rafmacalaba/data-use-mentions-extended huggingface dataset:
18621918, data comes from The Financial Review. From 1919 onwards, data is retrieved from the Federal Reserve Bank using 90-day Treasury bills. From 1920 until 1942, the data comes from the Federal Reserve Bank, National Monetary Statistics. From 1942 onwards, we retrieve 3-month US bills returns from the Center for Research in Security Prices (CRSP). CSRP calculates total returns based on a comprehensive database of historical US bills. In each month, a representative Treasury bill is selected that is closest to the target maturity.

### <u>US Treasury bonds, monthly, since 1815</u>

The long-term US Treasury bond return index is from GFD and constructed using data by Homer (1967) and Martin (1886) for the years 1815-1862, from The Economist (1854-1861) and from the periodical Hunt’s Merchants Magazine (1843-1853). For 1862-1919 data comes from The Financial Review. From 1920 onwards, the data comes from the Federal Reserve Bank and Salomon Brothers (1995). Whenever possible, we use an index of 10-year bonds. During the 19<sup>th</sup> century, the available indices also contain A1
sup> to footwear and leather.

- c) Includes 7,148.0 million pesos (excl. indirect taxes and subsidies) for petroleum refining which are not shown in the Resumen General but taken from Sistema de Cuentas Nacionales de Mexico, 1981.

- Source: Figures derived from the production censuses, i.e. Censo Industrial for<sup>Brazil,</sup> Resumen General for Mexico and Annual Survey of Manufactures for the USA; see for the universe table 2.1 (Brazil), table 2.3 (Mexico) and table 2.5 (USA); see for the sample figures tables 3.11 and 3.12
Suryahadi, A. (2001). International economic integration and labor markets: The case of Indonesia.

# **Annex A: Data**

# _Sakernas_

The Sakernas data, also known as the National Labor Force Survey (Survei Angkatan Kerja Nasional), is a comprehensive dataset for Indonesia. It is a survey conducted by the Indonesian Central Bureau of Statistics (Badan Pusat Statistik or BPS) and provides detailed information on various aspects of the labor force in the country. The survey is typically conducted bi-annually and collects data from households across Indonesia.

The Sakernas data covers a wide range of topics related to employment, unemployment, and labor force participation. It includes information on individuals' demographic characteristics, educational attainment, occupation, industry, employment status, working hours, wages, and other relevant labor-related variables.

For this study, we built a repeated cross-sectional dataset between 1993 and 2014 using the August rounds of data. We harmonized industry codes to the Klasifikasi Baku Lapangan Usaha Indonesia (KBLI) 1990 classification, the Indonesian industrial classification based on ISIC Revision 2.

# _UN COMTRADE_

The UN Comtrade dataset, officially known as the United Nations Commodity Trade Statistics Database, is a comprehensive international trade database maintained by the United Nations Statistics Division (UNSD). This dataset provides detailed information on global merchandise trade, encompassing the import and export of goods and services between countries and regions. In this study, we limit the analysis to trade in merchandise only, since data on trade in services is very limited for the pre-2000s period.

# _UN Statistics Division (ISIC-HS Concordances)_

The UN Statistics Division (ISIC – HS) concordances refer to a set of tables or mapping systems that establish a link between two international classification systems: the International Standard Industrial Classification of All Economic Activities (ISIC) and the Harmonized System (HS).

ISIC is a global standard for classifying economic activities. It provides a hierarchical structure that categorizes industries based on their primary economic activities. ISIC codes are used for various statistical and analytical purposes
outpatient primary care 6 times per year, which is the OECD average for in-person physician consultations in 2021.<sup>5</sup> In the African Region, there are 1.55 providers per 1,000 people,<sup>4</sup> and if each provider worked 250 days a year, this would imply that the average provider would therefore have to see just 15.5 patients per day. Even at the highest LMIC observed PHC consultation length of 9 minutes,<sup>6</sup> the average provider would see patients for 140 minutes each day, or two hours and twenty minutes. After paperwork and administrative duties, the typical PHC provider in SSA must still spend substantial time not seeing patients, or “excess capacity”. Published statistics are therefore consistent with the idea that the workforce is in fact _under_ -utilized.

The first part of our study investigates this apparent contradiction: Academic consensus, personal observation, and patient reporting all indicate that SSA primary care facilities are highly overcrowded, but published national and regional statistics are instead consistent with substantial underutilized time for PHC providers.

Our investigation uses nationally representative PHC facility survey data covering 7,915 health facilities in 10 Sub-Saharan African countries, collected between 2014 and 2019 through the World Bank’s Service Delivery Initiative (SDI).<sup>7, 8</sup> We first show that,

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Northeast, despite the fact that the region experienced a similar decline in SWB as other regions. This result is expected given the relatively strong support for the Partido dos Trabalhadores in the region, which has benefited from the redistributive policies of the party during the Golden Decade (Amaral, 2020).<sup>25</sup> In addition, we find that religious denomination and ethnicity – not available in the Gallup World Poll – are strong predictors of a vote for the Partido

> 24 Although the party was no longer officially the incumbent party in 2018, it was treated as such.

