metadata: authors: 0: author: Francis,David C.
1: author: Karalashvili,Nona
2: author: Maemir,Hibret Belete
3: author: Rodriguez Meza,Jorge Luis
count: World
docna: 0: docna: Measuring Total Factor Productivity Using
            the Enterprise Surveys : A Methodological Note
docty: Policy Research Working Paper
subtopic: Common Carriers Industry,Food & Beverage Industry,Plastics & Rubber Industry,Textiles, Apparel & Leather Industry,Pulp & Paper Industry,Construction Industry,Business Cycles and Stabilization Policies,General Manufacturing,Employment and Unemployment,Information Technology,Transport Services
teratopic: Macroeconomics and Economic Growth,Industry,Social Protections and Labor,Information and Communication Technologies,Transport
abstracts: cdata!: Total factor productivity is a key
            element of economic growth and an important performance
            metric for policy makers. This note describes the
            methodology for measuring firm-level total factor
            productivity using the World Bank's Enterprise Surveys
            cross-country data. It also presents some estimates
            recovered from the production function. Two versions of the
            production function are estimated: one Cobb-Douglas, the
            other a more flexible translog specification. Both
            estimations are at the two-digit industry level pooling all
            the Enterprise Surveys data across economies. Evidence is
            found against using a Cobb-Douglas specification, which is
            more parsimonious, and in favor of using the flexible
            translog specification. The resulting firm-level estimates
            are all published in the Enterprise Surveys database with a
            unique firm identifier to link to the rest of the Enterprise
            Surveys data; because the estimates are reliant on new data,
            they are updated periodically as new Enterprise Surveys data
            become available. The results show that: (i) median firms
            operate close to constant returns to scale; (ii)
            gross-output and value-added production functions provide
            similar ranking of sectors in terms of output elasticities,
            capital intensity, and returns to scale; (iii) there is
            large, firm-level heterogeneity in output elasticities; and
            (iv) gross-output-based total factor productivity measures
            are less dispersed than the value-added ones.
repnme: repnme: Measuring Total Factor Productivity Using
            the Enterprise Surveys : A Methodological Note
display_title: Measuring Total Factor Productivity
            Using the Enterprise Surveys : A Methodological Note
listing_relative_url: /research/2020/12/32649055/measuring-total-factor-productivity-using-enterprise-surveys-methodological-note
topicv3: Transport Equipment m1326014 1812,Manufacturing Sectors m1326521 1281,Information and Communication Technologies
            m1326624 133,Transport m1326661 135,Enterprise Surveys m1504072 2762
publishtoextweb_dt: 2020-12-08T00:00:00Z
historic_topic: Macroeconomics and Economic Growth,Industry,Social Protections and Labor,Information and Communication Technologies,Transport
origu: Global Indicators Group - WB (DECIG)
no_of_pages: 26
author_keys: 0: author_key: 1099768
1: author_key: 1304920
2: author_key: 1365974
3: author_key: 144470
model_extractions: Policy Research Working Paper 9491

# Measuring Total Factor Productivity Using the Enterprise Surveys

## A Methodological Note

_David C. Francis Nona Karalashvili Hibret Maemir Jorge Rodriguez Meza_ Development Economics Global Indicators Group December 2020
Policy Research Working Paper 9491

# **Abstract**

Total factor productivity is a key element of economic growth and an important performance metric for policy makers. This note describes the methodology for measuring firm-level total factor productivity using the World Bank’s Enterprise Surveys cross-country data. It also presents some estimates recovered from the production function. Two versions of the production function are estimated: one Cobb-Douglas, the other a more flexible translog specification. Both estimations are at the two-digit industry level pooling all the Enterprise Surveys data across economies. Evidence is found against using a Cobb-Douglas specification, which is more parsimonious, and in favor of using the flexible translog specification. The resulting firm-level estimates are all published in the Enterprise Surveys database with a unique firm identifier to link to the rest of the Enterprise Surveys data; because the estimates are reliant on new data, they are updated periodically as new Enterprise Surveys data become available. The results show that: (i) median firms operate close to constant returns to scale; (ii) gross-output and value-added production functions provide similar ranking of sectors in terms of output elasticities, capital intensity, and returns to scale; (iii) there is large, firm-level heterogeneity in output elasticities; and (iv) gross-output-based total factor productivity measures are less dispersed than the value-added ones.

