rafmacalaba/datause-encoder-data huggingface dataset:
# References

- Barnett, A. (1980), “The Pigouvian tax rule under monopoly” American Economic Review, 70, 1037-41.

- Biglaiser, G. and J. K. Horowitz (1995), “Pollution regulation and incentives for pollution-control research”, Journal ofEconomics & Management Strategy, 3, 4, 663-84.

- Bohm, P. and C.F. Russell (1985), "Comparative analysis of alternative policy instruments", in A.V. Kneese and J.L. Sweeney (eds), Handbook ofnatural resource and energy economics, Amsterdam: north-Holland, 395-460.

- Cropper, M.L. and W.E. Oates (1992), "Environmental economics: a survey", Journal of Environmental Economics and Literature, 30, 675-740.

- Downing, P.B. and L. J. White (1986), “Innovation in pollution control”, Journal ofEnvironmental Economics and Management, 13, 18-29.

- Jenkins, G.P. and R. Lamech (1992), “Fiscal policies to control pollution: international experience”, Bulletin for International Fiscal Documentation, 46, 10, 483-502.

- Laffont, J-J. and J. Tirole (1994), “A note on environmental innovation”, Nota di Lavore 42.94, Fondazione ENI Enrico Mattei.

- Lee, D. (1975), “Efficiency of pollution taxation and market structure”, Journal of Environmental Economics and Management, 22, 69-72.

- Lovei (1994), M. Pollution Abatement Financing: Theory and Practice, mimeo, ENVPE, The World Bank.

- Malueg, D.A. (1984), “Emission credit trading and the incentive to adopt new pollution abatement technology”, Journal of Environmental Economics and Management, 16, 52-57.

- Milliman, S.R. and R. Prince (1989), “Firm incentives to promote technological change in pollution control”, Journal of Environmental Economics and Management, 17, 247-265.

- Requate, T. (1994), “Incentives to innovate under emission taxes and tradeable permits”, paper presented at the 50th Congress of the International Institute of Public Finance, Cambridge, MA, August.

31
_How Fait Has Ch;na_ **_Irdalwvy_** _Grown?_ **_21_**

# TABLE 1: ALL INDUSTRY

# A. Level of Industrial Gross Output, 100 million yuan

|Year|GVIO<br>Iadusuy|GVIO<br>_Viage_|GVIO<br>Industzy +|CvIO<br>Indus +|Price<br>Iadex|Annual %<br>Inflazion|
|---|---|---|---|---|---|---|
|Data in 1|970 prices||||||
|1978|4231|161|4392|4237|96.47||
|1979|4591|184|4775|4681|98.03|1.62|
|1980|4992|222|5214|_5155_|98.87|0.85|
|1981|5199|241|5440|5400|99.26|0.40|
|Data in 1|980 prices||||||
|1981|5178|246|5424|5400|99.56|N.A.|
|1982|5577|277|5854|5811|99.26|-0.29|
|1983|6164|325|6489|6461|99.57|0.30|
|1984|7030|460|7490|7617|101.70|2.14|
|1985|8295|661|8956|9717|108.50|6.69|
|1986|8979|841|9820|11194|113.99|5.06|
|1987|10307|1150|11457|13813|120.56|5.76|

|B. Index|of Output Growth, 1978- 100|||
|---|---|---|---|
|1979|108.51<br>114.28|108.72|110.48|
|1980|117.99<br>137.89|118.72|121.67|
|1981|122.88<br>149.69|123.86|127.45|
|1982|132.35<br>168.55|133.68|137.15|
|1983|146.28<br>197.76|148.18|152.49|
|1984|166.83<br>279.91|171.04|179.77|
|1985|196.85<br>402.21|204.52|229.34|
|1986|213.08<br>511.74|224.25|264.20|
|1987|244.59<br>699.77|261.63|326.01|

(continued)
18 (to ease interpretation), subjects in the pay-for-ability and pay-for-performance exert 5 percent more effort than the flat pay treatment. This is expected since, as shown in the earlier section, both pay-for-ability and pay-for-performance attract statistically identical ability-motivation profiles, and are more likely to attract motivated subjects. Thus, effort under the low-powered pay for ability system and the high-powered pay for performance system is statistically indistinguishable.

