rafmacalaba/datause-encoder-data huggingface dataset:
in this area, the risks are still high. In May 2009, GreenFuel Technologies had to close its operations as it was unable to raise the money needed to continue their research efforts to lower costs of production (F.O. Licht 2009a).<sup>21</sup> In short, widely divergent estimates of feedstock costs have been published in the past few years. Ranges of feedstock production costs obtained from the representative studies surveyed are summarized in Figure 3.

**Figure 3: Cost of various feedstocks for first and second-generation biofuels** .<sup>a</sup>

<!-- Start of picture text -->
1.40 3.00<br>1.20 2.50<br>1.00<br>2.00<br>0.80<br>1.50<br>0.60<br>1.00<br>0.40<br>0.50<br>0.20<br>0.00<br>0.00<br>$/llt of gasoline equiv.<br>$/lt gasoline equiv.<br>(E U) W ood* (US) Jatropha<br>S ugarc ane (B raz il) M aize (US A ) S ugarbeet W heat (E U) Crop Res idue Herb. crop Soybean oil  Rape oil (EU) Microalgae<br><!-- End of picture text -->

Source:<sup>a</sup> For cellulosic ethanol, a yield of 0.3 liters/ton of feedstock is assumed (see Table 1). A one to one conversion was assumed for vegetable oils into biodiesel. * Includes forest residues and dedicated woody energy crops. Feedstock costs for first-generation ethanol were obtained from IEA (2008) for the 20052007 period (co-product credits are not assigned). Rapeseed oil and soybean oil prices are from FAPRI (2009) for the 2005-2007 period.

# **3.2     Biofuel capital investment and production costs**

Based on the current state of technology, second-generation biofuels will come at very high capital cost, over five times that of similar capacity starch ethanol plants (Wright and Brown 2007). The estimated capital investment cost for a 220 million liters per year cellulosic ethanol plant using wood or switchgrass as feedstock along with the magnitude

> 21 These costs have been estimated in 2007 by Dimitrov to result in biodiesel costs exceeding $800 per barrel, even using very optimistic (in the words of the author) assumptions

27
The relationship between absolute mobility and national income may similarly be non-monotonic as opposing forces are at play. On the one hand, we hypothesize that absolute mobility decreases with national income on the grounds that richer countries have more educated populations, making it harder for children to surpass the educational attainment of their parents. On the other hand, however, the capacity to educate children increases as countries become richer, which suggests a positive effect of national income on absolute mobility. The relationship between absolute mobility and national income will thus be determined by which of these two effects is stronger at different stages of national development.

# **3. Data**

## **3.1. Identifying relevant surveys**

To construct our Global Database on Intergenerational Mobility (GDIM), a comprehensive review of surveys that ask respondents about their parents’ educational attainment was conducted. Eighty percent of the surveys selected were conducted after 2011 such that respondents born in the 1980s had a chance to complete their education by the time the survey data was collected. Only individuals aged 21 or more are included in the sample to minimize the fraction of individuals still enrolled in school while not removing too many individuals from the sample. If more than one relevant survey was found for a country, the survey was selected based on sample size and the quality of the parental education information.

Broadly speaking, three different types of surveys are used. For most developing countries, crosssectional household income or expenditure surveys are used. For most countries in Europe and Latin America, social surveys such as the European Social Survey (ESS), the Latinobarometro, and the Life in Transition Survey (LITS) are used.<sup>8</sup> For a selected number of high-income countries, annual panels such as the Panel Study of Income Dynamics in the United States and the Labor & Income Panel Study in the Republic of Korea are used. A table with the list of surveys and years used can be found in Table B.1 in Appendix B.

The social surveys tend to have smaller sample sizes, so when multiple waves of the same survey contain relevant information on educational attainment, these waves are pooled. This includes three waves of the ESS (from 2010-2014), six waves of the Latinobarometro (from 2008-2015), and two waves of the LITS (2006 and 2011).

