rafmacalaba/data-use-mentions-extended huggingface dataset:
- 2 Low Top income tax rate greater than 0 percent and less than or equal to 25 percent.  Marginal rate for the average taxpayer greater than 0 percent and less than or equal to 10 percent

- 3 Moderate Top income tax rate greater than 25 percent and less than or equal to 35 percent.  Marginal rate for the average taxpayer greater than 10 percent and less than or equal to 15 percent.

- High Top income tax rate greater than 35 percent and less than or equal to 50 percent.  Marginal rate for the average taxpayer greater than 15 percent and less than or equal to 20 percent.

- 4

- 5 Very high Top income tax rate greater than 50 percent.  Marginal rate for the average taxpayer greater than 20 percent

- Corporate Tax Grading Scale

- 1 Very low Corporate tax rate less than or equal to 20 percent

- 2 Low Corporate tax rate greater than 20 percent and less than or equal to 25 percent.

- 3 Moderate Corporate tax rate greater than 25 percent and less than or equal to 35 percent.

- High Corporate tax rate greater than 35 percent and less than or equal to 45 percent.

- 4

Based on: Ernst & Young, 2002 The Global Executive and 2002 Worldwide Corporate Tax Guide; International Monetary Fund Staff Country Report, Selected Issues and Statistical Appendix, 2000 to 2002; Economist Intelligence Unit, Country Commerce, Country Profile, and Country Report for 2001 and 2002; U.S. Department of State, Country Commercial Guide 9 ; and official government publications of each country. Sources other than Ernst & Young are noted in the text. For information on government expenditures, the authors’ primary sources were Organisation for Economic Co-operation and Development data (for member countries); International Monetary Fund, Government Finance Statistics Yearbook for 2001, and International Monetary Fund Staff Country Report, Selected Issues and Statistical Appendix, 2000 to 2002; Standard & Poor’s, Sovereigns Ratings Analysis; Asian Development Bank, Key Indicators of Developing Asian and Pacific Countries 2001; African Development Bank
Table 8 presents these results. We find that market concentration is not related to any of the three market growth variables for the global sample. The coefficients of the stock market total value traded to GDP and equity market capitalization to GDP variables are positive, albeit not significant. Conversely, the coefficients of stock market turnover ratio are negative, but also not statistically significant. For LMICs, however, it is significant, implying that greater share turnover in the market is related to lower concentration in less developed markets. Note, though, that the magnitude of the association is marginal in economic terms. Results are similar when we use an alternative control for the macroenvironment in a country as a robustness check (Table A-6 in the Annex).

# **6. Concluding remarks**

The paper examines how issuer composition changes as stock markets grow, focusing on low- and middle-income countries. Specifically, we study whether such growth is associated with new firms listing in the stock market, the sectoral diversity of issuers and the market concentration among issuers. To investigate these relationships, we use a novel IFC dataset that includes data on various measures of stock market growth (size and activity) and issuer composition (number, concentration, and sectoral diversity) for over 150 countries for the period 2015-2020.

Our results show that stock markets may not necessarily become more inclusive as they grow. Specifically, we find that stock market capitalization, often the most popular headline indicator for market development, tends not to be associated with new equity issuances. Instead, a growing stock market is related to an increase in the number and size of seasoned equity offerings, indicating growth at the intensive margin. Greater trading activity, however, is associated with a higher number of issuances both by existing and new issuers.

These findings hold for low- and middle-income countries. The paper additionally finds that higher stock turnover is related to lower market concentration in these countries. This result is of particular relevance given that less developed markets are more likely to be
# **3 Baseline Estimates from the Pre-Crisis Period**

Table 2 presents our first set of results for the “pre-crisis” period 1988:Q1 - 2008:Q3. We utilize a panel data set comprised of five economies _j_ – the United States, the European Union, South Korea, Australia and Canada – and fifteen of each economy’s top trading partners _i_ . The dependent variable is defined as the count of HS-06 imported products against which the government of economy _j_ has newly initiated a temporary trade barrier investigation against trading partner _i_ in quarter _t_ .

