rafmacalaba/data-use-mentions-extended huggingface dataset:
development outcomes (World Bank, 2015a), and these differences limit richer analyses that could establish linkages across domains of household and individual welfare.

The quality, reliability, relevance and cost-effectiveness of household survey data depend on the availability of well-documented, validated data collection tools and methods, which must be continuously adapted to account for new data needs and to take advantage of new technologies and linkages between data sources. Moreover, emphasis should be placed on promoting greater harmonization in content and methodology used in cross-country household survey operations. Establishing best practices and international standards for cross-country data collection requires methodological research, which is an international public good that must be supported in a systematic fashion by countries and their international development partners. The costing of the direct and in-kind contributions of client countries, the World Bank and development partners towards a global methodological research agenda coordinated by the Intersecretariat Working Group on Household Surveys (IWGHS)<sup>23</sup> are outside the scope of the current study. However, the multi-topic, multi-purpose nature of the recommended questionnaire instrument and the need for greater harmonization make it imperative to invest in methodological research, further develop data production and analysis standards, as well as to work across sectors and institutions in order to marshal the greatest expertise towards better harmonization of methodologies.

> 23 United Nations Economic and Social Council (2015) provides the terms of reference for the IWGHS.

17
statistics for each variable. The sample mean for the dependent variable electricity production per capita is 2,639 kwh per capita, and for the telephone lines per capita it is 195. For the purpose of estimation, per capita infrastructure and income measures were transformed into natural logarithmic form.

**Table 1: Summary Statistics of the Panel Data, 1980-2002, 52 low- and middle-income countries**

||**gdppc**|**yag**|**yman**|**yserv**|**eleconpc**|**elepropc**|**telpc**|
|---|---|---|---|---|---|---|---|
|**mean**|8336.2|15.2|17.6|53.1|2343.6|2638.6|195.4|
|**median**|4788.9|12.9|17.1|53.2|784.6|955.3|57.8|
|**max**|57740.8|62.6|40.5|87.5|24858.4|31086.2|1857.4|
|**min**|644.4|0.1|3.5|17.2|11.4|1.9|0.4|
|**stddev**|8641.5|11.7|6.7|11.3|3920.4|4364.8|297.0|
|**skewness**|1.6|1.1|0.4|-0.2|3.1|3.4|2.3|
|**kurtosis**|2.5|1.3|0.1|0
# **Figure 1: Density of log of per capita expenditure over time**

<!-- Start of picture text --> 4 6 8 10 12 14<br>Log of per capita expenditure<br>VHLSS 2004 VHLSS 2012<br>VHLSS 2020<br>.8<br>.6<br>.4<br>Density<br>.2<br>0<br><!-- End of picture text --> Note: i) Authors’ estimation based on VHLSSs data 32
The bulk of our work is conducted on data from the Annual Survey of Industries (ASI) by the Indian Ministry of Statistics and Programme Implementation (MoSPI). The survey spans over many years and covers the entire manufacturing sector comprising industrial units (“factories”) registered under Sections 2(m)(i) and 2(m)(ii) of the _Factories Act_ , 1948.<sup>14</sup> This includes all firms employing 10 or more workers using power, and those with 20 or more workers regardless of the use of power. The ASI data collect detailed information on employment, material inputs, and output that can be conveniently used to recover mark-ups and estimate production functions. The data set also provides information on the location of the factory at the state and district level.

The ASI data are available both as detailed, unit-level cross-sections without firm identifiers, and as panel data with firm identifiers.<sup>15</sup> Both these data sets have been widely used in the literature, most notably by Allcott et al. (2016), Hsieh and Klenow (2009), and Martin et al. (2017). In our work, we use the panel version for the period 1999-2014 for the sample of the major Indian states.<sup>16</sup> For our analysis on adjustment costs, we use four main outcome variables from the ASI – wage bill of permanent labor, wage bill of contract labor, value of land (owned and leased), and value of fixed capital excluding land. In line with our discussion in Section 3.2.3, we further divide all our outcome variables by the expenditure on materials consumed (or on electricity consumed and purchased in our robustness checks), which is our reference input for measuring relative input adjustment costs.<sup>17</sup> Descriptive statistics by year on the main ASI variables used in our analysis are reported in Table 3.2.

