rafmacalaba/datause-encoder-data huggingface dataset:
that there is considerable overlap between infrastructure and household attribute variables. This is expected as sorting of household attributes is supposed to be outcomes of attractiveness of communities in terms of access to infrastructure and services as well as agro-climatic conditions. What is interesting to note is that though the handful of infrastructure and geography variables can explain a signi...cant proportion of spatial variations in LRPCE, the overlap between these variables and household attributes is not complete.

In contrast to urban areas, the explanatory variables are less successful in explaining spatial di¤erences in welfare in rural areas in either years. This is partly because sorting of human capital is less important in rural areas where agriculture predominates. Though regressions control for agro-climatic conditions, these measures are de...ned at much larger spatial resolutions due to lack of information on micro agro-climatic conditions at the community level. More importantly, one would expect access to market to have larger roles in rural areas but our measure of distance is again de...ned at sub-district level and does not capture recent improvements in road transport.

To provide an indication of which of the individual regressors are important in explaining spatial variations in welfare, we report the regression results from the mixed e¤ects model in Table 6. The regression results highlight importance of three variables in explaining spatial variations in LRPCE. These are education level, access to phone and electricity. The coe¢ cients of these three variables are large in magnitudes, positive in sign and highly statistically signi...cant in all of the six regressions. Note that access to electricity and phone are statistically signi...cant even after controlling for possible indicators of sorting and income such as education and skills. While wealthier community may be argued to have better access to these services due to income e¤ect, the regression results suggest direct in‡uence of electricity and phone connections beyond what would have been possible if it were due only to income e¤ect.

# 4.5 Sources of Spatial Convergence in Welfare

To explore the sources of change in spatial di¤erence in welfare, we draw from the estimates 24
availability of key inputs) indicate the existence of favorable dimensions for successful investments, others convey the higher costs of an investment climate. To ease the interpretation of the estimated coefficients, therefore we assign a positive or negative sign to each value depending on the assumed theoretical direction of influence of a given variable on the probability of successful investment, such that higher values of all included variables imply positive effect on the likelihood of receiving the FDI.

_Table 2. Results for FDI data_

|Group|Variable|Beta|Standard<br>deviation|Transformation<br>sign|
|---|---|---|---|---|
|1|GDP growth - 5Y forecast|0.931|0.319|1|
|1|Final consumption expenditure (% of GDP)|2.058|0.375|1|
|2|Labor force, total|0.697|0.382|1|
|2|Tertiary education enrollment, gross %|0.849|0.498|1|
|2|Labor tax and contributions (% of profit)|0.928|0.341|-1|
|3|Urban population growth (annual %)|0.662|0.436|1|
|4|Industry (including construction), value added (annual % growth)|1.908|0.733|1|
|4|Scientific and technical journal articles|0.784|0.269|1|
|4|Availability of scientists and engineers, 1-7 (best)|0.766|0.473|1|
|5|Total natural resources rents (% of GDP)|0.697|0.242|-1|
|5|Forest rents (% of GDP)|0.604|0.179|-1|
|5|Mineral rents (% of GDP)|0.590|0.202|-1|
|5|Crude Oil Proved Reserves (Billion Barrels)|0.495|0.219|1|
|6|Logistics performance index: Ability to track and trace consignments (1=low to 5=high)|0.998|0.617|1|
|7|Soundness of banks, 1-7 (best)|1.006|0.223|1|
|8|Profit tax (% of commercial profits)|0.719|0.458|-1|
|8|Total tax and contribution rate (% of commercial profits)|0.818|0.489|-1|
|8|Time to pay taxes (hrs/year)|0.616|0.284|-1|
|8|Registering property: Cost, % of property value|0.881|0.284|-1|
|9|Commencement of proceedings to resolve insolvency index (0-3)|0.799|0.342|1|
|9|Judicial independence, 1-7 (best)|0.942|0.609|1|
|10|Cost to Import: Documentary Compliance (USD)|0.423|0.173|-1|
|10|Cost to Export: Border Compliance (USD)|0.936|0.298|-1|
|10|Cost to Export: Documentary Compliance (USD)|0.725|0.302|-1|
|10|Prevalence of trade barriers, 1-7 (best)|1.033|0.639|1|
|10|Tariff rate, most favored nation, simple mean, all products (%)|1.031|0.519|-1|
|10|General government final consumption expenditure (% of GDP)|2.423|0.853|-1|
|11|Inflation, consumer prices (annual %)|1.281|0.503|-1|
|11|General government net lending/borrowing, Percent of GDP|1.697|0.317|1|
|11|General government gross debt, Percent of GDP|0.656|0.352|-1|
|11|Political Stability No Violence|1.613|0.416|1|
|11|Business costs of crime and violence, 1-7 (best)|0.672|0.345|1|
|12|Foreign Direct Investment: Inward stock (USD per capita)|0.543|0.316|1|

Note: The table reports the results for variables with statistically significant estimated coefficients (at the 10% significance level). The first column profiles the variables with respect to groups outlined in Table 1. The results are obtained using the outlined methodology with country/sector panel data. The dependent variable is the binary indicator of FDI performance over 2015-19 period. The regressors are the policy indicators (average value over the 2010-14 period).

