rafmacalaba/datause-encoder-data huggingface dataset:
A Literature Review on Productivity and Growth in Brazil Fernando Veloso<sup>*</sup> and Gabriel Zaourak<sup></sup> World Bank

**JEL Classification** : D22, F17, O11, O30, O40, O47.

Keywords: Growth, misallocation, Innovation, Empirical Studies of Economic Growth, Cross-Country Convergence, Trade, Informality.

> *Email: fernando.veloso@fgv.br

> This paper was prepared as a background paper for the report Brazil 2040. The views expressed herein are only my own and should not be attributed to the World Bank, its executive directors, or the countries they represent. All errors are omissions are our own.

> Email: gzaourak@worldbank.org
Table 6: The Effect of TRA Agent Presence on Tax Morale

||Direct|Not Punishable|Avoid Paying|Refuse|Own Index|
|---|---|---|---|---|---|
|Direct Treatment|_−_0_._025|_−_0_._048|0_._019|_−_0_._077<sup>_∗∗_</sup>|_−_0_._082<sup>_∗_</sup>|
||(0_._021)|(0_._035)|(0_._038)|(0_._037)|(0_._045)|
|Number of Observations|2035|1898|1742|1737|2059|
|Adjusted R<sup>2</sup>|0_._000|0_._000|_−_0_._000|0_._002|0_._001|
|Control Group Mean|0.11|0.47|0.54|0.59|0.01|

Notes:<sup>_∗∗∗_</sup> _p <_ 0 _._ 01;<sup>_∗∗_</sup> _p <_ 0 _._ 05;<sup>_∗_</sup> _p <_ 0 _._ 1. The dependent variables are based on surveys, are all Likert scale variables based on the following statements: (i) ’Please tell me whether the following is true or not: My firm does not pay all the [Direct taxes] it is required to pay.’, (ii) ’Please tell me whether you think that the following action is not wrong at all, wrong but understandable, or wrong and punishable: Firms not paying the taxes they owe to the government.’, (iii) ’Please state your level of agreement with the following statements: If I was sure I would not get caught, I would not pay all the taxes that I owe.’, (iv) ’Please state your level of agreement with the following statements: Firms should refuse to pay taxes until they get better services from the national government.’ and (v) The mean of the aforementioned 4 variables. The independent variable is one for any respondent who were treated themselves in the first survey wave in December 2022, and 0 for everyone else. We cluster standard errors at the ward level.

Table 7: The Effect of TRA Agent Presence on interactions with the TRA

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|Direct Treatment|_−_0_._038|0_._225<sup>_∗_</sup>|0_._145<sup>_∗_</sup>|0_._003|0_._140<sup>_∗∗_</sup>|0_._222<sup>_∗∗_</sup>|
||(0_._105)|(0_._115)|(0_._076)|(0_._071)|(0_._066)|(0_._097)|
|Number of Observations|2044|1638|1633|1774|2069|2100|
|Adjusted R<sup>2</sup>|_−_0_._000|0_._002|0_._002|_−_0_._001|0_._002|0_._002|
|Control Group Mean|5.09|4.22|3.39|3.43|4.27|3.69|

Notes:<sup>_∗∗∗_</sup> _p <_ 0 _._ 01;<sup>_∗∗_</sup> _p <_ 0 _._ 05;<sup>_∗_</sup> _p <_ 0 _._ 1. The dependent variables are based on surveys, are all Likert scale variables based on the following statements: (i) ’In the last six months, how many times (if at all) did a tax inspector or tax agent from the TRA visit your business to ask you to pay taxes?’, (ii) ’Was the interaction with the TRA friendly?’, (iii) ’Which of the following best describes how you view these visits?’, (v) ’C8. Was an informal gift or payment ever expected or requested during these visits?’, (vi) ’TRA makes it easy for you to pay your taxes’, and (vii) ’TRA treats all taxpayers fairly’. The independent variable is one for any respondent who were treated themselves in the first survey wave in December 2022, and 0 for everyone else. We cluster standard errors at the ward level.

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Table A.4: Other farmers' characteristics

|Variables|Mean|SD|
|---|---|---|
|Is the head of household|0.590|0.492|
|Age|38.243|14.430|
|Years of schooling completed|2.054|2.798|
|Single|0.056|0.229|
|Married|0.849|0.359|
|Divorced, separated, or widowed|0.095|0.293|
|Number of children [ages < 15 years]|2.830|2.041|
|Total hectares of owned land|2.171|2.064|
|Number of rooms in the house|1.423|0.724|
|Number of observations|10,960||

Sources: Household survey, 2012, 2013.

