rafmacalaba/data-use-mentions-extended huggingface dataset:
importance of health policy, and is still used to motivate discussions of health policy strategy (Jamison and Murray, 1997, World Bank, 1997a). There are two interesting points.

First, the ranking of countries depends heavily on factors besides income. Table 3 presents the "ten best" and "ten worst" countries from the "income alone" (I) and the "income plus other variables" (II) regressions in Table 1. Since the variables in addition to income add considerable explanatory power, it is not surprising the regressions identify different outliers. Among the "ten best" countries from (I), only five reappear in the "ten best" list for (II) (Jamaica, Sri Lanka, Costa Rica, Singapore, and Trinidad and Tobago). Similarly only five of the "ten worst" countries identified with income alone reappear in the "ten worst" list for the full regression (Indonesia, Mozambique, Peru, Brazil, and Bolivia). Income alone clearly does not allow the identification of countries that are doing well (or poorly) with respect to mortality in a way that can be used to identify good or bad health policies.

Second, the difference in public spending on health between the "ten best" and the "ten worst" is very small, while the differences in public spending among countries within the "ten best" or "ten worst" groups are very large. The average fraction of GDP devoted to public spending on health among the "ten best" is 2.04 percent, versus an only slightly lower average of 1.81 percent among the "ten worst". Jamaica, with mortality 80 percent better than predicted spends 2.89 percent of GDP while Brazil, with mortality almost 50 percent worse than predicted, spends 2.97 percent. Sri Lanka, the second best, spends 1.67 percent compared to South Korea, which spends 1.88 percent. These are stark examples of the similarities.
a new trademark, patent, utility model or industrial design and for the latter, we review the use of technology for quality and control management and introduction of new products and processes; and (3) the different types of obstacles the firms face during their operations.

_Figure 3: Firm performance in Slavonia is lower than that in Zagreb and other regions_ <!-- Start of picture text --> Productivity<br>Croatian Firms, 2017<br>Labour Productivity GVA per Employee<br>Other Regions Slavonia Zagreb Other Regions Slavonia Zagreb<br>5,000<br>150<br>4,000<br>3,000 100<br>2,000 Real GVA per Employee<br>50<br>Real Labour Productivity (Sales/Employee)<br>1,000<br>0 0<br><!-- End of picture text --> Comparing real labor productivity (sales per employee) and real GVA per employee of firms across Zagreb, Eastern Croatia and other regions of Croatia, we note that both the indicators of performance are highest in Zagreb, and lowest in Slavonia, especially in terms of GVA per employee (Figure 3). Labor productivity in Eastern Croatia is half that of Zagreb and other regions of Croatia; while, in the case of gross value-added it is one-third of that observed in Zagreb and one-half relative to other regions of Croatia.

8
19|
|**Developing countries only (excluding h**|**igh-income**|**countri**|**es)**|||||
|Net oil importer in 2009 (33)|91|3|24|12|12|33|15|
|Net oil exporter in 2009 (17)|1|47|29|0|6|6|12|
|East Asia & Pacific (8)|58|0|50|13|13|25|0|
|Latin America & the Caribbean (16)|128|12|18|6|0|29|35|
|Middle East & North Africa (7)|1|43|43|0|14|0|0|
|South Asia (5)|88|0|40|0|40|20|0|
|Sub-Saharan Africa(12)|64|33|8|17|8|25|8|

_Sources_ : Tables A2.1 and A2.3, and author’s calculations. _Notes:_ The median is that for pass-through coefficients. The income category does not include high-income countries because there were only two. Europe and Central Asia is similarly excluded because there was only one country. a. Includes pass-through coefficients equal to 0 percent.