> 25 In this regard, Pereira and Melo (2015) also show for the Brazilian state of Pernambuco that the negative effect of corruption disappears when public expenditures in a particular area increase.

30
# **1. Introduction and Main Findings**

Between 2002 and 2008, the European Union (EU) experienced significant structural changes, including the introduction of the Euro, the 2004 expansion of the European Union, and the proliferation of international linkages worldwide. Responses to these changes among the EU member states included technological upgrades, adoption of new management processes, and regulatory reform.<sup>2</sup> Recent research efforts have revealed extensive heterogeneity in productivity growth across countries and sectors, even within narrowly defined industries.<sup>3</sup> The twelve newest members of the European Union (EU12, the “New Europe”)<sup>4</sup> experienced vigorous productivity growth, three to four times greater than the growth of the fifteen elder members of the European Union (EU15)<sup>5</sup> . However, as New Europe raced to catch up with Old, the southernmost states of Western Europe fell drastically behind, and experienced productivity contractions. What factors led to these disparate outcomes across members of the European Union? This paper disentangles the effects of country- and firm-level variables on productivity to answer the policy question of what countries may do to encourage greater productivity growth.

We use the 2010 Amadeus database,<sup>6</sup> which provides firm-level data on employment, sector, age, and international affiliations. We augment this with country-level business environment indicators from the World Bank’s Doing Business (DB) database, foreign direct investment (FDI) data from Eurostat, infrastructure quality indicators from the Global Competitiveness Report, and credit availability data from the World Development Indicators (WDI). Using ordinary least squares (OLS) regression, we estimate the contribution of each factor to productivity growth between 2003 and 2008, both individually and as sets of either firm- or country-level variables.

For the EU12, country-level characteristics contribute the most toward explaining productivity growth. Of the variables included, the most influential are DB indices
rights and that women lag men in all rights. Comparing descriptive statistics between surveys relying either on selfreporting (IHPS) or on responses by a proxy (IHS4) suggests that similar figures in the aggregate conceal important discrepancies between surveys. Compared to the data provided by individuals directly, proxy respondents (i) overstate levels of male (co-)ownership, (ii) understate levels of female (co-)ownership, and (iii) for parcels that are reported as (co-)owned, introduce an upward bias regarding the rights held by women and conversely, a downward bias regarding the rights held by men. The latter finding is consistent with informational imperfections as well as strategic responses. Econometric analysis to ascertain the net effects investment and land use is thus needed and we discuss the empirical framework to be used.

> 5 While historically, matrilineal inheritance regimes are related to the physical force required for food provision (BenYishay _et al._ 2017), there is some evidence of short-term changes in Malawi which are particularly relevant as divorce rates are high (Cherchye _et al._ 2016). 6 The law aims to demarcate traditional land management areas (TLMAs) and establish customary land committees (CLCs) with at least 50% female participation, normally at Group Village Headman level.

8
is considered poverty in 2022 may not have been seen as such in 2004. However, we have opted for absolute poverty lines in order to better track developments over time. By extension, we should note that our estimates do not correspond to the official Malaysian poverty figures, which are based on poverty lines that change between surveys. Hence, our findings may appear surprising given that the DOSM Poverty Report (DOSM, 2023) shows that poverty has risen recently, from 5.6 percent in 2019 to 6.2 percent in 2022. However, this may be

> 16 Note that all lines are given in 2016 Malaysian ringgit, so they represent the purchasing power of the ringgit in that year. The poverty line used in scenario 1 was selected because discussions about poverty and mobility in Malaysia often refer to the living standards of the bottom 40 percent of the income distribution. When our previous study commenced, the official Malaysian poverty line was very low and poverty virtually non-existent by those standards.