19
**Landsat Data** The Landsat program is managed by the United States Geological Survey (USGS) and consists of several satellites that capture global imagery at frequent intervals. Unlike satellites that measure nightlights, the Landsat satellites records daytime reflection of sunlight at different spectral bands. These bands include wavelengths of visible and invisible light, and thus rich information on objects on the ground. Depending on their structure, objects on earth reflect different portions of the electromagnetic spectrum, and therefore form a “spectral signature”. Landsat data is organized into individual georeferenced and time-coded images and covers all of Earth except for the polar regions.

The Landsat program dates back to the early 1970s and has launched several satellites since then. The currently active satellites (Landsat 7 and Landsat 8) provide a spatial resolution of 30x30 meters, and their output can therefore be classified as “medium-resolution” imagery. While Landsat imagery lacks the detail of commercial mapping services such as Google Maps, the spatial resolution is fine enough to visually recognize certain features like cities and other land use. Besides visible light that can be recognized by the human eye, the satellites also capture non-visible light such as infrared and thermal heat. While widely used in environmental sciences, the vast Landsat dataset has only recently been introduced into the social sciences, mostly in urban studies. Several papers have examined the predictive power of Landsat imagery for population counts and urban boundaries, e.g. Goldblatt et al. (2016). There is very little usage of Landsat in Economics despite the fact that Landsat imagery is available easily at no cost.<sup>4</sup> This paper uses Landsat 7 which launched in 1999 and is still operating to produce data.<sup>5</sup> Landsat 7 records eight different spectral bands and has a temporal resolution (the time until the satellite revisits a certain position on earth) of 16 days. Table 1 provides an overview of the recorded bands and their resolution. The analysis uses annual simple composite images

> 4The major exception is Burchfield et al. (2006)’s study employing Landsat 5 imagery to measure urban sprawl.

> 5The newest satellite, Landsat 8, collects data for additional bands, but was only launched in 2013.

8
# **The Long-Term Impact of High School Financial Education:**

# **Evidence from Brazil**

Miriam Bruhn, Gabriel Garber, Sergio Koyama, and Bilal Zia<sup>*</sup>

**JEL Codes:** D14, D91, I25, O12

**Keywords:** Financial Education, Financial Literacy, Youth Economic Development, Brazil

> * Bruhn and Zia are from the World Bank, mbruhn@worldbank.org and bzia@worldbank.org, respectively. Garber and Koyama are from the Central Bank of Brazil, <u>gabriel.garber@bcb.gov.br and sergio.koyama@bcb.gov.br,</u> respectively. We thank Jose Alexandre Vasco and Rogerio Vicentin Ferraz de Oliveira from CVM, Claudia Forte and Thiago Nascimento from AEF, Caio Piza from the World Bank and Cristina Oliveira Roriz, Mirena Gentilezza de Figueiredo Brito, Elisangela Batista Vieira Salgueiro Dias, Fabio de Almeida Lopes Araujo, Paulo Jose de Castro Jatahy, Rodrigo Augusto Silva de Andrade and Thiago Almeida Franco from the BCB and the participants of the 2022 Cherry Blossom Financial Education Institute for their support and comments. Laís Camargo Bartalotti provided excellent research assistance.
are in creating new jobs. In calculating the net job creation rates, firms’ own responses can dominate the average for the sector if the number of firms in the sector is small. In order to control for this effect, I drop country-sector cells with less than 20 observations<sup>9</sup> .

The hypothesis I want to test is whether firms are less likely to export in sectors where they are more discouraged to create new jobs. I estimate a probit model with the specification given in equation 1, Pr  _zijc_  1   Pr  1 _JN jc_   2 log( _L_ ) _ijc_   3 _Ageijc_   4 _Foreignijc_   _I j_   _I c_   _I t_   _ijc_  0  .<sup>(1)</sup> In this equation _z_ ijc is a discrete random variable equal to one if the firm _i_ in sector _j_ and country _c_ exports any part of its output. In Melitz (2003), driving force of a firm’s exporting decision is its intrinsic efficiency level. I use log of employment level as a proxy to the efficiency level. In addition to size, age and foreign ownership levels are other important characteristics of firms that can affect their exporting decisions. Foreign owned firms are more likely to be engaged with the rest of the world, hence more likely to export. There is also a vector of variables controlling the sector, country, and survey year fixed effects listed in respective order _I_ j, _I_ c and _I_ t. Sectors are likely to vary in intrinsic volatility of demand and supply shocks due to differences in technological or market characteristics. These differences might lead to different demands of job creation and destruction across sectors. As a result, sectors might be affected differently by labor regulations. Moreover, sectors are likely to vary in how engaged they are in international trade. I include sector dummies to control for these possible differences across sectors. I include country dummies to control for country level trade and other policies differences. With the inclusion of these controls, I explore the extent to which differences in restrictiveness in creating new jobs explain the remaining variation in exporting. In the estimations all standard errors are clustered to allow for possible correlations across firms within the same sector, country, and year.

