rafmacalaba/datause-encoder-data huggingface dataset:
**Table A3** Predictors of Achieving Goal

||(1)|(2)|
|---|---|---|
||**Number of**<br>**Strategies Set**|**Number of**<br>**Strategies Achieved**|
|Major depressive disorder likely|-0.235*<br>(0.121)|-0.219*<br>(0.127)|
|Self-efcacy|0.103***<br>(0.036)|0.102***<br>(0.035)|
|Will take risk (top quintile)|0.055|-0.002|
||(0.057)|(0.067)|
|Impatient now and patient later|-0.028|-0.018|
||(0.076)|(0.082)|
|Age|-0.002|0.008|
||(0.009)|(0.013)|
|Currently enrolled in school|-0.185|-0.168|
||(0.114)|(0.135)|
|Completed school|-0.093|-0.054|
||(0.106)|(0.125)|
|Household has electricity|-0.044|0.021|
||(0.051)|(0.055)|
|Household has earthen foor|-0.232***<br>(0.065)|-0.121<br>(0.088)|
|Outcome mean|1.772|1.640|
|Observations|789|644|

_Notes._ The outcome in column 1 is the number of strategies the female set and committed to during the goal setting activity, ranging from 1 to 3 strategies; the outcome in column 2 is the number of strategies that the female reports she achieved at the endline survey, ranging from 0 to 3 strategies. The covariates are from the baseline survey (age, currently enrolled in school, completed school, household has electricity, household has earthen floor) or the goal setting activity, 1st visit (major depressive disorder likely, self-efficacy, will take risk). Major depressive disorder likely is a binary indicator measured using the Patient Health Questionnaire-2 (PHQ-2), where a score of 3 or higher is indicative of depression. Self-efficacy is measured using the General Self-Efficacy Scale developed by Schwarzer and Jerusalem (1995). This scale generates a total self-efficacy score that ranges from 10-40, which we standardized using the mean and standard deviation among females in ELA only communities. Will take risk is based on a question that asked females how willing they were to take risks on a scale from 1 to 10. We generate a binary indicator equal to one if the female gave an answer in the top quintile of responses. Age is age in years at the time of baseline, and currently enrolled in school, completed school, household has electricity, and household has earthen floor are binary indicators. Standard errors, clustered at the club level, are presented in parentheses below coefficient estimates. .01 ***, .05 **, .10 *. _Source._ Column 1: Female goal setting participants, 1st visit. Column 2: Female goal setting participants, endline data.

62
3 This paper examines the correlates of success of the World Bank’s development policy lending in achieving its intended development results, focusing mostly on loan design characteristics while controlling for country factors. The paper examines these correlates only for the post-2004 development policy lending (hereafter policy-based loans).

To do so, the paper applies straightforward regression models on a data set that compiles performance of World Bank policy-based loans, loan design characteristics as well as country factors, between September 2004 and June 2012. The set of loan design variables constructed is based on a thorough desk review of each loan<sup>2</sup> Program Document and on the basis of task team leader skills and past performance.

To place this study in context, it is important to highlight the changes that occurred in 2004 to the policy-based lending instrument of the World Bank, which included the following: (a) the conditions for loans were generally to be drawn from the governments’ own development strategies; (b) the conditions were to be reduced in number, and were to become significantly more selective; (c) no loans were to be granted to countries that did not have an adequate macroeconomic policy framework; and (d) there had to be more focus on development results as a consequence of the policies supported. A further important change in 2004 was the introduction of the _programmatic series of loans_ .

There is a marked difference in performance between the old loans and the new, as is shown by the bottom line ratings of Implementation Completion Reviews (a review that all World Bank loans undergo after they close) and the validations of the latter by the Independent Evaluation Group (IEG) of the institution. As Dollar and Svensson (2000) report, in the structural adjustment era 36 percent of fast-disbursing loans failed; the failure rate of development policy loans in our data set, which extends from September 2004 to June 2012, was 19.6 percent using the same measure<sup>3</sup> . Hence the success rate of the “new” policy-based loans is high, and is higher than that of its predecessor.

> 2 This paper uses “loans” as a generic term for loans, credits (from the heavily subsidized IDA window), and grants.

