rafmacalaba/data-use-mentions-extended huggingface dataset:
in their tariffs, a procedure which encouraged them to make larger cuts in their smaller tariffs. The Doha tariff-cutting formulas have the economically desirable feature of making larger cuts in the higher—and hence more costly—tariffs.<sup>1</sup> In line with longstanding practice, developing country cuts in each band are two-thirds those of the industrial countries. The bands are also wider, in part to allow for the fact that many developing countries would otherwise have more tariffs included in the higher bands.

Special provisions apply for tariff escalation products. Here the general principle is that processed products subject to tariffs higher than their raw or intermediate forms are moved to the next higher tier. If they are in the highest tier, the cut is increased by 6 percentage points. If the gap between the processed and unprocessed product is less than 5 percentage points, then the tariff escalation procedure is not used, and the tariff on the processed product should not be brought below the tariff on intermediates.

A list of ―tropical‖ and diversification products will be subject to deeper-thanformula cuts. Two lists of products have been considered—one includes highly sensitive products such as rice, sugar and bananas (see Appendix G of the agricultural modalities), and another is the more limited list used in the Uruguay Round. Two alternative treatments have been discussed. Under the first, tariffs below 25 percent would be reduced to zero, and no sensitive product treatment permitted. Under the second, tariffs below 10 percent would be reduced to zero, while higher tariffs would be reduced by 70 percent, except for products already in the top tier, which would be cut by 78 percent. Under the second alternative, sensitive product treatment will not be ruled out. The Uruguay Round set, without sensitive products, is used in the empirical analysis.

Several groups of developing countries listed in Table 2 are allowed smaller tariff reductions. Least Developed Countries (LDCs) are not required to make any reductions. Small and Vulnerable Economies (SVEs
duty/registration fees was not readily available. Nonetheless, government officials generally acknowledge that such discounts do lead to revenue losses they considered insignificant. Furthermore, some government officials noted the benefits of stamp duty/registration fees, as both of these measures facilitated formalization of properties and resulted in an increased number of taxed properties on their records.

# 6.3 Policy implications

States in India levy stamp duty on registration of property while cities and towns levy recurrent property tax. India is one of the few countries offering discounts on property-related taxes to female property owners. This study attempts to analyze the impact of such incentives on female property ownership in India, and tax revenues from property-related taxes. The study used qualitative tools, such as focus group discussions with female and male property owners. To verify information collected through focus group discussions, the study also conducted interviews with government officials from land registries and revenue authorities at the city and state level. Wherever possible, efforts to collect gender-disaggregated data on property ownership and property-related tax revenues were also made.

The predominantly qualitative design of the study does not allow drawing conclusions by statistical inferences. However, the information collected from interviews and in focus group discussions is sufficient to draw a number of valuable conclusions on the impact of gender-based discounts for property-related taxes in India. One of the study’s findings is that gender-disaggregated data on both property ownership and property-related tax revenues is not readily available. While it may be collected during registration or otherwise, it is not systematically tracked or published. As a result, government officials are unable to accurately assess the impact of incentives on the level of property ownership among females or on property-related taxes.

