rafmacalaba/data-use-mentions-extended huggingface dataset:
5 Estimates from the Luxembourg Income Study (LIS) previously corresponded to total health expenditures (“hcmed” in LIS terminology). These, it turns out, include nonmonetary expenditures, such as the imputed value of publicly financed health insurance in some countries, e.g. the US. In the 2019 HEFPI database, expenditures are – wherever possible – monetary expenditures only (“hmcmed” in LIS terminology).<sup>2</sup> This process led to some datapoints being no longer available from LIS. For example, in the case of the US, out-of-pocket expenditures before 2012 reflect only the imputed value of Medicare and Medicaid; actual out-of-pocket expenditures are not available. Of the 54 LIS points that were included in the 2018 HEFPI database, 15 were affected by this revision. Aside from the US, the other dramatic change was Taiwan whose catastrophic health expenditure rate felt from 30 or so percent to under 7% and is now in line with values reported elsewhere (van Doorslaer et al. 2007).

Over 30 of the surveys we use ask about out-of-pocket expenditures in both the consumption and health modules. Very often there are differences across them in the number of items and the recall period. Lu et al. (2009) find that use of the single-item question leads to a smaller estimate of out-of-pocket expenditure than the survey’s multi-item question and a 4-week recall period leads to a larger (annualized) estimate of out-of-pocket spending than a 12-month recall period. Our sense is that a single item is unrealistic and that – at least up to a point – asking more items is better. Ideally, the list should explicitly separate outpatient and inpatient care. It also seems clear that a while a 4-week recall period might be appropriate for frequently consumed items, such as over-thecounter medicines, it is too short for most items of health expenditure, and far too short for inpatient care, where a 12-month window seems more appropriate. We ended up switching modules in several surveys we used in the
|Travel time to work for breadwinner|27.02|45.4|18.37|***|581|
|---|---|---|---|---|---|
|Volume of drainage system per capita by sub-<br>ward|0.06|0.13|0.07|**|581|
|Elevation of dwelling location|26.84|34.12|7.285|*|581|
|Electricity|77%|90%|13%|**|581|
|Piped water in dwelling|20%|31%|12%|**|581|
|Access to informal lending|53%|76%|23%|***|581|
|Proof of ownership dwelling|79%|88%|10%|**|581|
|Holds a bank account|33%|45%|13%|**|581|
|Access to community saving group|18%|26%|8%|*|581|
|Option of lending towards income generating<br>activities within saving group|10%|16%|7%|*|581|
|Owns insurance against floods|2%|6%|4%|*|581|
|Source of income breadwinner: Private<br>company|13%|22%|10%|*|574|
|Source of income breadwinner: Self-employed|65%|55%|-11%|*|574|

_Figure 13 - Distribution of the expenditure proxy and comparison between households that recovered and did not recover from flood_ <!-- Start of picture text --> Affected - recovered Affected - not recovered<br>0 200000 400000 600000 800000<br>Monthly expenditure proxy in Tsh<br><!-- End of picture text --
rural India. From 1999 until its replacement with a new program in 2012, the Indian central government operated a “flagship” rural sanitation program called the Total Sanitation Campaign (TSC). Averaging over implementation heterogeneity throughout rural India, Spears (2012a) finds that the TSC reduced infant mortality and increased children’s height, on average. In a follow-up study, Spears and Lamba (2012) find that early life exposure to improved rural sanitation due to the TSC additionally caused an increase in cognitive achievement at age six. Similarly, Hammer and Spears (2012) report a randomized field experiment in Maharashtra, in which children living in villages randomly assigned to a treatment group that received sanitation motivation and subsidized latrine construction grew taller than children in control villages. Section 5.1 considers the estimates of these causally well-identified studies in the context of this paper’s results.

