rafmacalaba/datause-encoder-data huggingface dataset:
CIMS Drug Information System (in print), which they used to record exact medicine names and classes. For drugs that were not immediately confirmed, they consulted local chemists and pharmacists and obtained correct names to the extent possible. Along with names, field staff coded if the medicines belonged to any of the following categories âĂŞ ayurvedic, homeopathic, antibiotics, analgesics, anti-ulcer medication, steroids, anti-allergy medicines, cardiac medicines, psychiatric medicines, other.

To construct our main treatment variables - correct treatment, palliative treatment and unnecessary treatment - we obtained from a panel of doctors in the United States and India a full list of correct and palliative treatments/medicines for each case. These include nitrates, aspirin, clopidogrel, anti-platelet agents, blood thinners, beta blocker, morphine, other pain control, ACE inhibitor and vasodilator for unstable angina; ORS, electrolytes and zinc for asthma; and inhaled-corticosteroids, leukotriene inhibitors, cromones, inhaledanticholinergics and oral-corticosteroids for asthma (see Table A4).

After medicine coding in the field, members of the ISERDD team in Delhi verified the codes assigned to all medicines and recoded if them when necessary. To further ensure the coding was correct, we used a third party, a pharmaceutical consulting firm in Delhi, to independently verify our classification of medicines.

Medicine coding is relatively straightforward in instances where providers prescribe and SPs receive a written prescription. In cases where providers dispense, it is obviously easier to obtain names when medicines come with packaging than when they do not. In the 1,123 complete SP interactions, SPs were recommended a total of 2,772 medicines corresponding to 969 unique medicines (by medicine names, ignoring unlabeled ones). We are unable to classify 14.18 percent of the all 2,772 medicines because they were unlabeled (providers dispensed them as loose samples or in crushed powder form). We are further unable to classify 3.64 percent of medicines (93 unique medicines by name) because we could not match them to secondary information sources. SPs received at least one unclassifiable medicine in 268 interactions (23.9 percent). However, in 211 of these interactions (18.8 percent), SPs received classifiable medicines along with the unclassifiable medicines. In only 57 interactions (5.1 percent) were all medicines unclassifiable.

We construct our main treatment variables - correct treatment, palliative treatment and unnecessary treatment - after completing the medicine coding process described above. For each interaction, we determine if any recommended medicines fall into correct, palliative and/or unnecessary treatments, treating all unlabeled and unidentifiable medicines as unnecessary. It is possible that the unlabeled and unidentifiable medicines construe correct or palliative treatment. But our results are robust to excluding interactions that include unclassifiable medicines. Nonetheless, the likelihood that the provider dispenses medicines, and 47
# **5.3.Technical Efficiency, Potential Gains, and Economic Implications**

Technical efficiency scores, representing the distance from the potential production frontier, are computed in accordance with Equation 6. Figure 4 presents the distribution of technical efficiency scores under each suitability class for both MAPS- and AFSIS-based suitability classifications while Table 6 summarizes the scores, the potential production (in kilograms), and the potential yields (kilograms/hectare). As with the stochastic frontier analysis, AFSIS results are only reported for the overall sample and S2 classification (see Annex Table A5 for technical efficiency scores of S1 and S3 classes).

Farmers cultivating MAPS-based S3 plots exhibit the highest technical efficiency scores, indicating they are operating most closely to the production frontier given their soil suitability. Farmers cultivating S1 and S2 plots have higher realized yields and lower technical efficiency scores, suggesting they have the potential to achieve the most significant gains in their maize yields vis-a-vis their already superior observed levels. T-tests reveal that while the technical efficiency scores of S1 and S2 plots are not different to a statistically significant degree, the scores between S1 and S3, and between S2 and S3, are statistically different at the 10 percent level. The resulting potential output per plot and potential output per hectare are significantly different at the 1 percent level, between all classifications.

The gap between mean realized yield and potential yield is 1,394 kg/ha, or 86 percent of the realized mean yield, on S1 plots. S2 plots only have the potential to increase yields by 69 percent on average, while S3 plots are constrained to 59 percent yield growth from the 828 kg/ha realized average. Figure 5 presents the potential yield gains for each of the MAPS-based classifications and AFSIS S2 plots, illustrating the greater level and percentage increase in potential yields for more highly suitable plots.

