rafmacalaba/datause-encoder-data huggingface dataset:
**Figure 1: Recent Estimates of Costs of Production of Algal Oil (triglyceride)**

<!-- Start of picture text --> Chisti (2007)-Pond*<br>Chisti (2007)-Pond Large Scale<br>Chisti (2007)-PBR<br>Chisti (2007)-PBR Large Scale<br>Huntley and Redalje (2006)<br>Benemann Low (1996)<br>Benemann High (1996)<br>NREL current<br>NREL aggressive<br>NREL maximum<br>NMSU low demo<br>NMSU high demo<br>NMSU low commercial<br>NMSU High commercial<br>Solix Current<br>Solix PI<br>262<br>Solix PII<br>NBT Ltd, Israel<br>Seambiotic/IEC Israel<br>Bayer AG<br>General atomics (case1)<br>General atomics (case 2)<br>CA Polytechnic Institute<br>Sandia (Pond 2007)<br>Sandia (PBR 2007) 298<br>Molina Grima et al (2003)<br>0 4 8 12 16 20 24<br>$/lt<br><!-- End of picture text --> Source: Pienkos (2009). *Assumes that 50% of the oil is accounted for by the recovery process. PBR=Photobioreactor. Pond=refers to open ponds and raceways. Estimates in the chart without a year assigned were presented in 2008. Low and High refer to algae productivity. Benemann and Oswald (1996) is cited as Benemann (1996) to reduce clutter in the figure. To the best of our knowledge, of the number in the graph, only Molina Grima et al. (2003) and Chisti (2007) have been published in peer reviewed journals.

The average and standard deviation of cost across the studies presented in Figure 1 are $25 and $72 per liter, respectively.<sup>20</sup> However, the mean is strongly affected by the (large) cost estimates of Molina Grima ($298 per liter) and NBT Ltd ($262 per liter). The median (which is less affected by extreme values) is $4.3 per liter. The costs uncertainties are large, with uncertainty in capital cost (facility investments) being more substantial than for operating cost (Pienkos 2008). It also should be emphasized that most of the lowest estimates in the figure (e.g., Benemann 1996, NREL aggressive, NREL maximum, and NMSU High commercial) refer to targets costs to be achieved at different points in the future, contingent on the possibility of realizing significant and consistent yield gains. Estimates of costs given current technologies are clearly much higher, amnd there is no assurance the hoped-for advances will materialized as assumed.

> 20 A list of the main assumptions behind the disparate results presented in figure can be found in Pienkos (2008).

25
interviews to gather essentially anecdotal information. However, that would be very valuable, especially if repeated across a number of studies, in providing some sense of the ‘shape’ of expenditure responses. If different studies produced reasonably coherent results, that would be really useful. But even if they did not, that would itself be germane to our understanding of the problems, and might provide a basis for sensitivity analysis. A second avenue may be to consult IMF country reports for low-income countries, both to check on the statistical information they contain, but also to utilize the narrative account they provide of policy recommendations and actions, including those in respect of supplementary budgets, and of any revenue and trade policy responses to disasters.

The second issue is even more difficult. It would almost certainly require a substantial exercise in contingent valuation, and this might be of dubious value, as already noted. It might also involve recourse to expert judgement. Once again, if these two avenues produced relatively similar assessments, that would be really useful, but even if not, that would still be germane, as before.

15
# **Table 1: Baseline Summary Statistics and Balance by Treatment Assignment**

