rafmacalaba/datause-encoder-data huggingface dataset:
# 3 Empirical Strategy

Our analysis is based on comparing the national COVID-19 vaccine coverage rate<sup>5</sup> for a given population of interest estimated from survey data to the coverage rate reported in administrative data. Neither source can be regarded ex-ante as bias free or (close to) the “true” rate, so that we cannot observe data accuracy directly. Instead, we observe how different survey estimates vary in response to design choices and vis-à-vis the administrative data. This approach is common in the measurement literature in the absence of an objective truth against which estimates under different design choices can be benchmarked (Bardasi et al. 2011; Laajaj and Macours 2021; Das et al. 2012; Beaman and Dillon 2012; De Weerdt et al. 2020b). It is also in line with health policy practice in which the size of the gap between survey and administrative estimates of vaccine coverage is taken as the key metric by which data quality as a whole is judged (Gavi, The Vaccine Alliance 2022c) .

An important caveat is that the two data sources usually refer to different reference populations. The phone surveys generally cover the adult population (aged 15+)<sup>6</sup> whereas the administrative data is reported for the entire country population. As age-disaggregated administrative data is not consistently available across our sample, we assume that the administrative data contains no vaccinated children (younger than 15 years). This assumption is likely more accurate at the start of the pandemic but will possibly underestimate the true gap between survey data and administrative records as countries lowered the age threshold for COVID-19 vaccination.<sup>7</sup> We structure our analysis along six hypotheses and associated predictions that we empirically test by randomly varying one aspect of the survey design at a time (Table 1). We conduct each analysis on a per-country basis. In the following, we introduce each hypothesis, its predicted effect on the estimated COVID-19 vaccine coverage rate and our empirical strategy to test it. Our first two hypotheses relate to possible errors of _representation_ whereas the remaining four hypotheses cover errors of _measurement_ .

> 5 Defined as the share of the total population that has received at least one dose of a COVID-19 vaccine.

> 6 Some phone and in-person survey rounds collect information on the vaccination status of all household members, rather than just the survey respondent, in which case the survey and administrative data refer to the same reference population. We analyze these survey rounds below.

> 7 Among the countries in which we conduct our survey experiments, children under the age of 16 started to be vaccinated in Ethiopia (from Nov 17, 2021), Malawi (from December 4, 2021), and Uganda (from July 26, 2021). Burkina Faso and Nigeria had not started vaccinating children yet at the end date of our data collection (Mathieu et al. 2021).

7
# **Bibliographical References**

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- Becker, Gary S., Kevin M. Murphy, and Michael Grossman (2006). “The Economic Theory of Illegal Goods: The Case of Drugs.” Journal of Political Economy, 114(1), 38-60.

- Bernardo, N. “Caffeine and Other Adulterants in Seizures of Street Cocaine in Brazil.” International Journal of Drug Policy , 14(4): 331

- Bhattacharya, Jay and Darius Lakdawalla (2005). “Time-inconsistency and Welfare.” NBER Working Paper 10345.

- Byrd, William and Christopher Ward (2004). “Drugs and Development in Afghanistan.” Social Development Papers – Conflict Prevention and Reconstruction n.18.

- Cameron, Samuel and Alan Collins (2006). “Addict Death. A Lacuna in the Welfare Economics of Drug Policy.” American Journal of Economics and Sociology 65 (4): 963–969.

- Caulkins, Jonathan P. and Sara Chandler (2006). “Long-Run Trends in Incarceration of Drug Offenders in the US.” Crime and Delinquency 52:4, 619-641.

- Colombian Government (2003). “Defense and Security.” Colombian Embassy, Washington DC (downloaded from www.colombiaemb.org/defense).

- Dave, Dhaval (2006). “The Effects of Cocaine and Heroin Price on Drug-related Emergency Department Visits.” Journal of Health Economics, 25(2006), 311–333

- Dills, Angela K. and Jeffrey A. Miron (2004). “Alcohol Prohibition, Alcohol Consumption, and Cirrhosis.” American Law and Economics Review, 6(2), 285-318.

- Dills, Angela K., Jeffrey A. Miron, and Garrett Summers (2008). “What Do Economists Know About Crime?” NBER Working Paper 13759.

