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- Gross, Wendelin, Cristina Hayden, and Christian Butz (2012). “About the impact of rising oil price on logistics networks and transportation greenhouse gas emission”. In: _Logistics Research_ 4, pp. 147–56.

- Guerriero, Marta (2019). “The Labor Share of Income around the World: Evidence from a Panel Dataset.” url: https://www.adb.org/publications/labor-share-income-around.

- world-evidence-panel-dataset

- Gygli, Savina et al. (2019). “The KOF GLobalization Index – revisited”. In: _The Review of International Organizations_ 14, pp. 543–574.

- Harrison, Ann (2005). _Has Globalization Eroded Labor’s Share? Some Cross-Country Evidence_ . Working Paper.

- Henry, Peter (2007). “Capital Account Liberalization: Theory, Evidene and Speculation”. In: _Journal of Economic Literature_ 45(4), pp. 887–935.

- ICTD/UNU-WIDER (2020). _Government Revenue Dataset_ . url: https://www.wider.unu. .

- edu/project/government-revenue-dataset

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- 5C%20distribution.pdf

- ILOSTAT (2021). _Statistics on labour income and inequality._ url: https://ilostat.ilo.org/topics/ labour-income/.

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- Prospects”. In: _Working Paper_

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37
this may actually induce banks to take on more risk with potentially negative repercussions for the stability of the financial system.

In the remainder, section 2 discusses the data, and section 3 presents the empirical results. Section 3 starts with an analysis of the relationships between corporate governance and overall bank insolvency risk and bank returns. Then it considers the ‘channels’ on the assets and liabilities sides of the bank’s balance sheet through which corporate governance affects overall bank risk. Finally, it considers the relationship between corporate governance and bank risk taking over the business cycle. Section 4 concludes with policy implications.

# **2. The data**

In this study, we relate measures of bank risk to summary indicators of corporate governance for an international sample of banks over the 2004-2008 period. Accounting and market data necessary to construct our bank risk measures are taken from Bankscope of Bureau Van Dijk, CRSP and Compustat Global. Data on bank corporate governance are from the Corporate Governance Quotient data base assembled by Institutional Shareholder Services (ISS).

We examine two main measures of bank insolvency risk. First, the Z-score represents the number of standard deviations that a bank’s rate of return on assets has to fall for the bank to become insolvent. The Z-score is constructed as the sum of the rate of return on assets and the equity to assets ratio divided by the standard deviation of the return on assets. Table A1 in the Appendix describes variable definitions and data sources. A higher Z-score signals that a bank has lower insolvency risk. We calculate a Z-score 7
**Table 3. The role of connectivity in economic upgrading through GVCs**

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|VARIABLES|_DVA_|_DVA_|_DVA_|_DVA_|_DVA_|_DVA_|
|**Backward links**|||||||
|_FVAX_|0.0453**<br>(0.0182)|0.0189<br>(0.0546)|-0.0260<br>(0.0491)|-0.0167<br>(0.0599)|0.0903***<br>(0.0233)|0.0930***<br>(0.0210)|
|_FVADP_|0.253***|0.273***|0.273***|0.273***|0.252***|0.252***|
||(0.0269)|(0.0238)|(0.0238)|(0.0238)|(0.0270)|(0.0271)|
|_FVAX*Internet_|0.0008<br>(0.0005)||||||
|_FVAX*LPI logistics_||0.0141<br>(0.0166)|||||
|_FVAX*LPI customs_|||0.0302*<br>(0.0158)||||
|_FVAX*LPI overall_||||0.0251<br>(0.0180)|||
|_FVAX*Time to export_|||||-0.0013<br>(0.0014)||
|_FVAX*Time to import_||||||-0.0014<br>(0.0012)|
|_Constant_|5.735***|5.735***|5.863***|5.745***|5.440***|6.150***|
||(0.175)|(0.168)|(0.167)|(0.167)|(0.160)|(0.190)|
|Observations|7,156|7,057|7,057|7,057|7,156|7,156|
|R-squared|0.854|0.859|0.860|0.860|0.854|0.854|
|**Forward links**|||||||
|_DVAR_|0.190***<br>(0.0248)|0.0666<br>(0.0694)|0.0638<br>(0.0638)|0.0291<br>(0.0770)|0.303***<br>(0.0301)|0.273***<br>(0.0259)|
|_FVADP_|0.232***|0.251***|0.250***|0.250***|0.232***|0.231***|
||(0.0259)|(0.0227)|(0.0227)|(0.0227)|(0.0258)|(0.0260)|
|_DVAR*Internet_|0.0010*<br>(0.0006)||||||
|_DVAR*LPI logistics_||0.0513**<br>(0.0211)|||||
|_DVAR*LPI customs_|||0.0550***<br>(0.0204)||||
|_DVAR*LPI overall_||||0.0621***<br>(0.0232)|||
|_DVAR*Time to export_|||||-0.0053***<br>(0.0019)||
|_DVAR*Time to import_||||||-0.0033**<br>(0.0014)|
|_Constant_|4.844***|4.671***|4.742***|4.965***|5.059***|5.029***|
||(0.165)|(0.166)|(0.157)|(0.159)|(0.174)|(0.181)|
|Observations|7,164|7,060|7,060|7,060|7,164|7,164|
|R-squared|0.872|0.879|0.879|0.879|0.872|0.872|

