rafmacalaba/datause-encoder-data huggingface dataset:
-44 -

The very fact that the value of shares outstanding in the Turkish capital market has been rather small as compared to government securities implies that the impact of the collapse of an asset bubble will be limited, particularly to the banking sector. The stock market in Turkey is known for its volatility, and at times the market seemed to be overvalued, only to be corrected some months later, without too much effect on the rest of the financial sector or real economic activity.

**Table 21 TURKEY**

|**Capital Ma**|**rkets: Selected**|**Indicators**|**, 1992-199**|**7**|||
|---|---|---|---|---|---|---|
||1992|1993|1994|1995|1996|1997|
|||(i|n trillion of|Turkish lira|)||
|New issues of securities|||||||
|Total|172|402|926|1,840|4,787|6,638|
|Private|23|72|85|175|162|378|
|of which: shares|5|10|38|51|102|306|
|asset-backed securities|15|53|42|114|42|23|
|T bills and Govt. bonds|150|330|841|1,666|4,625|6,260|
|Trading volume in secondary markets|||||||
|Total|682|2,031|6,163|22,248|73,446|186,515|
|of which: shares|56|256|652|2,375|3,040|9,086|
|govt. bonds|209|658|1,679|4,580|12,741|85,389|
|T bills|376|1,028|3,479|14,831|56,808|90,729|
|Outstanding securities|||||||
|Total|196|382|729|1,498|3,221|7,025|
|Private|61|112|131|295|441|932|
|of which: shares|49|71|109|224|425|909|
|Public|135|270|598|1,202|2,780|6,093|
||||(in pe|rcent)|||
|Memorandum items:|||||||
|Public sector share of:|||||||
|New issues|86.9|82.1|90.9|90.5|96.6|94.3|
|Trading volume|85.7|83.0|83.7|87.3|94.7|94.4|
|Outstanding securities|69.0|70.7|82.0|80.3|86.3|86.7|
|Public sector outstanding securities|||||||
|As percent of GNP|12.2|13.5|15.4|15.3|18.6|20.6|
|As percent of M2Y|42.7|56.8|50.5|_50.5_|53.8|59.3|

Source: own calculation based on data from Capital Markets Board and Treasury.
# **<u>Table 8. Mechanism: the impact of the program on enterprise and economic activity</u>**

||||Dependent<br>|variables<br>|||
|---|---|---|---|---|---|---|
|Explanatory variables|The number of<br>firms per 100<br>square km|The number of<br>micro firms (1-<br>9 workers) per<br>100 square km|The number of<br>small firms<br>(10-50<br>workers) per<br>100 square km|The number of<br>medium firms<br>(51-299<br>workers) per<br>100 square km|The number of<br>large firms<br>(300+<br>workers) per<br>100 square km|Log of<br>nighttime light<br>intensity|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Program * Post-program period|-0.7263|-0.6645|-0.4307|0.3238|0.0452|0.3178|
||(3.0742)|(1.7593)|(1.0410)|(0.2868)|(0.0809)|(0.2501)|
|(Poverty rate – 50) * Post-|-0.6688|-0.3703|-0.1740|-0.0937|-0.0308|-0.0055|
|program period|(0.9449)|(0.5366)|(0.2915)|(0.0922)|(0.0294)|(0.0262)|
|Program * (Poverty rate – 50) *|0.5014|0.2798|0.1230|0.0710|0.0277|0.0103|
|Post-program period|(0.7820)|(0.4410)|(0.2448)|(0.0762)|(0.0258)|(0.0290)|
|District fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|
|Year fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|
|Constant|0.4518|0.1390|0.2238|0.0707|0.0183|-2.6308***|
||(1.4249)|(0.5715)|(0.5626)|(0.2691)|(0.0394)|(0.0786)|
|Observations|2,625|2,625|2,625|2,625|2,625|2,279|
|R-squared|0.578|0.490|0.604|0.633|0.359|0.840|
|The mean value of the dependent<br>variables before the program|9.016|0.908|0.923|0.348|0.067|0.277|

Note: This table reports district fixed-effect RDD-DD regressions of density of firms and nighttime light data intensity of districts using district-level observations. The number and density of firms at the district level are computed using the annual Vietnam Enterprise Censuses from 2000 to 2017. Robust standard errors in parentheses. Standard errors are clustered by district level. *** p<0.01, ** p<0.05, * p<0.1.

47
Klein, M and N. Marion, "Explaining the Duration of Exchange Rate Pegs," NBER Working Paper No. 4651 (Cambridge, MA:NBER, February 1994).

