Silviase/jgov_v1.4_full_ann huggingface dataset:
the five sectors we focus on, discussing measurement issues and comparing changes in Japan's sector TFP and gross output deflator with those in the United States and the UK. Moreover, based on the comparisons, we make conjectures about the extent to which measurement issues affect estimated TFP growth. Specifically, Section 2 focuses on construction, Section 3 on wholesale and retail, Section 4 on education and health care, and Section 5 on public administration and defense, and compulsory social security. Finally, Section 6 summarizes our findings. $ ^{7} $

### 2. Construction

In Japan’s GDP statistics, nominal output of construction is calculated by summing up all intermediate input costs (which are estimated from commodity flow data) plus gross value added (labor costs plus operating surplus). $ ^{8} $ Real output is obtained by using an input price index as the deflator. Under this convention, which essentially equates output with inputs, if the input price index covers all inputs, measured TFP growth will be close to zero, which in fact is the case in the statistics on many general government activities (see Section 5). However, in the case of Japan’s GDP statistics on construction, since the “output=inputs” convention is not fully applied in at least three aspects, measured TFP changes over time. That is, given that the efficiency with which inputs in any given sector are used can be expected to change over time, TFP of that sector should change accordingly. However, due to the “output=inputs” convention, such changes are not captured and measured TFP should remain unchanged. Yet, in practice, the “output=inputs” convention is applied only incompletely, so that measured TFP does change, but for reasons that are not necessarily related to any changes in “true” TFP.

First, the output measure does not entirely consist of information on factor inputs but also includes the operating surplus in nominal output. Therefore, when the construction sector enjoys a high profit rate, its measured TFP growth accelerates. Figure 1 shows changes in TFP (measured on a value added basis; 1973=1) and the gross output deflator/GDP deflator ratio for the construction sector in Japan, the United States, and the UK. Consistent with our conjecture, Figure 1 (a) shows that in
Japan, measured TFP increased substantially during the period of the bubble economy from 1986 to 1991. (Developments in the United States and the UK are considered at the end of this section.)

Second, Japan’s deflator for the construction sector takes account only of intermediate input prices and wage rates (Takayama et al. 2013) and takes neither capital service prices ((interest rate + capital depreciation rate − capital gains)’ capital stock price) nor the cost of capital depreciation into account. Since capital service prices and the cost of capital depreciation tend to increase slower than intermediate input prices as well as wage rates, Japan’s deflator for the construction sector overestimates the increase in overall input prices. It is probably for this reason that the gross output deflator/GDP deflator ratio for Japan’s construction sector has increased substantially (Figure 1 (b)). This results in an underestimation of TFP growth in the construction sector.

Third, Japan’s deflator for the construction sector does not take account of changes in labor quality (Takayama et al. 2013). Since labor quality in the construction sector has increased over time, wage rate increases are overestimated and growth of real output and TFP are underestimated. According to the JIP Database 2015, during the period 1970–2012 labor quality increased at an annual rate of 0.60% (Figure 2). $ ^{9} $ Since

Figure 1: Developments in TFP and the Gross Output Deflator in the Construction Sector: Japan-US-UK Comparison

Sources : World KLEMS Data, EU KLEMS Data, and JIP Database 2015.
Figure 2 : Quality of Labor in the Construction Sector

labor costs / (labor costs + capital service costs) in the construction sector are quite high (around 0.90), both real value added growth and TFP growth (on a value added basis) for the period 1973–2012 are underestimated by 21 percentage points $ (0.60 \times 0.90 \times 39 = 21) $ . $ ^{10} $ Given that the average share of the gross value added in this sector in GDP during this period was 8.0%, this means that Japan's GDP statistics underestimates real GDP growth by 1.7 percentage points for the period 1973–2012, and the JIP Database underestimates TFP growth in the economy as a whole by 1.7 percentage points for the same period.

To sum up, these considerations suggest that both the decline in the TFP level of Japan's construction sector over the period as a whole as well as the hump during and after the period of the bubble economy are statistical artifacts caused by deficiencies in Japan's GDP statistics. Moreover, Japan's GDP statistics also substantially underestimates real GDP growth.

