rafmacalaba/datause-encoder-data huggingface dataset:
correlated with growth, but the relationship between any particular indicator or group of indicators and cross-country growth rates is fragile. After this subsection illustrates these findings, the following subsections conduct a systematic series of extreme bounds tests to determine the robustness of past findings.

Tables 2 and 3 anticipate this paper's findings. Country's that grew faster than average over the 1960-89 period tended to have a higher share of exports in GDP, a higher share of investment in GDP, larger primary and secondary school enrollment rates, a lower black market exchange rate premium, and lower inflation rates than slower growing countries. Similarly Table 3 shows that the investment share, the export share, the black market premium, and the indexes for revolutions/coups and civil liberties are significantly correlated with the average real per capital growth rate. Importantly, however, none of these variables is significantly correlated with the residuals from the regression of growth on the I-variables (the investment share, initial income, initial secondary school enrollment rate, and population growth). Thus, while many policy indicators are significantly related to growth, this relationship depends importantly on what factors are being held constant.

Kormendi and Meguire (1985) and Barro (1991) use a variety of macroeconomic variables to explain growth, and they present intuitively appealing results. Table 4 presents equations based on these studies. Equaticn 2 is nearly a replication of Barro’s (1991) work and includes the share of investment in GDP, a measure of initial human capital, population growth, and initial income along with the ratio of government consumption expenditures to GDP, a dummy variable for socialist economic systems, indicators for revolutions and coups, and dummy variables for Latin America and Africa. Barro's data is 12
To capture any broader strategic use of the MIS in the government, we also interviewed representatives from the Federal Small and Medium Manufacturing Industries Development Agency because they expressed interest in developing an agency‐wide MIS with the same structure as that of the WEDP MIS. We also interviewed the Association of Ethiopian Microfinance Institutions (AEMFI) because they were planning a similar MIS for connecting MFIs and because they attempt to coordinate MFI M&E and reporting systems.

Conceptually, one can think of developing an M&E framework for a project on three levels. On the most basic level, the M&E framework needs to deliver as required from the project perspective. This includes the donor’s, client’s, implementers’, and other stakeholders’ needs to be informed about project progress and likely outcomes. In this case, the standard approach entails hiring an external M&E consultant to develop the framework.

The second level involves more‐integrated development of the M&E framework using the client’s M&E systems. The donor and client together map out the indicators, modes of data collection, and other components of the system. Although learning may not be part of the objective, working closely together usually entails some capacity building to meet the required standards.

On the third and most‐advanced level, there is an explicit capacity‐building aim, with the client learning through the project how to develop its own M&E systems and systematically using the information provided to manage the project. In this latter case, the system for generating the data for the M&E is often referred to as an MIS.

# **5. Effectiveness: How to Establish an Effective MIS**

## **5.1 An effective MIS**

By September 2018, the MIS contained a database of approximately 25,611 female business owners who had applied to the WEDP and fulfilled the participation criteria. Data are entered in the MIS at three main points: when an eligible client registers with the WEDP, takes training, and takes a loan from the MFI. We present information from each of the sources and provide a graphic overview of some of the functions that can be applied to display the overview of the data and trends.

The MIS has an easy‐access user‐friendly interface that is suitable even for people without basic computer skills. Most users at the entry level have had no prior experience with computers, and only some have experience with the Internet through mobile phone applications. The start page when accessing the webpage (available here: http://197.156.90.249:8100/) The website start page contains real‐time data on project indicators, number of clients, number of clients trained, and number of loans that these clients have taken out. Refreshing the browser provides instant changes in the figures when a new client has been registered, some training has been completed, or a loan has been disbursed. Everything can be viewed in Amharic to ensure that nothing is lost in translation for individuals without strong English skills, for instance, lower‐level government staff.

13
# Table 28: Share of Eastern Europe in US Imports of Textites Restrictiveness of the NFA, 1985 - 1989 (percent) and Ctothing and Indicators of the Coverage nd

