Trends in Household Portfolio Composition

We use data from the Survey of Consumer Finances (SCF) to explore how household asset portfolios in the United States have evolved from 1989 to 2016. Throughout this period, two key assets—housing and financial market assets—have driven the aggregate household balance sheet evolution. However, aggregates mask great heterogeneity in balance sheet composition across the wealth distribution, and most families hold a relatively small share of assets in financial markets and larger shares in housing and other nonfinancial assets. We also describe the typical life cycle asset accumulation processes among low, middle, and high-income families which—though varying dramatically by level—are quite similar. Finally, we use household balance sheets to describe how financial vulnerability has changed over time.

Introduction
Understanding the composition and distribution of household balance sheets-and how they have evolved over time-is important for a host of economic policies. The allocation of assets and debts will influence household exposure to unexpected economic shocks (Mian, Rao, and Sufi, 2013) and the preparedness of the current workforce for future retirement (Henriques, Jacobs, Llanes, Moore, and Thompson, 2018), and portfolio composition has implications for the transmission of monetary and fiscal policy (Mian, Rao, and Sufi, 2013;Benmelech, Miesenzahl, and Ramcharan, 2017;and Poterba and Samwick, 2003), and can influence the future path of wealth and income inequality (Piketty, 2012;Stiglitz, 2012).
In this chapter we use data from the Federal Reserve Board's Survey of Consumer Finances (SCF) to explore how household asset portfolios in the United States have evolved from 1989 to 2016. Overall, household assets have grown from around $25 trillion in 1989 to just above $100 trillion in 2016. Because the aggregated wealth and asset data in the SCF are consistent with the Financial Accounts of the United States (FA), the National Income and Product Accounts (NIPA), and other macroeconomic data sources (Dettling, Devlin-Foltz, Krimmel, Pack, and Thompson, 2015), we can use the SCF to better understand the distribution of aggregate U.S. household assets. 1 Throughout this period, two key assets-housing and financial market assets-have driven the household balance sheet evolution. The relative importance of housing and financial market assets has fluctuated over time. From 1989From to 2001  However, we also describe a great heterogeneity in household balance sheets that is overlooked in averages and aggregates. For example, in 2016, housing makes up about 30 percent of the average household asset portfolio and financial market assets make up about 40 percent. However, most families hold a relatively small share of assets in financial markets, and instead hold primarily housing assets. Families near the median, for example, hold about 60 percent of assets in housing and only 20 percent in financial markets. The asset portfolio of the average household most closely resembles families near the 90th percentile of assets.
We also observe that ownership of assets has become more concentrated over time.
Increased concentration is shown within the distribution of assets, the distribution of income, and also by age. Since the processes that create wealth for a household unfold over time, the relationship between assets and age can be complicated. We explore the trends in asset levels and portfolio composition over the lifecycle later in this chapter using a birth-year/income-group cohort analysis of the SCF.
We observe the typical life-cycle asset accumulation processes among low, middle, and high-income families. Though the levels are different magnitudes, assets generally increase throughout the typical working life in each income group, and peak just before the typical retirement age. Afterwards, asset levels de-cumulate, but only partly. We also observe movement out of "risky" assets (asset classes especially exposed to asset price shocks), particularly business and equities, as households transition into retirement. Homeownership remains high up through the early eighties -the last point where we observe the oldest cohorts. The housing share of total assets actually rises in retirement as families remain in their homes and start to consume out of their other assets.
Finally, we focus on quantifying financial vulnerabilities in family balance sheets. We first show that families with a combination of two potential vulnerabilities-high debt payments and high loan-to-value ratio on their primary residence-peaked in the late 2000s, and are at the lowest level in 2016 since the early 1990s. We also show that nearly all of the time series variation is due to middle income families, who also hold most of their assets in housing and are often the most highly-levered in the housing market.

II. SCF data and comparison to aggregates
The SCF is a cross-section survey, conducted every three years by NORC on behalf of the Federal Reserve Board (FRB) and with the cooperation of the Department of Treasury (SOI). 2 The SCF provides the most comprehensive and highest quality survey microdata available on U.S. household wealth. SCF families respond to questions about financial and nonfinancial assets, debts, employment, income, and household demographics.
The concentrated nature of wealth means that a random sample of U.S. families is unlikely to capture the small minority of families that hold the large majority of wealth. Wealthy families are also less likely to participate in a survey, meaning that a random sample of families may incorrectly estimate the mean and variance of wealth. The SCF provides unbiased and efficient estimates of the distribution of household wealth by using a dual-frame sample design, whereby a nationally representative set of families (the area probability or "AP sample")-selected from an address-based frame-is supplemented with an oversample of wealthy families selected from administrative records derived from income tax returns (the "list sample").
The AP sample provides a nationally-representative sample of families. 3    for results from the most recent triennial SCF. 3 See Tourangeau, et al. (1993), andO'Muircheartaigh et al. (2002) for more information on the AP sample selected by staff at NORC at the University of Chicago. 4 The only official wealth record that exists in the U.S. comes from an estate tax. This tax applied at death and only to families with estates greater than $5 million (since the mid-2000s). 5 The INSOLE file consists of a sample of individuals and sole proprietorship tax filings from the IRS administrative tax data, statistically edited for quality by SOI. Tax filings with unique income are oversampled and many highincome records in the INSOLE file are sampled with certainty (Statistics of Income, 2012). The INSOLE file is a sample of the IRS administrative tax data, so the LS is a sample from a sample. No correction for this is made during the LS sampling procedure, though, because the certainty sample and rare incomes found in the INSOLE file are a near certainty sample of the LS target population (Kennickell and Woodburn, 1999). The unit of observation in sample of tax filings from the IRS administrative tax data, statistically edited for quality by SOI.
In the list sampling process, wealth is inferred from these income records through two models that relate wealth to income. 6 The SCF not only oversamples wealthy families but also ensures adequate response rates through careful field work (Kennickell, 2007). Response rates for list sample families range from about 35 percent (for families near the 99 th percentile percent of the wealth distribution) to about 10 percent for the very wealthiest families (the 99.9 th percentile). These response rates are constructed before the field period begins, and should not be taken as evidence that differential non-response biases the upper end estimates in the SCF. In fact, high-end families that respond to the SCF are observably similar to those that do not respond .
The SCF AP and list families are woven together by a set of sample weights Woodburn, 1999 andKennickell, 1999). In each sample, the base sampling weight is adjusted to account for non-response, population targets, and the strengths of each sample. Overall, the sampling and weighting mechanisms serve to select a set of high-wealth tax filers and fit those families into a set of US families.

Wealth measurement: Comparing the SCF to Macro Aggregates
A test of the success of the SCF sample design is a comparison of aggregate assets and debts measured in the SCF to those measured in other data sources used to track the macro-economyincluding the Financial Accounts of the United States (FA). There are some conceptual measurement differences between the SCF and the other sources, though, which make a direct comparison inappropriate. However, once these concepts are put into comparable terms the SCF and the other data sources produce very similar aggregate measures (Henriques andHsu, 2013 andDettling et al, 2015).
the INSOLE data is a tax unit while the SCF unit of observation is a family. In practice, there are millions more tax units than families because several members of a family can file distinct tax returns; without a correction, these multi-filer families would have a disproportionately large chance of being selected. To account for this in the SCF LS sampling process, the INSOLE sampling weight of tax units that filed "married filing separately" is divided in half. Further, all filers below the age of 18 are dropped (a family headed by someone less than age 18 is ineligible for the SCF). Still, to a certain extent, the discrepancy between tax units and families remains in the adjusted INSOLE sampling frame. 6 See Bricker, Henriques and Moore (2017) for more detailed information on SCF sampling.
Notably, the SCF asset measure used in the paper does not measure the family's implied asset claims for defined benefit (DB) pensions, while national wealth measured in the FA does include DB pension wealth. After adjusting the FA measure to exclude DB pension wealth, the level and trend in aggregate total financial assets in the SCF is similar to that from the Financial Accounts ( Figure 1). Various other studies have added DB pension wealth to the SCF measure of wealth; those studies find that DB pension wealth is claimed by families in the upper-middle part of the wealth distribution (Devlin-Foltz, Henriques, Jacobs, Llanes, Moore, and Thompson, 2018;. The primary differences across the two sources are that the SCF find greater business assets and housing values (Figure 1).  (Gallin, Molloy, Nielsen, Smith and Sommer, 2018). This shows that aggregate asset valuations can potentially differ considerably across data sources.

