rafmacalaba/data-use-mentions-extended huggingface dataset:
Page 17 56. Conscious of the non-experimental nature of the data employed in this section of analysis, and unresolved endogeneity issues, in the sections that follow, care is taken to present results as correlations rather than causation. Moreover, in order to limit the problem of possible multicollinearity among the independent variables, principal factor analysis is used on sets of variables that are meant to proxy for similar school, household or community characteristics and that appear highly correlated.

57. The survey was designed to: (i) quantify educational resources and institutions for accountability, including both external (“top down”) and participatory (“bottom up”) mechanisms; (ii) measure the awareness of the different stakeholders of the school institutional framework in general, and participatory institutions in particular; and (iii) measure actual parental involvement with the school. In what follows, we present descriptive statistics drawn from the survey to measure or proxy school outputs, school and teacher characteristics, “top-down” accountability and transparency, participatory “bottom up” accountability and parental involvement, and parents‟ and community characteristics. In the first part of this section, we explain the measures that have been selected from the survey data. In the second part, we present descriptive statistics and the results of tests for significant differences between community groups of interest.

# _2.b.1. Measures of students’ performance at the school level_

58. Until the 2006-2007 academic year primary school leaving examinations were designed and administered by school teachers. This system generated opportunities for irregularities and prevented MoES from carrying out objective comparisons of students‟ performance across schools. Starting from June 2008, the locally administered final year examinations were replaced by standardized tests, centrally administered by the division of the MoES that is also in charge of _State Matura_ high school final year examinations. Therefore, since 2008 the performance of primary school students on leaving examinations has been comparable across schools.

59. We focus on five education outcomes at the school level. Three of them are generated
# **5.1 Predicting the Sector-County Profile of Non-VAT Firms**

To incorporate informal firms into the network, we first update the firm-type probabilities _p_ ( _θ_ ) for each sector-county-size cell, this time accounting for the entire firm size distribution. To update _p_ ( _θ_ ), we ideally would want to observe the number of firms _Ncs_ in each sector _s_ , county _c_ , and size cell – irrespective of their formality status. However, none of the KNBS records available to us feature a breakdown of the firm count along both the sector _s_ and the county _c_ dimension, let alone size dimension. Therefore, instead of the firm count, we rely on granular sector and region level information on formal and informal employment in the 2019 census labor force module to compute our alternative entry probabilities _p_ ( _θa_ ): 23
# TABLE 6

# OUTSTANDING BANK CREDIT: TOTAL ECONOMY, AGRICULTURE, INDUSTRY (Selected years, end of year balances in real values)

||In Pe|rcent of|1969 credit|In percent of|total credit|
|---|---|---|---|---|---|
|Year|total<br>credit|credit<br>agricult|to<br>credit to<br>ure<br>industry<br>...|credit to<br>agriculture<br>------------------------------|credit to<br>-<br>industry<br>-------------------------__---|
|1970|138|136|<br>124|10|32|
|1974|236|209|<br>219|9|33|
|1979|351|335|<br>327|10|33|
|1984|427|491|<br>352|12|29|
|19137|535|655|<br>402|13|27|

SoLrce: Annual Bank Statistics, various issues.

# TABLE 7

-

RATIO OF OUTSTANDING DEBT TO GROSS PRODUCT AND NET CAPITAL (Selected years, end of year data, real values)

|Year<br>----------------------------|total<br>credit,<br>% of gnp<br>--------------------------------
3. Global estimates of population dispersion and sparsity for more than 100 countries Estimates of the geographical scale of population dispersion and dispersion-adjusted population sparsity were calculated for each country with sufficiently detailed data (a minimum of five sub-national areas) in the World Bank’s subnational geography database.

