rafmacalaba/datause-encoder-data huggingface dataset:
by investments in the energy sector. In India, there are 543 PPPs in the transport sector and 414 in the energy sector.

Only 2.8 percent of the infrastructure PPPs initiated in South Asia were canceled. The cancellations have mostly been in the Indian transport sector, which accounts for 27 out of the 35 canceled PPPs in the region (table 1). Even though the highest number of cancellations occurred in the transport sector, the sector with the largest share of canceled PPPs is the ICT sector, with 19 percent. The share of canceled PPPs in the region is similar to the global share, which is 3.7 percent.

The PPP transport program in India showed signs of distress in 2013 and 2014. Twenty-four of the 27 cancellations in the Indian transport sector occurred in 2013 and 2014. At that time, there were about $7 billion of highway PPPs in operation and roughly $34 billion of highway PPPs under construction. About one-third of the PPPs under operations and two-thirds of those under construction were showing signs of distress. Infrastructure developers and banks (mainly public) that financed highways PPPs were also stressed. As a result, the national highways development program experienced a sudden stop in 2013 and 2014 after a period of rapid growth between 2010 and 2012 (figure 3).

# **Figure 3 Number of national highway public-private partnership projects in India by financial closure year, 2001–2018**

<!-- Start of picture text --> 2001 2003 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018<br>40<br>30<br>20<br>number of projects<br>10<br>0<br><!-- End of picture text --> _Source_ : PPI database.

The distressed projects in India also coincide with the slowdown in PPP investments in South Asia, as can be seen in figures 1 and 2. Although it can be argued that it is coincidental, there is evidence that the cancellations led to more caution in initiating new PPPs. For example, a federal statute, which came into effect in 2014, set the requirement that a PPP must obtain the consent of at least 70 percent of the affected persons for land acquisition and made compensation more generous— and thus more costly for the project. Earlier projects were undertaken with as little as 30 percent of the land having been secured (Pratap and Chakrabarti 2017). Furthermore, after 2010, in India, traditional procurement of infrastructure was favored to PPPs at an increasing rate (figure 4).

9
- Philippines (Cagas et al., 2006; Salas, 2006);

- Poland (Sznajderska, 2014; Greszta et al., 2011,Nojkovi ́c and Petrovi ́c, 2015);

- Thailand (Taguchi and Wanasilp, 2018; McCauley et al., 2006);

- Turkey (Gozgor, 2012;Kaytanci, 2009);

- And South Africa (Naraidoo and Paya, 2012; Ortiz and Sturzenegger, 2007; Naraidoo and Raputsoane, 2011; Steinbach et al., 2009; Aron et al., 2000).

The Taylor Rule used in this paper is specified as: where _it_ is the nominal policy rate, and responds to the deviation of inflation ( _πt_<sup>_e_)fromitstarget</sup> ( _πt_<sup>_∗_) and the output gap ( ̃</sup><sup>_yt_).The rule specified above is a forward looking version of the rule by Taylor</sup> (1993). This paper uses private sector inflation forecasts ( _πt_<sup>_e_)asthevariabletargetedbythecentral</sup> bank because it is alternative source of information than model expectations, and since the model is more parsimonious. Although EMDE central banks have different horizons to achieve their inflation targets and can target slightly different definitions of annual inflation, the Taylor Rule specified above should be a good approximation of most central banks’ behavior. Taylor Rules have also been extended in a number of directions for use in EMDEs including nonlinear specifications (Caporale et al., 2018) and additional variables such as the exchange rate (Taylor, 2001; Batini et al., 2003). While individual central banks’ behavior may be better defined by a non-linear rule or one that includes other variables, the Taylor Rule as specified above should indirectly capture other variables including the exchange rate through its impacts on inflation and output.

The Taylor Rule is embedded in an unobserved components model that estimates the output gap based on: in conjunction with equations 5 and 6 above. The output gap is defined as _y_ ̃ _t_ = _yt − yt_<sup>_∗_.The neutral</sup> real interest rate is treated as another unobserved component to be estimated in conjunction with the output gap and model coefficients.