If reliable data on construction output prices were available, this would allow us to derive more reliable real output and TFP measures. Unfortunately, however, there are no government statistics in Japan providing construction output prices. $ ^{11} $

The situation in the United States and the UK stands in stark contrast to that in
Japan. In the United States, the Bureau of Economic Analysis (BEA) creates (quality adjusted) price deflators for construction output based on broad types of structure, using price indexes such as the Producer Price Index (PPI) for new health care building construction (published by the Bureau of Labor Statistics). $ ^{12} $ Similarly, in the UK, the Office for National Statistics (ONS) estimates real output of the construction sector using price deflators for construction output such as the construction price and cost indices (CPCIs) of the Department for Business, Innovation and Skills (BIS). $ ^{13} $

That being said, as can be seen in Figure 1 (a), according to the World KLEMS Data, the derived TFP index for the United States steadily declined throughout the period covered by the data. Similarly, Figure 3, which is based on TFP estimates (on a gross output basis) obtained by Jorgenson, Nomura, and Samuels (2016), also suggests that TFP in the US construction sector steadily declined from around 1970 onward. Since it is difficult to believe that the technology level of the US construction sector or the efficiency of resource allocation within that sector continued to deteriorate for 40 years, it seems that the US statistics also suffer from some serious problems. On the other hand, in the case of the UK, estimated TFP grows in a plausible manner, as shown in Figure 1.

Figure 3 : Absolute TFP Level of the Construction Sector (on a Gross Output Basis) Based on PPP Data: US–Japan Comparison

Source : Jorgenson, Nomura, and Samuels (2016).
### 3. Wholesale and Retail

Next, we examine GDP statistics on wholesale and retail. As OECD/Eurostat (2014) points out, if the quality of wholesale or retail services per unit of traded commodities does not change over time, the margin price per unit, i.e.,

Margin price per unit = Selling price per unit - Purchase price per unit (1)

will be the appropriate measure of the price of services provided by wholesalers and retailers. The BEA recently started to use margin price data collected by the Bureau of Labor Statistics (BLS) as part of the Producer Price Index (PPI) for their estimation of deflators for wholesale and retail activities (Mayerhauser and Strassner 2010, Gilmore, et al. 2011). $ ^{14} $ Canada also started to use margin price data for their estimation of deflators for wholesale and retail activities (OECD/Eurostat 2014). In order to correctly measure changes in the margin price per unit by adjusting for changes in the quality of trade services, statistical offices collect margin price data for transactions in specified groups of commodities with specified groups of suppliers/customers for each of the traders surveyed. Therefore, when the trade share of a certain type of transactions with a high margin price increases, this is not regarded as an increase in the price of trade services but as an increase in service output. However, if service quality, such as the hours of operation or the variety of commodities offered for sale, improves in each transaction in a specified group of commodities with a specified group of suppliers/customers for each of the traders surveyed, such quality improvement is not recorded as output growth. One way to resolve this problem and measure such quality change is to collect information on numerous service characteristics, such as the hours of operation, the variety of commodities offered for sale, etc., and estimate a hedonic model. However, few countries employ this approach, with the United States not being an exception (OECD/Eurostat 2014).

While the United States and Canada have started to use margin prices, most other countries, including Japan and the UK, $ ^{15} $ still use the prices of traded commodities as deflators for the wholesale and retail sector. Since the margin price per unit (selling price per unit – purchase price per unit) is equal to the margin rate ((selling price per
unit-purchase price per unit) / purchase price per unit) multiplied by the purchase price per unit, this approach implicitly assumes that the margin rate is constant over time.