Across the global database, the geographical scale of population dispersion ranges from a maximum of 3,600km in Russia, to a minimum of 40km in Trinidad and Tobago (Figure 5, Appendix E shows the numerical values and country rankings according to the new dispersion measures). None of the PICs lies at the extremes of this range, but dispersion for FSM and Kiribati is at the higher end of the distribution, while Fiji and Tonga are at the lower end. The scale of dispersion in Russia is considerably larger than every other country across the database, with the estimated average distance between every two Russians more than 3,600km. The three countries with the next highest dispersion levels are Brazil (1,300km), China (1,300km) and Kazakhstan (1,200km). Dispersion is smallest in the Caribbean countries of Trinidad and Tobago (40 km) and Jamaica (60 km).

After accounting for population size, the PICs stand out as having extremely low levels of dispersionadjusted population density compared to other countries (Figure 6), and equivalently, an extremely high degree of population sparsity (Figure 7). Figure 6 illustrates the cross-country distribution of dispersionadjusted population density. This measure ranges from a maximum of 1,200 people per square kilometer in Bangladesh, to a minimum of 0.05 people per square kilometer in FSM. The inverse of this density measure, the dispersion-adjusted population sparsity, is illustrated in Figure 7. With the exception of Fiji, the degree of population sparsity in the PICs completely dwarfs that recorded for other countries in the database (the first panel shows a complete scale on the vertical axis; the second panel reduces the scale to better reveal the variation across the non-PIC countries).
, 2006_ for Kenya by the Central Bureau of Statistics (2007a, p. 198)) and the single financial services sector into insurance, and banking and other financial services, from data in Central Bureau of Statistics (2007a, pp. 95-98. We assumed that the input output structure for all sectors using these services was identical for the disaggregated sectors. A full listing of the sectors and factors of production is provided in table 1. Kiringai et al. (2007) also provide a

> does not. On the other hand, for sectors that are low in R&D intensity, their results suggest that for technology diffusion trade with developing countries can be as important as trade with industrialized countries.

21
access to services, to estimate the cost of asset reconstruction or replacement, to identify the most urgent needs, and on that basis to establish a recovery strategy.

This second approach tends to be comprehensive and has produced useful estimates of the cost of disasters, often in a matter of just weeks or months. However, one of the main challenges it faces is to convert the information on physical destruction into monetary values. A baseline reporting of the market value or investment cost of public and private assets prior to the disaster is generally unavailable and educated guesses on the spot may carry a significant margin of error. Assessing in monetary terms the change in earnings opportunities and in access to services can be even more challenging. Because of these difficulties, the precision and relevance of studies based on this second approach has been questioned (Jeggle and Boggero 2018).

An alternative to using field survey data is relying on remote-sensing technology. Proceeding this way allows for greater measurement consistency across geographic areas and provides real-time estimates. Studies based on this third approach have leveraged satellite images on built-up areas and nighttime light intensity to infer the impact of natural disasters at local levels. For example, an analysis of Chinese typhoons shows that when 50 percent of properties in an area are destroyed, local economic activity falls by 20 percent (Elliott et al. 2015).

The advantages of this third approach include the speed at which cost estimates can be generated, and their high spatial granularity. However, there are disadvantages as well. Built-up data allows assessing the extent of physical destruction, less so the market value of the assets destroyed. Similarly, nighttime light intensity provides information on long-term trends in economic activity, especially across countries and regions, but is less well-suited to assess its short-term fluctuations <mark>(Ezran et al. 2023)</mark> . And satellite images may not be sufficient to disentangle the impact of disasters across population groups.

On the other hand, the approach proposed in this paper is closely linked to the existing
proficiency.</mark> On student performance, girls have slightly outperformed boys in all subjects tested in the national exam ( _Ujian Nasional_ , UN) for students from 2016 until 2018 for grade 9. The UN data show the differences in exam scores between girls and boys were largest for Indonesian language ( _Bahasa Indonesia, 66.52 and 71.19_ ), followed by English (57.23 and 54.86) with average score differences of 4.67 and 2.37, respectively, of 100 points. The average scores for mathematics and science are generally low. Girls’ average mathematics score in the past three years is 48.3, while the average score for boys is slightly lower at 46.9. Similar trends are also observed in science, where the average score difference between girls and boys is only 1.3 percentage points, with girls slightly outperforming boys. While variations of the score differences are observed at the local level, overall 96 percent of districts have similar trends in the past three years, where the average score for girls is higher than the average score for boys. In 2018, the highest score gap observed was in Bantul, where girls outperformed boys by 6.6 percentage points across all UN subjects.

