rafmacalaba/data-use-mentions-extended huggingface dataset:
# **V. Conclusions**

The global trend of school-based management follows the principle of decentralized decision-making, putting power in the hands of the frontline providers and parents to improve their schools, aiming at improving education beyond providing more classrooms, more teachers, and more textbooks. The idea behind decentralized decision making and management is that the frontline providers and clients know best what they want, and what goes on in schools and other public service provision facilities.

This paper examines the key aspects of the practices of school-based management currently in Indonesia, and its effect on education quality. Using a conceptual framework of an accountability system of public service delivery, this paper explores the relations among parents, school committees, schools, and government education supervisory bodies from three tenets: participation and voice; autonomy; and accountability. The paper’s empirical approach allows a close examination of how these aspects affect school decision-making on key educational inputs, which significantly affect student learning outcomes.

Using the data from a nationally representative survey of about 400 public primary schools in Indonesia, the paper finds that the level of parental participation and voice in school management is quite low in Indonesia. While the role of school committees is still limited to community relations, school facilities, and other administrative areas of schools, the school principal, together with teachers, is very much empowered to assert professional control of the school. The most important finding of this paper is that the accountability system is very weak in Indonesia’s school system, which is reflected in inadequate information flow to parents, as well as seemingly low parental awareness of holding schools accountable. The accountability arrangement of the Indonesian school system currently puts more emphasis on top-down supervision and monitoring. Even though the interactions between district or subdistrct education offices and schools are generally frequent, the lack of rewards and sanctions for good or bad performance also leaves the system weak.

The empirical work of this paper shows that although the scope of school-based management in Indonesia is limited today, it has begun to help schools
|3237|
|CC Yields|ERIVaS|500.68|380.06|0.00|423.44|2472.50|577|
||EACI 2017|760.19<br>***|482.30|0.00|720.00|3200.00|980|
|GPS measured area (ha)|ERIVaS|1.56|1.25|0.05|1.20|12.02|577|
||EACI 2017|2.65<br>***|2.49|0.02|1.94|17.83|3237|
|Quantity of Seeds used (kg/ha)|ERIVaS|8.30|5.90|0.56|6.56|51.02|577|
||EACI 2017|13.38<br>***|42.57|0.00|5.57|1300.00|3237|
|Household labor on plot (persons-days/ha)|ERIVaS|159.30|267.37|8.36|76.40|2711.45|577|
||EACI 2017|69.03<br>***|146.20|0.54|34.04|2700.00|3237|

_Notes:_ To ensure comparability of the variables, the EACI 2017 sample is limited to pure stand plots. The stars. below the sample mean values of the two surveys denote the statistical significance level for a t-test comparison of the means in the two samples. Significance levels: *p _<_ 0.1; **p _<_ 0.05; ***p _<_ 0.01 In terms of the non-classical measurement errors that were uncovered in the ERIVaS data, similar patterns emerge from the EACI sample. Table 11 shows the means of alternative yield and harvest measures for pure stand
consumption. Table II summarizes the labor supply statistics of children aged 12-15 years in 1997 (pre-Progresa grants). It can be seen that of those children not in school, 63% of boys report having a job, while 20% of girls report having a job. Conversely, of those children who report they attend school, only 7.5% of boys and 2.9% of girls report having a job. Further, when we look at hours worked for those children with a job, those not attending school work an average of over 40 hours per week, whereas those attending school report an average of less than 15 hours per-week. Thus, it appears that going to school places a major constraint both on labor market participation and on hours worked in the labor market for children.

23
LaLonde, Robert J. and Robert H. Topel. 1997. “Economic Impact of International Migration and the Economic Performance of Migrants.” In M.R. Rosenzweig and O. Stark (ed.) “Handbook of Population and Family Economics.” Elsevier Science Long, James E. 1980. “The Effect of Americanization on Earnings: Some Evidence for Women.” Journal of Political Economy, 88(3): 620-629.

Lubotsky, Darren H. 2007. “Chutes or Ladders? A Longitudinal Analysis of Immigrant Earnings.” Journal of Political Economy, 115(5): 820-867.

