Does a worker choose not to work when collecting Social Security?

Campaigns against social security usually claim that Social Security Benefits discourage workers from being employed. Many right wing policy advocates point their fingers at Social Security Benefits as being expensive and further making the labor force lazy – to say the least. In this article I analyze to what extent the number of unemployed people is determined by the number of people collecting Social Security Benefits given out by disability claims. That is, workers’ own disability; workers’ spouse disability; and, workers’ children disability. I use the term workers because, in spite being disable, I assume they are willing to work. Thus, the argument from the right would be that people readily available to work will remain unemployed whenever they can secure an income from the Social Security Administration. Furthermore, workers will do so too before the scenario in which their spouses collect benefits. And third, workers will not work in the case in which social security benefits are being collected for their children. In other words, workers would rather take care of the disable children or spouse and live out of public transfers. Then, the question that possesses this analysis is the following: does a worker choose not to work when collecting some form of Social Security Benefit for her family?

Social Security and Unemployment levels.

Social Security and Unemployment levels. By Catherine De Las Salas (Summer 2015).

The data:

So, by looking at the correlation between number of unemployed people and number of people claiming benefits for the above mentioned three reasons, I am able to capture the “willingness” of disable workers, whom are collecting social security benefits, to work. I take data at the United States county level from the U.S. Social Security Administration database which contains the number of beneficiaries by type of benefit. Also, I take observations pertaining to the number of people claiming benefits for disability reasons. In addition, I take the number of unemployed people at the county level (data from the U.S. Bureau of Labor Statistics). Both data correspond to 2014. The only counties excluded from the sample are the ones at U.S. Virgin Islands. All other counties, and independent cities are included in the sample regression.

One could argue, correctly, some sort of multicollinearity in the data since people collecting benefits usually do not work. However, unemployment statistics from the Bureau of Labor Statistics interestingly count as unemployed persons those who have looked actively for a job during the recent past weeks of the application of the survey. This means that what the unemployment statistics is capturing here is the “willingness” of disable people to work while collecting social security benefits. Given that the answers to BLS Household Survey data have no conditional effect on social security benefits, it is reasonable not expect the survey to be corrupted by the interest of keeping the benefit on the beneficiaries’ end. In other words, in spite of the statistical identity, data can be further interpreted given the nature of the question being asked by BLS Household Survey.

Results:

What I found at the county level is that as the number of disable workers rise by 2.9, the number of unemployed persons do so by one. This is an obvious outcome of the effect that disabilities have on the labor market. So, this should not surprise anyone. However, what turned out to be interesting is the fact that disable people collecting social security benefits are counted as unemployed. This basically means, to some extent, that disable people are “willing” and actively looking for jobs. Although the logic is counterintuitive at first glance, it may reveal something thought-provoking. On one hand, if the person is disable to work, and at the same time collecting social security benefits, such a person should not be looking for a job. But, what the data show is that they actually, and actively, looked for a job despite their condition. Although interpretations have to be carefully examined, either disable persons are cheating the system, or they are just eager to be incorporated to the labor market. Further, given the statistical significance at 95% confidence level for all of the estimated coefficients, there is little room for concluding the variation is due to sampling error only.

Likewise, unemployment levels are affected by workers’ disable spouses. For every increase of roughly 46 people collecting benefits for their spouses, there is a unit increase in the number of unemployed people. Clearly, having a disable spouse does little discouragement for the worker to work. Finally, unemployment levels decrease with increases of disable children. That is, disable children make workers look for jobs eagerly. As the number of disable children increases by 10.5, the number of unemployed people drops by one.

One obvious limitation of the analysis is the type of disability that beneficiaries may have, which certainly mediates the “willingness” of the disable person to work. Nonetheless, some narrow conclusions can be drawn from this regression. First, even though disable people get support from social security, it does not translate necessarily in quitting the labor force, which means neither disabilities, nor public transfers make them lazy. Also, data show that paying for a disable children encourages parents to work.

Regression Output, Social Security Benefits and Unemployment levels

Regression Output, Social Security Benefits and Unemployment levels

 

Where are the teenager workers? An answer to The New York Times.