This paper is a product of the Global Indicators Group, Development Economics. 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://www.worldbank.org/prwp. The authors may be contacted at hmaemir@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
Measuring Total Factor Productivity Using the Enterprise Surveys: A Methodological Note<sup>_∗_</sup> David C. Francis Nona Karalashvili Hibret Maemir Jorge Rodriguez Meza<sup>_†_</sup> _Keywords_ : TFP, Enterprise Surveys.

_JEL Classification_ : 012

> _∗_ This note uses the dataset “Firm Level TFP Estimates and Factor Ratios ~~S~~ eptember ~~1~~ 0 ~~2~~ 020.dta” which is published at `www.enterprisesurvyes.org` in the Firm-Level Datasets for Researchers section. The estimation procedure of this note uses the full cross-country ES data, which is continuously being updated as new data becomes available. Every month estimates are updated, and older datasets are archived and available upon request.

> _†_ The authors are from the Enterprise Analysis Unit of the World Bank. We would like to thank Arvind Jain and Rita Ramalho for very helpful comments. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.
# **1 Introduction**

Total factor productivity (TFP)—the ability to generate greater value or output with fewer inputs—is one of the key elements of economic growth. There is now a broad consensus that TFP differences account for the bulk of observed cross-country income differences (Klenow & Rodriguez-Clare 1997, Hall & Jones 1999). As Krugman (1994) succinctly put it “productivity isn’t everything, but, in the long run, it is almost everything”.

Considerable scholarly analysis has been devoted to measuring productivity. The recent, increased availability of detailed firm-level datasets has further intensified the interest in the subject, including investigations of how productivity varies by characteristics of firms (see, for instance, Syverson (2011)). To the extent that data can be disaggregated, researchers can delve into within-economy differences; if data are comparable across economies and across time, cross-economy differences can be examined. For example, a large and growing body of work explores the link between within-industry productivity dispersion across firms and cross-country differences in aggregate productivity (Banerjee & Duflo 2005, Restuccia & Rogerson 2008, Hsieh & Klenow 2009, Bartelsman et al. 2013).

In the absence of comparable census data, researchers have attempted to estimate productivity using survey-based data, which are often the only available data in less developed economies. The Enterprise Surveys (ES), detailed firm-level data collected by the World Bank’s (WB) Enterprise Analysis unit, are well suited for such inquiry. To facilitate the study of productivity by researchers and policymakers, the data published with this paper provide estimates of both TFP and factor ratios. The latter, more straightforward estimates are provided as TFP estimation may be troublesome for multiple reasons, e.g. the endogeneity of input choice (Olley & Pakes 1996, Levinsohn & Petrin 2003, Ackerberg et al. 2015). Unlike TFP estimates, some of these ratios are also available for most non-manufacturing firms.

The rest of this paper is organized as follows. Section 2 briefly summarizes the ES data, including steps taken for comparability and regarding outlier observations. Section 3 discusses the estimation of revenue-based TFP, so-called TFPR. Estimates of output elasticities and their derived characteristics are presented in section 4.

# **2 Data**

The World Bank’s Enterprise Analysis unit has been conducting surveys using a methodology that allows cross-economy analysis since 2006. To date, over 168,000 face-to-face interviews with top managers and business owners in 144 economies have taken place under this “Global Methodology”. This note uses these data to estimate TFP. Surveys not using the Global Methodology are excluded, as are the surveys conducted earlier than 2006. The data from Zimbabwe 2011 are excluded from analysis due to the complications due to hyperinflation just prior to the data collection. An additional 25 surveys are dropped because at least one of the key variables used in the analysis was not collected in these surveys.<sup>1</sup> This leaves 267 surveys in 134 economies and more than 161,000 interviews with top managers and business owners of firms spanning more than 40 different industries (specified by two-digit ISIC

> 1These are: Bangladesh 2007, Benin 2009, Bhutan 2009, Cambodia 2013, Cabo Verde 2009, Central African Republic 2011, Chad 2009, Congo 2009, Eritrea 2009, Fiji 2009, Gabon 2009, Lesotho 2009, Liberia 2009, Malawi 2009, Micronesia 2009, Niger 2009, Pakistan 2007, Rwanda 2011, Samoa 2009, Sierra Leone 2009, Timor-Leste 2009, Togo 2009, Tonga 2009, Vanuatu 2009, and the Rep ́ublica Bolivariana de Venezuela 2006.

