rafmacalaba/datause-encoder-data huggingface dataset:
similar to those obtained from panel VARs. That is, the two most important country characteristics that affect the shocks’ impact are trade openness and labor market flexibility, the former magnifying the impact and the latter reducing it. Financial depth and ease of firm entry carry positive coefficients, but not large enough to be statistically insignificant. Financial openness carries a negative coefficient, implying a stabilizing effect, and this effect is close to achieving statistical significance.

# **Asymmetric effects**

The analysis above allows us to determine if structural characteristics have a stabilizing (or destabilizing) effect for all shocks, whether positive or negative. In principle, however, this symmetric treatment could mask important differences on the effects of structural characteristics for positive and negative shocks. For instance, an ideal structural characteristic --one that in reality magnifies positive shocks and reduces negative ones-- could be found to be ineffectual under a symmetric analysis. We now consider separately the output response to negative and positive terms-of-trade shocks.

The results of the asymmetric analysis are presented in Table 7 and the corresponding panels of Figures 2 and 3. The first thing to notice is that only increases in trade openness and in labor market flexibility and only in the case of _negative_ shocks produce statistically significant results. The estimation of asymmetric shocks presents larger standard errors as it uses fewer observations and suffers from wide data variations associated to sign transitions. While the following discussion takes the point estimates at face value, we acknowledge that small effects (those below 0.3 percentage points, as a simple rule of thumb) are likely to be statistically irrelevant.

There is some evidence of asymmetric effects. The asymmetry in the effect of trade openness is not present in the direction of the impact but in its magnitude. Larger trade openness increases the (absolute) impact of both negative and positive shocks on per capita GDP. However, its effect on the impact of negative shocks is larger than that on positive ones. In fact, comparing the effects at the first and third quartiles of trade openness, its negative effect is about four times as large as the positive one.

Higher financial depth has no significant effect on the impact of either positive or negative terms-of-trade shocks. Therefore, its lack of relevance as shock stabilizer 14
coefficient is between Instructional Support from CLASS and Instruction from _Teach_ (0.31). The aggregate measures derived from these two instruments are correlated with an overall correlation coefficient of 0.36.

# **_Principal components analysis_**

Table 11 reports the results of a principal components analysis of all the Level 1 variables simultaneously. Note that this analysis can only be carried out on the 107 observations (lessons) where we have data for all four instruments. The Eigenvalues suggest that the data contain a number of dimensions; seven of them are greater than 1. At the same time, the difference between the first and the second is large, again suggesting that the main thrust of these variables is captured in the first principal component. The component loadings for this first component are generally positive and mostly range between 0.15 and 0.42 for the behaviors and practices variables, so this component could be characterized as “overall good teaching practices” (the component loadings are highest for the CLASS variables).

Proving an interpretation to the other principal components is more difficult. The second component loads most heavily on variables linked to the various Share of Time Teaching and Learning variables, as well as to the CLASS variable of Classroom Organization—suggesting that “good time use” is one dimension of the data. Component 3 loads heavily on the Classroom Environment variables from SDI and Stallings, suggesting “good classroom environment” as a third dimension (the Classroom Environment variable is built from measures of student work or other materials being displayed on the walls). Component 4 seems to be recovering the teacher practices as captured in SDI (loading positively on Good Teacher Demeanor, Good Pedagogical Practices, and Use of Materials) as well as more modestly on Instruction from Teach—suggesting a dimension related to “good instruction” that is not captured in the first component. The fifth component loads mostly on Materials and Infrastructure (from SDI and Stallings) along with Classroom Culture from Teach—suggesting it is capturing something along the lines of “material circumstances.” The main finding to emerge from this is that the data sort themselves somewhat neatly into dimensions that one might expect. First, a general quality dimension; second a dimension linked to good time management; third, a positive classroom environment (with materials displayed on the walls); fourth, a dimension linked to instructional practices (at least those not directly captured in the first component); and fifth, a dimension linked to the material circumstances in the classroom.

# **6. How do measures from these instruments correlate with student test scores?**

The final step in this analysis is to investigate the degree to which the various dimensions of teacher behaviors and practices identified in these observation systems are related to student performance. As 24
the FGI of the previous year, controlling (among other things) for financial turbulence, which we proxy by the annualized monthly volatility of the global factor we used to compute the FGI ( _σGF_ ), which we plot in Figure 3 together with the VIX index, an alternative measure of global financial market volatility, which we use in the robustness section below.

