rafmacalaba/datause-encoder-data huggingface dataset:
# Clean-Development Investments:

# An Incentive-Compatible CGE Modeling Framework

Christoph Böhringer<sup>a</sup> , Thomas F. Rutherford<sup>b</sup> ,<sup>Marco Springmannc</sup>

> a University of Oldenburg, Germany; E-mail: <u>christoph.boehringer@uni-oldenburg.de;</u> b University of Wisconsin-Madison, USA, E-mail: <u>rutherford@aae.wisc.edu;</u><sup>c</sup> University of Oldenburg, Germany, E-mail: marco.springmann@uni-oldenburg.de

**Keywords:** Clean Development Mechanism, Computable General Equilibrium Modeling

**JEL:** C68, Q58

**Sector** : Energy, Climate Finance

**Acknowledgements** : This working paper is an output of a World Bank research project on low carbon economic growth opportunities in developing countries led by Govinda R. Timilsina. We are indebted to him for fruitful research cooperation and guidance. We thank Ian S. Wing and Zhong X. Zhang for constructive comments and acknowledge the financial support from the Knowledge for Change Program (KCP) of the World Bank. The ideas expressed here remain those of the authors who remain solely responsible for errors and omissions.
Also, it can be observed from Figure 22 that there a general trend across sectors whereby the distance has been reduced and the product income has increased. In this graph, the rightmost point for all series corresponds to year 1994, the middle one to year 1998, and the leftmost point to year 2004.

# **Figure 22**

<!-- Start of picture text --> Product Income and Distance for Argentina, by sectors<br>13,000<br>11,000<br>9,000<br>7,000<br>5,000<br>3,000<br>1,000<br>-1,000<br>-3,000<br>1.4 1.5 1.6 1.7 1.8 1.9 2.0 2.1 2.2 2.3<br>Distance (Mean of Log of Inverse Density)<br>Ch. 01-05, Animals and Animal Products Ch. 06-14, Vegetable Products<br>Ch. 15, Animal or Vegetable Fats Ch. 16-24, Prepared Food Stuffs<br>Ch. 26-27, Mineral Products Ch. 28-38 Chemical Products<br>Ch. 39-40, Plastics and Rubbers Ch. 41-43, Hides and Skins<br>Ch. 44-46, Wood and Wood Products Ch.47-49, Wood Pulp Products<br>Ch. 50-63, Textikes and Textiles Products Ch. 64-67, Footw ear and headgear<br>Ch. 68-70, Articles of Stone, Plaster, Cement, Asbestos Ch. 71, Pearls, Precious or Semi-precious Stones, Metals<br>Ch. 72-83, Base Metals and Articles Thereof Ch. 84-85, Machinary and Mechanical Appliances<br>Ch. 86-89, Transportation Equipment Chpater 90-92, Instruments - Measuring, Musical<br>Ch. 93, Arms and Ammunition Ch. 94-96, Miscelaneous<br>Ch. 97, Works of Art<br>PPP, constant 2000 international $<br>Mean of (Product Income-Arg. Export Income),<br><!-- End of picture text -->

# **IX. Effect of Export Income on Growth: Econometric Analysis**

In this section we examine in more detail the relationship between economic growth and export income. Specifically, we estimate the effect of EXPY on growth of GDP per capita applying different econometric techniques at the cross-section and panel level. For the former we use the measures calculated using the COMTRADE dataset and for the latter we calculate PRODY and EXPY using the World Trade Flows dataset (Feenstra et al. 2005) described in Section II.

# **Cross-national Analysis**

All specifications include the initial level of GDP per capita and EXPY in logs as covariates. Additionally, we include in some specifications the log of average number of years of secondary school attained as proxy for human capital<sup>5</sup> , the Rule of Law Index as proxy for institutional quality,

> 5 Similar results are obtained including years of primary school attained instead.

