rafmacalaba/datause-encoder-data huggingface dataset:
The contribution of the services sector on growth in output per capita can mostly be attributed to improvements in within-sector productivity. Further, Figure 17 shows that increases in employment in Malaysia largely correspond to increases in employment in the services sector, particularly in the period from 2007 to 2018. As has already been mentioned, while the manufacturing and services sectors were sources of growth between 1987 and 2018, the agricultural sector had a negative contribution to the growth in output per capita. Even though output per worker in this sector increased over time, it remained relatively less productive than the economy as a whole. As a result, the declining share of employment in agriculture had a positive growth effect amounting to 3.3 percent of overall growth in output per worker according to Table 12.

For all three sub-periods, Table 13 lists the average output per worker at the beginning and end of each subperiod, the change in the employment share of the seven sectors, and the effect of between-sector shifts on output per worker by sector. The table once again shows that labor in Malaysia is not necessarily reallocated to sectors with the highest levels of productivity. For example, the share of employment in manufacturing declined both between 1997 and 2007 and between 2007 and 2018, despite the sector’s high level of average output per worker. In contrast, the share of employment in trade services increased in both periods, even though the sector had lower-than-aggregate average output per worker. As a result, changes in the share of employment in _both_ the manufacturing and the trade services sectors had negative effects on output per worker between 1997 and 2007 and between 2007 and 2018.

**Table 13: Average output per worker, change in employment share and effect of between-sector shift on output per worker by sector, 1987-2018**

|||**1987-1997**||
|---|---|---|---|
|**Sector**|**Average output per worker in**<br>**1987 and 1997(RM)**|**Change in employment share**<br>**1987-1997(%)**|**Effect of between-sector shift on**<br>**outputper worker(RM)**|
|Agriculture|35,925.16|-13.6|863.61|
|Manufacturing|50,410.19*|7.8|642.10|
|Construction|34,958.07|3.6|-265.61|
|Trade|35,788.23|0.2|-9.78|
|Transport|67,542.54*|0.7|184.01|
|Business|75,560.11*|1.2|393.89|
|Other|35,120.33|0.1|-6.60|
|_Aggregate_|_42,245.85_|_0.0_|_1,801.61_|
|||**1997-2007**||
|**Sector**|**Average output per worker in**<br>**1997 and 2007(RM)**|**Change in employment share**<br>**1997-2007(%)**|**Effect of between-sector shift on**<br>**outputper worker(RM)**|
|Agriculture|46,707.48|-2.5|343.23|
|Manufacturing|82,784.60*|-4.6|-1,041.51|
|Construction|35,280.39|-0.5|127.16|
|Trade|51,531.45|5.1|-445.04|
|Transport|99,938.62*|0.2|65.38|
|Business|100,561.37*|2.8|1,111.63|
|Other|47,819.77|-0.3|40.35|
|_Aggregate_|_60,326.15_|_0.0_|_201.21_|
|||**2007-2018**||
|**Sector**|**Average output per worker in**<br>**2007 and 2018(RM)**|**Change in employment share**<br>**2007-2008(%)**|**Effect of between-sector shift on**<br>**outputper worker(RM)**|

22
log ( g ) : Random θ , m = 7 <!-- Start of picture text --> log ( g ) : Random θ , m = 7 Number of Models: Random θ , m = 7 Model Size: Random θ , m = 7<br>G G G G G G G G G G G G<br>G G G G G G G G GG GG G G G G G GG G G G GGG G<br>G GG<br>G G G G G G G G GG G G G<br>G G G G G GG GG G G G G G GG G G G G G G G G G G G G G<br>1 2 3 4 5 6 7 8 9 11 13 15 1 2 3 4 5 6 7 8 9 11 13 15 1 2 3 4 5 6 7 8 9 11 13 15<br>8 250000 16<br>7 14<br>6 12<br>150000<br>5 10<br>8<br>4<br>6<br>3 50000<br>4<br>2 0<br><!-- End of picture text -->

**Fig. 9.** SDM data: log posterior medians of _g_ , number of models visited and posterior mean model size.

Predictions for these data seem to be quite similar for all priors, as judged by the LPS boxplots in Figure 7. Here we have used prediction subsets of 13 observations each. Interestingly, these comparable prediction accuracies are obtained with regression models that are rather different, since the posterior inclusion probabilities corresponding to the benchmark priors with _c_ = 1 and _c_ = 0 _._ 1 and to the choice of _g_ = _k_<sup>2</sup> ( _i.e._ , the models leading to the highest values for _g_ ) are quite different from those using the other priors. BMA predicts best in 76% to 92% of the subsamples, with the exception of the benchmark priors with _c_ = 1 and _c_ = 0 _._ 1 and the case with _g_ = _k_<sup>2</sup> , where BMA outperforms the highest posterior probability model, the null model and the full model in roughly half the subsamples. BMA is only beaten by the null model in up to 8% of the cases,<sup>16</sup> except for the benchmark priors with _c_ = 1 and _c_ = 0 _._ 1 and the fixed- _g_ specifications, where the null model outperforms BMA in 10% or 12% of the cases.