For several countries, including some of the developing world’s largest countries such as Bangladesh and a notable number of countries in Sub-Saharan Africa, there are no surveys with retrospective data on parental education. In these cases, high-quality household surveys without

> 8 Like Neidhöfer et al. (2018), Latinobarometro is used instead of Lapop, which is another social survey covering the Americas. Lapop only asks for mother’s education. Conversely, Latinobarometro records the highest education level of father and mother, which is the variable used in this paper.

10
has expanded in recent years, and improvements have occurred in indigenous areas. Nevertheless, educational levels remain higher in non-indigenous areas. In 2002, 12.8 percent of children between 6 and 14 years living in indigenous municipalities did not enroll in school and 32.5 percent of individuals older than 15 years did not know how to read or write (Instituto Nacional Indigenista 2002).

According to Psacharopoulos and Patrinos (1994), the child labor force is greater in indigenous areas than in non-indigenous areas. This can be partially explained by the rural concentration of the indigenous population. The impact of parental education is greatest in less indigenous municipalities. The household head employment conditions have a clear impact on a child’s average educational attainment. Heads of household who work in non-agricultural pursuits, in either indigenous or non-indigenous areas, have children with higher levels of educational attainment than otherwise employed heads of household. The contribution of the income of working children to total family income is substantial.

As expected, the contribution of child labor to family income increases with age, while increasing educational attainment reduces the contribution. Child income plays a slightly greater role in total family income in indigenous areas than in non-indigenous areas. Also, indigenous children are expected to have a higher likelihood of being employed and not in school because of language problems, school access (distance) and “cultural” differences. Guarcello, Mealli and Rosati (2003) show that indigenous households have a lower school attendance and higher work participation rate than the rest of the population.

In the academic literature, the phenomenon of child labor is often analyzed in conjunction with mothers’ fertility, especially for rural areas. Within the framework of fertility determinants and the economic value of children, it is hypothesized that children are economic assets of rural families in developing countries. These models are developed from the general household production theory. According to the quantityquality trade-off for children hypothesis, the household demand for child quality, as 3
most comparable benchmark possible for updating the $1.25 line to 2011 PPPs. Given the nature of the 2011 ICP revision of PPP conversion factors, this is the line that least changes the goalposts, by the standards of the world’s poorest countries.

However, using the average of these 15 countries does have potential limitations, in part due to weak statistical support, as reviewed by Jolliffe and Prydz (2015). Deaton (2010) similarly critiques this approach, arguing that the average should be weighted by the poor population (where this population is defined by the national poverty line) to better reflect the poverty line faced by the average poor person. Deaton (2010) also criticizes the selection of the 15 countries, which can similarly be interpreted as a critique of the statistical support for the line. He provides an example whereby when India’s national income grew, they were dropped from the set of countries determining the international poverty line, and this caused the international poverty line to increase. So, because India’s economy had grown, the number of poor as identified by the national poverty line had declined, but the number of extreme poor as defined by global poverty had increased. Their growth caused an increase in the number of Indians who were deemed extreme poor by the global poverty count.

Kakwani and Son (2015) have proposed an alternative approach to calculate a new global poverty line based on 2011 PPPs, using what they call “equivalent poverty lines”, which are poverty lines for each country that produce the same poverty headcount in 2011 PPPs, as with 2005 PPPs. In effect, their approach converts the $1.25 in 2005 PPP-adjusted dollars into 2011 PPP-adjusted dollars for each country.<sup>53</sup> Because of divergent changes in the CPIs and PPP conversion factors across countries between the 2005 and 2011 rounds, this leads to a wide range of values in 2011 PPPs. To calculate a single poverty line in 2011 PPPs, they propose using the weighted average of the equivalent poverty lines of 66 countries with weights proportional to their populations, giving them a value of $1.78 in 2011 PPP. As a robustness check to the $1.90 line, we expand on this approach using our own data.