# **1.2 Open defecation is common in India**

Of the 1.1 billion people who defecate openly, nearly 60 percent live in India, which means they make up more than half of the population of India. These large numbers are roughly corroborated by the Indian government’s 2011 census, which found that 53.1 percent of all – – Indian households and 69.3 percent of rural households “usually” do not use any kind of toilet or latrine. In the 2005-6 National Family Health Survey, India’s version of the DHS, 55.3 percent of all Indian households reported defecating openly, a number which rose to 74 percent among rural households.

These statistics are striking for several reasons. First, open defecation is much more common in India than it is in many countries in Africa where, on average, poorer people live. UNICEF and the WHO estimate that in 2010, 25 percent of people in sub-Saharan Africa openly defecated. In the largest three sub-Saharan countries – Nigeria, Ethiopia, and – the Democratic Republic of the Congo in their most recent DHS surveys, 31.1,
br>100 1000 10000 100000<br>Per capita GNP (in 1987 US$)<br><!-- End of picture text --> _Source:_ **Ingram** and Liu, "Vehicles, Roads, and Road Use." 32 These results are based on panel data from 35 cities in developing and industrial countries, and the elasticities take into account the strong negative relation between per capita income and population density at the urban level.
# 5.3) Additional considerations

We highlight a series of additional practical considerations that UTAS users may find useful when planning their simulation exercises.

## Choice of modeling framework

The choice of modeling framework should be guided by two main considerations. First, for users predominantly interested in modeling the effects of a tariff reform on a few selected sectors, the choice of framework should consider the homogeneity and the tradability of these sectors. For tradable sectors that produce relatively homogeneous goods, the perfect pass‐through assumption of the product homogeneity is more suitable than for less homogeneous good that are more difficult to trade across borders (e.g. services). Second, data requirements are considerably higher for the product differentiation framework. Users are recommended to at least obtain domestic consumption shares for each sector before embarking on simulations under the product differentiation framework. This should also be combined with a careful sensitivity analysis based on alternative substitution and supply elasticities.

## Duty exemptions and duty drawback schemes

In many developing countries, duty on imported intermediate products is exempt to encourage exports. To ensure that the estimations in UTAS are accurate, the baseline tariff schedule should thus ideally reflect effective (i.e. actually collected) tariffs and not statutory (i.e. de jure) tariffs. An ‘effective’ tariff schedule can normally be constructed using customs transactions data, which report the amount actually collected for each transaction at the import stage. In some countries, however, exemptions are not granted at the import stage but in the form of duty drawback schemes for exporters. Data on such schemes are much more challenging to collect, making it more difficult to factor in drawback schemes. When comprehensive duty drawback schemes are in place, UTAS will most likely overestimate the size of upstream tariffs on downstream costs for exporting industries. Note, however, that in most developing countries duty drawback schemes tend to work imperfectly. There are also large implicit costs associated with using them. This diminishes the possibility of estimation bias in UTAS even when drawback
90% of bank branches opened in Ethiopia between 2000 and 2015; and digitization of all annual reports in the five years around the policy change. First, we obtained access to confidential data on the monthly composition of bank balance sheets, collected and confidentially released by the NBE, which allows us to explore the effect of the policy on the key modeling variables (safe assets, deposits, loans). The high frequency of the data permits us to track tightly the policy change, which has large and clear effects, as the empirical section shows. Secondly, because we are especially interested in financial development in terms of branch expansion, we have constructed a branch map for Ethiopia for the years 2000–2015, in which we were able to observe more than 90% of all bank branches and to find their date of opening (month and year), their city and region, telephone numbers and other information. This permits us to verify what happens to the long-term trend in branch expansion and to the numbers of new towns connected by a branch, both of which show significant increases after the policy introduction. Finally, in order to test the effects on profits and other yearly variables (number of employees), we have collected annual report data for all bank between 2008 and 2013. Though the power of this test is low because the number of observations is limited, we find that profits do not seem to decline and the number of employees expands, as expected.

