rafmacalaba/datause-encoder-data huggingface dataset:
# **Analysis of Teacher Stock versus Flow in Primary Education in East Asia and the Pacific Middle-Income Countries:**

**A Simple Model and Results from Simulation between 2020 and 2030**<sup>1,2</sup>

Nobuyuki Tanaka and Lars Sondergaard The World Bank JEL Codes: I21 (Analysis of Education), I28 (Education Government Policy), J10 (Demographic Economics General), J20 (Demand and Supply of Labor General) _Keywords: Teacher Training, Teacher Workforce Planning, Primary Education_ Acknowledgments: The authors benefited from useful comments on the analysis from Andrew D. Mason, Toby Linden, and Mary E. Breeding. The authors also appreciated Tara Béteille and Ezequiel Molina for their comments and suggestions on the draft version of this paper, and Cristian Aedo for his guidance. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the countries they represent. Please address correspondence to ntanaka1@worldbank.org

> 1 This paper was prepared as a background paper for East Asia and the Pacific Regional Flagship Report “Bridging the Learning Gap: Supporting Teachers in East Asia and the Pacific” jointly prepared by Education Global Practice and Office of the Chief Economist in East Asia and the Pacific Region, The World Bank. (P180710)

> 2 We simulated primary school teachers’ stock and flow up to 2040 and examined the situation as of 2030 (SDG4 target year).
<!-- Start of picture text --> 50<br>40 :<br>= 30<br>2<br>g<br>2<br>o<br>3<br>a 20<br>10 :<br>0<br> a<br>1990 1995 2000 2005 2010 2015 2020<br>— Africa —— Rest of the World<br><!-- End of picture text -->
# **Table A30: OLS; Effects of refugee presence on household welfare disaggregated by change in main source of income, with analysis at cluster level and the distance weighted refugee index at -** **<u>50km from the clusters (Panel 2009 2012)</u>**

||Log of welfar|e(consumption aggre<br>equivalent)|gate per adult|
|---|---|---|---|
||(1)|(2)|(3)|
|Log of refugees (50km)|0.0183<br>|0.0183<br>|0.0175<br>|
||(0.0132)|(0.0131)|(0.0188)|
|Log of refugees(50km)#To Commercial farming|0.0772**|0.0772**|0.0731**|
||(0.0313)|(0.0313)|(0.0306)|
|Log of refugees(50km)#To Wage employment|0.0206|0.0206|0.0400**|
||(0.0166)|(0.0167)|(0.0172)|
|Log of refugees(50km)#To Non-Agric self-employment|-0.0048|-0.0046|0.0129|
||(0.0153)|(0.0153)|(0.0209)|
|HH head age||0.0033|0.0141**|
|||(0.0061)|(0.0070)|
|Age squared||-0.0000|-0.0001|
|||(0.0001)|(0.0001)|
|HH head sex (male)||-0.0077|0.0092|
|||(0.0385)|(0.0469)|
|Household size|||-0.0220***|
||||(0.0065)|
|**_HH head marital status_**||||
|Married monogamously|||-0.5175***|
||||(0.1943)|
|Married polygamously|||-0.4436**|
||||(0.2025)|
|Separated (divorced/widowed)|||-0.5121**<br>(0.2062)|
|**_HH head education level_**||||
|Completed primary sch|||0.2557***|
||||(0.0418)|
|**_HH main source of income_**||||
|Change to Commercial farming|0.1746|0.1761|0.2540|
||(0.1614)|(0.1623)|(0.1605)|
|Change to Wage employment|-0.1500**|-0.1503**|-0.1562**|
||(0.0598)|(0.0600)|(0.0616)|
|Change to Non-agricultural self-employment|0.0845|0.0835|0.0577|
||(0.0713)|(0.0714)|(0.0599)|
|**_Other source of HH income_**||||
|Property income and investments|||0.2266**|
||||(0.0912)|
|Transfers and other benefits|||0.1209**|
||||(0.0508)|
|**_HH Asset_**||||
|Own > median size of land (2.5 acres)|||0.1818***|
||||(0.0363)|
|Observations|3,734|3,734|2,763|
|R-squared|0.4057|0.4059|0.4213|