# **5.3 Time-varying vector autoregressive model**

A third approach uses a time-varying parameter vector autoregressive (TVP-VAR) model to estimate the neutral real interest rate. Time-varying parameter models in economics started with the seminal work of Canova (1993) and Stock and Watson (1996) and was first applied to monetary policy by Primiceri (2005).<sup>12</sup> Subsequent work using this methodology included Cogley and Sargent (2005), Sims and Zha 12He used a TVP-VAR to model the time varying aspects of United States monetary policy proving that US monetary policy has evolved over time and become more aggressive towards inflation and unemployment since the 1970s and 1980s.

10
**Table 10:** The estimated impact of ‘Vietnam’ on Mathematics PISA test scores

|Variables|(|1)|(2|)|Math<br>(|ematics<br>3)|(|4)|(|5)|
|---|---|---|---|---|---|---|---|---|---|---|
|VIETNAM|128.05|(5.65)|108.91|(5.32)|97.46|(5.48)|95.13|(5.87)|77.26|(7.84)|
|PRESCHOOL|-|-|45.86|(3.92)|40.54|(3.95)|39.21|(4.09)|24.90|(3.80)|
|REPEAT|-|-|-50.57|(2.59)|-47.55|(2.56)|-45.05|(3.19)|-36.96|(3.00)|
|ST08Q01|-|-|-8.59|(1.20)|-8.41|(1.18)|-8.38|(1.33)|-7.84|(1.32)|
|ST115Q01|-|-|-4.94|(1.70)|-4.57|(1.73)|-6.10|(1.80)|-5.40|(1.86)|
|BOOK<br>~~N~~|-|-|-|-|0.09|(0.01)|0.08|(0.01)|0.07|(0.01)|
|PARPRESSURE|-|-|-|-|10.73|(5.01)|12.51|(4.78)|10.02|(4.40)|
|FUNDMOM|-|-|-|-|0.27|(0.06)|0.24|(0.06)|0.19|(0.07)|
|COUNCILMOM|-|-|-|-|-0.14|(0.06)|-0.18|(0.06)|-0.10|(0.07)|
|DUTYMOM|-|-|-|-|-0.07|(0.06)|-0.12|(0.07)|-0.10|(0.07)|
|PROPCERT|-|-|-|-|-|-|16.08|(5.50)|16.32|(6.87)|
|SMRATIO|-|-|-|-|-|-|-0.01|(0.01)|-0.03|(0.01)|
|TCSHORT|-|-|-|-|-|-|-1.91|(1.97)|2.24|(1.87)|
|TCFOCST|-|-|-|-|-|-|0.30|(2.19)|-1.45|(1.88)|
|TCM<br>~~S~~TUASS|-|-|-|-|-|-|10.85|(7.45)|-0.18|(7.85)|
|TCM<br>~~P~~EER|-|-|-|-|-|-|-1.53|(6.59)|-5.61|(5.65)|
|TCH<br>~~I~~NCENTV|-|-|-|-|-|-|-0.92|(2.73)|-2.75|(2.72)|
|ASS<br>~~P~~ROG|-|-|-|-|-|-|-0.51|(15.51)|-22.58|(8.04)|
|ASS<br>~~P~~ROM|-|-|-|-|-|-|7.60|(6.11)|14.09|(5.80)|
|ASS<br>~~S~~CH|-|-|-|-|-|-|5.51|(6.51)|0.51|(7.31)|
|STU<br>~~F~~EEDB|-|-|-|-|-|-|3.66|(5.27)|2.20|(5.07)|
|PCGIRLS|-|-|-|-|-|-|-|-|14.59|(13.65)|
|COMP<br>~~U~~SE|-|-|-|-|-|-|-|-|-1.57|(5.30)|
|TXT<br>~~B~~OOK|-|-|-|-|-|-|-|-|-9.51|(7.05)|
|TOWN|-|-|-|-|-|-|-|-|-9.53|(3.76)|
|CLSIZE|-|-|-|-|-|-|-|-|0.81|(0.23)|
|COMPWEB|-|-|-|-|-|-|-|-|15.28|(6.31)|
|SCMATEDU|-|-|-|-|-|-|-|-|5.58|(2.94)|
|SCMATBUI|-|-|-|-|-|-|-|-|3.46|(2.45)|
|EXC2<br>~~P~~LAY|-|-|-|-|-|-|-|-|8.69|(3.96)|
|EXC6<br>~~M~~ATHCOMP|-|-|-|-|-|-|-|-|-1.70|(5.37)|
|EXC10<br>~~S~~PORT|-|-|-|-|-|-|-|-|-5.65|(9.15)|
|EXC11<br>~~U~~NICORN|-|-|-|-|-|-|-|-|6.81|(5.59)|
|SCL<br>~~E~~XTR<br>~~C~~L|-|-|-|-|-|-|-|-|10.90|(5.08)|
|SCORE<br>~~P~~UBLIC|-|-|-|-|-|-|-|-|10.10|(4.75)|
|QUAL<br>~~R~~ECORD|-|-|-|-|-|-|-|-|6.99|(6.77)|
|SCHSEL|-|-|-|-|-|-|-|-|1.57|(3.29)|
|R2|2|7.21|37.|24|40|.03|43|.15|43|.93|
|N|48|483|462|67|44|046|30|051|25|612|

