rafmacalaba/datause-encoder-data huggingface dataset:
Girls’ enrollment in public schools Unadjusted difference between treatment and comparison districts <!-- Start of picture text --> ie)<br>Ww 7 at os<br>2 +t ee ee ee ge<br>[yn] TM<br>O N a SS SS cs es eS<br>oOo ae<br>oT peters eee pecewecorecceess=<br>—_ aaa aaa ae<br>0 eee Te<br>= oc eassnenanecnecesnenennensenenens<br>DO 4 nnn nnnrrr<br>£<br>Ot<br>c<br>Lu<br>N<br>2003 2004 2005 2006 2007 2008<br>Year<br>--------- Lower bound ame Comparison District<br>oe Treatment District <----------- Upper Bound<br><!-- End of picture text --> Source: Author's calculations using EMIS School Census Data
|**Table 2: Time U**|**se, Maternal education and**<br>(1)|**Learning (minutes/d**<br>(2)|**ay)**<br>(3)|
|---|---|---|---|
||Mother Not Educated|Mother Educated|Difference in Means|
||(N=1096)|(N=341)||
|**Panel A: Maternal Time Use**||||
|Rest|608.69|575.5|33.19|
||(5.77)|(7.4)|(2.98)|
|Housework|560.46|596.2|-35.75|
||(7.49)|(10.62)|(-2.44)|
|Paid-Work|44.28|29.08|15.20|
||(4.23)|(5.38)|(1.86)|
|Children’s General Needs|93.16|98.67|-5.50|
||(4.29)|(7.54)|(-0.63)|
|Children’s Educational Needs|0.3|20.32|-20.02|
||(0.17)|(2.29)|(-15.29)|
|Other|137.82|124.37|13.45|
||(5.84)|(6.98)|(1.20)|
|Mother’s Time outside the house|<br>113.40|<br>54.10|<br>59.29|
||( 6.25)|(5.02)|(6.14)|
|Mother’s time inside the house|<br>475.71|<br>563.15|<br>-87.44|
||(7.79)|(14.05)|(-5.47)|
|**Panel B: Child Time Use**||||
||Child Not Enrolled|Child Enrolled|Difference in Means|
||(N=1026)|(N=3305)||
|Rest|650.92|598.83|52.09|
||(4.89)|(2.10)|(11.20)|
|School|<br>0|<br>356.27||
||(0)|(1.36)||
|Housework|197.31|24.14|173.17|
||(8.23)|(1.17)|(34.37)|
|Paid Work|62.46|1.57|60.88|
||(5.68)|(0.35)|(18.87)|
|Educational Activities|<br>8.74|<br>151|<br>-142.25|
||(1.14)|(1.25)|(-61.00)|
|Play|<br>278.48|<br>154.63|<br>123.85|
||(8.41)|(2.05)|(20.80)|
|Other|<br>226.30|<br>153.57|<br>72.73|
||(7.73)|(2.60)|(11.45)|
|**Panel C: Maternal Education and C**|<br>**hild Test Scores**|||
||Mother Not Educated|Mother Educated|Difference in Means|
||(N=483)|(N=193)||
|English|-0.18|0.25|-0.43|
||(0.04)|(0.07)|(0.08)|
|Urdu|<br>-0.14|<br>0.22|<br>-0.35|
||(0.05)|(0.06)|(-0.08)|
|Math|<br>-0.13|<br>0.12|<br>-0.25|
||(0.05)|(0.07)|(0.09)|

_Notes:_ Mean and Standard errors in parentheses, t-stats in parentheses for difference in means. Time allocations are based on a flexible time-use surveys where respondents tell the surveyors what they did in a typical day during the last week. Test scores are from a school-based test administered to all children in Grade 3 in all schools in the village. The results reported are Item-Response scaled scores where the distribution was standardized with respect to the universe of test-takers. Numbers reflect standard-deviations from the mean.