|Exporter<br>|(I)<br>Import<br>Share'|(11)<br>Restricted<br>Inports<br>(trade<br>coverage<br>ratio or<br>REST/|(111)<br>Imports<br>Subject<br>to<br>Binding<br>Restrictions<br>BIND/<br>TOT)Toc|(IV)<br>Iports<br>Subject<br>to<br>Binding<br>Restrictions<br>BIND/<br>REST"|(V)<br>Average<br>Quota<br>Utilization<br>Rate|
|---|---|---|---|---|---|
|EAST EUROPEAN FIVE<br>1985|0.83|62.61|704|1125||
|1986|0.81|<br>6329|.<br>2794|.<br>|19.59<br>|
|1987|<br>0.87|.<br>6288|.<br>2382|44.14<br>|26.07<br>|
|1988|0.86|.<br>7013|.<br>719|37.89<br>|32.97<br>|
|1989|<br>0.56|.<br>5991|.<br>1058|10.25<br>|23.75<br>|
|**1985-1989**|0.78|.<br>64.13|.<br>1554|17.66<br>2423|15.08<br>2269|
|Bulgaria<br>1985<br>1986|0.02<br>0.02|0.00<br>37.29|.<br>0.00<br>000|.<br>0.00<br>000|.<br>|
|1987|0.02|<br>17.71|.<br>000|.<br>000|86.93<br>|
|1988|0.01|<br>40.13|.<br>000|.<br>|51.63<br>|
|1989<br>|0.01|<br>n.a.|.<br>n.a.|0.00<br>na|24.58<br>|
|1985-1989|0.01|1779|<br>000|..<br>|n.a.<br>|
|Chlki||.|.|0.00|_A4.81_|
|zecosovaa<br>1985|0.06|0.00|000|*-||
|1986|0.04|<br>1629|.<br>1344|||
|1987|<br>0.04|.<br>721|.<br>000|82.51<br>|64.65<br>|
|1988|0.05|.<br>1340|.<br>1286|0.00<br>|19.21<br>|
|1989|0.04|.<br>na|.<br>na|96.01<br>|49.77<br>|
|1985-1989|0.05|..<br>7.21|..<br>516|n.a.<br>7166|n.a.<br>*****|
|Hnr|||.|.|**5.96**|
|ugay<br>1985|0.16|27.82|1396|||
|1986|0.18|<br>22.85|.<br>904|50.19<br>3955|52.87<br>|
|1987|0.23|<br>33.57|.<br>18.82|.<br>5606|66.14<br>9068|
|1988|0.24|4222|<br>31|.<br>|.<br>|
|1989|<br>0.23|.<br>4136|5.<br>1354|12.57<br>|38.51<br>|
|1985-1989|<br>0.21|.<br>3540|.<br>1205|32.74<br>|47.<br>|
|Pl||.|.|**34ap4**|47.k|
|oand<br>1985|D.14|69.91|0_(r_|000||
|1986|b.10|<br>6875|. <br>351|.<br>|6.74<br>|
|1987|<br>0.13|.<br>7215|.<br>402|5.11<br>|6.77<br>|
|1988|0.23|.<br>77.94|.<br>1429|5.57<br>1833|10.89<br>|
|1989|0.18|<br>78.21|.<br>1645|.<br>2104|15.75<br>1|
|1985-1989|0.16|74.89|.<br>9.95|.<br>13.29|0.58<br>10.41|