> 3 By “failure” is meant a rating by the Independent Evaluation Group (IEG) of “moderately unsatisfactory” or poorer; “success” is a rating of “moderately satisfactory ” or better.
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Table 9. Pakistan: Caital-Output and Capital-Labor Ratios and Per Capim Output. 1972-92 (000' Rs., 1987 prices)

||KIGDP|Overll<br>Economy<br>KIL|ODP/L|MKIMGDP|Manulacmnng<br>MKIML|MGDPIML|
|---|---|---|---|---|---|---|
|1972|-|-|-|-|-|12.4|
|1973|1.37|17.7|12.9|1.33|17.4|13.0|
|1974|1.42|18.4|13.0|1.33|17.7|13.4|
|1975|1.47|19.3|13.3|1.46|18.6|12.7|
|1976|1.53|20.3|13.4|1.70|20.9|12.3|
|1977|1.61|21.3|13.4|1.89|23.7|12.5|
|1978|1.61|22.0|13.8|2.01|26.5|13.1|
|1979|1.67|22.7|13.8|2.13|2B.8|13.5|
|1980|1.64|24.0|14.8|2.18|32.4|14.9|
|1981|1.62|25.0|15.3|2.34|35.1|IS.0|
|1982|1.62|26.0|_15.8_|2.19|37.3|17.0|
|1983|1.62|27.2|16.8|2.18|39.7|1B.2|
|1984|1.64|28.4|17.1|2.15|41.D|19.0|
|1985|1.63|29.6|18.2|2.10|41.7|19.9|
|1986|1.65.|31.6|19.2|2.07|45.7|22.0|
|1987|1.66|32.0|19.6|2.03|42.7|21.13|
|1988|1.63|33.7|20.6|1.94|40.3|25.4|
|1989|I.66|34.9|21.0|2.00|51.0|25.5|
|19900|1.70|36.0|21.2|1.97|53.0|27.1|
|1991|1.71|37.1|22.4|2.00|6.0|27.9|
|1992|1.70|38.7|23.5|-|_|.|

Source: Annex Tables Al and A2.

Table 10. Contribution of Total Factor Productivity Change to Pakistan's Economic Growth (% p.a.)

||Average<br>growth of<br>GDP|Average<br>Growth of<br>Capital|Average<br>Growth<br>of<br>Labor|Average<br>TFP<br>change|
|---|---|---|---|---|
|1973-1977|4.8|8.4|3.5|-1.2|
|1978-1992|6.4|6.8|2.6|1.6|

Source: Growth of labor capikal and output are estimated from Anne Table Al. To estmate TBP change, the she of capital was assumed at 0.52 and die share of labor 0.48. These are based on dxe numbers repored in Bumey (1986); an average of the 1969-70-1979-80 period is used.
levels of TRI and OTRI only depend on tariffs, according to Equations (15) and (18).<sup>33</sup> However, welfare and trade changes are larger in magnitude because they increase with size of the average elasticities, according to Equations (16) and (19). This shows that the upward biases are even larger (in magnitude) for the calculations of the overall welfare and trade impacts when the assumed homogeneous trade elasticity is larger.

# **7.2 Different Elasticity Estimates**

As an additional robustness check, we substitute our trade elasticity estimates with the estimates from Caliendo and Parro (2015) and then of Ossa (2015). There are some fundamental differences between these trade elasticity estimates. First, their estimates are at more aggregated sectoral levels: 2-digit ISIC Rev. 3 for Caliendo and Parro (2015) with 20 sectors, and 3-digit SITC for Ossa (2015) with 200 industries. Our elasticity estimates are at the HS 6-digit level for about 5000 products. Second, their elasticity estimates are homogeneous across countries, while ours are specific to each bilateral importer-exporter pair.<sup>34</sup> Finally, the elasticity estimates from Caliendo and Parro (2015), denoted as _θ,_ capture the percentage change in the trade value of a sector with respect to a one percent increase in the tariff, while the elasticity estimates of Ossa (2015), denoted as _σ,_ are defined as the elasticity of substitution from the CES aggregate. Our elasticities, denoted as _ε,_ measure the percentage change in the imported quantity of a product with respect to a one percentage increase in its price. The following relationship holds for these elasticities (ignoring the differences in

