rafmacalaba/datause-encoder-data huggingface dataset:
**Table 12:  Effect of Factors on TFP, Brazil**

||**Textile**|**Apparel**|**Leather**|**Electronics**|**Auto-Parts**|
|---|---|---|---|---|---|
|**Lage**|0.10|**0.12**|0.02|**-0.11**|0.09|
||_1.75_|**_2.90_**|0.46|**-2.58**|1.57|
|**Lcaput**|**0.46**|0.14|**0.72**|0.10|0.26|
||**_2.62_**|1.08|**_4.07_**|0.94|1.18|
|**Lncompd**|0.00|-**0.02**|0.01|**-0.10**|0.02|
||-0.04|**-1.99**|0.67|**_-4.10_**|0.37|
|**Lccia**|**-0.31**|-0.02|-0.02|**-0.16**|**-0.36**|
||**_-3.65_**|-0.54|-0.68|**_-3.18_**|**-2.55**|
|**Lccea**|0.09|**-0.15**|-0.08|**-0.11**|-0.01|
||1.25|**_-2.59_**|-1.16|**-2.14**|-0.06|
|**Llostelec**|**0.16**|**-0.09**|-0.05|-0.11|**0.20**|
||**2.32**|**-2.50**|-0.93|-1.40|**2.47**|
|**Llostbts**|**0.23**|0.06|-0.10|-0.05|**-0.26**|
||**2.13**|1.30|-1.53|-0.63|**_-2.69_**|
|**Lemail**|0.11|**0.09**|0.06|0.05|0.10|
||1.39|**_3.15_**|1.10|0.85|1.45|
|**Lcolgrad**|0.12|**0.11**|-0.02|**0.11**|0.06|
||1.63|**_3.26_**|-0.38|**2.49**|0.85|
|**Northeast**|-0.16|**-0.23**|0.00|**1.21**|-0.31|
||-0.95|**_-3.14_**|-0.04|**_3.34_**|-0.51|
|**Nobs**|263|1091|411|158|339|
|**R-Squared**|0.12|0.20|0.23|0.36|0.07|
|**F-statistic**|5.27|9.56|2.57|6.97|3.65|
|**Prob >F**|0.0000|0.0000|0.0054|0.0000|0.0002|

_t-scores are shown below parameter estimates. Estimates significantly different from zero at .01 level are shown in bold and italic; and at .05 level in italic._ 20
**Table 4: Do Women invest less in the business and more on children?**

||Sch|ool Attenda|nce|Monthly Ho|usehold|Expenditure|Index|
|---|---|---|---|---|---|---|---|
||5 to 12|12 to 15|17 to 18|Groceries|Health|Education|Household<br>assets|
||FE|FE|FE|FE|FE|FE|FE|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|Treatment Amount|-0.0310|-0.0110|-0.0050|-5.00|-0.56|-0.11|0.282**|
||-(0.060)|(0.062)|(0.113)|(5.63)|(1.40)|(0.75)|-(0.110)|
|Amount*Female|-0.0120|0.0218|0.1230|10.50|0.40|-0.27|-0.259*|
||(0.081)|(0.081)|(0.148)|(8.64)|(1.83)|(0.98)|-(0.148)|
|Baseline mean of dependent<br>variable|0.923|0.915|0.533|6810|678|474|0|
|Observations|605|433|272|1328|1328|1328|1393|
|Number of Firms|210|158|135|361|361|361|365|

Notes: Robust Standard errors in parentheses, clustered by enterprise *, ** and *** indicate significance at the 10%, 5% and 1% levels respectively. Data from household module conducted each April from 2005 through 2008. Household asset index is the first principal component of 19 household 17 household assets.

37

-  -
Hansen, H. and F. Tarp (2001), “Aid and growth regressions”, _Journal of Development Economics_ , Vol. 64, 2, 547-570.

- Hauk, W.R. and R. Wacziarg (2004), “A Monte Carlo Study of Growth Regressions”, _NBER Working Paper Series._

- Heckelman, J. and S. Knack (2005), “Political Institutions and Market-Liberalizing Policy Reform”, _Mimeo_ , The World Bank.

