rafmacalaba/datause-encoder-data huggingface dataset:
# **4.1 Policies to remove the regulatory, bureaucratic, and informational barriers that inhibit return migration**

There are a number of regulatory and bureaucratic barriers that can make it more difficult and costly for migrants to return to their home countries. One important example is citizenship or residency rights. Migrants from countries without dual citizenship may have had to give up their home country citizenship in order to gain citizenship abroad. If they wish to return later to their home countries, they may face difficulties in being allowed to move permanently back, as well as the disincentive of having to close off the option of migrating again by potentially giving up their newly acquired foreign citizenship. Individuals with only legal residency abroad may be even more reluctant to move back than those with foreign citizenship, since returning home can result in violating continuous residence requirements required for them to retain permanent residence status. For example, permanent residents in the United States can lose their permanent residence status if they remain outside the U.S. for more than one year.<sup>8</sup> Le Blang (2011) uses cross-sectional data on migrants in Spain and Germany, and finds an association whereby migrants from countries which offer dual citizenship send more remittances and express higher intents to return. Dual citizenship also can confer benefits on migrants if they do not return. Mazzolari (2009) uses a difference-in-differences approach to compare immigrants in the U.S. from five Latin American countries which granted dual citizenship rights in the 1990s to migrants from other Latin American countries, and finds the migrants granted dual citizenship rights increased employment and wage earnings, and reduced their use of welfare benefits relative to the comparison group. The number of countries which allow for dual citizenship has grown dramatically, from only 26 in the mid-1970s to 84 countries in 2006 (Le Blang, 2011).

Dual citizenship makes it easier for migrants to return. However, many migrants marry citizens of other countries, and so another constraint they face are limits to the ability of their non-citizen spouse and children to live, work, and attend schools in the migrant’s country of origin. Malaysia gives permanent residence status to foreign spouses and children as part of its efforts to facilitate return migration (Lowell, 2001). It is likely such efforts help, and they are certainly unlikely to reduce development impact, but we are unaware of any evaluation evidence.

> 8 <u>http://www.uscis.gov/green-card/after-green-card-granted/maintaining-permanent-residence</u> [accessed March 26, 2014].

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15

# 3. <u>Regulations that empower the private sector</u>

The private empowerment view suggests that bank regulations should focus on enhancing the ability and incentives of private agents to overcome informational barriers and exert corporate governance over banks (Grossman and Hart, 1980; Hay and Shleifer, 1998). Furthermore, this view argues that many empowered bank creditors will be less susceptible to capture by politicians and banks than a single government supervisory agency. Thus, special connections and corruption may play less of a role in countries that foster private monitoring.

Evidence tends to support the view that regulations that power the private sector work best to improve the governance of banks. BCL construct a measure of the degree to which regulations empower the private sector. This measure includes information on regulations that force banks to disclose information to the public and impose severe penalties on a bank’s directors or officers for providing inaccurate or misleading information. BCL find that strong private empowerment regulations boost overall bank development and efficiency. Furthermore, BDL find that (1) strong private empowerment reduces the reliance on corrupt ties with bank official by firms to receive bank credit and (2) strong private empowerment tends to lower the financing obstacles faced by the average firm.

Work on deposit insurance emphasize that it is important to give private creditors sound incentives. As noted earlier, more generous deposit insurance tend to induce banks to assume greater risks and fail more frequently (Demirguc-Kunt and Detragiache, 2003). Beyond bank fragility, BDL find evidence consistent with the view that less generous deposit insurance will reduce the diversion of bank credit toward riskier firms and thereby facilitate the financing of firms in general.