As is common practice for negative binomial regression models, the tables report estimates for incidence rate ratios (IRRs) for the explanatory variables. That is, we report the ratio of counts predicted by the model when the first lag of an explanatory variable of interest is one unit above its mean value (and all other variables are at their means) to the counts predicted when all variables are at their means. An estimated IRR with a value that is statistically greater than 1 is evidence of a positive effect of the determinant of interest; i.e., the explanatory variable is associated with an increase in the number of imported products from trading partner _i_ in quarter _t_ that economy _j_ subjects to a new TTB investigation. Conversely, an estimated IRR that is statistically less than 1 is evidence that an increase in the explanatory variable is associated with a reduction in the count of imported products subject to new import protection. The table also reports _t_ -statistics (in parentheses) for whether the estimated IRR is statistically different from 1.

The three macroeconomic determinants of interest are the percent change in the bilateral real exchange rate, the change in the domestic unemployment rate, and the foreign trading partner’s real GDP growth. The baseline model also includes a time trend as well as importer and exporter combined (pair-wise) fixed effects to control for time-invariant, bilateral relationship-specific heterogeneity in policy treatment. For example, China’s designation as a non-market economy
seven industries. We group these seven industries into two sets. The first set is “textiles” which includes weaving and spinning activities (ISIC 1710), manufacturing of wearing apparel (ISIC 1810) and the footwear industry (ISIC 1920). The second set is “other” industries which includes wood processing (ISIC 2000), manufacturing of plastic products (ISIC 2520), manufacturing of structural metal products (ISIC 2811), and furniture making (ISIC 3610).

> 4 This panel is compiled from the Ethiopian Survey of Large and Medium Scale Manufacturing Industries, which is in effect a census of all manufacturing firms with 10+ employees.

> 5 Data from 2005 are dropped because a survey was conducted during that year rather than a census.

13
we aggregate the data by city to obtain the percentage of households with sanitation access in each city. We therefore consider panel data by city from 1986 to 2009. The time span includes the preprivatization period in the case of Maldonado because the two privatizations in Maldonado occurred in 1993 and 2000, as discussed above.

The ECH survey is only representative at the department level or at the city level for the largest cities in terms of population. Therefore, and to ensure a representative sample, we only retain the capital cities of the different departments and other large cities in our sample. We have a total of 35 cities, three in the treatment group (Maldonado, Pan de Azúcar, and San Carlos) and 32 in the control group (Artigas, Bella Unión, Canelones, Carmelo, Colonia, Dolores, Durazno, Florida, Fray Bentos, Lascano, Libertad, Melo, Mercedes, Minas, Montevideo, Paso de los Toros, Paysandú, Periferia Canelones, Rivera, Rocha, Rosario, Río Branco, Salto, San José de Mayo, San Ramón, Santa Lucía, Sarandí del Yí, Sarandí Grande, Tacuarembó, Tranqueras, Treinta y Tres, Trinidad, and Young).<sup>13</sup> Because some of the selected cities were not surveyed in certain years (primarily in the older edition of the ECH), we have an unbalanced panel that may imply panel attrition bias. For instance, of the 24 time periods in the treatment group, Pan de Azúcar appears 15 times, and San Carlos appears 18 times. In the control group, we have observations for Lascano in 10 of the 24 time periods; for Bella Unión, Libertad, and Rosario in 12 of the years; for Santa Lucía in 15 of the years; for Carmelo, Dolores, Paso de los Toros, Río Branco, San Ramón, Sarandí del Yí, and Sarandí Grande in 17 of the years; and for Young in 18 of the years. Therefore, we have a total of 735 observations
measures that are also correlated with _Aij_ . The public inputs for which we have data are:

- Electricity

- Telecoms

- Transport

- Customs

- Unfair competition

- Access to land

- Crime/theft/disorder

- Tax administration

- Business licensing

- Political instability

- Government policy instability

- Corruption

- Operation of the courts and legal system

- Macroeconomic instability

- Labor regulation

- Skilled labor shortages

There is another question that is best interpreted along with the reported costs measures. Firms are asked about the adequacy of their access to water. When they report “insufficient water”, this can be interpreted as meaning that “insufficient water” is an obstacle to production. Had they answered “sufficient water” then this would be equivalent to the answer above of “not an obstacle”. We therefore consider water together with the other public input constraints.