- Holtz-Eakin, D. and W. Newey, H. Rosen (1988), "Estimating Vector Auto regressions with Panel Data", _Econometrical_ , Vol.56, 1371-1395.

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- Huang, Y., and J. Temple (2005), “Does External Trade Promote Financial Development?” _Discussion Paper #05/575, University of Bristol_ .

- Kilby, C. (2005), "Aid and regulation", The _Quarterly Review of Economics and  Finance_ , Vol. 45, 2, 325-345.

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- Levine, R., N. Loayza, and T. Beck, (2000), “Financial Intermediation and Growth: Causality and Causes”. _Journal of Monetary Economics_ .

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- Putnam, R.D. (1994),”Making democracy at work: civic traditions in modern Italy”, Princeton University Press.

- Rajan, R., and A. Subramanian (2005), “Aid and Growth: What Does the Cross-Country Evidence Really Show?”, _IMF WP 05/127_ .

37
Table A.3: Robustness: Subsamples, alternative levels of clustering, and measures of variables

||Drop P<br>Total|Ss with<br>Tenure|Deputy-prov<br>Cities Dropped|Hu’s<br>Tenure<br>(2003–12)|Matched by<br>Longest Tenure|Cluster<br>the Lev|ing at<br>el of|
|---|---|---|---|---|---|---|---|
||(1)<br>≤1 Year|(2)<br>≤2 Years|(3)|(4)|(5)|(6)<br>Prov-Year|(7)<br>Province|
|PS YearsInOffice|0.099<sup>∗∗∗</sup>|0.102<sup>∗∗∗</sup>|0.104<sup>∗∗∗</sup>|0.112<sup>∗∗∗</sup>|0.090<sup>∗∗∗</sup>|0.096<sup>∗∗∗</sup>|0.096<sup>∗∗</sup>|
||(0.030)|(0.031)|(0.032)|(0.035)|(0.031)|(0.029)|(0.036)|
|PS YearsInOffice<sup>2</sup>|−0.009<sup>∗∗</sup>|−0.010<sup>∗∗</sup>|−0.010<sup>∗</sup>|−0.012<sup>∗∗</sup>|−0.010<sup>∗∗</sup>|−0.009<sup>∗∗</sup>|−0.009<sup>∗</sup>|
||(0.005)|(0.004)|(0.005)|(0.006)|(0.004)|(0.004)|(0.005)|
|City Controls|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Leader Controls|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|City FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Province-Year FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|# of Observations|3620|3362|3447|2806|3642|3642|3642|
|# of Cities|281|281|266|281|281|281|281|
|Adj_R_<sup>2</sup>|0.60|0.60|0.60|0.63|0.60|0.60|0.60|

_Notes_ : This table presents several robustness checks for the political cycle of GDP manipulation. The dependent variable is the difference between reported real GDP growth and predicted real GDP growth imputed based on the nighttime lights data of prefecture-level cities. City controls include city-level time-varying economic controls (the share of industrial and tertiary value added in GDP, log population, and growth of nighttime lights) and leader controls are the same as the ones displayed in the table of baseline results. Standard errors clustered at the city level (except columns (6) and (7)) are reported in parentheses.<sup>***</sup> ,<sup>**</sup> and<sup>*</sup> denote significance at 1%, 5% and 10%, respectively.

54
intentions to migrate indicate that migration to achieve a higher wage abroad plays a significant role.

The example of the brain gain analyzed here represents a very narrow focus on the gross impact of migration opportunities on human capital formation. Clearly the net brain effect will depend also on outward flows of skilled labor. However, if recent policy trends towards a focus on increasing temporary and return migration are successful, the gross effect of migration on human capital formation, identified in this paper, could enable migration to be utilized as a powerful tool for development.

# **Bibliography**

Auriol and Sexton (2001). “Human Resources in Science and Technology: Measurement Issues and International Mobility”, International Mobility of the Highly Skilled,OECD Abadie (2003) “The Economic Costs of Conflict: A Case Study of the Basque Country” _American Economic Review_ , 93(1) p113-132.