In a recent article published by the New York Times, Patricia Cohen and Ron Lieber made a brief inquiry on youth employment during summer 2015. In their writing piece, Cohen and Lieber open two windows for interpretation about factors affecting teenagers’ employment during summer school break. One of them is to believe that people between 16 and 19 years old are not interested in working at all, and instead they are doing “other stuff” (going to summer school, traveling or volunteering). The other window for interpretation is that the rivalry between teenagers and 20-years-older people for summer jobs has intensified in recent years. In their own words “Adults, desperate for second and third jobs to make ends meet, may be crowding out many teenagers”. The former rationale has to be ruled out from the analysis given that the BLS Household Survey barely allows for such an interpretation, which leads to only speculations. Otherwise, the latter issue about adults crowding out effect on teenagers may explain the picture better.

So, the argument goes the following way: youth summer employment is being taken by 20-years-and-older people. In other words, level of employment of 16-19-years-old gets affected by level of employment of 20-years-and-older people. If the American Economy creates certain number of jobs per month (average 221,000), those employments must be distributed among the population actively looking for jobs. Thus, variance in 20-years-and-older people must explain variance in youth employment. In addition, since the question is appropriately posed for summer jobs, the comparison must be run among comparable months of the year. Furthermore, given that gender plays a role in the number of hours worked by employees, and the hypothesis proposes 20-years-and-olders are chasing second jobs, it makes sense to look at 20-years-and-older women and men disjointedly.

Therefore, it also makes sense to regress 16-19-years-old’s level of employment on 20-years-and-older Women and Men’s level of employment for the months of January through June using BLS data from 2000 to 2015.

The results show that level of employment of teenagers get affected negatively by women level of employment. This effect can be interpreted as women competing fiercely against teenagers looking for a summer job. Data reveal that women tend to take jobs traditionally “meant” for teenagers. These results are twofold. First, data show that the crowding out effect maybe indeed happening. Second, data show that women might be the ones crowding out teenagers’ employment.

The situation exacerbates for young as the labor market reaches the summer. Generally speaking, women level employment affects teenagers’ less at the beginning of the year than by the summer. Coefficients in this regard show an increase from -0.58 in April to an estimated -0.75 in June. The meaning of the estimates is that teenagers have 75% less chances to get a job when women 20-years-and-older apply for it too in June. Thus, results on June data actually reinforce the hypothesis given that June and summer are supposed to have  jobs temporarily filled by teens.

The following table summarizes findings of regressions. Asterisk means 95% significance level.

Youth employment

Is Construction Investment Holding Back Job Creation?

Employment level statistics for the month of June 2015 looked a bit worrisome for some economists. At a glance, Construction was one of the missing sector in the list of industries significantly contributing to job creation. Indeed, Construction was the last sector in joining job creation after the Great Recession. Though ADP, the payroll company, reported that the sector added an estimated figure of 19,000 jobs during the month of June 2015 -which reflects a slight decline from the month of April-, establishment survey data from the U.S. Bureau of Labor Statistics (BLS) showed no addition for the Construction payroll data. More in detail, BLS employment data on Construction sector showed that it contracted at several specific specialties. The table below shows awful figures for a season which is said to be appropriate for outdoor works.
Employees on Construction Nonfarm Pay Roll
Specifically, activities that cut back in employment were nonresidential specialty trade contractors (-5.6K estimated employees), specialty trade contractor (-1.9K estimated employees), Residential Building (-6.1K estimated employees). Although there is much of a mix in the employment data for the sector, the aggregate figures suggest that a brief revision is worth doing in order to see whether there is an industry slow down, or just a deferred process due to weather conditions.

Well, the latest data on Construction put in place –May 2015- in the United States show no change in construction investment on month-to-month basis. Estimated change in construction spending for May 2015 was about 0.8% ($1,035.8 billion), with a margin error of +/- 1.5%. Furthermore, most of the estimated values do not support alternative hypothesis in order to reject the null hypothesis. In other words, there is no statistical evidence to claim that construction spending was different than zero (0) in the United States from April to May 2015. At the least, we could say that weather has not played a deferring factor for Construction activities, thereby affecting employment levels for June 2015.

