rafmacalaba/datause-encoder-data huggingface dataset:
details from the interaction to see how responsive public sector doctors are to patient characteristics.

In Tanzania, where all doctors studied largely followed the same science-based approach to diagnosis, the researcher (who was also a doctor) filled a checklist of expected items, including history taking questions and physical examination procedures. The research team in Tanzania designed instruments to compare the effort provided in consultation for three common conditions to nationally designed protocols for those conditions. Thus, if a patient presented with a cough, fever or diarrhea, there are a series of history taking and physical examination procedures that are clearly rational and useful; doctors who provided more of these items were deemed to provide higher quality care. In addition, doctors who were observed consulting patients with any of these three conditions could be compared to other doctors in the sample, even if their case-mix was otherwise very different. Thus, the percentage of items provided from among those required by protocol was used as a process quality measure and compared across doctors within the sample to examine the determinants of variation in process quality.

One concern with measuring doctor effort through direct observation is that the doctor may work harder in the presence of the research team. The “Hawthorne effect,” as the effect of observation on performance is called, is well documented in health care studies.<sup>3</sup> Leonard and Masatu (2006) examine data on process quality using exit surveys that ask specific questions about the doctors’ activities just before the research team arrived and compare it to data on process quality when the doctor realizes that he is being observed. When the research team arrives at a facility, process quality increases by 20 percent on average. However, for the average

> 3  The Hawthorne effect is named after a study of the Hawthorne Works, an industrial plant located outside Chicago, from 1924 to 1932. The study was originally intended to determine the optimal lighting for the plant. However, the researchers found that workers increased their effort when lighting went either up or down – as long as someone was paying attention to them.

16
**Figure 1. Gender Gap in Hourly Earnings in Three ECA Countries**

<!-- Start of picture text --> 0.60<br>0.50<br>0.40<br>0.30<br>Armenia<br>0.20<br>Georgia<br>0.10<br>Ukraine<br>0.00<br>5 10 25 35 50 65 75 90 95 99<br>-0.10<br>-0.20<br>Earnings percentile<br>earnings in USD<br>Male earnings minus female<br><!-- End of picture text --> _Note_ . Earnings have been converted to 2011 PPP-adjusted U.S. dollars. The sample includes 25- to 64year-olds in self-employment or wage work. Employers, unpaid workers, part-time workers, and the top 1 percent of earners are excluded from the sample. ECA = Europe and Central Asia. _Source_ : STEP Surveys (2014).

**Figure 2. Gender Gap in Hourly Earnings in Bolivia and Colombia**

<!-- Start of picture text --> 0.60<br>0.50<br>0.40<br>0.30<br>Bolivia<br>0.20 Colombia<br>0.10<br>0.00<br>5 10 25 35 50 65 75 90 95 99<br>Earnings percentile<br>earnings in USD<br>Male earnings minus female<br><!-- End of picture text --> _Note_ . Earnings have been converted to 2011 PPP-adjusted U.S. dollars. The sample includes 25- to 64year-olds in self-employment or wage work. Employers, unpaid workers, part-time workers, and the top 1 percent of earners are excluded from the sample. _Source_ : STEP Surveys (2014).

14
performance oriented management, if properly implemented, would facilitate the identification of objectives and, consequently the efficiency of the company. This is particularly the case for SOEs where because of the lack of private investors, incentives for performance are difficult to create.

Together with the levels of professional management, the performance-orientation of SOEs is one of the aspects where SOEs in LAC show the largest deficiencies. This applies not only to the poor focus on external accountability but also to the lack of performance-orientation of the enterprise, especially in the evaluation of its staff.

Our measure of professional management finds three SOEs (CAGECE and Grupo CEEE from Brazil and AySA from Argentina) among the top state corporations of the region in terms of the quality of its bureaucracy. Paraguay’s ANDE is one of the few companies with performancebased schemes (although our measure does not reflect actual levels of implementation).

<!-- Start of picture text --> Performance Based PERFORMANCE ORIENTATION<br>40<br>35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>T1 T2 T3<br>Source: LAC SOEs Governance Database, The World Bank, 2009. THRESHOLD LEVELS<br>NUMBER OF COMPANIES<br>1<br>.8<br>.6<br>.4<br>.2<br>0<br>T1 PAR JAM T2 COL MEX URU BZL D R PER T T ECU C R ARG BOL<br><!-- End of picture text -->

# **6. IMPACT OF CORPORATE GOVERNANCE ON PERFORMANCE**

This section explores the correlations between various dimensions of corporate governance and the operational performance of utilities in the water and electricity distribution sectors of LAC. We correlated the dimensions described in the previous sections with the level and growth rates of the main performance indicators for utilities in the water and electricity sectors. We ran the governance indicators with the pool of utilities and analyzed the relationship between the governance indicators and each sector separately.

