rafmacalaba/data-use-mentions-extended huggingface dataset:
Policy Research Working Paper 8580

# **Abstract**

Despite the obvious positive health impacts of tobacco taxation, an argument raised against it is that poor households bear the burden of the increased prices because of their higher share of spending on tobacco. This note includes estimates of the distributional impacts of price rises on cigarettes under various scenarios using the Household Income and Expenditure Survey 2016/17. One contribution of this analysis is to quantify the impacts by allowing price elasticities to vary across consumption deciles. This shows that an increase in the price of cigarettes in Bangladesh has small consumption impacts and does not significantly change the poverty rate or consumption inequality. These findings stem from relatively even cigarette consumption patterns between less and more well-off households. These results hold even considering some small substitution through the use of bidis, which are largely consumed by the poor. The short-term consumption impacts are also negligible compared with the estimated gains because of savings in medical costs and the greater number of productive years of life.

This paper is a product of the Poverty and Equity Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/research. The authors may be contacted at afuchs@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._
13 straightforward. We were able to obtain from the National Village and Urban Community Fund Office the date that each village registered with the Thai government. However, we could not identify the date that each village actually received its 1-million baht fund. What we do know is that, on average, villages received their money within 2-3 months of registration. We therefore assume that the “implementation” date of VF is 3 months after the registration date. We define the VF variable at the provincial level because we do not know the identity of the village in our LFS data; we define the VF variable as the proportion of villages in the province that had received the first tranche of VF money. The first fund was transferred in July 2001; by the end of 2002, about 73,941 villages (or 93.79 percent of all villages) received the money (Satsanguan 2006). Our VF variable – unlike the UC variable – exhibits variation within each of the blocks of provinces that comprise the four waves of the UC rollout; this extra geographic variation helps us separate the effects of UC from the effects of the VF program.

Table 3 shows the descriptive statistics of our dependent variables and covariates. The statistics are calculated from the total sample size of 4,770,735 individuals (who are older than 15 years old) over the time period 1997-2005. For each variable, we report their population-weighted mean and standard deviation. The dependent variable is whether the individual is too ill to work. For each survey period, about 31 percent of the respondents would report that they were not available to work. About 5-7 percent of this pool provided "too ill or disabled" as the reason (see Table 4). In other words, about 1.5-2 percent of the population over age 15 was too-ill-too-work at a given time.
identify spatial variations in methane emissions that are not already captured by the IFPRI and EDGAR data?

# **7.  S5P CH4 Value Added**

We assess the “value added” of SP5 CH4 measures in a two-step exercise. First, we regress those measures on IFPRI field extents and EDGAR CH4 emissions. Table 3 shows that both variables contribute significantly to explaining SP5 CH4 variations. The regression residuals are the “value added” of S5P – the component of spatial variation in methane emissions not explained by field extents or EDGAR emissions.<sup>12</sup>

# **Table 3:  Determinants of local SP5 CH4 anomalies**

Dependent variable: CH4 Anomaly Percentile

||OLS|Robust|Cluster|
|---|---|---|---|
|Field Extent|0.199***|0.199***|0.199***|
|Percentile|(21.92)|(21.24)|(4.96)|
|EDGAR Emissions|0.400***|0.400***|0.400***|
|Percentile|(44.36)|(44.94)|(10.74)|
|Const.|20.34***|20.34***|20.34***|
||(57.28)|(58.31)|(8.61)|
|R<sup>2</sup>|0.3247|0.3247|0.3247|
|Obs.|19,755|19,755|19,755|
|t statistics in parentheses:<br>* p<0.05 ** p<0.01 *** p<|0.001|||

Figure 7 displays the global distribution of the value-added component. Large positive outliers (colored light and
In a few instances, the fraction is relatively low using aggregate exports, but higher if we calculate the ratio limited to manufacturing firms in the numerator and to manufactured products in the denominator. For example, the firms surveyed in Angola account for less than 1% of aggregate exports, which are dominated by oil. Limited to manufacturing, the ratio is 16.8% in 2006 and 2.4% in 2010.

The rich information in the enterprise surveys opens up a wealth of opportunities for research. Information is collected for the following broad modules, which focus on the composition of inputs and outputs, interactions with the external environment, and the identification of obstacles to operation or growth. The standard questionnaire contains the following modules:

- A. Control information: stratification variables, type of accounting, interviewer,...

- B. General information: owner, year of establishment, quality certification,...

- C. Infrastructure and services: problems, connection requests,...

- D. Sales and supplies: type and value of sales

- E. Degree of competition

- G. Land: also questions related to construction permits

- I. Crime

- J. Business-government relations: type spent, incidence of corruption,...

