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Fix hilda_w14_hh to be per household
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@HughParsonage
HughParsonage committed Jan 28, 2019
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57 changes: 29 additions & 28 deletions CGT_and_neg_gearing_parent-CHUNKTIMINGS.txt
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@@ -1,6 +1,6 @@
20.5 loadPackages
0.1 loadHILDA
1.2 EarlySessionInfo
1.3 EarlySessionInfo
0.0 make_negatives_NA
0.0 prohibit_quiet_censoring
0.0 downloadGrattex
Expand All @@ -12,15 +12,16 @@
0.0 omit_repeated
0.0 cgt_reducer_due_to_policy
0.0 load_sample_file
0.4 create-age-imp
0.5 target_sample_file
0.5 create-age-imp
0.6 target_sample_file
12.4 Import-HILDA
0.0 Import-HILDA-all-waves-step-1
2.5 Import-SIH
1.2 Import-SIH
0.0 Import-misc-ATO
0.0 individuals_table1_201516
0.0 individuals_table1
0.0 CGT-by-entity-get
5.3 Determine_future_rental_deductions
4.9 Determine_future_rental_deductions
0.1 cgt_discount_density
0.0 cost-new-CGT-discount
0.0 revenue_abolish_NG
Expand All @@ -29,27 +30,27 @@
0.0 revenue_quarantine_NG
0.0 revenue_quarantine_NG_2
0.0 henry_taxstats
12.7 Summary-chunk
1.4 revenue_foregone_CG_and_NG
12.2 Summary-chunk
1.2 revenue_foregone_CG_and_NG
0.1 CostofNGtotal
0.0 CGT-by-entity
2.1 CGT-by-entity-asset
2.2 CGT-by-entity-asset
0.0 CG-delay-EMTR
0.3 CG-marginal-tax-rates
1.8 CGT-by-age-taxable-income
1.4 CGT-by-age-income
1.6 CGT-by-age-taxable-income
1.3 CGT-by-age-income
0.0 meanPositive
1.6 CGT-by-age-income-weight-CG
0.0 ROR_CG
0.0 emtr_savings
1.3 delaying-realization-causes-reduction-in-effective-tax-rate
1.4 delaying-realization-causes-reduction-in-effective-tax-rate
0.0 should_we_get_ABS
0.0 ABS_GET_RES_VALS
0.0 house-prices
0.0 surya
0.0 surya-low-returns
0.0 return_rental
0.6 emtr_rental
0.5 emtr_rental
0.7 EMTR-nominal-real-excess-historic-vs-lower
0.0 emtr_nominal_historic_lower_assumptions
0.2 assumptions-table
Expand All @@ -66,14 +67,14 @@
0.0 tot_intrst_unincorp_biz
0.1 total_bizlosses
0.6 EMTR-by-gearing
0.9 number-NG-time-series
0.6 number-NG-time-series
0.6 avg-net-losses-time-series
0.8 number-of-investors-by-gearing
0.9 number-of-investors-by-gearing
0.1 prop_rental_deductions_gross_rent
0.0 NG-basic-stats
0.7 Net-rent-time-series
0.0 prop_le_80k
1.7 tx-inc-distr-by-NG
2.1 tx-inc-distr-by-NG
1.9 tx-inc-distr-by-NG-latest
1.0 Decile_constructor
0.8 Benefit-NG-before-after-deductions
Expand All @@ -83,36 +84,36 @@
0.0 value_of_property
0.0 impact-of-policy-changes-on-after-tax-returns-tbl
0.1 impact-of-policy-changes-on-after-tax-returns-tbl-2heading
3.5 Impact_of_NG_on_house_prices
2.8 What_price_change_req_to_obtain_same_return
3.4 Impact_of_NG_on_house_prices
3.2 What_price_change_req_to_obtain_same_return
0.8 Owner_occupiers_loans_are_being_outnumbered_by_investors
0.0 percent_of_prop_value_due_NG
1.3 Capital_city_rents_1ROW-2COL
1.6 Capital_city_rents_direct_abs
1.7 Capital_city_rents_direct_abs
0.0 ABS-lending-data
0.0 cost_NG_limited_to
0.0 number_affected
0.0 cost_of_policy_carry_fwd
0.0 HILDA-Number-of-properties
0.1 HILDA-Number-of-properties
0.0 Number-households-SIH
0.8 bizLosses-by-occ
0.7 bizLosses-by-occ
0.1 bizLosses
0.1 bizLosses-for-PP
0.3 PP-losses-by-occ
1.0 PP-losers-salary-comparison-evil-chart
1.4 PP-losers-salary-comparison-horiz-bar
0.8 Proportions-PP-losses
2.3 PP-losers-salary-comparison-ggrepel
4.9 density-salary-by-PP-losses
1.1 PP-losers-salary-comparison-horiz-bar
0.9 Proportions-PP-losses
2.5 PP-losers-salary-comparison-ggrepel
5.0 density-salary-by-PP-losses
0.0 bizLosses-for-NPP
0.1 costing-quarantining-losses
0.0 benefit-due-NG
2.4 NG-vs-salary
3.2 Results
2.6 NG-vs-salary
3.6 Results
0.0 prop_landlords_n_properties
65.7 HILDA-duration-of-NG
62.4 HILDA-duration-of-NG
0.0 ggsurv-NG
1.6 Survival-NG-analysis-hilda
1.7 Survival-NG-analysis-hilda
1.0 NG-survival-curve
0.0 haley-box
0.0 percent1_01_TeX
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8 changes: 6 additions & 2 deletions CGT_and_neg_gearing_parent.Rnw
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Expand Up @@ -379,6 +379,8 @@ hilda_w14_hh <-
