rafmacalaba/datause-encoder-data huggingface dataset:
crucial advantage of the corruption experiences question is that it is less likely to suffer from reverse causality, in the sense that individuals‟ confidence in institutions affects their corruption experiences. This is important for our interpretation of the empirical results that follow.

Figures 1 and 2 illustrate the country-level variation in these two measures of corruption from the GWP. Figure 1 plots country average corruption perceptions versus corruption experiences. All countries in the sample fall above the 45-degree line, indicating that average corruption perceptions are higher than average corruption experiences in every country. In some cases this gap is extreme. Countries such as Japan or Italy have low rates of personal experience with corruption, but nevertheless strong perceptions of widespread corruption in government. This suggests low rates of petty or administrative corruption but a greater incidence of high-level or political corruption. In Figure 2 we plot the two corruption questions from the GWP against a broad perceptionsbased measure of corruption, the Worldwide Governance Indicators „Control of Corruption‟ variable (Kaufmann et al., 2008). Both corruption questions display a fairly strong negative correlation with the Control of Corruption measure. However, this correlation is far from perfect, in part due to the fact that the Control of Corruption measure aggregates information from a large number of different data sources.

Our main objective in this paper is to document the links between corruption and confidence in public institutions. We measure the latter using another question in the GWP, which asks respondents about their confidence in a variety of institutions at the national level. Specifically, the GWP asks “ _Do you have confidence in each of the following?: (a) the military, (b) judicial system and courts, (c) national government, (d) health care or medical systems, (e) financial institutions or banks, (f) religious organizations, (g) quality and integrity of the media, and (h) honesty of elections._ In our core specifications we sum together the responses to (a), (b), (c) and (h) to obtain an index of confidence in public institutions that ranges from 0 (respondents who report no confidence in any of the four institutions) to 4 (respondents who report confidence in all four institutions).

9
a more developed financial system positively contributes to the process of technological diffusion associated with foreign direct investment.

_IIIg. Facilitating Access_ Access to financial services has increasingly been receiving greater emphasis over the recent years, becoming a focal part of the overall development agenda. One reason is the accumulating evidence on the importance of finance for growth, and the belief that limited access to finance is a contributor to not being able to escape poverty. Another is the observation that small enterprises and poor households face much greater obstacles in their ability to access finance all around the world, but particularly in developing countries.

What does access to finance mean? There are many dimensions of access, including availability, cost, and range and quality of services being offered. Morduch (1999) defines these dimensions as (a) reliability, whether finance is available when it is needed or desired; (b) convenience, how easy it is to access finance; (c) continuity, ability to access finance repeatedly; and (d) flexibility, whether the product is tailored to the needs of the household or enterprise.

While there is much data on financial sector development more broadly, there is very little data on usage and access to finance, both for households and firms. Hence, there is also very limited analysis on the impact of access to finance on economic development. Research using firm level survey data suggests that financing obstacles are the most constraining among different barriers to growth (Ayyagari, Demirguc-Kunt and Maksimovic, 2005). Financing obstacles are also found to be highest and most constraining for the growth of smaller firms (Beck, Demirguc-Kunt and Maksimovic, 28
Table 3: Impact of UCTs on Education Expenditures with Controls

||1|2|3|4|5|6|7|8|9|10|11|
|---|---|---|---|---|---|---|---|---|---|---|---|
|Treatment|0.732**|0.667***|0.607***|0.573***|0.601***|0.629***|0.612***|0.633***|0.637***|0.636***|0.586***|
||-0.255|-0.224|-0.221|-0.22|-0.219|-0.219|-0.219|-0.219|-0.219|-0.218|-0.213|
|Urban_×_Dept||✓|✓|✓|✓|✓|✓|✓|✓|✓|✓|
|HH Indig|||✓|✓|✓|✓|✓|✓|✓|✓|✓|
|Age||||✓|✓|✓|✓|✓|✓|✓|✓|
|Gender|||||✓|✓|✓|✓|✓|✓|✓|
|Children HH||||||✓|✓|✓|✓|✓|✓|
|# Children_<_5|||||||✓|✓|✓|✓|✓|
|# Adults||||||||✓|✓|✓|✓|
|Parent Present|||||||||✓|✓|✓|
|Parent_>_65||||||||||✓|✓|
|Parent educ|||||||||||✓|
|N|3645|3645|3645|3645|3645|3645|3645|3645|3645|3645|3645|
|_R_<sup>2</sup>|0.025|0.216|0.232|0.261|0.263|0.271|0.274|0.275|0.279|0.281|0.329|