- K. Finance: source and value for different types

- L. Labor: employment by type

- M. Investment climate constraints: rank the severity of various obstacles

- N. Performance: cost components

# **2.2 Extent of firm-level integration in GVCs**

The different modules of the questionnaires allow one to zoom in on a variety of topics that are relevant to GVC integration. For example, the questions make it possible to assess the importance of the following issues:

- Growth constraints: some frequently-mentioned constraints, e.g. lack of demand, could be much less of a problem for firms active in GVCs.

- Conflict resolution: weak domestic institutions are often an impediment to business development. For firms active in GVCs, relational
- 12 -

The social rates of return is the highest for primary education (14.4%), followed by secondary education (12.9%), implying that investment in these levels of schooling will promote both equity and efficiency objectives. These findings are consistent with those elsewhere that additional years of primary education will increase productivity and income among the poor 4. In secondary and higher education, the social rates of return are not very high due partly to higher government subsidy in terms of increased operating budget and teachers' salaries. This is also reflected in the index of subsidization particularly for higher education, which indicates the degree to which costs are borne out by the public sector compared to the private sector. In China, the index is only 4 percent for secondary education and 31 percent for higher education . The high private returns to secondary and higher education imply that investment in these levels lead to high wagepaying jobs, an important paradigm in a rapidly growing economy such as China. But, high wages may also reflect institutional pressures (minimum wages) rather than augmented productivity resulting from human capital formation by these levels of education'.

_Table 5: Rates of Return to Education (Percent), 1993_

||_Private_<br>_Rate of Return_|_Social_<br>_Rate of Return_|_Index of_<br>_Subsidization_<br>_(a)_|
|---|---|---|---|
|Primary|18.0|14.4|1.25|
|Secondary|13.4|12.9|1.04|
|Higher|15.1|11.3|1.31|

Note: see Technical Annex A for detail (a) The ratio of private to social rates of return. _Source:_ Staff calculations from MOL Survey 1993 Table 6 reports the basic Mincerian earnings functions, showing that the overall marginal private rate of return to one year of extra schooling, irrespective of education
Policy Research Working Paper 5873

# **Abstract**

This paper proposes two related measures of educational inequality: one for educational achievement and another for educational opportunity. The former is the simple variance (or standard deviation) of test scores. Its selection is informed by consideration of two measurement issues that have typically been overlooked in the literature: the implications of the standardization of test scores for inequality indices, and the possible sample selection biases arising from the Program of International Student Assessment (PISA) sampling frame. The measure of inequality of educational opportunity is given by the share of the variance in test scores that is explained by pre-determined circumstances. Both measures are computed for the 57 countries in which PISA surveys were conducted in 2006. Inequality of opportunity accounts for up to 35 percent of all disparities in educational achievement. It is greater in (most of) continental Europe and Latin America than in Asia, Scandinavia, and North America. It is uncorrelated with average educational achievement and only weakly negatively correlated with per capita gross domestic product. It correlates negatively with the share of spending in primary schooling, and positively with tracking in secondary schools.

This paper is a product of the Poverty and Inequality Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at fferreira@worldbank.org and gignoux@pse.ens.fr.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do
*Suitable for**<br>**phone**<br>**surveys **|**Comments**|**References**|
|---|---|---|---|
|Food security|Yes|Higher frequency data collection, including<br>over the phone, may be beneficial. The<br>metadata for the Food Consumption Score<br>(FCS) recommends implementation every 1<br>to 6 months if the objective is to monitor<br>food security, and food frequency data are<br>collected over the phone. For FIES, FAO<br>recommends to randomly rotate the sample<br>amongrounds.|Knippenberg et al.<br>(2019); Amankwah and<br>Gourlay (2021); Horjus<br>(2010); Picchioni et al.<br>(2021); personal<br>communication with FAO<br>FIES Team<sup>8</sup>|
|Consumption|Not for full-<br>fledged,<br>expenditure<br>module|Phone surveys unlikely to be suitable to<br>administer either weekly diaries or a full-<br>fledged 7-day recall module. Even the 24-<br>hour recall has notperformed well in tests.|Abate et al. (2021);<br>Aggarwal et al. (2021)|
|Land inventory,<br>plot<br>characteristics,<br>and land area|No|Plot level interviews too burdensome for<br>phone interviewing. Best-practice GPS land<br>area measurement requires in-person visit.|Dillon et al. (2021)|

8 The Food Insecurity Experience Scale (FIES) proved to be a valid tool when applied in repeated surveys. More specifically, in case of surveys repeated in short intervals and more importantly in case of panels, FAO recommends
studies is that on average, farmers tend to over-report plot areas, and more so at the lower end of the plot area distribution. As a result of using GPS-based plot area measures, as opposed to their self-reported counterparts, research has shown that the IR, though weakened, remained statistically significant.