50
8

**Table 2. Determinants of Sixth Grade Spanish Test Score:**

**PARE, 1994**

||**Regression**|**t-value**|
|---|---|---|
||**Co-efficient**||
|**Dependent:**<br>|||
|6<sup>th</sup>Grade Spanish test score|||
|**Independent Variables:**<br>|||
|Child’s past Academic Record<br>|-.139|-1.68|
|Per student cost of PARE|.005|2.08|
|Assistance<br>|||
|Score in 4<sup>th</sup>Grade|.251|9.91|
|Family’s Cultural Capital<br>|.105|3.95|
|Teacher’s Performance 5<sup>th</sup>grade|.095|2.39|
|Teacher’s Performance 6<sup>th</sup>grade|.126|2.93|
|Director’s Quality|.092|2.20|
|Supervision Quality<br>|.047<br>|2.76<br>|
|Parent’s Participation|-.062|-2.80|
|DUMMY for Rural<br>|-8.882|-9.08|
|DUMMY for Indigenous|-12.422|-8.85|
|(Constant)|23.079|4.72|
|Adjusted R square||.26|
|N=2114|||
|Estimation method|||
|Squares|||

This analysis indicates that, learning achievement elasticity with the respect to cost of intervention stood at about 0.33. This is lower than the previous estimate since the second analysis allowed for the many imperfections in implementation to be factored in, particularly the urban implementation problems. Still, the learning effect relative to cost for the combined indigenous, rural and urban samples remains substantial.

This analysis held constant a number of school and student background characteristics. It is worth mentioning that the quality of school principals, teachers, and supervisors appears to be a significant determinant of Spanish test score. An unexpected result is that parental participation appears to have significantly negative correlation. This probably reflects simultaneity bias, reflecting the fact that parents tend to be more active when children are not doing well in school. A recent U.S. study shows that parental participation takes on the expected positive effect when it is treated as an endogenous variable.<sup>4</sup>

# **4** . **Hypothesis**

> 4 Robert McMillan, Parental Pressure and Competition: An Empirical Analysis of the Determinants of Public School Quality, January 10, 1999 (Processed).
contribution of displaced workers to aggregate welfare is higher out of public sector than in it, downsizing has positive economic returns.

On pure efficiency grounds, the optimal allocation of labor in the economy occurs when the marginal productivity is equalized. Using the estimated production functions, we can solve for the needed transfers of labor and capital that achieves this equality. The results are shown in Table 5.

Our 1996 results show that within the industry, 43 percent of labor (and 70 percent of capital) needs to be transferred to the non-state sector to ensure efficient resource allocation and overall higher productivity growth.<sup>15</sup> The 1997 results suggest that even larger amounts of labor and capital need to be transferred to the non-state sector (57 percent and 74 percent of state industrial labor and fixed capital asset). The estimates for labor are consistent with Chinese officials' assessment of the extent of surplus labor in SOEs at between one third and one half of the work force (See Fan, O'Cornor and Lunati, 1998).

The estimated gains in GDP are large. A 43 percent transfer of labor to the non-state sector will produce a gain of 2.56 percent of 1996 GDP, and a 70 percent of capital transfer will produce a gain of 8.29 percent of 1996 GDP. This of course assumes full employment of redundant workers, if we consider that laid-off workers remain unemployed for a while, or not simply not re-employable in non-state sector, and are retired early, the total social gain could be smaller. If

> **15** Note that 43 percent of industrial **SOE** workers represent 21 percent of total SOEs workers and 11 percent of urban workers.

12
**Table 4.1. Elements for quality assessment at airports**

<!-- Start of picture text --> Passengers | Airlines | Others<br>Subjective approach: Quality surveys monitoring<br>assessment Objective approach: Establishment of standards and<br>measurement of performance<br><!-- End of picture text -->

# 4.2.1.- Services to passengers

As mentioned above, BAA controls the level of quality in passenger services through quality survey monitoring. This constitutes an application of the subjective approach. The survey measures passengers' perception of the service they receive in departures, arrivals and retail areas. Over 250,000 passengers are interviewed each year. The interview takes 8 to 12 minutes, and passengers are asked to assess services on a fivepoint scale from "extremely poor"(1) through "average"(3) to "excellent"(5). At Heathrow, Gatwick and Stansted, information has been collected throughout a six-year period on customers perception of twelve basic aspects of services in the departures area, and seven basic aspects of services in the arrivals area. Departing passengers are interviewed as they enter the gateroom, and arriving passengers as they exit the terminal. In a similar way, perceptions about various aspects of service and value for money provided by retail outlets, car parks and restaurants, are also collected.

