rafmacalaba/datause-encoder-data huggingface dataset:
8 services. Moreover, job separators have a relatively short employment relationship with the firm (that is, their tenure is less than four years). Knowing the length of tenure is important in assessing the potential costs associated with the severance regulations, particularly since Manpower Law 13/2003, Article 156, raised the rates of severance pay for employees with three or more years of tenure.<sup>6</sup> The majority of job separators worked in the domestic private sector either for a domestic firm (36.4 percent) or as a domestic worker (45.8 percent).<sup>7</sup> There was little difference, however, in the size of the firm in which they previously worked. Those persons who separated from their job tend to be employed in agriculture, trade and social services where the turnover rates are 21.5 percent, 11.9 percent, 21.2 percent, respectively.

The firms in which all job separators were employed tend to be concentrated in Bali and Java.<sup>8</sup> Most of the turnover occurs amongst employees with low job tenure. While the proportion of all wage and salary employees with tenure between 0 – 3 years is 0.4660, it is 0.7489 for employees with a job separation. The turnover rate for those with tenure between 0 – 3 years is 19.5 percent. Compared to all wage-and-salary employees, job separators tend to earn lower monthly wages. In addition, they tend to be concentrated at the lower end of the wage distribution and face a higher turnover rate.

# 3.2 _Scope of severance pay_

Employees who were eligible for severance pay and received a payment after a job separation tend to be young, fairly well-educated and working in large firms in industrial regions of the country. The data indicates that few are aware of their entitlement to severance pay. This lack of awareness may be due to the complexity of the legislation regarding severance pay but, more than likely, it is exacerbated by the lack of formal contracts that explicitly define the employment relationship.

> 6 See figure 4.4 in World Bank (2011) for a detailed description of severance pay regulations

> 7 Question R11a of the Sakernas (2008) survey asks individuals to identify the type of business concern where they worked before the job separation occurred: government agency; state-owned firm; domestic private firm; foreign firm; and domestic worker.

> 8 The questions in the firing module of Sakernas (2008) pertain only to the industrial districts of Indonesia.
Central-West (0.58), is more spatially concentrated in 2000 than the North (0.46) and Northeast (0.57).

**Table 3: Spatial Gini Coefficients in 1970 and 2000**

||**1970 (a)**|**2000 (b)**|**(b-a)**|
|---|---|---|---|
|Total (123)|0.692|0.700|0.008|
|North (3)|0.456|0.463|0.007|
|Northeast (25)|0.561|0.569|0.008|
|Southeast (60)|0.760|0.761|0.001|
|South (24)|0.626|0.658|0.032|
|Central-West(11)|0.441|0.583|0.142|

Number of cities in parentheses.

Another way to examine changes in agglomeration size in Brazil is via a transition matrix. It helps examine the degree of mobility of cities up and down the urban hierarchy and test for the stationarity of the processes affecting the 123 agglomerations (Eaton and Eckstein, 1997; Dobkins and Ioannides, 2001). Following Black and Henderson (2003), we divide the 1970 agglomeration size distribution into five groups or cells containing approximately 35%, 30%, 15%, 10% and 10% of all cities starting from the bottom, with fixed relative cell cut-off points.<sup>2</sup> Table 4 presents the resulting transition matrix. The transition probabilities of the transition matrix, P _jk_ , are calculated as the total number of cities moving from cell _j_ to _k_ over three decades divided by the total number of cities starting in cell _j_ in the three decades. Diagonal elements are the probabilities of staying in the starting state, and off-diagonals the probabilities of moving lower or upper cells.

> 2 The relative size (city population/mean(city population)) upper cut-off points are 0.256, 0.469, 0.812, 1.340 and the maximum.

10
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Roodman, D. (2012). _An Index of Donor Performance_ (Working Paper No. 67). Washington, D.C.: Center for Global Development.

- Sachs, J. (1989). The Debt Overhang of Developing Countries. In G. Calvo, R. Findlay, P. Kouri, & J. Braga De Macedo (Eds.), (p. 80-102). Cambridge, MA: Basil Blackwell.