The second strand of research has focused on the numerator in the formula for crop yields: total crop production, which, in low- and middle-income countries, has traditionally been based on self-reported survey data. There are, however, several complications in eliciting accurate self-reported information on crop production, including (i) potential recall biases, in part as a function of when data are collected vis-`a-vis the harvest period (Wollburg et al., 2021), (ii) inclination to round off numbers (as shown by Gourlay et al. (2019) for crop production and by Carletto et al. (2015) for land area), and (iii) reliance on non standard measurement units, such as bunches and carts, and gaps in the availability and quality of conversion factors that are required to express non-standard measurement units in kilogram-equivalent terms and that are ideally differentiated for different conditions in which crop harvest may be reported by farmers (Oseni et al., 2017). Researchers have demonstrated that self-reported crop yields are subject to non-classical measurement errors, and are over-estimated on smaller plots, in comparison to objective measured yields based on crop cutting, i.e. the gold standard, albeit resource- and supervision-intensive, approach to crop yield estimation based on weighing the harvest of a randomly selected sub-section of a farmer’s plot. The key cross-country finding of interest is that at the plot-level the IR is strong and statistically significant when self-reported crop yields are used, and that the IR ceases to exist when crop cutting yields are used instead (Desiere and Jolliffe, 2018 and Abay et al., 2019 for Ethiopia; Gourlay 2
concerning resettlement and rehabilitation in urban (and rural) development.

Other stakeholders are likely to oppose reforms in the existing system of land conveyancing and land administration, unless specific steps are taken to make reforms more attractive to them. Such stakeholders include the village record keepers _(paiwaris_ or revenue inspectors), large land owners who manage illegally to evade land ceilings, those in the legal profession who stand to profit from litigation, and urban development corporations with vested interests in the circulation of so-called 'black money' to facilitate developments that evade legal regulations (e.g. the Urban Land Ceiling and Regulation Act). The legal profession, for example, could be expected to benefit from the additional work generated by land adjudication under revisional surveys for some considerable time to come. It is difficult to make generalizations about the likely balance between those supporting reforms and those opposing them, but it is essential that this type of stakeholder and institutional analysis be conducted to guide the preparation of concrete proposals in selected states. To the extent that pro-reform stakeholders in urban areas have greater political voice than those in rural areas, urban-rural coalitions are likely to prove useful in galvanizing support for reforms in land administration.

General consensus has emerged from preliminary work on urban land markets in India that the legal and regulatory framework - chiefly the Land Acquisition Act 1894 and the Urban Land Ceilings and Regulation Act 1976 - is over-complex, impedes urban development, and severely constrains access to serviced land by the urban poor (Bijlani and Rao 1993, Muttagi 1996). Evasion is widespread, and it is common practice to delay land acquisitions through

> 32 Professor D C Wadhwa, personal communication. Criteria for this selection included: expectation of support from state government, and current status of records of rights in land (e.g . recent revisional surveys conducted).

32
predict whether a deceased worker is non-white or white. Thus, when comparing peer groups with the same initial racial composition, the race of the deceased worker provides us with an exogenous shock to the racial composition among the remaining incumbent workers.<sup>1</sup> Our analysis concentrates on small peer groups of no more than 30 workers in the same occupation within an establishment. We verify that the death of a non-white vs. white worker leads to a quantitatively meaningful and lasting shift in the racial composition of these peer groups. For our estimation strategy, we rely on simple cross-sectional regressions that relate incumbents’ retention after the death to a dummy for whether the deceased worker is nonwhite, controlling for the pre-death racial composition and other incumbent characteristics. Using comprehensive administrative data from Brazil, this set-up allows us to study the causal effects of the racial coworker composition on workers’ retention for a large set of firms.<sup>2</sup> We find that experiencing the loss of a non-white coworker – relative to the loss of a white coworker – significantly reduces the retention of non-white incumbents. In quantitative terms, our results show that the likelihood of non-white incumbents staying at the same firm decreases by 1.1 percentage points (1.8% relative to the control mean) in the three years after the death. In contrast, we find no significant effects on the retention rates of white incumbents. Our results highlight that peer effects are not symmetric across racial groups: Non-white workers are more strongly affected by the racial composition of their peers than white workers. Several robustness checks, in which we vary the inclusion of control variables in the specification, estimate duration models, or use alternative peer group definitions, confirm our main results.