In related work, CLL examine whether laws that protect minority shareholders boost the valuation of banks. They find the investor protection boosts the valuation of banks. At the same time, they find that strong regulatory agencies do not boost bank valuations. This implies that shareholders are willing to pay more for bank in countries where investor protection laws protect their interests, but strong regulatory agencies do not ease investor concerns that bank managers will exploit small investors.
of employment—as “not in work,” and also has some effect on working hours. Both boys and girls are reported to have lower participation in agriculture and more in domestic duties using the short module. Response by proxy seems to have no effect on employment statistics compared with the self-reported response by the child. These observations are confirmed when controlling for a wide range of individual, household, and village characteristics. When we use probit analysis to estimate the marginal effect of the two survey types, we find that the short module yields 17 percentage points lower participation rates for boys and 23 percentage points lower for girls. Using a multinomial logit, we find that both boys and girls are less likely to be reported in agriculture and other sectors than in no work and domestic duties when using the short module. However, response by proxy produces statistics that are not significantly different from self-response. This is in sharp contrast with the effect of survey methods on labor statistics of adults, where response by proxy appears to have the largest impact (see Bardasi et al., 2010). Our finding that there is no significant discrepancy in child labor force participation statistics between proxy and self-reports (that is, between the situation in which questions are asked to adults or the children themselves) is particularly reassuring.<sup>17</sup> When discussing the choice of the respondent, in particular the use of household surveys to obtain information on child labor, the ILO guidelines state that “...With regard to respondents, the general practice is to address survey questions to the most knowledgeable adult member of the household (or sometimes the head of household, who is often also the parent or guardian of the working child). However, sections of the questionnaire may be addressed to the children themselves, particularly on hazards at the work place, and the main underlying reason for working.” (ILO, 2008, para 49) The ILO document also states the importance of respecting ethical standards to make sure that children are not adversely affected by their participation in the survey, when they are respondents. So, in situations where it is not possible to interview the children directly or it is considered inappropriate, our results indicate that employment statistics should not be significantly affected.

> 17 An alternative view is that children and parents (and other proxy respondents) are equally disinclined to reveal the actual extent of child labor due to social stigma – that it is hidden from surveyors. As discussed in the Introduction, we consider this stigma to be minimal in this setting. This does imply that these results may not be germane in contexts where people would want to hide or deny the extent to which children work.

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# **Medium and Large State-owned Enterprises and Organizational Change**

The population of firms in China is vast and the diversity of organizational forms is on the rise. Not only are there SOEs, cooperatives, collectives and township and village enterprises (TVEs), to these have been added joint ventures (JVs) of many different kinds, wholly owned foreign subsidiaries and growing numbers of privately owned firms, following a constitutional amendment in 1988 legalizing individual ownership. Such proliferation makes it difficult to neatly trace the effects of reform or to typify organizational change. For this reason, we have chosen to examine one class of enterprises, the medium and large sized state-owned enterprise (MLSOEs). In addition, our attention is mainly focused on the major coastal cities where urban and organizational changes are in full swing and which stand at the apex of China’s urban hierarchy.

There are advantages of such a selectivity as well as drawbacks but the former, we will argue, outweigh the latter. Among the advantages first of all is the weight of the MLSOEs in the urban industrial economy. The total of some 7,500 enterprises are responsible for close to a fifth of industrial GDP and this share has shrunk relatively little over the past decade even though the overall share of SOEs output has declined from 55% in 1990 to 24% in 2002 as a result of the privatization or shuttering of tens of thousands of small SOEs and because of the rapid output growth registered by a variety of non-state firms. Moreover, MLSOEs are the source of 40% of China’s exports and they account for nearly two-thirds of China’s industrial fixed assets. A second consideration is the availability of information and detailed research on some of the leading MLSOE companies such as Baoshan Iron and Steel, CIMC, Lenovo, TCL International Holdings, and Kelon (see Table 2). The research is especially valuable because it adds depth and nuance to the dry statistics on the state sector. Third, the MLSOEs provide employment to a 6
<!-- Start of picture text --> 2 Data and Stylized Facts<br><!-- End of picture text --> Given the potential importance of export diversification for developing countries, what do the data say about its extent and cross-country variation? What might be some of the explanatory factors behind these observations? After a brief discussion of our approach to measurement issues, this section presents some initial impressions from the cross-country data. We then use non-parametric techniques to examine the links between export diversification and its potential determinants, such as country size and level of development, trade costs, international distance, and the costs of domestic market entry. 2.1 Measuring Export Diversification At its simplest, export diversification means broadening the range of products that a country exports. We therefore equate it with export growth at the extensive margin, and draw on the recent literature in that area for guidance as to the ways in which diversification can be measured.' As a starting point, we use a direct measure, namely a count of the number of products that a country exports. In practice, this approach is not as simple as it seems because individual "products" identified in the trade data usually map in reality to a number of distinct varieties. The most detailed trade data available on a worldwide basis are at the 6-digit level of the Harmonized System (HS), and distinguish amongst 5,000 or so different products. Casual empiricism suggests, however, that counts based on 6-digit data are likely to understate the true level of export diversity due to aggregation effects. In this paper, we improve on the level of detail provided by the 6-digit HS classification by using an 8-digit classification that provides roughly twice as much product-level detail: 10,753 distinct product lines.? We extract these data from a freely available Eurostat database covering 'We limit consideration to growth in the number of products exported, and do not examine the number of markets to which they are exported. This is an important question in its own right, and we expect that the methods used in this paper could be adapted to build on the insights developed in this area by Evenett and Venables (2002). The data are classified using the EU’s Combined Nomenclature (CN), which is based on the Harmonized System (HS) but contains additional subdivisions at the 8-digit level. 5
the surface temperature and the temperature in each of the two layers immediately above the surface. If the surface temperature is lower than the temperature in either the first layer _or_ the second layer we classify that day as experiencing an inversion. The second compares surface temperature to the temperature in all the layers in our sample. Again, if surface temperature is lower than the temperature in _any_ of these layers we classify that day as experiencing an inversion. We also split inversions into winter and summer inversions as meteorological conditions and the emissions of pollutants vary across seasons. In general winter inversions have a larger impact on pollution levels (for example because there are more emissions from burning for heating in the winter which can be trapped by inversions). We count the number of days with inversions in each district and year or season and calculate the share of days each district experienced an inversion in the whole year as well as separately by winter and summer. We also measure the strength of each inversion as the difference in temperature between the higher and lower layer on days when there is an inversion. Larger differences indicate stronger inversions (Chen et al., 2022). For each district and year or season we calculate the average inversion strength averaging across all inversions that occur in the relevant time period.

