rafmacalaba/datause-encoder-data huggingface dataset:
89 percent. The intensity of coverage expanded more slowly - 22 percent for all India and 30 percent for Scheduled Tribes<sup>15</sup> . The improvement was slower in rural areas where most of the tribal groups are concentrated. This expanded coverage of immunization is perhaps responsible for the fact that tribal children are at similar risk as other babies of getting sick from certain causes.

**Table 6. While breadth of immunization coverage (% children receiving** **_any_ basic vaccination) improved for ST children, intensity of coverage (% children receiving** **_all_ basic vaccinations) expanded more** **<u>slowly</u>**

||Urb|an||R|ural||Ov|erall|||
|---|---|---|---|---|---|---|---|---|---|---|
|||ST|Other|Total|ST|Other|Total|ST|Other|Total|
|All basic** vaccin|ations, % (Inte|nsity)|||||||||
||1992-93|36|51|51|24|32|31|25|37|35|
||1998-99|43|57|57|22|39|37|25|43|41|
||2005-06|52|58|58|30|40|39|32|45|44|
|_Change 1993-2006, %_||_45_|_13_|_13_|_27_|_25_|_25_|_30_|_22_|_23_|
|Any of the basic** vacci|nations, % (Br|eadth)|||||||||
||1992-93|79|84|84|56|67|66|58|71|70|
||1998-99|85|95|95|75|86|85|76|88|87|
||2005-06|94|97|97|89|95|94|89|95|95|
|_Change 1993-2006, %_||_19_|_15_|_15_|_57_|_41_|_43_|_53_|_34_|_35_|

# Source: NFHS

* Children 12 to 23 months old born to ever married women, 15 to 49 years old.

** Basic vaccinations include three rounds of Polio 1-3 and DPT 1-3, BCG, Measles.

**Mothers of tribal children are much less likely to get health care and perhaps this affects the care of their children.** While improvements for tribal women occurred at a faster pace than those for other women, the low base from which the former started has driven their faster progress.  Moreover, gaps between tribal and other women on a range of indicators related to access to maternal health care continue to be wide (figure5).  For instance, the proportion of tribal women going for ante-natal visits or using contraception remained lower than the population average or the average for women belonging to other social groups. Overall, almost 60 percent of all women and 80 percent of tribal women give birth at home.

The comparisons with SC and OBC women are particularly instructive. Fifty-five percent of tribal women in the 2005 NFHS reported having ever used contraception compared to 63 percent of SCs and 62 percent of OBCs and the all-India figure of 65 percent (see figure 5). In comparison to SCs and OBCs, a relatively smaller proportion of ST women reported three or more ante-natal visits (40 percent compared to 44 percent for SC women and 48 percent for women from the OBC group). Women belonging to Scheduled Tribes also remained less likely to receive pre-natal care from doctors. Only one-third received such care in 2005 as compared to the population average of 49 percent. Worse, the proportion of ST women to have received such care actually declined marginally from 1998 levels (from 35 percent to 32 percent).

> 15 This is not surprising given that improvements in intensity of coverage are considerably more difficult to bring about, insofar as they are more costly and require a more coordinated immunization policy.

14
any important concerns (see supplemental table S1.1b).<sup>9</sup> The implementation of the text message campaigns was less successful. Because of technical limitations, only 80 percent of the SMS messages that were sent reached their destination. The technical difficulties were especially problematic in the first wave of the study, when approximately 60 percent of the messages were received by the households.<sup>10</sup> We randomly assigned the households found in the baseline to 10 groups (nine treatment and one control), stratifying by street and household presence during the prestudy survey. The treatment groups conformity wealthy, conformity poor, signaling wealthy, signaling poor, authority religious, authority municipal, environmental emphasis, and social emphasis accounted for 8.75 percent of the sample each, whereas the signaling proximate treatment was assigned to 12.5 percent of the sample. We explain each treatment below. Because we will conduct the analysis by combining related treatments (conformity wealthy with conformity poor, signaling wealthy with signaling poor, and environmental emphasis with social emphasis), we randomized such that the signaling proximate treatment would be overrepresented to maximize power. The remaining 17.5 percent of the sample was assigned to the control group, which did not receive a flier or a text message and was canvassed according to PRISMA’s usual procedure. The division of the sample into treatment groups is presented in table 1, panel A.

