rafmacalaba/datause-encoder-data huggingface dataset:
|**Authors**|**Topic and country of the**<br>**study**|**Dimensions of**<br>**aspirations**|**Underlying**<br>**concept of**<br>**aspirations**|**Measures of aspirations**|
|---|---|---|---|---|
|Bernard and<br>Taffesse (2014)|Validation of a survey-based<br>measure of aspirations of<br>rural households in Ethiopia|1. Education<br>2. Income<br>3. Wealth<br>4. Social status|Wishes|One aggregate index representing the sum of weighted standardized sub-indexes<br>of the following four dimensions of aspirations, and weighted according to<br>people's self-reported relative importance (with 20 beans), each based on the<br>question "what is the level of [dimension] that you would like to achieve in your<br>life?":<br>1. Annual income<br>2. Durable consumer goods (monetary value)<br>3. Years of schooling to complete<br>4. Percentage of their fellow community members who would ask for their<br>advice at times of important decisions.|
|Bloem et al. (2018)|Assessment of survey-based<br>measures of aspirations and<br>hope of households in<br>Myanmar|1. Education<br>2. Income<br>3. Agricultural<br>landholdings<br>4. Remittances<br>5. Giving<br>donations|Wishes|Five continuous variables, one for each of the dimensions, based on the question<br>“How much [dimension] would you like to complete?”<br>(1) is educational attainment expressed in years of schooling, (2) is the monthly<br>income in local currency, (3) is in acres, and (4) and (5) are in the local<br>currency.|
|Carlana, La<br>Ferrara, and<br>Pinotti (2018)|Impact of a counselling<br>intervention on the school<br>choices of high-performing<br>immigrant adolescent<br>students in Italy|Education and job|Perceivably<br>feasible wishes|One index from a factor analysis of the following answers to the following two<br>questions:<br>1. Answering “Enroll in university” among four options to the question<br>“Thinking about your future education-wise, what objectives do you intend to<br>achieve?"<br>2. Answering positively to the questions “Independently from your educational<br>aim but thinking about your abilities, do you think you could get<br>a...(“university degree”/“white collar job”/“managerial job”)?|
|Chiapa, Garrido,<br>and Prina (2012)|Impacts of a conditional cash<br>transfer program on parents’<br>educational aspirations for<br>their children in Mexico|Education|Wishes|One categorical variable based on the coding in years of education of the<br>responses in levels to the question “Up to what level would you like your [child]<br>to study?”|

40
In the empirical work that follows, we shall depend almost entirely on social tables or tax census data obtained from secondary sources, including some estimates of our own. Detailed explanations for each country’s social table are provided in the Appendix 1.

[Table 1 about here] Table 1 lists 14 ancient pre-industrial societies for which we have calculated inequality statistics.<sup>10</sup> These societies range from early first-century Rome (Augustan Principate) to India just prior to its independence from Britain. Assuming with Angus Maddison an annual subsistence minimum of $PPP 400 per capita,<sup>11</sup> and with GDI per capita ranging from about $PPP 500 to $PPP 2,000, then α would range from 1.3 to 5. A GDI per capita of $PPP 2,000 is a level of income not uncommon today, and it would place 1732 Holland or 1801-03 England and Wales in the 40<sup>th</sup> percentile in the world distribution of countries by per capita income in the year 2000. With the possible exception of 1732 Holland and 1801-3 England, countries in our sample have average incomes that are roughly compatible with contemporary pre-industrial societies that have not yet started significant and sustained industrialization. The urbanization rate in our sample ranges from less than 10 to 45 percent (the latter, again, for Holland). Population size varies even more, from an estimated 983,000 in 1561 Holland to 350 million or more in India 1947 and China 1880. Finally, the number of social classes into which distributions are divided, and from which we calculate our Ginis, varies considerably.

population and their income shares are the only data given. In that case, of course, any member of a richer group must have a higher income than any member of a lower group. This is unlikely to be satisfied when the fractiles are not income classes but rather social classes as is the case here. The Gini2 formula is due to Kakwani (1980).