The marginal information in the data on _g_ , as presented in Figure 8, is quite similar to that for the LPS data, with a slight shift to smaller values and a heavier tail. As a consequence, the pattern of Bayes factors between the models with different priors on _g_ is rather similar to what they were for the FLS data (see Table 4).

# _8.2. Returns to Schooling_

Here we investigate the area of returns to education, where again many potential model specifications have been proposed, and BMA was introduced in this context by Tobias and Li (2004). As these are microeconomic data, the number of potential observations is much larger. We will focus on the log of hourly wages of _n_ = 1190 white males in the U.S. in 1990, which we model as a function of _k_ = 26 possible regressors. In order to simplify the computations, Tobias and Li (2004) always include seven of these regressors in each model and restrict themselves to a smaller model space characterized by 18 possibly included regressors, but we allow for inclusion or exclusion of all regressors. In addition, we have added the local unemployment rate (which only appears in Tobias and Li, 2004, through an interaction term with education) as a potential regressor, giving us a total of 26 candidate regressors. More details on the application can be found in Tobias and Li (2004).

> 16 For the hyper- _g_ prior with _a_ = 4, BMA is actually never beaten by the null model.

27
_Business_ report, is assessed by the Freedom House’s Freedom of the Press rating as not free (Pakistan, Russian Federation, Malaysia, United Arab Emirates)<sup>8</sup> ; in five as only partially free (India, Mexico, Philippines, Italy, Nigeria) and in only one – Spain – as completely free. As further discussed in the analysis section, to eliminate the bias of authoritarian governments’ control over media, we perform panel data analysis excluding authoritarian and semiauthoritarian governments.

**Figure 4:** _Doing Business_ media coverage by income group and reforms implemented in 2004-17

|**Income group**|**Media articles**<br>(number, Factiva 2004‐17)|**Number of**<br>**implemented reforms**<br>(2004‐18)|
|---|---|---|
|Lower middle income|6,530|972|
|High income|5,684|839|
|Upper middle income|4,759|882|
|Low income|503|495|

# **Variable selection and summary statistics**

One of the key outcome variables used to test the set hypothesis is “number of _Doing Business_ reforms” in a given country in a given year. The number of implemented _Doing Business_ reforms is a simple count of reforms carried out by each country per _Doing Business_ cycle. The sum of this variable can fluctuate from 0 (no reforms) to 10 reforms,<sup>9</sup> which is the maximum possible number of reforms, assuming a country reformed on each of the _Doing Business_ indicators.

Aside from the reform count, _Doing Business_ presents data in terms of two main aggregate measures: the _Doing Business_ score and the ease of doing business ranking, which is based on the Doing Business score. The ease of doing business ranking compares economies with one another; the Doing Business score benchmarks economies with respect to regulatory best practice, showing the absolute distance to the best performance on each _Doing Business_ indicator. When compared across years, the _Doing Business_ score shows how much the regulatory environment for local entrepreneurs in an economy has changed over time in absolute terms, while the ease of doing business ranking can show only how much the regulatory environment has changed relative to that in other economies. Therefore, for the purpose of this paper, we mostly use the _Doing Business_ score as one of the main outcome variables. In the historical data set the minimum _Doing Business_ score is 19.98 percentage points (the country is far removed from the perfect score of a100), while the highest _Doing Business_ score is 90.41 (see Table 1 in the Annex).

> 8 Average of the overall grades of the level of press freedom by year from 2004 to 2016. Source: Freedom House.

> 9 In general terms, a reform is defined as a legal or regulatory change that makes it easier for local firms to do business. Reforms can have different impact magnitudes and _Doing Business_ counts only one reform per topic per country in any given year.

