rafmacalaba/data-use-mentions-extended huggingface dataset:
pendix Table A3, I present these estimations using different standardizations of academic grades and behavior reports.

Finally, the IVV predicts both intensive and extensive margins of future misbehavior. Using data from students in the control group, I find that the correlation between IVV and bad behavior at the end of the academic year is positive and statistically significant at 5%. The estimation strategy and main results are in Appendix Table A4.

# **Treatments**

After estimating the IVV, enrolled children were randomly assigned to two groups—control (C, 25%) and treatment (T, 75%)—within each school-by-educational-level “block.”<sup>14</sup> Then, in a second randomization stage, treated children were randomly assigned to two treatment arms— heterogeneous (HT, 25%) and homogeneous (HM, 50%)—as shown in Figure 1.

Next, students in the HM set were ranked and assigned to subgroups according to their index: all students with an IVV above the median at the HM-stratum level were assigned to the High-IVV group (HM-High, 25% of the full sample) and the rest were assigned to the Low-IVV (HM-Low, 25%) group. The HM-Low and HM-High groups are defined using the median in each schoolby-educational-level block because I wanted variation in the cutoff. Moreover, an uniform cutoff across all randomization blocks could generate differences in group sizes that would probably confound the effects of tracking with group size. This design permits me to judge if targeting helps to improve the ASP’s effects and allows me to test the potential existence of peer effects and heterogeneity by initial propensity for violence in the program. In addition, this strategy utilizes a regression discontinuity (RD) design approach to measure the impact of tracking on the marginal student and contrast it to the average impact.

> 14Each education level consists of three years of schooling: the first is from first to third grades, the second from fourth to sixth grades, and the third from seventh
Figure 15 shows that boys in the affected cohort were less likely to hold a highschool degree in 2008. That effect is likely to be noise, given that, in other years, the point estimate was relatively small and imprecisely estimated. A similar pattern is observed in figure 16. Interestingly though, the estimates for 2008 and 2012 in figure 17 suggest that the affected cohort had fewer years of schooling. The estimates for 2013 and 2014 suggest they catch up later on. This would explain why, when we pooled all years together, we found no difference in completed years of schooling between the two cohorts.

Although we found no impact on work incidence and log earnings, figure 19 suggests that the ban might have affected individual’s occupation to some extent. According to table 5, the affected cohort was about 2 p.p. (or 3.5 percent) less likely to have a formal job than the unaffected cohort. We also found some indication of negative effect on log earnings, but the point estimate was statistically insignificant. Even pooling seven years of survey data, it seems we did not have the statistical power to detect such small effects for log earnings and occupation.<sup>31</sup>

# **7.1. Heterogeneous Effects**

It may be the case that a particular sub-group of individuals is strongly affected by the child-labor ban but these impacts are overshadowed in our long-term estimates for the overall treated cohort. Unfortunately, we are unable to identify the group of compliers in the long-term in our database since we work with repeated cross-sections. We could still try to estimate the impact of labor-force participation in the short term on, say, log wages in the long term using the two-sample instrumental strategy originally applied by Angrist and Krueger (1992) (see Inoue and Solon 2005 for a discussion). However, since the impact of the law on participation rates is partial and we also observe an increase in school attendance among those boys banned from the labor force,
wider donor and Government comrnunity) that having the PPAs available to inform the national diagnosis of poverty has been extremely valuable and illuminating. There have been certain limitations to the usefulness, however:

- I . The four PPAs were chosen to illustrate the problems of the poor in a range of different circumstances. Four studies are inadequate to capture some poverty situations, however. The Central Highlands is an extremely poor area with a particular set of problems, which are overlooked by not having a PPA in this region. This is an important limitation to the PPA findings;