2
Rev. 3.1 code). Of these interviews, more than 90,000 are with top managers and business owners of manufacturing firms, for which we provide TFPR estimates. Factor ratios using labor costs and revenues are provided in the associated dataset for most firms in the sample, including firms in selected services sectors covered by the ES.

To construct TFPR, we need information on sales (Y), employment (L), capital stock (K), and intermediate inputs (M). These variables are proxied using the questions available in the data. More precisely, Y is proxied by total annual sales of the establishment (variable d2); K is proxied by the replacement value of machinery, vehicles, and equipment (variable n7a); L is proxied by the total annual cost of labor (variable n2a); and M is proxied by the total annual cost of inputs (variable n2e). For value-added (VA) specifications (presented below), VA is proxied by the difference between total annual sales of the establishment (variable d2) and annual cost of inputs (variable n2e); K and L are the same as in the gross output specification (also elaborated below). The appendix presents each variable used along with the exact wording of the questions.

# **International Comparability**

All the variables used for the productivity estimation are collected in local currency units (LCUs), which are specific to the survey and year. Consequently, the data span different fiscal years. For the estimation of cross-economy regressions all data must be transformed to a common currency-year. To do this, all variables are first converted into U.S. dollars (USD) using the official exchange rate (period average) from the World Development Indicators (WDI).<sup>2</sup> The data are then deflated to 2009 using the GDP deflator for the United States from the relevant reference fiscal year.<sup>3</sup> Note that information on the closing month of the firms’ fiscal year is used to adjust exchange rates and deflators for each firm.<sup>4</sup>

# **Treatment of Outliers**

In order to minimize sensitivity to extreme values, outlier firms are eliminated from the analysis. More specifically, outliers in d2 (capturing Y), n7a (capturing K), n2a (capturing L), n2e (capturing M), and VA (d2 minus n2e), as well as outliers in ratios n7a/VA and n2a/VA were turned into missing before estimating the production function. To find outliers in levels, we first transform variables as ln( _x_ +1), and group observations by economy and broadly defined sector (more precisely, manufacturing and services). Next, we calculate (unweighted) means and standard deviations of these transformed variables within each group. Observations that are more than three standard deviations away from the mean are then marked as outliers and turned into missing. To find outliers in ratios, we first transform variables as ln( _x_ ), and group observations by industry. The three-standard-deviation rule is then applied (unweighted) and the corresponding observations are turned into missing.

> 2WDI indicator code: PA.NUS.FCRF

> 3WDI indicator code: NY.GDP.DEFL.ZS

> 4The fiscal year and its closing month information are given in variables “ _d_ 2 _~~l~~_ 1 _~~y~~ ear perf_ _~~i~~ ndicators_ ” and “ _d_ 2 _~~n~~_ 3 _~~l~~ ast_ _~~m~~ onth_ _~~f~~ y_ _~~p~~ erf_ _~~i~~ nd_ ” respectively.

3
## **Item Nonresponse**

Another challenge in estimating total factor productivity is dealing with item nonresponse: i.e., sampled firms do not answer specific questions of the survey. For example, respondents may answer the employment question but not sales. To reduce item non-response, the Enterprise Surveys team follows a strict quality control process that identifies non-responses and contacts firms to complete the data.

