rafmacalaba/data-use-mentions-extended huggingface dataset:
-9-

The difference between World Bank and other statistics seems to result chiefly from the fact that adjustments are effected at the World Bank. Some of these adjustments are intended to correct for the fact that the method of preparing the accounts does not always conform to the United Nations standard system or for inconsistencies in the accounts. Other, more delicate, adjustments take account of country economists' opinions concerning the reliability of the official data. Subject to the quality of these adjustments and uniformity of the principles underlying them, it seems preferable to work with the World Bank statistics. The advantage of the other series is that they are available more rapidly and more regularly.

As the illustrative model shows (cf. table 3, equations 3 and 4), the coefficient of determination is in fact a little better using the World Bank data.

The use, still rare, of the statistics of the international comparison project, perfectly justified for comparing standard of living leveis and trends, is much less justified for measuring economic efficiency through growth of product. This efficiency cannot be judged in each country without taking into account the structure of costs and scarcities, which is reflected in the country's price system.

# 1.1.3 The choice of the base year

The growth rates are calculated on CDP series measured at constant prices. The base year in which these series were originally established naturally varies from country te country. The series of aggregates published by the international agencies, including the World Bank, have used a base year varying with the country, which nevertheless, as we have seen, ensured a certain harmonization of the statistics. The next edition of the World Tables will present aggregates on a uniform base year for all countries, the year 1980. These data could not be used up till now and all the cross-sectional analyses of growth studied here use series established on base years varying from country to country. This practice raises a problem that has been ignored by all of the studies examined.

For example, ths
# **7 Conclusions**

Using a newly assembled dataset that does not suffer from fundamental limitations of FDI statistics, and guided by a simple analytical framework that highlights the possibility of implicit profit shifting through real investment decisions, the purpose here has been to revisit the questions of the extent to which, and through precisely which channels, international corporate tax arrangements affect aggregate cross-border tangible investment. Addressing that has also enabled an analysis of what the implications for tangible investment might be of what is likely the most profound of the fundamental reforms of the international tax architecture now underway: the adoption of a global minimum corporate tax.

There emerge effects of statutory tax rates on tangible investment that, across a wide range of specifications, are strongly significant and large relative to prior consensus values. Moreover, spillover effects from tax rates elsewhere appear to be about as large as the investment effects of the host country’s own tax rate. Our preferred estimated semi-elasticity, with respect to both the host statutory rate and the weighted average of rates elsewhere, if pressed to provide one, would be around 3.6. This is noticeably higher than many previous estimates. The reason, we have argued, is that a dampening of apparent tax effects is an inherent consequence of the conduit arrangements that are reflected in the FDI data that have commonly been used, but which are not present in FAI statistics. These effects from statutory rates, moreover, are consistent with the relatively little recognized channel of implicit profit shifting through tax-induced changes in real investment decisions. Indeed effects through this route appear empirically to dominate any associated with profit shifting by means of artificial arrangements lacking real substance, which have received much more attention both in the literature and from policy makers. Spillover effects from statutory rates appear, as one might expect, to be stronger within the relatively integrated market of the EU, but they are nonetheless also marked outside.

The impact of the traditional closed economy King-Fullerton marginal effective tax rate, in contrast, is weak at best. This
take the data on consumption patterns from household expenditure surveys, which contain relatively detailed information on the type of goods and services consumed by different households. Depending on the country under consideration, consumption is disaggregated into 7 or 8 major categories, although finer partitions are usually possible. As for the matching price data, we rely on the disaggregation of the overall CPI by category of expenditure, which is usually available from national sources (typically the Central Bank).

Table 1 offers summary information on the expenditure surveys used in the empirical analysis. The surveys correspond to four Latin American countries and allow constructing nine inflation spells: one each for Brazil (1988-1996) and Colombia (1997-2003), four for Mexico (1984-1989, 1989-1994, 1994-1996, and 1996-2002) and three for Peru (1995-1999, 1999-2001, and 2001-2003). The nine spells span periods of time of varying length: 8 years in Brazil, 6 years in Colombia, between 2 and 6 years in Mexico (for a grand total of 18 years), and between 2 and 4 years in Peru (for a combined total of 8 years).