- Dills, Angela K., Mireille Jacobson, and Jeffrey A. Miron (2005). “The Effect of Alcohol Prohibition on Alcohol Consumption: Evidence from Drunkenness Arrests.” Economics Letters, 86(2), 279-284.

Drug War Chronicle (2007). “Latin America: Brazilian Governor Says Legalize Drugs to Fight Crime.” March 9th, 2007.

- Executive Office of the President (2001).  “What America’s Users Spend on Illegal Drugs: 19882000.”  Washington: Office of National Drug Control Policy (December). http://www.whitehousedrugpolicy.gov/publications/drugfact/american_users_spend20 02/index.html

- Executive Office of the President (2004).  “The Economic Costs of Drug Abuse in the United States: 1992-2002.”  Washington: Office of National Drug Control Policy (December).

21
- 16 -

## **<u>Table 2. Summary statistics</u>**

|Variable|Abb.|Obs|Mean|Std. Dev.|Min|Max|
|---|---|---|---|---|---|---|
|Number of "enterprises" registered at location_i_at time_t_ <sup>1</sup>|_N_|1374|<br>7637|<br>27500|<br>1|331224|
|Travel time required to go to the nearest large regional<br>city with more than 1 million inhabitants (hour)|_HRREGION_|1374|<br>11.15|<br>9.88|<br>0.02|<br>44.97|
|Travel time required to go to the nearest local city with<br>more than 100,000 inhabitants (hour)|_HRLOCAL_|1374|<br>3.40|<br>2.89|<br>0.02|<br>16.18|
|Transport costs to convey goods to the nearest large|_TCREGION_|1374|<br>25.20|<br>21.88|<br>0.02|116.50|
|regional city with more than 1 million inhabitants ($/ton)|||||||
|Transport costs to convey goods to the nearest local city<br>with more than 100,000 inhabitants ($/ton)|_TCLOCAL_|1374|<br>8.29|<br>7.53|<br>0.02|<br>48.27|
|Population density at location_i_at time_t_(population per<br>km<sup>2</sup>)|_POPD_|1374|197.39|<br>909.73|<br>0.28|7840.24|
|Time dummy variables:|||||||
|_t_=2013||1374|<br>0.13|<br>0.34|<br>0|<br>1|
|_t_=2014||1374|<br>0.13|<br>0.34|<br>0|<br>1|
|_t_=2015||1374|<br>0.13|<br>0.34|<br>0|<br>1|
|_t_=2016||1374|<br>0.13|<br>0.34|<br>0|<br>1|
|_t_=2017||1374|<br>0.14|<br>0.35|<br>0|<br>1|
|_t_=2018||1374|<br>0.18|<br>0.39|<br>0|<br>1|
|_t_=2019||1374|<br>0.07|<br>0.26|<br>0|<br>1|
|_t_=2020||1374|<br>0.07|<br>0.26|<br>0|<br>1|

1 The definition of enterprises differs across countries.

# **IV. Main Estimation Results**

36. First of all, the OLS and fixed-effects models are performed to check how our agglomeration equation behaves. The estimation results are broadly consistent with prior expectations ( **Table 3** ). The agglomeration variable is found to have high persistence. The first three lagged agglomeration variables are all significant in the OLS models. As expected, firm agglomeration is positive related to transport connectivity. Both coefficients of regional and local connectivity are negative. Note that our transport variables represent the level of deterrence of transportation. Thus, the negative coefficients mean that the number of firms increases with transport connectivity.

37. Certainly, however, there are a number of unobservables omitted from our model. The fixed-effects models can control for time-invariant unobservables. The results turned out to be
# FIGURE 7 Pass-through associated with domestic shocks

