rafmacalaba/data-use-mentions-extended huggingface dataset:
the focus of the Mexican antitrust agency on absolute monopolistic practice cases, though does not indicate it should give up on relative monopolistic practices entirely. Though some have advocated for greater enforcement against relative monopolistic practices (Khan, 2017) actions in the United States have been rare since the 1970s (Areeda, Kaplow, Edlin and Hemphill, 2021). As a result, economics research on price discrimination and vertical control (e.g., Mortimer, 2007, 2008) does not observe them in the context of an enforcement action, as could become more common. To the best of our knowledge, this is the first paper to systematically compare the quantitative effects of antitrust enforcement actions across these different types of conduct.

Finally, our paper contributes to the literature focused on competition authorities themselves and their decision-making. This literature has focused primarily on merger control, and so we complement it by providing evidence on enforcement related to illegal conduct. Kwoka (2014) conducts a meta-analysis of all available merger retrospectives in the United States, concluding that enforcement has been too lenient and that more mergers should be blocked on the margin. Our result that the Commission has made Type II errors is similar in spirit, suggesting that there are gains from more sanctions on the margin. Affeldt, Duso and Sz ̈ucs (2021) use a comprehensive database of European merger reviews to document how the approach to merger control in Europe has changed over time. Similarly, our analysis of closed cases describes the characteristics of industries that are targeted for investigation by the Commission.

The paper unfolds as follows. Section 2 provides background on the Commission and the data. Section 3 describes our approach to estimating difference-in-differences outcome changes after each case, the triple difference approach to identifying the effects of sanctions, and three case examples. Section 4 reports the results, including the triple difference analysis of the effects of sanctions on sales, wages, employment, and profit margins, as well as the heterogeneity by cases of absolute and relative monopolistic practices. Section 5
20% of municipality-years have an average payment delay above 30 days, and many are above 45 or even 60 days. Overall, approximately 15% of the total amount paid in the procurement of goods and materials in recent years is made in more than 30 days.

In Figure 10, we present a map of the regions in Brazil included in our paper and color municipalities according to their average payment delay in 2018. Consistent with the distributions we plot above, we see that the majority of municipalities are paying their suppliers on average below 30 days, which are represented in dark and light green colors. Slow paying municipalities are often spread across the geography, but some clusters are clearly seen - the northeastern part of MG, for example, in the upper part of the "Southeast and South" region, is home to several municipalities that pay on average in more than 30 days.<sup>27</sup> This is also a region of lower-income municipalities, which suggests that perhaps payment delays are consistently correlated with local income – either because these are areas of lower state capacity or because local governments face budget constraints, for example.

We first document that, in that raw data, this relationship seems to be present: in Figure 11, we document a negative correlation between a municipality’s per capita GDP and their average payment delay – those with higher incomes pay their suppliers systematically faster. We then present in Table 5 a series of regressions documenting that this relationship is robust to other measures of payment timeliness. In all specifications, we control for the log of population and include state and year fixed-effects. In column (1), we show that municipalities with 1 log-point higher income (which is roughly moving from the 1st to 3rd quartile in the per capita GDP distribution) pay their suppliers on average 1.7 days faster - this is approximately an 8% increase in payment timeliness when compared to the sample mean of 21 payment delay. While the average payment delay is important, it
. They mainly consider the impact of dynamic travel supply and demand on transportation network vulnerability and resilience<sup>52</sup> .

# **Methods**

To understand the impact of pluvial and fluvial floods of varying intensities on road transportation networks worldwide under multiple climate scenarios, we develop a framework with three main components: network boundary delineation and network model creation, global flood mapping, and travel simulation (Fig. 1). First, we extract the boundaries of settlement clusters around the world leveraging the global human settlement mapping effort by the European Commission. These boundaries delineate all human settlements, from hamlets and villages, to towns, cities, and metropolitan areas. The size of these clusters 4
to use or not to use these systems. When the use of the Comprasnet system is voluntary we do not observe the full purchasing activity of a buying organization potentially biasing the sample.