As regards
approach ensures the greatest possible consistency. We updated Arriola’s data set to 2014 and expanded the countries included to 48, with a total of 1,797 observations. Median cabinet size over this period is 22 and the mean is 23, but there is tremendous dispersion both within and between countries. Cabinet size in our sample ranges from 5 (the Comoros in 2002) to 53 (Cameroon in 2006). Figure 1 summarizes the data.<sup>7</sup>

> 7 The correlation of our data with Banks’ is .73. In 55 cases, the ministerial count in the Banks data set differs by more than 10 from ours.

5
*|**-3.5760*****<br>**(0.9164)**|**-3.7197*****<br>**(0.9234)**|**-3.9317*****<br>**(0.8032)**|**-3.6712*****<br>**(0.9496)**|
|**HC15**|**4.4661****<br>**(1.9854)**||**4.5116*****<br>**(1.5779)**||||
|**Sq of HC15**<br>**HC15 with extended**<br>**educational returns**|**-1.0965****<br>**(0.4726)**|**1.5718****<br>**(0.7644)**|**-1.0374*****<br>**(0.3752)**||||
|**Sq (HC15 with**<br>**Extended**<br>**Educational Returns)**||**-0.2617****<br>**(0.1139)**|||||
|**HC25**||||**3.8946***<br>**(2.0153)**||**4.3664*****<br>**(1.5611)**|
|**Sq of HC25**||||**-0.9925*
of excess mortality, an emerging challenge even for high-resource health systems in this current pandemic. Collectively, the strengths and the limitations of the existing large-scale health facility surveys in assessing pandemic preparedness signal the pressing need to reflect on the future of health service delivery measurement, especially in light of the future threat of large-scale disease outbreaks or other public health emergencies.

# Health service delivery measurement: The future

## More and better measurement is required to inform resource allocation and response

Comprehensive measurement to understand pandemic preparedness is paramount to inform resource allocation and pandemic response strategies. Up-to-date information on facility capacity, including referral systems and transport, coordination, and communication; health service personnel; and availability of key supplies, including PPE, medicines, and backup stockpiles, is needed to identify facilities that can safely and effectively deliver care in a pandemic. Identified high-capacity facilities can both serve as models for others and potentially as treatment/referral centers as part of a national preparedness strategy. Conversely, facilities with inadequate pandemic preparedness need to be identified so that they can either be supported with additional resources for retooling, or only utilized in a manner befitting their capacity.

Beyond preparedness to address acute, pandemic-related care needs, information on facility capacity to continue to provide normal services and to recover from pandemic-related strains is also a key part of comprehensive preparedness. Specifically, extreme pressure on curative services during the pandemic peak periods could result in disruption of essential preventative services and curative services for health problems other than COVID-19. Survey data like that from the SDI could help identify services and platforms that require reinforcement and reorganization, so that health systems are prepared to respond effectively to population needs overall, and not just those related to the pandemic.

20
16

# 5. DETERMINING THE SIZE OF THE SUBSIDY

The preceding sections served to establish the overall need for a water subsidy in Panama and to define who should be considered eligible to receive it. Another important design question concerns the size of the subsidy that should be awarded. There are two aspects to this problem:

- the value of the subsidy that should be paid out in any given period; and

- in the case of a recurring use-of-service subsidy, the period which the payment should cover.

In deciding upon this issue, the following considerations need to be taken into account.

- the affordability of current and future charges;

- the administrative costs associated with the scheme;

- the incentive properties of different payment rules.

# **Affordability of Charges**

From an economic standpoint, the value of the subsidy should be set as the difference between the cost of purchasing a subsistence level of consumption and the household's willingness to pay for the service.

In order to determine the subsidy required on this basis, it is necessary to have information about the following issues.

- 3 _Tariffs-the_ cost of purchasing a basic level of consumption will depend on current tariff levels and any proposed changes to the current tariff structure. As noted above, IDAAN's tariffs have been frozen in nominal terms since 1982. During this period a number of different tariff studies have demonstrated the need for significant price increases to assure the financial sustainability of the company and to fund necessary investments.

- _Willingness to pay-a_ number of methods for estimating the willingness to pay of households in the target group have already been presented and discussed. As well as informing the overall assessment of the need for a subsidy, this information is invaluable in determining the appropriate size of the subsidy. The results from the survey conducted in Panama, as reported above, indicate a willingness to pay equal to 4% of household income.

