rafmacalaba/data-use-mentions-extended huggingface dataset:
effect of the information sheet on business sales or profits. We also accessed administrative follow-up data from the Brazilian Ministry of Labor and Employment and the Federal Tax Authority to investigate impacts on number of formal employees and business exit. We find no effects on these two outcomes either.

Finally, the follow-up survey asked firms who did not use SEBRAE services after the intervention for their reasons. Most firms in both the treatment and control group reported lack of time as the main reason for not using these services, but treatment firms were statistically significantly less likely to report that they were happy with their business’ performance (16 percent in the treatment group vs. 29 percent in the control group).

Our paper is related to recent randomized experiments that examine whether simply telling firms about the existence of best business practices (Dalton et al 2019) or benchmarking their performance helps them to improve by themselves (Grohmann, Menkoff, and Seitz 2022; Hou and Png 2022; Seither 2021). Although the overall evidence in these papers is mixed, the findings suggest that providing information can help improve firm performance at least in some settings, or when it is offered together with training. However, these papers don’t help explain why firms do not take-up more business assistance that already exists in the market. We test whether providing information on firms’ adoption of business practices increases their use of business services in the market, an intervention that is more scalable than also implementing training.

In a related study, Anderson and McKenzie (2021) gave small firms in Nigeria information on the market for professional business service providers and provider’s quality, without including information on firms’ business practices or past performance. Distributing this information on providers had no significant impact on the use of business services.

Our paper contributes to the literature by showing that lack of information on business practices is a reason why not more firms take up business services that exist in the market. When firms received information on how their business practices compared to other firms and
by Seeking Alpha analysts. We also do not consider announcements about U.S. oil inventories because oil inventories could reflect both aggregate demand factors and current or future oil supplies.

To ensure that the event days are primarily oil-specific news or events about current and future oil supplies, we also use U.S. news coverage as a check. We use Dow Jones’s FACTIVA news database to count how many U.S. newspaper articles have the words “ _economy_ ” or “ _economic growth_ .” The number of articles represents how intensively news about the economy, or “aggregate demand news,” is covered. The assumption is that the higher the count in a day, the more important aggregate demand news is for that day. We collect article counts for all the days from 1/1/2020 to 6/30/2020. Then, we check econometrically if the log of article count for the event days is higher than that for the non-event days. We find they are not significantly different.

Second, OPEC announcements could reflect worries about global aggregate demand by OPEC. For example, an announcement that OPEC countries will be meeting to cut production could reflect their concerns that aggregate demand is low now or will be low in the future. Should this announcement be treated as an event about a possible current or future oil supply cut or aggregate demand decline? The reaction of oil prices in the market can help us answer this question. An aggregate demand decline shifts the demand curve for oil to the left, reducing its price. A cut in oil production shifts the supply curve for oil to the left, raising its price. We thus verify that oil prices increased following an OPEC announcement to cut production because investors perceive this news as a sign of possible cuts in current or future oil supply.

# **III. Results**

In this section, we present the main results from our estimation of the effect of oil prices on the returns of U.S. oil stocks.
sup> For test scores which are drawn from MLSS, our sample is all public primary schools in the eight disadvantaged districts and the 17 comparison districts which are also part of the MLSS sample and for which baseline and endline data is available (a total of 444 schools). The MLSS sample is constructed using stratified probability proportional to size (PPS) sampling was used, with strata defined based on the six educational divisions. From each stratum, a random sample of schools was selected using PPS, using the number of schools in each stratum as measure of size. The PPS process generated a recommended sample size of 700 schools, 571 within the 25 study districts. From these

> 16 MESIP-Extended extended the MESIP interventions focused in the eight disadvantaged districts to four more districts which were similarly disadvantaged: Dowa, Mulanje, Nkhotakota, and Rumphi. MESIP-Extended did not extend the RBF incentives to new districts.

> 17 There were 1,856 schools in the eight disadvantaged districts and 2,572 schools in the 17 comparison districts. We include schools which are present in both EMIS 2016 and EMIS 2020 data.