**Table 5:  Post-selection treatment effects on effort in pro-social organizations**

|Dependent Variable: Effort|for Charity<br>|Relative to Abi<br>|lity<br>|
|---|---|---|---|
|Dependent Variable:|Ability-<br>adjusted<br>Effort<br>I|Ability-<br>adjusted<br>Effort<br>II|Ability-<br>adjusted<br>Effort<br>III|
|Pay-for-Ability (D)|0.033*|0.032*|0.031*|
||(0.02)|(0.02)|(0.02)|
|Pay-for-Performance (D)|0.032|0.034|0.033|
||(0.02)|(0.02)|(0.02)|
|Amount Sent in Dictator Game||0.009|0.006|
|||(0.02)|(0.02)|
|Gender (D)||-0.007|-0.005|
|(1 = Female)||(0.02)|(0.02)|
|Age (in years)||0.009|0.008|
|||(0.01)|(0.01)|
|Religious Attendance|||0.012|
|(5 = More than Once a Week)|||(0.01)|
|Family Income (Relative to<br>Others)|||0.001|
|(5 = Much Above Average)|||(0.01)|
|Accounting Major (D)|||0.011<br>(0.02)|
|Tax Major (D)|||0.007<br>(0.02)|
|Belief that Charity was Paid|||0.014|
|(5 = Complete Confidence)|||(0.01)|
|Constant|1.037***|0.851***|0.751***|
||(0.01)|(0.15)|(0.17)|
|R-squared|0.020|0.030|0.050|
|P-Value|0.115|0.263|0.363|
|Observations|219|219|219|

Note: * p<0.1, ** p<0.05, *** p<0.01. Dependent variable is ability-adjusted effort in the choice task, restricted to subjects that selected the Mission Sector. OLS specification, standard errors in parentheses. The dictator game variable is divided by 1000 for presentational convenience.
Sessou, F. E. and C. H. C. A. Henning. 2024. "Conflict and Farm Inputs Investment : Can Social Safety Nets Have Any Mitigation Effect?" _Applied Economics_ 56 (17): 1991-2007.

van Campenhout, B. 2022. "Icts to Address Information Inefficiencies in Food Supply Chains." _Agricultural Economics_ 53 (6): 968-975.

Wilson, W. W., P. Lakkakula and D. W. Bullock. 2024. "Implications of the Russian Invasion on the Logistical Competition for Corn Shipments from the United States and Ukraine." _Journal of Agricultural & Resource Economics_ 49 (1): 162-184. Yacoubou Djima, I. and T. Kilic. 2024. "Attenuating Measurement Errors in Agricultural Productivity Analysis by Combining Objective and Self-Reported Survey Data." _Journal of Development Economics_ 168: 103249. Zant, W. 2024. "Mobile Phones and Mozambique Farmers: Less Asymmetric Information and More Trader Competition?" _World Development_ 180: 106574.

34
implies greater substitution possibilities between factors of production and, ceteris paribus, greater demand for factors of production and vice-versa. On the one hand factors of production can be defined to be inter-sectorally immobile, hence not responsive to relative price changes. On the other hand, factors of production can be allowed to be mobile between sectors, thereby responding to changes in relative prices.

Another part of the model with implications for the flexibility of the capital market relates to the mechanism for investment allocation. In the GTAP model, investment funds are allocated across regions through a “global bank”, in accordance with the rates of return to capital across regions. Investors in the model behave as if they expect that a region’s rate of return in the next period (RORE(r)), will decline with positive additions to the capital stock. The rate at which this decline is expected is however a function of a flexibility parameter (RORFLEX). The behavioral equation describing this relationship is: _rore_ ( _r_ ) = _rorc_ ( _r_ ) − _RORFLEX_ ( _r_ ) *[ _ke_ ( _r_ ) − _kb_ ( _r_ )]...................................................................3 _Where:_ _rore(r) is the expected net rate of return on capital stock in region r rorc(r) is the current net rate of return on capital stock in region r kb(r) is the beginning-of-period capital stock in region r ke(r) is the end-of-period capital stock in region r_ _RORFLEX is the flexibility of expected net rate of return on capital stock in region r with respect to investment_ RORFLEX (r) is defined such that if a region’s capital stock increases by 1%, then its expected net rate of return on capital will decline by RORFLEX%. Hence a small value implies that the rate of return on capital is relatively insensitive to net investments. Higher RORFLEX thus imply that the expected rate of return on capital is low; in a certain sense this may reflect a weak investment climate.