The third step was to generate new aggregates used in our models. The main variables generated during the data preparation phase included measures of: (i) price; (ii) duration of the auction’s random phase; and (iii) competition characteristics. Several control variables reflecting market characteristics and trends were also included in the final data set.

Besides the typical challenges associated with the data cleaning process, the large volume of transactional data managed in Comprasnet also stretched the capacity of the server-based infrastructure<sup>2</sup> provided by the Brazilian Ministry of Planning, Budget and Management. Eventually, an upgrade of the R server from 2 GB to 112 GB of RAM significantly improved the computational capacity, allowing us to successfully complete all the tasks of the data preparation phase.

## B. Indicators used in the analysis

To test the four hypotheses mentioned above, we use the unit price (in reais) as our dependent variable, and the duration of the random phase (in seconds) as our main independent variable. To test the second, third

> 2 An R instance running on a remote server with 2 GB of RAM.

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# **1. INTRODUCTION**

Accurate measurement of business outcomes is critical for understanding the dynamics of firm growth, especially in emerging market economies where self-employment constitutes the majority of income generating activities.<sup>1</sup> Such reliable measurement is also important for assessing the impact of the myriad policies and programs aimed at improving entrepreneurial performance in these economies (World Bank 2013; de Mel, McKenzie, and Woodruff 2010). Further, multinationals looking to emerging markets for customer expansion must often rely on small firms as distribution channels, despite serious issues in measuring the suitability and growth prospects of these potential business partners.<sup>2</sup> Yet, the lack of record-keeping by small-scale entrepreneurs and dearth of administrative data in government agencies make the measurement of business outcomes extremely challenging in these contexts. Relevant data on small firms, such as sales and profits, are notoriously noisy – as evidenced by the high coefficient of variation<sup>3</sup> seen in cross-sectional performance data and the relatively low autocorrelation over time seen in panel data (Fafchamps et al. 2012). Consequently, impact evaluation studies involving small firms often do not use sales and profit outcomes due to poor data quality (Drexler, Fischer and Schoar 2014; Dupas and Robinson 2009); or do not bother to collect it (Valdivia 2012; Klinger and Schündeln 2011). Studies that do examine business outcomes are often unable to detect impacts due to high variance in sales and profit values and the resultant low statistical power (McKenzie and Woodruff 2017).

In addition, studies that attempt to calculate profits from detailed questions on sales and costs often show weak correlations between calculated profits (sale minus costs) and self-reported profits (Vijverberg 1992; Vijverberg and Mead 2000; Daniels 2001). This can be caused by unreported costs and sales, likely reflecting the fungibility of resources between the business and household (Samphantharak and Townsend 2012), or by mismatching of costs and sales due to the timing of
that, particularly self-employment, may be desirable. Being one's own boss is valued in the industrialized world, and there is no reason to think that developing country workers value their independence less. There is support in the extensive interviews ofMonterrey workers undertaken by Balan, et al. (1973), and increasingly elsewhere, for a "life cycle" pattern where workers enter into salaried work, accumulate knowledge, capital, and contacts, and then quit to open their own informal businesses. The low formal sector productivity and firm specific capital reduce the opportunity cost of such moves and should lead to an increased share of self-employment in total work.

