rafmacalaba/datause-encoder-data huggingface dataset:
extreme global poverty to less than 3 percent by 2030 remained essentially just as ambitious with the 2011 PPPs as with the 2005 PPPs.

# **3. Data and methodology**

A number of choices regarding data sources and methods are made in the construction of the global poverty estimates. These decisions reflect a mix of theoretical foundations, empirical analysis, and knowledge of country and region context. The present update of global poverty numbers (for the year 2012) follows closely the methods used by Chen and Ravallion (2010) based on the 2005 PPPs and PovcalNet data. While these estimates have been widely used for the monitoring of the MDGs and World Bank goals, some of the details that underpin them have not always been well understood. This section aims to provide an extensive description of the data, assumptions and methods used, and to highlight their merits and limitations.<sup>14</sup>

# **_3.1 Individual-level welfare measures from household surveys_**

At the core of the World Bank’s global poverty measurement is a growing collection of income and consumption distributions, primarily derived from national household survey data. For the present global poverty estimates, the World Bank uses income and consumption distributions from more than one thousand surveys covering 131 developing countries. The estimates are based on survey interviews of more than 2 million households, representative of 95 percent of the population in the developing world. While there has been a dramatic improvement in the availability of consumption and income data over the past two decades, this improvement in the stock has been accompanied by widening heterogeneity of the surveys in terms of instruments (for example, questionnaires) and methods used by countries to measure household consumption, expenditures and income. This widening heterogeneity poses an important challenge to the fundamental assumption in measuring global poverty, that the underlying distributions of wellbeing (whether income or consumption) are comparable across countries (and within countries, over time).

There are several reasons why this assumption of comparability is challenging, but one key reason is that the surveys underlying the welfare distributions used in PovcalNet are carried out by National Statistical Offices (NSOs) as part of national efforts to monitor poverty and other

> 14 A less extensive, but more in-depth, discussion of select methods and data used in national and global poverty monitoring is available in Jolliffe et al. (2014).

10
improving governance and transparency would also be important contributors to increasing the growth in FDI/GDP. Making progress on all of these areas could help to substantially improve FDI flows into South Asia and enhance the region’s growth and productivity, as indicated by the experience of other regions.

# **Global FDI and South Asia’s FDI Experience**

**Global FDI has increased substantially since the 1980s, and it is now among the largest forms of cross-border capital flows.** World FDI inflows totaled US$1.4 trillion in 2010, 27 times larger than the 1980s FDI of US$53 billion. Relative to GDP, FDI has grown fivefold. Since the early 1990s, private capital inflows, mostly in the form of FDI, have become a major factor in development, exceeding official foreign assistance provided to developing countries.

**Developed countries continue to be major source of FDI to developing countries, although the trend is changing as more FDI moves between the developing countries themselves** . Except for a few brief periods, portfolio flows (both debt and equity) and bank lending have exceeded FDI for the world as a whole during the past two decades.  However, FDI has risen to dominate all flows to developing countries (Figure 1). FDI inflows have also been a relatively stable form of investment.  As a source of capital flows, FDI may exhibit lower volatility than other types of capital flows, such as debt and portfolio equity. The stability of FDI is especially relevant during “sudden stops,” or interruptions on capital flows. Capital flows skewed toward non-FDI types, such as bank lending and portfolio investments, may lead to increased vulnerability to economic shocks. This pattern certainly played out in South Asia during the global financial crisis that began in 2008 (see chart below).  Regardless of the source of capital flows, significant volatility suggests that there is room for countries to invest in institutions and programs that would help reduce their populations’ vulnerability to increased exposure to global (and regional) economic shocks.

**Growing international capital flows have become an increasing share of employment in developing countries, including those in the South Asia region.** Globally, employment in wholly or partly foreignowned companies has increased in the recent years, accounting for 69 million jobs in 2011, an 8 percent increase over the previous year. By contrast, overall job growth in the same period was 2 percent (UNCTAD 2012).

5
**Figure11: Investment share of GDP in the conflict scenario over 2010-2025.**

<!-- Start of picture text --> 35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025<br>Nigeria Ethiopia South Africa<br><!-- End of picture text -->

# **Figure 12 Output value shares in Nigeria in the conflict scenario 2010-2025.**

<!-- Start of picture text --> 40<br>35<br>30<br>Agriculture<br>25 Oil<br>Nat Resources<br>20<br>Food<br>15 Manufacturing<br>Infrastructure<br>10<br>Services<br>5<br>0<br>2010201120122013201420152016201720182019202020212022202320242025<br><!-- End of picture text --> Source: Authors’ simulations and calculations.

18
self-employed households in rural Ethiopia. However, rather than measuring aspirations for incomegenerating activities directly, they focus on the aspirations of these households for their general wealth, social status, and children’s education.