_Table 6_ . Technical Efficiency and Productivity Potential

|||**MA**|**PS**||**AF**|**SIS**|
|---|---|---|---|---|---|---|
||Overall|S1|S2|S3|Overall|S2|
|Technical Efficiency|0.53|0.51|0.53|0.58|0.53|0.53|
|Potential Output (KG)|288.00|599.90|264.30|161.00|289.04|282.66|
|Potential Yield (KG/Ha)|1,804|3,009|1,714|1,315|1,811|1,754|
|Mean Output (KG)|176|333|162|97|176|175|
|Mean Yield (KG/Ha)|1,068|1,614|1,015|828|1,068|1,053|
|Potential-Mean Yield Difference|736|1,394|699|487|743|702|
|Difference as a % of Mean Yield|69%|86%|69%|59%|70%|67%|
|N|840|106|634|100|840|736|

23
are also in either Yin or Yang state. The Rat is Yang, while the Ox is Yin. As celestial pre fi xes and terrestrial appellations need to be in the same state (Yin or Yang), the Chinese and Vietnamese calendars are characterized by 60-year cycles.

The horoscope is then determined depending on the compatibility between the gender of the newborn, and the celestial and terrestrial attributes of the year of birth. For example, a year characterized by Yin is on average more compatible with girls than with boys, while the reverse holds for Yang years. On top of that, there are compatibilities based on the elements. Table A1 in the appendix displays the horoscope for the entire 60-year cycle. To read this table, let’s consider the top-left corner, “Canh Ty”, which corresponds to year 1960 (modulo 60). That year is an inauspicious year for boys and neutral for girls. A one-year increment would then consist of moving one cell (modulo 10) to the right, and one cell (modulo 12) down. Table 1 shows for the time period we are interested in, which years are auspicious, inauspicious, or neutral for boys and girls respectively. A complete astral theme would also look at parents’ dates and times of birth and their compatibility with their child’s to re fi ne the horoscope.

# 3 Astrology, Fertility and Human Development Outcomes

Most Vietnamese would acknowledge that they are superstitious, and that astrology signi fi cantly affects their daily decision-making. They would have a family fortune-teller whom they would consult before making important decisions. Having a child is one of them. However, whether this behavior translates into observed changes in patterns of birth that can be captured in nationally representative surveys is an open question. This section investigates whether astrology is associated with important decisions such as fertility choices, and how this translates into human development outcomes.

We therefore look at how fertility decisions are correlated with the Vietnamese horoscope. The main sources of data are the two rounds of the Vietnam Living Standards Measurement Study Survey undertaken in 1992-1993 and 1997-1998 respectively. We henceforth refer to these data sets as VLSS93 and VLSS98, or VLSS more generically. Unless explicitly speci fi ed, we will use data from both surveys. VLSS are household surveys that cover topics ranging from health to education, labor activities and agricultural production, income and consumption, etc. The fi rst wave consists of 4800 households while the second interviews 6000 households sampled across the country. Some 5
- 32 -

# TABLE 3: MEXICO: Data Used In Estimating

# the Regressions

# (In Billions of 1980 MexS)

|obs|PRIV|k<br>|PUI<br>|a<br>|m<br>|
|---|---|---|---|---|---|
|1960|151.0791|2908.505|64.74820<br>|44.44444<br>|177.7778<br>|
|1961|151.0792|2960.570|71.94245|35.71429|178.5714|
|1962|142.8572|2895.862|78.57143|41.66667<br>|201.3889<br>|
|1963|166.6667|3198.969|93.33334|54.05405|229.7297<br>|
|1964|190.7894|3412.867|118.4211|50.95541|235.6688<br>|
|1965|232.1429|3577.585|95.23810|43.47826|254.6584<br>|
|1966|219.6532|3788.942|121.3873|47.90419|269.4611|
|1967|238.8889|4012.842|133.3333|52.32558<br>|261.6279<br>|
|1968|248.7047|4310.642|145.0777|56.81818|278.4091|
|1969|286.4322|4628.684|140.7035|54.64481|355.1913|
|1970|355.3300|4755.270|162.4365|54.18720|349.7537|
|1971|376.2376|5072.652|118.8119|65.11628|353.4884|
|1972|372.6415|5414.887|174.5283|74.56141|429.8246|
|1973|392.4051|6271.057|236.2869|69.76744|492.2481|
|1974|482.3944|6752.759|257.0423|60.31746|552.3810|
|1975|463.1268|7053.831|312.6844|57.69231|598.9011|
|1976|460.5911|8080.573|298.0296|50.57471|600.0000|
|1977|483.1262|8035.553|273.5346|-0.530972|1020.708|
|1978|530.3951|8630.012|316.1094|-8.333333|1146.212<br>|
|1979|583.6431|9432.747|411.4003|-6.717368|1296.198<br>|
|1980|727.0000|10499.42|487.0000|-42.89999<br>|1386.100<br>|
|1981|797.5102|11686.32|595.0208|-85.71429<br>|1534.127<br>|
|1982|580.6299|15103.02|474.3771|-295.3649|1506.410<br>|
|1983|487.9618|14753.65|282.0673|-182.2890|1280.425|
|1984|524.8465|15316.92|292.1552|-81.20423|1355.899|
|1985|622.3652|16582.56|292.6184|-168.4484|1238.817|
|1986|487.5311|19905.28|240.4868|-214.6600|1268.250|
|1987|540.4833<br>= = = = = =<br>= = = =|19981.49<br>= = = = = =<br>= = = =<br>= = = = =<br>=|220.5079<br>= = = =<br>= = = = =<br>=|235.8072<br>= = = =<br>= = = = =|1290.177<br>**==**=<br>=<br>=<br>=|
CONAFE students of 22.3 points, a linear projection of score increases would predict CONAFE to eliminate test score inequality in about six years. It is likely, however, that test score increases are logarithmic and not linear, and thus actual time required for test scores to become equal may be much longer.

