rafmacalaba/data-use-mentions-extended huggingface dataset:
Using this new counterfactual in a triple difference, we estimate a positive effect of antitrust sanctions on industry sales and a negative effect on the operating profit margin. In industries with closed investigations, we find that sales are declining and the margin is rising relative to other industries in the same sector, so our estimates using the new counterfactual are larger than estimates using the prior approach, which assumes counterfactual trends are the same as industries in the same sector.<sup>1</sup> In addition to this novel identification approach, our paper measures industry performance using an economic census, which in Mexico enumerates every firm in the economy with a fixed location, including those that are small, informal, and privately held. These data enable us to quantify the aggregate effects of antitrust sanctions, and also illustrate the mechanisms behind these effects, informing current debates about how antitrust may or may not address an aggregate productivity slowdown in the United States and Europe that has coincided with a rise in markups. We estimate the effects of sanctions on industry total factor productivity (TFP), employing recent insights from Ackerberg, Caves and Frazer (2015) and De Loecker and Syverson (2021) to flexibly allow for different factor shares across industries, potential decreasing or increasing returns to scale, and imperfect competition. After antitrust sanctions, productivity increases for all firms in an industry. Aggregating this effect and accounting for the small share of output the Commission has resources to investigate each year, we attribute roughly half a percent (or 1/200) of the steady state per capita gross domestic product growth to antitrust sanctions.

The mechanisms behind this effect are very interesting. De Loecker et al. (2020) document that two-thirds of the growth in markups in the United States is due to the reallocation of sales towards high markup firms. If lax antitrust enforcement is responsible for rising markups in the United States, one might expect that antitrust enforcement would cause a reallocation of sales across firms. In Mexico, this is not what we
A final measure of institutional quality is the Government effectiveness index, which captures perceptions of the quality of public services, the quality of the civil service and the degree of its independence from political pressures in 1996. Higher values of this variable indicate greater government effectiveness. Government effectiveness is important for private sector efficiency. Hence, this variable is an indication of how efficiently private credit transactions can be completed. Greater government effectiveness is expected to reduce the costs and risks associated with especially short-term credit, as short-term financing implies a sequence of refinancing transactions. The Government effectiveness index is available from the World Governance Indicators database of the World Bank.

Table 2 provides the correlations among the various variables. We see that two volatility measures are correlated negatively and significantly with Maturity, potentially because long-term finance reduces liquidity risk. Conversely, the volatility variables are correlated positively and significantly with the DEF variable, which could reflect moral hazard created by external finance regarding firms’ risk choices as well as liquidity risks. The correlation between DEF and Maturity is negative and significant at -0.158, perhaps reflecting some trade-off between the total availability of external finance and its maturity. The correlations between the firm volatility and financial development variables, except the bonds variable, are negative and significant. The tendency for firm volatility and financial development variables to be negatively correlated could reflect that external finance can prosper in less risky environments, or alternatively that the provision of external finance enhances risk monitoring which reduces firm volatility.

Figure 1 plots the average ratio of long-term debt to total assets over the 1995-2013 period separately for firms located in developing and in high-income countries according to World Bank classification in 1995. Long-term debt use in developing countries has generally been lower than in high-income countries, possibly reflecting greater macroeconomic 12
and electricity use and monthly bills from utilities’ customer billing records with socioeconomic data collected through a household survey.

Using a sample of households with both private electricity and water connections for the exclusive use of household members in Addis Ababa, we find that both the electricity and the water IBT subsidies disproportionately target richer households. When these poor targeting outcomes are considered jointly, the regressive results are striking. Our sample’s 366 households with private water and electricity connections were substantially richer than the general population of Addis Ababa. Yet even within this middle- and upper-income sample, the cumulative electricity and water subsidies were disproportionately received by the richer households. Households in the wealthiest quintile received an average combined subsidy that was more than twice as large as households in the lowest wealth quintile ($38 vs. $16 per month).

The water IBT’s targeting of subsidies was somewhat worse than that of the electricity IBT. One reason could be that the water consumption of most households was within the first (lifeline) block of the IBT; in contrast, the electricity consumption of households was more evenly spread throughout the seven blocks of that utility’s IBT. This difference might reflect the greater ease with which people can visualize the extent of their water consumption (which is measured in cubic meters) than that of their electricity consumption (measured in kilowatt-hours).

However, we find no difference between water use and our wealth indicator (+0.48, p value=0.00) and between electricity use and our wealth indicator (+0.48, p value=0.000).<sup>22</sup> Figure 7 and Figure 8 illustrate the distribution, by wealth quintile, of (respectively) electricity and water use among our sample of households with private connections. These figures confirm the relatively low correlation between resource use and wealth.

