rafmacalaba/datause-encoder-data huggingface dataset:
# Financial Structure in East Asia

Before commencing a discussion of recent thiking on how best to promote financial development **in** East Asia, we will look at the region's current financial structure Developing economies are generally more dependent upon bank lending than on capital markets for financing This **is** largely true for the developing economies of East Asia as well Figure 5 1 below shows the breakdown of total extemal finance by source **(i** e, bank loans, equities and corporate bonds) for selected East Asian countries and the U S for 1990 to 1999 It is obvious that among the lesser-developed economies bank loans and equities are a much greater source of external financing than are corporate bonds. In the case of Thailand, Indonesia and China, bank loans are the largest source while Singapore, Malaysia and the Philippunes are more reliant on the equities markets In Korea, bank loans are the most important source although equities also play a sigmficant role However, when compared with the U S , for which bank loans make up only about 13 percent of extemal financing, the heavy reliance on bank lending by the other countnes shown (from around 30 percent for Singapore and Malaysia to over 80 percent for China) becomes obvious Flgure 5 1 <!-- Start of picture text --> Breakdown of External Financing 199O-1999 Average<br><!-- End of picture text --> <!-- Start of picture text --> 80<br>70<br>60<br>20 30 -- -|- - - - - -<br>US Singapore Koam Malayaa Thailand Indonea Philixprms China<br>Source Adapted from Shirai (2001b Chart 2c, 15)<br><!-- End of picture text -->

Table _5_ 9 presents a dtfferent perspective of financial structure (and includes some countnes not shown above), but the implications are the essentially same. Financing source as a percentage of GDP<sup>166</sup> (I998) for the developing countrnes similarly reveals a heavy reliance on bank loans Among these countries Indonesia, Korea, 68
fer to the tax department.<sup>13</sup> A belief that international donors and NGOs have just the right amount of influence, rather than too little influence, corresponds to a percentage point increase of 2.8 in the predicted probability that one will defer to the tax department. Analyses point to a similar effect of domestic civic groups and NGOs on citizens’ willingness to defer to the tax department. The predicted probability that one will defer to the tax department is 2.9 and 4.1 percentage points higher for respondents who believe that international businesses and domestic civic groups and NGOs, respectively, have too much influence, rather than too little influence, over their government.

This finding supports either one of two arguments. Citizens may view the influence donors and non-state actors exert over their government positively. Alternatively, it may be the case that citizens who believe that donors and non-state actors exert “too much influence” want to strengthen the state. By quasi-voluntarily complying with government rules and regulations, citizens are increasing the amount of revenue flowing to the state, thereby increasing the strength of the state.

Next, I examine whether including country-level indicators of the provision of services by donors and non-state actors, including a measure of the number of NGOs and IGOs present in each of the 19 countries, as well as foreign aid flows, food aid, and health aid, improves model 2’s fit. None of the country-level measures are significant at the p _<_ 0.05 level when I include the variables in the full model. The variable indicating the number of NGOs and IGOs per country is positive but only significant at the p _<_ .01 level. Including these country-level variables do not affect the predicted probabilities and of individual-level measures of donors and non-state actors.

To summarize, findings from multilevel logistic regressions suggest that the relationships between government effectiveness, procedural justice and deference to the tax department do not change once I account for citizens’ perceptions of donors and non-state actors. There is little evidence that donors and non-state actors are undermining rather than strengthening citizens’ willingness to defer to the tax department.

13 Alternatively, a belief that international donors and NGOs have too much influence, rather than too little influence, corresponds to a percentage point increase of 4.4 in the predicted probability that one will defer to the tax department.

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# **Figure B26: Natural rubber**

<!-- Start of picture text --> Log scale<br>2.5<br>Log price Permanent component<br>1.5<br>0.5<br>-0.5<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>1.0<br>Medium-term cycle Transitory component<br>0.5<br>0.0<br>-0.5<br>-1.0<br>1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020<br>Source : Authors’ calculations<br><!-- End of picture text --> _— 67 —_
practical policy purposes, such “transitory” growth rates are just as important as long-run growth. In a developing economy with chronic under-supply of infrastructure, transitory growth could conceivably last for decades.