Tables 2 to 5 report for each country the budget shares devoted by each quintile of the expenditure distribution to the various categories of goods and services: apparel, education, food 5
elderly, the HCA would not assign an economic value to old individuals that have either retired from the workforce or do not make significant contributions to household work. This obviously is a serious shortcoming of the HCA approach.

The estimated cost of mortality in India based on HCA is presented in Table A1.16. Average annual income is approximated by GDP per capita, corresponding to around 57 thousand Rs. per year. The estimates are from equation (1).

# **Table A1:** **<u>Cost of Mortality (per Death) using HCA</u>**

||Average Number of<br>Years Lost|Thousand Rs|
|---|---|---|
|**_Adults:_**|||
|Mortalityfrom Urban Air Pollution|8|430|
|Mortalityfrom Indoor Air Pollution|7|390|
|Mortalityfrom Lead Exposure|8|430|
|**_Children:_**|||
|Mortalityfrom Urban Air Pollution|65|1,148|
|Mortalityfrom Indoor Air Pollution|65|1,148|
|Mortality from Diarrheal Illness, Typhoid , children<br>under 5|65|1,148|
|Mortality from Diarrheal Illness, Typhoid , children<br>under 19|55|1,863|

# **B. Value of Statistical Life.**

While the HCA involves valuation of the death of an individual, VSL is based on preferences for reducing mortality risk by a small amount. Everyone in society is constantly facing a certain risk of dying. Examples of such risks are occupational fatality risks, risks of traffic accident fatality, and environmental mortality risks. It has been observed that individuals adjust their behavior and decisions in relation to such risks. For instance, individuals demand a higher wage (a wage premium) for a job that involves a higher than average occupational risk of fatal accident, individuals may purchase safety equipment to reduce the risk
in manufacturing. Second, we obtained from the SENER fuel consumption data in each NAICS 4-digits manufacturing sector, converted fuel consumption in kWh equivalents and derived sector-specific shares.<sup>28</sup> Third, we combined these data with monthly national prices for diesel from SENER, for coal and coke from INEGI and monthly-regional prices for gas from SENER. Doing that allowed us to compute for each NAICS-4 digits sector monthly sector-specific composite price indices for fuels, weighted by sectoral consumption shares in 2009: where _αs_<sup>_d_arethesharesoffueltype</sup><sup>_d_intotalconsumptionoffuelsinsector</sup><sup>_s_</sup> and _Pm_<sup>_d_arethenationalpricesoffuel</sup><sup>_d_inmonth</sup><sup>_m_forcoal,coke,anddieseland</sup> monthly regional ( _R_ ) prices for natural gas. The final step consists of weighting these monthly indices with plant-level monthly production from EMIM using the same approach as equation 1. This generates estimates of the yearly price of fuels faced by each plant.

# **3.3 Performance Measures**

We are interested in the impact of energy prices on alternative measures of firm performance. Our main productivity indicator is an index of revenue-total factor productivity, as in Aw et al. (2001).<sup>29</sup> For each plant _f_ and year _t_ , In the above equation, _V Aft_ is the value added of the plant, _Sfjt_ its revenue share for input _j_ and _Xjft_ real value of the same input. As it is standard, we consider capital and labor as factors of production. Upper bars represent averages within a sector and year.

> 28We use the following conversion factors: 1 liter of diesel corresponds to 10 kWh; 1 kg of coal is equivalent to 8.1 kWh; 1 _m_<sup>3</sup> of natural gas is equal to 11.7 kWh, and 1 kg of coke is equal to 8.8 kWh.