Standard errors in parentheses, * _p <_ 0 _._ 05, ** _p <_ 0 _._ 01, *** _p <_ 0 _._ 001
identified in broad categories; clearly, two households can each own a “fridge” but in one case it is 30 years old and works poorly while in the other case it is a fancy new model. Second, an important exclusion is that one cannot use fine geographic effects, such as at the level of the village, since one is constrained to estimating on a sample survey that will typically only cover a sample of villages (typically determined by the first stage of a two-stage sampling design). One does not know the geographic effect for the population, as required for implementing the PMT.<sup>23</sup> However, in one version we include community-level variables that go some way toward addressing this concern.

There is a degree of judgement required in selecting covariates. Here we consider various options, starting with a “Basic PMT” that seems to capture well the set of variables found in practice. We also consider “Extended PMT” methods that include variables that have extra explanatory power; while this provides a useful indication of the gains from more data, it is acknowledged that this version may not be easily implemented in the field. The <u>Statistical Addendum</u> provides descriptive statistics.

# **3** . **Measures of targeting and poverty**

An early strand of the literature formulated the targeting problem as that of choosing a schedule of transfer payments across types of households to minimize a measure of poverty subject to a budget constraint.<sup>24</sup> The subsequent literature has instead emphasized “targeting efficiency,” defined in terms of reducing targeting errors as defined below. Here we shall study both types of measures. We start with targeting measures.

**_Measures of targeting performance:_** The relevant counts corresponding to the joint distribution of _yijt_ and _y_ ˆ _ijt_ are shown in Table 1, which helps clarify our notation and some of the properties of our measures.

We focus on three main measures of targeting performance. The first is the Inclusion Error Rate ( _IER_ ), defined by the proportion of those identified as poor who are not. This can be written as:<sup>25</sup>

> 23 The same limitation is shared by small-area estimation methods (“poverty mapping”) as in Elbers et al. (2003).

> 24 The idea was developed in theoretical terms by Kanbur (1987) and the problem was formulated and solved numerically in Ravallion and Chao (1989) for the squared poverty gap index of Foster et al. (1984). Glewwe (1992) generalized this approach to allow for continuous variables.

> 25 The indicator function 1(.) takes the value unity when the condition in parentheses is true and zero otherwise.

10
# **1 Introduction**

Recent evidence of increasing market power and growing profits (De Loecker, Eeckhout and Unger, 2020; De Loecker and Eeckhout, 2021) has led to the recommendation of vigorous antitrust enforcement (Khan, 2017; Shapiro, 2018; Wu, 2018; Berry, Gaynor and Scott Morton, 2019; Marinescu and Posner, 2019; Rose, 2019; Salop, 2021). Implementation of this recommendation raises practical questions about which there is limited empirical evidence. How much can an antitrust authority expect to shape microeconomic outcomes using tools available under the law, like investigations and judicial discretion? If an antitrust authority can shape microeconomic outcomes, what could be its contribution to aggregate outcomes like productivity, inequality, and employment, and what are the underlying mechanisms?

This paper answers these questions using outcomes of investigations by Mexico’s Federal Competition Commission (in 2013, renamed the Federal Economic Competition Commission, abbreviated as COFECE in Spanish) into alleged illegal monopolistic practices. A distinctive practice in Mexican competition law allows us to identify the causal effects on economic outcomes of antitrust sanctions, which involve a financial penalty for agents involved and a monitored order to cease illegal conduct. In jurisdictions like the United States, a description of the relevant market in an antitrust case is not always known to researchers except after prosecutors bring it to a public trial. In Mexico, to officially start an investigation, the Commission has the obligation of publishing in the Federal Official Gazette (Diario Oficial de la Federaci ́on in Spanish) a document ( _acuerdo de inicio_ ) that, among other information, has to describe the relevant market. This applies to all ex-officio antitrust investigations and credible public complaints, including closed investigations that do not produce sufficient evidence to find agents responsible for illegal conduct and so do not go to trial. These closed investigations allow us to observe and measure changes in markets where illegal conduct was suspected but not punished and to use these changes as a counterfactual for assessing the impact of sanctions.