Further, both focus group discussions and interviews with government officials indicate that stamp duty and registration fee discounts are very effective in stimulating property registration in females’ names (for both joint and single ownership). The one-time stamp duty is a significant amount and creates an economically significant incentive to spur female property ownership. This finding supports earlier studies by Ayalew Ali et
i<br>é88<br>da00 500,000 1,000,000 1,500,000 2,000,00¢ ° z ma ae as<br>Per capita expenditure (LCU/person/year) capita expenditure (LCU/person/year) expenditure (LCU/person/year) Percapitacapita expenditure (LCU.personyear) (LCU.personyear)personyear)year) .<br><!-- End of picture text --> <!-- Start of picture text --> 4<br>: g<br>Z<br>2 2<br>z. 2<br>a3 a<br>2aa<br>6<br>° °<br>° 500,000 1,000,000 1,500,00¢ 0 50,000 100,000 150,000<br>Per capita expenditure (LCU/person/year) Per capita expenditure (LCU/person/year)<br>i<br>i:: g<br>8388 tBeBe<br>5 i<br>é88<br>a0da00 500,000 1,000,000 1,500,000 2,000,00¢ ° z ma ae as<br>Per capita expenditure (LCU/person/year) capita expenditure (LCU/person/year) expenditure (LCU/person/year) Percapitacapita expenditure (LCU.personyear) (LCU.personyear)personyear)year) .<br><!-- End of picture text -->
The choice of the appropriate matching variables should be dictated by economic theory and eventually adjusted based on information available in the data. Rosenbaum and Rubin (1983) argue that only variables measured before the treatment should be included to avoid any endogeneity with respect to the exposure to the treatment. Lechner (2008) shows that this condition can be relaxed as long as the effect of the treatment on the covariates is nonsystematic; variables observed after the treatment thus only induce a measurement error in . In the empirical application in the study, the choice of the appropriate variables is guided by the hedonic theory of consumption (Freeman 2003; Lancaster 1966), which shows how housing can be thought of as a XX commodity composite of different characteristics, and by consolidated empirical evidence. (See Hill 2013 for a survey of hedonic models for housing prices.) This includes location of the dwelling; structural attributes of the dwelling, such as whether it is a detached home or an apartment, type of construction, age of the dwelling, dimensions and number of rooms, and so on; and neighborhood characteristics, such as the quality of schools, accessibility of public transport, proximity of streets, crime rate, poverty rate, traffic congestion, and so on.

# **5. Data**

The study relies on data from the 15 rounds of the National Household Survey on Living Conditions and Poverty (Encuesta Nacional de Hogares sobre Condiciones de Vida y Pobreza) over 2003–17. This long time span includes the period of the rapid expansion of the housing market and the implementation of the progressive housing policies of the second half of the 2000s. Over the period, the survey was collected using the same methodology, including the questionnaire and sampling design, though there was an update of the sampling frame following the 2007 population census.

The National Household Survey is a continuous survey that collects detailed information on demographics, education, health, labor market status, farm and nonfarm income, participation in social programs, and perceptions of governance
most important determinants of firms’ compliance with environmental regulations in India.

In this paper, firm-level data are used from India to compare the compliance level of firms when they are inspected by agencies versus the times when they self-report. The paper also looks at the other factors that may determine regulatory compliance such as age, size, sector, location, etc. Newer and privately-owned firms come out to be more compliant. There are also differences between complying with air and water pollution. Such a detailed characterization can help regulators facing budget constraints to better target the most polluting firms and maximize abatement.

The main takeaway from this paper is that regulator information can be improved through a clear understanding of firms’ characteristics and how these characteristics affect the firm’s probability to comply with environmental regulations. The paper finds that there is ample scope to improve the environmental regulation system to create positive dynamics that can then increase the compliance levels in India since more (past) inspections are found to increase the level of compliance in a few industries.

Firms are more in compliance when they self-report than when they are inspected, as expected, but the self-reporting system needs to be reformed to serve as a more accurate policy tool in environmental regulation. Self-reporting can potentially be a valuable instrument in the context of limited resources, but its design is weak in India. In the future, if there are similar levels of penalties for self-reported noncompliance as for inspections, one may potentially see the firms’ compliance going up in general. Findings from the United States suggest that the firms that voluntarily disclose regulatory violations and commit to self-policing improved their regulatory compliance and environmental performance, suggesting that the enforcement relief they received was warranted. Collectively, our results suggest that self-reporting can be a useful tool for reliably identifying and leveraging the voluntary self-policing efforts of regulations.

20
less so in Henan and Sichuan. According to census data of 2000 (Table 2), there were 20.15 percent of population living in towns in Zhejiang province; in contrast, there was only 8.31 percent of population living in towns in Henan, and 12.26 percent in Sichuan. Zhejiang is also more urbanized than other two provinces. There was 28.5% of population living in cities whereas there was only 15% of population living in cities in Henan and Sichuan respectively, even lower than national average; majority of population, three quarters, lived in rural areas in Henan and Sichuan.