Con't.
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<!-- Start of picture text --> 8<br>6 sane2 ~ sesceceneeee?SSS<br>4 Tee sae aoe mee ee eee<br>0<br>2000q1 2005q1 2010q1 2015q1<br>—— Allcountries ——— Advanced economies<br>—--'- - EMEs s+-++++-+ Developing countries<br><!-- End of picture text --> <!-- Start of picture text --> 1<br>8<br>6<br>4 eee_ aLeUmT -Ne==—<br>2 Tt eset eee Woe wees ae<br>0 ceceeecteeeeseene seen seen eraneseneaessanaesaanersataneesenns 25 —<br>1995q1 200091 200591 201091 2015q1<br>—— Banks ——= Corporates<br>—-:-- Sovereigns --------- Central bank<br><!-- End of picture text --> <!-- Start of picture text --> 1<br>8<br>6<br>4 - pee eoa eee~<br>2 SR nn<br>1995q1 200091 200591 201091 2015q1<br>—— Banks ——= Corporates<br>—-:-- Sovereigns --------- Central bank<br><!-- End of picture text --> <!-- Start of picture text --> 1<br>not edewat este,<br>— oT see .<br>és. -- ee<br>6 ST<br>4 ~~ ==<br>2<br>1995q1 2000q1 2005q1 201091 2015q1<br>7 ~~" EMES srrieerer_Developing countries<br><!-- End of picture text --> <!-- Start of picture text --> 1<br>8<br>6<br>4<br>. pry eee =<br>240 emwe—. power a peste eens<br>0. ce eeee qeaten edi snesvsnnesenan TeFerErer TS DVS 2 eT<br>1995q1 2000q1 2005q1 201091 2015q1<br>—— Banks ——=— Corporates<br>—-:-- Sovereigns ---+----» Central bank<br><!-- End of picture text --> <!-- Start of picture text --> 1<br>8<br>6<br>4 > > ~ => oS a<br>2 a poet ttre.<br>Ne We ee te ee.<br>1995q1 2000q1 2005q1 201091 2015q1<br>—— Banks ——=— Corporates<br>—-:-- Sovereigns ---+----» Central bank<br><!-- End of picture text --> <!-- Start of picture text --> 1<br>8<br>8 ; ==<br>4 Pw aaa ‘ne ot Ne<br>= ew ——TM<br>0<br>1995q1 200041 2005q1 201091 2015q1<br>—— Allcountries ——— Advanced economies<br>- -- > EMEs seseeese+ Developing countries<br><!-- End of picture text --> <!-- Start of picture text --> 1<br>8<br>6<br>4 cate taal oN ‘SATweeIINSTaal<br>2 <Awe - “Nee<br>O]__nnesssseseeeceeescceeeseeeseseeeeeseeoteeeteeeesteeesse eneee tenes eneeeeeee<br>1995q1 2000q1 2005q1 2010q1 2015q1<br>Banks ——- Corporates<br>= -'= = Sovereigns -+++++--» Central bank<br><!-- End of picture text --> <!-- Start of picture text --> 1<br>8<br>a Let a eee ep te<br>6 was<br>4<br>a) pe TTT Rare nnee<br>0 Seriiretteateseveereresesceeatenersatenereoestoneroneesorscssrenese<br>1995q1 2000q1 2005q1 2010q1 2015q1<br>Banks ——- Corporates<br>= -'= = Sovereigns -+++++-» Central bank<br><!-- End of picture text -->
2 world prices and the prices farmers receive. The key steps In the transformation from dollar world prices to local currency value producer prices can be explained with the aid of the data preserted In Flgure 2 for the case of rice In Thailand. All variables are valued In constant prices, a practice followed throughout this paper.

The transformation from world price to producer price begins with the average export price received by a country, the export unit value or EUV, which need not closely follow the world price. Dlfferences between the world price quotation and the EUV can be explained by differences In quality, by the seasonal distribution of exports, by forward pricing contracts and by the particular world market location used. For some crops such as coffee and sugar, preferential access to protected markets for all or part of a country's exports can also account for dilfferences between the EUV and the world price. Since they may also vary over time, these factors not only explain average price differentials but also account for less than perfectly correlated price movements.

she transformation of EU%Vs from U.S. dollar to local currency units is determined by the real exchange rate. Of course, the real exchange rate Is affected by macroeconomic forces and policies, but agricultural export performance, and hence movements In EUVs, can themselves be an important factor. In some cases, It Is possible that movements In EUVs Induce offsetting movements In the real exchange rate that tend to
distribution of such enterprises by sector suggest that reforms led to a significant increase in the incidence of agricultural enterprises and a corresponding decrease of those in the tertiary sector.

# **5. Conclusion and policy implication**

Use of village-level data, mostly from administrative records, allows us to assess the impact of far-reaching factor market reforms that were implemented in Chengdu prefecture through a ‘rural-urban integration’ experiment in 2008. We find that reforms significantly reduced the threat of reallocation or expropriation, thus facilitating more efficient land use, either through investment or by transferring land form less to more efficient uses and users. This resulted in higher shares of agricultural and construction land being used for arable and economic purposes. Many of the reform-induced land use changes were mediated through land markets, suggesting that reform efforts to improve transferability of land and reduce the transaction costs of land markets operation were not without impact. Beyond this, a significant increase in new enterprise startups may point towards independent impacts from labor market reforms. Most reform effects are estimated to have been larger closer to Chengdu city center. Given the evidence in support of reform-effects at village level, analysis of the productivity of land use, distributional effects, and more rigorous costbenefit analysis of the reform using household level data could provide a promising area for future research.

15
# III. NONFORDAL BDUCATION AND **TDAININGs** INVUSTNBNT oWJEcTIVB, ST3RA2eGI3S AND LESSONS

Nonformal education and training (NFET), defined as learning that is not part of a country's regular school and training system, provides basic literacy to adults who have little or no formal schooling and to children who do not attend formal schools because they live too far away or their parents need them to work. In addition, NFET programs teach adults basic knowledge about health and nutrition, and agricultural and craft skills that can help them improve their living standards.

