rafmacalaba/datause-encoder-data huggingface dataset:
38

# reported in the health-related literature.

Because the LSMS surveys were conducted six years after the period used for the comparison with EPR and IFS results, we can conclude that the quality of the 1985 LSMS mortality data is relatively good. No similar statement can be made, however, for the 1986 LSMS mortality data. We have no knowledge about the relations between the omission of deaths and socioeconomic characteristics of respondents in Africa, which knowledge could serve as a base for hypothesizing a direction for potential biases on parameter estimates of interest in this paper. But if the omission of deaths is not related to family characteristics, these biases would be minimal.

The data suggest associations between infant and child mortality and proximate determinants of child health that are quite consistent with what is reported in the literature. These results add to our confidence in the LSMS mortality data.

Methods. The methodology adopted in this section rests on the assumption that a family's characteristics, such as education, type of residence, and relative position in the income scale, are fixed during the expansionary period and the economic crisis-adjustment period. In considering aggregates of families, this assumption appears very mild: the mobility of individual families is unlikely to affect significantly the relative position of aggregates of families over a limited period.

The probability of dying in the first year of life is used as an indicator of child health. The age-dependent nature of child-health-promoting activities
The advantage of such a methodology is that the procedure is highly standardized and comparable across economies and entails lower measurement errors for what it intends to measure, and the indicators provide good reflections of the de jure regulatory environment. However, a limitation, as documented in the _Doing Business_ report (2019), is that these indicators assume that entrepreneurs have knowledge of and comply with applicable regulations. As a result, _Doing Business_ indicators cannot capture what is actually happening on the ground in real business settings. Hallward-Driemeier and Pritchett (2015) document significant differences between closely related indicators in _Doing Business_ (which focus on the de jure regulatory environment) and in the Enterprise Survey (which reflects the de facto regulatory environment). Another limitation is that the survey cases in _Doing Business_ are located in the largest business city of each country,<sup>5</sup> which cannot capture the heterogeneity across a country.

The Global Competitiveness Index is produced by the World Economic Forum (WEF). It aims to present comprehensive insights into a country’s drivers of economic growth. A portion of the Global Competitiveness indicators is based on the Executive Opinion Survey conducted by WEF in cooperation with its partner institutes in each country. In the 2018 survey, for example, more than 12,000 business executives from companies of various sizes and from the various sectors in 140 countries were surveyed. The other indicators are drawn from external data sources, such as the International Energy Agency and the International Telecommunications Union. The Global Competitiveness project uses detailed data to construct 12 pillars of competitiveness, and its indicators capture broader aspects of the business environment. However, because of the limited sample size in each country (for example, in 2018, the median number of respondents in a country was 83), the representativeness of the indicators drawn from the Executive Opinion Survey can be an issue.

We now turn to the construction of our variables. The business environment has many aspects. In this paper, we rely on the guidance of Reyes et al. (2017) and focus on five main categories: (1) Government Protection, (2) Infrastructure, (3) Human Capital, (4) Access to Finance, and (5) Barriers to Entry. Government Protection refers to the control of corruption and provision of physical safety. Infrastructure includes traditional infrastructure (captured here by the quality of the provision of electricity) and modern infrastructure (captured here by the extent of access to the internet). Human Capital focuses on the skill level of labor, which is mainly affected by education quality. Access to finance refers to access to basic financial 5 For the countries with a population exceeding 100 million, DB also covers the second biggest city.

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IPOLICY **RESEARCH WORKINC PAPIER** 1422

# Summary findings

In Eastern European countries in large social sectors such as education, inefficiency and technical deficiencies are the lcgacy of the old command cconomy.

Bogetic and Chattophadyay examine the technical efficiency of classroom use (defined as the number of classes per classroom) in one transitional economy: Bulgaria. They examine that concept of efficiency in 199 urban and rural municipalities, usirn data envelopment analysis to generate efficiency scores. Those scoresdiscussed in terms of frequency and regional distribution - are then regressed on several socioeconomic variables. The researchers find significant relationships between the efficiency scores, on the one hand, and, on the other, the proportion of students in the population under age 20 (demand indicator), the number of teachers (variable input), the percentage of the municipal budget spent on education, and the degree of urbanization.

Efficiency in the use of classrooms (in terms of classes) varies considerably among municipalities, and efficiency is highest in the capital city of Sofia. To the extent that some variation in efficiency reflects demand or demographic factors, there is little that policy can do to change the pattern. But some changes in municipal policy could increase the efficiency of classroom use without jeopardizing the fundamental learning objective. In some rural areas, for example, where there arc few students and classroom utilization is low, it may be possible to consaidate several grades into multigrade classes and reduce the size of the treaching (and nonteaching) staff, while maintaining the quality of learning and maximizing the use of such fixed inputs as classrooms.

