rafmacalaba/datause-encoder-data huggingface dataset:
predicted year of universal access is computed in a very simple way by taking into account the difference for each country and each service between the growth rate in coverage (in terms of number of connections made available) and the growth rate of the population over time. The average Africa-wide annual growth rates in coverage for the different services in the countries in the sample is 5 percent for electricity, 1.4 percent for piped water, 7 percent for flush toilet, and 12 percent for landline telephones during the period 1996-2005 (Table 9). Figures 6 to 8 provide data showing the share of countries with different levels of expansion rates for the various services at the national, as well as the urban and rural levels. It is striking that for piped water and flush toilets, around a quarter of the countries in the sample actually show evidence of negative growth rates in coverage, while another third report only modest growth rates of 0-4 percent per year. The strongest performers in terms of piped water service expansion are Benin, Burkina Faso, Chad, Ethiopia, Mali and Senegal, all showing growth rates of 4-8 percent per year. A significant minority of countries are expanding flush toilet service at a rate in excess of 12 percent per annum. These are a subset of the countries that are performing well for piped water service expansion: Burkina Faso, Chad, Ethiopia and Mali. However, this growth is taking place from a very low base, and hence does not amount to a great deal in absolute terms.

The rate of expansion of electricity service is more encouraging, with almost half of the countries reporting average annual growth rates in the 4-8 percent range. The fast expanding countries, once again, shows considerable overlap with that of the countries registering rapid expansion of piped water service: Benin, Burkina Faso, Chad, Madagascar, Mali, Senegal and Tanzania. The most rapid rates of coverage expansion are for landline service, where about half of the countries are expanding at over 12 percent per year, albeit from a very low base. The list of high performing countries is somewhat different in this case: Ethiopia, Ghana, Guinea, Kenya, Madagascar and Mali. The household surveys do not yet provide a time series for cellular telephones; however it is known from sector statistics that the rate of expansion for that service is much higher than for landlines. At the other extreme, one country that stands out as falling behind demographic growth in expansion of all its modern infrastructure services is Zambia, which reports a negative growth rate for piped water, flush toilet and electricity, and has been expanding landline service slowly.

Using the data on the annual growth rates in coverage at the country level, it is possible to project the year in which each country would reach universal access for each of the modern 11
# **Table 5 Manufactures GVC workers, 1995 and 2008, by sector**

||Manufa<br>GVC wor<br>(%) shar|ctures<br>kers as<br>e of all|Manu<br>2008(in|factures<br>thousan|GVC work<br>ds)empl|ers in<br>oyed in|Chan<br>worker<br>(in t|ge in ma<br>s betwe<br>housand|nufacture<br>en 1995 an<br>s)employ|s GVC<br>d 2008<br>ed in|
|---|---|---|---|---|---|---|---|---|---|---|
||workers<br>econ|in the<br>omy|Agricul<br>ture|Manufa<br>cturing|Services|All<br>sectors|Agricul<br>ture|Manufa<br>cturing|Services|All<br>sectors|
||1995|2008|||||||||
|Germany|26.8|26.4|400|5,481|4,766|10,647|-161|-666|1,388|561|
|France|22.0|18.7|303|2,195|2,355|4,853|-96|-423|368|-151|
|United Kingdom|20.1|12.6|115|1,946|1,931|3,992|-128|-1,148|-347|-1,624|
|Italy|29.1|25.5|333|3,553|2,559|6,444|-192|-234|517|91|
|Spain|23.2|17.5|271|1,827|1,494|3,592|-97|185|353|440|
|Netherlands|22.8|19.0|89|643|929|1,661|-42|-87|158|29|
|Belgium|25.0|20.9|31|399|503|933|-18|-86|72|-32|
|Sweden|22.7|21.0|36|481|443|959|-23|-49|94|22|
|Austria|24.8|22.6|104|463|393|960|-46|-35|120|40|
|Greece|21.0|15.0|97|374|247|717|-202|17|34|-151|
|Denmark|23.9|19.6|41|271|257|569|-25|-66|51|-41|
|Finland|23.6|19.7|39|248|211|498|-25|-12|51|14|
|Ireland|31.5|18.8|59|168|168|394|-35|-17|40|-11|
|Portugal|28.9|21.7|191|602|343|1,136|-57|-139|20|-176|
|Luxembourg|20.3|17.4|1|23|36|61|-1|1|16|17|
|all EU15|24.4|20.4|2,110|18,674|16,632|37,416|-1,149|-2,758|2,936|-971|
|Poland|31.0|28.8|917|2,278|1,347|4,542|-468|81|368|-19|
|Czech Republic|30.8|30.9|93|990|553|1,636|-59|74|35|50|
|Romania|34.0|27.6|684|1,388|517|2,588|-356|-222|-68|-646|
|Hungary|31.6|29.3|129|675|400|1,204|-145|13|63|-69|
|Slovak Republic|28.2|29.6|22|392|249|663|-35|19|85|69|
|Other EU12|29.0|24.2|362|1,121|616|2,098|-86|-217|98|-205|
|all EU12|31.2|28.0|2,207|6,844|3,682|12,732|-1,150|-251|580|-820|
|Total EU 27|25.9|21.9|4,316|25,518|20,314|50,148|-2,298|-3,009|3,517|-1,791|

