metadata: authors: 0: author: Sinha,Rishabh
count: World
docna: 0: docna: Sectoral productivity gaps and aggregate productivity
docty: Policy Research Working Paper
subtopic: Consumption,Economic Theory & Research,Fiscal & Monetary Policy,Industrial Economics,International Trade and Trade Rules,Food Security,Economic Growth
teratopic: Macroeconomics and Economic Growth,Industry
abstracts: cdata!: This paper examines the role of changes
            in sectoral productivity gaps over time in accounting for
            growth realized by countries over the past few decades. To
            quantify the productivity impact of the sectoral gaps, a
            simple model of resource allocation is developed in which
            the gaps arise due to distortions in the form of
            asymmetrical taxes across sectors. The paper finds a limited
            role of changes in distortions over time in accounting for
            actual growth. Implied growth from changes in distortions
            accounts for less than 2.5 percent of actual growth for the
            median country. To check if the lower contribution of
            changes in distortions is because of unrealized gains
            suggested by high levels of present distortions,
            productivity gains are estimated when distortions across
            countries are reduced to the US levels. Barring a couple of
            cases, the gains in aggregate productivity are modest across
            the sample of countries.
repnme: repnme: Sectoral productivity gaps and aggregate productivity
display_title: Sectoral productivity gaps and
            aggregate productivity
listing_relative_url: /research/2016/06/26538638/sectoral-productivity-gaps-aggregate-productivity
topicv3: Agriculture and Food Security m1326205 435,Economic Growth m1326241 471,Trade m1326924 698,Macroeconomic and Structural Policies
            m1327380 439,Population, Aging and Health m1327736 640
publishtoextweb_dt: 2016-06-29T00:00:00Z
historic_topic: Macroeconomics and Economic Growth,Industry
no_of_pages: 53
author_keys: 0: author_key: 1411342
model_extractions: WPS7737 Policy Research Working Paper 7737 Sectoral Productivity Gaps and Aggregate Productivity _Rishabh Sinha_ Development Research Group Macroeconomics and Growth Team June 2016
Policy Research Working Paper 7737

# **Abstract**

This paper examines the role of changes in sectoral productivity gaps over time in accounting for growth realized by countries over the past few decades. To quantify the productivity impact of the sectoral gaps, a simple model of resource allocation is developed in which the gaps arise due to distortions in the form of asymmetrical taxes across sectors. The paper finds a limited role of changes in distortions over time in accounting for actual growth. Implied growth from changes in distortions accounts for less than 2.5 percent of actual growth for the median country. To check if the lower contribution of changes in distortions is because of unrealized gains suggested by high levels of present distortions, productivity gains are estimated when distortions across countries are reduced to the US levels. Barring a couple of cases, the gains in aggregate productivity are modest across the sample of countries.

This paper is a product of the Macroeconomics and Growth Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at rishabhsinha@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **Sectoral Productivity Gaps and Aggregate Productivity**

Rishabh Sinha<sup>∗</sup> .

## JEL Classification: O11, O47, O50

Keywords: Sectoral productivity gaps, Misallocation, Labor Shares, Aggregate productivity, Agricultural productivity

> ∗Email: rishabhsinha@worldbank.org. I thank Aart Kraay, Dino Leonardo Merotto, Todd Schoellman, Luis Servén and conference participants at North American Productivity Workshop (Quebec City) for their valuable comments and suggestions.
# **1 Introduction**

A well-documented fact in the development accounting literature is that labor productivity is lower in Agriculture compared to productivity elsewhere in the economy (Caselli (2005), Restuccia _et al._ (2008) etc.). A number of studies interpret these gaps as misallocation in which too much of resources are allocated in the relatively unproductive Agricultural sector. Moreover, as the gaps in productivity across sectors are large, these studies often find enormous gains from a more efficient allocation of resources.1 However, these findings are highly sensitive to model specification and measurement errors (Restuccia & Rogerson (2013)). Therefore, ascertaining whether measured gaps indeed imply misallocation is of vital importance. In this paper I conduct two simple quantitative exercises that are easy to interpret.

First, I ask how much of the countries’ growth since the last few decades can be attributed to convergence in gaps in productivity across sectors. This becomes important as many countries have seen sizable increases in incomes over the last few decades. For instance, the median growth in GDP per capita over the last two decades for the sample of countries for which I am able to measure sectoral gaps from the available data has been around 40%. The country at the 90<sup>th</sup> percentile of economic growth during the same period has realized a doubling of its GDP per capita. Hence, this exercise sheds light on whether changes in sectoral gaps over time are quantitatively important in accounting for growth observed in these countries. I find that changes in distortions over time play limited role in accounting for actual growth. For the median country, implied growth is less than 2.5% of actual growth attained over long periods of time. Nonetheless, changes in distortions can account for larger share for some countries with implied growth ranging over 17–82% of actual growth for 10% of the sample countries. Of course, this may be a _lower bound_ on possible gains to the extent that countries are leaving many possible reductions in barriers on the table. As a second exercise I ask how much richer poor countries would be if they had instead the barriers of rich countries. While the gains are high for some countries (>50% for China, >125% for Guinea and Puerto Rico), I find only modest gains for most countries.

A contribution of this paper is that it measures the effect of allocation of resources by comparing the gaps in marginal productivity. While many studies have tried to study the potential gains in productivity through a reallocation of resources, most have used measures of average productivity in their calculations. However, allocative efficiency requires an equalization of marginal productivity rather than an equalization of average productivity across sectors and a gap in average productivity 1Many papers have also looked at variations in productivity within sectors and have quantified the productivity gains from a reallocation of resources within sectors. For example, Hsieh & Klenow (2009) find that the variation in labor and capital productivity across manufacturing plants is much higher in China and India compared to the variation observed in the US.

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across sectors is neither a necessary nor a sufficient condition of inefficiency. When production follows a Cobb-Douglas technology in presence of perfect competition, marginal productivity is simply the product of average productivity and the labor share of income. The deviation in labor income shares across sectors drives a wedge between marginal and average productivity gaps. If the sector with higher average productivity also has a higher relative labor share of income, then marginal gaps are larger than observed average gaps. To estimate labor shares at the sectoral level, I use the data from Global Trade Analysis Project (GTAP). I find significant deviation between marginal and average gaps and as such considering the gaps at margin is not only a theoretical concern. On average, distortions estimated using average gaps in productivity are around 35% and 22% lower than when estimated using marginal gaps for Industry and Services respectively. Vollrath (2009) also considers differences in marginal productivity to measure impact of misallocation. Though, in his calculations he assumes the labor shares to be invariant across countries and hence uses the US labor shares to estimate marginal productivity in each country. However, labor shares obtained from GTAP show that there is a considerable variation in sector labor shares across countries. Thus, using a common set of labor shares may not be an appropriate strategy.

In order to quantify the productivity impact of the sectoral gaps, I develop a simple model of resource allocation in which the gaps arise due to distortions in the form of asymmetrical taxes on labor inputs across three broad sectors: Agriculture, Industry and Services. These taxes are a stylized way of capturing the net effect of a menu of factors that create barriers to movement of resources across sectors. A model of resource allocation is essential in contrast to a shiftshare analysis of aggregate productivity because productivity in both sectors change as a result of resources flowing from the low productivity sector to the high productivity sector. An important point is that the growth impact of changes in distortions depends crucially on the amount of resources that get reallocated. If the sector-specific distortions in Industry and Services are positive, any decline in distortions in either sector leads to an outflow of resources from Agriculture. However, the gains are capped by the resources in Agriculture initially. An economy that begins with fewer resources in Agriculture is bound to realize lesser gains compared to an economy that begins with a larger share of its resources allocated to Agriculture.

I use the labor shares data from GTAP together with sector value added and employment share data from the World Development Indicator (WDI) database to estimate distortions for many country-years. There are two major concerns with regards to using the sectoral share of employment data obtained from the WDI database. First, the source survey used to estimate the sectoral share of employment for many country-years is not representative in certain respects. For example, the surveys often were conducted in urban areas only, or only sampled workers in a very restrictive 2
age group. Second, for some surveys the shares refer to sector of current employment rather that the sector of usual employment. I filter out the spurious data and the cleaned data set consists of more than 1,800 country-year observations spread across 150 countries. Using this data, I estimate distortions in Industry and Services relative to Agriculture. I smooth out the annual time series of distortions by averaging distortions in a decade and analyze the impact of changes in decennial distortions. There is a significant variation in distortions across countries with distortions at the 90<sup>th</sup> percentile being more than twice as large as distortions at the median. I also find that the distortions are higher in developing countries in both sectors. Moreover, the relationship between GDP per capita and distortions is statistically significant.2 The change in distortions realized by the median country over any two decades is small and distortions in any decade remains in the range of ±13% of the previous decade. However, countries located at the extreme report changes that are orders of magnitude larger than the modest change observed at the median. Interestingly, for many countries the distortions increase over the decades.

The benchmark quantitative exercises assume that labor shares remain constant over time within a country. In order to check the robustness to changing labor shares over time, I consider a case in which each sector experiences a differential change over decade and use the findings from Karabarbounis & Neiman (2014) to discipline the magnitude of change. The ability of changes in distortions in accounting for growth is essentially unchanged for the median country. Though, I find that the share of growth explained by changes in distortions increases for a few countries.

The paper is related to several strands of literature that study sectoral gaps in productivity. Closely related is the literature that explore the barriers to movement of labor out of agriculture (Todaro (1969), Bryan _et al._ (2014)). Another strand studies imperfections in credit markets that affect non-Agricultural sectors disproportionately driven by acute need of finance (Caballero _et al._ (2008), Buera _et al._ (2011), Song _et al._ (2011)). Credit market imperfections thus create hurdles for Industry and Services and stunt their growth leading to a depressed demand for resources. This work is also related to a large literature that tries to explain low agricultural productivity in poor countries (Restuccia _et al._ (2008), Adamopoulos (2011), Adamopoulos & Restuccia (2014), Gollin & Rogerson (2014) etc.). Finally, it also relates to the literature that argues that the measured gaps in productivity across sectors can be in large part explained by accounting for differences in human capital across sectors (Lagakos & Waugh (2013), Young (2013), Herrendorf & Schoellman (2015)). The measurement of labor inputs in this paper abstracts from differences in hours worked, human capital and talent across sectors due to the unavailability of time series data. Given the

> 2The estimates of distortions for the most recent decade are available for 84 countries. There is a large variation in GDP per capita across this sample with the country at 90<sup>th</sup> percentile being 15 multiples richer than the country at 10<sup>th</sup> percentile.

3
findings of the papers mentioned earlier, it is very likely that a finer measurement of labor inputs will further chip away the gains estimated from bringing the present level of distortions to some common benchmark.

The paper is most closely related to Duarte & Restuccia (2010) who examine the role of structural transformation in aggregate productivity. The analysis of the paper differs from them in two important ways. First, while they study the role of differential productivity growth across sectors in accounting for the catch-up in aggregate productivity across countries, I analyze the role of changes in sectoral productivity gaps within a country in accounting for aggregate changes in productivity. Second, the sample of countries in the paper is much larger and covers a significant breath of world income distribution. In a similar analysis, Cai (2015) examines the role of reduction in sectoral gaps in generating structural transformation. In contrast, I analyze the importance of such changes in generating growth. Additionally, the sample of countries in this paper spans a much broader world income distribution in this case too.

The rest of the paper is organized as follows. In the next section, I present the simple model of resource allocation in presence of asymmetrical labor taxes across sectors. Following, I discuss the data used before embarking on the quantitative exercises. Next, I perform robustness checks of the main findings of the paper to address concerns regarding changes in labor shares over time before concluding.

# **2 Model**

In this section, I develop a simple model that can be used to calculate implied productivity growth due to changes in levels of sector-specific distortions.

