rafmacalaba/datause-encoder-data huggingface dataset:
|0_, X_)<br>(5)<br>IV<br>0.027<br>(0.044)<br>0.001<br>(0.011)<br>0.052**<br>(0.025)<br>-0.023<br>(0.037)<br>0.000<br>(0.033)<br>-0.012<br>(0.014)<br>-0.012<br>(0.011)<br>-0.019<br>(0.022)<br>Yes<br>1) and (2) report<br>fference between<br>ho report to have<br>treatment group<br>e, education and<br>ristics, sub-zone<br> we run a probit<br>gnifcance of the|
|---|
|1_, X_)_−E_(_Y |T_ =<br>(4)<br>OLS<br>0.016<br>(0.033)<br>0.005<br>(0.008)<br>0.038**<br>(0.018)<br>-0.007<br>(0.028)<br>0.009<br>(0.025)<br>-0.001<br>(0.012)<br>-0.007<br>(0.008)<br>-0.019<br>(0.015)<br>Yes<br>ents only. Columns (<br>and (4) report the di<br>etween households w<br>participation with the<br>s include gender, ag<br>ter, dwelling characte<br>or joint signifcance, <br>F-test for the joint si|
|_E_(_Y |T_ =<br>(3)<br>OLS<br>0.056<br>(0.045)<br>-0.001<br>(0.009)<br>0.062**<br>(0.025)<br>-0.004<br>(0.041)<br>0.020<br>(0.035)<br>0.006<br>(0.011)<br>-0.001<br>(0.009)<br>-0.048**<br>(0.022)<br>No<br>ailable for respond<br>ackets. Columns (3)<br>tes the difference b<br>umenting program<br>arentheses. Control<br>about access to wa<br>lage). To control f<br>eport p-values of an|
|(2)<br>Treatment<br>0.711<br>[0.454]<br>0.021<br>[0.144]<br>0.866<br>[0.341]<br>0.368<br>[0.483]<br>0.313<br>[0.464]<br>0.035<br>[0.183]<br>0.033<br>[0.180]<br>0.067<br>[0.250]<br>household, data av<br>d deviations in br<br>Column (5) estima<br>who didn’t by instr<br> are reported in p<br>igion, information<br>n living in the vil<br>variables and we r|
|(1)<br>Control<br>0.655<br>[0.476]<br>0.022<br>[0.148]<br>0.804<br>[0.397]<br>0.372<br>[0.484]<br>0.293<br>[0.455]<br>0.029<br>[0.168]<br>0.034<br>[0.182]<br>0.115<br>[0.319]<br>observation per<br>nt group, standar<br>ssion (model2). <br>onths and those<br>d at village level<br>ize, tribe and rel<br> share of wome<br>ected groups of|
|1. Malaria is a problem in the village<br>Yes<br>Respondent doesn’t know<br>2. Most affected by malaria:<br>Children<br>Pregnant women<br>Children and pregnant women<br>Adult men<br>Adult women<br>Respondent doesn’t know<br>Controls<br>Note: *** p_<_0.01, ** p_<_0.05, * p_<_0.1. One<br>sample means restricted to control and treatme<br>treatment and control groups using OLS regre<br>received the spraying campaign in the last 5 m<br>indicator (model 3). Standard errors clustere<br>marriage status of the respondent, household s<br>dummies and village characteristics (size and<br>regression of the treatment indicator on the sel<br>coeffcients on the selected variables.|

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Preferences over policy might change not only with age but also across cohorts. For example, if younger generations are more supportive of increases in government spending on the environment, they may also be more supportive of these policies when they grow old than the current generation of old individuals. In other words, the age patterns that emerge in a cross‐sectional survey could be a mix of both life cycle effects and preferences changing across generations.

Empirical evidence underscores the importance of controlling for age or cohort effects. To our knowledge, the only paper that attempts to disentangle age, cohort and time effects in attitudes towards public spending priorities in European economies is Sorensen (2013). Using data from the International Social Survey Program (ISSP) – which comprises four repeated cross‐sectional sample surveys for the years 1985, 1990, 1996 and 2006 covering 22 countries, 16 of them in Europe – the author finds that people do shift their public spending priorities over the life cycle, but not by as much as cross‐sectional age comparisons would suggest. In particular, older people tend to be less (more) supportive of an increase (decrease) of government expenditure on education (pensions) than their younger peers. However, controlling for cohort effects significantly reduces, but not entirely eliminates, these age patterns. As mentioned in the literature review section above, one limitation of the data used by Sorensen (2013) is that the alternatives available in the questionnaire for additional government expenditure are not mutually exclusive.