**Figure 3** : Global factor volatility and VIX (monthly frequency)

<!-- Start of picture text --> month<br> Average VIX Global factor volatility<br>4<br>.6<br>.5 3<br>.4<br>2<br>.3<br> Average VIX<br>1<br>Global factor volatility<br>.2<br>.1 0<br>1992m1 1993m3 1994m5 1995m7 1996m9 1997m11 1999m1 2000m3 2001m5 2002m7 2003m9 2004m11 2006m1 2007m3 2008m5 2009m7 2010m9 2011m11 2013m1 2014m3 2015m5 2016m7<br><!-- End of picture text --> Source: Authors’ computation using Datastream.

In table 3, below, we regress the (annualized) monthly volatility of stock market returns in domestic currency<sup>14</sup> on the FGI, controlling for the lag dependent variable, for financial turbulence, the US monetary policy stance (measured by the US Federal Reserve fund rate, _FED_ ), and country fixed effects. We find that an increase in financial globalization has a negative impact on financial volatility. While the effect is relatively small, an increase of one percentage point in the FGI yields a decrease of 3 basis points, equivalent to a 0.2% decrease in the average volatility of stock market returns, it is highly significant, see column 4. To see whether such an average effect hides differences between country groups, we run separate regressions for developed, emerging, and frontier markets. Performing such an exercise, we find that, in developed markets, financial globalization has no significant effect on financial market volatility; in emerging and frontier markets, instead, we find negative and significant coefficients. More precisely, we find that an increase of one percentage point in the FGI yields a 4 basis points decrease in the volatility of stock market returns (0.2% of average volatility) in emerging markets, and an 8 basis point decrease in frontier markets (0.5% of average volatility). Hence, financial globalization is more likely to be a stabilizer the less financially developed a country is.

The fact that financial globalization appears to smooth financial volatility, and the more so the less financially developed a country is, is an interesting finding, which provides support to the view that

> 14 In the robustness section, we discuss the case in which returns are measured in USD. See Appendix table A3.

8
- 12 -

where the dissimilarity parameter to adjust the scaling across subgroups is defined by  _k_  1   _k_ . _ρ_ is correlation within nest _k_ . The inclusive value is defined by ' _IV j_  ln  _j_  _Gk_ exp( _X ij_  /  _k_ ) .

In the following analysis, seven crop choices classified into two subgroups, domestic food crop and export crop, are examined: maize, rice, cassava, groundnuts, tobacco, cotton and sesame. The selection reflects the significance of these products in Tanzania as well as data availability in the household survey used. The geographic distribution of the current and potential production areas is also taken into account. The first four crops are among the largest agricultural commodities produced in Tanzania (Figure 14). These crops account for about 30 percent of the total agriculture production in value terms.

The last three crops are among the major export crops, accounting for about 35 percent of the total agriculture exports from Tanzania (Figure 4 above). Tobacco is the largest export crop, and its production more than doubled from 50,000 tons in 2005 to 130,000 tons in 2011 (Figure 15). Sesame is also a growing export crop in the southern region of Tanzania. Cotton is one of the most traditional export crops in the country, dating from the early 1900s.<sup>9</sup> Several major crops are excluded from the analysis, mainly because of the lack of sufficient data. Bananas and two important export crops, coffee and tea, are excluded, because our primary data source of the analysis, which is the 2010 Living Standards Measurement Study (LSMS), does not include many households producing these crops.

> 9 See for instance Baffes (2002).
# **METHODOLOGY**

The Global Findex indicators measure the use of financial services, which is distinct from access to financial services. _Access_ most often refers to the supply of services, while use is determined by demand as well as supply.<sup>1</sup> _Use_ refers to the levels and patterns of use of different financial services among different groups, such as poor people, youth, and women.

## **Indicators**

The first set of indicators focuses on formal accounts; the mechanics of the use of these accounts (frequency of use, mode of access); the purpose of these accounts (personal or business, receipt of payments from work, government, or family); barriers to account use; and alternatives to formal accounts (mobile money).

The account penetration indicator measures individual or joint ownership of formal accounts—accounts at a formal financial institution such as a bank, credit union, cooperative, post office, or microfinance institution. It includes those who report having a debit or ATM card tied to an account.

Indicators relating to the receipt of payments measure the use of formal accounts to receive wages (payments for work or from selling goods), payments or money from the government, and family remittances (money from family members living elsewhere).