as.data.table %>%
select_grep(c("hhrhid", "hhwth$", "hweqini", "oprntn")) %>%
.[, lapply(.SD, make_negatives_NA), keyby = "nhhrhid"] %>%
# make household
unique %>%
.[, .(nhhwth = sum(nhhwth, na.rm = TRUE)), keyby = c(grep("(hhrhid|hhwth)$",
names(.),
value = TRUE,
Expand Down Expand Up @@ -3022,6 +3024,7 @@ Of course, not all investments are negatively geared. `Positively geared' proper
Negative gearing is used much less for investments outside of housing. Investors in assets other than real estate, such as equities or unincorporated businesses, are less likely to borrow some of the funding, and usually do not borrow so much that these investments are negatively geared.
<<individuals-share-holdings>>=
individual_dividends <-
# taxstats 2013-14
indiv_by_taxable_status_residency_taxable_income_range %>%
.[variable %ein% c("Dividends unfranked",
"Dividends franked")] %>%
Expand All @@ -3034,9 +3037,10 @@ hh_equity_tot <-
hilda_w14_hh %>%
.[, sum(nhweqini * nhhwth)] # Equity investments * WEIGHT
@
Total lending to individuals for share investments is at most \$19 billion, compared to individuals' direct share holdings of around \$550~billion,\footnote{The ATO reported that the 2013-14 dividend income for individuals was \Sexpr{texNum(individual_dividends, dollar = TRUE)}. The HILDA survey showed in the same year \Sexpr{texNum(hh_equity_tot, dollar = TRUE)} for household wealth in equities.} and compared to borrowings of \$548~billion from banks for housing investor lending.
Total lending to individuals for share investments is at most \$19 billion, compared to individuals' direct share holdings of around \$550~billion,\footnote{The ATO reported that the 2013-14 dividend income for individuals was \Sexpr{texNum(individual_dividends, dollar = TRUE)}. The HILDA survey showed in the same year \Sexpr{texNum(hh_equity_tot, dollar = TRUE, .suffix = "billion")} for household wealth in equities.} and compared to borrowings of \$548~billion from banks for housing investor lending.%
\footnote{2013-14 figures.}

When individuals do borrow to invest in equities, they are seldom negatively geared. Equities investors will only be negatively geared if they are at least 70 per cent leveraged. The average leverage of those who do have a margin loan is around 27 per cent. Few margin lending investors leverage more than 65 per cent. Usually the maximum leverage permitted is around 75 per cent. Most margin lending customers dislike margin calls, and so they tend to create a buffer of at least 10 per cent less than this.
When individuals do borrow to invest in equities, they are seldom negatively geared. Equities investors will only be negatively geared if they are at least 70 per cent leveraged. The average leverage of those who do have a margin loan is around 27 per cent. Few margin lending investors leverage more than 65~per cent. Usually the maximum leverage permitted is around 75 per cent. Most margin lending customers dislike margin calls, and so they tend to create a buffer of at least 10 per cent less than this.

<<tot_intrst_unincorp_biz>>=
tot_intrst_unincorp_biz <-
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58 changes: 29 additions & 29 deletions CGT_and_neg_gearing_parent.tex
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Expand Up @@ -442,7 +442,7 @@ \subsection{Not distorting decisions between consumption today and saving for to
\caption{Assumptions for \Vref{fig:EMTR-savings}}
\small
% latex table generated in R 3.5.2 by xtable 1.8-3 package
% Sun Jan 27 18:53:14 2019
% Mon Jan 28 14:27:54 2019
\begin{tabularx}{\linewidth}{llrrrrrrr}
\toprule
& & & & & \multicolumn{4}{c}{\textbf{Constant}}\\
Expand Down Expand Up @@ -642,7 +642,7 @@ \section{Negative gearing provides a tax shelter against

Negative gearing is used much less for investments outside of housing. Investors in assets other than real estate, such as equities or unincorporated businesses, are less likely to borrow some of the funding, and usually do not borrow so much that these investments are negatively geared.