All fliers included both a generic message about PRISMA’s program—“Do you know that an association of recyclers is starting a recycling program in your area? By recycling, you help both the environment and the informal collectors to get a formal and decent job”—and a treatment message (except for the control group). The text message only contained the treatment message. The treatment messages printed on the fliers and 10
19 Figure 2 Bulgaria: Concentration coefficients of various sources of income Figure 3 Bulgaria: composition of disposable income (in percent) <!-- Start of picture text --> 60 au<br>private<br>50 a030 oN<br>40<br>wages<br>40<br>30<br>a0<br>BO pret Ciéasters<br>10<br>. | 10<br>Re 1988 1989ae1390 1991 1992tigeenets 1993 bar 1988 1989 «1390 1391 1992 1993<br>More unequally distributed income source<br>a higher coefficient. The concentration<br>also shows how much a given source<br>up the overall inequality.<br>Figure 4<br>Percentage of a transfer received by three lowest deciles<br>in Chile, UK, Bulgaria, and Poland<br>60<br>UVEB (Chile)<br>50 ~.<br>Pub. works (Chife}TM,<br>40 an<br>SA(UK}. OS.<br>SA (Pol) a<br>10 ° me<br>0<br>1 Ma q<br>Incave deciles (fran poorest to richest)<br><!-- End of picture text -->

Note: More unequally distributed income source has a higher coefficient. The concentration coefficient also shows how much a given source “pushes” up the overall inequality.

Note: SA=social assistance; UEB=unemployment benefits. Source: Countries’ household budget surveys; Chilean data for 1987; UK 1991; Poland 1993; Bulgaria 1992.
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**Table 1: Country Comparison for Selected Variables**

|**Country**|**No. of Fee types with Easy**<br>**Access**|**GNI per capita in USD**|**Internet Users**<br>**(per 100 persons)**|
|---|---|---|---|
|Equatorial Guinea|0 out of 4|14,540|1.56|
|Bahrain|1 out of 4|23,132|32.91|
|Greece|2 out of 4|25,030|29.98|
|Slovenia|3 out of 4|23,610|53.09|
|Portugal|4 out of 4|21,250|39.75|
|Eritrea|0 out of 4|430|2.51|
|Niger|1 out of 4|360|0.39|
|Mozambique|2 out of 4|470|0.91|
|Sierra Leone|3 out of 4|340|0.24|
|Tanzania|4 out of 4|540|0.97|

_Source: Doing Business 2013 surveys, World Development Indicators_

# **IV.2. What are the commonalities among governments that make information easily accessible?**

Further we would like to explore what factors other than resources might explain differences between countries on our new composite index. It is, for example, conceivable that governments choose to invest in transparency simply in order to be more responsive to the needs of their citizens. This could, for example, be the case in political systems that are more democratic and where political and civil rights such as the freedom of expression are more strongly valued and citizens demand more access to information.

We therefore examine how resources (as proxied by income levels) and different political systems correlate with the transparency efforts of different governments by adding the income variable as a control to correlations between our new RTI and indicators on different forms of government, such as the Democracy and Autocracy Scores of the POLITY IV database and the Freedom House Political Rights and Civil Rights indicators (Table 2). For our analysis we use a Poisson specification because the dependent variable is a non-negative integer. We find that economies that make more information easily accessible tend to be also more democratic (and hence less autocratic), and have stronger political and civil rights guaranteed to the citizens, even after controlling for income per capita.

10
Policy Research Working Paper 5035

# **Abstract**

This paper examines the relationship between the type of senior high school attended by Indonesian youth and their subsequent labor market outcomes. This topic is very timely, given the government’s recent decision to dramatically expand vocational enrollment. The analysis controls for an unusually rich set of predetermined characteristics, and exploits longitudinal data spanning 14 years to separately identify cohort and age effects. There are four main findings. First, students are sorted into different school types largely on the basis of their entering exam score. Public schools attract the highestscoring students, while private vocational schools serve the lowest-scoring students. Second, after controlling for a variety of characteristics, including test scores, male public school graduates earn a substantial premium over their privately schooled counterparts. Third, private vocational school graduates fare at least as well as private general graduates, despite coming from more disadvantaged socioeconomic backgrounds. Finally, the returns to public vocational education have declined sharply for the most recent cohort of men. This raises important concerns about the current expansion of public vocational education, and the relevance of the male vocational curriculum in an increasingly serviceoriented economy.

