rafmacalaba/datause-encoder-data huggingface dataset:
This paper seeks to make a first contribution to the literature in this direction by exploring the relationship between data transparency and economic growth for developing economies for a unique time period of considerable uncertainty- during and after the global financial crisis.<sup>1</sup> Countries that have credible and timely data systems are likely to perform better during periods of uncertainty than countries that have poor data systems – being transparent is a good antidote for uncertainty. Our work builds on the study by Hodelin (2022) that explores how data density – availability of country-level data in the World Development Indicators (WDI) increases long term economic growth. We define data transparency as the regular publication of credible statistics by the state. Beyond just data density at the country level, data transparency also encompasses the availability of micro data and the credibility of data through adherence to international standards. Our choice of time period is largely dictated by the availability of the data transparency indicator. At the time of writing, we are not aware of other existing empirical analyses of the link between data transparency and subsequent economic growth in developing economies during and after the period of global financial crisis.

Historical data collection can be traced to the first recorded census undertaken by Babylonians around 3800 BC (Grajalez et al., 2013). The purpose and nature of data collection have evolved over time. Early data collection served rulers with the aim of accounting for wealth and power. Information was gathered for taxation purposes, counting of men for military recruitment and workforce, and ascertaining conquered populations and territories (World Bank 2021). The data were kept secret from the public and not meant to improve their lives. This raised general distrust of data collection activities among the populace. In contrast, the enlightenment ideals in eighteenth century Europe emphasized objective scientific inquiry. The role of data evolved to a means of examining society and became more public. In the late eighteenth century, statistical agencies were set up in Europe and North America to publish official statistics and inform the public.

> 1 The World Uncertain Index developed by Ahir, Bloom, and Furceri (2022) has an average score of 19,553 between 2005 and 2018 in contrast to a score of 12,829 between 1990 and 2004.

3
<!-- Start of picture text --> 4,500<br>4,000 3.801<br>3.500 3,242<br>3,000<br>2,500<br>2,000<br>1,500 1,381 1,500<br>1,000 1010 1218 4,196<br>| = 700<br>500<br>0<br>i)<br>2010 2011 2012 2013 2014 2015 2016 2017 2018<br><!-- End of picture text -->
Table 8 looks at attrition of firms in the sample. It shows both the number of firms that dropped out that year, as well as the mean non‐compliance rate of those firms that dropped out. It also shows the total number of firms, and the dropout rate. It is interesting to note that in 2015, the year of disclosure program announcement, there was a high incidence of poorly performing firms dropping out. The higher number reported for 2017 results from data only being available for half of 2018.

**Table 8: Summary statistics of firm attrition**

|**Year**|**Number of firms**|**Number of firms dropping out**|**Dropout rate**|**Mean noncompliance rate**|
|---|---|---|---|---|
|2010|65|4|0.062|0.162|
|2011|116|11|0.095|0.094|
|2012|136|15|0.110|0.102|
|2013|139|19|0.137|0.110|
|2014|177|29|0.164|0.144|
|2015|219|25|0.114|0.179|
|2016|253|31|0.123|0.132|
|2017|286|211|0.738|0.084|

Source: Author’s calculations using data from ILO‐IFC Better Work Vietnam.

Note: Attrition is defined as dropping out of the ILO‐IFC Better Work Program.

Table 9 provides the summary statistics of the other firm characteristics available in the data set. The average age of firms that participate in the Better Work Vietnam program is about 13 years, though with substantial variation across firms, ranging from only 1 year to more than 50 years in operation. There is also substantial variation across firm size. The largest firm in the sample had more than 15,000 employees, compared to the average of about 1,300 employees.

**Table 9: Summary statistics of firm characteristics**

||**N**|**mean**|**p50**|**sd**|**min**|**max**|
|---|---|---|---|---|---|---|
|Firm age|454|12.7|12|8.0|1|52|
|Total employees|458|1314.7|819|1574.1|50|15057|
|Noncompliance rate at baseline|458|0.125|0.112|0.064|0.012|0.379|

Source: Author’s calculations using data from ILO‐IFC Better Work Vietnam.