Figure 4 shows the “equivalent poverty lines” of the 101 countries in which we measure extreme poverty, and which also participated in both the 2005 and 2011 ICP rounds, plotted against the countries income level. Using the “equivalent poverty lines” approach would result in

> 53 Conversion of 1.25 from 2005 PPP-adjusted dollars to 2011 PPP-adjusted dollars is simply the product of $1.25 and � for each country. This way the poverty line is held constant in local currencies.

31
Table 1 presents key household characteristics of beneficiaries of programs in the Main and EGPP sample, respectively. From the program participation statistics, it is clear that many of EGPP participants (about 60 percent) are coming from traiditional social assistance benefiaries. Familiarity with the safety net system and connection with local committees determining the selection of beneficiaries in case of over-subscription, may explain the large share of EGPP participants being former or current beneficiaries of traiditional social assistance programs in line with the finding from Sharif and Rubath (2017). The heads of the households in both samples show similar demographic characteristics, with about one-quarter of them being illterate and 70 percent having no formal education. The poverty scores based on the Proxy Means Test (PMT) method — a composite measure of overall economic well-being — and per capita monthly expenditure among these samples are similar.<sup>10</sup> Given the more robust enforcement of EGPP of the eligibility criteria, however, the shares of day laborors and those who do not own any land are slightly higher among individuals in the EGPP sample than those in the Main sample. Similarly, due to the requirement to include at least 30 percent women beneficiaries imposed in EGPP, the share of female participants in the program is higher among the EGPP sample.

# **3.2 Characteristics of Beneficiaries of Traditional Social Assistance Programs**

In the following sections, focusing only on the Main sample, we assess the economic lives of the poor who benefit from the traditional social assistance programs in rural Bangladesh and examine the impact of EGPP participation on these households.

## **3.2.1 How poor are they?**

The overall economic well-being of a household is reflected in both income and expenditure (Deaton and Grosh, 2000). Expenditure, as an outcome of household earnings, savings and loans, and nonlabor income including social assistance benefits, can capture the economic status of a household. Table 2 presents the socioeconomic characteristics of households in the Main sample. The average monthly household earnings are about BDT 6,400, yielding per worker earnings of less than BDT 3,000 (about US$38 per month). Considering that the average monthly earnings of a paid employee from the Bangladesh Labor Force Survey in 2013 is about BDT 8,400 per worker, labor earnings in these households appear to be very low. Monthly expenditures are more than 35 percent higher than the household labor earnings, which indicates use of non-labor incomes for consumption. Per capita

> 10 See Sharif (2009) and Bangladesh Bureau of Statistics (2014) for the PMT discussion.

9
# **Table A2: Robustness Checks of Estimates of Employment Determinants, 1983-2002**