> 29This methodology
01)|(0.01)|(0.01)|(0.01)|(0.01)|
||008|009|001|008|-000|
|**Household owns a refrigerator/freezer**|.<br>(0.01)|.<br>(0.02)|.<br>(0.02)|.<br>(0.01)|.<br>(0.02)|
||007|007|001|006|-000|
|**Household owns an air c/fans**|.<br>(0.01)|.<br>(0.01)|.<br>(0.02)|.<br>(0.01)|.<br>(0.02)|
|**Household owns a radio**|0.49<br>|0.50<br>|0.01<br>|0.50<br>|0.01<br>|
||(0.03)|(0.03)|(0.04)|(0.03)|(0.04)|
|**Household owns a mosquito net**|0.85<br>|0.84<br>|-0.00<br>|0.84<br>|-0.01<br>|
||(0.02)|(0.02)|(0.03)|(0.02)|(0.03)|
||044|047|003|046|002|
|**Log of perca residential area**|.<br>(0.01)|.<br>(0.01)|.<br>(0
the automated<br>reporting of the results of all physical<br>examinations. Data also be assessed, when more<br>reliable, on a monthly basis.|
|**Border crossings**|||
|Truck examinations|Number of trucks actually<br>opened (i.e. seals broken)<br>compared to the total<br>numberoftrucks processed|Information is compiled through the use of<br>computerized traffic logs and the requirement to<br>prepare an automated report of the results of each<br>truckexamination.|
|Irregularities/Number<br>of examinations|Number of irregularities<br>discovered during<br>examinations compared to<br>the total number of trucks<br>examined|Information is extracted from the automated<br>reports of truck examinations.|
|Average border exit<br>time|For trucks exiting the<br>country, it is the time<br>between joining the queue<br>and crossing the border|Information on truck identification and times is<br>recorded continuously with the use of computer<br>terminal s or time clocks installed at appropriate<br>locations.|
|Average border entry<br>time|For trucks entering the<br>country, it is the time<br>between crossing the<br>border and departing the<br>station|Same as exit time.|
|Survey occurrence of<br>corruption|Number of cases when a<br>driver makes or is asked to<br>make an unauthorized<br>payment compared to the|Information to calculate this indicator is derived<br>from surveys of truck drivers using the crossing<br>point. This survey is conducted on a continuous<br>basis throughcooperating transport companies.|

55 ____________________________________________________________________________________________________________
To get more detailed information on how reaching Decision Point of the HIPC Initiative affects child health, I examine how a number of individual-level health measures other than mortality are associated with HIPC. The country-specific PRSPs and Decision Point documents more or less exhaustively describe which areas in the health sector are targeted for improvements in relation to HIPC and interim debt relief. The areas include health care provision for children, mothers, and disadvantaged group (rural, poor, uneducated) and sanitation measures. In some cases particular diseases are identified for prevention and treatment measures, e.g. Measles and Tuberculosis, and most countries aim for better provision of vaccines. A number of these measures are known to reduce child mortality (death before reaching five years of age) (Jones et al., 2003) and vaccines which are often given to babies before they turn one year of age, most notably Bacillus Calmette-Gurin (BCG) (against Tuberculosis), Measles, Diarrhea, and Polio, are associated with non-specific health effects and linked to reduced infant and neonatal mortality from causes other than specific diseases they are aimed at preventing (Cooper, Boyce, Wright, & Griffin, 2003).

In the individual-level health measure analysis I make use of older DHS data for the countries in the sample. Because mothers report on various health measures of their babies born within five years of the survey date, combining the newest survey data with the older surveys enables analysis of how health variables relate to debt relief through HIPC. I pool all available surveys from the countries in the sample<sup>9</sup> to obtain a panel of the following health measures: BCG vaccine, the first shot of Diphtheria, Pertussis and Tetanus (DPT) vaccine, Measles vaccine, the first shot of Polio vaccine, Tetanus vaccine for pregnant mothers, access to skilled assistance<sup>10</sup> at delivery, prenatal care by skilled health worker, oral rehydration solution for treating Diarrhea, and access to improved sanitation facilities (flush toilets
, we have a large number of observations across time asking the same set of participants the same survey questions with varying recall periods (though, of course, with fewer observations for monthly rather than weekly recall periods). These data will be used to compare responses given for weekly versus monthly recall periods. The size of the discrepancies between survey responses in the weekly and monthly data indicates the magnitude of forgetting that takes place with the monthly recall period. Given the structure of the data, we will also be able to account for seasonal effects by including responses from the group of households administered a monthly survey concurrently. Finally, since monthly surveys were administered _after_ the completion of weekly surveys, we are able to avoid ―telescoping‖ effects (survey reports capture events preceding the recall period considered) in the monthly data as well.

