rafmacalaba/data-use-mentions-extended huggingface dataset:
some deaths due to COVID-19 are not attributed to the disease. The magnitude of under-attribution may also vary by age. Differences across countries in the relative levels of under-attribution by age group could contribute to generating differences in the observed COVID-19 age-mortality curves. Figure 10 shows the rate of increase of the COVID-19 mortality rate plotted against a measure of the overall quality of death reporting data.<sup>7</sup> A few of the middle-income countries with flatter age-mortality curves—Pakistan, the Philippines, and Peru—rate low by this index, but other middle-income countries have death reporting systems considered equal in quality to those of highincome countries. Thus, the variation across countries in the slope of the age-mortality curves cannot be fully explained by the overall quality of cause of death data. However, differences in COVID-19 death attribution among older people could be responsible for part or even all of the observed difference between advanced and developing countries. It is likely that deaths due to COVID-19 are more likely to be correctly attributed to the disease when the death occurs in a hospital. In wealthy countries, a large share of deaths occurs in hospitals and other institutions. In developing countries, where a larger fraction die outside of hospitals, if deaths of older people are seen as more expected (given higher mortality rates for older people generally), their deaths may be especially likely to be under-attributed to COVID.

If the flatter COVID-19 age-mortality curves in developing countries do reflect in part the underlying reality and not only differences in data collection and attribution, the possible explanations fall into two broad categories. The varying patterns by age across countries may reflect 1) differences in infection rates and/or 2) mortality rates among those infected.

> 7 The Vital Statistics Performance Index is a country-level composite index of measures of cause of death data: quality of cause of death reporting, quality of age and sex reporting, internal consistency, completeness of death reporting, level of cause-specific detail, and data availability/timeliness.

14
requirements with treated and nontreated sectors are captures by the coefficients on T T T × L and T T T × L , respectively. TT mm mm NNTT mm mm

# 5. Data

## _5.1 Locations, distance and employment_

The Economic Census of India is used to compute the total employment and number of firms for towns and villages and for sectors in each town or village (MOSPI 2013). We use the 2013 round of the Economic Census for our baseline results and the 1990 and 1998 rounds for robustness checks. We geo-reference all three rounds of the Economic Census to digitized boundaries of administrative units in India, available down to the town or village level. These boundaries are based on India’s Administrative Atlas _2011_ (ORGI 2011b) and were generated as part of a broader research project, the Spatial Database for South Asia (Li et al. 2015).

For each round of the Economic Census, we achieve the geo-refence through two steps. First, we create a concordance between the town/village codes used by the Economic Census and those of the Population Census of India 2011 (ORGI 2011a). 7 Second, we match the town/village codes used by the Population Census with those of the digitized administrative units, using an official concordance and addressing some additional mismatches not covered by the concordance (ORGI 2011b).

We obtain the spatial coordinates of all towns and village from the digitized boundaries of administrative units. Thereafter, we use the Open Source Routing Machine and take advantage of the road network data from OpenStreetMap to calculate the shortest driving distance from all towns and village to the border between Uttarakhand and Uttar Pradesh.<sup>8</sup> The Economic Census of India enumerates almost all non-farm establishments, including single-worker to the largest establishments, formal and informal, from both manufacturing and services sectors. For our analysis, we include all non-agricultural economic sectors, excluding public administration and defense. The 2013 Economic Census uses the three-digit National Industrial Classification 2008 (NIC 2008)
we compare the period 1995-2008 with 2014-18, sovereign bond spreads have decreased for most of the sample groups except EA while bond spreads in EA are the lowest. For instance, the lowest average spread among the different country groups is 240 for EA in the periods of 1995-2008 and 239 of 2014-18. The highest average sovereign spread is 999 for SSA in the period of 1995-2008 although SSA successfully reduces its spread by 561points in the period of 2014-18.

# **4. Empirical Evidence**

This section analyzes the empirical results from the fixed effects and instrumental variable estimations. Our results from fixed effects robustly show that improving data transparency benefits a domestic economy by reducing sovereign bond spreads when the country has a higher institutional quality.

