1. Productivity or Employment: Is it a choice? Andrea De Michelis Federal Reserve Board Marcello Estevão International Monetary Fund Beth Anne Wilson Federal Reserve Board January 4, 2013

2. The views in this presentation are solely the responsibility of the authors and should not be interpreted as reflecting the views of the International Monetary Fund or the Board of Governors of the Federal Reserve System or of any other person associated with the Federal Reserve System.

3. Background In general, economic theory assumes that TFP growth follows an exogenous process. In general, economic theory assumes that TFP growth follows an exogenous process. Low TFP growth is seen as worrisome, as many associate it with poor economic performance. Low TFP growth is seen as worrisome, as many associate it with poor economic performance. In reality, not a one-to-one relation between TFP and output growth  key motivation for this paper: TFP growth may be a “choice” variable. In reality, not a one-to-one relation between TFP and output growth  key motivation for this paper: TFP growth may be a “choice” variable. 2

4. Take the case of Canada: TFP growth has been particularly low. 3

5. In contrast, employment growth has been quite strong. 4

6. Same with hours of work. 5

7. As a result, Canadian GDP growth has outperformed the G7 average. 6

8. More generally, growth in TFP and labor input are negatively correlated across the OECD. 7

9. Data: Labor Input and TFP The Conference Board Total Economy Database: total economy annual data, main 20 OECD countries, 1970-2007 The Conference Board Total Economy Database: total economy annual data, main 20 OECD countries, 1970-2007 The Conference Board Total Economy Database The Conference Board Total Economy Database World/EU KLEMS: annual data, 14 OECD countries, 10 sectors, various sample ranges, but 1980-2007 available for most countries of interest World/EU KLEMS: annual data, 14 OECD countries, 10 sectors, various sample ranges, but 1980-2007 available for most countries of interest World/EU KLEMS World/EU KLEMS EU AMECO: total economy annual data, European and other G-7 countries, 1960-2013 EU AMECO: total economy annual data, European and other G-7 countries, 1960-2013 EU AMECO EU AMECO (no hours data, used only for robustness analysis) 8

10. Other Data Sources for tax data Sources for tax data McDaniel (2007): payroll, income, and consumption taxes, 15 OECD countries, 1950/70-2007 McDaniel (2007): payroll, income, and consumption taxes, 15 OECD countries, 1950/70-2007 McDaniel Sources for population data Sources for population data United Nations United Nations United Nations United Nations The Conference Board Total Economy Database The Conference Board Total Economy Database The Conference Board Total Economy Database The Conference Board Total Economy Database 9

11. Negative correlation of TFP and hours growth is robust, holding across datasets and labor inputs... 10 DatabaseTED KLEMS † TED KLEMS † Labor InputEmployment Hours Constant1.35***0.86***1.07***0.74*** (0.17)(0.18)(0.10)-0.12 Coefficient-0.53***-0.36*-0.49***-0.37** (0.15)(0.17)(0.11)(-0.09) Observations 20142014 Adjusted R20.360.210.480.33 †KLEMS data spans the time period 1980-2007. Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1

12. …and across time. Correlation remains negative and significant decade by decade (except 90s.) 11 Hours Growth vs. TFP Growth Period1970-20071970s1980s1990s2000-2007 Constant1.07***1.67***1.01***0.60***0.91*** (0.10)(0.13) (0.15)(0.22) Hours Growth-0.49***-0.57***-0.41***-0.19-0.63*** (0.11)(0.13) (0.18) Observations 20 Adjusted R0.480.490.330.010.36 Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1

13. 12 Dotted lines represent averages over 1970-2007. Countries relative relationship between TFP and H growth fairly stable.

14. 13 But, some drift toward lower TFP/ stronger hours growth in Europe. (1970s and 1980s) (1970s and 1980s) Dotted lines represent the averages over 1970-2007 on all charts.

15. 14 1990s and 2000-2007 Dotted lines represent the averages over 1970-2007 on all charts.