31
that it fails to reflect the possibility that a particular population group may exhibit considerable within-group inequality but at the same time represent an entirely distinct partition of the overall income distribution. If such a non-overlapping partitioning of the income distribution occurs this would seem to constitute evidence of some kind of social exclusion occurring. And yet conventional decomposition techniques are not designed to capture this.

A recent paper by Elbers, et al (2008) proposes a simple adaptation to the standard inequality decomposition calculation. This yields a new statistic which has been designated here as a _partitioning index_ and which sets out to capture exactly to what extent a given breakdown of the population into groups results in a partitioning of the income distribution into non-overlapping intervals. One important advantage that this approach for the study of ethnicity over previous inequality decomposition methods is that it allows for valid comparisons to be made even if group categorizations differ. This makes it useful to compare the level of between-group inequality across different populations, and also makes it much more amenable to analyses that are informed by instrumentalist and constructivist approaches to ethnicity.

The paper demonstrates that when this index is calculated for Palanpur there is evidence that during the 26-year period covered by the first four surveys of the village, one group of village households – the Jatabs – has failed to share in the overall steady, albeit slow, rise in prosperity experienced by the bulk of the population. This statistical evidence matches rather well the findings from the much more in-depth analysis of economic and social development in Palanpur that was reported in Bliss and Stern (1982) and Lanjouw and Stern (1998), and that was based on detailed fieldwork in the village. The fact that a simple statistical analysis, based only on income data and an indicator of social group membership, 27
|Cell phone (%)|24.3|55.8|40.8|58.2|71.2|
|---|---|---|---|---|---|
|Bicycle (%)|40.1|34.1|47.8|50.7|46.1|
|Motorbike (%)|3.1|3.9|2.4|4.8|6.2|
|<br>**Access to basic services**<br>**Education**||||||
|Primary net enrolment ratio|84|78|83|80|76|
||15|29|23|28|29|
|Secondary net enrolment ratio (forms I–IV)||||||
|**Access to water, Sanitation and Electricity**<br>||||||
|Proportion of households using protected water source<br>in the rainy season|‐|69|42|42|45|
|Proportion of households using protected water source<br>in the dry season*|52|61|43|49|52|
|Proportion of households with any toilet facility|93|88**|90|87|87|
|Households with electricit(%)|10|15.7|11.5|13.0|18.5|
|y<br>**Economic Activities, Land and Farm Implements**||||||
|Percentage of adult persons (15+ years) with<br>household farming as their main occupation|‐|74||74|74|

* Previous HBS did not distinguish between dry and rainy seasons.

** HBS 2011/12 introduced the use of a show card to demonstrate different types of toilets. This may contribute to the different results.

# **D. Aggregate consumption**

Table 5 compares the consumption aggregates per adult equivalent per month between NPS and HBS, as well as their distribution by quintile. All values are presented in nominal terms, real terms adjusted for the spatial and seasonal differences in the cost of living, and in real terms adjusted for the temporal price variations across survey years. <u>Nominal values: are the weighted averages of</u> consumption per adult equivalent in nominal terms. <u>Real consumption values: are the weighted</u> averages of real consumption per adult equivalent, where real consumption is estimated by adjusting nominal consumption with spatial deflators as well as temporal deflators within each survey year. The deflation uses the Fisher price index calculated from unit values of the survey data to adjust for cost of living differences between stratum (Dar es Salaam, other urban and rural) and quarters (a period of three consecutive months within each survey round). Note, while the adjustment involves a temporal element within the year, henceforth for conciseness, the deflator will be referred to as a “spatial price deflator”. Real consumption, temporally adjusted (survey price indicators): here real consumption is deflated by a temporal price deflator to adjust for inter‐year (between survey rounds) price variation. The temporal adjustment allows for comparisons of real values between survey years, where the real consumption values (above) are adjusted by the inter‐year Fisher price 21
**Table 11: Ratio of students to teaching positions by gender of school**