This paper—a product of the Social Protection and Labor Division, Human Development Network—is part of a larger effort in the network to understand the relationship between training and labor market outcomes. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at dnewhouse@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
yi,c,s = α + βAgentc,s + ∑γsds + δAgei,c,s + εi,c,s (1) where yi,c,s, is a follow-up survey measure of mobile money usage or other outcome of household i, in cluster c and randomization strata s. The variable Agentc,s indicates whether the cluster was randomly selected for the agent rollout and is thus equal to one for the treatment group and equal to zero for the control group. We control for randomization strata dummies ds, as well as for household head age Agei,c,s since table 1 shows that average household head age is slightly lower in the treatment group than in the control group. Standard errors are clustered at the EA cluster level. To deal with multiple hypothesis testing, we calculate sharpened q-values that hold constant the false discovery rate (Anderson 2008).

In heterogenous treatment effect regressions, we examine whether the effect of the agent rollout varies by Airtel priority rating using the following equation yi,c,s = α + β<sup>High</sup> Agentc,s ∗Highc,s + β<sup>Low</sup> Agentc,s ∗Lowc,s + ∑γsds + δAgei,c,s + εi,c,s (2) where Highc,s is a dummy variable equal to one if the EA cluster had a priority rating of three or four and equal to zero otherwise. Similarly, Lowc,s is a dummy variable equal to one if the EA cluster had a priority rating of one or two and equal to zero otherwise.

As described above, we stratified the randomization by priority rating. In clusters with higher priority, the professional services firm spent more time rolling out agents and a larger fraction of these clusters received an agent than clusters with low priority. Appendix Table A1 shows that most household characteristics are similar across high priority and low priority areas. One significant difference is that households in high priority areas are slightly more likely to have heard of mobile money (62 percent vs. 58 percent, respectively). Appendix Table A2 reveals that high priority areas tended to have a greater number of Airtel Money transaction at baseline, although these differences are not statistically significant.

13
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<!-- Start of picture text --> All Votes 2009-2010<br>AprilPn 20710Cala Ap Til 2070Caja<br>Min. Salud Min. Salud<br>Obras Obras<br>One Week One Week<br>Two Weeks Two Weeks<br>One-Six MonthsMonth One-Six MonthsMonth<br>More Six More Six<br>Sin Plazo Sin Plazo<br>Clear Order Glear Order<br>0 1 2 3 i) 1 2 3<br>Hazard Ratio Hazard Ratio<br>N=2556 with 2347 failures. N=2680 with 2491 failures.<br>Prior to 9/2010 2/2010-5/2010<br>April 2010 April 2010<br>_ Gaja _ Caja<br>Min. Salud Min. Salud<br>OneObrasWeek OneObrasWeek<br>Two Weeks Two Weeks<br>One-Six MonthsMonth One-Six MonthsMonth<br>More Six More Six<br>Sin Plazo Sin Plazo<br>Clear Order Glear Order<br>0 1 2 3 0 1 2 3<br>Hazard Ratio Hazard Ratio<br>N=2100 with 1972 failures. N-785 with 743 failures.<br><!-- End of picture text --> Figure 5: Cox Proportional Hazard Models: Panels display estimated hazard ratios with 95% confidence intervals. From the northwest to the southeast, panels show results for increasingly restricted samples, closing the window around the votes taken in the months immediately before and after the press conference.
Annex V-B: Distribution of poverty status by household asset ownership

|||1994||1998|
|---|---|---|---|---|
|Assets Scores|Poor|Non Poor|Poor|Non Poor|
||_(%)_|**(%)**|_(%)_|_(%)_|
|0|63.33|42.41|55.66|34.28|
|1|28.28|34.95|40.66|41.4|
|2|3.04|12.51|3.38|11.55|
|3|.35|4.86|.20|5.06|
|4|0|3.06|.11|2.24|
|5|0|1.76|0|3.39|
|6|0|.45|0|2.08|
|All|100|100|100|100|

Annex VI: Correlation analysis between selected determinants of welfare and poverty across socioeconomic groups _(P(E,Wf,SIG), P(A,wisrG))_

||_Education a_<br>_w_|_nd housebold_<br>_elfare_|_Asset ow_<br>_househo_|_nership and_<br>_ld welfare_|
|---|---|---|---|---|
|_Socioeconomic Groups_|_PS -1994_|_PS -1998_|_Ps -1994_|_PS -1998_|
|_Wage Public_|.792|.8189|.7508|.5883|
||[.111]|[.090]***F|[.0517]*""|[.165]|
|_Wage Private_|.895|.7791|.902|.9096|
||[.0401]'*|[.1204]|[.005]'*|[.0045]-|
|_Commerce_|.8206|.8762|.916|.7932|
||[.088]*-|[.0513]-*|[.0038]*|[.0599]-*|
|_Other Activities_|.7228|.8349|.8586|.9273|
||[.168]|[.0785]***|[.0286]1|[.0026]1|
|_Export Farmers_|.4716|.911|.7918|.8445|
||[.528]|[.0896]**|[.1104]|[.0712]**|
|_Subsistence Farmers_|.957|.3528|.7816|.1734|
||[.0105]11|[.5603]|[.066]**|[.711]|
|_Inactive_|.918|.8044|.899|.9012|
||[.0271|[.4007]|[.00581]|[.0056]|

* Significant at the I percent level.