- Sufficient supply of water

_Mitigation outlays_ Measures of mitigation costs _Gij_ * will also be increasing in _Aij_ . The following indicators of mitigation outlays are included in the Enterprise Surveys.

- Bribes (=1 if the firm paid bribes, =0 if not)

- Managerial time dealing with regulatory issues (%)

- Generator (=1 if the firm has its own generator, =0 if not)

_The flow of public input services_ Measures of the flow of services _Eij_ * that are available in the Enterprise Surveys are: 10
# **Appendix 3: Descriptive Statistics**

<u>Table A1. Firms integrating into GVCs</u>

||Number of firms|Percentage of total firms|
|---|---|---|
|GVC1|28,681|19.97%|
|GVC2|12,485|8.69%|
|GVC3|6,083|4.24%|
|GVC4|3,288|2.29%|
|Total|50,537|35.18%|

Source: Constructed by the authors using the WBES.

Note: GVC1 refers to firms that export and import simultaneously, GVC2 = GVC1 + international certification, GVC3= GVC1+ share of its capital owned by a foreign firm, GVC4 combines the four criteria altogether.

<u>Table A2. The Average Number of Female Employees, by region</u><sup>11</sup>

|Region|Female Employees|Female Production Workers|Female Non-Production Workers|
|---|---|---|---|
|SSA|14.17|14.99|5.10|
|EAP|28.49|72.15|13.85|
|ECA|24.09|27.84|8.28|
|LAC|39.77|25.59|12.98|
|MENA|11.03|17.55|5.10|
|SAR|9.33|42|4.25|

Source: Constructed by the authors using the WBES.

Note: SAR stands for South Asia, MENA Middle East and North Africa, EAP East Asia and Pacific, SSA Sub-Saharan Africa, LAC Latin America and the Caribbean and ECA Europe and Central Asia.

Figure A1. The Distribution of Firms’ integration into GVC, by region <!-- Start of picture text --> 100%<br>90%
# **The Silence of Corruption: Identifying Underreporting of Business Corruption through Randomized Response Techniques**<sup>**1**</sup>

Nathan M Jensen Aminur Rahman

# **Keywords:  Corruption, investment, Bangladesh, survey experiment, randomized response technique**

# **JEL Codes: C10, C18, C90, D22, D73**

> 1 Jensen is with the Department of Political Science, Washington University in St. Louis (njensen@wustl.edu) and Rahman is with the Investment Climate Advisory Services of the World Bank Group (arahman@ifc.org). The authors thank Aart Kray and Eddy Malesky for comments and suggestions.
believe (teacher beliefs) are more strongly predictive of student learning gains than other teacher, student, and school factors, especially in Math. For Math, CIF results show that the teacher practice of providing written feedback on homework/tests and reviewing key concepts at the end of class (measured through student surveys); the teacher belief that they can help disadvantaged and struggling students learn; and teacher preparation around teaching foundational concepts are the most important predictors of student learning gains. However, one counter-intuitive result that merits further investigation is that teachers who score high on fostering critical thinking in classroom observations (for instance by asking more open-ended questions) have lower student learning gains.

For Kiswahili, even though teacher (and other observable) covariates matter less for predicting student learning gains, teacher preparation on teaching foundational skills, teacher practice of providing additional support to struggling students, and teacher belief that they have good relationships within school still emerge as important. Consistent with existing literature, commonly used teacher characteristics such as education, experience, assessment scores etc. do not emerge as important predictors of student learning gains. Outside of teacher variables, rural schools (for both Math and Kiswahili) and older students (for Kiswahili) have stronger student learning gains; but these factors are still less important than the top teacher factors.

> 32 Examples of questions include (= 1 if 'Agree'; = 0 if 'Disagree'): (i) I have a good relationship with my students, (ii) I have a good relationship with my colleagues, (iii) I have a good relationship with my Head Teacher.