_Note_ : Manufactures GVC workers are workers directly and indirectly involved in the production of final manufacturing goods. The first two columns indicate the share of GVC workers in the total number of workers in the economy. Next four columns indicate the total number of GVC workers by sector in 2008. And the last four columns indicate the change in the number of GVC workers by sector between 1995 and 2008.

_Source:_ Author’s calculations based on World Input-Output Database, April 2012.

32
Implementation Plan (AIP) at the beginning of the school year and a report card to be shared with the community at the end of the school year. Project inputs for infrastructure, training, textbooks, and so forth, were partially based on the SIP. Principals and head teachers received training in leading the development and implementation of the SIP and the AIPs in collaboration with teachers and key members of the larger community. SBM schools also received funds for maintenance and operating expenses directly in cash rather than in kind, as had been the case previously. These cash funds could be used by the schools based on their AIP, The cash allocation was based on a formula that provided each school with a flat amount of funds plus a prorated figure based on the number of student and teachers as well as other criteria, such as percentage of indigenous student population in the school. Schools not participating in the SBM received no SBM-related training and no cash funds, and they were not required to develop SIPs and AIPs.

The SBM was designed to improve student outcomes through two main venues: by empowering the school community to identify education priorities and to allocate the school maintenance and operating budgets to those priorities (such as curriculum enrichment programs); and by enhancing transparency and accountability through the annual implementation plans and school report cards. However, the SBM program articulated no explicit assumptions regarding the timeframe within which improvements in student achievement were expected to take place. Systematic data on the level of uptake and implementation of the key features of the reforms are also not available.

The SBM training, funds, and requirements were rolled out in three batches and eventually covered almost all (84 percent) of the 8,613 schools in the 23 project districts. The first batch comprised 1,666 schools in 2003-2004, largely because they were perceived to be more capable, although no explicit assignment mechanism was designed.<sup>10</sup> The next batch of 2,700 schools was targeted for SBM rollout in 2004-2005, and another batch of 1,529 was included in 2005-2006.

# **3.  Analytic Approach**

We assess the effects of SBM on student performance using average school-level test scores from all schools and school-level indicators between 2003 and 2005 from the 23 TEEP districts in the Philippines. We base our analysis on a pipeline comparison strategy. Since we have student achievement data over a three year period, but schools were inducted into the program in three batches over time, we identify the

> 10 No written materials were available on the identification strategy.  Project officials interviewed as part of this paper mentioned that schools were chosen on the basis of the perceived strength of their capabilities.

4
## _Panel c. Kakwani Coefficient and Redistributive effect_

<!-- Start of picture text --> Kakwani Marginal contribution to redistributive effect<br>1.60 0.0160<br>1.40 0.0140<br>1.20 0.0120<br>1.00 0.0100<br>0.80 0.0080<br>0.60 0.0060<br>0.40 0.0040<br>0.20 0.0020<br>0.00 0.0000<br>-0.20 -0.0020<br>Redistributive effect<br>Kakwani<br>unequalizing <-------> equalizing<br>Regressive<--------------------> Progressive Other<br>Old Age NC Social Monthly Assistance GMI Family NC NMT Other Heating Allowance Family C NMT Other Family NC MT Child Benefit Unemployment Disability Family NC MT Other<br>Family NC NMT Child Benefit For... Family NC MT Benefit For Raising... Family NC NMT Birth Child Grant Family NC MT Children 1st Grade<br>Family NC NMT Mothers In Tertiary... Family C NMT Pregnancy Childbirth<br>C NMT Maternity Bring Up Child Of 2<br><!-- End of picture text --> _Source_ : Authors’ estimates based on 2021 EU-SILC, 2021 HBS and CEQ Methodology.