The objectives of the NFET programs supported by the Bank can be classified in three categories: (i) development of practical skills; (ii) promotion of basic literacy; and (iii) preparation for income generating activities.

Through 1989, 92 of the 375 Bank education projects financed by the Bank have contained NFET components;3 only 45 contained support for basic literacy training. Many of theme components were small, loes than US$1 million. Only six projects included MFET components of US$10 million or more, and in only one third of the projects did NFET comprise more than 10% of the total cost.

About two thirds of the NFET programs included support for the development of new curricula. Less than one fifth included financing for reading materials. About one fourth provided for audio-visual aids and materials.

Specific arrangements for evaluation were made in about 30% of the projects, and, in almost all instances, entrusted to existing regular permanent agencies. Nost of these evaluations focused on the effectiveness of delivery mechanisms and on assessment of the number of clients served. There are no reports of successful efforts to evaluate the achievement of learners.

While there are a few successes, the outcomes of many of these programs have been disappointing. This has been a major factor in the decline of Bank lending for NFET since 1985. The experience of the Bank, limited as it may be, contains many important lessons which need to be considered in re-examining ways to tap the potential of these programs to improve the quality of life and the productivlty of the poor.

# Ob1eet$ve 1s Development of Practical Skills

Practical skills training is the most common type of program for which the Bank has lent, comprising about 80% of all NFET programs. The teaching of agricultural or farming skills was the predominant area of interest, although a number of projects supported training in other rural vocational skills. Most 31 The discussion **in** this chapter draws very heavily on Romain and Armstrong, "Review of World Bank Operations in Nonformal Education and Training." 26
# <u>Decomposition Results</u>

Table 9 reports the results of decomposing the non-indigenous/indigenous achievement gap for Peru, Mexico and Guatemala using the traditional Oaxaca-Blinder decomposition method. Achievement decompositions are based on separate regressions for indigenous and nonindigenous students (Tables 6-8). The gross standardized gap for the language test is 0.77, 0.73 and 1.06 standard deviations for Peru, Mexico and Guatemala respectively, in favor of nonindigenous (Spanish-speaking) students. The gross standardized gap in the mathematics test is about 0.69 standard deviations for Peru as well as Mexico and 0.85 standard deviations for Guatemala.

Two issues worth commenting on before proceeding with the discussion of the test score decomposition results relate to how representative are the survey data available of the indigenous and non-indigenous populations of 3<sup>rd</sup> and 4<sup>th</sup> year pupils in the two countries, as well as the severity of selection bias due to dropping observations with partial background information. With respect to the first, it should be noted that pupils who drop out of school before the 3<sup>rd</sup> grade are not part of the sample, while the probability of a child dropping out of school is higher among the indigenous population. Furthermore, pupils who are more often absent (and hence more likely to be absent the day of the test) are not part of the sample.

The severity of selection bias due to dropping observations with partial background information can be assessed by comparing the average test score gap in the original sample to that after dropping observations with partial background information (that is the one that appears in the test-score decomposition in Table 9). Based on this comparison, the selection bias is 20
**Figure 3.  Policy Tradeoffs in Sudden Stop Episodes**

<!-- Start of picture text --> 1991‐2002 Monetary Policy<br>1<br>2003‐2014<br>0.6<br>0.2<br>‐0.2<br>‐0.6<br>Structural<br>‐1 Fiscal Policy<br>Reforms<br>Exchange Rate<br>Regime<br><!-- End of picture text --> We assign either a zero, one, or negative one to a country in each episode, with a one when a country tightened monetary policy, tightened fiscal policy, made its exchange rate regime more flexible, or committed to structural reforms. Zero when there is no change, and minus one when a country eased monetary policy or fiscal policy. Countries with all minus one are at the center of the figure, whereas countries with all ones are at the four vertexes (they trace out the diamond).

17
**<u>Table 4: Details on land takings in sample villages (household level information)</u>**