Construction put in place Adjusted

Although data released by the U.S. Census Bureau is subject to constant revision, it seems unlikely that those figures change given the data on employment level. That is, employment levels data are sort of “confirming” that current investment in Construction is not enough for the sector to keep up with economic growth, at least for the summer season.

Employment statistics cannot be interpreted in a vacuum.

Employment statistics cannot be interpreted in a vacuum inasmuch as other economic indicators do determine employment growth. There are many nuances that require attention to detail. Indeed, details on June’s 2015 report on labor market are twofold. First, mining related industries –including utilities- started to adjust to low oil prices, as well as low demand for several manufacturing goods tempered high expectations risen before summer season. Likewise, spring low levels of investment on residential construction realized a decrease on employment for the summer. Second, Professional Business and Services continues to lead job creation in United States. In other words, oil prices do continue to affect the economy, though the industry started to adjust; strong dollar dragged international demand for U.S. metals products thereby affecting employment in manufacturing; third, low levels of investment in construction are taking a toll in employment creation.

Employment level June 2015.

Employment level June 2015.

Since Construction Investment slowed down in the spring, employment in the industry drops in the summer:

The latter factor should worry the most labor economist. Given that oil prices and exchange rates are beyond strictly control of United States institutions, and are also well known phenomena, investment in construction should call the attention of economic policy leaders. Since the beginning of the summer of 2015, when the statistics about GDP 2015Q1 were released, economists started to look at Investment in non-residential and residential structures. This sector is key for the seasonal employment since, as soon as weather allows for, construction and outdoor activities rebound. However, early in the spring 2015, this was not the case. Total residential construction put in place for the month of April 2015 decreased to $353,086 billion of dollars from 360,826 billion put in place the same month 2014, which equals 2 percent decrease over the year.

Residential Construction Put in Place, April 2015.

Residential Construction Put in Place, April 2015.

So, it should not surprise anyone that construction-contractors cut back employment as they see investment dropping. That very fact shows up in employment statistics astonishingly. Data from BLS show construction did not contribute significantly to augment employment levels nationally. Instead, 6.1KResidential construction building workers were dismiss from work; 5.6K Nonresidential specialty trade contractors were also cut from duty. That makes up to roughly 12K seasonal jobs that are crucial for year-round labor statistics.
These statistics are relevant for economics given that construction of new homes has many job spillovers in manufacturing industries. Another way to say the same is that whenever a new home is built, new sofas, TV’s, Kitchens, furniture, so on and so forth, are needed. Furthermore, the housing market was the sector that initiated the Great Recession, and construction as a sector was the latest in joining the path to recover. This issue helps to introduce the other weak flank of the current employment situation: manufacturing.

Manufacturing feels the spillover of strong dollar and low local demand:

The other drop in employment levels for June 2015 was seen in manufacturing, more precisely on metal related products which decreased employment level by 4.5K persons. In this case, apparently, it is not only internal demand which is cutting back employment levels, but also international demand for U.S. manufacture goods. In other words, last six months of strong dollar reduced the demand for U.S. manufacturing thereby affecting employment locally. Several sources have noted the extent to which the exchange rate is affecting negatively U.S. competitiveness and employment. Especially for metal products. Recent data on Current Account –Exports and Imports- released by the Bureau of Economic Analysis showed that during the first quarter of 2015 Goods exports decreased to $382.7 Billion from $409.1 billion. The drop in manufacture exports was mostly driven by a decrease in industrial supplies such as Petroleum, Chemicals and…. Metals products. This effect obviously spills over employment levels nationally. Once again, it is not a surprise.

Unemployment rates, June 2015.

Unemployment rates, June 2015.

The surprise:

Finally, what really happens to be a surprise is the revision of employment levels for the months of April and May 2015. The Bureau of Labor Statistics corrected its initial estimates on those figures by stating that April’s 2015 employments added were 187,000, and May’s employment added were 254,000. At first glance, April statistic fell below the threshold of 200,000 jobs per month, which should worry analysts to begin with. Then, when computed, the total amount of jobs not realized statistically amounts to 60,000. Thus, the monthly average for job gains is 221,000, which is slightly above the 200,000 threshold.