> � 2

> 33Equations (15) and (18) can be verified empirically when elasticities are homogeneous: TRI = (0 _._ 046 _/_ 1 _._ 046)<sup>2</sup> = 0 _._ 0019 _. τ_  ̄<sup>2</sup> = (0 _._ 027)<sup>2</sup> = 0 _._ 0007 is the square of the import weighted average of tariff/(1+tariff); _σ_<sup>2</sup> = 0 _._ 0012 _,_ and _ρ_ = 0 _._ Thus, TRI does not depend on the levels of the average homoegeneous  ̄ elasticity. Likewise, OTRI<sup>�</sup> = (0 _._ 028 _/_ 1 _._ 028) = 0 _._ 027 = _τ,_ suggesting that OTRI does not depend on the levels of the average homogeneous elasticity.

> 34For example, Caliendo and Parro (2015) have one elasticity estimate for “Auto” industry for all countries, while we have an elasticity estimate for HS 870332, which is “Vehicles principally designed for the transport of persons (excl. of 87.02 & 8703.10-8703.24), with C-I internal combustion piston engine (diesel/semi-diesel), of a cylinder capacity >1500cc but not >2500cc” imported by the EU from the UK.

21
All these said, high asset inequality does not have to necessarily translate into high disposable income inequality unless taxes and transfers do not have significant corrective effects. In this regard, it may worth looking at the role played by the government in Latin America and compare it with some countries like the Europeans that are well known for having inequality as a policy concern.

**<u>Figure 13. Disposable and Market income in Latin America and Europe</u>**

<!-- Start of picture text --> Disposable Income<br>Panel A. Latin America Panel B. Europe<br>0.65 0.65<br>0.55 0.55<br>0.45 0.45<br>0.35 0.35<br>0.25<br>0.25<br>0.15<br>0.15<br>Market Income<br>Panel C. Latin America Panel D. Europe<br>0.65 0.65<br>0.55 0.55<br>0.45 0.45<br>0.35 0.35<br>0.25 0.25<br>0.15<br>0.15<br>Brazil Chile Mexico Peru LAC 6 Austria Belgium Denmark Finland France Germany Greece Ireland Italy Portugal Spain Sweden UK EURO 15<br>Argentina Colombia<br>Luxembourg Netherlands<br>Italy UK<br>Brazil Chile Mexico Peru LAC 6 Austria Belgium Denmark Finland France Germany Greece Ireland Portugal Spain Sweden EURO 15<br>Argentina Colombia<br>Luxembourg Netherlands<br><!-- End of picture text --> Source: Goñi, Lopez, and Serven (2008) In a recent paper, Goñi, Lopez and Serven (2008), elaborating on a topic highlighted in Perry et al (2006), argued that whereas in Latin America the distribution of _market income_ (i.e. the income before taxes and government transfers and thus a measure that is largely determined by market rewards to the private assets and efforts of individuals, and by the underlying distribution of those private assets) and _disposable income_ (i.e. the income after government cash benefits such as pensions, unemployment insurance, and social assistance transfers have been received and direct taxes have been paid) are very similar, in Europe this is not the case. Figure 13 (taken from Goñi, Lopez and Serven, 2006) reports the value of the gini coefficient of the distributions of disposable income (Panels A and B) and market income (Panels C and D) in Latin America (Panels A and 18
# **Infrastructure Development**

# **in Sub-Saharan Africa: A Scorecard**

**César Calderón, Catalina Cantú, Punam Chuhan-Pole**<sup>1</sup>

_The World Bank, 1818 H Street NW, Washington DC 20433, USA_

**Key Words:** Economic Infrastructure, Sub-Saharan Africa, Development **JEL Classification:** H54, O40, O55

> 1 * E-mail: Calderón: ccalderon@worldbank.org. Cantú: ccantu@worldbank.org. Chuhan-Pole pchuhan@worldbank.org. The paper is part of the background analysis for the Africa’s Pulse, Volume 15 (April 2017). The views expressed in this paper are those of the authors, and do not necessarily reflect those of the World Bank or its Boards of Directors.