# **Data**

The model used for this study draws data from the GTAP 6 database. Although this version of the GTAP database allows for 87 regions and 57 commodities, its coverage of MENA is rather limited. The database has separate data for Morocco and Tunisia, while the rest of MENA is aggregated into “the rest of North Africa” (RONAF), and “the rest of the Middle East” (ROMIDE). This study uses a 13 region by 16 commodity aggregation, which captures all the MENA sub-regions, key trading partners and key commodities. Details of the aggregation are provided in appendix table A1.

9
proportional profit share, each farmer’s supply is a very small fraction of the total cane purchased by the cooperative, making his contribution to the profit negligible. This situation creates incentives to free ride and a moral hazard problem (Holmstrom 1982). Cooperative members have no incentive to improve their cane quality above the minimum specified level, as is the case for farmers with ex ante contracts in the theoretical model. Thus, the testable hypothesis is that cultivation costs are higher for farmers who sell to a private factory than for farmers who sell to a cooperative, all else equal.

A second, more nuanced variant of this hypothesis is then tested based on the differences in incentives facing farmers in reserve and non-reserve areas within a private factory’s canecollection area. Due to the greater distance to the factory and the associated decline in cane quality due to the longer transit time, farmers in a private factory’s non-reserve area must have higher-quality cane to receive a permit than reserve-area farmers must have. This implies the testable hypothesis that cultivation costs are higher in a private factory’s non-reserve area than in its reserve area, which, in turn, has higher cultivation costs than in a cooperative’s cane collection area.

The final component of the empirical analysis focuses on the processors themselves. Using factory-level data, this analysis tests the theoretical model’s prediction that private factories receive higher-quality cane than do cooperatives due to the incentives facing farmers under the ex post contracts.

# _Survey and Data Description_

Farm-level data are from a household survey administered in fall 2008. The household survey involved in-person interviews that provided farm-level data for the 2007–8 cropping season. The 15
Policy Research Working Paper 10619

# **Abstract**

Based on global datasets, 4.5 billion people were exposed to extreme weather events (flood, drought, cyclone, or heatwave) in 2019, an increase from 4 billion in 2010. Among exposed people in 2019, 2.3 billion people lived with less than $6.85 per day and about 400 million lived in extreme poverty (on less than $2.15 per day). This paper presents a methodology to estimate the number of people who are _at high risk from extreme weather events,_ defined as the people who are exposed to these events and highly vulnerable to them. Vulnerability is proxied by a set of indicators measuring (1) the physical propensity to experience severe losses (proxied by the lack of access to basic infrastructure services, here water and electricity) and (2) the inability to cope with and recover from losses (proxied by low income, not having education, not having access to financial services and not having access to social protection). Estimates from 75 countries for which data on all indicators are available suggest that, in 2019, 42 percent of the total population (and 70 percent of people exposed) are at high risk from extreme weather shocks, if one indicator is enough to be considered as highly vulnerable. If high vulnerability is defined based on being vulnerable on two dimensions or more, then 12 percent of the total population (and 20 percent of people exposed) are at high risk from extreme weather shocks. The trend between 2010 and 2019 can be explored in a subset of countries covering 60 percent of the world population. In these countries, even though the population exposed to extreme weather events has been increasing, the number of people at high risk has declined. The exception is Sub-Saharan Africa where the number of people at high risk has increased between 2010 and 2019.

This paper is a product of the Poverty and Equity Global Practice and the Climate Change Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at rhill@worldbank.org or shallegatte@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
the corresponding pixel value from one map to another was greater than would be expected by chance alone. The t test statistic tell us that we can not reject the null hypothesis which provides some evidence of similarity between the two models using all the global pixels. Figure 10 displays three global maps: the two models and their Spearman correlation.<sup>16</sup> We exclude areas from the analysis with values that are less than 200,000. The correlation shows both areas of high and low correlation as the input of the models draws from the relationship of agriculture from productions values or a (rural) population perspective.

The cross-entropy model can also propagate errors from the ancillary data that are inputs to the components. For the SPAM model, the CGIAR network held expert consultation and validation workshops according to each crop and subsequently incorporated their feedback with modifications of the priors used in the model (You, Wood, et al., 2014). The authors of Gridded Livestock Of the World (GLW) note regional differences in accuracy (i.e. RMSE values) are the result of the variation of production intensity and thus dependence on the initial conditions of the land upon which the prediction variables are mainly drawn (spatial agro-ecological variables)(Robinson et al., 2014). Lastly, the models integrate higher spatial resolution data to inform the spatial disaggregation procedures, which is subject to the MAUP (Openshaw, 1981).