2. Certain vulnerable groups were not really covered in depth in all PPAs (e.g. children) or at all in some PPAs (e.g. the disabled); 3. Many have commented that the information is not representative in any statistical sense. This is, of course, true. It worked very well to use the PPA information alongside nationally representative household survey data. It is difficult to imagine how the PPAs might have been set in the national context effectively and convincingly _without_ the household survey data. Attention often focuses on the value 15
England was the prototypical and the most advanced capitalist economy. But we have the data for Chile in 1861 which reveal the same, or even sharper, polarization.<sup>4</sup> Working classes, from the poorest (female fisherman) to the richest (shoe-makers), covered the range of income that goes from less than one-seventh of the national mean to two-thirds of the mean. Only the artisans, who mixed ownership of some capital with their own labor, and possibly even hired a few workers, achieved an income higher than the national average. At the high end of the income distribution were owners of manufactures and mines with incomes between 20 and 55 times the mean, and land owners and large-scale merchants with respectively 35 and 9 times the mean. These top groups comprised only 2 percent of the population while working classes (including peasants) accounted for more than 90 percent.

But if workers filled in most (or perhaps in all) countries, that part of the income pyramid which ran from the subsistence to just somewhat under the mean, their real incomes in various countries could still have differed a lot if country means were very different. But, as we shall see, around the 1850s, they were not. Angus Maddison has estimated that around 1850, the mean income in the poorest countries in the world (Ceylon and China) was around $PPP 600.<sup>5</sup> At the top were the Netherlands and the United Kingdom with a GDP per capita of about $PPP 2,300. Thus, the ratio between the top and the bottom (of country mean incomes) was less than 4 to 1. Consequently, the better-off workers who earned incomes close to the national means, could not, in terms of their standard of living, differ from each other by more than the ratio of 4 to 1. And the bulk of workers who lived at less than their countries’ average income and closer to the subsistence, could not have
political borders (Alesina 2002, Alesina and Wacziarg 1998). If trade is more important to small compared with large countries, we might suspect that the relatively smaller countries are more likely to invest in more efficient documentation system. Hence, we hypothesize that a unit increase in the number of required documents leads to a smaller increase in the time cost in the small compared with large countries.

We test the two hypotheses mentioned above using panel data on 125 countries for which data are available on our main variables. The regression results strongly confirm both the stated hypotheses.

The present paper is restricted to a regulatory aspect of trade facilitation; that is, the number of documents required to trade and the associated time cost. The importance of such regulatory measures for trade cannot be denied, although formal empirical work on this issue is still in its infancy. For example, a recent study by Djankov et al. (2010) uses the same time cost measure as we do in the present paper and finds it has significant effects on the volume of trade. The study estimates that for each additional day that a product is delayed prior to being shipped reduces the volume of trade by more than 1 percent. Alternatively, each day is equivalent to a country distancing itself from its trade partners by about 70 kilometers on average. The present paper complements such studies in highlighting an important determinant of the time cost of clearing all the required procedures for shipment.

Notwithstanding the importance of time costs of shipments and the number of required documents, trade facilitation extends to a number of other dimensions that we do not include in the present study. Examples include internet availability (Freund and Weinhold, 2000) and standards harmonization and automating customs procedures (Herter et al. 2001). Hence, we caution that our results discussed below should be treated with due caution and not generalized 3
have prevailed if plants were distributed completely at random. Greater values of the **EG** index are associated with greater geographic concentration of the industry.<sup>7</sup> Dumais et al. (2002) show that the index can be generalized to a dynamic setting. Note that we compute the Herfindahl index **_Hj_** based on our plant level data for Indian industries, rather than take it from external sources as in other studies. This is a particularly important advantage of

> 5The main prediction of the model is that the relationship between average levels of concentration and industry characteristics is the same whether concentration is caused **by** from spillovers, from natural advantage, or from a combination of both. This prediction implies that one can construct an index that controls for industry differences, regardless of the cause of concentration. 'The Herfindahl index is defined as usual covering all plants in the industry without a geographic dimen- **S10n. 'A** value of zero for the **EG** index indicates that industry employment is as concentrated as would be expected from a random location process. Ellison and ( I **-** i **(1997)** suggest that industries with **EG** index values higher than **0.05** are **highly** concentrated while those with **EG** index values below 0.02 are not very concentrated.