|||||||J|oint Orthogonality|
|---|---|---|---|---|---|---|---|
||Total|Control|Insourcing|Outsourcing|Training|Consulting|F-test|
|Last month sales in USD ($1 = 365N)|3265|3885|3591|3646|2954|2251|1.507|
||[242]|[645]|[641]|[567]|[461]|[321]||
|Sales in best month in USD|7581|9477|9050|6353|8207|4777|1.446|
||[725]|[1916]|[2122]|[1177]|[1734]|[676]||
|Sales in worst month in USD|1108|1264|1135|1315|867|963|1.135|
||[81]|[207]|[191]|[192]|[139]|[165]||
|Sales in average month in USD|2942|3817|2776|2798|2821|2503|0.977|
||[227]|[664]|[459]|[457]|[489]|[443]||
|Full-time employees|4.3|4.2|4.2|4.4|4.4|4.2|0.183|
||[0.097]|[0.206]|[0.198]|[0.228]|[0.254]|[0.196]||
|Baseline finance practices|16.6|16.9|16.5|16.3|16.4|16.6|1.161|
||[0.095]|[0.218]|[0.208]|[0.217]|[0.217]|[0.203]||
|Baseline marketing practices|8.6|8.5|8.9|8.5|8.3|8.6|1.923|
||[0.067]|[0.139]|[0.150]|[0.149]|[0.158]|[0.151]||
|Baseline HR practices|7.6|7.6|7.7|7.7|7.5|7.5|0.389|
||[0.066]|[0.150]|[0.146]|[0.147]|[0.148]|[0.144]||
|Baseline overall business practices|6.3|6.4|6.4|6.2|6.2|6.3|1.478|
||[0.027]|[0.063]|[0.059]|[0.062]|[0.060]|[0.055]||
|Baseline insources or outsources accounting|0.34|0.28|0.32|0.36|0.39|0.34|1.134|
|Baseline insources or outsources marketing|0.20|0.19|0.22|0.19|0.19|0.21|0.209|
|<br>Baseline used an HR consultant in last 12 months|0.04|0.01|0.03|0.07|0.05|0.03|1.705|
|Baseline Sector is Construction|0.10|0.11|0.08|0.09|0.11|0.08|0.472|
|Baseline Sector is ICT|0.15|0.15|0.13|0.15|0.14|0.16|0.188|
|Baseline Sector is Entertainment|0.12|0.15|0.09|0.09|0.16|0.12|1.418|
|Baseline Sector is Hospitality|0.13|0.12|0.18|0.11|0.15|0.10|1.333|
|Baseline Sector is Manufacturing|0.50|0.46|0.52|0.55|0.44|0.54|1.398|
|Baseline location in Lagos|0.58|0.58|0.59|0.59|0.58|0.58|n.a.|
|Baseline firm age|4.3|4.1|4.6|4.1|4.8|4.1|1.088|
|Baseline registration with CAC|0.86|0.89|0.90|0.84|0.84|0.84|1.115|
|<br>Baseline percent of sales within state|29.5|30.0|29.8|28.3|29.6|29.6|0.124|
|Baseline Owner is Female|0.44|0.42|0.49|0.48|0.40|0.40|1.204|
|Owner's Age at Baseline|37.7|37.9|37.6|37.9|38.0|36.9|0.545|
|Owner is married at baseline|0.67|0.65|0.76|0.67|0.64|0.64|1.833|
|Baseline owner has completed undergraduate education|0.87|0.89|0.86|0.87|0.82|0.91|1.292|
|Baseline owner has masters or more education|0.48|0.50|0.45|0.45|0.52|0.46|0.490|
|Baseline owner has previously had salaried job|0.79|0.77|0.80|0.75|0.83|0.77|0.901|
|**Sample Size**<br>Notes: n.a. denotes not applicable, since location is part of s|**753**<br>tratification b|**149**<br>atch.|**152**|**150**|**153**|**149**||

Notes: n.a. denotes not applicable, since location is part of stratification batch. Standard errors in parentheses, not shown for binary variables.

F-test of joint orthogonality controls for stratification batch fixed effects. ***, **, and * indicate significance at the 1, 5, and 10 percent critical level.

36
<!-- Start of picture text --> eet am<br>4 eerte) ginast)y aeD<br>Y apr coe<br>pot tn, 2)<br>ot ee eae |Bim “<br>¢ + od a | Deo<br>+s<br><!-- End of picture text -->

<!-- Start of picture text --> oo<br>m<br>&<br>ra<br>=<br><!-- End of picture text --> <!-- Start of picture text --> TM<br>oa<br><!-- End of picture text --> <!-- Start of picture text --> O 5 10 15<br>Log of household per capita income<br>—— Census —-——- PNAD'<br><!-- End of picture text -->
**11**

# 3.1 **US house prices relative to per capita household income**

<!-- Start of picture text --> 220 220<br>In the sun belt as house prices rose,<br>perceptions of risk declined and<br>200 credit standards declined boosting Los Angeles 200<br>demand and driving prices higher.<br>As the bubble burst, seemingly 'safe<br>as bricks and mortar' investments Miami<br>180 lost almost half of their value, one of 180<br>the root causes of the current<br>financial problems. But prices are<br>160 now much closer to fair value. 160<br>140 Phoenix 140<br>120 120<br>National<br>100 100<br>80 80<br>Nov-1987 Nov-1990 Nov-1993 Nov-1996 Nov-1999 Nov-2002 Nov-2005 Nov-2008<br>Index 2000=100<br><!-- End of picture text -->