# **3.2 Usage Notes**

We provide descriptive statistics of the data and modeling from a fitness-for-use perspective (e.g. Leyk et al., 2019). The data are most appropriate for applications at global, continental and regional scales (You and Wood, 2006). Decisions regarding the use of this version over smaller spatial extents should be carefully considered in relation to the underlying assumptions and characteristics of a particular area. However, as the spatial refinement of ancillary data advances along with greater currency, coverage and representativeness, we expect validation possibilities to increase and inform a better understanding of the uncertainty and the associated fitness-for-use. Also, we intend to improve spatial and temporal coverage when it is feasible.

The data disaggregation model from source to target level does impose spatial relationships and is subject to error (T. Li et al., 2007). The measurement of GDP is also challenging (Angrist, Goldberg, and Jolliffe, 2021), especially agricultural production (Carletto, Jolliffe, and Banerjee, 2015). The level of uncertainty associated with these results includes the thematic, spatial and temporal accuracies. Below, we discuss these data and modeling issues in relation to two aspects: regional accounts and the regional components of AgGDP (mainly

> 16The raster correlation in R performs a simple moving window correlation between two grids with a 3x3 pixel window.

22
# Transformation of Agriculture in Central-Eastern Europe and the Former USSR: Major Policy Issues and Perspectives

Central-Eastern Europe and the former USSR are undergoing fundamental economic and political transformation. Far-reaching changes, surpassing the reforms of earlier years, characterize the agrarian economy of Central-Eastern Europe, where the creation of a new agricultural structure based on private ownership, real cooperatives, and a market economy has begun. The former USSR is also striving to overcome serious economic difficulties with comprehensive economic and political reforms. This process has not yet been completed in any of the countries concerned: many details h?ve yet to be clarified, especially in the former USSR, and there is much uncertainty regarding future developments. All these changes, however, will fundamentally reshape agriculture in the region and influence its behavior and role in international agrarian relations.

In analyzing the major policy issues related to the transformation of agriculture in the region, this paper surveys Bulgaria, Hungary, the former GDR, Poland, Romania, Czechoslovakia, and the former USSR. First, the paper examines the region's agriculturi sector, from both domestic and international perspectives. It then explores the sequence of agricultural reforms that has brought the region to its current transitional stage. Major issues in the transformation to a market economy are identified, and finally, future scenarios are outlined. (Because of the preliminary stage of transformation in the former USSR, the discussion of major issues of transformation is mainly based on the experience of the CentralEastern European countries.)

## **Agriculture in Central-Eastern Europe and the Former USSR**

### _Agricultural Production_

Central-Eastern Euiope and the former USSR account for about 20 percent of the world's arabie land and 8 percent of the world's population (table 1). The region has 12 to 16 percent of the world's livestock, with production of pigs and poultry dominant and cattle raising lower than average. Over 17 percent of the global pig stock and 12 percent of cattle stock are found in the region.
Table 9: Impact on Leadership

||||Child is Lea|der (Yes=1)|B l|Gil l|
|---|---|---|---|---|---|---|
||All t|eams|Mixed-gen|der teams|oys ony<br>team|rs ony<br>team|
||(1)|(2)|(3)|(4)|(5)|(6)|
|CHOICES|0.040|0.079**|0.054|0.061|0.103*|-0.042|
||(0.03)|(0.04)|(0.04)|(0.06)|(0.06)|(0.06)|
|Girl||-0.064||-0.103*|||
|||(0.05)||(0.06)|||
|CHOICES # Girl||-0.076||-0.017|||
|||(0.05)||(0.07)|||
|Number of CHOICES peers in team (0-4)|-0.010|-0.010|-0.013|-0.014|-0.026*|0.011|
||(0.01)|(0.01)|(0.01)|(0.01)|(0.01)|(0.01)|
|Control group mean|0.17|0.17|0.17|0.17|0.13|0.23|
|Adjusted R<sup>2</sup>|-0.01|0.00|-0.01|0.01|-0.01|-0.03|
|Observations|1,000|1,000|495|495|250|255|

Notes: * significant at 10% level ** significant at 5% level *** significant at 1% level (1) CHOICES=1 if adolescent was assigned to CHOICES only or CHOICES+EMB treatment groups.