To the extent that it is possible to use such classrooms more efficiently, savings could be generated in the municipalities that need them most: in demographically sparse, poor municipalities with a weak economic nase. Those savings could then be reallocated to other educational essentials, such as equipment and materials.

This paper-a product of the Country Operations Division, Europe and Central Asia, Country Department I-is part of a larger effort in the region to study social issues during the transition in South-Eastern European countries. Copies of the paper are available frce from the World Bank, 1818 H Street NW, Washington, DC 20433. Pleasc contact Faithlyn Smith, room _H5-245,_ extension 36072 (16 pages). February 1995.

_The Policy_ **_Researcb_** _Working Paper_ **_Series_** _disseminates_ **_the_** _findings of ulork in progress to encourage the exchange of ideas about developm.ent issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polisbed2 The papers carry the narnes of the authors and should be rsed and cited accordingly. The findings, interpretations, and conclusions are ihe authors' oum andshould not be attributed to the World Bank, its Executive Board of Directors or asny of its menber countries-_

## Produced by the Policy Research Dissemination Center
period (46 observations). The analysis is supplemented with 10 price indices—a common level of aggregation used in the literature on commodity price behavior. A brief description of the indicator price of the 35 commodities along with the price indices is given in the statistical appendix.

The Manufacture Unit Value (MUV) is used as an inflation proxy. The MUV, often considered as a developed country deflator indicator, represents the unit value index in dollar terms of manufactures exported from five industrial countries—France, Germany, Japan, the United Kingdom, and the United States—weighted proportionally to the countries’ exports to developing countries.<sup>**4**</sup> Admittedly, such a simplified modeling framework cannot capture the complex interplay and diverse nature of primary commodity markets. However, its simplicity along with the fact that all types of internationally-traded commodities are included in the analysis not only gives us a rough idea of the nature of the pass-through but will also guide us as to what shape future research should take.

The use of low frequency data was motivated by the desire to identify and compare the effect of crude oil prices on the prices of all primary commodity groups. Because most agricultural commodities (as opposed to other commodities such as metals) are subject to crop cycles, only annual frequency is relevant. For example, the decision of how much land to allocate to each commodity and how much inputs to use is taken once a year, typically prior to planting. On the other hand, although a higher frequency would add more observations, the extremely volatile nature of commodity prices allows even annual observations to have large information content.<sup>**5**</sup>

# **Empirical results**

The first step was to examine the stationarity properties of the series under consideration using the augmented Dickey-Fuller (ADF) and Phillips-Perron (PP) testing procedures. (In order to conserve space, unit roots results are not reported here.)

**_— 4 —_**
Table C5: Results: Network Details by wealth

||(1)<br>Family|(2)<br>Friends|(3)<br>Social Media|(4)<br>Schoolmates|(5)<br>Train mates|
|---|---|---|---|---|---|
|Offered Training|0.07<sup>_∗∗_</sup>|0.07<sup>_∗∗_</sup>|0.10<sup>_∗∗∗_</sup>|0.15<sup>_∗∗∗_</sup>|0.30<sup>_∗∗∗_</sup>|
||(0.03)|(0.03)|(0.04)|(0.03)|(0.03)|
|Offered * High Ubudehe|-0.01|-0.02|0.02|-0.10<sup>_∗_</sup>|-0.00|
||(0.05)|(0.04)|(0.05)|(0.05)|(0.04)|
|Control Mean: High Ubudehe|0.39|0.71|0.45|0.50|0.16|
|Control Mean: Low Ubudehe|0.46|0.72|0.44|0.54|0.18|
|Observations|2,597|2,597|2,597|2,597|2,597|
|p-value high Ubudehe|0.06|0.08|0.00|0.15|0.00|

Notes: Offered Training is the coefficient from a pooled OLS regression of the outcome (including three rounds of follow-up data, 4, 10, and 15 months after the end of the program) on an indicator for being offered the training. We control for the baseline value of the dependent variable, stratification dummies and survey wave dummies. Standard errors are clustered at the respondent level.

