Lecture 8 - CERGE-EI

Technology and efficiency

Lecture 8Technology vs Efficiency

Tomas Lichard

UPCES

Spring 2019


Technology vs efficiency

Coming back to Y = AKαL1−α

We can express TFP as A = T ×E (technology and efficiency)

Imagine that Czechia lags behind Germany in technology by Gyears, e.g.:

T2016,CZ = T2016−G ,GER

where G is the size of the lag in technology in years

The technology lag can be then expressed as:

T2016,GER = T2016,CZ × (1 + g)G

Ratio of Czech and German TFPs:

ACZ

AGER=

TCZ

TGER× ECZ

EGER

It is estimated that ACZAGER

= 0.68 (see previous lecture);technology growth in Germany 1995-2012 is estimated to bearound 0.7% per year.


Technology lag vs EfficiencyNumerical example

Lag in years (G ) TCZTGER

ECZEGER

10 0.933 0.72912773520 0.870 0.78180478430 0.811 0.83828757540 0.757 0.8988510650 0.706 0.96379005460 0.658 1.033420675


Efficiency

Definition

Efficient use of resources means that we cannot increase outputwithout increasing at least one of the inputs. It is such a use ofresources that minimizes waste.

Unfortunately, we cannot observe efficiency more directly, butby studying case studies, we can get a sense of its importance

Soviet Union had only 1/3 of the US level of GDP p.c.,although having huge amount of physical capital

over the decade before the collapse it grew less than 1% peryear

Result of central planning – bureaucrats were setting pricesand quantities of goods and services

state-owned firms had to fulfill these plans


EfficiencyEfficiency in Poland


EfficiencyInstitutions matter


EfficiencyImpact of central planning on common people


InefficiencyDifferent industries can have different efficiency within one country

10.2 Differences in Efficiency: Case Studies 297

close to that of the United States in three of the four industries examined but fell far behind in telecommunications. The differences in productivity at the industry level were much larger than those in aggregate productivity.

What was the source of these large differences in productivity? It seems un-likely that they were the result of differences in technology, because the three countries considered are clearly at the frontier of world technology, and ideas flow easily among them. Further, if technology differences were at the root of these dif-ferences in productivity, it would be hard to explain the pattern of productivity differences observed. As one commentator put it, “If we are going to attribute these productivity differences to differences in technology, it is hard to understand how Japanese businessmen can be so successful at learning and developing technologies for making automobile parts, and so inept at learning and developing technologies for freezing fish.”7 Another piece of evidence that something other than differences in technology explains these differences in productivity among countries is that some of these productivity differences hold true even within a single company. For example, Ford Europe failed to adopt Japanese just-in-time production techniques (by which a manufacturer holds small inventories of parts), even though these tech-niques were adopted by Ford USA. Finally, in some cases, productivity was low in a country even though the country clearly had better technology than its rivals. For example, productivity was low in the German beer-brewing industry, even though the most advanced brewing machinery was made in Germany and exported.

A better explanation for these productivity differences—and the one that the studies’ authors seize on—is differences in the organization of production among the countries. Japanese automobile makers, for example, work closely with their parts suppliers to streamline procedures and improve productivity. In Germany and the United States, by contrast, the relationship between automakers and their suppliers is more antagonistic. Suppliers fear that if they improve their productivity, the firms to which they sell their parts will simply negotiate lower purchase prices,

TABLE 10.2

Productivity in Selected Industries in the Early 1990s

United States Japan Germany

Automobiles 100 127 84Steel 100 110 100Food Processing 100 42 84Telecommunications 100 51 42Aggregate Productivity 100 67 89

F

7DeLong (1997).