(2) Standard errors are clustered at the community session level and are reported under the coefficient in parentheses. All OLS regressions includes district fixed effects and controls for whether household has multiple adolescents, the age of respondent, and peer effects given by the number of CHOICES adolescents in the adolescent's team excluding the respondent themself (0-4). Regressions in Table 6 columns (1) and (2) also include a control for team composition (whether team is mixed-gender, all boys, all girls).

(3) Outcomes: Dummy variable for whether the adolescent was chosen to be leader by their team (4) Table 6 Columns (2) and (4) present gender differences by including dummy variables for gender of the adolescent "girl" and an interaction term between treatment and gender (CHOICES # girl).

38
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Ss a)
development for our sample of countries with the ratio of deposits to GDP ranging from 1.74% to 161.40% and the ratio of credit to GDP varying from 0.46% to 121.46%.

_Rem_ refers to the ratio of remittances to GDP. The data on remittances are obtained from the IMF’s 2005 World Economic Outlook. With some exceptions, these data are constructed as the sum of three items in the _Balance of Payment Statistics Yearbook_ (IMF): _workers’ remittances_ (current transfers made by migrants who are employed and resident in another economy); _compensation of employees_ (wages, salaries and other benefits earned by nonresident workers for work performed for resident of other countries); and _migrant transfers_ (financial items that arise from the migration or change of residence of individuals from one economy to another).<sup>7</sup> Figures 3 and 4 show the top ten remittance recipient countries in our sample based on averages for the period 1975-2003, measured both in U.S. billion dollars and as a proportion of GDP. India ($U.S. 4.26 billion), Mexico ($U.S. 4.82 billion), Egypt ($U.S. 3.27 billion), Philippines ($U.S. 2.95 billion) and Turkey ($U.S. 2.44 billion) are among the largest recipients of remittances in absolute terms as shown in Figure 3. Relative to the size of the economy, remittances are especially high among low-income, small economies such as Jordan (18.61%), Tonga (17.86%), Moldova (11.66%), Haiti (10.09%), Vanuatu (8.03%), and El Salvador (8.01%) as shown in Figure 4.

The matrix _X_ refers to a set of variables that the literature has found to affect financial development. In all estimations we control for country size, defined as the log of GDP in constant dollars, and the level of economic development, as measured by GDP per capita. These variables are included on the grounds that financial sector development requires paying fixed costs that become less important the larger the size of the economy and the richer the country.

> 6 In Appendix 3 we also show estimates for financial development defined as the ratio of liquid liabilities to GDP.

9
Table 4: COVID-19 and Electricity Consumption in Rwanda: Urban vs Rural

|||R|esidential||
|---|---|---|---|---|
||Urb|an||Rural|
||(1)|(2)|(3)|(4)|
|I (Mar-Dec 2020)|0.070***|0.068***|0.035***|0.033***|
||(0.001)|(0.001)|(0.001)|(0.001)|
|I (Mar-Dec 2019)|0.030***|0.029***|0.007***|0.006***|
||(0.001)|(0.001)|(0.001)|(0.001)|
|I (Mar-Dec 2018)|0.003***|0.002|-0.003***|-0.004***|
||(0.001)|(0.001)|(0.001)|(0.001)|
|DID|0.054***|0.053***|0.033***|0.032***|
||(0.001)|(0.001)|(0.001)|(0.001)|
|Climate Ctrls|No|Yes|No|Yes|
|Customer FE|Yes|Yes|Yes|Yes|
|District X Year FE|Yes|Yes|Yes|Yes|
|R-squared|0.821|0.821|0.793|0.793|
|Mean dep. Var|37.415|37.415|11.670|11.670|
|Obs|5815537|5815537|11279386|11279386|

Notes: Dependent variable is the log of monthly electricity consumption. I (Mar-Dec 2020) is an indicator variable equal 1 for the period March 1- December 31 2020. Likewise, I (Mar-Dec 2019) and I (Mar-Dec 2018) are indicator for the same period March 1- December 31 in 2019 and 2018 respectively. The reference group is the period Jan-Feb in 2018, 2019, and 2020. DID estimates are derived from the expression _DID_<sup>_ω_</sup> = _β_<sup>ˆ</sup> 2020<sup>_ω−_</sup><sup><u>1</u></sup> 2 �2019 _k_ =2018<sup>_β_ˆ</sup> _k_<sup>_ω_.Climate Ctrls include the monthly average temperature</sup> and total precipitation. Standard errors clustered at district level in parenthesis. _∗_ Significant at 10 percent level _∗∗_ Significant at 5 percent level _∗∗∗_ Significant at 1 percent level 30