Effect of Business Environment on Productivity of Informal Manufacturing Enterprises in Kenya Florine Mwiti and James Kimunge Kenya Institute for Public Policy Research and Analysis KIPPRA Discussion Paper No. 228 2019 Effect of business environment on productivity of informal manufacturing enterprises in Kenya KIPPRA in Brief The Kenya Institute for Public Policy Research and Analysis (KIPPRA) is an autonomous institute whose primary mission is to conduct public policy research leading to policy advice. KIPPRA’s mission is to produce consistently high-quality analysis of key issues of public policy and to contribute to the achievement of national long-term development objectives by positively influencing the decision-making process. These goals are met through effective dissemination of recommendations resulting from analysis and by training policy analysts in the public sector. KIPPRA therefore produces a body of well-researched and documented information on public policy, and in the process assists in formulating long-term strategic perspectives. KIPPRA serves as a centralized source from which the Government and the private sector may obtain information and advice on public policy issues. Published 2019 © Kenya Institute for Public Policy Research and Analysis Bishops Garden Towers, Bishops Road PO Box 56445-00200 Nairobi, Kenya tel: +254 20 2719933/4; fax: +254 20 2719951 email: admin@kippra.or.ke website: http://www.kippra.org ISBN 9966 817 31 0 The Discussion Paper Series disseminates results and reflections from ongoing research activities of the Institute’s programmes. The papers are internally refereed and are disseminated to inform and invoke debate on policy issues. Opinions expressed in the papers are entirely those of the authors and do not necessarily reflect the views of the Institute. This paper is produced under the KIPPRA Young Professionals (YPs) programme. The programme targets young scholars from the public and private sector, who undertake an intensive one-year course on public policy research and analysis, and during which they write a research paper on a selected public policy issue, with supervision from senior researchers at the Institute. ii Abstract A favourable business environment enables easy entry and exit of domestic and multinationals from the markets, lessens the cost of doing business, and hence leads to higher productivity and consequently job creation. This study uses firm level survey data of informal manufacturing enterprises in Kenya to study the effect of business environment on total factor productivity (TFP) using data on micro, small and medium enterprises collected by KNBS in 2016. The sector contained 1,044 observations/enterprises, which comprised 13 sub-sectors. Out of these sub-sectors, the study focused on five major sub-sectors in which a total of 998 enterprises were interviewed (95.6%). These were 370 firms in the wearing apparel, 206 firms in the manufacture of food products, 203 firms in the manufacture of furniture, 157 firms in the manufacture of fabricated metal products (except machinery and equipment) and 62 firms in the manufacture of wood and of products of wood and cork (except furniture, manufacture of articles of straw and plaiting materials. The study employed a Cob-Douglas production approach to derive the total factor productivity. The factors that affect this TFP, which were categorized into business environment related factors, entrepreneurial and enterprise characteristics, were aassesed. The study found that access to water, access to electricity, access to computer and training were significant business environment factors that influence productivity of informal enterprises in the manufacturing sector of Kenya. Other factors that were found to be important in determining the productivity of the informal sector were the level of education of the owner/manager, gender, age of the business, market outlet, and expenditure on research and business size. The study recommends increased distribution of electricity to ease access in the informal sector. Also, the study recommends decentralization of training centres to bridge skills gap in the informal sector. There is also need for renewed effort by policy makers to bridge the gender gap in the sector, since male-owned enterprises were found to be more productive than female-owned enterprises. To ease credit access by businesses in the informal sector, the study recommends relaxation of minimum requirements for accessing credit, especially the collateral. The study finally recommends that businesses should increase on their spending on research and development, since it is also a key determinant of productivity in the informal sector. iii Effect of business environment on productivity of informal manufacturing enterprises in Kenya Abbreviations and Acronyms DEA Data Envelopment Analysis GDP Gross Domestic Product ILO International Labour Organization KNBS Kenya National Bureau of Statistics MSMEs Medium, Small and Micro Enterprises OLS Ordinary Least Squares R&D Research and Development SFA Stochastic Frontier Analysis TFP Total Factor Productivity iv Table Of Contents Abstract ...................................................................................................................iii 1. Introduction ...................................................................................................... 1 1.1 Statement of the Problem ................................................................................2 1.2 Objectives of the Study ....................................................................................2 2. Literature Review .............................................................................................3 3. Methodology .....................................................................................................6 3.1 Theoretical Framework ....................................................................................6 3.2 Conceptual Framework ....................................................................................6 3.3 The Model Estimated ....................................................................................... 7 3.4 Data Source ..................................................................................................... 13 4. Results ............................................................................................................ 14 4.1 Descriptive Statistics of Variables used in the Regression Model ................. 16 4.2 Results from Cobb–Douglas Production Function ....................................... 19 5. Summary, Conclusion and Policy Recommendations ...................................25 References ..............................................................................................................28 v Effect of Business Environment on Productivity of Informal Manufacturing Enterprises in Kenya List of Tables Table 3.1: Description of independent variables used in the regression equation 11 Table 3.2: Description of independent variables used in the production function 12 Table 4.1: Distribution of sub-sectors in the manufacturing sector ..................... 14 Table 4.2: Descriptive statistics for enterprises in the manufacture of fabricated materials and manufacture of food product sub-sectors ........................................................................................17 Table 4.3: Descriptive statistics for enterprises in the manufacture of furniture and wearing apparel ......................................................17 Table 4.4: Descriptive statistics for enterprises in the manufacture of wood products sub-sector ............................................................ 18 Table 4.5: Results of the Cobb- Douglas production function ............................. 19 Table 4.6: Regression results for determinants of productivity in the informal manufacturing sub-sectors ..............................................................20 List of Figures Figure 3.1: Conceptual framework .......................................................................... 7 Figure 4.1: Main constraint experienced by enterprises in the last one year ....... 15 vi Introduction 1. Introduction The informal sector has been highly recognized as a pathway to economic growth and reduction of poverty in both developed and developing countries. The sector has been proven to be of great importance especially in creating employment in developing economies. For instance, in Kenya, the informal sector accounted for 83.4 per cent of total employment in 2018. Some 898,000 and 787,800 new jobs were created in the informal sector in 2017 and 2018, respectively (Government of Kenya, 2018). The total number of people engaged in this sector (excluding agriculture) was approximately 16.9 million. Although the informal sector is a major contributor to employment in Kenya, the sector still faces several constraints that weaken its productivity. Productivity is a measure of how resources are managed to achieve intended goals in terms of quality and quantity. One of the constraints depressing productivity in the informal sector is the high cost of doing business, which is determined by the business environment. It has been argued that a poor business environment constrains the growth of businesses in Africa. Literature has also proven that inefficiencies in the business environment lead to distortions in the allocation of resources at the firm level and are responsible for the country differences in the level of output and total factor productivity (TFP) growth. For instance, Bah and Fang (2013) found that crime, corruption, inadequate infrastructure and poor access to credit facilities reduce TFP by between 7 and 19 per cent in 30 Sub-Saharan African countries. Diagne (2013) established that addressing the factors that negatively affect the business environment, more so power outages, corruption, crime, burdensome regulations, poor infrastructure and tax burden that negatively affect investment, TFP and output in Senegal would increase manufacturing firms investment and output by 94 and 79 per cent, respectively. In Kenya, businesses still face an adverse business environment. According to a World Bank (2018) report, businesses in Kenya still incur huge losses due to theft, power outages, theft, damages during transportation and corruption, which were higher compared to other middle income countries in Africa, India and China. Moreover, the same report highlights some of the top obstacles identified by business managers, which were licences, tax rates, access to credit, corruption, security and infrastructure. However, there is scanty evidence on how this business environment influences productivity of enterprises especially if the enterprise is operating informally. This is the main focus of this study. The study sought to find out the effect of business environment on TFP of informal enterprises using manufacturing firms in Kenya as case study. 1 Effect of business environment on productivity of informal manufacturing enterprises in Kenya 1.1 Statement of the Problem A conducive business environment enables easy entry and exit of domestic and multinational businesses from the markets, leads to higher productivity and job creation. Several studies have found that poor business environment constrain the growth of businesses in Africa. Inefficiencies in business environment lead to distortions in allocation of resources at the firm level, increases firms’ indirect costs and are responsible for the country differences in the level of output and TFP growth. Although business environment has been identified as a major productivity constraint for many firms, there is scanty empirical evidence on the effect of business environment on productivity of enterprises especially in the informal sector. Moreover, studies that have established that the productivity of the enterprises in the informal sector is low have not explored whether the business environment has an influence on the productivity of the sector. This study therefore sought to establish the effects of business environment on productivity of enterprises in the informal sector, with focus on informal manufacturing enterprises. 