Friday, March 17, 2017

Trump’s budget proposal plans a disaster for public investment [feedly]

Trump's budget proposal plans a disaster for public investment
http://www.epi.org/blog/trumps-budget-proposal-plans-a-disaster-for-public-investment/

Trump's budget proposal plans a disaster for public investment

Today the White House laid out its priorities in its first budget blueprint. And these priorities are simple enough to describe: paying for increased spending on defense and border security with cuts across the board to nondefense discretionary spending (NDD). Among other reasons why these are bad decisions, they would have devastating consequences for public investment.

It's worth looking at one specific cut that seems fairly telling. Despite campaigning on a $1 trillion infrastructure program, the president's budget actually cuts the Department of Transportation's funding by 13 percent. Coupling this cut with the fact that the campaign's original proposal was simply not a serious plan, and the rumors that the president and Congress are punting infrastructure to next year, it starts to become increasingly clear that increased infrastructure investment isn't a promise that the Trump administration is taking seriously.

The broader cuts in the budget blueprint foreshadow an even worse fate for overall public investment. NDD is only about 16 percent of all federal spending, but fully half of it is public investment. The Trump budget essentially puts a long-run decline in NDD spending on overdrive. NDD budget authority fell from almost 7 percent of GDP in 1977 to about 3 percent by 1990. It has hovered around 3 percent since then, beginning a slow decline in recent years. The administration's budget intends to accelerate this decline, reducing NDD spending swiftly and sharply from 2.8 percent of GDP in 2016 to 2.3 percent by 2018.

FIGURE A

The administration's budget blueprint would sharply accelerate the decline of nondefense discretionary spendingNondefense discretionary budget authority as a share of GDP, excluding supplemental spending, 1979-2018

YearHistorical and president's Budget
19765.60%
19776.73%
19786.26%
19795.80%
19805.96%
19815.11%
19824.18%
19834.05%
19844.00%
19853.79%
19863.27%
19873.30%
19883.12%
19893.07%
19903.26%
19913.50%
19923.61%
19933.64%
19943.48%
19953.14%
19962.96%
19972.89%
19982.87%
19993.09%
20002.79%
20013.08%
20023.19%
20033.32%
20043.29%
20053.19%
20063.00%
20072.99%
20083.01%
20093.37%
20103.62%
20113.24%
20123.05%
20132.85%
20142.86%
20152.77%
20162.82%
20172.63%
20182.32%

Source: Author's analysis of Office of Management and Budget data

With cuts to higher education funding, worker retraining programs and basic research, the administration seems to have a single-minded focus on cutting any research that may help us learn something for the future. To put it bluntly, this is the exact opposite of what the economy needs. Economic productivity has decelerated substantially over the past four years, associated with a steep decline in growth of the nation's capital stock. And with interest rates still low, now is not the time to exacerbate this decline, but rather to reverse it, with an ambitious increase in public investment that can financed by historically cheap borrowing costs.


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Thursday, March 16, 2017

Eliminating Income Taxes in Favor of Higher Sales Taxes Will Not Boost West Virginia’s Economy [feedly]

Eliminating Income Taxes in Favor of Higher Sales Taxes Will Not Boost West Virginia's Economy
http://www.wvpolicy.org/eliminating-income-taxes-in-favor-of-higher-sales-taxes-will-not-boost-west-virginias-economy/

West Virginia grows its economy and creates good jobs by investing in the things that help communities and individuals thrive, like our schools, health care, and public safety. Our state needs tax reform that generates the resources we need to pay for these things and asks everyone to pay what they owe. Eliminating the state income tax does just the opposite.

Similar to Kansas's failed tax experiment, Senate Bill 335 puts West Virginia on a path to eliminate the state's personal and corporate income taxes, reduce severance taxes, and replace with them with a broad-base sales tax that would be the highest in the nation. In Kansas, this has resulted in an ongoing fiscal crisis that has forced cuts to schools and other services people rely on every day, without the promised economic benefit. Kansas legislators are now trying to reverse course.

SB 335 would dramatically increase taxes on low-income populations and the middle-class to pay for large tax breaks for the richest West Virginians. If enacted, it would be the largest redistribution of wealth in the state in the last century.

Cutting the state income tax and replacing only some of the lost revenue with higher sales taxes will have immediate and harmful effects on our state. Instead of growing our economy, it is almost certain to lead to deep cuts in investments in schools, roads, and other critical building blocks of strong economy – similar to what happened in Kansas.

SB 335 will also reduce what people spend in local stores, slowing the state's economic growth and job creation. A higher sales tax makes the price of everything that is subject to a tax go up, and that is likely to reduce spending at local businesses. When they can, people will instead cross the border or buy from out-of-state Internet merchants who don't charge sales tax or charge a lower rate, causing West Virginia small businesses to layoff workers.