# A. Monetary policy shocks

# B. EMDEs: Monetary policy shocks

<!-- Start of picture text --> Ratio Ratio<br>0.5 Interquartile range Median 0.8 Interquartile range Median<br>0.6<br>0.4<br>0.4<br>0.3 0.2<br>0.0<br>0.2<br>0.1<br>0.0<br>All Advanced EMDEs Trade Exch. Inflation Energy Current<br>economies openness regime target account<br>C. Domestic demand shocks D. EMDEs: Domestic demand shocks<br>Ratio Interquartile range Median Ratio Interquartile range Median<br>0.0 0.0<br>-0.2<br>-0.4<br>-0.1 -0.6<br>-0.2<br>All Advanced EMDEs Trade Exch. Inflation Energy Current<br>economies openness regime target account<br>E. Domestic supply shocks F. EMDEs: Domestic supply shocks<br>Ratio Ratio<br>0.2 Interquartile range Median 0.4 Interquartile range Median<br>0.2<br>0.0<br>0.1<br>-0.2<br>-0.4<br>0.0 -0.6<br>-0.1<br>-0.2<br>All Advanced EMDEs Trade Exch. Inflation Energy Current<br>economies openness regime target account<br>Low High Float Peg. Non-IT IT Importer Exporter Deficits Surpluses<br>Low High Float Peg. Non-IT IT Importer Surpluses<br>Low High Float Non-IT IT Importer Exporter Surpluses<br>Exporter Deficits<br>Peg. Deficits<br><!-- End of picture text --> Note: Pass-throughs are defined as the ratio between the one-year cumulative impulse response of consumer price inflation and the one-year cumulative impulse response of the exchange rate change estimated from factor-augmented vector autoregression models for 29 advanced economies and 26 EMDEs over 1998-2017. A positive pass-through means that a currency depreciation is associated with higher inflation. Bars show the interquartile range and markers represent the median across countries. EMDEs = emerging market and developing economies; IT = inflation targeting.

B.D.F. Countries with “high” trade openness are defined as those with above median trade-to-GDP ratios; all others are considered to have “low” trade openness. Exchange rate and IT regimes are based on IMF classifications. Energy exporters are defined as in World Bank (2018); all other countries are considered energy importers. Countries with current account deficits are those with a negative average current account balance over 1998-2017.

30
Figure 12. Penalty of having children at home on time use variables. Balancing of covariates. Unmatched and matched sample <!-- Start of picture text --> Women 15-19 Women 20-44 Women 45-64<br>Unmatched Unmatched Unmatched<br>.2 .4 .6 .8 1 0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1<br>Matched Matched Matched<br>.2 .4 .6 .8 1 0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1<br>Treated Untreated Treated Untreated Treated Untreated<br>6<br>4<br>2<br>0<br>6<br>4<br>2<br>0<br>5<br>4<br>3<br>2<br>1<br>0<br>5<br>4<br>3<br>2<br>1<br>0<br>4<br>3<br>2<br>1<br>0<br>4<br>3<br>2<br>1<br>0<br><!-- End of picture text --> <!-- Start of picture text --> Men 15-19 Men 20-44 Men 45-64<br>Unmatched Unmatched Unmatched<br>Appendix<br>Table A1. Surveys included in the analysis<br>.2 .4 .6 .8 1 0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1<br>Matched Matched Matched<br>.2 .4 .6 .8 1 0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1<br>Treated Untreated Treated Untreated Treated Untreated<br>5<br>4<br>3<br>2<br>1<br>0<br>4<br>3<br>2<br>1<br>0<br>5<br>4<br>3<br>2<br>1<br>0<br>6<br>4<br>2<br>0<br>6<br>4<br>2<br>0<br>6<br>4<br>2<br>0<br><!-- End of picture text --> 49
# **4. 2005 India Human Development Survey**

The 2005 India Human Development Survey (IHDS) is a nationally representative sample of 41,554 urban and rural households covering a wide-ranging set of topics, including energy use, income, expenditure, education, health, time allocation, and irrigation. The 2005 IHDS covers all of India’s key states and union territories, with the exception of Andaman/Nicobar and Lakshadweep. The sampled households were selected from 33 states and union territories, 383 districts, 1,503 villages, and 971 urban blocks.

# **4.1** **<u>Household Selection</u>**

Urban and rural households were selected using various sampling designs. The urban sample was drawn from all of a state’s urban areas, listed according to their size, with the number of blocks drawn from each urban area allocated based on probability proportional to size. Once the number of blocks for each urban area was set, the enumeration blocks were randomly selected with the assistance of the Registrar General of India. From these Census Enumeration Blocks of some 150 households, a complete household listing was formulated, and a sample of 15 households was selected per block. For ease of sampling, some smaller states were combined with larger neighboring states. The final urban sample covered more than 13,000 households.