15
poorly performing districts in Pakistan to Executive District Officers and Dal Bó et al. (2018) who provide evidence that the superior local information of middle managers improves the targeting of monitoring interventions aimed at agricultural extension workers in Paraguay.

At the same time, the scale of errors made by officials does not systematically predict the service delivery indicators we have information on. This finding is consistent with Dhaliwal and Hanna (2017) in which detailed information on the absence rate of doctors provided to local officials in India has limited effects on service delivery. Together, these results highlight the need for more research on how the information of public officials and its equilibrium distribution in government relate to the delivery of public goods.

> The rest of this paper proceeds as follows. Section 2 details the approach we took to measuring information in the public sector. Section 3 presents detailed descriptives of the determinants of information

> 5The results echo those of Olken (2007) in which top-down government monitoring reduces corruption substantially more than grass-roots monitoring, Muralidharan and Sundararaman (2011) where effectively designed incentive schemes improved student learning outcomes far more than unconditional grants, and Pomeranz (2015) in which pre-existing incentives for tax compliance substitute for government audits.

> 6This paper also contributes to the literature on public sector decentralization. We provide direct empirical evidence that decentralized organizations have superior information, which is often assumed in the literature (Oates, 1999, 2005; Bardhan and Mookherjee, 2006). Our evidence implies that within layers of an organization, information is relatively homogeneous, while across organizational tiers it is relatively asymmetric. Our finding that the impact of decentralization on information is mediated by management practices provides one potential explanation for the divergent findings of the decentralization literature on service delivery (Bardhan, 2002; Crawford and Hartmann, 2008).

6
<br>60.7%<br>75.0%<br>Direct Indirect Re-imported Direct Indirect Re-imported<br>Automotive 2011 BRA Chemicals 2011 BRA<br>0.2% 0.1%<br>24.3%<br>32.4%<br>67.5%<br>75.5%<br>Direct Indirect Re-imported Direct Indirect Re-imported<br><!-- End of picture text --> Source: Authors’ calculations using OECD/WTO TiVA.

The direct shares in other countries continue to be higher than in Brazil across the GVC sectors. In short, for Brazil the indirect supplying industries play a relative larger role in generating 8
|This|user guide is divided intothreemain sections:|
|---|---|
|1.|Inputting project data into INFRISK|
|II.|Using the dialog boxes to specify desired setings for the simulation|
|III.|Customizing and understanding the simulation output|

# 1. **Inputting Project Data into** **_INFRISK_**

In _INFRISK,_ the project data for the simulation is inputted both through an input sheet and the _INFRISK_ dialog boxes. The input sheet contains fundamental project data for each year of the project. The data on this sheet is divided into two sections: one for the construction period and one for the operational period.

The program offers a large degree of flexibility when the user is creating her own input sheet. Different types of inputs can be placed anywhere within the appropriate section (construction or operation) and in the order of the user's choice. However, the two sections must be separated, and each of them starts with the _heading line_ that labels the subsequent columns with the year identifiers (i.e., 1992, 1993, etc.). For the _Construction Phase_ table, the heading line must contain _YRCON_ in the second column (as shown on the example sheet below). Correspondingly, for the _Operation Phase_ table, the heading line must contain _YROPER_ in the second column. To identify the data, the user must include the appropriate label (for example, "OR" for operating revenues) in the second column as in the example below. The label in the first column is for descriptive purposes only and may be customized as desired by the user. Annex 1 provides a typology of flow and stock variables familiar to _INFRISK._ Figure 7: _INFRISK_ Input Sheet <!-- Start of picture text --> FinancongCoslsdoringC .. n.t-o-o IK USD 0 .50.6 98.e88<br>C.pilaIConsW1lIoeCos- .CK USO 109.8825 105.6825 1096825
interviewers, with fieldwork spread over 12 months to balance each module over both time and space. Each interviewer implemented all eight modules in equal proportion in order not to confound module effects with interviewer effects. To prevent potentially uncontrollable crossmodule spillovers within households, each household faced just a single survey design.<sup>3</sup> Since the ‘gold standard’ measure of consumption is not available for each household in the experiment, we use two indirect methods to study the nature of measurement errors in these data.<sup>4</sup> First, we average the logarithm of total household consumption, for each sampling unit (hereafter ‘village’) and each survey design. To the extent that measurement errors in consumption are random, averaging should reduce bias, with estimates converging to the true village average. We find that errors are not random. If the error is calculated by subtracting the benchmark average from the average calculated with each of the other seven survey designs, the error is found to be negatively correlated with the benchmark (true) village average. Exactly the same pattern, found with earnings data, is referred to by Bound and Krueger (1991) as _meanreverting measurement error_ .