> 10 Joseph Massie’s famous social tables for 1759 England and Wales are not used here since he did not give them in a form consistent with our needs. In addition, we excluded 1752 Jerez (Andalusia) since it was primarily an urban observation. In the near future, we expect to augment the sample by adding 1861 Chile, 1924 Java, late Tokugawa and early Meiji Japan, 1427 Tuscany, 1788 France, Tsarist Russia and others. 11 All dollar data, unless indicated otherwise, are in 1990 Geary-Khamis PPPs.

13
through the survey. In selecting the final set of photos, we eliminated those that have extreme scores on either the objective or the subjective beauty measure, and obtained two sets of photos that have no significant difference in mean objective or subjective beauty scores by gender. More details on how we do this are given in Appendix A.

We create two dummy variables to be used in the regressions, one on objective and the other on subjective beauty. The one on objective beauty takes on the value 1 if the rating is above 0.6, the score threshold that the PrettyScale website considers the face to be “pretty”. For the subjective scores, the variable takes on the value 1 if the beauty score is above the median score obtained in the survey.

Other signals: All candidates have an advanced level of English proficiency, but we introduced random variation in the exact levels of listening, speaking, writing, and reading between levels 4 and 5, the two highest levels in the online job portal.

Computer skills are included only for the IT cluster, where we provide the same list of software for all candidates, but in randomized order.

As it is customary in Turkey to include marital status in CVs, we indicate that all our candidates are single.

# _Final CV construction and application_

To ensure comparability across genders, we create duplicate CVs for men and women that have exactly the same information in terms of all background characteristics, except name, photo, contact information, and the date of birth (see the above discussion on the imposed 6-month difference in age by gender due to military service).

For each selected vacancy, we randomly chose four profiles without replacement from our set of 16 profiles in the cluster/location. These four profiles correspond to the four cells in the experiment: male control, female control, male treatment, and female treatment.

# 2.3. Data

The online job portal used in this experiment allows us to collect information on selection at three different stages of the hiring process. Figure 1 provides the details on these three screening stages. The first stage consists of making the longlist. In screening the applicants, employers can use filtering, whereby they enter search criteria to create a filtered longlist of all applicants. For job seekers, the online portal provides information on whether their application made it to this filtered longlist. In other words, for each application of our fictitious candidates, we observe whether the fictitious candidate made it to the longlist. This indicator is used as a dependent variable for this first recruitment stage.

After creating the longlist, the employer can observe the name, photo, current position, and city of residence for all longlisted applicants. The employer can then click on a candidate from the longlist to obtain more information (i.e. view the candidate’s full CV). Note that clicking on a candidate is costly for the employer as there is a fee associated to clicking on a CV. For jobseekers, the portal provides information on whether the employer from a specific vacancy viewed their full profile. In other words, for each 11
**I. Introduction**

To reduce the wastage of resources caused by school repetition and dropout, some educators and policy makers have argued for liberal grade promotion policy in developing countries. Liberal promotion, also known as social promotion, is the practice of promoting students to the higher grade regardless of academic performance.

At the heart of the debate on the pros and cons of lenient promotion policy is the tradeoff between economic efficiency and social well-being of the child on the one hand and student learning and incentives to learn on the other. Grade repetition imposes fiscal burden on the public education system as more resources have to be devoted on the same child to complete a grade level. From the psychological perspective, separating a child from his or her peers may have harmful effects on his or her self-esteem, socialization, and self-expectation. From this point of view, grade retention may be counterproductive, leading to higher dropout rate and lower student achievement (Jimerson, 2001).

Others maintain that it is necessary to hold students back until they master the content before advancing to the higher grade. Automatic promotion may create a gap between student learning level and the syllabus, a gap that only grows over time in absence of remedial education (Pritchett & Beatty, 2015). Progressing through school without learning may only postpone the inevitable, setting students up for failure in school leaving examinations and beyond. Rather than being a burden on students, the “threat” of grade retention is a necessary motivator; without the implicit threat of the penalty, students do not have the incentive to put in the requisite effort to succeed in school.