The pricing policy of each government is briefly summarized in table A2.2 in appendix 2. Bolivia, Cameroon, Egypt, Indonesia, and República Bolivariana de Venezuela froze the prices of the four fuels in local currency between January 2009 and January 2012. Currency depreciation in Cameroon, Egypt, and Venezuela made pass-through coefficients based on U.S. dollars negative, while Bolivia’s currency appreciated slightly during the interval and Indonesia’s appreciably, but even in Indonesia not nearly enough to offset the price freeze. Gabon also effectively froze fuel prices in local currency despite slight currency depreciation—
Maruyama-George and Zellner-Siow priors (leading to a zero posterior density at _g_ 0), the F-Z with _a_ = 2 + 2 _/n_ , the hyper- _g/n_ and horseshoe- _n_ priors and the case with _g_ = _n_ , as a consequence of the very different value of _n_ for this application. In this application, the truncation induced by the Forte _et al._ prior means that the prior support does not include the chosen value of _g_ 0 (and is truncated quite a bit above that), which certainly makes this prior less appropriate for large values of _n/k_ . Thus, prior and posterior density values are both zero at _g_ 0 and the Bayes factor can not be computed for this case.<sup>17</sup> Let us summarize the information provided by these applications to real economic datasets. Predictive performance seems quite similar across priors, although possibly a bit worse for the benchmark prior with _c_ = 1 and the case with _g_ = _k_<sup>2</sup> . There is more variation in the Bayes factors, with most consistent data support provided for the Bottolo-Richardson, horseshoe, hyper- _g/n_ and horseshoe _/n_ priors, whereas the Maruyama-George, Forte _et al._ and Zellner-Siow priors (as well as the case with fixed _g_ = _n_ ) do quite well in two of the three applications, but rather badly in the one with large _n_ .

# **9. Concluding Remarks and Recommendations**

Combining the properties listed in Table 1 with the evidence from both simulated and real data, we can now come up with a recommendation for practitioners. We assume that users will want their priors to be consistent, to avoid the information paradox and to perform well in a wide variety of situations. The Bottolo-Richardson and horseshoe- _n_ priors are consistent (albeit at a slow rate) and do well on real data, but underperform on the simulated data. The truncation of the Forte _et al._ .
.00|
|Budget spent on non-exporters|2.166|1.130|1|6.00|
|Budget spent on established exporters|4.309|1.241|1|6.00|
|Budget spent on small firms|3.759|1.020|1|6.00|
|Budget spent on medium-size firms|3.807|0.982|1|6.00|
|Share of private sector over total at board|0.477|0.295|0|1.00|
|Rank of EPA responsibility|2.532|1.094|1|5.00|
|Strategy targets exports in all sectors and destinations|1.665|1.245|1|7.00|

_Source_ : Exports, GDP per capita and population are from WDI. EPA variables are from the three EPA surveys _Note_ : Units for nonoil exports are thousand of US dollars, units for EPA budget and GDP per capita are US dollars. For the construction of variables relating to EPA’s functioning see the text.

- 6 The response rates is around 80 percent, which is astonishing for an email survey. The high response rate is probably explained by the numerous follow-ups done by phone.

- 7 These are Austria, Belgium, Bosnia, Cyprus, Denmark, Estonia, Iceland, Italy, Lithuania, Malta, Netherlands, Slovenia, Switzerland, and the United Kingdom.

- 8 The survey is available from the authors upon request.
|2.06|1.90|1.84|1.90|1.86|
|Profit Margin|4.89|5.58|5.12|4.82|4.42|4.29|4.44|4.34|

Source: Own calculations, based on CBK data The effects of the government’s program for the development of the banking sector are reflected in both the decline in spreads and their determinants. The decomposition for privatelyowned banks (both domestic and foreign) in Panel B shows a steady decline in the interest rates charged on loans and a substantial reduction in the interest rate paid for deposits. The decline in lending rates is consistent with greater competition, while the drop in deposit rates likely reflects 13
are based on the consumption of users already participating in the market (that is, households with positive consumption levels), so they might be underestimating potential welfare gains due to the entrance of new consumers, which is more likely to occur among households in the bottom of the income distribution.

**Table 4. Expenditure on mobile telecommunication and potential distributive impact of higher competition**

||**All**|Poor|est||Hou|sehold in|come dec|iles||Ric|hest|
|---|---|---|---|---|---|---|---|---|---|---|---|
|||1|2|3|4|5|6|7|8|9|10|
|Average pc income<sup>a</sup>|3,499|558|1,012|1,326|1,634|1,987|2,395|2,933|3,772|5,338|14,041|
|Absolute impact<sup>b</sup>|6.43|1.00|1.77|2.09|2.80|3.55|4.71|5.54|7.47|10.71|24.72|
|Relative impact<sup>c</sup>|0.18|0.18|0.18|0.16|0.17|0.18|0.20|0.19|0.20|0.20|0.18|