To disentangle cohorts and age effects we need a panel data set covering a sufficiently long time period or a pseudo‐panel of repeated cross‐sectional data that would allow us to follow the same individuals or group of individuals over time. As far as we know, the best data set to meet these requirements is Eurobarometer, from which we use repeated cross‐sections with annual frequency between 2004 and 2013 for 27 European countries.<sup>9</sup> An advantage of this data set is that it allows us to study perceptions towards a broader set of issues. The Eurobarometer survey includes the question “ _What do you think are the two most important issues facing (OUR COUNTRY) at the moment?_ ”, and respondents can choose within a set of 16 mutually exclusive categories. Figure 4 shows the average results for the top choices,<sup>10</sup> which includes general contextual considerations, such as unemployment, the economic situation and prices, followed by more specific subject areas, such as crime, health care, pensions and immigration. While this question does not specifically try to gauge preferences regarding the allocation

> 9 The group of countries includes Netherlands, Germany, Italy, Luxembourg, Denmark, Ireland, Great Britain, Greece, Spain, Portugal, Finland, Sweden, Austria, Cyprus, Czech Republic, Estonia, Hungary, Latvia, Lithuania, Malta, Poland, Slovakia, Slovenia, Bulgaria, Romania, Turkey and Croatia.

> 10 Other categories include Public Transportation, Defense, Foreign Affairs, Others, Don’t know.

12
infant and child mortality with these countries, as well as with the other BRICS countries, remain large (Figure 15).

**Figure 15: India lags behind many of its comparators in reducing infant and child mortality**

<!-- Start of picture text --> (a) Infant mortality trend: India and selected (b) Child mortality trend: India and selected<br>comparators comparators<br>100 120<br>80 100 India, 112<br>India, 80 India, 75<br>80<br>60 India, 56<br>60<br>India, 39<br>40 India, 50<br>40<br>20 20<br>0 0<br>1994 2005 2014<br>1994 2005 2014<br>Nepal Bangladesh Nepal Bangladesh<br>Cambodia India Cambodia India<br>Vietnam Nicaragua Vietnam Nicaragua<br>(c) Infant, child and maternal mortality, 2014: the BRICS<br>50 200<br>IMR<br>Under‐5 MR<br>40<br>150<br>MMR<br>30<br>100<br>20<br>50<br>10<br>0 0<br>India China S. Africa Brazil Russia<br>Infant mortality rate Under 5 mortality rate<br>estimate<br>rate (per 1,000)<br>Infant, Under 5 mortality (per 100,000), modeled Maternal mortality ratio<br><!-- End of picture text --> _Source:_ World Development Indicators (WDI).

Relatively slow progress in sanitation in India has also meant that the country compares poorly with comparators including South Asian neighbors in access to sanitation and the incidence of open defecation (see Figure A‐ 6, Annex). In undernourishment among children of age 5 and under, stark differences across states mean that a number of states have stunting and underweight rates that are above not only the average for its “peer group” of low‐middle income countries but also the average for Sub‐Saharan Africa, a group with much lower average per capita GDP (Figure A‐ 6, Annex). Low‐income states like UP and Bihar fare particularly poorly, but challenges are not restricted to these states alone.

# 6. The global goal of ending extreme poverty by 2030 – what will it take in India?

India has the largest number of poor people in the world, accounting for roughly one‐fourth of the world’s extreme poor (at the $1.90 poverty line) in 2012. If rural and urban India were considered as separate entities, both would be among the top 10 “countries” of the world in terms of the number of extreme poor – urban India would rank sixth while rural India would top the list (Figure 16). The number 19
consulting diagnosed opportunities for improvement in a canonical set of management practices during the first month, followed by four months of intensive support for the implementation of these recommendations. The control plants received only the one month of diagnostic consulting.