Scores for BAA airports are presented at Tables 4.2 and 4.3. However, these are results only for those areas that are common to all three airports. Each table shows the constituent factors for each airport ranked according to the quality survey monitoring. Results show that, on average, passengers perceive most areas to be at least "average." Many areas are ranked between "good" and "excellent," and no area is assessed as "extremdly poor." Overall, Stansted scores consistently well, and Gatwick still scores slightly better than Heathrow, although Heathrow has shown more improvement than Gatwick since 1991.

In addition to recording subjective measures about passengers' perceptions, various performance standards have also been established by BAA . An example of check-in queue targets is presented at Table 4.4.

38
(Group 0 mean = 2.17), while experts who increased their estimates had Round 1 estimates that were lower relative to their country/regional means (Group 1 mean = 0.27). Among the other variables tested, only two had means that differed significantly (P < 0.05, one-sided test) between Groups 0 and 1: experts who believed more strongly that the Delphi estimates were close to actual WTP in their countries were more likely to reduce their estimates; the same pattern holds for experts who published more CV and CE papers.

**Table 10. Tests of mean differences between experts who decreased their predictions for Plan A between rounds 1 and 2 (Group 0) and experts who increased their predictions (Group 1).** Table shows means for each group and _P_ -values for one-sided _t_ -tests (null hypothesis: Group 0 mean equals Group 1; alternative hypothesis: Group 0 mean is greater than or less than Group 1 mean, depending on which mean is larger). Sample size varies slightly from variable to variable, but in most cases is 65 for Group 0 and 39 for Group 1.

|_Variable_|_Group 0_<br>_mean_|_Group 1_<br>_mean_|_P-_<br>_value_|
|---|---|---|---|
|Round 1 prediction|$116.47|$20.16|0.000|
|Discrepancy between expert’s Round 1 prediction and mean for|$61.43|$44.38|0.000|
|his/her country/region||||
|Ratio of expert’s Round 1 prediction to mean for his/her|2.17|0.27|0.000|
|country/region||||
|Proximity of Delphi estimate to result of an actual SP survey|6.48|5.78|0.043|
|Difficulty of successfully implementing a SP survey on this topic|5.47|5.85|0.230|
|Number of CV surveys carried out|7.22|7.18|0.490|
|Number of CE surveys carried out|3.77|3.63|0.448|
|Number of surveys (CV+CE) carried out about biodiversity and|3.22|3.56|0.363|
|ecosystem services||||
|Number of benefit transfer exercises|2.58|2.20|0.329|
|Journals: 1<sup>st</sup>principal component|-0.079|-0.272|0.261|
|Journals: 2<sup>nd</sup>principal component|-0.118|0.112|0.166|
|Journals: 3<sup>rd</sup>principal component|-0.104|-0.215|0.291|
|Journals: 4<sup>th</sup>principal component|0.072|0.148|0.352|
|Number of CV and CE papers published in national and|4.65|2.72|0.012|
|international journals in past 5 years||||
|Number of CV and CE papers reviewed in national and|9.48|8.66|0.379|
|international journals in past 5 years||||
|Highest degree: BSc|0.015|0|0.161|
|Highest degree: MSc|0.077|0.026|0.113|
|Gender: female|0.354|0.282|0.224|

# **5. Other Issues**

In this section we provide some information about certain key characteristics of experts, by region, that may have had systematic influence on valuation answers. Table 11 indicates some of 26
cancellation rates could be underestimated if one-year operational projects were included, as these projects are likely too young to reveal inherent problems that could lead to distress or cancellation.<sup>6</sup> Comparing the percentage of projects with multilateral support that are canceled (6.1%) to the rate of cancellation of non-MLS projects (5.8%), the rates are not statistically different (see Table 1 and Figure 2).