Figure 4 presents developments in Japan's TFP and gross output deflator for the wholesale and retail sector with those in the United States and the UK. In the case of Japan, probably reflecting the worldwide boom in natural resource prices in the 2000s, the gross output deflator–GDP deflator ratio increased substantially until the global financial crisis, which contrasts with developments in the United States, where this ratio continued to decline in the 2000s. Moreover, probably partly reflecting this increase in the gross output deflator–GDP deflator ratio, Japan's TFP growth in the sector slowed in the 2000s. These results are confirmed by Figure 5, which presents Jorgenson, Nomura and Samuels' estimation of TFP levels. The figure again indicates that Japan's TFP level stagnated in the 2000s. Turning to the UK, Figure 4 shows that the gross output deflator–GDP deflator ratio declined substantially, which probably reflects the appreciation of the pound in 2006–2007. However, the decrease in the gross output deflator–GDP deflator ratio did not cause a substantial slowdown in estimated TFP growth during this period.

When we compare the long-run trend of the gross output deflator-GDP deflator ratio in the three countries, what clearly stands out is the decline in the ratio in the United States. Moreover, the decline in this ratio and the increase in TFP accelerated

Figure 4 : Developments in TFP and the Gross Output Deflator in the Wholesale and Retail Sector : Japan-US-UK Comparison

(b) Gross output deflator/GDP deflator (1973=1)

Sources : World KLEMS Data, EU KLEMS Data, and JIP Database 2015.
Figure 5 : Absolute TFP Level of the Wholesale and Retail Sector (on a Gross Output Basis) Based on PPP Data : US–Japan Comparison

Source : Jorgenson, Nomura, and Samuels (2016).

in the second half of the 1990s and the first half of the 2000s. This phenomenon has been regarded as important evidence of the information and communication technology (ICT) revolution in the United States (Inklaar, Timmer, and van Ark 2006). In this context, it should be noted that the BEA introduced the use of margin price data around 2010 and made retrospective corrections for past data (Mayerhauser and Strassner 2010, Gilmore, et al. 2011). However, the acceleration in TFP growth in the US wholesale and retail sector was already observed in 2006 (Inklaar, Timmer, and van Ark 2006), so it seems unlikely that this acceleration is simply a statistical artifact caused by changes in the way deflators are calculated. Nevertheless, it is important to bear this issue in mind in future international comparisons for this sector.

Looking at Japan, it is difficult to assess how much of the increase in the gross output deflator–GDP deflator ratio in the 2000s (Figure 4) is caused by the fact that Japan uses the prices of traded commodities as deflators for the wholesale and retail sector. However, the Bank of Japan (BOJ) recently started a wholesale service price survey and estimated the margin price per unit in domestic wholesale trade for three commodity categories – food and beverages, plastics, and electronic parts and devices – on a trial basis as a part of its work on the Services Producer Price Index (Research and Statistics Department, Bank of Japan 2014). $ ^{16} $ In the case of plastics, we can
Figure 6 : Comparison of the PPI for Plastic Resins and Materials and the Margin Price per Unit in Wholesaling of Plastics : 2010=100 (Consumption Tax is Excluded)

Sources : Research and Statistics Department, Bank of Japan (2014) and Bank of Japan, Producer Price Index.

compare the BOJ's margin price per unit in wholesaling with the BOJ's PPI for plastic resins and materials that are included in chemical products.

This is done in Figure 6, which shows the PPI for plastic resins and materials and the margin price per unit in the wholesaling of plastics. Unfortunately, the data are available only from 2010. Therefore, we cannot compare the two series for the period before the global financial crisis. For the period after 2010, the figure indicates that the margin price per unit in the wholesaling of plastics increased more than the PPI for plastics. This means that, for this period, if we measure the TFP of wholesaling of plastics using the margin price per unit as deflator, the measured TFP growth rate will become lower than measured TFP growth based on the PPI. $ ^{17} $

### 4. Education and Health Care

Measurement of output and deflators in non-market service sectors such as education and health care is problematic because in these sectors prices are not determined in competitive markets. In education and health care, governments often intervene into the market mechanism through regulations, social insurance systems,

and other measures. Even without government interventions, prices determined in the
market probably do not reflect true consumer preferences because of asymmetric information and externalities.