Mattoo, Aaditya, Ileana C. Neagu and Çağlar Özden. 2008. “Brain Waste? Educated Immigrants in the US Labor Market.” Journal of Development Economics, 87(2): 255-269.

McKenzie, David. 2003. “Asymptotic theory for Heterogeneous Dynamic PseudoPanels.” Journal of Econometrics, 120: 235-262.

McKenzie, David and Hillel Rapoport. 2007. “Self-Selection Patterns in Mexico-US migration: the role of migration networks.” World Bank Working Paper no. 4118.

Moffitt, Robert. 1993. “Identification and Estimation of Dynamic Models with a Time Series of Repeated Cross-Sections.” Journal of Econometrics, 59: 99-123.

Powers, Mary and William Seltzer. 1998. “Occupational Status and Mobility among Undocumented Immigrants by Gender.” International Migration Review 32(1): 21-25.

Powers, Mary, William Seltzer and Jing Shi. 1998. “Gender Differences in the Occupational Status of Undocumented Immigrants in the United States: Experience Before and After Legalization.” International Migration Review 32(4): 1015-1046.

Ruggles, Steven, Matthew Sobek, Trent Alexander, Catherine A. Fitch, Ronald Goeken, Patricia Kelly Hall, Miriam King, and Chad Ronnander. 2009. Integrated Public Use Microdata Series: Version 4.0 [Machine-readable database]. Minneapolis, MN: Minnesota Population Center [producer and distributor].

Schoeni, Robert. 1997. “New Evidence on
number of times that the participant chose the equitable allocation in the two DI trials. The measure of cognitive function is the Wechsler Intelligence Scale for Children-IV. All regressions control for gender. _N_ = 1,006.

> 43 Exams are also administered to 7th graders but our cohort had not reached the 7th grade yet.

44 Survey data were linked to the test scores data using the child’s name and, if necessary, the child’s date of birth and the names of the child’s mother and father. Of the 2,113 children who participated in our study, 1,213 children were linked. For 1,006 of them, we also have a measure of IQ available. We suspect that many children could not be linked because they are either attending private schools or are attending public schools run by the state government rather than the local government.

26
Come Under Scrutiny. TheEpochTimes.com. s.l. : The Epoch Times, October 12, 2018.

58. Chastised by E.U., a Resentful Greece Embraces China’s Cash and Interests. Horowitz, Jason and Alderman, Liz. August 26, 2017, The New York Times.

59. Tornos News. Record January-October 2018 results for Piraeus container terminal. TornosNews.gr. Athens : Tornos News, November 20, 2018.

60. China Pakistan Economic Corridor. China Pakistan Economic Corridor Maps. CPEC official website. [Online] http://cpec.gov.pk/maps.

61. CPEC. CPEC Projects Progress Update – CPEC Infrastructure Projects . CPEC.gov.pk. s.l. : Government of Pakistan, November 2018.

62. Senate of Pakistan Secretariat. Questions for oral answers and their replies, 108th Session. Islamabad : s.n., October 23, 2014. p. 13.

63. Cui, Jia. China studying new Silk Road rail link to Pakistan. China Daily. June 28, 2014. Xinjiang dir. of regional development quoted.

64. Pakistan-China Institute. Pak Railways to conduct feasibility study for Havelian-Khunjerab Railway line. CPECInfo.com. s.l. : Pakistan-China Institute CPEC Portal, September 20, 2017.

65. The News International. Cost of CPEC’s Railways ML-I project brought down by $2 bn: Sheikh Rashid . TheNews.com.pk. [Online] The News International, October 2, 2018. https://www.thenews.com.pk/print/375438-cost-of-cpec-s-railways-ml-i-project-brought-down-by-2-bn-sheikh-rashid.

66. Duhalde, Marcelo, et al. China's super link to Gwadar Port. SCMP.com. [Online] South China Morning Post, 2018. http://multimedia.scmp.com/news/china/article/One-Belt-One-Road/pakistan.html.