The SCF and other household finance research
As more attention has been paid to household finance, the SCF is often the source for household balance sheet information in the United States (Campbell, 2006). The SCF has been used to examine the impact of the Great Recession on the balance sheet of young people (Dettling and Hsu, 2014), mortgage borrowing leading up to the Great Recession (Foote, Lowenstein, and Willen, 2016), entrepreneurship and asset diversification (Moskowitz and Vissing-Jorgensen, 2002;Cagetti and Di Nardi, 2006), the marginal propensity to consume (Carroll, Slacalek andWhite, 2017), precautionary saving (Cagetti, 2003), and the equity participation puzzle (Bertaut and Starr-McCluer, 2001;Haliassos and Bertaut, 1995), among other issues.

III. Composition of Average Household Portfolios
Households can choose to hold two main types of assets: those financial in nature-such as those held at depository institutions and brokerages-or non-financial, such as housing and businesses. Throughout the 1989-2016 sample period, nearly all families owned assets of some kind ( Table 1). Early in the sample period ownership rates were about 90 percent for each main type; by the end of the period, financial assets are more widely-held by families than nonfinancial assets. Throughout this section, we use averages to describe how household portfolios have changed between the 1989 and 2016 sample period.
Mean asset holdings in the early (1989)(1990)(1991)(1992)(1993)(1994)(1995) part of the sample period were about $375,000 thousand (in $2016) ( Table 2). In total, financial assets were about 30 percent of the aggregate household balance sheet at that time (described in the gray striped areas of Figure 2), and nonfinancial assets are about 60 percent of the total (the solid gray areas of Figure 1). The remaining "other" assets-such as vehicles, cash-balance life insurance, and savings bonds-are about 10 percent of the total. Average debt levels were about $52,000, and about three-quarters of total debt owed was in the form of mortgage debt. Though the balances are smaller, credit card debt and vehicle debt were widely-owed by families at the time, too.
Mean real assets grew by 50 percent from the early sample period to 1998-2001. The growth was led by financial assets, which grew to be almost 40 percent of assets (Figure 2), up from about 30 percent in the previous period. Financial asset growth was led by directly-held financial market assets (such as stocks, bonds, and mutual funds) and indirectly-held financial market assets held for retirement (such as IRAs and 401(k)-type pension plans). 7 The growth in directly-held and indirectly-held financial market assets occurred at both the intensive and extensive margins, and for voluntary and involuntary reasons. At the intensive margin, the mean value of financial market and retirement accounts more than doubled from the earlier period (Table 2), reflecting the growth of in the value of publicly-traded equities. At the extensive margin, the share of families that owned equity-whether held directly or through a retirement account-grew from about 35 percent to 50 percent ( Table 1). Part of the growth at the extensive margin was due to families entering the financial markets through employersponsored defined contribution (DC) pension plans, such as 401(k)s. DC pension plans gained in popularity during the 1990s, and employee coverage by DC pension plans overtook coverage by 7 The other group of financial assets are bank-type assets (such as checking and saving accounts, and CDs). The mean value of housing assets also increased in the 1998-2001 period (Table 1), as homeownership rates and mean housing values both increased ( Along with the increase in housing assets was an increase in mortgage debt (Tables 1 and   2). Relative to the 2001-2004 period, about 10 percent more families owed mortgage debt-48 percent of families now held a mortgage-and the real value of average mortgage debt increased by 50 percent-from $52,000 to $80,000. The increase in mortgage debt was partly due to new entrants in the housing market, and also to repeat buyers, and cash-out refinancing. Other debts, though, were still widely held at the time, especially through credit cards and vehicle loans.
During the 2004-2007 period, the average value of financial market and retirement assets remained near their 1998-2001 levels, but shrank as a share of the balance sheet. The share of families directly holding financial market assets fell, but the share of families holding any type of equity remained at around 50 percent (Table 1).

IV. Household portfolios across the asset distribution
Though the average household asset portfolio is mixed between housing and financial market assets, the main asset for most families is housing, with financial market assets representing a relatively small part of the portfolio. In fact, because asset holdings are highly Electronic copy available at: https://ssrn.com/abstract=3473055 concentrated at the top of the asset distribution, much of the change in the aggregate portfolio is driven by asset changes in the top 10 percent of the asset distribution. Figure 3 shows the asset composition of households at various points in the asset distribution and the average asset composition in 2016. The most noticeable feature is how much asset portfolios vary across the distribution. Housing is the most common asset for most families, while families in the top 1 percent of the asset distribution hold mostly private equity in businesses and financial market assets. 9 The main assets of families in the bottom 25 percent of the asset distribution are "other" assets, which include vehicles, savings bonds, cash value life insurance, and other miscellaneous assets. Transaction accounts, such as checking and savings accounts, are the next most common assets, and only about 10 percent of the portfolio is in housing and retirement assets. The debt to asset ratio of these families is more than 100 percent, and the mean asset value is about nine thousand ( Table 3). There are families of all ages in this group, but the average age is 43, which is seven years younger than the average age of household heads in 2016. In the next section we consider age and cohort may be influencing these results, but for now it is left untreated.
The asset portfolio of families near the median-in the 25 th to 50 th percentiles or the 50 th to 60 th percentiles-is heavily skewed toward housing. 10 About 60 percent of the asset portfolio of these households is housing, compared to 30 percent of the average household portfolio. The portfolio of households near the median is also less composed of financial market and retirement assets, which make up less than 15 percent of assets, while they are about 35 percent of assets in the average portfolio. The average value of assets in the 25 th to 50 th percentiles is about 97 thousand and is about 228 thousand in the 50 th to 60 th percentiles ( Table 3).
The portfolio composition of families in the 80 th to 90 th percentiles, and 90 th to 99 th percentiles is similar to the average portfolio, underscoring how different the aggregate asset portfolio is from most families' asset portfolios. However, the portfolio composition of the top 1 9 The composition of assets across the U.S. asset distribution is remarkably similar to that found in Sweden (Bach, Calvet, and Sodini, 2016), where the wealthiest families are invested in private equity and families from the 50 th to 95 th percentiles are mainly invested in housing. In Sweden, as in the U.S., leverage ratios also decline as assets increase. 10 The asymmetry of the groups we use to show portfolios across the distribution is largely a function of the concentration of assets in the right tail of the distribution. percent is dominated by business and financial market assets, highlighting the concentration of these assets at the top the distribution.
The composition of assets across the distribution in 1989 is fairly similar to that at the end of the sample in 2016 (bottom panel of Figure 3). The most common asset for families in the bottom 25 percent, for example, is "other" assets in both years, the most common asset for most families is housing in both years, and the most common assets for the top 1 percent are privatelyheld businesses and financial market assets. However, we can see the rise of retirement account assets across the distribution (in the light gray striped bars), and in financial market assets (in the medium gray striped bars). The leverage ratio is typically higher in 2016 for all percentile groups below the 80 th percentile, underscoring the increase in debt observed for most families. Second, leverage ratios are lowest at the top of the asset distribution and rise steadily moving down the asset distribution. Increases in the leverage ratio are incremental over the top three-quarters of the asset distribution, and rise sharply for the bottom 25 percent. In 2016, for example, the leverage ratio for the 90 to 99 th percentiles was less than 0.10, but was about 0.45 for the 50 to 60 th percentiles. Families in the middle percentiles hold mostly housing assets, which is typically a highly levered asset.
Because the main asset of families in the middle percentiles is housing, and housing is typically financed by debt, we re-ran Figure 3 and included home equity-the asset value of the home minus the outstanding debt on the home-in place of the asset value of the house (not shown). Using this measure, housing occupies a smaller share of the asset bundle for families in each asset percentile, and where average leverage ratios are highest there is typically a greater reduction in share. But the qualitative takeaways from Figure 3 remain unchanged, and housing continues to be the dominant asset for most families, especially those in the 25 th to 80 th percentiles.