Beine, Docquier and Rapoport, (2001) “Brain Drain and Economic Growth: Theory and Evidence” _Journal of Development Economics_ 64 p275-289 Beine, Docquier and Rapoport, (2003) “Brain Drain and LDC’s Growth: Winners and Losers” IZA Working Paper Beine, Defoort and Docquier, (2007) “A Panel Data Analysis of the Brain Drain”, Discussion Papers 2007024, Universite catholique de Louvain, Departement des Sciences Economiques.

Bertrand, Duflo, and Mullainathan (2004) “How Much Should we Trust Differences-in-Differences Estimates?” _Quarterly Journal of Economics_ p249275 Bevan, Estrin and Grabbe (2001) “The Impact of EU accession Prospects on FDI Inflows to Central and Eastern Europe”, ESRC Policy Paper Bhagwati and Hamada (1974) “The Brain Drain, International Integration of Markets for Professionals and Unemployment” _Journal of Development Economics_ . 1(1) p19-42.

Borjas (1990) “Self-Selection and the Earnings of Immigrants: Reply”, _American Economic Review_ . 80(1): p305-308.

25
|||**Table 22: DID R**|**egression (8)**||||
|---|---|---|---|---|---|---|
||Bicycle ownership<br>Linear probability|Probit|Motorcycle owners<br>Linear probability<br>|hip<br>Probit|Car ownership<br>Linear probability|Probit|
|D|-0.110 (0.031) ***|-0.290 (0.082) ***|-<br>0.001 (0.024)|-0.015 (0.098)|0.026 (0.018)|0.172 (0.121)|
|T|-0.284 (0.034) ***|-0.767 (0.095) ***|0.089 (0.030) ***|0.305 (0.103) ***|-0.037 (0.020) **|-0.211 (0.134)|
|D*T|0.070 (0.041) *|0.171 (0.119)|-<br>0.008 (0.037)|-0.002 (0.130)|0.041 (0.025) *|0.224 (0.164)|
|lnHH size|0.102 (0.027) ***|0.308 (0.080) ***|0.091 (0.023) ***|0.359 (0.088) ***|0.045 (0.018) **|0.263 (0.112) **|
|Adult share|-0.100 (0.059) *|-0.255 (0.165)|0.100 (0.049) **<br>-|0.403 (0.177) **|0.156 (0.037) ***|0.945 (0.223) ***|
|HH head sex: Male|-0.003 (0.030)|-0.025 (0.088)|0.109 (0.022) ***|-0.480 (0.113) ***|-0.025 (0.019)|-0.137 (0.137)|
|HH head education: Elementary|0.063 (0.023) ***|0.182 (0.068) ***|0.074 (0.019) ***|0.298 (0.078) ***|0.037 (0.013) ***|0.267 (0.097) ***|
|HH head education: Secondary|0.090 (0.043) **|0.267 (0.123) **|0.210 (0.041) ***|0.702 (0.126) ***|0.092 (0.031) ***|0.536 (0.154) ***|
|HH head education: University|0.089 (0.085)|0.263 (0.252)|0.219 (0.092) **|0.743 (0.261) ***|0.284 (0.085) ***|1.129 (0.249) ***|
|Appliance ownership: Refrigerator|0.007 (0.032)|0.027 (0.093)|-<br>0.038 (0.027)|-0.147 (0.099)|0.020 (0.018)|0.108 (0.127)|
|Appliance ownership: Color TV|0.023 (0.031)|0.071 (0.088)|0.045 (0.026) *|0.152 (0.094) *|0.013 (0.018)|0.084 (0.115)|
|Appliance ownership: Radio|0.072 (0.021) ***|0.208 (0.062) ***|0.027 (0.018)|0.092 (0.067)|0.022 (0.013) *|0.141 (0.085) *|
|Appliance ownership: Gas stove<br>Appliance ownership: Washing|-0.051 (0.030) *|-0.146 (0.082) *|0.079 (0.022) ***|0.355 (0.100) ***|0.008 (0.015)|0.091 (0.125)|
|machine|-0.039 (0.029)|-0.107 (0.083)|0.054 (0.028) **|0.157 (0.084) *|0.033 (0.021)|0.156 (0.100)|
|Gas use for cooking|-0.055 (0.026) **|-0.166 (0.078) **|0.014 (0.025)|0.054 (0.081)|0.031 (0.018) *|0.152 (0.095) *|
|Power use for lighting|-0.002 (0.032)|-0.012 (0.091)|0.029 (0.024)<br>-|0.128 (0.098)|0.016 (0.016)|0.116 (0.122)|
|Tap water for cooking|-0.019 (0.022)|-0.056 (0.063)|0.026 (0.019)|-0.100 (0.069)|0.019 (0.014)|0.109 (0.085)|
|Constant|0.504(0.089)***|-0.041(0.254)|-<br>0.041(0.072)|-1.806 (0.299)***|-0.153 (0.056)***|-2.954(0.382)***|
|Obs.|2,064|2,064|2,064|2,064|2,064|2,064|
|R-squared|0.128||0.076||0.050||
|F-stat|21.38||11.16||4.27||
|Pseudo-R2||0.100||0.076||0.072|
|Wald-stat||262.54||148.35||79.86|
|Z-statfor interactioneffect (mean&|std.dev.)|1.692(0.331)||-0.048 (0.014)||1.093 (0.096)|