# **4.1 Fixed Effect Results**

# _Baseline Specifications_

Table 2 presents the regressions of sovereign bond spreads on domestic and global factors using fixed effects estimation. We find that lower GDP per capita and lower economic growth are associated with widened sovereign bond spreads in developing countries. For instance, in Table 2 a one-point increase in GDP per capita would reduce sovereign spreads by 0.64 point in regression [1] of Table 2 when controlling for WB data transparency (SCI) and they decline by 0.753 point 9
# **What Are the Empirical Determinants of International Tourist Arrivals and Expenditures? An Empirical Application to the Case of São Tomé and Príncipe**

Gabriel Montes-Rojas<sup>∗</sup> Rafael Barroso<sup>♦</sup>

**JEL:** C33, F14, L83, O14, Z32

**Keywords:** Service Trade, Tourism, Panel Data, Small-Island States

> ∗ Professor of Econometrics at Universidad de Buenos Aires and Universidad Nacional de La Plata, Argentina. He holds a PhD in Economics from University of Illinois at Urbana-Champaign and works on theoretical and applied econometrics with applications to labor and development economics.

> ♦ Senior Economist at the World Bank.
consumed for cooling purposes, depending on the latitude of the country (i.e., tropical, temperate, cold).

In what follows, the impact of increasing average temperature on energy demand is computed, taking into account all these factors.

# _7.1 Methodology_

Our estimates are based on De Cian, Lanzi and Roson (2013), who computed parameters of a model for household energy demand, by energy source and season, using econometric techniques and a global panel database. Energy demand is expressed as dependent, among other factors, on the (natural logarithm of) seasonal average temperature, expressed in °F.

Seasonal long run temperature elasticities by energy source and by climate region (Table 4) are those estimated by De Cian, Lanzi and Roson (2013.). Since we are interested in the variation of total energy demand, elasticities in Table 4 have been scaled down by considering the share of energy used for heating and cooling purposes (Table 5). The adjusted elasticities are shown in Table 6.

Data on average seasonal temperature by country are obtained from the Weather Database (www.weatherbase.com), whereas each country has been classified as Cold, Mild or Hot, according to its latitude.<sup>3</sup> Applying the model estimated by De Cian, Lanzi and Roson (2013), to the percentage variation in temperature corresponding to 1°C (and 2, 3, 4, 5°C) increase in seasonal average temperature has been multiplied by the elasticities reported in Table 6.

> 3 Hot countries: latitude<27°; mild countries: 27°<latitude<63°; cold countries: latitude>63°. For aggregated regions the latitude has been computed as a weighted sum of the latitude of each single country.

16
in deciles 4-7 and 38 percent in the upper 3 deciles. Age and marital status of eligible candidates are extremely similar across bootcamps locations. Our sample in Argentina has a somewhat smaller share of mothers relative to Colombia, and a household income distribution relatively skewed to the right.

To summarize education, we show the percentage of individuals who have a college degree in our sample. This is 61 percent, and is very similar between Argentina and Colombia. Last, employment variables show that 83 percent of our sample is employed at baseline, with 22 of them being self-employed, and about 60 percent of them being employed in the formal sector.<sup>19</sup> On average, individuals in our sample work about 27 hours a week, and their wage and salary earnings are US$ 392 a month. While the employment figure

> 18Data Appendix A.1 details how we construct each variable in our data.

> 19Our self-employed and formal sector dummies assign zeros for those not employed.

11
and Rose (1998) and Rose (2000) who examined the association between female birth, savings, consumption, parents’ time allocation, and income in India, but they did not show that dowry was the underlying mechanism for their findings. We are the first study that estimates the causal impact of dowry on household savings and parental labor supply in a causal manner.

Our findings are also relevant to the literature on barriers to savings in low-income countries. Household savings are a crucial determinant of welfare, especially in developing countries, where credit and insurance markets are imperfect. Although the relationship between marriage and savings has been previously recognized,<sup>10</sup> we highlight a hitherto under-appreciated motive for savings in dowry-paying societies. Finally, we make a modest contribution to the large literature on income and consumption smoothing (Morduch (1995)). Our finding that households use savings and adjust labor supply to smooth the negative income shock due to dowry is consistent with classical life-cycle and permanent income models (Franco and Brumberg (1954), Friedman (1957), Campbell (1987), Jappelli and Pistaferri (2010)).