Across time
Looking at bookended years can obscure the changes in the distribution across the whole 1989-2016 sample. Figures 4 and 5 shows these changes across survey years by plotting the share of assets in two key assets-housing and financial markets-for families near the median (the 25 th to 50 th percentiles or the 50 th to 60 th percentiles), near the 75 th percentile (the 70 th to 80 th percentiles), the 90 th to 99 th percentiles, and the average.
The most noticeable feature of figure 4 is that for families in the 80 th percentile and below (represented by the 25 th to 50 th , the 50 th to 60 th , and 70 th to 80 th percentiles), housing has always been the key asset, usually ranging from 55 to 65 percent of assets. However, each group shows the same cyclical trend as the average, as housing became less important in the late 1990s, more important in the mid-2000s, and then less important again by 2016.
The share of assets in financial and retirement assets ( Figure 5) has slowly trended up for families in the 25 th to 50 th and 50 th to 60 th percentiles, but has remained relatively low-between 10 to 15 percent of assets-from 1998 to 2016. Families in the 70 th to 80 th percentiles have a higher share of financial and retirement assets-around 20 percent-since 1998, though the share has ticked up to about 25 percent by the end of the sample.
The share of assets in housing and the share in financial and retirement assets for the families in the 90 th to 99 th percentiles almost perfectly tracks the average share of assets in housing over time, both in level and in trend.
An important factor in the accumulation of wealth (distinct from net savings and inheritances) over time is accrual of capital gains. The longer assets are held, and the greater the asset price increases over the period of ownership, the larger will be the gains in wealth. For several major asset classes (real estate, directly-held businesses, and equities) the SCF includes information that allows us to measure unrealized capital gains. Unrealized gains account for an important share of total assets, particularly for wealthier families. Between 1989 and 2007, unrealized gains accounted for 25 to 35 percent of total assets for the 90 to 99 th percentiles ( Figure 6). Over this same period, unrealized gains were about one-third of total assets for households around the middle of the asset distribution. In

V. Asset concentration
During our 1989-2016 sample period, there has been a well-known increase in wealth concentration, documented by the rising share of wealth held by the "top 1 percent" of families in the SCF (Bricker, Henriques, Krimmel, and Sabelhaus, 2016), in capitalized income tax data (Saez and Zucman, 2016;Bricker, Henriques, and Hansen, 2018), and in a combination of the SCF and other wealth surveys (Fisher, Johnson, Smeeding, and Thompson, 2018). The focus of this chapter is household assets while the focus of these papers is wealth-the difference between household assets and debts.
As shown in Figure 7, though, asset concentration has increased along with wealth concentration during our sample period. Electronic copy available at: https://ssrn.com/abstract=3473055 The asset concentration plateau from around 1995 to 2010 is generally consistent with wealth concentration estimates from the SCF Kuhn Schularick, and Steins, 2018) and from estate tax data (Kopczuk and Saez, 2004;Saez and Zucman, 2016). The SCF also shows a notable increase in wealth concentration from 2010 to 2016; there are no recent estate tax data estimates, though. These wealth concentration estimates stand in contrast to those first published from capitalized income tax data (Saez and Zucman, 2016), where wealth concentration estimates rise fairly steadily from 1989 to 2013, though tweaking the capitalization model assumptions generally reproduces the SCF and estate-tax wealth concentration trend (Bricker, Henriques, and Hansen, 2018).
In each survey year, the share of assets held by the top 1 percent-ranked by assets-are smaller than the corresponding share of wealth held by the wealthiest top 1 percent, usually by about 3 to 5 percentage points (not shown, see . Leverage tends to increase as assets decrease (Figure 3), which reflects the leading role of housing in the asset bundle held by families outside of the top groups and which serves to equilibrate asset concentration relative to wealth concentration.
The asset share for the top 10 percent is similar to the top 1 percent-described abovewhich started rising slowly in the 1990s, and then somewhat more rapidly in the 2010s, such that the top ten percent held 70 percent of total assets by 2016.
Concentration of assets and wealth has also occurred along other dimensions, including by income and by age. Higher-income families' share of total assets has risen, as has the share of assets held by older families. For example, young families (with a head under age 45) now hold only 13 percent of total assets, down from about 30 percent in 1989 (Figure 8). The share of assets held by older families (head age 55 or older) rose from nearly 50 percent in 1989 to 70 percent in 2016.
Some of this shift in the distribution, certainly by age but also by income and asset group, are a result of the general trend toward an aging population that we have experienced over the last three decades. Assets are accumulated and wealth is built over time, and the life-cycle processes that influence household-level decisions could have important implications for the economy-wide levels, composition, and distribution of assets.

VI. Cohorts
Life cycle processes are important to understanding the evolution of wealth and its composition over time. The balance of debt and assets change over the life cycle as does the composition of assets. Young people take on debt, first, as they build their human capital, and then as they take ownership of a primary residence. Accordingly, residential real estate dominates the balance sheets of young families. Progressing through life, families accumulate wealth that they will ultimately draw upon in retirement and potentially pass on to heirs in the form of bequests. 12 Financial assets, including directly held equities and employment-related pension plans, become increasingly prominent over time. While these processes play out across individual households, as society ages they also influence overall composition of wealth.
One thing we seek to understand in this section is the ways in which the stylized life-cycle processes described above are different for different parts of the distribution. Since wealth is so highly concentrated, the life-cycle patterns observed at mean of the data are driven primarily by wealth dynamics of households at the top of the distribution. We are interested to see if households at the bottom or the middle of the distribution exhibit the same broad life-cycle patterns exhibited in the aggregate data. In addition, we would like to know how the financial crisis affected life-cycle patterns across the wealth distribution and for different birth cohorts.
The composition of wealth also differs dramatically across the distributions on wealth and income. Businesses and directly-held financial assets account for a substantial portion of assets for families at the top, but only a minor share at the middle of the distribution, and are almost completely absent at the bottom. Housing dominates the portfolios of households at the middle of the distribution, and illiquid assets-primarily vehicles-account for the lion's share at the bottom. Since the portfolios of low, middle, and high-wealth families differ substantially, the long-term trends toward rising concentration in the distribution of wealth might also be influencing the overall composition of wealth. Part of the cohort analysis will explore how these different components of wealth evolve over the life-cycle for households at different part of the 12 Households tend to not exhaust their wealth, with substantial numbers leaving inheritances for future generations. Past research has concluded that the bequest motive is powerful, and accounts for one-third to as much as one-half of all savings (Davies and Shorrocks, 2000;Kopzcuk and Lupton, 2007). Whether it is best to view bequests as a distinct motive for saving that is separate from the life-cycle model (Kotlikoff and Summers, 1981) one that can be incorporated into the into the standard model through extension is beyond the scope of this chapter. As a practical matter, we follow Dynan, Skinner, and Zeldes (2002) and think of the motivation for saving at older ages (precautionary vs. bequest) as potentially impossible to disentangle. The focus of this chapter is less about the precise motive for saving as it is using the life-cycle process as a way to separate age effects from wealth trends.
wealth distribution, and examine whether recent cohorts are following similar patterns compared to previous cohorts.
Household saving decisions are, of course, not solely motivated by life-cycle considerations.
The need to smooth consumption against adverse shocks -through precautionary savings -is ubiquitous. Previous research using the SCF indicates that precautionary savings accounts for a relatively small but important part of total savings, particularly among younger households and business owners (Cagetti, 2003;Hurst, Lusardi, Kennickell, and Torralba, 2010).
In this section we explore the joint influence of these factors -age and distribution -on trends in wealth and the composition of wealth using a cohort analysis of the SCF. We identify cohorts based on ten-year bins of birth-year; the earliest of the six cohorts analyzed was born The SCF asks households not just about wealth and income, but also about the income that households "usually" receive in a "normal year." Normal income is viewed as a proxy for permanent income, and has been shown in previous research to be a stable classifier that smooths transitory shocks and ranks households by the resources that are typically available to them (Ackerman and Sabelhaus, 2012). Since the questions about normal income have only been included in the SCF since 1995, the analysis in this section is limited to the eight cross-sections between 1995 and 2016.

