rafmacalaba/datause-encoder-data huggingface dataset:
# **7. Estimation of standard errors**

The field protocols used by BBS to collect the HIES 2016/17 data are based on the concept of terms. Each term consists of 20 days, the time needed for a team of two enumerators to cover an entire PSU (20 households). The 12-months of data collection are divided across 18 terms (18*20 days=360 days). As the 18 terms of data collection planned cannot be divided exactly into quarters, PSUs in terms 5 and 14 will need to be randomly split to avoid sampling bias so that each quarter corresponds to exactly 4.5 terms. This random split is conducted by randomly allocating PSUs in term 5 to quarters 1 and 2, and those in term 14 to quarters 3 and 4.

The HIES 2016/17 sample was designed so that each of the 132 sub-strata includes at least 4 PSUs during the entire four quarters of planned data collection. The allocation of PSUs was conducted in such a way that each quarter includes at least one PSU per sub-strata to ensure that each of the quarterly poverty estimates includes observations from each of the 132 substrata. This implicitly means, however, that standard errors for quarterly poverty rates and welfare statistics cannot be estimated for sub-strata that have only one PSU assigned. This is not an issue when estimating annual poverty rates or annual welfare statistics, as each of the sub-strata includes at least four PSUs, but this poses a challenge when trying to estimate standard errors for quarterly statistics.

To address this issue, we consider four alternative ways of estimating standard errors for quarterly poverty rates and welfare statistics based on the HIES 2016/17. In the first option, we propose to use the method called the “centered approach”. This method calculates the standard error for the sub-strata based on the distance from the grand mean rather than the substrata means. In the second method, we propose to use the “certainty method”. This method assumes that a sub-strata with a single PSU do not contribute to the overall standard error and is equivalent to specifying a finite population correction of 1. The third method we propose is called the “scaled one”. This method assumes that the contribution to the standard error for a sub-stratum with a single PSU is equal to the mean estimate for all of the other (multiple PSU) strata and it yields essentially the same results as those that would be obtained using mean imputation. Lastly, in order to maintain full comparability across years in the estimation of 36
indicates more financial stability, while greater variability in earnings reduces the Z-index and thereby increases the bank’s overall risk.

The results of Models 1, 2 and 3 show that the estimated quadratic functions have a downward oriented parabola shape with a positive linear term. The inflection points (0.37, 0.15 and 0.21 respectively for the Lerner index, the HHI deposits and the HHI loans respectively) all occur above the 99<sup>th</sup> percentile of the Lerner index data, implying a positive relationship between all proxies of market power and the Z-index. This suggests that more market power is associated with significantly higher overall bank stability. The results lend support to the “competitionfragility” view that an increase in competition in banking is likely to erode the franchise value of firms and encourage banks to increase their overall risk exposure. We do not interpret this result as a contradiction to the previous finding that more market power leads to a riskier loan portfolio. When banks enjoy higher franchise value derived from their market power, they are likely to command higher loan rates, thereby increasing the riskiness of their loan portfolio. However, the fact that banks with more market power also enjoy greater overall financial stability seems to suggest that they protect their franchise value from the higher loan risk with other risk management methods.

In Table 5, we seek to establish whether banks enjoying a higher degree of market power do in fact hold more equity capital as a cushion to absorb unexpected losses resulting from their loan portfolio risk. The results of Model 1 indicate that the inflection point occurs around the 50<sup>th</sup> percentile of the data and no significant positive or negative relationship can be established between the Lerner Index and the equity-to-assets ratio. In contrast, the inflection point for Model 2 is evaluated at -0.03 and lies below the 1<sup>st</sup> percentile of the HHI deposits data (because the quadratic function is upward oriented), so that a significant and positive relationship emerges between the degree of market power and bank capitalization levels. The positive sign in Model 3 indicates that this result is maintained using the HHI loans as well. Thus, the findings indicate that bank capitalization levels are higher for banks with more market power using the HHI deposits and loans proxies of market structure, while the results of the Lerner index seem to imply that half of banks with more market power do hold more equity capital.