27
The family support service features professionally trained Family Counselors, to which a number of families is assigned. The Family Counselor plays a crucial role of connector between the family and social services local network. For those families that sign a required Participation Agreement, the Family Counselor will regularly visit families at home and also refer them to local social services. One Family Counselor works with between 60 and 100 families simultaneously, some of them in the initial intensive phase (weekly or biweekly) and others in the phase of monitoring and follow-up (monthly, bimonthly or quarterly visits).

_Chile Solidario_ provides regular training to Family Counselors. Twice a year, Family Counselors go through a binding performance evaluation process, and only those who achieved satisfactory scores can continue in the job. The performance evaluation assesses 3 dimensions: personal abilities, knowledge of social services and ability to connect with them, and productivity (coverage; number of families graduating; social empowerment of families). 95% of family counselors are university graduates.

# **System management and coverage**

Since _Chile Solidario_ is a management model, it was necessary to develop a set of management tools that facilitated its operations, which comprised:

1. An _Interagency Coordination System_ that aims at achieving both horizontal (between institutions) and vertical (between levels of government administration) coordination. The daily management of _Chile Solidario_ occurs at the local level. However, a national coordination process focuses on the articulation and coordination of resources (programmatic, managerial and financial) necessary for an effective and efficient local implementation.

2. An _Integrated Social Information System_ (SIIS) containing data of families and their members. In addition to being the backbone of Chile Solidario’s targeting system (based, for the most part, on a proxy means approach), the SIIS is the device that allows calculating the demand for services and monitoring the available supply, family support service and changes in the living conditions of the beneficiary families.

14
**Table 5. Conditional Correlations of Labor Earnings with Measures of Skills, across Subsamples**

|Outcome|||Log|hourlylabor ear|ning||
|---|---|---|---|---|---|---|
|Method||||OLS|||
||||||Less educated|More educated|
|Subsample|Men|Women|Younger<br>(15–34)|Older<br>(35–64)|(maximum,<br>incomplete<br>secondary)|(minimum,<br>complete<br>secondary)|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Reading proficiency|**0.160****|**0.140****|**0.134***|**0.173*****|0.019|**0.180****|
||(0.07)|(0.06)|(0.07)|(0.06)|(0.05)|(0.08)|
|Extraversion|-0.030|0.024|0.011|-0.011|0.007|0.008|
||(0.05)|(0.05)|(0.05)|(0.04)|(0.04)|(0.05)|
|Conscientiousness|-0.037|-0.017|**-0.102***|0.044|-0.040|-0.004|
||(0.05)|(0.05)|(0.05)|(0.04)|(0.04)|(0.05)|
|Openness to experience|**0.130*****|0.046|0.030|**0.152*****|0.065|**0.066***|
||(0.04)|(0.05)|(0.04)|(0.05)|(0.04)|(0.04)|
|Emotional stability|-0.031|0.047|-0.013|0.036|0.011|-0.050|
||(0.05)|(0.05)|(0.05)|(0.05)|(0.05)|(0.05)|
|Agreeableness|0.028|0.013|-0.006|0.037|-0.003|0.050|
||(0.04)|(0.05)|(0.04)|(0.04)|(0.04)|(0.04)|
|Grit|-0.033|-0.040|-0.040|-0.059|-0.015|-0.084|
||(0.06)|(0.06)|(0.06)|(0.05)|(0.05)|(0.05)|
|Hostile attribution bias|-0.044|0.029|0.048|-0.053|0.036|-0.008|
||(0.04)|(0.05)|(0.05)|(0.04)|(0.05)|(0.05)|
|Decision making|0.046|-0.030|0.033|0.019|-0.024|0.006|
||(0.05)|(0.05)|(0.05)|(0.04)|(0.04)|(0.05)|
|Observations|686|686|678|694|438|934|
|R-squared|0.12|0.09|0.13|0.16|0.06|0.14|

_Source:_ Authors’ calculations based on Colombia STEP Household Survey (2012).