On international assessments and in the classroom, girls similarly outperform boys in Indonesia. Following Indonesia’s participation in the 2018 PISA, results showed that girls scored higher in all subjects, most significantly in reading. The average scores for Indonesian boys in math, science and reading, respectively, were 373, 392 and 358, while the respective scores for girls were 383, 399 and 383.<sup>21</sup> Results from the 2015 TIMSS showed that girls scored 10 percentage points higher than boys in mathematics and 8 percentage points higher in science (Mullis et. al, 2015).

> 20 Susenas 2018.

> 21 OECD Education GPS: Indonesia Student Performance (PISA 2015).

> <u>http://
investors was located but also that it was quantitatively large: compared to an annual rate of area expansion in Africa of some 1.8 million ha in 1961-2007, demand for land in Africa in 2009 alone amounted to some 39.7 million hectares--greater than the total agriculturally cultivated area of Belgium, Denmark, France, Germany, the Netherlands, and Switzerland combined.

Two comments are in order to interpret this figure. First, it refers to demand for land deals rather than actual transactions or area brought under production. Second, as reports of land acquisitions are less likely in countries where press freedom is limited or where land acquisition is not ―new‖ or ―noteworthy‖, it may be biased downward and covering only the largest projects. Efforts to cross-check the information from press reports with administrative data in the countries where such information is available supports this in the sense that while not all the projects mentioned in press reports could be identified in official data–presumably because some had never made it beyond expressions of interest–most lands actually transferred could be traced to press reports. Preliminary results from a recent effort to more systematically cross-check press reports confirm this finding.<sup>17</sup> For each project, we code origin and destination country,<sup>18</sup> size, commodities involved, investor type, and whether any activity had started.<sup>19</sup> The universe

> 16 The data can be accessed at www.farmlandgrab.org.

> 17 Results will soon be posted on ILC‘s website http://www.commercialpressuresonland.org/monitoring-land-transactions. 18 In Sub-Saharan Africa, key target countries are Sudan, Ethiopia, Nigeria, Ghana and Mozambique, which account for 23% of projects. 21% of projects are in Latin America and the Caribbean (mainly Brazil and Argentina), 11% in Europe and Central Asia (Kazakhstan, Russia, and

10
regional dummy variables, it has a strongly significant quadratic impact on reading and mathematics scores, the impact being stronger for those with higher school attainment" (p.380). These results, showing the significance of the interaction terms, reveal that schooling years work in conjunction with other factors. The interaction with Raven's score shows that more talented individuals benefit more from school years-as judged by the labor market. The interaction with age captures the secular decline in the quality of education. Persons who attended school many years ago were able to enhance their earnings per year of schooling more than recent cohorts. The result that the regional dummies, which are insignificant in themselves, show strong and significant variation in the attainment of reading and mathematical skills across regions, when interacted with years of schooling, point to the importance of regional difference in the quality of schools.

The total effect of education on earnings were calculated from theses estimated equations as shown in equation (3). The rate of return to school years, calculated at the mean value of both schooling and Raven's score was higher in the public but no longer zero in the private sector. In the former they ranged between 5.4 percent and 6.2 percent, and in the latter between 3.4 and 6.3. As already noted, the rates of return rise with age are higher in Accra reflecting the higher school quality in the capital city at all age groups.

# **5.3 Family Background, Private-Public Choice and Returns to Education**

Canagarajah and Liang (1996) address a different set of issues in the study of private-public differences in the returns to education. They consider the labor market all together, including the self-employed, the private sector employees, and those employed in the public sector. We can estimate separate earnings functions for these three categories, but the resulting estimates might be biased because we are not allowing for the selectivity factors which govern the allocation of the workers to the t-hree types of employment
Brothers in September 2008. While GSDP growth recovered in the post-crisis years, credit growth, investment, and manufacturing growth remained subdued.