> 22 When the correlations are measured not with respect to our wealth quintiles but instead to self-reported income, we obtain values of 0.
Taxes by activity explicitly considered are the following:

- Corporate income tax IRES (“Imposta sul reddito delle società”) and the regional production tax IRAP (“Imposta regionale sulle attività produttive”) are added together and disaggregated by activity using tax return data available at DF (the Department of Finance). The implicit tax rates are calculated using the operating surplus as a tax base and will increase the cost of capital used by each firm.

- Social Security contributions paid by the employer (SSCs Employer) are retrieved from the use table, defined as the difference between employee income of regular workers and gross wages by sector of economic activity; subsequently, this value is classified according to the type of skill category: low skilled, skilled, and high skilled.

- Other taxes on production are published by Istat by sector. As IRAP is considered with IRES as a separate entry in the SAM, its amount is subtracted to obtain the other taxes on production as the residual value.

Finally, households’ direct taxes are included in the model and treated as a single entry that combines income taxes (imposta sui redditi delle persone fisiche (IRPEF)) and IRPEF surcharges, substitutive taxes (forfeit mixed, rents, capital income taxes etc.) and other special regimes and social security contribution paid by the employee.

# **2.2 The IRENCGE-DF Model**

## **_Model Overview_**

The Italian Regional and Environmental Computable General Equilibrium of Department of Finance (IRENCGE-DF) Model is a (recursive) dynamic single country computable general equilibrium (CGE) model that is developed by the Italian Ministry of the Economy with technical assistance from the World Bank. It is based on the MANAGE-WB model of the World Bank (World Bank, 2023), which is in turn based on the MANAGE model that is documented in van der Mensbrugghe (2021).

IRENCGE-DF is designed to focus on energy, emissions and climate change. In addition to the standard features of a single country CGE model, the
To vary the detail of the questionnaire, we developed a short and a detailed labor module focusing on differences in screening questions used to determine economic activity and labor force participation. The detailed questionnaire reflects the approach that is generally considered to be best practice and is typically used in multipurpose household surveys, such as the Living Standard Measurement Surveys (LSMS). Increased demands from policy makers to evaluate changes over time, requires frequent data collection that is also simple to implement. Many countries therefore collect data on an annual basis using short questionnaires. The short module used in our survey experiment reflects the approach followed by more concise surveys used in many low‐income countries, such as the Core Welfare Indicator Questionnaire (CWIQ) and the Welfare Monitoring Surveys (WMS), as well as other surveys listed in Table 1.

Specifically, the detailed module contains three questions at the start to determine employment status, namely, (i) whether the person has worked for someone outside the household (as an employee), (ii) whether s/he has worked on the household farm, and (iii) whether s/he has worked in a non‐farm household enterprise. In each case the response is either yes or no.<sup>17</sup> In the short module there was only one question to determine employment status, namely whether s/he did any type of work, which also invited a response of yes or no. In both cases the questions were asked with respect to the last 7 days (the reference period for identifying those who are “employed” and the set of detailed questions on that employment) and, if not reported to work in the last 7 days, then asked for the last 12 months. Those identified as working in the last 7 days in either module were then asked identically the same questions to gather information on their occupation, sector, employer, hours, and wage payments in their main job. The short and detailed employment modules are reported in
# **5 Results**

In this section, we first present results from individual-level regression analysis concerning the relationship between individuals‟ cooperativeness and the short and the long routes to accountability, and then turn to district-level analysis.

# **5.1 Individual-level analysis**

_The short route to accountability_ We estimate individual-level probit regressions where the dependent variable is a dummy equal to 1 if the parent participated in the latest elections of parent class representatives, and zero otherwise. Table 5 reports marginal effects for continuous explanatory variables and the effect of a change from 0 to 1 for dichotomous explanatory variables. In all regressions, we employ district fixed effects and we control for the four geographical areas identifying different economic realities within the Albanian territory. Finally, in all regressions we cluster the standard errors at the school level. The district dummies and the geographical area dummies are particularly important given the significant differences in behavior in the public goods game across districts identified in the previous section.

In column 1 of Table 5 we do not include any controls beside the geographical area dummies. In column 2 we control for standard demographics, the urban or rural location of the surveyed school, and the distance of the parent‟s house from the school. In column 3 we add survey-based measures of social capital. Finally, in column 4 we add measures of parents‟ involvement with their children‟s education.