_Infrastructure in Sub-Saharan Africa - 1_
# **4. Data**

In this study, we match data on monthly retail prices of six major cereals (teff, wheat, maize, sorghum, barley, and millet) and wages of unskilled workers in 82 markets from 1997 to 2013 with annual weather shock data for the same period. In addition, the price and weather information is combined with data on the type of PSNP transfers households in the beneficiary districts since 2005 (if any) and data on access to towns with population greater than 50,000 in 1997 and 2004. The data is introduced in section 4.1. In section 4.2, the key features of weather shocks are described.

## **4.1. Data sources**

_Grain prices and wages_ : We use monthly price data collected in 119 markets from 1996 to 2013 for the analysis.<sup>12</sup> The data come from the Retail Price Survey, which is conducted every month by the Ethiopian Central Statistics Agency (CSA). CSA has selected 119 representative markets to be visited monthly in this survey. The markets surveyed have stayed remarkably consistent across time. More markets used to be covered prior to 2001, but this data is not included in the analysis. Although there are about 119 markets that were followed in this survey, this analysis focuses only on those located in rural districts for which crop loss data is available. In addition, most of the analysis is focused on 82 markets that are in districts which experience only one major rainy season, _Meher_ season. This is done by excluding markets that also experience another rainy season, _Belg_ . We use nominal prices in the analysis and include year and monthly dummies to control for inflation and seasonal price patterns.<sup>13</sup> _Weather shocks_ : Data on weather shocks is taken from the Livelihoods, Early Assessment and Protection project (LEAP). LEAP combines ground and satellite rainfall data collected throughout the year to provide rainfall data for each district in Ethiopia.

We use a weighted average of the rainfall during the growing season for the analysis. The relevant rainfall measure for this analysis is not necessarily the total rainfall that falls in the season as the timing of the rainfall also matters for crop production. In order to aggregate decadal rainfall data into one number which accurately reflects both the quantity and the timing of the rainfall experienced in the district in that season, we use a crop model, chosen based on the crops usually grown in a given place. The crop model measures how sensitive the crop is to rainfall losses at that moment in the production cycle. Rainfall losses in that 10-day period are given more weight. The

> 12 Occasionally there were months in which prices of certain commodities were not collected on certain markets. This data is treated as missing.

> 13 Specifically, the prices are expressed in log of Ethiopian birr per kilogram, and birr per day for wages.

10
(2011) found signs that the process of economic growth is changing in India, making urban economic growth more pro-poor in the post-reform period up to 2005-06. It is important to know whether this pattern has continued in more recent data—to assess whether stronger linkages from urban economic growth to rural poverty reduction have continued, alongside a more economically diversified rural economy.

For the purpose of this paper, we have compiled a new data series on poverty and related data spanning 60 years, extending the period of analysis in past research.<sup>22</sup> With the benefit of nearly two decades of post-1991 data, we believe there is now sufficient data for the post-1991 period to test the poverty implications of the new rate and pattern of growth in post-reform India. Attribution to reforms _per se_ is problematic, but further scrutiny of the emergent properties of the changing growth process with respect to poverty reduction is clearly important.

The sectoral structure of NDP growth in the post-1991 period is also of interest, as is the role played by population urbanization, including the Kuznets process that has been so influential in past thinking about the distributional implications of economic growth in poor countries.<sup>23</sup> We provide a decomposition of poverty reduction by sector of NDP and a decomposition method that allows us to identify the difference between population urbanization effects with constant within-sector distribution (as in the Kuznets process) versus changing within-sector distributions.

This enquiry delivers the most robust evidence to date that economic growth has not only come with a lower incidence of absolute poverty in India but that there has been an acceleration in the pace of progress against poverty in the post-1991 period. The new pattern of growth has brought greater benefits to India’s poor. While there has been rising inequality within the rural and (especially) urban sectors, growth within sectors has delivered sufficient gains to poor people to mitigate the higher inequality. Population urbanization played a role, but not in the way assumed by the Kuznets process. Instead we find that urbanization came with distributional changes within sectors; these were pro-poor in the pre-1991 period, but that have not been so since 1991. Another difference is that the sectoral pattern of growth in NDP mattered less to progress against poverty post-1991 than was the case in the pre-1991 period.

> 22 The period of analysis in Ravallion and Datt (1996) ended at about the time (1991) when India’s process of economic reform started in earnest. Datt and Ravallion (2011) updated Ravallion and Datt (1996) by incorporating an extra 14 rounds of the National Sample Surveys (NSS) taking the series up to 2006.  This paper extends the series to 2012.

