rafmacalaba/datause-encoder-data huggingface dataset:
23 Table 1: Descriptive statistics for 189 NCMS pilot counties, 2005

|||NCMS ‘m<br>|odel’<br>|Inpatient||
|---|---|---|---|---|---|
|||Inpatient|Inpatient|expenses|All 4|
||Inpatient<br>expenses only|expenses<br>and<br>catastrophic<br>outpatient<br>expenses|expenses<br>and pooling<br>account for<br>outpatient<br>expenses|and<br>household<br>account<br>for<br>outpatient|<br>‘models’<br>combined|
|||||expenses||
|Number of counties|16|16|46|111|189|
|GDP per capita (RMB)|12,301.6|22,315.0|13,626.7|9,498.4|11,825.5|
|Share of rural population|73.7%|76.7%|71.7%|76.9%|75.4%|
|Share of rural population in NCMS|69.8%|84.6%|78.4%|86.1%|82.7%|
|Total NCMS budget per member|47.3|75.3|100.9|47.5|62.9|
|_of which_Central|9.2%|0.0%|5.2%|23.0%|12.8%|
|Province|17.9%|7.5%|4.7%|13.0%|9.5%|
|City|8.0%|4.5%|3.6%|9.3%|6.5%|
|County|20.3%|19.1%|18.0%|12.4%|15.8%|
|Township|9.0%|14.8%|13.8%|2.7%|8.7%|
|Member|35.2%|39.3%|28.3%|34.6%|32.7%|
|Other|0.4%|14.8%|26.4%|5.0%|14.1%|
|Co-insurance rate (inpatient care)||||||
|Township|59.0%|58.2%|56.0%|56.8%|56.9%|
|County|61.4%|60.7%|60.4%|61.1%|60.9%|
|Above county|66.3%|64.6%|65.8%|66.7%|66.3%|
|Deductible (average)|817.1|785.2|532.7|420.7|516.6|
|Gate-keeping in place|43.8%|25.0%|41.3%|37.8%|38.1%|
|NCMS contracts with specific providers|62.5%|87.5%|69.6%|73.0%|72.5%|
|Provider payment methods||||||
|Fee-for-service|81.3%|87.5%|73.9%|64.9%|70.4%|
|Case-based|0.0%|0.0%|2.2%|2.7%|2.1%|
|Mixed|6.3%|0.0%|10.9%|22.5%|16.4%|
|Other|12.5%|12.5%|13.0%|9.9%|11.1%|
age and age of death vary by gender<sup>and country and benefits from the joint annuitystart</sup> flowing to widows late in old age.

_The counterfactual._ In Parts III-V<sup>we discussthe new systemsonly, sothere is no</sup> counterfactual. In Part VI we apply<sup>the DBformulaeof theoldsystemstoassessthe</sup> gender impact of the new<sup>versustheoldsystems.Thisintroduces anadditionalsetof</sup> methodological problems. The old<sup>systems wereactuariallyunbalancedso could not have</sup> delivered their promised benefits<sup>inthelongormediumterm.Argentinawasalready</sup> defaulting on its payments. What,<sup>then,isthecounterfactualtothenewsystem?We</sup> avoid this problem by applying<sup>the DBformulaethatwereinplacejustpriortothe</sup> reform, and focusing on relative rather<sup>than absolute gains and losses todifferentgender-</sup> education-marital groups. Thus<sup>we abstractfromefficiencyeffectsthatmightlead</sup> everyone to be better or worse<sup>off. Insteadwe ask:Whichgroups gainedor lost the most</sup> from the reform? Did gender<sup>ratios improveor deteriorate?hnplicitly,this is tantamount</sup> to a counterfactual in which the<sup>fiscaladjustmenttoinsolvencyinthe oldsystems is</sup> distributionally neutral, involving<sup>equi-proportional benefitcuts or taxincreasesforeach</sup> group, while leaving relative positions<sup>unchanged.9</sup> _Taxes._ Throughout, this analysis<sup>concentrateson the benefitsideratherthan the</sup> finance side, because we don't<sup>knowwhat thefullcostofthepublicpillar,its</sup> intergenerational burden or<sup>itsgender incidencewillturn outtobe,eitherin the oldor</sup> new systems. Our comments<sup>on netredistributions(transfersminustaxes)arebasedon</sup> the assumption that within each<sup>cohort,the tax burden is distributedroughly proportional</sup> to earnings.