The estimates in all columns show a strong and robust correlation between parents‟ decisions to invest in the group account in the public goods game and their participation in the elections of parent class representatives. Among the other determinants of participation, Table 5 shows that both years of schooling and household wealth increase the likelihood that a parent participates in the elections, whereas the rural location of the school lowers such likelihood. With respect to the survey-based measures of social capital, it seems that, as expected, parents who participated in community activities and parents that are better connected with other parents are more likely to participate in the elections. In
# References

Afridi, F., Mukhopadhyay, A. & Sahoo, S. Female labor force participation and child education in India: evidence from the National Rural Employment Guarantee Scheme. IZA J Labor Develop 5, 7 (2016). https://doi.org/10.1186/s40175-016-0053-y Allen, Joseph P. 2008. “The Attachment System in Adolescence.” In Handbook of Attachment: Theory, Research, and Clinical Applications, 2nd Ed, 419–35. New York, NY, US: The Guilford Press. Alto, Michelle E., and Christie L. M. Petrenko. 2017. “Fostering Secure Attachment in Low- and MiddleIncome Countries: Suggestions for Evidence-Based Interventions.” Evaluation and Program Planning 60 (February): 151–65. https://doi.org/10.1016/j.evalprogplan.2016.11.003.

Baker, Michael, Jonathan Gruber, and Kevin Milligan. “Universal Child Care, Maternal Labor Supply, and Family Well‐Being.” Journal of Political Economy 116, no. 4 (August 2008): 709–45. <u>https://doi.org/10.1086/591908.</u> Currie, J. (2009). ‘Healthy, Wealthy, and Wise: Socioeconomic Status, Poor Health in Childhood, and Human Capital Development’. Journal of Economic Literature, 47(1): 87–122. <u>https://doi.org/10.1257/jel.47.1.87</u> Cheryl Doss, Intrahousehold Bargaining and Resource Allocation in Developing Countries, The World Bank Research Observer, Volume 28, Issue 1, February 2013, Pages 52–78, https://doi.org/10.1093/wbro/lkt001 Duflo, Esther. 2003. “Grandmothers and Granddaughters: Old Age Pension and Intra-Household Allocation in South Africa.” World Bank Economic Review 17 (
Gračner, and Rothenberg (2022) evaluate the impacts of Jakarta’s BRT system, the _TransJakarta_ , on vehicle ownership, travel times and commuting flows. Kreindler _et al._ (2023) add to this by showing how the recent expansions of the BRT network affected bus ridership and overall commuting flows. Meanwhile, Hanna, Kreindler, and Olken (2017) identify the effect of Jakarta’s so-called “3-in-1” high occupancy vehicle policy, aimed to reduce congestion, on travel speeds in the city.

The remainder of the paper is structured as follows. Section 2 provides an overview of Jakarta’s MRT system. Section 3 describes our event study design, which leverages the highly spatially and temporally granular data that we describe in Section 4. Section 5 presents our results. Finally, Section 6 concludes.

# **2. Overview of Jakarta’s MRT System**

Jakarta is Indonesia’s largest city. In 2016 it had a population of 10 million in the “city proper”, and of 31 million in the wider metropolitan area, also known as Jabodetabek.<sup>5</sup> It is also consistently rated one of the 10 most congested cities in the world (Roberts, Gil Sander, and Tiwari, 2019). Around 3.2 million (or 11 percent) of Jabodetabek’s inhabitants are commuters (BPS, 2020). Most of them (63.3 percent) use their own motorbike for travel, followed by “public” transport (26.9 percent) (BPS, 2020). The latter

> 5 Jakarta’s “city proper” corresponds to the district of DKI ( _Daerah Khusus Ibukota_ ) Jakarta.

5
For each indicator, there is a threshold and continuous variable option. In theory, the threshold option ‘percent zero’ on either reading comprehension or oral reading fluency is most plausibly comparable. This measures the most basic foundational skills attained which should be relatively invariant to details which would affect performance higher up the learning margin. It is also easily interpretable and meaningful across contexts. However, since this variable is a threshold rather than a continuous measure of learning it is difficult to convert this to a score we can place on an international scale.

Instead we produce mean scores using a continuous variable that is plausibly comparable, the ‘percent of answers correct’ on reading comprehension. We use the percent correct since the number of test items varies slightly for each EGRA. If students did not attempt any question this is recorded as a zero. We further confirm that the threshold and continues variables are tightly correlated, with an inverse correlation of -0.9 (see Figure 2.0). We compute a standardized Z-score from the continuous reading comprehension metric and scale it with a mean of 500 and standard deviation of 100, akin to RSAT and ISAT scales.