Source: OECD ICIO database. *** p<0.01, ** p<0.05, * p<0.1. Notes: Robust standard errors in parentheses. _FVAX_ and _DVAR_ lagged. All level variables in natural logarithms.

Looking at the income group specific results, we observe again that upper middle and high-income countries drive these forward links results, but, overall, the results suggest that connectivity is particularly important for economies that have strong forward links. If these countries want to benefit from GVC participation, connectivity is an important policy field and must be included in GVC strategies.

21
**Figure 1.  United States Housing Prices, Building Costs & Interest Rates, 1900-2010**

<!-- Start of picture text --> 250 1000<br>900<br>200 800<br>700<br>150 600<br>Home Prices 500<br>100 400<br>Building Costs 300<br>50 200<br>Population Interest<br>Rates 100<br>0 0<br>1880 1900 1920 1940 1960 1980 2000 2020<br>Year<br>Index or Interest Rate Population in Millions<br><!-- End of picture text --> Source: Robert Shiller http://www.econ.yale.edu/~shiller/data.htm The recession in the United States quickly spread to other economies. In 2009, world output fell by almost 2 percent; in the US it fell by 2.6 percent, in Japan by 5.2 percent, in the UK by 4.9 percent and in EU countries by 4.3 percent. In emerging markets, GDP growth slowed down to about 0.5 percent, while developing countries maintained a reasonably good growth performance (above 4 percent) until 2011. World trade in 2009 was 65 percent lower than in the previous year. Equity markets collapsed. Unemployment around the world rose sharply. It surpassed 15 percent in countries like Spain and Greece that already had high levels of unemployment before the crisis. The US financial crisis spread through three main channels. One channel was money markets. Following concerted efforts by central banks to inject liquidity into the system, commercial banks significantly decreased lending rates. Trade was another channel. As a result of the freeze of credit markets, exports collapsed—on a year-to-year basis—by 30 percent in China and Germany, and by 45 percent in Japan, for instance. Remittances from some workers, in particular laid off migrant workers from Central America in the US construction sector dropped. The collapse in international commodity prices, and especially oil, as a result of the fall in demand in advanced economies was another channel through which the crisis spread.

The global slowdown heightened vulnerabilities that had already been in place before the crisis. Notably, countries that had also experienced housing booms, like Ireland and Spain, or had high fiscal deficits before the crisis, like Greece and Portugal, now teetered on the brink of a sovereign debt crisis and required support from the European Central Bank and the International Monetary Fund. In the Eurozone, the crisis turned into a sovereign debt crisis, with potentially significant consequences for the world economy as a whole. In the developing world, a large 5
differences in the quality of parent-child interactions during early childhood are likely to explain part of the observed gender gaps in children’s cognitive and non-cognitive skills in the early years.

In light of the literature reviewed above, in this paper, we investigate gender differences across an array of cognitive and non-cognitive skills for children in the first few grades of primary school, and examine the extent to which early schooling experiences and parenting practices account for this observed gender gap in rural Indonesia.

# **3. Country context**

Indonesia has the fourth largest education system in the world with over 50 million students, 2.6 million teachers, and more than 250,000 schools. In 2012, the government expenditure on education was 4% of its GDP. Net enrollment rates in primary, secondary, and tertiary education are 92%, 77% and 32% respectively (World Bank 2015). There are no significant differences in primary and secondary education enrollment rates between girls and boys (Suryadarma 2015).