For the Antebellum USA case study, we developed a dataset in which we follow a repeated cross-section of more than 1,200 banks for 60 years. Several historical sources were used for this 5
298|298|337|337|188|188|

Note: City-level pollution reflects PM2.5 data for 2014 for 298 cities (78 in developing countries). Congestion reflects the annual additional hours spent driving in rush hours, measured in 2018, and contains data for 337 cities (69 in developing countries). Homicide rate data reflects 124 cities (63 in developing countries) in 2015. Population density reflects population per square km of built up area in 2015. The top and bottom 1 percent of population density observations are excluded. Non-HIC reflects a dummy variable for developing countries using World Bank country classifications in 2015. Log population density is demeaned such that the constant terms are interpreted at the mean city density. Robust standard errors in parentheses, ***, ** and * reflect significance at the 1, 5 and 10 percent levels respectively.

# 7. Conclusions

15 Our estimate is somewhat higher than Ahlfeldt and Pietrostefani (2019) who report the pollution elasticity to be +22 percent, using OLS estimation and OECD data. They also find a congestion elasticity of +8 percent, using a different measure of congestion to our paper, average travel speeds using OECD data. Their meta-analysis of 13 high income country studies of crime elasticities (not focusing on homicides as we do) finds a mean elasticity of -24 percent.

16 Census data is available for only about half the cities in our sample.

24
||0.428***<br>(0.145)|||
|SBFs - Extensive margin|||0.131*<br>(0.0717)||
|FTM*SBF EXT|||0.0829<br>(0.146)||
|SBFs - Intensive margin||||0.0665<br>(0.0946)|
|FTM*SBF INT||||0.294<br>(0.203)|
|Controls|Yes|Yes|Yes|Yes|
|Sector|Yes|Yes|Yes|Yes|
|Region|Yes|Yes|Yes|Yes|
|Observations|6717|6717|4370|4362|
|R-squared|0.495|0.496|0.501|0.497|

Note: All estimates control for capital stock, employment, sector, country, and additional controls, including formality and the management human capita index. FTM = Female Top Manager, GBF EXT = General Business Function Extensive Margin, GBF INT = General Business Function Intensive Margin, SBF EXT = General Business Function Extensive Margin, SBF INT = General Business Function Intensive Margin. Estimates were performed using sample weights. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.

Table 9 provides additional results from the 2SLS model estimates. As we include inter19
.761|0.750|0.756|0.750|
|Orig._×_product_×_dest. FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Origin_×_year FE|Yes|Yes|Yes|Yes|No|No|No|No|
|Origin_×_product_×_year FE|No|No|No|No|Yes|Yes|Yes|Yes|

> _c_ significant at 10 percent; _b_ significant at 5 percent; _a_ significant at 1 percent. Unit values computed from BACI at the exporter-importer-HS6 product-year level. Standard errors clustered by product-origin-destination-year are in parentheses. ln RER is the real exchange rate of the destination country vis-a-vis the USD.

In this section we test whether our results on the effect of trade policy on market power obtained at the firm-level hold at a more aggregate level. To do so, we perform two additional exercises. We start by replicating our analysis using BACI data at the HS 6-digit product-country pair level. The results are reported in Table 9 below. At the product-level, we still find that NTMs magnify pricing-to-market while tariffs reduce it. Although the magnitude of the effects is lower than what we found at the firm level, it is still economically meaningful. The coefficient in column (1) suggest that moving from zero to a 10 percent ad valorem equivalent NTM raises the price elasticity to exchange rate from 5 percent to 6 percent, i.e. by 20 percent (against one third in Table 4). For tariffs, moving from a zero to a 5 percent tariff, would reduce the price elasticity to exchange rate by 10 percent (against 50 percent in Table 5) from 1.2 percent to 1.1 percent. Online appendix Section C.19 shows that these results are not specific to our sample of origin countries: we expand the database for all countries for which tariffs / NTMs are available, i.e. a total of 169 exporters serving 146
Espitia, A., Ruta, M., Rocha, N., Winkler, D.E., Mattoo, A., 2021. Pandemic Trade : Covid-19, Remote Work and Global Value Chains (No. 9508), Policy Research Working Paper Series, Policy Research Working Paper Series. The World Bank.