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
||Fil|led|Sanct|ioned|
|**School fixed effects regressions**|Girls|Boys|Girls|Boys|
|2004|3.758***|-0.261|6.412***|0.0348|
||(1.012)|(0.640)|(0.782)|(0.141)|
|2005|3.909***|0.225|7.893***|0.122|
||(1.026)|(0.648)|(0.801)|(0.142)|
|2006|3.680***|2.220***|2.829***|0.0134|
||(0.258)|(0.280)|(0.201)|(0.0531)|
|2007|0.894***|-0.326|0.577*|0.0517|
||(0.238)|(0.204)|(0.259)|(0.0575)|
|2008|1.188***|-0.329|-0.0789|-0.00941|
||(0.251)|(0.209)|(0.190)|(0.0553)|
|Stipend District * 2004|1.383***|1.432***|1.370***|0.0890|
||(0.266)|(0.215)|(0.270)|(0.0526)|
|Stipend District * 2005|3.030***|2.169***|2.390***|0.0279|
||(0.314)|(0.259)|(0.293)|(0.0548)|
|Stipend District * 2006|4.421***|2.537***|3.945***|0.0981|
||(0.398)|(0.345)|(0.314)|(0.0543)|
|Stipend District * 2007|6.521***|4.045***|4.886***|0.209**|
||(0.355)|(0.277)|(0.287)|(0.0691)|
|Stipend District * 2008|6.912***|3.824***|5.121***|0.0577|
||(0.389)|(0.281)|(0.240)|(0.0538)|
|Constant|22.70***|26.14***|16.29***|0.153|
||(1.007)|(0.630)|(0.766)|(0.138)|
|Adjusted R-squared|0.063|0.033|0.094|0.002|
|R-square within|0.0636|0.0334|0.0943|0.00281|
|Observations|32,545|33,017|32,577|33,024|
|Number of Schools|5,718|5,683|5,719|5,683|
|Controls included|Yes|Yes|Yes|Yes|
|MultanandKhanewal Excluded|Yes|Yes|Yes|Yes|

* p<0.05 ** p<0.01 *** p<0.001. Standard errors in parentheses are robust to heteroskedasticity and clustered at the level of the school. Sample excludes mosque and primary schools and two districts whose treatment status was interchanged. Estimates from a fixed effects specification at the school level. Controls are a series of indicators for how many of the following three facilities a school has: toilets, drinking water and electricity. The excluded group is no facilities. The excluded year is the baseline year 2003.

49
# **Table A21:  Latinobarometro (LBO)**

**Data Provider** Latinobarometro **Description** Nongovernmental organization based in Santiago, Chile **Website** www.latinobarometro.org **Data Source** Latinobarometro surveys **Type** Survey **Respondents** Households **Frequency** Annual since 1995 **Coverage** Sample of Latin American countries **Public Access** Some country-level aggregates are freely available, rest of dataset is commercially available **Description** Latinobarometro administers a common questionnaire to households in Latin America with questions on areas such as Economy and International Trade, Integration and Regional Trading Blocks, Democracy, Politics and Institutions, Social Policies, Civic Culture, Social Capital and Social Fraud, the Environment, and Current Issues.

|**Voice and Accountability**|2006|2005|2004|2003|2002|2000|1998|1996|
|---|---|---|---|---|---|---|---|---|
|Satisfaction with democracy|X|X|X|X|X|..|X|X|
|Trust in Parliament|X|X|X|X|..|..|X|..|
|**Political Stability**|||||||||
|NA|..|..|..|..|..|..|..|..|
|**Government Effectiveness**|||||||||
|Trust in Government|X|X|X|..|X|..|..|..|
|**Regulatory Quality**|||||||||
|NA|..|..|..|..|..|..|..|..|
|**Rule of Law**|||||||||
|Trust in Judiciary|X|X|X|X|X|X|X|X|
|Trust in Police|X|X|X|..|..|X|X|X|
|Have you been a victim of crime?|X|X|X|X|X|..|..|..|
|**Control of Corruption**|||||||||
|Frequency of corruption|X|X|X|X|X|X|X|..|
|**Country Coverage**|**18**|**18**|**18**|**17**|**17**|**17**|**17**|**17**|
|**Year of publication**|**2006**|**2005**|**2004**|**2003**|**2002**|**2000**|**1998**|**1996**|