# Health and education in-kind benefits

**Health and education in-kind benefits significantly contribute to lower inequality, as they are progressive and equalizing, except tertiary education, which does not reduce inequality.** Health and education benefits account for 15.3% of market income plus pensions (MIPP), the largest share of any program. The Gini coefficient decreases by 0.031 points due to health services and by 0.026 points because of education services. These are the most effective fiscal instruments for redistribution. The reason is that health and education services are equally available to all income groups, but they are more valuable for the lower-income groups concerning their MYPP. For example, health services account for 35.6% of the income of the poorest quintile but only 3.9% of the income of the wealthiest quintile. This makes these services quite progressive and redistributive. All education levels, except for tertiary, benefit the poor more. The tertiary level is only relatively progressive and does not reduce inequality. The most equalizing marginal contribution comes from upper-secondary (almost 1 Gini point), followed by lowersecondary (half a Gini point). Health is relatively progressive, but their marginal contributions are significant. Health from Social Security insurance reduces the Gini by two points (Figure 6).

26
vendor supply analysis and other statistics.<sup>4</sup> Specifically, WITSA reports ICT spending for four categories: (1) computer hardware, (2) computer software, (3) computer services, and (4) communications. The sum of these four categories gives a fairly comprehensive picture of ICT _expenditure_ around the world. However, as we are interested in constructing measures of the physical _stock_ of ICT capital, it is important to notice that, conceptually, some of these WITSA spending measures represent investment spending but others consist primarily of rental fees. For example, while spending on internet subscriptions or telecommunication fees may comprise a substantial amount of ICT spending, it does not constitute investment: from a macro perspective, these are transfers between users of ICT capital and owners of ICT capital, more appropriately viewed as rental fees. From an aggregate perspective, an internet subscription does not require the sacrifice of resources today for the purpose of increasing aggregate production capacity tomorrow, which is the defining characteristic of investment.

More specifically, of the four WITSA spending categories, computer services is in fact the only category that consists primarily of true aggregate investment spending, taking the form of custom software development and equipment maintenance. This category also includes some services that may be more appropriately viewed as rental payments, such as web hosting, but these likely represent a small share of spending in this category.

The categories of computer hardware and computer software include the total value of purchases and leases. Ideally, one would like to count hardware and software investment as the purchase of new machinery or software. However, the WITSA measure includes secondary markets as well, as it takes into account the value of leases. Bluntly, if a computer is purchased and then leased, it is double counted. We therefore adopt an approach similar to Vu (2005) and assume that hardware investment is 0.57 times computer hardware spending, which is roughly the coefficient of proportionality in US data.<sup>5</sup> The coefficient of proportionality for software is greater than one, suggesting that software spending is lower than software investment in the United States. This is probably due to the omission of computer services spending, which includes some forms

> 4For instance, both the Penn World Table and the Conference Board’s Total Economy Database use WITSA as the main source for information on ICT spending.

> 5See the Appendix of Vu (2005) for year-by-year estimates of this factor of proportionality.

5
# to be unreliable in the end-line Madang dataset.<sup>4</sup>

The Early Grade Reading Assessment was conducted four times, both before and after the interventions in the two provinces. In Madang, the intervention occurred in 2013, and the assessments were conducted in 2011 and at the end of the school year in 2013. In Western Highlands Province, the intervention occurred in 2014, and the assessments were conducted in 2013 and at the end of the school year in 2014. The data sets were not implemented as a panel as a new sample of students was drawn in each round.