13
**Table 5. Results from estimating** **_β_ -convergence models**

||**1985 arrival**<br>||**1975 arrival**||
|---|---|---|---|---|
||**1990-2000**|**1980-1990**|**1980-2000**|**1990-2000**|
||**(1)**|**(2)**|**(3)**|**(4)**|
||**EDUCATION**|**INDEX 1**|||
|**Predicted Log of OP in 1990**|-0.0291***<br>[0.00522]|||-0.0278***<br>[0.00726]|
|**Predicted Log of OP in 1980**||-0.0523***<br>[0.00442]|-0.0297***<br>[0.00166]||
|**Constant**|0.202***<br>[0.0341]|0.359***<br>[0.0281]|0.205***<br>[0.0105]|0.189***<br>[0.0481]|
|**Observations**|79|80|80|80|
|**R-squared**|0.554|0.834|0.906|0.343|
||**PRESTIGE**|**INDEX**|||
|**Predicted Log of OP in 1990**|-0.0290***<br>[0.00377]|||-0.0183**<br>[0.00706]|
|**Predicted Log of OP in 1980**||-0.0341***<br>[0.00566]|-0.0177***<br>[0.00210]||
|**Constant**|0.188***<br>[0.0236]|0.218***<br>[0.0353]|0.114***<br>[0.0132]|0.118***<br>[0.0447]|
|**Observations**|79|80|80|80|
|**R-squared**|0.562|0.419|0.546|0.178|
||**EDUCATION**|**INDEX 2**|||
|**Predicted Log of OP in 1990**|-0.0234***<br>[0.00492]|||-0.0198***<br>[0.00677]|
|**Predicted Log of OP in 1980**||-0.0382***<br>[0.00469]|-0.0214***<br>[0.00172]||
|**Constant**|0.0660***<br>[0.0133]|0.107***<br>[0.0125]|0.0603***<br>[0.00457]|0.0552***<br>[0.0184]|
|**Observations**|79|80|80|80|
|**R-squared**|0.414|0.647|0.773|0.213|

Robust standard errors in brackets *** p<0.01, ** p<0.05, * p<0.1 40
mean and finite variance. Parameter estimation is carried out by putting the models in state-space form, using the Kalman filter and optimizing the likelihood function. Estimation can be carried out using STAMP 6.0.18 Tests for normality, autocorrelation and heteroskedasticity of residuals are used a specification tests. Model specification is also assessed in light of conventional goodness of fit and information criteria.

## **ESTIMATION AND STATISTICAL** RESULTS

# **General comments on estimation and results**

Quarterly data used range from 1993.1 to 2001.4 (please see Annex I for a description of the data set).. Results are presented for real MI balances, which was the only money monetary measure for which consistently good results were obtained using all modeling approaches. The same estimation procedures presented here for MI, were applied to data for base nmoney and M2, with generally unsatisfactory results.'<sup>9</sup> The measure of real economic activity was real GDP in all models. Relatively satisfactory results were obtained for iniflation, log of inflation and log of the nominal depreciation rate, as measures of the opportunity cost of holding money. In all cases, the magnitude and signs of the estimated long-run elasticities (or semi-elasticities) conformed to our expectations based on economic theory. We also attempted to use (nominal) deposit interest rates as a measure of the rate of return to holding money, but results were not very satisfactory (mainly because the deposit rate tended to be negatively associated with demand for Ml, suggesting that deposit rates are if anything a weak measure of opportunity cost not of the own rate of return to holding MI -type assets).

# **Estimation results followiong the structural time series approach**

A model taking as endogenous variables log real MI, log real GDP and the inflation rate was estimated and convergence was achieved in 100 iterations. The value of the loglikelihood was 271.43. The estimated (common) stochastic cycles have periods of 2.78

> 18 See Doornik, J., et al (2000).

> '9 For a limited sample (1997.1 io 2001.4), moderately satisfactory results could be obtained for M2 using the VECM approach; these results, however, are based on a very short sample and could not be strengthened by using altemative modeling approaches. We have therefore chosen to disregard these results.

14 Money Demand in Venezuela, Mario A. Cuevas
though there are differences in the explanatory power across the different targets, there are not large differences in characteristics in the targets themselves. This actually is a warning sign as it indicates that, in the aggregate, there may be little difference in the targets, but there can still be systematic differences in who is likely to achieve the target.