For example, the amount of medical care provided per person,adjusting for differences in the age of the population, isalmost three times as high in Miami as it is in Honolulu


CEE example


Forms of inefficiencyUnproductive activities

Resources that could be used in production of goods andservices are diverted to uses without economic value

Given that nothing is produced, these activities involve somekind of redistribution (usually not the desirable kind)

Some are outright illegal:

theft, smuggling, bribery

In addition to wasting resources directly, they force others tospend further resources unproductively

(e.g. hiring security guards, installing expensive securitysystems etc)

Another example, kidnapping for ransom, banditry

Civil wars in many resource rich countries


Forms of inefficiencyUnproductive activities

Rent seeking – rent is a payment to a factor of productionthat is to the excess of market price on a functioning market(licenses, quotas etc)

one damaging type of rent seeking is associated withcorruption and bribery

misallocation of talent (ambulance-chasing lawyers in the US)

Overeducation


Forms of inefficiencyIdle resources

Unemployment/underemployment and idle capital

often result of recessions (labor hoarding – but it may berational)overstaffing is often done for political reasons

Air Afrique had 4200 employees for 8 aircraft in 2001 (500employees per airplane)Examples could be found during central planning in CEEcountries


Forms of inefficiencyMisallocation among sectors

304 CHAPTER 10 Efficiency

the marginal product of labor in Sector 1 as a function of the quantity of labor in Sector 1, and the other curve shows the marginal product in Sector 2 as a function of labor in Sector 2. The curve describing Sector 2 is drawn with the horizontal axis reversed, so that a movement from left to right corresponds to a reduction in the quantity of labor used in Sector 2. The point where the two curves meet repre-sents the optimal allocation of labor between the sectors.

Figure 10.4 illustrates a case in which the marginal products of labor in the two sectors are not equalized. In this case, labor is overallocated to Sector 1. The marginal product of labor in Sector 1 (MPL1) is thus lower than the marginal product in Sector 2. Were one worker to move from Sector 1 to Sector 2, output in Sector 1 would fall by MPL1, but output would rise in Sector 2 by MPL2. The change in output would thus be MPL2 - MPL1. Moving from the inefficient allocation to the efficient allocation would raise output by an amount given by the shaded triangle in the figure.

In a well-functioning market economy, reaching the optimal allocation of la-bor between sectors occurs automatically, as a result of two forces. First, as we saw in Chapter 3, labor will be paid its marginal product as a wage. Second, if the mar-ginal product (and thus the wage) for the same unit of labor is different in different sectors of the economy, then workers will have a strong incentive to move from the sector with low marginal product to the sector with high marginal product. This movement of labor will lower the wage in the sector that has high marginal product and will raise it in the sector with low marginal product. The movement of labor will continue until the two marginal products are equalized, and thus the

FIGURE 10.4

Overallocation of Labor to Sector 1

Marginal productof labor in Sector 1

Output lost dueto misallocation

Marginal productof labor in Sector 2

Labor allocatedto Sector 2

Labor allocatedto Sector 1

Units of output

F


Forms of inefficiencyMisallocation among sectors

Reasons:Barriers to mobility

Geographical (share of owner-occupancy is positivelyassociated with unemployment rate)

Sector-speficic centrally set wages

Wage not equal to marginal product

overallocation of family members to family farms in developingcountries

segmentation/discrimination


Reallocation among sectors

This implies that there may be efficiency gains fromre-allocating factors from one sector to another (or onegeographic area to another)


Forms of inefficiencyMisallocation among firms

Some firms may have inferior technology or poor organization

In a well functioning economy, more productive firms willattract more resources

Reasons why inferior firms continue to exist

high and low productivity firms may collude to keep priceartificially high

low productivity firms may get government subsidies orcontracts

use of connections or bribery

low productivity firms may evade taxes, which will put them onan equal footing with tax complying high productivity firms

state-owned firms do not have to make a profitmonopoly


Case study: Central planning

102 THE REVIEW OF ECONOMICS AND STATISTICS

Table 1. - Wage Grids in the Communist and Postcommunist Periods A. 1985-1989 Wage Grid for White-Collar Workers in Czechoslovakia*

Wage

(a) All Industries except Those in (b) (b) Heavy Industry and Construction Salary - Class I la Ib Bonus II Ila ... Vb

1 1,000 _ _ 300 - - ... - 2 1,100 - - 300 - - ... - 3 1,200 - - 350 - - ... - 4 1,300 _ _ 400 - - ... - 5 1,450 - - 450 - - ... - 6 1,600 1,750 - 500 1,700 1,850 ... - 7 1,750 1,950 - 550 1,850 2,050 ... - 8 1,950 2,150 2,350 600 2,050 2,250 ... 3,100 9 2,150 2,350 2,600 650 2,250 2,450 ... 3,400