111. Aaunnaie5 Tor Men<sup>animdWomeEnfromthe ]PrivatePffligar</sup> Work anim wage experiiece of men<sup>versus womenm</sup> Based on our cross-sectional<sup>analysiswefind that,onaverage,womenaffiliates</sup> in Chile work and contribute<sup>tothesystemonly70%asmanyyearsasmen(Table3).</sup> Using secondary school graduates<sup>as an example,by the time they reach the age of 65 the</sup> average male affiliate has contributed<sup>38yearswhiletheaveragefemaleaffiliatehas</sup> contributed only 27 years. In<sup>Argentinamen tendtowork moreand womenless,sothe</sup> relative experience of women<sup>is</sup> lowerO%<sup>forsecondaryschoolgraduatesand less</sup> 15
# **Introduction**

The role of family background in determining the economic opportunities of a child has been the focus of a large literature in economics and sociology.<sup>1</sup> There has been a surge in the interest in reliable estimates of intergenerational persistence in economic status, motivated by the evidence that economic inequality has increased in recent decades in many countries (World Bank (2006)). The concern is that the observed increase in inequality reflects worsening deep-seated inequality of opportunities in education and the labor market. There is growing evidence of a Great Gatsby curve, showing that crosssectional inequality and intergenerational mobility are negatively correlated (Corak (2013), Fan et al. (2015), Neidh ̈ofer (2016)).

A major constraint on the research on intergenerational mobility has been data limitations in the standard household surveys. A substantial literature in the context of developed countries shows that the estimates of intergenerational income persistence can be severely biased downward due to measurement error (Solon (1992), Mazumder (2005)). A small but growing literature analyzes how different measures of intergenerational persistence used in the literature are affected by data limitations. In a widely-acclaimed paper, Chetty et al. (2014) show that the standard measure of intergenerational income persistence, called intergenerational income elasticity (IGE) and estimated as the slope of a regression of log children’s income on log parental income, is fragile. The lack of robustness reflects the fact that the conditional expectation function of children’s log income given parent’s log income is highly nonlinear. They show that, in contrast, rank correlation, estimated as the slope of a regression of children’s percentile rank in income distribution on parental percentile rank, is approximately linear, and it is much more robust to measurement error and treatments of zero income.<sup>2</sup> Dahl and Deleire (2008) show that the rank-based measures

> 1For excellent surveys of the economics literature, see Solon (1999), Black and Devereux (2011), Bjorklund and Salvanes (2011), and for the sociological literature, see Erikson and Goldthorpe (2002), and Fox et al. (2016).

> 2The advantages of rank-transformed variables in economic analysis have been appreciated since Hotelling and Pabst (1936). They emphasized the robustness with respect to violation of the normality assumption. For analysis of intergenerational mobility, the rank-rank specification was introduced in

1
20 employees. About 31% of the firms are medium size (with between 20 and 99 employees), while the share of large firms is 16 percent (more than 99 employees). There are representative firms from each sector: 40 percent of the firms are from the manufacturing sector; 17 percent from the retail sector; 25 percent of firms are from the other service sectors; 2 percent of firms are from other sectors; and the remaining sectors are not identified.

One of the limitations of the ES database is that it does not cover all countries (about 60 less than the Doing Business for the years in consideration). Therefore, we do not get a worldwide dataset. Another limitation is that the ES does not do a survey in each country every year, the way the Doing Business is conducted. This fact requires using a technique to fill out missing data points for the missing years from the ES database.