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# **Table A5: Robustness test – Alternative samples**

# _Panel A: Poverty_

|||Dropping|De|pendent variable|:Povertyrate at $1.<br>Eldi 10|90<br>Eldi 10|S.E.||
|---|---|---|---|---|---|---|---|---|
||Balanced<br>panel|countries with<br>few<br>subregions|Excluding<br>USA|Excluding<br>India|xcung<br>percent cold<br>countries|xcung<br>percent hot<br>countries|clustered<br>country<br>level|Spatially-<br>corrected<br>Conley S.E.|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Temperature|0.920***|0.922***|0.785***|0.791***|1.036***|0.526***|0.648***|0.920***|
||(0.160)|(0.179)|(0.116)|(0.113)|(0.158)|(0.100)|(0.090)|(0.279)|
|Controlling for rainfall|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Subnational FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Number of countries|134|134|134|134|134|134|134|134|
|Number of regions|1,594|1,594|1,594|1,594|1,594|1,594|1,594|1,594|
|Observations|4,604|3,990|4,448|4,888|4,547|4,674|4,957|4,958|

_Notes:_ Results of panel fixed effects model. Robust standard errors in parentheses. Standard errors are clustered at the subnational level. Regressions are weighted by region population. In column (8), we use the Conley standard error (Conley, 1999) to adjust for spatial correlation based on a radius of 200 km following Graff Zivin et al. (2020). *** p<0.01, ** p<0.05, * p<0.1 52
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# **Appendix C: Sources of Raw Data**

|**Variable**|**Source**|
|---|---|
|GDP (constant LCU)|W orld Bank W orld Development Indicators (W DI)|
|GDP is the sum of gross value added by all resident producers in<br>the economy plus any product taxes and minus any subsidies not<br>included in the value of the products. It is calculated without<br>making deductions for depreciation of fabricated assets or for<br>depletion and degradation of natural resources. Data are in<br>constant local currency.||
|GDP per capita (constant LCU)|W DI|
|GDP per capita is gross domestic product divided by midyear<br>population. GDP is the sum of gross value added by all resident<br>producers in the economy plus any product taxes and minus any<br>subsidies not included in the value of the products. It is calculated<br>without making deductions for depreciation of fabricated assets or<br>for depletion and degradation of natural resources. Data are in||
|Population, total|W DI|
|Total population is based on the de facto definition of population,<br>which counts all residents regardless of legal status or citizenship--<br>except for refugees not permanently settled in the country of<br>asylum, who are generally considered part of the population of<br>their country of origin.||
|Average Years of Schooling of Population 15 years and older|Barro and Lee (1993, 2000); Cohen and Soto (2001)|
|Average Years of Schooling of Population 25 years and older|Barro and Lee (1993, 2000); Cohen and Soto (2001)|
|Rates of Return to Education|Psacharopoulos (1994); Bils and Klenow (2000)|
|Educational Quality<br>USPTO Patent Counts|Hanushek and Kimko (2000)<br>USPTO website|
|USPTO Utility Patent Counts|Hall, Jaffe and Trajtenberg (2001)|
|USPTO Utility Patent Citations|Hall, Jaffe and Trajtenberg (2001)|
|Scientific and Technical Journal Articles|W DI|
|Royalty and license fees, payments (BoP, current US$)|W DI|
|Royalty and license fees, receipts (BoP, current US$)|W DI|
|Gross Foreign Direct Investment (% of GDP)|W DI|
|Manufactures imports (% of merchandise imports)|W DI|
|Merchandise imports (current US$)|W DI|
|Manufactures exports (% of merchandise imports)|W DI|
|Merchandise exports (current US$)|W DI|
|Personal computers (per 1,000 people)|W DI|
|Internet users (per 1,000 people)|W DI|
|Internet hosts (per 10,000 people)|W DI|
|Telephone mainlines (per 1,000 people)|W DI|
|Mobile phones (per 1,000 people)|W DI|
|Television sets (per 1,000 people)|W DI|
|Radios (per 1,000 people)|W DI|
|Daily newspapers (per 1,000 people)|W DI|
|Economic Openness Index|Sachs and W arner (1995)|
|Institutional Quality Index|Knack and Keefer (1995)|
# **Appendix II: Highway development and management model (HDM-4)**

The Highway Development Management Model (HDM-4) considers several different variables in order to estimate the cost of traveling along each segment of the road network. The data used for the estimates used in this paper was collected specifically for DRC, to best characterize the transportation conditions one would find there.