**=** **<u>Table 1.  Probit Estimates Using Alternative Targets (Meeting target 1, 0 otherwise)</u>**

||Secretary of Education<br>Target|Target Based on<br>Quantile Regression at<br>67<sup>th</sup>Percentile|Target based on JTPA-<br>type adjustment to value<br>at 67<sup>th</sup> percentile|
|---|---|---|---|
|Constant|4.176***<br>(14.84)|.194<br>(0.78)|-.178<br>(-0.72)|
|2007 Portuguese<br>proficiency|-.021***<br>(-14.90)|-.001<br>(-.085)|.001<br>(0.89)|
|2007 Number of<br>students|-.0001*<br>(-1.93)|.00001<br>(0.19)|-.0001**<br>(-2.36)|
|2008 Participation Rate<br>in PROEB|.0002*<br>(-1.86)|.0003**<br>(2.42)|.0003**<br>(2.45)|
|Dummy for Grande<br>Norte|-.131**<br>(-2.08)|.096<br>(1.60)|.161***<br>(2.68)|
|Pseudo R2|.0887|.0052|.0090|
|N|2204|2255|2255|
|Mean of target|199.9|199.8|200.0|
|Standard Deviation of<br>target|22.2|15.4|14.8|
|Range of target|130.1-300.4|148.1-264.5|150.2-259.9|
|Percentage of Schools<br>that met target in 2008|52.3%|53.1%|52.3%|

Looking at patterns of coefficients in probit estimations is one way of judging whether the probability of achieving the target is systematically related to the particular circumstances of the agents. However, there is some value to having a summary measure that could be used to judge whether there is any significant relation to the circumstances. A summary measure is readily available because the problem that is posed in judging the adequacy of the target is an exact parallel to the problem of determining whether opportunities are equally distributed in the population in the literature of equality of opportunity. The metric developed for the equality of opportunity, the human opportunity index, is an inequality-adjusted coverage rate, with the adjustment or penalty determined by the extent to which there is a deviation from randomness in who receives the opportunity and who does not. If there is no systematic relation between who gets the coverage and a set of circumstances, then there will be no difference between the estimated coverage rate and the human opportunity index. The relevant test, then, is a test of whether the penalty is equal to zero.

11
charged for their services in public units, there was little change in observable quality indicators.<sup>11</sup> Even though there was a slight decline in availability of antibiotics and malaria drugs in public hospitals bed capacity did actually increase slightly and there is no change in the number of hours per day during which the facility was open. Comparing public to private hospitals clearly illustrates that the policy change was limited to the former. As table 4 illustrates, no such changes are visible in private facilities almost all of which charged for medicines both before and after the abolition of user charges, with slightly more than half also charging for consultations.

Taking these figures together allows us to obtain an estimate of the reduction of revenue to the public system through elimination of user charges which amounts to about US$3.4 million per year.<sup>12</sup> This was more than compensated by an increase in the health budget of more than US$ 20 million. A key element of the econometric analysis to be performed below is to obtain an estimate of the benefits the policy shift provided to individuals that would allow to compute a social cost-benefit ratio.

# **4. Econometric results**

While administrative data point toward a significant expansion in the provision of services, they do not allow us to make inferences on who in the population benefited and how the better provision of services translated into improved outcomes. Analysis of household level data to provide insight into these questions shows that there was a clear reduction of rationing, especially for children; there is no appreciable impact on the choice of provider between public and private establishments, and morbidity was reduced in a pro-poor way as a result of the policy change.

# **4.1 Administrative data**

Regressions using administrative data as reported in table 5, suggest significant, though regionally uneven, increases in the provision of services by health units following the abolition of user fees. In particular, we find that, on average, the number of new cases treated by the health centres increased by 18.5% for under 5-year olds and by 31% for individuals of the age of 5 and above while the number of referrals increased by 26%. The increase was particularly pronounced in the West (50% for adults, 25% for children) and the East (42% for adults and 30% for children) but not at all visible in the North. Provision of simple services such as weighing and vitamin A supplementation for children increased everywhere (by 38% for weighing and 61% for supplements ), one observes significant differences in this

> 11 In interpreting these figures, one should note that responses were given by officials who may have an interest in overstating the quality of service provision and that a cross-check with information from households who actually needed such medicines would be desirable in future rounds of the survey.

14
# **5. References**

- Ahmad, E., and N. Stern (1991) _The theory and practice of Tax Reform in Developing Countries_ , Melbourne: Cambridge University Press.

- Aldy, J.E., E. Ley, and I.W.H. Parry (2008) “A Tax-Based Approach to Slowing Climate Change;” _National Tax Journal_ 61, 493–517.

- Amundsen, E. S. and R. Schob (1999) ”Environmental Taxes on Exhaustible resources”, _European Journal of Political Economy_ 15, 311-329.

- Atkinson, A.B., and J.E Stiglitz (1980) _Lectures on public economics_ , New York: McGrawHill Book Co.

- Bacon, R. and others (2007) “Growth and co2 Emissions: How do Different Countries Fare?” (unpublished: The World Bank).