The economy features a representative firm that produces a single final good _Y_ combining the output _Yj_ from the three broad sectors - Agriculture (A), Industry (I) and Services (S). The representative firm is endowed with a Cobb-Douglas production technology given by Each sector in turn consists of a stand-in firm that employs capital _K j_ and labor _L j_ to produce sectoral output _Yj_ according to the following Cobb-Douglas technology 4
where _Aj_ denotes the sector-specific TFP term.

I model sector-specific distortion in spirit of Restuccia & Rogerson (2008) and Hsieh & Klenow (2009) as a tax τ _j_ on per-unit labor hired by a sector _j_ . There are a few comments that need to be made here. First, these taxes are a stylized way of capturing the effect of barriers to allocation of resources across sectors and do not represent actual taxes on hiring labor. On the other hand, they denote the net effect of a menu of factors that may lead to such frictions. Second, the allocations are independent of where the incidence of these taxes lie. For example, the results of the analysis will not change if these taxes are levied on capital inputs or revenues instead of labor inputs. This is not to say that the estimate of taxes are identical in all cases. Rather, the allocation of resources are identical given that same sectoral gaps in productivity are used to estimate taxes in each case. Third, the sector-specific distortion τ _j_ is not restricted to be positive. A negative value of the distortion can be interpreted as a subsidy to that sector.

I assume perfect competition in the factor markets and accordingly the sectoral firms take the labor wage w and the rental rate of capital _R_ as given when maximizing profits. The optimization problem of the firms can be summarized as where _Pj_ denotes the price of the good produced in sector _j_ .

Given this simple set up, I now characterize the relationship between the sectoral productivity gaps and the sector-specific distortions. I also outline how this relationship is used to estimate the implied distortions from the data on value added per worker and labor income shares.

# **2.1 Sectoral Productivity Gaps**

The first order condition with respect to _L j_ from the firm’s optimization in 3 yields where _K_<sup>∗</sup><sup>_L_∗Equating the wage rate w using 4 from</sup> _j_<sup>,</sup> _j_<sup>and</sup><sup>_Y_</sup> _j_<sup>∗represent the equilibrium quantities.</sup> the firm optimization across any two sectors gives 5
We take the above equation to data to obtain our estimates of sector-specific distortions. Given data _P Y_<sup>∗</sup> _j j_ on sector value added and total employment in that sector, maps to value added per worker in _L_<sup>∗</sup> _j_ that sector. Moreover, the capital share of value added within a sector is given by α _j_ .3 However, in practice we estimate (1-α _j_ ) from the share of income accruing to labor in any sector. For brevity, we express the above equation as follows where _LSj_ and _V Aj_ denote the sectoral labor income share and sectoral value added per worker respectively.

# **2.1.1 Marginal vs. Average Productivity**

Equation 6 highlights why it is important to use gaps in marginal productivity (labor share _LSj_ × value added per worker _V Aj_ ) across sectors rather than gaps in average productivity (value added per worker _V Aj_ ) in estimating gains from reallocation. Large gaps in average productivity between two sectors do not imply inefficiency in allocation of resources. It is possible that these gaps in average productivity reflect differences in labor shares across sectors rather than differences in distortions. In the special case when relative shares are inverse of relative average productivity, there are no distortions4 present in the economy. This means that there are no possible gains to be realized by moving resources across sectors. On the other hand, absence of gaps in relative average productivity do not imply efficiency of resource allocation. Even if the average productivity is same across sectors, there are gains from reallocation if the labor shares vary across sectors. In line with this, an exercise that considers gaps in marginal productivity would lead to quantitative differences with respect to an exercise that considers gaps in average productivity to the extent that

> 3To see this, note that the first order condition with respect to capital _K_ j from the firm optimization gives: _P_ j _Y_ j ∗ _RK_<sup>∗</sup> j α j = _R_ ⇒ α j = . � _K_<sup>∗</sup> j � _P_ j _Y_ j<sup>∗</sup>

> 4Or all sectors face the same level of distortions.

6
labor shares vary across sectors. I discuss the magnitude of these differences when I present the quantitative results.

# **2.2 Equilibrium Allocation and Productivity Gains**

Once I have the estimates of distortions, I use them to analyze how they affect aggregate productivity. To do this, I first identify the relationship between equilibrium allocations and distortions and show that resources flow from a low productivity sector to a high productivity sector as the high productivity sector experiences a decrease in distortion relative to the low productivity sector. This reallocation leads to an increase in aggregate productivity.

First, consider the optimization problem of the firm that aggregates the sectoral output to produce the final good. The final good firm’s first order condition gives where _P_ is the price of the final good _Y_ . Now, using 4 and 7, we can solve for the the equilibrium labor allocation _L_<sup>∗</sup> _j_<sup>in any sector</sup><sup>_j_</sup> _L_ denotes the aggregate endowment of labor in the economy. The above equation captures the resource allocation dynamics of the economy. More resources are allocated to a sector as the sector-specific distortion τ _j_ faced by that sector goes down with distortions in other sectors remaining unchanged. Aside from distortions, the final good production function parameter θ _j_ and the labor share of income within a sector (1-α _j_ ) also determine the sectoral allocation of resources. The allocation of capital can be solved for using the same steps and the equilibrium allocation of capital in any sector _j_ is given by where _K_ is the aggregate endowment of labor in the economy. Finally, the total amount of final good produced in an economy endowed with capital _K_ and labor _L_ is given by 7
|IT<br>(<br>dD<br>dD<br>dD<br>dD<br>dD|
|---|
where δ<sup>∗</sup> _j_ k<sup>and δ∗</sup> _j_ l<sup>denote the equilibrium share of capital and labor allocated to sector</sup><sup>_j_respectively.</sup> The above equation is flexible enough so that the marginal effects of changes in capital and labor stock (through changes in participation and employment rates) on aggregate productivity can be isolated in addition to marginal gains from allocation of resources across sectors. Nonetheless, it is clear from equations 8 and 9, that the relative allocation of resources across sectors is independent of changes in stock of aggregate resources.5 Hence, given the focus of this paper on sectoral gaps in productivity, I abstract from marginal gains due to growth in stock of resources and consider only the marginal effects of reallocation of resources across sectors.6 Finally to take this equation to data, I normalize the final good as the numeraire. I discuss the data that I use to carry out the quantitative exercise in the next section.

# **3 Data**

The bulk of the data used in the analysis are taken from the World Development Indicator (WDI) online database. Specifically, I use the following series from the database: value added at sector level (provided in constant 2005 US dollars) and employment at sector level (provided as percentage of total employment). Apart from these six series, I use data from Global Trade Analysis Project (GTAP) to estimate the sector labor share of income at sector level.

The WDI data on sectoral share of employment are originally sourced from the International Labor Organization’s (ILO) Key Indicators of the Labour Market Database. This annual series on sectoral share is often constructed using different sources for the same country over time. The annual series starts from 1980 for some countries and there is a wide variation with respect to the annual observations available across countries. However, there are two major concerns with regards to using the data as provided for the analysis of this paper.

1. For many country-years, the source used is not representative of the country. For instance, the sectoral share of employment in Argentina for many years is constructed from sources that surveyed only the Greater Buenos Aires area. Even in recent years, the data is based on surveys carried out in main cities or metropolitan areas. As such, it is highly likely that the

> 5It is important to note that this independence is a direct result of the model specification that abstracts from differences in resource accumulation across sectors. The independence would cease to exist if one allows for different accumulation process across sectors in presence of barriers on marginal flow of resources from period to period. For example, if the rate of population growth is different across sectors and there are frictions to movement of additional labor that flows to a particular sector, then the resource allocation would not be independent of population growth. On the other hand, the model abstracts from dynamic adjustments and focuses on allocation of aggregate resources which helps in isolating the marginal gains from a better allocation.

> 6The second and the third terms of equation 13.

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sectoral share of Agriculture in total employment is severely under reported and using such data would lead to spurious estimates of sectoral gaps in productivity.

2. For many country-years, the source used to construct the series refers to a particular period or month. Consider a case in which such a survey was conducted in a period in which many workers changed their activity from the sector in which they usually work to a sector in which they shifted temporarily to satisfy the transitory high demand. For instance, many workers leave their usual jobs to satisfy the increased demand for labor in agriculture during harvest season. Such a scenario will lead to under estimation of sectoral share of employment in the sector from which the workers moved temporarily.

The two concerns above necessitates the cleaning of the data before it could be used for analysis. The specific details of how the data on sectoral share are adjusted from the raw data are provided in the data appendix. Data correction basically consists of two main steps: first, for each country selecting a data source that is free of any major concerns (for example, limits with regards to geographical coverage, period of the year, age groups, sector/occupations etc.) and second, including annual data constructed from other sources provided the average annual change in sectoral shares of employment remain reasonable. The final dataset consists of more than 1,800 data points spread across 150 countries. Yet, the number of data points available for a country varies significantly: from 35 in Switzerland to a single observation in around 40 countries.

The next step is to harmonize the sectors across WDI and GTAP. The WDI reports value added and employment data for the three broad sectors of Agriculture (A), Industry (I) and Services etc. (S) which can be aggregated from the constituents sectors in the International Standard Industry Classification (ISIC, revision 3). On the other hand, the GTAP reports data using a much finer classification of 57 sectors and uses a combination ISIC and Central Product Classification (CPC) sectors to aggregate its sectors. To harmonize the sectors, I map the 57 GTAP sectors to the three broad sectors used in the WDI database using the United Nations Statistics Division correspondence tables. The mapping is near perfect with the following three exceptions:

1. GTAP sector 1 ( _pdr_ ) consists of 2 sectors, of which one (husked rice) maps to Industry in the WDI classification while the other (rice, not husked) maps to Agriculture

2. GTAP sector 10 ( _oap_ ) consists of 7 sectors of which one (hides, skins and furskins, raw) maps to Industry in the WDI classification while the rest map to Agriculture 3. GTAP sector 12 ( _wol_ ) consists of 4 sectors of which one (pulled wool, greasy, including fleece-washed pulled wool) comes under Industry in the WDI classification while the rest map to Agriculture 10
In all of the above cases, I map the respective GTAP sectors to Agriculture assuming that the larger share of value added accrues to it as compared to Industry. In the next section, I discuss how I use this data to quantify the gains from changes in distortions.

# **4 Quantitative Analysis**

I begin the quantitative analysis by calibrating the technology parameters θ _j_ and α _j_ . Equation 7 pins down the value of θ _j_ which is the share of sector _j_ ’s value added in the total value added. Using the sector value added data from the WDI database, I am able to estimate θ _j_ for multiple country-periods.7 As discussed before α _j_ maps to the capital share of income within a sector. I estimate α _j_ indirectly by estimating the labor share of income within a sector. To do this, I employ the GTAP data that reports both the value added as well as the labor income at the sector level. However, the GTAP data are available for only three years – 2004, 2007 and 2011. I estimate the labor shares for all the three years and then consider the average of the three estimates for our analysis. The estimated labor shares for the three years vary over a narrow range for a large number of countries. Hence, I take the average of the three years to serve as a reasonable benchmark and use a time-invariant set of sectoral labor shares for each country in the quantitative exercises that follow.

## **4.1 Estimating Distortions**

Once I have the estimates of φ _j_ and α _j_ , I employ equation 6 to arrive at the estimates of sectorspecific distortion τ _j_ . However, note that for any fixed country-period I have two independent equations and need to solve for three sector-specific distortions. But, since these distortions only determine the allocation of resources across sectors, I can normalize τ _j_ for any one sector and solve for distortions in the other two sectors relative to the distortion in the chosen sector. In this case, I set distortions in Agriculture at zero, i.e., τ _A_ = 0.8 Using this normalization, equation 6 can be rewritten in terms of τ _j_

> 7See data appendix for more details on the series used from the WDI database.