In order to estimate the unit cost (in ton per km), the cost of transporting a vehicle with an average weight of 25 tonnes, one kilometer, was first estimated. The unit cost per ton-km was derived from the costs per vehicle using a factor of 15 ton per vehicle (average net weight). This factor was obtained based on the assumption of a 30 ton gross vehicle weight, with a 10 ton tare weight and a 75% loading factor.

## **Characterization of network type and terrain**

The road network of DRC includes three classes of roads: primary, secondary, and tertiary. Average vehicle speed and width of the main carriage road were used to characterize the differences among network types as follows:

|**Paved Roa**<br>|**d Speed(km/hr) **<br>|**by Network & Co**<br>|**ndition**<br>|
|---|---|---|---|
|Road Condition|Primary7m|Secondary6m|Tertiary5m|
|Flat|100|80|70|
|Rolling|80|70|60|
|Mountainous|60|50|40|

|**Unpaved Ro**<br>|**ad Speed(km/hr**<br>|**) by Network & C**<br>|**ondition**<br>|
|---|---|---|---|
|Road Condition|Primary7m|Secondary6m|Tertiary5m|
|Flat|80|70|60|
|Rolling|60|50|40|
|Mountainous|40|30|20|

where terrain type is defined using the following concepts and road geometry parameters:

- Flat. Mostly straight and gently undulating

- Rolling. Bendy and gently undulating

- Mountainous. Winding and gently undulating

|||**Number**<br>|||
|---|---|---|---|---|
|**TERRAIN**<br>**TYPE**|**Rise &**<br>**Fall**|**Rise &**<br>**Fall**|**Horizontal**<br>**Curvature**|**Super_**<br>**elevation**|
||**(m/km)**|**(#)**|**(deg/km)**|**(%)**|
|**FLAT**|10|2|15|2.5|
|**ROLLING**|15|2|75|3.0|
|**MOUNTAINOUS**|20|3|300|5.0|

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# **_Access to finance_**

Another large concern of would-be entrepreneurs is access to finance. External finance, that is, resources other than the entrepreneur’s own savings, is often necessary to start a business --one needs to rent office space, buy computers, or maybe test a prototype. External finance is also important to expand an existing business.

However, entrepreneurs have generally difficulty securing bank loans. The reason is that banks want a positive track record and collateral –which new firms generally do not have.

The scarcity of seed capital is even more acute in transition economies. A study of the entrepreneurial activity in Croatia<sup>5</sup> found out that business opportunities were exploited mostly by high-income individuals. This finding suggests that potential entrepreneurs are credit constrained. The reasons are the high cost of credit, large collateral required by banks, or lack of credit available to the private sector. Data on those indicators have been used to construct an index of access to finance in each of the countries under analysis. For more details, please refer to Appendix 2.

The lack of development of the financial sector in many of the ECA countries is the consequence of a combination of factors: the lack of proper regulation of the banking sector, the lack of competition in some countries because the State still holds majority ownership in large banks, the low deposit rate, due to the mistrust of the banking sector, and the risk-aversion of banks before new or small businesses among others.

Although another possible source of finance is risk capital, venture capital funds are invested mainly in the expansion phase of high growth businesses, ignoring the initial stages of firms all together. Besides, the development of venture capital goes hand in hand with that of the stock markets, which are still underdeveloped in transition economies.

# **_Exit costs_**

Bankruptcy costs, and the more elusive stigma of failure, might have a large effect on the entry decision of would-be entrepreneurs. That is the reason why we have included the exit costs in the section dedicated to factors preventing firm entry.

The importance the of bankruptcy costs as an explanatory factor of entrepreneurial activity is based on the observed large differences in the perception and the cost of failure between Europe and the US. While around 50% of the Europeans surveyed in an empirical study affirmed that “one should not start a business if there is a risk it might fail”, only 25% of North-Americans thought the same.<sup>6</sup> Among the risks most

> 5 Slavica Singer et al. (2003): What makes Croatia a (non) entrepreneurial country? Global Entrepreneurship Monitor 2002.