# Results

## Graphical analysis

Figure 1 and Figure 2 present a graphical overview of trends in the average noncompliance rate of firms that participate in Better Work Vietnam between 2010 and 2018. Figure 1 shows trends in average compliance by year, and Figure 2 shows average compliance by valuation cycle (duration of participation in the program). Each graph presents trends for all compliance questions, as well as compliance questions mapped to broader compliance clusters of child labor, compensation, contracts, discrimination, forced labor, freedom of association, occupational health and safety, and work time.

The top left panel of Figure 1 presents some evidence that non‐compliance rates are falling soon after the announcement of Better Work Vietnam Public Disclosure Programme. However, this follows increasing trends in non‐compliance a few years prior to the program. These trends may or may not be the direct 17
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Figure 3. Average distance and Indirect Trade Cost Measures for 124 countries, 1970-2006 <!-- Start of picture text --> Figure 3a – Overall Figure 3b – by Income Tercile<br>9000 ADOT P - Poor 1.2 1.2<br>ADOT P - All<br>8000 1.1 1.1<br>ADR- Rich<br>ADR - All<br>7 000 1 1<br>ADOT - Poor<br>6000 0.9 0.9<br>ADR- Poor<br>5000 ADOT - All 0.8 0.8<br>4000 0.7<br>0.7<br>Distance (km)<br>1970 1975 1980 1985 1990 1995 2000 2005 1970 1975 1980 1985 1990 1995 2000 2005<br><!-- End of picture text --> Note: 5-years periods over 1970-2004 and a 2-years period 2005-2006 _Source: authors’ calculations on data from UN-COMTRADE and WITS._ This changing pattern is reflected in diverging paths of the ADR ratios for the whole sample and for the lowest tercile. If the gravity model is an adequate of representation of the determinants of trade, then we get in figure 3a confirmation of the meta-analysis results of figure 2a: the costs of barriers to trade for the poorest countries have gone up in relative terms with a fall of 15% in the average distance of trade over the sample period.

Why did poor countries trade relatively more with geographically closer partners?<sup>17</sup> In this simple setting, the only two possibilities are changing weights of existing trading partners, or changing trading partners. First, it could be that close trading partners (e.g. China and India in Asia) grew fast. This would result in the observed regionalization of trade for the poorest tercile. If so, we should then also observe a decrease in the average potential distance because of the increasing GDP weights for the close partners. However, figure 3a indicates that the potential distance of trade barely increases, so this effect cannot be a major factor.

> 17 To illustrate this point, we report in appendix A4 the 3 main import suppliers of each of the 39 poorest countries of the sample with their trade share and distance, for 1970-1975 and 2005-2006