**a** Notes: Series shown are house prices over per capita household income. For comparison series are indexed to a common base 2000=100. Individual cities are from the S&P Case Schiller index. National prices are from the OFHEO. OFHEO data has a complete coverage of the US while the Case Schiller index only covers the largest 20 cities. OFHEO and Case Schiller data for individual cities indicate similar movements – i.e. differences in the series largely reflect the coverage differences. (The widely reported Case Schiller index shows much larger falls than the OFHEO index.)

_Data source:_ Standard and Poors, Office of Housing Enterprise Oversight, OECD Economic Outlook Database.
In 1999, regular public schools near concession schools had, on average, fewer resources than other public schools. For instance, the number of computers in schools near concession schools was only 17, whereas it was 25 in schools in other parts of the city. Also, the absolute numbers of teachers, administrative personnel, physicians and psychologists were lower in regular public schools near concession schools. The educational attainment of teachers in 1999, measured as the proportion of teachers with secondary studies to those with college or graduate studies, was very similar across the two school types. Finally, regular public schools near concession schools had, on average, 24 students per teacher, compared to 26 students per teacher in the rest of the public system. In sum, there is evidence that regular public schools near concession schools were smaller and poorer than the rest of the public system, although the quality of the teachers was similar. Given that concession schools are located in low-income areas, these differences are not surprising and it is likely that any result based on an unmatched sample will be biased.

In 2003, in general, the regular public schools near concession schools were similar to the rest of the public schools, with some exceptions. The proportion of secondary schools with an academic focus was higher in nearby schools than in public schools further away. Also, a higher percentage of the nearby schools used the evening schedule (0.61 versus 0.43, respectively). Furthermore, the nearby schools have lower overage ratios.

Regular public schools near concession schools tended to have lower rates than other public schools in 2003. The dropout rate increases from 2.1 percent in grade 5 to 5.1 percent in grade 6, whereas the dropout rate for other public schools rises from 2 percent in grade 5 to 7.1 percent in grade 6. However, the dropout rates in regular public schools near concession schools tend to be higher than the ones in the rest of public schools at higher grade levels. Again, these results are based on the unmatched samples and thus the controls may not be a good counterfactual.

# **c. Results**

Estimates of Equation (2), the average treatment impact on the treated, are presented below. There are several different estimators, ranging from the nearest neighbor to kernel estimators, that can be used to determine the counterfactual. In this paper the estimation is based on the 10 21
in Madagascar; Nicita, 2006), 64 percent had daily or periodic markets, and 96 percent had basic health services (as opposed to 31 percent in Malawi; Balat and Porto 2006). Among the individual households, 36 percent of rural households had electricity at home which increased to 71 percent of households by 1998. Similarly, almost 14 percent of households had access to formal finance, and had borrowed from a Government Bank, and this ratio had increased to 30 percent by 1998<sup>10</sup> .

**Figure1. Average Access to Major Services**

<!-- Start of picture text --> 100%<br>80%<br>60% 1993<br>40% 1998<br>20%<br>0%<br>Electricity (commune)RoadElectricity (hh access)MarketLoan govt bank (hh)Clinic Loan any (hh)<br><!-- End of picture text --> The coverage of basic services in other areas is also very extensive. In 1993, 98 percent of rural communes had primary schools, 88 percent had lower secondary schools and 8 percent had upper secondary schools<sup>11</sup> . Almost 20 percent of communes had agricultural extension services within the commune and even more within 10 kilometers. And 50 percent of communes had public transport that passes through the commune.

These numbers are extremely high for low income countries whereas most other countries have not been able to supply basic services to their rural households. In a sense this makes the analysis of the impact of basic services and real income or supply response very difficult because almost all areas are covered by these services.

> 10 While initial ratio for access to credit is similar in Madagascar, 10 percent in 1993, the access did not change much over time and is around 9.5 percent in 2001 (Nicita, 2006).

> 11 The ratios for the school coverage are not exactly comparable between 1993 and 1998 due to different questionnaire design, thus have not been presented here. However, it is unlikely that the access to educational services declined between 1993 and 1998.

