rafmacalaba/data-use-mentions-extended huggingface dataset:
consider two proxies for local resource constraints. The first is the amount of local taxes. We have data on prefectural business taxes _(Likin_ ) and land taxes, the two major sources of local fiscal income, in the year 1925. The second proxy for resource constraints is total warlord resources, measured as the sum of fiscal revenue of all the

> 13 An illustration might be useful here. Suppose a province had 5 prefectures, A, B, C, D, and E. Initially A, B, and C were under Warlord 1, D and E were under Warlord 2. Then in the middle of the warlord period, C changed hands to Warlord-2. Then the set of unique warlord history regions are: A and B; C; D and E.

23
near extraction sites for heavy raw materials. Wages are often low in these areas, so poverty and dirty production will reveal a natural, if accidental, spatial correlation.

Second, recent research suggests that pollution and labor are complements in production (Lucas, 1997; Hettige, Mani and Wheeler, 1998). Dirty industry should therefore have a tendency to locate in low-wage regions.

Third, a number of recent contributions indicate that pollution regulation is weaker in poor regions (Pargal and Wheeler, 1996; Wang and Wheeler, 1996; Dasgupta, Mody, Roy and Wheeler, 1996). This is partly because the poor assign lower relative value to ambient quality, and partly because low-income communities may be poorly-informed or unable to regulate pollution effectively. Weaker pollution regulation lowers the 'price of polluting' for emissions-intensive industries, providing an incentive to locate in poor regions.

## 2.4 **Summing** Up: **Three hypotheses**

From this brief review of prior research it may be fair to extract three null hypotheses to bring to the data:

# _1: Small plants are more pollution-intensive than large plants in developing countries._

_2: The small-plant share of industrial activity falls during the process of economic development._ _3: The share of dirty sectors in industrial activity declines with economic development._

## **3. Data**

Two cross-sectional data sets are deployed in the following sections to explore these hypotheses -- one from Brazil the other from Mexico.

# 3.1 Mexico

The SNIFF (Sistema Nacional de Informacion de Fuentes Fijas) database, maintained by the Instituto Nacional de Ecologia (INE) in Mexico's Environment Ministry (SEMARNAP), records emissions of conventional air pollutants (particulates, SO2 , etc.), sector of production and number of employees for approximately 6,000 plants. These data are unusually rich in covering very small manufacturing plants as well as medium and large facilities. In fact three classifications of plant size, according to 6
# **8. Discussion and Conclusion**

We investigated the role that early education plays in children’s economic preferences and decision-making abilities. In 2007, the local government of the city of Rio de Janeiro used a lottery system to determine admissions into oversubscribed public free-of-charge daycare centers, which was exploited to identify intent-to-treat effects of daycare attendance. Nine years after those who were admitted started attending daycare, we surveyed about 2,100 of the applicant-children. Four incentivized experiments were conducted to measure both economic preferences as well as the quality of children’s decision-making.

Overall, daycare attendance affected neither economic preferences nor decision-making abilities. There was one exception however: daycare attendance did increase children’s aversion to disadvantageous inequality. More specifically, it increased the aversion of girls to being behind with no effect on the aversion of boys. Notably, this differential result by gender replicates in Cappelen et al.’s (2020) sample, which was randomized into a preschool program.

A natural question then is why the daycare lottery changed the social preferences of girls but not of boys. One possible explanation is that it changed girls’ perceptions of gender roles, including perceptions about how accommodating they are expected to be and how acceptable it is for girls to be more concerned with their own personal well-being and less concerned with the well-being of others.

But why would their perceptions have changed? One reason is that in Brazil families and daycare centers seem to model gender roles differently (see e.g. Santos 2017). Another reason is the empowerment that the mothers of treatment children seemed to have experienced.<sup>46</sup> Using data from the 2008 survey, Attanasio et al. (2022) show that, after gaining access to daycare services, mothers of treatment children reduced the amount of time they spent on childcare and increased their labor supply, both in the extensive and intensive margins.<sup>47</sup> The families of treatment children also had higher incomes, suggesting
, Taube, and Tsyvinski to provide estimates of the quasi-fiscal deficits (QFDs) of the power sectors in countries in Europe and Central Asia. Saavalainen and ten Berge defined the QFD of state-owned public utilities as _[t]he value of the implicit subsidy computed as the difference between the average revenue charged and collected at regulated prices and the revenue required to fully cover the operating costs of production and capital depreciation_ .<sup>1</sup> This QFD, or implicit subsidy, was termed a hidden cost<sup>2</sup> by Ebinger. Not all the elements of the QFD relate to a monetary cost being too large. For example, a low rate of bill collection is a cost in the sense of a loss of revenue. For the set of 20 countries analyzed by Ebinger based on 2003 data, underpricing accounted for 67 percent of total hidden costs, excess transmission and distribution (T&D) losses for 22 percent, and poor bill collection for 11 percent. Total hidden costs declined from 5.4 percent of GDP in 2000 to 2.6 percent in 2003.