# **3.3 Meteorological controls**

We collect weather re-analysis data from ERA5. ERA5 is produced by the European Commission’s Copernicus Climate Change Service.<sup>11</sup> We use data from the ERA5 Land hourly product. This provides data at an hourly level on a grid of 0.1<sup>_◦_</sup> _×_ 0.1<sup>_◦_</sup> , which translates to a 9km resolution over India. We use data on temperature, dew point, surface pressure, wind direction, wind speed, and precipitation over the full sample. We calculate relative humidity from dew point, temperature and surface pressure. We aggregate these weather variables to the district level by averaging over all grid-points that fall within a district boundary using the same population weighting we use to calculate inversions.

# **3.4 Air pollution data**

We use pollution data from the global data produced by the Van Donkelaar research group (Van Donkelaar et al., 2019). This provides monthly average pollution levels on a 0.01<sup>_◦_</sup> _×_ 0.01<sup>_◦_</sup> grid covering the entire planet from 1998 to 2020. We calculate annual average pollution in Indian districts by assigning grid-points to their respective district and then averaging across all grid-points within a district in each month. We weight grid-points by the population

> 11Data available here: https://cds.climate.copernicus.eu/cdsapp!/dataset/reanalysis-era5land?tab=overview

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**Table 10 Inflation rate by** **<u>population quintile: alternative indices</u>**