1.2 Objectives of the Study The main objective of this study is to find out whether business environment has an effect on performance of manufacturing enterprises in the informal sector in Kenya. To address this objective, the study will answer the following research question: What is the effect of business environment indicators on total factor productivity of informal manufacturing firms in Kenya? 2 2. Literature Review The literature on measurement of total factor productivity (TFP) growth and its determinants is quite extensive (Lundvall and Battesse, 2000; Bernard et al., 2003; Melitz, 2003; Redding and Reenen, 2004; Griffith, Biesebroeck, 2005; Doraszelski and Jaumandreu, 2013; Voutsinas and Tsamadias, 2014; Kafouros, 2005 and Ray, 2014). However, only few studies have focussed on the effect of business environment on total factor productivity. Gasiorek et al. (2010) investigated the effect of business environment on firms in Morocco. Their study focussed specifically on credit access, water outages, regulatory and institutional environment and infrastructure. The authors found that water outages, infrastructure, regulations (measured by the number of permits per year) were negatively related with firm productivity. Bah and Fang (2011) established that regulations, crime, corruption and poor infrastructure were the main factors that dragged productivity in Africa. The authors found that these factors decrease productivity by between 18 to 44 per cent and a decrease of between 40 and 77 per cent in output. In another study (2013), the same authors investigated the quantitative effects of business environment, specifically crime, corruption, infrastructure and access to credit facilities in 30 Sub-Saharan Africa countries. The study found that inefficiencies in these areas reduce TFP by between 7 and 19 per cent. Diagne (2013) found similar observations: power outages, corruption, crime, burdensome regulations, poor infrastructure and tax burden negatively affect investment, TFP and output in Senegal. The study established that addressing these problems would increase manufacturing firms investment and output by 94 and 79 per cent, respectively. Nguinmkeu (2013) identified that quality of labour, administrative delays and corruption, infrastructure, illicit trade, inadequate access to credit and regulatory burden as the key challenges facing businesses in Cameroon, and hence affecting their productivity negatively. Essmui et al. (2014) found that limited access to credit, corruption and high crime are the major factors that limit the growth of manufacturing enterprises in Libya. The study failed to find any significant relationship between business regulation and corruption on productivity of the firms. Lasagni, Nifo and Vecchione investigated how institutional quality affects productivity of firms in Italy. Some of the institutional quality indicators considered by the study were corruption, rule of law, regulatory quality, government effectiveness and voice and accountability. The study found that better institutions lead to higher firms’ productivity. Corruption was found to be negative and significant while government effectiveness and voice and accountability were found to affect productivity positively. Giang et al. (2018) 3 Effect of business environment on productivity of informal manufacturing enterprises in Kenya did a study in Vietnam and discovered that lack of access to finance, bribery, low employees educational level and administrative burden were the main constraints to productivity among manufacturing businesses. There is vast empirical literature on the other factors that can affect TFP growth at firm’s level. Diaz and Sanchez (2008) argued that increase in the size of firm results to increase in organizational and managerial complexity. They found that inverse relationship between firm size and productivity can be expected, but also large firms easily access market and they also have better technology hence a positive association between these two can also be expected (Lundvall and Battesse, 2000; Biesebroeck, 2005). Some studies have established that embodied technological intensity helps to improve productivity by importing capital goods and thereby infusing better technology to the firm and disembodied technological intensity affects productivity by fostering the quality of technology (Hasan, 2002 and Mendi, 2007). Considering data from manufacturing sector in Malaysia, Jajri (2007) found that the rate of growth in output, foreign investment and exports affect the productivity of the firms in a positive way. Gaitan, Herera and Pablo (2017) find that the main determinants of productivity in a firm are size, gender diversity and ownership. Size and institutional ownership (firms owned by shareholders) were positively related with productivity while a higher share of female directors was negatively associated with productivity. Considering the innovation-based growth models, Aghion and Howitt (1998) and Grossman and Helpman (1991) argued that research and development (R&D) activities help to motivate innovations, which in return affects the TFP of a company. Other empirical studies have also supported that R&D activities have a significant role in the determination of productivity (Doraszelski and Jaumandreu, 2013; Leachman, Ray, 2014; Voutsinas and Tsamadias, 2014). Another strand of studies pays attention on the relationship between total factor productivity and the intensity of market regulations. In their view, inadequate regulations can create perverse incentives that in return reduce TFP (Bridgam et al., 2009). Other related studies (for example Eslava et al., 2004, Bernard et al., 2006 and Bloom and Van Reenen, 2010) focus on the relationship between the competition intensity and productivity. These studies evidenced a positive relationship between competition intensity and productivity. According to Taymaz (2010), other factors that determine productivity are firm characteristics such as composition of employees by age matter for firms productivity (firms with older employees are more productive), education of the entrepreneur (more educated entrepreneurs are more productive), type of 4 Literature review consumer and geographical market (firms that sell to institutions and government are more productive) and vocational training (training increases productivity). However, the author failed to find any effect of gender on productivity. 5 3. Methodology 3.1 Theoretical Framework 3.1.1 Theory of production In every production process, resources are used in order to obtain some output. Resources comprises of several factors such as raw materials, land, machinery, tools and human resources. Enterprises or companies then combine these factors and transform them into outputs, which could be services or goods. A combination of certain inputs results into some specific quantity of output, which can be shown in a production function equation. Production function demonstrates the relationship between output and input used in a given production. For instance, an enterprise producing output Q by use of inputs (Xi, X2, X3, ...Xn) the production function can be expressed as: Q =ƒ(X1, X2, X3,... Xn) Where: Q = Output Xi, X2, X3 ...Xn= inputs Assuming that in the production process only capital (K) and labour (L) are used, this production function can be can be mapped as: Q =ƒ (L, K) Where: Q= Output, L= Labour and K=capital 3.2 Conceptual Framework Enterprises do not function in isolation, but they are surrounded by an environment known as business environment. These business environments consist of both internal and external environment, with external environment comprising of both macro and micro environment. Businesses may have to some extent control of the internal environment but the external environment is beyond its control. Therefore, since the businesses lack control over its environment, it may pose threats or opportunity for enterprises, thus impacting positively or negatively on the performance/productivity. 6 Assuming that in the production process only capital (K) and labour (L) are used, this production function can be can be mapped as: Q =ƒ (L, K) Where: Q= Output, L= Labour and K=capital 3.2 Conceptual framework Enterprises do not function in isolation, but they are surrounded by an environment known as business environment. These business environments consist of both internal and external environment, with external environment comprising of both macro and micro environment. Businesses may have to some extent control of the internal environment but the external environment is beyond its control. Therefore,since the businesses lack control over its environment, it may pose threats oropportunity for enterprises thus impacting positively or negatively on the performance/ productivity. Methodology Figure 3.1: Conceptual Framework Fig ure 3.1: Conceptual framework Productivity Business environment External Environment Internal Environment Quality of labour Physical resources Technology Business regulations Micro factors Macro factors Management Competition Economic environment Suppliers of raw Political environment materials Legal environment Customers Technological Social Cultural Source: Authors own conceptualizing Source: Authors own conceptualization 3.3 The Model Estimated 3.3.1 Measure of total factor productivity 10 The re are two main directions for measuring TFP in the literature: the non- parametric approaches which include Data Envelopment Analysis (DEA) and TFP index; and the parametric approaches which include Stochastic Frontier Analysis (SFA) and estimation of the production function. Different studies have tried to estimate TFP using these various statistical techniques. However, due to certain limitations of these techniques, such as the assumption of constant returns to scale and perfect market conditions, the semi- parametric approach, such as the Levinsohn–Petrin (L-P) approach, has become popular over the years to estimate TFP compared to ordinary least squares (OLS) (Blalock and Gertler, 2004; Vial, 2006; Ghosh, 2009; Kato, 2009; Coricelli, Driffield, Pal and Rolland, 2012; Sharma, 2014). Hasan (2002), Mendi (2007), Saliola and Şeker (2012), Doraszelski and Jaumandreu (2013), Leachman and Ray (2014), Voutsinas and Tsamadias (2014) measured total factor productivity (TFP) by estimating the Cobb-Douglas 7 333..33.3TTThhhee e m mooodddeelel lee seststitimimaaattetededd 222..11.1 M Meeaeaassusuurrere e oo off f TT Tooottataall lFF Faaacctctotoorr r PP Prrorooddduuucctctitivviviittiytyy (( T(TTFFFPPP))) TTThhheerereer e aarareer e ttwwtwooo mmmaaiainnin dddiirrieercectctiitooionnnss s ffooforr r mmmeeaeasasusuurriirnninapproaches which include Data Envelopment Ang gg TTTFFFPPP iinnin tthhthee e lliilttieetreraartatuuturreer:e: : tthhthee e nnnooonnn-p-paararammeetrtircic aapppprrooaacchheess wwhhiicchh iinncclluuddee DDaattaa EEnnvveellooppmmeenntt AAnnaalalyylsysisissi s (( DD(DEEEAAA)) )aa nannddd TT TFFFPPP ii nnindddeexexx;; ;aa nannddd tt hhth -epae p p ra aarar mametertirci metricc aapappppprrooroaacachchheeses s ww whhhiiccihchh ii nnincclcluuludddee e SS Sttootocchchhaasaststiitcci c F FFrrooronnnttiiteeirer rAA A e parametric function. n nnaalalyylsysisissi s ( (SS(SFFFAAA)) )aa nannddd ee seststiitmmimaatatiitooionnn oo off ftt hhthee e pp prrooroddduuucctctiitooionnn ffuunnccttiioonn.. 