Our second data source is an experiment carried out in 2008 to verify the large recall effects discovered in the observational data. For this survey, we began with a sub-sample of 205 households who could still be located from the original sample and surveyed them for a total of 2 months. In the first month, the entire sub-sample

> 7 See (Das and Hammer 2009) for more detailed discussion of how households identify and handle chronic illness.

6
Figueiredo and Larson (2021) found a positive association between belief the government was handling the crisis well and willingness to be vaccinated in a sample of 32 countries with various income levels. Lazarus and colleagues reported the same, but only in bivariate odds ratios. Future analysis is needed to investigate this relationship, preferably using an individual level measure of trust in government rather than the national-level measure used in this analysis.

With respect to our results on changes over time, which showed that changes were minimal in all countries except Iraq, Malawi, and Uzbekistan, where we report relatively large increases. It is difficult to identify the factors that led to increased hesitancy in these cases. The larger body on changes over time in vaccine hesitancy during the same timeframe as the HFPS data were collected is mostly from highincome countries and shows mixed results: increased rates of vaccine hesitancy reported in Australia, Canada and the United States between April and December 2020 (To et al. 2021, Lavoie et al. 2022, Szilagyi et al. 2020), rates remained stable in Italy between June 2020 and January 2021 (Basio et al. 2022) and decreased in Greece between November 2020 and May 2021 (Sypsa et al. 2022).

The main reasons reported in the HFPS regarding COVID-19 vaccine hesitancy, which mainly revolve around safety and to a lesser degree efficacy, are consistent with the few studies that report reasons for COVID-19 vaccine hesitancy in low- and middle-income countries and with the main reasons for vaccine hesitancy historically. Solis Arce and colleagues (2021) also reported concerns about side effects to be the most frequently expressed reason in their sample of low- and middle-income countries. Facebook survey results for the same month were available for 21 countries, although reasons for not getting vaccinated were collected separately for respondents who would “definitely not” and “probably not” take the COVID19 vaccine and multiple reasons were collected. Nevertheless, they show that concerns about safety (worded as “concern about side effects” or “wait and see if
cities of a small and relatively open economy like Bolivia. We measure the size and the speed of the price transmission, both internationally and within Bolivia, and test for upward and downward symmetry in that speed. We also shed light on the determinants of geographical price differences and price transmission. We focus on ten key products for the median Bolivian household: bananas, beef, cooking oil, chicken, milk, onions, potatoes, rice, sugar and wheat flour, and on the main four cities that accumulate more than half of the population: Cochabamba, El Alto, La Paz and Santa Cruz.<sup>2</sup> The main results of the paper are that domestic and foreign goods are imperfect substitutes, as transmission from world to domestic prices is imperfect and slow. Marked asymmetries in transmission patterns also point to departures from perfectly competitive settings. Finally, we identify a clear association between regional production levels and prices for non-industrial products, which point to the importance of transportation costs as determinant of price differences and a possible barrier to market integration.

The remaining of this paper is structured as follows. Section 2 presents the data and the methodology used. In section 3 we present the main results of the spatial integration analysis, both within Bolivian cities, and between world and domestic markets, and explores whether transmission from world to domestic prices is symmetric with respect to increases and decreases of world prices. Section 4 provides an informal discussion of possible determinants of geographical price differences. Section 5 provides some concluding remarks.

# 2. Data & Methodology

Domestic price data for the ten products analyzed here were obtained from the National Statistics Office of Bolivia (INE) for the period 1991m1 - 2008m9.<sup>3</sup> World price series for the corresponding products were obtained

> 2 El Alto and La Paz are virtually the same market, as they are contiguous.