> 8This normalization does not alter the quantitative results obtained in the paper. A separate strategy that does not estimate distortions at sector level, but rather defines relative distortions as ratio of undistorted marginal products of any two sectors yields the same resource allocations. See appendix section on normalization for more details.

11
where now _j_ ∈{ _I_ , _S_ }. It is now possible to estimate the implied distortions in a country for many periods by plugging the value added per worker in a sector relative to agriculture in any period together with the time-invariant sectoral labor shares α _j_ .9 Since it is also worthwhile to analyze the long-term trends in the distortions within a country in addition to the cross-country variation, I calculate the average distortions by decade in a country. This helps in smoothing out the yearly fluctuations in distortions and isolating the long-term trend. The decennial averages that I report here do not necessarily represent the 10-year averages and as it is not possible to obtain estimates of distortions in many periods due to lack of data.10 Estimated distortions for the complete set of countries by decade are listed in Table 1.

The distortions are not constrained in either direction and can take positive as well as negative values. So, what do positive and negative distortions mean with respect to sectoral allocation? Note that the distortions are modeled as taxes on hiring labor. If these taxes are higher in some sector relative to Agriculture, then less labor would be hired by that sector compared to a case in which the taxes are same across sectors. In poor countries, a disproportionately large fraction of the workforce is engaged in Agriculture. A fraction of this workforce arguably should instead flow to other sectors under efficiency. Within the framework of the model, this implies that taxes on hiring labor in sectors outside Agriculture are higher compared to taxes in Agriculture. Relative distortions in such cases would be greater than zero which corresponds to what is obtained from the data.

Before embarking on my counterfactual exercises to quantify the effects of distortions on aggregate productivity, I briefly discuss the behavior of distortions across countries and over time.

1. **Variation across countries:** Figure 1 shows the distortions at 10<sup>th</sup> , 50<sup>th</sup> and 90<sup>th</sup> percentile by decade for both Industry and Services.11 There is a significant variation in distortions across countries. This implies that resource misallocation is more pronounced in some countries compared to others. For both sectors, distortions at the 90<sup>th</sup> percentile are more than twice as large as distortions at the median. Distortions at the 10<sup>th</sup> percentile are negative in the 1990s indicating that distortions are relatively larger in Agriculture. The distortions at the 10<sup>th</sup> percentile remain well below the median distortions in other decades. An interesting observation is that the variations in distortions across countries have not changed much from

> 9Calculating value added per worker by sectors is not required for this calculation. What suffices is to calculate the relative value added per worker in a sector relative to agriculture which I obtain using the sectoral distribution of workforce and the sector value added.

> 10In some cases, there is a single estimate available for a decade.

> 11To keep analysis consistent across decades, I consider only the 55 countries for which estimates are available for each decade. The decade of 1980s is dropped because including it significantly reduces the sample of countries to 19 from 55.

12
the 1990s to the 2010s. The gap in distortions between the 90<sup>th</sup> percentile and the median decreased from 3.0 to 2.6 in Industry and declined marginally from 2.6 to 2.5 in Services. In contrast, the gap between median and the 10<sup>th</sup> percentile increased for Industry during the same period.

2. **Variation across GDP per capita:** Figures 2 – 5 plot distortions in each decade against the GDP per capita at 2005 constant International dollars in that decade for Industry and Services. The number of countries for which estimates are available vary over decade and ranges from 28 in the 1980s to 88 in the 2000s. As correlations between distortions and GDP per capita across decade are likely to be affected by changes in composition of countries in each decade, it is spurious to gauge changes in correlations over time. However, I find that distortions in both sectors are negatively correlated with GDP per capita for the two most recent decades, both of which contains estimates for over 80 countries.12 The negative relationship between the variables is particularly more evident in the most recent decade. It appears from the figure that the relationship between the variables is driven by some outliers. To check for this concern, I estimate the correlations after dropping observations for which the estimates of distortions lie beyond ±2 standard deviations of the mean value. The negative relationship between GDP per capita and distortions becomes stronger for Industry and is still significant at 5% level of significance. The magnitude of correlation drops a lot for the case of Services and the relationship is no longer significant. This suggests that the poor countries exhibit higher distortions, at least in Industry, than the rich countries and hence are positioned to reap larger benefits from elimination of these distortions.

3. **Variation over time:** In figures 6 and 7, I show how distortions in a decade stand relative to the previous decade for Industry and Services respectively. If the distortions in the two decades are identical, they lie on the 45-degree line. Given that the sign of distortions don’t change over time, a contraction in absolute value points to a better allocation of resources. Points below the line correspond to a contraction in distortions and the points above correspond to an expansion across decades provided that the sign of the distortions remain unchanged over the decades. Around 88% – 92% of the countries report the same sign over any two consecutive decades for both Industry and Services. The distortions for majority of these countries contracted during the 1980s – 1990s and the 2000s – 2010s. In contrast, the distortions in both sectors increased for more than two-fifths of countries in the sample during the 1990s – 2000s. Table 2 shows the extent of variation in decennial changes

> 12The estimates are available for 88 and 84 countries in the decades of the 2000s and the 2010s respectively. The small difference in sample size across the decades masks much larger changes in replacement with 17 of the 84 countries in the 2010s being missing in the 2000s.

13
in distortions across countries. The change in distortions realized by the median country over any two decades is small and remains in the range of ±0.2 in absolute terms. Contrarily, countries that are located at extremes report changes that are orders of magnitude larger than the modest change seen at the median.

# **4.2 Average and Marginal Gaps**

At this moment, it is worthwhile to compare the estimates of distortions that take into account the convergence of marginal productivity gaps to estimates that are obtained while using convergence in gaps in average productivity as a measure of efficiency. Figure 8 shows the estimated distortions using the two approaches for the 2000s.13 The horizontal and the vertical axes report the distortions estimated using the marginal gap and the average gap approach respectively. If the estimates from the two approach are similar, they lie on or close to the solid 45-degree line. The points below this line refer to cases where the average gap approach underestimates the distortions and the points above the line refer to cases where the average gap approach overestimates the distortions. The two gray lines helps in identifying how large the deviations are in estimates from the two approaches.

In general, the distortions estimated using average gaps in productivity are around 35% and 22% lower than when estimated using marginal gaps for Industry and Services respectively. This divergence in estimates is widespread. For the 88 countries in the sample, the distortions estimated using the average gaps are lower in 41 (60) countries in the case of Industry (Services). What is more important here is that the magnitude of difference in distortions estimated from the two approaches are quite large for many countries. For example in a quarter of the sample, the average gap method overestimates the distortions in Industry by around 50% and underestimates the distortions in Services in excess of 40%. Such large deviations imply that there are bound to be large deviations between quantitative exercises that employ the different approaches. Hence, considering the marginal gaps in productivity across sectors as a measure of efficiency is not only important for theoretical reasons, but also important for quantitative analysis as there are significant variation in labor shares across sectors.

I now begin with the two main quantitative exercises. The first quantitative exercise isolates the contribution of changes in distortions over time to growth realized during the same period. In the second quantitative exercise I explore the relationship between GDP per capita and counterfactual growth resulting from reducing current distortions to a common benchmark for all countries. An

> 13I present the distortions for the 2000s as an illustration, as the findings in this period would extend to other periods given that I assume invariance of factor shares over time. The choice of the 2000s is due to the fact that the distortions for maximum number of countries (88) are available for in this decade.

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important aspect of the quantitative analysis is the fact that the measurement of labor inputs is done at a worker level. As such, the differences in labor inputs across sectors does not account for differences in hours worker, human capital or talent across sectors. Given the evidence of differences in these factors across sectors14, the estimates of distortions are somewhat upward biased. This would lead to even lower gains in productivity in the second counterfactual exercise. To the extent that these differences across sectors remained stable over time, they would not affect the findings obtained in the first counterfactual in any significant way.

# **4.3 Contribution of Changing Distortions to Growth**

A number of studies have found large gaps in productivity across sectors and have interpreted these gaps as suggestive of misallocation of resources across sectors. Based on this interpretation, these studies quantify the impact of closing of gaps on aggregate productivity. However, these findings are highly sensitive to model misspecification and measurement errors. Therefore, it is important to ascertain whether measured gaps in productivity truly imply misallocation. The goal of this paper is to examine the role of distortions in a historical perspective. Over the last few decades countries have seen sizable changes in GDP per capita over time. For instance, the median growth in GDP per capita over two decades has been 40% for the sample of countries for which estimates of distortions are available. The country at the 90<sup>th</sup> percentile has realized a doubling of its GDP per capita over the same period. A fundamental issue of interest here is to discern whether changes in distortions over time have been important drivers of growth or not.

Using data from WDI and GTAP, I have obtained consistent estimates of sectoral gaps and have estimated resulting distortions for a large number of countries for relatively long periods. In the first exercise, I ask how much of the economic growth experienced by countries over long stretches of time can be accounted for by observed changes in distortions. For example, what fraction of growth in GDP per capita witnessed by Thailand from 1980s – 2010s can be explained by changes in distortions over the same period. The change in productivity over time I obtain here is driven solely by changes in distortions as everything else – capital stock, labor stock and technology, is fixed at the initial decade. As the estimates of distortions are available for four decades, there are six different time periods for which this exercise can be conducted. Figure 9 reports the result of the exercise with panels 9a– 9c, 9d– 9e and 9f corresponding to duration of one, two and three decades respectively. The vertical axis represents the implied change in productivity from the intertemporal changes in distortions while the horizontal axis represents the actual growth in GDP per capita. The closer an observation is to the 45-degree line, the greater is the contribution

> 14See Lagakos & Waugh (2013), Young (2013) and Herrendorf & Schoellman (2015).

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of changing distortions in explaining the growth. The 0.25X and 0.10X lines act as a reference for illustrating the impact of changing distortions.

In general, the points lies far from the 45-degree line across all time periods implying the limited role of changes in distortions in explaining growth. This is reflected in the fact that while the median growth rate across all time periods has been close to 25%, the median implied growth rate is less than a quarter of a percent. For many countries the impact of changes in distortions has been in the opposite direction.

Table 3 summarizes the results for various time periods. As seen in column (3), the median implied growth lies below 1% for all time periods. Implied growth at the 90<sup>th</sup> percentile are somewhat higher and lie in the range of 2.6–6.3%. In contrast, implied growth at the 10<sup>th</sup> percentile are firmly in the negative territory. Columns (5)–(7) report the implied growth as a percentage of actual growth. Broadly speaking, there is limited evidence supporting a dominant role of changes in distortions in accounting for actual growth. For the median country, implied growth is less than 2.5% of actual growth across all periods. During 1990s–2000s, implied growth occurred in opposite direction of the actual growth at the median.15 However, changes in distortions can account for larger share for some countries. Implied growth as a percentage of actual growth can account for 17–82% of actual growth at the upper end of sample for various periods.

A possible reason why changes in distortions are able to explain larger share of growth at the higher end may be because these correspond to cases where the actual growth is fairly small in magnitude. To check for this, I restrict the sample to those cases in which the growth exceeds 10% and show the results of the restricted sample in columns (9)–(11). I find that the contribution of changing distortions decreases somewhat at the median and now remains within 2% of actual growth across all time periods. In sharp contrast, the ability to explain growth at the 90<sup>th</sup> percentile shrinks considerably and varies in the range of 7–22%. The deviations from actual growth become much larger at the 10<sup>th</sup> percentile compared to the unrestricted sample. In summary, the changes in distortions cannot account for a significant share of actual growth experienced by countries. Implied growth from changes in distortions constitute for less than 2.5% of actual growth for the median country.

In the next counterfactual exercise, I show that productivity gains for poor countries are larger compared to rich countries when the present level of distortions across countries are counterfactually changed to the same benchmark.