_Note:_ Standard errors are in parentheses. The bottom and top 1 percent of the log hourly labor earnings distribution are trimmed. Ordinary least squares (OLS) calculations control for being a woman (dummy), age, age-squared, mother’s education (dummies; primary education is the reference category), and cities of living and their metropolitan areas (dummies; Bogota-Barranquilla-Villavicencio is the reference category). Measures of reading proficiency and socioemotional skills are standardized. Regression coefficients and standard errors of reading proficiency are the average of the 10 estimations using plausible values.

> * _p_ < 0.1  ** _p_ < 0.05  *** _p_ < 0.01

19
In a recent study, Dihman et al. (2011) applied PRECIS under the A1B emission scenario for projecting malaria expansion into 2030s with a focus on the Indian Himalayas. With the transmission windows for temperature as 18-32 °C and a relative humidity >55%, Dihman et al. (2011) not only find an increase in the number of open months (months favorable for malaria vector survival and transmission) in 2030s as compared to baseline (1961-1990), but also opening of transmission windows in some Himalayan states where they are non-existent earlier. This finding that Indian Himalaya is projected to be more vulnerable in the future (Dhiman et al., 2011) has implications for both Nepal and Bhutan. In Nepal, MoPE (2004) suggests temperatures 22-32 °C are very favorable for malaria vectors to develop and complete their cycle. Similarly, Japanese encephalitis thrives at the average annual temperature range of 23-26 °C, while kalazar occurs mainly in summer season due to optimum breeding environment (MoPE, 2004); thus, climate warming may have implications for these diseases’ vectors to shift upland as increasing temperature makes higher altitudes more favorable. However, there are no reported modeling efforts or other studies for such impact assessments.

# **4.4 Impacts on biodiversity**

Research on climate change impacts on ecosystem, forests and wildlife in the Himalayas are notably lacking (Xu et al., 2009, Scheidegger et al, 2011). Most recently, Charturvedi et al. (2011) used a dynamic global vegetation modeling approach (Kucharik et al., 2000) to assess the impact of projected climate change on forest ecosystems in India under SRES A2 and B2 scenarios. Most mountainous forest (including sub-alpine, alpine, dry temperate, and moist temperate forests) were susceptible to climate change impacts, which Charturvedi et al. (2011) attribute to higher rates of climate change at higher elevations. Additionally, forests in northwestern Indian states are highly vulnerable to projected climate change (Charturvedi et al., 2011). Recently, an ICIMOD study (Tse-ring et al., 2010) did an integrated analysis of current vulnerability of biodiversity and ecosystem services considering exposure, sensitivity, and adaptive capacity in the eastern Himalayas (east of Kaligandaki Nepal to northwest Yunnan in China). Tse-ring et al. (2010) identified the Terai belt in southeast Nepal as highly vulnerable, together with other vulnerable regions such as the Brahmaputra Valley, the lower Gangetic plain of northeast India and a few highly localized sites. The most vulnerable ecosystem identified included the Brahmaputra Valley’s semi-evergreen forests (Tse-ring et al., 2010). MoPE (2004), based on the Holdridge model under 2xCO2, projected that Nepal’s current 15 forest types 19
of a discriminatory education system. For the attentive observer, these issues are still felt today.

Table 2 shows that the country still performs poorly for indicators of education and health, and ranks highest among states in the region for child malnutrition, despite some improvements over time.<sup>8</sup> In addition, Guatemala spends less on education than any other country in the region. Based on household survey data comparing the education level of age cohorts, the Inter-American Development Bank (2001) finds that the educational gap between Guatemala and other Latin American countries is widening.