Table 2 City, town, and county <u>population in three provinces in China (in million): 2000</u>

|Province|total|city|town|county|
|---|---|---|---|---|
||(pe<br>|rcentage in pa<br>|renthesis)<br>||
|Zhejiang|45.93|13.10|9.25|23.57|
|||(28.53)|(20.15)|(51.33)|
|Henan|91.24|13.80|7.58|69.851|
|||(15.13)|(8.31)|(76.56)|
|Sichuan|82.35|12.21|10.10|60.04|
|||(14.83)|(12.26)|(72.91)|
|National|1,242.61|292.63|166.14|783.84|
|||(23.55)|(13.37)|(63.08)|

________________________________________________________________________ Source: NBS “Tabulation on the 2000 population census of the People’s Republic of China. Table 1-1
rates, e.g., when the dynamics in a labor market favor certain kinds of workers. Thus, a series of personal characteristics, namely, gender, ethnicity,<sup>37</sup> educational attainment (measured by total education years) and potential experience,<sup>38</sup> are included.

Changes in Export Index may be systematically correlated with province specific characteristics that affect wage growth (Topalova, 2010). For instance, if provinces which had more initial industrial base were enjoying higher wage growth even in the absence of exports and also experienced larger industrial exports expansion, the coefficient of the change in Export Index might capture a spurious relationship. In order to overcome this concern, I included the initial provincial employment composition at a more aggregate level, namely the proportion of workers in agriculture, construction, industry, government, and service sector in total workers constructed from the VHLSS 2002 (McCaig, 2011; Topalova, 2010). The growth path also may differ between rural and urban areas. Thus, Model B also includes a dummy variable for whether or not the individual lived in an urban area.

Columns 4 through 6 in Table 1 report the results of Model B. Relative to the results obtained by Model A, the inclusion of the additional control variables led to an increase in R<sup>2</sup> . However, the coefficients for the changes in Export Index in Model B turn out to be similar to that obtained in Model A and the results appear to be robust to the inclusion of control variables. Some personal characteristics are found to have affected wage growth. For instance, the coefficients for education are found to be highly positively significant, highlighting the importance of educational attainment in wage growth. The positive impact of education on wage growth is also consistent with the rise in skill premium in Vietnam’s economy as a whole.

> 37 The minority is defined to represent all ethnic groups other than the Kinh majority and Hoa (
.1|201.5|275.9|299.0|227.1|
|From international capital|19.4|26.2|52.2|100.0|89.6|96.1|149.5|_135.5_|72.1|
|Markets||||||||||
|Private debt flows|15.7|18.6|38.1|49.0|54.4|60.0|100.3|105.3|58.0|
|Commercial bank loans|3.2|4.8|16.3|3.3|13.9|32.4|43.7|60.1|25.1|
|Bonds|1.2|10.8|11.1|37.0|36.7|26.6|53.5|42.6|30.2|
|Others|11.4|3.0|10.7|8.6|3.7|1.0|3.0|2.6|2.7|
|Portfolio equity flows|3.7|7.6|14.1|51.0|35.2|36.1|49.2|30.2|14.1|
|Foreign direct investment|24.5|34.4|46.1|67.0|88.5|105.4|126.4|163.4|155.0|

a. Preliminary Source: World Bank, Debtor Reporting System 6
in Central and Northern Mozambique and in rural areas nationwide. Compared to the official figures, this represents a complete re-ranking of provinces based on poverty incidence. In addition, our calculations do not support the ostensibly enormous spatial price variations that are the basis for the large differentials in location-specific PLs and consequently call into question the observed changes in poverty headcount rates between 2002/03 and 2008/09.

In terms of poverty dynamics, our revised estimates indicate very different trends in poverty reduction than those recorded in the official statistics. Rather than suffering economic stagnation, Mozambique is continuing to experience a general decrease in poverty, though this has been heavily concentrated in urban areas where only 30 percent of population resides; meanwhile, rural poverty has remained largely intractable, with the notable exception of rural areas in the greater Maputo region. In sum, our analysis strongly suggests that not only are the Southern and urban areas of Mozambique considerably richer than the official figures would indicate, but growth in these areas has been relatively robust and consistent over time. Meanwhile, poverty in the Central and Northern regions of the country, and in rural areas nationwide, appears to be systematically underestimated in the official statistics. Ostensibly high rates of poverty reduction in these areas, as recorded in the most recent national surveys, are likely to be the result of changes to the survey itself and its analytical methodology, and do not necessarily reflect real improvements in the economic conditions of the nation’s poor. If the evident weaknesses in the official statistics are not adequately addressed they may ultimately become the foundation for illtargeted or even potentially ineffective economic growth and poverty-reduction policies.