Given these problems, recent years have seen growing interest in the use of direct measures of the output of non-market service sectors such as education and health care. For instance, the Atkinson Review (2005) recommends to measure output directly by counting the number of units for whom services are provided instead of measuring output by aggregating inputs for the production of the service. In addition, the review recommends adjusting output for changes in the quality of services. Similarly, Eurostat (2001) recommends the use of direct measures with quality adjustments. In the UK, the ONS estimates and publishes direct and quality-adjusted output indexes for public sector activities. In the United States, important research has been conducted, particularly at the National Bureau of Economic Research (NBER), to measure the quality of health care and adjust estimated deflators by changes in quality (Cutler and Berndt 2001). The BEA is now developing a Health Care Satellite Account, which is based on detailed information about health care treatments (Dunn, Rittmueller, and Whitmire 2015). Interestingly, although many important studies on how to measure the quality of public services are authored by scholars and statisticians in the United States, it seems to be the government of the UK that is the most proactive and advanced in implementing the results of such studies.

Therefore, in this section, we first take a look at the new output indexes of the ONS in the UK. We also try to obtain rough estimates of the output and TFP of Japan's education and health care service sectors using the direct method and adjusting for quality changes.

An explanation of the methodology and the results of the new measurement of output and productivity is provided in various ONS publications (ONS 2013, 2015b, 2015c, 2015d). Activities covered in these estimates include health care, education, social security administration, adult social care, children's social care, public order and safety, and some other areas.

The method of measuring output is chosen based on the availability of data for each type of activity. $ ^{[18]} $ In the case of health care and education, the direct measure of output with quality adjustment is used. $ ^{[19]} $ In the case of social security administration, adult
social care, and children's social care, the direct measure of output without quality adjustment is used. $ ^{20} $ Public order and safety and other areas are measured based on the traditional input approach using the “output=inputs” convention.

The data used for the direct measure of output for each type of activity are as follows. For health care, hospital inpatients, general practitioner (GP) consultations, and drugs prescribed by GPs are used. For education, the number of pupils or students in preschools and in primary, secondary, and higher education adjusted for absence are used. For social security administration, recipients of pension and unemployment benefits are used. For adult social care, weeks of residential care, meals provided, etc., are used. For children's social care, days spent in short term placement, the number of children in secure accommodation or children's nursing homes, etc., are used. For public order and safety, the number of fires responded to by fire brigades, court cases, prisoners in prisons, etc., are used.

The direct measure of output without quality adjustment is calculated by aggregating the growth rate of the output of each type of activity using cost share weights:

$$ Q_{t}=Q_{t-1}\Big(\sum_{j}\Big(\frac{a_{j,t}-a_{j,t-1}}{a_{j,t-1}}\frac{a_{j,t-1}x_{j,t-1}}{\sum_{j}a_{j,t-1}x_{j,t-1}}\Big)+1\Big) $$

Q : Quantity index at time t.

$ a_{j,t} $ : Quantity of service j at time t.

$ x_{j,t} $ : Unit cost of service j at time t.

$ \frac{a_{j,t-1}x_{j,t-1}}{\sum_{j}a_{j,t-1}x_{j,t-1}} $ : Cost share of service j at time t.

The above equation can be transformed into

$$ Q_{t}=Q_{t-1}\Big(\frac{\sum_{j}a_{j,t}x_{j,t-1}}{\sum_{j}a_{j,t-1}x_{j,t-1}}-1\Big)+1. $$

In the case of health care, factor that are taken into account to adjust for quality are patients' health gains, short-term survival rates, waiting times, patient satisfaction, etc. In the case of education, test scores are used for quality adjustment.