**Table 2. Guatemala, Central and Latin America: Comparison of Human Capital Indicators, 1998-2002**

|Indicator<br>G|uatemala|Nicaragua|Honduras|El<br>Salvador|Costa<br>Rica|Mexico|Latin<br>America|
|---|---|---|---|---|---|---|---|
|Public spending on education<br>(in percent of GDP) (average<br>1998-2000)<sup>c/ d/</sup>|1.7|5.0|4.0|2.3|5.7|4.4|N.D.|
|Average years of schooling<br>(2000)<sup>b/</sup>|4.8|6.3|5.3|5.1|6.7|7.9|7.3|
|Net primary school<br>enrollment (in percent)<br>(2000-2001)<sup>c/</sup>|84|81|88|81|91|103|97|
|Net secondary school<br>enrollment (in percent)<br>(2000-2001)<sup>c/</sup>|26|36|N.D.|39|49|60|64|
|Adult illiteracy (in percent<br>of total population) (2002)<sup>a/</sup>|30.1|32.9|23.8|20.3|4.2|8.3|10.5|
|Infant mortality (per 1000<br>births) (2001)<sup>a/</sup>|43|36|31|33|9|24|28|
|Life expectancy at birth<br>(years) (2002)<sup>a/</sup>|65.5|68.7|66.1|70.1|77.6|73.6|70.7|

_Source:_ a/ World Bank (2003c). b/ Cohen and Soto (2001). c/ UNDP (2003b). d/ Notice that Guatemala’s public spending in education has increased recently. UNDP (2003a) reports a figure of 2.6 percent in 2002. N.D. = no data available.

Historically, it may be that a certain degree of development and growth in Guatemala was attainable with a skilled elite and a large amount of unskilled workers. Since the economy has diversified over time and is now less dependent on agriculture than before (Segovia and Lardé 2002), the past exclusionary education policies may present an obstacle for future growth. On the micro level, there is evidence suggesting that insufficient human capital constitutes a constraint for production. For

> 8 Anderson (2001) provides a brief synopsis of recent developments in the education sector.

-7-
**Figure 16: Macroeconomic impacts from ongoing historical drought shocks**

<!-- Start of picture text --> Real GDP GVA - Agriculture<br>% % % %<br>1.0 1.0 5 5<br>0.5 0.5<br>0 0<br>0.0 0.0<br>-5 -5<br>-0.5 -0.5<br>-1.0 -1.0<br>-10 -10<br>-1.5 -1.5<br>-15 -15<br>-2.0 -2.0<br>-2.5 -2.5 -20 -20<br>2020 2040 2060 2080 2100 2020 2040 2060 2080 2100<br>Mean Median Mean Median<br>Exports Fiscal balance<br>% % ppts ppts<br>2 2 0.2 0.2<br>1 1 0.1 0.1<br>0 0 0.0 0.0<br>-1 -1 -0.1 -0.1<br>-2 -2 -0.2 -0.2<br>-3 -3 -0.3 -0.3<br>-4 -4 -0.4 -0.4<br>-5 -5 -0.5 -0.5<br>-6 -6 -0.6 -0.6<br>2020 2040 2060 2080 2100 2020 2040 2060 2080 2100<br>Mean Median Mean Median<br><!-- End of picture text --> Note: Confidence intervals of 68 percent (dark green) and 95 percent (light green) are shown.

