rafmacalaba/datause-encoder-data huggingface dataset:
power, with _R_<sup>2</sup> substantially lower than in columns (C.1) through (D). Notice, however, that (despite the overall low explanatory power) this is the only specification in which we get a marginally significant coefficient on income per capita. This result is consistent with column (C.2), which suggests that the coefficient on our industry predictions is slightly above one.

# 4.1. Reduced Form Within Industry Evidence

As a final test of our model’s predictions we investigate whether there is a notable within-industry relation between ICT abundance and real income. Note that, if cross-country differences in ICT abundance were truly driven primarily by variation in industrial composition, then we should not expect a strong relationship between ICT abundance and real income per capita _within_ a given industry. Due to data limitations we cannot perform a direct test of this prediction but we provide some suggestive evidence using reduced form analysis based on an approximation.

Specifically, we assemble a sector by country by year panel with a proxy for ICT abundance based on WITSA’s detailed tables on sector specific total ICT spending and total capital stocks based on the World Input Output Database (WIOD).<sup>16</sup> Note that we would ideally like to construct relative ICT stocks as in Section 2. However, at the sector level, we only have aggregate WITSA ICT spending data. We do not have any data on telecommunication spending from the ITU at the sector level. Thus, as a crude proxy for ICT abundance we simply divide WITSA’s measure of sector-specific nominal ICT spending by the corresponding nominal sector-specific capital stock reported in the WIOD (in current USD).<sup>17</sup> The resulting panel spans the period 2003-2009 for a total of 14 WITSA sectors in 32 countries.

Table 5 summarizes our results. The first column reports the within industry correlation of our relative ICT abundance proxy and log real income per capita. While there is some overall correlation (measured by the correlation coefficient pooled over all years), we only find a statistically significant relationship for two out of fourteen sectors when controlling for time fixed effects. Considering that the dependent variable in this exercise is only a very crude proxy for true ICT

> 16 This datasource is publicly available at http://www.wiod.org.

> 17Note that we manually build a concordance between the SIC sectors in the WIOD and the sectors reported in WITSA.

27
## **Figure 4  10 Year Government Bond Yields and Guaranteed Interest Rates**

<!-- Start of picture text --> 10<br>9 Yield after tax<br>Yield before tax<br>8<br>guaranteed interest rate<br>7<br>6<br>5<br>4<br>3<br>2<br>1<br>0<br><!-- End of picture text --> Source: Bloomberg, Danish FSA and Tax Authorities (Told & Skat) Although pension funds offer guaranteed rates of return these rates have, but for a brief period in the early 1990’s been well below market interest rates. However the narrowing gap between guaranteed interest rates and markets rates in the late 1990’s and the fact that earlier guarantees must be honored highlighted risks inherent in providing interest rate guarantees that were neither priced nor hedged.

The need to become more risk focused was underscored by the collapse of equity prices between 2000 and 2002 and the substantial fall in interest rates.

# **IV. Adaptation of Regulatory Framework**

Adaptation of the regulatory framework to the risk-based approach involved a gradual increase in the effectiveness of the powers of the supervisor and changes in the rules regarding the licensing, governance, financial management, disclosure and supervision of pension institutions. The trend toward risk-based regulation and supervision started in the late 1980s with the creation of the DFSA and accelerated over the 1990s in response to the financial difficulties faced by banks and pension institutions **.**

# **1. The Danish Financial Supervisory Authority (DFSA)**

The Danish FSA (Finanstilsynet) is the main supervisor of the financial sector, including the pension sector. In its present form, the DFSA was established on 1 January 1988 when the Supervisory Authority for Banks and Savings Banks and the Insurance Supervisory Authority were merged. In January 1990, the Supervisory Authority for Mortgage Credit Institutions was also transferred to the DFSA from the Danish Housing Agency, creating an integrated supervisory authority for the entire financial sector.