26
Specific monthly grant amounts range in 2003 from $US9.50 ($105 pesos) in the third grade of primary to about $US53 ($580 pesos) for boys and $US60 ($660 pesos) for girls in the third year of senior high school (grades 10-12). By the senior year of high school, the grant amount represents about two-thirds of Mexico’s minimum wage. All monetary grants are given to the mother of the family, with the exception of scholarships for senior high school, which the youth can receive themselves subject to their mothers’ authorization. The program is means tested, with both geographic and household-level targeting. The geographic targeting uses aggregate census indicators to select poor rural communities based on the marginality index.

Table 1: Monthly Amount of Schooling Grants, 1997 and 2003

||2<sup>nd</sup>semester|1997|2<sup>nd</sup>semester|2003|
|---|---|---|---|---|
||Boys|Girls|Boys|Girls|
|Primary school|||||
|3<sup>rd</sup>year|60|60|105|105|
|4<sup>th</sup>year|70|70|120|120|
|5<sup>th</sup>year|90|90|155|155|
|6<sup>th</sup>year|120|120|210|210|
|Secondary school|||||
|1<sup>st</sup>year|175|185|305|320|
|2<sup>nd</sup>year|185|205|320|355|
|3<sup>rd</sup>year|195|225|335|390|
|High school|||||
|1<sup>
problem.<sup>22</sup> To minimize the bias, researchers should check the balance in the distribution of enumerator characteristics with respect to the treatment status when assigning enumerators to subjects.<sup>23</sup> Yet, balancing enumerator characteristics can be difficult to implement in the field. When this is not possible, at least researchers should balance enumerators themselves between treated and control groups.

In the case of surveys collecting sensitive information and for which there is not a clear identification strategy (for example Afrobarometer, World Bank Living Standard Measurement Survey (LSMS), etc.), the results suggest that these data need to be interpreted cautiously. Also in this case, simply including enumerator fixed effects in analysis is not enough. Data documentation for these surveys should provide, at a minimum, a clear description of how enumerators and teams were assigned. This would allow researchers to check for the presence and the size of the enumeration effect before conducting any analysis.

Note that, unlike other improvements in measurement, the suggestions from this study do not necessarily involve significant additional survey costs and does not generate tradeoffs between accuracy or bias and cost.<sup>24</sup> On the contrary, for most studies, limiting the possible bias from the enumerator effect can be done quickly, relatively easily, and inexpensively, and so represents a low-cost improvement in the quality of collected data.

> 22 In this case, controlling for enumerator dummies, would not prevent the enumerator effect from biasing the program impact. This bias could be even more problematic if combined with self-report data (e.g. see Barrera-Osorio et al. (2011) and Baird and Özler, (2012)).

> 23 For example, the study for this experiment – in which enumerators were randomly assigned - has an R2 for treatment status on observable and personality enumerator characteristics of 0.020 and on fixed effects of only 0.024. This indicates a minimal - if any – bias due to the
efcient|things to reduce|||
||today|appliance|energy use|||
|Years of education|0.071|0.060|0.041|||
||(0.010)|(0.014)|(0.016)|||
||[0.000]|[0.000]|[0.012]|||
|Observations|17240|16888|17059|||
|Clusters|36|36|36|||
|Mean|0.688|0.664|0.706|||
|Percent change|10.3 %|9.0 %|5.8 %|||
|_Panel C: pro-climate_|Favor increase|Favor|Favour ban sale|Green||
|_policy preferences &_|taxes on fossil|subsidise|of inefcient|Voting||
|_voting_|fuels to reduce|renewable|household|||
||CC|energy|appliances|||
|Years of education|-0.008|0.024|0.022|0.036||
||(0.017)|(0.011)|(0.010)|(0.013)||
||[0.631]|[0.028]|[0.037]|[0.005]||
|Observations|16417|16734|16611|52493||
|Clusters|36|36|36|39||
|Mean|0.544|0.748|0.589|0.103||
|Percent change|-1.5 %|3.3 %|3.7 %|35.0 %||