A third contribution of this paper is methodological. We provide a unique and novel dataset on entrepreneurial aspirations that combines insights from different literatures. We study both aspirations for a fixed and an open time horizon, and in four key business dimensions. We elicit the time estimated by the entrepreneur to realize their aspired business (adapted from Laajaj, 2017). In addition, we introduce two new measures for imagination and planning failure (in the spirit of Dalton, Ghosal, and Mani, 2016), distinguish aspirations from hope (in the spirit of Lybbert and Wydick, 2017), measure the extent of realism of aspirations and how they are revised with time (Selten, 1998). Using this comprehensive framework, we provide first and comprehensive evidence of the growth aspirations of small-scale entrepreneurs in a developing country setting.

The remainder of the paper is organized as follows. Section II introduces the conceptual framework and hypotheses. Section III describes the methodology and data. Section IV presents the results and Section V concludes.

# **II Framework and Hypotheses**

Since Simon (1955) and Selten (1998) and more recently Bogliacino and Ortoleva (2014); Dalton, Ghosal, and Mani (2016); Genicot and Ray (2017), aspirations have been conceptualized as reference points. Thus, losses and gains relative to the initial level of aspirations are what determine effort.<sup>1</sup> Understanding the determinants of reference points, in this case of entrepreneurial aspirations, has both research and policy relevance. This section lays out hypotheses for both, the static and dynamic determinants of business aspirations of entrepreneurs. We also study the type of entrepreneurial future-oriented behavior that can be predicted by aspirations and the business outcomes that can be influenced by aspirations. Since the literature on entrepreneurial aspirations is still in its infancy, the hypotheses tested in this paper are inspired by several strands of the existing empirical and theoretical literature on household aspirations, and by insights from research in psychology.

> 1This literature builds on the contribution of K ̈oszegi and Rabin (2006) who model reference dependent preferences with reference points endogenous to the economic environment.

5
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28
# **Appendix 2 (continued)**

||Accounts per<br>1,000 adults<br>in commercial<br>banks (logs)|Loans per<br>1,000 adults in<br>commercial<br>banks<br>(logs)|Value of<br>deposits in 2009<br>/ GDP|Value of loans<br>in 2009 / GDP|Branches per<br>100,000 adults<br>in commercial<br>banks<br>(logs)|Branches per<br>squared km in<br>commercial<br>banks<br>(logs)|
|---|---|---|---|---|---|---|
|Low-fee accounts|0.0891|0.0448|0.0684|0.1297|0.2242*|0.1128|
|documents_~p|-0.4726*|-0.4169*|-0.2488|-0.2892|-0.2728|-0.4013*|
|deposit_mi~n|-0.4541*|-0.4192*|-0.0914|-0.1334|-0.3377*|-0.1876|
|deposit_an~e|-0.5103*|-0.4168*|-0.2548|-0.3168*|-0.5768*|-0.3889*|
|Places to submit loan<br>application|0.4542*|0.6272*|0.2551|0.4411*|0.5317*|0.2418|
|Minimum loan amount|-0.4297*|-0.5719*|0.0044|-0.0969|-0.3393*|-0.1196|
|Fees - consumer loan|0.0011|0.07|-0.1063|-0.1617|0.0486|-0.1332|
|Days to process loan<br>application|-0.2547|-0.2263|-0.221|-0.2364|-0.2544|-0.0826|
|Lending rate|-0.3806*|-0.2648|-0.4211*|-0.4791*|-0.3973*|-0.3776*|
|Branch approval|-0.2912*|-0.1121|-0.1186|-0.2785*|-0.2818*|-0.0999|
|Tax incentives|0.3372*|0.3871*|0.1899*|0.3212*|0.2038*|0.054|
|Agents (savings)|0.3126*|0.1832|0.1506|0.2047*|0.0727|0.0603|
|Agents (credit)|0.3041*|0.1986|0.1399|0.2180*|0.1288|0.0656|
|Postal system (private<br>operator)|0.4027*|0.2007|0.2157*|0.3023*|0.3262*|0.3164*|
|Capital account<br>openness|0.3785*|0.4541*|0.2809*|0.3787*|0.5462*|0.3814*|
|Deposit rate|0.1024|-0.1224|-0.4114*|-0.4332*|-0.1179|-0.1591|
|Credit legal rights|0.3693*|0.1356|0.1367|0.2390*|0.2989*|0.2909*|
|Credit rank|-0.5196*|-0.5180*|-0.1708|-0.3482*|-0.5166*|-0.4075*|

53
<!-- Start of picture text --> 60,000<br>50,000<br>40,000<br>30,000<br>20,000<br>10,000<br>fi<br>2015 2020 2025 2030 2035 2040 2045 2050<br>Bi Biomass Bcoal lignite<br>crude@Nuclearoil @ Renewable Natural Gas<br><!-- End of picture text --> <!-- Start of picture text --> 60,000<br>50,000<br>40,000<br>30,000<br>20,000<br>10,000<br>fi<br>2015 2020 2025 2030 2035 2040 2045 2050<br>Biomass m Coal BLignite<br>mcrude Oil MRenewable GNatural<br>@ Nuclear Gas<br><!-- End of picture text -->
-6-

months after program completion, has been used in several reports written by the STPS.Z/ A second, more complete retrospective survey, was administered to the 1990 cohort of trainees in February 1992.&1 It elicited a wealth of information on all jobs held since completion of training until February 1992, including the start and e-d dates for each job, monthly salary, hours worked per week, occupation and industrial sector. Our evaluation is based on the second PROBECAT survey.