> 15Table 4 reports the proportion of countries within each sample period for which the implied change in GDP per capita was in the same direction as the actual change. Barring the 1990s–2000s decade, roughly three in every five countries in the sample reported implied change that was in line with the direction of actual change.

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# **4.4 Counterfactual Productivity Gains across GDP per Capita**

As discussed previously, distortions in both Industry and Services in 2010s are higher in poor countries (Figure 5). Moreover, the negative relationship between distortions in either sectors and GDP per capita is significant for the 84 countries for which the estimates of distortions are available. The countries in the sample cover a breadth of world income distribution and there is an enormous variation in GDP per capita across the sample. The country at the 90<sup>th</sup> percentile of world GDP per capita distribution is around 15 times richer than the country located at the 10<sup>th</sup> percentile with the gap in GDP per capita exceeding 30 multiples for the 95<sup>th</sup> and 5<sup>th</sup> percentile countries.

Given that distortions are lower in rich countries, it is interesting to analyze what productivity gains can be realized by poor countries if they experienced a convergence in productivity gaps so that the distortions in these countries resembled those in the developed world. Moreover, as all of these countries have a significant share of workforce in agriculture, a drastic reduction in distortions could cause a drastic outflow from agriculture to other sectors. Based on such a motivation, I use the estimated distortions in the United States in 2010s as a benchmark for distortions in the developed world.

I present the result of this exercise in figure 10 where the log of implied growth for a country is plotted against the log of its GDP per capita in 2010 in 2005 International dollars terms. As seen in the figure, the relationship between GDP per capita and the implied growth from changing distortions to US levels is negative and is statistically significant at 1%. The median implied growth for countries having GDP per capita below 4,000 International dollars is much higher (12.5%) compared to the median of countries having GDP per capita above 16,000 International dollars (1%). Implied growth is particularly large in China, Guinea and Puerto Rico where the growth breaches the 50% mark. Implied growth exceeds 25% for another set of seven countries. It is interesting to note that for almost a third of the sample the implied growth is negative implying that the net distortions in these countries is lower compared to the net distortions in the United States. The majority of these countries belong to the Europe and Central Asia region. However, the magnitude of impact is marginal with the maximum contraction being just over 5%.

Distortions are present in all countries even for the most recent decade. This is also true for countries that have seen a steady decline in distortions over time. As a second benchmark of counterfactual changes in distortions, I consider the case where the distortions are completely eliminated. Like in the previous exercise, I present the findings in figure 11 by plotting the log of implied growth such obtained against the log of GDP per capita in 2010.

As distortions are present in all countries in 2010s, a move to no distortions leads to an efficient 17
allocation of resources and net positive gains for all countries in the sample. Implied growth ranges over a wide range (<1% to >300%) with a median growth of 7% and the implied growth at 90<sup>th</sup> percentile exceeds 45%. Like in the previous benchmark, the variation in implied growth is inversely correlated with GDP per capita. However, the relationship is much stronger than before. The median implied growth for countries having GDP per capita below 4,000 International dollars (37%) is more than 15 times larger than the median implied growth for countries having GDP per capita above 16,000 International dollars (2%) in 2010. Implied growth is particularly large for Puerto Rico and Guinea with GDP per capita expanding by a multiple of three and two respectively if distortions are eliminated entirely. In all, seven countries report implied growth in excess of 50%.

An important point is that the growth impact of changes in distortions depends crucially on the amount of resources that get reallocated. If the sector-specific distortions in Industry and Services are positive, any decline in distortions in either sector leads to an outflow of resources from Agriculture. However, the gains are capped by the resources in Agriculture initially. What this means that if two countries witness the same decline in distortions, the country with fewer resources in Agriculture is bound to realize lesser gains compared to the country that begins with a larger share of its resources allocated to Agriculture. For instance consider the case of Cambodia and United States. Distortions in Cambodia in 2010s are smaller than distortions in United States for both Industry and Services. Yet, the growth impact from eliminating current distortions is more than 8 times as high for Cambodia. This is due to the fact that Cambodia employs around 54% of its workforce in Agriculture compared to United States that has less than 2% of its workforce in Agriculture. A massive reallocation out of Agriculture occurs in Cambodia due a decline in distortions which is not the case for United States because it has fewer resources in Agriculture to begin with. Hence, the productivity impact of similar changes in distortions can vary depending In summary, the gains from reducing distortions to a common benchmark are higher for poor countries owing to the fact that distortions in both Industry and Services are higher in these countries. Moreover, as relative distortions in Industry and Services are in general higher compared to Agriculture, the poor countries reap larger productivity gains from a common change in distortions because they have a much higher share of resources in the relatively unproductive Agriculture sector from which resources flow to other sectors. The median implied growth for countries with GDP per capita below 4,000 International dollars ranges between 12–37% depending on whether distortions are leveled to match distortions in the United States or eliminated altogether.

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> | | x | |
2000s, I increase the labor shares for the decades of 1980s and 1990s, and decrease them for the decade of 2010s.

To access the importance of changes in factor shares, I repeat the first counterfactual exercise in presence of declining decennial labor shares. Figure 12 plots the implied productivity growth from the repeated exercise against the benchmark case in which labor shares were time invariant. Panel 12a corresponds to the case in which labor shares in all sectors decline by 2% every decade while panel 12b corresponds to a higher decennial decline rate of 8%.

As expected, the larger deviations from earlier estimates are realized when I use a higher decennial decline rate of 8%. However, the difference in productivity gains relative to the benchmark case of time invariant labor shares is small even when a higher rate of decline is used. Median productivity gain is around 3.8 percentage points higher for the benchmark case for the longest period under consideration and is around 1.4 percentage points higher for all country-periods. Estimated productivity gains from the two approaches differ by more than 5 percentage points in less than 3% of the 263 country-periods and never surpass 8 percentage points. When using the lower decennial decline of 2%, the difference in productivity change never exceeds 2 percentagepoints barring a single country-period.

Columns (5) – (7) of table 5 show the percentage of growth accounted for by changes in distortions for country-periods in which growth was more than 10% when labor shares decline by 8% per decade. Compared to the benchmark where labor shares were time invariant, the ability of distortions in explaining growth reduces with proportionate decline in labor shares. In fact, for all periods the direction of implied growth is opposite to the direction of actual growth. Contribution of changing distortions at the 90<sup>th</sup> percentile also diminished and ranges over 3 – 17% of actual growth compared to 7 – 22% in the benchmark.

# **5.2 Disproportionate Changes in Factor Shares across Sectors**

As discussed earlier, the distortions are unchanged if the changes in factor shares are proportionate across sectors. As such, the differences in productivity estimates from the benchmark case are to an extent insulated against such changes in factor shares. In this exercise, I carry out the first counterfactual exercise with disproportionate factor shares changes across sectors that lead to a divergence in estimated distortions from the benchmark case.

Once again I need reasonable estimates of sector-specific changes in labor shares and in this case also I use the findings in Karabarbounis & Neiman (2014). Using the EU KLEMS data starting from 1975 to 2007, Karabarbounis & Neiman (2014) estimate the trends in labor share for 10 non20
overlapping sectors and report the per decade change in labor shares for each of these sectors. Four and five of these sectors aggregate to Industry and Services respectively. Moreover, the per decade decline in labor shares for sectors aggregating to Industry and Services vary over a large range. The per decade decline in Mining, which is a component of Industry, is 4.5 percentage-points compared to almost no change realized in Construction which is also a component of Industry. In Transport and Financial and Business Services, two of the five sectors that comprise Services, the labor shares have moved in opposite direction. The per decade decline in the former is 4.5 percentage-points compared to a 2 percentage-points increase in the latter. To carry out the exercise with disproportionate changes across sectors, I consider the mid-point of the estimates within a sector group (2.5 and 1.5 percentage-points decline in labor share per decade for Industry and Services respectively). For agriculture, I use a 2 percentage-points decennial increase in labor share as reported in the paper.

The productivity change obtained when factor shares are disproportionately changed across sectors is plotted against the benchmark productivity change in Figure 13. The productivity gain lies above the 45-degree line for more than two-thirds of the country-periods indicating larger productivity gains relative to the benchmark. This is in contrast to the previous case where productivity gains with proportionate changes in labor shares across sectors were generally lower compared to the benchmark case. The reason behind this is that the changes in factor shares over time occur in opposite directions for Industry and Services relative to Agriculture. If labor shares in Agriculture are lower than in Industry and Services to begin with, then the gaps in labor shares between Agriculture and other sectors decreases over time. This in turn leads to a higher estimate of distortions in earlier decades and lower estimates of distortions in the later decades. As such, changes in distortions over time increases and larger productivity changes are realized. Median productivity gain is around 2.2 percentage points higher relative to the benchmark for the longest period under consideration and is around 0.4 percentage points higher for all countryperiods. Like with proportionate changes in labor shares, differences in productivity gain remain close to the benchmark and deviate by more than 5 percentage points for less than 5% of the total country-periods.

Columns (8) – (10) of table 5 show the percentage of growth accounted for by changes in distortions for country-periods in which growth was more than 10% when labor shares decline disproportionately across sectors. The ability of changes in distortions in explaining growth increases at the 90<sup>th</sup> percentile. Contribution of changing distortion ranges over 9 – 32% of actual growth compared to 7 – 22% in the benchmark. The deviations from actual growth also somewhat diminish at the 10<sup>th</sup> percentile with deviations ranging over 6 – 30% compared to 14 – 34% in absence of changes in labor shares over time.

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In summary, declining labor shares are of second-order importance in quantifying the effects of changes in productivity gaps across sectors on aggregate productivity. Using disproportionate changes in labor shares increases the share of actual growth accounted for by changing distortions at the upper end.

# **6 Conclusion**

The goal of this paper is to examine the role of distortions in a historical perspective. Over the last few decades countries have seen sizable changes in GDP per capita over time. A fundamental issue of interest is to understand whether changes in distortions over time have been important drivers of growth or not. To quantify the effect of changes in distortions over time in accounting for actual growth, I develop a simple model of resource allocation in which these gaps arise due to distortions in the form of asymmetrical taxes on labor inputs across sectors. A model of resource allocation is essential in contrast to a shift-share analysis of aggregate productivity because productivity in both sectors change as a result of resources flowing from the low productivity sector to the high productivity sector.

Using sector level data from WDI database and labor share estimates from GTAP database, I estimate distortions in Industry and Services relative to Agriculture for a number of countries that span a spectrum of world income distribution. The main finding of the paper is that there is limited evidence supporting a dominant role of changes in distortions in accounting for actual growth. For the median country, implied growth is less than 2.5% of actual growth across all periods. However, changes in distortions can account for larger share for some countries. To the extent that reductions in distortions represent a _lower bound_ on possible gains because large reductions in barriers are yet to be realized, I perform a second counterfactual in which barriers across countries are reduced to what is observed in rich countries. Once again, I find only modest gains in productivity for most countries. The findings of the paper are also robust to changes in labor shares over time. However, disproportionate changes in labor shares across sectors somewhat increases the share of actual growth accounted for by changes in distortions for some countries. The analysis suggests that a simple reallocation of resources across sectors without any corresponding changes in TFP and resource accumulation will not deliver large gains in aggregate productivity as suggested by observed large gaps in productivity across sectors.