- Schmid, J. P. (2009). Is Debt Relief Good for the Poor? The Effects of the HIPC Initiative on Infant Mortality. In C. A. P. Braga & D. D ̈omeland (Eds.), _Debt Relief and Beyond - Lessons Learned and Challanges Ahead_ (p. 49-69). Washington, D.C.: The World Bank.

- The UN. (2015). _The UN - Millennium Development Goals._ Retrieved 2015-06-12, from `http:// www.un.org/millenniumgoals/`

- Thomas, M. R., & Giugale, M. M. (2015). African Debt and Debt Relief. In C. Monga & J. Yifu Lin (Eds.), _The Oxford Handbook of Africa and Economics: Volume 2: Policies and Practices._ Oxford: Oxford University Press.

- UNICEF. (2015). _Multiple Indicator Cluster Survey (MICS)._ New York, NY: UNICEF.

- WHO. (2005). _Report of a WHO Technical Consultation on Birth Spacing_ (Tech. Rep.). Geneva: Author.

World Bank. (2015). _World Development Indicators Database._ Washington, D.C.: World Bank.

20
ability to pay. In these countries, it is important to consider affordability of higher education, including student assistance, for different kinds of households. Therefore, the public debate on financing of higher education could be more nuanced and discuss more than just for or against tuition in public universities. The discussion could among other things take into account affordability for low-income families, affordability and ability to pay of high-income families, affordability of private education, and distribution of public subsidies.

# **4. MAIN FINDINGDS AND POLICY IMPLICATIONS**

This paper explored affordability and accessibility of tertiary education in four selected countries in Latin America – Brazil, Colombia, Mexico, and Peru – within a global context. We relied on information from household surveys, expenditure surveys, administrative data, and institutional data through primary data collection.

We learned that the available information is far from perfect. We therefore urge caution when interpreting the results. In particular, the estimates of education costs for Peru and Brazil are not necessarily representative of the true national average. However, it is to our knowledge, the best available estimates. We believe the paper brings value-added by estimating accessibility and, in particular, affordability. The analysis can contribute to improved policymaking and stimulate public debate based on real quantifiable trade-offs as opposed to the occasionally ideologically driven debate in Latin America.

# **Main Findings**

<u>Affordability of tertiary education in LAC is low compared to high-income countries. We</u> examine education costs and living costs expressed as a share of GDP per capita, a measure of ability to pay. We find that the average family in Latin America pays the equivalent of 60 percent of GDP per capita for tertiary education in LAC. This is the outof-pocket costs. It compares to 19 percent for families in high-income countries. This is explained by the difference in GDP per capita, comparatively higher education costs due to a higher share of non-governmental education in Latin America, and a low level of student assistance.

<u>Living costs are a significant cost of tertiary education. We estimate that the cost of living</u> amounts to 29 percent of GDP per capita for the typical Latin American family. This compares to 19 percent for high-income countries.

<u>There is a relatively low level of student assistance in Latin America. In the four</u> examined Latin American countries, Brazil, Colombia, Mexico and Peru, student assistance—loans and scholarships—amounted to four percent of GDP per capita, divided into two percent for loans and two percent for grants. This compares to nine percent for high-income countries (and 22 percent for Anglo-Saxon countries and 15 percent for Northern Europe).

<u>There is relatively low access to tertiary education.</u> Our estimation confirms existing evidence of a relatively low accessibility to tertiary education in Latin America, 30
Corruption Perception Index and the World Bank’s Control of Corruption Index described in Kaufmann, Kraay, and Mastruzzi (2007). We find that both the cross-country measures show similar patterns as our data. The TI measure relates to perceptions of the degree of corruption as seen by business people and country analysts and ranges between 0 (highly corrupt) and 10 (highly clean). In our sample, the TI measure ranges from 6.21 in high income countries to 2.50 in low income countries. The World Bank’s Control of Corruption Index measures the degree to which corruption is perceived to exist among businesses, public officials and politicians and ranges from -2.5 (highly corrupt) to +2.5 (non-corrupt). Specifically it is meant to “capture the extent to which public power is exercised for private gain, including both petty and grand forms of corruption, as well as "capture" of the state by elites and private interests.” In our sample of countries, the Control of Corruption Index ranges from 1.13 in high income countries to -0.72 in low income countries. Thus we see that the reported bribe payments by firms vary across countries in a similar fashion as other cross-country indicators do. Corresponding cross-country indicators of Tax Evasion are not available for comparison purposes.