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JJaJajajrrjiir i ((22(2000000777)) ) utilized 2007) utilized auu mttiiloliizrzee dod u aa at p mm mooorut orrerei e oueoonuutttpptpuuutt ted daoo otraririented data enve ieennvtteedldo pddmaatteaan ete nnavnveaellloyolopppsi s a a a(nnnDaaElalyylsysisisis method (DEA) and MalmquisA) ms memtheetothhdoo addn (d(D DMEEaAAlm)) q aaunnidsdt Maallmmqquuiisstt taa pappppprrooroaacachchh tt ootoestima aeepsspttirimmoaaactteete TFP and theh tTToF FePsPt iaamnnaddt etth hTeeF dPd dee eattneetrermmrmd thiinenin aadnannetttsst s oo off fT TFP.erminTFaFnPPt.s. of TFP. TTFTFFPFPP iP si isusi s su uusasusulluaayla llollyylb y to oaobibnbttaeatdiainn ifneerdedod m f fr rfotorhommem et tshhttheiem e e easesttestiitdmmi mpaartateoetdded u p cptprrioorodnddu ufuccutctniitocoiontnin o f nfuuf unannncctdctii tooiisonn n u a asnaenndd d ti oissi s u uussesededd t tooto m mmeeaeasasusuurreer e f fiifrrimmrm mpppreroaorodsduduuruccetct ifiitvvirviimttiyyt .yp. .rT ToThdhheue ec it ininivndditdeeyxex. x T o ohoffe f ri rneerdlelaaeltaxtiitv voiveef erTeTTlFaFFtPPiPv ef fo ofTorrF rPe ea eafcaochcrh h e f afiifcrrihmmr mfi 𝑖𝑖𝑖𝑖r𝑖𝑖𝑖𝑖 m𝑖𝑖𝑖𝑖aata t att tii tmtmiimmee ee 𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡 𝑡𝑡𝑡𝑡cccaacannan bn eb b bee e g ggeenenneereraarlallllyyl y d ddeefefiifnnineededd a asas s gffeoofnolllelloorlowawlwlssy:s: d:efined as follows: 𝜃𝜃𝜃𝜃 = 𝑌𝑌𝑌𝑌𝑌𝑌𝑌𝑌𝜃𝜃𝜃𝜃 = 𝑌𝑌𝑌𝑌 𝑖𝑖𝑖𝑖 𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡𝜃𝜃𝜃𝜃 𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡= ∫(𝐾𝐾𝐾𝐾𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 𝑡𝑡𝑡𝑡 ,𝐿𝐿𝐿𝐿 ) (1) ((11(1))𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 ∫∫((𝐾𝐾𝐾𝐾𝐾𝐾𝐾𝐾𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡𝑖𝑖𝑖𝑖,,𝐿𝐿𝐿𝐿𝑡𝑡𝑡𝑡𝐿𝐿𝐿𝐿 𝑖𝑖𝑖𝑖)𝑡𝑡𝑡𝑡 )𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡) WWhehree rYeit 𝑌𝑌𝑌𝑌 is tihse tohuet pouut topf ufitr mof i afitr tmim 𝑖𝑖𝑖𝑖e at,t Ktiimt ise t 𝑖𝑖𝑖𝑖h,e𝐾𝐾𝐾𝐾 capiitsa lt hinep ucta opfi tfiarlm i ni paut tiWhh t mofe tf,i rm 𝑖𝑖𝑖𝑖 at time 𝑡𝑡𝑡𝑡, L ise etrhreee 𝑌𝑌𝑌𝑌l𝑌𝑌𝑌𝑌a𝑖𝑖𝑖𝑖b𝑡𝑡𝑡𝑡𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡oiisus rtt hihneep uootuu ottpfp ufiutrt m oo ffL fafiitrr mtmim 𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖eaa tt, attinimmde eθ 𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖,=,𝐾𝐾𝐾𝐾 𝐾𝐾𝐾𝐾1 𝑖𝑖𝑖𝑖i𝑡𝑡𝑡𝑡𝑖𝑖𝑖𝑖n𝑡𝑡𝑡𝑡idissi cttahhtee s c ctahapep iicttaeanll tirinanplp utuettn odoeff n ffciiyrrm m 𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 aatt ttiimmee 𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡,,𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡iisis ththee liatlabor in 𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 labboorr iinnpppuuutt to ooff ff fiifrrimmrm 𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 𝑖𝑖𝑖𝑖aatat tt tiitmmimee e 𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡 ,𝑡𝑡𝑡𝑡, ,a ana 𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 s the of TFP. If a firm’s θ value is above 1, nnddd 𝜃𝜃𝜃𝜃𝜃𝜃𝜃𝜃 𝜃𝜃𝜃𝜃 it it in𝑖𝑖𝑖𝑖d𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡== =111iinnindddicciacatateetses s t thhthee e c cecenennttrrtaarlal lt teetnenndddeenennccycy y o ooff fT TTFFPP. . IfI f aa fifrimrm’s’s 𝜃𝜃𝜃𝜃 𝜃𝜃𝜃𝜃 vvvaalaluuluee e i issi s a ababbooovvvee e 1 11,, ,i itti ti innindddiiccicates icates a high TFP rela aatteess a a a h hhiiggihghh 𝑡𝑡𝑡𝑡 T TTFFFPPP r reerlelaaltatiitvvivee e t tooto t thhthee e o tiovteh teootthheerr r tfhierm other ffiirrmmss,s, ,w wwhhh FP. eerereeraeasas s a a a v vv Iaf lua ef ibrmelo’s 𝜃𝜃𝜃𝜃 firms, whereas a value below aalluuee bbeelloowww 1 11 iinnindddiicciacatateetses s aa a ll ooloww TT TFFFPPP.. .RR Reeaeara 1 rrrr rinandgiciataannggiinnngg g es a low TF ee qeqquuuaatatiitooionnn P (.1 R)e aarranging equation (1) as an ((11)) aass s aa nann ee qeqquuuaatatiitooionnn oo off f 𝑌𝑌𝑌𝑌 𝑌𝑌𝑌𝑌 𝑌𝑌𝑌𝑌𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡𝑖𝑖𝑖𝑖,𝑡𝑡𝑡𝑡, ,ww wee e hh haavavvee:e::equation of Yit, we have: 𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 𝑌𝑌𝑌𝑌𝑌𝑌𝑌𝑌𝑌𝑌𝑌𝑌𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡=== ∫ ∫∫((𝐾𝐾𝐾𝐾(𝐾𝐾𝐾𝐾𝐾𝐾𝐾𝐾𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡𝑖𝑖𝑖𝑖,𝑡𝑡𝑡𝑡,𝐿𝐿𝐿𝐿,𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡𝑖𝑖𝑖𝑖)𝑡𝑡𝑡𝑡))𝜃𝜃𝜃𝜃𝜃𝜃𝜃𝜃𝜃𝜃𝜃𝜃𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 (2) (2(2))𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 (2) TThTheh ene e nxnetex fxeature is thThe nextt tf feefaeatatuuturreer e i issi es tthethcehe nt olog the teetcechchhnnn yo ooll oolfo gpgyry o odofu fc ptpirooronogy of prodd ,du wuucctc htitioiconhn, c, awwnh hbiciech eh x cpcalanan in bbeede ebexyxp uplaslaeini needd bbyy uussee oof f ddififfefererennt t ohhfhy ydpypipofofotetrhe ion, which can be explained by use of different thheesneseste sesh.s. y .TT pThohheteh e CeC Csooeobsb.bbb Tb––h–DDeDoo Couuuoggblglabalsas- Ds ff uoufunnncgcltctaiitosoi onnfnsus sn aa cnantndidod ntt hshthe ea e ntt rrdtaar nanthnss-es-ll -ootlroggagaanrarsiirt-tihlhtohmgmairciic tph prmoroidcdu ucctitoionn a arere t hthee t wtwoo m moosst t pccrocooommdmummcmtion layr eu mic production are the two most oonnllyy uuss esetdehdd e mm mtewetetohht hoomoddodss.s. t .T T cThhohimissi s mss tstuoutnuddldyyy y aau dadsdoeodopp pttmeetdeded ttth hhtoheed e psp .prr oorToddhduuisucc tcstiittooiuonndn ytt e etacecdhchohnnpnootoleloodlog tgyhy ef ofolllolowweedd C Coobbbb––DDoouugglalass ppprprrooordodduduucuctcitctio gy followed Cobb–Douglas toiionon ntn e ff cuufhunnnnccotctilitoooigonnynss sfdoddululueoe ew tt ooitno igi ttis st s hff lelfee lCxexxioibbbibiibllii–ilttiyDyt,y,o,aualalgggllegaebseb brpraarriaioccidctutrrtacartcacitoctaantbab bifiulliiinlttiycyttyaiaonannnddsd d gg uogoeooo odtdod aa pappppprrooroxxxiimmimaatatiitooionnn produ o o off f tt hhthee e iptpsrr oflodeduxuiccbtctiiiltooiitonnyn , pap lprrgooreoccbecerseassssi cs (( RtR(rReaecyeytnynanèbèsèisl,s,i t,22y 20 0a011n177d7)) ) ..g .HoH Hoeednen ncacecpe ep ee rqeqoquuxuaiamtatiitoaoitonnin o ((n 22( 2) ) o )ccf ac a natnhn beb be e ep ww rworridirttiuttteectnetnin o aa nsas s ff oofollllloolowwwss:s: : process (Reynès, 2017). Therefore, equation (2) can be written as follows: 𝛽𝛽𝛽𝛽 𝑌𝑌𝑌𝑌𝑌𝑌𝑌𝑌𝑌𝑌𝑌𝑌𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡===𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴 𝛼𝛼𝛼𝛼𝛼𝛼𝛼𝛼 𝛽𝛽𝛽𝛽 𝐾𝐾𝐾𝐾𝐾𝐾𝐾𝐾𝐾𝐾𝐾𝐾𝛼𝛼𝛼𝛼𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡𝑖𝑖𝑖𝑖𝐿𝐿𝐿𝐿𝑡𝑡𝑡𝑡𝐿𝐿𝐿𝐿𝛽𝛽𝛽𝛽𝐿𝐿𝐿𝐿𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡𝑖𝑖𝑖𝑖𝜃𝜃𝜃𝜃𝑡𝑡𝑡𝑡𝜃𝜃𝜃𝜃𝜃𝜃𝜃𝜃𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 (3 (3) ((33)))𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 TThhee T TFPF Pd ifdfeifrfeenrceensc reesfl ercetf lsehcitf ts hinif otsu tipnu ot uwthpiulet hwohldiline gh aolll dthineg i naplul ttsh ceo ninsptauntts. constant. Therefore, to TchToemhreeef oTurFep,P tw od icitofhmf eaern eu npoc wuetispt hur eat/fniln eopcutut pts uhrtai/ftitinsop iutnht aroatu timtop etuhata swtu 1m1rh11e1ei s1 la e sT uhFroePls,d T itFnhPge, taihnlled iitnvhdiedi vuiinadplu uaitnls p cuotsn smtaunstt. bTeh ewreefiogrhet,e dto i an c popumrtso epm ruiuapst tew lbyiet h ww haeneign ho gtueetdpn ueatrp/aiptnirnpogup rt aiar atsetiinlyog lwtehh-adetni mm geeeansseiuroarnetiasnl g Ti nFap Psu,i tn tighnleed -eidnximd. iIevni dstihuoiansla cli naspeu,t sth me uCsot bbbe– Dwoeuigghltaesd ipnapropudpt uriocnptdireoixan.t efIluny n twchtihisoe ncna psgere,o nvtehireda etCidno gba bna– esDaionsugygl ela-nsd dipm rcoeodnrurseciotcinto anwl efiunignphcuttio ninng d.p eTrxor.va iIndnse fdtoh raimsn cinagse e, qthuea tCioonb b(3–)D ionutog laas elaipnsryeo adrnu dec xctipoornree scfstui ownnce tibigoyhn tti anpkgr.io nTvgria dtnhesedf o laromng aienraigts heyqm ua anotdfi o bnco o(t3rhr) e sicnidtt oew sa,e lwiignehe athirna evgxe.p :Tr ersasniosnfo brym ing equation (3) into a talkiinnega trh eex lpogreasrsitihomn bofy b toatkhi nsigd eths,e w leo ghaarviet:h m of both sides, we have: 𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑌𝑌𝑌𝑌 = lnA + 𝛼𝛼𝛼𝛼𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝐾𝐾𝐾𝐾 + 𝛽𝛽𝛽𝛽 ln 𝐿𝐿𝐿𝐿 + ln𝜃𝜃𝜃𝜃 (4) 𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑌𝑌𝑌𝑌𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 = lnA + 𝛼𝛼𝛼𝛼𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝐾𝐾𝐾𝐾 𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 + 𝛽𝛽𝛽𝛽 ln 𝐿𝐿𝐿𝐿𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 + ln𝜃𝜃𝜃𝜃 𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 (4) (4) AAsssusumminigng 𝜃𝜃𝜃𝜃 = 𝑢𝑢𝑢𝑢Assumi n, gw e𝜃𝜃𝜃𝜃𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 can= r ew𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡r , iwte ee qcuaant rewrite equation 4 as: 𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 𝑢𝑢𝑢𝑢𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 , we cani orne w4 raist:e equation 4 as: 𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑌𝑌𝑌𝑌 = lnA + 𝛼𝛼𝛼𝛼𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝐾𝐾𝐾𝐾 + 𝛽𝛽𝛽𝛽 ln 𝐿𝐿𝐿𝐿 + 𝑢𝑢𝑢𝑢 (5) 𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑌𝑌𝑌𝑌𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 = lnA + 𝛼𝛼𝛼𝛼𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝐾𝐾𝐾𝐾 𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 + 𝛽𝛽𝛽𝛽 ln 𝐿𝐿𝐿𝐿𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 + 𝑢𝑢𝑢𝑢𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 (5)𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 (5) FFrroomm e qeuqautaiotino n(5 )(,5 t)h,e tnhaet unraatlu lroagla rliothgmar iothf mth eo tfo ttahl ef atcototarl p fraocdtuocrt ivpirtoyd inudcteixv iisty index is equal to the erqeFusraiodl mutoa let hqteeur amrtei so𝑢𝑢𝑢𝑢ind u(ai5nl ) t,te hrtemh ee icnno antthouemr aeelc torlniocog mparreiottdrhiumc cp toiroofn dt uhfcuetni otconti toafuln n.f caItncio tponrr. a Ipcntr iopcdreau, ccettiqicvuei,a ttyio inn d5e cxa is e𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 n beq eusatilm toat ethde euqrsueiasntigido unLa l5e vtceianrnms ob h𝑢𝑢𝑢𝑢en 𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡e siatninm dtha etPe edec turoisnnino gm(2 Le0et0rvi3icn) spoarhopndp uraocnatdico hPn.e ftTruinhnce (t 2imo0n0a.3i nI)n a ippdprearaco taibcchee.h, Tienhqdeu aLtieovni n5s ochann baen eds tPimetartiend maupasipnirn oigda ecaLh ebviesih ninsthodah Ltn e avaninn sdion htPnee ratmrniednd Pi(ae2tte0ri 0ni3 na)p pupatpr opcarcaohna cishb .et h Tauths aeen d minaatsei nrma iedcdeoiaan tterb oienlh pifunotdr Ltheev inusnoohbns eravnedd Pfeirtmrin cpaarnop bdpeur oucatsicevhdi tayiss a c cthhoaanrtta rcoatln ef roirisn ttthiecers mu nteohdbuisaset erve enidns upfirurimtn g pc aronud nubbcietai vsueitsdye cdhe asartisam ctaae treicssot incotsr,f o thl tuhfseo r ptrhoed uucntioobns erfvuendc tifoinrm. eLnpesruvoridninusgoc thuinvn ibatiyna dse cPdh eaetrsratiincm t(ea2rti0ess0t i3oc)fs pthrteehs upesrn otededun csttuhiorein np gfruo nducuntcibotiniao.s nLe dfeuv innecsstotiiohmnna iatnen sdE qPouefat rtiitnoh ne (5p)r oads ufcotliloonw sf: unction. Levinsohn and Petrin (2003) presented the production function in Equation (5) as follows: 𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑌𝑌𝑌𝑌𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 = lnA + 𝛼𝛼𝛼𝛼𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝐾𝐾𝐾𝐾 + 𝛽𝛽𝛽𝛽 ln 𝐿𝐿𝐿𝐿 + 𝛾𝛾𝛾𝛾 ln𝑀𝑀𝑀𝑀 + 𝜔𝜔𝜔𝜔 + 𝜀𝜀𝜀𝜀 (6)𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑌𝑌𝑌𝑌𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 = lnA + 𝛼𝛼𝛼𝛼𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝐾𝐾𝐾𝐾 𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 + 𝛽𝛽𝛽𝛽 ln 𝐿𝐿𝐿𝐿𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 + 𝛾𝛾𝛾𝛾 ln𝑀𝑀𝑀𝑀𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 + 𝜔𝜔𝜔𝜔𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡+ 𝜀𝜀𝜀𝜀𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 (6) Where: 8 Where: 𝜔𝜔𝜔𝜔𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 is productivity and𝜔𝜔𝜔𝜔𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 is productivity and 𝜀𝜀𝜀𝜀𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 is unpredicted shocks. In equation (6), the residual 𝑢𝑢𝑢𝑢𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 was split into two elements, 𝜔𝜔𝜔𝜔 and 𝜀𝜀𝜀𝜀 𝜀𝜀𝜀𝜀𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡. Tish ue nipnrteedrmiceteddia tseh oinckpsu.t Iwna esq aulastoio 𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 and d(6ed), aths ea rferseiedluya lv a𝑢𝑢𝑢𝑢r𝑖𝑖𝑖𝑖i𝑡𝑡𝑡𝑡awblaes insppluitt . iTnthoe tiwntoe remleemdieanttes ,i n𝜔𝜔𝜔𝜔p𝑖𝑖𝑖𝑖u𝑡𝑡𝑡𝑡at’nsd d e𝜀𝜀𝜀𝜀m𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 .a nTdh efu innctteiromn eids igaitve einn paus:t was also added as a freely variable input. The intermediate input’s demand function is given as: ln𝑀𝑀𝑀𝑀 = ∫M (𝜔𝜔𝜔𝜔 ln𝑀𝑀𝑀𝑀𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 = ∫M (𝜔𝜔𝜔𝜔𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 , 𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝐾𝐾𝐾𝐾𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡) (7) 𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 , 𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝐾𝐾𝐾𝐾𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡) (7) The intermediate inputs demand function must be monotonic in the firm productivity element 𝜔𝜔𝜔𝜔The in𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 for taelrl mreeldeivaaten t i𝑙𝑙𝑙𝑙n𝑙𝑙𝑙𝑙p𝐾𝐾𝐾𝐾uts tdoe qmuaanlidf yf uansc ati𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 ovna lmidu pstr obxey .m Loenvoitnosnoihcn i na ntdh eP efitrrmin (p2r0o0d3u)c atisvsiutym eedle mtheant t in𝜔𝜔𝜔𝜔p𝑖𝑖𝑖𝑖u𝑡𝑡𝑡𝑡 tf oanr da lol urtepluevt awnet r𝑙𝑙𝑙𝑙e𝑙𝑙𝑙𝑙 c𝐾𝐾𝐾𝐾o𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡mtmo oqnu aalcirfoys as sf iarm vsa laidn dp trhoaxty d. eLmevaninds ofuhnnc tainodn Pine terqinu(a2t0io0n3 )( 7a)s saubmoveed htahsa t nion peurtr oarn.d Aousstpuumt iwnge rem coonmotmonoinc iatycr ohsosl dfisr,m tsh aen din tthearmt deedmiaaten di nfupnuct’tsio nd einm eaqnuda tfiuonnc (ti7o)n a bcoavne bhea s innvoe reterrdo tro. yAieslsdu m𝜔𝜔𝜔𝜔inags am founnocttoionnic oit𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 fy c ahpoiltdals ,a ntdh ei ntienrtmeremdeiadtiea tien piuntpsu: t’s demand function can be inverted to yield 𝜔𝜔𝜔𝜔𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡as a function of capital and intermediate inputs: 𝜔𝜔𝜔𝜔𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 = ∫𝜔𝜔𝜔𝜔 (ln𝑀𝑀𝑀𝑀𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 , 𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝐾𝐾𝐾𝐾𝜔𝜔𝜔𝜔 = ∫𝜔𝜔𝜔𝜔 (ln𝑀𝑀𝑀𝑀 , 𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝐾𝐾𝐾𝐾𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 ) (8) 𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡) (8) Substituting equation (8) into Equation (6), we can rewrite the production function as follows: Substituting equation (8) into Equation (6), we can rewrite the production function as follows: 𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑌𝑌𝑌𝑌 𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑌𝑌𝑌𝑌𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 = 𝛽𝛽𝛽𝛽 ln 𝐿𝐿𝐿𝐿𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 + 𝜑𝜑𝜑𝜑(ln𝑀𝑀𝑀𝑀𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 , 𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝐾𝐾𝐾𝐾𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡)+ 𝜀𝜀𝜀𝜀 (9) 𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 = 𝛽𝛽𝛽𝛽 ln 𝐿𝐿𝐿𝐿𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 + 𝜑𝜑𝜑𝜑(ln𝑀𝑀𝑀𝑀𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 , 𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝐾𝐾𝐾𝐾𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡)+ 𝜀𝜀𝜀𝜀 𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 (9) Where Where 𝜑𝜑𝜑𝜑(ln𝑀𝑀𝑀𝑀𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 , 𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝐾𝐾𝐾𝐾𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡) = lnA + 𝛾𝛾𝛾𝛾 ln𝑀𝑀𝑀𝑀 + 𝜔𝜔𝜔𝜔 (ln𝑀𝑀𝑀𝑀 , 𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝐾𝐾𝐾𝐾 ) (10)𝜑𝜑𝜑𝜑(ln𝑀𝑀𝑀𝑀𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 , 𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝐾𝐾𝐾𝐾𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡) ∫ = lnA + 𝛾𝛾𝛾𝛾 ln𝑀𝑀𝑀𝑀𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 + ∫𝜔𝜔𝜔𝜔 (ln𝑀𝑀𝑀𝑀𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 , 𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝐾𝐾𝐾𝐾 𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡) (10) Equation (9) can be used to estimate 𝛽𝛽𝛽𝛽 the labour coefficient, but not the other capital and inEtqerumateiodnia t(e9 ) incapnu tsb e puasreadm etote ress. timThatee 𝛽𝛽𝛽𝛽estthime altaiboonu ro fc oe𝛽𝛽𝛽𝛽ffiicsi enret,a sbounta bnloet tshien coet hethr ec afpuitnaclt iaonnd intermediate inputs parameters. The estimation of 𝛽𝛽𝛽𝛽 is reasonable since the function 12 12 TThhhee e TTFFPP d ddiififfffefeerrerenennccceeses s r rerefeflflelecectct t s shshhiififtftsts s i ininn o oouuuttptppuuutt t wwhhhiililele e h hhooolldlddiininnggg a alallll l t ththhee e i ininnpppuuuttsts s c cocoonnnsststatananntt.t. . TThhheererereeffofoorrere,e, ,t oto cTcTocohhomeem e eTT e uF uFupPpPp dwdwiiiiftfitfhtfheh e ra raeenannn coc oeoueusustt p tprrpueueutftf/lt/lie/ieninccnptptp u usustth ht ri rifarfattatstisti oi oio in nt t ht hohaoautaut tt tp mpmuueettae a sawsusuhurhrierielselse s hThToFoFllPddP,,i i ,ntn thgthghe e a ea il ilnlinl nd dttdhihiviveevi id idiidnunuupapalaulu l tit isnsin n pcpcpuououtntnstss s t tma to amnunutust.s.t st Tt Tb bhbehee e r rweweefefoieoigirgrgheheh,t,t e tettdeodod acacpaopopppmprroeroeo p pupurrpirpiaia tatewteleiliytlythy h w waahnhnhe e neonon u ug gtgtepepneununetet/er/riairanntatpiptininuunggttg ar ara a st stiisiionoinn g gtgtlhlheleae-a-td-td diimimmeeeaanenssnusuisirorioeoennssna a laTlT l iF iFninPnpPp,pu,u utttt ht hi ienein nd diidenenxedxdx.i. iv .vI IinIindnd ut uththaahilisl is si icn cncapapsasuueset,ets, s , t ththmheeue u s CsCtot o obbbbebeb b– –w–DDeeooiioguguuhghggtltleaeldsad s papaprrporoppoddrrduououcpcptcrtriitioiaioaontnten e lf lfyuyfu un nwncchtchiteoeinon n ggp eeprnonreovervriadaitdteiindengd ga ana n sse iinaensaggyslley e-a -dnadindimd ce eocnnorssrriieoroecnntca atl l wiienniepgpiughuthtt i itniningddg.e as e.x xT.r. aIrInann s tftshhofioirssr mccianaisnsgeeg ,, e tqethhqueeua taCitooioonbbn bb( ––3(3D) )oio nuiuntggotlol aa ss a lplpilinrinroneoedaedauraur rc ece txetixixopopnprnre re sefsfussusisninoiococnnt tiioonn pprroovviiddeedd aann eeaassyy aanndd ccoorrrreecctt weeiigghhttiinngg.. TTrraannssffoorrmiinngg eeqquuaattiioonn ((33)) iinnttoo aa tn ibb obyyny tt aptakarkkoiininvnggigd tt ehthdhee e lal olonogg gaearariristithythh mam n oo doff f bcb boootrthrthhe scs isitdid dewesese,s, i ,w gwhee et hih nhaagvav.ve e:eT: : r ansforming equation (3) into a lliinneeaarr eexxpprreessssiioonn bbyy ttaakkiinngg tthhee llooggaarriitthhm ooff bbootthh ssiiddeess,, wee hhaavvee:: 𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑌𝑌𝑌𝑌𝑌𝑌𝑌𝑌𝑖𝑖𝑖𝑖𝑌𝑌𝑌𝑌𝑡𝑡𝑡𝑡𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡==lllnn𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 AA++ 𝛼𝛼𝛼𝛼𝛼𝛼𝛼𝛼 𝛼𝛼𝛼𝛼𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝐾𝐾𝐾𝐾𝐾𝐾𝐾𝐾𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑌𝑌𝑌𝑌 lnA 𝛼𝛼𝛼𝛼𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝐾𝐾𝐾𝐾𝐾𝐾𝐾𝐾𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡 𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡++𝛽𝛽𝛽𝛽𝛽𝛽𝛽𝛽𝛽𝛽𝛽𝛽l ln𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 + l ln𝜃𝜃𝜃𝜃𝜃𝜃𝜃𝜃𝛽𝛽𝛽𝛽 llnn𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡 + llnn𝜃𝜃𝜃𝜃𝜃𝜃𝜃𝜃𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 ((4(44))) 𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑌𝑌𝑌𝑌𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡 = lnA + 𝛼𝛼𝛼𝛼𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝐾𝐾𝐾𝐾 𝑡𝑡𝑡𝑡 𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡 + 𝛽𝛽𝛽𝛽 ln 𝐿𝐿𝐿𝐿𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡 + ln𝜃𝜃𝜃𝜃𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡 ((44)) AAsssssusuummiininngg 𝜃𝜃𝜃𝜃 𝜃𝜃𝜃𝜃𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 = 𝑢𝑢𝑢𝑢 𝑢𝑢𝑢𝑢 , we can rewrite equation 4 as: ssu ingg 𝜃𝜃𝜃𝜃𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡 = 𝑢𝑢𝑢𝑢𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡 𝑖𝑖𝑖𝑖,𝑡𝑡𝑡𝑡, wee ccaann rreewrriittee eeqquuaattiioonn 44 aass:: Assuming 𝜃𝜃𝜃𝜃𝜃𝜃𝜃𝜃𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡 = 𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡,, wee ccaann rreewrriittee eeqquuaattiioonn 44 aass:: 𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑌𝑌𝑌𝑌𝑌𝑌𝑌𝑌𝑖𝑖𝑖𝑖𝑌𝑌𝑌𝑌𝑡𝑡𝑡𝑡𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡==lllnnAA++ 𝛼𝛼𝛼𝛼𝛼𝛼𝛼𝛼 𝛼𝛼𝛼𝛼𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝐾𝐾𝐾𝐾𝐾𝐾𝐾𝐾𝐾𝐾𝐾𝐾𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡++𝛽𝛽𝛽𝛽𝛽𝛽𝛽𝛽𝛽𝛽𝛽𝛽lllnn𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡++ 𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑌𝑌𝑌𝑌𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 l 𝑢𝑢𝑢𝑢 𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 ((5(55))) 𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑌𝑌𝑌𝑌𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡 = lnnAA + 𝛼𝛼𝛼𝛼𝛼𝛼𝛼𝛼𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝐾𝐾𝐾𝐾𝐾𝐾𝐾𝐾 𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 +𝛽𝛽𝛽𝛽𝛽𝛽𝛽𝛽llnn𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 + 𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 ((55)) FFrroroomm 𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 e eqeqquuuaatatitioioonnn ( (5(55)),), ,t ththhee e n nnaatatutuurraralal l l lolooggga 𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 aarriritiththhmm o ooff f t ththheee t totoottatalal l f fafacactctotoorr r p pprorodFro equation rod duuucctctitivivviititytyy i ininndddeexexx i isis s e eqeqquuuaalal l t totoo t ththhee e rrFeresersoisididmduuu aaelal ql tt euterearrmtmi o 𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢 n𝑢𝑢𝑢𝑢 (5)𝑖𝑖𝑖𝑖 𝑡𝑡𝑡𝑡𝑖𝑖𝑖𝑖(𝑡𝑡𝑡𝑡i5inin),n , t t hththheee e e e cencnocaoaontntunuoororamamll 𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 ee te ltlrotroirigcgica ca prp rpirirtotrohhoddmduuu c cotcotitfiofio ontnthn h fef eufu untntoncocttcttaitaioliol on nfn.fa. a .Ic IcnInttno o pp rrpr r arpapaccrrtcotoiticdicdecueu,ec, c ,ete tiqeiqvvquuiiutatayytati ti oiioinonndn d 5e5 e5 x cxc ac ainaisnsn beb ebeqeq eu eue aseastlstli ti titomoa tattehthdeeed urureusessisinidual t r mated iningdgg u aLLle etvevevirininmnss os o𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢ohh𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖h𝑡𝑡𝑡𝑡n𝑡𝑡𝑡𝑡nn i in ana na nttnhdhdde e ePePececteotortrnirnioinon m ((2e(2e20t0tr0r0i0ic03c3 3) p)p ) r raoaopadpdppupuprcrocrototaiaioacocnhcnh h. .f f .u uTnnThchcheteti ei oo mnnm.a. a IiaIinninnn p piirdirdadaeecaecat ati ic bcbebee,,he h eheiiqnqinnududda a t tiiLoeonven vi 5n5i ns ccosaaohnnh bn be ae na eendssdt ti imPeaatetrtteierndidn auaupaspsppipinpnrrgorgoo a acaLcLhcheeh v v iiisinisn s ss totohthhhaantant t aaaanannnn d d i ininPPntteeteeretrtrrmiminnee de d(di(i2a2ia0ta0tete00 e 33 ii)n)in nppapauupputtp pt r crcooacaanancnc hh bb.. be eeT T uhuhusesees e dedmd aaain idea behind L Leevinsohn and Petrin saisn s aa ai d cceocoaonn nttbrtroeroolhl l i nffofdoor r r L tthethvhevei ei nn ususunononohohobnbnbs esaarenvnrdved de Pd P efeitftrriririnmn papaprrporoppoddrrduououcacatctcitihvhiv vi itiityitssy Methodology served firm y tctchhchahaattra r araacacntcnte te reiriinrisnisttstteieticricrscms s e etdthdthihuiauaustste se eeineinnnspspusuuurrtitri n inncgcgag a n nu u unnbbnbeebi ia iausausesesdeded d eeaseasststi ti imamaa tacectoseon sn ttorrofo lfl tfhftohoerer ptthphreore od duuuncntcooitbobiosnsen er rvfvufeeunddnc tcfiftioiirronmn. . pLpLrereoveovdvidinuinunscscostotiohihvvhnniint t aya yna n nddccd h hP aPeaertertaratricricninttne e (r( 2r(istics thus ensuring unbiased esti a atteess ooff tthhee pprroodduuccttiioonn ffuunnccttion. 