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<!-- Start of picture text --> LUX 64000<br>NOR<br>CHE USA ISL<br>JPN AUT GERDNK BHSAUSSWEFRABELITAGBR<br>FIN IRL ESPNZL<br>GRC PRI<br>PRT SAU<br>KOR CYP PLW 16000<br>TTO HUN<br>MEX GRD SVK<br>POL JAM VEN<br>MYS URYSUR<br>CUB MUS IRN<br>THA DOM<br>DMA<br>SLV<br>(b) Services Correlation = 0.099 BOL HND EGYPRY SYR 4000<br>GUY<br>LKA PHL MNG GDP per Capita: 2000s (2005 I$)<br>NIC<br>PAK<br>GHA<br>1000<br>LUX 64000<br>NOR<br>CHE USA ISL<br>JPN AUT GER SWEDNKAUSBHSBELFRAGBRITA<br>FIN ESPIRLNZL<br>PRI GRC<br>PRT SAU<br>KOR CYP PLW 16000<br>TTO HUN<br>MEX GRD SVK<br>POL JAM VEN<br>MYS URYSUR<br>CUB MUS IRN<br>THA DOM<br>DMA<br>SLV<br>(a) Industry Correlation = 0.118 PRY BOLHND EGY SYR 4000<br>GUY<br>PHL MNGLKA GDP per Capita: 1990s (2005 I$)<br>NIC<br>PAK<br>GHA<br>1000<br>10 8 6 4 2 0<br>Distortions in Services<br>10 8 6 4 2 0<br>Distortions in Industry<br><!-- End of picture text --> 27
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<!-- Start of picture text --> LUX<br>NOR<br>64000<br>CHE JPN IRLAUTUSADNKFINCANSWEAUSFRA GR EBBEL ISL<br>KOR GRC ESPITANZL<br>MLTPRI<br>PRT CYPSAUSVK<br>POL HUN<br>CUB MEX URYMYS 16000<br>TURMUS BGR SUR<br>WSM GTMTHA JAMECUZAFCOL SLVTUN VENIRN CHN<br>NAM EGY<br>JOR<br>(b) Services Correlation = −0.309** LKA IND MARHND IDN MNGPHL PRY 4000<br>LAO<br>PAK<br>GDP per Capita: 2010 (2005 I$)<br>ZMB<br>GIN 1000<br>LUX<br>NOR<br>64000<br>CHE JPN AUTIRLUSAFIK N DSWECANGERAUSGBRFRABELISL<br>KOR GRCESP ITANZL<br>PRI MLT<br>SAUPRT CYPSVK<br>POL HUN<br>CUBMEX MYSURY R 16000<br>MUSTURSUBG<br>THA COLJAMZAF VEN IRNCHN<br>WSM GTM ECU SLV TUN<br>NAM EGY<br>JOR<br>(a) Industry Correlation = −0.305*** INDIDN MNGLKAHNDMARPHL PRY 4000<br>LAO<br>PAK GDP per Capita: 2010 (2005 I$)<br>ZMB<br>GIN 1000<br>20 15 10 5 0<br>Distortions in Services<br>20 15 10 5 0<br><!-- End of picture text --> 29
<!-- Start of picture text --> 20<br>15<br>45−Degree Line<br>PRI<br>10<br>CHE<br>KAZ<br>NAM THA<br>WSMAZE KORJPNCUBMNGMEXSVNLKANORGTMHNDCOLPOLMARJAMPHLAUTIRLUSASAUFINHRVLUXDNKPRTSWEARMPRYCANSLVUKRVENGERGRCLTUESPAUSMUSLVAGBREGYMYSPAKURYFRARUSTURBELCZEESTITAKGZTUNHUNNZLMKDSURBGRISLCYPSVKIRNMNEJORMLT 50 Distortions in Industry (2000s) (c) 2000s–2010s<br>Industry<br>20<br>15<br>45−Degree Line<br>10<br>THA<br>PRI<br>TTO<br>CHE<br>GEO NOR POLAZEJPNKORNICMEXJAMPRYPHLHNDCUBAUTFINSVNUSAIRLBOLDNKVENMNGDOMSWELKAHRVGERPRTLTULUXSLVMYSSURPAKLVASAUAUSGRCGBRFRAEGYCZEPE S RUBELESTURYABWNZLITA G MUSZKUYCYPISLHUNSVKGHAIRNBHSPLW 50 Distortions in Industry (1990s) (b) 1990s–2000s<br>Changes in Distortions Over Time:<br>20<br>Figure 6:<br>15<br>45−Degree Line<br>10<br>THA<br>TTO<br>PRI JPN 5<br>NOR BOL Distortions in Industry (1980s) (a) 1980s–1990s<br>MEX FIN VENAUTPHL SWESLVAUSMYSLKABHSCYPGRDFRANZLEGYMMRSURSYR 0<br>20 15 10 5 0<br>Distortions in Industry (2010s)<br>20 15 10 5 0<br>Distortions in Industry (2000s)<br>20 15 10 5 0<br>Distortions in Industry (1990s)<br><!-- End of picture text --> 30
<!-- Start of picture text --> 15<br>45−Degree Line 10<br>CHE<br>THA<br>WSM POL<br>LKA CUBKAZ MAR HNDJPN MEX 5<br>NAM GTMSVN JAMPRTPAKHRVIRLLUXKORCOLAUTNORMNGUSATURGRCPHLMUSDNKEGYSLVFINAZECANTUNLVAUKRSWEMKDLTUARMAUSFRAPRYGERURYGBRESPBELMYSKGZCZEESTVENCYPITAIRNRUSSAUBGRHUNNZLSVKISLMLTMNESURJORPRI 0 Distortions in Services (2000s) (c) 2000s–2010s<br>Services<br>15<br>THA<br>45−Degree Line 10<br>CHE POL MEX<br>TTO JPN NORBOL USA 5<br>LKACUBHND JAMSVNGEOPRTNICIRL HRVPAKKORAUTLUXDNKPHLDOMFINLTULVASLVGRCEGYGHAGERPRYMUSSWEFRAVENMNGMYSCZEESTABWAUSGBRSURIRNBELBHSESPURYCYPITARUSAZENZLSVKHUNGUYKGZISLPLWSAUPRI 0 Distortions in Services (1990s) (b) 1990s–2000s<br>Changes in Distortions Over Time:<br>15<br>Figure 7:<br>45−Degree Line 10<br>THA<br>TTOJPNNOR 5<br>BOL<br>MEX LKA<br>AUTFIN VENEGYCYPBHSMMR Distortions in Services (1980s) (a) 1980s–1990s<br>GRDSLV PHL AUSSWEMYSFRA NZL SYRSUR<br>PRI 0<br>15 10 5 0<br>Distortions in Services (2010s)<br>15 10 5 0<br>Distortions in Services (2000s)<br>15 10 5 0<br>Distortions in Services (1990s)<br><!-- End of picture text --> 31
<!-- Start of picture text --> 20<br>0.75X Line<br>45−Degree Line<br>15<br>MDG<br>1.25X Line<br>10<br>CHE<br>(b) Services THA<br>POL VNM<br>5 Distortions: Marginal Gap<br>NOR<br>IRL USA<br>SEN<br>0<br>Marginal and Average Gaps<br>20<br>Distortions: 45−Degree Line<br>0.75X Line<br>Figure 8: 15<br>1.25X Line MDGTTOPRI<br>10<br>CHE<br>GEO<br>KAZ<br>(a) Industry<br>THA<br>NAM<br>NOR<br>AZE 5 Distortions: Marginal Gap<br>MEX JAM<br>USA<br>SEN<br>IRN GHA<br>0<br>20 15 10 5 0<br>Distortions: Average Gap<br>20 15 10 5 0<br>Distortions: Average Gap<br><!-- End of picture text --> 32
Figure 9: Productivity Impact from Changing Distortions Over Time <!-- Start of picture text --> (a) 1980s - 1990s (b) 1990s - 2000s (c) 2000s - 2010s<br>45−Degree Line<br>45−Degree Line<br>45−Degree Line<br>0.25X Line<br>SUR TTO VENMEX PHL BOL BHSSYRSWE FIN NZLFRA SLV AUTAUS NORJPN CYPPRILKA GRD EGYMYS0.25X Line0.10X LineTHA PLWPRYJAM VENJPN SAUHNDICHE SL NICSUR ITA URY GER BOLPHL FRA MEX BHS PAKKGZ DNK PRT BELU CYP AUT THAMNG S NZL GHAPRI NORSWELVESPAAUS IRN EGYGBRRUSMYS CUB HUNCZEFINGRC HRVGUY LUXMUS LKADOM SVKSVNKORPOL LTU GEOIRL LVA EST TTO 0.10X Line0.25X Line AZE ISLIRL JAM ITAESPDNK PRT USAFRGRCJPNVEN MEX CYPGBRC FINWSMNOR LUXNZLHUNSLVBELGER A MLTCHE THRV GTMSAU AUSSWE HNDLVA NSVN U IRNKGZ JORTNEST NAMLTU TUR M COL PAK PHL CZE PRYYSKD M THA KOREGYS MAR BGR U SVK POL SMNER UKRCUB RUS LKAARMMNG URY KAZ 0.10X LineAZE<br>PRI<br>−20 0 20 40 60 80 −25 0 25 50 75 100 125 −50 0 50 100<br>Actual Growth: 1980s − 1990s (%) Actual Growth: 1990s − 2000s (%) Actual Growth: 2000s − 2010 (%)<br>(d) 1980s - 2000s (e) 1990s - 2010s<br>45−Degree Line 45−Degree Line<br>0.25X Line 0.25X Line<br>0.10X Line<br>SUR VEN JOR GTMBOLPHLMEX BHS FRASWENZLJPN SLVTUN AUT FINTTO AUS NORCYP LKAEGYMYSTHA JAMISLVENJPNITA PRYSAU DNKFRPRT HNDCHE GER MEX USAESPCYPBEL A NZLSLV KGZNORPHL SUR G PAK UTSWEFINAUSHUNIRNBRHRVRCLUX THAURY IRLMYSCZEEGYSVN MNG MR CUB US LTUKORSVK LKAPOLLVA EST 0.10X Line AZE<br>PRI PRI<br>−25 0 25 50 75 100 125 −50 0 50 100 150 200 250 300<br>Actual Growth: 1980s − 2000s (%) Actual Growth: 1990s − 2010s (%)<br>(f) 1980s - 2010s<br>45−Degree Line<br>0.25X Line<br>0.10X Line<br>SUR VENJOR GTMBOLPHLMEX BHSFRASWENZLJPN SLVTUN AUT FINTTO AUSNOR CYP LKAEGY MYS THA<br>PRI<br>−50 0 50 100 150<br>Actual Growth: 1980s − 2010s (%)<br>80 125 100<br>100<br>60<br>50<br>75<br>40<br>50<br>20<br>25 0<br>Implied Growth: 1980s − 1990s (%) 0 Implied Growth: 1990s − 2000s (%) Implied Growth: 2000s − 2010s (%)<br>0<br>−20 −50<br>−25<br>125<br>300<br>100<br>250<br>75 200<br>50 150<br>100<br>25<br>50<br>Implied Growth: 1980s − 2000s (%) Implied Growth: 1990s − 2010s (%)<br>0<br>0<br>−25 −50<br>150<br>100<br>50<br>Implied Growth: 1980s − 2010s (%) 0<br>−50<br><!-- End of picture text --> 33
Figure 10: Productivity Impact from Changing Distortions to the US 2010s Levels <!-- Start of picture text --> Correlation = −0.289***<br>PRI<br>GIN<br>CHN<br>ZMB<br>LAO INDLKANAM AZESUR<br>UGA WSM<br>MN E IRN<br>TZA PAK HNDM AR IDNJOR GTM THA CUBKAZ SAU<br>MLT ISL<br>MWI PHLMNG EGY TUNECUCOLZAFMKDJAMVENMUSTURBGRMEXLVABLRHRVRUSHUNPOLESTCYPSVKPRTCZESVNGRCKORESPNZLITAFJPN FINRAD DEU G IRLBNKSWEBRCANELAUTCHEAUS NOR LUX<br>LTU<br>SLV<br>KGZ PRY ARM UKR URYMYS<br>KHM<br>1000 4000 16000 64000<br>GDP per Capita: 2010 (2005 I$)<br>EAP ECA LAC MENA NAM SAS SSA<br>125<br>25<br>5<br>0<br>Implied Growth: Country Level 2010s − US 2010s −5<br><!-- End of picture text --> Both variables are plotted using the log scale. As the implied growth for some countries lies in negative territory, a constant was added to the series so that the minimum value corresponded to unity. EAP: East Asia and Pacific, ECA: Europe and Central Asia, LAC: Latin America and Carribean, MENA: Middle East and North Africa, NAM: North America, SAS: South Asia, SSA: Sub-Saharan Africa.