Following this line of argument, infrastructure policies might play a role in explaining East Asia’s relative growth success if East Asia is more effective than other regions in relieving infrastructure constraints as they emerge. A small piece of evidence to this effect may be seen in the results of enterprise surveys (Table 2), which indicate that new connections are provided to firms more quickly and that service interruptions are lest costly in East Asia than in most developing regions.

||**Table 2: Impact**<br>|**of Infrastructure Shor**<br>|**tages on Firms**<br>||
|---|---|---|---|---|
|Region|Electricity<br>connection delay<br>(days)|Value lost to power<br>outages (% of sales)|Water connection<br>delay (days)|Mainline telephone<br>connection delay<br>(days)|
|East Asia & Pacific|21|2.6|18|16|
|Europe & Central Asia|15|3.0|9|16|
|Latin America &<br>Caribbean|34|4.1|35|36|
|Middle East & North<br>Africa|62|4.3|44|49|
|South Asia|49|7.4|29|50|
|Sub-Saharan Africa|38|5.9|42|54|
|OECD|10|2.3|—|9|

Source: The data are derived from World Bank Investment Climate Assessments, and reported at <u>www.enterprisesurveys.org</u> (last visited January 10, 2008) However, this could again reflect causality running the other way round, as economies with stronger growth have readily available resources to address such bottlenecks as they become apparent.

Beyond the neoclassical growth framework, endogenous growth theory envisages instances where an aggregate economy may exhibit increasing returns to scale, notwithstanding the presence of diminishing or constant returns to individual factors.<sup>5</sup> If infrastructure stocks play a role in the realization of these economies of scale, infrastructure policy has a role in determining long-run growth.

An important feature East Asia’s infrastructure history has been the construction of major transport links between cities. Korea’s Seoul-Pusan highway built in the 1960s, Malaysia’s road network built in the 1970s and 1980s, China’s rail network and more recent expressways

> 5 See Aghion and Howitt (1998) and Barro and Sala-i-Martin (2005) for a review of endogenous growth models.

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16 _Table 3: Independent variables_

|VARIABLE|Description|1991<br>|1993<br>|1997<br>|2000<br>||Ally<br>|ears<br>||
|---|---|---|---|---|---|---|---|---|---|
|||Mean|Mean|Mean|Mean|Mean|sd|Min.|Max.|
|AGE01|Average age of household members on Jan.<br>1, 2001|58.5|58.1|55.0|53.2|56.1|14.8|25.0|100.0|
|AGE01_SQ|Average age squared|3,653.2|3,593.7|3,237.3|3,019.2|3,369.5|1,764.6|625.0|10,000.0|
|AGE01_CUB|Average age cubed|242,348.6|235,770.7|202,769.9|181,611.1|215,026.4|167,564.1|15,625.0|1,000,000.0|
|HHSIZE|Number of household members|2.97|4.04|3.78|3.61|3.61|1.44|1.00|12.00|
|HHSIZE_SQ|Number of household members squared|10.41|18.46|16.23|15.07|15.09|12.18|1.00|144.00|
|HHSIZE_CUB|Number of household members cubed|42.43|93.73|77.56|70.88|71.42|93.19|1.00|1,728.00|
|POOR|Per capita household income is among<br>bottom 40% of sample in last period|-|0.40|0.44|0.39|0.41|0.49|0.00|1.00|
|HI|Head of household is covered by either<br>urban or rural health insurance|0.31|0.28|0.27|0.24|0.28|0.45|0.00|1.00|
|URB_HI|Head of household is covered by 'public<br>insurance' or 'workers insurance', or by<br>other type of insurance and resident in<br>urban area|0.26|0.25|0.18|0.17|0.21|0.41|0.00|1.00|
|RUR_HI|Head of household is covered by<br>'cooperative medical insurance' or 'work<br>unit insurance', or by other type of<br>insurance and resident in rural area.|0.05|0.04|0.10|0.07|0.06|0.25|0.00|1.00|