Our strategy corrects an issue with the difference-in-differences approach used in prior ex-post evaluations of antitrust (for examples of this approach, see Sproul (1993) and other papers reviewed by Ilzkovitz and Dierx (2015)). In the prior approach, the counterfactual is taken to be another industry in the same sector with similar cost and demand drivers rather than a market in which illegal activity is suspected but not punished. If illegal conduct emerges in industries with unique trends that make them different from other industries in the same sector, the prior approach is not valid. Our new approach also ensures that the counterfactual includes any potential deterrent or encouragement effect of a “shot across the bow” investigation that is made public to market participants but does not go to trial.

2
If asked to guess the demand elasticities facing small countries such as Austria, Belgium and Denmark in the world market, most trade economists will pick very large numbers and, for purposes of deriving policy prescriptions, show no hesitation in relying on the smallcountry assumption. Yet, the actual estimates of demand elasticities in international trade for these as well as other countries are disturbingly low. Thus, in Table 1, taken from Goldstein and Khan's (1985) detailed survey, the highest estimate of demand elasticity across Austria, Belgium and Denmark is 1.56. Many of the estimates are less than 1.

If we believe these estimates, the case for unilateral trade liberalization is seriously undermined. The estimates imply a considerable market power on the part of even small countries and, beyond a point, make unilateral liberalization by them a welfare-reducing proposition. The estimates also raise doubts about exports serving as the engine of growth. For, even after we take into account the expansion of world demand due to growth in income, if price elasticities are as low as those shown in Table 1, a 20% per annum expansion of a country's exports is bound to worsen substantially her terms of trade. Alternatively, given these elasticities, it is difficult to reconcile the fast growth in the exports of several East Asian countries with relatively stable terms of trade during the last three decades.

To our knowledge, Riedel (1988) is the only author who seriously questions the low elasticity estimates on the ground that they suffer from a simultaneity bias. He notes that researchers commonly assume, incorrectly, that the elasticity of supply of exports is infinity which makes the price exogenous and allows them to estimate the demand equation independently of supply. Riedel drops this assumption, models the supply equation explicitly and then estimates the elasticity of demand for Hong Kong's exports. He reaches the dramatic conclusion that the elasticity of demand for Hong Kong's exports is infinity.
# D. Valuing QALYs

QALYs are typically valued using the Value of a Statistical Life Year (VSLY), calculated on the assumption that the VSL is strictly proportional to remaining life expectancy (equation (7)). As noted above, the assumption of a constant VSLY has not been borne out by the empirical literature (see, e.g., Alberini et al., 2004). It is also inappropriate to use a VSLY because it is based on a tradeoff of consumption/wealth for longevity, whereas a QALY measures the tradeoff between health and longevity. Hammitt (2002) discusses the theoretical difficulties in using the VSLY to monetize a QALY, and a recent NAS-IOM panel has cautioned against this practice (Miller et al., 2006). Nevertheless, the practice of valuing QALYs (or DALYs) using a VSLY is practiced by the US Department of Transportation, and other agencies (Robinson, 2007). At this point, the alternative is to use estimates of the medical costs and productivity losses associated with injury as a lower bound to the value of an injury, which would undervalue injuries relative to lives saved.

# E. Lessons from the US Re: Injury Valuation

As noted above, there have been detailed analyses of the costs of injuries in the US, especially for injuries associated with road traffic crashes. The results of these analyses indicate the relative importance of various components of injury costs (medical costs, productivity losses and QALY losses) and whether total costs vary in proportion to one component of costs, thus suggesting a possible rule of thumb for computing costs. The analyses also indicate how costs vary with injury severity level. The latter information can be used to determine equivalent lives saved, for various injury severity categories.