** Significant at the 5 percent level.

*** Significant at the 10 percent level.

26
Table 2: The distribution of the share of land allocated to coffee in 2011

|Sample|Mean|Median|75%|90%|95%|SD|N|
|---|---|---|---|---|---|---|---|
|All farmers|0.05|0.00|0.00|0.13|0.37|0.16|3466|
|Land share of cofee >5%|0.34|0.25|0.50|0.79|0.93|0.27|492|
|Land share of cofee>10%|0.41|0.32|0.59|0.85|0.94|0.27|389|
|Land share of cofee>25%|0.56|0.51|0.75|0.93|1.00|0.24|244|
|Land share of cofee>33%|0.63|0.60|0.79|0.95|1.00|0.21|190|
|Land share of cofee>50%|0.75|0.75|0.89|1.00|1.00|0.16|125|

Notes: This table provides the distributive statistics of the share of household land allocated to coffee production in the year 2011 for different sub-samples of farmers defined based on the farmers’ intensity of coffee farm.

Table 3: Pass-through of coffee price shock to household consumption

||Total AEC|Food AEC|Non-food AEC|
|---|---|---|---|
||Pa<br>|nel A: Regres<br>|sion results<br>|
|Land share of cofee * LogPrice|0.822***<br>(0.205)|0.834***<br>(0.207)|0.580*<br>(0.300)|
||Panel B: Im<br>between 2|plied efects o<br>011 and 2013|f 50% price decrease<br> for average farmer|
|all sample|-2.06%|-2.09%|-1.45%|
|_≥_5% land share of cofee|-13.49%|-14.18%|-9.84%|
|_≥_10% land share of cofee|-16.85%|-17.1%|-11.89%|
|_≥_25% land share of cofee|-23.02%|-23.35%|-16.24%|
|_≥_33% land share of cofee|-25.89%|-26.27%|-18.27%|
|_≥_50% land share of cofee|-30.83%|-31.28%|-21.75%|
|_N_|9525|9525|9525|
|_R_<sup>2</sup>|0.650|0.608|0.710|

Notes: This table is based on three rounds (2011, 2013, and 2015) of ESS survey. Standard errors are clustered at zone level. All regressions include household and zone-year fixed effects, and log rainfall. EAC stands for adultequivalent consumption. All dependent variables are in log units of monetary value adjusted for variation in living expense across regions. _Land share of coffee_ is the fraction of household land allocated to coffee in 2011. _LogPrice_ is the log of average coffee price over the four quarters before the survey month. * _p <_ 0 _._ 10, ** _p <_ 0 _._ 05, *** _p <_ 0 _._ 01 26
**Table 12. Cross section, Rail, regional dummies interactions.**

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
||OLS|OLS|2SLS|2SLS|
||pcGDPgrowth|pcGDPgrowth|pcGDPgrowth|pcGDPgrowth|
|Constant|-3.245|-2.387|-0.320|-0.489|
||(1.257)**|(1.138)**|(2.900)|(1.867)|
|GDPpc71|-0.000|-0.000|-0.000|-0.001|
||(0.000)|(0.000)|(0.000)|(0.000)***|
|prim_expen_70|-0.043|-0.046|0.001|-0.049|
||(0.061)|(0.061)|(0.121)|(0.081)|
|second_expen_70|0.001|0.004|0.000|0.012|
||(0.004)|(0.004)|(0.010)|(0.010)|
|Invt_GDP7195|0.241|0.224|0.024|0.172|
||(0.058)***|(0.049)***|(0.194)|(0.071)**|
|rail7195|0.661|52.269|4.073|-19.234|
||(1.771)|(16.292)***|(2.968)|(9.942)*|
|EA.rail|22.630||101.633||
||(11.833)*||(78.180)||
|LI.rail||-57.714||0.000|
|||(16.315)***||(0.000)|
|MI.rail||-50.803||23.624|
|||(16.076)***||(10.950)**|
|Observations|41|41|36|36|
|R-squared|0.55|0.61|||
|Wu-Hausman F test,<br>p-value|||0.04|0.12|

Robust standard errors in parentheses. * significant at 10%; ** significant at 5%; *** significant at 1%. Regional dummies: EA (East Asia), LI (low income countries), MI (middle income countries). Instruments: see Table 3 and 4.