Businesses will also face higher sales taxes that will eliminate a portion of any savings from the income tax cuts. A substantial share of business purchases – such as electricity, heat, water, machinery, equipment, computers, furniture, and shipping supplies – will now be taxed at eight percent, hurting West Virginia small businesses by adding thousands of dollars in annual expenses.

Our tourism industry will suffer as the state lodging tax increases to 11 percent. Combined with the six percent local hotel occupancy tax, West Virginia would have the highest lodging tax in the nation.

Because the increased sales tax revenue is unlikely to fully replace the lost income tax revenue, the state will very likely have to cut back on funding for schools and other services provided at the local level, and local governments, in turn, likely will increase business taxes to make up some of the difference.

The idea that economists agree that taxing consumption is better for economic growth than taxing income is just not true. There is no consensus among economists that shifting from reliance on state income taxes to sales taxes would improve state economic growth.

Most economists who favor a pure consumption tax also acknowledge that it will increase taxes for low- and moderate-income households and support taking steps to alleviate this problem. Yet the proposal in our state fails to do this.

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Here Come the Robots; Your Job Is at Risk [feedly]

Here Come the Robots; Your Job Is at Risk
http://triplecrisis.com/here-come-the-robots-your-job-is-at-risk/

Martin Khor

The new automation revolution is going to disrupt both industry and services, and developing countries need to rethink their development strategies.

A news item caught my eye last week, that Uber has obtained permission in California to test two driverless cars, with human drivers inside to make corrections in case something goes wrong.

Presumably, if the tests go well, Uber will roll out a fleet of cars without drivers in that state. It is already doing that in other states in America.

In Malaysia, some cars can already do automatic parking. Is it a matter of time before Uber, taxis and personal vehicles will all be smart enough to bring us from A to B without our having to do anything ourselves?

But in this application of "artificial intelligence", in which machines can have human cognitive functions built into them, what will happen to the taxi drivers? The owners of taxis and Uber may make more money but their drivers will most likely lose their jobs.

The driverless car is just one example of the technological revolution taking place that is going to drastically transform the world of work and living.

There is concern that the march of automation tied with digital technology will cause dislocation in many factories and offices, and eventually lead to mass unemployment.

This concern is becoming so pervasive that none other than Bill Gates recently proposed that companies using robots should have to pay taxes on the incomes attributed to the use of robotics, similar to the income tax that employees have to pay.

That proposal has caused an uproar, with mainstream economists like Lawrence Summers, a former United States treasury secretary, condemning it for putting brakes on technological advancement. One of them suggested that the first company to pay taxes for causing automation should be Microsoft.

However, the tax on robots idea is one response to growing fears that the automation revolution will cause uncontrollable disruption and increase the inequalities and job insecurities that have already spurred social and political upheaval in the West, leading to the anti-establishment votes for Brexit and Donald Trump.

Recent studies are showing that deepening use of automation will cause widespread disruption in many sectors and even whole economies. Worse, it is the developing countries that are estimated to lose the most, and this will exacerbate the already great global inequalities.

The risks of job automation to developing countries is estimated to range from 55 to 85%, according to a pioneering study in 2016 by Oxford University's Martin School and Citi.

Major emerging economies will be at high risk, including China (77%) and India (69%). The risk for Malaysia is estimated at 65-70%. The developed OECD countries' average risk is only 57%.

From the Oxford-Citi report, "The future is not what it used to be", one gathers there are at least three reasons why the automation revolution will be particularly disruptive in developing countries.

First, there is "premature deindustrialisation" taking place as manufacturing is becoming less labour-intensive and many developing countries have reached the peak of their manufacturing jobs.

Second, recent developments in robotics and additive manufacturing will enable and could thus lead to relocation of foreign firms back to their home countries.

Seventy per cent of clients surveyed believe automation and 3D printing developments will encourage international companies to move their manufacturing close to home. China, Asean and Latin America have the most to lose from this relocation.

Thirdly, the impact of automation may be more disruptive for developing countries due to lower levels of consumer demand and limited social safety nets.

The report warns that developing countries may even have to rethink their overall development models as the old ones that were successful in generating growth in the past will not work anymore.

Instead of export-led manufacturing growth, developing countries will need to search for new growth models, said the report.

"Service-led growth constitutes one option, but many low-skill services are now becoming equally automatable."

Another series of reports, by McKinsey Global Institute, found that 49% of present work activities can be automated with currently demonstrated technology, and this translates into US$15.8tril in wages and 1.1 billion jobs globally.

About 60% of all occupations could see 30% or more of their activities automated. But more reassuringly, an author of the report, James Manyika, says the changes will take decades.