However, results of educational achievement data during primary and secondary schooling show some evidence of gender gaps. Girls significantly outperform boys in reading in the fourth grade (Mullis et al. 2012) and by age 15, this female advantage is equivalent to approximately 10 additional months of schooling (OECD 2015). In contrast, results in mathematics are mixed. Using a longitudinal household survey from Indonesia, Suryadarma (2015) finds that girls score 0.08 SD higher in numeracy tests than boys at age 11 and this gap increases to 0.19 SD when the sample of children are 18 years-old in 2007. In contrast, results from the most recent round of PISA show that the difference between boys and girls in mathematics at around age 15 is small in magnitude and not statistically significant (OECD 2015). Thus, the existing evidence of educational outcomes of Indonesian children shows mixed evidence of gender gaps during late primary and secondary school. This paper adds to the gender gap literature in Indonesia by focusing on the early childhood years and examining both cognitive and non-cognitive outcomes.

# **4. Data and measures**

# **4.1 Data**

Data for this study were collected in 2013 as part of an impact evaluation of the Indonesia ECED Project (see Pradhan et al., 2013 for a detailed description of the study protocol). The Indonesia ECED Project was designed to improve poor children’s school readiness by establishing community-based early childhood education programs.

6
# **Appendix. Variable definitions and data sources**

|Variable|Description|Sources|
|---|---|---|
|Assets|Log of assets in millions of 2000 US dollars|Bankscope and<br>WDI|
|Liabilities|Log of liabilities in millions of 2000 US dollars|Bankscope and<br>WDI|
|Liabilities over GDP|Bank liabilities divided by GDP|Bankscope and<br>WDI|
|Big0.1|Dummy variable that equals one if ratio of bank liabilities to GDP exceeds 0.1 and zero otherwise|Bankscope and<br>WDI|
|Big0.25|Dummy variable that equals one if ratio of bank liabilities to GDP exceeds 0.25 and zero otherwise|Bankscope and<br>WDI|
|Big0.5|Dummy variable that equals one if ratio of bank liabilities to GDP exceeds 0.5 and zero otherwise|Bankscope and<br>WDI|
|Big1|Dummy variable that equals one if ratio of bank liabilities to GDP exceeds 1.0 and zero otherwise|Bankscope and<br>WDI|
|Return on assets|Pre-tax profits divided by assets||
|Z-score|Index of bank solvency constructed as<br>_SROA_<br>_CAR_<br>_ROA_<br>, where ROA is return on assets, CAR represents capital<br>assets ratio and SROA stands for standard deviation of return on assets|Bankscope|
|Fee income|Share of non-interest income in total operating income|Bankscope|
|Interest income|Net interest income divided by assets|Bankscope|
|Other operating income|Other operating income divided by assets|Bankscope|
|Loans|Total loans divided by earning assets|Bankscope|
|Non-deposit short-term funding|Share of non-deposit short-term funding in total deposits and short-term funding.|Bankscope|
|Interest expense|Interest expense over bank liabilities excluding non-interest bearing debt|Bankscope|
|Deposit growth|Growth rate of a bank's customer and short term funding after dividing by the GDP deflator in percent|Bankscope and<br>WDI|
|Equity|Ratio of equity to assets|Bankscope|
|Liquidity|Ratio of liquid assets to total assets|Bankscope|
|Overhead|Ratio of personnel and other non-interest expenses to assets|Bankscope|
|Short term debt|Bank's customer and short term funding divided by total interest-bearing debt|Bankscope|
|Investment bank|Dummy variable that equals 1 if bank is investment bank or securities firm, and zero otherwise|Bankscope|
|Non-banking credit institution|Dummy variable that equals 1 if bank is non-banking credit institution, and zero otherwise|Bankscope|
|Other bank|Dummy variable that equals 1 if bank is cooperative bank, Islamic bank, medium and long term credit bank ,|Bankscope|

23
agriculture to the manufacturing, construction, retail, and service sectors outside extractive industry would be conducive for TFP growth.