The treatment intervention led to significant improvements in quality, inventory and production output. The result was an increase in productivity of 11% and an increase in annual profitability of about $230,000. Firms also spread these management improvements from their treatment plants to other plants they owned, providing revealed preference evidence on their beneficial impact.

Given these results, the natural question is why firms had not previously adopted these practices. Our evidence suggests that informational constraints were an important factor. Firms were often not aware of the existence of many modern management practices, like inventory norms and standard operating procedures, or did not appreciate how these could improve performance. For example, many firms claimed their quality was as good as other local firms and so did not need to introduce a quality control process.

We also find two other major impacts of better management practices. First, owners delegated greater decision making power over hiring, investment and pay to their plant managers. This happened in large part because the improved collection and dissemination of information that was part of the change process enabled owners to monitor their plant managers better. As a result, owners felt more comfortable delegating.

Second, the extensive data collection and processing requirements of modern management led to a rapid increase in computer use. For example, installing quality control systems requires firms to record individual quality defects and then analyze these by shift, loom, and design. So modern management appears to be a skill-biased technical change (SBTC), as increased computerization raises the demand for educated employees. A large literature has highlighted SBTC as a key factor increasing income inequality since the 1970s. Our experiment provides some evidence on the role of modern management in driving SBTC.<sup>3</sup> The major challenge of our experiment is the small cross-sectional sample size. We have data on only 28 plants across 17 firms. To address concerns over statistical inference in small samples we implement permutations tests that have exact finite sample size. We also exploit our large time series of around 100 weeks of data per plant by using estimators that rely on large T

> 3 See, for example, the survey in Autor, Katz and Kearney (2008).

2
<!-- Start of picture text --> 8.c. Market sophistication 8.d.Business sophistication<br>70 60<br>60 50<br>50<br>40<br>40<br>30<br>30<br>20<br>20<br>10<br>10<br>0 0<br>Observed Market Sophistication index value Observed Business Sophistication index value<br>Median value Median value<br>Predicted value Predicted value<br><!-- End of picture text --> Source: Own elaboration based on GII dataset and WDI dataset Note: Predicted value dots show the benchmark predicted by a (linear) regression with (the log of) GDP per capita adjusted for purchasing power parity (in 2011) as the explanatory variable; the dependent variable is the enabling innovation factor of interest. The regression used all countries available in the GII and WDI dataset. The median value (for each enabling factor) is calculated within the 127 countries covered by the GII dataset

# **_3. Demand side of innovation: The role of firm characteristics and use of innovation inputs_**

The analysis of the demand side of innovation draws from the World Bank Enterprise Survey (ES) implemented in 2015-2016 for a selected set of developing East Asian countries, as follows: Cambodia, Indonesia, Lao PDR, Malaysia, Myanmar, the Philippines, Thailand, and Vietnam. The survey collects substantial balance sheet and other information regarding the investment climate and includes an innovation section (section H) containing questions covering innovation outputs and inputs to the innovation process. Table 3 lists the year the survey was conducted for each country as well as the total number of firms surveyed. The sample for each economy is stratified by industry, firm size, and geographic region. Stratification by industry follows a different level of detail depending on the size of the economy. Stratification by size follows three levels: 5 to 19 (small), 20 to 99 (medium), and 100 or more (large); firms with fewer than five employees are ineligible for the survey. Regional stratification covers the main economic regions in each economy. Through this methodology estimates for the different stratification levels can be calculated on a separate basis and inferences can be made for the non-agricultural private economy.

**<u>Table 3. List of developing East Asian countries covered by the Enterprise Survey</u>**

|**Country**|**Survey year**|**# of firms**|
|---|---|---|
|Cambodia|2016|373|
|Indonesia|2015|1320|
|Lao PDR|2016|368|
|Malaysia|2015|1000|
|Myanmar|2016|607|
|Philippines|2015|1335|
|Thailand|2016|1000|
|Vietnam|2015|996|

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**Figure 5b: LEAPS Math Test Score Data – Sample Size 100**

<!-- Start of picture text -->
-1 -.5 0 .5 1<br>Difference in average math test score (weighted by std dev)<br>1 Draw 8 Strata Matched Big Stick Minmax<br>8<br>6<br>4<br>Baseline<br>2<br>0<br>3<br>2<br>1 year later1<br>0<br><!-- End of picture text -->