Interpreting the Cohort Figures
Below we examine trends in a select group of wealth indicators by birth-year & income group cohorts. First we explore trends in median cohort assets, then the share that own homes, equities, and business, the share holding a mortgage on their primary residence, and finally the share of total assets accounted for by houses, equities, and businesses. Each of the nine figures in this section uses the same format, which we will discuss in detail before introducing the first cohort figure in order to aid interpretation. The numbers on the horizontal axis represent the lower-bound of the 10-year age range for the birth cohorts when they appear in an SCF survey year. When multiple birth-year cohorts have adjacent lower-bound ages represented in the data, we show the mean of the lower-bound age.
For example, the first time that we see the most recent cohort (born 1974 to 83) represented in the data is 2001, when the youngest member of this cohort is 18. The 1974-83 cohort is the only one that we see at that age. Three years later, the youngest age from that birth cohort is 21. The first time we observe the second-youngest birth cohort (born 1964 to 73) is in 1994, when the lower bound of its age range is 22. We only observe these two cohorts around that age, and represent the lower bound age on the horizontal axis with 22 (the rounded value of 21.5). Figure 9 shows trends in the log(10) of median assets across the life-cycle -adjusted for inflation using 2016 dollars -for eighteen birth-year/income-group cohorts using SCF data from 1995 to 2016. For each normal income group we observe the anticipated growth in assets as the cohorts approach retirement age. Total assets peak for each income-group when the mid-point of the age range is at or slightly above 60. 13

Median Household Assets
13 One issue that could affect the cohort analysis is the role of intergenerational transfers in asset accumulation, as younger households may be implicitly accumulating assets via their parents. Any transfers already received by household are included in their assets, but expected transfers are not included. Modeling the expected transfers is Several differences between the high and low-income cohorts stand out. First are the substantial differences in wealth across the entire life cycle. Peak median assets -reached between the ages of 54 to 63 for the cohort born between 1944 and 1953 -were $6 million for the top-income group, $698,000 for the next 45 percent, and $170,000 for the bottom-half of the normal income distribution. Similarly, wide differences across the normal income groups are present at the younger ages and in retirement years.
Another notable difference is the relative performance of successive "generations" of cohorts and how they were affected by the financial crisis of 2008-09. The period when the financial crisis occurs can be identified easily in Figure 9, Evaluated at the same age levels, we see that more recent cohorts in the high-income group had higher median assets than the previous cohort in the period leading up to the financial crisis.
For example, for the two successive birth-year cohorts born between 1944-53 and 1954-63, we observe them at the same ages five different times. In the survey years preceding the financial crisis, the median assets of the younger cohort were substantially higher. Following the crisis median assets are approximately the same across successive cohorts.
Prior to the financial crisis lower-income cohorts appeared to have very similar median assets as previous cohorts at the same age. Since the crisis more recent cohorts have fallen behind the previous generation. For example, median assets over the ages of 44 to 53 were $130,000 for the cohort born in 1954-63, slightly larger than the median of $114,000 for the one born in 1944-53. Following the financial crisis, we see the 1954-63 cohort at ages 50 to 59 when their median assets had fallen to $70,400, compared to median assets of $141,000 for the previous cohort at the same ages.
beyond the scope of this chapter, see Feiveson and Sabelhaus, 2018 for evidence on the role of intergenerational transfers from the SCF.
In the "next 45" percent of the distribution more recent cohorts consistently had higher median assets than previous cohorts in the years leading up to the financial crisis. Post-crisis we see that more recent cohorts have lower median assets than previous cohorts.

Ownership of Risky Assets: Business, Equity, and Housing
There are a number of different ways to think about the risk -the potential for losing valueassociated with different assets. Very few -if any -assets are truly free of any risk. In an economy where inflation has been low and stable for decades, bonds seem like a relatively safe asset, but that assumption of relatively low-risk is dependent on inflation. The most dramatic form of risk, and the one that has dominated recent financial crises is sharp, unexpected declines in asset prices. Using this concept of risk -exposure to sharp declines in asset prices -we identify three risky assets -businesses, equity, and housing -and explore how exposure to these assets varies across income group and over the life cycle.

Business ownership
A substantial majority of high-income households have business assets, including both directly and passively run businesses (Figure 10). Business assets consist of privately-held active or passive businesses, such as sole proprietorships, partnerships, limited-liability companies, subchapter S and C corporations. 14 Business ownership is much less common among the next 45 percent, and quite rare among those in the bottom half of income. We consider business assets as risky assets due to exposure of businesses to competitive market forces, such as raw material input prices, labor costs, and output prices.
For all three income groups we see business ownership peaking in the 40s and 50s, and then declining markedly in the 60s. We see the pattern most clearly at top-incomes. In their twenties and early thirties, roughly one-third of top five percent income families have any business assets.
This share climbs sharply with age, such that by the time high income cohorts are in their forties and fifties nearly seventy percent own businesses. As they head into retirement, high-income business ownership falls off considerably, dropping back to about one-third by the time they are in their seventies. All three income groups follow a similar arc over the life-cycle, with rates of 14 Note that business assets are measured as the equity value of the business.
business ownership in retirement returning to levels that earlier cohorts from the same income group had exhibited in their twenties.
For families in the top-half of the income distribution, we see that business ownership is less common among the 1944-53 and 1934-43 cohorts than those that followed. This is particularly evident in the top five percent, where business ownership in the 1944-53 cohort is greater than that in the 1934-43 cohort at every comparable age. Likewise, business ownership among the 1934-43 cohort consistently outstrips that of the cohort born in 1924-33. For the most recent cohorts, and in the bottom-half of income in general, there is no general movement toward or away from business ownership.

Equity Ownership
Ownership of equity is considerably more common than businesses for all income groups ( Figure 11). Equity includes both direct holdings of stocks and stocks in pooled investment funds and indirect holdings in retirement accounts (401k, IRA and other defined contribution accounts) and other managed assets (trusts, annuities and managed investment accounts). We consider equities risky assets due to exposure to price volatility in equity markets. By age 30, equity ownership is nearly universal among top-income households. Equity holding falls off only slightly as high-income families enter retirement age.
Large majorities of families in the next 45 percent also hold equities in one form or another.
For most cohorts and ages the rate of equity ownership among the middle-income group only lags that of the top-income group by ten to fifteen percentage points. A non-trivial share of families in the bottom half of income also own equities; the share with equities peaks at just over forty percent when household heads are in their forties. The decline in equity ownership is more pronounced among the low and middle-income groups than for those with top-incomes. As they enter retirement age, the share owning equities falls off nearly twenty percentage points.
One pattern shared broadly across the income groups is the rise in equity ownership across successive generations of cohorts. Expanded equity holding is particularly evident after age thirty -is limited to the cohorts born prior to 1964 -and occurs primarily between 1989 and 2001. Within the middle-income group these changes are especially dramatic. Equity ownership for the cohort born in 1934-43 is thirty points higher than for the previous birth-year cohort when we see them both at ages 61 to 70.