The main objective of the DFSA is to maintain confidence in the stability and efficiency of the financial sector by regulation, supervision and dissemination of information. Apart from the registration and review of the articles of association of new companies, which are carried out by the Danish Commerce and Companies Agency, the Danish FSA is 10
19 parts, it is expected that integration via trade in components and parts should be even more intense (Yi, 2003; Ng and Yeats, 2003). Global and regional production sharing and vertical specialization have become increasingly important. In the next Table we wish to highlight intra-regional trade of manufactured goods and components and parts within these two regions for various years. Table 4 shows clearly that for both manufactured goods and components and parts, intra-regional trade is relatively more pronounced in East Asia than in Latin America. 48.5% of East Asian exports of manufactured goods were shipped to other East Asian economies in 2005. The corresponding share for Latin America (LAIA) was only 17.1%. However, if we compare ASEAN to ANDEAN and MERCOSUR countries, results are different: 25.1% of ASEAN exports of manufactured goods were shipped to other East Asian economies in 2005; the corresponding share for ANDEAN countries was 34.3% and 20.2% for MERCOSUR. As for parts and components, the share of East Asian intra-regional exports of components and parts amounted to 59.3% in 2005, as compared to 14.00% in Latin America (LAIA). But if we compare ASEAN to ANDEAN and MARCOSUR countries, results are different: 29.6% of ASEAN exports of parts and components were shipped to other East Asian economies in 2005, while the corresponding share for ANDEAN countries was 39.5% and 22.6% for MERCOSUR.

**Table 3: East Asia and Latin America Intra-Regional Trade in Various Years**

||Share of<br>East|Intra-Region<br>Asia to the W|al Exports in<br>orld (%)||Share of Intra-R<br>Latin America|egional Ex<br>to the Wo|ports in<br>rld (%)||
|---|---|---|---|---|---|---|---|---|
|Year|ASEAN|NE Asia|East Asia|ANDEAN|MERCOSUR|LAIA|NAFTA|LAC|
|1985|18.7|23.3|34.2|2.6|5.5|10.0|42.9|46.1|
|1990|19.0|27.8|39.8|4.2|8.9|10.9|41.3|44.5|
|1995|24.9|34.5|48.7|12.1|20.5|17.2|46.0|50.5|
|2000|23.0|34.5|47.3|9.1|20.9|13.1|55.7|58.8|
|2005|25.2|39.2|50.5|8.2|12.9|13.2|55.8|57.2|

Notes: ASEAN includes Cambodia, Indonesia, Malaysia, Philippines, Singapore, Thailand and Vietnam due to missing data for Brunie, Laos and Myanmar. Cambodia and Vietnam are missing data in 1985-1995. NE Asia includes China, Hong Kong, Japan, Macao, Korea Rep., and Taiwan. East Asia includes those countries in ASEAN and NE Asia. ANDEAN includes Bolivia, Colombia, Ecuador, Peru and Venezuela. MERCOSUR includes Argentina, Brazil, Paraguay and Uruguay (Venezuela is excluded due to new member in 2006). LAIA includes those countries in ANDEAN and MERCOSUR plus Chile and Mexico. NAFTA includes Canadia, Mexico and United States. LAC includes all countries in LAIA and NAFTA.

Source: Computations by authors based on UN COMTRADE Statistics.
even smaller in the near future. Therefore, PSM reform is not expected to be perceived as a critical threat to rents that are central for the political survival of the current elites.

**Figure 4: Expected tax revenues from mineral royalties, 2011-2015**

<!-- Start of picture text -->
-<br>Production<br>US$ million ( mill. tons)<br>1,000 35.0<br>900<br>30.0<br>800<br>700 25.0<br>Production<br>600<br>20.0<br>500<br>15.0<br>400<br>Dividend Witholding<br>Corporate  Tax<br>300 income tax 10.0<br>200 Foreign Aid<br>Royalty (3%) Loans 5.0<br>100<br>Grants<br>0 0.0<br>2011 2012 2013 2014 2015 (2008 - 10 Avg.)<br><!-- End of picture text -->

_Source_ : Bank staff‘s estimates.

**_The pressures generated from within the civil service open-up new opportunities for leveraging donor resources_** . The government is currently under political pressure to increase public sector pay in the face of binding fiscal constraints. Recognizing that enhanced pay alone will not improve the effectiveness of the public service, the GoSL has also embarked upon a performance contracting process. This situation offers a strategic  ̳entry point‘ for the Bank and other development partners to leverage financial resources and technical knowledge to support the government in pushing through a pay and performance reform agenda. Moreover, while the electoral process might be potentially destabilizing, both the content and the sequencing of suggested reform paths is such that the risks associated with the electoral cycles can be successfully mitigated. On the one hand, evidence suggests that the Public Sector Reform agenda cuts across party lines. Both parties have publicly expressed the intention to improve the performance of public sector in the country as part of their programmatic agenda. Accordingly, a change in the administration and shift of party in power is not expected