When we look at the distribution of firm types across different regions, we find that Abiders make up the largest fraction of firm types in each region. The highest percentages of Perpetrators are found in South Asia where as the largest percentage of firms that pay bribes to public officials but do not evade taxes, the firms that we refer to as Victims, are found in East Asia Pacific. Interestingly the pattern of Retaliators somewhat resembles that of the Victims. Similar to that of the Victims, the largest percentage of Retaliators are found in East Asia Pacific.

In Panel B, we report summary statistics depending on the level of bureaucratic regulation in the economy. Following De Soto (1990) and Djankov et al. (2002), we use entry regulation to capture the extent of bureaucratic regulation in an economy. Specifically, we use the Number of procedures required to start a business averaged over 2004-2005 from the World Bank Doing Business Indicators, which in our sample of countries ranges from 4 (Ireland) to 18 (Uganda) with a median value of 10.7. We classify countries into high and low regulation depending on whether the number of procedures required to start a business in that country lies above or below the median value respectively. We find that the level of bribe payments and tax evasion is higher in the high regulation countries. When we look at firm types, we find that the the high regulation countries have a higher percentage of retaliators and lower percentage of 18
# **ABBREVIATIONS**

|CEQ|Commitment to Equity|
|---|---|
|CIF|cost, insurance, and freight<br>|
|CIT<br>|corporate income tax<br>|
|CSO|Central Statistical Office (Zambia)|
|ECE|early child education|
|FGP|Fiscal Gains to the Poor|
|FI|Fiscal Impoverishment|
|FISP|Farmer Input Subsidy Program|
|GEWEL|Girls Empowerment and Women’s Livelihoods Project|
|kWh|kilowatt hour|
|MoH|Ministry of Health|
|IMF|International Monetary Fund|
|NSSP|National Social Protection Policy|
|PAYE|Pay as You Earn (personal income tax)|
|PER|public expenditure review|
|PIT|personal income tax|
|SADC|South African Development Community|
|SCTS|Social Cash Transfer Scheme|
|SSA|Sub-Saharan Africa|
|VAT|value-added tax|
|WBG|World Bank Group|
|WDI|World Development Indicators|
|WEO|World Economic Outlook|
|WHO|World Health Organization|
|ZESCO|Zambian Electricity Corporation|
|ZMoAL|Zambian Ministry of Agriculture and Livestock|

2
**Figure 3. Excess reserves and foreign currency reserves**

# **A. Excess reserves held at central banks in the B. Foreign reserves held by central banks in euro area and Japan Denmark and Switzerland**

<!-- Start of picture text --> Billions of Euros Trillions of Yen Billions of US$ Billions of US$<br>600 300 100 600<br>80<br>400 200 400<br>60<br>40<br>200 100 200<br>20<br>0 0 0 0<br>Euro Area Japan (RHS) Denmark Switzerland (RHS)<br>2007 2010 2013 2016 2007 2010 2013 2016 2007 2010 2013 2016 2007 2010 2013 2016<br><!-- End of picture text --> Sources: European Central Bank, Bank of Japan, Danmarks Nationalbank, Swiss National Bank. A. Excess reserves are the difference between total reserve balances and minimum reserves held by banks. Last observation is June 2016. B. Foreign reserves are foreign currency assets held by central banks. Last observation is May 2016 for Switzerland, and April 2016 for Denmark.

or a “deposit rate.” During normal times, banks minimize such excess reserves, as deposit policy rates are typically below money market rates. However, since the global financial crisis, a wide range of factors, including persistent macroeconomic and financial uncertainty, and bank balance sheet repair, have encouraged commercial banks to maintain higher-than-usual balances in central bank deposit facilities in some advanced economies (Figure 3).