Databases based on survey studies may have incomplete data points. Such missing information raises uncertainty associated with data aggregation and negatively affects the possibility of obtaining proper conclusions. Several techniques are suggested in the literature to estimate incomplete data points. In this paper the data imputation technique of expectation maximization is exploited (Dempster, Laird, and Rubin, 1977; Anderson, Basilevsky, and Hum, 1983; Rubin, 1987; Ruud, 1991; and Honaker and King, 2010). This technique estimates missing data points with the help of a predictive model that incorporates the available information, and any prior information on the data, as well as relationships between variables included in the process. The imputation technique is a two-stage iterative method. In the first stage, called the expectation stage, a log-likelihood function for missing data points is formed and their expectations are taken. In the second stage, which is named as the maximization stage, the expected log-likelihood from the first stage is maximized. Before the imputation is applied, all variables used in the process are standardized to enhance the distributional features of the series. If there are any negative numbers in the series, a constant number is added to data points to guarantee that the imputation of negative values can be realized.

The data imputation technique of expectation maximization requires including different related variables as predictors of series that needs to be completed. In this paper, because the tax corruption ratio is the variable with missing data points, the candidates of predictors must 10
Policy Research Working Paper 5033

# **Abstract**

Satisfaction surveys offer a potentially convenient and cost-effective means for measuring the quality of services. However, concerns about subjectivity and selection bias impede greater use of satisfaction data. This paper analyzes satisfaction data about health and educational services from the 2006 second round of the Governance and Decentralization Survey in Indonesia to assess whether satisfaction data can serve as reliable indicators of quality, despite dubiously high levels of reported satisfaction. The authors use an expectation disconfirmation model that posits that a user’s satisfaction with a facility improves with the (positive) difference between the actual quality of the facility and the household’s expected standard for quality, which is influenced by its socioeconomic characteristics. The findings show that, after taking into account the expectations of households, reported satisfaction does vary significantly with objective indicators of quality. The analysis also checks for possible selection bias affecting the results by using a two-stage selection model. The model yields policy-relevant insights into the aspects of service delivery that most affect satisfaction, highlights differences across rich and poor districts, and shows that once the role of expectations has been factored in, the variation in user satisfaction can be highly informative for policymakers and researchers alike.

This paper—a product of the Poverty Reduction Group, Poverty Reduction and Economic Management Network —is part of a larger effort in the group to analyze poverty and monitor and evaluate the effectiveness of poverty reduction programs. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at eskoufias@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
**Table 8. Under five years old children's anthropometric in refugee areas in 1996 (after) as compared to 1991 (before the influx)**

||**Height for Age (HAZ)**|**Weight for Age**<br>**Weight for Height**<br>**Body Mass Index**|
|---|---|---|
|c.refugee#c.after|0.0315|-0.176<sup>**</sup><br>-0.259<sup>***</sup><br>-0.207<sup>***</sup>|
||(0.0861)|(0.0685)<br>(0.0647)<br>(0.0609)|
|_N_|11232|11515<br>11176<br>11188|
|adj._R_<sup>2</sup>|0.042|0.015<br>0.017<br>0.026|
||**Stunting**|**Wasting**<br>**Underweight**|
|c.refugee#c.after|-0.0118|0.0443<sup>**</sup><br>0.0552<sup>***</sup>|
||(0.0193)|(0.0163)<br>(0.0197)|
|_N_|11232|11176<br>11515|
|adj._R_<sup>2</sup>|0.035|0.008<br>0.006|
||**Under five mortality**|**Mortality (1 month)**<br>**Mortality (12 months)**<br>**Mortality (24 months)**|
|c.refugee#c.after|-0.0133|-0.000409<br>-0.0202<sup>**</sup><br>-0.0162|
||(0.0153)|(0.00757)<br>(0.00818)<br>(0.0145)|
|_N_|13879|13879<br>13879<br>13879|
|adj._R_<sup>2</sup>|0.006|-0.000<br>0.002<br>0.005|
||**Had diarrhe**|**a recently**<br>**Had fever in the last two weeks**|
|c.refugee#c.after|0.27|2<sup>***</sup><br>0.0965<sup>*</sup>|
||(0.03|23)<br>(0.0506)|
|_N_|119|57<br>11912|
|adj._R_<sup>2</sup><br>**Source:** 2015/2016 Tanzania D<br>of the child but also child year o|0.0<br>HS. Standard errors in parentheses. Standar<br>f birth fixed effects. * p < 0.10, ** p < 0.05|36<br>0.019<br>d errors are clustered at the region level., the regression controls for the gender of the head, the birth order and gender<br>, *** p < 0.01. *After= in 1996 after refugees’ arrival.|