Note: Standard errors are shown in parentheses. *, **, *** indicate the statistical significance at 10%, 5% and 1%, respectively.

41
# **I. Introduction**

Should changing price levels across countries affect the overall count of poor people in the world? If so, how should the new data on price levels across the world be incorporated into global poverty estimates? These questions have prompted intense disagreement within the international development community. The disagreement is not without good reason – the share of people living in extreme poverty has become one of the most prominent indicators for assessing progress in global economic development. It has been a central indicator for the Millennium Development Goals and is emerging as an important indicator among the Sustainable Development Goals. The World Bank (2013) recently announced a new goal of the institution to reduce the share of the world's population living in extreme poverty to no more than 3 percent by 2030. Moreover, many other international organizations and bilateral development actors are focusing their efforts on reducing and, ultimately, ending extreme poverty, as defined by the international poverty line. These global goals and targets have been based on an understanding of the current level of global poverty based on prices as measured by the 2005 purchasing power parity (PPP) data and the poverty line of $1.25. Therefore, large revisions to the global profile and rate of poverty as a result of integrating the 2011 PPP measures of country-level prices may cause confusion and undermine the confidence in the poverty indicator and also the associated goals.

In 2014, the International Comparison Program (ICP) released an update to the PPP data based on an extensive data collection effort to measure price differences across the globe.<sup>1</sup> The resulting PPP conversion factors for 2011 were immediately integrated into the World Bank’s World Development Indicators (WDI) database and applied to key macroeconomic indicators,

> 1 See World Bank (2014c).

2
MULTILINGUAL CHILD ASSESSMENT Table 2. Descriptive statistics of child measures at baseline

||2 y|ears|3 ye|ars|4 ye|ars|5 y|ears|6 y|ears|Overall|
|---|---|---|---|---|---|---|---|---|---|---|---|
||N =|106|N =|76|N =|110|N =|113|N =|105|N = 505|
||mean|sd/n|mean|sd/n|mean|sd/n|mean|sd/n|mean|sd/n|%|
|Receptive vocabulary||||||||||||
|English (out of 34)|3.40|3.41|4.26|3.44|6.51|3.76|8.58|3.92|9.17|3.70||
|Swahili (out of 31)|3.45|3.40|5.19|3.96|8.44|4.81|11.04|5.00|14.48|5.39||
|Luo(out of 27)|5.08|4.20|5.99|3.87|8.77|3.87|11.53|4.90|11.94|5.43||
|Expressive vocabulary (out||||||||||||
|of 30)|1.71|2.02|2.60|2.37|4.82|3.86|8.73|5.33|9.78|5.83||
|% of responses in English|4.44%|15.29%|6.42%|16.30%|15.57%|24.08%|28.34%|26.04%|29.02%|27.10%|19.21%|
|% of responses in Swahili|7.02%|25.54%|4.90%|18.24%|5.03%|16.13%|4.83%|12.97%|3.80%|7.28%|4.96%|
|% of responses in Luo|88.54%|28.78%|88.68%|24.97%|79.39%|30.25%|66.83%|29.00%|67.18%|29.13%|75.83%|
|% answered only in English|0.94%|1|0.00%|0|2.73%|3|3.54%|4|4.76%|5|2.55%|
|% answered only in Swahili|3.77%|4|1.32%|1|0.91%|1|0.00%|0|0.00%|0|1.18%|
|% answered only in Luo|44.34%|47|59.21%|45|47.27%|52|21.24%|24|20.00%|21|37.06%|
|Multiple language<br>response|50.94%|54|39.47%|30|49.09%|54|75.22%|85|75.24%|79|59.22%|
||N =|106|N =|76|N =|110|N =|113|N =|105|N = 505|
|Adapted MDAT fine motor||||||||||||
|(out of 43)|5.52|3.62|12.99|5.34|18.34|4.85|22.65|4.85|25.98|5.05||
|Adapted MDAT language<br>(out of 36)|10.22|1.93|13.81|3.28|16.93|3.27|20.78|2.92|22.22|2.64||