We ask whether there were any specific state-level characteristics that correlated with the impact of the GFC on the states. We define the states’ dependence on agriculture, the relative importance of manufacturing in economic activity, and the credit-to-GSDP ratio as an indicator of the states’ dependence on credit, and the rate of credit growth prior to the GFC (between 2004 and 2008) as an indicator of the prevalence of a credit boom in states in years prior to the GFC. While in our main specifications we compare states above and below median for these characteristics, in

> 26 Data on Gross State Domestic Product are from the CMIE’s states of India database. While India has a total of 36 states and union territories, we restrict our analysis to the 20 large states, including Andhra Pradesh, Assam, Bihar, Chhattisgarh, Gujarat, Haryana, Himachal Pradesh, Jammu and Kashmir, Jharkhand, Karnataka, Kerala, Madhya Pradesh, Maharashtra, Odisha, Punjab, Rajasthan, Tamil Nadu, Uttar Pradesh, Uttarakhand, and West Bengal. Our sample covers the years 1990 to 2015 for all states except the younger states Jharkhand, Chhattisgarh, and Uttarakhand, for which credit data are only available from 2001 onward.

> 27 Due to the unavailability of data for exports for each state, we cannot confirm the patterns in exports.

20
17 developing a commercially viable natural gas futures contract, but the development of a short-term gas contract has been postponed due to communication problems between the IPE and BGT.

Figure 3 Argus Monthly Sell-Buy Index, October 1995-September 1996 <!-- Start of picture text --> Pence per therm<br>14<br>12<br>10<br>8<br>6<br>4<br>2<br>0 4 I - -II [!, -<br>Oct. Nov. Dec. Jan. Feb. Mar. Apr. May Jun. Jul. Aug. Sep.<br>95 95 95 96 96 96 96 96 96 96 96 96<br><!-- End of picture text --> Source: Petroleum Argus data
previous household survey was used to target the household head as the respondent, while in Lao PDR and Mongolia, the targeted respondent in the first survey round was an adult member of the household who answered the phone or was available to be surveyed. In Myanmar, the Philippines, and PNG, employment data was collected for both the respondent and the household head when the respondent was not the head, but only in PNG was the pooled sample used to measure employment impacts.<sup>8</sup> Thus, in some countries, household heads are overrepresented, though at varying degrees (Table 2).<sup>9</sup> To the extent that household heads differ in terms of age, gender, type and formality of work, and other job characteristics from the average worker, the sample of respondents may not fully capture the pandemic’s impact on the labor force. To control for these differences across countries in respondent characteristics, we conduct the analysis controlling for respondent’s characteristics including relation to the household head, gender, and age. Results are generally not qualitatively different whether these controls are included, so unconditional means are reported throughout the paper unless otherwise noted. Regression results including controls are available in the appendix.

The second data set is a cross-sectional household data set that is used to analyze household-level indicators of income, coping mechanisms, food insecurity, and access to services. While it is possible to build a panel data set of households, cross-sectional data are used in order to maximize the number of

> 6 For Indonesia, Mongolia, Philippines, and Vietnam, we use an individual-level panel data set. However, building a true panel was difficult or purposely not done for Lao PDR, Myanmar, and PNG. Lao PDR and Myanmar added new respondents in each round to account for attrition and maintain representativeness, and thus restricting the sample to just panel respondents across rounds would have limited the sample size substantially. Instead, new respondents were included in the sample, which
# **1 Introduction**

While coronavirus (COVID-19) is primarily a health crisis, its ramifications extend to social and economic aspects of countries. Trade collapsed and demand and supply shocks became widespread after pandemic-induced measures were imposed globally to contain transmissions. Supply-side shocks on exports arose from COVID-19 related deaths and infections, and lockdown policies such as border closures, slow clearance of goods and workplace closures that reduced labor participation resulted in a slump in overall firm and country production capacity. Demand shocks then compounded the supply shocks from declines in aggregate demand due to job redundancies, business closures, cautionary spending and investments. These shocks were in turn transmitted and augmented through disruptions in the global supply chains (Hayakawa and Mukunoki, 2021a; Hayakawa and Mukunoki, 2021b).