Krugman, P. (1979) "A Model of Balance of Payments Crises," _Journal ofMoney Credit and Banking_ 11,3, August.

Krugman, P. (1991), "Target Zones and Exchange Rate Dynamics," _Quarterly Journal of Economics,_ Krugman, P. and J. Rotemberg (1992), "Speculative Attacks on Target Zones," in P. Krugman and M. Miller (eds.) _Target Zones and Currency Bands,_ Cambridge: Cambridge University Press.

Lutkepohl, H. (1994) "Interpretation of Cointegrating Relations" _Econometric Reviews 13:391-_ 4.

Obstfeld, M. (1986), Rational and Self-Fulfilling Balance of Payments Crises," _American Economic Review,_ 76:72-81. Otker, 1. and C. Pazarbaioglu (1994), "Exchange Market Pressures and Speculative Capital Flows in Selected European Countries." IMF Working Paper WP/94/21.

Sachs, J. A. Tomell and A. Velasco (1996), "Financial Cdses in Emerging Markets :The Lessons form 1995" in _Brooking Papers on Economic Activity,_ 1: 147-215.

Salant, S. And D. Henderson (1978), Market Anticipation of Government Policy and the Price of Gold, _Journal of Political Economy,_ 86:627-648.

Svensson, L.(1994), "Fixed Exchange Rates as a Means to Price Stability: What Have We Leamned?" _European Economic Review,_ Vol 38. pp 447468.

Williamson, J. (1996). _he Crawling Band as an Exchange Rate Regime. Lessonsfrom Chile, Colombia and Israel._ Institute for International Economics. Washington DC.

# Appendix **I:** Data

_MO:_ All from line 14 of IFS statistics except: Italy 1995 from Banca D'Italia, Economic Bulletin, Number 22, Feb 1996.Colombia, Banco de la Republica.

_MI:_ line 34 IFS or, if unavailable, Ml. Colombia, Banco de la Repuiblica. Not available for U.K. or Sweden.

_M2:_ line 34 + line 35 quasi money, IFS. VK: Building Societies pay interest on demand deposits. In 1989, some became banks. Bank of England continued calculating Ml without new banks 18
Policy Research Working Paper 5120

# **Abstract**

In the Philippines, an important part of income inequality is associated with the wage difference between the less educated and the better educated. The majority of the least educated are employed in low-paid services jobs and the agricultural sector. Tertiary education is to a large extent a prerequisite for high-paid occupations.

Using the Labor Force Survey 2003–2007, this paper examines disparities in human capital endowment, returns to education, and the role of education in wage differentials in the Philippines. The empirical results show that returns to education monotonically increase— workers with elementary education, secondary education, and tertiary education earn 10 percent, 40 percent, and 100 percent more than those with no education.

The results also show that education is the single most important factor that contributes to wage differentials. At the national level, education accounts for about 30 percent of the difference in wages. It accounts for a higher percentage of the difference for female workers (37 percent) than male workers (24 percent). There are also differences across regions and sectors.

As an economy develops, the demand for skills increases. In the Philippines, efforts to improve education to increase the supply of highly educated people are important not only for long-term growth, but also for helping to translate growth into more equal opportunities for the children of the current generation.

This paper—a product of the Poverty Reduction and Economic Management Department, East Asia and Pacific Region—is prepared as a background paper for the Philippines Inclusive Growth report. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at xluo@worldbank.org and tnterada@umd.edu.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **3. Quantity of Schooling and Economic Growth**

Given the microeconomic evidence of the productivity-enhancing effects of education, it seems natural to extend the view to the macroeconomic perspective of long-run economic growth of countries. From a theoretical viewpoint, there are at least three mechanisms through which education may affect economic growth. First, just as in the micro perspective, education increases the human capital inherent in the labor force, which increases labor productivity and thus transitional growth towards a higher equilibrium level of output (as in augmented neoclassical growth theories, cf. Mankiw, Romer, and Weil (1992)). Second, education may increase the innovative capacity of the economy, and the new knowledge on new technologies, products and processes promotes growth (as in theories of endogenous growth, cf., e.g., Lucas (1988); Romer (1990a); Aghion and Howitt (1998)). Third, education may facilitate the diffusion and transmission of knowledge needed to understand and process new information and to successfully implement new technologies devised by others, which again promotes economic growth (cf., e.g., Nelson and Phelps (1966); Benhabib and Spiegel (2005)).