# **VL Trade Reform and Sub-Contracting:**

In phase II, contract work is not obviously a disadvantaged sector. Figure 3 suggests that contract earnings follow the same increase relative to formal salaried wages shown by the selfemployed. Mobility between the formal and contract sectors is heightened during the upturn of phase II. It is possible that contracting exists partially as a Pareto improving way to avoid the inefficiencies in the labor legislation (or duplication ofbenefits coverage) discussed above, rather than to avoid the legislation per se, and that efficient workers may do better on a piece rate basis in good times than working in a factory."9 However, the data after 1991 may suggest a less sanguine interpretation of increased outsourcing to reduce labor costs in the face of increased global competition. Figure 1 reveals a secular decline in the contribution offormal salaried work, from over 38% to 35% of the interviewed 19 Roberts'(1989) Guadalajara interview **s** ugges **t** hat given the very weak unions and low wages, informalization is not primarily a strategy for reducing remuneration and worker control over production: 'Market uncertainty **and** the large number of income opportunities in the city mean that it is usefiu for _both_ employees and employers to **have** flexibility in allocating labor."(italics added, p. 48
# **Table 1: Baseline Summary Statistics and Balance by Treatment Assignment**

|||||||J|oint Orthogonality|
|---|---|---|---|---|---|---|---|
||Total|Control|Insourcing|Outsourcing|Training|Consulting|F-test|
|Last month sales in USD ($1 = 365N)|3265|3885|3591|3646|2954|2251|1.507|
||[242]|[645]|[641]|[567]|[461]|[321]||
|Sales in best month in USD|7581|9477|9050|6353|8207|4777|1.446|
||[725]|[1916]|[2122]|[1177]|[1734]|[676]||
|Sales in worst month in USD|1108|1264|1135|1315|867|963|1.135|
||[81]|[207]|[191]|[192]|[139]|[165]||
|Sales in average month in USD|2942|3817|2776|2798|2821|2503|0.977|
||[227]|[664]|[459]|[457]|[489]|[443]||
|Full-time employees|4.3|4.2|4.2|4.4|4.4|4.2|0.183|
||[0.097]|[0.206]|[0.198]|[0.228]|[0.254]|[0.196]||
|Baseline finance practices|16.6|16.9|16.5|16.3|16.4|16.
) reduction in utilization of curative care and (ii) reduction in utilization of preventive care. We consider the responses of households to the following two questions in the baseline data: _(i) “Have you had to reduce visits to the doctor between October 2008May 2009?” and (ii) “Have you had to reduce the utilization of preventive health services_

> 10 Note that private insurance holders make up less than 1 percent of the urban sample and have hence been excluded from the summary statistics in this section.

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# **Online Appendix**

## **A Data Appendix**

In this section, we list and describe the multiple data sources we use in this paper, as well as the necessary data cleaning and adjustment procedures we perform. Where relevant, we show descriptive statistics based on the data. We cover not only the data sources mentioned in Section III, but also other ancillary data sources.

### **A.A Trade Policy**

We obtained Sri Lanka’s Extraordinary Gazettes on Imports and Exports (Control Regulations) from March 2020 to October 2022, which allow us to identify the HS8 products that were subject to import bans.<sup>95</sup> We digitized the gazettes and manually inspected all entries to create a database with HS8 products and start and end dates of import bans. Appendix Figure A1 shows the timing of the fertilizer import bans, while Appendix Table A1 shows the list of HS8 product codes corresponding to fertilizers subject to the import ban.

Figure A1: Timing of Fertilizer Import Bans <!-- Start of picture text --> Jan/2017 Jan/2018 Jan/2019 Jan/2020 Jan/2021 Jan/2022<br>Month<br>.6<br>.4<br>.2<br>Fraction of fertilizer products banned<br>0<br><!-- End of picture text --> _Notes_ : For each month between January 2017 and September 2022, the chart shows the fraction of HS8 fertilizer products that were subject to an import ban in that month. The denominator of the fraction is 25, which is the number of fertilizer products that were imported by Sri Lanka at any point between 2017 and 2022. A fertilizer product is defined as any HS8 product that is listed in Chapter 31 of the HS system. The data comes from Sri Lanka’s Extraordinary Gazettes.