In this context, this paper assesses the trade effect of the pandemic on the largest economy in Southeast Asia, Indonesia. The study investigates the broad effects of lockdown measures (domestic, i.e., imposed by Indonesia, and external, i.e., imposed by Indonesia’s trading partners) on Indonesia’s imports and exports, including the survival of products in import and export markets. Then, we differentiate between sea and air trade as well as assess the role of specific non-tariff measures (NTMs) on the effect of lockdown measures on trade performance in Indonesia. Additional analyses are also conducted to establish the sensitivity of our results to the type of lockdown policy and the effect of lockdowns on export and import prices. Lockdown is a binary composite indicator variable where one indicates the month any of the following measures were imposed by Indonesia and its trading partners: workplace closure, the closure of public transport, stay-at-home requirements, restrictions on internal movement, and international travel controls. This is done using a monthly series of productby-country data for the period 20 months before and 20 months after January 2020, i.e., May 2018 to August 2021, in survival analysis and event-study designs. For imports, domestic lockdowns represent demand factors
, as was the case in many of the COVID-19 phone surveys. However, responses may be biased positively or negatively depending on the sources of bias at play (for example, social desirability bias or perceived benefits to indicating poor outcomes). Moreover, the loss of information is significant, so that this strategy is not suitable for many types of agricultural surveys. For instance, it would not be feasible for a large-scale crop production survey seeking to generate national statistics.

A third approach is to retain some detailed and disaggregated data collection but reduce the response burden by asking fewer questions or covering only a sub-sample of crops, plots, or individuals. IFPRI’s vegetable value chain phone surveys in Ethiopia asked detailed questions focusing on the most important vegetable household’s grow (Hirvonen et al., 2021b; Minten et al., 2020). The LSMS-supported HFPS followed the same approach, focusing on the ‘main crop’ in the reference agricultural season. In contrast, the World Bank’s Africa Gender Innovation Lab used phone calls to collect input data for one selected parcel in a survey of rural agricultural households in Western Uganda (Sharma et al., 2021). In the context of their survey experiments, Arthi et al. (2018), Gaddis et al. (2021), Kilic et al. (2021) successfully collected plot-level input and output data over the phone, focusing on one specific topic (labor inputs and extendedharvest crop production). In all three settings, the plot-level data collected through phone interview was deemed reliable and even superior to the standard end-of-season labor module administer in a one-off in-person visit. These examples illustrate that it is possible to retain a certain level of complexity if focusing on a narrow set of issues or a sub-sample of plots, crops, or individuals, thus allowing for short phone interviews. Sub-sampling has implications for the representativeness of the data so this approach may not be suitable to replace an extensive agricultural sample survey in full but may be used to complement inperson
.0<br>0.5<br>0.0<br>2006 2007 2008 2009 2010 2011 2012<br><!-- End of picture text -->

- (E) Domestic and global staple food prices during 2007-08 and 2010-11

- (F) Average increase in world and domestic price index, 2010-11

<!-- Start of picture text --> Index, 1=2006 Percent Domestic World<br>3.0 120<br>2.5 100<br>2.0 80<br>1.5 60<br>1.0 Domestic prices: 2007-08 40<br>World prices 2007-08<br>0.5 Domestic prices: 2010-11 20<br>World prices: 2010-11<br>0.0<br>0<br>Rice Maize Wheat<br>t-12 t-10 t-8 t-6 t-4 t-2 0 t+2 t+4 t+6 t+8 t+10 t+12<br><!-- End of picture text --> Sources: Ivanic and Martin (2014b), World Bank. A. Rice, wheat, maize, oil and sugar prices.

E. Event study based on monthly cross-country average domestic staples prices (average of wheat, rice and maize prices) and global staples prices (average of wheat, rice and maize) during 2007-08 and 2010-11. Period 0 represents the month of the peak of the world food price spike.

F. Average percent increase in the price index.