# **3. Data Description**

The source of the data used in this paper is World Bank’s Enterprise Surveys<sup>10</sup> . In the surveys, a random sample of firms is selected that is representative of non-agricultural private sector of each country. The data cover firms from 26 countries from Eastern Europe and Central

> 9 The value of 20 was arbitrarily chosen, alternative values like 15, 25 didn’t affect the results. 10% of the dataset was excluded as a result of this selection. When I focus on industries in manufacturing sector, since the sample size is much smaller, I use 10 observations instead of 20 in order to have enough variation per country-industry cell. 10 See www.enterprisesurveys.org for detailed description of the data and methodology used for data collection.

8
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**Figure 10: Effect of shared prosperity premium on the world’s headcount under a zero growth scenario**

<!-- Start of picture text --> Zero growth<br>2010 2015 2020 2025 2030<br>year<br>m=0% m=-1% m=-2% m=1% m=2%<br>Graphs by Growth scenario<br>30<br>20<br>10<br>0<br>Share of population in poverty ($1.25/day) (%)<br><!-- End of picture text -->

# **4.2 The dynamics of shared prosperity and poverty reduction**

It should be noted that although some of the simulated estimates for poverty in 2030 are similar across shared prosperity scenarios, their trajectories differ. In other words, although the 2030 endpoints may look similar, the number of poor is reduced sooner under scenarios with a higher shared prosperity premium. This is illustrated well by comparing the trajectories of and for South Asia in Figure 8 and Figure 9. Both simulations result in a low regional headcount in 2030, however they follow mm= 1% mm= 2% different trajectories up to this point. For example, in 2020, the scenario is already at 39 million people, while the scenario has more than twice the number of poor people at 84 million. Given mm= 2% this seemingly asymptotic shape of the trajectories, it might be more appropriate to compare the mm= 1% average number of poor people over any period, rather than comparing only the endpoints. A steeper poverty trajectory implies that fewer people live fewer years in poverty up to 2030, even if the final headcount in 2030 may appear similar.

In several cases, our method of redistributing growth for the purpose of ‘boosting shared prosperity’ leads to an increase in the poverty rate (or a slowdown in poverty reduction) in the medium term. In countries where the initial poverty headcount is above 40%, boosting shared prosperity through a positive growth premium for the bottom 40%, while maintaining growth in the mean, would slow down 17
PRS Group, 1999. _International Country Risk Guide._ The PRS Group, East Syracuse, NY.

- Ros, Agustin J., 1999. "Does Ownership or Competition Matter? The Effects of Telecommunications Reform on Network Expansion and Efficiency." _Journal of Regulatory Economics 15_ (1), 219-244.

- Rose-Ackerman, Susan, 1978. _Corruption: A Study in Political Economy._ Academic Press, New York, NY.

- Schaffer, Mark E., 1998. "Do Firms in Transition Economies Have Soft Budget Constraints? A Reconsideration of Concepts and Evidence." _Journal of Comparative Economics 26_ (1), 80-103.

- Shirley, Mary, and Lixin Colin Xu, 1998. "Information, Incentives, and Commitment: An Empirical Analysis of Contracts between Government and State Enterprises." _Journal of Law, Economics, and Organization_ 14 (2), 358-78.

- Shirley, Mary and Patrick Walsh, 2000. "Public versus Private Ownership: The Current State of the Debate." Policy Research Working Paper #2420, World Bank, Washington DC.

- Shleifer, Andrei, and Robert Vishny, 1993, "Corruption." _Quarterly Journal of Economics_ **108** (3), 599-617.

- Sudman, Seymour, Norman Bradburn, and Norbert Schwarz, 1996. _Thinking about Answers: The Applications of Cognitive Processes to Survey Methodology._ Jossey-Bass, San Francisco, CA.

- Tanur, Judith, ed., 1992. Questions about Questions: Inquiries into the Cognitive Bases of Surveys. Russell Sage, New York, NY.

- Treisman, Daniel, 2000. "The Causes of Corruption: A Cross-National Study." _Journal of Public Economics_ 76 (3), 399-457.

- Wallsten, Scott, 2001. "An Empirical Analysis of Competition, Privatization, and Regulation in Africa and Latin America." _Journal of Industrial Economics 49_ (1), 1-19.