24
road; but this turns out not to be so. For residents of villages with all-weather roads, the average distances travelled for treatment at PHCs and hospitals were 9.6 and 14.4 km., respectively; for those resident in villages without, the corresponding distances were 8.0 and 14.7 km., respectively. While the gap between the average distance to the PHC and the hospital was a bit smaller for the former group, the hospital was equally far off for both groups. This suggests that those households with such a road at their disposal decided to sacrifice some of the savings in time in favor of a longer journey to the hospital, presumably in the hope of getting better treatment there than at the PHC that served as their alternative.<sup>8</sup> In order to investigate this further, it should be noted that villagers often have some limited choice among facilities, so that the reported distance travelled to get treatment then reflects the choice made and may not be (econometrically) exogenous. In virtually each and every village, however, there was a sharply defined and strong modal choice, whose distance from the village in question has been taken as representing a single available option. The time taken to reach a health facility for treatment, whatever kind it might be, was regressed on the variables used above in connection with morbidity together with village dummy variables to capture any fixed effects other than the existence of an all-weather road and the distance to the modal choice of facility. OLS yields the finding that an all-weather road reduced the duration of the journey by about 130 minutes, an estimate that is statistically rather precise ( _|t|_ = 2 _._ 62, robust standard errors). An increase of 1 km. in distance alone increased the duration of the trip by an estimated 1.5 minutes, which is likewise statistically highly significant ( _|t|_ = 4 _._ 37). Other than the head of household’s years of education, each of which lengthened the trip by 2.1 minutes (perhaps the better-educated are more likely to decide for the longer trip to the hospital), no regressor other than the village dummies is remotely significant at conventional levels. In contrast, all but four of the said dummies are highly so, and the estimated values of their coefficients vary strongly, which confirms the initial impression of a very heterogeneous network of connections to health facilities.

Turning to outlays, the sick must often be accompanied by relatives or friends, whose time has an opportunity cost. Of the 566 cases, 381 were so accompanied. A Tobit regression with the same set of regressors yields an estimated saving of 43.2 minutes ( _|t|_ = 6 _._ 72) when there is an all-weather road, whereby one may wish to interpret the ‘saving’ as the ability and willingness to pay for faster transport rather than a decision to leave the patient to his own devices. An increase in distance of 1 km. reduces this

> 8Klemik _et al._ (2009) find strong evidence of such behaviour in rural Tanzania.

28
<!-- Start of picture text --> °<br>3<br>iaCc °<br>=<br>&—<br>a<br>zo<br>ome)<br>a<br>£&<br>Ss<br>a °<br>at<br>—<br>io)<br>&<br>°<br>N<br>°<br>% time employed wage<br>HE All quality determinants GW Student characteristics<br>WE Program characteristics QO HE! characteristics<br>MN Students' admin variables | [J Peers' admin variables<br>PY) Field of study<br><!-- End of picture text -->

<!-- Start of picture text --> % time employed Wage<br>Costs |<br>=“ [| _<br>Infrastructure |<br>eee<br>Other Practices | |<br>— _ is<br>0 5 10 15 0 2 4 6 8 10 12<br>% of explained variation % of explained variation<br><!-- End of picture text -->
- Perez-Heydrich, C., Warren, J.L., Burgert, C.R., Emch, M.E., 2013. Guidelines on the Use of DHS GPS Data, Spatial Analysis Reports No. 8. ICF International, Calverton, Maryland.

- Peters, P.E., 2013. Land appropriation, surplus people and a battle over visions of agrarian futures in Africa. The Journal of Peasant Studies 40, 537-562.

- Place, F., Garrity, D., 2015. Tree-based Systems in Africa’s Drylands. World Bank Group, Washington, DC.

- Pouliot, M., Treue, T., 2013. Rural People's Reliance on Forests and the Non-Fforest Environment in West Africa: Evidence from Ghana and Burkina Faso. World Development 43, 180-193.

- Ribot, J.C., 1999. A history of fear: imagining deforestation in the West African dryland forests. Global Ecology and Biogeography 8, 291-300.

- Sayer, J., Sunderland, T., Ghazoul, J., Pfund, J.-L., Sheil, D., Meijaard, E., Venter, M., Boedhihartono, A.K., Day, M., Garcia, C., van Oosten, C., Buck, L.E., 2013. Ten principles for a landscape approach to reconciling agriculture, conservation, and other competing land uses. Proceedings of the National Academy of Sciences 110, 8349-8356.

- Scherr, S.J., 1995. Economic Factors in Farmer Adoption of Agroforestry: Patterns Observed in Western Kenya. World Development 23, 787-804.

- Scherr, S.J., 2004. Building opportunities for small-farm agroforestry to supply domestic wood markets in developing countries. Agroforest Syst 61-62, 357-370.