Feenstra, R.C., Inklaar, R., Timmer, M.P., 2015. The Next Generation of the Penn World Table. American Economic Review 105, 3150–3182. https://doi.org/10.1257/aer.20130954 Javorcik, B., 2020. Global supply chains will not be the same in the post-COVID-19 world, in: COVID-19 and Trade Policy: Why Turning Inward Won’t Work. CEPR Press.

Kilic, K. , Marin, D., 2020. How COVID-19 is transforming the world economy. VoxEU, 20 May 2020.

Lund, S., Manyika, J., Woetzel, J., Barriball, E., Krishnan, M., Alicke, K., Birshan, M., George, K., Smit, S., Swan, D., 2020. Risk, resilience, and rebalancing in global value chains. McKinsey Global Institute.

Mayer, T., Zignago, S., 2011. Notes on CEPII’s Distances Measures: The GeoDist Database (SSRN Scholarly Paper No. ID 1994531). Social Science Research Network, Rochester, NY. https://doi.org/10.2139/ssrn.1994531 Martin J., I. Mejean, M. Parenti, 2021. Relationship Stickiness, International Trade, and Economic Uncertainty, Working Papers ECARES 2021-03, ULB -- Universite Libre de Bruxelles.

Rodrik, D., 2008. The real exchange rate and economic growth. Brookings papers on economic activity 2008, 365–412.

UNCTAD (2020), World Investment Report 2020: International Production Beyond the Pandemic, New
tricity infrastructure stock _Gt_ , as a consequence of the relatively lower intensity of electricity infrastructure in services and in agricultural output production, shifts the structural transformation process towards these last two sectors. This effect can be observed, for example, in the bottom chart of Figure 4 that depicts the evolution of the sectoral consumption shares. We see that, in the absence of public infrastructure growth, the manufacturing share rises whereas the shares of the other two sectors shift down. The model predictions, in particular, imply that the consumption share rises 0.71 and 1.52 percentage points in agriculture and services, respectively, and declines 2.23 percentage points in manufacturing.

Second, the growth in the electricity infrastructure reduces fixed costs in manufacturing, but increases them in farming and services. This means that, ceteris paribus, the income share of the secondary sector will decrease as the public capital is accumulated as a less rapid increase in fixed costs in manufacturing increases the relative price in the other two sectors. Therefore, both supply-side effects that cause changes in relative prices push down the manufacturing share. This is precisely the qualitative effect that the IV estimates for electricity access in Tables 2 and 3 mainly capture, with the exception of agriculture. Hence, the electricity instrument tends to isolate the effect of the electricity-supply component on the sectoral production capacity and market-entry barriers.<sup>26</sup> Importantly, these findings reinforces the choice of the IV estimates made in the calibration section to assign values to the production-function and fixed-costs parameters.

A growing _Gt_ also contributes to sectoral reallocations through the larger demand for investment goods induced by the grid construction. In this instance, the manufacturing share is the one that gains, because 73 percent of the total investment is supplied by the secondary

> 26In Tables 2 and 3, the two electricity infrastructure proxies, electricity access and generation capacity, enter the regression separately, and electricity access is the only electricity proxy that is instrumented. Therefore, the access variable captures the supply-side channel,
# 4.1 Heterogenous mortality under no-intervention

In this section, we perform a cross-country comparison of potential mortality burdens _LY_<sup>_NI_</sup> under the no-intervention scenario. This exercise reveals the large quantitative dierences in the potential number of lost-years between developing and developed countries.

We compute a country's potential number of lost-years under herd-immunity as follows. We assume an 80 % infection rate in all age categories of the population. The number of infected individuals in each age-category is computed from the country's population pyramid. The probability that an infected individual of a given age dies is deduced from age-specic infection-to-fatality ratios (IFR), once again using the IFR estimates for China from Verity et al. (2020) for developing countries, and those for France from Salje et al. (2020) for developed countries. Then, the number of lost-years for a given death is the country's residual life-expectancy at the age of death. Summing the lost-years over all deaths provides the potential number of lost-years.