Table 1. Summary statistics of project data set, PPPs from 1990-2010

|PPP Status|Multilate<br>With MLS|ral Support<br>Without MLS|Total|
|---|---|---|---|
|Canceled PPPs|24|149|173|
|Total operational or concluded|368|2,409|2,777|
|Total(canceled,operational,or concluded)|392|2,558|2,950|
|Cancellation rate|6.1%|5.8%||

Source: PPI Database, Feb 2016 Figure 2. Relative cancellation rates of MLS and non-MLS projects, 1990-2010 <!-- Start of picture text --> Cancellation rate<br>0% 1% 2% 3% 4% 5% 6% 7% 8% 9% 10%<br>With MLS Without MLS<br><!-- End of picture text --> Source: PPI Database, Feb 2016 These observed patterns could lead one to conclude that the cancellation rate of PPP projects is not reduced with financing from multilateral organizations. However, the effect of ‘not having MLS’ is not directly observable in the group of PPPs that do not have MLS. To this end, a proper non-MLS comparison group (counterfactual) for projects _with_ MLS needs to be established.

To do this, a probabilistic model was first estimated to calculate the probability of receiving multilateral support for the full set of PPP projects. Second, a group of projects without MLS but with a very similar (statistically identical) probability of receiving MLS to the group that actually received MLS was identified. Details of the methodology and the results of comparing projects with MLS with their counterfactual are presented in the following sections.

# **_Methodology_**

In randomized controlled experiments, a randomly assigned intervention divides the individuals under study into two groups: those with intervention (treatment group) and those without intervention (control group). Because of the randomization, both groups have exactly the same likelihood of being treated and

> 6. To this point, it is important to recognize also that younger projects which are canceled are less likely to be recognized in the PPI Database due to lags in project status updates. As a robustness check, analysis was also performed on sets excluding PPPs with less than seven and less than five years of potential operability. See sensitivity analysis section and Annex 4 for more detail.

5
8 To address these issues in our case, we proceed in two stages. First, Montecarlo simulations are used to quantify the power of the most-used parametric unit-root test, the Dickey-Fuller (DF) test, in the presence of level and trend breaks. The design of the Montecarlo experiment follows that of Hendry and Neale (1991), but it has been adjusted to fit the characteristics of the commodity price data. Second, we test for the existence of breaks in the Grilli-Yang commodity prices database, using a sequential test developed by Zivot and Andrews (1992).

Table 1 presents the results of the Montecarlo simulation of the power of the DF test for different sample sizes (ranging from 64 to 256 observations), competing alternatives (AR(1) and ARIMA(1,1,1) models) and types of structural breaks (changes in the level or the trend of the series).<sup>7</sup> Considering first the no-breaks case, it can be seen that the power of the test when the true data generating process is a stationary AR(1) model, is low in samples of less than 128 observations, even if the specification is not close to the unit-root process (when p =0.85 the DF test correctly rejected the null hypothesis of random-walk only in 48.2% of the cases, at 10% significance levels). As expected, the power of the test declines as the model approaches the unit-root specification; when the autoregressive parameter p increases to 0.96, the power of the test is less than 25% for 128 observations. The more complicated ARIMA(1,1,1) model, suggested by Summers (1986) to capture slow mean-reverting processes, proves the DF test to have very low power, even for large samples of 256 observations.

To analyze the role of structural breaks we introduce two types of breaks --a level shift equivalent to 2 times the size of the standard deviation of innovations and a trend break of size 5%-- located at the mid-point of each sample size.<sup>5</sup> The results illustrate the significant reduction in the power of the test, in particular when the sample has 128 or fewer observations. In the 7 We used the pseudo random-generator routine of GAUSS to create 10,000 artificial series of two specific processes: a stationary AR(1) model with homoskesdastic innovations, and an ARIMA(1,1,1) model, formed by adding to the former a nonstationary (random-walk) component. The standard deviation of the innovations in the latter was chosen to be substantially smaller than that of the stationary component (25%). We apply a standard DF test to the simulated series and use McKinnon critical values to compute the number of cases in which the test (correctly) rejects the null hypothesis of a random walk. A detailed description of the methodology and the complete set of results is presented in Le6n and Soto (1994a).