Since economists rarely work with village averages, our second exercise for assessing the nature and consequences of the measurement error is to estimate a widely used regression on household consumption data – a food Engel curve. We treat the estimates from the intensively supervised sample given the individually-kept diaries as being closest to what true consumption would reveal. We then compare with coefficients estimated using the data from the other designs, and with patterns of bias expected from simulated random and non-random measurement errors. The food Engel curve is useful because of extant results on expected biases from different types of measurement error that are motivated by an Engel curve puzzle raised by Deaton and Paxson (1998).<sup>5</sup> This exercise also shows that the most plausible model of

- 3 Most previous attempts to compare different consumption survey
_concave but subject to scale effects_ . In contrast, the quality of the contractual environment (creditor and property rights) is _convex but not subject to scale effects_ .

This suggests that informational frictions are of a mostly technological nature. Because of fixed costs, they are easier to implement in larger countries. However they are solvable with adequate investments and ready-made imports from abroad. Moreover, once the required investments are in place, there are decreasing returns to further informational improvements as developmental levels rise. In contrast, contractual frictions cannot be solved by technological investments or imports. They are mostly institutional and reflect collective action frictions that

> 27 Source: Depth of Credit Information Index (Doing Business Indicators, World Bank)

> 28 Source: we merge Number of Procedures, Time to Resolution, and Costs as percent of Claim into a single indicator using the first principal component (all three indices are from Doing Business Indicators, World Bank).

> 29 Source: Strength of Legal Rights, Doing Business Indicators (World Bank).

> 30 Source: Property Rights, Heritage Foundation.

> 31 We depart from most of the crisis literature by studying multiple _annual_ credit crashes instead of focusing on a “before and after analysis” based on financial crisis _periods_ . All three crash variables are defined as the percentage of years a country experienced a given type of crash. We average all data for 2005-07 and add each crash variable separately to the regressions of private credit to GDP.

13
**Table 5. Shared prosperity numbers for three simulation scenarios (negative values) based on EUSILC data**

||||shar|edprosperityind<br>|icator,%<br>||∆<br>|
|---|---|---|---|---|---|---|---|
|countrycode|base|end|negative<br>numbers<br>(1)|zeroes replace<br>negative<br>numbers(2)|excluding<br>negative<br>numbers(3)|change in %,(<br>(3)/(1)‐<br>1))*100|Absolute<br>difference in<br>pp, (3)‐(1)|
|Austria|2006|2011|0.71|0.71|0.71|‐1|‐0.01|
|Belgium|2006|2011|0.20|0.29|0.33|59|0.12|
|Bulgaria|2007|2011|1.40|1.39|1.39|0|0.00|
|Cyprus|2006|2011|‐1.14|‐1.14|‐1.14|0|0.00|
|Czech Republic|2006|2011|1.85|1.85|1.85|0|0.01|
|Denmark|2006|2011|‐0.57|‐0.25|0.13|‐123|0.70|
|Estonia|2006|2011|1.40|1.43|1.51|8|0.11|
|Finland|2006|2011|1.97|1.97|1.98|0|0.00|
|France|2006|2011
in reality one needs to consider the</sup> whole operation of the bank. Ex-post spreads,<sup>the difference between banks' actual interest</sup> revenues and interest expenses, on the other<sup>hand, allows us to analyze the determinants of</sup> interest margins in a more general equilibrium<sup>setting and to quantify the net effects of a broad</sup> set of specific distortions imposed on banks,<sup>depositors and borrowers.</sup> Using bank-level data for 80 countries over<sup>the 1988-95 period, Demirgu -Kunt and Huizinga</sup> (1999) identify the importance of a number of factors<sup>determining ex-post interest margins and</sup> the incidence of intermediation costs imposed<sup>on banks. Their empirical analysis controls for</sup> country-specific and time specific effects and<sup>for the variation in the number of banks across</sup> countries. Although cross-country analysis does<sup>not allow us to include the richness of</sup> institutional details, such as tax codes, it enables<sup>us to compare the determinants of interest</sup> margins in Brazil relative to other countries.<sup>Applying their findings to Brazil, the following</sup> main factors can explain the high interest<sup>margins in Brazil:</sup>