**Figure 5c: LEAPS Math Test Score Data – Sample Size 300**

<!-- Start of picture text -->
-1 -.5 0 .5 1<br>Difference in average math test score (weighted by std dev)<br>1 Draw 8 Strata Matched Big Stick Minmax<br>10<br>8<br>Baseline 64<br>2<br>0<br>5<br>4<br>3<br>2<br>1 year later<br>1<br>0<br><!-- End of picture text -->
# **5.4 The fiscal consequences of minimum income guarantees**

As stated earlier, income guarantees were necessary in order to attract private investment to the sector. By reducing traffic and policy risk, they also serve to lower the risk premium used by firms in evaluating the project.

However, many authors have pointed out the potential risks associated with these guarantees." As seen in the previous section, the government is already paying some amount of guarantees. If current traffic trends do not change, these payments are expected to disappear in the near future. However, from a fiscal perspective, the real danger is a simultaneous downturn in usage across a multitude of concessions. If traffic levels across different concessions are highly correlated-as they would be if they are sensitive to the general economic business cycle-then a recession could trigger these guarantees over a wide range of contracts, forcing up fiscal expenditure just when government finances are least able to afford it.

Compounding the above problem is the fact that these contingent liabilities are generally not treated as such by current fiscal accounting practices. In most cases, no budgetary provisions are made for the eventuality that the government will have to pay the guarantees. From a budgetary perspective, guarantees are ignored until they are made effective, after which payments are recorded as current expenditure.

Due to its importance, MOP commissioned a study to address the fiscal issues related to the minimum income guarantees. The basic objective of the study was to determine the value of these contingent liabilities and whether budgetary provisions were necessary to cover expected future payments. Using MOP's traffic forecast models, Monte Carlo analysis found that the 70% guarantee level is sufficiently low so that only a very unlikely event would simultaneously trigger a substantial number of guarantees. Therefore, provisions were not considered to be necessary at that moment. However, ongoing research is being undertaken to implement the contingent liability valuation method for MOP and to continuously monitor the fiscal risks associated with the minimum income guarantees.

Furthermore, as noted earlier, the programme in Chile has so far generated a positive cash flow to the government. The proceeds deposited in the Investment Fund provide some form of provision for future minimum income guarantee payments.

Another problem with income guarantees is that if they are designed-as in Chile-to be a debt guarantee, then creditors may have a reduced incentive to monitor the operations of the concession firm. EFG (1997a) also argue that income guarantees remove one of the main advantages of private infrastructure provision, namely the effective screening by the private sector of 'white elephant' projects. These are projects that are not socially justified based on 31 See Mody and Patro (1996), Engel, Fisher and Galetovic (1997c) and other studies in Irwin, Klein, Perry and Thobani (1997).

35
legally permitted to own and operate private schools in Tanzania. However, according to the Education Act of 1978 (which has not been amended, to the best of the authors’ knowledge), there are 14 criteria upon which a school can be denied registration. The challenge here is that some of these criteria and minimum requirements are opaque and are open to distortion (World Bank 2014, 2015). For instance, a school can be refused registration if “the premises of the proposed school or the equipment will not allow of effective tuition in the subjects to be taught in the school.” On the other hand, policies governing private schools’ autonomy and quality assurance are well-formulated and enforced through inspections. Also, policy affords private schools moderate levels of autonomy. They can set teacher salaries, deploy and dismiss teachers (subject only to labor laws) but they have to adhere to centralized requirements on teacher qualifications, class sizes, and pedagogy.

Adding to the complexities around private school provision, Tanzania currently offers no tax subsidies or cash transfers for families and children attending independent private schools. Schools set their own tuition fees, but they are subject to review from the government. In order to operate, independent private schools are required to pay an inspection fee of 5,000 Tanzanian shillings (US$3) per student per year, as well as an examination fee of 15,000 Tanzanian shillings (US$9) for each student in a grade where standardized examinations are administered. As a result, private secondary enrollment varies from 47 and 44 percent of total enrollment in Dar es Salaam and Manyara to 6 percent in Lindi and Mbeya as their availability is more pronounced in wealthier regions (Baum and Cilliers 2018).