Dependent Variable: Log US Manufacturing Sector Employment Variable

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|(13)|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Lagged Log US Manufacturing||||||||||||0.348***|0.348***|
|Sector Employment||||||||||||(0.07)|(0.07)|
|Lagged log of low income|-0.022**|-0.024**|-0.027**|-0.029**|-0.022**|-0.022**|-0.027**|-0.029**|-0.030**|-0.027**|-0.021***|-0.0270*|-0.0270*|
|affiliate employment<sup>1</sup>|(0.011)|(0.011)|(0.011)|(0.012)|(0.011)|(0.011)|(0.011)|(0.012)|(0.012)|(0.011)|(0.007)|(0.016)|(0.016)|
|Lagged log of high income|0.076**|0.084***|0.087***|0.097***|0.083***|0.075**|0.087***|0.092***|0.091***|0.088**|0.024|0.100***|0.100***|
|affiliate employment<sup>1</sup>|(0.03)|(0.03)|(0.032)|(0.031)|(0.03)|(0.031)|(0.032)|(0.033)|(0.034)|(0.036)|(0.089)|(0.031)|(0.031)|
|Lagged log of price of|-0.071|0.061|-0.039|0.102|0.105|-0.073|-0.039|-0.023|-0.009|-0.055|-0.008|0.370*|0.370*|
|investment using 1979 weights|(0.17)|(0.159)|(0.188)|(0.182)|(0.096)|(0.172)|(0.188)|(0.206)|(0.207)|(0.187)|(0.219)|(0.221)|(0.221)|
|Lagged total factor productivity|-0.164**||-0.172**||-0.005|-0.170**|-0.172**|-0.195**|-0.189**|-0.141*|0.009|0.046|0.046|
|level using 1979 weights|(0.069)||(0.072)||(0.026)|(0.069)|(0.072)|(0.075)|(0.073)|(0.08)|(0.088)|(0.074)|(0.074)|
|Lagged export share using 1979|-0.279|-0.336|-0.567**|-0.648**|-0.365||-0.567**|||||||
|weights|(0.27)|(0.267)|(0.249)|(0.247)|(0.269)||(0.249)|||||||
|Lagged import penetration|-0.597*|-0.639*|||-0.592|-0.747***||||||||
|using 1979 weights|(0.323)|(0.347)|||(0.363)|(0.275)||||||||
|Lagged log of real price of|0.149**|0.051|0.148**|0.045||0.155**|0.148**|0.163**|0.157**|0.125*|0.034|-0.064|-0.064|
|shipments using 1979 weights|(0.064)|(0.042)|(0.072)|(0.049)||(0.063)|(0.072)|(0.076)|(0.075)|(0.07)|(0.088)|(0.068)|(0.068)|
|Log price of exports|||||||||-0.017|0.148|-0.168|||
||||||||||(0.156)|(0.141)|(0.285)|||
|Log price of imports|||||||||-0.043<br>(0.134)|-0.001<br>(0.148)|0.548**<br>(0.254)|||
|Prices at 1-digit level|||||||||Y|N|N|||
|Prices at 2-digit level|||||||||N|Y|N|||
|Prices at 3-digit level|||||||||N|N|Y|||
|Industry-Specific Time Trend|||||||||||||-0.013***<br>(0.003)|
|Number of observations|6,427|6,427|6,427|6,427|6,427|6,427|6,427|6,427|6,382|5,284|1,596|1,173|1,173|
|R-squared|0.86|0.86|0.86|0.86|0.86|0.86|0.86|0.86|0.86|0.86|0.87|.|.|

_Source_ : See Table A1.

_Note_ : Robust standard errors reported in parentheses below coefficient estimates. All employment specifications include industry and year fixed effects. Low-income affiliate employment is defined according to the World Bank income categories. Regressions in columns 12 and and 13 are Arellano-Bond dynamic panel data regressions include current rather than lagged offshoring to low and high income countries.
**Table 1 Causality relationships** **<u>(developing</u> countries)**

|Variable|local accountability<br>_reg_|global accou<br>_institutional_|ntability<br>_checks_|
|---|---|---|---|
|_ml_<br>|Yes|Yes|Yes|
|_cel_<br>|No<br>|Yes<br>|Yes<br>|
|_eff_|No<br>|Yes<br>|No<br>|
|_p_<br>_res_|Yes|Yes|Yes|
|_p_<br>_cel_|Yes|Yes|No|

the data on developing countries. Indeed, from the results shown in Tables A12-A14, we see that there are some estimations where no lag length and hence no Granger-causality relationship is accepted. More specifically, when testing whether regulatory governance causes regulatory performance and the latter is measured by mainline penetration or price of cellular, no lag length is accepted (see Table A12). Hence, we conclude that regulatory governance does not cause regulatory performance in either of these two cases. In the same vein, these data on developing countries do not show causality relationships between the institutional environment and regulatory performance when the latter is measured by mainlines per employee or price of cellular (see Table A13) and between the political process and regulatory performance when the latter is measured by price of cellular (see Table A14).