Home ownership
The final risky asset, housing, is one that some might not categorize as a risky asset.
However, we feel that given housing is the largest asset held by most households and the volatility in house prices over the past few decades, housing should be considered a risky asset.
Over the age of 40 home ownership is nearly universal for households in the top half of the income distribution (Figure 12). 15 Ownership climbs sharply when households are in their twenties and thirties, peaks at over 90 percent after age forty and remains high in retirement.
Home ownership is much less common for lower-income households, but even among the bottom half of income home-owning climbs steadily with age. The rate of ownership at the bottom finally peaks when households are in their late 60s, with nearly 80 percent owning homes.
Since the financial crisis the rate of ownership outside of the top-income group has fallen relative to previous birth-year cohorts. For households in the "next 45" percent of income, for example, between the ages of 34 and 43 the home ownership rate of the most recent cohort (born between 1974 and 1983) was 71 percent, compared to an ownership rate of 85 percent for the previous birth-year cohort. The decline in ownership is even more widespread in the bottom half of income. Following the crisis each of the three most recent cohorts of lower-income families (born after 1953) have ownership rates ten percentage points lower than the preceding birth-year cohort.

Mortgage Holding
The share of households with a mortgage for a primary residence rises sharply when household heads are in their twenties and thirties, in parallel with the rise in home ownership ( Figure 13). Mortgage holding, though, begins to decline after age 40, while home ownership remains steady or, in the case of lower-income households, continues to rise. As they enter their forties, roughly 8 in 10 households in the top half of the income distribution hold mortgages, but this share falls sharply as households approach and enter retirement. Less than fifteen percent of households in the oldest cohort, regardless of income level, hold mortgages when we see them between the ages of 73 and 82. and 9 percent more likely, depending on their income level, to be holding a mortgage than the previous birth-year cohort.

The Risky Asset Share
This section explores that share of total assets held in these three risky asset types, and how those shares evolve over the life cycle for different income groups.

Business Share
Businesses represent a substantial share of assets only for high-income cohorts. Unlike the likelihood of owning a business, however, there is little curvature in the business share of assets over the life cycle (Figure 14). For the high-income group, businesses account for approximately one-third of total assets when household heads are in their thirties, forties, and fifties, and as these households transition into retirement the business share declines modestly. One implication is that the businesses shed in retirement by high-income households are relatively low-value companies, with more valuable businesses possibly held onto for bequest purposes.
For low and middle-income households, business account for roughly 10 percent of total assets over most of the life-cycle. As these households enter retirement, the business share declines and approaches zero.
In comparing business shares across successive cohorts by income group, there appear to be few systematic differences moving from one birth-year cohort to the next. The two differences that do stand out can be found within the high-income group at the tails of the age distribution.
The business asset share is higher among more recent of the two represented cohorts when we see households at the youngest and at the oldest age levels. When we see them between their early-20s to mid-30s, the most recent cohort (born 1974-83) has a business share that is between 10 and 20 points higher than the previous birth-year cohort. At the other end of the life cycle, the 1934-43 cohort has between two and six percent higher business share than the previous birth-year cohort in four out of the five points when we see them at the same ages.

Equity Share
In both levels and patterns over the life-cycle, the equity share is quite similar across income groups (Figure 15). For the three different birth cohorts when we see household heads from ages 54 to 63 the equity share of assets averages 27 percent among top-income households, 22 percent for the "next 45" percent of households, and 11 percent in the bottom half of the income. The equity share also rises slowly and steadily across the life-cycle. As households hit retirement age, the equity share appears to flatten out in the top-half of the income distribution and modestly decline in the bottom half.

Housing Share
The housing share of total assets rises sharply for all income groups at the very youngest ages when observe households (Figure 16). When households are in their twenties, the housing share is also similar across income. For the 1964-73 cohort the housing share at ages 22 to 31 was 43 percent for top-income households, 46 percent for the next-45, and 35 percent for the bottom half. In their thirties and beyond the housing share behaves quite differently by income.
Among high-income households, the housing share falls steadily with age until it accounts for slightly more ten percent of total assets for heads in their late forties through their early eighties. For the middle-income group the housing share remains mostly flat -at around 50 percent -in the thirties and starts to steadily fall as household heads enter their forties. The housing share appears to bottom out at roughly 30 percent of total assets when the middleincome group hits their mid-50s. For the oldest cohort (born 1924-33) the housing share starts to rise as household heads start to enter their seventies, presumably as these retirees continue to live in their home and begin to draw down other assets.
Across the entire life-cycle housing remains the dominant asset for households in the bottom half of income. From the mid-twenties through their thirties, housing accounts for just over half of all assets of the low-income group. Starting in their forties, the housing share declines slightly, but never falls below forty percent of total assets. As low-income households approach retirement age, though, the housing share starts to rise markedly, climbing to well over 60 percent for the older cohort when we see them in the seventies. It looks as is if the draw-down of non-housing assets is happening earlier and more rapidly in the bottom half of the income distribution than in the next-45 percent of the distribution.

Combined Risk Asset Share
Despite the clear differences in the business share and the housing share of total asset between low, middle, and high-income groups, the combined risky asset share is very similar over the life-cycle for the different income groups. The combined risky asset shares rises in the twenties for each of the income groups, peaking between 70 and 80 percent of all assets in the thirties (Figure 17). Between the thirties and the sixties, the combined risky asset share slowly and steadily declines, hitting 60 to 70 percent at the point of transition into retirement. In their seventies, each of the income groups exhibits a small up-tick in the combined risky asset share.

VII. Financial vulnerability, shocks, and the health of the household balance sheet
In the previous sections we saw that asset composition varies considerably across the asset distribution (section V) and across the age and income distribution (section VI). In this section we discuss how these portfolio differences influence the level of financial risk to which families are exposed, and how this exposure has evolved over time. First, we discuss how housing and equity price shocks would affect wealth across the distribution. Second, we discuss exposure to risk that takes housing leverage, consumer debt and income into account.

Risk from asset price shocks
The composition of assets for middle class families by assets (Figure 3) and by income ( Figure 16) are heavily skewed toward housing. Housing is a highly levered asset for these families: average debt levels for these families are about 40 percent of total assets (Figure 3) and mortgages are about 75 percent of total debts for these families (Appendix Figure 1). On the other hand, families with high levels of assets and income often hold extensive public and private equity positions (Figures 3, 14, and 15).
While equity is the traditional barometer of portfolio riskiness, house price declines often affect a family's wealth position to a greater degree because of leverage. Here we calculate the share of total household wealth lost from two stylized asset price shocks-a 10 percent decline in house prices and a 10 percent decline in equity prices-and the share of households experiencing large losses from these stylized shocks.
Families outside of the top 5 percent by income are particularly susceptible to a housing shock. In 2016, on average these families would lose between 4 and 6 percent of their net worth from a 10 percent decline in housing prices. Further, nine percent of the bottom 50 percent by income and 24 percent of the 50 th to 95 th percentile by income would lose more than 10 percent of net worth ( Table 4).
The top 5 percent by income would lose about 1 percent of total net worth and only about 4 percent of these families would lose more than 10 percent of wealth from this hypothetical shock. These families, though, would lose about 7 percent of wealth based on a hypothetical equity price decline, while lower income families would lose between 3 and 4 percent. 16 Applying these shocks to past waves of SCF, we can see that the impact of leveraged housing on middle income (50 th to 95 th percentile) family net worth in the early to mid-2000s. In 2007, for example, 33 percent of these families would have lost 10 percent of wealth in a 10 percent house price decline. 17 In general, though, the fraction of these families that would lose more than 10 percent of wealth is lower in 2016 than in 2010 or 2013, indicating that these families are a bit less susceptible to a house price shock (as in Haughwout, Guttman-Kenney, andFuster, 2018, andBhutta, Bricker, Dettling, Kelliher, andLaufer, 2018).

Financial vulnerability: income and asset price shocks 18
These stylized asset-price shocks are suggestive, but do not identify all of the households whose portfolios are vulnerable to adverse shocks. For one thing, asset price shocks do not usually happen without being accompanied by a labor market shock-whether as a cause or as an effect. Given the large role of housing and housing debt in shocks in Table 3, we investigate further the health of the household balance sheet by seeing how vulnerable families are to simultaneous house price and income shocks.

