Electricity and the jobless recovery from the Great Depression

Transcription

1 Electricity and the jobless recovery from the Great Depression Supervisor: Ricardo Reis The weak recovery from the Great Depression in the United States remains a puzzle: in the words of Kehoe and Prescott (2008), a satisfactory theory of the U.S. Great Depression... needs to explain why hours... stayed so depressed [after 1933] even though productivity recovered. Explaining the joint dynamics of employment, output, and productivity in the recovery from the Great Depression is crucial to our understanding of the economy during bad times, which has implications for policy. For example, Cole and Ohanian (2004) suggested that the cartelization policies of the National Industrial Recovery Act kept wages high above trend and may have delayed the recovery of employment. Alternatively, Reinhart and Rogoff (2009, page 270) emphasize that the recovery from a global financial crisis such as the Great Depression is weaker than a regional crisis because for a country to be pulled out of a post-crisis slump is far more difficult when the rest of the world is similarly affected than when exports offer a stimulus." These explanations have different implications for policy-making in difficult times. This paper takes an alternative route and asks whether the adoption of electricity can explain the jobless recovery from the Great Depression. It builds on a previous paper (Morin, 2014), that used geography as an instrument for electricity adoption and found that, in reaction to cheaper electricity prices, firms reduced employment and the labor share of income and increased productivity and capital intensity. Rather than the medium-term implications, this paper looks at the cyclicality of employment and productivity changes, especially over the period The baseline results are that the adoption of electricity can account for both facts of low job creation and high productivity growth during the recovery from the Great Depression. The identification strategy follows from Morin (2014) and consists of two parts. First, it uses natural variation in the price of electricity depending on the power source hydro power or 1

2 coal power. Hydroelectric power was highly efficient from the beginning and extracted 90% of the potential energy of falling water, leaving no margin for technological improvement. States like California have cheap electricity but the price of electricity is constant. Coal power was relatively inefficient and extracted 25% of the thermal energy of coal, leaving a wide margin for technological progress. States like New Jersey have expensive electricity but the price of electricity is falling. A state s initial loading on the coal technology is an instrument for changes in the price of electricity. The second part of the identification strategy consists of choosing the concrete industry to provide measurements of labor market outcomes. Given the natural variation in electricity prices, it could still be a problem if plants chose endogenously to locate in regions with cheaper electricity prices. The concrete industry provides a close approximation to the ideal random assignment of plants across regions. It is a local industry selling a non-traded good (Syverson, 2004): downstream of the cement industry, it produces heavy products with high transport costs or a limited time to reach its destination (e.g., ready-mix concrete has to be delivered in a few hours before it hardens). Accordingly, concrete is among the most spatially dispersed industries. The non-traded quality of concrete products ensure that this industry locates near its customers, as opposed to industries selling traded goods and able to choose their location. Concrete plants locate in New Jersey or California to be close to their customers, after which they react to the change in the price of electricity in each state. The location decision of concrete plants is orthogonal to the geography of the price of electricity, rules out geographical sorting, and strengthens the validity of the instrument. The concrete industry is a quasi-experiment to assess the causal effect of technical progress in electric utilities on the cyclicality of employment and productivity. The dataset for the regressions is the universe of concrete plants from the Census of Manufactures every two years from 1929 to 1935 (Morin, 2014). The micro-data have information on employment, wage-bill, revenue, the quantity of concrete tons, and the horsepower of electric motors. Linking plants across years produces a set of 630 continuing plants between 1929 and 1935 and a set of 561 continuing plants between 1933 and This paper also uses the coal share of capacity and the average price of electricity by state from the Census of Electric Light and Power Stations (1927 and 1937). Furthermore, the McGraw directory of central stations of 2

3 1928 contains 800 pages with the details of all generating stations by source of power, which was digitized for the first time for this project. Table 1 shows the 1 baseline results: a higher coal reliance in 1927 caused a decrease in employment from 1933 to 1935 and an increase in labor productivity (the quantity of concrete tons per worker) over the same period. The coefficients are both statistically significant, with a significance level of 5% for labor productivity and 10% for employment, and economically significant, with the average coal reliance causing a 13.5% decrease in employment and a 30% increase in productivity over the 2 years of the period (These numbers equal the regression coefficient, e.g for employment, multiplied by the average coal share of 77.9% and by the two years of the period.) This regression answers the research question and confirms that cheaper electricity from coal prices caused a divergence between employment and productivity, potentially explaining the puzzle of the jobless recovery from the Great Depression. Figure shows the scatter plot of the regression for labor productivity. Table 2 and Figure confirm these results with the coal share of power at the city-level from the McGraw directory of central stations. The point estimates from the regressions are similar to the baseline results and predict similar magnitudes for the change in employment and productivity after the Great Depression. Note that these results use quantities and are thus robust to problems from nominal variables, such as deflation and other price channels. Tables 3 to 5 suggest that the coal share of power and technological convergence mainly affects concrete firms after the recession, not during the recession: the coal share of power has no statistically significant effect on productivity or employment between 1929 and These tables therefore point at the productivity gains from technology adoption occurring at the cyclical frequency with firms in coal states hiring fewer workers and increasing productivity more than firms in hydroelectric states. Finally, Tables 6 to 8 show evidence that the effects of electricity on the labor market are stronger for counties with a deep recession: firms in counties with a deeper recession have a larger regression coefficient on the coal share and were more sensitive to the technical progress in electric utilities. The depth of the recession is measured by the change in housing construction between 3