67. APP. Ministry to submit PC-1 for new railway tracks soon. Tribune.com.pk.
|**Table 31 Share of Stapl**<br>_Expenditure Categories_|**e Foods in House**<br>_Share of_<br>_maize, millet,_<br>_manioc and_<br>_yams in food_<br>_expenditure_|**hold Expenditu**<br>_Share of_<br>_rice and_<br>_plantain in_<br>_food_<br>_expenditure_|**re Based on th**<br>_Food share_<br>_in total_<br>_expenditure_|**e 1993 Survey**<br>_Share of_<br>_maize, millet,_<br>_manioc and_<br>_yams in total_<br>_expenditure_|**Results**<br>_Share of rice_<br>_and plantain_<br>_in total_<br>_expenditure_|
|---|---|---|---|---|---|
|_Region_||||||
|Abidjan|12.0|20.1|34.8|4.2|7.0|
|Other Cities|18.4|33.6|43.9|8.1|2.7|
|East Forest|29.8|23.4|56.8|16.9|13.3|
|West Forest|28.7|33.5|59.9|17.2|10.3|
|Savannah|43.5|17.2|61.5|26.8|4.6|
|CPI||||||
|Blue collar worker CPI|10.3|7.2|48.0|4.9|8.3|
|White collar worker CPI|6.8|15.5|38.8|2.6|6.0|

Offsetting this difference, however
Given their comprehensive nature, the survey results can provide an overall health system diagnostic, shedding light on the system’s ability cope with and recover from unexpected shocks, such as the COVID19 pandemic.

The three most recently completed health SDI surveys are from Kenya, Sierra Leone, and Tanzania. Kenya’s survey is the largest to date, with a sample of 3,094 facilities, and was conducted between March and July 2018. Of the sampled facilities, 1,781 are public and 1,313 are private. The survey team observed 13,026 workers for absenteeism and assessed 4,430 health workers for competence using patient case simulation. The data collected are representative of the 47 counties, facility location (urban/rural areas), facility ownership (public/private), and level of facility (first level hospital/health center/dispensary and clinic).

The SDI survey in Sierra Leone was conducted between January and April 2018 and consists of a sample of 547 health facilities, 501 public and 46 private facilities. The survey team observed 1,700 workers for absenteeism and assessed 818 health workers for clinical competence. The data collected are representative of the 14 districts, facility location (urban/rural), facility ownership, and level of facility (hospital/health center/health post).

The survey in Tanzania took place between August and September 2016 and is comprised of a sample of 400 health facilities, which included 273 public and 127 private facilities. The survey included an assessment of 2,093 health care workers for absenteeism and 563 for clinical competence. The data collected are representative at the national level and by location (Dar es Salaam, other urban areas, and rural areas).

Each of these three surveys adheres to a core set of questions that are standardized across all SDIs, ensuring international comparability of results. However, in response to specific country contexts and policy interests, the surveys also have questions that differ. This report presents the results from analyzing data from
# **3. The Data**

We study corporate diversification in nine countries: Hong Kong, Indonesia, South Korea, Japan, Malaysia, Philippines, Singapore, Taiwan and Thailand.<sup>6</sup> Our primary data source is the Worldscope database. Worldscope contains financial and segment informnation on companies from 49 countries. The database has been used in several international studies of corporate diversification (Lins and Servaes (1997) and Fauver, Houston, and Naranjo (1998)).

We initially selected all companies from the nine countries covered by the June 1991-1998 CD-Rom version of annual Worldscope database. In each of the annual database, Worldscope provides historical financial data. We are able to assemble several years of financial data for most of the companies. Historical segment data for many of the companies are missing, however, since Worldscope reports only the latest available segment data. To increase sample size, we collected additional segment data for Worldscope companies from the autumn edition of the 1994-1998 _Asian Company Handbook_ and _Japan Company Handbook._ All financial data were converted to US dollars using fiscal year end foreign exchange rate for each firm.

In order to determine the types of the companies' businesses, we group the companies' segments according to the two-digit Standard Industry Classification (SIC) system. This procedure involves two steps. In the first step, we assign the four-digit SIC codes reported by Worldscope to appropriate segments. In many cases we are able to obtain one-to-one matches between SIC codes and segments. For some companies, the number of reported SIC codes is not the same as the number of reported segments. If a segment can not be associated with a reported SIC code, we determine the segment's SIC code according to its business description. If a segment is associated with multiple SIC codes, it is broken down equally so that each segment is associated with one SIC code. In the second step, we redefine segments at the two-digit SIC level and aggregate segment sales to that level.