Indigenous students learn a significant amount of new material as they proceed through primary school. CONAFE students gained in math scores by 6.5 points against non-CONAFE non-indigenous students and by 5 points annually against comparable CONAFE non-indigenous students. However, CONAFE had no discernible and consistent effect on indigenous Spanish scores.

While CONAFE was more effective for math instruction in primary school, CONAFE was more effective for Spanish instruction in secondary school. CONAFE appeared to increase _telesecundaria_ Spanish scores by 0.8 to 3.4 points per year, while CONAFE increased math scores by 0.02 to 1.4 points per year. That effect represents an annual decrease in test score inequality between less-disadvantaged CONAFE and non-CONAFE students of 24 percent in math scores and 38 percent in Spanish scores.

Predictably, CONAFE students had significantly higher repetition and failure rates than did non-CONAFE students. CONAFE appeared to decrease repetition rates by an average of 0.13 percent per year, which represents an elimination of 6 percent of inequality in repetition rates between comparable CONAFE and non-CONAFE students. Overall, CONAFE decreased failure rates by 0.3 percent per year, but that effect was largest for the less-disadvantaged group.

These results imply that the Mexican government’s provision of extra resources to particularly disadvantaged schools does result in measurable improvement in the learning of students in those schools. One should not draw from this result the conclusion that other 38
# **Table 1 Annual Percentage Changes in Employment in China, 2008–09**

||June 2008|December 200|8<br>June 2009|
|---|---|---|---|
|All firms|3.03|-0.53|2.87|
|Nonexporters|3.27|0.68|3.20|
|Exporters|2.76|-1.92|2.48|
|Byownership||||
|State/collective|-6.05|-0.83|1.78|
|Private|2.61|0.99|5.40|
|Joint/Ltd/Other|3.70|0.65|1.70|
|Foreign|3.84|-4.55|4.30|
|Bysize(no. employees)||||
|Smallestquartile|2.11|0.48|3.41|
|Secondquartile|3.00|0.28|3.20|
|Thirdquartile|3.00|0.16|4.16|
|Largestquartile|3.05|-0.72|2.63|

_Source_ : CNFS (2009).

27
**Figure 1.1: Growth and Volatility Relationship, 1960–2000**

<!-- Start of picture text --> Figure 1. Grow th and Volatility, 1960 - 2000 Figure 2a. Growth and Volatility, 1960 - 2000<br>8 Low-income Economies<br>Gr = 3.1695 - 0.3355*Vol 4<br> (-3.02)<br>6<br>3<br>R2 = 0.1958<br>4 2<br>1<br>2<br>0<br>0 -1<br>-2<br>-2 Gr = 2.9793 - 0.5088*Vol<br>R2 = 0.119<br>-3 (-2.47)<br>-4 -4<br>0 2 4 6 8 10 0 2 4 6 8<br>Source: Authors' calculations. Std dev (GDP grow th) Source: Authors' calculations Std dev (GDP growth)<br>Figure 2b. Growth and Volatility, 1960 - 2000 Figure 2c. Growth and Volatility, 1960 - 2000<br>Middle-income Economies High-income Economies<br>7 7<br>Gr = 1.7987 + 0.0463*Vol<br>6 (0.29) 6 Gr = 1.0986 + 0.715*Vol<br>5 (2.83)<br>5<br>4<br>4<br>3<br>3<br>2<br>2<br>1<br>R2 = 0.0029 1 R2 = 0.3651<br>0<br>0<br>-1<br>0 2 4 6 8 10 1.5 2 2.5 3 3.5 4<br>Std dev (GDP growth) Std dev (GDP growth)<br>Source: Authors' calculations Source: Authors' calculations<br>Av GDP growth, %<br>Av GDP growth, %<br>Av GDP growth, % Av GDP growth<br><!-- End of picture text --> _Source:_ Hnatkovska and Loayza (2005).