7
<!-- Start of picture text --> Table 6: Effect of girls’ scholarship program on test scores<br><!-- End of picture text --> <!-- Start of picture text --> outcome English & Math<br>sample girls boys all girls boys<br>Q) (2) (3) (4) (5) (6) (7) (8) (9) (10) (1)<br>girls’ scholarship program 0.09 0.11 0.12 0.05 0.13* = 0.18* = -0.05 0.05 0.12* 0.10 ~0.01<br>(0.03) (0.06) (0.06) | (0.02) (0.03) (0.05) (0.08) | (0.04) (0.03) (0.06) (0.10)<br>interaction of program with: [uj] = [37]—s.38] | 33) .10)—f.06)~—f86) | (45) = 10) f.65] 88]<br>male -0.02<br>(0.02)<br>[42]<br>low enrollment 0.04 0.05<br>(0.03) (0.06)<br>rural [.35] -0.23 [.75] -0.22*<br>(0.03) (0.02)<br>distant [.14) -0.16 [.07] 0.03<br>(0.09) (0.17)<br>[45] [75]<br>wealthy 0.13 0.09<br>(0.10) (0.14)<br>[.83] [.64]<br>N 7,997 15.811 23,808 | 7,234 7,234 7.997 7,234 | 13.813 13,813 15,811 13,813<br>R-squared 0.35 0.27 0.29 0.37 0.37 0.35 0.37 0.31 0.31 0.27 0.31<br>treatment effect 0.10 0.09** = -0.10 0.02 0.09 0.10 -0.10 0.13 0.07<br>with interaction (0.05) | (0.03) (0.02) (0.06) (0.03) | (0.06) (0.04) (0.13) (0.05)<br>[34] | [02] [52] [83] [30] | (31) [.32) [69] 54]<br>mean outcome -0.28 -0.14 -0.19 -0.27 -0.27 -0.28 = -0.27 -0.16 -0.16 -0.14 -0.16<br><!-- End of picture text -->

Outcome is Grade 12 exam score in English and Math, standardized by universe of scores in that year. Sample is students taking grade 12 exit exam in Regions 2-6, 1998-2012. Interaction with female based on individual student. Distant is dummy for above national median distance from school to main road. All other interactions are district characteristics from 1998 household survey. Low enrollment = below national median secondary-age (13-18) enrollment rate. Rural is above national median proportion of 1998 district population living in rural area. Wealthy = at or national above median household assets, by first principal component. “Treatment effect with interaction” is sum of program coefficient and coefficient on interaction term. All regressions include age, age squared, school and year fixed effects. Regression with both male and female students in sample also includes female dummy. Sample sizes differ across regressions because not all districts surveyed in 1998. Standard errors in parentheses, clustered by region. p-values in brackets, calculated from 200 iterations of wild-t cluster bootstrap of Cameron et al (2008) with Webb (2013) 6-point distribution of weights. Starred significance levels are based on these p-values. * significant at 10%; ** significant at 5%; *** significant at 1%.
|_______________<br>Country|_______________<br>Model|________|______________<br>All<br>firms|________________<br>10 - 19<br>employees|_______________<br>20 – 49<br>employees|______________<br>50 – 249<br>employees|______________<br>250 – 499<br>employees|___________________<br>500 and more<br>employees|
|---|---|---|---|---|---|---|---|---|
|_________________<br>UK|________________<br>Pooled|__________<br>ß1|_______________<br>0.052|__________________<br>0.490|________________<br>-0.325|________________<br>0.129|________________<br>-0.069|_____________________<br>0.170|
|1995 - 2004||p|[0.064]|[0.003]|[0.000]|[0.001]|[0.341]|[0.018]|
|||ß2|0.017|-0.330|0.450|-0.080|0.062|-0.110|
|||p|[0.638]|[0.085]|[0.000]|[0.098]|[0.487]|[0.213]|
||Fixed|ß1|0.028|0.023|0.085|0.087|-0.098|-0.088|
||effects|p|[0.488]|[0.919]|[0.410]|[0.110]|[0.371]|[0.367]|
|||ß2|0.050|0.196|0.115|-0.030|0.116|0.122|
|||p|[0.284]|[0.417]|[0.302]|[0.646]|[0.326]|[0.298]|
||N||9,903|600|1,950|5,180|1,070|1,103|
||NxT||55,286|2,228|9,594|29,421|7,122|6,921|

___________________________________________________________________________________________________________________________________________________________________ Note: Results are for firms from ISIC industries 15 – 36 with at least 10 employees at the median over the years covered in the panel. The firms with the bottom / top one percent of labour productivity (defined as total sales per employee) in a year are excluded from all computations. Firms are classified into size classes according to the median of the number of employees over the years covered. ß1 and ß2 are the estimated regression coefficients from an OLS-regression of log (labour productivity) on the share of exports in total sales and its squared value, respectively, controlling for the log of the number of employees and its squared value, the log of wages and salaries per employee, and a full set of interaction terms of 4-digit industry-dummies and year dummies; the fixed effects model adds firm fixed effects. p is the prob-value, N is the number of firms, NxT is the number of observations.