The approach of Ebinger was utilized in the Africa Infrastructure Country Diagnostic (AICD), a knowledge program that undertook extensive data collection in the 2000s (primarily between 2001 and 2008) in all key infrastructure sectors, including electricity (http://www.infrastructureafrica.org/). Recognizing that the weak infrastructural base in SSA is constraining economic growth, the AICD estimated the magnitude of the QFDs for the power sector in SSA.

> 1 This definition assumes that the costs of production are the same in the hypothetical case of efficient operation and the actual case. The difference in revenues then measures the QFD.

> 2 The term hidden cost is used in other contexts for the energy sector. For example, in the evaluation of externalities of power generation (Biegler 2009), and of costs of power cuts and battery backups (Seetharam et al. 2013).

8
described in the Morningstar database or in the fund’s website. Instead of minimizing the variance of the portfolio, we minimize the variance of the difference

> 35 For robustness, we also perform these simulations with a more restricted sample. We use rolling windows of 240 business days. The results are robust to this change and are reported in Appendix Tables 6A and 6B.

> 36 These results are not driven by significant changes in the weights of the simulated global funds over time. The time series of the portfolio weights change smoothly and the simulated portfolios do not require large shifts in holdings, which could entail large transaction costs.

> 37 This is analogous to what others in the literature do. See, for example, Roll (1992) and Chan, Karceski, and Lakonishok (1999).

30
differences in the labor market characteristics between older and younger workers will likely influence the relationship between the employment of older persons and the labor market outcomes of younger persons, which will be analyzed further in the upcoming sections.

# **3 Literature review**

**At the macroeconomic level, research in a number of high-income countries has found no adverse relationship – and even a positive relationship – between the employment of older persons and the labor market outcomes of younger persons.** A study of 12 OECD countries<sup>7</sup> between 1960 to 2006 finds that an increase in the employment of older persons is estimated to decrease the unemployment rate of younger persons (Gruber et al. 2010). These findings hold both for analyses conducted within and between countries, as well as for causal analyses using natural experiments (Gruber et al. 2010). Focusing on the United States in the years 1977 to 2011, Munnell and Wu (2013) find no evidence that greater employment among older persons decreases either the job opportunities or wage rates of younger persons. This finding is consistent across genders and for groups with different levels of education. Munnell and Wu (2013) also conduct causal analysis using older male mortality rates as an instrumental variable, and do not find any consistent evidence that the employment of older persons adversely affects the labor market outcomes of younger persons. In Japan, using a firm-level survey, Kondo (2016) finds that increased elderly employment is not associated with a decline in the hiring of young full-time workers. They did, however, find that hiring elderly workers may lead to a decline in the hiring of female part-time workers (Kondo 2016). This is consistent with findings from the United States that suggests that age-friendly jobs (i.e. jobs that are more conducive for older workers) are also more desirable among women, who may prefer flexibility (Acemoglu et al. 2022). That aside, these findings generally suggest that in high-income countries, older and younger
|915|763|276|598|5|2,557|
|Ponding|691|-|245|-|-|936|

_- Not available. Source:_ Mian and Mirza 1993 Salinity and sodicity (hereafter referred to as salinity) and waterlogging dominate the environmental agenda in irrigated agriculture. Other problems include soil productivity losses and excessive use of agricultural inputs, especially chemicals.

# **_Waterlogging, Salinity, and Groundwater Management_**

Salinity is partly the result of naturally occurring geological processes and depends on the soil material, landform, relief, climate, and land use. In Pakistan, salinity has almost certainly increased as a result of the canal irrigation system as water containing dissolved salts from the lower parts of the soil profile seep from the canal system, and evaporate from the surface of adjoining soils. WAPDA has estimated that 2.12 million hectares of land are waterlogged, and Sandhu (1993) estimates that an additional 40,000 hectares per year are lost to waterlogging. These reports are based on outdated information; regular monitoring of waterlogging and salinity using standardized assessment criteria is needed so that more accurate estimates can be made.

