Tag Archives: microeconomics

MINIMUM WAGES AND EMPLOYMENT

MINIMUM WAGES AND EMPLOYMENT: A CASE STUDY OF THE FAST-FOOD INDUSTRY IN NEW JERSEY AND PENNSYLVANIA

D. Card & A. B. Krueger

The American Economic Review, Vol 84, No.4 (Sept., 1994)

A Summary

 

Principal Research Question Do rises in the minimum wage reduce employment?
Theory In a competitive labour market, if the government sets a minimum wage that is higher than the equilibrium wage the supply of labour will exceed demand at the higher minimum wage. The increased supply will be complimented by contracted demand and therefore employment levels will fall.
Motivation To be right, and everyone else wrong!!!The rise of minimum wage from $4.25 to $5.05 in NJ provided a good quasi-experiment in that they could do a difference in difference estimation using PN as a control group. They are geographically proximate, and starting wages, meal price, and employment indicators were substantially similar in the pre-treatment survey responses. Additionally, employment in restaurants in the band that paid starting salaries above the (new) minimum wage in both periods fell by an equivalent amount in both NJ and PN. This is significant, as they were paying above the equilibrium price already and so should not have been affected by the wage hike in NJ. Thus they were affected by general economic conditions which appear to have been the same in both states. The distribution of starting wages in the stores was very similar before.

Additionally, as both states were in a recessionary environment with rising unemployment, it is doubtful that any rise in employment found after the wage hike could be attributable to general economic conditions.

Data Data on Fast Food restaurants – they are leading employer of low-wage workers; they comply with regulations; the job requirements are homogenous across restaurants so particular characteristics not an issue; no tips means income levels are easily measurable.They only consider the big chains, 410 of them, in two rounds of survey.
Method Difference in difference with PN as control.
Results Full time employment increased in NJ after the rise. Employment expanded most at low wage stores, and contracted at high wage (those already paying above the minimum).
Robustness
  • Set employment at 0 for temporarily closed stores.
  • Exclude 35 stores on jersey shore
  • Redefined full time work
  • Exclude stores they called more than twice  – none had major effect
  • They test subgroups to see if demand shocks are making up the rise in employment but they find it is not.
  • They test opening hours, number of cash registers with no significant effect.
  • They see no evidence that non-wage compensation decreased, indeed in NJ the amount of free and subsidized meals given to employees actually rose.
  • They look at macro employment data which showed that NJ employment actually was worse in the period than elsewhere in the US, but teenage employment did better.

 

Interpretation The results are not compatible with standard theory. This indicates that although firms are price takers in the product market, they have some power in the factor market. If they are facing an upward sloping labour supply curve a rise in wage can mean a rise in emplyment.Ambiguous as to whether the age increase increased full time employment. It would potentially do so because employers want to substitute low wage earners for full-time employees who are probably older and more skilled. Additionally they may be more productive as they have more time to learn by doing.

There is mixed evidence that the wage increase was passed on to customers through product prices. Prices did rise around 3% which would cover the wage increase, but it did so even at stores already paying more than the minimum wage, and they did not rise faster at the stores most affected by the minimum wage. This could be because the market is very competitive.

Problems Reliability of the data is only 0.7 for employment (based on accidentally doing the same interviews twice).They only consider the big chains, who are most likely to be able to pass on prices, and otherwise squeeze suppliers, engage in advertising to boost sales etc. The story could be different at small, independent outfits.

There are external validity issues in other words.  

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THE DEMAND FOR FOOD CALORIES

THE DEMAND FOR FOOD CALORIES

S.Subramanian & A. Deaton

Journal of Political Economy Vol. 104, No.1 (Feb., 1996) pp.133-162

 

Principal Research Question How elastic is caloric intake with respect to expenditure?
Theory Elasticity measures the responsiveness of quantities demanded to price changes. It is the % change in quantity divided by the % change in priceIt is calculated as   ΔQd/Q ÷ ΔP/P

In this case we are looking at how caloric intake rises with total household expenditure so     ΔCalories/Calories  ÷  ΔExpenditure/Expenditure

If elasticity >|1| then demand is elastic

If elasticity <|1| then demand is inelastic

Motivation Bouis & Haddad, together with others have claimed that the elasticity of caloric intake with respect to income is close to zero. This goes against the idea that nutrition responds to income and that economic policies that are good for growth do not imply an elimination of hunger. It questions whether real income is a good proxy for thinking about welfare. The promotion of real income would therefore not be conducive to development.
Data
  • National Sample Survey for rural households in Maharashtra, western India.
  • 5,630 households, 10 in each of 563 villages
  • Report expenditure on over 300 items including 149 food items.
  • No income data collected, so total household expenditure is used as the welfare measure.

 

Method They regress total available calories on the number of meals given to guests, employees and those taken at home to find out how many calories are contained in each type of meal. They then subtract/add those meals given away/received to the total calories available, to create an adjusted figure. If they did not do this the elasticity for richer households would be grossly overstated as they have a large number of available calories as they give away many more meals to employees/guests. When the data are tabulated it becomes clear that the poor spend a lot less money per 1000 calories than the rich. This is because coarse cereals provide a much larger share of caloric intake. As people get richer people substitute between food groups away from cereals toward dairy and meat products etc. which have many fewer calories. Thus although the total food elasticity if 0.772, the price of calories elasticity of 0.32 drives a wedge between the food and calories elasticites. This is due to substitution.

They regress log calorie intake on log expenditure. Non-linearity would be a problem as it is possible that those with insufficient food would have much larger elasticities than those with more income. So they plot the regression line using a grid of 100 different data points. Although it is somewhat steeper at lower expenditures it is fairly close to a straight line.

This method is not appropriate if controlling for other variables, so they also run an OLS regression with additional variables (no. in household etc.)

Results There is no evidence that elasticities are close to zero unlike in Strauss & Thomas’s findings for Brazil. This could be because for all households surveyed wealth is such that calories are still an issue whereas in brazil they were not.Elasticity declines from 0.65 to 0.4 over the range of incomes from lowest to highest.

Once household composition is controlled for other variables are not significant in the OLS regression. Both calorie consumption and price elasticities are around 0.35.

Robustness Various controls added. They check for non-linearity using the specification noted above.
Interpretation Income does constrain caloric consumption but not by very much, at least within the range of household incomes surveyed here. Poor households tend to purchase cheaper calories but also fewer calories overall.
Problems Endogeneity: if hunger caused poverty as well as poverty causing hunger there would be reverse causality problems. The argument is that lack of hunger reduces productivity and thus wage earning capability which prevents calories from  being purchased. Hunger thus creates a “poverty trap”. They cannot rule this out by using e.g. IV regression, but they claim that as the 600 extra calories that are needed to sustain physical work, could be purchased for 4% of the daily wage, that the barriers to sufficient nutrition are not high enough to create a poverty trap.