rafmacalaba/data-use-mentions-extended huggingface dataset:
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# **2.  Severance Pay and Employment Rigidity**

The first concern about severance pay in Indonesia is that the costs associated with the program are high and, as a consequence, distort firms’ hiring and firing decisions. Business representatives argue that these high costs discourage them from hiring new employees on a permanent basis, influence their lay-off decisions in response to contemporaneous economic conditions and lead to a deterioration in the investment climate.

Severance pay compensation rates in Indonesia began to increase in the mid1990s. There were three changes to these regulations in the last decade of the last century and he first decade of this century: 1996, 2000, and 2003. Figure 1 shows the severance pay rates associated with these three legislative changes to the regulations. In 1996, the severance payment was estimated to be equivalent to a “hiring tax” of about 2.0 monthly wages per employee.<sup>4</sup> The legislative changes in 2000 led to an increase in the “hiring tax” to an average of 3.4 months. With the changes introduced in 2003, the average severance payment is about 4.1 monthly wages per employee or 34 percent of an employee’s annual wage. The maximum severance pay is close to 30 months of wages in the case of a dismissal for an economic cause beyond the control of the employee.

The current employment protection legislation, Manpower Law (No. 13/2003), regulates severance pay rates in Indonesia. In terms of severance regulations, Indonesia falls into the group of economies in East and South Asia with more generous statutes, in the same range as Bangladesh, Korea, Nepal, the Philippines, and the People’s Republic of China. As stated above, business representatives in Indonesian claim that severance pay costs are high and constrain the workforce decisions that firms make.

One way to evaluate these claims is to examine the trends in various indicators of labor market rigidity. Using unemployment data and work experience data from the annual Sakernas
# **Technical Note 1: Creating a poverty map using the imputed consumption method**

## **Poverty mapping using imputed welfare**

The ‘imputed welfare’ poverty mapping technique developed by Elbers et al.(2002) uses the strength of both the detailed information about living standards available in the household budget survey and the more extensive coverage of a census to derive spatially disaggregated poverty estimates based on a consumption welfare indicator.

## **Overview of Methodology**

Survey data are first used to estimate a prediction model for consumption and then the parameters are applied to census data to derive an imputed value for consumption, employing a set of explanatory variables which are common to the survey and the census. This allows defining a set of welfare indicators based on consumption such as headcount poverty. Finally, the welfare indicators are constructed for geographically defined subgroups of the population using these predictions.

Although the approach is conceptually simple, properly accounting for spatial autocorrelation in the first stage model and estimating standard errors for the welfare estimates requires additional elaboration. The method can be thought of as being divided into three stages. There is a commonly defined “zero stage”, which involves the careful selection of a set of comparable variables common to both the survey and census (this stage is discussed further under implementation in section). The first stage of analysis then involves the use of survey data to derive a model for predicting household welfare. This model is then applied to the census dataset in the final stage. Stages one and two are further elaborated below.

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# **References**

- Akresh, R., Halim, D., & Kleemans, M. (2018). Long-term and intergenerational effects of education: Evidence from school construction in Indonesia. _NBER Working Paper Series_ .

- Alderman, H., Hoddinott, J., & Kinsey, B. (2006). Long term consequences of early childhood malnutrition. _Oxford Economic Papers_ , _58_ (3), 450–474.

- Andrabi, T., Das, J., & Khwaja, A. I. (2012). What did you do all day? Maternal education and child outcomes. _Journal of Human Resources_ , _47_ (4), 873–912.

- Attanasio, O., Cattan, S., Fitzsimons, E., Meghir, C., & Rubio-Codina, M. (2020). Estimating the production function for human capital: Results from a randomized controlled trial in Colombia. _American Economic Review_ , _110_ (1), 48–85.

- Baumrind, D. (1991). The influence of parenting style on adolescent competence and substance use. _The Journal of Early Adolescence_ , _11_ (1), 56–95.