Using a simulation model, the willingness to pay for different income groups was compared with the corresponding water bill under alternative tariff levels
(ANOVA).

_Source: Authors’ calculation based on data from Brauer et al., 2015, Hansen et al., 2013, Vu et al,. 2014, Harmonized World Soil Database, Climate Research Unit, Bangalore et al., 2016, Winsemius et al., 2015, Lanjouw et al., 2013 and Geographic Information Science and Technology, 2015._

# **4. Commune‐level environmental risks across household groups**

This section explores whether poorer households, as measured by consumption expenditure, live in communes with higher levels of environmental risks based on descriptive statistics and nonparametric analyses.

## **4.1 Methods**

To compare environmental risks across households, the level of environmental risks is calculated for each commune. Whereas the geographical location of communes is known, the household data from the VHLSS is not geo‐coded so that it is not possible to track the exact location of households within communes. Although there can be considerable variation in environmental conditions within communes, environmental risks measured at commune level can measure the wider risk environment the household is exposed too.<sup>7</sup> For each commune, the area‐weighted average of the high‐resolution environmental variables is calculated,<sup>8</sup> as explained in section 2.2. Also the standardized value of each environmental risk is calculated by subtracting from each commune value the mean and dividing by the standard deviation, producing a value distribution with the mean of 0 and a standard deviation of 1. Such normalization produces a scaled version of the original value which allows comparison between the different risk variables.

Descriptive statistics and non‐parametric analyses are conducted. First, the mean risk level is calculated for different household groups in each survey year, differentiating between consumption quintiles, households below the national poverty line, ethnic minorities and female‐headed households (Figures 2 and A.2 for the standardized values and Table A.2 for the absolute values). Households
|ln(TFPR) (BKR)|22,061|0.56|0.44|-1.14|2.31|
|---|---|---|---|---|---|
|ln(Age)|35,723|2.72|0.69|0.00|5.61|
|ln(Employment)|35,730|4.02|1.18|2.30|9.61|
|Foreign owned?|35,730|0.002|0.04|0|1|
|**_Vietnam_**||||||
|ln(TFPQ)|142,630|5.18|1.82|0.04|9.2|
|ln(TFPR) (HK)|106,413|0.71|0.96|-2.99|3.41|
|ln(TFPR) (BKR)|106,413|0.56|0.85|-2.99|3.72|
|ln(Age)|106,967|1.98|0.69|0.00|4.23|
|ln(Employment)|142,630|3.93|1.30|2.30|11.35|
|Foreign owned?|142,630|0.18|0.38|0|1|
|SOE?|142,630|0.02|0.15|0|1|

Source: Country data as detailed in Table 1 and authors’ calculation.

# **4. Results**

# **4.1 Baseline results**

The descriptive statistics in Table 2 suggest a few discrepancies between TFPR obtained using HK or BKR. A more nuanced picture emerges from inspecting the full distributions of TFPR for each country
goods and materials - this is a sample we exploit in more detail in the coming Sections. We also show that for an overwhelming proportion of commitments values are non-zero and can be matched to some verification and payment – allowing us to track the entire cycle of budget execution. We also highlight that our dataset currently encompasses over 3.5 trillion BRL in payments (in 2021 prices) or the equivalent of 38% of GDP in 2021, made to over 9 million different unique agents (identified by national tax IDs).

We provide an illustrative example of the nature of our data in Figure 2, highlighting a case in which we can track the entire process of public procurement and budget execution.<sup>18</sup> In this figure we zoom in on one case involving the town hall of Abatiá – a municipality with less than 10,000 inhabitants but relatively high income in the state or Paraná – which initiated a tender to procure uniforms for basic health employees, identified by ID 1336360. The tender was officially published in November 2018, with an initial budget of 17,116 BRL. The auction took place on December 13 of the same year, during which bids were received for 23 items, comprising different types of t-shirts and bags. The tender resulted in two winners, one for supplying t-shirts and another for supplying bags. Subsequently, the tender was officially homologated on December 18.

One day after the tender’s homologation, four separate commitments were made – meaning that the local government set apart funds to pay for the purchases, once goods were delivered. Verification of delivery happened in mid-May 2019, five months after the end of the tender. Less than 30 days later, three separate payments were made to different agents that won the contracts, with total values of 11,377 BRL and 3,890 BRL. For the purchase of this relatively simple good, a total of 235 days elapsed between the tender publication and the last payment.