Which jobs are most susceptible? The McKinsey study lists accommodations and food services as the most vulnerable sector in the US, followed by manufacturing and retail business.

In accommodations and food, 73% of activities workers perform can be automated, including preparing, cooking or serving food, cleaning food-preparation areas and collecting dirty dishes.

In manufacturing, 59% of all activities can be automated, including packaging, loading, welding and maintaining equipment.

For retailing, 53% of activities are automatable. They include stock management, maintaining sales records, gathering customer and product information, and accounting.

A technology specialist writer and consultant, Shelly Palmer, has also listed elite white-collar jobs that are at risk from robotic technologies.

These include middle managers, commodity salespeople, report writers, journalists, authors and announcers, accountants and bookkeepers, and doctors.

Certainly, the technological trend will improve productivity per worker that remains, and increase the profitability of companies that survive.

But there are adverse effects including loss of jobs and incomes for those who are replaced by the new technologies.

What can be done to slow down automation or at least to cope with its adverse effects?

The Bill Gates proposal to tax robots is one of the most radical. The tax could slow down the technological changes and the funds generated by the tax could be used to mitigate the social effects.

Other proposals, as expected, include training students and present employees to have the new skills needed to work in the new environment.

Overall, however, there is likely to be a significant net loss of employment, and the potential for social discontent is also going to be large.

As for the developing countries, there will have to be much thinking about the implications of the new technologies for their immediate and long-term economic prospects, and a major rethinking of economic and development strategies.

Originally published in The Star.

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The great Chinese inequality turnaround

The great Chinese inequality turnaround

Ravi Kanbur, Yue Wang, Xiaobo Zhang

15 March 2017


Alongside the spectacular growth and extraordinary reductions in poverty – perhaps the most dramatic in human history – the evolution of Chinese income inequality since the start of the reform process in 1978 has been a focus of interest among analysts and policymakers. Sharply increasing inequality became an integral part of the narrative on Chinese development, with some commentators arguing that this was the inevitable price to be paid for the high rates of growth, while others warned of the social consequences of these growing gaps.

However, as more data have accumulated, attention has shifted to the evolution of inequality in China in the 2000s, including the present decade – the years after 2010. From the mid-2000s onwards, a number of studies began to argue that the rise in inequality was being mitigated, and that inequality could be plateauing or even going down (an early example is Khan and Riskin 2005).

Our recent research attempts to provide a comprehensive assessment of what the data show, a deeper look into the patterns of inequality change, and preliminary explanations for the trends observed (Kanbur et al. 2017). Our basic conclusion is that there does indeed appear to be a turnaround taking place in Chinese inequality from the latter part of the first decade of the 2000s, and that the explanations lie in policy changes and in the nature of structural transformation in China.

Our assessment uses household-level survey data from the Chinese Household Income Project (CHIP) for 1995, 2002, and 2007, and from China Family Panel Studies (CFPS) for 2010, 2012, and 2014.1 A number of issues of completeness and comparability have to be addressed in order to use these data, but between them they provide a 20-year perspective on income distribution in China, from 1995 to 2014. This analysis of household-level data is complemented with analysis of regional inequality using the provincial level income and population data from the National Bureau of Statistics and Provincial Yearbooks. Full details of data sources and adjustments are provided in Kanbur et al. (2017).

Table 1 presents the Gini coefficient for the six years covering 1995 to 2014. We see that the Gini coefficient has an inverted U-shaped pattern with the turning point at 0.533 in 2010. Similar trends are found for other inequality measures (Kanbur et al. 2017). Figure 1 shows that the income share of the top 10% reached its highest point in 2010, at around 0.4, and then declined.2 And the top-bottom decile income ratio went up from 1995 to 2012 and declined a little thereafter.

Table 1. Inequality measures from household survey data

Note: We adjusted CHIP income by excluding the components that are not in CFPS. CHIP 2007 uses the NBS survey data. Full details in Kanbur et al. (2017).

Figure 1. Decile income share (adjusted income)

The combination of CHIP and CFPS data give us six observations spanning 1995 to 2014, all based on household surveys. An alternative methodological approach, useful for capturing long term annual trends, was introduced in Kanbur and Zhang (1999, 2005). This method uses NBS data on provincial consumption per capita broken down by rural and urban areas for each province. Combining this with rural-urban population data for each province, we can construct a synthetic national consumption distribution which assumes equality within rural areas and within urban areas of each province. Clearly, this will understate the level of inequality, but the trend over time may nonetheless convey useful information on how inequality has evolved.