The second factor in accounting for TFP growth, which naturally emerges from Africa’s own experiences, is improved political stability. It is believed that instability due to conflicts and dysfunctional government is a barrier to economic development. We use the index (Freedom in the World data) from 1980 to 2010 from Freedom House to examine whether improved freedom and political stability have played a role in TFP growth and thus the turnaround of the economic growth.<sup>39</sup> As shown in Figure 9A, there has been a significant change in freedom and political stability in SSA since the mid-1990s: many countries shifted from the “not free” to “partly free” or “free” status.<sup>40</sup> These “stabilized countries,” such as Burkina Faso, Ethiopia, Mozambique, and Tanzania, experience faster TFP growth than others by 1.4 percentage points from pre- to post-1995 period (see Figure 9B).<sup>41</sup> The “stabilized countries” include some resource rich countries: Mali and Burkina Faso (gold) and Mozambique and Ghana (oil). Considering that resources have been often the source of instability,<sup>42</sup> maintaining stability and managing the revenues from the resources to harness investment in human and social capital should be prioritized in these countries for sustained growth.

In addition to these two factors – structural transformation and political stability – we consider potentially important determinants of TFP that went through a large change from pre- to post-1995 period when data permit. They include external debt and FDI. External debt as a percent of Gross National Income (GNI) among SSA countries peaked at 134 percent in 1993 but rapidly decreased to 31 percent in 2009. The increase in FDI is as shown above. However, we do not find any relationship between these factors and TFP growth.

> 39 The Freedom in the World survey, which includes both analytical reports and numerical ratings, measures freedom according to two broad categories: political rights and civil liberties. Political rights ratings are based on an evaluation of three subcategories: electoral process, political pluralism and participation, and functioning of government. Civil liberties ratings are based on an evaluation of four subcategories: freedom of expression and belief, associational and organizational rights, rule of law, and personal autonomy and individual rights.

> 40 See Table A1 in Appendix for the full list of countries that experienced “stabilization.”

> 41 The TFP growth rates for stabilized countries improved from 0.03 to 1.04 percent from pre- to post1995 periods, while they changed from -0.18 to -0.58 percent for others.

> 42 See Sachs and Warner (1995).

18
established, existing stations may be the only available means of looking into hydroclimatic trends in the region.

# **2. Climate Change Observations**

# **2.1 Strong evidence of warming**

Temperature data in the Himalayas overwhelmingly show a warming trend, albeit at different rates in different periods depending on the regions and seasons (Table 1). In a very recent regional study using Climate Research Unit’s reconstructed temperature dataset (Brohan et al., 2006), Diodato et al. (2011) show that in the last few decades the Himalayan and Tibetan Plateau region have warmed at a rate higher than that in the last century. They show a 0.5 °C in annual average maximum temperature (Tmax) warming over 1971-2005 compared to 19011960. Dash et al. (2007) report that the western Indian Himalayas saw a 0.9 °C rise over 102 years (1901-2003). They report that much of this observed trend is related to increases after 1972. Using winter (Dec-Feb) monthly temperature data from 1975-2006, Dimri and Dash (2011) also found a warming trend over the western Indian Himalayas, with the greatest observed increase in Tmax (1.1-2.5 °C). Over the northwest Indian Himalayan region, Bhutiyani et al. (2007) found 1.6 °C warming (0.16° C /decade) in the last century. Singh et al. (2008) observed increasing trends in Tmax and seasonal average of daily maximum temperature for all seasons except monsoon over the lower Indus basin in the northwest Indian Himalaya. Fowler and Archer (2005) report increasing trends in winter temperature during 1961-2000 in the upper Indus basin (Pakistan) with varying warming rates of 0.07-0.51 °C/decade in annual mean temperature (Tav) and 0.1-0.55 °C/decade in Tmax. Increasing winter maximum temperature in the upper Indus basin was also reported by Khattak et al. (2011) who found an increasing warming trend of 0.45,0.42,0.23 °C/decade in Tmax for the upper, middle, and lower regions, respectively, during 1967-2005.