59
25 Table 5: Representadtve land poor and land rich countries as classified by arable land per rural person (in 1980).

|Land Poor (mor<br>density)|e than twice the average|Land rich (less t<br>density)|Wan half the average|
|---|---|---|---|
|Country|Rural population per<br>square arable<br>kdlometer|Country|Rural population<br>per square arable<br>kilometer|
|Sri Lanka<br>lEgypt|1324<br>1004|Botswana<br>Zambia|_56_<br>66|
|Liberia|969|Brazil|67|
|Malaysia|904|Senegal|69|
|Bangladesh|846|Venezuela|81|
|Indonesia|810|Syria|87|
|Korea|796|Tunisia|_95_|
|Kenya|777|Bolivia|95|
|HXaiti|752|Turkey|98|

Notes: Average density is 376 wral people per square amable kilometer.

How do the regressions with the sample divided by "land poor' and "land rich" behave? _Not_ very well. In table 6 the estimated effect is more severe in the land rich han the land poor counties. In the multivariate regressions the effect was even positive for the land poor (.21) while negative (-.10) in the land rich (although again, both are isignificant). Since creating this empmical division into land rich and land poor was a major part of the research and franldy, our best idea, this lack in intfitive results is very disappointing.
Table 3.4: TANZANIA, Representativity of sampling frame, complete case and sample with replacement

||Samp<br>|lingframe<sup>(a)</sup><br>|Com<br>|plete case<sup>(b)</sup><br>|With r<br>|eplacement<sup>(c)</sup><br>|
|---|---|---|---|---|---|---|
||# Obs|Perc over total|# Obs|Perc over total|# Obs|Perc over total|
|||**a) by Industr**<br>|**y**<br>||||
|Food & beverages|243|29.3|108|33.2|170|30.3|
|Textiles & apparel|93|11.2|29|8.9|58|10.3|
|Chemicals,rubber& plastics|102|12.3|42|12.9|69|12.3|
|Paper, edition& publishing|75|9.1|33|10.2|55|9.8|
|Machinery & equipment/Metallic products|87|10.5|26|8|65|11.6|
|Wood &furniture|195|23.6|68|20.9|117|20.9|
|Non-metallic products|33|4|19|5.8|27|4.8|
|Total|828|100|325|100|561|100|
|||**b) by size**|||||
|Small|381|46|144|44.3|271|48.3|
|Medium|320|38.6|125|38.5|207|36.9|
|Large|127|15.3|56|17.2|83|14.8|
|Total|828|100|325|100|561|100|

Notes: Same as Table 3.1.

# **3. Imputation of missing values:  The** **_ICA method_**

Rubin (1976) rigorously defined the assumptions that might plausibly be made about missing data mechanisms (MDM).<sup>18</sup> When the MDM is ignorable, the objective of the replacement methods is not to augment the sample size, but to preserve the sample representativity, to gain efficiency in the estimation and to retrieve for the analysis a large number of very expensive interviews. The alternative to these methods is the _listwise deletion_ , which is not a panacea even when the MDM is ignorable. Operating with the complete case is only acceptable if incomplete cases attributable to missing data comprise a small percentage, say 5% or less, of the number of total cases (Schafer, 1997), and when the complete case preserves the representativeness of the original sampling frame. In addition, in models with a large number of regressors, missing data problems may encourage analysts to leave out of the regression some explanatory variables with a high proportion of missing values. As Cameron and Trivedi (2005) point out, this practice may be misleading as it leads to an omitted variables problem, which is more serious than the missing data problem _per se_ .