16
# **Table 1**

# **Descriptive Characteristics of Analysis Samples from China, Indonesia and Korea**

|**Variable**|**Obs**<br>CHARLS Chi|**Mean**|**Std. Dev.**|**Min**|**Max**|
|---|---|---|---|---|---|
|Work|(na)<br>2595|0.66|0.47|0.00|1.00|
|1 Urban 2 Rural|2595|1.81|0.39|1.00|2.00|
|Women|2595|0.52|0.50|0.00|1.00|
|Age|2595|59.12|10.61|34.00|93.00|
|Years of Education|2595|2.95|4.13|0.00|19.00|
|Pension Eligible|2595|0.09|0.29|0.00|1.00|
|<br>Married|2595|0.84|0.37|0.00|1.00|
|Spouse Pension Eligible|2595|0.08|0.28|0.00|1.00|
|Spouse Working|2595|0.58|0.49|0.00|1.00|
|Average Education of Spouse and Adult Children|2595|1.87|2.11|0.00|15.00|
|Housing Wealth Per Capita|2595|5.74|16.00|-0.67|213.33|
|<br>Number of Grandchildren|2595|3.33|3.89|0.00|30.00|
|Number of Living Parents|2595|0.81|1.04|0.00|4.00|
|(I)ADL Z-Score(W/Difficulty)|2595|0.01|1.01|-0.63|6.01|
|(I)ADL Z-Score(Unable)|2595|0.00|1.01|-0.25|11.55|
|Spouse (I)ADL Z-Score(W/Difficulty)|2595|-0.04|0.86|-0.63|6.01|
|Spouse (I)ADL Z-Score(Unable)|2595|-0.04|0.77|-0.25|11.55|
||IFLS (Indonesia)|||||
|Work|7283|0.78|0.42|0.00|1.00|
|I Urban 0 Rural|7283|0.48|0.50|0.00|1.00|
|Women|7283|0.57|0.49|0.00|1.00|
|Age|7283|55.52|10.26|34.00|100.00|
|Years of Education|7283|5.26|4.57|0.00|22.00|
|Pension Eligible|7283|0.06|0.24|0.00|1.00|
|Married|7283|0.77|0.42|0.00|1.00|
|Spouse Pension Eligible|7283|0.04|0.20|0.00|1.00|
|Spouse Working|7283|0.61|0.49|0.00|1.00|
|Average Education of Spouse and Adult Children|7283|7.38|4.31|0.00|22.00|
|Housing Wealth Per Capita|7283|2.23|1.27|0.00|6.91|
|Number of Living Parents|7283|0.81|0.97|0.00|4.00|
|(I)ADL Z-Score(W/Difficulty)|7283|-0.05|0.88|-0.54|7.91|
|(I)ADL Z-Score(Unable)|7283|-0.13|0.59|-0.34|8.07|
|Spouse (I)ADL Z-Score(W/Difficulty)|7283|-0.09|0.67|-0.54|6.97|
|Spouse (I)ADL Z-Score(Unable)|7283|-0.14|0.42|-0.34|8.07|
||KLoSA (Korea)|||||
|Worker|10036|0.38|0.49|0.00|1.00|
|1 Urban 0 Rural|10036|0.77|0.42|0.00|1.00|
|Women|10036|0.56|0.50|0.00|1.00|
|Age|10036|61.52|11.08|45.00|105.00|
|Years of Education|10036|8.51|4.42|0.00|21.00|
|Pension Eligible|10036|0.10|0.30|0.00|1.00|
|Married|10036|0.78|0.41|0.00|1.00|
|Spouse Pension Eligible|10036|0.07|0.25|0.00|1.00|
|Spouse Working|10036|0.29|0.45|0.00|1.00|
|Average Education of Spouse and Adult Children|10036|11.54|4.01|0.00|21.00|
|Housin Wealth Per Caita|10036|461|1002|000|30000|
|g p<br>Number of Grandchildren|<br>10036|.<br>3.32|.<br>4.04|.<br>0.00|.<br>34.00|
|Number of Living Parents|10036|0.67|0.94|0.00|4.00|
|(I)ADL Z-Score(W/Difficulty)|10036|-0.01|1.00|-0.28|10.43|
|(I)ADL Z-Score(Unable)|10036|0.00|0.99|-0.18|9.27|
|Spouse (I)ADL Z-Score(W/Difficulty)|10036|-0.03|0.72|-0.28|10.43|
|Spouse (I)ADL Z-Score(Unable)|10036|-0.02|0.77|-0.18|9.27|

Notes: We use the following cross section surveys for analysis: 2008 CHARLS Pilot for China; 2007/8 IFLS for Indonesia; and the 2006 KLoSA for Korea. Units for housing wealth per capita are Korea (10,000,000 Won), China (10,000 RMB), Indonesia (1,000,000 Rupiahs). The definitions are slightly different given the design of the questions. Whether or not collecting pension is used as proxy for pension eligibility 34
independent of access to functioning equipment, there is no provider who can diagnose and treat both conditions.

