rafmacalaba/datause-encoder-data huggingface dataset:
# **Online Appendix B: The Evolution of the Value of Suharto Connections**

# **(Extending Fisman, 2001)**

A key assumption of our paper is that the fall of Suharto reduced the value of political connections to him. Given that he was replaced by his protégé, B. J. Habibie, and that many people, including Suharto family members, managed to maintain their positions of power and prominence, it is important to assess whether this is indeed the case. If political connections to the Suharto regime remained valuable – or if our measure of political connections is a measure of generic political connectedness, rather than connectedness to Suharto specifically, we might anticipate that these connections continued to impact how firms’ stock prices responded to political developments (at least to some extent). By contrast, if these connections lost their relevance, then political connections to the Suharto regime should not predict firms’ share price responses to news about political developments.

To discriminate between these competing hypotheses, we use Fisman’s (2001) event study methodology and data to assess how firms with connections to him respond to political news after his fall, with the caveat that we have to confine our attention to firms that survived up until 2019.<sup>26</sup> We use the Castle Suharto Dependency Index which Ray Fisman generously shared with us. This indicator is a numerical rating of the degree to which the profitability of the 25 largest industrial groups in Indonesia was dependent on political connections to Suharto, ranging from 1 to 5, that is based on the subjective assessments of consultants at the Castle Group, a leading economics consultancy firm based in Jakarta. Rank 5 is given to conglomerates with direct ownership links to the Suharto family. Most of these groups had multiple companies listed on the Jakarta Stock Exchange. The total sample of firms for which we can revisit Fisman’s analysis is 51, which is lower than the original sample of 79 firms analyzed by Fisman (2001) because Bloomberg data were only available for surviving firms.

26 Recall that Bloomberg does not retain information on the stock prices of de-listed companies. This forces us to reduce our analysis to 51 firms instead of the 79 firms that Ray Fisman included in his original analysis. These 51 surviving firms do not seem to be systematically more or less connected than those included in Fisman’s original sample as is shown in Table B1 in the online appendix. Replicating key regressions from Fisman’s (2001) seminal paper using our smaller sample, as is done in Table B3, offers additional suggestive evidence that survivor bias is limited.

42
TANZANIA’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Table 4. Detailed evolution of TANESCO’s hidden costs, 2006-2010**

||Power<br>billed|System<br>losses|Collection<br>ratio|Average<br>total cost|Average<br>effective<br>tariff|To|tal hidden costs||
|---|---|---|---|---|---|---|---|---|
||(GWh/year)|(%)|(%)|(US$/kWh)|(US$/kWh)|(US$m/year)|(% revenue)|(% GDP)|
|**TANESCO**|||||||||
|2006|2,628|26|94|0.16|0.06|344|175|2.37|
|2008|3,377|26|94|0.14|0.07|332|132|2.11|
|2010|3,923|15|94|0.14|0.08|305|87|1.36|

**Notes:** System losses for 2008 was not available and was estimated as the average of losses for 2006 and 2010; **Source:** TANESCO annual reports and World Bank staff estimates

**Figure 7. Evolution of Tanesco’s hidden costs, 2002–08**

<!-- Start of picture text --> 180%<br>160%<br>140%<br>120%<br>100%<br>80%<br>60%<br>40%<br>20%<br>0%<br>2002 2003 2004 2006 2008 2010<br>under-pricing under-collection unaccounted losses<br>percentage of revenues<br><!-- End of picture text --> _Source:_ Briceño-Garmendia and others 2009 updated with information from World Bank country staff.

12
**Table 4A: Adjustments Using Markups**

||||SL|MS||
|---|---|---|---|---|---|
|||round 8|rounds 2-8|retail|manufacturing|
||Mean|7334|6322|8508|6214|
|Adjusted Profits|S.D.|5956|5433|6044|5659|
||Median|6000|5000|7000|5000|
||% negative|0|0|0|0|
|Adjusted Revenue|Mean|5368|4582|7431|3401|
|<br>Minus Exenses|S.D.|16143|13412|20967|9110|
|p|Median|3700|2850|5775|2100|
||% negative|21|23|21|20|
|Markup-Adjusted|Mean|5543|4935|7227|3938|
|Revenue Minus|S.D.|6213|5847|6859|5039|
|Expenses|Median|4000|3127|5540|2188|
||% negative|9|8|8|9|
|Correlations: Adjusted|Pearson|0.362|0.329|0.363|0.383|
|Profits vs. Adjusted|Spearman|0.541|0.519|0.538|0.513|
|Rev-Exp|p-value|0.000|0.000|0.000|0.000|
|Correlations: Adjusted|Pearson|0.673|0.646|0.609|0.733|
|Profits vs. Markup|Spearman|0.718|0.692|0.677|0.724|
|Adjusted|p-value|0.000|0.000|0.000|0.000|