The rural sample selection was based on both the random sampling design used in the 2005 IHDS and the method used in the 1993–94 Human Development Profile of India (HDPI) survey, which covered 33,320 households in 16 states. About half of the sampled households were newly selected for the 2005 IHDS, while the other half had been interviewed initially as part of the 1993– 94 HDPI (Shariff 1999). Both surveys follow a similar sampling design. In states where the 1993– 94 survey had been conducted and contact details for future survey were available, 13,593 households were randomly selected for re-interview in 2005. To select the new households, the HDPI was consulted where districts in each state had been divided into high-, medium-, and low8
health agencies. Instead of food calorie requirements, they base their estimates on the technical provision of energy services. This is essentially the method used in the earliest approaches that classified and estimated the minimum quantities of energy to have a reasonable quality of life (Bravo et al. 1979). According to this approach direct energy includes provisions for cooking, lighting, heating/cooling, preservation of food, hot water, ironing, pumping of water, plus recreation and social occasions. Indirect energy needs refer to energy that is embodied in additional goods and services that households use. The Bravo measure goes into considerable details to quantify household’s direct energy needs, considering variations in energy sources and their efficiencies, urban and rural areas, and climate conditions. This method defines the average essential household needs for direct energy requirements in rural Bangladesh to be about 27.4 kgOE per capita per month.<sup>5</sup> Based on another interpretation of physical energy needs, Goldemberg (1990) includes an even wider range of energy-using activities, and based on that measure the energy poverty line for rural Bangladesh is 32.1 kgOE per capita per month.<sup>6</sup> As indicated, these two measures are based on physical energy requirements for households.

A simpler method that is more universal examines the physical needs of daily cooking and lighting based on various surveys around the world, and the minimum energy need according to this measure is much lower at 50 kgOE per capita per year (Modi et al 2005). This is based on the absolute minimum requirement of 40 kgOE for cooking and 10 kgOE is for lighting. It is obvious that this measure is very basic and does not include energy use for other purposes such as transport, heating/cooling, and other more essential services. Thus, the energy quantity approach is very interesting, but the differing assumptions yield vastly different results. They also do not take into

> 5 Bravo and others (1979) expressed it as 9.2 thousand kcal/day/person which converts to 27.4 kgOE per capita per month for a tropical country like Bangladesh.

> 6 Goldemberg (1990) proposed roughly a little over 500 watt per capita which is equivalent to 32.1 kgOE per capita per month.

6
<!-- Start of picture text --> = Bangladesh: Education<br>2<br>3<br>S<br>Pa3°5re)<br>3<br>Ss2<br>iUrban<br>&<br>doc5<br>oO<br>8<br>it]<br>re)<br>r<br>1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995<br>Year of birth<br>Generated by regressing years of education on wealth, urban, higher education and interactions with<br>year-of-birth and state fixed effects. Standard errors clustered by state.<br><!-- End of picture text --> <!-- Start of picture text --> a5 India: Education<br>S<br>3<br>o<br>r a<br>x)<br>ge<br>5<br>&c<br>ie}<br>3 Urban<br>Eo<br>oO<br>=£<br>6<br>22)hae)<br>2 2<br>1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995<br>Year of birth<br>Generated by regressing years of education on wealth, urban, higher education and interactions with<br>year-of-birth and state fixed effects. Standard errors clustered by state.<br><!-- End of picture text --> <!-- Start of picture text --> Pakistan: Education<br>5<br>g<br>a o ]<br>2°<br>o=<br>xe)<br>5<br>s Urban<br>8<br>So<br>o<br>3<br>5<br>2<br>Cwm<br>2 2<br>1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995<br>Year of birth<br>Generated by regressing years of education on wealth, urban, higher education and interactions with<br>year-of-birth and state fixed effects. Standard errors clustered by state.<br><!-- End of picture text --> <!-- Start of picture text --> : i<br>5 Nepal: Education<br>a”<br>Ss<br>3<br>-S<br>=8.0<br>S<br>6 Urban<br>3<br>3<br>Bo<br>oc<br>B<br>5<br>2<br>o<br>2"<br>1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995<br>Year of birth<br>Generated by regressing years of education on wealth, urban, higher education and interactions with<br>year-of-birth and state fixed effects. Standard errors clustered by state.<br><!-- End of picture text -->
**Appendix 1:** _Summary statistics of selected tradable procedures_