50% (95% CI 0.44-0.57) of the facilities with antibiotics in stock have at least one provider employed which has the knowledge to diagnose and treat pneumonia, while in 51% (95% CI 0.44-0.58) of the facilities with ORS in stock, there is at least one provider that has the knowledge to diagnose and treat diarrhea. About half of the facilities have at least one provider employed which has the knowledge to diagnose and treat pneumonia, and about one in four of the facilities have at least one provider employed which has the knowledge to diagnose and treat diarrhea, but they do not have the drugs in stock to treat them (antibiotics and ORS, respectively).

# **Discussion**

This analysis demonstrates the value of measuring clinical knowledge, provider availability, and other structural dimensions of quality in order to provide a diagnostic measure of key challenges, most clearly (and perhaps most damagingly) manifested at the facility level, in a health system. The evidence, and more generally the Service Delivery Indicators (SDI) instrument, provides a useful way for governments to benchmark health systems performance on such measures. The SDI is complementary to other survey instruments, such as the Service Availability and Readiness Assessment (SARA) and the Service Provision Assessment (SPA). The distinguishing characteristics of SDI are the clinical vignettes to assess providers’ clinical knowledge and the use of unannounced visits to quantify absenteeism.

This study uses the lowest common denominator across the country-specific protocols to define comparable care readiness criteria across countries. Moreover, there is the wellknown gap between what health workers can do and what they actually do (the “can-do” gap).<sup>26</sup> For these reasons, we expect that in most surveyed countries the probability of providing minimum quality care according to country-specific guidelines is even lower than 16
TABLE 15 Impulse responses of potential growth to recessions

|**Dfiiti f ttil**||**Recessio**|**ns: Baselin**|**e definition**|<br>**Reces**|**sions: Alt**<br>**definition**|**ernative**<br>|
|---|---|---|---|---|---|---|---|
|**enon o poena**<br>**output**|**h**|**World**|**AEs**|**EMDEs**|**World**|**AEs**|**EMDEs**|
||0|-0.042|0.066|-0.138|-0.046|0.042|-0.123|
||1|-1.153***|-0.773***|-1.499***|-1.123***|-0.792***|-1.414***|
|**Production-function**|2|-1.573***|-1.407***|-1.738***|-1.432***|-1.402***|-1.454***|
|**approach**|3|-1.542***|-1.444***|-1.645***|-1.401***|-1.432***|-1.371***|
||4|-1.521***|-1.421***|-1.639***|-1.348***|-1.386***|-1.308***|
||5|-1.431***|-1.257***|-1.635***|-1.244***|-1.193***|-1.296***|
||0|-0.355***|-0.354***|-0.352***|-0.348***|-0.342***|-0.352***|
||1|-2.082***|-1.782***|-2.465***|-2.014***|-1.709***|-2.419***|
|**Multivariate filter**|2|-1.298***|-1.485***|-0.947***|-1.215***|-1.372***|-0.91***|
||3|-0.734***|-1.033***|-0.192|-0.647***|-0.848***|-0.272|
||4|-0.442*|-0.699**|0.06|-0.356*|-0.488**|-0.103|
||5|-0.133|-0.215|0.025|-0.123|-0.143|-0.089|
||0|-0.058|-0.06|-0.057|-0.04|-0.037|-0.042|
||1|-0.208**|0.055|-0.356***|0.08|0.128*|0.052|
|**Expectations (WEO)**|2|-0.33**|-0.143|-0.425**|-0.036|-0.042|-0.032|
||3|-0.315*|-0.144|-0.403|-0.282|-0.08|-0.395|
||4|-0.251|-0.072|-0.348|-0.282**|-0.022|-0.433**|
||5|-0.262*|-0.125|-0.336|-0.269**|-0.078|-0.378*|
||0|-0.208***|-0.215***|-0.2***|-0.215***|-0.238***|-0.184***|
||1|-1.83***|-1.605***|-2.102***|-1.794***|-1.597***|-2.037***|
|**Unobserved component**|2|-0.638***|-0.711***|-0.532***|-0.599***|-0.67***|-0.497***|
|**model**|3|-0.279***|-0.256**|-0.316*|-0.275***|-0.217**|-0.362**|
||4|-0.3***|-0.298**|-0.301**|-0.297***|-0.262**|-0.358***|
||5|-0.198*|-0.143|-0.288***|-0.19**|-0.118|-0.314***|