The World Bank provided four data sets for each of the four rounds of the assessment. These data included scores for each of the 9 reading skill domains, sample weights, and several variables about the schools and students, which are described below. The reading domain scores contain a proportion of zero scores, which vary depending on the domain, suggesting a truncated distribution. As a result, the reading domain scores are standardized using a mean and standard deviation of the control group, baseline students estimated using a Tobit model.

# _B. Sample sizes and attrition_

Table 1 presents the number of schools sampled in each round of EGRA. In Madang province, the baseline sample included 11 of the 15 control schools and 10 of the 16 treatment schools. Endline assessment data are missing for 5 of the sample control schools and 3 of the sampled treatment schools. The unpublished government report states that this is due to logistical, financial and weather issues; it is unlikely that the interventions had any effect on school attrition in Madang. Four of the remaining control schools and 5 of the remaining treatment schools were added to the sample, but there are no baseline data for these additional schools. In Western Highlands Province (WHP), 10 each of the 23 treatment and control schools were sampled at baseline and all 23 treatment and control schools were sampled at end-line. For the additional 13 control and 13 treatment schools included in the end-line data, there are no baseline data.

> 4 An unpublished reliability analysis found that the letter sounds domain was negatively associated with the initial sounds domain controlling for all other domains for the Madang end-line sample.  For other samples, it is strongly positive.  We replicated this analysis and found the same result.  If it were included in this paper, it would yield the largest effect size (over 1 standard deviation) compared to the effect sizes on the other domains.

6
-3-

additional output (above that required by the plan) as well as purchase additional inputs (above those allocated by the plan) at market prices. Taxes on controlled price sales have to be borne by the enterprise and reduce investment, welfare facilities and bonuses. In contrast, taxes on free market sales can be passed on to the consumer and only harm the enterprise through a reduction in the demand for its output. The coexistence of forward shifting of taxes on market sales and bacio.'ard shifting on price-controlled sales makes significant non-uniformity uf tax rates desirable; this is because, with price controls, backward shifting would cause uniform tax rates to lead to highly unequal profitability and workers' bonuses across sectors. The China model is used to show that the losses from uniform taxation can increase rapidly with the extent of price controls. These losses are much larger than those computed in economies without such controls. This implies that while simplification of rates is desirable on administrative grounds and to reduce opportunities for rent seeking, a unification of value added tax rates is not appropriate in the presence of price controls. The exercise may also be seen as providing a strong argument for price reforms.

The third part of the paper, drawn from an ongoing study on India, pertains to the coordinated reform of tariffs and indirect taxes. Trade liberalization, recommended ia the interests of promoting an outwardoriented development strategy, can, in the absence of offsetting fiscal measures, have negative consequences for revenue and, hence, macroeconomic stability. Tracing the consequences of tariff reductions on macroeconomic aggregates requires an aggregated model capable of incorporating economywide linkages. On the other hand, the language of tariff reform analysis is disaggregated and requires that the effects of policy changes be quantified in sectoral detail. These objectives are reconciled by implementing two
22

|**....**<br>**, ...**<br>|**Table**<br>**6:Euain 3:-Total**<br>|**Earing Reges**<br>|**o**<br>**for the self em**<br>|**ployed--**<br>|
|---|---|---|---|---|
|**Year**|**1976**|**1981**|**1985**|**1996**|
|Intercept|4.319|4.192|3.853|4.250|
||(0.0001)|(0.0001)|(0.0001)|(0.0001)|
|Education|0.196|0.148|0.165|0.114|
|_____________|_<br>(0.0001)|(0.0001)|(0.0001)|(0.0001)|
|Education<sup>2</sup>|-0.206|0.021|0.012|0.219|
|(x 100)|(0.0001)|(0.4892)|(0.6545)|(0.0001)|
|Experience|0.074|0.079|0.084|0.063|
||(0.0001)|(0.0001)|(0.0001)|(0.0001)|
|Experience<sup>2</sup>|-0.101|-0.108|-0.111|-0.082|
|(x 100)|(0.0001)|(0.0001)|(0.0001)|(0.0001)|
|Gender|-1.092|-1.148|-1.131|-0.714|
||(0.0001)|(0.0001)|(0.0001)|(0.0001)|
|______________|0.431|0.434|0.438|0.336|

Source: Authors' calculations based on the "Pesquisa Nacional por Amostra de Domicilios" (PNAD). P-values in parentheses.