15
Policy Research Working Paper 10972

# **Abstract**

Against the backdrop of aging, Indonesia has started to address important policy challenges, including by gradually raising the retirement age. However, the increase in the retirement age and the prospect of longer employment of older persons raises concerns about its potential impact on the employment of younger persons. This paper is the first to analyze the relationship between the employment rate of older persons and the labor market outcomes of younger persons in Indonesia. Using data from the Indonesia Labour Force Survey (Sakernas) for 2016 to 2023, the analysis explores the relationship between the labor market outcomes – that is, the employment rates, unemployment rates, hours worked, and income – of younger persons and older persons. The findings show that overall, an increase in the employment rate of older persons is significantly associated with an increase in the employment rate of youth and prime-aged persons. The positive relationship is robust and is found across most of the specifications tested, that is, across genders, education levels, sectors, and within the formal sector. Encouragingly, there is no evidence of a significant negative relationship between the employment of older persons and younger persons. When there is a significant negative relationship between the employment of older persons and other employment outcomes of younger persons, the magnitude is small. These findings support the notion that raising the retirement age can address some of the challenges faced by an aging society. Finally, lifelong learning, upskilling, and reskilling are important to facilitate longer working lives by ensuring that older workers remain employable in a changing economy.

This paper is a product of the Social Protection and Labor Global Practice. 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://www.worldbank.org/prwp. The author may be contacted at aabdurrahman@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
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<!-- Start of picture text --> 50.0<br>45.0 444<br>. 40.7 41.5<br>40.0<br>35.0<br>29.6<br>30.0 278<br>24.7 24.9<br>25.0<br>20.0 16.3<br>15.0<br>10.0<br>5.0<br>0.0<br>1992 1998 c.2000 c.2010<br>TM$2.5 a day poverty line "$4 day poverty line<br><!-- End of picture text -->
Kakwani, N. 1980. On a class of poverty measures. _Econometrica_ 48 (2): 437–446.

- Kihiu, Njoki. 2020. “Kenya: 80% of Students Missing Virtual Learning Amid School Closures – Study”. Available online at https://allafrica.com/stories/202005180774.html

- Kim J. S., Quinn D. M. (2013). The effects of summer reading on low-income children’s literacy achievement from kindergarten to grade 8 a meta-analysis of classroom and home interventions. Review of Educational Research, 83(3), 386–431. Available online at <u>http://journals.sagepub.com/doi/10.3102/0034654313483906</u>

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- Kuhfeld, Megan, James Soland, Beth Tarasawa, Angela Johnson, Erik Ruzek and Jing Liu. 2020. Projecting the potential impacts of COVID-19 school closures on academic achievement. EdWorkingPaper No. 20-226.

- Kulbir Singh, Ruchi; Abu Alghaib, Ola. 2019. Every Learner Matters : Unpacking the Learning Crisis for Children With Disabilities (English). Washington, D.C. : World Bank Group. <u>http://documents.worldbank.org/curated/en/756811560851384787/Every-Learner-MattersUnpacking-the-Learning-Crisis-for-Children-With-Disabilities</u>

- Lloyd, D. N. 1978. Prediction of School Failure From Third-Grade Data: Educational and Psychological Measurement. https://doi.org/10.1177/001316447803800442

- Nicola, M., Alsafi, Z., Sohrabi, C., Kerwan, A., Al-Jabir, A., Iosifidis, C., Agha, M., & Agha, R. (2020). The socio-economic implications of the coronavirus pandemic (COVID-19): A review. International Journal of Surgery (London, England), 78, 185–193. <u>https://doi.org/10.1016/j.ijsu.2020.04.018</u>

- Macdonald, K. A. D. 2012. Indigenous Peoples and Development Goals. In G. H. Hall & H. A. Patrinos (Eds.), Indigenous Peoples, Poverty, and Development (pp. 17–72). Cambridge University Press. https://doi.org/10.1017/CBO9781139105729.002

- Marcotte, Dave E. and Steven W. Hemelt. 2008. Unscheduled School Closings and Student Performance. Education Finance and Policy, Volume 3 | Issue 3 | Summer 2008, p.316-338. Available online at https://www.mitpressjournals.org/doi/pdf/10.1162/edfp.2008.3.3.316

- Maria A. Cattaneo, Chantal Oggenfuss, Stefan C. Wolter. 2016. The More, the Better? The Impact of Instructional Time on Student Performance.  IZA DP No. 9797 March 2016. <u>http://ftp.iza.org/dp9797.pdf</u>

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37
In the MENA context, geospatial data can be particularly useful in conflict affected countries to prioritize interventions aiming to provide basic services. For welfare measurement, geospatial data can help design surveys by providing population estimates and help create sampling frames in countries where this information is obsolete or the displaced population is large, such as in Iraq, Lebanon, Djibouti and others (Hoogeveen and Pape, 2020). Finally, geospatial data can improve modeling for consumption imputation for poverty maps (small area estimation).