# **2 Data and Descriptive Statistics**

We use the most recent, 2006 round of REDS. REDS is a nationally representative survey of rural Indian households first carried out in 1968. Uniquely among household surveys, REDS collects detailed information on savings and marriage payments, along with data on labor supply and other economic and demographic variables.

## **2.1 Sample-selection criteria**

We restrict our sample to households that have unmarried individuals under the age of 18 (the minimum age at marriage for women in India) at the time of the survey since this is the group for which our research question is relevant. In addition, we impose several other restrictions that are either necessitated by our research question or the conceptual framework underlying the identification strategy. For instance, we restrict our sample to nuclear families, i.e., families where all children
**Table 9**

# **Reasons Given in Caskey Survey for Why Households Do Not Have Deposit Accounts**

|**Reason/reasons given for why households do not**<br>**have deposit accounts**|**Percentage**<br>**Giving this**<br>**Reason **|
|---|---|
|Don't need account because I have no savings|53.3|
|Bank account fees are too high or banks require too<br>much money to open or maintain an account|45.2|
|I want to keep my financial records private|21.6|
|Not comfortable dealing with banks|17.6|
|Banks won't let us open an account|9.5|
|No bank has convenient hours or location|8.5|

Source: Caskey (1997) As discussed earlier, large percentages of lower-income urban residents see themselves as having severely limited access to credit or turn to non-bank lenders, such as finance companies or payday loan shops. Although the OCC survey did not examine why people lack access to bank credit, other data support the conclusion that the main reason that people are excluded from bank credit is that they have impaired credit histories or, in some cases, no credit histories. In addition, many households have reached the limit of the credit that banks are willing to extend them.

A comparatively large percentage of lower-income and minority households report a history of failing to fulfill payment obligations on time, heavy debt-payment burdens, bankruptcy, or liens placed on their property. For example, Fair Isaac and Company Inc., one of the largest U.S. credit scoring bureaus, reported that it examined loan application data from "tens of thousands" of individuals applying for installment loans between July 1992 and December 1994 (Martell et al). Using its proprietary data base, Fair Isaac assigned scores for each
The second stage of the analysis studies the association between the evolution of access to basic education and public services during childhood and the level of income achieved in adulthood in several countries in the LAC region. For each subgroup described above, we compare access to each opportunity using information from censuses from the 1980s—and in some cases from the 1970s—with per capita household income of the same cohort in the most recent household survey. To the best knowledge of the authors, this is the first time that such a comprehensive data set has been organized.

There are two advantages of breaking down the population by gender, geographical areas, and urban/rural zones instead of using past subnational economic performance from national accounts. First, the population breakdown suggested in this paper provides a finer level of disaggregation than common subnational administrative records. At best, subnational GDP or aggregate consumption in the 1980s might be found at the level of aggregate geographical location but not by urban/rural zones, let alone by demographic categories like gender. Second, this breakdown considers some of the circumstances explored in the Human Opportunity Index (HOI), according to which differences in characteristics out of the control of the individual—known as circumstances—like race, gender, household composition, geographic environment, location, parental education, and wealth, among others, are associated with the unequal distribution of economic opportunities such as access to basic services and education. This analysis explores three such circumstances.