_Source:_ World Bank estimates using ENIGH 2014. a. According to current income (MX pesos of August of 2014). b. The average impact on well-being (moving from concentrated to competitive market) c. The average
on socioeconomic inequalities in health within a developing country. Though incomes are not available, Hurt et al., (2004) report death rates among those with no education that are about double those found for people with five or more years of formal education.<sup>9</sup> Estimates with more comprehensive coverage are provided by Gwatkin (2000), who reports death rates by age group for the poorest 20% of the population of the developing world. This follows a method outlined in Gwatkin et al. (1999) in which the country is the unit of observation for income and death rates. However, this method has the drawback that differences <u>within</u> countries are ignored; the differentials are driven entirely by between-country differences in average incomes (though states were used for China and India). By this method, deaths in the poorest 20% account for 35% of all deaths at age group 0-4 years, falling to 32% for ages 5-29, 27% for 30-44, 23% for 45-59 and 20% for 60+. The implied values of _DRD_ for each age group are 1.8, 1.6, 1.2 and 1.0 respectively.

Recognizing the aforementioned data problems in studying the socioeconomic differences in social indicators, Bidani and Ravallion (1997) use an indirect method of inferring the conditional means from cross-country (or cross-regional) aggregates. An econometricdecomposition method on cross-country data is used to estimate the differences in various social indicators between the poor and non-poor; hereafter this is termed the BR method. In essence, the regression coefficient of the social indicator on the poverty rate across countries estimates the difference between the mean social indicator for the poor and that for the non-poor.

Bidani and Ravallion did not implement their decomposition for overall death rates. However, it is not difficult to do so for the purpose of this paper. Figure 2 gives a scatter plot of the CDR for 2002 against the latest Chen-Ravallion “$1 a day
study period; suggesting that variation in exposure is driven by variation in domestic trade costs, which generated persistent decreases in mask prices in sub-Districts more exposed to licensed mask manufacturing. Our magnitudes support this interpretation: we show our estimates imply a 10% decrease in distance to mask manufacturing causes a 0.6% decrease in mask prices, in line with existing estimates from the trade literature (Donaldson, 2018).

Second, while exposure to licensed domestic mask manufacturing increased purchases of formally traded masks, these effects dissipate rapidly. Our estimates imply price elasticities of demand for masks of between 3 and 13 in April and May. These elasticities are larger than demand elasticities for other durable preventive health products (Berry et al., 2020), which we interpret as driven by the availability of a close substitute for formally manufactured masks in these early months of the pandemic—informally produced non-certified masks, which we do not observe in our data on formal transactions. These elasticities converge toward zero, coinciding with the June gazetting of a decree requiring that all masks sold in Rwanda meet the same quality standards as certified manufactured masks, which targeted the informal sale of non-certified masks. We therefore interpret this fade-out as driven by substitution away from informal low-quality masks in low-exposure sub-Districts to formally manufactured masks following the decree.

Third, increases in certified mask supply slowed the spread of COVID-19. As an alternative to sub-District data on COVID-19 cases, we use Electronic Billing Machine (EBM) data on purchases of fever medicine as a proxy for active COVID-19 infections. We find average manufactured mask exposure caused a 12% reduction in the monthly growth rate of infections through June, and no impacts on growth rate in July and August while level impacts on infections persisted. These implied impacts of certified mask manufacturing on the transmission of COVID-19, the first in the 3
|**United Kingdom**|15.1|15.5|12.6|**Turkey**||42.6|34.6|26.5|
|_Source:_<br>OECD<br>Family<br>http://www.oecd.org/els/fami<br>_Notes:_For 2011, data for Swi<br>data for Japan refer to 2014, a|Database,<br>ly/database.h<br>tzerland refe<br>nd for Chile|Indicator<br>tm.<br>r to 2009, fo<br>to 2017. The|CO3.5<br> <br>r Lithuania t<br>OECD ave|“Young<br>people<br>o 2010, and for Co<br>rage excludes Chil|not<br>lombia<br>e and|in<br>education<br>and Costa Rica to<br>Korea.|or<br>empl<br>2013. For|oyment”,<br>2018,|

As shown in Figure 1, NEET rates are generally higher for young people in their 20s than for those in their teens. In the OECD countries, on average about 18 percent of those aged between 25 and 29 years old, compared to less than 6 percent of 15-19 year-olds, are NEET. This difference may be the result of the expansion of upper secondary education in many OECD countries. Furthermore, as the OECD reports, NEETs are less likely to live with their parents than non-NEETs. About 50 percent of NEETs, compared to about 75 percent of non-NEETs, live with their parents.