To shed light on the potential mechanisms driving the lower retention rates of non-white workers following the reduction in the share of same-race coworkers, we conduct several additional analyses. First, we show that the effects are driven by worker-initiated quits rather than employer-initiated layoffs. Given
## WPS3628

# **Deposit Insurance around the World: A Comprehensive Database**

Asli Demirgüç-Kunt (World Bank) Baybars Karacaovali (University of Maryland) Luc Laeven* (World Bank and CEPR)

### World Bank Policy Research Working Paper 3628, June 2005

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._

* Demirgüç-Kunt: World Bank (E-mail: ADemirguckunt@WorldBank.org); Karacaovali: University of Maryland (E-mail: Karacaov@econ.umd.edu); Laeven: World Bank, and Centre for Economic Policy Research (E-mail: LLaeven@worldbank.org). We are very grateful to Guillermo Noguera for providing excellent research assistance and to numerous colleagues at the World Bank, the International Association of Deposit Insurers, and officials of deposit insurance agencies, Ministries of Finance, and Central Banks around the world for providing input for the deposit insurance database.
# D. Central Bank transparency in EMDEs

<!-- Start of picture text --> Index<br>6<br>5<br>4<br>3<br>2<br>1<br>0<br>1999 2014<br><!-- End of picture text --> F. Bankruptcy rights protection in EMDEs Distance to frontier score, 100=best <!-- Start of picture text --> 80 Advanced-economy average<br>60 2018 2010<br>40<br>20<br>0<br>ECA LAC EAP SAR MNA SSA<br><!-- End of picture text --> Source: Cerutti, Claessens and Laeven (2017); International Monetary Fund; Kose et al. (2017); Dincer and Eichengreen (2014); Ha, Kose, and Ohnsorge (2019); World Bank.

A. An economy is considered to be implementing a fiscal rule if it has one or more fiscal rules on expenditure, revenue, budget balance, or debt.

B. Inflation targeting as classified in the IMF’s Annual Report of Exchange Arrangements and Exchange Restrictions (AREAER).

C. An economy is considered to have a flexible exchange rate if it is classified as “floating” or “free floating” in the IMF AREAER.

D. Dincer and Eichengreen Transparency Index. The index ranges from 0 (least independent and transparent) to 15 (most independent and transparent).

E. Sample includes 123 EMDEs. Unweighted average of the macroprudential policy index of Cerutti, Claessens and Laeven (2017). The macroprudential policy index measures the number of tools used by authorities and is based on a simple sum of up to 12 including, but not limited to, the countercyclical capital buffer and loan-to-value ratios. F. Distance to frontier score for strength of insolvency resolution. A higher index indicates reforms that improve the business climate. EAP, ECA, LAC, MNA
|[0.047]|[0.058]|
|Constant|0.01<br>[0.006]*|0.01<br>[0.003]***|||
|Observations|800|432|793|418|
|R-squared|0.122|0.158|||
|Number of countries|39|18|39|18|
|Country Fixed Effect|Yes|Yes|Yes|Yes|
|Adjusted R-squared|||0.071|0.112|
|**First Stage IV Estimates**|||First Stage|First Stage|
|Trade Weighted Growth * Exp|||1.17<br>[0.03]***|1.27<br>[0.02]***|
|Trade Weighted Growth * Rec|||1.35<br>[0.03]***|1.43<br>[0.04]***|
|**Underidentification - Weak Identification - W**|**eak Instrume**|**nts Tests**|||
|LM test statistic for underidentification|||84.20|33.38|
|p-value of underidentification LM statistic (And|erson or Kleibe|rgen-Paap)|0|7.58e-09|
|F statistic for weak identification (Cragg-Donald|or Kleibergen|-Paap)|918|813.8|
|Anderson-Rubin chi-sqared test of significance|of endogenous r|egressors|65.43|22.66|
|p-value of Anderson-Rubin chi-sqared test of en|dogenous regre|ssors|0|1.20e-05|

Source: Authors’ calculations based on World Economic Outlook database 2019.

Notes: Robust standard errors in brackets. *** p<0.01, ** p<0.05, * p<0.1 Underidentification tests. Ho
_Table 5 - The unequal effect of shocks on expenditure and income. The sample includes households in the HEIS data. Shock is measured as the yearon-year difference in log(USD to IRR rate) in each month._

**Panel A: Regression with all HEIS households**

||**Total exp**|**enditure**|**Food ex**|**penditure**|**Total i**|**ncome**|**Earned**|**income**|**Wag **|**e rate**|
|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|
|**Afghan dummy**|-0.217***|0.032|-0.191***|-0.082***|-0.007|-0.048**|0.164***|0.181***|0.006|0.131***|
||(0.023)|(0.022)|(0.019)|(0.019)|(0.025)|(0.022)|(0.030)|(0.029)|(0.023)|(0.023)|
|**Shock**|-0.024***|-0.027***|-0.081***|-0.083***|0.059