34
The proportion of the interview month which lies within the Ramadan period which is defined by the Islamic lunar calendar is also controlled for. Ramadan advances through the calendar underlying the LSMS‐ISA data with the consequence that lunar seasonality may confound the solar seasonality reflected in the crop year and food prices. Many urban households return to their villages at Christmas and it is plausible that this results in higher caloric intake at that time. This departure is controlled for by including a dummy variable which takes the value one if the household was interviewed in December. The seasonality observed in food consumption is reported net of these confounding effects. Caloric intake is obtained by converting reported food expenditures or food quantities from the expenditure module into calories. Food expenditures and intake are reported at the household level for the past 7 days.

Columns 1 and 2 in Table 3 report the pooled OLS estimates of equations (8) and (9) respectively (for comparison). The pooled OLS estimates indicate significant seasonality. The trigonometric specification is preferred over the dummy variable approach (lower BIC). Figure 3 charts the dummy variable (solid line) and trigonometric (broken line) seasonal factors from pooled OLS. The dummy estimates show caloric intake to be low from November to January and high from March to June with an estimated seasonal gap of 10.6 percent. The estimated trigonometric factors are (inevitably) much smoother. Relative to the dummy estimates, they give a lower estimate the depth of the November and January troughs but also the extent of the March to May recovery. The vertical bars in Figure 3 show the 95% confidence intervals for the dummy variable seasonal factors. November is the only month in which this confidence interval excludes zero. Although the trigonometric factors are generally much lower than the dummy variable estimates, there is only one month (again November) in which the trigonometric estimate falls outside the 95% dummy variable confidence intervals.

The FE seasonality estimates are reported in columns 3 and 4 of Table 3. The dummy variable specification does not show significant seasonality,<sup>19</sup> unlike the more parsimonious

> 19 The choice between pooled OLS and the FE estimator involves balancing possible inconsistency arising from unmodeled household heterogeneity and loss of information in the FE estimation procedure. The information loss results in two ways. First, the FE transformation results in a loss of approximately one third of the observations through differencing or by taking deviations about household means. Second, the data are only informative about seasonal effects for those household who were interviewed in different months in at least one of the three waves.

18
Tanzania, Uganda, and Zambia, national level semi-autonomous revenue authorities are in charge of collecting a limited number of taxes. The Ugandan Revenue Authority (URA), for example, collects all major indirect taxes and some direct taxes (income tax, but not property or social security tax). Even with the presence of national revenue authorities, there is often little or no coordination in setting and collecting taxes between the various levels of government in Africa (Fjeldstad and Rakner, 2003, vi).

The Afrobarometer asks respondents whether they have paid five different types of taxes and fees within the year preceding the survey: fees for a government service, such as education or health care; licence fees to local government, such as a bicycle, cart, or market stall; property rates or taxes; public utility fees; and, income taxes. The majority of respondents, 68 percent, have experience with paying a fee for a government service. 47 percent of respondents have had experience with paying a public utility fee. One-quarter of respondents have experience with paying a license fee to a local government. 24 percent of respondents have experience with paying a property rate or tax. Only 22 percent of respondents have had experience with paying an income tax.

Table 2 shows a large amount of variation between countries in the percentage of respondents who report having paid a tax or fee to government. For example, 83.7 percent of respondents from Senegal have experience with paying a fee for a government service compared to 38.4 percent of respondents from Mali. Forty-two percent of respondents from Ghana have experience paying a license fee to their local government compared to 3.5 percent of respondents from Mozambique. Although the percentage of respondents who have experience paying an income tax is low, there is a substantial difference between Liberia and Mali. Forty percent of respondents from Liberia have experience paying an income tax compared to 10.3 percent of respondents from Mali. I control for respondents’ experience with paying an indirect tax, an income tax, and a direct tax, property rates or taxes.<sup>6</sup>

# **6.2.3 Government**

Quasi-voluntary compliance is most likely among those who believe that a government is trying to meet its end of the fiscal contract by delivering

> 6I also included controls for experiences with paying fees for licenses, government services and utilities. Because these variables were not significant at the p _<_ 0 _._ 05 level, I left them out of the regression tables.