_n = 2,719 (1991), 2,715 (1993), 3,089 (1997), 2,798 (2000)_
Table 2: Summary statistics

|Variable|All|Treatm|Comp|t-stat|nobs|
|---|---|---|---|---|---|
|Pre-treatment||||||
|School-level variables||||||
|Enrollment, Primary grades 1-8 (1)<br>|313.3|331.9|294.6|0.88|50|
|Preschool enrollment (1)|55.0|55.2|54.8|0.05|50|
|Preschool avg participation (1999)|33.2|33.6|32.8|0.16|50|
|No. of children in baseline|205.7|204.9|206.5|-0.08|50|
|Fee charged / pupil / term (2)|163.0|157.0|169.0|-0.44|49|
|Total fees collected, 1 year (3)|5530|5109|5952|-0.69|50|
|Ethno-linguistic fractionalization index (4)|0.24|0.23|0.25|-0.38|50|
|Classroom equipment (as of term 1, 2000)||||||
|Structure Usable in Rain?|0.52|0.60|0.44|1.12|50|
|Blackboard|0.86|0.80|0.92|-1.22|50|
|Storybook(s) in Kiswahili|0.06|0.04|0.08|-0.59|50|
|Storybook(s) in local language|0.66|0.68|0.64|0.29|50|
|Alphabet chart|0.96|1.00|0.92|1.44|50|
|Number chart|0.96|1.00|0.92|1.44|50|
|Teachers||||||
|Number of preschool teachers|1.48|1.40|1.56|-0.92|50|
|Quality of nursery teachers (5)|1.89|1.83|1.96|-0.64|50|
|Experience in years (6)|5.59|5.29|5.87|-0.56|74|
|Education in years (6)|10.55|10.31|10.77|-1.13|74|
|Any training or seminars (6)|0.68|0.66|0.69|-0.30|74|
|2-year ECD training (6)|0.34|0.31|0.36|-0.39|74|
|Baseline children||||||
|Age (6)|3.74|3.76|3.72|0.49|9244|
|Female (6)|0.50|0.51|0.49|2.01|10220|
|Standard of sibling (6)|4.90|4.96|4.83|0.90|8114|
|Female head of household (6)|0.10|0.12|0.08|2.06|10284|
|Enrolled in preschool in February 2000 (6)|0.41|0.41|0.40|0.27|10284|
|Post-treatment||||||
|Nr of partic. children who had breakfast at home|9.32|8.80|9.84|-0.69|521|
|Ratio of partic. children who had breakfast at home|0.22|0.17|0.26|-3.24|518|
|Non-salary expenditures in preschool (7)|217.3|357|185.6|0.78|50|
|Non-salary expend. as % of total fees collected (7)|3.38|5.75|1.32|1.59|43|

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- Hillegeist, S., E. Keating, D. Cram, and K. Lundstedt (2004): "Assessing the probability of bankruptcy," Review of Accounting Studies , 9, 5-34

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minimum wages (rather than average wages) to output per capita. The sample of countries is somewhat smaller than in section 3, because there are fewer data on minimum wages in the data base being assembled at the Research Department of the World Bank. Most of these data were originally collected by US embassies for the US Department of Labor, and refer mainly to the years 1985-88.7 The sample includes now seven countries from the CFA zone.

As in the case of average wages, minimum wages tend to represent a higher multiple of per capita output in poorer countries. More generally, the coefficients multiplying most of the variables considered in Table 6 have the same sign as those estimated in Table 2 for average wages, but they tend to be statistically insignificant.

The most interesting feature of Table 6 is, of course, the positive and statistically significant coefficient associated with the dummy variable for CFA countries. As before, this coefficient does not vary much with the set of explanatory variables included in the regression. Although the coefficients in columns (C) and (D) seem to be 50 percentage points higher than those in columns (A) and (B), this is due to the inclusion of a Sub-Saharan African dummy in the specification. The coefficient on this variable is significantly negative, at around -50 percentage points. Since CFA countries do also belong to the SubSaharan African region, the net gap with other developing countries is still around 100 percentage points, which is roughly the same as in columns (A) and (B).

A few countries (e.g. the UK) do not have a minimum wage policy. The minimum wage is set equal to zero in their case. Strictly speaking, therefore, the dependent variable in Table 6 is censored. However, the number of countries with no minimum wage policy is small enough to justify the use of ordinary least squares in the estimation.

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The model uses data from the input-output (IO) table which provides more details about intermediate demand between sectors and the composition of final demand across sectors. The IO table maps the different intermediate products (notably energy projects) used to produce the goods and services that comprise final demand. By using the technical coefficients from this table (how many units of electricity and coal are needed to produce a unit of transportation) and time series data for final demand and aggregate sectors, we are able to generate pseudo time series for the intermediate demands of each sector (assuming that the technology of production is constant over time).