We consider two no-intervention scenarios. Under the saturation scenario, we assume that contagion is fast and hospitals are overwhelmed by the ow of infected individuals in need of care. In order to reect this, the IFR used assumes that all patients who would need intensive care cannot access ICU and die. This is our reference no-intervention scenario. Under the no-saturation scenario, we assume that contagion is slow and hospitals are not overwhelmed by the ow of infected individuals in need of care. Then, the IFRs used assume that all patients who would need intensive care have access to ICU. See Appendix A2 for more details.

Our estimates of the potential number of lost-years can dier across countries for three reasons: population pyramids, residual life-expectancies and IFR ratio used. We nd that the potential mortality burden is several times larger in high-income countries than in low-income countries. This is despite the use of IFR from China in the latter countries, which implies more lost-years
6 weights defined as the consumption shares. The second term accounts for stratification or overlap between the groups. It is typically negative because the stratification indices Q k tend to be positive, and because the population shares P k are less than one. The third term accounts for the between group inequality. It is a direct extension of the covariance based expression for the Gini index given in (2) as applied to group mean consumption and ranks. Given equation (4), changes in the Gini index over time can be decomposed into changes in within group inequality, between group inequality, and stratification.

# _1.12 Results_

Tables I to 3 give the welfare ratios, poverty measures, and Gini indices at the national, rural, and urban levels for the five survey years, as well as the results of the above decompositions.

Looking first at the sectoral decomposition for poverty, it is seen that urban and rural poverty moved hand in hand in all years, first decreasing from 1983-84 to 1985-86, then increasing from 1985-86 to 1991-92, and then decreasing again from 1991-92 to 1995-96. Given these joints movements, national trends are probably at work. Note that there may be problems with the 1985-86 survey since the growth in per capita consumption observed for that year is not observed in the National Accounts of Bangladesh. Downplaying the results for that year, one finds relatively stable poverty in both urban and rural areas between 1983-84 and 1991-92, and a significant decrease thereafter. Note also that migration contributed to the decrease in poverty over time, but only for about half a percentage point. In the decomposition, it is assumed that households migrating to urban areas face a change in their probability of being poor equal to the difference in poverty between the two sectors. While this is ad hoc, it is coherent with expected positive returns from migration. All results are similar with the lower and upper poverty lines.

What happened to inequality? It increased in both sectors over time (with a small drop in 1991 - 92
1999; Pons and Gonzalo 2001). Parental education can be said to be related to the schooling level of an individual through genetic or environmental effects when an individual is a dependent child in a parent’s household. However, the direct influence of parental education on adult earnings, independent of the influence on schooling, would be mild. In such a case it has been shown that the findings would not substantially deviate from the benchmark case of a strictly exogenous instrument.

The current paper exploited retrospective RLMS questions, asked in 2006 and 2011, about mother’s and father’s occupation (J216AC08, J216BC08), and their highest achieved education level (J217A, J217B) at a respondent's age of 15. Occupational categories were converted to indices with the help of The Standard Occupational Prestige Scale (SIOPS) (Ganzeboom and Treiman 2019). The final family background measures represented maximum values for the two SES dimensions between two parents. Besides, following the lead of several past studies (Angrist and Krueger 1991; Card 1999; Kim et al. 2019) we make use of dummies for the Russian regions, in which individuals reside at the time of the interview (STATUS), as instruments. The analysis was performed, using 2018 RLMS data to capture the most recent labor market situation.

The general TSLS specification of interest can be written by the following equations.

First stage:
<br>**working hours**|
|---|---|---|---|---|
|Lower Secondary|50.9|0.0|50.9|49.1|
|Secondary|30.5|3.2|69.5|19.9|
|University|32.5|0.0|55.0|15.0|
|Post-Graduate|22.1|2.1|54.2|22.0|
|Source: GHFPS.|||||

41