Trends in Vulnerability Across time
Using the same color scheme from the heat map in Figure 18, we can track fluctuations in this multidimensional vulnerability measures over time. Figure 19 displays the cell totals in a stacked bar (omitting the white group) to facilitate a comparison over time. We see that fewer families meet any degree of vulnerability/susceptibility to an economic shock in 2016 than in previous surveys back to 2001. This decline is mainly driven by fewer families occupying the dark and medium gray areas -where LTVs and PIRs are the most elevated. The fraction of families in the dark gray area has fallen by a third since its peak in 2010, and the dark and medium gray area combined has halved since 2010. In the aftermath of the financial crisis, just over 13 percent of all households were identified as financially vulnerable. 19 See, for example, evidence for the double trigger theory of mortgage default in Bricker and Bucks (2016) or Foote, Gerardi, and Willen (2008).
Both the levels and the trends in financial vulnerability vary by income group. This makes sense, as homeownership, income and debt each vary differently across these groups.
For the bottom half of the income distribution, vulnerability has remained flat over most of the last two decades, fluctuating around 20 percent (Figure 20). 2016 marks a modern low-point in financial vulnerability for households in the bottom half of the distribution, with only 7.5 percent of families shaded as either dark or medium gray. At the 2010 peak, nearly 11 percent of families had extreme (dark gray) or moderate (medium gray) financial vulnerability.
The highest levels of and the greatest degree of variation over time in financial vulnerability is seen among the families in the "next 45" percent of the income distribution (Figure 21). These families have high rates of home ownership and carry substantial debt, particularly tied to their homes, so joint income and house price shocks hit them hardest. By 2010-after a large house price shock-nearly 35 percent were vulnerable in some way, with 16.5 percent in dark or medium gray areas. After seven years of economic expansion (by 2016), financial vulnerability among the "next 45" had receded to 2001 levels, particularly for dark and medium gray areas.
Financial vulnerability is unsurprisingly lowest among high-income families (Figure 22).
Between 2007 and 2010, the share of high-income families coded as either dark or medium grey jumped from 3.5 percent to more than 7 percent. By comparison, the combined post-crisis measure of vulnerability among high-income families remains below that of middle and lowerincome families in the very best of economic times. By 2016 financial vulnerability at the top had receded sharply, with just 2.5 percent coded as dark or medium gray.

VIII. Conclusion
Understanding developments in household portfolios are arguably more important than ever.
To identify the causes and consequences of long-term rise of inequality in the distribution of wealth, and to monitor the degree of financial vulnerability that households face it is crucial to understand the allocation of assets, across asset types, age groups and the distribution of income.
The data from the Survey of Consumer Finances that we reviewed in this chapter establishes several clear patterns and allows us to draw number of conclusions. First, the household asset portfolio inferred from aggregate data-which is spread across housing and financial assetsdoes not look like the portfolio of most households, which is composed mostly of housing assets.
Second, the time series dynamics of the aggregate portfolio-which has swung toward and from financial assets-does not appear in the time series of most families, which has always been mostly composed of housing. This is also the case when taking a longer view of household assets (Kuhn, Schularick, and Steins, 2018).
Third, we also see a slow and steady increase in the concentration of assets which has picked up in the 2010-2016 period. Since the processes that create wealth for a household unfold over time, the relationship between assets and age can be complicated. We explore the trends in asset levels and portfolio composition over the lifecycle later in this chapter using a birth-year/incomegroup cohort analysis of the SCF.
Fourth, viewing the data through birth-year cohorts by income, we observe the typical lifecycle asset accumulation processes among low, middle, and high-income families. Though the levels are different magnitudes, assets generally increase throughout the typical working life in each income group, and peak just before the typical retirement age before partially decumulating.
However, recent cohorts have fallen behind in homeownership and asset accumulations. In general, though, recent cohorts are also more highly educated than previous cohorts (Figure 23 ), so these gaps may close if past relationship between education and wealth persists. Another chapter in this volume (Kurz, Li, and Vine) shows that the relationship between consumption, income, and wealth is similar in younger cohorts (Millennials) compared to older cohorts (Generation X and Baby Boomers).
Finally, we focus on quantifying financial vulnerabilities in family balance sheets. We first show that families with a combination of two potential vulnerabilities-high debt payments and high loan-to-value ratio on their primary residence-peaked in the late 2000s, and are at the lowest level in 2016 since the early 1990s. We also show that nearly all of the time series variation is due to middle income families, who also host most of their assets in housing and are often the most highly-levered in the housing market.   1924-1933 born 1934-1943 born 1944-1953 born 1954-1963 born 1964-1973 born 1974-1983 Bottom 50 Next 45 Top 5 Electronic copy available at: https://ssrn.com/abstract=3473055


extfieldsofstudy: Physics Business

text:
Transit oriented development of light rapid transit palembang

Light Rapid Transit (LRT) is one of the public mass transport which is necessary to break the problem of the urban transportation system in developing countries. LRT has many promises in the development of the city, in addition to solving the transportation problem is an increase in economic activity. This current case study of the paper is Palembang, one of the cities in Indonesia; the government develops mass public transport to solve the transportation problems. The government planned LRT construction to optimize the development of demand for public transport. This paper studies the concept of LRT construction by the development of Transit Oriented Development (TOD), include the development of estate management at stations area. The concept of Transit Oriented Development (TOD) for the develop stations location to spur the growth of economic activity around the locations. This concept needs to analyze the socio-economic impact study to determine the feasibility of developing TOD level especially at stations and the land use impact of the construction of LRT. Survey of Palembang City land use shown that 23 location can use as stations. The analysis by the impact of the socio-economic condition, land use, and feasibility of LRT construction; present that there are 13 locations can be used as TOD and shown as stations of Palembang LRT.

Introduction
The importance of an economic sector that requires a reliable, efficient, and effective transportation system. Effective transportation means that transportation systems that are compatible with capacity, connected or integrated with modes of transport, orderly, safe, convenient, precise, safe, secure, convenient and economically affordable. Efficient in the sense of community burden as the user of transportation service becomes low and has high utility.
In the context of the rapid economic growth in Palembang City, the demand for transportation increasing, so needed the appropriate mode to realize. From the last two years, the government trying to optimize the demand by developing light rail transit as a new mode to solve the problem.
The concept of development for transit-oriented must necessary to support the installation of light rail transit (LRT). Analysis of transit-oriented will be a great opportunity to investigate as research, since the Palembang light rail transit (PLRT) as the first LRT implementation in Indonesia. Transitoriented development in PLRT will apply the concept of development of Transit Oriented Development (TOD).
Sriwijaya international Conference on Science, Engineering, and Technology IOP Conf. Series: Materials Science and Engineering 620 (2019) 012023 IOP Publishing doi:10.1088/1757-899X/620/1/012023 2 Nowadays, TOD has become an increasingly popular planning idea [1]. Many arguments for pursuing TOD even though that are similar despite the different contexts [2]. Meanwhile, there is no single which all-encompassing definition that represents the TOD concept [3]. TOD facilitates increased accessibility because it provides alternatives to automobile-based land uses [2]. In that point of view, this study analysis about the development of TOD in PLRT, in which to analysis socioeconomic condition, land use, and feasibility of LRT construction context to suggest TOD locations of PLRT and comparing with current stations. This research expected to make an effort for the success of integrated mode for future development of Palembang city. TOD will give the sustainable transit and benefits to users and government.