2i20s00t00i03c33)s) ) pp ptrrhereusesesene nnttetendedds tu htrhein ep gpr orodudunucbtciiatoisonen df u funencstctiitomionan it neins Eqoqufua tatithoioen n ( 5p(5)r o)a dsau sfc oftolilololno wsf:su :n ctiioonn.. LLeevviinnssoohhnn aanndd PPeettrriinn ((22000033)) pprreesseenntteedd tthhee pprroduction function in Equation (5) as follows: the proodduuccttiioonn ffuunnccttiioonn iinn EEqquuaattiioonn ((55)) aass ffoolllloowss:: 𝑙𝑙𝑙𝑙(𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙2𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙0𝑌𝑌𝑌𝑌𝑌𝑌𝑌𝑌0𝑖𝑖𝑖𝑖𝑌𝑌𝑌𝑌𝑡𝑡𝑡𝑡𝑖𝑖𝑖𝑖3𝑡𝑡𝑡𝑡)= =plrlelnnsAeAn+t+e 𝛼𝛼𝛼𝛼d𝛼𝛼𝛼𝛼 𝛼𝛼𝛼𝛼 𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙t𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙h𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝐾𝐾𝐾𝐾e𝐾𝐾𝐾𝐾𝐾𝐾𝐾𝐾 p𝑖𝑖𝑖𝑖r𝑡𝑡𝑡𝑡o++du𝛽𝛽𝛽𝛽𝛽𝛽𝛽𝛽𝛽𝛽𝛽𝛽cltlilonn𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑌𝑌𝑌𝑌𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 𝐿𝐿𝐿𝐿 lnA 𝛼𝛼𝛼𝛼𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝐾𝐾𝐾𝐾𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡 𝛽𝛽𝛽𝛽 lnn𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿 𝐿𝐿𝐿𝐿 f𝑖𝑖𝑖𝑖u𝑡𝑡𝑡𝑡n++ct 𝛾𝛾𝛾𝛾i𝛾𝛾𝛾𝛾 o𝛾𝛾𝛾𝛾lnlln nin𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀 eq𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡u+a𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 + t 𝜔𝜔𝜔𝜔i𝜔𝜔𝜔𝜔 o𝜔𝜔𝜔𝜔n𝑖𝑖𝑖𝑖 𝑡𝑡𝑡𝑡+(+5 )𝜀𝜀𝜀𝜀𝜀𝜀𝜀𝜀 𝜀𝜀𝜀𝜀a𝑖𝑖𝑖𝑖s𝑡𝑡𝑡𝑡 follows: 𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 ((6(66))) 𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑌𝑌𝑌𝑌𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡 = lnA + 𝛼𝛼𝛼𝛼𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝐾𝐾𝐾𝐾𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡 + 𝛽𝛽𝛽𝛽 ln 𝐿𝐿𝐿𝐿 𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 + 𝛾𝛾𝛾𝛾𝛾𝛾𝛾𝛾llnn𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 Wh 𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 + 𝜔𝜔𝜔𝜔𝜔𝜔𝜔𝜔𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡+ 𝜀𝜀𝜀𝜀𝜀𝜀𝜀𝜀𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡 (6) ((66)) Whhehee rerere:e:: Whheerrere:e:: 𝜔𝜔𝜔𝜔𝜔𝜔𝜔𝜔𝜔𝜔𝜔𝜔𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 is produ𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡iiss productcitvivitiyty a nanddω𝜔𝜔𝜔𝜔𝜔𝜔𝜔𝜔it𝑖𝑖𝑖𝑖 𝑡𝑡𝑡𝑡isi ipss r p po rodu prdroouddcutui cvtitiyv 𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 ccttiivvi iiat t tyn y and y d aanndd ε 𝜀𝜀𝜀𝜀𝜀𝜀𝜀𝜀 𝑖𝑖𝑖𝑖𝜀𝜀𝜀𝜀 𝑡𝑡𝑡𝑡i𝑖𝑖𝑖𝑖s𝑡𝑡𝑡𝑡i istis𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 sh u euu nnunppnpr 𝜀𝜀𝜀𝜀 is unprrp er eer dedediidcicitctcetetdeedd ds hshoit dicted s sshhhooo occckckksckss .s. .I IInnIn ee eqqeququuauaattaitiotioiononn n ( 6( (6)(6,6) ),t), h ,t tehth here ee r sreiredsesuisididadulu uaµalal l 𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢 w𝑢𝑢𝑢𝑢𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡a𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡s wwsapasalsi s ts spispnplltiliotit t i tinwinnttooto o t ttwwooo e eleleleemmeenennttsts,s, ,𝜔𝜔𝜔𝜔𝜔𝜔𝜔𝜔 𝜔𝜔𝜔𝜔𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡𝑖𝑖𝑖𝑖a𝑡𝑡𝑡𝑡anannddd e 𝜀𝜀𝜀𝜀 𝜀𝜀𝜀𝜀l 𝜀𝜀𝜀𝜀𝑖𝑖𝑖𝑖e𝜀𝜀𝜀𝜀𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡m𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡.𝑡𝑡𝑡𝑡. .ie TsnT hhtuhseen, e ipω ininrnteteatedrenrir 𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 mdcm teeεededdd it 𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 .ii aia . In equation (6), the res Tstathhete eeo i icniinknpptspueu.u trt mIt n wwe adaesaisq as au taelals lstiosiono pan auda dtd(d d6wdee)dead,d s ta hasasle ss a oar a efa frsfieriddeeuleuyalay ll v 𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢va𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖ra𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡iraiwbaablaesls e si snpipnpllipuitt ut .iti n.n ttToho he t etwi nioont e terelrleememdeeedinanittatsest, e, i𝜔𝜔𝜔𝜔 𝜔𝜔𝜔𝜔ninp𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡puaautnn’tds’d s d d 𝜀𝜀𝜀𝜀d𝜀𝜀𝜀𝜀ee𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖e𝑡𝑡𝑡𝑡m𝑡𝑡𝑡𝑡.m. a aTTnanhndhded e f f ufiuinun ittn ctceetctrirt i iomioonneen d id t i siisi as agg ttgeieiv iv veieinennpn p aua usatt: s: : w aass aallssoo aaddddeedd aas a fr rdeedeeelldyy avvsaa arri iafarbbelleeel y i nvapruiat.b lTe he intermediate input’s input. The intermedia s a freely variable iinnppuutt.. TThhee iinntteerrmeeddiiaattee iinnppuutt’’ss ddeemaanndd ffuunnccttiioonn iiss g tei vinepnu ats’s: demand function is given as: given as: lllnn𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡== ∫∫MM ( (𝜔𝜔𝜔𝜔(𝜔𝜔𝜔𝜔𝜔𝜔𝜔𝜔𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡𝑖𝑖𝑖𝑖 𝑡𝑡𝑡𝑡 ,, , 𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝐾𝐾𝐾𝐾𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝐾𝐾𝐾𝐾𝐾𝐾𝐾𝐾𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑖𝑖𝑖𝑖)𝑡𝑡𝑡𝑡)) (7) (7(7) )ln𝑀𝑀𝑀𝑀 (7) Tlnhe𝑀𝑀𝑀𝑀 𝑖𝑖𝑖𝑖i𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡n𝑡𝑡𝑡𝑡 = ∫∫M ((𝜔𝜔𝜔𝜔𝜔𝜔𝜔𝜔𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡 ,,𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝐾𝐾𝐾𝐾𝐾𝐾𝐾𝐾𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡)) ((77)) TThhhee e i 𝑡𝑡𝑡𝑡 ini termediate inputs demand function nnttetererrmmeededdiiaiatatete e i ininnpppuuuttsts s d ddeeemmaanannddd f fufuunnncctctitioioonnn m umusuts ts btb ebe em moonononotootontonicni cii cni n in tht htehe efif rifmrir m p prorodduuctcitvivitiyty e leelemenent t 𝜔𝜔𝜔𝜔pTrhoed uicnttievr must be monotonic in the firm productivity element 𝜔𝜔𝜔𝜔T𝜔𝜔𝜔𝜔𝑖𝑖𝑖𝑖h𝑡𝑡𝑡𝑡𝑖𝑖𝑖𝑖e𝑡𝑡𝑡𝑡ff ofoiornr r ta eala irtllmy l ell r rerd l edle e leili m eaveavtvta eenana n nit ti tn nt𝑙𝑙𝑙𝑙 𝑙𝑙𝑙𝑙 p f𝑙𝑙𝑙𝑙p𝑙𝑙𝑙𝑙or 𝑙𝑙𝑙𝑙u𝑙𝑙𝑙𝑙u𝐾𝐾𝐾𝐾𝐾𝐾𝐾𝐾t𝐾𝐾𝐾𝐾ts a 𝑖𝑖𝑖𝑖s l 𝑡𝑡𝑡𝑡𝑖𝑖𝑖𝑖 l r 𝑡𝑡𝑡𝑡dtdtoetoe eo mlq eqauvanuaandnladil tf i fyf utyu ona n csaqc tsutia ioao alify as a valid proxy. Levinsoh qualify as a nv nvv a alamilduiud ssp tt pr borboexe xy ym. .o oLnenoveotvitonoinsnoisicoch hniin na tnatnhd nede affPneiirdterrmt irn i np(p2(rr2o0o0d0d0u3u3c)c t)tia ivvsaissittusyyu meelleded mt hetehanntatt t iP𝜔𝜔𝜔𝜔 𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 i𝜔𝜔𝜔𝜔ninenp𝑖𝑖𝑖𝑖tp𝑡𝑡𝑡𝑡rpuuifutn𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 fto ot a ra(rna 2an 𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 n da0dld ll0 ol o 3orurue)uet tlpltpaeepusuvvustta uat n wnmwtet ee 𝑙𝑙𝑙𝑙re𝑙𝑙𝑙𝑙rd𝑙𝑙𝑙𝑙e𝑙𝑙𝑙𝑙re e𝐾𝐾𝐾𝐾 𝐾𝐾𝐾𝐾tc ch𝑖𝑖𝑖𝑖oco𝑡𝑡𝑡𝑡aomttmt inpu𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 oom m qoqoonunant a al alaicaifcnfrcyryodro o sasaossss us f fatiafpir ir uvrmvmtaa ssl lwiidde r pep rrcoooxy. Levinsohn and Petrin(2003) assumed thatl s ia adnan ndpdd r t tohthxxhamaytayt .mt . d dLdeLoeenmemv vaaaiinancnnnrdsdsod o sfh fhsufun unnfi n carcatcmntintiodiosdon n Pan P in ieneindtn tr er eiieqnqnqu(u(2ua2a0ta0ti0tio0io3on3n)n ) ( (a7a(7s7)s)s s) au aubabmbooeovevdvdee e tht hhhaaaasstt s ntinihnonoapo p tu uedetret er rarrmaoronnorard.dr.n . o doAu Aufstustspsspnusuuuctmtt mi oiwinninenge grigr ne e me cmcqooouonmnanotomoittootoononnn ni(i c7 icaaic)ict citayrtyrbyo o o shshvshsoo e ofl fldhildirdrsasm,ss, ,sn st toh atha heneen erd dr i io ntinthrntht.eat At sdseu earertrm mdeeedemdmdiiaaiiaantatnegtedd e m ififnuionunpnpnpucoucuttttt’iot’ios’osn sn ni cd d iideitnenye m meeaqaqnanuunddaad t ti fiofoufununn n c(c(7tc7titi)o)io onananb b ococvcavanaenen hh bbaabesn error. ssu ing onotonicity holds, the i ter e iate input’s e se ihininonovlvdv eesrer,tt retertdehodd er tt . oto io n yAy tyieiseierslemlduldd me 𝜔𝜔𝜔𝜔𝜔𝜔𝜔𝜔 d𝜔𝜔𝜔𝜔iina𝑖𝑖𝑖𝑖gat𝑡𝑡𝑡𝑡aesa s s miaan a pfof ufuununtno’ncscttc otidtinoioeoinma𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 ncn ioo tofy nf f dc ac hfpaoupilntdiactslat, il ao nanthnd ced ia nniint netbtreeer rimendevediedairitataeett edei n itnpoipnu yuptsitue:s tl:d’ s adsd eeam aanndd ffuunnccttiioonn ccaann bbee fiiunnnvvceetrirtoteendd o tfto oc ayypiieietladl 𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 ld 𝜔𝜔𝜔𝜔a𝜔𝜔𝜔𝜔n𝑖𝑖𝑖𝑖d𝑡𝑡𝑡𝑡a aisns t aae r ffmuunencdctitiaiootenn in𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 oofpf uc catapital and intermediate inputs: caspp: iittaall aanndd iinntteerrmeeddiiaattee iinnppuuttss:: 𝜔𝜔𝜔𝜔𝜔𝜔𝜔𝜔𝜔𝜔𝜔𝜔𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡== ∫𝜔𝜔𝜔𝜔𝜔𝜔𝜔𝜔𝜔𝜔𝜔𝜔 ( (l(ln𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 , 𝑙𝑙𝑙𝑙,𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝐾𝐾𝐾𝐾𝐾𝐾𝐾𝐾𝑖𝑖𝑖𝑖)𝑡𝑡𝑡𝑡)𝜔𝜔𝜔𝜔 ln𝑀𝑀𝑀𝑀 , 𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝐾𝐾𝐾𝐾 )∫∫𝜔𝜔𝜔𝜔 ( 𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 (8) ((8(88))) 𝜔𝜔𝜔𝜔𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡 = ∫𝜔𝜔𝜔𝜔 (llnn𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀 𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡 ,,𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝐾𝐾𝐾𝐾𝐾𝐾𝐾𝐾 𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡)) ((88)) SSSuububsbttsiitttuiuttutiintnigng eg eq qeuquatuaitaoitnoi on(8n () 8 (i8n) )tio ni netoqto u aEqtiquouanta (ito6io)n,n w( 6(e6) c,) a, nw er eecw carnaint re e rtehwer iprtireto etd htuhec etp iporonrod fduunctccittoiioonnn f ufunnctcitoionn a sa sf ofolllolows:s: aSSsu ufbobs sttiittuuttiinngg eeqquuaattiioonn ((88)) iinnttoo EEqquuaattiioonn ((66)),, wee ccaann rreewrrite the production function as follows:lslotiwtuting equation (8) into Equation (6), we can rewriittee tthhee pprroodduuccttiioonn ffuunnccttiioonn aass ffoolllloowss:: 𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑌𝑌𝑌𝑌𝑌𝑌𝑌𝑌𝑖𝑖𝑖𝑖𝑌𝑌𝑌𝑌𝑡𝑡𝑡𝑡𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡== s: 𝛽𝛽𝛽𝛽𝛽𝛽𝛽𝛽𝛽𝛽𝛽𝛽lllnn𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡++ 𝜑𝜑𝜑𝜑 𝜑𝜑𝜑𝜑(l(ln𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 , 𝑙𝑙𝑙𝑙,𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝐾𝐾𝐾𝐾𝐾𝐾𝐾𝐾𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 𝑖𝑖𝑖𝑖)𝑡𝑡𝑡𝑡)+ 𝜀𝜀𝜀𝜀 𝜀𝜀𝜀𝜀𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 (9(9) )𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑌𝑌𝑌𝑌𝑌𝑌𝑌𝑌𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡 =𝛽𝛽𝛽𝛽𝛽𝛽𝛽𝛽llnn𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡 + 𝜑𝜑𝜑𝜑𝜑𝜑𝜑𝜑((llnn𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡 ,, 𝜑𝜑𝜑𝜑(ln𝑀𝑀𝑀𝑀𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡 ,𝑙𝑙𝑙𝑙 𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝐾𝐾𝐾𝐾𝐾𝐾𝐾𝐾𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡))+ 𝜀𝜀𝜀𝜀𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 (9)𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝐾𝐾𝐾𝐾 )+ 𝜀𝜀𝜀𝜀𝜀𝜀𝜀𝜀𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡 𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡 (9) ((99)) WW Where Wh hheeerreree hhe erree 𝜑𝜑𝜑𝜑𝜑𝜑𝜑𝜑𝜑𝜑𝜑𝜑((l(llnn𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 , ,𝑙𝑙𝑙𝑙,𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝐾𝐾𝐾𝐾𝐾𝐾𝐾𝐾𝐾𝐾𝐾𝐾𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡𝑖𝑖𝑖𝑖)𝑡𝑡𝑡𝑡))== ll lnnAA++ 𝛾𝛾𝛾𝛾𝛾𝛾𝛾𝛾 𝛾𝛾𝛾𝛾lllnn𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡++ ∫∫𝜔𝜔𝜔𝜔𝜔𝜔𝜔𝜔𝜔𝜔𝜔𝜔 ( (l(llnn𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 , ,𝑙𝑙𝑙𝑙,𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝐾𝐾𝐾𝐾𝐾𝐾𝐾𝐾𝐾𝐾𝐾𝐾𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡𝑖𝑖𝑖𝑖)𝑡𝑡𝑡𝑡)) (10) ((1(11000))𝜑𝜑𝜑𝜑𝜑𝜑𝜑𝜑((llnn𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡 ,,𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝐾𝐾𝐾𝐾𝐾𝐾𝐾𝐾 𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡)) = llnnAA + 𝛾𝛾𝛾𝛾𝛾𝛾𝛾𝛾llnn𝑀𝑀𝑀𝑀 ) 𝑀𝑀𝑀𝑀𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 + ∫∫𝜔𝜔𝜔𝜔𝜔𝜔𝜔𝜔 ((llnn𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 ,,𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝐾𝐾𝐾𝐾𝐾𝐾𝐾𝐾𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡)) ((1100)) EEEqqququuauaattaitiotioiononn n ( 9 ((9)(9 9)c) ) a cn 𝑖𝑖𝑖𝑖 cc 𝑡𝑡𝑡𝑡 aa nabnn e bbubees e uduus steseoded des 𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 ttottoio m eeasesttsteiti imβma attahtete e 𝛽𝛽𝛽𝛽 𝛽𝛽𝛽𝛽l𝛽𝛽𝛽𝛽abtthothheue er llaclaobabeboofoufiuurcr ir e ccnocotoe,e fefbfffuificictic ienieneonnttt, t, t ,h bbebuu utot t nhnnoeorott t tththhee e oootththheerer r ccapapitiatla l anandd icE𝜑𝜑𝜑𝜑iEnainqn𝜑𝜑𝜑𝜑tp(qteuteliu(retnraalrmtni capital and l𝑀𝑀𝑀𝑀mt ieoeao𝑀𝑀𝑀𝑀dendndi𝑖𝑖𝑖𝑖 di a 𝑡𝑡𝑡𝑡ia,( ta( 𝑙𝑙𝑙𝑙t9ie9,te𝑙𝑙𝑙𝑙n) e𝑙𝑙𝑙𝑙) t 𝐾𝐾𝐾𝐾𝑙𝑙𝑙𝑙 eicic𝐾𝐾𝐾𝐾rninamanp𝑖𝑖𝑖𝑖)np𝑡𝑡𝑡𝑡npeu )uc dutbtobsictsea sneo t tenpurpu topasirasnlearoersapdrald asu mf m toetsofeorte ot ep tereruea rssrsnsu.tsa.t oin .i mboTesabTahethtsthereeerv e sr 𝛽𝛽𝛽𝛽e 𝛽𝛽𝛽𝛽v. edeeTset stdhtshtpi etierimp m oelraladasoatttbuidbtiiomioocuontuncuain r vtr i iovooctcoyfniofto f. ye eo𝛽𝛽𝛽𝛽.f𝛽𝛽𝛽𝛽Aff 𝛽𝛽𝛽𝛽f fiAβnicci ioisnisie teso nh ntrrtehrte,er,re ea ae abrsbsais oumosuointonmtn pa nanabonpbabolroobletltet rale etn tat hthns seiestian in snocaoectsths eheu etrmrh t hecpc eata ippofiuifnttuana lncli stcai atonitnonhddn a t sip𝜑𝜑𝜑𝜑ininrpt(otcerledenror umdt𝑀𝑀𝑀𝑀hcueete 𝑖𝑖𝑖𝑖cd𝑡𝑡𝑡𝑡tiiavtiet y if𝑖𝑖𝑖𝑖n𝑡𝑡𝑡𝑡oplluotws cseu mtphteio nf uins ctihoant id vf iu,iat𝑙𝑙𝑙𝑙nty𝑙𝑙𝑙𝑙ec 𝐾𝐾𝐾𝐾 tfiooiln)lo pcφwuo(tsnlsn sM paa ,M rla𝜑𝜑𝜑𝜑(ln𝑀𝑀𝑀𝑀 , 𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝐾𝐾𝐾𝐾 ) con t art orMpoiltsal asra nfa rokm Krke frooi etrv )tout evcro spn. rtorToclehs enpurornsob.o csbeTsrsevhs s ee.efs d oA . rAec peuscsrctnoticiodromdburasiacdntettiriignovovlngeinytl dy, y .