This paper makes three main contributions. This is the first paper that compiles historical data on human opportunities to analyze the relationship between access to education and basic public services early in life and incomes at adulthood. Second, this work takes a wide-ranging approach 3
_Mexico: The 1997 Pension Reform_ _25_ <!-- Start of picture text --> Assumptions: Insured/EAP Ratio<br>38%<br>36% - ---------- - - -- - ---- - 0 . _ _ ._ _--<br>32% --- 0--- 0-0-0 ---.------ - -<br>-30% ---- - -- NN<br>28% S ~---- -------- I------ ---..-.-.-<br>2 6 % -- - - - - -- -- --- - .-..-..--.--....<br>24% - -- -- - --- -.-- .. -.-- - - - -- - - - - - -<br>a)~~~~c aw gg o CN o-_N CN N4 N<br><!-- End of picture text --> These amounts ar exresseD as a ' ulIplen ofOtheminimu wae The avilbl daacmrie h

**E:stimating workers' salaries.** The wage of a person of a given age was determined by using the data for average salary history, i.e. the average age/salary couplets recorded by the IMSS for a given year. These amnounts are expressed as a multiple of the minimum wage. The available data comprised the salary histories reported for 15 years (1977-86 and 1990-94). To these observations a curve was fitted using a polynomial to describe the average salary history of IMSS members. Average salary history expressed as a multiple of the minimum wage is constant throughout the projection period but it is assumed that the minimum wage rises in real terms
work permit is then given by: rr ww = ∑ U UU (4) mm ss mm=1 mm NN 1[rr. ww < pp] In setting the replacement rate, we draw on the surveys that have been done to determine what share of pre-retirement income will make a retiree no worse off on a pension. Binswanger and Schunk (2012) report their estimates from such surveys for the U.S. The mean replacement rate is 80%, but they find that it is higher for lower-income workers (as one would expect). Based on their results, we set A Mexican migrant purchases a work permit if his expected earnings in the U.S. ( MU ) rr= 0.9. is higher than the price of the permit and additional fees and costs associated with moving to the jj ww� U.S. The net earnings reflect the standard taxes on earnings levied by the U.S. government. An

> 9 We use the Mexico PPP rate for 2018 of 9.38 (World Bank 2021).

7
), 585-618.

Checchi, D., Peragine, V. (2010). Inequality of opportunity in Italy, _Journal of Economic Inequality_ , 8(4), 429-450.

Deaton, A. (1997). “ _The analysis of household survey. A microeconometric approach to development policy_ ” Johns Hopkins University Press (Baltimore and London) DeFraja, G. (2002). "The Design of Optimal Education Policies," _Review of Economic Studies_ , vol. 69(2), pages 437-466.

Ferreira, F.H.G., Peragine, V. (2016). Individual responsibility and equality of opportunity, in M. Adler and M. Fleurbaey (eds.), _Handbook of Well Being and Public Policy_ , Oxford: Oxford University Press.

Fleurbaey M., Peragine, V. (2011). Ex ante versus ex post equality of opportunity, _Economica,_ vol. 80(317), pages 118-130.

Heckman, James J., Lochner, Lance J. and Todd, Petra E., 2006. “Earnings Functions, Rates of Return and Treatment Effects: The Mincer Equation and Beyond” in Erik Hanushek and F. Welch (ed.), _Handbook of the Economics of Education_ . Elsevier, vol.1 Kramarz, Francis and Oskar Nordström Skans, 2014. "When Strong Ties are Strong: Networks and Youth Labour Market Entry," _Review of Economic Studies_ , Oxford University Press, vol. 81(3), pages 1164-1200.

Ramos, X., Van de Gaer, D. (2016). Approaches to inequality of opportunity: Principles, measures and evidence, _Journal of Economic Surveys_ , 30(5), 855-883.

Roemer, J.E., A. Trannoy (2015). Equality of opportunity, in A.B. Atkinson and F. Bourguignon (eds). _Handbook of Income Distribution_ , vol.2B, Amsterdam: North Holland.

12
households. By contrast, more than half of Rwandan households have an account. This is also reflected in a relatively higher share of formal loan experience: 16 percent have received a loan from a commercial bank or a microfinance institution in the past 3 years. In Senegal and Burkina Faso, people rely more on informal credits, e.g. at a shop, from relatives or friends, or informal savings associations (tontines).