Transit-Oriented Development
Transit-oriented development (TOD) is a mixed-use residential or commercial area intended to maximize access to public transportation [4]. TOD formally refers to mixed-use, relatively intense development concentrated around the transit station in a 1⁄4-to 1⁄2-mi radius and oriented to transit riders with a pedestrian and cycle-friendly environment [4], [5]. TOD strategies also integrate land use and transport functions to aimed at urban sprawl [6].
Limited studies have examined localities plan for and implement transit-oriented projects [1]. Belzer & Autler (2002), mention that framework to use for planning and analysis of TOD projects as three main focus; a focus on the desired functional outcomes of TOD, not just physical characteristics, acknowledgment of a continuum of success, adaptation to different locations and situations. Planners identified three essential zoning strategies for TOD, they called as the ABC's of TOD zoning, where is: (1) active pedestrian-friendly streets, (2) building intensity and scale, and (3) careful transit integration [4]. TOD planning can be adopted for new construction or redevelopment [6]. Holmes & Hemert (2008) mention that measures TOD potential for sustainability can be assessed by: 1. Income variance in TOD neighborhoods (the greater, the better) 2. Number of housing units nearest to transit stations (increasingly higher nearer station) 3. Percentage of person whos living in TOD using transit 4. Increased income levels of TOD residents over a period 5. Harmony of mixed uses 6. Thriving enterprise in TOD communities

Challenges for Transit-Oriented Development
Generally, TOD can create a community more desirable for people to live and work in it [4]. TOD can be one of the approaches to developing an urban area that adopts mixed spatial layout and maximizes the use of mass transit. TOD's provide unique benefits on a regional, local, and individual scale with overall planning, thus can promote local businesses and retail area, capture increased land values from public investment in new transit routes, and replace costly surface parking costs and associated automated infrastructure with usage that generates more revenue for local governments and businesses [4]. In reality, many projects fail to provide the full range of synergies and benefits of TOD are announced successes because there is no standard or criterion for success [7].

Methodology
The study method includes four parts: (a) review of the literature on the TOD experience in the selected cities; (b) data collection for supporting the analysis; (c) TOD analysis from socioeconomics of Palembang City and land use that related to PLRT project; and (d) Evaluate the TOD of PLRT concept. Evaluation for TOD of PLRT concepts are adopted with this measurement: a. Socio-economic of the city [4], [6] b. Origin-destination [3] Sriwijaya international Conference on Science, Engineering, and Technology IOP Conf. Series: Materials Science and Engineering 620 (2019) 012023 IOP Publishing doi:10.1088/1757-899X/620/1/012023 3 c. Mode used [8] d. Land use and Facilities in TOD [3], [4] e. Thriving enterprise in TOD [4] In socio-economics analysis, influence factor support for deciding on TOD planning. The factors are a socio-economic area, the condition of transportation, potential locations for business development, and regulation. Data attraction and generation from survey assessed with correlated by linear regression method with socioeconomic parameters. The socio-economic parameter in this study are 1) population, 2) population growth, 3) education and 4) economic. Furthermore, the condition of transportation observed by identification of feeder mode, access, and infrastructure facilities. After that, selection of potential location with the possibility of business development and connect the assessment with regulation. Based on the linear regression method, correlation above generation and attraction with socio-economic parameter are used to origin-destination analysis. The socio-economic parameter used is population. Furthermore, analysis of mode in Palembang, analysis of land use facilities and a thriving enterprise in 23 TOD points by the survey. Hereafter, pursed the analysis results to obtain the best TOD and make a comparison with the planning of PLRT stations location.
To support this study conducted data retrieval in the form of primary and secondary data. Primary data is the most important and useful data in the study to get a proper review in solving the problem. Primary data retrieval with two methods of the survey; survey origin-destination and survey of land use. Secondary data collected by statistic data of Palembang city and government spatial plan of city and province.
Primary data collected at the end of 2015 before the installment of PLRT. Origin-destination (OD) survey conducted to analyze the types of people movements in 16 sub-district in Palembang city. To ease of analysis, Palembang city divided by zones which zone based on sub-district and narrowed it to four zones as seen in figure 1. OD Survey held in a week in October 2015 by home interview survey, this conducted before the PLRT installment to get the valid data. Furthermore, primary data collected by a survey of land use. This survey held during the research by analysis of Palembang spatial plan and observation of the current situation.

Analysis of Socio-economics
Population in Palembang City on the year 2010 to 2016 has increased with the average growth of 1.41% from 1,468,007 people in 2010 to 1,602,071 people in 2016 as seen in table 1. This number of growth level is relatively low compared to the growth of the South Sumatera Province population which are 1.85% per year but almost equal with the National Population Growth of Indonesia above 1.49% per year.  From Population data, projected using linear regression with base data of origin-destination matrix by data collection. Data collection was provided in 2015 before construction of PLRT start to get the valid data about origin-destination without any due to construction impact. Furthermore, prediction of inter-zone movement in Palembang can be formed in matrix of origin-destination with growth factor model.

Analysis of Origin-Destination
Correlation analysis was present to assess the relationship between the dependent and independent variable. Population data used as an independent variable to projected using linear regression with Origin-destination data. The data showed that most of origin-destination in Palembang are short distances and inter-zone trips. Based on the origin-destination data, most of the trip in a screen line of Palembang which is shown in figure 1. Furthermore, the new matrix provides with four-zone as shown in Table 1. This allocation of the zone to analyze the trips and determine the TOD location for PLRT. The four-zone provide based on socio-economic data which present before.
From the table 3 shown that total trips in Palembang about 83102 trips per day, with interzone trips are the most trips that prevail in.

Analysis of Mode-Used
To see the use of modes based on the origin of the destination and to estimate the number of passengers, the city of Palembang is divided based on the four zones. Four zones were analyzed the number of modes of use to obtain the destination matrix based on the mode and origin based on as shown in this following table.
From the data of matrix origin-destination and mode, shown that zone 2 and zone 3 more high than another zone. Taxi bike and motorcycle are the most popular mode that used in Palembang. Otherwise, this table shows that most of the people in Palembang using motorcycle and private car for their daily routine activity. PLRT will give the new effect of Palembang residents by using a new mode of transportation. This mode is expected to reduce the using of private vehicles and give the effective benefit of the mass transit system. Furthermore, based on this analysis which shown the highest of origin-destination in zone 2 and zone 3, it means that most of TOD locations will be in that zones.

Analysis of Land Use and Thriving Enterprise
To see the use of modes based on the origin of destination and to estimate the number of Based on the Government Palembang Spatial Plan, origin-destination analysis, mode-used analysis, and land use data; there are 23 locations suitable for TOD location. Thus are 3 locations in zone 1, 7 locations in zone 2, 8 locations in zone 3, and 5 locations in zone 4. The Strategic location area for TOD location in table 6 shown current land use from the survey and future land use from Government Palembang Spatial Plan.

TOD Evaluation
Furthermore, analysis of TOD location and selection of PLRT station location based on current land use, attraction and generation area, feeder, and mode integration. PLRT station must integrate with an understanding of this basic following: a. Connectivity: integrated doesn't simply mean that it is a collection or connection between identical modes. Integrated also means that there is a connection between motor engined vehicles and train. In addition, integrated also means connecting stations to ease the movement of passengers. b. Comfort; Integrated means that passengers do not have to exit the station and take pains (face social risks) while moving between routes in their pursuit of comfort. Square Mall, A.rivai Road, Cinde Traditional Market, Ampera Bridge, Jakabaring Intersection, Jakabaring Stadium, and OPI Mall. This location was chosen by selecting station locations and integrating with evaluation from the analysis. Thus, the chosen TOD locations must include: a. Analysis of demand, namely based on busy spots in the city and centers of movement. On the selected spots, identification of development plan must be made or alternative development to become a transportation mode solution. b. Must exist every 2 to 4 km, to make a development spread planning that transforms PLRT into a transportation backbone in the future of Palembang City. c. Spots with TransMusi stations or other stops must be prioritized, integrating Trans Musi with PLRT thus is suitable with the principles of Integrated Transportation as explained before. d. Spots necessary such as mall, important places (sport, worship, event, etc) and integrated shopping spots and Public Service Centers. The 13 locations of TOD are similar to PLRT station, only two locations are different which are Palembang Square Mall and Jakabaring Intersection. Those locations near the installation of PLRT stations as shown in figure 2.