Total lending to individuals for share investments is at most \$19 billion, compared to individuals' direct share holdings of around \$550~billion,\footnote{The ATO reported that the 2013-14 dividend income for individuals was \$23.8~billion. The HILDA survey showed in the same year \$0.969~trillion for household wealth in equities.} and compared to borrowings of \$548~billion from banks for housing investor lending.
Total lending to individuals for share investments is at most \$19 billion, compared to individuals' direct share holdings of around \$550~billion,\footnote{The ATO reported that the 2013-14 dividend income for individuals was \$23.8~billion. The HILDA survey showed in the same year \$0.387~trillion for household wealth in equities.} and compared to borrowings of \$548~billion from banks for housing investor lending.

When individuals do borrow to invest in equities, they are seldom negatively geared. Equities investors will only be negatively geared if they are at least 70 per cent leveraged. The average leverage of those who do have a margin loan is around 27 per cent. Few margin lending investors leverage more than 65 per cent. Usually the maximum leverage permitted is around 75 per cent. Most margin lending customers dislike margin calls, and so they tend to create a buffer of at least 10 per cent less than this.

Expand Down Expand Up @@ -866,7 +866,7 @@ \section{House price impacts will be modest}
% latex table generated in R 3.5.2 by xtable 1.8-3 package
% Sun Jan 27 18:53:29 2019
% Mon Jan 28 14:28:09 2019
\begin{tabularx}{\linewidth}{lRRRR}
\toprule
& & \multicolumn{3}{c}{\textbf{Return}}\\
Expand Down Expand Up @@ -985,26 +985,26 @@ \section{\TBD{Number of properties}}
% Jim QC
% eval=FALSE due no access to HILDA
% latex table generated in R 3.5.2 by xtable 1.8-3 package
% Sun Jan 27 18:53:39 2019
% Mon Jan 28 14:28:20 2019
\begin{tabularx}{\linewidth}{rr}
\toprule
{\textbf{Investment properties owned}} & {\textbf{Households}} \\
\midrule
1 & 376,377 \\
2 & 670,296 \\
3 & 224,579 \\
4 & 120,203 \\
5 & 54,685 \\
6 & 25,331 \\
7 & 9,216 \\
8 & 3,218 \\
9 & 8,674 \\
10 & 2,113 \\
13 & 3,664 \\
14 & 1,790 \\
27 & 6,242 \\
58 & 2,813 \\
& 21,392,249 \\
1 & 130,104 \\
2 & 228,686 \\
3 & 76,895 \\
4 & 35,493 \\
5 & 17,498 \\
6 & 8,190 \\
7 & 3,253 \\
8 & 1,073 \\
9 & 3,466 \\
10 & 1,057 \\
13 & 1,832 \\
14 & 895 \\
27 & 1,248 \\
58 & 1,406 \\
& 8,292,691 \\
\bottomrule
\end{tabularx}
Expand All @@ -1014,7 +1014,7 @@ \section{\TBD{Number of properties}}
\begin{table}
% latex table generated in R 3.5.2 by xtable 1.8-3 package
% Sun Jan 27 18:53:39 2019
% Mon Jan 28 14:28:20 2019
\begin{tabularx}{\linewidth}{rr}
\toprule
\midrule
Expand All @@ -1030,7 +1030,7 @@ \section{\TBD{Number of properties}}
\begin{table*}
\caption{Taxpayers with wage and salary income claiming business losses by occupation}\label{tbl:bizlosses}
% latex table generated in R 3.5.2 by xtable 1.8-3 package
% Sun Jan 27 18:53:40 2019
% Mon Jan 28 14:28:21 2019
\begin{tabularx}{\linewidth}{lrrrrr}
\toprule
{\textbf{Occupation}} & {\textbf{Type}} & {\textbf{No. claiming losses}} & {\textbf{\%}} & {\textbf{Average loss}} & {\textbf{Average salary}} \\
Expand Down Expand Up @@ -1184,7 +1184,7 @@ \subsection{Capping loss deductions}
\begin{table}
\caption{Budgetary impact of caps to negative gearing}\label{tbl:cap-NG}
% latex table generated in R 3.5.2 by xtable 1.8-3 package
% Sun Jan 27 18:53:55 2019
% Mon Jan 28 14:28:37 2019
\begin{tabularx}{\linewidth}{rrrS}
\toprule
{\textbf{Threshold}} & {\textbf{Costing}} & {\textbf{Number affected}} & {\textbf{\%}} \\
Expand All @@ -1210,7 +1210,7 @@ \subsection{Limits on number of properties}
Only 31~percent of landlords have two or more properties. A fraction of these would not be taxed as they would the `first property' for a landlord. The relatively small number of taxpayers affected substantially reduces the potential budget gains.