> 3 For Bananas, we use price series No. 11016 from INE, for Beef, 112010, for Chicken, 112059
public school students. Using data from the government and the World Bank, we estimate that 36.3% of these students were from the lowest two income

> 5 See for instance http://www.ilo.org/public/english/region/asro/bangkok/events/sis/download/paper24.pdf

> 6 <u>http://go.worldbank.org/BEQZ2K3MR0</u>

26
help boost competition and innovation. In fact, in Indonesia, the road freight sector, for example, could benefit substantially from reducing restrictions on foreign participation, which would bring the sector into line with those of comparator countries, such as Malaysia, thus better positioning local firms to produce at competitive prices.

Although in this paper we report evidence suggesting downstream vertical spillovers from service sector reform on manufacturing performance, it is likely not only that manufacturers benefit, but also that service users across the economy also benefit. It is known from the literature that these spillovers tend to increase with the amount of interaction between foreign and domestic firms. To maximize the positive spillovers, the government could consider incentivizing interaction between foreign service providers and domestic firms, either those that use their services or that supply inputs to them. One option would be for the government to substitute restrictions on foreign ownership with requirements for new foreign entrants to undertake, for example, explicit supplier development programs.

# **References**

Amity, M. and J. Konings (2007), “Trade Liberalization, Intermediate Inputs, and Productivity: Evidence from Indonesia”, American Economic Review, Vol. 97, No. 5, pp. 1611-1638.

Arnold, J., B. Javorcik, M.Lipscoom and A. Mattoo, (2010), “Services Reform and Manufacturing Performance: Evidence from India”, CEPR Discussion Papers, 8011, C.E.P.R. Discussion Papers.

Arnold, J., B. Javorcik, and A. Mattoo, (2011), “Does Services Liberalization Benefit Manufacturing Firms? Evidence from the Czech Republic”, Journal of International Economics, Vol. 85, No.1, pp. 136-146.

Arnold, J., A. Mattoo, and G. Narciso (2008) “Services Inputs and Firm Productivity in SubSaharan Africa: Evicdence from Firm-Level Data”, Journal of African Economies, Vol. 17, No.4, pp. 578-599.

Aw, Y
,37|86,28|78,74|
|Organizational rigidities within the<br>enterprise|35,48|36,67|31,94|
|Lack of qualified personnel|44,40|43,55|46,90|
|Lack of information on technology|32,04|31,30|34,24|
|Lack of information on markets|33,96|33,12|36,44|
|Insufficient flexibility of regulations<br>or standards|32,70|31,17|37,20|
|Lack of customer responsiveness to<br>new goods or services|39,48|39,24|40,17|

_Source_ : Latvian Innovation Survey Finally, the innovation survey suggests that a firm’s organizational capacity appears to be a critical determinant of whether it innovates. Simply stated, firms with more sophisticated organizational structures tend to be more innovative than firms with unsophisticated structures. This is also consistent with the finding from the recent FIAS survey showing that firms with more extensive linkages to foreign or multinational suppliers and foreign or multinational customers tend to be more innovative. Both studies conclude that a firm’s internal capacity to get out of its cocoon and look beyond the most immediate horizon seems to be a critical determinant of whether it will be 38
.2520***|||
|||||[0.0492]|||
|Lagged EWAP|||||||
|Total||-0.0622***|||||
|||[0.0124]|||||
|Female|||-0.0589***||||
||||[0.0101]||||
|Male||||-0.0607***|||
|||||[0.0189]|||
|Non-agriculture|||||-0.0512***||
||||||[0.0190]||
|Agriculture||||||-0.0485***|
|||||||[0.0146]|
|Constant||4.4334***|3.4728***|5.2174***|2.2074***|1.4587***|
|||[0.7700]|[0.5260]|[1.4031]|[0.6268]|[0.4167]|
|Observations||2058|2058|2058|2058|2058|
|Numbers of countries||103|103|103|103|103|
|Adjusted R-squared||0.234|0.192|0.208|0.082|0.154|
|Year dummies||Yes|Yes|Yes|Yes|Yes|
|Countryfixed effects||Yes|Yes|Yes|Yes|Yes|

Sources: Authors’ estimates. International Labour Organization; Penn World Tables (database); WDI (database); World Bank.

Note: *** indicates significance at the 1 percent level, ** at the 5 percent level, and * at the 10