> 6 Flash Eurobarometer 134 “Entrepreneurship”.  Done by EOS Gallup Europe upon request of the European Commission (DG XXIII), 2000, 2001 and 2002.

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Table 3: Robustness to sample selection

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
|Dep. Var.:Growth inper capita i|ncome(1996-2|011)||||
|Homeland (=1)|1.256**|0.660|0.841**|1.433***||
||[0.508]|[0.454]|[0.423]|[0.454]||
|Share Employed mining1996|-0.120|-0.447*|-0.497***|-0.356**||
||[0.243]|[0.258]|[0.190]|[0.154]||
|Homeland*Share Employed|0.604**|0.840***|0.952***|0.766***|0.363***|
|mining1996|[0.267]|[0.284]|[0.219]|[0.188]|[0.111]|
|Observations|7,338|9,583|12,003|15,854|10,340|
|R-squared|0.646|0.634|0.636|0.644|0.614|
|1996 Controls|Yes|Yes|Yes|Yes|Yes|
|Municipality FE|Yes|Yes|Yes|Yes|Yes|
|Sample|<15 km|<30 km|<60 km|All SA|onlyHL|

Notes: Data from the 1996 and 2011 population census at the community (sub-place) level. Each column corresponds to a separate OLS regression where the sample is restricted to communities within _X_ kilometers of the former homeland borders (on both sides), as indicated in the sample row. Column 5 restricts the sample to communities inside former homelands. The dependent variable is the difference in log per capita income for 2011-1996. All regressions include 1996 controls and municipality fixed effects. 1996 controls include: linear distance to former homeland border, share of white, colored and Indian/Asian population, share by age group (0-9, 10-19, 20-39, 40-59, 60+), household head education, share employed, share male, per capita income, total population, share connected to electricity grid, share no refuse, share with piped water. All control variables are interacted with the homeland indicator. Regressions are weighted by community population in 1996. Heteroscedasticity robust standard errors in brackets clustered at the municipality-homeland level. ***p<0.01, **p<0.05, *p<0.1
infection is expected to affect schooling levels because the disease stunts fetal development, impairs cognitive ability, and raises school absenteeism. The author found significant positive effects on adult earnings and literacy in all three economies, but years of schooling only rose significantly in Colombia.

In a similar study that focused on India, Cutler et al. (2010) compared outcomes across pre- and post-eradication-campaign cohorts in areas with varying pre-campaign prevalence. Their differences-in-differences estimates revealed no relative gains in either literacy levels or primary school completion in areas that experienced the largest reductions in malaria. Only when the authors distinguished between the most and least malarious areas did they find a positive effect of the campaign on primary completion and literacy. Lucas’s (2010) study of women born before and after a malaria eradication campaign in Sri Lanka and Paraguay found that women born after the malaria intervention gained more years of schooling and improved their literacy.<sup>3</sup> Other studies have examined how information regarding life expectancy affects human capital. For example, Oster, Shoulson and Dorsey (2013) used knowledge of Huntington’s Disease, a life-shortening genetic illness, to show that individuals who test positive for the disease were 30 percentage points less likely to complete college. There were no differences in high school completion since testing occurred later in life after high school completion. The estimated elasticity of demand for education with respect to life expectancy was around 1.0.<sup>4</sup>

> 3 Miguel and Kremer (2004) found that deworming drugs raised completed schooling primarily by reducing student absence rates.  Benefits also extended to the surrounding community because there was lower transmission of infections.  Unclear was how much of the increased schooling was due to the physiological effect of reducing helminth infections and how much was due to the Ben-Porath effect.

> 4 This study provides a clearer test of the Ben-Porath hypothesis than those studies that have used weather conditions (Maccini and Yang, 2009) or randomized nutrition programs (Maluccio et al., 2009) to measure changes in health status.  For example, Maccini and Yang (2009) found that the amount of rainfall <mark>around the time of birth was linked to the health, education, and socioeconomic outcomes of Indonesian adults born between 1953 and 1974. The effect could be due to better nutrition and health from improved harvests, but it could be also due to better incomes from greater agricultural productivity.</mark>