- Schroeder, R.A., 1999. Shady Practices: Agroforestry and Gender Politics in The Gambia. Univ of California Press.

- Schroth, G., da Fonseca, G.A., Harvey, C.A., Gascon, C., Vasconcelos, H.L., Izac, A.M.N., 2013. Agroforestry and biodiversity conservation in tropical landscapes. Island Press, Washington, DC.

- Sendzimir, J., Reij, C.P., Magnuszewski, P., 2011. Rebuilding resilience in the Sahel: regreening in the Maradi and Zinder regions of Niger. Ecology and Society 16.

- Shorrocks, A.F., 2013. Decomposition procedures for distributional analysis: a unified framework based on the Shapley value. The Journal of Economic Inequality, 99-126.

- Sunderlin, W.D., Dewi, S., Puntodewo, A., Muller, D., Angelsen, A., Epprecht, M., 2008. Why Forests Are Important for Global Poverty Alleviation: a Spatial Explanation. Ecology and Society, 1-21.

- World Bank, 2015. The Living Standards Measurement Study - Integrated Surveys on Agriculture (LSMS-ISA), http://www.worldbank.org/lsms.

- Wunder, S., Angelsen, A., Belcher, B., 2014. Forests, Livelihoods, and Conservation: Broadening the Empirical Base. World Development 64, S1-S11.

- Zomer, R.J., Trabucco, A., Coe, R., Place, F., Van Noordwijk, M., Xu, J.C., 2014. Trees on farms: an update and reanalysis of agroforestry's global extent and socio-ecological characteristics. ICRAF, Bogor, Indonesia.

19
school students for secondary school bursaries? Innovations for Poverty Action Kenya, _Unpublished Report_ , 2008.

Duflo, Esther, Pascaline Dupas, and Michael Kremer. School governance, teacher incentives, and pupil-teacher ratios: Experimental evidence from Kenyan primary schools. 2014.

Gertler, Paul, Harry Patrinos, and Marta Rubio-Bodina. Empowering parents to improve education: Evidence from rural Mexico. Journal of Development Economics 99(1), 2012.

Gugerty, Mary Kay and Michael Kremer. Outside funding and dynamics of participation in community associations. _American Journal of Political Science_ , 52:585–602, 2008.

Hanushek, Eric A. and Ludger Woessmann. The role of education quality for economic growth. _World Bank Policy Research Working Paper_ No. 4122, 2007.

Hanushek, Eric A. and Ludger Woessmann. Schooling, cognitive skills, and the Latin American growth puzzle. _NBER Working Paper_ 15066, 2009.

Holmstrom, Bengt, and Paul Milgrom. Multitask Principal-Agent Analyses: Incentive Contracts, Asset Ownership, and Job Design. _Journal of Law, Economics, & Organization_ , 7:24-52, 1991.

Jimenez, Emmanuel and Yasuyuki Sawada. Do community-managed schools work? an evaluation of El Salvador’s educo program. _The World Bank Economic Review_ , 13:415– 441, 1999.

Lee, David S. Training, wages, and sample selection: Estimating sharp bounds on treatment effects. _The Review of Economic Studies_ , pages 1071 – 1102, 2009.

Miguel, Edward and Michael Kremer. Worms: Identifying impacts on educational and health in the presence of treatment externality. _Econometrica_ , 72:159 – 217, 2004.

Pugatch, Todd and Elizabeth Schroeder. Incentives for Teacher Relocation: Evidence from the Gambian Hardship Allowance. _Economics of Education Review,_ 41:120-136, 2014.

Reinikka, Ritva and Jakob Svensson. Local capture: Evidences from a central govern- ment transfer program in uganda. _The Quarterly Journal of Economics_ , 2004.

USAID, The World Bank, & Eddata. _The Gambia Early Grade Reading Assessment (EGRA): Results from the 1,200 Gambian Primary Students Learning to Read in English—Report for the World Bank._ January 2008.

24