21
# **Figure B16: Urea**

<!-- Start of picture text --> Log scale<br>8<br>Log price Permanent component<br>7<br>6<br>5<br>4<br>1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020<br>Source : Authors’ calculations<br><!-- End of picture text --> <!-- Start of picture text --> Log scale<br>1.5<br>Medium-term cycle Transitory compnent<br>1.0<br>0.5<br>0.0<br>-0.5<br>-1.0<br>-1.5<br>1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020<br>Source : Author’s calculations<br><!-- End of picture text --> _— 57 —_
**Table 1: Macro performance in emerging markets of the G20, period averages, 1990-c2014**

||Real<br>GDP<br>growth|Standard<br>Deviation of<br>Real GDP<br>growth|<br>Per capita<br>GDP<br>growth|Inflation<br>(CPI)|General<br>government net<br>lending/borrowin<br>g (% of GDP)|General Government<br>Gross Debt (% of<br>GDP)|
|---|---|---|---|---|---|---|
|Argentina|3.95|**_5.68_**|2.97|9.8|-1.00|51.7|
|Brazil|2.68|2.58|1.35|345.8|-3.88|66.6|
|China|9.92|2.45|9.03|4.4|-1.22|33.9|
|India|6.51|2.24|3.63|7.9|-7.43|73.6|
|Indonesia|5.06|4.12|4.79|10.3|-0.82|41.2|
|Mexico|2.79|3.02|1.28|11.0|-2.88|43.0|
|Russia|1.98|6.44|0.93|78.7|1.26|26.9|
|Saudi Arabia|4.36|3.28|1.88|1.6|9.44|40.7|
|South Africa|2.48|2.09|2.75|7.5|-2.08|35.8|
|Turkey|4.25|4.84|0.69|41.8|-4.03|50.6|
|Average|4.40|3.67|2.93|51.9|-1.27|46.4|
||External<br>Debt Stock|<br>Curre<br>Balance|nt Account<br>(% of GDP)|Total<br>reserves|Total reserves<br>in months of|Sovereign Bond<br>Interest Rate Spread,|
||(% of GNI)|||(in % of<br>GDP)|imports (in %<br>of GDP)|basis points over US<br>Treasuries|
|Argentina|44. 8||-0.1|8.6|7.7|**_912* _**|
|Brazil|27.8||-1.5|8.6|8.4|545|
|China|13.2||4.1|25.6|12.9|120|
|India|22.3||-1.3|12.0|7.6|N/A|
|Indonesia|62.0||0.2|12.5|5.4|288|
|Mexico|28.4||-2.2|7.5|3.3|276|
|Russia|41.3||5.8|17.4|8.0|706|
|Saudi Arabia|N/A||6.9|36.4|13.5|N/A|
|South Africa|24.4||-1.6|6.8|2.8|226|
|Turkey|42.9||-2.9|9.8|4.6|395|
|Average|34.1||0.7|14.5|7.4|365|

*Excluding the period 2002-2004 when Argentina was in default with foreign creditors. Sources:  WDI;  WEO Database; Authors’ Calculations

4
the union figures do not reflect free trade unions, we have coded unionism as zero. Our second type of data are subjective evaluations of the extent of institutional intervention in an economy taken from the Heritage Foundation’s Index of Economic Freedom. We have taken as an indicator the score given the country in the extent of wage/price interventions in the economy. The score ranges from 1 to 5, reflecting an increasing intensity of institutional intervention. We have taken these variables from the 1998 Index of Economic Freedom, because this gives us the maximum number of country coverage.

Columns 1 and 5 of Table 8 show that wage-setting institutions have a large impact on the gender wage gap in richer countries, but not in poorer countries.<sup>27</sup> For richer countries it is estimated that an increase in union density of 10% points will decrease the gender wage gap by 0.5% point. An increase in the regulatory score from 1 to 2 would lower the gender wage gap by 5% point. Hence, relative to trade, the impact of wage-setting institutions is substantial.

We do not find any significant impact of wage-setting institutions on the gender wage gap for the poorer countries. This insignificance may reflect the fact that these institutions tend to be weaker and less binding because of weaker enforcement and the large size of the informal sector. Significantly, the coefficients for the trade intensity variable are relatively unaffected (compare with columns 1 and 5 in Table 6), suggesting that omitted variable bias is no problem.

# **Intra-country trade**

The gender wage gap may not only be affected by international trade but also by within-country trade. Increases in international trade may reduce the gender wage gap because of an increase in the relative demand for female labor or a decrease in discriminatory practices. The same effects can be expected from increases in withincountry trade. Also large countries may have relatively low intensity of international trade but a high intensity of within-country trade. Frankel and Romer (1999) therefore include trade intensity as well as proxy measures for within-country trade in their

> 27 The reported results are for trade intensity (as a % of GDP, current prices). Similar results hold if one uses one of the other trade variables or the FDI net inflow variable.