We
, it was estimated by deducting food and energy inflation multiplied by their corresponding weights from headline CPI inflation and dividing this contribution from the core by the weight of core inflation in the total CPI. The following formula for calculating core inflation was utilized: where π , π F, and π E are the current monthly inflation rates for headline, food, and energy, respectively, and ω F and ω E are the current weights for food and energy, respectively. Weights of the sub-indexes in the total index were obtained from the Consumer Price Index database published by the IMF as well from OECDstat and Haver Analytics.

Rainfall is used as an instrumental variable in identifying supply-driven changes in domestic food prices. Rainfall monthly data come from the World Bank’s Climate Change Knowledge Portal: Historical Data. The data set is produced by the Climatic Research Unit of the University of East Anglia, and reformatted by the International Water Management Institute. The monthly mean historical rainfall data can be mapped to show the baseline climate and seasonality by month. Rainfall is measured as millimeters per month for all countries for 1970-2016. To test the relevancy

> 11 The 104 countries included in the data set satisfy the panel SVAR condition that for a country to be included in the sample, it must contain at least 36 months of continuous data for the intersection of the country block, for all variables, namely (i) headline CPI, (ii) food CPI, (iii) energy CPI, (iv) core CPI, (v) NEER, and (vi) rainfall.

13
The selection was done on the basis of several criteria. First, all enterprises were state-owned in 1991 and were listed among the largest 200 Slovak manufacturing enterprises (the average size was over 2,000 workers in 1991). Second, we mostly selected enterprises which had difficulties in the early transition period. Such enterprises were followed by the Slovak Ministry of the Economy starting in 1992. In 1993, the Ministry commissioned major consulting firms to study twenty-seven large firms. A detailed report with recommendations for further restructuring steps was issued in each case. Based on the reports, firms were classified in three categories (Table 2): non-viable loss makers (category NL), potentially viable loss makers (category VL), viable profit makers (category VP). We revisited seven firms in category NL, nine firms in category VL, and two firms in category VP included in the original survey. Three other firms had become part of holding companies by 1996. We visited the respective holding companies and obtained information on the individual firms which participated in the original survey, as well as the other firms in the holdings. Six firms from the Ministry list remained outside the scope of this study. Their exclusion was dictated solely by timeconstraints.6 The case studies provide both quantitative and qualitative evidence. Balance sheet and income statement data were obtained for 1991-96. The interviews with managers and owners contain information on production and marketing strategy, firm-specific input and output prices, technology acquisition, sale/disposal of social and dubious assets, labor shedding, wage policies and severance packages, cooperation with foreign firms, financing, and export performance. The interviews followed a structured questionnaire (available from the authors). A presentation on the history of the enterprise preceded each interview. Since the industrial conglomerates were broken down in 1990, we followed

- 6 Those are Hydrostav Bratislava, Kinex Bytca, Vihorlat Snina, VSS Kosice, ZSNP Ziar nad Hronom, and ZTS Dubnica nad Vahom.

8
been recently upheld by the Supreme Court.<sup>20</sup> On the other hand, concerns have been raised that the wage rate on MGNREGS is being set too high, relative to actual casual labor market wages. The concern here is that the scheme will attract workers from market work and so bid up the market wage rate.<sup>21</sup> (Though to many supporters of the scheme this is counted as a benefit.) What does the evidence suggest? Table 6 gives the average wage rates from the administrative data. These are calculated as total MGNREGS spending on unskilled labor divided by total person days of employment provided.<sup>22</sup> The table also reports estimates for average wages in (private) casual labor from the NSS for the same year.<sup>23</sup> We see that it is not the case that the MGNREGS wage rate is everywhere well above the market wage rate. Indeed, for India as a whole the two wages are quite close. If rural India was one labor market one might conjecture that the scheme has indeed brought the two wage rates into parity. However, rural India is not one labor market, as mobility is clearly imperfect. Across states we see that for half of them the MGNREGS wage rate in 2009/10 is actually lower than the average wage rate for casual labor (Table 6).