- Wei, Shang-Jin, 2000. "Natural Openness and Good Government." Mimeo, World Bank, Washington DC.

- World Bank, 1999. "Non-Payment in the Electricity Sector in Eastern Europe and the Former Soviet Union." World Bank Technical Paper # 423, World Bank, Washington DC.

World Bank, 2001. _World Development Indicators._ World Bank, Washington DC.

28
**21**

# 3.8 **Japanese manufacturing stocks to sales ratio**

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1.30 1.30<br>1.25 1.25<br>1.20 1.20<br>1.15 1.15<br>1.10 1.10<br>1.05 1.05<br>1.00 1.00<br>0.95 0.95<br>0.90 0.90<br>0.85 0.85<br>0.80 0.80<br>Mar-1991 Mar-1994 Mar-1997 Mar-2000 Mar-2003 Mar-2006 Mar-2009<br>Index 2000=1<br><!-- End of picture text -->

_Data source_ : OECD Main Economic Indicators and CIE calculation.

The key feature about the inventory cycle for this study is that it will account for some of the large monthly drops in trade volumes that have generated headline grabbing attention. Year-on-year effects, the focus of this model, are likely to be much less.

# **_Trade credit_**

At the time of the onset of the financial crisis in September 2008 there was much publicity given to the shortage of credit as banks stopped lending. For example, Auboin from the WTO Secretariat described a $25 billion shortfall in trade credit in November 2008<sup>18</sup> . A shortage of trade credit would impact on world trade on the supply side but it also seems much of the contraction was a collapse on the demand side as orders were cancelled once business realised they were holding excess inventories as described above. A study by the World Bank estimates the shortfall in trade finance accounting for 10 – 15% of the fall in world trade<sup>19</sup> . Being of small magnitude this effect is omitted from this study.

# **_4. Effects of crisis without a fiscal policy response_**

> 18 Auboin, M, 2009 , _The collapse of global trade, murky protectionism, and the crisis: Recommendations for the G20_ , Centre for Economic Policy Research, London.

> 19 Reported in International Economics Weekly, _What’s happening to world trade? Part II_ , 24 April 2009
using these definitions must go beyond income-support measures designed to target the income poor, as they will not reach a large share of this group.

_Figure 11: Overlap of energy and monetary poor using expenditure-based measures (over expenditure, when applicable)_ <!-- Start of picture text --> 100%<br>80%<br>60%<br>40%<br>20%<br>0%<br>LIHC 2M M/2 M/2 P10<br>Neither nor (equ)<br>Only energy poor<br>Only monetary poor<br>Energy and monetary poor<br><!-- End of picture text --> _Figure 12: Overlap of energy and monetary poor using expenditure-based measures (over income, when applicable)_ <!-- Start of picture text --> 100%<br>80%<br>60%<br>40%<br>20%<br>0%<br>LIHC 2M M/2 M/2 P10<br>Neither nor (equ)<br>Only energy poor<br>Only monetary poor<br>Energy and monetary poor<br><!-- End of picture text --> _Notes: Monetary poverty is the at-risk poverty rate using the income measure reported in the HBS 2021 and, therefore, does not correspond to the official poverty measure. The resulting poverty estimate is slightly above the estimate from the EU-SILC._ _Source: Own estimates based on HBS (2021)._

**In summary, the large differences in the incidence of energy poverty between the alternative expenditure-based measures demonstrate how sensitive these measures are to the underlying definition** ; **the most effective public policies that address energy poverty will, therefore, be highly conditioned by the definition adopted by the government** . The differences in energy poverty incidence rates between the measures confirm the challenge of identifying sound and precise indicators. If policymakers plan to target those who experience low income and high costs, the lowincome-high-costs measure might be most appropriate. If they want to generate indicators that can be compared across countries, the 10 percent measure might be most appropriate. Suppose the goal is to capture those that under-consume energy in absolute terms or abnormally high energy expenditure relative to the distribution. The M2 and ( equivalized) M/2 measures might be best suited in that case. Finally, since these expenditure-based measures rely on data from the Household Budget Survey (HBS), and this data is only representative at the national, urban, and rural levels, additional geographical disaggregation (NUTS2 and below) is not possible.

**Characterizing energy affordability within the group experiencing income poverty or estimating the intersection between official income poverty and energy poverty poses challenges.** In particular, the above indicators rely exclusively on the Household budget survey, the main source of energy expenditures among households, but use the income reported in this survey. However, official income poverty in Bulgaria, as in other EU countries, relies on the EUSILC survey. A different approach to this problem involves the statistical matching of these datasets (Rude and Robayo-Abril, 2024).<sup>39</sup> Using

39 Through data fusion, they create a unique dataset incorporating details on energy spending shares, incomebased poverty indicators, inequality measures, and additional variables related to households' living conditions 25