34
Figure 11: Productivity Impact from Eliminating Distortions <!-- Start of picture text --> Correlation = −0.625***<br>PRI<br>GIN<br>LAO<br>UGA ZMB IND<br>TZA<br>MWI PAK HNDMARIDNLKANAM GTMWSMTHAAZE<br>KHM PHLMNG ECUCHNJAMIRNTURSURCUBKAZ SAU<br>P RY EG ARY TUSLVM N COLMKDVENMUSMEXHRVPOLPRT SVNKOR CHE<br>JOR UKRMNEZAF MLTGRC NOR<br>LVA CYP JPNFINIRLISLSWEAUT<br>KGZ URYMYSBLRLTURUS DDEU NK CANUSA LUX<br>ESPFRA AUS<br>ESTSVK<br>BGR CZE<br>ITA BEL<br>GBR<br>NZL<br>HUN<br>1000 4000 16000 64000<br>GDP per Capita: 2010 (2005 I$)<br>EAP ECA LAC MENA NAM SAS SSA<br>125<br>25<br>5<br>1<br>1/5<br>1/25<br>Implied Growth: Country Level 2010s − No Distortions (%)<br><!-- End of picture text --> Both variables are plotted using the log scale.

EAP: East Asia and Pacific, ECA: Europe and Central Asia, LAC: Latin America and Carribean, MENA: Middle East and North Africa, NAM: North America, SAS: South Asia, SSA: Sub-Saharan Africa.

35
<!-- Start of picture text --> 40<br>45−Degree Line −5 PP<br>20 1980s − 2010s 2000s − 2010s<br>+5 PP<br>0<br>1980s − 2000s 1990s − 2000s<br>−20<br>Productivity Gains: Constant Factor Shares (%) (b) Decennial Decline = 8%<br>−40 1980s − 1990s 1990s − 2000s<br>−60<br>40<br>20 Changing Factor Shares (Proportionate Change Across Sectors)<br>1980s − 2010s 2000s − 2010s<br>45−Degree Line<br>0<br>1980s − 2000s 1990s − 2010s<br>Productivity Gains:<br>−20<br>Productivity Gains: Constant Factor Shares (%) (a) Decennial Decline = 2%<br>−40<br>Figure 12:<br>1980s − 1990s 1990s − 2000s<br>−60<br>40 20 0 −20 −40 −60<br>Productivity Gains: Proportionate Changes in Factor Shares (%)<br>40 20 0 −20 −40 −60<br>Productivity Gains: Proportionate Changes in Factor Shares (%)<br><!-- End of picture text --> 36
<!-- Start of picture text --> 40<br>−5 PP<br>45−Degree Line<br>20 1980s − 2010s 2000s − 2010s<br>+5 PP<br>0<br>1980s − 2000s 1990s − 2010s<br>−20<br>(a) 1980s – 2010s<br>Productivity Gains: Constant Factor Shares (%)<br>−40<br>Changing Factor Shares (Disproportionate Change Across Sectors)<br>1980s − 1990s 1990s − 2000s<br>−60<br>Productivity Gains:<br>Figure 13:<br>40 20 0 −20 −40 −60<br>Productivity Gains: Dispropotionate Changes in Factor Shares (%)<br><!-- End of picture text --> 37
|2010s|.<br>.<br>0.84<br>.<br>0.76<br>2.23<br>1.34<br>.<br>-0.01<br>0.45<br>.<br>0.11<br>1.20<br>1.17<br>.<br>-0.77<br>2.33<br>2.62<br>5.03<br>0.32<br>0.35<br>1.56<br>.<br>.<br>2.75<br>1.55<br>1.44|_(continued)_|
|---|---|---|
|2000s|2.04<br>0.91<br>0.82<br>0.81<br>0.80<br>2.50<br>0.31<br>0.56<br>.<br>0.63<br>3.41<br>-0.06<br>.<br>1.20<br>4.90<br>.<br>2.10<br>2.77<br>4.41<br>0.48<br>0.82<br>2.12<br>.<br>1.97<br>.<br>1.36<br>1.74||
|ervices<br>1990s|.<br>.<br>.<br>-0.17<br>1.11<br>2.39<br>0.08<br>1.23<br>.<br>0.71<br>3.70<br>.<br>.<br>.<br>.<br>.<br>.<br>3.34<br>3.19<br>1.27<br>0.75<br>1.58<br>1.06<br>1.47<br>.<br>1.29<br>1.96||
|S<br>1980s|.<br>.<br>.<br>.<br>0.43<br>2.09<br>.<br>1.58<br>.<br>.<br>3.62<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>1.60<br>.<br>.<br>.<br>.<br>.<br>1.90<br>0.60||
|2010s|.<br>.<br>1.60<br>.<br>0.65<br>2.47<br>6.34<br>.<br>0.26<br>0.33<br>.<br>-0.01<br>1.35<br>1.41<br>.<br>-0.76<br>2.95<br>1.79<br>3.61<br>-0.18<br>0.26<br>1.74<br>.<br>.<br>2.22<br>0.62<br>1.27||
|stry<br>2000s|2.00<br>0.54<br>2.61<br>0.33<br>0.66<br>2.72<br>4.04<br>-0.17<br>.<br>0.49<br>2.18<br>-0.25<br>.<br>1.45<br>1.72<br>.<br>2.12<br>1.71<br>2.82<br>0.16<br>0.56<br>2.16<br>.<br>1.89<br>.<br>0.59<br>1.46||
|Indu<br>1990s|.<br>.<br>.<br>-0.38<br>0.93<br>2.09<br>0.91<br>0.72<br>.<br>0.63<br>1.49<br>.<br>.<br>.<br>.<br>.<br>.<br>2.41<br>1.03<br>0.71<br>0.48<br>1.14<br>0.48<br>1.60<br>.<br>0.21<br>1.29||
|1980s|.<br>.<br>.<br>.<br>0.60<br>1.35<br>.<br>1.14<br>.<br>.<br>2.79<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>0.90<br>.<br>.<br>.<br>.<br>.<br>0.41<br>0.22||
|Country|Albania<br>Antigua & Barbuda<br>Armenia<br>Aruba<br>Australia<br>Austria<br>Azerbaijan<br>Bahamas, The<br>Belarus<br>Belgium<br>Bolivia<br>Bulgaria<br>Cambodia<br>Canada<br>Chile<br>China<br>Colombia<br>Croatia<br>Cuba<br>Cyprus<br>Czech Republic<br>Denmark<br>Dominica<br>Dominican Republic<br>Ecuador<br>Egypt, Arab Rep.<br>El Salvador||

38
|2010s|0.35<br>1.38<br>0.76<br>.<br>0.61<br>.<br>1.87<br>.<br>3.98<br>5.19<br>.<br>3.85<br>.<br>0.04<br>-0.39<br>5.76<br>2.74<br>0.27<br>2.59<br>0.30<br>3.17<br>3.84<br>-0.66<br>4.83<br>2.42<br>0.38<br>4.27<br>_(continued)_|
|---|---|
|2000s|0.81<br>1.87<br>1.00<br>3.62<br>1.26<br>1.32<br>1.38<br>.<br>2.47<br>.<br>0.05<br>4.24<br>2.24<br>0.12<br>-0.12<br>.<br>.<br>0.71<br>3.16<br>0.42<br>3.75<br>4.40<br>-0.12<br>4.39<br>2.62<br>-0.10<br>.|
|ervices<br>1990s|1.25<br>2.22<br>0.93<br>1.41<br>1.73<br>0.26<br>1.41<br>2.05<br>.<br>.<br>-0.32<br>1.87<br>.<br>0.01<br>-0.13<br>.<br>.<br>0.21<br>1.15<br>0.69<br>2.71<br>4.00<br>.<br>.<br>2.82<br>-0.36<br>.|
|S<br>1980s|.<br>1.70<br>1.17<br>.<br>.<br>.<br>.<br>0.90<br>3.61<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>4.17<br>-0.36<br>.<br>.<br>.<br>.|
|2010s|0.20<br>1.79<br>0.51<br>.<br>1.16<br>.<br>1.13<br>.<br>3.01<br>20.22<br>.<br>2.97<br>.<br>0.06<br>-0.14<br>4.17<br>3.94<br>-0.47<br>2.25<br>0.20<br>2.78<br>3.70<br>-0.60<br>6.99<br>4.14<br>0.18<br>6.14|
|stry<br>2000s|0.47<br>2.69<br>0.66<br>7.05<br>1.60<br>-0.05<br>0.66<br>.<br>0.64<br>.<br>0.19<br>2.84<br>0.73<br>0.04<br>0.04<br>.<br>.<br>-0.16<br>2.28<br>0.28<br>3.40<br>3.85<br>-0.25<br>6.48<br>3.57<br>0.20<br>.|
|Indu<br>1990s|0.80<br>2.64<br>0.59<br>3.45<br>1.81<br>-0.14<br>0.57<br>0.62<br>.<br>.<br>0.19<br>1.42<br>.<br>-0.08<br>0.03<br>.<br>.<br>-0.44<br>0.56<br>0.48<br>3.69<br>3.70<br>.<br>.<br>3.35<br>-0.14<br>.|
|1980s|.<br>1.80<br>0.70<br>.<br>.<br>.<br>.<br>-0.12<br>1.40<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>4.26<br>-0.43<br>.<br>.<br>.<br>.|
|Country|Estonia<br>Finland<br>France<br>Georgia<br>Germany<br>Ghana<br>Greece<br>Grenada<br>Guatemala<br>Guinea<br>Guyana<br>Honduras<br>Hong Kong<br>Hungary<br>Iceland<br>India<br>Indonesia<br>Iran, Islamic Rep.<br>Ireland<br>Italy<br>Jamaica<br>Japan<br>Jordan<br>Kazakhstan<br>Korea, Rep.<br>Kyrgyz Republic<br>Lao PDR|