Given the extent of rationing we have reported earlier in this paper, it does not seem plausible that the scheme would be having a large impact on wages for other casual work, let alone resulting in a higher casual wage than for MGNREGS in half the states. For example, with only 17% of those who wanted work on the scheme in Punjab getting that work, it is hard to believe that the fact that the casual (non-PW) wage rate is above the MGNREGS wage rate is due to competition with the scheme for workers.

That said, we do find that
Other possible identification approaches. For example, Favero and Giavazzi (2010) note that impact estimates of fiscal policy are larger when fiscal shocks are identified in a narrative approach than when they are identified in a VAR framework. Romer and Romer (2010) use a narrative approach to identify exogenous fiscal shocks for the United States. For a broader set of countries, Guajardo, Leigh, and Pescatori (2014) and Gupta et al. (2017) have compiled data for fiscal consolidation episodes. However, we would not necessarily expect the interest rate channel, especially, to operate during a fiscal consolidation if investors remain cautiously risk averse during consolidations, whereas their risk aversion may lead them to respond sharply to fiscal stimulus. Hence, a dataset of fiscal consolidations is less useful for our purposes.

2.4 Database: Our main database comprises an unbalanced panel that covers 34 countries (19 advanced and 15 developing), at the quarterly frequency over the period 1980:1 to 2014:1. Real government consumption and real GDP are based on the quarterly database in Ilzetzki, Mendoza, and Vegh (2013) which are extended to 2014:1 by splicing data from the OECD and Haver Analytics. Real effective exchange rates are from the narrow (wherever available) and broad indices of the BIS, and current account balances are from Haver Analytics. The short-term rate used for discounting the multiplier is drawn mainly from the IMF-IFS database. We obtain quarterly real private consumption and private investment series from the OECD, Haver Analytics, and Eurostat. CDS spreads are in basis points and taken from Kose et al. (2017). We draw the conditioning variable (government debt as percentage of GDP) from the IMF-WEO database.<sup>9</sup> The government consumption, private consumption and GDP series are converted into logarithmic form, and detrended using a linear quadratic trend. The exchange rate is transformed into quarter-on-quarter growth rates, and the current account series is seasonally-adjusted using the X11 routine. All these series
Moreover, we observe gender disparities in aspirations between male and female youths. Female youths aged 15-18 exhibit higher educational aspirations than their male peers. However, their aspirations decline and become lower than those of male youths when they reach 19-25 years old, irrespective of their activity. Conversely, male youths report higher career and migration aspirations than female youths across all ages and activities, except for young female students who demonstrate higher migration aspirations than young male students.

This paper presents two significant contributions to the existing literature. Firstly, it provides a comprehensive assessment of youth aspirations, utilizing data from nationally representative multi-topic household surveys conducted in three SSA countries. Our study offers robust insights into youth aspirations as they relate to occupational demand in SSA, providing valuable information for policy making aimed at aligning youth skills and migration desires with labor market opportunities. Secondly, by employing high-frequency phone surveys (HFPS) based on pre-pandemic sampling frames derived from nationally representative face-to-face household surveys supported by the World Bank’s Living Standards Measurement Study – Integrated Surveys on Agriculture (LSMS-ISA), the analysis links youth aspirations to a rich array of demographic and socio-economic characteristics. The inclusion of an aspiration module in household surveys allows for the tracking of aspirations over time and facilitates a deeper understanding of the youth and their families. This is crucial for comprehending how aspirations shape the future of young individuals and identifying the appropriate age for intervention in shaping aspirations, among other considerations.

The rest of the paper is organized as follows: section 2 presents a literature review that helps to conceptualize aspirations. Section 3 describes the data, survey instruments, sampling strategy and statistical analysis. Section 4 presents descriptive results, while section 5 examines the empirical results. Finally, section 6 sheds light on the policy implications of the study and concludes.

# **2. Conceptualizing Aspirations: A Literature Review**

The notion of aspiration, in its broadest sense, refers to the ambition of achieving something that may or may not be within the range of outcomes that the individual’