SLMS retail and manufacturing breakdown is for round 8

**Table 4B: Markups and Inventory Turns**

|||> 75% of<br>inventories<br>sold in same<br>month|< 60% of<br>inventories<br>sold in same<br>month|
|---|---|---|---|
|Correlations: Adjusted|Pearson|0.698|0.138|
|Profits vs. Adjusted|Spearman|0.767|0.296|
|Rev-Exp|p-value|0.000|0.000|
|Correlations: Adjusted|Pearson|0.702|0.659|
|Profits vs. Markup|Spearman|0.742|0.697|
|Adjusted|p-value|0.000|0.000|

**Table 5: How important are recall errors?**

||Mean<br>|Median|25th|75th|Pearson<br>Corre|Spearman<br>lations|
|---|---|---|---|---|---|---|
|_One month vs Four month recall_|||||||
|SLMS March Sales Asked in Round 1 (April)|12358|7100|3000|15000|0.491|0.654|
|SLMS March Sales Asked in Round 2 (July)|11185|6000|2050|15000|||
|% difference|-9.5|-15.5|-31.7|0.0|||
|_Quarterly recall vs Annual recall_|||||||
|SLMS 12 month sales as sum of quarterly|256598.7|142922|66700|334000|0.763|0.841|
|SLMS 12 month sales as recall over year|248167.3|132000|60000|300000|||
|% difference|-3.3|-7.6|-10.0|-10.2|||

26
Their analysis finds considerable heterogeneity in the earnings of Filipina, Asian Indian and nonHispanic white women, adding nuance to the generalization that women in major cities earn less than men. Examining the role of intersectionality through interaction effects, they show that the observed earning gaps are driven by the migration backgrounds of each group of women (as refugees, migrant workers, spouses of migrants or native-born residents), which position them differently in the labor market.

In addition to quantitative evidence from formal markets, qualitative literature focusing on domestic work also documents how race, gender, class, immigration and citizenship status exacerbate the disadvantages of poor, migrant and ethnic minority women. Most of these studies use historical analysis or in-depth interviews to show cumulative disadvantages in terms of pay, flexibility and amount of autonomy experienced by minority women in domestic jobs. These disadvantages are attributed to norms governing gender-division of domestic work; class- and ethnicity-based justifications for paying minority groups lower wages; and the legal status of migrant groups in host countries, which weakens their bargaining position vis-à-vis employers (Browne and Misra 2003). Qualitative studies and historical accounts in racially divided societies similarly show that gender, race, ethnicity and class operate together to relegate certain groups to the lowest economic status, such as African descendant women in Zimbabwe (e.g. Moyo and Kawewe 2002).<sup>12</sup> The literature on intersectionality is much thinner with respect to developing countries, cutting across multiple disciplines and fields. Many of these studies are also experimental, in the sense that they introduce new methods to analyze intersectionality using existing data sets (e.g. Iyer, Sen and Ostlin 2008; Sen, Iyer and Mukherjee 2009; Sen and Iyer 2012). For example, Sen, Iyer and Mukherjee (2009) propose a quantitative methodology to study gender- and class-based inequalities in access to health, which they apply in Karnataka, India using a specialized survey to capture inequalities in healthcare during pregnancy and illness. Unlike most studies that focus on the disadvantages of a particular group (e.g. poor women) vis-à-vis all other groups (e.g. poor men, non-poor men, non-poor women), their methodology allows them to compare health

> 12 See a special issue of Feminist Economics (Vol. 8, Issue. 2, 2002) for a set of empirical studies on the intersectionality of gender, race and class.

6
81 to the atmosphere. _Spillage losses_ refer to gasoline that is unintentionally spilled during refueling, which then evaporates.

About 90% of all refueling emissions consist of vapors displaced from the vehicle fuel tank by the incoming gasoline. The mass of these emissions depends on the volume of vapor displaced and its density, which in turn are determined by the temperature of the fuel being dispensed and of that already in the tank, the tank size and geometry, the volatility of the fuel, and a number of other minor factors.