39
|2010s|1.03<br>0.87<br>2.54<br>0.91<br>.<br>2.40<br>0.44<br>-0.41<br>1.65<br>3.27<br>2.02<br>-0.48<br>4.21<br>.<br>5.21<br>-0.01<br>.<br>2.03<br>2.81<br>.<br>0.68<br>1.87<br>3.24<br>2.98<br>-0.84<br>0.23<br>5.64<br>_(continued)_|
|---|---|
|2000s|1.84<br>1.86<br>2.42<br>0.92<br>12.43<br>.<br>0.85<br>-0.35<br>1.16<br>4.12<br>0.97<br>-0.32<br>4.58<br>.<br>3.37<br>0.29<br>3.23<br>3.57<br>2.70<br>-0.12<br>1.21<br>2.05<br>5.16<br>3.30<br>-0.39<br>0.41<br>4.95|
|ervices<br>1990s|3.18<br>1.17<br>1.56<br>.<br>.<br>.<br>0.94<br>.<br>0.73<br>5.26<br>0.48<br>.<br>.<br>1.01<br>.<br>0.51<br>2.43<br>3.79<br>1.98<br>0.87<br>1.19<br>1.53<br>5.00<br>2.31<br>-0.46<br>0.12<br>.|
|S<br>1980s|.<br>.<br>.<br>.<br>.<br>.<br>0.84<br>.<br>.<br>2.89<br>.<br>.<br>.<br>1.86<br>.<br>0.65<br>.<br>4.23<br>.<br>.<br>.<br>1.21<br>.<br>.<br>-0.44<br>.<br>.|
|2010s|0.64<br>1.10<br>1.78<br>0.01<br>.<br>1.53<br>0.59<br>-0.62<br>0.64<br>3.38<br>3.43<br>-0.56<br>2.80<br>.<br>8.81<br>0.04<br>.<br>3.05<br>0.58<br>.<br>1.58<br>2.57<br>2.83<br>1.73<br>11.5<br>0.49<br>6.38|
|stry<br>2000s|0.86<br>1.58<br>1.55<br>-0.12<br>11.15<br>.<br>1.17<br>-0.49<br>0.20<br>3.51<br>2.01<br>-0.42<br>2.99<br>.<br>5.42<br>0.32<br>3.55<br>4.73<br>0.92<br>-0.27<br>3.01<br>2.91<br>4.06<br>1.58<br>10.76<br>0.51<br>2.35|
|Indu<br>1990s|2.33<br>0.86<br>0.74<br>.<br>.<br>.<br>0.87<br>.<br>-0.03<br>4.06<br>0.88<br>.<br>.<br>-0.07<br>.<br>0.53<br>2.88<br>2.95<br>0.83<br>-0.21<br>2.58<br>2.08<br>4.10<br>1.27<br>6.83<br>0.22<br>.|
|1980s|.<br>.<br>.<br>.<br>.<br>.<br>1.18<br>.<br>.<br>1.63<br>.<br>.<br>.<br>0.34<br>.<br>0.66<br>.<br>3.01<br>.<br>.<br>.<br>2.36<br>.<br>.<br>4.16<br>.<br>.|
|Country|Latvia<br>Lithuania<br>Luxembourg<br>Macedonia, FYR<br>Madagascar<br>Malawi<br>Malaysia<br>Malta<br>Mauritius<br>Mexico<br>Mongolia<br>Montenegro<br>Morocco<br>Myanmar<br>Namibia<br>New Zealand<br>Nicaragua<br>Norway<br>Pakistan<br>Palau<br>Paraguay<br>Philippines<br>Poland<br>Portugal<br>Puerto Rico<br>Russian Federation<br>Samoa|

40
|2010s|0.19<br>.<br>-0.12<br>3.69<br>2.41<br>0.47<br>6.74<br>-0.62<br>0.91<br>8.38<br>.<br>2.08<br>3.96<br>.<br>1.13<br>1.88<br>4.16<br>1.00<br>0.58<br>1.89<br>0.60<br>.<br>0.35|.<br>7.18|
|---|---|---|
|2000s|-0.34<br>1.16<br>0.22<br>3.65<br>.<br>0.55<br>4.60<br>0.71<br>1.10<br>7.08<br>.<br>.<br>6.45<br>5.25<br>1.39<br>1.87<br>.<br>0.42<br>0.73<br>2.69<br>0.51<br>.<br>0.98|5.37<br>.|
|Services<br>1990s|-0.23<br>.<br>0.74<br>3.06<br>.<br>0.74<br>2.37<br>-0.52<br>1.05<br>5.07<br>-0.17<br>.<br>10.16<br>4.05<br>.<br>.<br>.<br>.<br>0.55<br>3.71<br>-0.28<br>0.33<br>1.35|.<br>.|
|1980s|.<br>.<br>.<br>.<br>.<br>.<br>2.91<br>0.52<br>0.89<br>.<br>0.34<br>.<br>7.83<br>4.63<br>2.06<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>2.05|.<br>.|
|2010s|1.87<br>.<br>-0.24<br>3.13<br>2.60<br>0.72<br>3.08<br>-0.01<br>1.66<br>10.27<br>.<br>4.80<br>4.05<br>.<br>0.14<br>0.48<br>4.23<br>1.24<br>0.62<br>2.11<br>0.53<br>.<br>1.23|.<br>9.87|
|stry<br>2000s|0.74<br>0.54<br>0.02<br>2.68<br>.<br>0.55<br>1.77<br>1.12<br>1.78<br>8.04<br>.<br>.<br>5.87<br>10.97<br>0.34<br>0.50<br>.<br>1.52<br>0.66<br>2.32<br>0.46<br>.<br>2.08|4.68<br>.|
|Indu<br>1990s|0.15<br>.<br>0.33<br>1.49<br>.<br>0.70<br>0.76<br>-0.52<br>1.29<br>5.09<br>-0.54<br>.<br>8.19<br>5.83<br>.<br>.<br>.<br>.<br>0.50<br>3.04<br>-0.41<br>0.54<br>2.11|.<br>.|
|1980s|.<br>.<br>.<br>.<br>.<br>.<br>1.26<br>1.95<br>0.94<br>.<br>-0.27<br>.<br>6.64<br>4.82<br>0.43<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>2.17|.<br>.|
||abia<br>epublic<br>rica<br>a<br>e<br>nd<br>rab Rep.<br>and Tobago<br>ingdom<br>ates<br>an<br>a, RB||
|Country|Saudi Ar<br>Senegal<br>Slovak R<br>Slovenia<br>South Af<br>Spain<br>Sri Lank<br>Surinam<br>Sweden<br>Switzerla<br>Syrian A<br>Tanzania<br>Thailand<br>Trinidad<br>Tunisia<br>Turkey<br>Uganda<br>Ukraine<br>United K<br>United St<br>Uruguay<br>Uzbekist<br>Venezuel|<br>Vietnam<br>Zambia|

41
Table 2: Decennial Changes in Distortions

|||Ind|ustry|||Ser|vices||
|---|---|---|---|---|---|---|---|---|
|||P|ercentile|||P|ercentil|e|
|Period|N|10|50|90|N|10|50|90|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|1980s – 1990s|22|-0.54|-0.09|2.17|23|-0.66|-0.02|1.94|
|1990s – 2000s|58|-0.55|-0.20|1.91|60|-0.66|-0.07|1.22|
|2000s – 2010s|66|-0.87|-0.10|1.34|65|-0.80|-0.18|0.83|

Includes only those countries in which distortions have the same sign across the decades.

Table 3: Productivity Impact of Changes in Distortions Over Time (%)

|||Implie|d Grow|th (%)|Percen<br>Grow|tage of<br>th Acc|Actual<br>ounted||Perc<br>Gro<br>(Actua|entage o<br>wth Acc<br>l Growt|f Actual<br>ounted<br>h≥10%)|
|---|---|---|---|---|---|---|---|---|---|---|---|
|||P|ercenti|le|P|ercenti|le|||Percent|ile|
|Period|N|10|50|90|10|50|90|N|10|50|90|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|
|1980s – 1990s|24|-8.5|0.6|5.7|-54.5|2.0|62.1|16|-34.4|1.2|7.4|
|1980s – 2000s|25|-6.2|0.4|6.3|-27.5|0.5|44.6|22|-20.5|0.4|13.3|
|1980s – 2010s|22|-12.5|0.9|5.7|-13.1|1.7|81.6|20|-14.0|1.4|9.2|
|1990s – 2000s|64|-5.6|-0.1|3.4|-31.9|-0.2|16.6|57|-22.5|0.1|12.0|
|1990s – 2010s|55|-6.9|0.4|5.2|-20.1|1.5|23.3|50|-19.4|1.2|11.5|
|2000s – 2010s|71|-2.1|0.2|2.6|-33.2|2.2|42.6|33|-22.3|1.3|22.2|

42
Table 4: Contribution of Changes in Distortions in Explaining Growth (%)

||N<br>(1)|Fraction for which Implied Growth is<br>Postive Share of Actual Growth (%)<br>(2)|
|---|---|---|
|1980s – 1990s|24|63|
|1980s – 2000s|25|64|
|1980s – 2010s|22|64|
|1990s – 2000s|64|48|
|1990s – 2010s|55|64|
|2000s – 2010s|71|61|

Table 5: Contribution of Changes in Distortions to Growth: Changing Labor Shares (%)

|||Pe|rcenta|ge of Ac|tual Gro|wth Ac|counted(|Actual G|rowth|≥10%)|
|---|---|---|---|---|---|---|---|---|---|---|
|||Be|nchma|rk|Propo<br>in<br>(Decen|rtionate<br>Labor S<br>nial Decl|Change<br>hares<br>ine = 8%)|Dispro<br>in|portion<br>Labor|ate Change<br>Shares|
|||Pe|rcenti|le||Percenti|le||Percen|tile|
|Period|N|10|50|90|10|50|90|10|50|90|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|
|1980s – 1990s|16|-34.4|1.2|7.4|-40.8|-7.1|3.8|-29.7|0.8|9.2|
|1980s – 2000s|22|-20.5|0.4|13.3|-30.2|-5.3|2.6|-8.3|0.5|30.1|
|1980s – 2010s|20|-14.0|1.4|9.2|-47.3|-6.5|9.0|-5.7|3.9|17.2|
|1990s – 2000s|57|-22.5|0.1|12.0|-28.7|-3.9|7.1|-14.8|-0.2|22.2|
|1990s – 2010s|50|-19.4|1.2|11.5|-24.2|-4.2|10.9|-14.0|1.1|14.9|
|2000s – 2010s|33|-22.3|1.3|22.2|-28.2|-2.4|16.7|-16.5|2.4|31.6|

Statistics populated from those country-periods in which the actual growth was greater than or equal to 10%.

43
# **A Appendices**

## **A.1 Data: Share of Employment by Sectors**

The data on employment share by sectors have been taken from the WDI database which in turn are sourced from the ILO’s Key Indicators of the Labour Market database. The specific series for Agriculture, Industry and Services are _SL.AGR.EMPL.ZS_ , _SL.IND.EMPL.ZS_ and _SL.SRV.EMPL.ZS_ respectively. The annual data series for a country is often constructed using different sources for different years. However, as discussed before there are two serious concerns with regards to using the data as provided in the database. First, the source used for a particular year may not be representative with regards to geography, working age population, gender etc. Second, the source survey may have been conducted in a period that captures seasonal allocation of labor force rather than the usual allocation of labor across sectors. Both these factors are bound to interfere with estimation of sectoral gaps in productivity.

The challenge is to filter the spurious data out from the good data and to do this I proceed as follows. For each country, I first identify the years in which the employment share is constructed from a source that is free from any of the concerns listed above. For example, such a source should be nationally representative, should not exclude any age group within the working age population, corresponds to the sector of employment of workers’ usual activity etc. For the purpose of exposition, I name the filtered data points thus obtained as Consistent Data Points (CDP). In the next step, I identify which of the data sources for a particular country contains maximum CDP and term the series generated from this data source as the Longest Reasonable Series (LRS). In case the maximum CDP come from more than one data sources, I pick the one that corresponds to the most recent data. The LRS serves as the skeletal for including or excluding data points from other sources.

Next, I define an objective criterion to determine whether to include a specific data point or not. The motivation behind designing this criterion comes from that the fact that the movement of workers across sectors is a relatively slow process and it is unlikely that there would be large variations in sectoral shares year over year. Hence, inclusion of data points that amount to large deviations should be avoided. Yet, the process may be faster in some countries compared to others with the speed of transformation depending on several factors including where the country is at with respect to its development path. This means that using a common threshold for deviation across countries may not be the best strategy. To overcome this hurdle, I use different thresholds across countries where these thresholds are selected based on changes observed in the LRS. Specifically, the steps that determine the inclusion of a data point are as follows: 44
1. Find the maximum annual change in sectoral share from the LRS. If change over two consecutive observations occurred over several years due to gap in data, arrive at an average by dividing the total change by number of years. If the maximum annual change is less than or equal to 5 percentage points, go to step 2. Else drop the country from the analysis. Assume a maximum annual change of 5 percentage points for a country that has a single observation in its LRS.

