rafmacalaba/datause-encoder-data huggingface dataset:
# Table A3.4: Simulations

## **<u>Household Head</u>**

|||**2002**|**20**|**12/13**|
|---|---|---|---|---|
||**Actual **|**Simulated**|**Actual**|**Simulated**|
|**Education Structure**|||||
|Illiterate or Incomplete<br>primary|23.76|23.78|17.99|17.51|
|Primary and low<br>secondary|52.85|53.57|53.97|55.08|
|OL|14.60|14.20|15.74|15.40|
|AL|8.78|8.46|12.31|12.01|
|_P-value of Pearson chi-_<br>_square_|_0_|_.9984_|_0_|_.9970_|
|**Occupation**|||||
|non-employed|20.91|20.86|33.57|31.82|
|Salaried farm|9.91|10.07|7.28|7.31|
|Salaried non-farm|34.89|34.61|30.97|31.79|
|Self-employed farm|20.84|20.53|13.04|13.14|
|Self-employed non farm|13.46|13.92|15.14|15.94|
|_P-value of Pearson chi-_<br>_square_|_0_|_.9999_|_0_|_.9971_|

## **<u>Other Members</u>**

|||**2002**|**20**|**12/13**|
|---|---|---|---|---|
||**Actual **|**Simulated**|**Actual**|**Simulated**|
|Education Structure|||||
|Illiterate or Incomplete<br>primary|24.61|24.77|22.78|23.28|
|Primary and low<br>secondary|51.92|52.43|48.05|48.63|
|OL|14.14|13.84|15.88|15.50|
|AL|9.34|8.96|13.29|12.60|
|_P-value of Pearson chi-_<br>_square_|_0_|_.9988_|_0_|_.9958_|
|Occupation (pop 15+)|||||
|non-employed|12.87|12.82|9.31|9.23|
|Salaried farm|50.98|51.85|56.96|56.89|
|Salaried non-farm|16.51|14.99|7.86|7.51|
|Self-employed farm|9.97|10.39|13.82|14.17|
|Self-employed non farm|9.68|9.94|12.04|12.19|
|_P-value of Pearson chi-_<br>_square_|_0_|_.9961_|_0_|_.9999_|

41
does not guarantee domestic biodiesel consumption because the subsidy does not mandate consumers to buy biodiesel. Whether or not domestic consumers will buy biodiesel under the subsidy depends on the relative magnitudes of consumers’ willingness to pay for biodiesel and its world price<sup>11</sup> . For example, if the consumer willingness to pay is lower than the world price, no biodiesel will be consumed domestically, and the biodiesel production subsidy acts as an export subsidy for biodiesel.

# _Soybean Production Subsidy_

This policy instrument by itself does not generate any biodiesel production provided a compensatory payment is required. It is because soybean producers are independent of biodiesel producers and respond only to the prevailing (world) market prices of soybean.

# **4. A Numerical Illustration for Zambia**

# _Data_

To assess the market and welfare impacts of various biodiesel policies, we calibrate a stylized model for Zambia, using the 2010 market data reported in Appendix 1. Biodiesel is currently not blended with fossil-based diesel nor does commercial biodiesel production exist in Zambia (Sinkala, 2011). Of the several biodiesel feedstocks being considered in Zambia (e.g., jatropha, palm, sunflower), this study focuses on soybeans and soybean oil. The analysis can, however, be readily extended to other oilseed feedstocks.

In 2010, Zambia produced 41,000 mt of soybeans and net exports represented 14,418 mt.<sup>12</sup> This leaves 26,582 mt for domestic crushing. To be able to determine the net trade position in soybean oil and meal in the ensuing analysis, we assume a constant crushing-to-production ratio for soybeans of 0.65 that reflects the current state of industry infrastructure in Zambia (we do, however, change the ratio in sensitivity analyses).

Zambia is not self-sufficient in soybean oil production: to meet the demand for nonbiodiesel soybean oil of 10,952 mt, net imports of 7,352 mt were required on top of domestic production of 3,600 mt. We use the above values to calculate the implied extraction rates for soybean oil and meal for Zambia in 2010 (noting that these rates very likely vary by year,

> 11 See discussion on equation (6).

> 12 Soybean imports of 27 mt are very small relative to exports and most likely represent the seed material for the next year’s crop.

9
Psacharopoulos and Patrinos (2018) also report higher returns for females than for males as a general pattern for many countries. The earnings functions are relatively well behaved.