We use utilities’ sector performance data from 250 public and private utilities in the electricity sector, covering 26 countries and 89 percent of the connections in the region. It contains more than 20 variables indicating output, input, operating performance, quality and customer services, and prices. Data for the water sector includes annual information on 1700+ public and private utilities of 16 countries, covering 59 percent of the water connections in the region. Variables for water are similar to those in electricity. The results are presented in the tables in Annex A.

_Legal Framework:_ When we analyzed the correlation between these governance indicators and the growth rates of the set of performance indicators, results suggested that our measure of legal framework is associated with a decrease in average quality of service and an increase in average tariffs. When we evaluated each of the sectors separately, we observed that in water utilities there are some differences in terms of labor productivity where higher soundness is associated with 19
from http://www.fao.org/gaez/

- Fuglie, K., & Rada, N. (2013, 02). Resources, policies, and agricultural productivity in sub-saharan africa. _SSRN Electronic Journal_ . doi: 10.2139/ssrn.2266459

- Gadedjisso-Tossou, A., Avell ́an, T., & Sch ̈utze, N. (2020, 01). Impact of irrigation strategies on maize (zea mays l.) production in the savannah region of northern togo (west africa). _Water SA_ , _46_ . doi: 10.17159/wsa/2020.v46.i1.7894

- Gebregziabher, G., Namara, R., & Holden, S. (2009, 08). Poverty reduction with irrigation investment: An empirical case study from tigray, ethiopia. _Agricultural Water Management_ , _96_ , 1837-1843. doi: 10.1016/j.agwat.2009.08.004

- Gebregziabher, G., Villholth, K. G., Hanjra, M. A., Yirga, M., & Namara, R. E. (2013, October). Cost-benefit analysis and ideas for cost sharing of groundwater irrigation: evidence from north-eastern Ethiopia. _Water International_ , _38_ (6), 852-863. Retrieved from https://ideas.repec.org/a/taf/rwinxx/v38y2013i6p852 -863.html doi: 10.1080/02508060.2014.847

- Giordano, M., de Fraiture, C., Weight, E., & van der Bliek, J. (2012). _Water for wealth and food security: Supporting farmer-driveninvestments in agricultural water management_ (Tech. Rep.). Colombo, Sri Lanka: International Water Management Institute. Retrieved from https://www.iwmi.cgiar.org/Publications/Other/ Reports/PDF/Water ~~f~~ or ~~w~~ ealth ~~a~~ nd ~~f~~ ood ~~s~~ ecurity.pdf

- Goyal, A., & Nash, J. (2017). _Reaping Richer Returns [Obtenir de meilleurs r ́esultats]_ (No. 25996). The World Bank Group. Retrieved from https://ideas.repec.org/b/ wbk/wbpubs/25996.html

- Ground Water Resource Estimation Committee. (2015). _GEC 2015 Detailed Guidelines._ Retrieved 2023-18-12, from https://cgwb.gov.in/sites/default/files/ inline-files/2020-11-17 ~~D~~ etailed ~~G~~ uidelines ~~G~~ EC2015.pdf

- Heckman, J. J. (1979, January). Sample Selection Bias as a Specification Error. _Econometrica_ , _47_ (1), 153-161. Retrieved from https://ideas.repec.org/a/ecm/emetrp/ v47y1979i1p153-61.html

- Inocencio, A., Kikuchi, M., Tonosaki, M., Maruyama, A., Merrey, D., Sally, H., & de Jong, I. (2007). _Costs and performance of irrigation projects: A comparison of sub-saharan africa and other developing regions_ (IWMI Research Reports No. H036214). International Water Management Institute. Retrieved from https://EconPapers.repec.org/ RePEc:iwt:rerpts:h036214

- Jambo, Y., Alemu, A., & Tasew, W. (2021, 07). Impact of small-scale irrigation on household food security: evidence from ethiopia. _Agriculture Food Security_ , _10_ . doi: 10.1186/ s40066-021-00294-w

- Jones, M., Kondylis, F., Loeser, J., & Magruder, J. (2022, July). Factor market failures and the adoption of irrigation in rwanda. _American Economic Review_ , _112_ (7), 2316-52. Retrieved from https://www.aeaweb.org/articles?id=10.1257/ aer.20210059 doi: 10.1257/aer.20210059

- Khan, A. M., Kuate, L., Pongou, R., & Zhang, F. (2024, June). _Weather, Water, and_

22
confidence interval on the value of a result from a competing model. The confidence intervals _cannot_ overlap.