Despite this effort, like many other firm-level datasets, the ES also suffers from item nonresponse in variables needed to calculate TFPR. One way to handle the item nonresponse is through imputation. For example, in the U.S. Census Bureau’s 2007 manufacturing data 73% of observations have imputed data for at least one variable used to compute TFPR (White et al. 2018). While item non-response may be consequential for most analysis, we do not attempt to address it in the data. We do not employ any of the available imputation or re-weighting methods that assume that data “missingness” is not ignorable and is related to underlying firm characteristics (Little & Rubin 2019). Additionally, the survey (probability) weights included in the data are agnostic to item non-response and the missingness of productivity estimates: weights are not re-adjusted or scaled to account for this missingness.

We exclude observations missing any one of the main production function variables (i.e., sales, labor, capital, or materials). Additionally, note that in cases of negative value-added, logarithms cannot be defined, and thus these observations are not included. This leaves 50,754 observations in 134 countries .

# **3 Estimating Firm-Level Total Factor Productivity**

We begin with a Cobb-Douglas production function (1) for ease of exposition. Throughout this paper, all lowercase variables denote the natural logarithms of the corresponding uppercase variables, representing raw, level values.

Where firm-level value-added ( _vai_ ) is a function of inputs of capital ( _ki_ ), and labor ( _li_ ). Firms’ (logged) TFP is estimated as a sum of the constant and the residual, i.e., _tfpi_ = _β_<sup>ˆ</sup> 0 + ˆ _εi_ . We refer to the above model as value-added specification (VAKL). In addition to VAKL, TFP is also estimated using gross-output specification (YKLM) where _vai_ in (1) is replaced with _yi_ , output in terms of total revenues, and the right-hand-side has an additional input variable, expenditure on material inputs ( _mi_ ).<sup>5</sup> The coefficients _βk_ and _βl_ estimate the elasticities of capital and labor, respectively. Throughout, we will denote estimated elasticities by _θ_<sup>ˆ</sup> _input_ ( _i.e., θ_<sup>ˆ</sup> _l_ ), where _input ∈ k, l, m_ .

While analytically straightforward, this estimation of TFP bears several caveats. First, ordinary least squares estimates of equation (1) may suffer from a simultaneity problem: firms’ input choices may be guided by their productivity (Marschak & Andrews 1944). Several methods have been developed in order to address this endogeneity problem (Olley & Pakes 1996, Levinsohn & Petrin 2003, Ackerberg et al. 2015). In these methods, past input decisions (for instance the choice to invest in capital) are

> 5Note that other versions of (1) are also possible, e.g. YKL, or YKELM with E for Electricity. We do not analyze these here as adding a fourth input into a translog production function substantially increases the number of parameters to be estimated.

4
used to proxy for the current production and use of inputs. To extend future analysis to include these estimations, the ES has started collecting information on these lagged variables of inputs. This will be used in the future as the data become available. Other, firm-level fixed effects methods have been used to estimate average firm-level productivity over time. The ES has constructed longitudinal/panel data for a large number of countries: TFP estimates based on the panel data will also be provided in the future.

Second, there are issues associated with the fact that often only monetary (as opposed to physical) output and input expenditure are observed in typical firm-level data. Such revenue-based TFP is often referred to as _TFPR_ ( _TFPRi_ = _Pi × TFPQi_ ), where R stands for revenue and Q for quantity, and _Pi_ denotes the firm’s product price. Market dynamics are inseparable within TFPR estimates, which incorporate clearing prices of inputs and revenue-based outputs and can conflate productivity and market (e.g. negotiating) power. As in the case for most firm-level datasets, the ES collect information on revenues and firm-level line item costs (rather than physical inputs and outputs), and hence TFPR is the only measure that can be estimated using the ES data. For a recent discussion of these and other issues in estimation see for instance Foster et al. (2008), Hsieh & Klenow (2009).