> 95The Extraordinary Gazettes were accessed at: http://www.imexport.gov
when programs are implemented (Devereux et al., 2017). While implementation flaws (including implementation lags) have been mentioned as caveats emerging from basic simulations (see for example Brown et al., 2018), other factors such as geographical targeting and the arbitrary choice of selection thresholds have been largely ignored. In contexts such as the Sahel, PMT or CBT methods are almost always applied after geographical targeting has been applied to focus the program on the neediest areas. Because of this, PMT targeting is implemented in places that significantly differ from those that are used to simulate their performance – usually based on national or regional samples. If geographical targeting selects areas that are poorer and more homogenous, then simulations will likely overstate the contributions that PMT targeting or other household-level selection methods can make. Besides, a series of implementation issues affect the actual targeting outcome. This is well-documented in the literature (Devereux et al., 2017; de Sardan & Piccoli, 2018; Stoeffler et al., 2020) and in the reports we reviewed (see Table A1). Finally, simulations often rely on selection thresholds that differ from practice. Given the significant role of selection thresholds in the performance of targeting, this is not trivial.

As an illustration, we compute targeting errors for selection thresholds set at 20% and 40% and provide a comparison with results from Brown et al. (2018) who use these thresholds in Burkina Faso, Mali and Niger (Table A3 and Table A4). As expected, the level of targeting errors is much larger in our data sets than in their study based on nationally representative data sets: our targeting error rates are approximatively 50% larger, based on the same selection thresholds in the same country. In sum, our results suggest that the role of geographical targeting and other implementation issues need to be carefully considered when conducting simulations, and that results obtained based on program data are largely different from those obtained from nationally representative surveys. For future studies based on simulations,
revenues collected within their borders, while poorer, transfer-dependent regions have been supportive of more centralized finances. Many of the heavily ethnic regions have supported more autonomy from Moscow, while those regions with heavy Russian populations identified more with the Russian state. The dominant political color of the regions has also played a role; those regions with a high level of communist party support have been traditionally more antagonist toward Moscow (Martinez-Vazquez, 2002). Because of these differences, Alm et al. (2006) stress that attitudes toward paying taxes may differ across these regions during the transition years. Subsequently, their data sets for 1995 and 1999 allow us to analyze the different levels of tax morale in 11 different territories of the Russian Federation. Their results indicate a decay in tax morale in the first four years of the transition from 1991 to 1995, and a small recovery in 1999 (see _Figure A2_ in the Appendix). Interestingly, Hanousek and Palda (2008) observe a similar pattern exploring tax evasion using survey data from 2000, 2002, 2004, and 2006 from the Czech Republic to measure its development for the years 1995 to 2006. Their results indicate that the number and percentage of evaders increased until the early millennium and started to decrease calling such an inverse-U shape ―an evasional Kuznets curve‖ (p. 3) stressing also that this might be an indication that hysteresis ―may not be a feature of evasion in a transition economy‖ (p. 3).