|||**Lasp**|**eyres**|**Paa**|**sche**|**Fis**|**her**|
|---|---|---|---|---|---|---|---|
|**Period**|**Quintile**|**Inflation**|**Inflation**<br>**differential**|**Inflation**|**Inflation**<br>**differential**|**Inflation**|**Inflation**<br>**differential**|
|**1988-1996**|**1**|725.47|4.48|722.37|3.85|723.92|4.16|
|**l**|**2**<br>|726.43<br>|3.51<br>|722.69<br>|3.53<br>|724.56<br>|3.52<br>|
|**azi**|**3**|727.59|2.36|723.96|2.26|725.77|2.31|
|**Br**|**4**|729.06|0.89|725.04|1.18|727.05|1.04|
||**5**|732.02|-2.08|728.42|-2.20|730.22|-2.14|
||**All**|729.95||726.22||728.08||
|**1997-2003**|**1**|10.53|0.37|9.85|0.30|10.19|0.33|
|**ia**|**2**|10.50|0.40|9.77|0.38|10.13|0.38|
|**mb**|**3**|10.61|0.29|9.92|0.23|10.27|0.25|
|**olo**|**4**|10.76|0.14|10.02|0.12|10.39|0.13|
|**C**|**5**|11.20|-0.30|10.42|-0.28|10.81|-0.29|
||**All**|10.90||10.15||10.52||
|**1984-1989**|**1**|77.58|0.30|77.99|1.05|77.79|0.67|
||**2**<br>|77.60<br>|0.29<br>|77.68<br>|1.36<br>|77.64<br>|0.82<br>|
||**3**|77.65|0.23|78.11|0.94|77.88|0.58|
||**4**|77.73|0.16|78.30|0.75|78.01|0.45|
||**5**|78.15|-0.27|80.11|-1.07|79.13|-0.67|
||**All**|77.88||79.04||78.46||
|**1989-1994**|**1**|15.94|0.83|15.34|0.63|15.64|0.72|
||**2**|16.17|0.59|15.44|0.53|15.81|0.56|
||**3**<br>|16.42<br>|0.34<br>|15.58<br>|0.40<br>|16.00<br>|0.37<br>|
||**4**|16.51|0.26|15.63|0.34|16.07|0.29|
|**o**|**5**|17.26|-0.49|16.46|-0.49|16.86|-0.50|
|**xic**|**All**|16.77||15.97||16.37||
|**1994-1996**<br>**Me**|**1**|35.12|-1.34|36.06|-1.53|35.59|-1.44|
||**2**|34.92|-1.14|35.60|-1.08|35.26|-1.11|
||**3**|34.47|-0.69|35.27|-0.75|34.87|-0.72|
||**4**|34.17|-0.39|34.85|-0.32|34.51|-0.36|
||**5**|32.92|0.86|33.61|0.91|33.27|0.88|
||**All**|33.78||34.53||34.15||
|**1996-2002**|**1**|12.08|0.39|11.87|0.38|11.97|0.39|
||**2**|12.17|0.30|11.99|0.26|12.08|0.28|
||**3**|12.30|0.17|12.06|0.18|12.18|0.18|
||**4**|12.38|0.09|12.16|0.08|12.27|0.08|
||**5**|12.69|-0.22|12.47|-0.23|12.58|-0.23|
||**All**|12.47||12.24||12.36||
|**1995-1999**|**1**|7.79|0.37|7.69|0.39|7.74|0.38|
||**2**|7.86|0.30|7.70|0.38|7.78|0.34|
||**3**|7.98|0.18|7.79|0.29|7.89|0.23|
||**4**|8.13|0.03|7.89|0.19|8.01|0.11|
||**5**|8.41|-0.25|8.40|-0.32|8.41|-0.29|
||**All**|8.16||8.08||8.12||
|**1999-2001**|**1**|3.75|0.87|1.93|0.35|2.84|0.41|
|**ru**|**2**<br>**3**|3.89<br>4.16|0.73<br>0.46|1.99<br>2.12|0.29<br>0.16|2.94<br>3.13|0.31<br>0.11|
|**Pe**|**4**|4.52|0.10|2.33|-0.05|3.42|-0.17|
||**5**|524|-062|270|-042|396|-071|
||**All**|.<br>4.62|.|.<br>2.28|.|.<br>3.25|.|
|**2001-2003**|**1**|1.28|0.32|0.95|0.40|1.12|0.36|
||**2**|1.35|0.25|0.92|0.43|1.14|0.34|
||**3**|1.46|0.14|1.09|0.26|1.28|0.20|
||**4**|1.58|0.02|1.22|0.14|1.40|0.08|
||**5**|1.81|-0.20|1.71|-0.35|1.76|-0.28|
||**All**|1.60||1.36||1.47||

Note: The table reports the inflation rate by population quintile of the expenditure distribution and the difference between the inflation rate corresponding to each quintile and the average inflation rate for alternative indices.

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trustworthiness, and our local bank density IV is unlikely to reflect omitted local characteristics.

Table 3 presents the definitions and summary statistics of our key variables. Full sample descriptive statistics are presented in panel A. Overall, 3.8 percent of Chinese urban households were fraud victims in 2014, and the counterpart was 16 percent for Dutch citizens in 2007, 11 percent for U.S. residents in 2011, 8 percent for U.K. residents in 2006, 4 percent for Canada residents in 2006 or for Australian residents in 2012 (Anderson, 2013; Whitty and Buchanan, 2012; Jorna and Hutchings, 2013).<sup>14</sup> The average amount of economic losses associated with fraud victimization is 897 yuan ($126.5), and the maximum amount of losses is 3 million yuan ($423,131). Descriptive statistics on approached urban households are presented in panel B. Among the approached urban households, 5.7 percent became victims. The average amount of economic losses for approached urban households is 1,345 yuan ($192), and 23,961 yuan ($3,423) for victimized urban households. For victimized urban households, the 25<sup>th</sup> , 50<sup>th</sup> and 75<sup>th</sup> percentiles of economic losses are 300 yuan ($42.3), 1,000 yuan ($141), and 6,000 yuan ($846.2), respectively. Among the approached urban households, 15.4 percent face credit constraint, the average years of schooling is 10.9 years, 38 percent have at least one member with chronic illness, nearly 5 percent are living alone, and 69 percent of urban household heads are male.