A third caveat relates to the importance of the functional form of the production function. The Cobb-Douglas specification in equation (1) assumes a constant elasticity of output, regardless of other output choices. These elasticities are constant and in the form of _βk_ and _βl_ . In other words, a firm’s elasticity of capital output, for example, does not depend on its use of labor: labor-intensive firms expect the same elasticity of output of capital as non-labor-intensive ones. This assumption may be unrealistic in two ways. First, imposing one production function on firms in different industries is almost surely too restrictive. And indeed, most of the empirical literature on productivity defines industries as narrowly as data permit (Syverson 2011). The ES estimations address this point with a very practical solution: TFPR is estimated separately for each industry — grouped by two-digit ISIC codes — over pooled economies.<sup>6</sup> Second, the assumption of constant elasticities of output for all three (or two) inputs may be too stringent even within industries defined at the two-digit level: so, we also consider a more flexible functional form, the translog specification, which does not impose this restriction.<sup>7</sup> Table A.3 in the Appendix presents the 16 industries with separate estimations.

Hence, the estimations assume that the production function, either Cobb-Douglas or translog, is sector-specific but common across countries. To address this rather restrictive assumption, the specification is enriched as follows. First, in order to control for potential differences in production technology between countries, wherever possible, the production coefficients are allowed to vary by the income-level grouping of the corresponding economy by adding interaction terms between income group and factor inputs. The income levels are grouped according to the WB classification (low-income and lower middle income grouped as low-income and upper-middle income and high income grouped as high income) as of the year in which each survey was conducted and are denoted with _Ic_ (equals 1 for high-income). Empirical investigation of the stability of our estimates revealed that this income grouping is appro-

> 6The production functions could in principle be estimated by country and sector. However, estimating production coefficients separately by country-sector is difficult with the ES data since there are few observations for most countries within each sector.

> 7Due to low number of observations in the current dataset, four groups of industries are defined: group 15 and 16: food, beverages and tobacco; group 23 and 24: refined petroleum, nuclear fuel and chemicals; group 30,31,32 and 33: electrical machinery and electronics; and group 34 and 35: transport equipment.

5
<!-- Start of picture text --> Low Income High Income<br>21 428 21 (257<br>34135 '‘450 27 3341<br>30133 717 19 437<br>1 '<br>19 728 34135 |480<br>27 1 834 20 1 575<br>22 959 22 653<br>= 29 1,030 = 30133 1,021<br>oO ' oOo '<br>> 20 1,062 > 36 1,050<br>reg T & T<br>oO 25 1,584 O 26 1,167<br>Q 36 1,802 Q 2 1,369<br>17 1,822 17 1,482<br>2324 1,981 23124 1,755<br>28 2,008 29 2,002<br>26 2,308 18 2,657<br>18 3,536 28 2,810<br>15t16 6,405 15t16 5,051<br>11<br>||<br>0 2,000 4,000 6,000 ) 1,000 2,000 3,000 4,000 5,000<br>Number Number<br><!-- End of picture text -->
The value-added production function (VAKL), which imposes a fixed proportion assumption on material inputs (Leontief), is estimated as follows: where subscripts _i_ , _c_ , _t_ index establishments, countries, and year, respectively; _c_<sup>_V AKL_</sup> , and _c_<sup>_Y KLM_</sup> are constants which are common across establishments within each industry.

To test whether either the Cobb-Douglas or translog production specification was more appropriate, the joint significance of the translog terms (all interaction and square terms) was tested. Table 1 reports the results of these joint significance tests. Under the gross-output (YKLM) specification, the translog terms are jointly different from zero for all sectors, suggesting that the translog specification fits the data better. Under the value-added (VAKL) specification, the translog terms are jointly significant at the 10% significance level for all except four sectors (17, 21, 29, and 34-35). To ensure comparability across sectors, only translog estimates are reported for all sectors under both specifications in the associated dataset and are used in the rest of the note.