The empirical evidence on the relative impact of grade retention and automatic promotion on subsequent school outcomes is mixed. Some studies find positive effect of grade retention on student achievement and the probability of academic success (Gomes-Neto & Hanushek, 1994; Eisemon, 1993; Allensworth, 2004). Other studies, however, show negative impact of grade retention on school dropout rate and educational attainment (Manacorda, 2012; Grissom & Shepard, 1989; Roderick & Nagaoka, 2005). Meta-analyses of grade retention research also suggest that the practice has either negative or no effect on student performance and emotional and behavioral measures (Jimerson, 2001; Holmes, 1989).

A dimension that is missing from the discussion of liberal promotion policy is the magnitude of the practice: What proportion of students who advance to the next level of the education system 2
agreements. For example, only 22 agreements have TBT measures, whereas almost all of them have a provision regarding customs. Figure 4 plots the share of agreements that include �- and �-provisions by country. All the agreements signed by the EU contain customs provisions but only 11 percent of them cover consumer protection. On the other hand, all agreements signed by the United States and Japan deal with consumer protection. Provisions regarding GATS and customs are included in all Japanese agreements, whereas all U.S. agreements include TRIPS. While the least frequent provision in the agreements signed by Japan is export taxes (45 percent of agreements), in U.S. agreements they are investment, movement of capital, and TBT (around 80 percent of the agreements). Finally, the less frequent provisions in EU agreements are TBT and SPS, covering less than one-third of the mapped agreements.

# **3.2. Identification and Measurement of Vertical FDI**

The model outlined in Section 2 ultimately gives predictions on the share of firms engaging in vertical FDI relative to outsourcing. Since we do not have information about the total number of firms and on the extent of outsourcing in an economy, we test the first part of the predictions regarding the profitability of vertical integration by focusing on a measure of positive flows of vertical FDI.

In order to quantify vertical FDI flows we apply the methodology proposed by Alfaro and Charlton (2009), used also by Lanz and Miroudot (2011) , using firm level data obtained from the ORBIS dataset. The Bureau van Dijk collects information about location, ownership, detailed sector level, and operational data (e.g. revenues) for more than 100 million firms in Europe, Americas, and Asia-Pacific region.

We restrict our analysis to subsidiaries in any country of the world owned by parent firms located in Germany, Japan, or the United States, respectively, for the years 2003, 2007, and 2011.<sup>17</sup> Alfaro and Charlton (2009) methodology allows to identify three types of foreign direct investment, namely vertical, horizontal and complex.<sup>18</sup> Simply put, horizontal FDI is an activity of a foreign-owned subsidiary

> 17 We select firms such that only industrial parent firms are included in our dataset. This is done in order to exclude individuals, government, or financial institutions owners.

> 18 See appendix A for a definition of ownership and additional details about our measure of vertical FDI.

15
-33-

<!-- Start of picture text --> STEP 2 STEP 3<br>Before Tax<br>BALANCE SHEET, Period 1<br>ASSETS A1' L1 ' LIABILITIES<br>. . . ~~~~~Income in Period 1<br>MA1' 480 0 ML1' NW1' -NWO<br>- 2940 - 2520<br>NMA1 2940 NW1'<br>Land 2400 - 420<br>Inventory 60<br>Tax liability<br>A1' 2940 2940 L1 ' - 0.5 (420) -210<br><!-- End of picture text --> STEP 4 <!-- Start of picture text --> After Tax BALANCE SHE T, Period 1<br>ASSETS A1 L1 LIABILITIES<br>. . ~~~~~~~~~Net Income in Period 1<br>MA1 270 0 NLl - NW1 - NWo'<br>- 2730 - 2520<br>NMA1 2730 NW1<br>Land 2400 - 210<br>Inventory 60<br>A1 2730 2730 Li<br><!-- End of picture text --> The net worth adjustment therefore yields a measure of taxable income of 420 and after-tax income of 210.
- [6] Driscoll, John C., and Aart C. Kraay, 1998. “Consistent covariance matrix estimation with spatially dependent panel data,” The Review of Economics and Statistics 80 (4), 549-560.