# <u>Summary of relevance and feasibility of different methods</u>

To summarize the discussion of methods/data to fill HBS data gaps, table 6 ranks approaches from “1” to “3” indicating both relevance and feasibility in each country context. Lower numbers indicate the highest relevance and feasibility. We put “0” when the method is irrelevant or not feasible in a particular country.

Table 6. Ranking different methods and types of data for measuring well-being in the MENA countries

||measuring<br>pove<br>collecting|monetary<br>rty|measuring no|n-monetary poverty or<br>well-be<br>|use as auxiliary da<br>ing|ta to measure|
|---|---|---|---|---|---|---|
|||||Food|||
||||||||
||ouseo<br>budget<br>survey|Imputation|asse<br>index|security/subjective<br>well-being|mnsrave<br>data|er g<br>data|
|Algeria|1|0|2|2|3|3|
|Djibouti|1|1|0|2|3|3|
|Egypt, Arab Rep.|1|1|0|2|3|3|
|Iran, Islamic Rep.|1|2|2|2|3|3|
|Iraq|2|1|1|1|2|2|
|Jordan|1|1|2|2|2|2|
|Lebanon|1|0|1|2|2|3|
|Libya|0|0|0|1|2|2|
|Morocco|1|0|0|2|2|3|
|Syrian Arab Republic|0|0|0|1|1|1|
|Tunisia|1|2|0|2|2|3|
|West Bank and Gaza|1|1|2|2|2|3|
|Yemen,Rep.|0|0|0|1|2|1|
|Source|||||||

Algeria, for example, conducted its latest HBS in 2011, its latest labor force survey (LFS) in 2014, its MICS in 2017, and its Arab Barometer in 2019. Using this information, we ranked different methods and data for measuring well-being the following way. “Conducting a new household budget survey” as a top priority, and calculation of an MPI/asset index and measuring subjective well-being as second-best options. Alternatively, we note that imputation is not feasible because the existing HBS is too old. Using administrative and big data can help, but it is unclear how we might access credible welfare information.

Overall, collecting new HBS data is a top priority for nine of the 13 countries. Imputation is feasible in a number of countries for years they have not conducted HBSs. As expected, conflict affected countries (highlighted by grey color in Table 6) use different strategies to measure well-being, with most relying on non-traditional methods and data.

26
<!-- Start of picture text --> 100%<br>90%<br>80%<br>70%<br>60%<br><!-- End of picture text --> <!-- Start of picture text --> 50%<br>40%<br>30%<br>20%<br><!-- End of picture text --> <!-- Start of picture text --> 10%<br>0%<br>1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015<br>= Retail market competition = Wholesale market competition<br>@ Bilateral contracting with Third Party Access @ Single Buyer Model<br>@ Independent Power Producers & Monopoly<br><!-- End of picture text -->
# FIGURE 23: SHARE OF HOUSING AND CONSUMER SERVICES IN EMPLOYMENT: SOVIET REPUBLICS AND OECD

<!-- Start of picture text --> 25%-<br>20%.<br>15%-<br>10%-<br>5%-<br>~~ARGESR IAE<br>_ - Z MO<br>0%- , I<br>7.5 8 8.5 9 9.5 10<br>LOG OF PER CAPITA INCOME<br><!-- End of picture text -->
TABLE 8: DESCRIPTIVE STATISTICS FOR TRAINER CHARACTERISTICS

|||_Trainer Chara_|_cteristics_||
|---|---|---|---|---|
||Mean|Std. Dev|Min|Max|
|_Demographics_|||||
|Age|35.24|10.53|25|56|
|Female teacher|.53|.51|0|1|
|Household members|4.24|2.11|1|8|
|Years at school|18.71|2.71|15|25|
|_Entrepreneurial experience_|||||
|Ever been a business owner|.41|.51|0|1|
|Years as business owner|.85|1.33|0|4.5|
|Number of businesses started in life|.47|.62|0|2|
|Number of businesses running|.18|.39|0|1|
|HH members with business|.59|1.01|0|3|
|_Cognitive abilities_|||||
|Digit-span score (forward + backward)|9.29|2.52|6|17|
|Raven test total|9.12|2.42|3|12|
|_Non-cognitive skills_|||||
|Personal Initiative (PI)|4.55|.44|3.43|5|
|Error competence|4.73|.34|3.8|5|
|Prosocial orientation|4.82|.43|3.5|5|
|Learning motivation|4.91|.26|4|5|
|_Job satisfaction and involvement_|||||
|How satisfied are you with your job?|4.06|.89|3|5|
|Given a choice, I would still become a teacher|3.94|1.29|1|5|
|Career satisfaction|4.35|1.06|2|5|
|Job involvement|3.98|.87|2|5|
|Organizational commitment|4.10|.83|2.44|5|
|_Years at TVET College and perceived skills_|||||
|Years as teacher|12.85|11.72|.5|36|
|Years at TVET College in general|8.97|5.72|.5|23|
|Perceived entrepreneurial skills|4.24|.75|3|5|
|Observations|17||||
20