**6**
obtain data on “General Government Total Expenditure, Percent of GDP” (GGEY), “Gross Domestic Product, Current National Prices” (NGDP), and “Inflation, Average Consumer Prices” (CPI) from the International Monetary Fund (IMF) World

> 46 Commodity export data are taken from the UN Comtrade Database. See Appendix Table 1 for details on commodity exports for each of the 56 countries included in the sample.

34
value with inflation. In the case of savings clubs and “lay-away plans” at stores, informal savings can be shown to lose a margin in excess of the inflationary loss, since savings are frequently traded in for certain items sold above list price. In addition, informal savings systems do not benefit from any institutional deposit insurance, having to rely on personal trust mechanisms instead.

**Table 3. 2  Banked versus unbanked users of informal savings services**

||**Banked**|**%**|**Unbanked**|**%**|**Total **|**%**|
|---|---|---|---|---|---|---|
|Uses informal savings|318,211|19.0%|237,424|9.1%|555,635|12.9%|
|Does not use informal savings|1,359,091|81.0%|2,379,201|90.9%|3,738,292|87.1%|
|_Total_|1,677,302|_100%_|2,616,625|_100%_|4,293,927|_100%_|

_Source: DANE household study of Bogotá, 2003_ 48 The savings rate in Colombia has been constantly declining since 1990 from 24% of GDP to 14% of GDP in 2003.

49 The DANE did not gather sufficiently robust information on the savings behavior of the interviewed people. A second application of the survey with revised questions is therefore recommended. In contrast, the México City survey included several in-depth questions on savings behavior.

53
based on 2022 EU-SILC. We report the survey year, and not the income year.

**The incidence of our extended version of multidimensional poverty is substantially high among all types of vulnerable households, but some families are particularly more affected in some countries.** All vulnerable households considered have AROPE + rates above national rates –with the exception of households with a disability recipient in Bulgaria and Croatia (Table 9). Individuals living in households receiving social aid, with an unemployed member or singleelderly households are considerably more prone to be AROPE + in all countries. In addition, individuals living in families with a pensioner are particularly more likely to be AROPE + in Croatia, whereas this is the case for large families in Bulgaria and Romania.

**Table 9.** AROPE + rates for individuals living in vulnerable households (%), by country <u>(2022)</u>

|**vulnerable households**|**Bulgaria**|**Croatia**|**Poland**|**Romania**|
|---|---|---|---|---|
|household with unemployed|76.4|61.1|65.0|85.8|
|single-elderly household|73.2|71.7|47.3|71.4|
|Female-headed household|47.0|46.2|41.7|58.0|
|household with 3 or more children|78.5|47.4|42.8|78.1|
|household with 5 or more members|65.0|54.3|48.4|74.1|
|household with disability recipient|44.1|43.5|40.5|57.2|
|household with pensioner|52.1|59.9|45.3|63.9|
|household with social aid
payments enable deeper financial usage**

Previous iterations of the Global Findex database found that digital payment recipients tended to cash out the money they received into an account. Data from the Global Findex 2021 finds that is changing, with 83 percent of digital payment recipients in developing economies also making a digital payment. Furthermore, 63 percent of payment recipients stored money using an account and 42 percent saved formally. In other words, receiving a digital payment is associated with making a digital payment, and with using an account to save, borrow, and store money. This has 13
or the resource mobilization approach to uprisings. Nevertheless, this analysis suggests that _suffering_ is a relatively universal basis for non-violent uprisings.