Our approach builds on the extreme bounds approach to assessing difference in estimates from Levine and Renelt (1992) and the graphical methods to visualize these differences in Sala-i-Martin (1997b,a).

While the three metrics are formal statistics, our weak and strong tests are not and we do not treat them that way. Rather, we use the combination of metrics and informal tests as heuristics in evaluating what weather metrics matter, for what countries, from what remote sensing products, and from what extraction method. Again, since we lack data on the objective fact which each data source is trying to measure, all comparisons of one obfuscation/metric/source combination are made relative to a different obfuscation/metric/source combination. Our data and heuristics do not allow us to make claims regarding the accuracy of a remote sensing source. Rather, we quantify the significance and magnitude of measurement error in remote sensing data by comparing results from each product with results from the other sources always bearing in mind that, for a given metric and country, if there was no measurement error the results from our tens of thousands of regressions would be exactly the same regardless of the obfuscation/source combination.

An important caveat to bear in mind with respect to our results, and all results focused on _p_ -values, is that the significance of a point estimate does not imply that the model is correctly specified, that the point estimate is agronomically meaningful, or that the point estimate has the correct sign. It may very well be the case that the skew of temperature does not matter for agricultural production and that remote sensing products which result in a significant coefficient are therefore products with measurement error, while products which do not result in a significant coefficient are the more accurate products. These results and the associated figures simply allow us to visualize the variability in the number of significant coefficients across these specifications of interest. And any variability in results is a sign that obfuscation/source combinations provide measures of weather and therefore measurement error exists.

# **4 Results**

Due to the large number of regressions and estimated values produced from our analysis, we do not present our results as one would for a traditional economics paper, with coefficients, standard errors, and stars signifying _p_ -values, all reported in tables corresponding to each regression. Instead, we present a series of figures, which allow us to evaluate the significance and magnitude, per our heuristics, of the effect of various sources of measurement error in remote sensing weather data. We start by examining measurement error due to extraction methods used to preserve the privacy of households. We then examine measurement error in relation to the choice of weather metrics to measure precipitation and temperature. Finally, we investigate the degree to which specific remote 20
Table 5: Program Impact on Learning Outcomes: Answers to Math Questions

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
|---|---|---|---|---|---|---|---|---|---|
|||6-15 years old|||6-12 years old||1|3-15 years ol|d|
||All|Boys|Girls|All|Boys|Girls|All|Boys|Girls|
|Meals*||||||||||
|Follow-up|-0.038|-0.152|0.087|0.028|-0.077|0.124|-0.300|-0.505|-0.131|
||[0.070]|[0.099]|[0.100]|[0.079]|[0.111]|[0.112]|[0.153]*|[0.215]**|[0.228]|
|Rations*|-0.078|n.a.|-0.042|-0.084|n.a.|-0.063|-0.054|n.a.|0.044|
|Follow-up|[0.092]|n.a.|[0.105]|[0.099]|n.a.|[0.113]|[0.244]|n.a.|[0.276]|
|Follow-up|0.044|0.076|0.005|0.040|0.061|0.018|0.073|0.106|-0.034|
||[0.049]|[0.071]|[0.070]|[0.055]|[0.079]|[0.078]|[0.109]|[0.157]|[0.161]|
|Girl|-0.050|||-0.045|||-0.098|||
||[0.034]|||[0.038]|||[0.073]|||
|Constant|0.554|0.496|0.545|0.533|0.474|0.533|2.933|3.053|2.802|
||[0.052]***|[0.080]***|[0.063]***|[0.056]***|[0.085]***|[0.065]***|[0.120]***|[0.173]***|[0.156]***|
|Observations|5548|2302|3246|4467|1826|2641|1081|476|605|
|R-squared|0.39|0.42|0.39|0.34|0.36|0.34|0.16|0.18|0.22|

# Robust standard errors in brackets

* Significant at 10%; **significant at 5%, *** significant at 1% Dependent variable is number of correct answers (varies from 0 to 4). Regressions also control for child age, orphanhood, parents education, ethnicity, religion, household assets and village fixed effects (not reported).