Figure 7: Share of total enrollment in private schools, by level <!-- Start of picture text --> 30%<br>25%<br>20%<br>15%<br>10%<br>5%<br>0%<br>2012 2013 2014 2015 2016<br>Pre-primary Primary Lower secondary Upper secondary<br><!-- End of picture text --> Source: 2012-2014, 2016, Best Education Statistics in Tanzania (BEST); 2015, UNESCO Institute for Statistics.

So what kind of an education market have these policies created in Tanzania? A deep dive into the education market of one region, Morogoro, is presented in the next section.

# 6. What do private schools look like?

In order to understand private schools’ ability to fill the gap in secondary education, a survey of 53 public and 89 private schools, at different levels (pre-primary, primary, and secondary) was undertaken in Morogoro, Tanzania in March 2015. This survey covered all the schools in urban 10
Using _λ_<sup>_′_</sup> ( _e_ ) = 0 in (16) and (15), we find In the first equation, the coefficients of _dR_ and _dL_ are both strictly positive. Hence, _dR/dL >_ 0: an increase of the senior lending _L_ , has a positive impact on the private rate _R_ because of the higher risk of private loans. The higher private rate _R_ induces a reduction of investment but that reduction of the demand for loans is only a second round effect and does not change the sign of the initial impact.

In the second equation, the coefficient of _dR_ is positive. Therefore an increase of _L_ , which generates an increase of _R_ in the first equation, has a positive impact on the welfare of the borrowing country. Furthermore, assume that senior lending at the risk-free rate is at its maximum (to be risk-free), _a/R_<sup>_∗_</sup> . In the second equation of the system (19), the coefficient of _dR_ is equal to zero: a small decrease of _L_ has a first-order effect on the private rate _R_ , as expected, but it has no first-order effect on the welfare of the country. We have the following result.

# **Proposition 2 (Seniority lending with endogenous investment and exogenous effort)**

_In the model of this section where investment by the borrowing country is endogenous and the resources for debt servicing are above a fixed level a, the level of senior lending L at the risk-free rate R_<sup>_∗_</sup> _can take any value in the interval_ [0 _, a/R_<sup>_∗_</sup> ] _. The values of the rate R for other (junior) loans and of the welfare of the country are increasing functions of L ∈_ [0 _, a/R_<sup>_∗_</sup> ] _. The investment by the country is a decreasing function of L. At the maximum L_ = _a/R_<sup>_∗_</sup> _, the variation of L has a zero first-order effect on the welfare of the country._ We discuss this result after the analysis of the impact of _L_ on effort.

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associated with Leverage (i.e. high DaR by both Leverage and Net Debt to EBIT ratios) and debt service capacity (i.e. DaR based on the ICR).<sup>11</sup> The CVI has increased for several countries between 2009 and 2016: the median rise of the CVI is 2.9 percentage points in that period. The non-financial corporate sectors of some regionally important EMDEs such as Brazil, India, the Russian Federation, Nigeria, China, and Indonesia have also shown weaker conditions, with a median CVI growth of 6 percentage points in the period 2009-2016.

**Figure 10: Intensity of corporate vulnerability by region**

Debt at Risk for X or more indicators (DaR>=X) <!-- Start of picture text --> DaR>=X<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>EAP ECA LAC MENA SA SSA<br>2007 2013 2016<br>DaR>=1 DaR>=2 DaR>=3 DaR>=4 DaR>=5 DaR>=1 DaR>=2 DaR>=3 DaR>=4 DaR>=5 DaR>=1 DaR>=2 DaR>=3 DaR>=4 DaR>=5 DaR>=1 DaR>=2 DaR>=3 DaR>=4 DaR>=5 DaR>=1 DaR>=2 DaR>=3 DaR>=4 DaR>=5 DaR>=1 DaR>=2 DaR>=3 DaR>=4 DaR>=5<br><!-- End of picture text --> Regional country medians. Source: Own calculations 11 An analysis by industry shows that corporate financial vulnerability in MENA countries is highly concentrated in the Energy sector. Debt at Risk by both Leverage and Net Debt to EBIT are above 95% of the total sectorial debt in 3 MENA countries, while Debt at Risk by ICR is above 60% of the total sectorial debt also in 3 MENA countries.

17