The most neglected problem is that of tertiary salinity from the low-quality groundwater provided by tubewells. Surveys have shown that three-fourths of tubewells provide brackish water that is unfit or only marginally fit for agriculture. The cost of salinity in terms of reduced yields is hard to evaluate. Using farm-level data, Siddiq (1994) estimated that yield losses in wheat as a result of sodic irrigation water were 9 percent and 20 percent in two different locales in the central 14
–06|2.6|—|—|15|
||Swaziland|WHO|2003|12|24|7.2|46|
||Tanzania|WHO|2007|0.2|0.8|0|97|
||Togo|MICS3|2006|1.2|2.6|0.2|98|
||Uganda|HH surveys|2005–06|0.2|0.7|0|98|
||Zambia|WHO|2007|0.01|0.02|0|85|
||Zimbabwe|WHO|2003|0.5|1.5|0.08|67|

_Sources:_ WHO 2011; UNICEF various years for MICS3; World Bank various years for DHS; Bacon, Bhattacharya, and Kojima 2010 for HH (household) surveys; and Statistics South Africa 2008 for Statistics SA. _Notes:_ ECA = Europe and Central Asia; LAC = Latin America and the Caribbean; MNA = Middle East and North Africa; SSA = Sub-Saharan Africa; — = not available; + = no distinction made between natural gas and LPG, and hence the answer may apply to either fuel; * = total use across fuels greater than 100 percent, indicating that the question asked which fuels were used for cooking.
jurisdictions they must also rate their borrowers with a mandatory system prescribed by the regulator and based only on a few criteria, such as type of credit, days past due and projected cash flow.<sup>14</sup> These “prudential ratings” have typically been introduced as a means to homogenize the criteria to compute regulatory loan loss provisions and to assess the credit quality of different bank portfolios with the same metric, particularly in jurisdictions where banks’ rating systems were yet not widespread.<sup>15</sup> Although these “prudential rating systems” are not aimed at replacing or competing with banks’ rating systems,<sup>16</sup> they can play a subsidiary role. For example, smaller banks may be limited in their capacity to develop their own rating systems because of insufficient data, experience or resources; in such cases, the prudential ratings could supplement better risk analysis. Well constructed regulatory/supervisory rating systems may contribute to leveling the playing field in bank risk management practices and at the same time it would familiarize less advanced banks with modern credit risk management tools.<sup>17</sup> Statistical evidence for Argentina in Majnoni _et al_ (2004a) shows that a rating system built with PCR data benefits smaller banks with a larger reduction in their loan portfolio credit risk, as compared to that observed in bigger banks.

In jurisdictions where mandatory rating systems are in place, transition matrices coupled with assumptions regarding recovery rates can indicate to what extent provisioning ratios are aligned with average risk. Computing credit portfolio models with PCR data can also assist in optimizing the design and calibration of the regulatory framework, since they typically yield loss distributions that depict banks’ risk profile.<sup>18</sup> The output of credit portfolio models can be used to verify that credit loss provisions, obtained from applying the supervisory ratios, globally match the estimated expected loss and that regulatory capital covers most unexpected losses. Academic attempts to perform such comparisons can be
least one cash payment during the study period. Consequently, 75 percent of the households have direct experience with both cash and food payments. Reselling of food transfers is rare. In 2016, 93 percent of the households that received food transfers reported that they never sold any of them, and most of the remaining 7 percent did so rarely (Hoddinott, Stifel, Hirvonen, Minten, 2018).<sup>11</sup> Starting in 2010, the household surveys began collecting more detailed information on beneficiaries’ experiences associated with obtaining their payments. In order to receive their payments, beneficiaries are instructed – typically with 1‐2 days notice – to go to a “pay point”, nearly always a roadside location. These are supposed to be sited in such a way that beneficiaries do not have to travel more than three hours (one way) from their home. In practice, however, logistical considerations – most notably road conditions which determine where and how far vehicles carrying payments can travel – play a role in the siting of these pay points. Table 2 describes the transactions costs associated with receiving food and cash payments by year.<sup>12</sup> While there is some variability in these data from round to round, the following features emerge. First, generally pay points for food are farther away than those for cash, reflecting the difficulties that heavy goods vehicles face when carrying several tons of grains to remote areas. Second, irrespective of transfer modality, virtually all beneficiaries walk

> 11 Across all rounds, PSNP payments were equivalent to approximately 15 percent of household consumption.