# **Two- and Three-Wheeled Vehicles**

Two- and three-wheeled vehicles, such as motorcycles and auto rickshaws constitute a large portion of motorized vehicles in developing countries, particularly in East and South Asia. While they are responsible for a relatively small fraction of the total vehicle kilometers of travel (VKT) in most countries they may make a substantial contribution to air pollution from mobile sources, in particular motorcycles/auto rickshaws with two-stroke engines running on a mixture of gasoline and lubricating oil. For example, it has been estimated that uncontrolled motorcycles in industrialized countries emit 22 times as much hydrocarbons and 10 times as much carbon monoxide as automobiles controlled to U.S. 1978 levels [OECD 1988a]. In Taiwan, HC emissions from two-stroke engine motorcycles were 13 times higher than the emissions from new four-stroke motorcycles and over 10 times higher than the emissions from in-use passenger cars. The CO emissions from two-stroke motorcycle engines were similar to those from four-stroke engines [Shen and Huang, 1989].

Technologies available to control emissions from two- and three-wheeled vehicles are similar to those available for other Otto cycle powered engines. Reducing the content of lubricating oil in the fuel is one possible approach. Refining the fairly simple type of carburetors used would help significantly reduce HC, CO, and smoke emissions. Even catalytic converters are technologically feasible for these engines [OECD 1988a].
group and 381 for the control group. The control group was selected by INEGI using a probabilistic design based on sub-sector, firm size and geographic location to match the CIMO sample. Due to non-response, plant closures, and incomplete responses to questionnaires, the final sample sizes were 248 and 316 firms for the treatment and control groups, respectively. The combined sample had roughly equal numbers of manufacturing (284) and nonmanufacturing enterprises (280), and spanned three employment size categories: 30 percent micro (with less than 16 workers), 50 percent small (16-100 workers), and 20 percent mediumsize enterprises (101-250 workers).

**Figure 1.  Data Structure of the CIMO Evaluation Study**

|**First**<br>**CIMO**|**1991 1992**<br>**1993**|**1993**<br>**1994**<br>**+ control**<br>**group**||
|---|---|---|---|
|**Study**||||
|||**1995**<br>**1993 1994**|**+ control**|
|**Second**|||**group**|
|**CIMO**||**1996**<br>**1995**||
|**Study**||||

The second STPS study (November 1997), covering the period between 1993 and 1995, followed a similar design. Two groups, a CIMO and a control group, were identified based on similar characteristics, such as economic sub-sector, size, geographic location and sales in constant pesos of 1993, and tracked over time. One innovation of this second study was that it also included a sample of CIMO and non-participating firms from the first STPS study, so that one sub-set of firms could be followed over the entire 5-year period. Table 1 shows the final sample for the second study: 595 firms in the treatment group and 638 firms in the control group, of which 381 were firms that participated in both studies.

5
# _Relationship to Factors Influencing Development_

In Table 3 we have shown correlation coefficients for several factors that may influence, or may be influenced by, governance quality: per capita PPP income, GDP growth, and military spending. The strong positive correlation between income and governance quality supports the casual observations drawn above. The negative correlation of military spending with governance quality is not statistically strong but it does suggest that it may be governments that are being defended rather than countries. The positive colrelation between the ten year economic growth rate and governance quality supports the argument that the institutional focus of government is an important determinant in economic development. Also, since the highest income countries have generally not had the highest growth rates over the last decade, the positive correlation between higher growth and better governance suggests that good governance improves economic performance rather than vice-versa.

**Table 3: Governance Index Correlation Coefficients**

||Governance<br>Index|Income|Military (as %<br>govt spending)|Annual Growth<br>(85-94)|
|---|---|---|---|---|
|Governance|1.00|0.89|-0.16|0.25|
|||(0.00)|(0.22)|(0.03)|
|Income||1.00|-0.11|0.26|
||||(0.42)|(0.03)|
|Mi:litary|||1.00|0.10<br>(0.47)|
|Annual Growth||||1.00|

_Sou'rces:_ Income and Growth from World Bank (1996b), Military and Subnational Expenditures from International Monetary Fund (1995)

# III:. **Quality of Governance and Fiscal Decentralization**

During the past half a century, developing countries have, in general, followed a path of centralization and as a result, these countries are more centralized today than 9
Policy Research Working Paper 5103

# **Abstract**

The extent to which India’s poor have benefited from the country’s economic growth has long been debated. This paper revisits the issues using a new series of consumption-based poverty measures spanning 50 years, and including a 15-year period after economic reforms began in earnest in the early 1990s. Growth has tended to reduce poverty, including in the post-reform period. There is no robust evidence that the responsiveness of poverty to growth has increased, or decreased, since the reforms began, although there are signs of rising inequality. The impact of growth is higher for poverty measures that reflect distribution below the poverty line, and it is higher using growth rates calculated from household surveys than national accounts. The urbanrural pattern of growth matters to the pace of poverty reduction. However, in marked contrast to the pre-reform period, the post-reform process of urban economic growth has brought significant gains to the rural poor as well as the urban poor.