These results from Russia are consistent with the relevance of social norms in tax compliance. The widespread perception of tax evasion along with the economic convulsions revealed inadequate social institutions, and led to an initial crowding out of the intrinsic motivation to pay taxes from 1991 to 1995. These results also suggest restoration of higher trust levels in the state in 1999, after progress in the transition to a market economy had been made - a transition that positively influenced individual attitudes toward paying taxes. The analysis of disaggregated data for Russian regions also shows significant regional differences in
1988), or a binary variable identifying individuals living in a household whose per capita income lies below the national poverty or extreme poverty line. We estimate these models using OLS and probit models with robust standard errors,<sup>5</sup> respectively. _Tdt_<sup>+,</sup><sup>_T −_</sup> _dt_<sup>,</sup><sup>_P_+</sup> _dt_<sup>and</sup><sup>_P_</sup> _dt_<sup>_−_represent</sup> measures of temperature and precipitation shocks in district _d_ at time period _t_ , measured as positive and negative anomalies as explained in 4.1, _Wd_<sup>_′_isavectorofweatherandclimate</sup> variables, namely the historical average temperature and precipitation in district _d_ , and the average temperature and precipitation in the district during time period _t_ ,<sup>6</sup> _Xidt_<sup>_′_isavectorof</sup> household demographic variables, _ηdt_ are department-year dummies,<sup>7</sup> _θm_ are interview-month dummies, and _εidt_ is the error term. In all models, we use individual-level survey weights to obtain population representativeness. After estimating the models, we calculate marginal effects which allow us to obtain estimates of the changes in the expected value of the dependent variable associated with a one-unit change in the independent variable of interest, in this case temperature and precipitation anomalies, while holding other variables constant. To account for differences in incomes between urban and rural areas, and because we expect the impact of weather shocks to be structurally different given the different economic structure of production in rural vs. urban areas, we split the models by area of residence. Since districts are the lowest level of geographical aggregation in the EPH data, our results will capture the effects of an average weather shocks on income or poverty at the district level. In other words, there may
random effects. Model 3.1 employs non-standar<br>expenditures, household size and age in both surveys are|omplex survey design<br>f the error terms and<br>dized variables. Mode<br>transformed to norma|. All estimates ar<br>Method 2 uses th<br>l 3.2 employs<br>lity using the Bo|1<br>(1<br>e obtained with<br>e empirical dist<br>variables in TA<br>x-Cox method b|7.7<br>.9)<br>the population we<br>ribution of the err<br>2 standardized b<br>efore standardiza|ights. Method<br>or terms. Both<br>y those in NP<br>tion. The impu|1 uses the normal<br>methods employ<br>S 2019/20. Food<br>ted poverty rates|

**Note** : The standard errors in parentheses are adjusted for complex survey design. All estimates are obtained with the population weights. Method 1 uses the normal linear regression model with the theoretical distribution of the error terms and Method 2 uses the empirical distribution of the error terms. Both methods employ cluster random effects. Model 3.1 employs non-standardized variables. Model 3.2 employs variables in TA 2 standardized by those in NPS 2019/20. Food expenditures, household size and age in both surveys are transformed to normality using the Box-Cox method before standardization. The imputed poverty rates for TA 2 use the estimated parameters based on the 2019/20 data. 1000 simulations are implemented. The true poverty rate for TA 1 is the estimate directly obtained using the consumption data in TA1 and the poverty line in NPS 2019/20 deflated to 2022 prices with the annual WB deflator. The estimates shown in bold or with an “a” respectively fall within the 95% CI or one standard error of the
**Figure 3. Cognitive, Emotional, and Social Development Outcomes over Time**

<!-- Start of picture text --> 100<br>90<br>90<br>80<br>70<br>58<br>60<br>51<br>48<br>50 2006/7<br>40 34 2011<br>30<br>2012<br>20<br>20<br>10<br>0<br>ECCE Development Violent Work &<br>Activities Discipline Domestic<br>Work<br>Percentage<br><!-- End of picture text --> _Source:_ Authors’ calculations based on MICS 2006/7, ENPSF 2011, and ONDH panel 2012.

- _4.2 Risk Factors for Poor ECD over Time_

# 4.2.1 Health and Survival

A number of different risk factors contribute to children’s chances for poor ECD in health and nutrition. There has not been a systematic pattern in terms of rising or falling inequalities by children’s different circumstances over time in health and survival. Inequality has been rising, falling, and remaining steady across different dimensions of health. Table 1 (in annex 1) shows health outcomes over time and their relationship with different circumstances.

With regard to prenatal and delivery care, there have been substantial disparities depending on the families’ education and wealth and where the children live. Disparities have, however, been narrowing over time as health care coverage has risen. Figure 4 shows narrowing disparities in prenatal care by mother’s education over time. While children of uneducated mothers have the lowest chances of prenatal care and children of secondary and higher educated mothers have always had chances in the 90–100 percent range, these gaps have been narrowing. While in 2003/04, 56 percent of births to uneducated mothers had prenatal care, this rose to 74 percent by 2012.

11