Notes: Figures in parentheses are t-values (95% significance level). For a more detailed description of variables, please see Tables A2, A3, A4 in the Appendix.

The third set of variables, related to teachers is presented in column (4) and results in a reduction of the Vietnam dummy by only 3 points to a level of 95.13. This does not mean that teachers are unimportant as a reason for Vietnam’s superior performance in mathematics, only that the observed teacher related variables in the PISA dataset do not collectively help as an explanation. In the regression itself, one can see that PROPCERT, proportion of certified teachers affects mathematics scores positively. The same goes for a few of student assessment related variables, including TCM ~~S~~ TUASS and STU ~~F~~ EEDB, that relate to the use of student assessment and student written feedback to assess teachers.

22
ing sector is relatively high. The rm's capital wedge in the 90th percentile of the productivity distribution is 4 (U.S. Industry Share) or 3 (Brazilian Firm-Specic Share) times greater than the capital wedge for rms in the 10th percentile. The rm's labor wedge is considerably smaller, varying between 2 (U.S. Industry Share) and 2.75 (Brazilian Firm- Specic Share). De Vries (2014) analyzes the existence of resource misallocation in the Brazilian formal retail sector. The author uses rm-level data from the National Retail Survey (Pesquisa Anual de Comércio, PAC) constructed by Instituto Brasileiro de Geograa e Estatística (IBGE). The author applies the Hsieh and Klenow (2009) methodology to study changes in resource allocation in Brazil's retail sector during the period from 1996 to 2006. The ndings suggest there are large potential output gains from the reallocation of resources to the most ecient retailers. Improvements in resource reallocation may increase TFP levels by a factor of two. However, he does not nd any allocative eciency improvements for the retail sector between 1996 and 2006. Brazil opened up its retail sector in the World Trade Organization's 1995 General Agreement on Trade in Services, and also within MERCOSUR. Furthermore, the participation of foreign capital in Brazilian retail rms was freed from restrictions in the Sixth Constitutional Amendment of 1995. It could thus be expected that these reforms would result in a productive reallocation through the expansion of modern retail chains and the growth of small successful retail businesses. According to De Vries (2008), however, the Brazilian experience suggests a dierent pattern. Retail chains did not replace mom-andpop stores during the period following reforms. The author argues that this may be partly explained by business regulations, such as zoning laws, and diculties in setting up national distribution systems because of the low quantity and quality of rail and road networks. The limited role of reallocation in Brazil's retail sector may explain its low productivity growth.