Table C6: Results Pooled: Earnings and Work - Heterogeneity by Public/Private

||(1)<br>Earned Income<br>Last 7 Days|(2)<br>Main Job Earnings<br>Last 7 Days (USD)|(3)<br>Main Job Hours Worked<br>Last 7 days|
|---|---|---|---|
|Offered Training|0.09<sup>_∗∗_</sup>|3.65|5.87<sup>_∗∗∗_</sup>|
||(0.05)|(5.71)|(2.11)|
|Offered * Public|-0.05|3.18|-3.36|
||(0.05)|(6.56)|(2.42)|
|Control Mean: Public|0.59|36.25|22.95|
|Control Mean: Private|0.72|34.43|29.31|
|Observations|2,597|2,597|2,597|
|p-value public|0.07|0.03|0.04|

Notes: Offered Training is the coefficient from a pooled OLS regression of the outcome (including three rounds of follow-up data, 4, 10, and 15 months after the end of the program) on an indicator for being offered the training. We control for the baseline value of the dependent variable, stratification dummies and survey wave dummies. Standard errors are clustered at the respondent level. Earnings are top winsorized at the ninety-ninth percentile; we replace earnings with 0 for participants who do not work.

45
(0.6) that reflects the findings of Harrison (2002) on the labor share of income in low- and middle-income countries.<sup>6</sup>

# **4.  Model Implementation**

We transform all model variables to ranks for ease of use and interpretation, and to ensure robustness against outlier effects. To facilitate the nested procedure that we describe below, our rank-ordering for each variable ensures a positive relationship to the priority scores in (9). Accordingly, higher numerical ranks are assigned to lower values for subspecies numbers (T), subspecies distribution (E), land opportunity cost (L) and labor cost (W). Higher numerical ranks are assigned to higher values for subspecies’ overall habitat loss (D), habitat area (H), the percent of habitat remaining (F), and the effectiveness of country protection (P).

Our model incorporates equations (1) – (10) in a two-stage exercise. In the first stage, the user specifies relative weights for the determinants of ρ, Q and C in (2), (4) and (6) above.<sup>7</sup> The model allows the user to specify the parameters for each equation in arbitrary units. After the parameters are specified, the model standardizes to Cobb-Douglas (CD) parameters by dividing each parameter by the sum of parameters for that equation. Then it forms the product of the relevant rank-transformed variables, each weighted by its exponential CD parameter.

At the completion of the first stage, the model has created indices for ρ, Q and C. P (protection effectiveness) has only one component, so that is the second-stage index for this variable. At the beginning of the second stage, the user specifies relative weights for species risk (ρ), habitat quality (Q), protection effectiveness (P) and project cost (C). As before, the model allows the user to specify the weighting parameters in arbitrary units. Then it standardizes to CD

> 6  Formally, this index assumes a Cobb-Douglas (unit-elastic) cost function, internationally-traded capital and nontraded labor.  The cost elasticity of the average wage (proxied by income per capita) in this function is the labor share of national income.

> 7 There is no need to specify the constants (γ0 δ0 β0), since they do not affect the relative values that determine priority scores.

9
This would increase the proportion of college graduates choosing teaching profession to about one-third. Using the population projection by BPS (Bureau of statistics), and assuming constant proportion of population above age twenty with college education, this would lead to about 24-25 pupils per teacher with college education. This level of pupil-teacher ratio is higher than the current level, but still falls within the adequate range compared with other countries in the region.

Assuming this can be an acceptable pupil-teacher ratio such as _q*_ in Figure 3, it still requires increased amount of public resources. With increased pupil-teacher ratio from 16:1 to 24:1, but doubled teacher remuneration, the per-pupil cost would increase 33 percent. Without this commitment, there will be either teacher shortage, or a mix of teachers with high and low education background will continue to exist.

# **VI. Conclusions**

Aiming at attracting high caliber human resources into teaching, the latest Teacher Law (UU14/2005) in Indonesia promises a 100 percent teacher salary increase for certified teachers with a minimum 4-year college education or above. In the long run, whether the law will be successful in attracting the needed college-educated labor force into teaching depends foremost on how it would influence their occupational choice decision. Until now, there has been no basis for making such forecasts. The findings of this paper provide some empirical foundations for the latest teacher law in Indonesia.

25
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# **7. References**

Abraha, M, (2013). Assessment of Drought Early Warning in Ethiopia, a comparison of WRSI by surface energy balance and water balance. PhD Thesis. University of Twente, The Netherlands.

Anttila‐Hughes and Hsiang (2011) Destruction, disinvestment, and death: economic and human losses following environmental disaster. Working Paper.

Anttila ‐Hughes and Sharma (2014) Linking Risk Models to Microeconomic Indicators, World Bank publications, 2014.