# Source: World Bank.

Note: *** indicates significance at the 1 percent level, ** at the 5 percent level, and * at the 10 percent level. “Recessions: Baseline definition” are defined as the period from the peak preceding a business cycle trough to the trough, with the troughs defined as years of output growth that is both negative and one standard deviation below the long-term average (as in Huidrom, Kose, and Ohnsorge 2016). “Mild Recessions: Alternative definition” are defined as years of negative output growth only, regardless of the depth of the output decline. Sample includes unbalanced panel of 33 advanced economies and 77 EMDEs for 1981-2020.

61
**Table S4.10.** ITT effects on different knowledge outcomes, FD- and non-FD samples separately

|||Overall k<br>|nowledge<br>|||Principle<br>|s knowledge<br>|<br>||How-to k<br>|nowledge<br>||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||Non-FD|sample|FD sa|mple|Non-FD|sample|FD s|ample|Non-FD|sample|FD s|ample|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|
|T1 (extension)|0.013|0.014|0.112***|0.117***|-0.003|-0.001|0.076**|0.083***|0.025*|0.027**|0.156***|0.160***|
||(0.015)|(0.013)|(0.019)|(0.017)|(0.018)|(0.017)|(0.032)|(0.024)|(0.014)|(0.012)|(0.024)|(0.023)|
|p-value|0.368|0.298|0.000|0.000|0.878|0.969|0.021|0.001|0.066|0.026|0.000|0.000|
|T2 (extension + video)|0.043***|0.044***|0.135***|0.133***|0.032*|0.036**|0.126***|0.122***|0.044***|0.045***|0.139***|0.136***|
||(0.013)|(0.012)|(0.020)|(0.019)|(0.016)|(0.014)|(0.027)|(0.024)|(0.014)|(0.013)|(0.020)|(0.016)|
|p-value|0.001|0.000|0.000|0.000|0.053|0.015|0.000|0.000|0.002|0.001|0.000|0.000|
|Test T1 >= T2 (p-value)|0.016|0.013|0.143|0.229|0.070|0.019|0.079|0.098|0.115|0.103|0.730|0.836|
|Endline control mean|0.4|68|0.5|22|0.|541|0.5|84|0.4|04|0.|454|
|Additional controls|No|Yes|No|Yes|No|Yes|No|Yes|No|Yes|No|Yes|
|R-squared|0.010|0.135|0.134|0.264|0.004|0.101|0.068|0.219|0.012|0.136|0.165|0.282|
|Observations|1,550|1,550|382|382|1,550|1,550|382|382|1,550|1,550|382|382|

_Source:_ Authors’ analyses based on baseline and endline survey data.

_Note:_ OLS regressions of household heads' knowledge scores, ranging from 0 to 1, and calculated based on the number of correct answers relative to the total number of questions in a respective domain. FD stands for field day. FD- and non-FD samples consist of actual field day participants respectively non-participants in treatment mws, and their matched controls. Additional control variables identical to those listed in notes of Table 5. One-sided equality tests of T1 and T2 are F-tests. Robust standard errors in parentheses, clustered at the mws level. Significance levels indicated as following: *** p<0.01, ** p<0.05, * p<0.1.