28
The SBTC hypothesis predicts that technology shocks will spread the earnings distribution by increasing the returns to skills which are complementary with the technology. These skills should partly be reflected in educational attainment.<sup>14</sup> The relevant skills which are uncorrelated with education will also experience an increase in demand and in their returns. In the 1990s, the returns to both observed skills, especially university education, and unobserved skills worked strongly towards increasing the wage variance. As noted earlier in this paper, the literature on the United States shows that the returns to observed and unobserved skill have not grown at the same time, casting doubt on the hypothesis that SBTC is behind both changes. In Argentina, however, the two have evolved together. This is compatible with the hypothesis that SBTC is behind both sets of changes.

In the male and female wage distribution, increases in the variance occurred in both the upper and lower halves of the earnings distribution. The drivers, however, are fundamentally different. The increase in the 90:50 ratio is mostly caused by increasing returns to observed skills, especially college education, as college-educated individuals are concentrated in the highest deciles of the wage distribution. On the other hand, the returns to high school education did not increase strongly. As mostly workers with lower to middle education populate the lower half of the wage distribution, the effect of returns to education is weak in this part. Instead, the growth of the 50:10 can almost fully be explained by changes in the residual variance. This means that increasing returns to unobserved skills are the driver of the growth in dispersion between the median and the 1<sup>st</sup> decile. In other words, workers at the bottom and in the middle of the wage distribution must differ with respect to their unobserved skills in a way in which the middle does not differ from the top of the distribution.<sup>15</sup>

# **7. Informal-Formal Wage Gap**

This section considers what relevance the findings for the return to unobserved skill may have for the earnings gap between formal and informal salaried workers.<sup>16</sup> One of the puzzles in

> 14 This also means that they may be partially correlated with educational attainment. This will introduce a bias to the estimated coefficients to education which will partly reflect returns to unobserved skills.

> 15 The same patterns with very similar magnitudes of change hold for the much bigger sample of 16 Argentinean urban conglomerates between 1992 and 2002. The results are shown in Table A2 of the appendix.

> 16 The paper uses a three-way classification of employment: formal employees, informal employees, and independent workers. Independents are defined as the self-employed and those who are owners of microenterprises with 5 or fewer employees. Formality is defined in terms of worker benefits, specifically having the

19
# **Exporting from a small landlocked economy** **_An assessment of firm-product-destination survival rates in the Lao PDR_**

Liviu Stirbat, Richard Record and Konesawang Nghardsaysone The World Bank<sup>1</sup>

**Key words:** Exports, Trade, Firm Level, Extensive Margin

**JEL codes:** D21, F14

> 1 Respectively: Consultant, Trade Specialist and Trade Analyst, World Bank Office Vientiane. Corresponding author: Richard Record, rrecord@worldbank.org. Useful comments were provided on an earlier draft by Stéphane Guimbert, Geneviève Boyreau, Olivier Cadot, Michael Schultze and Sorana Toma. The paper was prepared with support from the Lao PDR Trade Development Facility, a multi donor trust fund financed by the Government of Australia and the European Union, and administered by the World Bank. The data used in this study were generously provided by the Lao Customs Department.
Figure 4 continued: Educational attainment profiles for ages 15-19 by gender and wealth (Vertical axis is proportion of children who have completed grade).