- Bayer, J. K., Ukoumunne, O. C., Lucas, N., Wake, M., Scalzo, K., & Nicholson, J. M. (2011). Risk factors for childhood mental health symptoms: National longitudinal study of Australian children. _Pediatrics_ , _128_ (4), e865–e879.

- Black, M. M., Walker, S. P., Fernald, L. C., Andersen, C. T., DiGirolamo, A. M., Lu, C., ... others (2017). Early childhood development coming of age: Science through the life course. _The Lancet_ , _389_ (10064), 77–90.

- Black, S. E., Devereux, P.
DATA**

This study uses two data sources: interpreted satellite data and survey data. Satellite-based data are used to study program impacts on land-cover change or avoided deforestation. For the land-cover analysis, we compiled data for the entire population of both private and common property applicants to the program in all 32 states between 2011 and 2014. We include applicants from each of these four individual years, but combine them into two groups for purposes of analysis – we suggest that those who applied in 2011 or 2012 represent “medium term” effects, while those more recent applicants (2013-2014) represent “short term” effects. The survey data are used to measure program impacts on land-cover management activities, as well as community-level social capital and household welfare indicators. The survey, which took place in 12 states, focused on common property communities, which currently receive the majority of the program’s budget. These states include 60% of the common property applicants to the program between 2011 and 2014, and our sample represents approximately 30% of the total applicants to the program during these years. Communal governance structures own more than 45% of land with forest or vegetative cover in Mexico (Registro Agrario Nacional _,_ 2012) and are crucial to the maintenance of Mexico’s forests and biodiversity.

> 7 It bears mentioning that in terms of sustainability of conservation, re-enrolling the same properties over time is likely to be the correct approach, since it may guarantee sustained conservation of lands at high risk of conversion.

> 8 Approximately two-thirds of the points assigned in each year are based on geographic covariates, such as location within an overexploited aquifer or being a majority indigenous municipality. The remaining points are based on observable applicant characteristics or documented certifications, such as whether the applicant has a property management plan or whether the applicant has a registered agroforestry program.

9
other response patterns overwhelms variance in responses that is driven by the latent traits that are intended to be measured. Clearly a factor analysis that is driven by phrasing rather than actual content is of little interest to the researchers, hence the prior correction for the acquiescence bias is fundamental. Although far from making it perfect, the correction improves the items’ ability to capture a somewhat coherent factor structure.

The findings in this paper hence suggest it is advisable to first correct for acquiescence bias, and systematically analyze the latent factor structure through exploratory factor analysis when using noncognitive skills data. Naïve interpretation of item aggregation following pre-existing constructs without such analysis is likely to lead to erroneous conclusions regarding noncognitive skills.

# **4.7 Explaining the noise in the measure of technical skill**

As the technical skills questions attempt to measure knowledge, one would expect them to be less affected by systematic response biases. They require respondents to choose between a series of answers that do not have a clear ranking, or to give open answers, and while respondents certainly can (and do) guess, systematic bias of all questions in a certain direction is less likely. The results in this paper indicate however that random measurement error is much more important for technical than for cognitive skills. This can be inferred from the low test-retest statistics, low Cronbach’s alpha, and the gains in precision and predictive power when using means of test and retest, or the gains in precision from using factor analysis.