The example above shows the potential of
after winsorization is similar.

# **A1.7 Descriptive Statistics**

The composition of firms across countries and two-digit sectors before and after winsorization is similar. Observations of services firms are generally missing capital stock and share of high-skilled labor. Using the World Bank classification (World Bank Data, 2020), only one country is classified as low-income (Tajikistan); we therefore group countries into three income categories: high, upper middle, and lower middle.

38
- Schultz, T. P. (1994). Human Capital, Family Planning, and Their Effects on Population Growth. _American Economic Review_ , 84(2):255–260.

- Schultz, T. P. (1997). Demand for Children in Low Income Countries. _Handbook of Population and Family Economics_ , 1:349–430.

- Seid, Y. (2016). Does learning in mother tongue matter? Evidence from a natural experiment in Ethiopia. _Economics of Education Review_ , 55:21–38.

- Snilstveit, B., Stevenson, J., Menon, R., Phillips, D., Gallagher, E., Geleen, M., Jobse, H., Schmidt, T., and Jimenez, E. (2016). The Impact of Education Programmes on Learning and School Participation in Lowand Middle-Income Countries. _3ie Systematic Review Summary_ , 7.

- Sundberg, R. and Melander, E. (2013). Introducing the UCDP Georeferenced Event Dataset. _Journal of Peace Research_ , 50(4):523–532.

- UNESCO (2007). World Data on Education. Geneva. UNESCO: International Bureau of Education.

- USAID (1987). Final Disaster Report: The Ethiopian Drought/Famine. Technical report, USAID, Addis Ababa.

- World Bank (2005). Education in Ethiopia: Strengthening the Foundation for Sustainable Progress. Washington D.C. World Bank.

- World Bank (2009). Abolishing School Fees in Africa: Lessons from Ethiopia, Ghana, Kenya, Malawi, and Mozambique. Washington D.C. World Bank.

- Zenebe Gebre, T. (2014). Effects of Mother Tongue Education on Schooling and Child Labor Outcomes. Working Paper.

- Zenebe Gebre, T. (2018). Free Primary Education, Timing of Fertility, and Total Fertility. _World Bank Economic Review_ , Forthcoming.

22
_|_(89.593)_|_(25.654)_|_(0.064)_|
|Demand: Household Characteristics|||||
|Proportion of households who are self-employed farmers|251.015***<br>_(80.795)_|597.81**<br>_(280.65)_|-20.972<br>_(52.068)_|0.746***<br>_(0.133)_|
|Expenditure per capita|11.171***||||
||_(4.023)_||||
|Loan terms and conditions|||||
|VF would borrow more if funds were short|-|||1.239***<br>_(0.092)_|
|VF could lend out another 1m baht safely|-|||<br>0.538***<br>_(0.086)_|
|VF wants to borrow more from BAAC, GSB, etc.|-|||<br>0.529***<br>_(0.067)_|
|VF borrows to on-lend to members|927.096***<br>_(40.860)_|||<br>-|
|Memo items:|||||
|Number of observations|2,528|||2,593|
|R2|0.328||||
|pfor endogeneity||||0.088|

Source: Village Fund Survey 2010 and SES 2009. Robust standard errors in parentheses. Variables included based on OLS backward stepwise with p=0.2 cutoff. * p<0.05, ** p<0.01, *** p<0.001.

32
Figure A1: Effect of Extended Provider Training on Patient Satisfaction <!-- Start of picture text --> A. Aggregated Index B. Testing Sub-Index C. Medication Sub-Index<br>.4 .4<br>.4<br>.2<br>.2 .2<br>0<br>0 0<br>-.2<br>-.2 -.2 -.4<br>-.4 -.4<br>0 .2 .4 .6 0 .2 .4 .6 0 .2 .4 .6<br>Predicted Malaria Risk Predicted Malaria Risk Predicted Malaria Risk<br>Basic Provider Training Extended Provider Training<br>Satisfaction Index Satisfaction Index Satisfaction Index<br><!-- End of picture text --> _Notes:_ Patient satisfaction data from home follow-up of the patient survey. Results from local linear regressions. Graphs omit results for top and bottom 2.5 percent of malaria risk distribution to avoid influence of outliers. Vertical dashed lines indicate 25th, 50th, and 75th percentiles of predicted malaria risk respectively. Shaded areas give 90% confidence intervals, based on bootstrapped standard errors clustered at the clinic-pair level. To account for the fact that predicted malaria risk is a generated regressor, we re-calculate predicted risk for each bootstrap replication.
security for the primary job, while an individual is considered to be informally employed if the individual did not have a work contract, social security, or neither. Public sector jobs include the following: public and government, while private sector jobs include the following: private, investment, international, and other.