> 23 Datt and Ravallion (2011) did not study the sectoral structure of NDP or identify the role played by the Kuznets process.

6
ONESQA’s evaluations are too rooted on bureaucracy and do not reflect school quality as related to student learning (Tangkitvanich and Sasiwuttiwat 2012: 8-9). While the first round of ONESQA school assessment indicated that 65 percent of OBEC schools did not meet the minimum standards, the second round of assessments in 2010 indicated that only 20 percent of schools in basic education failed to meet the minimum standards (UNESCO 2011: 30). However, learning outcomes declined during the same period, suggesting that the assessments did not reflect school quality (Tangkitvanich and Sasiwuttiwat 2012). For the third round of assessments ONESQA has developed a different methodology, which is aimed at producing recommendations for improving education quality.

|**Table 5: Thailand: Policy Intent**|**and Imple**|**mentation Indicators o**|**f School and St**|**udent Assessment**|
|---|---|---|---|---|
|**Sub Indicator**|**Intent**<br>**Score**|**Implementation**<br>**Score**|**Intent**|**Implementation**|
|**4.1 Existence and frequency of**<br>**school and student**<br>**assessments.**Process well defined<br>but needs improvement in its<br>implementation.|**4.0**|**3.6**|Advanced<br>|Established<br>|
|**4.2 Use of school assessments**<br>**for making school**<br>**adjustments.**Links between test<br>results and adjustments not always<br>clear.|**4.0**|**3.2**|Advanced<br>|Established<br>|
|**4.3 Frequency of standardized**<br>**student assessments.**Process<br>well defined but needs improvement<br>in its implementation.|**4.0**|**3.7**|Advanced<br>|Established<br>|
|**4.4 Use of student assessments**<br>**for pedagogical and personnel**<br>**adjustments.**Links between test<br>results and adjustments not well<br>understood by principals and<br>teachers.|**4.0**|**3.4**|Advanced<br>|Established<br>|
|**4.5 Publication of school and**<br>**student assessments.**Process<br>well defined but needs improvement<br>in its implementation.|**4.0**|**3.6**|Advanced<br>|Established<br>|
|**Overall**|**4.0**|**3.5**|Advanced<br>|Established<br>|

Source: World Bank 2013 and school survey data. The differences between the intent and implementation scores are statistically significant at the 0.001 level.

The National Institute of Educational Testing Service (NIETS) was established in 2005, with the mission of implementing standardized student testing to measure learning outcomes in basic and vocational education. NIETS administers the Ordinary National Educational Test (O-NET), a standardized achievement test for grades 6, 9, and 12 in eight subject areas (Thai language, math, science, social science, religion and culture, health and physical education, art, career and technology, and foreign languages). Each year 950,000 students take O-NET in the 6<sup>th</sup> grade, 16
- 16 -

(1986) **and** Ainsworth and van der Gaag (1987). An average visit to fill out half of the household questionnaire takes about 2-3 hours, though the questionnaire is designed so that no individual need be interviewed for more than an hour.

The **OteLc** two romponents of a typical LSMS survey are the price and community questionnaires, the latter of which is administered in rural areas only. In **order to measure** the **true purchasing power of household incomes,** it is **necessary** to **have data** on **prices faced by households. The LSHS price questionnaire gathers cata** on **consumer prices** from **local markets for both food** and non-food items. In some cases prices are also gathered for medicines and agricultural inputs as well. The community questionnaire collects information on local conditions in rural areas, including the nearest schools and medical facilities, common agricultural practices (including agricultural wage rates) transportation and communications, and other "infrastructure" data. These data are crucial for studying the effects of a wide variety of government policies on various aspects of living standards. For example, they have been used to analyze the effects of user fees on the demand for medical care and for schooling, and the effect of commodity prices on agricultural productivity.

The LSMS questionnaires are completed by several mobile survey teams, each of which contains one supervisor, two interviewers, one anthropometrist, and one data entry operator. Each team can cover two communities in four weeks, where 16 households are interviewed in each community. During the first week the first half of the household questionnaire is filled out in one community, and in the following week the first half of that questionnaire is filled out in the other community. In the third and fourth weeks the second