EGRA data is designed for grades 2-4, although certain countries will participate out of this range. We restrict data used for our database to grades 2-4 to be consistent with the design of EGRA. This range further optimizes the coverage-bias tradeoff. If we include additional grade levels to expand coverage, the data starts to skew due to availability of data for certain countries coming from a particularly low or high grade (grades 1 or 6). If we restrict our interval to reduce grade bias, we start to lose countries. In the grade 2-4 range, we optimize this trade-off.

Figure 2.0: Correlation between Percent Zero Reading & Percent Reading Comprehension <!-- Start of picture text --> Pearson Correlation Coefficient: -.8991<br>NIC<br>MKD<br>IDN IDNTJKKGZAFGKGZKGZIDNKGZHNDPHL<br>MKD LBR<br>
analysis with data from the Household, Income and Labour Dynamics in Australia (HILDA) survey over the past two decades (2001 – 2019).

# **3. Empirical strategy**

To investigate the relationship between refugee mental illness and their labor market outcomes, we estimate the following regression where Yit are labor market outcomes of refugee _i_ who was interviewed in wave _t_ . We focus on two main outcome variables: employment status and weekly labor income (real income in $1,000 Australian dollars). But we also examine other outcomes such as refugees’ LFP, employment type, employment skills levels and sector, life satisfaction and their partners’ and children’s outcomes (including their partners’ employment status and life satisfaction and their children’s mental health and school performance).

The coefficient of interest in Equation (1) is β, which measures the impacts of mental illness (MIit) on labor market outcomes. We also control for a range of (potentially) timevarying refugee characteristics in vector Xit including age groups, marital status, household size, language skills (listening, speaking, reading, and writing), home ownership, and residence areas (i.e., living in major cities versus remote areas).<sup>6</sup> These are important determinants of economic integration of refugees that have been examined in the literature (e.g., Lochmann et al., 2019; LoPalo, 2019).

We include the individual fixed effects (μi) to control for unobserved time-invariant characteristics at the individual level and the survey wave fixed effects (τt) to absorb the effects of unobservable time-varying characteristics that can commonly affect all refugees in each time period. εit is the error term. For continuous outcome variables, we estimate Equation (1) with

> 6 We follow previous studies and use a set of dummies for the age variable to account for its nonlinear relationship with employment (e.g., Staubli and Zweimüller, 2013; Laun,
Bank also uses income class poverty lines which facilitate comparisons between countries at similar stages of development. The income class poverty lines are defined for the lower middle-income and upper middle-income countries and are based on the national poverty lines of the countries in each group. As such, they provide a more appropriate threshold to measure poverty for countries in each income class. The lines are defined at $3.2 (for lower middle-income countries) and $5.5 (for upper middle-income countries). The welfare measures of income or consumption used are the same as those used for the international poverty line. Cross-country comparisons of poverty measured at these lines are presented in Figure 1.

> 2 For more details on the updated lines from Francisco Ferreira, Dean Joliffe and Espen Prydz, follow this link

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The rate of interest microenterprise owners will receive if they save the money in the bank instead of using it for microenterprise investment. For example, Grameen Bank pays its depositors an interest rate of 8 percent (Faruqee and Khalily, 2011), while the interest rate on savings in commercial banks ranges from 7.5-8.0 percent. Many other organizations pay interest rates below the market rate (Hossain and Ahmed, 2000), which is much lower than the average rate of return earned by microenterprises. However, because sometimes the needed startup capital is much higher than a micro-entrepreneur’s own savings, many households are constrained in starting up their enterprises. Therefore, expanding credit access is critical.

# <u>8. Conclusion</u>

This paper examines the nationally-representative data from three large household surveys conducted during the period 2000-2010 to study whether inadequate access to finance constrains microenterprise growth and profitability in Bangladesh. In doing so, we also address why microenterprise growth is important for a country such as Bangladesh. We find that rural households draw some 36 percent of income from an average microenterprise activity. Data show that households with income from microenterprises as an additional source of income are much better off in terms of income, consumption, and poverty than their counterparts that do not operate such income-earning activities. An econometric analysis that establishes the causality between microenterprise participation and household welfare shows that a household’s participation in a microenterprise activity raises income additionally by 6 percent and consumption by 5 percent, and consequently reduces moderate poverty by 2 percent and extreme poverty by some 4 percent per year. If microenterprise does help to reduce poverty by raising rural income, the question arises as to why microenterprise expansion does not occur more quickly.

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