dependence on higher- and lower-income countries. The model has been adjusted to include a dummy variable equal to 1 for low- and lower-middle-income countries and to 0 for high- and upper-middle-income countries. Within this framework, the coefficient associated with the interaction variable will measure the association between resource dependence, GDP per capita and the Gini index among lower-income countries, while the coefficient associated with the non-interaction variables permits to isolate the average relationship for higher-income countries. Table 8 summarizes the results obtained from the recursive model.

Table 8: Estimated Results from the Recursive Model Specification (income-group heterogeneity)

||**Fixed**<br>|**Effect**<br>|**SU**<br>|**RE**<br>|
|---|---|---|---|---|
||**Gini index**|**GDPc**|**Gini index**|**GDPc**|
|Nonrenewable resource dependence|-0.082***|-0.006|-0.190***|0.124|
|Lower income|dropped|dropped|4.386*|47.628***|
|Lower income* Nonrenewable resource dependence|0.068|-0.370***|0.115**|-0.720***|
|High-low education ratio|-0.622***|-|-0.179|-|
|High-low education ratio square|0.021***|-|0.010**|-|
|Consumer price index (2005)|-0.002|-|0.000|-|
|Services value added share|0.081*|-|0.071|-|
|Manufacturing value added share|-0.043|-|0.068|-|
|Agriculture value added share|-0.087|-|-0.110**|-|
|Log labor force|-|-6.538**|-|-17.058***|
|Log gross fixed capital formation|-|3.433***|-|7.237***|
|Log exports of goods and services|-|2.348**|-|3.519***|
|Log REER|-|5.852***|-|2.742|
|World oil price index|-|0.068***|-|0.066***|
|Worldmetalpriceindex|-|0.016|-|0.010|
|Observations|1015|1034|873|873|
|R2|0.060|0.392|0.911|0.863|

Note: * ** and *** denote significance at the 10% 5% and 1% levels, respectively.

The results show that higher levels of resource dependence are associated with lower levels of income inequality among countries in the higher-income group, with no negative effect on GDP per capita. Conversely, higher levels of resource dependence are associated with greater income inequality and lower GDP per capita in lower-income countries. Again, the estimated results of the non-recursive model, summarized in Table 9, confirm those of the recursive model.

12
Deaton, A. 1985. “Panel Data from Time Series of Cross-Sections.” Journal of Econometrics 30:109–26.

Deininger, K. and P. Olinto. 2000. “Asset Distribution, Inequality and Growth”. World Bank. Policy Research Working Paper 2375.

de Ferranti, D., G. Perry, I. Gill, and L. Servén, with F. Ferreira, N. Ilahi, W. Maloney, and M. Rama. 2000. Securing Our Future in a Global Economy. Washington, DC: World Bank.

de Ferranti, D., G. Perry, F. Ferreira, and M. Walton. 2004. Inequality in Latin America and the Caribbean: Breaking with History? Washington, DC: World Bank.

de Ferranti, D., G. Perry, D. Lederman, A. Valdes, and W. Foster. 2005. Beyond the City: The Rural Contribution to Development. Washington, DC: World Bank.

Engerman, S., and K. Sokoloff. 2004. “Factor Endowments, Institutions, and Differential Paths of Growth among New World Economies: A View from Economic Historians of the United States.” NBER Working Paper H0066, National Bureau of Economic Research, Cambridge, MA Fajnzylber, P., D. Lederman, and N. Loayza. 2002. “Inequality and Violent Crime”. Journal of Law and Economics, vol. XLV, April 2002. The University of Chicago.

Fatás, A. (2002). “The Effects of Business Cycles on Growth.” Central Bank of Chile Working Paper No. 156, May.

Fatas, A and I. Mihov. 2007. “Fiscal Discipline, Volatility and Growth” forthcoming in Prudence or Abstinence? Fiscal Policy, Stabilization and Growth, Stanford University Press.

Forbes, K. 2000. “A Reassessment of the Relationship between Inequality and Growth.” American Economic Review 90 (4): 869–87.

Galor, O., and J. Zeira. 1993. “Income Distribution and Macroeconomics.” Review of Economic Studies 60 (1): 35–52.

Glewwe, P., H. Jacoby and E. King (2001). “Early Childhood Nutrition and Academic Achievement: A Longitudinal Analysis”. Journal of Public Economics, 81: 345-368 Goñi, E., H. Lopez, and L. Servén (2005). “Getting Real about Inequality.” World Bank, Washington, DC.