- [7] Fazzari, Steven M., James Morley, and Irina Panovska, 2012. “State-dependent e¤ects of ...scal policy,”Australian School of Business Research Paper No. 2012 ECON 27.

- [8] Granger, Clive W., and Timo Terasvirta, 1993. Modelling non-linear economic relationships (Oxford University Press).

- [9] Ilzetzki, Ethan, and Carlos A. Vegh, 2008. “Procyclical ...scal policy in developing countries: Truth or ...ction?”NBER Working Paper No. 14191.

- [10] Ilzetzki, Ethan, Enrique G. Mendoza, and Carlos A. Vegh, 2013. “How big (small?) are ...scal multipliers? Journal of Monetary Economics 60, 239-54..

- [11] Jorda, Oscar, 2005. “Estimation and inference of impulse responses by local projections,” American Economic Review 95 (1), 161-182.

- [12] Kaminsky, Graciela, Carmen M. Reinhart, and Carlos A. Vegh, 2004, “When it rains it pours: Procyclical capital ‡ows and macroeconomic policies,” NBER Macroeconomics Annual.

- [13] Owyang, Michael T., Valerie A. Ramey, and Sarah Zubairy, 2013, “Are government spending multipliers greater during periods of slack? Evidence from 20th century historical data,”NBER Working Paper No. 18769.

- [14] Ramey, Valerie A. 2011. “Identifying government spending shocks: It’s all in the timing,” Quarterly Journal of Economics 126 (1), 1-50.

22
Countries face two types of constraints to mobilize revenues. One is the actual ability to raise taxes, which includes both whether resources are available to tax–the arithmetics of resource mobilization–as well as the capacity to collect those taxes. The second type of constraint refers to the notion that low fiscal revenues are often the result of a political equilibrium, where the incentives for stakeholders are aligned in such a way that there is a tendency to revert to such equilibrium. We refer to this as the politics of domestic resource mobilization. In this way, some countries may not have the resources to finance their own development; while the political equilibria in others may be such that, even though resources exist, it is unlikely that reforms will be passed to mobilize them towards reducing poverty.

This paper puts forth an accounting exercise to evaluate the feasibility to eradicate poverty using domestic resources–the arithmetic capacity–by mere redistribution. To assess the capacity of countries to eliminate poverty through redistribution we propose a new tool: an index of poverty eradication capacity. Our poverty eradication policy considers a set of transfers from those above the poverty (or affluence) line to those below the poverty line, aimed at filling up the shortfalls of poor individuals, without making anybody else poor and avoiding re-ranking.

Additionally, we propose a tool to estimate the concentration of political influence starting from the distribution of income in the society. The paper argues that the concentration of resources among few individuals can limit the feasibility of effective fiscal policies to eradicate poverty, both in terms of obtaining revenues as well as regarding social spending. We consider that richer individuals, who would lose out from redistributive reforms, would oppose their implementation; and that the strength of their political opposition is proportional to their income. To outweigh this opposition from the top, poorer individuals (the potential winners from redistributive policies) would need to form a coalition. Countries where political influence is more concentrated would need to mobilize larger coalitions of individuals to counter-balance the political influence in the hands of the few.

We apply this methodology, using the most recent data from the World Bank PovcalNet database for over 120 developing countries. Our results show that many countries do not have the resources to finance their own development out of poverty. Moreover, the countries that have the arithmetic capacity to do so would likely face great resistance to implement redistributive policies as political power is concentrated among a few rich individuals whose interests diverge from those of the poor. These results suggest that domestic resource mobilization, beyond an issue of political will, implies a challenge related to the concentration of resources among powerful groups and their ability to influence policy decisions.