10 2,350 2,600 2,850 700 2,450 2,700 ... 3,750

20 6,300 - - 1,800 6,500 - ... - 21 7,100 - - 1,900 7,200 - ... -

B. 1998 Wage Grid for the Public Sector in the Czech Republic*

Wage by years of experience Class <1 1-2 3^ 5-6 7-9 10-12 13-15 16-19 20-23 24-27 28-32 >32

1 3,250 3,390 3,550 3,700 3,850 4,000 4,170 4,330 4,490 4,660 4,820 4,980 2 3,560 3,720 3,880 4,050 4,210 4,380 4,540 4,720 4,900 5,080 5,250 5,430

11 8,800 9,250 9,710 10,170 10,620 11,080 11,540 11,980 12,440 12,910 13,370 13,840 12 10,000 10,520 11,030 11,560 12,070 12,590 13,120 13,640 14,170 14,710 15,230 15,760

*See text for description. Sources: Ministry of Labor and Social Affairs (1985, 1986, 1998).

The paper is organized as follows: In section II we provide a brief institutional background; in section III we describe our data and methodology. Section IV contains our empirical findings on returns to education under the communist grid and during the transition, and in section V we present the corresponding returns to experience. In section VI we analyze the returns in transition to human capital obtained under communism. The shift in interindustry wage differentials from the communist to the transition period is analyzed in section VII. In section VIII we present and apply a new method for decomposing the variance of worker-specific wages. We conclude the paper in section DC.

II. The Wage Grids

As in other centrally planned economies, after the 1948 communist takeover of Czechoslovakia, the government introduced a wage grid in an attempt to leave little discretion for managers or unions to set wages at the enterprise level. However, some discretion remained, as managers could award "personal evaluation bonuses" that varied across workers with the same observable characteristics and could represent as much as 30% of the base wage. While in principle the trade unions and government jointly deter- mined the grid and the level of wages within the grid, in practice the union and government officials by and large

implemented the communist party policies as set out in the central plan.6

In panel A of Table 1 we present the 1985 wage grid that was used for white collar workers in the last 5 years of communism.7 The columns represent wage levels by industry. Most workers were placed in wage tariff (class) categories I- Ib, but workers in heavy and construction industries were placed in wage tariff categories Il-Vb. Within each wage tariff category, workers were placed in salary classes 1-21 on the basis of their education, experience, and occupation and the number of employees that they super- vised. The grid was accompanied by a detailed handbook that permits one to determine the relationship between education or experience and wages.

The system underlying the grid evolved over time. For example, the earlier grids were sector-specific (e.g., the

6 See, e.g., Windmuller (1970), Svejnar (1974a,b), Adam (1984), and Flanagan (1998). In addition to personal evaluation bonuses, the managers could influence total compensation and hence compete for workers by offering various social benefits, such as subsidized housing. However, they could not change the centrally set wage rates. 7 We could not obtain a detailed grid for blue collar workers for the mid to late 1980s, but we believe that the experience profile was similar to that for white collar employees. As we show in table 9 below, the interpolated wage-experience grid estimates for blue collar workers in 1982 and white collar workers in 1985 are quite similar. The grids that we present in figure 1 for 1954, 1979, and 1998 cover both blue and white collar workers.

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Reallocation among sectorsTransition of Czechia and Estonia

32

Share

on T

ota

l Em

plo

yment

.

Figure 3: Employment Evolution by SectorYears of Transition

.

91 E

mpl.

Index

OLD Estonian NEW Estonian OLD Czech NEW Czech

-2 -1 0 1 2 3 4 5

0

.1

.2

.3

.4

.5

.6

.7

.8

.9

1

1.1. . . . . . . . . .

. . . . . . . . . . .. ES

. . . . . . . . . . . . . . . . . . .. CZ. . . .