Notes. English, Swahili, and Luo vocabulary are raw total receptive vocabulary scores, measured using three assessments based on the British Picture Vocabulary Scale (BPVS). Expressive vocabulary raw scores were measured using a tool developed from the PPVT. Percent (%) English, Swahili, and Luo expressive are the mean (and sd) percent of responses given in each language for the expressive vocabulary test. Only in English, Swahili, and Luo are the percent (and n) of children who answered the expressive vocabulary exclusively in each language. Multiple language response are the percent (and n) of children who answered in more than one language. The adapted MDAT are the raw scores from the Kenya adaptation of the Malawi Developmental Assessment Tool (MDAT).

36
interest in using randomized controlled trials (RCTs) for impact evaluation of projects. This raises the question to what extent RCTs can be used to evaluate programs, for instance by aggregating the impact of the components of the program. This question is particularly relevant for the evaluation of budget support or of NGOs which typically involve a wide variety of activities.

The strength of RCTs is in establishing proof of principle. Going further and using RCTs to estimate the impact of programs is possible in special cases but becomes problematic if the probability of assignment is correlated with the effectiveness of the intervention. For example, teachers may give more attention to children who they think can benefit more from it. An RCT which randomizes at the level of beneficiaries (children) would produce a biased estimate of the program effect by ignoring this correlation between assignment and treatment effects.

Alternatively, randomization at the appropriate level (teachers) would require a larger sample for the same precision. If confounders are important and correlated with characteristics of the program officers, the RCT-based estimate of the program’s impact would even be inconsistent.

The approach proposed in this paper requires observational panel data for a representative sample of beneficiaries rather than experimental data for randomly selected treatment and control groups. If treatment is exogenous this will correctly reflect the assignment process even under treatment heterogeneity. Instead of estimating average impact coefficients for each of the various interventions of the program, the expected value (across beneficiaries) of the total impact of the combined interventions is estimated. This gives the total program effect (TPE). The paper has shown how and under what conditions regression techniques can be used to estimate the TPE in the presence of selection effects. As an example TPE estimates for a simple intervention: a health insurance program in Vietnam were presented. The example shows that 16
Policy Research Working Paper 9734

# **Abstract**

Migration has been demonstrated by various studies to be closely linked to improvements in individual- and household-level outcomes. Rather than examining the effects of migration, this paper explores whether an economic shock in United States negatively affected migrant households in rural Guatemala. Treating the Great Recession as a natural experiment affecting migrant and non-migrant households differently, the paper puts the spotlight on the effect on child anthropometry, including longer-term indicators of height-for-age z-scores. Panel data on children and multiple children in households enable double- and triple-difference estimation. In relative terms, migrant households fared far worse than non-migrant households over the period. In particular, large advantages in child anthropometric status for the youngest children in migrant households in 2008, just prior to the crisis, were substantially diminished four years later. The findings underscore the possible fragility of the benefits of migration, particularly in the face of a substantial economic shock, and point to the potential importance of deepening social safety nets.

This paper is a product of the Development Data Group, Development Economics. 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 gcarletto@worldbank.org, jmalucci@middlebury.edu, savant.m.shrestha@gmail.com, and mackenzie.f.stewart@gmail.com.

_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
A further break down of the ENE data is presented in Table 2.2, where we consider among other things, the sector of activity and the participation of women and men separately over time in the rural labor force. Of the working-age population in rural Mexico, the share of people engaging in agricultural activities is slowly falling while the share engaging in nonfarm activities is slowly rising. In 2003 the vast majority of women (75 percent) worked in nonfarm related activities while the majority of men worked in farm related activities (67 percent).