12
Figure 2. Contributions of country groups to world output and growth <!-- Start of picture text --> A. World output<br><!-- End of picture text --> <!-- Start of picture text --> A. World output B. World output growth<br>Percent Advanced economies EMDEs Percent Advanced economies EMDEs<br>100 100<br>80 80<br>60 60<br>40 40<br>20 20<br>0 0<br>1950s 1960s 1970s 1980s 1990s 2000s 2010s 1950s 1960s 1970s 1980s 1990s 2000s 2010s<br>Sources: Feenstra, Inklaar, and Timmer (2015); Kose and Terrones (2015); World Bank.<br>Note: Each bar in Panel A shows the average distribution of world output among country groups in the decade<br>indicated (computed using market exchange rates). Each bar in Panel B corresponds to the average of each country<br>group’s contribution to growth in world GDP in the decade indicated. The 2010s period refers to 2010-19, which<br>includes estimates for 2019.<br><!-- End of picture text --> 31
# **5.5  Estimation results**

We obtained maximum likelihood estimates for the parameters using the Frontier 4.1 program (Coelli, 1996), and ran several likelihood ratio (LR) tests on the functional form, the stochastic specification and inefficiency effects (Table 5).<sup>7</sup> We report full results in Table 6. In row 1 of Table 5, we test the null hypothesis on the joint translog restrictions implied by the Cobb-Douglas specification. We find that the restrictions cannot be rejected at a very high confidence level, so we adopt the Cobb-Douglas form because its constant-elasticity results are more easily interpreted.

**Table 5. Tests of Hypotheses**

|**Null hypothesis**|**Restrictions**|**Log-Likelih**<br>**(Ho)**|**ood**<br>**(H1) **|λ|**# restr.**|**Critical**<br>**value**|**Outcome**|
|---|---|---|---|---|---|---|---|
|**Functional form**||||||||
|Cobb-Douglas|β_nk =_0 ∀k,∀n|-94.274|-54.790|78.970|36|47.212|Reject Ho at 10%|
|**Mean function (OLS)**||||||||
|Cobb-Douglas|γ_=_δo_=_δ1_= ... =_δ9 = 0|-113.525|-94.274|38.501|12|25.549|Reject Ho at 1%|
|**Accounting for inefficiency effects**||||||||
|Cobb-Douglas|δ1_= ... =_δ9 = 0|-113.525|-94.274|38.502|11|24.725|Reject Ho at 1%|

Row 2 tests the estimated stochastic frontier against the mean function estimated by ordinary least squares: Under _H_ o the error term _u_ is assumed to be non-stochastic and equal to zero. Failure to reject _H_ o implies that deviations from the frontier are purely random, and that variables chosen to model inefficiency effects can enter directly into the production function for estimation by ordinary least squares. Our LR test rejects _H_ o with high confidence, implying that deviations from the frontier are systematic in Bangladeshi farming.<sup>8</sup>

> 7 The likelihood ratio test is λ = -2[ln _LF_ ( _H_ o) – ln _LF_ ( _H_ 1)], where _LF_ denotes the likelihood function, _H_ o the null hypothesis and _H_ 1 the alternative hypothesis.  λ follows a chi-squared distribution with the number of degrees of freedom equal to the number of restrictions.  Estimation results report the 2 2 2 2 2 2 parameters σ<sup>2</sup> = σu + σv and γ = σu /( σu + σv ), where σ corresponds to the variance of the overall model, and γ is the share of inefficiency variance to overall model variance.  Since γ is the ratio of two variances, and is therefore always positive, the test statistic follows a mixed chi-square distribution, with the critical values to be found in Kodde and Palme (1996).

- 8 This result can be confirmed via the estimated value of γ (0.17 in Table 6, where γ = 1 implies no random noise) and the calculated variance-ratio parameter γ* (0.07).  This implies that only 7% of the difference between observed and frontier output is due to differences in farmer efficiency.  Statistical significance for γ implies that distributional variation in _u_ can be systematically explained by the regressors in the inefficiency model.  Although γ is not significant, the likelihood ratio test above suggested a mild effect.

14