𝜔𝜔𝜔𝜔o,p o rf𝜔𝜔𝜔𝜔Afo n𝑖𝑖𝑖𝑖d𝛽𝛽𝛽𝛽𝑡𝑡𝑡𝑡c𝛽𝛽𝛽𝛽ouatcnhatii iesnvbsr i ebt yirerwme.e awArapsisnortootiroenttntanahean bnebatrl slea ea: s s:s ssuiinmnccpe rved productivity. Another important assueti mo tnthph eei s ftfuhunantc cttiioonn 𝑖𝑖𝑖𝑖 ipmrpoodrutcatn𝑖𝑖𝑖𝑖 𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡 𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡ivt iatsys ufoml𝑖𝑖𝑖𝑖l𝑡𝑡𝑡𝑡potwiosn a i sM thaartk porvo pdruocctievsist.y Afocllcoowrds ian Markov p 𝑖𝑖𝑖𝑖r𝑡𝑡𝑡𝑡ocess. Acco tion is that pr𝜔𝜔𝜔𝜔odu=cti𝐸𝐸𝐸𝐸vi[t𝜔𝜔𝜔𝜔y 𝑡𝑡𝑡𝑡fo−llo1w] +s aξ Markov process. Accordginlyg,l 𝜔𝜔𝜔𝜔y,𝑖𝑖𝑖𝑖 𝑡𝑡𝑡𝑡𝜔𝜔𝜔𝜔canc abne wber iwtte rnd iansg: ly, ω𝜔𝜔𝜔𝜔 c𝑖𝑖𝑖𝑖a𝑡𝑡𝑡𝑡it𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 =n b𝐸𝐸𝐸𝐸e[ w𝜔𝜔𝜔𝜔r𝑡𝑡𝑡𝑡itt−en1 a]s+: ξ 𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 ritten as: 𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 (1 (11)1) 𝜔𝜔𝜔𝜔𝜔𝜔𝜔𝜔S𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡u==bst𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸it[[u𝜔𝜔𝜔𝜔𝜔𝜔𝜔𝜔ti𝑡𝑡𝑡𝑡 − 1122 S u𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡bstituting𝑡𝑡𝑡𝑡n g−E E 11q]]+ ξ𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 12 (11) qua ut+aiotiξno𝑖𝑖𝑖𝑖 𝑡𝑡𝑡𝑡n1 01 0in itnot oE Equqautaiot ino n(6 ()6, )w, we eg11 2eg2t e : t: (11) (11) SSS uu𝑙𝑙𝑙𝑙ub𝑙𝑙𝑙𝑙bbs𝑌𝑌𝑌𝑌sstitttiiuttuu=ttitniinlngngg e A EqEu+qqauu ta𝛼𝛼𝛼𝛼iato𝑙𝑙𝑙𝑙itn𝑙𝑙𝑙𝑙oio n𝐾𝐾𝐾𝐾1n 01 10in+0 it noi𝛽𝛽𝛽𝛽nt eotql 𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑌𝑌𝑌𝑌 𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡= lnA + 𝛼𝛼𝛼𝛼𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝐾𝐾𝐾𝐾 𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡+ 𝛽𝛽𝛽𝛽 lnon Eu Eqa𝐿𝐿𝐿𝐿tuqiaoutn+aio t(in6 o𝛾𝛾𝛾𝛾 )n(,l6 wn()6,e𝑀𝑀𝑀𝑀 )w g,eet :g et: 𝐿𝐿𝐿𝐿 𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡+ 𝛾𝛾𝛾𝛾 ln𝑀𝑀𝑀𝑀 𝑖𝑖𝑖𝑖 𝑡𝑡𝑡𝑡+w+e𝐸𝐸𝐸𝐸 g𝐸𝐸𝐸𝐸[e𝜔𝜔𝜔𝜔[t𝜔𝜔𝜔𝜔:𝑡𝑡𝑡𝑡 𝑡𝑡𝑡𝑡−−1]1+] +ξ ξ𝑖𝑖𝑖𝑖+𝑡𝑡𝑡𝑡+ 𝜀𝜀𝜀𝜀 𝜀𝜀𝜀𝜀𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 (1(21)2) 𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑌𝑌𝑌𝑌𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 = lnA + 𝛼𝛼𝛼𝛼𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝐾𝐾𝐾𝐾 𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡+ 𝛽𝛽𝛽𝛽 ln 𝐿𝐿𝐿𝐿 𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡+ 𝛾𝛾𝛾𝛾 ln𝑀𝑀𝑀𝑀 𝑖𝑖𝑖𝑖+𝑡𝑡𝑡𝑡 𝐸𝐸𝐸𝐸[𝜔𝜔𝜔𝜔𝑡𝑡𝑡𝑡 − 1] + ξ +𝑖𝑖𝑖𝑖 𝑡𝑡𝑡𝑡𝜀𝜀𝜀𝜀 𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 (12) W𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙W𝑌𝑌𝑌𝑌e𝑖𝑖𝑖𝑖 e𝑡𝑡𝑡𝑡c ac=na nlwn wrAirte+itee 𝛼𝛼𝛼𝛼qe𝑙𝑙𝑙𝑙uq𝑙𝑙𝑙𝑙au𝐾𝐾𝐾𝐾ta 𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡tio+n 𝛽𝛽𝛽𝛽(1l2n) 𝑖𝑖𝑖𝑖𝐿𝐿𝐿𝐿𝑡𝑡𝑡𝑡as 𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 (12)𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 𝑖𝑖𝑖𝑖 Wee c caann w wrirtiet eeqeuqautaiotinoio n(n1 (2 1()12 a2)s )a: sa:s : 𝑡𝑡𝑡𝑡: + 𝛾𝛾𝛾𝛾 ln𝑀𝑀𝑀𝑀𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 + 𝐸𝐸𝐸𝐸[𝜔𝜔𝜔𝜔𝑡𝑡𝑡𝑡 − 1] + ξ𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡+ 𝜀𝜀𝜀𝜀𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 (12) W ∗lnl𝑌𝑌𝑌𝑌ne 𝑌𝑌𝑌𝑌∗c𝑖𝑖𝑖𝑖a𝑡𝑡𝑡𝑡=n= 𝛼𝛼𝛼𝛼w𝑙𝑙𝑙𝑙𝛼𝛼𝛼𝛼r𝑙𝑙𝑙𝑙i𝑙𝑙𝑙𝑙t𝐾𝐾𝐾𝐾𝑙𝑙𝑙𝑙e𝐾𝐾𝐾𝐾e𝑖𝑖𝑖𝑖q𝑡𝑡𝑡𝑡+u+a 𝛾𝛾𝛾𝛾t i𝛾𝛾𝛾𝛾olnnln 𝑀𝑀𝑀𝑀(1𝑀𝑀𝑀𝑀2𝑖𝑖𝑖𝑖)𝑡𝑡𝑡𝑡+ a+s ∗ 𝐸𝐸𝐸𝐸:𝐸𝐸𝐸𝐸 [𝜔𝜔𝜔𝜔[𝜔𝜔𝜔𝜔𝑡𝑡𝑡𝑡 𝑡𝑡𝑡𝑡−−1]1+] +η∗η𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 (1(13)ln𝑌𝑌𝑌𝑌𝑖𝑖𝑖𝑖∗𝑡𝑡𝑡𝑡 = 𝛼𝛼𝛼𝛼𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝐾𝐾𝐾𝐾 𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 ∗ (13) 3)𝑖𝑖𝑖𝑖∗𝑡𝑡𝑡𝑡 𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 + 𝛾𝛾𝛾𝛾 ln𝑀𝑀𝑀𝑀𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 + 𝐸𝐸𝐸𝐸[𝜔𝜔𝜔𝜔𝑡𝑡𝑡𝑡 − 1] + η 𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 (13) lnW𝑌𝑌𝑌𝑌 ∗Whhe𝑖𝑖𝑖𝑖e𝑡𝑡𝑡𝑡hrree=e r e𝛼𝛼𝛼𝛼 𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝐾𝐾𝐾𝐾𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 + 𝛾𝛾𝛾𝛾 ln𝑀𝑀𝑀𝑀𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 + 𝐸𝐸𝐸𝐸[𝜔𝜔𝜔𝜔𝑡𝑡𝑡𝑡 − 1] + η𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 (13)Where lW nl𝑌𝑌𝑌𝑌nh𝑌𝑌𝑌𝑌e ∗ ∗ ∗∗ 𝑖𝑖𝑖𝑖 r𝑡𝑡𝑡𝑡=e =𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑌𝑌𝑌𝑌𝑙𝑙𝑙𝑙𝑌𝑌𝑌𝑌𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡−− 𝛽𝛽𝛽𝛽 𝛽𝛽𝛽𝛽lnln𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝑖𝑖𝑖𝑖a𝑡𝑡𝑡𝑡nadn dη ∗η𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡==ξ ξ𝑖𝑖𝑖𝑖+𝑡𝑡𝑡𝑡+ln𝑌𝑌𝑌𝑌𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 = 𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑌𝑌𝑌𝑌𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡− 𝛽𝛽𝛽𝛽 ln 𝐿𝐿𝐿𝐿 𝑖𝑖𝑖𝑖a𝑡𝑡𝑡𝑡nd η∗ 𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡= ξ +𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 𝜀𝜀𝜀𝜀 𝜀𝜀𝜀𝜀 𝜀𝜀𝜀𝜀𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 (14) (14) 𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 (14) (14) ∗ IlnnI 𝑌𝑌𝑌𝑌nt𝑖𝑖𝑖𝑖 h𝑡𝑡𝑡𝑡teh= ee 𝑙𝑙𝑙𝑙qe𝑙𝑙𝑙𝑙uq𝑌𝑌𝑌𝑌au𝑖𝑖𝑖𝑖t𝑡𝑡𝑡𝑡aiot−ino n 𝛽𝛽𝛽𝛽( 1(l41n)4,𝐿𝐿𝐿𝐿 ),t𝑖𝑖𝑖𝑖 𝑡𝑡𝑡𝑡htaehn edt ∗ e tηrem𝑖𝑖𝑖𝑖r𝑡𝑡𝑡𝑡m ∗η= ∗ηξ ∗ 𝑖𝑖𝑖𝑖w𝑡𝑡𝑡𝑡𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡wa+sa s𝜀𝜀𝜀𝜀 𝑖𝑖𝑖𝑖a𝑡𝑡𝑡𝑡ssumed to be uncorrelated with𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝐾𝐾𝐾𝐾 , but this g(Ind othees enqoutahtioolnd (f1o4r )t,h teh ec atseerm o fηln𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 𝑖𝑖𝑖𝑖w𝑀𝑀𝑀𝑀𝑡𝑡𝑡𝑡 as . aTsashsuesmrueemfde tdo tboe buen cuonrcroelrarteelda tewdi thw𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙it𝐾𝐾𝐾𝐾h𝑙𝑙𝑙𝑙 𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡𝑙𝑙𝑙𝑙, 𝐾𝐾𝐾𝐾b𝑖𝑖𝑖𝑖u𝑡𝑡𝑡𝑡 ,t bthuits tgheins egraeln 1een4re)ly a rla∗ly ll y Idnou entshc eon roertteqhluaoatletldiddo f nfowo rr(i tt1hht4he )le n,c cat𝑀𝑀𝑀𝑀hasse e o tofelrfnmlnw𝑀𝑀𝑀𝑀 η𝑀𝑀𝑀𝑀 ∗ a𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡s𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡. 𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 𝑖𝑖𝑖𝑖 𝑡𝑡𝑡𝑡 wuT. sah9Tesedh r.ae rsfToeshfuroeem,r oer,e t,o t of afcaicliitlaittaet e aclacluclualtaiotino,n t,h teh ea sassusmumptpiotino nth ∗that η is n te,o dt hftaeoc ielbqietua tuen ccaolrcruellaattieodn , wthieth a𝑙𝑙𝑙𝑙s𝑙𝑙𝑙𝑙s𝐾𝐾𝐾𝐾um𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 ,p tbiount tthaist ηgeanti esη r ∗a 𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡lliys duonecos rnreo 𝑖𝑖𝑖𝑖(𝑡𝑡𝑡𝑡−1)of thel tacthteoodel fd wf ificitothihre ln tnlhtns𝑀𝑀𝑀𝑀e 𝑀𝑀𝑀𝑀 αc𝑖𝑖𝑖𝑖(𝑖𝑖𝑖𝑖a 𝑡𝑡𝑡𝑡(−s𝑡𝑡𝑡𝑡ae−1n )1dow)fwalnsa su𝑀𝑀𝑀𝑀 su𝑖𝑖𝑖𝑖e𝑡𝑡𝑡𝑡sd.e .dT T.h hTeerhnee,f not,hr eet,h etqo ue fqaatucioailnt ation (13) will produce consistent estimates ii to(an1te 3( )1c wa3l)ic luwl lpialrtloi odpnuro,c deth uceco ean sscsiosutnmesnipts tteiosntnit m tehasateitm sη a∗𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡teiss uonfp crteohvreri eocluoaste effsdffit icecwipieei.nt nhtHst s len αnα 𝑀𝑀𝑀𝑀c aea𝑖𝑖𝑖𝑖n,(n d𝑡𝑡𝑡𝑡o−d n1γ ).γ .γcweF . auF ruFucso tuhrte rhtheseremdr.mo rmore, p Troeh,r epn, a, prtahmr aarematemert eeβtrer βe equat iowβn a ws waosbas(13) toa wib onit beatained fromlld inp efrrodod mufrc oeem qcu oae ntqi eouqnuation (9) in a sisat tei(on9nt) ei(sn9t i)am iant eas opfrf euvtnhicoetu iosc n os etitfenfp pie.c. q iHeuneaetntsnic oceαne, ,( oa6onn)ncd aec r eγec . o cdnoFesnuties nitsseitnsettn eetn stet ismetstirthermore,i ampt measta etosearamf s o pfoa frp aparameters α, β, and γ of the production eter amβr aemwtearesst e ffuncttiion iin eeqquuaattiioonn ( 6(6) )a arer ed deteetremrmirnmiendien,d ew,d ew, wwe eewr we raeebr elae ba tlboel ect ot nocs ocisnotsneins αiross,b teβtαna,, it nlaβyen, dd e a sfγtnir dmoo mfaγ t tehoe teqfh uetpah rfteoiio rdmpnur c-otl(ied9ovu)n ec iltn iTo Fna P praesv iao urse ssidteupa.l . HFernocme, tohne cde isccounsssiisotnen itt ’ess teivmidateenst othf apt atrhaem Letestrtlesy n αtels,y t iβme,s ataitmen dtah teγ ftoihrfem ft-hilreemv ep-llr eTovdFeuPl c TtiFoPn afauss n aac tirroeenssii ddinuu aaellq.. uFFartroiomomn t(ht6he)e a drdiesic sducsussisoi evinsohn and Petrin (2003) method requires variables, such as theete orm no niin t’eistd ’s,e vweidev eiwdnet that the Levinsohn and Petrin (2003) method rreeqquuiirreess utput, labour, e nrceta pathibtalaetl , ttiohn etc eoLrnmesveisidntiesanottehl ynin epasuntitdms (Parteaetw rtih nme (af2tier0mr0ia3-ll)es )vm.ele tThFoPd as a resivvdaaurriaiaalb.b lleFesrs,o ,s msuuc chthh ae as sdt htihesc eou uostuspituoptnu, tl,ai tbl’aosb uoeru,v crid,a epcnaitpta iltt,h ailan,tt ientrhmteeer mdLieaedtveii anintsepo uihntnsp u(artnaswd ( r mPaweatt remirniaa tl(es2r)0i.a0l3s)). method rAeAAddqddduiidinnriggen sgin vint intteearrrmermedmiaeebddliieaatsieate mt,e m smuacatehar itaaelsrsi attholse t thoe u tchtopebu cbto-,D blabob-uDoglouaurs,g fcluaanspc function presented i equation (3) gives:terials to the cobb-Douglas futinitocantli, o pinrne tpseerrenmsteeednd tieinad et eiqn ui aentqpiouunat ts(i 3o()nr a g(wi3v) e msg:iavteesr:ials). A𝑌𝑌𝑌𝑌𝑖𝑖𝑖𝑖𝑌𝑌𝑌𝑌d𝑡𝑡𝑡𝑡𝑖𝑖𝑖𝑖d𝑡𝑡𝑡𝑡=in=g𝐴𝐴𝐴𝐴 𝐾𝐾𝐾𝐾𝐴𝐴𝐴𝐴in𝐾𝐾𝐾𝐾 𝛼𝛼𝛼𝛼t𝛼𝛼𝛼𝛼e1 𝛼𝛼𝛼𝛼 𝛼𝛼𝛼𝛼 𝛼𝛼𝛼𝛼 𝑖𝑖𝑖𝑖1r𝑡𝑡𝑡𝑡𝐿𝐿𝐿𝐿m 1𝛼𝛼𝛼𝛼 2 3𝛼𝛼𝛼𝛼𝐿𝐿𝐿𝐿2e 𝛼𝛼𝛼𝛼32𝑖𝑖𝑖𝑖𝑀𝑀𝑀𝑀d𝑡𝑡𝑡𝑡ia𝑀𝑀𝑀𝑀𝛼𝛼𝛼𝛼te3𝑖𝑖𝑖𝑖 𝑡𝑡𝑡𝑡materials to the cobb-Douglas function presented in equation (3)( g1i5ves:𝑌𝑌𝑌𝑌𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 = 𝐴𝐴𝐴𝐴𝐾𝐾𝐾𝐾𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡 𝐿𝐿𝐿𝐿 𝑀𝑀𝑀𝑀 )(1(𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡 𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 5 1)5) E𝑌𝑌𝑌𝑌E𝑖𝑖𝑖𝑖Eqq𝑡𝑡𝑡𝑡uuq=aauttaii𝐴𝐴𝐴𝐴ooti𝐾𝐾𝐾𝐾nno n 𝛼𝛼𝛼𝛼( 1 𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡(1 1(5𝐿𝐿𝐿𝐿51) 𝛼𝛼𝛼𝛼 𝑖𝑖𝑖𝑖)5 2 𝑡𝑡𝑡𝑡 p) 𝛼𝛼𝛼𝛼 p𝑀𝑀𝑀𝑀r repe𝑖𝑖𝑖𝑖sr𝑡𝑡𝑡𝑡se 3 ensntestns t hsth et ehs eps epscepicfeiifciiafctiaicotainot ino fno tfoh efth tseht aesn tsdatnaardndad rCadro dCb bCo-boDbbo-buD-gDolauosgu lpgarlsoa sdp urpcrtoiodnu cfutino(c1nt5 iof)unn, catniodn , and ggiigvvieevsse saa arr eerllaeatltiaiootninossnhhsipihp i oduction function, and Einiqtneutraemtrimeodnei da(ti1ea5 teg) opgorodeoss de𝑀𝑀𝑀𝑀sn bpbe etbwtewetweenee neo nuo tuoptuptup 𝑦𝑦𝑦𝑦tu 𝑖𝑖𝑖𝑖𝑦𝑦𝑦𝑦t, 𝑦𝑦𝑦𝑦a, 𝑖𝑖𝑖𝑖n,ad na dtnh det h tfeha ecf tofarsc tofr sp roofd upcrotidounc ctiaopni tacla 𝐾𝐾𝐾𝐾pit, ala 𝐾𝐾𝐾𝐾bo,r 𝐿𝐿𝐿𝐿lab, oarn 𝐿𝐿𝐿𝐿d , and t𝑀𝑀𝑀𝑀sf otrhf oea r s ppaa erptciaicfruitcliacarut ilfoai𝑖𝑖𝑖𝑖nrm fo ifri mt(hS eia l(siSotalaana dcaatnorddr s S Coeokf beprb,r- oD2d0ou1uc1gt)il 𝑖𝑖𝑖𝑖 𝑖𝑖𝑖𝑖 𝑖𝑖𝑖𝑖 𝑖𝑖𝑖𝑖 .oa Insn tpcerarompdieutadclit ai𝐾𝐾𝐾𝐾toe𝑖𝑖𝑖𝑖n , intermediate goods 𝑀𝑀𝑀𝑀𝑖𝑖𝑖𝑖 liola and Seker, 2011). Intermed gfloiauaobntdeocs rt gi ao𝐿𝐿𝐿𝐿orn𝑖𝑖𝑖𝑖oe,, d aasnn addr e giniivcnlecusldu eadd e rdien lia noti roodnerdsr hetirop t bef 𝑖𝑖𝑖𝑖 roe𝑖𝑖𝑖𝑖 drtueowcdree u aece nenp daoeornutgidtceponuugeltaei t𝑦𝑦𝑦𝑦rny 𝑖𝑖𝑖𝑖e f,ibi traeymnt wdb ie ett(hwnSe ae tlehfinaoec laittnho paersun itdnso pfSa uneptkdsr oe tardh,nu ed2c e0titr1ohr1on)r .c tIeanrpmtiet.ar mlI t𝐾𝐾𝐾𝐾 ecd𝑖𝑖𝑖𝑖o,i arlrtaeebc otgsro fo𝐿𝐿𝐿𝐿od𝑖𝑖𝑖𝑖r,s aanrde iipnnopctteoleurtnmedtnieeaddl icianote ror egrldoaeotirdo snto 𝑀𝑀𝑀𝑀 bre𝑖𝑖𝑖𝑖etfdwouerc eean epthnaedrt oiicngupelnuatersi tfayirn mbd e ttihw e(eS euannli otbhlasee arivnnedpd u Stfseir kmaensrd, s 2pthe ee rerrorro rt etremrm. .I t Itc ocrorre0e1ci1f)ic. Icnhtaerramcteedriiasttiec sg or(etohcdet csts for s aforer pienorecrtroleruron drttie a tila lc ocrorrerlealtaiotino nb ebtewtweeeen the inputs and the unobserved firms specific characteristics (the trdem rim)n. )To. hrTdishe ircs a tnoca brne d beuexc peelx anepi nltaedhidoen geiinedn n pcieuinirtt csycu imarbcnesudtamw nteshcteeans nu cwtnhehoseeb rwisene hprfeviurretmesd s fa ifmnrimrdma syts hm res apsepyero crrnioefdris cpt ooec rnhlmdaar .rg taoIect t lpecaororisgrsirteteii cvcpseto s s( tfhoer eprorprotoredornud t itive tcuietacirlvmt iicvt)yoi. t rsyTrh ehsolhiacsotk iccso kanbns y bb bueyest i wunesgexi enpmngla o itmrnheeeo id rnei pni uinpnt uspct ui(srL tcsaeu nv(mLdin sesttvohaihnenn csu eoanshn ondwb Pahsneedtrrrve iPen fde,i t 2rrmf0iin0rsm,3 2m)s.0 a0syp3 e)r.ce isfpico ncdh atora lcatregries tpicoss i(titvhee perrroodru tcetrivmit)y. Tshhoisc kcsa nb yb ue seinxgp lmaionreed i ninp uctisr c(uLmevsitnasnochens wanhde rPee tfriirnm, s2 0m0a3y). respond to large positive All va AprAlol ldlvu avc ratiiarvibailtbeysl e riabless i sh n i no int c he tk htseh Cbe yoC bubos-biDnbgo-D umgolouarsg specification are expressed as logarithms, and the resulting coceofefficfiiceinetns tso fo ef acehac hoCf o obtfhb e-t hDieno puiungtplsa els ai nss ppsuptcesic f(iiLfciaectvaiiotninos no hraenr eae nxedpx rpPeresestsresidne ,d a2 a0s0 3lo).g arithms, and the resulting urtesp rreespernets etnhte tehlea steilcaitsyti coift yl aobfo ulsar ,b loocgaupar,ri tiatchla mpainstd,a aindte rtmhee drieastue lting cAmomlaelt faevftrieaicarriilieasan lbtsts of l and intermediate ol et posr poindroe utadchcuteihco tCnioo o(fne bq tb(hue-eaDq tuioioanuntpig ou1lan6tss) 1. rs6ep)p.e rceisfiecnatt