_Notes:_ This table shows point estimates for each of the elements of the indices. Panel A shows the elements of the beliefs index, Panel B the behaviors index, and finally Panel
A few remarks are in order regarding our migration variable. First, a general caveat is that it measures whether a household sent one or more migrants in-between waves, but it does not incorporate detailed information regarding the nature of these migration decisions. Therefore, based on the conceptual framework illustrated in Section 2, we are assuming that most of the migration observed in the data is voluntary and economically motivated. To be able to disentangle economically motivated versus involuntary and low-return migration and thoroughly identify the respective transmission channels, we would need more detailed data on migrant individuals e.g., regarding the purpose of their migration, their current location, work status, and financial links with the origin household. Second, despite these limitations, our definition of migration is more precise than other recent studies using household survey data (e.g., Di Falco et al., 2023; Kafle et al., 2020; Mueller et al., 2020) which simply used changes in household size or short-duration absence from the households as proxies for migration. Third, consistent with the conceptual framework outlined in Section 2, it is defined at the household rather than individual level. This is important not only from a conceptual point of view (the household as the decision-making unit, rather than the individual, in line with the NELM perspective), but also for technical and practical reasons due to the rare-event nature of the migration phenomenon. One of the main concerns when using nationally-representative household surveys for studying migration is that the migrant sample size is often (too) small. While this is certainly a limitation if one wants to infer individuallevel relationships, it is a less serious problem for household-level statistical analysis. As noted by Carletto et al. (2023), collapsing individual migration data at the household level provides enough of a gain in statistical power and sufficient outcome variation needed for estimation. In our case, we observe just above 3% of migrant _individuals_ across the panel (2010-2019) but almost 27% of migrant-sending _households_ over
that non-bank ratings for firm i at time t are negatively related to rating changes for firm i at time _t-1_ (RATING(T-1)) and are strongly positively related to sovereign rating changes at time t (ASOVEREIGN).1 4 i The relationship between sovereign ratings and ratings on domestic denominated instruments should be looser than that for foreign currency denominated instruments (Moody's, 1999). No relationship should instead exist between Moody's Financial Strength Ratings -assessing the firm's stand-alone financial strength - and sovereign ratings. Our empirical analysis will be devoted to assess the relationship between sovereign ratings and ratings on long term bank deposits and debt issues of non-bank corporations.

12 Specifically, we consider the year-minimum sovereign rating for: Argentina, Australia, Austria, Belgium, Brazil, Canada, Chile, China, Colombia, Czech Republic, Denmark, Finland, France, Germany, Greece India, Indonesia, Ireland, Japan, Kazakhstan, Korea, Luxembourg, Malaysia, Mexico, Netherlands, New Zealand, Norway, Panama, Philippines, Poland, Portugal, Russia, Singapore, Spain, Sweden, Switzerland, Thailand, UK, USA, Venezuela.

**_13_** All the regressions are run by OLS with random effects using RATS. The 18 dummies controlling for sector effects are not reported for convenience: they are generally not significant and omitting them doesn't qualitatively alter regression results. The fourth and fifth columns report the same specification as the second and third columns, respectively, the only change being that the specific effect is singled out for HICs instead than for NHICs.

**14** We must point out that SOVEREIGN(T-1) is non-significant in the non-bank panel. This suggests that, in our sample, we cannot identify a long-term relation between firm and sovereign ratings, although a short-term relation is still identified. As we will see below, this contrasts with what we find for the bank panel. Although we don't have a fully satisfactory explanation for this lack of a
70

# Figure 15b: A Complete Decomposition

<!-- Start of picture text --> 0.<br>00<br>-0.5<br>0<br>d.)<br>-2.0<br>- 2.0 1 _ _ _ _ _ _ _ _, _ _ _O _ _ _ _ _ _ _ _ _ _ _ _ _ _<br>Percentiles<br>Hmaiphas and: betas alphas, betas, gammas 996- 6<br>Source: "Pesquisa Nacional por Amostra de Domicilios" (PNAD), 1976 and 1996.<br><!-- End of picture text -->