# **Alternative measures of peaceful uprising**

Our measure of nonviolent uprisings based on the CNTS data set is collected through the New York Times and the coverage of such events is relatively limited in small, low-income countries with few ties to the U.S. Systematic time-invariant differences in coverage are filtered out by the country fixed effects, but the quality of New York Times news coverage might change over our sample period and such changes could be correlated with widespread human suffering in these countries, leading to selection bias. We therefore explore the robustness of our results using the data on nonviolent uprisings from the Major Episodes of Contention (MEC) data set (Chenoweth, 2015; Chenoweth & Ulfelder, 2017). Because nonviolent uprisings are identified through a review of global media (i.e. Associated Press and Agence France Press news stories within Factiva and LexisNexis), selection bias should be minimized.

The MEC measure of nonviolent uprisings differs from the CNTS variable in several ways. First, it measures whether an episode (i.e. more than one event, occurring within a week of one another) is ongoing in a certain year. Hence, their variable is a dummy instead of a count variable. This is disadvantageous, because we lose considerable variation in the intensity of nonviolent uprisings. Second, in order for an event to be considered it needs to have at least 1,000 participants (instead of 100 participants for protests in the CNTS data set), and hence they only include _mass_ nonviolent uprisings. Third, for an episode to be included, an uprising should not only be anti-government, but its explicit goal should be to seek the removal of the incumbent. Hence, whereas the CNTS data also include acts of resistance in opposition to certain policies, the MEC data only include them if they have the maximalist aim of bringing about a regime change. Fourth,
**_<u>________________________________________________________</u>_**

Global Logistics and Port indicators include:

- Customs Performance (e.g., customs clearance time and cost; percentage of consignments inspected)

- Bureaucracy/red-tape associated with typical trade transactions (e.g. number of signatures; time and costs for processes from order to delivery)

- Port performance (e.g., container waiting time at ports, time to enter port, vessel turn – around time)

# _<u>International Exhibition Logistics Associates (IELA)</u>_

IELA, a trade association has prepared average clearance time through customs for 67 countries. The data on customs clearance is collected from local agents in different countries, who report these times to the association based on their experience. The clearance is related to the exhibition in a particular country, and differs from commercial clearance. The procedures followed for clearance of cargo for an exhibition are normally longer, since the entry is on a temporary basis. Also, there is very little information on developing countries.

# _<u>Trade and Transport Facilitation in Southeast Europe Program (TTFSE)</u>_

The TTFSE regional program was established in 1998 with support from the World Bank, the EU, and bilateral partners. Based on face-to-face interviews and discussions with representatives of companies, the TTFSE is a continuation of the monitoring work during 2001-03 to ascertain whether improvements for customs users are possible or not. Its methodology is participatory in nature – seeking to give ownership to the different stakeholders. Operating in eight southeast European countries<sup>4</sup> , it aims to create a framework for facilitating trade by encouraging more efficient and less costly trade flows, and to modernize customs administrations and other border control agencies. It seeks to reduce non-tariff costs to trade and transport (processing time for traders and transporters, “facilitation payments”, smuggling and corruption at border crossings, reduced waiting times), as well as increase the
21

# **7. Corruption**

This section summarizes the literature on four policy issues related to corruption: its level in infrastructure, its effects, its drivers, and what can done about it.<sup>27</sup>

# **The level of corruption in infrastructure**

Favoritism, fraud, cronyism, patronage, embezzlement, bribes, and state capture are all concepts that have long been associated with the delivery of infrastructure services in many countries. In the early 1990s their perceived increase in the sector was, in many countries, one of the arguments used to motivate privatization. Ten to fifteen years and many reforms later, the same concerns are reemerging in discussions about the delivery of infrastructure services. Indeed, the level of corruption in the sector is one of the main concerns for policymakers and opinion-makers monitoring service delivery in developing countries. The main debate has shifted from the interactions between public operators and users to those between private operators and government. NGOs are releasing books expressing concerns about the issue (such as Finger and Allouche 2002 and Hall and Lobina 2002), documenting legal events that have demonstrated incidents of corruption in the sector. (Friends of the Earth 2001 and social scientists express concerns about the unusually high costs of renegotiations in the sector, while Flyvberg and others 2002, 2003a, 2003b and Mitlin 2002 point to their surprising high levels; see Guasch 2004.) The most powerful recent indicator of the importance of this issue may be that one of the main watchdogs in the business, Transparency International, has just released a survey of corruption and privatization in infrastructure in developing countries (Boehm and Polanco 2003). Unfortunately, this survey provides little new quantitative evidence and only covers concepts and useful anecdotes. Indeed, there is little applied or even theoretical research on this topic—maybe because there is even less data available than for all the other topics covered in this survey.