- _Overhead costs._ In general, about one sixth of a bank's<sup>overhead is at the margin passed onto</sup> its customers. Since Brazilian banks' overhead costs<sup>(10 percentage points of assets over</sup> 1988-95) were almost double Latin American average,<sup>and triple upper middle income group</sup>

8
>.1<br>Social Sustainability Index<br>0<br>1 .05<br>.8 0<br>.6<br>-.05<br>CCDF<br>.4 Difference<br>.2 -.1<br>0 -.15<br>.4<br>.3<br>.2<br>.1<br>Social Sustainability Index<br>0<br><!-- End of picture text --> _C12. Change in the indicators included for each dimension, one indicator removed at a time. Baseline scenario: All indicators included. Alternative scenario: Victim of crime indicator removed (weights within the Social Cohesion dimension were scaled from 6.25% to 8.33% so all weights add up to 1)._ South Africa C12.1. FOSD: Counting Vector C12.2. Dominance Analysis 61
Demand charges are widespread for non-residential customers, but are almost twice as frequent for industrial customers as for commercial customers (figure 4b). This suggests that most countries find it important to reflect load considerations in designing tariffs for nonresidential customers (table 3). Peak demand is a critical cost driver in the power sector, because it defines the amount of installed capacity needed to provide a given volume of electricity.

<!-- Start of picture text --> Figure 4. Prevalence of specific tariff schemes: Commercial and industrial users<br>a. Tariff type b. Fixed charges<br>20 20<br>15 15<br>10 10<br>5 5<br>0 0<br>FR TOU IBT DBT None Only fixed Only Demand<br>demand and fixed<br>commercial industrial commercial industrial<br>Source: Africa Infrastructure Country Diagnostic Power Tariff Database<br>Figure 5. Characterization of nonresidential block pricing<br>a. Numbers of pricing blocks b. Nonlinear tariff price differential between first and last<br>block (%)<br>20 6<br>5<br>15<br>4<br>10 3<br>2<br>5<br>1<br>0 0<br>One Two Three 4 or more negative no change 0-100% greater<br>than 100%<br>Commercial Industrial Commercial Industrial<br>number of countries number of countries<br>number of countries number of countries<br><!-- End of picture text --> _Source:_ Africa Infrastructure Country Diagnostic Power Tariff Database Volume charges for nonresidential customers are typically linear for at least 15 of the countries in the sample. In the cases where block tariff structures are used, they are more frequently decreasing blocks (DBT) rather than increasing blocks (IBT) and these are relatively simple with not more than three blocks (figure 5b). The reason
disaggregated micro-survey data often suffers from sample selection bias because electricity expenditures are only selectively observed for those households choosing to report.

Indeed, even though Turkey has near universal access to electricity, 4,051 households (27.7%) in the dataset did not report electricity expenditure. Furthermore, poorer households were more likely to have non-positive electricity expenditures (Table 2), suggesting that the sample selection is not random. Suppose that households are endowed with observable and unobservable characteristics, sample selection bias would arise if unobservable characteristics affecting reporting or non-reporting decisions are correlated with unobservable characteristics affecting consumption behavior. On the other hand, if non-reporting behavior and electricity consumption are correlated purely through the observables, we can control for this by including the appropriate observable variables in the demand equation.