Figure 2 presents the Gini coefficient over time for the synthetic distribution which focuses on regional inequality. Inequality went down a little after 1978 and started to climb after 1985. The Figure shows a peak in the mid-2000s, with a decline thereafter. Of course the values of the Gini coefficient in Figure 1 and Table 1 are not comparable since they come from different data sources and use different methods. However, the broad trends since the mid-1990s are similar for the two different time-perspectives – there appears to be an inequality turnaround sometime in the first decade of the 2000s.

Figure 2. Regional inequality in consumption per capita

What explains the turnaround? While a full explanation will require further detailed research, we can propose a number of hypotheses centred on the transformation of China from a rural/agrarian society towards an urban/industrial economy. In this framework, the national income distribution can be thought of as a weighted sum of the rural and the income distributions. The national inequality trend thus depends on three factors:

  1. The gap in average income between rural and urban sectors;
  2. Inequality within the rural and urban sectors; and
  3. The rate of urbanisation.

Zhang et al. (2011) have argued that China has now reached the 'Lewis turning point', where rural to urban migration begins to tighten rural labour markets and thereby mitigate the rural-urban wage differential. In addition, heavy government investment in infrastructure in the rural sector and lagging regions – a feature of Chinese policy since the 2000s (Fan et al. 2011) – would also raise economic activity and incomes in these areas.

A number of specific policy measures can be argued to have further mitigated inequality, by narrowing the spread within rural and urban areas. For example, in 2004 the Ministry of Labor and Social Security issued a 'Minimum Wage Regulations' law and the next decade saw rising minimum wages coupled with substantial improvements in compliance (Kanbur et al. 2016). Further, a number of social programs were introduced and strengthened from the 2000s onwards. Since 2004, China has introduced new rural cooperative medical insurance, currently covering more than 95% of the rural population. Rural social security has also been rolled out since 2009. Although the premiums of the rural medical insurance and social security are still much lower than for its urban counterparts, the programs have provided a cushion for rural residents against health risk and elderly care.

Concluding remarks

Thus, transfer and regulation regimes, combined with tightening labour markets in rural areas, have acted to mitigate inequality in China. Some evidence of this is found in Tables 2 and 3, which show the income share for each of a number of income sources and the Gini coefficients of income from each source. Inequality of wage income has fallen sharply, as has inequality of transfers. These are the dominant factors in total income, so their declining inequality is the dominant factor in the inequality turnaround. Further details of these calculations and interpretations are given in Kanbur et al. (2017).

Table 2. Share of income by source

Source: Kanbur et al (2017)

Table 3. Gini of income by source

Source: Kanbur et al (2017)

Finally, holding other factors constant, the rising share of urban population can, beyond a certain point, begin to contribute to inequality reduction. Kanbur and Zhuang (2013) have argued that while countries like India have yet to reach this turning point, China's urbanisation rate has now taken it even further.

Our argument thus suggests that a number of forces have come together to mitigate the sharp rise in inequality seen in the first quarter century after the reform process. Chinese inequality is still high, with a Gini of around 0.5. But the rising trend has plateaued, and even begun to decline. There has been a great turnaround in Chinese inequality, and we now need to better understand the reasons for it, and the lessons that can be learnt from it.

References

Alvaredo, F, L Chancel, T Piketty, E Saez and G Zucman (2017) "Global inequality dynamics: New findings from WID.world',' NBER Working paper 23119.

Fan, S, R Kanbur and X Zhang (2011) "China's regional disparities: Experience and policy", Review of Development Finance, (1): 47–56.

Kanbur, R, Y Li and C Lin (2016) "Minimum wage competition between local governments in China", processed, Cornell University.

Kanbur, R, L Wang and X Zhang (2017) "The great Chinese inequality turnaround", CEPR Discussion Paper No. 11892.

Kanbur, R and X Zhang (1999) "Which regional inequality? The evolution of rural-urban and inland-coastal inequality in China, 1983-1995", Journal of Comparative Economics, 27: 686-701.

Kanbur, R and X Zhang (2005) "Fifty years of regional inequality in China: A journey through central planning, reform, and openness ", Review of Development Economics, 9(1): 87–106.

Kanbur, R and J Zhuang (2013) "Urbanization and inequality in Asia", Asian Development Review, 30(1): 131–147.

Khan, A R and C Riskin (2005) "China's household income and its distribution, 1995 and 2002", The China Quarterly, 182: 356-384.

Zhang, X, J Yang and S Wang (2011) "China has reached the Lewis turning point", China Economic Review, 22(4): 542-554

Endnotes

[1] CHIP data was collected by China Academy of Social Sciences and Beijing Normal University, and CFPS data was collected by Peking University.

[2] Alvaredo et al. (2017) also show a recent decline in the share of the top of the distribution.


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John Case
Harpers Ferry, WV

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