- Baumol, W. (1972), “On Taxation and the Control of Externalities”’, _American Economic Review_ , 62(3), 307-321

- Baumol, W., and W. Oates (1971), “The Use of Standards and Prices for the Protection of the Environment”, _Swedish Journal of Economics_ , March, 73, pp. 42-54

- Baumol, W. and W. Oates (1988) _The Theory of Environmental Policy_ , (2nd edition) Cambridge University Press, Cambridge.

- Bergstrom, T.C. (1982) “On capturing oil rents with a national excise tax;” _American Economic Review_ , 72(1), 194–201.

- Carisma B. and Lowder S. (2008) “Economic Cost of Traffic Congestion: a literature review for multiple Locations”

- http://greenconsumerism.net/wp-content/uploads/2008/08/the-cost-of-traffic-congestion.pdf

- Connelly and Supangan (2006), “The economic costs of road traffic crashes: Australia, states and territories” _Accident Analysis & Prevention_ , 38(6), 1087–1093.

- Creutzig, F., He, D. (2009). “Climate change mitigation and co-benefits of feasible transport demand policies in Beijing”. _Transportation Research_ D 14, 120–131.

- Diamond, P.A. and J.A. Mirrlees (1971) “Optimal Taxation and Public Production I: Production Efficiency” and “II: Tax Rules” _American Economic Review_ 61, 8–27 and 261–278.

- Farzin, Y.H. (1996) “Optimal pricing of environmental and natural resource use with stock externalities”, _Journal of Public Economics_ , 62, 31-57.

- IEA (2007) _Energy Use in the New Millennium Trends in IEA Countries_ . International Energy Agency. Paris: OECD.

- IEA (2008) _Review of International Policies for Vehicle Efficiency_ . International Energy Agency. Paris: OECD.

- INFRAS/IWW (2000) _External Costs of Transport: Accidents, Environmental and Congestion Costs of Transport in Western Europe_ . Zurich/ Karlsruhe.

- INFRAS/IWW (2004) _External Costs of Transport: Update Study_ , October http://www2.uic.asso.fr/d ~~e~~ nvironnement/envissues/full-report.pdf

17
# **<u>Table 21 PTA members’ knowledge and participation, MP</u>**

|Percentage members that say yes orgive the correct response|Headmaster|PTAchair|Parents|
|---|---|---|---|
|Do you know if you are a member of the PTA executive<br>committee|98|97|70|
|Have you received any training on roles/responsibilities of<br>PTA|80|42|8|
|Has PTA met in this schoolyear|98|92|55|
|Has PTA inspected the school in this schoolyear|89|87|34|
|Doyou know who operates the school account|93|73|19|
|Doyou know which account scholarshipcomes to|74|18|8|
|Doyou know which account mid daymeal moneycomes to|94|74|24|
|How many other PTA members can you name (on average<br>PTA has10-13members): **no of members named correctly**|5|4|1|
|Do you know how much money came into the school account<br>inthis schoolyear|93|49|2|
|Do you know how much money remains in the school account<br>at present|86|27|1|
|Do you know if PTA chair checks/sees the attendance of|68|72|29|
|teachers everymonth||||
|**Table 22**<br>**Can PTA members list their roles/**|**responsibiliti**|**es?, MP**||
|Percentage members that list each item|Headmaster|PTAchair|Parents|
|Inspect schools|59|52|16|
|Manage civil works|46|21|5|
|Prepare schemes/plans for school improvement|26|5|2|
|Manage school accounts, decide how to spend funds in<br>schoolaccounts|17|4|1|
|Verify and sign on teachers salary slip/attendance register<br>each month|14|6|1|
|Ensure teachers come regularlyand on time|42|47|20|
|Ensure teachers teach satisfactorily|35|49|21|
|Recommend reducing/stopping teachers’ salary if teachers<br>performance/attendanceis unsatisfactory|6|3|1|
|Ensure distribution of textbooks|18|5|1|
|Ensure distribution of scholarships|15|4|1|
|Ensurepreparation and distribution ofqualitymid daymeals|73|70|28|
|Ensure distribution of uniforms|23|12|3|

34
# **4 Results**

## **4.1 Evaluation of the performance of the models**

Figure 4 shows the prediction errors across the six combinations of target variables from Table 1 by each of the five machine learning algorithms we use (regular lasso, post-lasso, CARF random forest, conditional inference random forest, and gradient boosting). Note that regardless of which target variable combination we used (i.e. which panel of Figure 4), all the predictions are turned into poverty rates and the errors are evaluated over poverty rates. Hence, they are comparable to each other. As one example, the very first bar shows that when predicting poverty rates directly using a CART random forest (abbreviated as _carf_ ), then we on average get a predicted poverty rate 4.99 percentage points off the truth.