18
# **Table 3. Correlations between Program Contributions, Average Outcomes, and Other “Quality” Measures**

||_Prog_<br>|_ram contribution_<br>|_to:_<br>|
|---|---|---|---|
||Graduation|Formal<br>Employment|Wages<br>(log)|
||(1)|(2)|(3)|
|_Panel A. Average Outcomes_||||
|Graduation|0.98<sup>***</sup>|-0.01|-0.02|
|Obs.|621|418|418|
|Formal Employment|-0.11<sup>**</sup>|0.94<sup>***</sup>|0.32<sup>***</sup>|
|Obs.|418|851|851|
|Wages (log)|-0.06|0.30<sup>***</sup>|0.93<sup>***</sup>|
|Obs.|418|851|851|
|_Panel B. Quality Measures_||||
|Tuition|0.02|-0.16<sup>***</sup>|0.04|
|Obs.|621|851|851|
|SABER 11|-0.12<sup>***</sup>|0.10<sup>***</sup>|0.19<sup>***</sup>|
|Obs.|611|832|832|

_Source_ : Authors’ calculations using administrative data. See Appendix 1 for the definitions of the variables. _Notes_ : This table shows the correlation between estimated program contributions and average outcomes (Panel A) and other commonly used quality measures (Panel B). We include tuition and SABER 11 as quality measures since they are available in administrative sources. Average outcomes in Panel A are estimated across all students that are part of the sample used for the underlying regression of program contributions. Average SABER 11 was calculated across all students in each program. Correlations are weighted by the program number of students. “Obs.” corresponds to number of programs. Differences in the number of observations is due to data availability.<sup>*</sup> _p_ < 0.10,<sup>**</sup> _p_ < 0.05,<sup>***</sup> _p_ < 0.01.

41
unemployment rate to 12.6 percent, starting from a 2012 rate of 15.7 percent (defined to account not only for open unemployment but also for underemployment). Both foreign and domestic government debts grow at rates that leave the debt-to-GDP ratios with little change.

Table 4.2 compares the government budgets (expressed as percent of GDP) in 2012 and 2022. By construction and intention, the changes are minor. The most important change in 2022 compared to 2012 is an increase in the GDP share of taxes by around 3.5 percent of GDP partly countered by a decline in domestic borrowing (by 2.4 percent of GDP). After accounting for other smaller changes, total government receipts increase by around 1.4 percent of GDP. On the spending side, consumption increases more than investment. In terms of functions, the GDP shares on human development and other government increase while the infrastructure share is virtually unchanged. Among the two other major macro balances, the main changes in the savings-investment balance (also expressed as percent of GDP) are increased government savings (from -2.9 to -0.5 percent of GDP) and a matching decrease in private savings, in effect a displacement of private savings by government savings in a setting with unchanged government and private investment and only a smaller change in foreign savings. The changes in the balance of payments are minor; the most noticeable change is an increase in profit remittances, driven by a gradual build of foreign-owned capital. (See Tables A2.3 and A2.4 for macro and balance of payments data, respectively.) The annual growth rates for aggregate sectors are also quite uniform, in the range of 4-5 percent (Table 4.3). In terms of ranking, the highest growth rates are for government services (for which government demands are supplemented by household demands), followed by private services and industry, with the lowest growth rate for agriculture. Among the more disaggregated government sectors, most growth rates are at 3-6 percent; the only exception is tertiary education, for which growth is driven by quality improvements and growing number of graduates from the secondary level.

Finally, MDG and education indicators improve across the board (Table 4.4). For headcount poverty, this reflects the impact of the disaggregated pattern of growth in real per-capita household consumption. For the education indicators and the non-poverty MDGs, the improvements are the result of increased real services, in education measured per student (referred to as “quality improvement”) and elsewhere per capita, provided by the government and the private sector, complemented by reinforcing positive developments for other determinants: growth in real household consumption per capita and in different government infrastructure capital stocks.

23