Our study has several parts. We begin by documenting the key features of Paraguayan business cycles during 1994-2012. Then we turn to a more formal analysis based on structural vector autoregressions (SVARs). This allows us to gain insights into the dynamic relationships between variables and to analyze and quantify the effects of various shocks on Paraguayan GDP. Lastly, we turn to the structural analysis to disentangle the results from the SVAR and to give them an economic interpretation. More precisely, we will formalize a model of a small open economy that replicates the key features of the Paraguayan economy – in particular, its dependence on agriculture. We will use this model to perform the business cycle accounting in Paraguay using the methodology of Chari, Kehoe and McGrattan (2007).
proxy respondents may not classify them as work activities when reporting information for someone else.

# **8 Cost Implications of the Survey Design**

As highlighted in Bardasi et al. (2011), different survey methods may have different implementation costs. We use the cost data related to this randomized survey experiment to estimate the cost implications of each treatment arm. The households’ members assigned to LOA underwent a marginally longer interview since they participated in the additional LOA module. It took participants an average of 3.1 minutes to complete this module, and each household assigned to LOA had on average, 2.5 members. Therefore, the additional interview duration per household is 7.8 minutes. Since the average total cost per interview in C was US$135.4 and the average duration was 111.6 minutes, the cost for the additional time required to complete the LOA module is US$9.44 (US$1.21/minute x 7.8 minutes), which corresponds to 6.9% of the total cost per interview for group C. In light of these calculations, the cost implications of adding this module are negligible.

The cost implications of ESR are not small compared to C. Although the average duration of the survey was similar between enforced self-reporting and proxy reporting, the additional costs of ESR are due to the fact that interviewers had to pay more visits to ESR households to ensure that they located all of the respondents. Households in C and LOA needed an average of 1.50 and 1.49 visits, respectively, to collect the necessary information from all respondents. However, households assigned to ESR required an average of 1.94 visits to complete the interviews. Since the average cost per visit per household was US$90.27, it costs 29.6% more to complete an interview in the ESR group compared to C. We do note, however, that these costs are lower than estimates for other settings. For example, Bardasi et al. (2011) calculated that their interviewers needed at least one extra day to track down the respondents, which led to a 50% increase in cost.

In sum, researchers, survey practitioners, and policy makers must evaluate the tradeoffs between more accurate reporting of labor outcomes of youth relative to older adults when selfreporting is enforced versus the additional cost that this survey method entails. If nationally 22
**Figure 39. Pre-funding the future increase in liabilities in constant tax rate approach: Baseline case**

<!-- Start of picture text --> 100 .<br>60<br>60<br>Net W . rss Financial<br>40<br>20<br>Gross Debt<br>% of GDP<br>-20 /97 00/01 1 20/21 30/31 40/41 / 0/51<br>-40 Net Debt\ot e<br>-40 /<br>-60<br>-80<br><!-- End of picture text --> The size of the forecasted earnings to the government from managing these financial assets is illustrated in the dynamics of non tax revenues, which is shown as the difference between the government total and tax revenues (figure 40).

Empirical evidence suggests, however, that governments often fail to insulate their pension funds from political (mis)use and thus do not always manage their financial assets profitably.27 27 Real returns on publicly managed pension reserves have been negative in most countries (World Bank 1994 and Mitchell 1993). The reasons include weak accountability and incentives, inefficient regulations and monitoring, political pressures to invest into public projects and government paper, low nominal interest rates, volatile inflation, and restrictions on international diversification (World Bank 1994 and Davis 1993). In many countries with a compulsory fully funded scheme the provident funds earned negative returns (World Bank 1994). For analysis of the associated risks, see also Bradbury, Brumby, and Skilling 1996.