Zaloshnja et al. (2004) report per capita injury costs for 137 injuries sustained in traffic crashes, broken down by body part, injury type (fracture/non-fracture) and maximum AIS code.<sup>38</sup> We have used these data to compute the fraction of total injury costs attributable to each cost component, by MAIS code. Two points are noteworthy: First, medical costs constitute, on average, only 16% of total injury costs. Productivity losses constitute 38% of total costs and monetized QALYs 46% of total costs.<sup>39</sup> Secondly, the ratio of each cost component to total costs is not significantly related to MAIS code. Thus, in the US, productivity and QALY losses constitute over 80% of the total costs of road traffic injuries. It is also interesting to note that variation in QALY losses explain 97% of the variation in total injury costs. The ratio of QALY losses to total costs is approximately 1.7:1.

Although there is, admittedly, significant variation in injury costs within an MAIS category, total costs are often summarized by MAIS category and the variation in costs across categories is noteworthy. Table 10 shows the per capita costs of traffic crashes in the US, by MAIS category, for the year 2000.

> 38 The per capita costs for a given injury reflect the age distribution of injury in the United States.

> 39 These ratios clearly depend on the VSL used to monetize QALYs, which is $3.3 million USD.

27
this parameter, we finally take the simplifying assumption of using a share of 35% stock losses in reported total losses.<sup>10</sup> <u>Table 2: Portion of total loss considered as direct, capital stock loss based on country cases</u>

|<br>Event|<br>Capital stock losses (productive sector and<br>infrastructure) as a share of total loss|
|---|---|
|Hurricane Stan in El Salvador, 2005|36%|
|Hurricane Stan in Guatemala, 2005|30%|
|Hurricane Mitch in Honduras 1998|48%|
|Hurricane Mitch in Nicaragua 1998|29%|
|Earthquake in El Salvador, 2001|21%|
|Arithmetic Average|33%|
|**Parameter used in this study**|**35%**|

Sources: ECLAC 1999, 2002; Telford et al., 2004; CRED, 2009 For the savings measures and socio-economic information, we use World Bank Development Indicators (World Bank, 2009) for calculating consumption, gross, net and genuine savings and GNI per capita.<sup>11</sup> The discount rate used for discounting future consumption was 5% for all series (in line with World Bank, 2006).<sup>12</sup> <u>Table 3: Overview over data used</u>

|**Variable**|**Data source**|**Time horizon**|
|---|---|---|
|Disaster losses (USD)|EMDAT, Munich Re|1971-2000|
|Consumption (USD)|WDI 2009|1970-2005|
|Gross National Income, GNI (USD)|WDI 2009|1970-2005|
|Population|WDI 2009|1970-2005|
|Gross savings (% of GNI)|WDI 2009|1970-2005|
|Net savings (% of GNI)|WDI 2009|1970-2005|
|Genuine savings (% of GNI)|WDI 2009|1970-2005|
|Aid (% GNI)|WDI 2009|1970-2005|
|GDP deflator<sup>13</sup>|WDI 2009|1970-2005|
|Countryincomegroups|WDI 2009|2005|

> 10 Varying this parameter within reasonable bounds did not importantly affect the analysis. 11 Note, those data were indicated as a share of GNI, not GDP, so the analysis is based on GNI. 12

> Conducting sensitivity analysis with discount rates related to country income groups, we find the discount rate to be relatively insensitive to marginal changes, yet, an extension of this work might explore using country-specific discount  rates.

> 13 In the absence of a GNI  deflator, we used the GDP deflator.

11
on forward GVC participation shares in Table 6 imply that such factors have more negative or smaller positive e¤ects on forward GVC trade than on traditional exports.