itohne aerlea setxicpirteys soefd laasb oluorg,a rciathpmitasl, aanndd tihnete rrmeseudltiiantge mcoaetfefriicailesn tto𝑦𝑦𝑦𝑦 = 𝛼𝛼𝛼𝛼 + 𝛼𝛼𝛼𝛼s p 𝐿𝐿𝐿𝐿o rfo deuactio+ 𝛼𝛼𝛼𝛼ch𝐾𝐾𝐾𝐾 o n +f ( equa𝛼𝛼𝛼𝛼the𝑀𝑀𝑀𝑀 i tnio+p nu 𝜀𝜀𝜀𝜀t 1s6 )r.e present the elasticity of labour, capital and intermediate m𝑖𝑖𝑖𝑖𝑦𝑦𝑦𝑦ate=ria𝛼𝛼𝛼𝛼ls+ to1 𝑖𝑖𝑖𝑖 2 𝑖𝑖𝑖𝑖𝑦𝑦𝑦𝑦 =𝑖𝑖𝑖𝑖 𝛼𝛼𝛼𝛼 + 𝛼𝛼𝛼𝛼𝛼𝛼𝛼𝛼 p𝐿𝐿𝐿𝐿1r𝐿𝐿𝐿𝐿o+𝑖𝑖𝑖𝑖d+u 𝛼𝛼𝛼𝛼c t𝛼𝛼𝛼𝛼io𝐾𝐾𝐾𝐾2n𝐾𝐾𝐾𝐾 +𝑖𝑖𝑖𝑖(e+q 3u𝛼𝛼𝛼𝛼a𝑖𝑖𝑖𝑖𝛼𝛼𝛼𝛼 𝑀𝑀𝑀𝑀3ti𝑀𝑀𝑀𝑀o+n𝑖𝑖𝑖𝑖 +1 𝑖𝑖𝑖𝑖 𝜀𝜀𝜀𝜀6𝜀𝜀𝜀𝜀). (16) 𝑖𝑖𝑖𝑖 (1(61)6) 𝑇𝑇𝑇𝑇𝑖𝑖𝑖𝑖𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑖𝑖𝑖𝑖 = 𝜀𝜀𝜀𝜀 = 1 𝑦𝑦𝑦𝑦𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 𝑖𝑖𝑖𝑖 − 𝛼𝛼𝛼𝛼� 2−𝑖𝑖𝑖𝑖𝛼𝛼𝛼𝛼�1𝐿𝐿𝐿𝐿 3 𝑖𝑖𝑖𝑖 − 𝛼𝛼𝛼𝛼 𝑖𝑖𝑖𝑖�2𝐾𝐾𝐾𝐾 𝑖𝑖𝑖𝑖 𝑦𝑦𝑦𝑦𝑇𝑇𝑇𝑇=𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝛼𝛼𝛼𝛼=+ 𝛼𝛼𝛼𝛼𝜀𝜀𝜀𝜀 𝐿𝐿𝐿𝐿= +𝑦𝑦𝑦𝑦 𝛼𝛼𝛼𝛼− 𝑖𝑖𝑖𝑖 − 𝛼𝛼𝛼𝛼�3𝑀𝑀𝑀𝑀𝑖𝑖𝑖𝑖 (17) 𝑇𝑇𝑇𝑇𝑖𝑖𝑖𝑖𝑇𝑇𝑇𝑇 𝑖𝑖𝑖𝑖 1𝑖𝑖𝑖𝑖 𝑖𝑖𝑖𝑖 𝑖𝑖𝑖𝑖Fin𝑇𝑇𝑇𝑇a𝑖𝑖𝑖𝑖lly=, e𝜀𝜀𝜀𝜀q𝑖𝑖𝑖𝑖u=ati𝑦𝑦𝑦𝑦o𝑖𝑖𝑖𝑖n −(1𝛼𝛼𝛼𝛼� 2𝛼𝛼𝛼𝛼𝐾𝐾𝐾𝐾�−𝑖𝑖𝑖𝑖−𝛼𝛼𝛼𝛼+�𝛼𝛼𝛼𝛼�𝛼𝛼𝛼𝛼𝐿𝐿𝐿𝐿13𝐿𝐿𝐿𝐿𝑀𝑀𝑀𝑀−𝑖𝑖𝑖𝑖 −𝑖𝑖𝑖𝑖 𝛼𝛼𝛼𝛼+� 𝛼𝛼𝛼𝛼�𝜀𝜀𝜀𝜀𝐾𝐾𝐾𝐾2𝑖𝑖𝑖𝑖𝐾𝐾𝐾𝐾𝑖𝑖𝑖𝑖 − 𝛼𝛼𝛼𝛼�3𝑀𝑀𝑀𝑀𝑖𝑖𝑖𝑖 (16) (17) 7), de1mo𝑖𝑖𝑖𝑖nstrat2es 𝑖𝑖𝑖𝑖th−at 𝛼𝛼𝛼𝛼�in3 𝑀𝑀𝑀𝑀th𝑖𝑖𝑖𝑖e Cobb-Douglas production function t(o1t7al) factor F𝑇𝑇𝑇𝑇pirF𝑇𝑇𝑇𝑇noi𝑇𝑇𝑇𝑇adn𝑖𝑖𝑖𝑖lualc=yllt,yi v,e𝜀𝜀𝜀𝜀 iqt𝑖𝑖𝑖𝑖eyuq= a𝑇𝑇𝑇𝑇uta𝑇𝑇𝑇𝑇i𝑦𝑦𝑦𝑦ot𝑇𝑇𝑇𝑇𝑖𝑖𝑖𝑖ino− no( f1𝛼𝛼𝛼𝛼�( 71a−) 7,p )𝛼𝛼𝛼𝛼a,d� r1edt𝐿𝐿𝐿𝐿miec𝑖𝑖𝑖𝑖muo−lnoasrn t 𝛼𝛼𝛼𝛼s�rfta2irrt𝐾𝐾𝐾𝐾amet𝑖𝑖𝑖𝑖se −cst aht nah𝛼𝛼𝛼𝛼� tab 3tie𝑀𝑀𝑀𝑀 n i cn𝑖𝑖𝑖𝑖ta hltech ueCl aCotebodbb -baDs-D otuhoegu lgarelsas sipd rpuoradol udtceutrcimotin o𝑖𝑖𝑖𝑖 o nff uftnuhcent cip(ot1irnoo7 nd)t uotctoattiloaprod lnf a fcatcotro r pFfurionndcatlui uc locynt,i v tsiipvteyity equc a𝑇𝑇𝑇𝑇itfi𝑇𝑇𝑇𝑇i 𝑇𝑇𝑇𝑇e𝑇𝑇𝑇𝑇 function specoif𝑇𝑇𝑇𝑇 d 𝑇𝑇𝑇𝑇 of a particular firm can be calculated as the residual term of the production nie𝑖𝑖𝑖𝑖 i(no𝑖𝑖𝑖𝑖1 fe7 q)a,u pation (16). function specified din i ne qeuq daueramttiicoounnlsa t(rr1 a6ftie)r.sm t hcaatn i nb et hcea lCcuolbabte-Dd oausg tlhaes prersoidduucatli otenr mfu nocft itohne tportoadl ufcatcitoonr productivity 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑖𝑖𝑖𝑖 of a paatritoicnu (l1ar6 )f.irm can be calculated as the residual term of the production function specified in equation (16). 13 13 13 13 𝜑𝜑𝜑𝜑(ln𝑀𝑀𝑀𝑀𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 , 𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝐾𝐾𝐾𝐾𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡) controls for unobserved productivity. Another important assumption is that p (𝜑𝜑𝜑𝜑𝜑𝜑𝜑𝜑r(oldlnnu𝑀𝑀𝑀𝑀c𝑀𝑀𝑀𝑀ti𝑖𝑖𝑖𝑖v𝑡𝑡𝑡𝑡 i ,t,𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 𝑙𝑙𝑙𝑙y 𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙 𝑙𝑙𝑙𝑙f𝐾𝐾𝐾𝐾o𝐾𝐾𝐾𝐾𝑖𝑖𝑖𝑖l𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡l𝑡𝑡𝑡𝑡)o )wconcos nat tMroalsrolsr k fofovr punobserved productivity. Anor urnoocbessesr. vAedcc oprrdoidnugcltyi,v 𝜔𝜔𝜔𝜔ity𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡. cAann boet hwerri ttiemnp aosrtant assumption is that productivity follows a Markov process. Accordingly, 𝜔𝜔𝜔𝜔 caont hbeer w irmitpteonr taa : sn:t assumption is that 𝜔𝜔𝜔𝜔prod=uc𝐸𝐸𝐸𝐸ti 𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 [ v𝜔𝜔𝜔𝜔it𝑡𝑡𝑡𝑡y −fo1ll]ow+sξ a Markov process. Accordingly, 𝜔𝜔𝜔𝜔𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 can be written as: 𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 (11) 𝜔𝜔𝜔𝜔𝜔𝜔𝜔𝜔𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 = 𝐸𝐸𝐸𝐸[𝜔𝜔𝜔𝜔𝑡𝑡𝑡𝑡 −Su𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡bs=tit𝐸𝐸𝐸𝐸ut[i𝜔𝜔𝜔𝜔ng𝑡𝑡𝑡𝑡 E−q1 1 u] ] + ξ𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 ( at+ioξn𝑖𝑖𝑖𝑖 𝑡𝑡𝑡𝑡10 into Equation (6), we get: (11 1)1) SSuubbssttiittuuttiinngg EEqquuaattiioonn 10 in𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑌𝑌𝑌𝑌 = lnA + 𝛼𝛼𝛼𝛼𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝐾𝐾𝐾𝐾 +10𝛽𝛽𝛽𝛽 inlnto to E Eqquuaatitoionn ( 6(6),) ,w wee g geet:t : 𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑌𝑌𝑌𝑌𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 𝐿𝐿𝐿𝐿𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 + 𝛾𝛾𝛾𝛾 ln𝑀𝑀𝑀𝑀𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 + 𝐸𝐸𝐸𝐸[𝜔𝜔𝜔𝜔𝑡𝑡𝑡𝑡 − 1] + ξ𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡+ 𝜀𝜀𝜀𝜀𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 (12) 𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑌𝑌𝑌𝑌𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 = lWe 𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡ca=n wln nAA++ 𝛼𝛼𝛼𝛼 𝛼𝛼𝛼𝛼𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝐾𝐾𝐾𝐾𝐾𝐾𝐾𝐾𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡++𝛽𝛽𝛽𝛽𝛽𝛽𝛽𝛽lnln𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 + 𝛾𝛾𝛾𝛾 ln𝑀𝑀𝑀𝑀𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 + 𝐸𝐸𝐸𝐸[𝜔𝜔𝜔𝜔𝑡𝑡𝑡𝑡 − 1] + ξ𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡+rite equatio𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡n (12) as:𝑖𝑖𝑖𝑖 𝑡𝑡𝑡𝑡 + 𝛾𝛾𝛾𝛾 ln𝑀𝑀𝑀𝑀𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 + 𝐸𝐸𝐸𝐸[𝜔𝜔𝜔𝜔𝑡𝑡𝑡𝑡 − 1] + ξ𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡+ 𝜀𝜀𝜀𝜀 𝜀𝜀𝜀𝜀𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 (1(21)2)𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 EWffecet ocfa bnu swinersist eenevqiruonamtieonnt o (n1 p2ro) dausc:ti vity of informal manufacturing enterprises in Kenya lWn𝑌𝑌𝑌𝑌e ∗ca=n 𝛼𝛼𝛼𝛼w𝑙𝑙𝑙𝑙r𝑙𝑙𝑙𝑙it𝐾𝐾𝐾𝐾e eq+ua 𝛾𝛾𝛾𝛾tilo𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡∗ 𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 n n𝑀𝑀𝑀𝑀 (12) as:𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 + 𝐸𝐸𝐸𝐸[ 𝜔𝜔𝜔𝜔𝑡𝑡𝑡𝑡 − 1] + η∗ ln𝑌𝑌𝑌𝑌 = 𝛼𝛼𝛼𝛼𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝐾𝐾𝐾𝐾 + 𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡∗ (13) ln𝑌𝑌𝑌𝑌𝑖𝑖𝑖𝑖∗𝑡𝑡𝑡𝑡 = 𝛼𝛼𝛼𝛼𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝐾𝐾𝐾𝐾 𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡+ 𝛾𝛾𝛾𝛾 𝛾𝛾𝛾𝛾llnn𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 +Wh + 𝐸𝐸𝐸𝐸 𝐸𝐸𝐸𝐸[[𝜔𝜔𝜔𝜔𝜔𝜔𝜔𝜔𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡−−11]]++ηη∗𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 (1(31)3) In e𝑖𝑖𝑖𝑖eq𝑡𝑡𝑡𝑡ruea tion (1𝑖𝑖𝑖𝑖4𝑡𝑡𝑡𝑡), the term𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 η*it was assumed to 𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 be uncorrelated with lnK Where it , but this gWenhe∗errael ly does not hold for th∗e case of lnMit. Therefore, to facilitate calculation, tlhnlne 𝑌𝑌𝑌𝑌 𝑌𝑌𝑌𝑌 𝑖𝑖𝑖𝑖a𝑡𝑡𝑡𝑡∗∗s =sum𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙p𝑌𝑌𝑌𝑌t𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡= 𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑌𝑌𝑌𝑌io −n 𝛽𝛽𝛽𝛽 ln− th 𝛽𝛽𝛽𝛽atl nη 𝐿𝐿𝐿𝐿*𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 aisn ud η𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 = ξ𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡+𝐿𝐿𝐿𝐿it andn cη∗o∗rr=elaξted 𝜀𝜀𝜀𝜀 + w 𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 𝜀𝜀𝜀𝜀ith lnMi(t-1) was used. Then, the equation (14) 𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 (14) (Il1nn3 𝑌𝑌𝑌𝑌)t𝑖𝑖𝑖𝑖h 𝑡𝑡𝑡𝑡we= ilelq 𝑙𝑙𝑙𝑙p𝑙𝑙𝑙𝑙ur𝑌𝑌𝑌𝑌ao𝑖𝑖𝑖𝑖t𝑡𝑡𝑡𝑡diou−nc e (𝛽𝛽𝛽𝛽 1c4lon)n,𝐿𝐿𝐿𝐿 sti𝑖𝑖𝑖𝑖h𝑡𝑡𝑡𝑡sateen ntdet rηem𝑖𝑖𝑖𝑖s𝑡𝑡𝑡𝑡 tηi=m∗ aξw𝑖𝑖𝑖𝑖t𝑡𝑡𝑡𝑡ae+ss oa𝜀𝜀𝜀𝜀fs𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 th𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 sume ecdo etfofi cbieen utsn cαo rarnedla tγe.d F wuritthhe𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙rm𝐾𝐾𝐾𝐾 (14) 𝑖𝑖𝑖𝑖o𝑡𝑡𝑡𝑡r, eb, ut this generally pdIInaonr e atsthmh ene eo eteteqqrhuu oaβalt dtiwio ofannos r (o (1t1bh44te)a), ,ic ntathehsedee tfoetrefrorlmmn 𝑀𝑀𝑀𝑀η eη∗q ∗ 𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡wu.w aaTtasihs𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 oe anarse ss(fsu9oum)rm eien,ed td aot otpfo arb ecbevile iiuo tanutncse oc scortraererlplcea.ul taTletahdete diowr enwif,to ihttrh𝑙𝑙𝑙𝑙he𝑙𝑙𝑙𝑙e𝑙𝑙𝑙𝑙,𝑙𝑙𝑙𝑙 𝐾𝐾𝐾𝐾oa𝐾𝐾𝐾𝐾𝑖𝑖𝑖𝑖sn𝑡𝑡𝑡𝑡s𝑖𝑖𝑖𝑖,c𝑡𝑡𝑡𝑡 ue,bm ubptu ttti hotinhs i tsgh eagnte eηnre ∗arlaliyslly 𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 cuddonoonceesossi rs nrntoeolntatthth eoeodls ldtdwi mfifotoahrr t tlethnshe e𝑀𝑀𝑀𝑀 o ccf𝑖𝑖𝑖𝑖a (as𝑡𝑡𝑡𝑡ps−eae 1ro )oawfmflnlanes𝑀𝑀𝑀𝑀t 𝑀𝑀𝑀𝑀eur𝑖𝑖𝑖𝑖s𝑖𝑖𝑖𝑖s𝑡𝑡𝑡𝑡e. .dT αT𝑡𝑡𝑡𝑡 .h , hTeβerh,er eefanofn,or derthe, ,eγt o te ofq faf uactacihtlieiilot iatnpta ert(o e1c d3acul)acc lwutciuloialln ∗ tlai topifnouro,n d,tc huttiehco eean s casiosnusnm usmipsttpieotninot nteh stathitam ηt a∗ηte𝑖𝑖𝑖𝑖i𝑡𝑡𝑡𝑡ss is eouuqfnn ucctaohotreirro ree 𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 nclla oa(tet6eefd)fd i a cwwrieiit nthdht sel lntneα𝑀𝑀𝑀𝑀r 𝑀𝑀𝑀𝑀ma𝑖𝑖𝑖𝑖(𝑖𝑖𝑖𝑖ni(𝑡𝑡𝑡𝑡n𝑡𝑡𝑡𝑡d−−e 1d1)γ)w,. w waFaseus urautsrhseeeed rad.m b.T loTehr heteo,n n ,pc ,to ahtnrhease mie seqteqeutenuartat iltyoβi on en ws( t1(ai1m3s3 )a o)wt bewit latilhli lnpe e rpfiodrrdo mufdrcu-oelcme evc eocelno qsnuissaittseitonentn et (s9etsi)m tiimant aeatse s TpooFrffe P vtt haihoeseu asc c rooeseestfeiffdpfiicu.c iaieHeln.n etFtsnsr c oαeαm , a oatnhndecd e dγ γi.sc . coFuFnususrsitrishtotheenern,mr tim to eiorser tee,i mv, pidapateareanrsamt m toehfeta etterp rta hβrβa em wLweatvsae isnro ssob obtαhat,in anβi ean,dn ea dnf Prdfoer motγrm inoe fqe uqtahuteai otipnor no( d9(u)9 c)it nioi nna a (fpp2urr0nee0cvvti3iioo)u unmss i ensstt theeeqpopu.d. a HrtHieeoeqnnuc ci(ere6,e ,) s o aonvrnacecer dei a ecbctoelonernmss isiinstuetecendhnt, t a wesee stoi tmwiumteapratueetes ta, s b ollaofe bf potopau rarcar,o macnmaesptieesittrteeasrn ls,αt liα,yn , βt ee,βs rt,mai mnaednad dtieγa tγteoh feo fft ihrtemh e-p lrepovrdoeudl cuTtciFotPino n iafnfusup nnuacc tttsirio eo(nsrnia d iwinun a emelq.qa uutFaeartrtioiioamonlns ( )t(6.h6)e) a adrreies d cdeuetsetserirmomnin inieted’ds, ,w ewvee iwd weenerter e ta hababltel e tt hotoe c ocLonensvissiintsestneotnhltynl yea sentsidtm imPateaett retih nteh (ef2 ifr0im0rm3-l)e- lvmeevele tTlh FTodPF P raaessq uaai rrereesss ividdauuraiall.b. lFeFrsro,o msmu c ththh eae s d dtihsiscecu ousussstipoiounnt , i tli’ats’bs o euevvri,di dceaenpnt itt tahtlha,at i tnt htteher emL Leedveivianitnseos ihonnhp nua tnas dn( draP wPet ermtirnai nt(e 2r(i02a00ls30))3. )m metehtohdo d Arredeqqduuiniirrgee si nvtaerrimabeldesia, tseu cmh aatse rthiael so uttop utth, ela bCooubrb, -cDapoiutgalla, sin tfeurnmcteidoina tep irnepseuntst e(dra win materials). eAqdudaitnigo sin vtearrmiaebdlieast,e smuacth n (3) gives: er aiasl sth teo othuet pcuobt,b l-aDboouugrl,a cs afpuintcatli,o inn pterremseendteida tien ienqpuuattsio (nr a(w3) mgiavteesr:ials). AAddddiinngg iinntteerrmmeeddiiaattee m maateterriaialsls t oto t hthee c coobbb𝛼𝛼𝛼𝛼1 𝛼𝛼𝛼𝛼2 𝛼𝛼𝛼𝛼3 b-D -Doouugglalas sf ufunnctcitoionn p prerseesnetnetde din i ne qeuqautaiotino n(3 ()3 g) igvievse:s: 𝑌𝑌𝑌𝑌𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 = 𝐴𝐴𝐴𝐴𝐾𝐾𝐾𝐾𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 𝐿𝐿𝐿𝐿𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 𝑀𝑀𝑀𝑀𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡 (15) (15) 𝑌𝑌𝑌𝑌 𝛼𝛼𝛼𝛼 𝛼𝛼𝛼𝛼 𝛼𝛼𝛼𝛼 E𝑌𝑌𝑌𝑌q𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡u==𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐾𝐾𝐾𝐾 𝛼𝛼𝛼𝛼11𝐿𝐿𝐿𝐿𝛼𝛼𝛼𝛼22𝑀𝑀𝑀𝑀𝛼𝛼𝛼𝛼33 (15) Equaattiio 𝐾𝐾𝐾𝐾 𝐿𝐿𝐿𝐿 onn 𝑖𝑖𝑖𝑖(𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡(1𝑡𝑡𝑡𝑡155)𝑖𝑖𝑖𝑖) 𝑡𝑡𝑡𝑡𝑖𝑖𝑖𝑖p 𝑀𝑀𝑀𝑀 (15) 𝑡𝑡𝑡𝑡preres𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡𝑖𝑖𝑖𝑖se𝑡𝑡𝑡𝑡enntsts t hthee s pspeceicfiicfiactaiotino no fo tfh eth set asntadnadrdar Cdo Cbbo-bDbo-Dugoluags lparso pdruocdtuiocnti on function, and fgEuEiqnvquceutasai totiaiono n,rn ae (ln(1a1d5t5i )go) i npvpsrerehsesi sapee n rnbettsels at twthtihoeee ne snssphp eoiecpuci fbitifpeciuctawtat it𝑦𝑦𝑦𝑦eoieo𝑖𝑖𝑖𝑖n,n o a oonfu fdtt h pthteuhe tes Yts atfi,na andcndtaodarrdr std h C oeCo ff obapbbcrtb-ooD-drDosu ucougtfi lgpoalrnsao spdc rpauoprcdotitiudaoculnt c𝐾𝐾𝐾𝐾i oti𝑖𝑖𝑖𝑖no, nlfa ufbnuocnrtc i𝐿𝐿𝐿𝐿oti𝑖𝑖𝑖𝑖no,, n aa, nnaddn d ciggnaiiptvveieretsmas l ae,a dl raireabeltloaeaut tigrio on,n oassdhnhsidip p 𝑀𝑀𝑀𝑀i n b𝑖𝑖𝑖𝑖bteeftorwtmwr eaeee dnpni a otrouetui tcgptupouluota dt𝑦𝑦𝑦𝑦r s 𝑦𝑦𝑦𝑦𝑖𝑖𝑖𝑖f ,𝑖𝑖𝑖𝑖 i f,ro amran n daid p t(ahSthreate lif icoafuacllacto traor finsr rsdom ofS fiep (kprSeorarodl,i duo2cul0taci1 toai1non) nd.c ISacnpeatkipeteraitmrla, 𝐾𝐾𝐾𝐾le d𝐾𝐾𝐾𝐾𝑖𝑖𝑖𝑖 ,i 𝑖𝑖𝑖𝑖a,lt aelb aogbro o𝐿𝐿𝐿𝐿r d𝑖𝑖𝑖𝑖𝐿𝐿𝐿𝐿,s 𝑖𝑖𝑖𝑖 ,aa nraedn d 2iiinn0nct1tele1ur)rmd. eIeedndd tieianartt meeo egrgdooeioaord tdestso 𝑀𝑀𝑀𝑀 g 𝑀𝑀𝑀𝑀or𝑖𝑖𝑖𝑖eo𝑖𝑖𝑖𝑖dffouosrc r ae ara e p pnaiandrrtocitlcgiucuedulnaeleardi r tf iyifnr im rbomer tid w ie( eSr( eSatnoali l otirholeaelda ua