Table 2.2 also reveals information on the labor status of the rural population. In 2003, employers and the self-employed constituted 40.9 and 36.5 percent of the male and female employed, employees 39.3 and 34.2 percent, and unpaid family workers 16.0 and 25.0 percent, respectively. Partly because of high unemployment in the formal labor sector, the number of informal-sector workers ballooned during the 1990s, reaching 27.5 and 17.8 percent of the male and female employees respectively in 2003.

The nonpoor people are relatively more likely to be employed in nonagricultural activities than the poor are (see Verner 2004). While 61.3 percent of the poor working population is employed in nonagricultural activities, more than 78 percent of the nonpoor are active in this sector; up from 42.3 and 50.6 percent respectively in 1992. This may indicate that nonfarm employment offers a route out of poverty in rural Mexico.

The average years of education of the employed 9.3 million people in the rural labor force increased from 4.4 years for both genders in 1995, to 5.1 and 5.4 years for males and females respectively in 2003 (Table 2.2). The level of attained education of the work force is rapidly increasing (Figure 2.2). The male and female workers that completed lower secondary education increased by 62.2 and 83.6 percent, respectively, during 1995-2003. The workers that had completed upper secondary education expanded by 118.5 and 75.3 percent for male and female workers, respectively, although the level is still very low. Only 2.6 and 3.1 percent male and female workers have completed secondary school.

**<u>Figure 2.2:  Education Attainment of Rural Labor Force</u>**

<!-- Start of picture text --> 60.0<br>1995 Male 1995 Female<br>50.0<br>2003 Male 2003 Female<br>40.0<br>30.0<br>20.0<br>10.0<br>0.0<br>No Complete Eduation Primary Complete Lower Sec. Complete Upper Sec. Complete Higher Education Technical<br>Source: Calculations based on ENE surveys.<br><!-- End of picture text --> 8
International Energy Agency (IEA) (2008) Worldwide trends in energy use and efficiency - Key insights from IEA indicators Analysis, 2008.

Levitt, S. 2007. The answer to the quiz on beer prices and violence. NY Times Freakonomics Blog.

http://www.freakonomics.com/2007/04/11/a-quiz-for-econ-graduate-studentsor-anyone-else-with-too-much-time-on-his-or-her-hands/ http://www.freakonomics.com/2007/04/11/the-answer-to-the-quiz-on-beerprices-and-violence/ Liebowitz S. and Zentner, A., “Clash of the Titans: Does Internet Use Reduce Television Viewing?,” The Review of Economics and Statistics, MIT Press, vol. 94(1), pages 234-245, 07.

Malik, Abdul, Grohmann, Elisabeth (2012) Environmental Protection Strategies for Sustainable Development McCarthy, Patrick & Lei Lei, 2010. Regional demands for pulp and paper products. Journal of Forest Economics, Elsevier, vol. 16(2), pages 127-144, April Onozuka D, Yoshimura T, Kaneko S, Furue M. (2009) Mortality after exposure to polychlorinated biphenyls and polychlorinated dibenzofurans: a 40-year follow-up study of Yusho patients.

Pesaran M., Shin Y., and Smith R. 1999. Pooled Mean Group Estimation of Dynamic Heterogeneous Panels. Journal of the American Statistical Association, Vol. 94, No. 446 (Jun., 1999), pp. 621-634 Simon, D.H., Kadiyali, V. 2007 The effect of a magazine’s free digital content on its print circulation: cannibalization or complementarity?, Information Economics and Policy, Vol. 19, Nos. 3–4, pp.344–361.

Sumathi S, Hung Y. 2006. Chapter in Hand Book of Industrial and Hazardous Wastes Treatment, Second Edition, Revised and Expanded (Wang, L. K., Hung, Y-T., Lo, H. H. and Yapijakis, C. Eds", Book (seco), 469-51 Technical Association of the Pulp and Paper Industry “Fundamental of the Kraft Recovery Process Technology” The Environmental Paper Network. (EPN) http://calculator.environmentalpaper.org/about 25