25
21 _Table 10. 13 coefficientsfor Pre-Transfer Regional Revenue, 1992-1995 (significant results)_

||**1992**|**1993**|1994|1995|
|---|---|---|---|---|
|**National economy (total)**|(N=86)<br>368|(N=87)<br>. 389|(N=87)<br>.396|(N=86)<br>.440|
|**Housing**|n.a.|(N=87)<br>.270|(N=87)<br>.259|(N=86)<br>.217|
|**Agriculture**|n.a.|(N=87)<br>--|(N=87)<br>.015|(N=86)<br>.013|
|**Transportation**|n.a.|(N=83)<br>.026|(N-85)<br>.022|(N=85)<br>.030|
|**Other (93/94)/Industry**(95)|n.a.|(N=86)<br>.074|(N=86)<br>.093|(N=85)<br>.107|
|**Food**|(N=86)<br>.011|(N=84)<br>.010|(N=65)<br>.005|n.a.|
|**Total investment**|(N=86)<br>.125|(N=86)<br>.153|(N=87)<br>.157|(N=78)<br>.199|
|**Budget loans**|(N=46)<br>.04|(N=63)<br>.033|(N=67)<br>--|(N=73)<br>.060|

"n.a." means "not applicable. ".--" means results were not significant.

_Table 11. 13 Coefficients for Federal Transfers, 1992-1995 (significant results)_

||**1992**|**1993**|**1994**|**1995**|
|---|---|---|---|---|
|**National economy (total)**|.136|.284|.362|.308|
|**Housing**|n.a.|.240|.295|.298|
|**Agriculture**|n.a.|.02|.015|--|
|**Transportation**|n.a.|--|.008|-.012|
|**Other (93/94)MIndustry**(95)|n.a.|.025|.038|.040|
|**Food**|--|--|.003|n.a.|
|**Total investment**|.054|.063|.031|--|
|**Budget loans**|--|--|--||

"n.a." means 'hot applicable." " -" means results were not significant. The number of observations is the same as in corresponding regressions in Table 10.
9 The first term in this decomposition accounts for the impact of changing returns over time, while the second term accounts for the impact of changing household characteristics. There is a reminder (covariance term) which is typically small. The decomposition can be implemented to assess the impact of changes in returns and characteristics for sub-groups of variables by partitioning the vector X.

Finally, the regressions can be used to assess the micro determinants of inequality. When using the unconditional group decomposition of the Gini index, we found that in 1995-96, the between group inequality term accounted for fifteen percent of the national Gini (see Table 3). This does not mean that urban-rural location in itself makes such a difference. The inequality between urban and rural households may be due to difference in household characteristics, and/or in returns to these characteristics (which are sector specific). An example will make this clear.

To estimate the inequality between households differing only in terms of sectoral location, the national sample means XN can be used to compute the urban and rural expected consumption for nationally representative households. These are given by exp(1<sup>3</sup> U'XN) and exp(13R'XN), which were equal to 1.479 and 1.236 in 1995-96. These conditional values are lower than the actual welfare ratios in the two sectors given in Table 1 (2.318 in urban areas and 1.286 in urban areas). This is because urban (rural) households tend to have characteristics such as education (land) which have a higher return in urban (rural) areas. When conditioning on national means, we impose nationally representative household characteristics which are less favorable in each sector than the sample mean characteristics of the sectors themselves. Next, note that if all rural households are assigned the same expected welfare ratio of 1.236, the mean expected rank for rural households, denoted by E(FR), must be equal to half the rural population share (0.4173 in 1995-96), and the mean rank for urban household, E(FU), must be equal to the rural population share plus half the urban population share (0.9173 in 1991-92). Given the expected consumption and rank for nationally representative households in the two sectors, and recalling the formulae for the between group Gini in equation (4), the urban-rural conditional between group Gini, denoted as CBGG, can be computed as 2 covEexp(13k'XN), E(Fk)]/E(yT), where k = U, R and E(yT) is
18 secondary) through its financing policies,<sup>concentration of resources, and</sup> enrollment coverage, relative to other Asian<sup>countries (Tan and Mingat, 1991).</sup> In the health sector, a very different picture<sup>is painted</sup> (Griffin, 1990 and World Bank,<sup>1991a). overall public spending is very low.</sup> It has risen considerably since 1975 (from<sup>about .2 percent of GNP) yet, in</sup> 1985, it still remained under 1 percent<sup>of GNP, below the Asian mean of 1.3</sup> percent of GNP. In addition, there<sup>is evidence that government per capita</sup> spending on health declined by about<sup>25 percent b^-ween 1982/83 and 1987/88,</sup> though it has increased again since<sup>(MOH, 1991b)</sup> t-ivate spending accounts for over half of total expenditures on<sup>health. At le3st half of total public</sup> outlays are disbursed to hospitals. Recently,<sup>spending on communicable</sup> disease control has been reduced, while curative<sup>care services have been</sup> buttressed. Griffin also finds that the distribution<sup>of central government</sup> health resources to the provinces is inversely<sup>correlated with need as</sup> revealed by provincial per capita<sup>incomes and infant mortality rates.</sup> Against this background, the paper now<sup>examines some of the</sup> existing evidence concerning the availability<sup>of social services and the costs</sup> of participation to households with different<sup>characteristics.</sup>