17
<!-- Start of picture text --> Boys Soccer<br>|________________________________________|<br>Census Supply<br>________ _____________________________________ Census<br>| | | |<br>Baseline Endline Goal, Goal, 1st visit 2nd visit<br>|____________| |________|<br>Jul Aug Sep Oct Nov Dec Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan May Jun Jul Aug<br> 2016 2017 2018<br><!-- End of picture text -->

**Figure 3** Study Timeline

_Notes._ This figure presents the study timeline.

<!-- Start of picture text --> Boys Arm Treatment Effect<br>IPV often index p=.024 / b=-0.229<br>IPV in last year index p=.056 / b=-0.150<br>Sexual activity index p=.032 / b=-0.125<br>With same partner as baseline p=.331<br>Sex partner quality index p=.265<br>Violence attitudes index (Males) p=.016 / b=-0.290<br>IPV often index (Males) p=.837<br>IPV in last year index (Males) p=.278<br>Sexual activity index (Males) p=.213<br>SRH perceptions index (Males) p<.000 / b=0.293<br>-0.5 -0.4 -0.3 -0.2 -0.1 0 0.1 0.2 0.3 0.4 0.5<br><!-- End of picture text -->

**Figure 4** ITT Effects of _Boys_ Treatment

_Notes._ This figure presents estimates of _β_ 1 from equation 1 for separate regressions with the outcome indices specified on the y-axis. Indices are
a) we investigate vulnerability functions specific to household “types” as noted in the description of the regression model above. We also investigate ii) whether the vulnerability function for all types should be non‐linear by including squared and cubed terms in the model.

For b) we use economic theory to choose the characteristics of households and communities that enter the function. These will be constrained by the availability of data in the household survey datasets.

- **d. How significant is the variation of the derived functional forms across geographical regions within Ethiopia?**

The first round of testing discussed above involved random division into k parts. However, also split groups according to characteristics that vary across the dataset, but that we have not explicitly identified as determinant of vulnerability, to test _external validity, or generalizability across ecological contexts_ . We use non‐random sampling for the k‐Fold Cross Validation method, separating the k groups according to geographical area (e.g. Woreda) but controlling for differences expected from changes in agro‐climatic conditions. This will tell us if, when we separate out households according to what we have determined to be the key vulnerability characteristics, the relationships hold across geographies.

However, we have reasons to believe that the relationship between rainfall, crop losses and consumption differs considerably in pastoral areas as discussed above so we exclude the pastoral regions from the analysis.

23
- C. Generation and transmission procurement index for Pakistan and comparators, 2015

||**India**|**Pakistan**|**International**<br>**benchmark**|
|---|---|---|---|
|**Procurement of Generation**|**95%**|**100%**|**85%**|
|There is a framework forprocurement|||82%|
|Country allows International competetive bidding or public<br>auctions forprocurement|||94%|
|Types ofprocurement methods allowed||||
|_Unsolicited bids_|||29%|
|_Direct negotiation_|||47%|
|_International competitive tendering_|||88%|
|_Public auctions_|||41%|
|_Stand-alone capacity market_|||0%|
|Auction design score|0.86|NAP|80%|
|_Country usespublic auctionsforprocurement_|||41%|
|Clear and comprehensive established rules||_NAP_|100%|
|Crediblepenalties for violatingthe rules||_NAP_|86%|
|Guarantees andpenalties to ensure timelycompletion||_NAP_|86%|
|Standard,non-negotiable contracts||_NAP_|86%|
|Stapled financingterms or risk mitigation instruments||_NAP_|86%|
|No concerns regardingthe transparencyand fairness of the auction||_NAP_|14%|
|Efforts to inform and attract bidders to the auction||_NAP_|100%|
|**Transmission Procurement**|**83%**|**100%**|**64%**|
|There is a framework for procurement of new transmission<br>lines|||59%|
|Methods used toprocure new transmission-|||69%|
|_Competitive tender_|||65%|
|_Direct negotiation_|||24%|
|_Allprojects are awarded to the incumbent
This paper maintains the purchased food expenditure aggregate as calculated in the INS file, with the proviso that it may lead to a downward bias in the expenditure data for 1995. It should be noted, however, that some of the puzzling trends noted in the next section of the paper (e.g. the large increase in poverty among West Forest export crop farmers, the larger growth in household expenditure among Savannah farmers relative to East Forest farmers) persist even if the 12 month estimate of purchased food expenditure is used in calculating total household expenditure.