7
Table 1: Joint Significance Test

||**Gross-ou**|**tput (YKLM)**|**Value-ad**|**ded (VAKL)**|
|---|---|---|---|---|
|**Sector (ISIC Rev 3.1)**|**F-Stat**|**p-value**|**F-Stat**|**p-value**|
|ISIC 15, and 16|43.01|0.00|4.22|0.01|
|ISIC 17|28.96|0.00|1.58|0.19|
|ISIC 18|29.48|0.00|7.39|0.00|
|ISIC 19|28.51|0.00|3.53|0.01|
|ISIC 20|18.22|0.00|7.63|0.00|
|ISIC 21|54.72|0.00|0.93|0.43|
|ISIC 22|7.82|0.00|3.57|0.01|
|ISIC 23, and 24|12.13|0.00|3.21|0.02|
|ISIC 25|10.98|0.00|8.54|0.00|
|ISIC 26|46.91|0.00|4.97|0.00|
|ISIC 27|37.77|0.00|10.45|0.00|
|ISIC 28|25.81|0.00|2.69|0.04|
|ISIC 29|7.32|0.00|0.87|0.45|
|ISIC 30, 31, 32, and 33|18.77|0.00|2.16|0.09|
|ISIC 34, and 35|20.29|0.00|0.89|0.45|
|ISIC 36|22.46|0.00|5.18|0.00|

Having adopted the translog specification, firm-level TFPR is estimated by: where _f ∈ V AKL, Y KLM_ . TFPR is estimated as a sum of the establishment-level residual _ε_<sup>�</sup><sup>_f_</sup> _ict_<sup>,</sup> constant term ( _c_<sup>�</sup><sup>_f_</sup> _s_<sup>)whicharecommonacrossestablishmentwithineachindustry,country-fixedeffects</sup> ( _FE_<sup>�</sup> _c_<sup>_f_),incomegroupfixedeffects(</sup> _FE_<sup>�</sup> _I_<sup>_f_),yearfixedeffects(</sup> _FE_<sup>�</sup> _t_<sup>_f_)whicharecommonacrossestablish-</sup> ments within industry-country, industry-income group, and industry-year, respectively. The database � � available on the ES portal contains the firm-level estimates, i.e. _TFPRicf_<sup>_Y KLM_</sup> and _TFPRicf_<sup>_V AKL_</sup> , alongside the variables used to estimate TFPR. All estimates take into consideration the survey design for the ES by incorporating both stratification and probability (survey) weight information.

# **4 Estimates**

The output elasticity is given by the first derivative of the production function with respect to each input. For instance, under the gross-output production function, the output elasticity of material inputs for ˆ high-income economies is estimated as _thetam_ = _β_<sup>ˆ</sup> _m_ +2 _β_<sup>ˆ</sup> _mmmict_ + _β_<sup>ˆ</sup> _kmkict_ + _β_<sup>ˆ</sup> _lmlict_ + _β_<sup>ˆ</sup> _klmkictlict_ + _β_<sup>ˆ</sup> _mi_ . The elasticity for low-income counties is the same except _β_<sup>ˆ</sup> _mi_ . The elasticities for material inputs depend on the level of use of all inputs used in the production, including labor and capital and not only on the level of use of material inputs.