# 3.2 Informality and productivity

The previous analysis provided evidence of signicant misallocation of factors of production among Brazilian formal rms. However, in order to have a broader picture of misallocation in Brazil, we have to consider the large informal sector.<sup>11</sup> Meghir et al. (2015a) use data from the Monthly Employment Survey (Pesquisa Mensal de Emprego, PME), which provides a rotating panel of individuals sampled from the six main metropolitan regions of Brazil.<sup>12</sup> They show that the lowest part of the productivity distribution is populated only by informal rms. However, over a large segment of the support of the productivity distribution, formal and informal rms coexist. The increased probability of detection for larger informal rms

> 11See Ulyssea (2020b) for a recent review of the literature.

> 12PME has been discontinued in 2016. It was replaced by PNAD Contínua, which also has a rotating panel but is representative at the national level.

12
December 2013, continued through May 2015, and payouts were made to SSP clients in June 2015.<sup>9</sup>

## **2.3 Data**

Our analysis combines administrative data from NVRB with six waves of survey data. The administrative data contain information on all transactions posted to respondents’ NVRB accounts from the time of account opening to one year after the end of the intervention (32 months after baseline).

The baseline took place before SSP was introduced. The first three follow-ups took place – while SSP was ongoing during program months 6, 10, and 14. The objective of these rounds was to collect data on savings and debt during the commitment period, and assess how commitment impacted expenditure. The last two follow-ups took place 3 and 5 months after the SSP payout. These surveys were designed to assess how respondents’ financial lives had changed after the commitment amount was released.<sup>10</sup> In total, 91 percent of study participants participated in all survey rounds and 98 percent of study participants participated in at least one follow-up survey round. Appendix Table A2 that attrition is uncorrelated with treatment status.

# **3 Main Results**

## **3.1 Take Up**

Seventy-two percent of individuals in the treatment group signed up for SSP. The median monthly contribution was GHS 30, while the average contribution was GHS 42. Completion rates were also high, with just 13 percent of respondents who signed up for SSP dropping out before the savings cycle was over. Appendix Table A3 studies correlates of take-up (in the treatment group) and dropout (among those in the treatment group who signed up). The first two columns study pairwise correlations between take-up/dropout and baseline characteristics, while the last two columns present results of regressions where all baseline

> 9All study participants – in both the treatment and the control group – who did not have a savings account with NVRB were given the opportunity to open one in May 2015. Treatment group individuals were significantly more likely to open a savings account, largely because the bank stated to customers that SSP deposits were meant to be sent to a savings account. As a result the treatment group had 0.45 more accounts with NVRB than the control group by endline. This should be kept in mind when interpreting treatment effects in the post-intervention period.

> 10Additional details on each survey round, including timing, participation, and modules contained, can be found in Appendix A.

7
<!-- Start of picture text --> Figure 1: Subjective well-being around time of devaluation<br>28may2005 04jun2005 10jun2005<br>Days of survey<br>Vertical line shows timing of devaluation. Dashed curves are 95%-confidence bounds.<br>.6<br>.5<br>.4<br>.3<br>.2<br><!-- End of picture text --> <!-- Start of picture text --> Figure 2: Food prices in period surronding devaluation<br>2004m7 2005m1 2005m7 2006m1 2006m7<br>Months<br>Note: Vertical line shows timing of devaluation<br>m1=January; m7=July<br>100<br>95<br>90<br>85<br>80<br><!-- End of picture text --> 14
**Table 3: Estimated Impact on Nominal Exports (% change of baseline)**