# **5. Empirical Results**

# **5.1 Baseline results**

Table 4 presents the baseline OLS results based on equation (1). The dependent variable for columns (1) to (4) is being a fraud victim; the dependent variable for columns (5) to (8) is the logarithm of the total amount of economic losses subject to fraud schemes. Full sample containing all urban households is used in columns (1), (2), (5) and (6). As households are approached by fraudsters before victimization and approached households may have distinct characteristics, we further restrict our sample to approached urban households in columns (3), (4), (7), and (8). Columns (1), (3), (5) and (7) report the results

> 14 Netherlands Consumer Authority, “Unfair Commercial Practices (UCPs) in the Netherlands: Survey Report,” Prepared by Intomart Gfk, November 2008. See also https://www.ic.gc.ca/eic/site/112.nsf/eng/01513.html.

16
However, there is a trade-off between the level of service offered and its costs. The higher the level of service, the higher the amount of resources required. If we could identify all or some costs associated with the time wasted by passengers at queues, and the economic resources wasted as a consequence of this waiting period, it would be possible to assess the losses arising from the level of services provided. In summary, the establishment of an inadequate level of service could negatively influence users and even airport interests. An illustrative example is provided by the check-in service. The more time spent by passengers in front of check-in counters, the less time available for shopping in the airport commercial area.

**Table 5.3. Quality of service indicators**

|ff;Ef-:XA;f!000<br>ttat00000ff;;;0T00 ft4 iV0<br>L;t;.i'Time<br>of service ':hckin<br>tm,luggage divfery timse<br>Delays<br>*Waiting time~~~~~~~intie,IUP&|
|---|
|0:0000 000;:5<br>t0tt0:<br>000000000**0**:;<br>0000 0000<br>;00t;i'0Costsfor ps eng ers offo da d0<br>0<br>dr<br>0ink<br>00i00);00;0<br>Costs<br>'Departure~~~*C<br>fee<br>*Conetd<br>e|

**Source: Lemaitrt (1997) adapted.**

The use of indicators as tools for assessing a given activity is ineffective if there are no reference standards delineating acceptable performance margins. However, once the particular features of each airport are taken into consideration, these "desirable" or "best practice" reference standards should be considered as provisional guides only. There is not a unique optimum level for a given indicator. The appropriate and optimal reference level depends upon the circumstances _of_ each airport. Furthermore, there may be conflicts between the different objectives pursued. For example, an improvement in the level of 52
regulations<sup>35</sup> , provided a strong impetus to the credit reporting industry. As a result, more than 1,000 institutions feed the databases of _Buró de Crédito_ with positive and negative information on more than 64 million individual accounts and 4 million company accounts. Banks and other lenders can obtain real-time data on customers that, in combination with their own proprietary information, allow them to better differentiate borrower risk (calibration of scoring models) and expand credit. The relatively early resumption of consumer credit (as compared to commercial and housing finance) in 2000 can, at least partly, be attributed to the presence and compulsory use of credit bureau information. Although the experience has been positive to-date, more can be done to further strengthen the credit reporting industry<sup>36</sup> .

> 35 Financial institutions are required to obtain a copy of the credit history of prospective borrowers as part of the loan paperwork for loans over USD 300. A provisioning requirement of 100 percent of the loan amount may be imposed upon failure to obtain such a report.

> 36 Examples include better coordination across agencies responsible for the oversight of different parts of the credit reporting industry, analysis of whether/how the governance structure and ownership of the _Buró de Crédito_ has affected competition, the speed of product innovation and expansion of coverage (especially the number of bureau clients), inclusion of payment information on public utility and Infonavit clients, bankruptcy or other judicial data etc.; see Western Hemisphere Credit and Loan Reporting Initiative (March 2005).

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