# _Access to Education Facilities_

Tables 7 and 8 give us some idea of how the<sup>availability of</sup> education facilities differentiated<sup>by whether they are public (Table 7) or</sup> private (Table 8), varies with household<sup>per capita expenditure levels and</sup> region of residence. The presented data<sup>is derived by linking the household</sup> level data of the 1987 SUSENAS survey with the<sup>1986/87 Potensi Desa, a survey</sup> of village level facilities.'2 The numbers in the tables indicate the percent of villages in each individual's district of<sup>residence which has the specified</sup> facility, averaged over all individuals in the<sup>decile. This is a useful</sup> indicator of access to facilities. Unfortunately,<sup>as these data are</sup> 12 Only the district of residence can<sup>e matched between the SUSENAS and</sup> Potensi Desa surveys.
# **Online Appendix A Data, Variable Definitions and Data Cleaning**

# **A1. Additional information on key variable sources**

**Stock market data:** To replicate and extend Fisman’s (2001) analysis testing for the value of political connections through the relationship between share prices and news of Suharto’s health, we use stock market data from Bloomberg. Unfortunately, Bloomberg does not retain information on the stock prices of de-listed companies, forcing us to focus on surviving firms only.

**Political connections:** Mobarak and Purbasari (2006a) identify politically connected firms as follows: First, they identify firms whose market value on the Jakarta Stock Exchange exhibited abnormally negative movements in response to news episodes about Suharto’s deteriorating health that occurred between 1994 and 1997 before the onset of the East Asian financial crisis (and thus not contaminated by confounding financial turmoil), following Fisman’s (2001) event study methodology. Whereas Fisman examined the stock price responses of 79 firms belonging to the 25 largest conglomerates for whom a Castle Suharto Dependency Index score was available, Mobarak and Purbasari examine the share price responses of all 285 firms traded on the Jakarta Stock Exchange at the time. Out of these, 29 firms were significantly adversely affected and thereby identified as politically connected. The authors used newspapers, and other media to confirm that these firms were indeed connected.  Second, they trace the shareholders and members of the boards of management and commissioners of each of the adversely affected firms. They subsequently list all conglomerates owned by each of the members as well as all firms that are part of these conglomerates. They then merge these data with the annual manufacturing survey in 1997. In our data we have 246 politically connected firms. A Suharto family member owned or served on the board of 86 (35 percent) of these firms. To identify such Suharto family firms, Mobarak and Purbasari used information obtained from the Castle Group on board membership. To avoid potential endogeneity, they excluded those that married into the family, thus focusing only on blood relatives. Moreover, to avoid the possibility a Suharto family member was invited to the board strategically they further restrict the definition of Suharto family firms to only those firms that are subsidiaries of business groups that belong to the Suharto family.

Some potential limitations of Mobarak and Purbasari’s (2006a) approach have to be borne in mind when interpreting our results. First, some publicly traded firms may have spuriously overreacted (underreacted) to news about Suharto’s health and consequently been incorrectly identified as (not) 36
Figure **1:** Time Allocated to All Types of Work by Gender <!-- Start of picture text --> 70.00<br>60.00<br>50.00 _ OILt<br>*40.00<br>30.00<br>20.00*****<br>0.00 lv E LEE<br>1 2 3 4 5 6 7 8 9 10<br>Per Capita Consumption Deciles<br><!-- End of picture text --> 27