|rtr|_Case 1(_<br>_Impor_|_2006)_<br>_ters_|_Case 2(_<br>_Impor_|_2006)_<br>_ters_|_HSW 2004_<br>_Importers_|
|---|---|---|---|---|---|
|expoes|_APEC_|_World_|_APEC_|_World_|_APEC_|
|_1 AUS_|7.9|3.3|10.9|4.9|11.4|
|_2 CAN_|-0.4|-0.5|-0.4|-0.4|1.2|
|_3 CHL_|6.7|0.5|8.8|2.1|10.7|
|_4 CHN_|26.7|23.4|22.6|19.5|3.8|
|_5 HKG_|8.8|1.2|3.6|0.2|16.9|
|_6 IDN_|17.3|12.8|15.1|9.7|7.7|
|_7 JPN_|11.4|6.4|10.8|5.8|10.9|
|_8 KOR_|18.4|12.0|16.8|10.7|14.1|
|_9 MEX_|10.3|9.1|9.3|8.1|0.5|
|_10 MYS_|10.8|6.2|10.2|5.4|7.8|
|_11 NZL_|5.2|1.2|6.2|2.4|5.0|
|_12 PER_|32.0|26.5|26.2|22.1|2.0|
|_13 PHL_|42.9|36.8|34.0|28.4|8.2|
|_14 RUS_|60.4|41.6|43.1|33.0|13.9|
|_15 SGP_|7.3|0.9|7.9|1.8|12.9|
|_16 THA_|26.3|30.4|-31.2|35.0|8.5|
|_17 CTP_|5.5|1.7|6.0|2.0|_n.a._|
|_18 USA_|10.6|5.0|9.3|4.3|8.5|
|_19 VNM_|5.7|0.2|5.6|13.5|5.4|
|_20 EU27_|_n.a._|0.2|_n.a._|0.3|_n.a._|
|_21 ROW_|_n.a._|0.9|_n.a._|1.3|_n.a._|
|_APEC Total_|12.7|9.3|8.3|7.1|_n.a._|
|_World_|_n.a._|4.3|_n.a._|3.5|_n.a._|

(Note) Authors’ calculation using the GTAP model and Gempack. GTAP database version 7 pre-release 6, and Gempack. Row denotes exporters, and Columns denotes importers.

The model estimates generally follow as the trade theory would indicate. Exports of the reforming economies significantly increase. The mechanism of this expansion is such that the cheaper import prices pass through the domestic production process, depressing the factor prices, and lead to lower the export prices to improve the competitiveness of their export industries to recover the trade balances<sup>18</sup> . Since the expansion of the reforming economies results from the improved competitiveness, their exports tend to orient toward all over the world. The impact of their exports to APEC

> 18 Macroeconomic closure of the general equilibrium model ensures the expansion of their exports to achieve recovery of the trade balance, as their trade balance is determined the international capital flows.

23
# **2.4.1 Education**

# <u>At the school</u>

# _Infrastructure (electricity, water, sanitation)_

School buildings often lack basic infrastructure, in particular public schools in rural areas. The indicators account for the three basic infrastructures: availability of electricity, water and sanitation.

# _Children per classroom_

In many countries or regions, students and teachers in schools lack access to basic primary school facilities and, in many cases, schools are overcrowded. The indicator of availability of classrooms is measured as the ratio of the number of primary school age children per available primary school classrooms.

# _Students/teacher ratio_

Teacher shortage is a problem in many SSA countries and regions, especially in poorer and rural areas. UNESCO has estimated that about 145,000 teachers should be recruited – 77% above the observed increase between 1999 and 2006 -- to fill the needs of basic education annually to meet the MDGs. The indicator of teachers’ availability is measured as average number of students per teacher.<sup>7</sup>

# _Books per student_

Lack of basic education material is often an important constraint for learning faced by children and teachers in SSA countries. For instance, a SACMEQ II survey found that over half of grade 6 students in many SSA countries did not have a single book (e.g. Malawi, Mozambique, Uganda, and Zambia). Similarly, a PETS in Namibia reported only 0.58 mathematics textbooks per student (Schade and Naimhwaka, 2004, p. 67). The indicator of learning material is measured as the overall number of books available within primary schools per student.