10
19

|Country,<br>L<br>economy,<br>or<br>rritory|ast eublis<br>P<br>Date|hed censu<br>opulation<br>(1000) *|s<br>Source for population<br>(mid-1985)|Source<br>for fertility<br>(1985-90, unless indicated)|Source<br>for mortality<br>(1985-90, unless indicated)|
|---|---|---|---|---|---|
|echoslovaki|a Nov 80|15283F|UNPVSR<br>4/87 (official<br>est.)|Basedon INED 10/87|Based on UNPVSR<br>4/87|
|Denmark|Jan 81|5124 J|UNPVSR<br>4/87 (official<br>est.)|Eurostat<br>1987|Eurostat<br>1987|
|ibouti|60-61|<br>81 F|U.N. 1988 revision<br>(prelim.)|<br>U.N. 1988 revision<br>(prelim.)|U.N. 1988 revision<br>(prelim.)|
|Dominica|Apr 81|75 F|Bank projection<br>from census|1980-85,<br>based on official<br>data, Ministry<br>of Health;<br>1985-90,<br>Bank projection|1980-85,<br>based on official<br>data, Ministry<br>of Health;<br>1985-90,<br>Bsank<br>projection|
|Dominican<br>Rep.|Dec 81|5648 F|U.N. 1988 revision<br>(prelim.)|<br>U.N. 1988 revision<br>(prelim.)|U.N. 1988
regard to the scope of the production census, it should be emphasized that physical quantities and output values are not specified sepazrately fOr all individual commodities of an industry. For some items only value figures are provided, and some items are not specified individually at all. This latter pc.int is one reason for differences sometimes found between total output values in the summary volume of the census and the values specified in the detailed volumes. These census limitations require an adjustment of the "matched" output to total output of an industry, which we will discuss below.

With regard to the definition of "industries" and "commodities" Rostas already recognized difficulties when he wrote in 1948:

- "These difficulties are mainly due to the fact t.iat individual industries, as classified by the censuses, each produce a group of products and by-products which are not identical in the different countries, either as regards type or quality or as regards the. relative importance of individual types within the group."

- (Rostas, 1948, p. 11).
reported using the 4-digit SITC Revision 2 classi...cation. Since our variable of interest, EFNXct, is constructed using information on total exports from each country in each industry, we ...rst aggregate bilateral ‡ows across countries to obtain total exports for each country and manufacturing sector. We then convert the trade ‡ows from SITC to 3-digit ISIC Revision 2 classi...cation.<sup>8</sup> This allows us to combine the trade data with the information on external dependence from Rajan and Zingales.

For the purposes of estimating the gravity equation (13), we retain information on bilateral trade, converting it once again into the 3-digit ISIC Rev. 2 classi...cation. We merge bilateral trade data with geography variables taken from Centre d’Etudes Prospectives et d’Informations Internationales

> 8 The conversion is based on the concordance found on the International Trade Resources website maintained by Jon D. Haveman: http://www.haveman.org.

11
# **1 Introduction**

Improving the quality of education is one of today’s main challenges to policy makers in Indonesia and elsewhere in the developing world. Despite the fact that Indonesia doubled real spending on education in the last decade<sup>1</sup> , the quality of instruction, as measured through international student comparisons like PISA and TIMSS, has not (or not much) improved. PISA researchers conclude for example that half of Indonesia’s 15-year-olds do not have “a basic level of proficiency, at which students begin to demonstrate the reading literacy competencies that will enable them to participate effectively and productively in life [p50. in OECD (2010)].”<sup>2</sup> This suggests that many teachers and schools fail to prepare the next generation for an ever-more demanding global labor market.

This paper presents two empirical results for Indonesia that potentially also apply to the broader developing world. First, section (2) shows that subject-matter knowledge of primary school teachers in Indonesia is arguably low on average, and most likely a binding constraint to learning in many Indonesian primary schools. Second, section (4) presents evidence that increases in teachers’ subject-matter knowledge can help increase student-learning outcomes. The inference is based on three types of value-added regressions: a standard model in levels, a school fixed-effects model, and a flexible student-teacher fixed-effects model. The latter estimates the parameters of a value-added model, only using variation within each student-teacher pair across the three different subjects – mathematics, science and Indonesian language.

The student-teacher fixed-effects model attempts to address important threats to conventional value-added models in levels. The fixed-effects results, for example, would be insensitive to situations in which intrinsically more able students students are matched to better schools or teachers. Moreover, models in levels are incapable of separating out a teacher’s subjectmatter knowledge from other (unobserved) teacher attributes, such as motivation, general intelligence, etc. The idea of looking at within-student variations across different subjects is
than 1,594 subnational units from 134 countries and spanning the period from 2003 to 2019. Our results suggest that temperature change has larger impacts over the short-term than over the long-term and more impacts on chronic poverty than transient poverty. These results are robust to various measures of chronic poverty and are more pronounced for poorer countries. These findings are relevant for policy advice and highlight the importance of tackling chronic poverty to more effectively reduce poverty.

Our findings also help emphasize the need of collecting better-quality data, which include panel household survey data (e.g., to better monitor poverty mobility patterns), more disaggregated data (e.g., to more accurately estimate climate change impacts), more use of administrative data in poorer countries, and better integration of different data sources to address their respective limitations. Data imputation methods may also offer promising alternatives in contexts where actual panel data are unavailable. Our analysis suggests that promising future research can further identify the specific channels through which climate change impacts poverty and inequality. It is also useful to better quantify the long-term impacts of climate change on various forms of poverty and inequality, including multi-dimensional aspects.