To see how the various mechanisms applied to deal with missing data perform, it is useful to depart from a population model of interest. A repeated task that applied researchers carry out in the context of IC data is the estimation of production functions to perform a variety of productivity analyses. Concretely, let us suppose the extended production function as in Escribano and Guasch (2005 and 2008). The population model is given by   log _Yit_   0   _L_ log _Lit_   _M_ log _M it_   _K_ log _K it_   _IC ICi_   _D Dit_  _uit_ , (1)

> 18 Data on Y variable is said to be missing completely at random ( _MCAR_ ) if _P(Y missing | Y, X)= P(Y missing)_ , where X is a matrix of other variables on data. Data is missing at random ( _MAR_ ) if _P(Y missing | Y, X)= P(Y missing | X)_ . Missing data is nonignorable if _P(Y missing | Y, X)= P(Y missing | X, Y)._

16
<!-- Start of picture text --> 35%<br>1987 1993 1999 2004 2009 2011<br>30%<br>25%<br>20%<br>15%<br>10%<br>5%<br>0%<br>Illiterate Lit < prim Primary Middle Secondary Graduate<br><!-- End of picture text --> Note: Married women age 25-54. Education is the highest level completed, see appendix Table A1.2 for definitions. _Source_ : NSS Employment and Unemployment Survey.

In line with India’s high growth rates, earnings data form the NSS surveys show that real wages roughly doubled between 1987 and 2011 (see Fig. A1.1 in the appendix). In absolute terms, real wages increased almost equally for men and women, but the ratio of male to female average weekly earnings declined from 1.6 in 1987 to 1.3 in 2011. Given the very high participation rates of married men in this age group and their higher average wages, one can safely assume that most women in urban India are secondary earners. Rising earnings of men most likely had a strong negative impact on female labor force participation. But with women’s wages increasing more than men’s, positive substitution effects could at least partly offset the negative impact of men’s income.

However, patterns in the data suggest there is no close link between women’s wages and their labor force participation. Figure 3 shows women’s real wages by education level. Returns to secondary and graduate education are high and rising, especially returns to graduate education. Real wages at lower levels of schooling, on the other hand, show a strong convergence due to relatively fast wage growth of illiterate women. If anything, own wage effects would thus have led to increases in labor force participation rates at both the ends of the educational distribution—for illiterate women on the one side and highly educated women on the other side. Going back to 12
country PFM systems. If donors’ decisions to use country PFM systems are influenced by the PD-endorsed measure of PFM Quality, then we should observe a stronger relationship in a sample limited to recipients with publicly-available CPIA ratings than in a sample that includes a mixture of public and non-public ratings.<sup>10</sup> Results from equations 1.1 and 1.2 are strongly consistent with this prediction.

The explanatory power of these regressions (R<sup>2</sup> = .05 in 1.1 and .08 in 1.2) suggests that donors’ decisions are influenced by many other factors, potentially including other recipient characteristics. The OECD (2011: 50, 52) report and Ellmers (2011) both emphasize the large variation in use of country systems across recipients, for a given level of PFM Quality, that is reflected in these low R<sup>2</sup> values. Nevertheless, this variation should not obscure the reality of a large, positive and significant relationship between the two variables.

Donor-specific factors such as tolerance for risk undoubtedly contribute to the large variation in use of systems at a given level of quality (Knack, 2013), but they cannot account for the bulk of variation in the data. A full set of donor-year dummy variables (in the absence of any other regressors) statistically explains only 20% of the variation in use of country PFM systems, compared to 23% for a full set of recipient-year dummies. Recipient characteristics thus appear to matter somewhat more than donor characteristics.<sup>11</sup> Henceforth, we control for donor-year fixed effects and for several additional recipient-level variables, in testing the robustness of our initial results from equations 1.1 and 1.2. Estimates are therefore informed only by cross-

> 10 We cannot entirely rule out the possibility that some donor agency officials may have obtained the confidential ratings for non-IDA countries through unofficial channels.

> 11 In some instances, particularly where line ministries have a larger role in managing aid and interacting directly with donors, recipient country officials may actually prefer using donor systems over their own central government systems (OECD, 2011a: 52).