This paper—a product of the Director’s Office, Development Research Group—is part of a larger effort in the department to monitor and explain progress against poverty in developing countries. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at mravallion@worldbank.org.

_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
**Figure 6: Average CLASS Scores for all Dimensions, Guangdong Pilot Counties and U.S. Comparator**

<!-- Start of picture text --> 7<br>6<br>5<br>4<br>3<br>2<br>1<br>0<br>Guangdong ‐ Elementary School Guangdong ‐ Middle School US Average<br><!-- End of picture text --> Source: Guangdong class observations; authors’ calculations; Teachstone.

Note: For comparability with the U.S. studies, the Negative Climate score has been reversed in the figure to make higher, better.

# **3.2 By county**

This section examines the Guangdong pilot results by county. The three pilot districts, while all a part of the 16 project counties, varied widely by several measures, including economic prosperity and academic achievement. Were the differences in outcomes reflected in the CLASS scores, or was there relative equality in teaching quality? The results may surprise.

Table 7 provides a breakdown of the three pilot counties. Each county has data on total population, GDP per capita, and Zhongkao scores, both the absolute values and the rank out of the 16 project counties. All values are from 2014. The Zhongkao is a countywide test taken by all students at the end of 9<sup>th</sup> grade and serves as the best available comparator for cross-county educational achievement. These tests are not identical between counties, however, making it hard to compare directly. For this ranking, the percentage score was used to make comparisons between the project counties.

15
Identification of the causal effect of income inequality on aggregate output is complicated by the endogeneity of the former variable. Income inequality may be affected by countries' GDP per capita as well as other variables related to deep-rooted differences in countries' geography and history. We address this issue by estimating a panel model with country and time fixed effects. We instrument income inequality with variation in that variable not driven by GDP per capita.

The remainder of the paper is organized as follows. Section 2 reviews related literature. Section 3 discusses the data. Section 4 presents the econometric model. Section 5 discusses the empirical results. Section 6 concludes.

# **2. Related Literature**

The literature on the relationship between income inequality and aggregate output is well established in economics and we refer the reader to Galor (2011) for an exhaustive survey. In this section we only discuss empirical papers that make an attempt to use plausibly exogenous variation in income inequality.

Galor et al. (2008) examine the impact that land inequality had on human capital for the United States at the beginning of the 20<sup>th</sup> century using state level data. Instrumenting land inequality with the interaction between nationwide changes in the relative prices of agricultural crops and variation in climatic characteristics across states, the authors find that inequality had on average a significant negative effect on human capital. According to Maddison (2013), the real GDP per capita of the United States at the beginning of the 20<sup>th</sup> century was around 5,000USD. Using a value of (the natural logarithmn) 5,000USD and plugging it into our estimates, we find that the effect of inequality on education and GDP per capita is negative (see Section 5). Our panel estimates, which are based on a sample of 104 countries during 1970-2010, are therefore consistent with the results in Galor et al. (2008).

Easterly (2007) uses the abundance of land suitable for growing wheat relative to that 4
**Figure  3.2: Liquidity Assets and Yield-Seeking Assets in an Investment Portfolio**

**Money that frequently inflows and outflows is marginal to the whole investment portfolio. The rest does not need to stay liquid at all times.**

<mark>Source: Author</mark>

# **_3.3 Buy and Hold_**

The “buy and hold” strategy, which is often viewed as responsible for the low liquidity of corporate bonds, is rational to many institutional investors. On many occasions, institutional investors are likely to achieve their investment objectives better by not trading corporate bonds frequently.

Most institutional investors do not need to keep a large part of their portfolio liquid. Only a small part of a portfolio may flow out on demand or with a short notice.<sup>9</sup> A substantial part of the liabilities of institutional investors is medium or long term, or both. Therefore, assets that they hold for liquidity purposes ( _liquidity assets_ ) can be relatively small<sup>10</sup> (Figure 3.2). In addition, they are usually able to fund temporary shortfalls in the

> 9 An exception is open-end mutual funds. They keep a larger part of their portfolios liquid to meet their unitholders’ redemption requests than other institutional investors do.

> 10 The actual size of this illiquid component varies and fluctuates, depending on investment objectives, restrictions, outlook for market conditions, and other factors.

10
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