As a control group for these trainees, we use a sample of unemployed individuals drawn from the 1990-91 quarterly urban labor force survey (ENEU). ENEU, a household-based survey of the sixteen main urban areas in Mexico, which elicits detailed information on employment status, jobs, monthly salary, and hours of work broadly comparable to the PROBECAT survey. ENEU uses a quarterly rotation system such that each rotation group (of households) remains in the survey for five consecutive quarters, and then leaves the sample. We obtained panel data for the rotation group that remained in the survey from the third quarter of 1990 to the third quarter of 1991--the same period spanned by the trainee data--and drew our control group from this sample. This control group includes all those who were unemployed in the third quarter of 1990 (who we then track for a year).2' For certain analyses, we will augment this control group with a second cohort who became u'nemployed in the fourth quarter of 1990 (and were not in the first cohort). For this latter cohort, only nine months of data are available.

Table 2 presents summary information on the demographi **c** haracteristics of our samples of trainees and controls. In 1990, the average PROBECAT male trainee was 28 years old, with the majority having completed primary schooling 2/ See STPS, Reporte de Seguimiento del Prozrama de Becas de Capacitacion Rara Trabajadores 1988, Reporte de Seguimiento del Prog^rama de Becas de Capacitacion Rara Trabaiadores 1989, and Reporte de Seguimiento del Programa de Becas de Capacitacion gara Trabajadores 1990 (preliminary). None of these reports include :ooparisons of post-program outcomes for trainees with those for a control group.

1/ The original 3 month follow-up survey had been administered to 1,995. trainees, but it was impossible to track down 273 of these. Some basic consistency checks provided a match for 1,722 individuals. Four individuals included in the new 1992 survey were not part of the original one.

2/ As discussed earlier, we use two alternative definitions of "unemployed". Under the "standard" definition of unemployed, we include all those individuals who report to be (a) out of work the previous week and (b) searching for a job. Under the "alternative" definition of unemployed for males, we also include those who report to be (a) out of work the previous week, (b) able to work, and (c) not students or retirees, whether they are searching for a job or not.
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39
To meet the rapidly growing demand, the interconnection of the North-Northeast subsystems was implemented during the 1970s, and the interconnection of the South and Southeast subsystems was achieved in the beginning of the 1980s. The fiscal crisis of the 1980s, which eroded the government investment capacity, led to a long period of stagnation. The absence of investments, coupled with unfavorable hydrologic conditions, culminated in the power outage of 2002 and the subsequent rationing regime. This energy crisis reinforced the importance of further integrating the electric system, as the excess energy potential in the South subsystem could not be transmitted to minimize the power outage impacts in the rest of the country. In 2003, various transmission facilities started operating, consolidating the North-South and the South-Southeast interconnections. Nonetheless, the quality of Brazil’s electricity infrastructure remains precarious. For example, the Global Competitiveness Report 2016-17 ranks Brazil’s quality of electricity supply at 91st place out of 138 nations. This figure represents a significant decline compared to the 76th position achieved in 2013-14.

In line with our model, Brazil’s poor electricity infrastructure status seems to be at least in part due to poor management of public investment.<sup>15</sup> As Amann et al. (2016), among many others, argue, this is related to Brazil’s deficient regulatory governance that has deterred or delayed infrastructure investment. World Bank (2012) gives a clear example of lost public capital as a consequence of bureaucratic barriers: no less than 15% to 20% of the budgets of hydroelectric investment projects in Brazil are a consequence of environmental licensing costs. Corruption is also behind the low quality of regulatory governance, with a prominent case of the recent Eletrobrás contractors’ scandal (U.S. Securities and Exchange Commission, 2018).<sup>16</sup> To get a better idea of the evolution of the Brazilian electrification, the right panel in

> 15 Though public management issues are important, Brazil is not an extreme case of mismanagement according to the public investment efficiency index developed by Dabla-Norris et al. (2012). With an index of 3.12 out of 4, it ranks second within their sample of 71 low- and middle-income nations.

> 16Officers at Eletrobras Termonuclear S.A (“Eletronuclear”), Eletrobras’s owned nuclear power generation subsidiary, engaged in an illicit bid-rigging and bribery scheme involving the construction of a nuclear power plant (“UTN Angra III”) from approximately 2009 until 2015. These officers used their influence at Eletronuclear in favor of a bid-rigging scheme among certain private Brazilian construction companies.

25
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*Per 1000 live births.

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