As discussed above, respondents’ difficulties in understanding the technical knowledge questions may partly explain the measurement error. Insights from the qualitative field work provide some additional insights for why the technical measure is noisy. To effectively assess a skill, a question needs to have only one correct answer and have enough variation in the responses to be informative of the respondents’ knowledge. However, after working with agronomists to identify the most fundamental knowledge that can affect the farmer’s productivity and piloting the questions, we found that most of them fell
discussed in Section 1, Grade 4 students in these schools were administered two rounds of tests (each covering the subjects of mathematics, Kiswahili, and English) and we use these to calculate an overall score averaging over subjects and the two rounds.<sup>34</sup> Specifically, we estimate two-parameter IRT models for each subject and each round separately. We then extract the latent ability parameter from each model, average across these, and normalize the resulting variable so it has mean 0 and standard deviation 1.<sup>35</sup> We estimate a series of regression models with the student test score as the dependent variable against various combinations of the variables that emerge from the observation instruments. We include two variables related to the share of time spent in teaching and learning: the Share of Time Teaching and a separate variable equal to the share of time lost due to students being off-task.<sup>36</sup> In addition, we include a test score for the teachers’ subject content knowledge. This is derived from a test administered as a part of the national SDI survey structured as a teacher correcting mock student tests in language and math. The test covered material at all grades of primary school.<sup>37</sup> The score we use is based on an item response theory analysis of these data.<sup>38</sup> We estimate each model first without controlling for other variables, and second including a set of student, household, teacher, and school characteristics. Student and household characteristics include age, gender, whether the student had eaten before school on the day of the test, and a number of characteristics that reflect socioeconomic status (having a separate room to sleep in at home; having electricity at home; having running/tap water at home; living in a dwelling with concrete/cement/stone walls; living in a dwelling with a metal roof; living in a dwelling with a toilet; household ownership
e captured in the LSMS survey where questions on participation in agricultural land markets appear in the agricultural module that is administered only to those who currently cultivate agricultural land (Deininger and Jin 2008).

> 17 A number of countries have initiated promising efforts to foster structural change and transfer of land to young producers by allowing those who transfer their entire land on a long-term basis (12 years or longer) to access pensions early, akin to early retirement schemes in other sectors.

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The regions of Bongolava and Sofia have the highest percentages of children weighing less than 2.5 kg, with 19% and 17%, respectively. On the other hand, the regions of Ihorombe and Anosy have the lowest percentages of low-birth-weight children, at around 8%. In other words, stunting seems prevalent in the least poor regions of Madagascar and less prevalent in the poorest regions. These findings emphasize the importance of regional context in addressing child undernutrition, highlighting the need for tailored approaches that consider socioeconomic factors, health care access, and nutritional practices to improve child health outcomes across Madagascar.

But these heterogeneities also exist within regions at the commune level. We therefore applied the SAE approach in the DHS data to produce stunting estimates at the commune level and produce the map presented under figure 3. The maps highlight higher stunting rates among communes located in high plateau of the regions in the center, also known for its better agro-ecological advantage for agricultural production. We also note the higher stunting rates among communes in the regions in the east and southeast of the country.

Linguistic, historical, and DNA research suggests that the central highlands of Madagascar were settled by people with an Austronesian background 1,200 years ago (most likely South Borneo); the coastal areas were settled later by people mainly from East Africa (Hurles et al 2005). Can this partly explain the stunting patterns? Our limited data does not allow us to verify this hypothesis.

The variables selected to predict stunting as well as the resulting coefficients from the linear mixed model is provided in table A2 of the annex. Table 4 presents a set of model diagnostics relating to the fit of the model and the consistency of the normality assumptions with the sample data. Overall, the model explains 7.5 percent of the variation in heigh for age Z score when not considering the conditional random effect, and 11.3 percent when the conditional random effect is included. The model was estimated in a subsample of 6,379 children again of approximately
rough estimate suggests that the debt and overall-deficit rules have been complied with a little more than half the time. This is not out of line with the experience of other regions and is consistent with the difficulty of enforcing national fiscal rules.

- An online survey conducted for this paper reveals public awareness of fiscal rules, but also suggests that understanding of the rules may not be sufficient to give governments strong political incentives to comply with them, partly explaining imperfect compliance.

- A qualitative assessment of the rules against established criteria finds that the rules have both strengths and weaknesses. Some rules require fairly prudent levels of debt, but others are arguably too permissive for countries that borrow mainly in currencies that they do not control. Some rules are admirably flexible or impressively comprehensive, but these benefits come at the cost of considerable complexity, which may explain why they are not always widely understood.