The reasons highlighted by central banks in their decisions to implement NIRP have varied somewhat. The main motivation stated by the ECB, BoJ, Riksbank, and MNB was the need to stabilize inflation expectations, and support growth. In the case of the SNB and DNB, a proximate motivation was the need to respond to currency appreciation and capital inflow pressures. In some cases, NIRP have been accompanied by complementary policy measures to ease financing conditions and stimulate activity while mitigating adverse effects on bank earnings (Table 1).<sup>6</sup> This section first presents evidence on the aforementioned motives behind the use of NIRP, then briefly discusses why NIRP were employed to respond to the perceived limits of previous policy measures.

> 6 The Riksbank’s deposit rate fell below zero in 2009 for the first time. However, its main policy rate for monetary transmission is weekly repo rate at which banks can borrow or deposit for a period of one week. Danmarks Nationalbank’s deposit rate fell slightly below zero (-0.2) in July 2012 for the first time, and returned to positive territory in April 2014. It remained positive until September 2014, and went below zero again after that. In figures presenting monetary policy rates, the reference period starts with the date at which weekly repo rate went below zero for Sweden, and the second period (starting with September 2014) for Denmark.

9
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based on data from the first national economic census for 2005. Ghana increased its estimate of nominal GDP for 2006 by around 60 percent in 2010, partly as a result of re-basing its volume estimates. Nigeria released re-based estimates in 2014, resulting in new estimates of its GDP for 2012 that are above that of South Africa.

To be useful for classification purposes, GNI per capita estimates must be converted into a common currency so that they can be compared on the same basis. The current analytical country classification system, and the related operational guidelines, uses the US dollar as the common currency or numeraire. Conversion factors are estimated from market exchange rates, adjusted to lessen the impact of any large transitory changes. A clear advantage of using market exchange rates compared with purchasing power parity exchange rates is that they are readily available on an annual basis for almost all countries (this issue is discussed further in section 5.3).

# **_5.2 Reducing Volatility of the GNI measure: The Atlas Method_**

The Atlas method is designed to smooth the effects of short-term transitory changes in exchange rates which introduce undesirable volatility into the classification system. The Atlas conversion factor for any year is the average of a country’s exchange rate for that year and its exchange rates for the two preceding years, adjusted for the difference between the rate of inflation in the country and international inflation.<sup>12</sup> Consistent with the threshold adjustment, international inflation is estimated using the SDR deflator, which is itself compiled as a weighted average of the inflation in the Euro Area, Japan, the UK, and the US (the SDR deflator is discussed in section 6 of this paper). The current methodology was introduced in 1984 following discussion by Executive Directors of a report prepared by the Economic Analysis and Projections Department, in consultation with a panel of experts (World Bank, 1983b).

There have been criticisms of the Atlas method and its theoretical underpinnings, but nonetheless the weighted three-year moving average is effective in achieving its aim of smoothing the series. The smoothing effect is achieved in two ways: the use of the moving

> 12 For more details, see https://datahelpdesk.worldbank.org/knowledgebase/articles/378832‐what‐is‐the‐world‐bank‐ atlas‐method.

18
is, if propensity does not at all reflect opportunity costs of investing in lower earthquake mortality, including learning about how to respond best to quake risks), then income and political characteristics should have no effect on the relationship between propensity and mortality. Only if learning-by-doing requires costly investments should we expect such contingent effects to emerge. In that case, however, one is immediately in the world of opportunity costs: countries with the greatest incentives to invest in learning-by-doing, and in preventing earthquake mortality generally, are those in which the opportunity costs of making these investments are low, and in which political incentives to make them are high. In other words, if we find evidence for our hypotheses that the effects of quake propensity on mortality are contingent on factors such as the income and political characteristics of countries, then this supports the conclusion that opportunity cost plays a large role, whether or not there is learning by doing.

# **4. Research Design**

To test these hypotheses, we analyze the determinants of earthquake mortality over the period 1960 to 2005. In this section, we describe our research design in detail.