2. Start with the data point that is nearest to the last/first observation in the LRS. Select the data point if the average annual change in sectoral share for all sectors is lower than the maximum annual change obtained in the previous step. However, if there is more than a year between the data point and the first/last data point from the LRS make sure that the total deviation between them remains below 7.5, 10 and 12.5 percentage points for gaps corresponding to 2, 3 and 4 or more years respectively. Exclude the data point if it fails to meet either of the two criteria. Proceed to the next data point and repeat the process but instead of using the nearest data point from the LRS, consider the nearest data point from the appended series that includes data points that have passed the inclusion criteria before.

The selection criterion leads to exclusion of just under 40% of the total observations. A significant portion of exclusion is driven by the fact that for roughly 20% of the 183 countries, there does not exist a data source that is independent of the concerns raised before.

I illustrate the how the selection process works with the help of an example. Consider the case of Bolivia for which sectoral allocation of workforce is available for 30 years from 1980 – 2009 with data missing for only one intermediate year. However, the series is constructed using different surveys for different years. For example, the data for 1980 – 1988 comes from official estimates and for 2004 – 2009 comes from Household/Labor Force Survey. But more importantly the data for 1989 – 1997 comes from surveys that were geographically constrained in main cities or urban areas.

The raw series from the WDI for Agriculture (red solid circles) and Services (blue solid squares) are shown in figure A.1. The share of employment in Agriculture during 1980 – 1988 hovers around 47% while the share of employment in Services rises gradually to just under 40% by 1988. However, both series observe a massive change in 1989 as the source used to construct the series changes. The massive jump in sectoral share of Services is driven by the fact the survey in 1989 from which the share is calculated was restricted in the main cities of the country. This led to enormous over representation of workforce in Services and an equally colossal under representation of workforce in Agriculture. The shares again return to trend levels beginning 1999 with a change in source survey used. The selection process outlined above helps in filtering out the spurious data 45
which are very clearly visible for the case of Bolivia.

<!-- Start of picture text --> 1980 1985 1990 1995 2000 2005 2010 2015<br>Raw Data: AGR Adj Data: AGR Raw Data: SER Adj Data: SER<br>80<br>60<br>40<br>Share of Employment (%) 20<br>0<br><!-- End of picture text --> Figure A.1: Employment Shares: Raw vs. Adjusted (Bolivia) The first step involves identifying the LRS. There are two major data sources that are free from any of the limitations. These refer to the official estimates for 1980 – 1988 and the estimates from household/labor surveys for 2004 – 2009. As the official estimates are available for more periods, they serve as the LRS for Bolivia. The maximum annual change in sectoral share within the LRS happened for Services during 1985 – 1986 when Services realized a 3.4 percentage points expansion in its share. To add data points from other sources to the LRS, I begin with the year closest to the LRS which is 1989. The deviation in sectoral share of Agriculture from 1988 to 1989 exceeds 45 percentage points. This is much higher than the maximum annual change of 3.4 percentage points observed in the LRS and hence, the raw data from 1989 is dropped. Similarly, data points from 1989 to 1997 are all excluded as the average annual change for each of them exceed 3.4 percentage points. However, the average annual change for all sectors from 1988 (the nearest data point that has passed the selection criteria) to 1999 is lower than 3.4 percentage points. Therefore, raw data from 1999 is appended to the LRS and serves as a reference point for 2000. Likewise, the process moves forward for the remaining data points.

The hollow circles and squares represent the subset of data points that pass the selection process for Agriculture and Services respectively. The dashed lines represent the path of selected data points 46
that smooths the big deviations in the raw data. In order to estimate distortions, one can use the interpolated shares of employment from the selected series for years in which sector value added data is available but the adjusted sectoral share is missing. The estimates from such an interpolation technique are estimated where possible and are listed in table A.1. Nonetheless, the estimates from non-interpolated series are not very different from ones estimated using the interpolated series and using the interpolated series do not affect results of the paper in any significant way.

# **A.2 Normalization of Distortions**

The distortions in Industry and Services are estimated relative to distortions in Agriculture by setting distortions in Agriculture τ _A_ = 0. However, this normalization is done purely for simplifying the analysis and the quantitative results of the paper will be affected if one abstracts from distortions at sectoral levels and instead just consider on marginal gaps.

To see this, note that the equilibrium allocation of labor _L_<sup>∗</sup> _j_<sup>in any sector</sup><sup>_j_is given by</sup> Multiplying the numerator and denominator of the RHS by (1 + τ _A_ ) gives where _Tj A_ =<sup>1 + τ</sup><sup>_j_</sup> . Using equation 6, _Tj A_ can be written in terms of labor shares and value added 1 + τ _A_ per worker All the analysis done in the paper can be carried out using such a transformation in which the distortions _Tj A_ are just the ratio of the marginal productivity in any sector _j_ to the marginal productivity in Agriculture. However, the allocations remain the same with respect to the exercises in the paper, as it is precisely this ratio that directly enters equation 6 and the allocations remain 47
independent of any normalization conditional on<sup>1 + τ</sup><sup>_j_</sup> being equal to the ratio of marginal 1 + τ _A_ products. Of course, the same equation is used to estimate the normalized distortions from the data. The relative distortions τ _I_ and τ _S_ represent the factor by which marginal productivity in Industry and Services deviates from the marginal productivity in Agriculture. The same argument applies for capital allocations.

48
||2010s|0.84<br>0.73<br>.<br>0.45<br>.<br>5.03<br>.<br>1.55<br>1.44<br>0.35<br>0.76<br>0.61<br>1.87<br>3.98<br>0.04<br>0.30<br>3.92<br>-0.66<br>0.38<br>2.54<br>0.44<br>1.69<br>3.27<br>2.02<br>-0.48<br>4.86<br>_(continued)_|
|---|---|---|
||2000s|0.84<br>0.80<br>0.75<br>0.63<br>3.49<br>4.25<br>2.00<br>1.28<br>1.74<br>0.83<br>1.00<br>1.26<br>1.33<br>3.66<br>0.13<br>0.41<br>4.40<br>-0.13<br>-0.11<br>2.42<br>0.85<br>1.13<br>4.12<br>1.04<br>-0.29<br>3.65|
|ervices|1990s|.<br>1.07<br>1.18<br>0.71<br>3.54<br>3.31<br>1.46<br>1.33<br>1.95<br>1.25<br>1.02<br>1.73<br>1.41<br>2.93<br>0.01<br>0.71<br>4.00<br>.<br>-0.36<br>1.56<br>0.96<br>0.73<br>5.09<br>0.48<br>.<br>.|
|S|1980s|.<br>0.40<br>1.58<br>.<br>3.65<br>.<br>.<br>1.79<br>1.07<br>.<br>1.17<br>.<br>.<br>3.61<br>.<br>.<br>4.17<br>-0.36<br>.<br>.<br>0.85<br>.<br>2.89<br>.<br>.<br>.|
||2010s|1.60<br>0.69<br>.<br>0.33<br>.<br>3.61<br>.<br>0.62<br>1.27<br>0.20<br>0.51<br>1.16<br>1.13<br>3.01<br>0.06<br>0.20<br>3.75<br>-0.60<br>0.18<br>1.78<br>0.59<br>0.66<br>3.38<br>3.43<br>-0.56<br>8.28|
|stry|2000s|2.86<br>0.66<br>-0.02<br>0.49<br>2.06<br>2.79<br>1.94<br>0.56<br>1.46<br>0.47<br>0.66<br>1.57<br>0.65<br>2.39<br>0.04<br>0.27<br>3.85<br>-0.26<br>0.16<br>1.55<br>1.17<br>0.19<br>3.51<br>1.92<br>-0.4<br>7.00|
|Indu|1990s|.<br>0.90<br>0.63<br>0.63<br>2.51<br>1.28<br>1.57<br>0.20<br>1.25<br>0.80<br>0.66<br>1.81<br>0.57<br>0.97<br>-0.06<br>0.55<br>3.70<br>.<br>-0.14<br>0.72<br>0.89<br>-0.03<br>3.84<br>0.88<br>.<br>.|
||1980s|.<br>0.55<br>1.14<br>.<br>2.91<br>.<br>.<br>0.32<br>0.48<br>.<br>0.70<br>.<br>.<br>1.40<br>.<br>.<br>4.26<br>-0.43<br>.<br>.<br>1.19<br>.<br>1.63<br>.<br>.<br>.|
|Country||Armenia<br>Australia<br>Bahamas, The<br>Belgium<br>Bolivia<br>Cuba<br>Dominican Republic<br>Egypt, Arab Rep.<br>El Salvador<br>Estonia<br>France<br>Germany<br>Greece<br>Guatemala<br>Hungary<br>Italy<br>Japan<br>Jordan<br>Kyrgyz Republic<br>Luxembourg<br>Malaysia<br>Mauritius<br>Mexico<br>Mongolia<br>Montenegro<br>Namibia|

49
||2010s|-0.01<br>2.03<br>2.81<br>0.62<br>1.87<br>3.24<br>2.98<br>-0.87<br>0.23<br>0.19<br>3.69<br>6.59<br>-0.62<br>0.91<br>.<br>.<br>0.94<br>0.58<br>1.89|0.60<br>0.35|
|---|---|---|---|
||2000s|0.29<br>3.53<br>2.68<br>1.15<br>2.03<br>5.16<br>3.28<br>-0.42<br>0.41<br>-0.34<br>3.43<br>4.61<br>0.71<br>1.10<br>-0.32<br>5.25<br>0.46<br>0.73<br>2.66|0.27<br>1.12|
|Series<br>ervices|1990s|0.51<br>3.79<br>1.89<br>1.19<br>1.4<br>5.00<br>2.46<br>-0.46<br>0.30<br>-0.23<br>2.91<br>2.33<br>-0.48<br>1.03<br>-0.18<br>4.05<br>.<br>0.55<br>3.71|-0.24<br>1.44|
|erpolated<br>S|1980s|0.65<br>4.23<br>.<br>.<br>1.24<br>.<br>.<br>-0.44<br>.<br>.<br>.<br>2.91<br>0.52<br>0.89<br>0.34<br>4.36<br>.<br>.<br>.|.<br>2.13|
|nt||||
|rtions: I|2010s|0.04<br>3.05<br>0.58<br>1.51<br>2.57<br>2.83<br>1.73<br>11.00<br>0.49<br>1.92<br>3.13<br>2.99<br>-0.01<br>1.66<br>.<br>.<br>1.25<br>0.62<br>2.11|0.53<br>1.23|
|of Disto<br>ustry|2000s|0.32<br>4.80<br>0.89<br>3.02<br>2.87<br>4.06<br>1.56<br>10.92<br>0.51<br>0.74<br>2.46<br>1.77<br>1.12<br>1.78<br>-0.47<br>10.97<br>1.52<br>0.66<br>2.31|0.13<br>2.37|
|Estimate<br>Ind|1990s|0.52<br>2.95<br>0.77<br>2.58<br>1.85<br>4.10<br>1.38<br>6.83<br>0.38<br>0.15<br>1.58<br>0.80<br>-0.50<br>1.25<br>-0.56<br>5.83<br>.<br>0.50<br>3.04|-0.39<br>2.03|
|le A.1:|1980s|0.66<br>3.01<br>.<br>.<br>2.36<br>.<br>.<br>4.16<br>.<br>.<br>.<br>1.26<br>1.95<br>0.94<br>-0.27<br>4.55<br>.<br>.<br>.|.<br>2.03|
|Tab<br>Country||New Zealand<br>Norway<br>Pakistan<br>Paraguay<br>Philippines<br>Poland<br>Portugal<br>Puerto Rico<br>Russian Federation<br>Saudi Arabia<br>Slovenia<br>Sri Lanka<br>Suriname<br>Sweden<br>Syrian Arab Rep.<br>Trinidad and Tobago<br>Ukraine<br>United Kingdom<br>United States|Uruguay<br>Venezuela, RB|

50