To correct for any selection bias, we apply the Heckman two-step correction model (see Annex Table A3). Conceptually, this is achieved by explicitly modeling the individual sampling probability of each observation (the so-called selection equation) together with the conditional expectation of the dependent variable (the so-called outcome equation). The results suggest that the returns to education for females are not biased.

We also estimate the returns to education by economic sector (Annex Table A5). The results show that the private rate of return to an additional year of schooling is highest in the manufacturing sector, at 8.0 percent, followed by services, at 5.0 percent, and agriculture, at 3.0 percent.

**Table 2: Private Mincerian (OLS) Estimates of the Returns to Education,**

# **Azerbaijan 2015**

|||||Females|
|---|---|---|---|---|
||All|Males|Females|Heckman|
|||||correction|
|Returns (%)|6.1|6.2|8.3|8.3|
|N|9,890|6,122|3,768|5,712|

Source: Authors’ calculation using AMSSW 2015; see full results in Annex Tables A3 and A5 12
**Figure 3:**

<!-- Start of picture text --> Vulnerability Distribution (Expected Poverty)<br>0 10 20 30 40 50 60 70 80 90 100<br>Estimated Probability to Fall Below the Poverty Line<br>Source: ELIM 2010<br>Density<br><!-- End of picture text --> While poverty and vulnerability to poverty are closely related, the two concepts are not the same and the set of vulnerable households does not overlap exactly with the set of poor households (Figure 4). Poor households are on average more vulnerable (61.8%) than nonpoor households (28.7%).

**Figure 4**

<!-- Start of picture text --> Predicted Vulnerability<br>Among the Poor and the Non-poor<br>0 20 43.2 50 60 80 100<br>Predicted Vulnerability (Covariate + Idiosyncratic Risk)<br>Poor Non-poor<br>Source: ELIM 2010 and authors'calculations.<br>The vertical line represents the aggregate level of vulnerability (43.2%).<br>Density<br><!-- End of picture text --> However, if we choose 0.5 for our vulnerability threshold (i.e. a household will be classified as vulnerable if its probability to fall below the poverty line is higher than 50%), we note that not all the poor households are classified as vulnerable (34% are not vulnerable), and some non-poor households are classified as vulnerable (19%). Figure 5 18
<!-- Start of picture text --> 0 1 2 3 6 7 8 0 1 2 3 4 5 6 7 8 9 10<br>Number of public colleges Number of private colleges<br>Very low household income High household income Very low household income High household income<br>Low household income Very high household income Low household income Very high household income<br>Panel (a) Panel (b)<br>Availability of public colleges and income Availability of private colleges and income<br>0 1 2 3 6 7 8 0 1 2 3 4 5 6 7 8 9 10<br>Number of public colleges Number of private colleges<br>Low ability Median ability High ability Low ability Median ability High ability<br>Panel (c) Panel (d)<br>Availability of public colleges and ability Availability of private colleges and ability<br>1.0 1.0<br>0.8 0.8<br>0.6 0.6<br>0.4 0.4<br>0.2 0.2<br>Probability of moving for college Probability of moving for college<br>0.0 0.0<br>1.0 1.0<br>0.8 0.8<br>0.6 0.6<br>0.4 0.4<br>0.2 0.2<br>Probability of moving for college Probability of moving for college<br>0.0 0.0<br><!-- End of picture text --> Figure 3: Marginal effects of college availability college, and declines further with additional public colleges. In contrast, the probability of moving for individuals in the highest income quintile (orange solid line) is only about 0.35 when their high school city has no public college, and the probability declines only slightly (to about 0.3) when one public college is present. These individuals are less responsive to the number of public colleges because they can afford private ones, should they exist in the high school city. A cross-over in the moving probabilities for the highest and lowest incomes happens when three public colleges are available, at a moving probability of about 0.25. When more than three public colleges are available, individuals from the highest income households move at a higher rate than all others, partly because they can afford to do so.

Taken together, our findings indicate that the decision regarding the first move (for college) is heavily affected by college access, as given by the combination of college availability and household income, and by high school city size. We now turn to the second move (for work).

18
- Taylor, W. L. (1953). Cloze procedure: A new tool for measuring readability _Journalism Quarterly, 30_ , 415-433.

- The Access Center. (2005). Early Reading Assessment: A Guiding Tool for Instruction. Retrieved June, 2008, from <u>http://www.ldonline.org/article/14510?theme=print</u>

- The Psychological Corporation. (2003). _Early Reading Diagnostic Assessment - 2nd Edition (ERDA-2)_ . San Antonio, TX: Harcourt Assessment, Inc.