Figure 4: Job Reallocation by Sector

Rat

e R

elat

ive

to T

otal

Em

ploy

men

t

OLD sectorYears of Transition

Est JD Est JC Cz JD Cz JC

-2 -1 0 1 2 3 4 50

.01

.02

.03

.04

.05

.06

.07

.08

.09.1

.11

.12

.13

.14

.15

NEW sectorYears of Transition


-2 -1 0 1 2 3 4 50

.01

.02

.03

.04

.05

.06

.07

.08

.09.1

.11

.12

.13

.14

.15

Rat

e R

elat

ive

to S

ecto

r E

mpl

oym

ent

OLD sectorYears of Transition


-2 -1 0 1 2 3 4 50

.05

.1

.15

.2

.25

NEW sectorYears of Transition


-2 -1 0 1 2 3 4 50

.05.1

.15.2

.25.3

.35.4

.45.5

.55.6

.65.7

.75


Case study: Corruption

ascertain the date when rumors first hit theJakarta Exchange—there was generally a spe-cific triggering event, which I take as the start ofthe episode. I assumed that each episode cameto an end when it was (1) explicitly put to restby the revelation of new information or (2) itwas reported that analysts had factored the newinformation about Suharto’s health into theirpricing of securities.

II. Results

Figure 1 shows the share price returns for thesix episodes, with the Suharto Dependency In-dex on the horizontal axis. The graph stronglysuggests that politically dependent firms, on av-

erage, lost more value during these episodesthan did less-dependent firms.To get a sense of the magnitude of the effect

of political dependence during each episode, Iran a set of regressions using the followingspecification:

(1) Rie ! " # $ ! POLi # % ie

where Rie is the return on the price of securityi during episode e, POLi is the firm’s SuhartoDependency Number, and %ie is the error term.5The results of this set of regressions are listed inTable 2; consistent with the raw pattern illus-trated in Figure 1, $ is negative in everyinstance.Now, in each episode, investors were reacting

to a different piece of news, so we expect thecoefficient on POLi to differ across events.More precisely, a more severe threat to Suhar-to’s health should intensify the effect of politi-cal dependence, hence the magnitude of $should be increasing with event severity. As ameasure of the market’s concerns regarding thethreat to Suharto’s health in each episode, I use

5 All regressions reported in this paper use standarderrors that correct for heteroskedasticity. I also ran regres-sions using an error structure that only allowed for thecorrelation of %eis for each company, i.e., Cov(%ei , %fj) ! 0 ifand only if i " j. The regressions were also run using anerror structure that allowed for the correlation of %eis withineach group. These various approaches yielded very similarsets of standard errors.

TABLE 1—SUMMARY STATISTICS BY DEGREE OF POLITICAL DEPENDENCE AS MEASUREDBY THE SUHARTO DEPENDENCY INDEX

POL 1 2 3 4 5 All firms Observations

Observations 5 34 10 16 14 79

Assets2,145.76(2,843.63)

2,228.57(3,989.85)

2,206.20(3,676.99)

1,634.08(2,561.07)

1,765.51(2,230.52)

2,033.19(3,321.59) 76

Debt707.18(702.84)

791.32(1,478.83)

813.25(976.28)

397.83(461.06)

712.57(1,070.83)

717.37(1,186.85) 70

Return on assets(net income)/(total assets)

0.038(0.031)

0.058(0.058)

0.043(0.023)

0.037(0.032)

0.050(0.029)

0.050(0.044) 76

Tax rate (taxespaid)/(pretaxincome)

0.23(0.05)

0.24(0.12)

0.16(0.14)

0.22(0.16)

0.15(0.12)

0.21(0.13) 74

Sources: All data are from the Financial Times’ Extel Database (1997); Assets and Debt are expressed in millions of 1995rupiah.

FIGURE 1. EFFECT OF POLITICAL DEPENDENCE ON SHAREPRICE RETURNS

1098 THE AMERICAN ECONOMIC REVIEW SEPTEMBER 2001


Forms of inefficiencyCorruption Perception Index 2015 (Source: Transparency International)

It is estimated that that a decrease of Corruption PerceptionIndex by 1 increases deficit by 0.2pp and long-run growth by0.5 pp (Hanousek and Kocenda (2011) – however they usedthe old index that was on a scale 0-10).


Forms of inefficiencyEffect of corruption

Source: Hanousek&Kochanova (2015)

http://www.voxeu.org/article/bribery-and-firm-performance-new-evidence


Forms of inefficiencyTechnology blocking

Artificial barriers to the spread of technology

classical example: Luddites vs textile mills in 19th centuryBritainmore recent: Microsoft trying to suppress Java languageUber vs. standard taxi companies

Lecture 8 - CERGE-EI

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