16. Correlation of growth in TFP and hours varies by sector (OECD 14) IndustryCoefficientConstantObservationsAdjusted R Hotels and Restaurants-0.60**(0.26)0.28(0.49)140.25 Manufacturing-0.46(0.35)1.19**(0.49)140.05 Total Economy-0.37**(0.14)0.74***(0.12)140.33 Other Services-0.35*(0.19)0.11(0.30)140.15 Wholesale and Retail-0.33(0.48)1.31***(0.40)14-0.04 Financial Services-0.23*(0.12)0.39(0.41)140.18 Electricity-0.23(0.26)0.81**(0.30)14-0.02 Agriculture, Forestry, and Fishing-0.21(0.31)2.77***(0.81)14-0.04 Construction-0.15(0.19)0.24(0.25)14-0.03 Mining and Quarrying-0.13(0.28)0.43(1.04)14-0.06 Transportation-0.11(0.37)1.37***(0.42)14-0.08 Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1 Source: World KLEMS, EU KLEMS. 15

17. But variation in industry composition does not explain cross-country variance. 16 TFP Growth vs. Hours Growth Baseline U.S. time-varying weight Constant0.74***0.82*** (0.12)(0.13) Hours Growth-0.37**-0.38*** (0.14)(0.09) Observations 14 Adjusted R 0.33 0.56 Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1 Sources: EU KLEMS, World KLEMS and authors’ calculations.

18. What could explain this negative correlation? 17 Measurement error? Probably not. Measurement issues with TFP more relevant at cyclical frequencies. Result does not depend on the database used (TED, World/EU KLEMS) Country mix of TFP and hours growth is relatively stable over time. Result holds within industry/country pair. TFP as a choice variable: Given the availability of labor inputs, TFP growth is “chosen”.

19. Causality: hypothesis 18 Factor endowment not only affects the choice of capital or labor-intensive technologies but also how much to invest in techniques and processes that boost TFP. Given that productivity innovations are costly, countries with abundant labor supply may “choose” less productivity growth. Given that productivity innovations are costly, countries with abundant labor supply may “choose” less productivity growth. Test: Is there a causality going from labor supply shocks to TFP growth? Test: Is there a causality going from labor supply shocks to TFP growth?

20. Causality: strategy 19 Find variables that affect TFP growth only through the decision of hiring labor. Use these variables as instruments in regressions linking TFP growth to hours growth. Good candidates: Tax wedge: differences in taxes influence labor supply and introduce a gap between MRS and MPL (Prescott, 2004, and Ohanian et al., 2007). Population growth: availability of labor input.

21. Causality: IV regressions using tax wedge and population growth 20 Step 1- Dependent variable -- Hours Growth Constant-2.42**-0.55*** (1.00)(0.16) Average Tax Wedge4.52** (1.60) Population Growth1.80*** (0.24) Step 2- Dependent variable -- TFP Growth Constant1.22***1.07*** (0.11) Predicted Hours Growth-0.71***-0.47*** (0.19)(0.15) Observations1520 Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1 Time period spans 1970-2007. Sources: Total Economy Database, McDaniel tax data.

22. Is the instrument independently correlated with TFP growth? 21 Dependent variable -- TFP Growth Constant2.18***1.03*** (0.54)(0.21) Hours Growth-0.31**-0.53** (0.12)(0.24) Average Tax Wedge-1.79* (0.90) Population Growth0.10 (0.49) Observations1520 Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1 Time period spans 1970-2007. Sources: Total Economy Database, McDaniel tax data.

23. Conclusions There is robust negative correlation between TFP growth and hours growth across OECD countries. There is robust negative correlation between TFP growth and hours growth across OECD countries. At least some of this negative correlation seems to be a result of reactions to shocks in labor input. So, TFP could, in part, be a “choice” variable. At least some of this negative correlation seems to be a result of reactions to shocks in labor input. So, TFP could, in part, be a “choice” variable. This mechanism makes more sense than explanations of TFP growth differences between, say, Germany and Canada based on institutions. These are all rich, mature societies with good institutions. This mechanism makes more sense than explanations of TFP growth differences between, say, Germany and Canada based on institutions. These are all rich, mature societies with good institutions. The endogeneity of TFP could also help explain longer-run developments in Europe and Canada. The endogeneity of TFP could also help explain longer-run developments in Europe and Canada. 22

24. Conclusions Looking ahead, population aging could trigger a wage adjustment and an endogenous increase in TFP growth in countries so far specialized in fast hours growth/low TFP growth. Looking ahead, population aging could trigger a wage adjustment and an endogenous increase in TFP growth in countries so far specialized in fast hours growth/low TFP growth. But no guarantee, look at Japan. But no guarantee, look at Japan. Good institutions that support innovation and product market competition are always good for TFP growth, and would raise incentives to be more productive and ease transition. Good institutions that support innovation and product market competition are always good for TFP growth, and would raise incentives to be more productive and ease transition. 23