- Thorne, C. (2005). Contexto sociocultural, desarrollo del niño y lectura inicial en el Perú. _Revista de Psicología de la PUCP, 23_ , 139-163.

- Torgesen, J., Wagner, R., & Rashotte, C. (1999). _Test of Word Reading Efficiency (TOWRE)_ . Austin, TX ProEd.

- University of Oregon. (2005). _Dynamic Indicators of Basic Early Literacy Skills -- 6th Edition (DIBELS-6)_ . Oregon: University of Oregon.

- Vaughn, S., & Linan-Thompson, S. (2004). _Research-Based Methods of Reading Instruction Grades K-3_ : Association for Supervision and Curriculum Development.

- Wiederholt, J. L., & Bryant, B. (2001). _Gray Oral Reading Test-4th Edition (GORT-4)_ . Austin, TX: ProEd.

- Williams, K. T. (2001). _Group Reading Assessment and Diagnostic Evaluation (GRADE)_ . Circle Pines, MN American Guidance Service.

- Wilson, M. (2004). _Constructing Measures: An Item Response Modeling Approach_ . Philadelphia: Lawrence Erlbaum Associates.

- WINSTEPS. Winsteps Help for Rasch Analysis: Reliability and separation of measures [Electronic Version]. Retrieved May 13, 2008 from <u>http://www.winsteps.com/winman/index.htm?reliability.htm.</u>

- Wood, F. B., Hill, D. F., Meyer, M. S., & Flowers, D. L. (2005). Predictive Assessment of Reading. _Annals of Dyslexia, 55 n°2 2005_ , 193-216.

- Wren, S. (2004). Descriptions of early reading assessments.   Retrieved June, 2008, from <u>http://www.balancedreading.com/assessment/assessment.pdf</u>

- Wright, B. D., & Stone, M. H. (1979). _Best Test Design. Rasch Measurement_ . Chicago: MESA Press.

59
30 Economic Parameters of Deforestation _fh,e) = eah w itho < a <l_ where h is the amount of land used for logging. With h normalized to }: <!-- Start of picture text --> INI q _ 4 9a(12) = 1 1, c<br>f > Pt ff a < I<br>QS2 pr<br>pr<br><!-- End of picture text --> Historical timber price time series indicate real rates of price increase of only one to two percent per annum. Assuming a future price increase of one percent and a discount rate of 5%, the results of the constant-effort model would hold for any a < 0.833. With a Cobb-Douglas production function and competitive mnarkets, a would be the share of timber revenues spent on all logging costs including labor and capital but cxcluding timber rents and royalties). From available data, it seems unlikely that a share as large as **83%** of log revenues would be spent on logging costs. Similar analysis can be performed for other functional specifications of £ The analysis would suggest that most of the comparative statics results of the constant effort model continue to hold. Clearly, additional empirical analysis of the production function would be helpful to verify this claim.

An additional result can be established for any timber price increase, k", and without any additional condition on the production functions: LPV is increasing in the timber price (since the profit function is increasing in output prices). Therefore, maximized LPV increases with any increase in the timber price path. _Thus,_ if max LPV **c O** (and this piece of land is not logged in finite time), also with any timber price reduction LPV < 0. Hence, with a lower timber price path, there is no unmanaged forest that is ultimately converted that would not also have been converted with higher prices. However, some lands that would ultimately be logged under higher prices may not be logged at all under
15 like the other partners of NAFTA retains the right to conduct its own antidumping, countervailing and safeguard practices. Peru administers antidumping with its Dumping and Subsidies Commission, now under INDECOPI, and under the jurisdiction of the Minister of Industry and supported by a technical staff. It is unique for considering both antidumping and domestic competition cases by the same body. In Uruguay, antidumping policy making is the responsibility of the Application Commission, which advises the Ministry of Economy on antidumping actions. Venezuela administers antidumping through the Commission on Antidumping and Subsidies, an autonomous entity within the Ministry of Development.<sup>9</sup> All these institutions follow a pretty standard pattern for proceedings with antidumping inquiries. These are transparent processes, all the applications are gazetted. Various time periods are stipulated for notification, for making decisions on the applications and communication of decisions on antidumping to the applicants and the public at large. Thus compared to the past, the region has adopted well set and open processes. This is not to say that the processes have been impeccable. It is most interesting to note that the two countries that seemed to have used antidumping more sparingly than any others in the region are Bolivia and Chile. They are also the more open trade regimes in the region, with low protection (i.e. 11% uniform tariffs in Chile and 5% and 10% tariffs in Bolivia).