Only 30~percent of landlords have two or more properties. A fraction of these would not be taxed as they would the `first property' for a landlord. The relatively small number of taxpayers affected substantially reduces the potential budget gains.
In any case, capping property numbers is a crude way to target `excessive' negative gearing. Taxpayers can increase their deductions by purchasing fewer but more expensive properties. Consequently the policy will distort investment decisions by discouraging investors from buying more affordable properties.
Expand Down Expand Up @@ -1343,7 +1343,7 @@ \chapter{Effective tax rates by investment return and tax bracket}
% latex table generated in R 3.5.2 by xtable 1.8-3 package
% Sun Jan 27 18:55:05 2019
% Mon Jan 28 14:29:44 2019
\begin{tabular}{Qlllrrrrr}
\toprule
& & & & \multicolumn{5}{c}{\textbf{Nominal annual capital gain}}\\
Expand Down Expand Up @@ -1382,7 +1382,7 @@ \chapter{Effective tax rates by investment return and tax bracket}
\begin{ocg}{ocgColored}{ocgColored}{0}
\node (Coloured) {%
% latex table generated in R 3.5.2 by xtable 1.8-3 package
% Sun Jan 27 18:55:05 2019
% Mon Jan 28 14:29:44 2019
\begin{tabular}{Qlllrrrrr}
\toprule
& & & & \multicolumn{5}{c}{\textbf{Nominal annual capital gain}}\\
Expand Down Expand Up @@ -1453,7 +1453,7 @@ \chapter{Session info of $\mathsf{R}$ used for compilation}
\centering
\caption{Platform}
% latex table generated in R 3.5.2 by xtable 1.8-3 package
% Sun Jan 27 18:55:05 2019
% Mon Jan 28 14:29:44 2019
\begin{tabularx}{\linewidth}{l}
\toprule
R version 3.5.2 (2018-12-20) \\
Expand All @@ -1464,7 +1464,7 @@ \chapter{Session info of $\mathsf{R}$ used for compilation}
English\_Australia.1252 \\
English\_Australia.1252 \\
Australia/Sydney \\
2019-01-27 \\
2019-01-28 \\
\bottomrule
\end{tabularx}
Expand All @@ -1475,7 +1475,7 @@ \chapter{Session info of $\mathsf{R}$ used for compilation}
% latex table generated in R 3.5.2 by xtable 1.8-3 package
% Sun Jan 27 18:55:05 2019
% Mon Jan 28 14:29:44 2019
\begin{longtable}{lllll}
\caption{Packages} \\
\toprule
Expand Down Expand Up @@ -1519,7 +1519,7 @@ \chapter{Session info of $\mathsf{R}$ used for compilation}
hms & 2018-03-10 & CRAN (R 3.5.1) & TRUE & \\
httr & 2018-12-11 & CRAN (R 3.5.2) & TRUE & * \\
hutils & 2019-01-27 & local & TRUE & * \\
hutilscpp & 2019-01-25 & local & & * \\
hutilscpp & 2019-01-28 & local & & * \\
knitr & 2018-12-10 & CRAN (R 3.5.2) & TRUE & * \\
lattice & 2018-11-04 & CRAN (R 3.5.1) & TRUE & \\
lazyeval & 2017-10-29 & CRAN (R 3.5.1) & TRUE & \\
Expand Down
2 changes: 1 addition & 1 deletion CGT_and_neg_gearing_parent_time.txt
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@@ -1,2 +1,2 @@
Start Finish Duration
2019-01-27 18:52:18 2019-01-27 18:55:06 00:02:48
2019-01-28 14:26:46 2019-01-28 14:29:45 00:02:58
3 changes: 2 additions & 1 deletion memory-usage.txt
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Expand Up @@ -14,6 +14,7 @@
0.1 load_sample_file
0.1 create-age-imp
0.4 target_sample_file
4.6 Import-HILDA
0.0 Import-HILDA-all-waves-step-1
0.1 Import-SIH
0.0 Import-misc-ATO
Expand Down Expand Up @@ -110,7 +111,7 @@
1.3 NG-vs-salary
1.5 Results
0.0 prop_landlords_n_properties
24.6 HILDA-duration-of-NG
22.9 HILDA-duration-of-NG
0.0 ggsurv-NG
0.0 Survival-NG-analysis-hilda
0.0 NG-survival-curve
Expand Down
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