44
delays in clearance at the border post or terminals, with that percentage being actually higher for medium/large freight forwarders.<sup>15</sup>

**Figure 10. Physical Inspections and Clearance Delays**

<!-- Start of picture text --> Do intensive physical inspections lead to extended delays?<br>80% 75%<br>70%<br>61% 63%<br>60%<br>50%<br>40% 39% 38%<br>30% 25%<br>20%<br>10%<br>0%<br>Yes No<br>Small Medium/Large Total<br>Share of freight forwarders<br><!-- End of picture text --> Source: Authors’ calculations based on data from the Survey of Serbian Freight Forwarders. Note: the statistics shown are based on responses by 152 freight forwarders (140 small and 12 medium/large).

Regarding the poor performance of customs and other border agencies and how that may lead to unexpected delays in the clearance process, Table 8 shows that freight forwarders report poor training (of officials), understaffing, and lack of automation as the top three reasons for that poor performance.

**Table 8. Ranking of Reasons for Border Agencies to Cause Unexpected Clearance Delays**

|Freight forwarders' ranking|
|---|
|1 ‐ Poor Training (2.80)|
|2 ‐ Understaffing (2.86)|
|3 ‐ Lack of automation (2.90)|
|4 ‐ Insufficient physical infrastructure at<br>border/terminal (3.00)|
|5 ‐ Corruption(3.93)|

Source: Authors’ calculations based on data from the Survey of Serbian Freight Forwarders.

Note: Mean rank reported in parentheses. Rank of 1 represents the biggest problem. The statistics shown are based on responses by 128 freight forwarders (117 small and 11 medium/large).

> 15 The risk of physical inspection is itself linked to shipment consolidation, more strongly so for small freight forwarders than for medium/large freight forwarders, as seen in Appendix B.

20
feasible generalized least squares (FGLS) were employed. The null hypothesis of homoscedasticity is not rejected at conventional levels of signi...cance.<sup>13</sup> However, the test derived by Wooldridge (2002), suggests that the null hypothesis of no ...rstorder autocorrelation is rejected in favor of AR(1).<sup>14</sup> The inferences drawn from the 1982, 1987, and 1992 temporal cross-sections are supported by the pooled data. Both OLS and FGLS regressions produce statistically signi...cant parameter estimates. We again ...nd that ...rm size dispersion decreases with increasing levels of rental and resale market activity and higher rates of capital depreciation. When (15) and (18) are estimated with OLS, the maintained hypotheses H01 : b1 = b2 and H02 : c1 = c2 are not rejected at the 5 percent level.<sup>15</sup> However, we do note a di¤erence in the estimated parameters when equations (15) and (18) are estimated with OLS and when they are estimated with FGLS. This is not surprising since OLS and FGLS are di¤erent estimation procedures; and, in any case, it is not easy to determine whether such di¤erences are statistically signi...cant.<sup>16</sup>

> 13 We perform the Bickel (1978) version of the Breusch-Pagan test on (18). This tests for both within and between heteroscedasticity. We regress the squared residuals on ��powers of the predictions: for � = 5; F =1:03 and Prob > F = :39: Thus for � = 5, the null hypothesis of homoscedasticity cannot be rejected at the 5% level. For � = 6 to 10, we ...nd that the null hypothesis of homoscedasticity cannot be rejected at the 10% level.

> 14 When we apply the Wooldridge test on (18) we obtain F = 57:31 and Prob > F = 0:00.

> 15 For H01, F = :73 < F1 :95;819<sup>= 3:85,andtheprobabalitylevelatwhichastandardF-testrejects</sup> the restriction is .39; for H02, F = 1:09 < F1<sup>:95</sup> ;819<sup>=3:85,andtheprobabalitylevelatwhicha</sup> standard F-test rejects the restriction is .30.

> 16 In the presence of serial correlation, FGLS is preferred because it is more e¢ cient and its test statistics are at least asymptotically valid.

15
|Concerned about food shortages|573|118|1.593|1.571|1.704|0.174|
|---|---|---|---|---|---|---|
|Concerned about price increases|573|118|1.065|1.068|1.051|0.594|
|Concerned about being sick with|573|118|1.844|1.850|1.814|0.748|
|COVID-19|||||||
|Concerned about quarantine|573|118|1.944|1.958|1.958|0.885|
|Concerned about lack of other|572|118|1.758|1.788|1.788|0.719|
|healthcare|||||||
|Satisfaction with government|569|117|0.961|0.966|.966|0.753|
|measures|||||||

_<mark>Source</mark>_ <mark>: Authors’ calculations from CIMS (2020)</mark> 36