I use the Heckman selection model to test for the selection bias. For the first stage regression, one needs variables that affect the chances of not reporting electricity expenditures but not the level of electricity consumption (variables that are not in the consumption equation). The selection variables used are the education level of the household head, whether the household head had a permanent job during the survey month, and whether the household head had health insurance. Education of the household head is putatively not included in the electricity demand estimation, but may plausibly affect household response to survey questions. Similarly, the job status and the possession of health insurance of the household head may not directly affect electricity consumption, but may affect the reporting decision.<sup>11</sup> In the results section, I present estimates of Equation (1) using an ordinary least square (OLS) model where I limit the observations to households who reported electricity consumption. I also present estimates from the selection model. I found the two models yield nearly identical results. Furthermore, both Wald and likelihood ratio tests<sup>12</sup> suggest that the unobserved variables influencing the reporting decision are statistically independent of unobserved factors of the electricity demand. Therefore,
58|-1.11|0.44|0.53|0.11|
|Teachers Elasticity|0.20|0.49|0.04|0.08|1.19|

_Source:_ Own calculations based on ENIGH 96 As can be seen on table 11, the probability of enrollment in upper secondary is much higher for both the top 40% of the income distribution and in urban areas as compared to those in the bottom 40% and in rural areas. The variable teachers (government effort) has a significant marginal impact which is many times larger for the 'Poor' as compared to for the 'Wealthy' and for rural areas as compared to urban areas. In elasticity terms, the teacher's variable is more effective for the poor and for the rural areas by factors of 12 and 15, respectively. The differential impact suggests that the goal of efficiency in terms of maximizing enrollments in upper secondary school does not have a trade-off with the goals of greater equity of educational opportunity. Indeed, the above findings indicate that increases in enrollment will be more readily obtained if resources are successfully targeted towards the poorer income group. It is of interest to note the negligible impact of educational transfers, which could probably be explained by the null variance of educational transfers among states. Investigation in this phenomenon would play an important note in policy decisions regarding the allocation of resources in upper secondary.

# **_5 CONCLUSIONS_**

Preliminary evidence suggests that the burden of educational expenditures on poor households is high. This finding suggest that actions aimed at increasing the participation of poor children should comprise subsidies for secondary textbooks, scholarships for transports and schools materials to reduce the burden on other schooling costs (i.e., unforeseen expenditures).

At primary level, non-poor students in public school spend four times the amount that extreme poor students spend in education. While at the university level non-poor individuals spend 1.4 times as much as poor students. The public subsidy
(3.88)|(0.90)|(1.32)|(0.94)|(2.92)|
|H1N1 versus||-0.03||||-0.03|||
|students per teacher||(0.06)||||(0.05)|||
|H1N1 versus|||-2.08**||||-1.74**||
|teacher absenteeism|||(0.94)||||(0.80)||
|H1N1 versus teachers that||||0.14***|||||
|correct Math homework||||(0.04)|||||
|H1N1 versus teachers that||||||||0.05|
|correct Portuguese homework||||||||(0.03)|
|N. schools|5329|5329|5295|5329|5329|5329|5295|5329|
|Adj. R2|0.7|0.7|0.7|0.7|0.6|0.6|0.6|0.6|

_Notes_ : The authors’ estimate is based on data from _Prova Brasil_ , Census of Education, and IBGE. Wild bootstrap p-values. 95% CI in brackets. ***, **, and * indicate significance at the 1, 5, and 10% critical levels. Standard error in parenthesis and clustered at the municipality level. All regressions are weighted by fifth-grade enrollment at the school level. Math performance on a scale from 0 to 350 (SAEB scale). reading performance on a scale from 0 to 325 (SAEB scale). Columns DiD show the estimates of differences-in-differences as described in subsection 4.0.1 and Columns Triple D show the estimates for triple difference-in-differences as described in