22
Unless domestic work is unproductive, these results suggest that the potential for ranking errors using pcc as a measure of living standards is significant. Part of the problem is the overall distribution of pcc. In common with the ranking by full time earnings discussed in Section 4.3, individual records are concentrated towards the median and the distribution is strongly positively skewed. As a result, a small change in pcc can lead to a large change in ranking over some ranges of pcc. For example, a two-parent low wage family in which the mother switches from domestic to market work could move up two quintiles in the ranking in Table 5, yet experience only a minimal improvement in the family’s standard of living. The family may, for example, appear in quintile 2 with a mean pcc of C$2664 pa with just one parent working full time. If their pcc doubles because the second parent or a child, or possibly both, go out to work, the family may then appear in quintile 4 (the lower limit of quintile 4 is only C$4,869). Equally problematic is the case cited in the World Bank (2000) Report, where children are taken out of school to cover for mothers in the labor force.

A policy implication of these results is that low wage households working long hours may be treated unfairly by programs targeted on pcc. Moreover, if market and domestic work become closer substitutes as pcc increases, the disincentive effects of such programs on female labor supply will tend to reinforce traditional gender roles.

# **6. Conclusions**

This paper has surveyed the main approaches to modeling the household to be found in the literature on developed and developing countries. Beginning with the standard model of the one-person household dividing its time between work and leisure, supplemented, in the developing economy literature, with a farm production activity, I considered two major generalizations of this model. The first was to the multi-person household, the second was by the inclusion of household production. It was argued that for theoretical and empirical analysis of issues concerning the intra-household allocation of resources and distribution of real income, the across-household welfare distribution, and the

> 56 Defined as 40 hours per week, 50 weeks per year.

57
# **Table 3d.  Trade Flows of Rwanda**

|||Imports|||Exports||
|---|---|---|---|---|---|---|
||USD in|||USD in|||
||<br>billion|% of total|% of supply|<br>billion|% of total|% of output|
|**Business Services**|||||||
|Air Transport|0.0|1.4|33.3|0.0|0.5|16.1|
|Communication|0.0|0.5|4.5|0.0|0.6|5.9|
|Insurance|0.0|0.4|9.0|0.0|0.2|3.7|
|Business Services nec|0.1|9.7|36.7|0.0|0.5|3.1|
|Financial Services nec|0.0|0.1|0.6|0.0|0.1|0.5|
|Transport nec|0.0|2.3|9.0|0.1|7.4|23.6|
|Water Transport|0.0|0.2|9.6|0.0|0.2|11.4|
|**Dixit-Stigliz Goods**|||||||
|Chemicals Mineral and Metal Products|0.2|21.5|48.5|0.0|1.1|4.7|
|Energy and Minerals|0.0|1.6|33.2|0.4|60.8|95.2|
|Food Products|0.1|12.8|18.0|0.0|3.6|5.1|
|Petroleum and Coal Products|0.1|10.3|71.2|0.0|0.4|13.9|
|Other Manufacturing|0.2|23.6|58.3|0.0|0.7|5.0|
|Textile and Apparel|0.0|2.8|35.2|0.0|0.5|8.7|
|Wood and Paper Products|0.0|3.3|46.4|0.0|0.1|2.9|
|**CRTS**|||||||
|Agriculture and Forestry|0.0|1.7|1.1|0.1|9.9|5.1|
|Other Services|0.0|4.1|3.0|0.1|10.4|6.2|
|Trade|0.0|3.3|7.2|0.0|1.6|3.3|
|Utilities|0.0|0.6|6.3|0.0|1.3|12.4|

**Source:** Authors’ calculations based on the GTAP 8.1 data set.

50
<!-- Start of picture text --> 80% ®<br>@<br>bh 60%<br>3<br>=° e<br>5 40% 5 ee<br>x° ° e e<br>20% te @@<br>e he e e Pe<br>0% e ete = 9 oo@ ® ee<br>5.00 6.00 7.00 8.00 9.00 10.00<br>(Log of) GDP per capita<br><!-- End of picture text --> <!-- Start of picture text --> 80% o—,<br>e<br>60%<br>3<br>2 ° ee<br>£ 40% ee<br>xeO° e ° eete wet<br>20% ee .°<br>e &<br>0% e.eoe e eee e<br>5.0 6.0 7.0 8.0 9.0 10.0<br>(log of) GDP per capita<br><!-- End of picture text -->