The pattern cf antidumping measures initiated in the region is revealing for their commodity composition. Textiles and garments, steel, some agricultural products (despite high bound tariffs in the region for agriculture), chemicals, plastic products and fertilizer have been subject to most antidumping actions. Obviously, the presence of domestic capacity is the overriding reason for the initiation of antidumping actions. In most countries there is a cut-off of at least 25% of the production capacity must support an antidumping application. This is not hard to find, as a successful antidumping action is a guarantee for profits or the avoidance of losses.

> 9 This paragraph draws heavily on the Trade Policy Review documents of GATT/WTO for the countries mentioned.
are useful measures. Furthermore, Demirgu, -Kunt and Levine (1999) show that countries with strong shareholde **r** ights and high accounting standards tend to have more market-based financial systems. Thus, key legal and regulatory differences match-up with the measures of financial structure that we use to assess the relationship between industrial performance and degree to which countries are bank-based or market-based.

3.2.3 The Legal Environment We use three indicators of the rights of outside investors and the degree to which these rights are enforced. These data are from La Porta, Lopez-de-Silanes, Shleifer and Vishny (1998). _Creditor_ is an index of the degree to which the legal codes of the country protect the claims of secured creditors in the case of reorganization or liquidation of a company. It ranges from zero to four and is the sum of four dummy variables that indicate whether (i) the reorganization procedure does not impose an automatic stay on assets, thereby not preventing secured creditors from taking possession of loan collateral, (ii) secured creditors are ranked first in the case of liquidation, (iii) management does not stay in charge of the firm during reorganization, thereby enhancing creditors' power, and (iv) management needs creditors' consent when filing for reorganization. In economies with higher values of Creditor, outside investors have more rights relative to the management and other stakeholders, and should therefore be more willing to provide the external resources that industries need.

_Anti-Director_ is an index of the degree to which the legal codes of the country protect minority shareholder rights. It ranges from zero to six and is the sum of six dummy variables that indicate whether (i) shareholders are allowed to mail their proxy vote to the firm, (ii) shareholders are not required to deposit their shares prior to the General Shareholders' Meeting,

> 9 For evidence on the impact of financial intermediation on growth, see, for example, Levine, Loayza, and Beck (2000). For evidence on the impact of stock markets on growth, see Levine and Zervos (1998) and Rousseau and

16
5. **Average labor earnings:** Data is taken from national household income surveys with the exception of Croatia and the UK from the Eurostat and the ILO; Malaysia from the Povcal Net database; and Hungary, Poland, Slovenia and South Africa from the ILO.

# 6. **Complementary Data for the Panel Analysis**

- a. Trade Shock: The trade shock series are based on data from the IMF’s Direction of Trade database.

- b. Unemployment Rate: The Unemployment rate data have been collected in the IMF’s World Economic Outlook database.

|Argentina|_www.mecon.gov.ar/hacienda_<br>_www.indec.gov.ar_<br>Useful|Mexico<br>Links for Data Sources|_www.apartados.hacienda.gob.mx_<br>_www.inegi.org.mx_|
|---|---|---|---|
|Australia|_www.abs.gov.au_|Peru|_www.bcrp.gob.pe_|
|Belgium|_statbel.fgov.be/en_||_www.mef.gob.pe_|
|Brazil|_www.tesouro.fazenda.gov.br_||_www.inei.gob.pe_|
||_www.receita.fazenda.gov.br_|Slovakia|_www.statistics.sk_|
||_www.ipeadata.gov.br_|Slovenia|_www.stat.si_|
|Bulgaria|_www.nsi.bg_||_www.mf.gov.si_|
|Canada|_www.statcan.gc.ca_|South Africa|_www.reservebank.co.za_|
|Chile|_www.dipres.cl_|Thailand|_nso.go.th/en_|
||_www.bcentral.cl_|UK|_www.ifs.org.uk_|
|Colombia|_www.banrep.gov.co_<br>_www.dane.gov.co_|USA|_www.bea.gov_<br>_www.dol.gov_|
|Croatia|_www.mfin.hr/en_|World Economic Outlook|_www.imf.org/weo_|
|Cyprus|_www.mof.gov.cy_|Direction of Trade|_www.imfstatistics.org/DOT_|
|Czech Rep|_www.czso.cz_|Povcal Net|_www.worldbank.org/poverty_|
|Dominican R.|_www.hacienda.gov.do_|World Development Indicators|_data.worldbank.org_|
||_www.one.gob.do_|Eurostat|_epp.eurostat.ec.europa.eu_|
|Estonia|_www.stat.ee/en_|International Finance Statistics|_www.imfstatistics.org_|
|France|_www.insee.fr/en_|OECDstats|_stats.oecd.org_|
|Latvia|_www.csb.gov.lv_|CEPALstats|_websie.eclac.cl/sisgen_|
|Lithuania|_www.stat.gov.lt/en_|LABORsta|_laborsta.ilo.org_|
|Malaysia|_www.statistics.gov.my_<br>_www.anm.gov.my_|OECD Economic Outlook|_www.sourceoecd.org_|
# **Figure B14: Potassium**