The technical relationships between elements of final demand and sectors of production that are summarized by the IO table are illustrated in the following diagram, which focuses on a single sector i.

Government Households ii,II ii,CC ii ii,ττ ααii,GGGG αα II αα CC VVAA + αα ττ<sup>KKIIDD</sup> Foreign countries Sector i Other sectors ii,XX ii,IIKKDDSS SS αα XX αα IICCTT ii,MM ii,IIKKD DD αα MM αα IICCTT Effectively, one unit of demand for exports will imply units of final demand for the output of sector I, which in turn would generate units of intermediate demand for the output of each sector j. In ii,XX αα this way each component of expenditure GDP is shared out to individual sectors. E.g. the exports of sector ii,XX jj,ii are written as the share of total exports, given by αα ∗ αα . It is important that = 1 , such that all of exports are shared out. Imports are given by . The intermediate goods supplied to and demanded from ii,XX ii ii,XX ii the other sectors is denoted as and , respectively. In addition, sector αα ∑αα supplies government MM consumption , investment goods and consumption goods . Value added ( ) and indirect taxes ( ) SS DD IICCTT IICCTT ii correspond to the value of capital and labor provided by households (or equal to GDP from the income GG II CC VVAA ττ<sup>KKIIDD</sup> side, although without capital and labor data, respectively). The equations for total source (on the left of the equation) and total use (on the right of the equation) with the equilibrium condition of Supply = Demand imposed can be written as, . For the purposes of this model there is no need to include all the intermediate demands generated for ii ii,ττ ii,MM ii,IIKKD DD ii,CC ii,II ii,GG ii,XX ii,IIKKDDSS SS VVAA + αα ττ<sup>KKIIDD</sup> + αα MM+ αα IICCTT = αα CC+ αα II+ αα GG+ αα XX+ αα IICCTT each final demand (the number of variables expands by the number of sectors and for each price, quantity and value equivalents). Instead we focus on the intermediate demands for energy from each sector but lump together all other intermediate demands. As is customary in CGE models, we assume a Leontief technology (fixed ratio) for intermediate demands, which implies that the share of intermediate supply to aggregate supply is constant over the policy and forecast horizons. We define these ratios as: 21
Between 2007 and 2009, the Marriage Transitions in Malawi (MTM) project collected innovative, longitudinal data from a random sample of nearly 1,200 initially never-married women and men in the Central Region of Malawi, ranging in age from 14-20 for females and 1725 for males. The study was designed to understand the links between pre-marital relationships and sexual activity, the transition into marriage, socioeconomic status, and HIV/AIDS.

Respondents provided detailed information on socio-economic characteristics, marriage and fertility, and sexual partnering. Two particular features of this longitudinal data set stand out. First, respondents were interviewed at short intervals, up to five times within a 24-month window. Most panel studies in Sub-Saharan Africa conduct survey rounds at a minimum of yearly intervals (and often longer), which necessitates a reliance upon retrospective reporting of events, and may bias estimates due to recall error (such as on dating and marriage).<sup>3</sup> Second – and most relevant for this study – respondents who left the sample, due to relocation outside of the study site, were tracked by the research team. Such tracking allows us to identify the socioeconomic characteristics and demographic behaviors of mobile respondents.

There are multiple ways to define migration. Using the MTM data to explore the socioeconomic dimensions of migration, we take several approaches to measuring migration. In the most general approach, we look at the distance in kilometers (based on GPS data) between the baseline (summer 2007) and follow-up (summer 2009) residence. That is, we compare the location of physical dwelling in which our sample of young adults resides using objective measures of location (rather than self-reported distances from baseline). A second approach is to look at moves out across administrative/economic boundaries, such as villages, Traditional

> 3 The issue of recall bias in reporting events is also relevant for migration data. For example, lacking panel data which follows movers over time, studies may need to use life history calendars to collect information on moves, the timing of moves, and the reason for the move. This is the approach of Reed, Andrzejewski, and White (2010) in their study of how migration links with education, employment, marital status, and childbearing in Ghana.