## **_3.1 Results of Initial Cross-country Growth Regressions_**

Just as in the literature on microeconomic returns to education, the majority of the macroeconomic literature on economic growth that tries to test these predictions employs the quantitative measure of years of schooling, now averaged across the labor force.<sup>27</sup> Early studies used adult literacy rates (e.g., Azariadis and Drazen (1990); Romer (1990b)) or school enrollment ratios (e.g., Barro (1991); Mankiw, Romer, and Weil (1992); Levine and Renelt (1992)) as proxies for the human capital of an economy. These were followed by attempts to measure average years of schooling based on perpetual inventory methods (cf. Lau, Jamison, and Louat (1991); Nehru, Swanson, and Dubey (1995)).<sup>28</sup> Then, it was particularly the work by Barro and Lee (1993, 2001) which provided internationally comparable data on average years of schooling for a large sample of countries and years, based on a combination of census or survey data on educational attainment wherever possible, using literacy and enrollment data to fill gaps in the census data.

> 27 More precisely, the most commonly used measure is average years of schooling in the working-age population, usually defined as the population aged 15 years and over, instead of the actual labor force.

> 28 For a survey of measurement and specification issues from early growth accounting to current cross-country growth regressions, see Wößmann (2003b).

20
secondary school-age children, compared<sup>to 23%fromthe fourthquintile and20%from thetop</sup> quintile. Thus on the Lorenz diagram,<sup>cumulatedshareof publicspending foreducationaccrued</sup> will be drawn against cumulated<sup>shareof school-agechildren insteadof cumulatedshareof</sup> population. The diagonal line (or<sup>45line)is alsoknownas the lineof absolute equallysinceit</sup> goes through those points where<sup>thecumulativeshareofschool-agechildrenequalsthe</sup> cumulative share of total public<sup>spendingon education.</sup> Targeting of public education spending<sup>tothe poor improvedsignificantly between1993</sup> and 1998 partly due to the surge<sup>in enrolmentsby poorchildren at primaryand lowersecondary</sup> education levels and partly due to the recent<sup>policyshiftfavoringprimaryeducation.In</sup> Vietnam, the majority of school<sup>enrolment hastraditionallyconsistedof primary-levelstudents.</sup> In 1993, students at this level<sup>comprised78percentof thetotalgeneraleducationenrolments,</sup> while in 1998 the share of these<sup>students reducedto 58percent.</sup> As a result, the share of public<sup>educationspendingreceivedbythepoorincreased</sup> between 1993 and 1998. The share<sup>of allpublic educationexpenditurereceivedby thepoorest</sup> expenditure quintile increased<sup>from17percentin1993to18percentin1998whiletheshareof</sup> the richest expenditure quintile<sup>fellfrom23percentin1993to21percentin1998.Primary</sup> education observed the most dramatic<sup>shift favoringthepoorbetween1993and1998.In1998,</sup> the poorest expenditure quintile<sup>received26percentof publicprimaryeducation expenditure</sup> compared to 20 percent in 1993.<sup>Thesharegoingtotherichestexpenditurequintilefellto13</sup> percent in 1998 from 17 percent<sup>in1993.Puttingthesecumulativesharesof publicspendingin</sup> primary education against cumulative<sup>sharesof school-agechildrenin Lorenz diagramsfor1993</sup> and 1998 shows that public spending<sup>for primaryeducationin1998wasquite neutral,thoughin</sup> 1993 it was still slightly biased<sup>against the poor.In1993,publicexpenditureat the primary level</sup> was slightly biased against the poor<sup>becauseits distributionwasbelowbutcloseto thediagonal</sup> line; which means that the poor<sup>receivea bit smallershareof total educationspendingthan their</sup> share of primary school-age children.<sup>In contrast,in1998 publicexpenditureat theprimary level</sup> was pro-poor because its distribution<sup>was just above thediagonal line (Figure6).</sup>

**Figure 6: Lorenz distributions**<sup>**of publiceducation expenditure bylevelof education,1993 and1998**</sup>

<!-- Start of picture text --> .- 45 degree line --45 degree line<br>100- 1993 100 Primary 1998<br>-<br>Pr-inary<br>80 - -Low er 80 - Low er<br>secondary secondary<br>- Ulpper - Upper<br>60 - secondary 60 - secondary<br>40 -40-<br>20 20-<br>0 0<br>0 20 40 60 80 100 0 20 40 60 80 100<br><!-- End of picture text --> 17
Withdrawing policy stimulus and replenishing fiscal buffers in good times has proven to be a challenge for most governments. Countercyclical actions pursued by Sub-Saharan African countries in the downturn failed to be subsequently followed by measures to boost revenues and either contain or rein in spending as they regained and consolidated their growth momentum. Moreover, the plunge in the price of oil, as well as the prices of metals and minerals, sharply reduced government revenues in resource abundant countries—thus leaving them with fewer resources to fund public spending. Consequently, many countries in the Africa region now face the need to undertake fiscal consolidation measures to narrow fiscal deficits and stabilize government debt.