14
<!-- Start of picture text --> Does your househctd grow:<br>Soya 76X 76X 50<br>Corn 93X 86S 50<br>Coffee 45X 52X 50<br>Does your houschold have a:<br>Radio a 5S 50<br>Sewing machine 10% 10X 50<br>Bicycle 34X 19X 50<br><!-- End of picture text --> The insarance premium for 1989 was set at 125 Z ($0.35) per person. In theory, if a family wishes to enroll in the plan, all members of the family must be enrolled, although this practice is not always followed (see discussion, section 2). The size and composition of the family are determined from the family health card, which is filled out for every family in the zone during the health zone census completed every two years. All adults wishing to enroll in the plan face the same premium. Table A.3 shows premiums for 1986-1989: Table A.3 **Amual** Premium **for Bwamada** Insurance Plin, Per Person '

|... ........<br>|.......................................<br>**NOMINA**<br>|......<br>**L**<br>|1986<br> <br>|1986<br> <br>|
|---|---|---|---|---|
|YEAR|ZAIRES|DOLLAR<br>I|<br> <br>ZAIRES|<br>DOLLARS|
|......................|..............................||||
|1986|20|**s0.33**|20|$0.33<br>|
|1987|30|**S0.23**|22.9|$0.38|
|1988|so|50.27|24.6|50.40|
|**1989**|125|S0.35|28.8|50.47|

'Exchange rates **and** infiation rates **are** found **in** Anex 111.

The insurance plan employs co-payments but no deductibles. In 1989 the co-payment was established at 20 percent of the standard flat-rate charge for 13 of the 14 covered charge categories. (Payor A, the most common payment category, is for non-insured zone residents whose health care is not paid through hospital billing to an employer, as described later in this section.) The exception is obstetrical care to women who were enrolled in prenatal care; in these cases, no co-payment is charged. The 20 percent co-payment applies to treatment of chronic illness cases at the health center level as well.

During 1988, the health zone experimented with a system of variable rates for co-payment, depending on the distance from the hospital. Plan members who lived furthest (over 50 km) away from the hospital of the patient were charged only a 5% co-payment, compared to 10 percent for those living 25 to 50 km and 20 percent for those who lived closest (under 25 km). The plan asked nurses to make a special effort at enrollment especially in outlying areas.

When the hospital staff reviewed the insurance plan at the end of 1988, they thought that the system had not increased access for members living far from the hospital. It was difficult to control, as well, leading the zone to abandon it in 1989. According to Figure A.l, however, which shows the relationships between insurance status and distance, the 1988 program changes (either the variable co-payment rates, the stepped-up awareness campaign, or both) may have influenced the decision of distant residents to enroll in the program. Enrollment declined less with distance from Bwamanda center in 1988 than in the three other years of the insurance plan. There is a clear decrease of the membership rate the further the villages are away from Bwamanda for 1986, 1987 and 1989. Regression analysis confirmed that the slope 15
Table 4: Willingness to Pay (in Terms of Estimated Impact)

||Pooled<br>(1)|Policymaker<br>(2)|Policy<br>Practitioner<br>(3)|
|---|---|---|---|
|Quasi-Experimental|-1.55|0.66|-2.89|
||(1.61)|(3.10)|(1.84)|
|Experimental|-4.83***|-5.26|-4.70**|
||(1.84)|(3.42)|(2.18)|
|Diferent country, same region|-2.07|-1.03|-2.88*|
||(1.57)|(2.95)|(1.74)|
|Same country|-6.70***|-5.77*|-7.55***|
||(1.73)|(2.97)|(2.09)|
|Recommended by local expert|-3.11**|-4.99*|-2.01*|
||(1.23)|(2.84)|(1.12)|
|Small C.I.|-3.67***|-1.21|-5.13***|
||(1.24)|(2.21)|(1.49)|
|Observations|818|407|411|

This table reports the results of conditional logit regressions in terms of the implied willingness-to-pay for programs with certain attributes. “Impact” is implicitly included in each estimate. For example, policymakers would only be willing to accept a program that was not recommended over one that was if the program that was not recommended had a 5.0 percentage point higher estimated impact on enrollment rates. The number of observations represents the total number of choices made across individuals. Standard errors are provided in parentheses. Standard errors are provided in parentheses. * pă0.10, ** pă0.05, *** pă0.01.

12