<!-- Start of picture text --> Central America and Caribbean<br>Dominican Rep. 1996 . Guatemala 1995 Haiti 1994-95 Nicaragua 1998<br>0.8 - 0. - 0.8~ 0.83-<br>086 - 0' 06 5 0.6 -<br>0.4 - \ 0.4 0.41 0.4-<br>0.2- 0.2 0.2 0.2<br>o 1, ,,, 0 0<br>1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9<br>Grade Grade Grade Grade<br>South America<br>Bolivia 1997 Brazil 1996 [Brazil, Northeast 1996] Colombia 1995<br>1 3 1 - 2 . 1 - 9 I 1 9<br>0.8 0.8 0.8 0.8 -<br>0.6 0.6 0.6 0.6<br>0.2 -0.2 -0.2 -0.2-<br>0 0 0 ,.,, , ,<br>1 3 5 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 7 9<br>Grade Grade Grade Grade<br>East Asia and Pacific<br>Peru 1996 Indonesia 1997 Philippines 1998<br>1 1- 1 - a +<br>0.8 - 0.8 -c0.8em<br>0.6 -60.6 - 0.6 -<br>0.4 - 0.4 - 0.4M a<br>0.2 - 0.2 - 0.2 -<br>1 3 5 7 9 1 3 5 7 9 1 3 5 7 9<br>Grade Grade Grade<br>Middle East and Central Asia<br>Kazakhstan 1995 Turkey 1993 Uzbekistan 1996<br>1 ------ 1 1 aRich/Male<br>0.8 0- - C 0.8 0.8 0 0 ,;,, f orMl<br>-4-Rich/Female<br>0.6 -0.6 -0.6-<br>Middle/Male<br>0.4 -0.4 -0.4-<br>0.2 -0.2. 0.2 - ~~~~~~~~~~~~~~0-Middle/Female<br>0 ~ . . . 0 ... 0 .-. . i ,, -rn-I-Poor/Male<br>1 3 5 7 9 1 3 5 7 9 1 3 5 7 9<br>Grade Grade Grade -.- oPoor/Femrale<br><!-- End of picture text --> 26
24 Dooley, M. 1995. "A Survey of Academic Literature on Controls over International Capital Transactions." National Bureau of Economic Research, Working Paper 5352.

Estrella, A., and G. Hardouvelis. 1991. "The Term Structure as Predictor of Real Economic Activity." _The Journal of Finance,_ 46: 555-576.

Estrella, A., and F. Mishkin. 1997. "The Predictive Power of the Term Structure of Interest Rates in Europe and the United States: Implications for the European Central Bank." _European Economic Review,_ 41: 1375-1401.

Evans, G. W. 1991. "Pitfalls in Testing for Explosive Bubbles in Asset Prices." _American Economic Review,_ 81: 746-54.

Fama, E. F. 1990. "Term Structure Forecasts of Interest Rates, Inflation and Real Returns." _Journal of Monetary Economics, 25:_ 59-76.

Flood, R., and R. Hodrick. 1990. "On Testing for Speculative Bubbles." _Journal of Economic Perspectives,_ 4: 85- 101.

Froot, K.A., and M. Obstfeld. 1991. "Intrinsic Bubbles: The Case of Stock Prices." _American Economic Review,_ 81: 1189-1214.

Guerra, A. 1997. "La Relevancia Macroeconomica de los Bienes Raices en Mexico." Research Departnent Working Paper 9707. Banco de Mexico.

Hall, S. G., Z. Psaradakis, and M. Sola. 1999. "Detecting Periodically Collapsing Bubbles: A Markov-Switching Unit Root Test." _Journal of Applied Econometrics,_ 14: 143-154.

Hargis, K., and W. Maloney. 1997. "Emerging Equity Markets: Are They for Real?" _Journal of Financial Research,_ 20: 243-262.

Hartzel, D. 1986. "Real Estate in the Portfolio." in F. Fabozzi, ed. _The Institutional Investor: Focus on Investment Management._ Ballinger, Cambridge.

Harvey, C. 1995. "Predictable Risk and Returns in Emerging Markets." _Review of Financial Studies,_ 8: 773-816.

Herrera, S., and C. Garcia. 1999. "User's Guide to an Early Warning System for Macroeconomic Vulnerability in Latin American Countries." Policy Research Working Paper 2233. The World Bank.

Herring, R., and S. Wachter. 1999. "Real Estate Booms and Banking Busts: An International Perspective." Processed. Wharton School, University of Pennsylvania.
-22-

**:ble 7.** Mean Earings, Yem of Schooling and Mincerian Returns to Education: by Gender, circa 1980

|Country|Year|Earnings (|ocal curr.)|Years of|Schooling|Rate of|Return(%)|
|---|---|---|---|---|---|---|---|
|||Males|Females|Males|Females|Males|<br>Females|
|Argentina|1980|1,234Q9O|0 333,000|8.1|7.8|8.0|9.8|
|Brazil|1979|10,943|6,813|_5.2_|_5.5_|11.8|8.9|
|Colombia|1980|10,124|5,626|7.0|6.8|18.6|17.3|
|Costa Rica|1981|1,539|3,334|_5.9_|_9.5_|14.8|10.4|
|Panama|1979|193|281|7.3|10.0|13.4|10.5|
|Paraguay|1983|51,043|28,678|8.4|7.7|10.5|11.7|
|Uruguay|1981|4,549|4,318|9.9|8.0|7.3|10.5|
|Venezuela|1981|2,545|2,799|6.7|8.7|12.1|10.9|

Source: Annex 3.