12 Total transactions costs would also be affected by the number of trips made to receive payments. Looking at payment data covering the five months prior to each survey round, there are always more cash payments than food payments, but the magnitude of the difference in most years is 0.3‐0.8 trips over a five‐month period. The exception is 2012 when there were 1.
solutions to (standardized) exams, a practice that clearly undermines the education system.<sup>9</sup> Furthermore, monetary transfers that are imposed on a per capita basis might also affect poor households disproportionately since they have to pay a higher share of their income (Emran <u>et al. 2013).</u> The form of corruption in schools that is locally perceived as most problematic is direct bribing with the purpose of increasing the attention or grades a teacher gives to an individual student at the expense of others. Furthermore, bribes can be necessary to gain access to the best public high schools and to universities.<sup>10</sup> In sum, while payments to teachers are in part motivated by grade-buying or seeking better treatment for the child, a larger share seems to operate as a per capita tax. In the latter case, the extent and magnitude of informal payments is more likely to be determined by local norms, the preferences and the bargaining power of teachers, parents, and school officials, and less by the pursuit of inflated grades or preferential treatment for the child. Both kinds of petty corruption, however, can be expected to affect incentives negatively, increase the socioeconomic gradient in educational outcomes, and contribute to a social climate where corruption is an everyday experience.

In this section, we discuss the data and present key descriptive statistics of our sample.

# _Data_

We use data from a nationally representative household survey conducted in Moldova in 2011– 12 (henceforth abbreviated CELB 2012) which was specifically designed to investigate the effects of migration on children and elderly left behind. The survey includes 3,568 households with 12,333 individuals, of which 2,501 are children of age 6–18.<sup>11</sup> In addition to socioeconomic characteristics of household members, detailed information on private financial and non-

> 9. This problem was so widespread that, sometime after our survey took place, the education minister
Other Risks are not well-covered by supervisors in many developing countries that have been assessed. In practice there is then a need to enhance supervision here and Basel II may provide an incentive for extra resources to be provided. Of course, whatever the precise regulation adopted, the real job of supervisors is to consider whether the bank has sufficient capital for its overall risk-profile.

In terms of simply checking the capital adequacy against the regulation, the Basic Indicator approach would not require large resources if bank’s income statements are believed. The Standardized Approach is slightly more problematic as banks may have the incentive to massage their income statements such that greater gross income appears against the business lines that attract the lower operational risk. If supervisors have little experience or expertise, low effective control over and confidence in the veracity banks’ income statements, there may be an argument to staying with the Basic Indicator Approach.

However, as countries have experimented with different levels of the basic capital requirement for credit risk, it may also be that countries will wish to consider whether the actual percentages suggested by Basel II (currently 15% under the Basic Indicator or the various percentages under the Standardized Approach) are relevant for their particular circumstances. The 15% figure was calibrated largely on the basis of G10 banks. However, gross income in developing country banks tends to be high compared to their G10 counterparts, perhaps reflecting higher risks but also perhaps higher costs. It is understood that the results of Basel’s quantitative impact study (known as QIS3) indicated that the operational risk charge for some developing country banks could be very high. The appropriate calibration of the operational risk capital requirement for developing countries is then an issue for continuing future research.

# **2.4.5 Securitization Risk**

The securitization of bank-originated claims has grown enormously in recent years. While, in terms of size, the markets in G10 swamp those in developing countries, some developing countries have made significant advances in terms of the volumes of these transactions. Moreover
# **Section 4: Empirical Analysis of Urban Movements in Economic Activity**

This section analyzes the factors promoting or discouraging changes in urbanization from 1994 to 2005 in districts, with particular emphasis on education and infrastructure. We conduct our estimations at the district-industry level. In all regressions, we control for industry fixed effects to absorb changes in urbanization that are simply following national changes in urbanization levels for given industries. In other words, we will be considering factors that promote or discourage urbanization for a district beyond the change that would be expected based upon its industry composition. We first describe the district sample and the district traits that we consider, and then we present the estimation results.

## **_District Sample_**

Our sample focuses on district-industry cells. Our core sample contains 262 districts out of a total district count for India of 630. This sizeable decline in district count is due to the steps taken to prepare a consistent estimation setting, with the primary decline being due to lack of information in the 2001 Census on the district’s traits (e.g., education, infrastructure). This Census information was only collected for about 400 districts.

In addition to our state-level restrictions noted earlier, our regression sample also requires that the district-industry be observed in all of our manufacturing surveys so that we can observe changes over time. Our explicit criteria with respect to district size are that the district has a population of at least one million in the 2001 Census and has 50 or more establishments sampled. Given our desire to study urbanization changes, we also exclude districts that had fewer than five urban plants or five rural plants. Finally, we exclude plants that have negative value add, which accounts for 6%-7% of employment.

These restrictions are not very significant in terms of economic activity. The resulting panel accounts for over 80% of plants, employment, and output in the manufacturing sector throughout the period of study. As important, the aggregate trends for these districts mirror that
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