5
Panel A). We also find no difference in the probability of students being assigned to a target teacher as a function of their incoming test scores (based on comparing Y0 scores in Y2 and Y2 scores in Y3), and whether they are above or below the median asset ownership (Table A.7 – Panels B-E).<sup>28</sup> Table VII presents IV estimates of the impact of being taught by a certified teacher for the full sample of students, as well as for the sample of students taught by target teachers (which will give us more precise IV estimates, since the first stage is more powerful in this case). Focusing on students who were taught by target teachers, we can reject a positive effect greater than 0.07σ at the 95% level in the Y2 data. In the Y3 data, our preferred estimate is the one where the sample includes students who were taught by a target teacher in either Y2 or Y3, and we find that we can reject a positive effect greater than 0.1σ at the 95% level.<sup>29</sup> Finally, we examine heterogeneity of treatment effects as a function of several school-level characteristics, including the fraction of target teachers, the number of target teachers, mean student affluence, measures of school size, mean baseline test scores, and find no evidence of any heterogeneous effects (Table A.8). Thus, the increase in teacher pay in treated schools had no impact on student test scores, either in aggregate or in any subset of the data.

# **V. COST EFFECTIVENESS AND POLICY IMPLICATIONS**

Before discussing cost effectiveness, we note that teacher salary increases do not represent a social cost, because they are a transfer from taxpayers to teachers. The social cost of the program is the deadweight loss of raising tax revenue for the increased salaries, combined with the cost of implementing the certification program. However, developing countries typically face hard budget constraints because of a limited ability to run deficits, and so the cost of the policy may best be thought of as the opportunity cost of potentially higher-return public spending that was crowded out.<sup>30</sup> To simplify our analysis, we limit the use of this "opportunity cost" framework to other

> 28 Note that we test for differential assignment of students to target teachers as a function of the household asset index because we do not have baseline test scores for many of the cohorts in our final estimation sample. 29 We also show the ITT effects for each estimation sample in Table VII to enable a comparison between ITT and IV estimates. These are almost identical since outcomes are similar across students taught by target and non-target teachers (as seen in Table VI).

> 30 In principle, governments should be able to borrow to finance any project that has a higher rate of return than the cost of borrowing. In practice, financial markets find it difficult to evaluate the quality of public spending and impose a sovereign-risk interest-rate penalty when fiscal deficits exceed a threshold. Thus, in practice, choosing one form of public spending will reduce the fiscal space for other policies, which motivates our "opportunity cost" approach.

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make the two touch, because they are pointed in the wrong direction. To deal with cases like these, “bridges” were created as follows. All dangles were paired with the closest other dangle if it was within 100 meters using the Spatial Join tool, and connecting lines were created between these pairs of dangles. These bridges were added to the rest of the roads.

The AICD roads database was gathered with explicit reference to inter-city roads. Unfortunately, this means that in many cases, information on roads within cities was not collected, greatly reducing the connectivity of the dataset in many countries. “Spiders” were created to model missing city roads. For every dangle falling within a city, a (paved) road was created between the city centroid and the dangle. The implicit assumption is that radial road travel within cities is comparatively easy. The resulting spiders were added to the roads, and all spiderlegs and any roads that intersected them were “planarized”. Before planarizing, the topology of the network was such that a spiderleg could cross a road without being connected to it. Planarizing ended this.

Manual edits were necessary for several reasons. Recall that Extend did not close holes if a dangle was not pointed at another road, and that bridges were only created between two nearby dangles, not a dangle and a non-dangle. So extra segments of less than 10 meters each were created in 6 other places to fix dangles affecting routes to 16 cities. Recall also that spiders were created between a centroid and any dangles within a city. So once a spider is created, the spiderleg is the network location of the centroid, so if it connects to a dead end, other nearby roads cannot be reached even if they are very close. In these cases, deleting one or more spiderlegs fixed the problem. Five spiderlegs affecting 4 cities were deleted.

# **A.4 Route calculation**

To prepare for building the network, the roads were intersected with all land borders, so that the resulting border posts could be used as barriers—non-traversable points on the network. Coastlines were not treated as borders in this operation, because the only reason a road would cross a coastline is because of misalignment—the resulting route is most likely legitimate.

A network dataset was built using the roads dataset. The “Closest Facility” solver was used with the following settings. All light centroids were used as the “Incidents”, centroids of primate cities were used as “Facilities”, and the intersections of the roads and the land borders were used as “Barriers”. Each city was assigned a network location on the closest road within 5 kilometers of its centroid.

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