||**All**||**By re**|**gion**||
|---|---|---|---|---|---|
||**China**|**N & NE**|**Coastal **|**Central **|**SW**|
|**Project level indicators**||||||
|Amount of land lost (mu)|1.23|1.52|1.33|1.06|1.05|
|Net income from land before taking (Y/mu)|780.95|526.69|1148.08|616.14|639.01|
|Received monetary compensation (%)|82.37|72.44|82.08|89.47|85.88|
|... before policy change (%)|79.67|75.38|76.70|82.61|82.74|
|... after policy change (%)|85.57|69.35|86.92|94.12|91.58|
|Received land as compensation|41.25|51.18|35.24|35.09|42.59|
|if yes, size relative to amount of land lost (%)|86.79|92.98|86.59|81.01|84.35|
|Monetary compensation received (Y/mu)|7729.9|4908.9|9635.1|4128.5|8240.9|
|...before policy change (Y/mu)|6642.2|4258.7|7272.1|2655.3|7669.4|
|...after policy change (Y/mu)|8949.8|5649.8|11642.4|5003.3|9154.0|
|Distance to county center (km)|20.15|16.89|19.71|23.90|21.31|
|Nearby national road (%)|14.31|16.54|11.91|5.26|17.11|
|Negotiation directly with private investors (%)|21.16|23.62|20.48|21.05|20.53|
|**Perceived impact of land taking**||||||
|Perceives taking to have improved welfare|22.91|19.69|12.04|31.58|31.66|
|... due to compensation|16.77|30.77|18.42|19.05|10.98|
|.... due to job creation|55.69|50.00|63.16|52.38|54.88|
|.... due to both|27.54|19.23|18.42|28.57|34.15|
|Perceives taking to have worsened welfare|18.36|24.41|19.91|10.53|15.83|
|... due to compensation|60.00|62.86|50.91|33.33|75.61|
|... due to compensation & harmful effects|40.00|37.14|49.09|66.67|24.39|

_Source:_ Own computation based on 665 households affected by takings from the 2005 NBS/WB survey.

26
Indonesia, and about half of them are primary school teachers, this translates to a large number of teachers that need to be upgraded or replaced.

# **Table 1: Number and percentage of teachers below required qualification before** **<u>and after the new teacher law</u>**

||Primary|Junior<br>secondary|Senior<br>secondary|
|---|---|---|---|
|**Minimum qualification starting in**<br>**2005**|**4-year degree**<br>**or diploma**|**4-year degree or**<br>**diploma**|**4-year degree**|
|% of teachers below min. qualification|<br>82%|<br>33%|13%|
|**Minimum qualification before 2005**<br>% of total teachers below min.<br>qualification|**2-year diploma**<br>33%|**3-year diploma**<br>15%|**4-year degree**<br>13%|

While the Ministry of Education is carrying out a massive upgrading program with recognition of prior experience for incumbent teachers, whether the law will be successful in the longer run depends foremost on how it would influence the occupational choice decision of college-educated workers. Until now, there has been no basis for making such forecasts. In the absence of empirical analysis, we can only guess at the impact of changes in teacher salaries on the composition of the teaching force.

Under a theoretical framework with a government-dominated market with a government-set wage rate and demand for teachers, this paper explores how teacher supply, particularly the resulting composition of the teaching force with low or high qualification, would be determined by current and future public policies. Using 2001 to 2008 Indonesian Labor Force Survey (SAKERNAS) data, this paper further estimates the potential effect of the latest teacher law, which gives high qualification teachers a significant pay increase, on the composition of the Indonesian teaching force with differentiated education backgrounds.

7
33 impact on GDP. This constellation of rates define the fiscal space of action for the government to have a positive impact on GDP through its spending.

|Table 7<br>Required Rates of Return for Alt<br>Rates of Discount (r), Recurren<br>Ratios (c), and Marginal Cost o<br>(MCF)<br>Project pays for itself (no ta<br>r<br> <br>|ernative<br>t Cost<br>f Funds<br>xes)<br>|
|---|---|
|0.03<br>0.05|0.09|
|c||
|0.03<br>0.06<br>0.08|0.12|
|0.05<br>0.08<br>0.10|0.14|
|0.12<br>0.15<br>0.17|0.21|
|Project needs full tax financ|ing|
|**MCF =**<br>**0.15**||
|r||
|0.03<br>0.05|0.09|
|c||
|0.03<br>0.07<br>0.09|0.14|
|0.05<br>0.09<br>0.12|0.16|
|0.12<br>0.17<br>0.20|0.24|
|**MCF =**<br>**0.25**||
|r||
|0.03<br>0.05|0.09|
|c||
|0.03<br>0.08<br>0.10|0.15|
|0.05<br>0.10<br>0.13|0.18|
|0.12<br>0.19<br>0.21|0.26|
|**MCF =**<br>**0.4**||
|r||
|0.03<br>0.05|0.09|
|c||
|0.03<br>0.08<br>0.11|0.17|
|0.05<br>0.11<br>0.14|0.20|
|0.12<br>0.21<br>0.24<br>Source: calculations based on Appe|0.29<br>ndix 4|