# _4.1. Data Sources and Operationalization_

Our dependent variable is the annual sum of earthquake deaths in a country, with data taken from EM-DAT (2008).<sup>7</sup> We take the sum of fatalities in a country year rather than fatalities from individual earthquakes as the unit of analysis since practically all of our control variables are measured at the country year level. Aggregation is unlikely to affect the

> 7 See Neumayer and Plümper (2007) for a discussion of the weaknesses of this dataset, which is,

> however, the only publicly accessible source of disaster type-specific mortality data.

19
Natural resources play an important role in export baskets and economic activity for a large number of African countries. The importance of natural resources in export activity will be measured by the value of natural resource exports (in US$ at current prices) normalized by either the GDP or total merchandise exports. The data are collected from the World Integrated Trade Solution (WITS) database on the SITC, Revision 1.0. The definition of natural resource exports includes commodities exported in the SITC sections 0 – Food and live animals, 1 – Beverages and tobacco, 2- Crude materials, inedible except fuels (excluding 22 – oil seeds, oil nuts and oil kernels), 3 – mineral fuels, lubricants and related materials, 4 – Animal and vegetable oils and fats, and 68 - No ferrous metals (Lederman and Maloney 2007).

_Natural resources exports as a percentage of GDP_ . Natural resource exports in Sub-Saharan Africa declined, on average, from 11.8 percent of GDP in 1990-94 to 7.9 percent of GDP in 2010-14. The decline in this ratio also took place among resource and non-resource rich countries. In the case of resource rich countries, the (median) ratio of natural resources exports to GDP among resource rich countries dropped by almost half from 46.2% of GDP in 1990-94 to 23.6% of GDP in 2010-14. The (median) ratio also fell among non-resource rich countries (from 9.2% of GDP in 1990-94 to 6% in 2010-14). Finally, the share of natural resource exports in total economic activity in resource rich countries was nearly four times as large as that in non-resource rich countries.

_Natural resource exports as a percentage of total merchandise exports_ . Table 1 shows that the share of natural resource exports in total merchandise exports decreased over time in all country groups at different speeds. For the region as a whole, this share declined from 91.4 percent in 1990-94 to 77.3 percent in 2010-14; that is a reduction of nearly 15 percentage points of total merchandise exports in the past two decades. For resource rich countries, the share of natural resource exports in total merchandise exports dropped from 98.6 percent in 1990-94 to 86.5 percent in 2010-14. Finally, natural resource export dependence among non-resource rich countries decreased from 83.6 percent in 1990-94 to 74.9 percent in 2010-14. Note that although the share of natural resource exports in total merchandise exports among nonresource rich countries is high, this ratio does not include services exports and their importance relative to the size of economic activity is small.

# _Sectoral trade: Trade in primary goods vs. Trade in Manufacturing Goods_

Assessing the linkages of the country with world goods’ markets also implies examining what you export —that is the type of products that the country sells abroad. Table 1 presents the amount of trade (in exports and imports) of primary goods vis-à-vis manufacturing goods of Sub-Saharan Africa over the past 9
# **OLS Index: CPI in Costa Rica**

<!-- Start of picture text --> 4<br>cpi<br>Linear GT Index<br>3<br>2<br>1<br>0<br>1<br>2<br>2006 2007 2008 2009 2010 2011 2012 2013<br><!-- End of picture text --> Figure 7: Linear OLS index created for CPI series from Costa Rica. Displays some evidence of overfitting.

20
<!-- Start of picture text --> Figure5. Proportion of workers in automation-prone, formal sector by<br>country, across time<br>non low income middle income high income<br>75% ~— ><br>~— — ii<br>ry 7<br>i YT)<br><!-- End of picture text --> <!-- Start of picture text --> 25%<br>eemel ><br>0% . > —= a,So -<br>Log GDP per capita (constant 2010 USS)<br>=—— Automation-prone sector<br>=—— Informal agriculture sector<br>(each line denotes a different country)<br><!-- End of picture text -->