Using a new comprehensive database developed by Kose et al. (2017), this paper examines the evolution of fiscal space in Sub-Saharan African countries over the past 15 years from different perspectives. First, it documents the evolution of indicators of fiscal sustainability, external debt position, and the composition of the public sector’s balance sheet for countries in SSA vis-à-vis industrial countries and non-SSA developing countries.

Next, it examines whether fiscal space–related indicators (that is, fiscal sustainability, external debt and balance sheet composition) improved or worsened in the post-global financial crisis period by comparing the magnitude of these indicators in 2010-13 vis-à-vis 2015-16. This comparative analysis is undertaken at the regional level (SSA compared to other developing regions), at the sub-regional level (SSA countries classified by their growth performance) and at the country level.

Finally, it evaluates public debt dynamics in the region by analyzing the evolution of fiscal sustainability gaps (Blanchard 1993, Ley 2009, Kose et al. 2017, World Bank 2017). This exercise compares the sustainability gaps of SSA vis-à-vis advanced economies and other developing regions. It also looks at the performance of public debt dynamics across SSA countries classified by their access to financial markets and their degree of natural resource abundance.

The remainder of the paper is organized as follows. Section 2 discusses the measurement of fiscal spaces. Section 3 describes the evolution of fiscal sustainability indicators in SSA (vis-à-vis other benchmark regions of the world) over the past 15 years. Section 4 characterizes the performance of fiscal space during the post-global financial crisis period. Specifically, it compares indicators of sustainability, balance sheet composition and external debt in 2010-13 vis-à-vis 2015-16. It looks not only at the performance of the region vis-à-vis other benchmark regions but also at SSA country groups classified by: (a) the resilience of their growth path, and (b) the extent of their natural resource abundance. Section 5 examines public debt dynamics in SSA by looking at the emerging pressures from the accumulation of primary deficits over time to unsustainable debt stocks —even if the initial debt by the public sector was low. Finally, section 6 concludes.

3
sample. Any sampling for the second source leads to additional error and should be avoided if possible. For the area-based estimates described in this report, publicly available satellite, station, and geographic data aggregated to the district-level are used, none of which suffers from additional constraints due to sampling.

To ensure that the definitions of key indicators defined in the microdata are as similar across countries as possible, the harmonization standard applied by the World Bank in its ECAPOV initiative in generating official World Bank estimates of poverty is used here.

# **II.I – Districts by Country**

Presenting the results and matching the survey data to satellite imagery require identifying the cartography of each country at the district level. This was accomplished using standardized map shapefiles and matching each to the disaggregated location data available for each survey.

## **Table 1: Survey Data Used**

|Country|Year|Hhlds|Svy<br>Dist.|Map<br>Dist.|Impute|
|---|---|---|---|---|---|
|KAZ|2017|<sup>47445</sup>|178|202|24|
|KGZ|2016|<sup>19575</sup>|40|42|2|
|TJK|2015|2999|149|159|10|
|UZB|2018|4013|126|190|64|
|TKM|None|.|.|.|.|

The analyses discussed in this report primarily use the data from the diary/list of food consumed, non-food expenditure, and household composition.

# **II.II – Kazakhstan HBS data for 2017**

Poverty estimates and other welfare indicators for Kazakhstan rely on the 2017 round of the HBS, conducted by the Statistics Agency of the Republic of Kazakhstan. For household consumption and expenditure, the survey is usually considered nationally representative, representative at the oblast (region) level, and separately representative for rural and urban areas. The survey uses a stratified sample design with strata corresponding to 14 regions + 2 large cities crossed by their urban and rural areas (except for Almaty and Astana cities, which are entirely urban). A complete consumption module is gathered in the HBS, covering both food and non-food items. Information on household composition, income, employment, and related topics is also collected. All data for the HBS in Kazakhstan are collected in person, except for consumption, which is collected using a diary-type instrument. The survey is continuous, and representative quarterly within the year for most welfare indicators.