77
S non-socialist countries); but we believe there is a logical classification into three distinct sub-sectors: infrastructure services (transportation and communic&tion), public services (education, health, and public administration), and business and coneumer services (all other jervices, including housing).' The international data used in the regression analysis rsported in Section C consist of two data sets: 25 developed cot-tries covered by the Organization for Economic Development and Cooperation (OECD) national accounts data, and 160 countries covered ay data compiled for the World Bank's World Tables. The latter data set is referred to as "world economies"; it includes aggregated OECD national accounte data, national accounts data for developing countries taken from country-specific sources, and data on employr.ent from the International Labor Offioe (ILO). The advantages of the "world economies" data set are that it is a larger sample and provides more natural comparators to some of the poorer Soviet republics; its limitation is that there is little disaggregation by subsector. The more detailed OECD iiational accounts data are used independently of the "world economies" data set because they permit a separation between pub3ic services, such as health and education, and private business and consumer services. The limitations of the OECD data are that thry represent fewer countries and these countries have higher per capita incomes than the former Soviet states.<sup>10</sup> 9. In principle, housing might best be classified with infrastructure services, but OECD data classify it with financial services and Soviet data classify it with community services- -hence the classification here with business and consumer services.

'° In addition to these two data sets, the discussion on End Use in Section C draws on estimates of real services for 60 countries by Heston and Summers in Griliches 1992.
higher likelihood of reference group substitution when migration takes place within Mexico.

Ravallion and Lokshin (2005) use perceived relative consumption by friends, by neighbors, and in the enumeration area as reference levels for perception data from Malawi. The authors assume that comparison-group welfare is unlikely to be endogenous, as in a low income country like Malawi individuals have less freedom in choosing their location and hence neighbors and friends than in a developed country. Ravallion and Lokshin find that in rural Malawi, where subsistence poverty is widespread, the positive effects of better-off friends and neighbors dominate rank concerns. Negative externalities are a concern only for the comparatively better off.

# **2. A Simple Model with a Proximate, Exogenous Reference Group**

To build more realistic models, it is critical to refine our knowledge about reference groups that influence utility. Bias can be avoided if reference groups are exogenous as is the case for one’s siblings, and if data is available for perceived relative income.

This paper proposes that individuals are likely to form reference points according to proximity in characteristics and interaction. The hypothesis is that individuals compare their incomes to the incomes of people with resembling traits and opportunities, and about whom they have sufficient information. This accords with the social psychologist Festinger (1954) who argued that the tendency to compare oneself to others is a decreasing function of relative differences.

In addition, it is likely that relative status matters more for groups whose members are fully informed about each other. For instance, Frank (1984) illustrates that the price of status is highest in professional groups whose members interact most intensively. Two papers assess the significance of relative income of proximate, exogenous comparator groups. Clark (1996) shows that measures of job satisfaction are strongly negatively correlated with both objective spouse’s income and the average income of all other 8
Policy Research Working Paper 7670

# **Abstract**

Early childhood development is a critical, but often neglected, phase of human development. Shortfalls in early childhood development have historically been a challenge in Morocco, with children facing unequal opportunities to develop because of the circumstances of their birth. This paper examines the roots of inequality in human and economic development in Morocco by focusing on the state of early childhood development, inequality in early childhood development, and trends over time, using several surveys conducted between 2003 and 2012. Large disparities exist in children’s chances of healthy early childhood development based on the circumstances of their birth. There have not been consistent improvements in the levels of early childhood development or decreases in inequality over time. This study discusses critical directions for future policy on early childhood development to address the deficits and disparities identified.

This paper is a product of the Education Global Practice 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://econ.worldbank.org. The authors may be contacted at selkogali@ worldbank.org or cgkrafft@stkate.edu.

_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