32
# **Annex 6. Graphs of Predicted Prices of Housing in Select Capital Cities**

# **<u>Figure 22. Predicted House Prices Based on Hedonic Estimates from Actual Rent</u>**

<!-- Start of picture text --> Predicted House Prices, Kigali (actual rent) Predicted House Prices, Abidjan (actual rent)<br>14,000 8,000<br>12,000 7,000<br>6,000<br>10,000<br>5,000<br>8,000<br>4,000<br>6,000<br>3,000<br>4,000<br>2,000<br>2,000 1,000<br>0 0<br>1 2 3 4 5 6 7 1 2 3 4 5 6 7<br>number of rooms number of rooms<br>Predicted House Prices, Dar es Salaam (actual rent) Predicted House Prices, Maputo (actual rent)<br>20,000 250,000<br>18,000<br>16,000 200,000 avg. rooms<br>14,000<br>150,000<br>12,000 confidence<br>10,000 interval<br>100,000<br>8,000<br>predicted<br>6,000 price<br>50,000<br>4,000<br>2,000<br>0<br>0<br>1 2 3 4<br>1 2 3 4 5<br>number of rooms<br>number of rooms<br>constant USD, 2005 constant USD, 2005<br>constant USD, 2005<br>constant USD, 2005<br><!-- End of picture text --> <!-- Start of picture text --> Predicted House Prices, Abidjan (actual rent)<br>8,000<br>7,000<br>6,000<br>5,000<br>4,000<br>3,000<br>2,000<br>1,000<br>0<br>1 2 3 4 5 6 7<br>number of rooms<br>constant USD, 2005<br><!-- End of picture text --> 37
# **2. The data**

Our study employs a unique, very detailed export firm-level dataset provided by the Dominican Republic Custom Agency. The data contain all transactions (i.e. amount and quantity) by product at the HS 12-digit level and by destination for all exporters<sup>6</sup> for the period 2002-2009. The sample includes 135,016 exporter-destination-product relationships. The firms’ universe during this period consists of 8,706 firms, among which not all export in every year.<sup>7</sup> Table 1 reports the number of exporting firms, products and markets for selected years.

Table 1: Summary statistics

||**2003**|**2005**|**2007**|**2009**|
|---|---|---|---|---|
|**exports by firm (10**<sup>**6**</sup>**US $):**|||||
|mean|1.4|1.5|1.4|1.5|
|standard deviation|9.4|9.9|9.0|9.8|
|**products by firm:**|||||
|mean|6.3|5.2|6.5|6.2|
|standard deviation|10.7|8.9|13.1|13.1|
|**markets by firm**|||||
|mean|2.1|2.1|2.1|2.2|
|standard deviation|2.7|2.9|2.9|3.1|
|**number of firms**|2,660|2,622|3,237|3,031|
|**number of products**|2,035|2,049|2,937|2,812|
|**number of markets**|133|131|147|147|
|**total exports(10**<sup>**6**</sup>**US$):**|3,742|3,969|4,474|4,563|

The data show that the number of exporters, products and markets increased until 2007, before declining in the next years (except for the number of markets). The number of exporters went from 2,660 in 2003 to 3,237 in 2007, which is a 21.6% increase. In addition, the number of products rose by 44.3% for the same period. Their number went from 2,035 in 2003 to 2,937 in 2007. It is also worth mentioning that while the change in both the number products and firms was very modest between 2003 and 2005, their number expanded rapidly during the following years. Indeed, the number of firms for 2005-2007 period went from 2,622 to 3,237 and the number of products from 2,049 to 2,937. As for the export markets, their number rose by 10.5% between 2003 and 2007.

Looking at the exporters’ profile, each year around 60% of firms are multi-product exporters (i.e. they export more than one product). Half of them export to a single market, while the other half exports to multiple markets (see Appendix 1). For the remaining firms, around 37% export only one product to one destination and 3% export one product to multiple destinations. In terms of exports, almost all exports

> 6 Exporters are identified through their names.

> 7 From this universe, only 459 export in every year.

5