Caring for children and/or living with a partner can also make a substantial difference. About 26 percent of NEETs, but only 9 percent of non-NEETs, live with a partner and one or more children— an almost 3:1 ratio.
.120|-11.08%|2.93%|4.85%|

Source: Computed by authors from EICV data

**Table 10. Determinants** **<u>of the Probability of Being a Coffee Farmer (propensity score)</u>**

|**Parameter**|**DF**|<br>**Estimate**|**Standard**<br>**Error**|**Wald 95%**<br>**Lim**|**Confidence**<br>**its**|**Chi-**<br>**Square**|**Pr > ChiSq**|
|---|---|---|---|---|---|---|---|
|Intercept|1|-3.1209|0.0318|-3.1832|-3.0586|9651.42|<.0001|
|Distance to road|1|-0.0353|0.0028|-0.0407|-0.0298|158.7|<.0001|
|Access to electricity|1|1.0688|0.0372|0.9959|1.1418|824.84|<.0001|
|Extension services<br>dummy|1|0.1843|0.0049|0.1747|0.1938|1439.03|<.0001|
|Head's Education|1|0.023|0.0014|0.0202|0.0258|257.34|<.0001|
|Dependency ratio|1|0.0441|0.0011|0.042|0.0462|1687.25|<.0001|
|Land area|1|0.0345|0
09|0.02|
|Finger Millet|0.03|0.01|0.05|0.03|0.02|0.03|0.01|0.06|0.04|0.02|
|Simsim|0.03|0.00|0.01|0.08|0.00|0.02|0.00|0.01|0.06|0.00|
|Sunflower|0.01|0.00|0.00|0.04|0.00|0.02|0.00|0.00|0.05|0.00|

Source: Staff calculations using RIGA 2010/11‐2011/12. Note: red indicates a share 10 percent and higher in a given region, green indicates a share between 3 and 10 percent in a given region.

**Figure 2.1. Share of crop income derived from crop sales, bottom 40 percent, 2006‐2012**

<!-- Start of picture text --> 1<br>0.8<br>0.6 2006<br>2010<br>0.4<br>2011<br>0.2<br>2012<br>0<br>0 10 20 30 40 50 60 70 80 90 100<br>Share of bottom 40 percent<br>Source: Staff calculations using RIGA income aggregates calculated from UNPS 2005/06‐2011/12.<br>crop sales<br>Share of crop income from<br><!-- End of picture text --> 4 The percent of households engaged in agriculture ranged between 75 and 82 percent throughout the 2005/06 ‐ 2013/14 period.

5
region _i_ after a change in trade costs _τ_ ˆ is given by: In the previous expression, (1 _− σ_ ) is the trade elasticity, _li_ , _s_ ,0 is the share of labor in market _i_ employed in sector _s_ in the base year (denoted with a zero even though in our implementation it will be the year 2019), and _θi_ , _s_ ( ˆ _τ_ ) is the shift in demand for the sector _s_ good of region _i_ : The variable _rij_ , _s_ ,0 denotes the share of market _i_ ’s sales in sector _s_ that go to market _j_ in the base year, _λqj_ , _s_ ,0 denotes the share of market _j_ ’s purchases in sector _s_ that come from market ˆ _q_ in the base year, and _τij_ , _s_ = ln( _τij_ , _s_ ,2022) _−_ ln( _τij_ , _s_ ,2019) denotes the log of the change in the iceberg trade costs between the base year and the high-trade-cost years (2020, 2021, and 2022).

_η_ ˆ _i_ ( ˆ _τ_ ) represents market _i_ ’s “revenue shock exposure”. It is the sum across sectors of the shock to the demand for the good of region _i_ in each sector, _θi_ , _s_ ( ˆ _τ_ ), weighted by that sector’s share in _i_ ’s employment in the base year _li_ , _s_ ,0. The sector-level demand shock, _θi_ , _s_ ( ˆ _τ_ ), is itself the sum across destinations _j_ of the impact of market _i_ ’s own trade shock on the demand for its good minus the demand shift caused by competitors’ trade shocks in that sector, weighted by the revenue importance of each destination in the base year _rij_ , _s_ ,0. Note that all components of _η_ ˆ _i_ ( ˆ _τ_ ) can be computed with information on bilateral trade flows in the base year plus measures of the bilateral trade shocks. In our implementation, ˆ ˆ _τij_ , _s ≈_ 12
# 5. How to close the data gap?

# <u>Closing the data gap when surveys exist or can be collected</u>

The World Bank tries to play an active role in promoting access to household budget surveys. If data sharing is inhibited by lack of experience or capacity, governments can benefit from the World Bank’s experience in anonymization and protecting sensitive information; practices of data sharing from around the world that establish rules that govern microdata use and appropriate citation. The World Bank can provide training on preparation of metadata under the Data Documentation Initiative (DDI), an international standard for describing surveys. The World Bank should help support prioritization and advocate for these types of technical assistance (TA) to national statistics offices.