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fish, crops, livestock, and handicrafts, the value of own-produced or own-captured food consumed by the household, and other income from investments, pensions, rentals, etc. Household expenditures are recalled over the previous week, month or six months, depending on the particular item and an estimate of household consumption is formed from the sum of cash expenditures and the value of own-produced or own-captured food consumed by the household. All of the income and expenditure components are adjusted to an annual basis.

We use poverty standards that are based on existing poverty lines set for Samoa from the 2002 Household Income and Expenditure Survey. The higher “basic needs” poverty line had a value of ST$37.49 per adult equivalent per week and 20.3 percent of households in Samoa had consumption expenditures below this level in 2002. The food poverty line, which was the required expenditure just for a minimum diet providing 2,200 kilocalories per adult per day, was calculated as ST$24.68 per week in 2002 (with 7.6 percent of households below this line). We use the Samoa CPI to update these poverty lines to October 2008 annual values of ST$2,962 and ST$1,850 which is equivalent to US$ $1,007 and $663 per adult equivalent per year.<sup>7</sup> In addition to these two objective poverty standards, the SLMS also asked the principal applicant in each ballot loser household and the adult aged 18-45 with the next birthday in ballot winner households about subjective poverty, using a 10-rung Cantril ladder question: “Please imagine a 10-step ladder where on the bottom, the first step, stand the poorest people and on the highest step, the tenth, stand the richest. On which step are you today?

# **3.4 Estimating the Impact of Migration Through the Samoan Quota**

The Samoan Quota enables us to overcome the triple- and quadruple-selectivity issues more credibly than existing studies. Consider first the problem of estimating the overall average impact of migration on household income per capita in Samoa, as in equation (3) above. The random selection among SQ applicants provides a means of overcoming the first selectivity issue – that households self-select into migration. Households self-select into whether or not a member applies for the SQ, but among these applicants, whether or not they can migrate is random. The SQ policy rules provide a means for overcoming the second source of selectivity. The rules specify which individuals can migrate with a Principal Applicant – the spouse and dependent

> 7 The average exchange rate during the period of the survey was 2.94 Samoan Tala per US Dollar. In the absence of any nutritional-based adult-equivalence scales we assume that children count as 0.5 of an adult.

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# Figure 11.  Country correlations for CE and level of bureaucracy quality

<!-- Start of picture text --> Low bureaucracy quality High bureaucracy quality<br>1.0<br>0.8<br>0.6<br>0.4<br>0.2<br>0.0<br>-0.2<br>Low bureaucracy quality = 0.47***<br>High bureaucracy quality = 0.33***<br>-0.4<br>-0.6<br>-0.8<br>Azerbaijan Yemen Côte d'Ivoire Ukraine<br>Oman Congo, Rep. Nicaragua Ecuador Uruguay Guyana Liberia Argentina<br>Brazil Namibia Niger Bolivia Kenya Zimbabwe Angola Qatar<br>Sudan Senegal Mali Burkina Faso<br>Ghana Saudi Arabia Mozambique The Gambia Colombia Tanzania Libya Gabon Iceland Ethiopia Mongolia<br>Algeria Uzbekistan Myanmar South Africa Paraguay<br>Iran, Rep. of Madagascar<br>Uganda Cameroon Indonesia Honduras Suriname Togo<br>Russian Federation Papua New Guinea<br>Bahrain Congo, Dem. Rep.<br>Nigeria Peru Iraq<br>Sierra Leone Kazakhstan Guinea-Bissau Costa Rica Botswana Armenia<br>Guinea<br>Chile<br>Zambia<br>Kuwait<br>New Zealand<br>Norway Malawi<br>Australia Guatemala<br>Brunei Darussalam United Arab Emirates<br>Canada<br>government spending and real GDP 1980-2020<br>Correlation between cyclical components of real<br><!-- End of picture text --> Notes: Each bar represents the correlation between the (HP) cyclical components of real GDP and real government spending for countries with high (low) bureaucracy quality, defined as bureaucracy quality above (below) the sample median. The difference between the two averages is not significant at the 10 percent level (p-value = 0.16). *, **, and *** denote significance at the 10, 5, and 1 percent level, respectively. Sources: Authors’ calculations based on International Country Risk Guide and WEO (IMF) data.