The second set of indicators focuses on savings behavior. This relates to the use of accounts, as people often save at formal financial institutions. Other indicators explore the use of community-based savings methods and the prevalence of savings goals.

The third set focuses on sources of borrowing (formal and informal); purposes of borrowing (mortgage, emergency or health purposes, and the like); and use of credit cards. The fourth focuses on use of insurance products for health care and agriculture. (See the questionnaire for the survey questions.)<sup>2</sup>

## **Data coverage**

The Global Findex indicators are drawn from survey data collected over the 2011 calendar year, covering more than 150,000 adults in 148 economies and representing about 97 percent of the world’s population. The survey was carried out by Gallup, Inc. in association with its annual Gallup World Poll, which since 2005 has surveyed about 1,000 people annually in each of up to 157 economies,<sup>3</sup> using randomly selected, nationally representative samples.<sup>4</sup> The target population is the entire civilian, noninstitutionalized population age 15 and above. Surveys are conducted in the major languages of each economy. (For details on the data collection dates, sample sizes, excluded populations, and margins of error, see the annex to this methodology section.) The 148 economies covered by the Global Findex indicators include both high-income economies and developing (low- and middle-income) economies. The regional and income group classifications are those used by the World Bank, available at http://data.worldbank.org/about/country-classifications. The regions exclude high-income economies.

**8**

MEASURING FINANCIAL INCLUSION
exports of higher-valued, sweeter crudes, such as those from North Sumatra (Figure 7). For example, exports of Sumatran Light Crude have declined from over 170,000 barrels per day in 2000 to less than 39,000 in 2007.

**Figure 6.  Prices of Asian sweet crude oils are drawing ahead of WTI, while the average price of Indonesia’s oil exports lags**

<!-- Start of picture text --> USD<br>Indonesia Export Crude Price<br>WTI Crude Price<br>70 Malaysia Tapis Crude Price<br>Indonesia Crude Anoa<br>60<br>50<br>40<br>30<br>20<br>2000 2001 2002 2003 2004 2005 2006 2007<br>Source: World Bank staff calculations and DGMIGAS<br><!-- End of picture text -->

**Figure 7.  High-value crude oils make up less of Indonesia’s exports** _Million barrels of selected crudes_

<!-- Start of picture text --> 250<br>High value crudes<br>200<br>150<br>100<br>Mid-value oils<br>50<br>Low-value oils<br>0<br>2000 2001 2002 2003 2004 2005 2006 2007*<br><!-- End of picture text --> Note: The type of crude is determined by its average price per barrel in 2007. High-value oils recorded an average price above US$90; mid-value, US$80-89; and the price of low value oils was below US$80. These data do not fully enumerate Indonesia’s oil exports. _Source: DGMIGAS; World Bank staff calculations_ Indonesia’s budget is becoming less dependent on oil and gas revenues, but these revenues remain highly important. With record prices in 2007, these revenues accounted for 23 percent of the total. Indeed, over a longer horizon oil and gas revenues have increased far less quickly than other sources in real terms (Figure 8). In 2007, total revenues and expenditures are estimated to have increased by 11.6 and 13.2 percent respectively, while the government projects 2008 revenues to grow by 7 percent, while expenditures are expected to increase by almost 11 percent (these figures are all nominal).

Oil and gas revenues flow to the government either as taxes or non tax revenues. Non-tax oil and gas revenues are far greater than oil and gas tax revenues (Table 3) **.** In the 2008 budget non-tax revenues contribute 16 percent of total government revenue, while tax revenues only account for 5.4 percent of total revenue. This difference has widened somewhat in recent years. Non-tax oil and gas revenues also represent the largest share in total natural resource revenues, accounting for 94 percent of the total. In turn, oil non-tax revenues were almost three times as large as gas non-tax revenues in 2007, although the difference is shrinking fast — non-tax oil revenues were almost four times as large as gas revenues as recently as 2006.