Although, studies specific to the eastern Indian Himalaya are not available in the literature, many of the studies discussed above include the eastern Indian Himalaya as part of the greater northeast Indian region. For 1901-2003, Dash et al. (2007) found a rise in Tmax of 1°C over the whole northeast India. The study reports annual average daily minimum temperature (Tmin) increasing after 1972 following a sharp drop of 1.4 °C during 1955-72. Other studies also 5
**Table 2: Biofuels Targets and Blending Mandates**

|**Country**|**Biofuel Targets**|**Blending Mandates**|
|---|---|---|
|Australia|350 million liters of biofuels by 2010|E2 in New South Wales, increasing to E10 by 2011;<br>E5inQueensland by2010|
|Argentina||E5 andB5 by2010|
|Bolivia||B2.5 by2007 andB20 by2015|
|Brazil||E22 to E25 existing (slight variation over time);<br>B3 by2008 andB5 by2013|
|Canada||E5 by 2010 and B2 by 2012;<br>E7.5inSaskatchewanandManitoba;E5 by2007inOntario|
|Chile||E5 andB5 by2008 (voluntary)|
|China|12 million metric tons ofbiodieselby the year 2020|E10in9 provinces|
|Colombia||E10 andB10 existing|
|Dominican Republic||E15 andB2by2015|
|Germany|5.75% share ofbiofuelsintransport by2010;10% by2020|E5.25 andB5.25in 2009;E6.25 andB6.25from 2010 through 2014|
|India||E5 by2008 andE20 by2018;E10in 13 states/territories*|
|Italy|5.75% share ofbiofuelsintransport by2010;10% by2020|E1andB1|
|Jamaica||E10 by2009|
|Japan|20% of total oil demand met with biofuels by 2030;<br>500million liters by2010||
|Korea||B3 by2012|
|Malaysia||B5 by2008|
|NewZealand|3.4% totalbiofuels by2012||
|Paraguay||B1by2007,B3 by2008, andB5 by2009;E18 or higher(existing)|
|Peru||B2 in 2009;B5 by2011;E7.8 by2010|
|Philippines||B1andE5 by2008;B2andE10 by2011|
|South Africa||E8-E10 andB2-B5 (proposed)|
|Thailand|3 percent biodiesel share by 2011; 8.5 million liters of biodiesel<br>productionby2012|E10 by 2007 and B10 by 2012|
|UnitedKingdom||E2.5/B2.5 by2008;E5/B5 by2010|
|United States|130 billion liters/year of biofuels nationally by 2022; 3.4 billion<br>liters/year by2017 Pennsylvania|E10 in Iowa, Hawaii, Missouri, and Montana; E20 in Minnesota; B5 in<br>NewMexico;E2 and B2 in Louisiana and WashingtonState;<br>|
|Uruguay||E5 by2014;B2 from 2008-2011andB5 by2012|

Source: REN21 (2009); USAID (2009); BNDES and CGEE (2008); Worldwatch (2007). Note:* Poor sugar cane yields in 2003 to 2004 forced India to import ethanol to meet state blending targets. It has postponed broader targets until adequate domestic supplies become available.
# **Table A1. Number of Firms, by Country and Year of Survey**

||||||Surve|y Year|||||# of|
|---|---|---|---|---|---|---|---|---|---|---|---|
||2002|2003|2004|2005|2006|2007|2008|2009|2010|Total|Surveys|
|Albania|167|||202||276||||645|3|
|Angola|||||421||||306|727|2|
|Argentina|||||1019||||1017|2036|2|
|Armenia|169|||351||||356||876|3|
|Azerbaijan|167|||345||||291||803|3|
|Bangladesh|948|||||1471||||2419|2|
|Belarus|247|||325|||240|||812|3|
|Benin|||182|||||143||325|2|
|Bolivia|||||603||||346|949|2|
|Bosnia-Herzegovina||||200||||343||543|2|
|Botswana|||||340||||260|600|2|
|Brazil||1619||||||1170||2789|2|
|Bulgaria|242||492|298||1008||269||2309|5|
|Burkina Faso|||||138|||357||495|2|
|Cameroon|||||168|||351||519|2|
|Chile|||941||989||||1003|2933|3|
|China|1353|1572||||||||2925|2|
|Colombia|||||990||||934|1924|2|
|Congo|||||338||||334|672|2|
|Croatia|169|||227||615||||1011|3|
|Czech Republic|258|||334||||223||815|3|
|El Salvador||463|||681|||||1144|2|
|Estonia|164|||216||||264||644|3|
|Georgia|172|||198|||334|||704|3|
|Guatemala||431|||503|||||934|2|
|Honduras||446|||421|||||867|2|
|Hungary|243|||605||||283||1131|3|
|<br>India|1461||||3086|||||4547|2|
|Indonesia||644||||||1313||1957|2|
|Kazakhstan|246|||582||||464||1292|3|
|Kenya||211||||653||||864|2|

26