18
A Conceptual Framework For Retirement Products: Risk Sharing Arrangements Between Providers And Retirees

# **Assumed frequency distribution of the 5 year yields at the end of 5 years for Country II**

<!-- Start of picture text --> 40<br>35<br>Fr 30<br>eq 25<br>ue<br>nc 20<br>y 15<br>10<br>5<br>0<br>-0.0300 -0.0187 -0.0074 0.0039 0.0151 0.0264 0.0377 0.0490 0.0603 0.0716 0.0829 0.0942 0.1054 0.1167 0.1280 0.1393 0.1506<br><!-- End of picture text -->

## **Interest rate**

Note: the distribution of interest rates was derived by callibrating a financial model using swaption data consistent with the yield curves in Appendix B

- 38 –
|EDUCATIONQUALITY AND SECONDARY EDUCATION<br>Juan Diego Alonso|
|---|
|EDUCATIONQUALITY AND SECONDARY EDUCATION<br>Juan Diego Alonso|
|EDUCATIONQUALITY AND SECONDARY EDUCATION<br>Juan Diego Alonso|
|EDUCATIONQUALITY AND SECONDARY EDUCATION<br>Juan Diego Alonso|
|EDUCATIONQUALITY AND SECONDARY EDUCATION<br>Juan Diego Alonso|
|EDUCATIONQUALITY AND SECONDARY EDUCATION<br>Juan Diego Alonso|
|EDUCATIONQUALITY AND SECONDARY EDUCATION<br>Juan Diego Alonso|
|EDUCATIONQUALITY AND SECONDARY EDUCATION<br>Juan Diego Alonso|
|EDUCATIONQUALITY AND SECONDARY EDUCATION<br>Juan Diego Alonso|
|EDUCATIONQUALITY AND SECONDARY EDUCATION<br>Juan Diego Alonso|
|EDUCATIONQUALITY AND SECONDARY EDUCATION<br>Juan Diego Alonso|
|EDUCATIONQUALITY AND SECONDARY EDUCATION<br>Juan Diego Alonso|
|EDUCATIONQUALITY AND SECONDARY EDUCATION<br>Juan Diego Alonso|
|EDUCATIONQUALITY AND SECONDARY EDUCATION<br>Juan Diego Alonso|
|EDUCATIONQUALITY AND SECONDARY EDUCATION<br>Juan Diego Alonso|
|EDUCATIONQUALITY AND SECONDARY EDUCATION<br>Juan Diego Alonso|
|EDUCATIONQUALITY AND SECONDARY EDUCATION<br>Juan Diego Alonso|
|EDUCATIONQUALITY AND SECONDARY EDUCATION<br>Juan Diego Alonso|
|EDUCATIONQUALITY AND SECONDARY EDUCATION<br>Juan Diego Alonso|
|EDUCATIONQUALITY AND SECONDARY EDUCATION<br>Juan Diego Alonso|
|EDUCATIONQUALITY AND SECONDARY EDUCATION<br>Juan Diego Alonso|
|EDUCATIONQUALITY AND SECONDARY EDUCATION<br>Juan Diego Alonso|
|EDUCATIONQUALITY AND SECONDARY EDUCATION<br>Juan Diego Alonso|
|EDUCATIONQUALITY AND SECONDARY EDUCATION<br>Juan Diego Alonso|
|EDUCATIONQUALITY AND SECONDARY EDUCATION<br>Juan Diego Alonso|
|National Urban Solid Waste Management Project<br>John Morton|
|National Urban Solid Waste Management Project<br>John Morton|
|National Urban Solid Waste Management Project<br>John Morton|
|National Urban Solid Waste Management Project<br>John Morton|
|National Urban Solid Waste Management Project<br>John Morton|
|National Urban Solid Waste Management Project<br>John Morton|
|National Urban Solid Waste Management Project<br>John Morton|
|National Urban Solid Waste Management Project<br>John Morton|
|National Urban Solid Waste Management Project<br>John Morton|
|National Urban Solid Waste Management Project<br>John Morton|
|National Urban Solid Waste Management Project<br>John Morton|
|National Urban Solid Waste Management Project<br>John Morton|
|National Urban Solid Waste Management Project<br>John Morton|
|National Urban Solid Waste Management Project<br>John Morton|
|National Urban Solid Waste Management Project<br>John Morton|
|National Urban Solid Waste Management Project<br>John Morton|
|National Urban Solid Waste Management Project<br>John Morton|
|National Urban Solid Waste Management Project<br>John Morton|
|National Urban Solid Waste Management Project<br>John Morton|
|BR Rio Grande do Norte Integrated Water Resources Management Paula Pedreira de Freitas de Oliveira|
|BR Rio Grande do Norte Integrated Water Resources Management Paula Pedreira de Freitas de Oliveira|
|BR Rio Grande do Norte Integrated Water Resources Management Paula Pedreira de Freitas de Oliveira|