7
**Table 1.  US Non-Agricultural Imports:  Preference Coverage,**<sup>**1**</sup> **Utilization,**<sup>**2**</sup> **and Average Nominal Tariff Preference,**<sup>**3**</sup> **2003**

||**AGOA C**|**overage **|**AGO**|**A Utilizati**|**on**|**AGOA**|**Av. Tariff**|**Pref.**|**GSP Cov**|**erage**|**GSP Utilization**|**GSP Av. Tariff Pref.**|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||**Overall**<sup>**4**</sup>|**Non-**<br>**App**|**Overall**<sup>**4**</sup>|**Non-**<br>**App**|**App**<sup>**4**</sup>|**Overall**<sup>**4**</sup>|**Non-**<br>**App**|**App**<sup>**4**</sup>|**Overall**|**Non-**<br>**App**|**Overall**|**Overall**|
|**_Congo (DROC)_**<sup>**_7_**</sup>|100|100|0|0||2.1|2.1||100|100|93|5.1|
|**_Ethiopia_**<sup>**_5,6_**</sup>|89|2|99|0|99|17.8|4.1|18.3|11|99|97|3.8|
|**_Gambia_**|0|0|||||||73|82|29|2.5|
|**_Guinea_**|5|5|0|0||7.5|7.5||97|99|21|3.6|
|**_Lesotho_**<sup>**_5,6_**</sup>|100|0|95|0|95|18.4||18.4|(.)|100|30|4.7|
|**_Madagascar_**<sup>**_5,6_**</sup>|99|20|95|81|95|15.6|8.3|15.9|1|75|71|4.9|
|**_Malawi_**<sup>**_5,6_**</sup>|100|10|97|100|97|18.6|5.3|19.1|(.)|90|100|3.2|
|**_Mali_**<sup>**_5,6,8_**</sup>|25|13|0|1|0|11.0|7.2|13.4|68|79|61|5.0|
|**_Mauritania_**|0|0|||||||4|100|93|2.2|
|**_Mozambique_**<sup>**_5,6_**</sup>|98|0|100|0|100|20.2||20.2|2|78|39|3.0|
|**_Niger_**<sup>**_5,6,8_**</sup>|4|2|7|16|0|8.2|8.6|6.7|79|81|12|4.0|
|**_Rwanda_**<sup>**_5,6_**</sup>|0|0|||||||98|98|4|4.0|
|**_Sao Tome/Prin._**|(.)|(.)|0|0|||||99|100|0|2.0|
|**_Sierra Leone_**|13|27|0|0||12.3|12.3||35|73|9|3.1|
|**_Tanzania _**<sup>**_5,6_**</sup>|41|(.)|90|0|91|13.8|5.0|14.7|16|27|90|4.8|
|**_Uganda_**<sup>**_5,6_**</sup>|94|0|88|0|88|22.3||22.3|6|98|44|3.7|
|**_Zambia_**<sup>**_5,6_**</sup>|1|1|0|0||3.0|3.0||97|97|100|5.1|

1 Ratio of eligible imports to total dutiable imports.

2 Ratio of imports entering under preference to total eligible imports.

> 3  Difference between nominal _ad valorem_ tariff equivalent and  nominal preferential tariff.  Covers all HS 8-digit lines with eligible US imports in 2003.

4 Apparel is defined as all lines within HS 61 and 62 (including the non-US value of production-sharing (HTS 9802.00.80)). For all countries in ATPA, CBTPA and AGOA (with apparel eligibility), overall calculations assumes all apparel is potentially eligible for apparel benefits. Thus “utilization” is actually the ratio of US imports entering under a preference to total US apparel imports. 5 Country is eligible for apparel benefits.

6 Country is eligible for special rule for apparel from LDC 7 AGOA benefits delayed until 10/31/03. 8 AGOA eligibility as of 12/03.

(.) Indicates less than one percent.

29
Stiglitz, J.E. et al., 2017: Report of the High-Level Commission on Carbon Prices. Carbon Pricing Leadership Coalition (CPLC), 68 pp.