|_Treatment_|_Number of_<br>_inpatient_<br>_discharges_<br>_in 2002_|_Mean_<br>_charges_|_Mean length_<br>_of hospital_<br>_stay_|_Risk of_<br>_surgical_<br>_mortality in_<br>_US (%)_|_Proportion_<br>_of Male_<br>_patients (%)_|
|---|---|---|---|---|---|
|KneeArthroplasty|399,139|28,032|4.0|0.1|37.2|
|Arthroplasty other<br>than hip or knee|46,037|20,898|2.7|0.2|41.2|
|TURP|111,936|12,501|3.1|0.3|100.0|
|Tubal ligation|78,771|9,018|2.2|*|0|
|Inguinal and femoral<br>herniarepair|40,553|22,609|5.6|0.9|80.5|
|Excision of skin<br>lesion|21,257|20,970|5.6|0.8|48.0|
|Tonsillectomy|17,251|10,250|2.0|*|56.0|
|Hysterectomy|640,565|13,448|2.8|0.1|0|
|Hemorrhoidectomy|12,787|12,656|3.3|*|50.8|
|Rhinoplasty|7,265|16,772|3.0|*|59.0|
|Bunionectomy|3,139|12,190|2.2|*|20.7|
|Lens and cataract<br>procedures|2,215|11,483|2.4|*|40.4|
|Varicose vein<br>stripping, lower limb|1,957|12,596|2.6|*|42.1|
|Glaucoma procedures<br>|*|9,955|2.0|*|*|
|Tympanoplasty<sup>37</sup>|754|11,983|1.3|*|*|

_Source:_ H-CUP Nationwide Inpatient Sample, 2002. Statistics based on estimates with a relative standard error (standard error / weighted estimate) greater than 0.30 or with standard error = 0 in the nationwide statistics are not reliable. These statistics are suppressed and are designated with an asterisk (*).

> 37 Volume pertains to year 2000.

29
for each country-pair separately. In a world where production chains spread many countries, a third country can be a key component in the GDP synchronization of any pair. Hence, it is important to include a “network” variable in the analysis in order to account for such a mechanism, which is consistent with the importance of trade in input in the rise of cross-country inter-dependence.

- The link between trade proximity and GDP comovement is very strong for country-pairs including at least one high income country. For low income countries, this results means that integrating in Global Value Chains with high income economies could potentially lead to an increase in business cycle synchronization with those trade partners.

- The association is also linked to GDP volatility: country-pairs including at least one low volatility country are also those where the relationship between trade proximity and GDP correlation is significant. This is related to the second point, since high income countries also have less volatile GDP fluctuations (i.e. GDP level and GDP volatility are strongly negatively correlated).

- Using recent decomposition of trade flows based on precise value added accounting from Borin and Mancini (2019), we show that various indices of GVC participation are strongly related to GDP correlation across countries. The association between trade in input and GDP comovement holds using both gross trade and value added trade data. Moreover, intermediate input that are absorbed by the destination country seem to play a more important role for GDP fluctuation than inputs that are re-exported further.<sup>4</sup>

- Results are _robust to a number of controls_ : we included constructed third country indices to control for common exposures to other trade partners, indices of similarity in the sectoral composition of GDP for each country-pair, as well as financial proximity indices such as foreign direct investment and bilateral financial interconnection indexes. In all cases, the main message emerging from the analysis does not change and cross-country production linkages, as proxied by trade in intermediate input, is associated with higher GDP co-movement, especially for all country-pairs that include a high-income country.

- Finally, based on a product characterization from Martin et al. (2018) that separates “sticky” from “non-sticky” products based on the duration of buyer-seller relationship,

> 4This finding shows that when considering cross-country production linkages, it could be misleading to only focus on the imported exports _that are re-exported further_ . Indeed, since exports typically are a small part of total GDP, the impact of imported input on domestic production goes far beyond the impact on exports.

3