# 5.3 Cross-Country Cross-Sector Di¤erence-in-Di¤erence Analysis

The cross-country regression models described in previous sections use IVs to establish causality between determinants and GVC participation. However, there may be a concern that the IV strategy is not su¢ cient given that some other determinants, such as factor endowments, institutions, and connectivity may also be endogenous. As an alternative approach, we use country-sector data with year-to-year variability and apply the country-sector di¤erence-in-di¤erence regression model inspired by the Rajan and Zingales (1998) framework. This framework relies on interactions between a country’s factor endowments and the intensity with which a sector uses a particular factor and has been used to link countries’ sectoral trade performance to various sources of comparative advantage: factor endowments (Romalis, 2004), institutions (Levchenko, 2007; Nunn, 2007; Costinot, 2009), and ...nancial development (Beck, 2003; Manova, 2008).<sup>37</sup> We use this di¤erence-in-di¤erences regression model to estimate the role of several determinants in explaining GVC participation and exports at the country-sector level: where c; s and t stand for country, sector, and year subscripts, respectively. Y is a measure of GVC participation in levels or a measure of gross exports, X is a vector of country factor endowments, m is a set of sectoral intensities of use of these endowments from a benchmark country (the US), Z is a vector of country-sector trade policies, which include tari¤s imposed at home on imports of ...nal goods, and on imports of intermediate inputs, and tari¤s imposed by destination markets and "; is an i.i.d. error.<sup>38</sup> The speci...cation controls for country-year ...xed e¤ects Ict that account for di¤erences across countries both in time-varying

> 37Theoretical underpinnings for this framework in a trade context are provided by Chor (2010) who extends the seminal Eaton and Kortum (2002) trade model and estimates the importance of various sources of comparative advantage for trade patterns in an integrated framework.

> 38The sectoral intensities and the tari¤s and FDI measures used are described in the Appendix.

23
with these children during the most critical ages of their life and are charged with promoting their development. Thus, it is of vast importance to understand what these teachers know about childhood development and how to promote it. If there are misunderstandings about any of the domains of development among the teachers, there are large consequences for children. Without teachers who understand the importance of these early stages, children’s future productivity, mental health, and success is at risk (Human Capital Index (2018)).

# **3 Data**

During the months of March-May, 2020, we collected data on 225 head teachers and 206 teachers in ECED centers in Eastern Nepal. In our district in Eastern Nepal, there are 367 ECED centers. A large proportion of teachers have not received government ECD training, and this is what our sample focuses on. We selected 200 schools where teachers had not received government ECD training and then conducted a phone based survey that covered demographics, knowledge of early childhood development, and leadership skills of teachers and head teachers. 4 We also collected data on mental health using questions from the GHQ-12, a 12-item questionnaire that measures anxiety, depression, and general mental health (Goldberg and Hillier (1979)).

Head teachers of 200 randomly selected schools in an Eastern district in Nepal were called, and, once surveyed, asked for information about teachers in their schools. Most ECED centers have more than one teacher; we interviewed one teacher per school that met our selection criteria. There were three selection criteria that were used. Teachers in our sample cannot be assistant teachers, must not have government ECD training, and must be government employed teachers. Assistant teachers do not have as large of a role in ensuring student success; thus, we eliminated them from our sample. Teachers in our sample have lots of interaction with children in the most important time frame in their lives: ages 3-5. It

> 4Due to COVID-19 and school closures, we had to collect these data over the phone. Teacher and headteacher interviews were limited to 45 minutes.

5
<!-- Start of picture text --> 80 G/GDP = 121.5*** - 29.9*** x In(GDP pc) + 2.1*** x In(GDP pc)?<br>[3.2] [-3.4] [4.3]<br>70 R?= 0.61 Finland<br>[VAT rate 24%6]<br>Greece<br>60 [VAT rate 23%]<br>e<br>Argentina . a<br>[VAT rate 21%] e -<br>50 Barbados * 6<br>[VAT rate 17.5%] ° e <<br>a Jamaica e e<br>[G} [VAT rate 16.5%] . . e 8<br>—~ 40 Honduras ° eo e<br>i) [VAT rate 15%] e aae e<br>30 Co wee<br>e . & Oe e<br>eo ef ww © e @<br>20 e 8 0 ot eee . ee ”® . Australia<br>a ee aow . ceee ° [VAT(AT raterate 10%10%]<br>e e 3 . e e e<br>10 e e . ee ° e . New Zealand<br>Philippines Thailand [VAT rate 15%]<br>[VAT rate 12%] [VATGuatemalarate 12%] [VATCostaRicarate 13%] Ivar rate 7%]<br>0<br>400 1,100 3,000 8,100 22,000 60,000 160,000<br>GDP pc, PPP (constant 2011 international $)<br><!-- End of picture text -->