In instances where a certain lag length is accepted, we proceed to examine Granger-causality. From Table A12, we see that regulatory governance causes regulatory performance when the latter is measured by cellular subscription or monthly subscription to fixed. From Table A13, we see that the institutional environment causes regulatory performance when the latter is measured by mainline penetration, cellular subscription, or monthly subscription to fixed service. Finally, we see from Table A14, that the data on developing countries show that the political process causes regulatory performance only when the latter is measured by cellular subscription. Figure 2 below summarizes our discussion of the existence of causality relationships in the data on developed countries.

From Tables 1 and 2, it is fair to say that, overall, the results presented 19
# Table 2: Differences between Foreign and Domestic Firms

|VARIABLES|(1)<br>TFP|(2)<br>TFP|(3)<br>TFP|(4)<br>TFP|(5)<br>TFP|(6)<br>TFP|(7)<br>TFP|(8)<br>TFP|(9)<br>TFP|
|---|---|---|---|---|---|---|---|---|---|
|Policy Restrictiveness in<br>Services FDI|-0.851*<br>(0.432)|-0.783*<br>(0.445)|-0.820*<br>(0.428)|-0.842*<br>(0.431)||||||
|Restrictiveness * Dummy<br>Foreign|||<br>-0.223<br>(0.651)|||||||
|Dummy Foreign|||<br>0.0707<br>(0.0636)|||||||
|Policy Restrictiveness in<br>Manufacturing FDI|-0.248|-0.527|<br>-0.233|-0.243|-0.219|-0.222|-0.161|-0.282|-0.326|
||(0.754)|(0.862)|(0.754)|(0.753)|(0.721)|(0.724)|(0.742)|(0.715)|(0.654)|
|Tariffs on Inputs (Effective)|<br>-0.00717|<br>-0.00754|<br>-0.00721|<br>-0.00719|<br>-0.00710|<br>-0.00708|<br>-0.00685|<br>-0.00695|<br>-0.00886*|
||(0.00501)|(0.00516)|(0.00502)|(0.00502)|(0.00503)|(0.00506)|(0.00507)|(0.00507)|(0.00521)|
|% of Output by Foreign Firms in<br>the Sector|<br>-0.000151|<br>-0.000624*|<br>-0.000171|<br>-0.000165|<br>-0.000282|<br>-0.000325|<br>-0.000309|<br>-0.000257|<br> <br>-0.000293|
||(0.00039)|(0.00037)|(0.00034)|(0.0004)|(0.0004)|(0.0004)|(0.0004)|(0.0004)|(0.0004)|
|Output Tariffs (Effective)<br>Restrictiveness * % Foreign<br>Own.|<br>-0.000758<br>(0.00339)|<br>-0.000972<br>(0.00359)|<br>-0.000774<br>(0.00338)|<br>-0.000767<br>(0.00338)<br>-0.000575<br>(0.00776)|<br>-0.000904<br>(0.00353)|<br>-0.00100<br>(0.00356)|<br>-0.00107<br>(0.00350)|<br>-0.00139<br>(0.00358)|<br>-0.000969<br>(0.00356)|
|Percent Owned (Foreign)||||<br>0.000364|0.000189|0.000160|0.000867|0.000541|0.000308|
|<br>Rest in Communications||||(0.000795)|(0.000432)<br>-5.970<br>(6.558)|(0.0005)|(0.0007)|(0.0005)|(0.0008)|
|Rest in Commun*% Foreign<br>Own.|||||<br>0.172<br>(0.151)|||||
|Rest in Construction||||||-9.161<br>(11.65)||||
|Rest in Const* % Foreign Own.<br>Rest in Distribution||||||<br>0.335<br>(0.393)|-0.173|||

22
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Figure 2 Density of Mobility Costs <!-- Start of picture text --> Developed and Developing Countries<br>0 2 4 6 8<br>Labor Mobility Cost<br>Developed Developing<br>.6<br>.4<br>Density<br>.2<br>0<br><!-- End of picture text --> Notes: Estimates of labor mobility costs _C_ using UNIDO data. Non-parametric density estimators for developed and developing countries.

33