34
over eight years, in the classroom disciplinary climate (see Appendix A, Table 6). In fact, trends in school safety indexes are quite consistent with what is observed when measuring classroom disciplinary climate. For instance, school safety indexes are mirrored in the disciplinary climate improvement patterns observed in Jordan and Qatar between 2012 and 2015 and the pattern of significant decline observed in Turkey and Tunisia (Table 1).

**Table 1. Trends in the PISA Disciplinary Climate in Countries Participating in 2009, 2012 and 2015**

||2009-2012|2012-2015|
|---|---|---|
|United Arab Emirates|-0.003|-0.034|
|Jordan|-0.442|0.079|
|Qatar|-0.321|0.231|
|Tunisia|-0.261|-0.052|
|Turkey|-0.096|-0.125|
|**Rest of the world**|**-0.033**|**-0.037**|

Source: Analysis using PISA data, 2009, 2012 and 2015.

**The rapid degeneration of school safety since 2011 indicates that the severity of the problem is recent.** Similar patterns to that witnessed in Egypt are observed in Tunisia, Syria and the Republic of Yemen. School safety was remarkably good in Tunisia in 2007, better than average in the Republic of Yemen, and around average in Syria in 2007. Four years later, in 2011, school safety dropped sharply

> 4 The index for Canada stands at 0.2 standard deviation above the world’s average.

5
sample weights.

The predicted expenditure for all households in the population census are subsequently aggregated to generate district- and province level welfare measures. In 2009, we apply the GSO-World Bank official poverty line of 7,836,000 VND/person/year, and in 1999 the line is 1,789,871 VND/person/year (Minot et al, 2003). As poverty measure we use the popular headcount rate. We also estimate the Gini coefficient as our primary indicator of income inequality at the local level.

6
# **II. Data Sources**

# **Trade Data (Values and Quantities of Imports)**

Bilateral data on the quantity and value of Russian imports from all partner countries except Belarus for the years 2001 to 2010 inclusive and for the first half of the year 2011, were acquired from the electronic database of the commercial company Academy-Service.<sup>7</sup> This dataset provides information on the value and quantity of imports at the tariff line level, i.e., the 10-digit level. The quantity data are available in physical units such as kilograms <u>and the value of imports is in US dollars. Since there</u> are more than 11,000 tariff lines and the data are reported by partner country at the tariff line level, the database contains about 450,000 or 500,000 observations per year.<sup>8</sup>

# **Tariff Data**

The source of the data on the tariff rates of the Russian Federation for 2001 to 2010, inclusive, is various editions of the Decree of the Government of the Russian Federation on import duties. For 2006 to 2010, it is decree number 718, dated November 27, 2006.<sup>9</sup> For 2001 to 2005, it is decree number 830. These documents are available from www.base.consultant.ru.<sup>10</sup>

> 7 <u>http://www.ftinform.com. Comparison of these data with those provided to us by the Russian Ministry of Economic Development and Trade have confirmed the accuracy of these data. Belarusian</u> imports are not included in the sample, which explains discrepancy between the aggregate import reported in the balance of payments and our data.

8 Seven observations were dropped from the sample as obvious outliers, based on highly implausible unit prices such as a 40 dollars per car, HS code 8703239027, imported from Germany in 2007
11 Ghana, Malawi and Uganda are under this threshold while South Africa, Algeria and Tunisia are well above it.<sup>4</sup> Column 3 of Tables 1 and 2 shows the results of the estimations testing the role of human capital in the trade-poverty relationship. The beneficial impact of an increase in trade openness on poverty reduction is larger when the investment in human capital is stronger. We find that when the share of the population over age 15 with completed primary education exceeds 46.7 percent (the average in the sample is 55 percent), trade openness starts being favorable to the poor. In other words, with appropriate learning skills, people are better able to take advantage of the new opportunities offered by trade.

Finally, we examine whether the relationship between the openness to trade and poverty may hinge on a country’s institutional environment (columns 4). Results suggest that the negative association between trade openness and poverty could diminish with improvements with respect to law and order, and even reverse. We find that trade openness could be favorable to the poor when institution quality (measured by the _Law and order_ variable) reaches 3.3 (the average of our sample is 2.9).<sup>5</sup> In other words, an environment where laws and contracts are enforced seems to be more favorable for the emergence of new enterprises and the closing of older ones, allowing the economy the needed flexibility to adjust.

# _Robustness tests_

We conducted several robustness tests:

- First, we used an alternative measure of absolute poverty, the poverty gap. The results using the poverty gap confirmed the observations for the poverty headcount ratio (see Table 2);

- We also removed outliers. The results reported in Table A.1 and Table A.2 confirm and reinforce our previous observations.

> 4 See Appendix 6.

> 5 We also used another ICRG indicator measuring Institutional strength and quality of the bureaucracy (ranging from 0 to 4, with a