Conclusion
The TOD idea is a comprehensive effort for using the planning of land use to support transit rail system [1]. TOD concept provides the referrals for transit planning which adjusted with land use thriving enterprise of the area. This concept gives sense very feasible to use to determine a suitable location for transit, especially for the mass rapid rail system. As the developing city, Palembang gets an opportunity as the first city has implemented LRT in Indonesia. The concept of PLRT planning should integrate with many aspects, neither modes but also the transit facilities. The better city planning always based on their transportation planning. It makes TOD concept being the best option to analyze transit system in Palembang. Furthermore, TOD also makes other chance for some area to develop especially suburbs area of Palembang city.
The results of this study suggest 13 locations of TOD for PLRT stations based on connectivity, comfort, accessibility, and convenience of Palembang people and especially the users of PLRT. The locations are quite similar with installment locations of PLRT stations. From analysis shown that TOD locations need integrations planning with other modes in Palembang city. The success of integrated planning with another mode will make a good effort to the future development of Palembang city.


extfieldsofstudy: Physics Geography

text:
Urbanization Phenomena of Small Inland City: Case of Parakan, Central Java

Urban development in Indonesia has been notably figured out with the development of coastal area as the place to provide regional-international trade having the control of labour. Hence, coastal cities and the surrounding areas are rapidly growing to urbanized area. However, rapid development with limited carrying capacity and climate related issues of coastal area has become the challenges of their development. Meanwhile, the growth of inland city also started to gain some attentions. In Central Java, for instance, the emerging of inland city centre has become a new phenomenon in urbanization. Parakan, one of the inland cities in Central Java selected as the study area due to the growing of rural-urban characteristic. This new inland city centre is a part of Temanggung Region, Central Java strategically located in the national road of Semarang to Purwokerto. The intention of this paper is to elaborate the growth of inland city in Central Java, especially in Parakan. The GIS is utilized to analyse the spatial changes in Parakan meanwhile the economic aspects are analysed to find out the indication of shifting economic activities within the city. From the analysis, it is found that changes in population appear in urban area as one of indicators of urbanization in Parakan inland city. As a consequence, the economic activities and the GDP also experience changes due to the growing of urbanized area. The growing of Parakan has been proven that the urbanization phenomenon in Central Java occurs in the inland city inferring that the urbanization pattern in Indonesia, specifically in Central Java, is not only limited to the growth of the coastal cities.

Introduction
The urbanization has been understood as the common phenomenon of urban development. According to the United Nation, the world urban population is expected to increase from 3.6 billion in 2011 to 6.3 billion in 2050 [1] which means that the growing urban population will reach 72% by 2050. Urbanization can also be simply understood as a demographic phenomenon [2]. However, the demographic phenomenon led to broader issues regarding with physical and environmental changes causing land use transformation. Land prices and land availability are the common factor behind land use transformation. Unbalance functional changes from farm use to non-farm uses emerge as the result of rapid modification and urbanization. Thus, the urbanization also leads to the environmental degradation in the new urbanized area.
In Indonesia, especially in Central Java, urbanization was started with the development of coastal city and the area surrounding it. For instance, the major big city in Central Java is Semarang City which is also the coastal city. Despite the rapid growth of Semarang as a coastal city, coastal area of Semarang faces its own vulnerability. In contrast with the development, coastal areas are mostly prone to the environmental hazard such as climate change, which would affect the urban development. Most climate-related disasters, such as floods, tidal flooding, and windstorms, affect coastal areas [3]. Hence, the emerging of inland city development should be encouraged to bridging the balanced urban development of coastal area and other urbanized area surround them [4]. Parakan as one of inland cities in Central Java is a unique sample of urban development. The development of this small city is influenced by the existence of national road to the tourism area and the plywood industry. This strategic position has implications for the role of the city as a service center for the region behind it, where the urban area of Parakan tends to be a distribution city for the other cities as the provider due to insufficient sources of primary raw material, for which the other regions acts as the provider. Growth and development of a region can also cause changes in spatial use of the area. Hence, this paper aims to further elaborate the urbanization of inland city in Central Java, especially in Parakan through analyzing the growth of the built up area, the changes of activities, growth of population in urban area, and the changes of GDP. By understanding the urbanization characteristic of the area, it is beneficial for the future development of Parakan as the growing inland city.

Study Area
The study area of this research is Parakan urban area. The urban area of Parakan includes Parakan Sub-district as the center of economic and government of Temanggung Regency. This sub-district is located on the slopes of Mount Sindoro-Sumbing and the urban area of Parakan sub-district is crossed by the route from Wonosobo Regency to Yogyakarta City/Semarang City and crossed by the route from Yogyakarta City to the Pantura Line/Jakarta. Urban and non-urban region in Parakan are identified through the latest data from the Central Bureau of Statistic (BPS) [5].

Data Collection
The data for this research was spatial data and socio-economic data of Parakan. The spatial data

Methods
There are two methods to further elaborate the urbanization in Parakan, through the Geographical Information System (GIS) analysis and from the analysis of economic and activities aspect. The series of maps are processed in GIS to see the changing of built up area. Meanwhile, descriptive analysis was applied to analyze the changing economic activity and the growth of GDP in Parakan.  The number of population in Parakan in 2017 was 52.346 with total of 42.089 people living in urban area meaning that more than 80% of people in Parakan are living in urbanized area. Figure 2 illustrates the number of growing urban village of Parakan. Based on the rural-urban data of BPS, the number of village with urban status are growing from only three villages in 1990 to eleven villages in 2016. The change of status from rural to urban mostly occurs in the village directly crossing to the national and provincial road. There are additional 8 villages which were adjanted to the national and provincial road that changed from rural to urban villages within 1990 to 2016. In line with the growing number of urban status of village in Parakan, the growing number of urbanized area in Parakan is also supported with the growth of the built up area (as referred to Table  1). By viewing the maps of built up area in Parakan from time to time, the growing of built up area as seen in Figure 3, is mostly along the national and provincial road. Those roads are the route of Wonosobo Regency to Yogyakarta City/Semarang City as well as crossed by the route from Yogyakarta City to the Pantura Line/Jakarta. Those roads attract many passerby hence the route connects the tourism activity in Wonosobo Regency, Borobudur Temple (Magelang, Central Java), Yogyakarta City and alsoto other cities like Purwokerto City to Semarang as the capital city of Central Java.

Activities and GDP Changes of Parakan
The existence of national and provincial road in Parakan is essential to the urbanization in Parakan. Figure 4 indicates the land use changes along the national road. It is apparent on Figure 4 that the lands use along the national road covers industrial cluster, central market and shop house. The growing of small industrial area is spreading along the road to Purwokerto and Wonosobo Regency. These are the changing land use from agricultural area to the industrial area although the numbers are not quite high. However, it is obvious that the changes are potentially getting wider along the road. Meanwhile, there is also growing of shop house along the national road, owned by private household   Table 1 illustrates the percentage of economic sector from Gross Domestic Product (GDP) of Parakan. Table 2 depicts that the agricultural sector is slightly decreasing from 17,33% in 2010 to 12,98% in 2014. In contrary, the processed industry sector has been growing from 23,95% in 2010 to 25,55% in 2014. In line with the processed industry, the building/construction as well as trading, restaurant and hotels have been growing more than 1% from 2010 to 2014. The percentage indicates that the agricultural sector in Parakan slightly decreases to the secondary and tertiary economic activities such as industry, trading, restaurant and hotels ( Figure 5).

Conclusion
This paper presents the findings that the urbanization occurs in the small inland city. Many scholars have been proved that the urbanization in Indonesia occurs in the coastal area [6] [7]. However, the elaboration of the growth of the built up area, the changes of activities, growth of population in urban area, and the changes of GDP in Parakan indicate that the urbanization phenomenon occurs in the small inland city such as in Parakan, at Central Java.