<!-- Start of picture text --> Log scale<br>7.0<br>Log price Permanent component<br>6.5<br>6.0<br>5.5<br>5.0<br>4.5<br>1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020<br>Source : Authors’ calculations<br><!-- End of picture text --> <!-- Start of picture text --> Log scale<br>1.20<br>Medium-term cycle Transitory component<br>0.90<br>0.60<br>0.30<br>0.00<br>-0.30<br>-0.60<br>1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020<br>Source : Authors’ calculations<br><!-- End of picture text --> _— 55 —_
Policy Research Working Paper 10449

# **Abstract**

Can the impact of justice processes be enhanced with the inclusion of a heterogeneous component into an existing cost-benefit analysis app that demonstrates how benefactors and beneficiaries are affected? Such a component requires (i) moving beyond the traditional cost-benefit conceptual framework of utilizing averages, (ii) identification of social group or population-specific variation, (iii) identification of how justice processes differ across groups/populations, (iv) distribution of costs and benefits according to the identified variations, and (v) utilization of empirically informed statistical techniques to gain new insights from data and maximize the impact for beneficiaries. This paper outlines a method for capturing heterogeneity. The paper tests the method and the cost-benefit analysis online app that was developed using primary data collected from a developmental crime prevention intervention in Australia. The paper identifies how subgroups in the intervention display different behavioral adjustments across the reference period, revealing the heterogeneous distribution of costs and benefits. Finally, the paper discusses the next version of the cost-benefit analysis app, which incorporates an artificial intelligence-driven component that reintegrates individual cost-benefit analysis projects using machine learning and other modern data science techniques. The paper argues that the app enhances cost-benefit analysis, development outcomes, and policy making efficiency for optimal prioritization of criminal justice resources. Further, the app advances the policy accessibility of enhanced, social group-specific data, illuminating optimal policy orientation for more inclusive, just, and resilient societal outcomes—an approach with potential across broader public policy.

This paper is a product of the Social Sustainability and Inclusion Global Practice. 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 cmahony@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
9 <!-- Start of picture text --> Table 4<br>Bias in Estimate by Degree of Malnutrition<br>Clinic Estimate Difference with "True" Survey Estimate<br>Gomez Standard<br>;K?--<>.,Normal x K<E^> IX A >9P'V'<br>1976/8 +22% -31% -40% -13%<br>1985-Ql +19 -28 -45 -58<br>WHO Standard -~ ' . ? .~ .<br>usingpulialculativeu Thae e1985xpfecated to fal<br>ais3i1~ai<br>in malnutrithan ruates' caused by esiain hmusn f childreniiih~~a h sml<br><!-- End of picture text --> mesues heant ountrieswl grew durin tuhe perod. satsis the use of publihcar but will have information on its availability as proxied by the number of clinics or medical personnel. In Jamaica, the number of mlnnics open was about constant. The number of nurses in the public health care system has declined gradually from 1984 to 1989. The number of community health aides, vho are responsible for home visits for growth monitoring, was reduced by 50 percent between 1984 and 1985. These differences in health care resources could be expected to affect the use of care and thus the bias in the malnutrition rate. In Jamaica, in spite of a decline in the number of personnel who are usually in charge of child health clinic sessions, the number attending them seems to be growing stead ; -, as discussed above.

29. Another factor which could be expected to affect the bias in the estimate of malnutrition is the malnutrition rate itself. In the Jamaican case the malnutrition rate, as measured by the survey data, falls throughout the series but the underestimation of the rate from clinic data rises and thnn falls.

30. The variable that looks most likely to explain the change in the amount of bias in clinic-based estimates of the malnutrition rate is the percent of children in the target population who are weiahed and whose weights are