Bohra‐Mishra, Oppenheimer and Hsiang (2014), Nonlinear permanent migration response to climatic variations but minimal response to disasters, PNAS, 2014.

Cavallo and Noy (2011) Natural Disasters and the Economy — A Survey, International Review of Environmental and Resource Economics, 2011.

Central Statistical Agency and The World Bank (2013) Living Standards Measurement Study Integrated Surveys on Agriculture (LSMS‐ISA): Ethiopia Rural Socioeconomic Survey (ERSS) Basic information Document. Addis Ababa, September 2013.

Chetty, R (2008). Sufficient statistics for welfare analysis: A bridge between structural and reduced‐ form methods. No. w14399. National Bureau of Economic Research, Colmer, J (2013) Climate Variability, Child Labour and Schooling: Evidence on the Intensive and Extensive Margin. Working paper.

D’Ayala D., Meslem A., Vamvastikos D., Porter, K., Rossetto T., Crowley H., Silva V. (2014) Guidelines for Analytical Vulnerability Assessment of low‐mid‐rise Buildings – Methodology, Vulnerability Global Component project.

Dercon, S. "Risk, crop choice, and savings: Evidence from Tanzania." Economic development and cultural change 44.3 (1996): 485‐513.

Diamond, P, et al (2015) Estimating Poverty Rates in Target Populations: An Assessment of the Simple Poverty Scorecard and Alternative Approaches. World Bank Group.

Doorenbos, J. & Kassam, A, (1979). Yield response to water. FAO Irrigation and Drainage Paper No. 33. Rome, FAO FEMA (2002). Rapid visual screening of buildings for potential seismic hazards. FEMA, 2002. Retrieved from: <u>http://www.fema.gov/media‐library‐data/20130726‐1646‐20490‐ 8071/fema_154.pdf.</u> Fermont, A, and Benson, T, (2011). Estimating Yield of Food Crops Grown by Smallholder Farmers: A Review in the Uganda Context. International Food Policy Research Institute, IFPRI Discussion Paper 01097, June 2011 Hill, Ruth and C. Porter (2014). The Changing Nature of Vulnerability In Ethiopia. In <u><mark>Ethiopia Poverty Assessment 2014. World Bank Group. 2015. Washington, DC.</mark></u> <mark>https://openknowledge.worldbank.org/handle/10986/21323</mark> 30
# **2.3 Variable Definitions and Descriptive Statistics**

Our sample of interest is the Turkish working-age population (15 to 64 years of age). Detailed descriptions of the variables used in this paper are provided in Appendix A. Summary statistics are presented in Tables 1, 2 and 3.

Our main employment indicator is all private sector, paid employment, including selfemployment. This measure is disaggregated into formal and informal employment, full and part-time, and those employed in regular and irregular workplaces.<sup>17</sup> We separately consider those employed in the public sector and employers. The other labor force status indicators we use are: unemployment, not in the labor force, in school and retired. We consider three education categories: low (less than completed primary education), medium (at least completed primary education but no high school completion), high (high school completion and above). We use monthly wages as our earnings measure and restrict the sample to respondents who report having usual working hours of less than 14 or more than 84 hours per week. Results are robust to using hourly wages and deflating wages by a regional consumer price index.

Table 1 provides descriptive statistics for the years 2011 and 2014.<sup>18</sup> Labor force participation is very low in Turkey, around 54 percent of the working-age population in 2011, though it has been rising. The reason is that female labor force participation is particularly low at about one-third. The majority of employment is private sector, around one-third of the working-age population, compared to 6 percent employed in the public sector. There are a large number of unpaid workers (7 percent) and unemployment is at 5 percent of the working-age population, an unemployment rate of about 10 percent. School attendance has been rising over the period, from 12 to 16 percent of the working-age population, and the fraction retired has been steady at about 5 percent. Correspondingly, educational attainment has been rising though still 13 percent of the working-age population has no formal education, 57 percent at least completed primary education but not high school, and high school completion has risen from 30 to 34 percent.

> 17 Of those who have an irregular workplace 60 percent are agricultural workers, 14 percent work in construction, 7 percent in transportation and 5 percent in retail and in manufacturing each, and 3 percent as household employees.

> 18 Note that in 2014 new regulations for the Household LFS were carried out within the framework of European Union criteria. Consequently, statistics are not necessarily entirely comparable across years. Since we do not use aggregate time-series variation for identification this does not affect our empirical strategy, see Section 3. For those interested, the Turkish Statistical Institute provides consistent time-series on their website.

9