The World Bank can also play a role in promoting the value of open data by facilitating exchange of ideas and experiences within the region and beyond. Inviting leaders in the field to join regional workshops or conferences can help. The World Bank organized such a conference at the Centre for Mediterranean Integration (CMI) in 2016. Keynote speakers and high-level participants from national statistics offices across MENA discussed the challenges involved in collecting and sharing micro-data for monitoring welfare and for evidence-based policy making.<sup>11</sup> The World Bank also provides TA and funding to help countries conduct HBSs and measure poverty. This TA is typically offered through country-level programs, drawing on long-established country engagements. In several notable examples in MENA, World Bank collaboration with country statistical agencies was very fruitful and resulted in the dissemination of HBSs and publication of national poverty rates. These examples include TA on household surveys in Djibouti, Iraq, Jordan, West Bank and Gaza, and Yemen and on poverty measurement in Egypt, Tunisia, and Lebanon.

Collecting new HBS data and enhancing data sharing are very different in terms of costs. Given the analysis above, collecting new HBS data is more of a priority in MENA than is irregular poverty measurement. Nevertheless, data sharing should be an indispensable part
Table D.1: Scaling Factors used on Calibrated Parameters

|Location|Scaling|based on:|
|---|---|---|
||Rent|Wage|
|1: Low Population – Low Income|0.688|0.906|
|2: Low Population – High Income|0.995|1.004|
|3: High Population – Low Income|1.021|1.030|
|4: High Population – High Income|1.297|1.059|

Note: Scaling factors computed using the 2000 Census, country-level data.

Table D.2: Proportion of Households with Available Relative Child Care.

|Location|Proportion|
|---|---|
|1: Low Population – Low Income|0.431|
|2: Low Population – High Income|0.391|
|3: High Population – Low Income|0.420|
|4: High Population – High Income|0.368|

Note: Values calibrated using proximity to grandparents by parents’ education levels estimated by Compton and Pollak (2015).

but using average wages instead of rents. They generate the scaling factors reported in the “Wage” column of Table D.1.

Fourth, we scale the the opportunity cost of non-relative care providers–set equal to the federal minimum wage of $5.15 an hour–so that it varies across locations. We use the same scaling factors as for transfers.

Finally, we allow the availability of relative care to vary across locations. As mentioned in Section 4.2.4, we use parental characteristics in our sample as well as estimates from Compton and Pollak (2015) to calibrate the aggregate sample’s proportion of households that have relative care available in close proximity. Those estimate show that not only parental education but also population density and labor market opportunities are related to this proximity. We therefore combine Compton and Pollak (2015
Brazil and Colombia also experienced an increase in their private investment shares; an increase that accorded with the small size of their privatization programs. Bolivia’s experience is a rather disappointing one, for despite a strong privatization effort, its private investment share actually dropped from its 1985 level, and then remained about half as large as that of other countries with a similar privatization effort. The decline of Venezuela’s private investment share in the ten years after 1985 cannot be associated with its privatization program, which started only recently; rather, this significant drop was initially due to macroeconomic instability, and subsequently, to an adjustment program. It is interesting to note that some of the countries that did not initiate a privatization effort already had private investment shares in the 1980s of around 80 percent. It is likely that for these countries, namely, Guatemala, Panama, and Paraguay, privatization was not needed as much as in other Latin American countries. Finally, a comparison with the Asian NICs’ reveals that, for the Latin America and Caribbean region on average, private-sector participation in investment is still below that of the NICs. The difference in this respect between the two groups of countries (five percentage points on average in the 1990s) is larger than it seems given that the productivity ofpublic investment is much greater in the Asian NICs than in Latin American countries.

# 2. Indicators on the efficiency of revenue generation

Clearly, the ability of the state to undertake an expenditure program without incurring an unmanageable deficit depends on efficient revenue generation. Latin American countries have relied excessively on trade taxes and seigniorage through money creation to finance their spending programs. High tax rates coupled with a weak tax administration induced many firms to become part of the informal sector. This undermined the ability of the state to generate revenues from income and domestic sales taxes, prompting the state to obtain revenues through both the inflation tax and taxes on imports and exports, which can be more easily monitored. In the late 1980s, many countries reformed taxes to