Translog coefficients ( _β_<sup>ˆ</sup> _m, β_<sup>ˆ</sup> _mm, β_<sup>ˆ</sup> _km, β_<sup>ˆ</sup> _lm, β_<sup>ˆ</sup> _mm, β_<sup>ˆ</sup> _klm, β_<sup>ˆ</sup> _mi_ ) are the same across establishments within an industry and income group. However, unlike the Cobb-Douglas production function, the output elasticities can vary across firms within the same industry/country, because they depend on the level of use of the inputs of production. Hence, small firms can have different elasticities than large firms (just the same, elasticities can vary between small firms, e.g.). This flexible form is an advantage of translog 8
<!-- Start of picture text --> Gross Output Specification (YKLM)<br>15<br>2<br>cS 1 aa , ] , |<br>D<br>0 eLAL SMLP PAPP HPA PPPSSX of HS ©WK GLP PAPP HA PPGSX of SF<br>Low Income High Income<br>G Capital (Labor (1 Materials<br>Value Added Specification (VAKL)<br>15<br>2<br>cS 1<br>a7)<br>&<br>w 5<br>0<br>NSK LOPS DMMP PED PPEKP CN POH DDH PED PPL YS &<br>%v SF of N v & of<br>Low Income High Income<br>G Capital (Labor<br><!-- End of picture text -->
<!-- Start of picture text --> Median Elasticity of Capital Median Elasticity of Labor<br>022 6<br>14 02353<br>02324 029<br>ne25<br>=~12 °5 o<br>aa 021a3033 °'? = 76 018o 26<br>< 026 Zz o3aae<br>= a 018017<br>>=s2 1 °2 027 27 034t034t3P0 >SB2> 4 02412726 oBSB9om028 8°<br>a 029 230133 v7 023124019<br>& &<br>w 02D Ww 022<br>= 08 94135 S 028<br>a 4<br>S) °8e 4 3 023t24 746333, 027<br>029 , 3,<br>028 072 919 025 034135<br>06 018<br>6 nt 027 030133<br>018 oie<br>.04 2<br>4 15 2 25 3 6 7 8 9 1<br>Capital Elasticity (VAKL) Labor Elasticity (VAKL)<br>© Lowlncome’ 4 High Income<br><!-- End of picture text -->
<!-- Start of picture text --> Capital Intensity Returns to Scale<br>5 1.2<br>022<br>028124<br>030133455 520<br>4<br>~ 5839 ~<br>S = 14<br>2x= 930343802324 z=<br>2 2 036<br>D 3 fe) az9<br>5 ?)) 03435<br>£ 021 2 oo7 017<br>£ 041 c 8269 1516<br>ao 026 027 = 1 3A nog 17<br>§ 26 @ 368<br>(6) 2 °36 036e74028020 oe 022019 039°3t8F2N3138838 2 335 15116<br>03435<br>027<br>1 04g0180294wee ° 030133<br>rs] 2 3 4 5 R:) 1 11 1.2<br>Capital Intensity (VAKL) Returns to Scale (VAKL)<br>© Lowlncome ' 14 High Income 45 degree<br><!-- End of picture text -->
<!-- Start of picture text --> Std. Dev. of Capital Elasticities Std. Dev. of Labor Elasticities<br>15 027 021<br>35<br>027<br>034t35<br>: 4 n19 34 A121... ogg<br>019 015116 027<br>S = 028<br>Z 02312630133 036 Z 034135<br>> 0238 020 = 25 838 26<br>538 01516 4As<br>.05 ( 017 ° 23124 19.<br>047 28°20 19<br>026018 °21 018 92324<br>028 & 2 33<br>RR 025<br>DIT ae<br>08983<br>025<br>) 15<br>) 4 2 3 .05 4 15 2 25<br>VAKL VAKL<br>© Low Income O High Income<br><!-- End of picture text -->
# Gross-Output Specification (YKLM)