- Quantitative analyses show that some countries in the region with high debt will have to do more than merely comply with their overall-deficit rules to get their debt down to prudent levels by the time of possible EU membership. That does not make the achievement of prudent debt levels impossible: the countries have sometimes run deficits

3
the middle of 1980s, and reached its lowest level in 1990-1995; during this period migrations into cities increased significantly thanks to urban market reforms initiated in the middle 1980s and flexibility of urban _hukou_ system (Yang and Zhou 1999). During the period of 1995-1996, the migration into towns started to pick up, reaching 19 percent. This recent increase in the flow of migrants into towns probably has been due to the more favorable migration policy toward small cities and designated towns residents by granting them urban _hukou_ , and the increase in FDI in many small towns, especially in the coastal areas.

Table 1 Percentage of flow of in-migration to cities, towns and rural townships.

|place of destination|1982-87|1985-90|1990-95|1995-2000|
|---|---|---|---|---|
|____________________|_____________|____________<br>(%)|____________|_______________|
|city|36.6|61.7|61.4|59.4|
|town|39.8|20.1|10.0|19.16|
|rural|23.6|18.2|28.6|21.43|

Source: Data for the period of 1982-87, 1985-90, and 1990-95 are from Yan (1998), data for the period of 1995-2000 are derived from the tabulation of 2000 population census data (the long form of 9.5% of census sample data).

# **3.  Economic opportunities in towns in three provinces**

Before we elaborate the town in-migration in these three provinces, we present the population composition and employment opportunities in towns in these three provinces. Towns are well developed in Zhejiang, and less so in Henan and Sichuan. According to census data of 2000 (Table 2), there were 20.15 percent of population living in towns in Zhejiang province; in contrast, there was only 8.31 percent of population living in towns in Henan
SOE). In some countries, they are made regularly, especially when governments compensate SOEs ex ante for specific QFOs and mandates.

- **Government injections of equity:** Governments can bail out and recapitalize SOEs by injecting equity. These equity injections increase the equity owned by the government (in SOEs that have other shareholders). We track them using a variable called Proceeds from Equity Injection from Government, which adds up the additions to equity from government either directly (by a domestic government entity, such as a ministry or a government agency) or indirectly (by a domestic government entity through its shareholding in another company, such as another SOE, or any other enterprise with government participation).

- **Credit from the government:** This variable is created by adding up long-term debt or loans from the government or other government-affiliated creditors except SOEs and stateowned banks. Credit usually has a direct impact on the budget and on the support governments report for their SOEs.

10
Summarizing, theory does not provide clear answers whether and how the business orientation, cost efficiency, asset quality and stability differ between conventional and Islamic banks. This ambiguity is exacerbated by lack of clarity whether the products of Islamic banks follow Sharia in form or in content. We therefore turn to empirical analysis to explore differences between the two bank groups.

# **3. Data and Methodology**

We use data from Bankscope to construct and compare indicators of business orientation, efficiency, asset quality, and stability of both conventional and Islamic banks.<sup>3</sup> We only include banks with at least two observations and countries with data on at least four banks. We restrict our sample to the largest 100 banks in terms of assets within a country so that our sample is not dominated by a specific country. Finally, we eliminate outliers in all variables by winsorizing at the 1<sup>st</sup> and 99<sup>th</sup> percentiles. We also double check the categorization of Islamic banks in Bankscope with information from Islamic Banking Associations and country-specific sources.

In our main analysis, we use two different samples, both over the period 1995 to 2007 and thus both pre-dating the recent global financial crisis. In the next section, we compare preand post-crisis performance of Islamic and conventional banks. The larger sample comprises 141 countries and 2,956 banks, out of which 99 are Islamic banks. Individual regressions, however, have significantly fewer observations, depending on the availability of specific variables. These samples include countries with (i) only conventional, (ii) only Islamic and (iii) both conventional and Islamic banks. Another, smaller, sample comprises only countries with both conventional and Islamic banks, which allows us to control for any unobserved time-invariant effect by introducing country dummies. This smaller sample includes 486 banks across 20 countries, out of which 89 are Islamic banks.