Strefler, J. et al. (2018) Between Scylla and Charybdis: Delayed mitigation narrows the passage between large-scale CDR and high costs. _Environmental Research Letters_ , 13(4), 044015 Tavoni, Massimo, Elmar Kriegler, Keywan Riahi, Detlef P. van Vuuren, Tino Aboumahboub, Alex Bowen, Katherine Calvin, Emanuele Campiglio , Tom Kober, Jessica Jewell, Gunnar Luderer, Giacomo Marangoni, David McCollum, Mariësse van Sluisveld, Anne Zimmer and Bob van der Zwaan (2015) Post-2020 climate agreements in the major economies assessed in the light of global models, _<mark>Nature Climate Change</mark>_ <mark>5 119– 26.</mark> Ueckerdt, F., L. Hirth, G. Luderer, O. Edenhofer (2013) System LCOE: what are the costs of variable renewables? _Energy_ , 63, pp. 61-75 van Vuuren, D.P. et al. (2018) Alternative pathways to the 1.5°C target reduce the need for negative emission technologies. _Nature Climate Change_ , 8(5), 391–397 van Sluisveld, M.A.E., S.H. Martínez, V. Daioglou, and D.P. van Vuuren (2016) Exploring the implications of lifestyle change in 2°C mitigation scenarios using the IMAGE integrated assessment model. _Technological Forecasting and Social Change_ , 102, 309–319 van Sluisveld, Mariësse A.E. Mathijs J.H.M. Harmsen, Detlef P. van Vuuren, Valentina Bosetti, Charlie Wilson, Bob van der Zwaan (2018) Comparing future patterns of energy system change in 2 °C scenarios to expert projections, Global Environmental Change, Volume 50, 201-211.

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41
- Public Trust of Politicians (Public trust in the honesty of the politicians is: 1 = very low; 7 = very high)

# A.3.4 Services sector infrastructure

Services sector infrastructure for each country is the average of two indexed inputs from GCR: ■ Quality of competition in Internet service providers (ISP) sector (Is competition among your country's ISP sufficient to ensure high quality, infrequent interruptions and low prices? 1 = no; 7 = yes, equal to world's best) ■ Extend of marketing (the extend of marketing in your country is: 1 = limited or primitive; 7 = high and among the world's best sophisticated)

# **A.4 Trade Flows**

The bilateral trade flows are bilateral trade in manufactured goods (source: COMTRADE), defined as commodities in categories 5 to 8 in SITC 1 digit industry except those in category 68 (non-ferrous metals). The Trade flow data aggregate the trade flows over the manufactured goods for a given importer-exporter. We deflate the trade flow using the world import index (source: IFS).

# **A.5 Tariffs**

We use weighted average of applied tariff rates (source: TRAINS) for the manufactured goods under the above definition where bilateral trade values corresponding to each tariff line are used as the weight.

# **A.6 Adjacency, language, colony, and distance**

Geographic data, together language and colonial links are extracted from the Centre d'Études Prospectives et d'Informations Internationales (CEPII) database (see www.cepii.fr). Distance data are calculated following the geat circle formula, which uses latitudes and longitudes of the relevant capital cities.
8 entire period to the replacement year not covered by the other phases. All countries with initial TFR under 3.0 exhibit only this phase. All other countries also go through this phase, though it may begin later.

The age pattern of fertility was allowed to change with its level. The age patterns when TFR is 7.6, 4.1, or 2.8 are shown in Figure 4. The - mean age at childbirth declines from 28.86 years when TFR 7.6, to 28.45 years when TFR - 4.1, to 27.98 years when TFR - 2.8. For intermediate TFRs, the age pattern falls between these curves.

# Migration level and trend

Estimates of international migration come from the U.N. 1984 assessment and the U.N. 1988 revision, documents prepared by the U.N. for the 1984 International Conference on Population, the U.S. Bureau of the Census (1985), Eurostat (1987), and Bank sources. To deal with the volatility of refugee movements in various parts of the developing world, only those are counted as migrants who are believed likely to remain permanently in the receiving countries.

Available data seldom include age-sex distributions of migrants, which were instead chosen in this manner. For each country, some estimate was made of the sex ratio among migrants. Based on this sex ratio, an appropriate age-sex distribution was chosen from among alternative models. This distribution was assumed to be constant over time, regardless of the total number of migrants.

Trends in the volume of migration are based on past trends, but are still essentially speculative. For most countries, net migration is assumed to decline to zero by the year 2000; for the remainder, net migration declines to zero by the year 2025.

# Summary of Results

Summary projection results are provided in Tables 2-12. (Tables 4-12 appear at the end of this Introduction.) In discussing these tables, we will summarize world population growth; note changes in these projections from previous versions; compare Oie four World Bank regions and 19 country departments; note other comparisons among geographic regions and groups of countries by income level; observe the results for some individual countries; and essay a brief conclusion.

# World poRulation

The projections show world population rising from 4.843 billion in 1985 to 6.205 billion in 2000 and 8.262 billion in 2025 (Table 2). From 1985 to 2025, the birth rate per thousand population will decline by 10 points, but the death rate will decline by less than 2 points. As a consequence, the annual growth rate will fall from 1.7 percent to less than 1 percent.