<!-- Start of picture text --> 15t16 °o @\}eo 15t16 ao 15t16 @d|Ho o<br>17 0 HH200 17 on 17 ood}po<br>18 +} om 18 Ho 18 aan-|}><br>19 o@oH|He 19 oof] 19 aH<br>20 a} 20 |} 20 a}<br>3 21 otf] 000 3 21 HH» 3 21 a@—f}Ho<br>& 22 {Hm 8 22 ao és 22 camt-|H><br>B 23124 po 5 23124 @|}400 5 23124 od|Ho<br>2 25 He 2 25 cen 2 25 aH<br>£ 2% at{} F 2% ole £ 2% at{Ho<br>3~ 27 oH{He ae~ |oe 3~ 27 o¢4H ©<br>28 a{}oo 28 to 28 aa}<br>29 oe | eked 29 oH 29 0 @t{Ho<br>30133 @HH» 30133 @HHo 30133 @|}2®<br>34135 o¢-|H4tm00 oo 34135 © @|Kpo 34135 o =6a} fH<br>36 ooat-}Hap 36 oat» o 36 0 ca} o<br>-1 0) 1 2 3 -5 0 5 1 2 A i) 1 2<br>Labor Elasticity (YKLM) Capital Elasticity (YKLM) Material Elasticity (YKLM)<br>Value-Added Specification (VAKL)<br>15t16 o \atfJF-00 15t16 coe |<br>17 ° EY{IF@ 00 17 00<br>18 ee | 18 o@{]Hm<br>19 oo E-{JH4@ 19 oo }—fiJo<br>20 o—f}E-00 20 af}<br>3 21 o+—iI c8@0 o 21 Hi<br>& 2 oom & 2x x0<br>2 23124 dH o g 23124 ato o<br>Q 25 HH Q 2 o@|Hemo<br>EF 2% oot] Ee 2 {00<br>3 27 o+—fiiMio 3 27 o +/—Sl °<br>28 oot» 8 HHom<br>29 ¢—{-mo o 29 @HH<br>30133 co | {JHom 3033 @ {JH oo<br>34135 ° &—fi—_4ooo ° 34135 ° @|}hoo<br>36 © 0 0¢}—ffj—mo0 36 o@{}-> o<br>-1 0) 1 2 3 -5 ) 5 1<br>Labor Elasticity (VAKL) Capital Elasticity (VAKL)<br><!-- End of picture text -->
<!-- Start of picture text --> 31<br>°<br>°<br>2<br>5< °° Z<br>= Booo z= ° °<br>> ° 0% © 2 B05<br>SsS @ °.€ peo%2 0 3S= Beena s ?<br>Ww ° pees MA 00 0 - Oa mo<br>2 ° ne BOO SE, = Pk: fo<br>Ss ° & A . 8 0 a. g<br>0 0 0 OMRON 0 e<br>° pm A ° oe e .<br>oom CU eee<br>%<br>e<br>-1 -.5<br>0 5 1 1.5 -5 0 5 1 1.5<br>Labor Elasticity (VAKL) Capital Elasticity (VAKL)<br><!-- End of picture text -->
<!-- Start of picture text --> Gross-Output Production Function Value-Added Production Function<br>Ke foo)<br>o<br>2<br>+<br>w<br>N<br>/| \<br>ZI\%<br>oO aaa a a Me oO —<br>-2 -1 0 1 2 -2 -1 0) 1 2<br>— Capital — — - Labor ------ Materials —— Capital —-—-- Labor<br><!-- End of picture text -->
<!-- Start of picture text --> Capital Elasticity (YKLM) Labor Elasticity (YKLM) Materials Elasticity (YKLM)<br>& 0.03 & 0.04 & 0.03<br>& & oe<br>x « sf<br>SsSS& 0.04 &oS& 0.06 Ae< 0.04<br>& ic)<br>& 0.31 & 0.08 & 0.07<br>gS&ss oeSSRS) SFNSRS)<br>& 0.35 & 0.14 & 0.10<br>s x s<br>ae ae ae<br>cs Ss Ss<br>& & &<br>SIx rr es es ee | x rs es ee | x rsee es ee |<br>i) 0 4 R-squared2 3 4 i)ss 0 0R-squared4a 15 ©x 0 .02 R-squared.04 06 08 .1<br><!-- End of picture text -->

<!-- Start of picture text --> Capital Elasticity (VAKL) Labor Elasticity (VAKL)<br>0.25 0.40<br>25 4<br>0.33<br>2<br>0.17 3<br>3 15 8<br>oO oO<br>ro] =]<br>oc Ina<br>4<br>0.08<br>4<br>.05 0.04<br>0.05<br>0.02<br>0 0<br>s& && Ss& ry& s& <& S& ry&<br>x KS & x x KS & x<br>o oy o Poy<br>& &<br>RS SS)<br>se se<br><!-- End of picture text -->
<!-- Start of picture text --> Dispersion of TFPR<br>/N<br>ae \\<br>/\<br>/<br>/<br>I \<br>ii \\<br>= = -<br>fo) // \\—<br>-10 5 0 5<br>—— YKLM__----- VAKL<br><!-- End of picture text -->
<!-- Start of picture text --> Food Sector (ISIC 15 and 16) Garment Sector (ISIC 18)<br>vt +<br>e e<br>om oO<br>4—<br><2 <<br>cae= e fn2> e<br>() c)<br>a ee e ee<br>ae} ne}<br>a e of @ a e of @<br>@. se © @, se y<br>e age e° oe<br>- & e - &, e<br>e e<br>oO Oo<br>0 5 1 1.5 2 2.5 0 5 1 1.5 2 25<br>Std.Dev tfprYKLM Std.Dev tfprYKLM<br><!-- End of picture text -->