# **Table 1  Comparison of LPG-using households who had abandoned polluting fuels and those who cited LPG as their primary cooking fuels and continued to use polluting fuels**

|**Item**|**Angola**|**Cameroon**|**Côte**<br>**d’Ii**|**Gabon**|**Ghana**|**Lesotho**|**Mauritania**|**Sudan**|**Kenya**<sup>**a**</sup>|**Nigeria**<sup>**a**</sup>|
|---|---|---|---|---|---|---|---|---|---|---|
|**Quintile 4**<br>People who live in households|that used LP|G and had aba|**vore**<br>ndoned po|lluting fuel|s||||||
|% of quintile 4 population|36|7|22|85|10|10|40|22|5|18|
|% LPG expenditure share|1.4|2.3|1.0|0.7|1.1|3.3|1.9|1.6|4.6|2.7|
|% Electricity expenditure<br>|2.1|2.5|2.2|2.8|5.3|3.9|7.9|1.4|2.1|1.8|
|share<br>Ratio of abandoning household<br>|s to those r|etaining pollut|ing fuels||||||||
|Spending on LPG|0.93|0.93|0.92|0.78|0.81|0.87|0.90|1.1|0.99|1.1|
|Spending on electricity|1.0|1.1|1.1|0.97|1.1|0.99|0.97|1.6|0.86|1.3|
|LPG expenditure share|1.2|1.2|0.92|0.67|1.0|1.0|1.0|1.2|1.2|1.2|
|Electricity expenditure share|1.3|1.1|1.2|1.3|1.2|1.7|1.1|1.5|1.0|1.4|
|Expenditures per capita|0.99|1.0|0.99|1.0|0.98|0.99|0.97|1.0|0.97|1.0|
|Household expenditures|0.86|0.89|0.99|0.84|0.85|0.90|0.90|0.99|0.79|0.95|
|**Quintile 5**<br>People who live in households|that used LP|G and had aba|ndoned po|lluting fuel|s||||||
|% of quintile 5 population|43|26|45|84|21|14|45|30|15|40|
|% LPG expenditure share|1.1|1.7|0.9|0.7|1.0|2.1|1.7|1.4|3.3|2.2|
|% Electricity expenditure<br>|2.0|1.8|2.0|3.2|4.3|2.7|8.7|1.2|2.2|1.9|
|share<br>Ratio of abandoning household|s to those r|etaining pollut|ing fuels||||||||
|Spending on LPG|1.4|0.74|0.88|0.87|0.66|0.81|0.77|1.0|0.85|1.0|
|Spending on electricity|0.89|0.62|0.95|1.0|0.78|1.2|1.1|1.4|0.96|1.1|
|LPG expenditure share|1.3|1.2|0.98|0.90|0.97|0.85|0.92|1.1|1.1|1.1|
|Electricity expenditure share|1.2|0.93|1.2|1.4|1.1|1.5|1.3|1.4|1.1|1.2|
|Expenditures per capita|1.3|1.2|1.2|1.0|1.3|1.0|1.0|1.1|1.4|1.2|
|Household expenditures|1.0|0.67|0.87|0.85|0.7|4<br>0.87|0.79|0.97|0.81|0.90|

_Source_ : World Bank staff calculations using household survey data. _Note_ : The ratio for a given item is that of those using LPG and had abandoned polluting fuels to that of those citing LPG as the primary cooking fuel and retained the use of polluting fuels. Expenditures on LPG and electricity are not deflated to avoid geospatial deflators markedly altering what households had spent. Ratios exceeding 1 (namely metrics for those who had abandoned polluting fuels were larger than those who had not) are shaded in light blue. To avoid giving a false sense of accuracy, only two significant figures are shown for all statistics except the percentage of the total population in the first line for each quintile, for which no decimals are retained.

a. Urban households only.

In a given expenditure quintile, households using LPG as their primary cooking fuel but retaining polluting fuels could even have nearly twice as many members as LPG-using households that had stopped fuel stacking, and in no country were fuel-stacking households on average the same size or smaller than