Let us now turn to the estimation of the multinomial logit in equation (4). This was estimated separately for household heads and for others, since the set of explanatory variables was slightly different in each case (see the description of vectors Z, and Z1 in Section 2 above). Table A3.1 in Appendix 3 presents the results for household heads, and Table A3.2 for other household members. In both tables, the results are presented as the effects of other choices versus that of remaining outside the labor force ('unoccupied').

For household heads, education was not significantly related to the likelihood of choosing to work in the wage sector vis-a-vis staying out of the labor force, at any time. In addition, the positive effect of education decreased from 1976 to 1996, to the point where it was no longer statistically significant. The dominant effect on the occupational choices of urban household heads over this period, however, was a substantial decline in the constant term affecting the probability of participating in either productive sector, as opposed to remaining outside the
Table 6: Components of Time-use Index

|||Panel A||
|---|---|---|---|
||(1)|(2)|(3)|
|Dependent Variable:|Studying/Homework<br>Outside School|Household work|Sleeping|
|Goal-Setting|0.042|-0.056|-0.087**|
||(0.040)|(0.038)|(0.038)|
|Goal-Setting + Recognition|0.014<br>(0.043)|-0.106***<br>(0.040)|-0.039<br>(0.041)|
|Observations|13,273|13,250|13,219|
|Baseline Outcome Controlled|Yes|Yes|Yes|
|||Panel B||
||(1)|(2)|(3)|
|Dependent Variable:|Games/Leisure time<br>Outside school|Studying extra for<br>Endline exam|Studying Math<br>Outside school|
|Goal-Setting|-0.043|0.022|0.067*|
||(0.027)|(0.048)|(0.039)|
|Goal-Setting + Recognition|0.004|0.023|0.059|
||(0.031)|(0.059)|(0.047)|
|Observations|13,258|13,310|13,241|
|Baseline Outcome Controlled|Yes|Yes|Yes|

_Notes_ : This table reports the result of estimating equation 1 on individual components of Time-use Index. All the components are discussed in Section 2. Standard errors are clustered at the level of school. *p<0.1; **p<0.05; ***p<0.01.

22
# **1. Introduction**

An increasing number of surveys in both developed and developing countries elicit the subjective expectations that individuals have about a wide range of future outcomes.<sup>1</sup> Such data are being used for understanding many economic and social behaviors, such as saving for retirement, contraceptive choice, migration decisions, and schooling.<sup>2</sup> In developed countries surveys usually collect subjective expectations by directly asking respondents questions like, “What is the percent chance your monthly income in one year will be below $1000?” (e.g. Dominitz and Manski, 1997). While this direct approach has been used in developing countries by McKenzie et al. (2007) and Attanasio and Kaufman (2009), in many settings it is felt that asking respondents with low levels of education directly for a probability is too abstract, and so visual aids are used to help individuals express probabilistic concepts. Such an approach could also be useful for surveying groups in developed countries with lower education levels, such as children or the poor.

The typical approach using visual aids is to give respondents physical objects like beans, balls or stones, and then ask them to allocate into intervals or bins in accordance with their subjective expectations of different events occurring.<sup>3</sup> In practice individuals are usually given 10 or 20 items to allocate, allowing them to express probabilities in units of 0.10 or 0.05. When eliciting a distribution, a major design issue is then how to specify a set of intervals for respondents to allocate these items to, and how to define the support over which expectations are elicited. Delavande et al. (2009)’s survey of the existing literature reveals two common methods for doing this. The first is to use a common, pre-determined support for all respondents. Pre-existing data or prior knowledge of the range of possible values of the outcome is used to define the support, and a relatively large number of intervals are often given within this support. The alternative method is to first ask individuals their perceived maximum and minimum for

> 1 Manski (2004) and Hurd (2009) review the experience in developed countries, while Delavande, Giné, and McKenzie (2009) review the experience of developing countries.

> 2 See for example Hurd et al. (2004) on retirement, Delavande (2008) on contraceptive choice, McKenzie et al. (2007) on migration, and Attanasio and Kaufman (2009) on schooling.

> 3 Delavande and Rohwedder (2008) use a similar approach to collect expectation data in the U.S. over the Internet.

2