On average, total household expenditures—expressed in USD—are almost twice as high in Senegal as in Rwanda, and are also 14 percent higher than in Burkina Faso. Adjusting for purchasing power, these differences reduce to 80 percent and 3 percent, respectively. However, Rwandan households have the highest per capita expenditure in the sample and Senegalese households have the lowest. Comparing these values to national income data, this sample does not reflect the differences at the national level, where per capita income is highest in Senegal and lowest in Rwanda.<sup>8</sup> The Senegal sample is hence a relatively poorer sample than the Burkinabe sample and especially the Rwandan sample when compared to the overall population of the respective country.

**Table 4: WTP electricity consumption levels**

|Scenario|Consumption level|Corresponds to<br>SE4All access tier|Corresponds to<br>technology|
|---|---|---|---|
|1|Electric lighting inside the house|< 1|Solar lamp|
|2|Electric lighting, radio, and TV, charging a<br>mobile phone|2 or 3|Pico‐PV kit/SHS|
|3|Electric lighting, radio, and TV, charging a<br>mobile phone, fridge, electric stove|3 or 4|Grid connection|

# _3.2 Contingent valuation design_

In all three data sets, respondents were asked about their monthly WTP for one of

> 8 According to World Bank data, GDP per capita in
reported in Section 4 demonstrate that among women _τ_ is not equal to zero: making investment returns observable impacts individual choices. However, the magnitude of the reduced form impact does not identify _τ_ because the treatment effect depends on an individual’s level of risk aversion. Fortunately, random assignment should insure that the distribution of individual risk parameters in each of the six treatments is representative of the population distribution. We can therefore estimate the parameters of that distribution using data from the private treatments, and estimate _τ_ while controlling for heterogeneity in risk aversion in a structural framework.

We model decisions in a discrete choice setting, where each subject decides how many ten shilling coins to allocate to the business cup, and allocates the remainder to the savings cup. Subjects in the small endowment treatments have nine options (0 _,_ 10 _,_ 20 _, . . . ,_ 80), while those assigned to the large endowment treatments have nineteen (0 _,_ 10 _,_ 20 _, . . . ,_ 180).

In what follows, we index experimental treatments with _t_ , individual subjects with _i_ , and investment options with _j_ and, when necessary, _k_ . We assume that subject _i_ ’s expected utility of investing _bj_ is given by: where _EVij_ denotes the explicitly-modeled expected utility of investing _bj_ (what Train (2003) terms “representative utility”) and _εij_ is a Gumbel-distributed preference shock.<sup>36</sup> Behavior in the private treatments suggests that individuals have heterogeneous risk preferences. As in Section 3, we assume these can be represented by a CRRA utility function, _vi_ ( _·_ ), parameterized by _ρi_ , and that individual _ρi_ parameters are normally distributed with mean _μρ_ and variance _σρ_<sup>2.The</sup> probability that subject _i_ chooses to invest _bj_ can then be written in the form of a mixed logit model:

> where _σε_<sup>2is proportional to the variance of</sup
- 10 -

course, the commute decision may also depend on the household member _i_ ’s characteristics, _Xi_ , as well.

22. To estimate Equations (1) and (2), the paper primarily uses the endogenous treatment effects model, with the cluster-robust variance-covariance combined. Our unit of analysis is household members, not households. In each household, more than one household member may work. For obvious reasons, there would likely be common unobservables shared within a household. For endogeneity, the standard Hausman exogeneity test is performed.

# **IV. DATA**

23. The current paper uses micro data from the Living Standards Measurement Survey (LSMS) carried out in Antananarivo in 2016. The survey interviewed about 2,300 households in 200 local areas of the city. The data cover 9,350 household members. In the sample, 2,953 people were working, of which 1,488 were wage employees. The rest were self-employed or engaged with family businesses. Since the wage information is always sensitive to those who are interviewed, only 879 household members reported their actual wages. Of those, 287 persons were commuting to work by public transit. With the observations with missing values excluded, the following analysis uses 858 observations.

24. The current paper aims to compare the level of wages between commuters and noncommuters. “Commuters” are defined by those who are employed formally (as opposed to those who work in the informal sector) and commute to their workplaces by public transportation. First, it is important to note that no information is available about wages of informal workers. Second, by this definition, those who commute by walking are not considered as commuters. Finally, those who commute by own car are not regarded as commuters, either.