Whenever relying on the cross-sectional rounds of ELMPS, we focus on individuals aged between 20 and 59 years old in each round. On the other hand, when we rely on the panel dimension, we focus on those aged at least 20 in 2012 and at most 59 in 2018 (hence 53 in 2012) to account for aging between the two surveys. Table A.1 in the Appendix reports descriptive statistics on the sample of men and women as well as discussions on the sampling bias resulting from focusing on the panel sample. We contrast raw data from each round of the ELMPS survey against our cross-sectional and panel samples, to which the age restrictions described above are applied.

# **2.2 Encuesta Nacional de Ocupación y Empleo (ENOE)**

The ENOE is Mexico’s labor force survey, nationally representative of the population older than 14. As does the ELMPS, the ENOE also includes comparable labor market characteristics of labor market states and dynamics, as well as demographic information on age, gender, education, among others. Notably, the ENOE is conducted at a much higher frequency than the ELMPS. In each 4
The production functions finally contain 18 countries times 52 sectors, which provides us with a total of 936 cells.<sup>13</sup> Note that the production functions were estimated twice: first using the approach of proxy materials and then, second, with reported materials and value‐added. They are also estimated only with firms that consistently report values for at least four years so as to remain as much as possible in line with previous works. As a result, we have tried to the feasible extent possible to come up with a balanced panel data set for our TFP estimates. In all, based on the three unbiased sets of estimates plus the TFPQ measure, we obtain eight firm‐level country‐specific time‐varying logarithmic TFP specifications as residuals from equation (1). Table A7 in the annex provides summary statistics for the variables used in equation (1) production function while Table A8 and A9 show summary statistics for our TFP estimates.

# **2.3 Input‐Output Tables**

Country specific input‐output tables are also taken from Eurostat and are constant price matrices with 2010 as reference point which is the earliest year for which these tables are available.<sup>14</sup> Several types of input‐output tables are provided on their website, but we choose the one with the best sector‐country coverage, namely the “Use Tables at Purchasers’ Prices”. One big disadvantage with these matrices is that the Retail sector is not well recorded for many countries which do not report any inputs in this sector. This may be due to reasons related to misreporting or perhaps due to an unclear sector definition of retail activities as the entire distribution sector to which retail belongs is also made up of two other sub‐sectors, namely Wholesale trade and Trade and repair of motor vehicles. There are several solutions with this problem. One is that we use the whole distribution sector as representative (excluding Trade and repair of motor vehicles) from where
the program is implemented transparently.

The program’s legislative basis is the 2005 National Rural Employment Guarantee Act. A total of 200, 130, and 285 districts started to implement the program in February 2006, April 2007, and April 2008, respectively, with roll-out in three phases from the poorest to the least poor districts, based on a poverty index by India’s Planning Commission. To deal with issues arising from non-random expansion, three main strategies to assess program impact have been used in the literature, namely (i) difference in difference (DID) estimates, mostly at district level, that use data from NSS’s household surveys (possibly aggregated to form a pseudo-panel) or administrative data from other sources; (ii) regression discontinuity (RD) designs using the discontinuity in the Planning Commission index of poverty level in districts in a state that

> 1 The work requirement differentiates MNREGS from programs such as PROGRESA (Attanasio _et al._ 2012) or Bolsa Escola (Glewwe and Kassouf 2012) that focus more on education.

> 2 Applicants are eligible to receive a job card containing photos of all adult household members free within 15 days of application. Job-card holders’ indicative work demand lead to elaboration of an annual plan which, once ratified by the village assembly, is transmitted for consolidation at district level although in practice a more top-down process is often followed, based on central budget allocations.

> 3 Village governments ( _gram panchayats_ ) and their staff are responsible to match job seekers to jobs throughout the year. These include preparation of a list of projects in reasonable distance from the village, measures to ease female participation (e.g. child care), supervision of ongoing projects, worker identification and assignment to specific sites, and financial management including wage payment.

4