In Table 1, we present data on 22 countries with both conventional and Islamic
to classroom observations in over 15,000 classrooms across 7 Latin American countries, Chang et al. (2014) who use video recording of 200 8<sup>th</sup> grade mathematics lessons in 200 classrooms with ex-post expert analysis in Indonesia, Seidman et al. (2018) who develop and apply the Teacher Instructional Practices and Processes System (TIPPS) instrument in 197 secondary schools in Uganda, Wolf et al (2018) who apply TIPSS in pre-primary classrooms in Ghana, the SDI surveys mentioned above which to-date have recorded observations for over 69,000 teachers in 10 Sub-Saharan African countries (www.sdindicators.org), and finally the development and application of the _Teach_ instrument in Mozambique, Pakistan, the Philippines, and Uruguay (Molina et al. 2018).<sup>3</sup> Fewer still are studies that explicitly compare how different observation instruments perform, with Bruns, De Gregorio and Taut (2016) who compare Stallings and CLASS for 51 teachers in the Santiago Metropolitan region and two adjacent regions in Chile being a rare exception.

# **3.  Sample, study design, and data**

# **_3.1 Setting, sample, and study design_**

The study setting is Tanzania where 106 schools were reached for this Extra Teacher Observation Study (ETOS). The sample was purposively chosen from the nationally representative sample of 400 schools that had been selected for the 2014 and 2016 rounds of the Tanzania SDI survey (Martin and Wane 2016).<sup>4</sup> The ETOS schools are located in 11 regions across the country. Data were collected across two rounds: the first round was carried out between August and November 2016 (with almost 65% of the sample in August itself), the second round was carried out in November 2016. Eighty-eight of the schools are located in rural areas, 9 are in Dar es Salaam, and 9 are in other (semi-)urban areas (in this analysis we
The econometric analysis examines how the evolution of urban form is closely tied to the processes of economic development. The econometric evidence is organized in three sub-sections. Section 4.1 examines the relationship between incomes and built-up area changes between 1990 and 2015. Section 4.2 examines links between income and city heights. Section 4.3 provides a new approach to look at the combined effect of population growth and income growth on floor space in a city.

# 4.1  The effects of income and population growth on built-up area

As population and income grow, cities expand by building more and occupying more land. We quantify the income and population elasticities of built-up using a panel of observations from the GHSL Urban Centers database. As described in section 2, built-up surface for all cities with more than 50,000 inhabitants in the world is available for four years: 1975, 1990, 2000, and 2015. City income and population estimates come from the same GHSL database.<sup>4</sup> Several econometric issues arise when estimating the impacts of a growing population and income on city built-up over the past decades. First endogeneity issues may arise as factors that are not observed may affect both the dependent variable, built-up surface, and the explanatory variables, population and income of the city. Second, there may be omitted variables that are key predictors of built-up but are not observed such as geographic or weather amenities. In each case, a simple OLS estimator will be biased. Therefore, the results from different specifications based on equation (1) are reported with each of them solving an econometric issue. The panel regression analysis is based on the following specification:

> 4 Similar estimates are obtained when using country income.

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|(0.060)|(0.056)|
|Refugee (d)||<br>0.186<sup>**</sup>||<br>0.259<sup>*</sup>|
|||(0.090)||(0.136)|
|Observations|243|243|180|180|
|Pseudo R^2|0.11899|0.14438|0.09456|0.13529|

Marginal effects; Standard errors in parentheses. Robust SE.<sup>*</sup> _p_ < 0.10,<sup>**</sup> _p_ < 0.05,<sup>***</sup> _p_ < 0.01 “Asset index above median” is computed using information about asset ownership in August 2014. Source: LDPS 2014-15 (d) for discrete change of dummy variable from 0 to 1 17