' We also tested for level and trend breaks located at 25% wod 75 % of each sample size, but results are affected only marginally. In addition, we tested bigger size breaks which, as expected, futer lowered the power of the test.
25

# **Table 3**

|Dependent variable:|Growth of Manuf|acturing Exports|from COMTRAD|E by Industry|
|---|---|---|---|---|
||1996‐2011|1996‐2003|2004‐2011|1996‐2011|
|Real Exchange Rate Change|**‐1.356 *****<br>0.261|**‐1.593 *****<br>0.303|**‐0.699 *****<br>0.084|**‐1.515 *****<br>0.317|
|Real Exchange Rate Change interacted with Participation of Industriesin GVCs||||**0.041 ****<br>0.016|
|Participation||||**‐1.087 ***<br>0.586|
|Lag real GDP|**0.174**|**‐10.774**|**‐24.578 *****|**0.608**|
||5.367|23.771|5.441|5.261|
|Foreign real GDP|**1.478 ****|**1.842 ****|**1.228 *****|**1.541 ****|
||0.721|1.464|0.411|0.748|
|Time Fixed Effects|Y|Y|Y|Y|
|Country‐Industry Fixed Effects|Y|Y|Y|Y|

Note: *** indicates statistically significant 1%, ** indicates statistically significant at 5%, and * indicates statistically significant at 10%.

Point estimates in bold letters and robust standard errors below.

Countries as in the Appendix.

Industries included basic metals, chemicals, electrical equipment, food, machinery, textiles, transport equipment and wood products.

# **Table 4**

## Exports <u>of Goods and Services</u>

|Independent variable/Dependent Variable|1996‐2011|1996‐2003|<br>2004‐2011|2011‐2004 minus<br>1996‐2003|
|---|---|---|---|---|
|Total Exports/REER|‐1.05*******|‐1.22*******|‐0.68*******|0.53|
|Value Added Exports/REER|‐0.88*******|‐1.00*******|‐0.60 ***|0.41|
|Value Added Exports/ REER in Value Added Terms|||||
|_(tasks value added)_|‐0.17 *|‐0.13|‐0.24*******|‐0.10|
|Value Added Exports/ REER in Value Added Terms|||||
|_(goods value added)_|‐0.23 **|‐0.18|‐0.31*******|‐0.13|

Note: *** indicates statistically significant 1%, ** indicates statistically significant at 5%, and * indicates statistically significant at 10%. Point estimates in bold letters and robust standard errors below. All Regressions include Time FE, Country FE, Lag real GDP and Foreign real GDP.

Countries include Australia, Austria, Belgium, Brazil, Canada, Chile, China, Denmark, Finland, France, Germany, Greece, Hungary, India, Indonesia, Ireland, Israel, Italy, Japan, Korea, Republic of, Luxembourg, Mexico, Netherlands, New Zealand, Norway, Poland, Portugal, Romania, Singapore, Slovakia, Slovenia, South Africa, Spain, Sweden, Switzerland, Thailand, Turkey, United Kingdom, United States.
Policy Research Working Paper 6724

# **Abstract**

The Government of Bangladesh, with help from the World Bank and other donors, has provided aid to a local agency called Infrastructure Development Company Limited and its partner organizations to devise a credit scheme for marketing solar home system units and making these an affordable alternative to grid electricity for poor people in remote areas. This paper uses household survey data to examine the financing scheme behind the dissemination of these solar home systems, in particular the role of the subsidy; the factors that determine the adoption of the systems in rural Bangladesh; and the welfare impacts of such adoption. The paper finds that while the subsidy has been declining over time, the demand for solar home systems has seen phenomenal growth, mostly because of technological developments that have made the systems increasingly more affordable. Households with better physical and educational endowments are more likely to adopt solar home systems than poor households. The price of the system matters in household decision making—a 10 percent decline in the price of the system increases the overall demand for a solar panel by 2 percent. As for the benefits, adoption of a solar home system improves children’s evening study time, lowers kerosene consumption, and provides health benefits for household members, in particular for women. It is also found to increase women’s decision-making ability in certain household affairs. Finally, it is found to increase household consumption expenditure, although at a small scale.

This paper is a product of the Agriculture and Rural Development Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at hsamad@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