4 Dependent variable: Change in employment, Change in productivity, state-level coal share in * 0.194** (0.0485) (0.0833) Constant 0.230*** * (0.0410) (0.0587) Observations R-squared Number of states/clusters Table 1: Baseline results: a higher coal reliance caused a decrease in employment and an increase in productivity over Change in labor productivity, ID MT AL AL GA SC AZ NC NC NC NH IA IA KY IA VA VA VA INDE KS CT IN FL CTAR DC LA LA MD DC IN INKS KS FL INDC KS FL LA MD MS MD MS ND LA FL OK OK WV SD SD SD SDIN KS Coal share of power by state in 1927 Slope: 0.19 t-statistic: 2.33 R2: 0.01 Observations: 410 Correlation: 11% Higher coal share in 1927 caused an increase in labor productivity after the Great Depression for the concrete industry. 4

5 Dependent variable: Change in employment, Change in productivity, city-level coal share in * 0.184** (0.0458) (0.0785) Constant 0.238*** * (0.0399) (0.0566) Observations R-squared Number of states/clusters Table 2: The baseline results are robust to using the city-level coal share of capacity. Change in labor productivity, IN KS KS NC CT FL AL AR LA IN IN IA AL CT DC ID GA IA IA IN KS AZ DC IA IA IN IN KSKS KS KY IA FL FL IA DC FL IN IN KY LA LA KS MD NC MS VA NH MD NCNC MD MD MS ND MT OK SD VA WV WV OK SC SD SD SD VA SD IN KS Coal share of power by city in 1928 Slope: 0.18 t-statistic: 2.34 R2: 0.01 Observations: 388 Correlation: 12% The baseline results are robust to using the city-level coal share of power in

6 Dependent variable: Change in employment, Change in productivity, state-level coal share in (0.0552) (0.0417) Constant *** *** (0.0489) (0.0329) Observations 1, R-squared Number of states/clusters Table 3: The coal share of power has no statistically significant effect on employment and productivity over Dependent variable: Change in employment, Change in productivity, state-level coal share in (0.0468) (0.0538) Constant *** *** (0.0298) (0.0349) Observations R-squared Number of states/clusters Table 4: The coal share of power has no statistically significant effect on employment and productivity over

7 and from Kimbrough and Snowden (2007), not just for a plant s county but also for an area of influence of 50 miles around the county with Geographic Information Systems. Counties with a shallow recession are those below the median of housing growth in the early 1930s. These results are consistent with the idea that firms used the Great Depression as an opportunity to fire workers, invest in electrical machinery, increase productivity, and not hire them back. In conclusion, this paper tested an explanation for the puzzle of the divergence between labor productivity and employment during the jobless recovery from the Great Depression. The instrument predicts a 13.5% decrease in employment and a 30% increase in productivity in only two years. The identification strategy of Morin (2014) restricted the sample to a specific industry but the anecdotal evidence from the narrative record suggests that the substitution of manual labor for electric capital may be more general than the concrete industry: for example, Frances Perkins, secretary of the Department of Labor, stated in a Congressional testimony in 1935 that you would be surprised at the number of labor-saving devices which have been introduced in industry in the last 2 or 3 years (Committee on Finance, 1935, page 206). This paper also has implications for current macroeconomic policy. Recent research has suggested three explanations for the jobless recoveries in the United States since 1990: technology adoption, or the substitution of jobs with machines; offshoring, or the shipping of domestic jobs abroad; and the decline in unionization, or the lower collective bargaining power of organized labor. This paper has the advantage of testing the technological explanation in a context when offshoring was infeasible and unionization was increasing, suggesting that technology adoption may play an important role in unemployment in the recent labor market as well. 7

8 Dependent variable: Change in employment, Change in productivity, state-level coal share in (0.0487) (0.0378) Constant *** ** (0.0413) (0.0310) Observations R-squared Number of states/clusters Table 5: The coal share of power has no statistically significant effect on employment and productivity over Dependent variable: change in employment, Full sample Deep recession Shallow recession State-level coal share of power *** ** (0.0136) (0.0242) (0.0210) Constant (0.0105) (0.0194) (0.0123) Observations R-squared Number of states/clusters Table 6: The effects of the coal share of power on employment are stronger in counties with a deep recession. 8

9 Dependent variable: change in labor productivity, Full sample Deep recession Shallow recession State-level coal share of power *** 0.136** ** (0.0190) (0.0485) (0.0271) Constant *** *** *** (0.0131) (0.0439) (0.0181) Observations R-squared Number of states/clusters Table 7: The effects of the coal share of power on productivity are stronger in counties with a deep recession. Dependent variable: change in electrical intensity, Full sample Deep recession Shallow recession coal k, *** 0.128*** (0.0368) (0.0434) (0.0456) Constant (0.0308) (0.0333) (0.0347) Observations R-squared Number of states/clusters Table 8: The effects of the coal share of power on electrical intensity are stronger in counties with a deep recession. 9

10 References Cole, Harold, and Lee Ohanian New Deal Policies and the Persistence of the Great Depression: A General Equilibrium Analysis. The Journal of Political Economy, 112(4): Committee on Finance, House of Representatives Economic Security Act. United States Government Printing Office. Kehoe, Timothy, and Edward Prescott Using the General Equilibrium Growth Model to Study Great Depressions: A Reply to Temin. Federal Reserve Bank of Minneapolis Research Department Staff Report 418. Kimbrough, Gray, and Kenneth Snowden The Spatial Character of Housing Depression in the 1930s Economic History Association Meetings Papers. Reinhart, Carmen M., and Kenneth S. Rogoff This time is different: eight centuries of financial folly. Princeton University Press. Syverson, Chad Market Structure and Productivity: A Concrete Example. Journal of Political Economy, 112(6): Morin, Miguel The labor market consequences of technology adoption: concrete evidence from the Great Depression. Manuscript, University of Cambridge. 10