With the exception of a database compiled by Clarke and Xu (2003) for Eastern Europe and
, for each of the 74 markets in MW, the closest markets in country B (where B is MW, TZ, MOZ, or ZM). MW-MW refers to a market pair where both markets are within Malawi, and MW-TZ, for example, indicates that one of the markets is in Malawi, and the other is across the border in Tanzania.<sup>4</sup> Figure 3 presents the median price dispersion of 327 market pairs, some of which are within Malawi and others across the border in TZ, ZM, and MOZ. The median price dispersion among market pairs within Malawi remains relatively flat across

> 3 The calculation of the travel time between every pair of markets within Malawi and its neighbors is implemented using the Stata package _georoute_ which relies on map data from HERE ((https://developer.here.com/) (Weber and Péclat 2017).

4 The nearest market pairs are identified in the following manner. First, for each of the 74 markets in MW, we identify all possible market pairs (by pairing it with the remaining 73 markets in MW and 79 markets across the border in TZ, ZM, or MOZ) and then impute the travel distance (and travel time) on the existing road network. Second, for market pairs in country B, that is, MW-country B, we identify the nearest market as the one with the decile of travel distance. Using this technique, we identified a total of 327 market pairs and the table below provides a summary of the travel distance. We present results using the lowest decile of travel distance, but the result is similar when we use the lowest quartile of travel distance or even travel time. The number of market pairs varies from 26 pairs in the case of MWMOZ to 112 pairs for MW-ZM. Within Malawi, the number of market pairs is 128.

|Travel distance (km) between<br>market pairs|Mean|Standard deviation|Minimum|Maximum|Number of pairs
populations and 131 PSUs from Kuchi populations were drawn. In the second and final stage, eight households were selected from each PSU.

The implicit stratification over time is a key element of the survey design. The population frame was sorted both spatially and temporally to ensure that (with a systemic interval selection) the selected sample would be seasonally representative. (See Kish (1965, pp. 235-236) for a discussion of implicit stratification.) Thus each quarterly sample of the NRVA survey is representative at the national level. In a country where agriculture is an important form of livelihood, seasonal variations in consumption patterns are to be expected; thus it is critical to capture nationally-representative measures of household food security throughout the year. (Online appendix Table A1 displays key demographic, educational and health, and infrastructure indicators across the four quarters. While we observe some statistical differences in means across quarters, there is little evidence of systematic differences in the samples based on these generally time-invariant characteristics.) Additionally, the year-long fieldwork enabled enumerators to access households in conflict zones without compromising the survey design. Specifically, enumerators would try to secure permission informally from local leaders; when a PSU was considered too insecure to interview at the scheduled time, it would not be replaced immediately, but would be reconsidered at a later date within the quarter.

The NRVA consists of household and community questionnaires and a district market price survey.<sup>2</sup> In this analysis, we exploit two key elements – the food consumption data and the price data. The consumption data include the frequency and quantity of consumption of 91 food items consumed over the previous week, including food bought on the market, produced, or obtained through other methods like food aid and gifts. The NRVA‘s broad coverage of foods, including seasonal varieties, allows for better calculation of caloric and nutrient intake than surveys with fewer items. The price data include prevailing prices of the food items included in the consumption section, as well as domestic and imported