37
Cash Transfers and Health: Evidence from Tanzania David K. Evans Brian Holtemeyer Katrina Kosec<sup>_∗_</sup> The World Bank IFPRI IFPRI _JEL Classification:_ I18, I13, O12, H42 _Keywords_ : Cash transfers, health, government policy, health insurance

> _∗_ This study benefitted at various stages from experts at the World Bank, the International Food Policy Research Institute (IFPRI), the Tanzania Social Action Fund (TASAF) and elsewhere. At TASAF, the evaluation has been supported by the Executive Director Ladislaus Mwamanga, as well as the former Executive Director Servacius Likwelile. Amadeus Kamagenge has led TASAF input to the evaluation, and his entire team has contributed with substantive and logistical support. We are also grateful to Harold Alderman, Domenico Fanizza, Margaret Grosh, Melissa Hidrobo, Jef Leroy, David McKenzie, Berk Özler, Anna Popova, Dena Ringold, Shalini Roy, and Magnus Saxegaard for comments and helpful discussions. We received financial support from the CGIAR Research Program on Policies, Institutions, and Markets, the International Initiative for Impact Evaluation (3ie), the Strategic Impact Evaluation Fund (SIEF), and the Trust Fund for Environmentally and Socially Sustainable Development (TFESSD). Any comments or suggestions are welcome; the authors may be contacted at k.kosec@cgiar.org and devans2@worldbank.org.
# **12b. By type Median Interest income**

<!-- Start of picture text --> 2007 5.75<br>Commercial 2011 6.20<br>2015 4.16<br>2007 4.90<br>Development 2011 3.54<br>2015 3.18<br>0 1 2 3 4 5 6 7 8 9 10<br><!-- End of picture text -->

# **Median non-interest income**

<!-- Start of picture text --> 2007 3.15<br>Commercial 2011 1.75<br>2015 1.20<br>2007 0.79<br>Development 2011 0.47<br>2015 0.47<br>0 1 2 3 4 5 6 7 8 9 10<br><!-- End of picture text -->

# **Median operating expenses**

<!-- Start of picture text --> 2007 3.27<br>Commercial 2011 3.21<br>2015 2.74<br>2007 1.37<br>Development 2011 0.59<br>2015 0.98<br>0 1 2 3 4 5 6 7 8 9 10<br><!-- End of picture text -->

# **12c. Across granular regions Median Interest income**

<!-- Start of picture text --> 2007 0.54<br>Benchmark Europe 2011 0.53<br>2015 0.50<br>2007 5.50<br>WEST ECA 2011 4.00<br>2015 3.18<br>2007 7.30<br>EAST ECA 2011 9.15<br>2015 9.30<br>0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15<br><!-- End of picture text -->

# **Median non-interest income**

<!-- Start of picture text --> 2007 0.06<br>Benchmark Europe 2011 0.27<br>2015 0.26<br>2007 1.20<br>WEST ECA 2011 1.20<br>2015 1.00<br>2007 3.80<br>EAST ECA 2011 1.76<br>2015 1.50<br>0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15<br><!-- End of picture text --> 30
|Financial<br>Institutions<br>(Maan)|High incoma|EastAsia<br>and Pacific|Europeand<br>Central Asia|LatinAmerica<br>and the Caribbean|Middlo East and<br>North Africa|South Asia|Sub-Saharan<br>Africa|
|---|---|---|---|---|---|---|---|
|Depth|69|43|37|37|33|32|17|
|Access|43|2a|35|30|14|16|10|
|Efficiency|a0|fo|65|62|83|81|51|
|Stability|4?|a|20|35|a7|38|a?|
|Financial<br>Markets<br>(Maan)|High income|EastAsia<br>and Pacific|Europeand<br>Central Asia|LatinAmerica<br>and the Caribbean|Middle Eastand<br>North Africa|South Asia|Sub-Saharan<br>Africa|
|Depth<br>|43<br>|38<br>|12<br>|2]<br>|24<br>|WF<br>|20<br>|
|Access|46|80|56|40|50|85|v7|
|Efficiency<br>|29<br>|40<br>|7<br>|Hs<br>|24<br>|49<br>|?<br>|
|Stability|66|60|43|64|81|56|bl|

|Financial<br>Institutions<br>(Maan)|High income|Uppermiddla<br>income|Lower middie<br>income|Lowincome|
|---|---|---|---|---|
|Depth|84|44|28|13|
|Access|55|az|19|5|
|Efficiency|86|75|61|A?|
|Stability|35|38|40|35|

|Financial<br>Markets<br>(Maan)|High income|Uppermiddla<br>income|Lower middie<br>income|Lowincome|
|---|---|---|---|---|
|Depth|51|27|16|10|
|Access|53|58|69|29|
|Efficiency|45|19|20|21|
|Stability|BG|60|63|4t|