The direct measure of output with quality adjustment, $ O_{t} $ , is obtained by multiplying the quantity index of each service j by the quality measure, $ q_{jt} $ .
$$ O_{t}=O_{t-1}\Big(\sum_{\mathrm{j}}\Big(\frac{q_{j,t_{j,t}}-q_{j,t-1}a_{j,t-1}}{q_{j,t-1}a_{j,t-1}}\frac{a_{j,t-1}x_{j,t-1}}{\sum_{j}a_{j,t-1}x_{j,t-1}}\Big)+1\Big) $$

The above equation can be transformed into

$$ O_{t}=O_{t-1}\Biggl\{\Biggl(\frac{\sum_{j}a_{j,t}x_{j,t-1}}{\sum_{j}a_{j,t-1}x_{j,t-1}}-1\Biggr)+\Biggl(\frac{\sum_{j}\frac{q_{j,t}}{q_{j,t-1}}a_{j,t-1}x_{j,t-1}}{\sum_{j}a_{j,t-1}x_{j,t-1}}-1\Biggr)\Biggr\}+1. $$

Figure 7 compares the annual average growth rate of TFP across public services categories from 1997 to 2010. In the case of health care and education, where the direct measure of output with quality adjustment is used, positive TFP growth is observed. In the case of categories where the direct measure of output without quality adjustment is used, the results are mixed. TFP growth was positive in social security administration. However, it was negative in the case of adult social care, children's social care, and public order and safety. The TFP growth in areas such as policing and defense as well as other areas is, by definition, zero because output in these areas is measured by inputs.

Next, Figure 8 shows the effect of quality adjustment in health care. The direct measure of output with quality adjustment shows a more rapid increase than the direct measure of output without quality adjustment. This means that the quality of health care services steadily improved.

Figure 7 : Annual Average Growth Rate of TFP in UK Public Services, 1997–2012 (in %)
Figure 8 : Developments in the Direct Measure of Output with Quality Adjustment and the Direct Measure of Output without Quality Adjustment: Healthcare, 1995–2013

Figure 9 : Developments in the Direct Measure of Output with Quality Adjustment, the Input Index, and TFP : Healthcare, 1995–2013
Figure 9 shows changes in TFP, the direct measure of output with quality adjustment, and the input index for the health care sector. Until the early 2000s, productivity barely increased due to strong increases in inputs. In the mid-2000s, with the continued growth of output and a deceleration of input growth, the TFP of health care increased. If the direct measure of output without quality adjustment is used to calculate TFP, virtually no TFP growth can be observed from 1995 to 2010.

Focusing on the period from 1996 to 2013, Figure 10 turns to developments in the direct measure of output with and without quality adjustment for education. The direct measure of output without quality adjustment stagnated throughout the period, while the measure with quality adjustment grew rapidly.

Figure 11 shows developments in TFP, the direct measure of output with quality adjustment, and the input index. After rapid growth from 1996–99, TFP declined by about 10% between 1999 and 2009. However, TFP started to increase again after 2009. This revival of TFP growth in education reflects a rise in output quality and a sharp slowdown of input growth. If the direct measure of output without quality adjustment is used in place of the measure with quality adjustment, no growth in TFP is observed from 2009.

Figure 10: Developments in the Direct Measure of Output with Quality Adjustment and the Direct Measure of Output without Quality Adjustment: Education, 1996–2013 Index numbers, 1996=100: In the UK
Figure 11: Developments in the Direct Measure of Output with Quality Adjustment, the Input Index, and TFP: Education, 1996–2013 Index numbers, 1996=100: In the UK

Turning to Japan and the United States, nominal output of most activities in the education sector is measured using total input costs. Moreover, real output is calculated by deflating nominal output using an input price index that covers intermediate, labor, and capital service inputs (BEA 2015, ESRI 2012). Meanwhile, wages are adjusted using changes in labor quality. $ ^{21} $ Under this output=inputs approach, measured TFP growth will be almost zero.

Figure 12 provides a comparison of developments in TFP and the gross output deflator in the education sector. The TFP results for the UK are not shown in the figure, because the results by the ONS shown in Figure 11 are more up to date. Contrary to our expectation that TFP growth in the education sector in Japan and the United States would be almost constant, Figure 12 shows there are some substantial changes in TFP over time. In the case of the United States, TFP declined by 20% from 1973 to 2010. On the other hand, in the case of Japan, TFP follows a hump-shaped pattern. How can these developments in TFP be explained?