# <u>Teachers</u>

# _Absence rate_

In several low income countries, highly centralized personnel systems, inadequate incentives systems and weak local accountability have resulted in high levels of staff absenteeism. Recent

> 7 As noted by UNESCO (2009), the student-teacher ratio could be viewed also as a proxy of education quality. The variation in learning outcomes In SACMEQ II for instance, it is explained at 36% by the student-teacher ratio.
## **2.2 Roll-out of the MGNREGA Program**

MGNREGA was implemented in three phases. Backwardness status of the districts was used to determine roll-out priority. However, each state was provided representation in Phase I. The Planning Commission of India calculated and ranked the backward status of Indian districts (Planning Commission, 2003). The official ranking of backwardness of the districts in each state was based on the Scheduled Castes and Scheduled Tribes population in 1991, agricultural wages in 1996-97, and output per agricultural worker in 1990-93. In Phase I, 200 backward districts implemented the program in February 2006. The program was then introduced in additional 130 districts in Phase II in April 2007,<sup>9</sup> and all the remaining 270 districts received the program in the last phase in April 2008.<sup>10</sup> This variation in the timing of the program roll-out enables us to identify the causal effect of this scheme on schooling outcomes.<sup>11</sup>

# **3 Data**

The principal source of data is the annual panel of Indian schools called the District Information System for Education (DISE).<sup>12</sup> The data covers grades 1 through 8 in 1.13 million schools. School characteristics include: staff characteristics such as gender and qualification of teachers, infrastructure measures including availability of common toilets, gender specific toilets, drinking water facilities, and electrification, and enrollment by gender and grade. The data also include appearance and pass rates for school ex-

> 9The program commenced in May 2007 for 17 Phase II districts in Uttar Pradesh due to state legislative assembly elections

> 10Due to splitting of districts for which data for the parent and split district was not available in all years, the number of districts in our sample are 193, 123, and 254, respectively.

> 11Prior to February 2006, the government experimented with a pilot program (the Food for Work Program) in November 2004 in 150 of the 200 Phase I districts. Field observations (Bhatia and Dreze, 2006) and research studies (Imbert and Papp, 2015) have found little evidence of increase in public works due to this pilot.

> 12DISE is collected every year in a joint collaboration between the Government of India, UNICEF, and the National University of Educational Planning and Administration (NUEPA). The data is publicly available from NEUPA.

7
children and increased economic stratification (Hsieh and Urquiola 2006). This, in turn, may reduce pressure on public schools to supply quality education (Fiske and Ladd 2003).

While the SDI data cannot be used to assess the validity of these various arguments, we can compare the performance of public and private schools in the sample. In general, indicators in private schools tend to be better: Private school teachers tend to put in more effort, show more knowledge, and exhibit better teaching practices than their public counterparts (Table 8). At the same time, it is important to note that private schools are not able to overcome many of the poor service delivery issues faced by public schools—the issues appear to be systemic. Indeed, even in the private sector, one-third of teachers are absent from the classroom. While the taught school day is four hours long on average, i.e., more than one hour longer than in the average public school, it is still well short of scheduled time. In addition, while teachers in private schools have significantly higher test scores, their pedagogical knowledge is similar to their public school counterparts.

The better performance of private school teachers is reflected in their students’ learning. The student score in mathematics and language is one-third and two-thirds higher in private schools. A student in private school is 50 percent more likely to be able to read a word, and his or her reading comprehension score is three times as high as those of public school students. However, there is also some evidence that at least some of this superior performance is due to sorting of students: the non-verbal reasoning score in private schools is 13 percent higher than in public schools.

# **10. Discussion and Conclusion**

In this paper, we report on what primary school teachers in Africa know and do, using representative data from an ongoing survey program: The Service Delivery Indicators data. The findings provide a concerning picture of teacher effort, knowledge, and skill, although there is significant variation in teacher quality, both within and across countries. Even taking this variation into account, however, if “adequate” teaching is defined by students that during most of their scheduled time are being taught by teachers with at least basic pedagogical knowledge and minimum subject knowledge in language and mathematics, then very few public primary school students, in the seven countries we surveyed, benefit from decent quality education.

We argue, and partly show, that this lack of quality education in turn can help explain why so many students learn little and complete their primary education lacking even basic reading, writing, and arithmetic skills. Potential human capital for cohorts of students is consequently lost.

23