# Shortcomings of the **CPI**

Published CPI information is available for three categories of households (blue-collar workers, white collar workers managers, and European-type consumption) in Abidjan. The expenditure shares are based on a 1976 consumption survey, while base prices currently in use are the mean November 1992-October 1993 prices. Prior to 1993, the CPI was based on the August 1984 to July 1985 prices. In addition in 1993, the INS shifted to actually purchasing goods at the market. So there may be some problem of comparability between the pre- and post-1993 consumer price indices. The major shortcomings of the deflators used to calculate real household expenditure and thus the poverty indicators are (1) the difference between the expenditure shares used to construct the CPI and the survey shares and (2) the absence of regional price indices. The large differences in expenditure patterns across the country illustrate the need for region-specific CPIs based on actual expenditure patterns and price increases.

There is a large difference in the expenditure shares on which the CPI is based and those derived from the surveys. Table 29 compares the consumption weights in the CPI basket for blue-collar Abidjan workers with the expenditure shares derived for Abidjan poor and non-poor households. The major difference is poor households spend a much greater proportion of their expenditure on housing and utilities than is indicated by the CPI basket. Even m non-poor Abidjan households the share of expenditure on housing and utilities as revealed by the survey exceeds the share in the
levels are most notable in states where illiteracy (or below primary level education) among adults is high. Reweighting is seen to be more successful in correcting biases in 2017 and 2018 than in 2019. But even in 2019, reweighting comes a long way in reducing the bias in states with high shares of illiterate or non-formal education. The estimates for higher education levels are largely scattered along the diagonal, confirming the successful bias correction. Figure 1 in Appendix 1.1 shows that the large bias in female illiteracy using reported CPHS data as documented in Somanchi (2021) is largely resolved after reweighting.

The NSS survey on education consumption conducted in 2017-18 provides another opportunity to compare education statistics derived from the (reweighted) CPHS against. As this survey is not used in the reweighting procedure, this comparison helps provide external validity of the adjustments made to the sampling weights. Panels (a), (b) and (c) from Figure 5 show the results for all adults, males and females above the age of 15, respectively. Reassuringly, all education level shares obtained using the adjusted CPHS sampling weights are within 1 percentage points from the benchmark survey. This denotes a notable improvement compared to the estimated obtained using the reported CPHS weights.

_Labor force indicators:_ Abraham and Srivastava (2019) observe a 3.2 percentage point gap in labor force participation rates among males between the CPHS-2017 and the PLFS from the same year. Labor force participation rate for females in the CPHS are about half that of what is estimated by the PLFS. Basole, et al.(2021) finds that the average real incomes in the CPHS of 2018 are about 30 percent higher when compared to the PLFS from the same year<sup>19</sup> . Despite the higher average incomes, wage inequality is lower in the CPHS relative to the PLFS: estimates of the Gini coefficient of income inequality for the two surveys are 0.42 and 0.44, respectively (excluding zero wage earners).
‐<br>respondent|Proxy‐<br>respondent|Diff|Number of<br>observations|
|**Old definition**|||||||||
|_Women_|43.96|26.21|17.75***|2,532|68|42.26|25.74***|7,703|
|_Men_|57.79|29.58|28.21***|2,919|74.6|50.8|23.8***|6,915|
|**Previous definition**|||||||||
|_Women_|20.36|12.68|7.68***|2,532|41.78|27.28|14.5***|7,703|
|_Men_|22.67|14.26|8.41***|2,919|45.55|33.94|11.61***|6,915|

Source: Based on 2013/14 Nigeria LSMS‐ISA data. Note: ***, ***,* indicate statistical significance at the 1%, 5% and 10% level.

> (own calculations). Hence, individuals who did not self‐report are less likely to be employed than individuals who self‐reported their activities. This is counterintuitive. One candidate explanation is underreporting by proxy respondents, as observed by Bardasi et al. (2011). An alternative explanation is that socioeconomic characteristics differ between the two groups. 20 The authors did test the sensitivity of child labor statistics to proxy reporting relative to self‐reports (Dillon et al., 2012). They did not observe significant differences.

20