25. Thanks! 24

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27. TFP Growth vs. Hours Growth by Sector (G7) IndustryCoefficientConstantObservationsAdjusted R Hotels and Restaurants-0.99**(0.27)1.07*(0.50)70.67 Other Services-0.72(0.36)0.72(0.50)70.33 Manufacturing-0.48***(0.12)1.12***(0.19)70.73 Wholesale and Retail-0.49(0.48)1.74***(0.39)70.01 Total Economy-0.47**(0.15)0.78***(0.11)70.59 Electricity-0.42(0.44)0.41(0.47)7-0.02 Construction-0.35(0.38)0.02(0.43)7-0.03 Mining and Quarrying-0.18(0.24)-1.20(0.96)7-0.08 Agriculture, Forestry, Fishing-0.17(0.66)3.06(1.86)7-0.19 Transportation0.16(0.69)0.98(0.79)7-0.19 Financial Services-0.16(0.19)0.075(0.60)7-0.06 Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1 Source: World KLEMS, EU KLEMS. 26

28. Results: Using Tax Wedge as an Instrument for Hours 27 Step 1 Regression Hours Growth vs. Average Tax Wedge† by Period Decades1970-20071970s1980s1990s2000-2007 Constant-2.42**-4.21**-2.88*-1.61-0.23 (1.00)(1.82)(1.35)(1.19)(1.33) Average Tax Wedge4.52**6.15**5.51**3.231.82 (1.60)(2.69)(2.14)(1.96)(2.21) Observations 15 Adjusted R 0.330.230.290.11-0.02 † Equal to (1- tax rate on labor income)/(1 + tax rate on consumption expenditures) Step 2 Regression TFP Growth vs. Predicted Hours Growth by Period Decades1970-20071970s1980s1990s2000-2007 Constant1.22***1.73***1.08***0.75***1.46 (0.11)(0.16)(0.20)(0.17)(0.84) Predicted Hours Growth-0.71***-0.83***-0.37-0.73*-1.13 (0.19)(0.27)(0.26)(0.37)(0.97) Observations 15 Adjusted R 0.490.370.070.170.09

29. 28 TFP Growth vs. Hours Growth and Average Tax Wedge Periods1970-20071970s1980s1990s2000-2007 Constant2.18***3.55***0.631.69*1.59* (0.54)(1.09)(0.67)(0.79) Hours Growth-0.31**-0.40**-0.53***-0.14-0.56*** (0.12)(0.14)(0.12)(0.17) Average Tax Wedge-1.79*-2.660.86-1.89-1.03 (0.90)(1.60)(1.11)(1.34)(1.35) Observations 15 Adjusted R0.640.600.630.140.46 Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1

30. 29 Step 1 Regression Hours Growth vs. Population Growth by Decade Decades1970-20071970s1980s1990s2000-2007 Constant-0.55***-1.31***-0.15-0.270.12 (0.16)(0.28)(0.23)(0.31)(0.17) Population Growth1.80***1.96***1.58***1.22**1.58*** (0.24)(0.36)(0.38)(0.46)(0.27) Observations 20 Adjusted R0.750.610.460.240.64 Step 2 Regression TFP Growth vs. Predicted Hours Growth by Decade Decades1970-20071970s1980s1990s2000-2007 Constant1.07***1.67***0.97***0.53**0.90*** (0.11)(0.16)(0.18)(0.20)(0.28) Predicted Hours Growth-0.47***-0.52**-0.34-0.02-0.62** (0.15)(0.20)(0.21)(0.34)(0.25) Observations 20 Adjusted R0.330.240.07-0.060.21 Results: Using Population Growth as an Instrument for Hours

31. 30 TFP Growth vs. Hours Growth and Population Growth Decade1970-20071970s1980s1990s2000-2007 Constant1.03***1.52***0.94***0.470.90*** (0.21)(0.39)(0.18)(0.28)(0.24) Hours Growth-0.53**-0.63***-0.48**-0.25-0.65* (0.24)(0.22)(0.18)(0.21)(0.33) Population Growth0.100.22 0.280.04 (0.49)(0.54)(0.41)(0.48)(0.63) Observations 20 Adjusted R0.450.460.31-0.030.32 Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1