Note that each bar in panels 5 and 6 of Figure 4 have several possible realizations depending on which growth incidence curve or distributional assumption is used to convert means and Ginis into poverty rates. In Figure B.1 in the Appendix we show the best way of converting predictions of growth/levels in the mean and Gini into poverty rates. The best way of converting predicted levels of the Gini and mean tends to be by using a log-normal distribution, while the best way of converting predictions of growth in the mean and Gini tends to be by applying a linear growth incidence curve. Figure 4 shows the best performing options from Figure B.1 in panels 5 and 6.

Figure 4 reveals that generating poverty rates by predicting from the last observed survey (right column) gives lower errors than predicting poverty rates or the mean (and Gini) without accounting for changes from the last survey (left column). Nearly all methods trying to predict poverty rates directly give a higher error than the worst method trying to predict from the last survey.

The poor performance of models trying to predict poverty rates directly is probably not due the fact that they have poverty rates on the left-hand side, but more likely that the models we have used with levels of poverty, mean and the Gini on the left-hand side do not use country fixed effects or lagged values. Under one interpretation, this treats these models equally to the models that predict changes from the last distributions, which likewise do not have country-fixed effects or lagged values. Such factors are less important when we have already taken first differences, though. Given that surveys within countries often are incomparable and often have many years between them, lagged information may not provide much additional information. Yet, to the extent that there are country specific effects in levels that are hard to predict, then any model that does not use 15
w TAble 2. Ic Relative inyouance of Merd ndis Trade Fows not nfluenced by RegonaTrade Aangemns in 1988

||Share|of 1988 Wodd|Trade(%M|Value|of 1988 Trade|($milEon)|
|---|---|---|---|---|---|---|
|Trsde Fhrv|||||||
||Items|G;oodsb|nflt|Titem|Ci2odb|mZaim c|
|TCOrAL WORLD EKPORTS|100.00|100.00|100.00|2,829,098|2,562,252|1,980,066|
|_Edatine_and Potenti_FrA_Trade Usd**in**Tabk 1|45.82|47.86|49.26|1.296.174|1.2263S0|97|
|Cokloed Arnnements|4.87|4.16|3.67|137.879|106.644|72.749|
|Maior Noi-PrefentialTrade FTlws|38.29|38.94|41.72|1.03.45|997|261|
|North America - Developed_Erope_<br>|7.14<br>|7.56<br>|8.31|202,105|193,717|164,561|
|North America-Japan<br>|4.94|5.35|5.90|139,846|137,091|116,890|
|North America- Deveoping South nd SE Asi<br>|4.77|S.12|S.82|134,999|131,9|115,152|
|Japan- Dvelopuwg Souh Ams<br>|3.96|3.98|4.73|112,134|102,04,|93,683|
|Developed Eurpc - Developing South and SE Asia<br>|3.46|3.52|4.47|97,856|90,303|88,498|
|lapan- Dveloped Europe<br>|2.84|3.12|3.92|80,206|80,040|77,712|
|Developed Europe- Develping We Aa<br>|2.00|1.69|1.85|56,460|43,388|36,702|
|Developed Europe - Developin Afiica<br>|2.44<br>|2.04<br>|1.81|68,935|52,27t|35,795|
|Deveoped Eurpe- Devwlopg Amenic|1.86|1.92|1.39|52,729|49,126|27,465|
|Eastern Europe - Developing Countries<br>|2.01|1.86|1.37|S6,727|47,769|27,214|
|North Amrika - Develping Americea<br>|1.62|1.47|1.21|46,014|37,587|23,906|
|Autraia/New ealand - Developed Europe<br>|0.61|0.64|0.52|17,216|16,510|10,238|
|lapan-Australa/New Zedand|0.64|0.63|0.42|18,178|16,134|8,347|
|bot-Regional Develocine Country|2.03|1.16|1.17|57,509|29,830|23,079|
|Total of Bilate Plows Lied Above|88.98|90.96|94.65|2,517,458|2,330,940|1,874,255|

1 Excludes CBI(Caribbean Bes Initiative) and CRIRCAN (Canada's Preferental Trade Scheme for the Co_monwealth Caribbean). 2 Although a prefeential trade famework among developng conti exists under UNCTAD's Global System of Trade Preferences (GSTP) thi systm has, ts far, been lte utilized.

Note: For convenience, regional groupg adopted we thos employed by the Statisd Office of the United Nation. See UNCTAD (1990).