inancendp d ueS tnsSed keaokengrede,r n,2t eh02ie10ty 11e )1br.r)e o.It nwrI tneetterermnrm me.d eIidat itaceto egr rogeoocdotssd sfa oraerr e tpihinonectc elliunudtdpieeauddlt s ic inoan nr oroderr dldtaehetrier o tnteoo r rbrroeeerdtd wutucecreeem n e e.n tnhIdtdeo o cgigoenrenprnueeicttistyt sy a bfnboedertw tpwtheoeetn eu nt nthtiohaebl e si cneionprrvpureutedstl as a ftniiaorndmn d ts bh tehstp ewee rcerrieofrniroc rtt ehctrehm ramr. a.Ic ttI etc rocisrortirecrcest cs(t tsfh oefro r iepnproportouteertn nstt teiiaranlml dc)c o.ot rhTrrrehe lilausat nticiooanbn s bebreevt wteewdxee epfienlnra m itnhtshee ed s i pnienpcp uicufitistrcs c aucanhmndads rt taahtcnhetce eue ruisns notwiobchbsees r(ervterh vefedei dr emf rifrsimo rmrs tasse yprs meprec)sic.fp iiTfocih nccid shc athoraa rlcatcregtreeisr itpsicotissc ist( itvh(teeh e cpeearrrnorro dorbur e ctt eteeirrvmxipt)y)l.a. TsiTnhhehoidiscs k ciscna a bnncy i b rbuecesu iemnexxgsp ptlmalanionicnreeeds di ni wnipnh uc etcisrri ecr( cuLfiumermvmsitsnsat sanmonchceanesy s aw rnwehdshe pPreoere netf rdiifr nimtr,om s2 s0lma 0mra3gy)ae. y rp erosespsioptinovdne dt ot ol alragreg ep opsoistiivtiev e pAprr roductivity shocks by using more inputs (Levinsohn and Petrin, 2 03). lool ddvuuaccrttiiiavvbiitltyey s ss hhinooc ctkkhsse b bCyy ou ubssibni-ngDg m omuogorlerae is ni npsppuuetscts i( f(LiLceaevtviinoinsnos ohahnren a aennxddp P rPeesetrstirenidn, ,2a 20s0 0l0o33g)).a. rithms, and the resulting AcAollel llv f fvaviracairiarieiabanblbtelslse e sois n fii nnte h atethch ehCe oCoCbofbo b-btbDhb-eo-D Duiognoulpuagugsl talssasp s re sescppiefirececcisafieiftciinocatant itt oiahonren e a eearlexrae pse rtxeiecxpsisprteyrds esos aesfsde d llaao bsag osal urolirgot,h agmrcaiartsihpt,im htaamnsl,d s a, a nnaddn dti hntethe err merseusdlutiialnttiegn g tmchcoeae etrfefferfisiicacuilielsetn intntotsgs p ocorofof e defeufiaaccccthiiheo n onotf s(f e otqhtfh ueee aa ticinionhpnp uo ut1fst 6st h )r.eer ep iprnerpesuseentsnt tr tehtphere e eselealnasttsistc itichtiytey eo lofa fsl talibaciobtuoyr uo,r f,c laacpbaipotiautlra ,l a nadn di nitnetremrmedeidaitaet e cmapaaittteearrlii aaa𝑦𝑦𝑦𝑦 = 𝛼𝛼𝛼𝛼 +ln lss d to𝛼𝛼𝛼𝛼to i n pptrerorodmduuction (equation 16). 𝑖𝑖𝑖𝑖 1𝐿𝐿𝐿𝐿𝑖𝑖𝑖𝑖 + e𝛼𝛼𝛼𝛼 cdtii𝐾𝐾𝐾𝐾oante +( meq𝛼𝛼𝛼𝛼autae𝑀𝑀𝑀𝑀triioa+nls 1t6o) p. roduction (equation 16). 2 𝑖𝑖𝑖𝑖 3 𝑖𝑖𝑖𝑖 𝜀𝜀𝜀𝜀𝑖𝑖𝑖𝑖 (16) 𝑇𝑇𝑇𝑇𝑦𝑦𝑦𝑦 𝑦𝑦𝑦𝑦 𝑇𝑇𝑇𝑇𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 = 𝛼𝛼𝛼𝛼 𝛼𝛼𝛼𝛼 𝐿𝐿𝐿𝐿 𝑇𝑇𝑇𝑇 𝛼𝛼𝛼𝛼=+ 𝜀𝜀𝜀𝜀𝛼𝛼𝛼𝛼=11𝐿𝐿𝐿𝐿𝑖𝑖𝑖𝑖𝑦𝑦𝑦𝑦𝑖𝑖𝑖𝑖+− 𝛼𝛼𝛼𝛼𝛼𝛼𝛼𝛼𝛼𝛼𝛼𝛼�22𝐾𝐾𝐾𝐾 𝐾𝐾𝐾𝐾𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖++𝛼𝛼𝛼𝛼𝛼𝛼𝛼𝛼33𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑖𝑖𝑖𝑖 𝑖𝑖𝑖𝑖++𝜀𝜀𝜀𝜀𝜀𝜀𝜀𝜀− 𝛼𝛼𝛼𝛼� 𝑖𝑖𝑖𝑖 𝑖𝑖𝑖𝑖 (16()1 (61)6) 𝑖𝑖𝑖𝑖 𝑖𝑖𝑖𝑖 𝑖𝑖𝑖𝑖 1𝐿𝐿𝐿𝐿𝑖𝑖𝑖𝑖 − 𝛼𝛼𝛼𝛼�2𝐾𝐾𝐾𝐾𝑖𝑖𝑖𝑖 − 𝛼𝛼𝛼𝛼�3𝑀𝑀𝑀𝑀𝑖𝑖𝑖𝑖 (17) F𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇i𝑇𝑇𝑇𝑇n𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇a𝑇𝑇𝑇𝑇l𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖ly=, e 𝜀𝜀𝜀𝜀𝜀𝜀𝜀𝜀q𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖u=ati𝑦𝑦𝑦𝑦o𝑦𝑦𝑦𝑦𝑖𝑖𝑖𝑖n𝑖𝑖𝑖𝑖− (1𝛼𝛼𝛼𝛼�𝛼𝛼𝛼𝛼7�−− (17) ), 𝛼𝛼𝛼𝛼d�𝛼𝛼𝛼𝛼�e11m𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝑖𝑖𝑖𝑖o𝑖𝑖𝑖𝑖−n−s t 𝛼𝛼𝛼𝛼r�𝛼𝛼𝛼𝛼a�2t2𝐾𝐾𝐾𝐾e𝐾𝐾𝐾𝐾s𝑖𝑖𝑖𝑖 𝑖𝑖𝑖𝑖 −t−h a𝛼𝛼𝛼𝛼 �t𝛼𝛼𝛼𝛼 �3i3𝑀𝑀𝑀𝑀n𝑀𝑀𝑀𝑀 𝑖𝑖𝑖𝑖t𝑖𝑖𝑖𝑖he Cobb-Douglas production functi(o1n(71 )t7 o)t al factor FpFirinionnadaalullllylcyy,t, ie, v qeeiqutqyuau at𝑇𝑇𝑇𝑇aitoti𝑇𝑇𝑇𝑇iono𝑇𝑇𝑇𝑇n n(𝑖𝑖𝑖𝑖 1 (o7(1f)17 d7a)e), ,mp ddaeoremntmiscotounrnlasasttrert arsfat itetrhesmas tt h tcihnaaat nt th inbein eCt htochebae lbC c-CuDolobaobtueb-gdD-lD aoasous p ugtrglhoaledsa surp ceprtsoiriododnudu cuafutlci ntoticenotr nimfo unf nuo, cnft citothinoe ntp orttooatdla ulfc aftciatocontro r tfpourtrnoaocdld tufiuaocccntttii ovsvripit tpeyyc r 𝑇𝑇𝑇𝑇oi𝑇𝑇𝑇𝑇f𝑇𝑇𝑇𝑇di𝑇𝑇𝑇𝑇eu𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇dc𝑖𝑖𝑖𝑖 𝑖𝑖𝑖𝑖tioinovf fi et ayqa u popaafatr iartoit pcincua u(lr1alta6irc r)u f.iflriamrrm fi crcamann cb abene c bcaeal cclacuullcaluateltaedtd ea dsa sat sht heth ere e rrseeisdsiiuddauulaa ltl e ttreemrrm o fo ft hteh ep rpordoudcutciotino n offfu ntnhccetti ipoonrno sdsppueeccctiiifofiineed df ui inn ce teqiqouunaa tsitopionenc ( i(1fi16e6)d.) .in equation (16). With the firm TFP having been estimated from1 3e quation (18), the relationship b Wetitwh etheen fibrmus TinFePs hsa evinnvgi rboeennm esetnimt aatenddf oprmro edquucattiiovnit (y1 8w),a tsh ee rsetliamtioantsehdip u bseitnwge eOn LbuSs.i nTesFsP wen 13 avsi rtohnem denetp aennd dperondtu vctaivriitayb wlea sw ehstiilme abteuds uinsiensgs O eLnSv.i rTo1FnP3m was the dependent variable while b usiness environment indicators were used as regressors with ot ehnert vinardiaibclaetso trhsa t wcoeurled uexspelda ina s rpergordeuscstiovritsy, gwapit.h other variables that could explain productivity gap. 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑖𝑖𝑖𝑖 = ?̂?𝛽𝛽𝛽0 + ?̂?𝛽𝛽𝛽1𝑊𝑊𝑊𝑊𝑊𝑊𝑊𝑊𝑡𝑡𝑡𝑡𝑊𝑊𝑊𝑊𝑊𝑊𝑊𝑊𝑖𝑖𝑖𝑖 + ?̂?𝛽𝛽𝛽2𝐶𝐶𝐶𝐶𝑊𝑊𝑊𝑊𝑊𝑊𝑊𝑊𝐶𝐶𝐶𝐶𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡𝑖𝑖𝑖𝑖 + ?̂?𝛽𝛽𝛽3𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑢𝑢𝑢𝑢𝑡𝑡𝑡𝑡𝑊𝑊𝑊𝑊𝑊𝑊𝑊𝑊𝑖𝑖𝑖𝑖 + ?̂?𝛽𝛽𝛽4𝐸𝐸𝐸𝐸𝑙𝑙𝑙𝑙𝑊𝑊𝑊𝑊𝑐𝑐𝑐𝑐𝑡𝑡𝑡𝑡𝑊𝑊𝑊𝑊𝑖𝑖𝑖𝑖𝑐𝑐𝑐𝑐𝑖𝑖𝑖𝑖𝑡𝑡𝑡𝑡𝑙𝑙𝑙𝑙𝑖𝑖𝑖𝑖 + ?̂?𝛽𝛽𝛽5𝑠𝑠𝑠𝑠𝑡𝑡𝑡𝑡𝑊𝑊𝑊𝑊𝑡𝑡𝑡𝑡𝑊𝑊𝑊𝑊 𝑐𝑐𝑐𝑐𝑜𝑜𝑜𝑜 𝑊𝑊𝑊𝑊𝑐𝑐𝑐𝑐𝑊𝑊𝑊𝑊𝐶𝐶𝐶𝐶𝑠𝑠𝑠𝑠𝑖𝑖𝑖𝑖 + ?̂?𝛽𝛽𝛽6𝐵𝐵𝐵𝐵𝑢𝑢𝑢𝑢𝑠𝑠𝑠𝑠_𝑊𝑊𝑊𝑊𝑎𝑎𝑎𝑎𝑊𝑊𝑊𝑊𝑖𝑖𝑖𝑖 + ?̂?𝛽𝛽𝛽7𝑀𝑀𝑀𝑀𝑊𝑊𝑊𝑊𝑊𝑊𝑊𝑊𝑀𝑀𝑀𝑀𝑊𝑊𝑊𝑊𝑡𝑡𝑡𝑡_𝑐𝑐𝑐𝑐𝑢𝑢𝑢𝑢𝑡𝑡𝑡𝑡𝑙𝑙𝑙𝑙𝑊𝑊𝑊𝑊𝑡𝑡𝑡𝑡𝑖𝑖𝑖𝑖 + ?̂?𝛽𝛽𝛽8𝑇𝑇𝑇𝑇𝑊𝑊𝑊𝑊𝑊𝑊𝑊𝑊𝑖𝑖𝑖𝑖𝑊𝑊𝑊𝑊𝑖𝑖𝑖𝑖𝑊𝑊𝑊𝑊𝑎𝑎𝑎𝑎𝑖𝑖𝑖𝑖 + ?̂?𝛽𝛽𝛽9𝑅𝑅𝑅𝑅𝑊𝑊𝑊𝑊𝑠𝑠𝑠𝑠𝑊𝑊𝑊𝑊𝑊𝑊𝑊𝑊𝑊𝑊𝑊𝑊𝑐𝑐𝑐𝑐ℎ_𝐶𝐶𝐶𝐶𝑊𝑊𝑊𝑊𝑑𝑑𝑑𝑑𝑖𝑖𝑖𝑖 + ?̂?𝛽𝛽𝛽10𝐵𝐵𝐵𝐵𝑢𝑢𝑢𝑢𝑠𝑠𝑠𝑠_𝑠𝑠𝑠𝑠𝑖𝑖𝑖𝑖𝑠𝑠𝑠𝑠𝑊𝑊𝑊𝑊𝑖𝑖𝑖𝑖+?̂?𝛽𝛽𝛽8𝐸𝐸𝐸𝐸𝐶𝐶𝐶𝐶𝑢𝑢𝑢𝑢𝑐𝑐𝑐𝑐𝑖𝑖𝑖𝑖 + ?̂?𝛽𝛽𝛽9𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑊𝑊𝑊𝑊𝑊𝑊𝑊𝑊𝑊𝑊𝑊𝑊𝑠𝑠𝑠𝑠ℎ𝑖𝑖𝑖𝑖𝑐𝑐𝑐𝑐𝑖𝑖𝑖𝑖 + ?̂?𝛽𝛽𝛽10_𝐺𝐺𝐺𝐺𝑊𝑊𝑊𝑊𝑊𝑊𝑊𝑊𝐶𝐶𝐶𝐶𝑊𝑊𝑊𝑊𝑊𝑊𝑊𝑊𝑖𝑖𝑖𝑖 + 𝜀𝜀𝜀𝜀𝑖𝑖𝑖𝑖 (18) The description of the variables used in the estimation of equations (17) and (18) ispresented in tables 3.1 and 3.2. 10 Table 3.4: Description of independent variables used in the regression equation Variable Description Measurement Business environment Water Dummy variable, indicates whether 1 if the business had access to water, an enterprises has access to water zero otherwise Credit Dummy variable, indicates whether 1 if the business had access to credit, 14 Methodology The description of the variables used in the estimation of equations (17) and (18) is presented in Tables 3.1 and 3.2. Table 3.1: Description of independent variables used in the regression equation Variable Description Measurement Business environment Water Dummy variable, If the business had access to water, indicates whether an zero otherwise enterprise has access to water Credit Dummy variable, If the business had access to credit, indicates whether a zero otherwise business has access to credit Computer Dummy variable, If the business has access to indicating whether a computer for official uses, 0 business has a computer otherwise Electricity Dummy variable, If the business had access to indicating whether a electricity, zero otherwise business has access to electricity State of Categorical variable A categorical variable showing roads showing the state of the state of roads, 1-bad, 2-fair, 3-good roads, whether good, fair or bad Firm characteristics Business age It is a measure of how It was measured in years as a long a business has been difference between when the in operation business was established and the year of the survey Market The main customer of the It was measured as a dummy: 1 business if the business sells to the other business and zero otherwise Research This is the amount It was measured in Ksh as the and of money (Ksh) that amount the business spent on development the business spends research and development. on research and development. It includes process and product innovation 11 Effect of business environment on productivity of informal manufacturing enterprises in Kenya Business size This was measured by Business size was measured number of employees as a categorical variable: 1 working for the enterprise represents micro business (0–9 employees), 2 small businesses (10–49 employees) and 3 medium businesses (50–99 employees) Ownership It refers to the nature It was measured as a dummy of the ownership of the variable with 1 as sole business proprietorship and zero otherwise Education The highest level of Education was measured as a education of the business categorical variable: 0 was used to owner, co-owner(s) or represent those with no education, manager 1 primary, 2 secondary and 3 post- secondary Entrepreneur characteristics Gender The sex of the business It was measured as a dummy: 1 if owner(s) male and zero otherwise Training Dummy variable showing 1 if training was received and zero whether employees have otherwise received training in the last six years Source: Author’s illustration Table 3.2: Description of independent variables used in the production function Labour The total number of workers It was measured as total in a business number of employees working in a business Output The total amount of goods Following several studies, it produced by a firm was proxied by total sales of a business Capital These are the physical It was measured as the initial and financial assets that a capital plus the added capital in business uses to produce a firm goods and services for sale Intermediate The raw materials and inputs It was measured as the value of inputs used in the production a firm’s inputs and raw material process Source: Author’s illustration 12 Methodology 3.4 Data Source The study used secondary data collected by the Kenya National Bureau of Statistics (KNBS) on Medium, Small and Micro Enterprises (MSMEs) in 2016 at national and county levels. The sample size was 24,164 enterprises. Among this, 17,895 businesses are not registered (informal enterprises) and 6,269 are registered (formal enterprises). The unit of observation was the establishment and the survey targeted those firms that engaged at most 99 persons. This survey used household-based approach and also interviewed businesses identified from business registers maintained by county governments. The survey was cross- sectional and was designed to provide estimates at national and county levels. The survey design was a representative probability sample that aimed at producing estimates at national, counties and urban and rural residence (for unlicensed businesses only). The survey covers firms that were registered (formal) and those that were not registered (informal), which formed the basis of the analysis. The survey adopted a stratified random sampling method for the establishment- based sample in which a systematic random sample of establishments was drawn using equal probability selection method. For the household-based sample, a two-stage stratified cluster sampling design was used where the first stage involved selection of 600 clusters (354 in rural and 246 in urban areas) with equal probability. In the second stage, a uniform random sample of 24 firms in each cluster was selected using systematic random sampling method (KNBS, 2016). 13 4. Results From the data set a total of 14,377 enterprises were interviewed, which were not registered by the Registrar of Companies, hence were considered as informal enterprises. Out of these informal enterprises, 2,330 enterprises were in the manufacturing sector. Data cleaning was then done to remove the inconsistent values in each of the variables included in the study. This resulted into 1,044 valid observations. The manufacturing sector had 13 different sub-sectors out of which we selected 5 sub-sectors which had large observations (at least 60 observations). These were manufacture of wearing apparel, manufacture of furniture, manufacture of food products, manufacture of fabricated metal products and manufacture of wood and wood products and articles of straw and plaiting materials. Table 4.1: Distribution of sub-sectors in the manufacturing sector Manufacturing sub sectors Frequency Percent 1 Manufacture of wearing apparel 370 35.44 2 Manufacture of furniture 206 19.73 3 Manufacture of food products 203 19.44 Manufacture of fabricated metal products, 4 157 15.04 except machinery and equipment Manufacture of wood and of products of wood 5 and cork, except furniture; manufacture of 62 5.94 articles of straw and plaiting materials 6 Manufacture of textiles 16 1.53 Repair and installation of machinery and 7 10 0.96 equipment 8 Manufacture of leather and related products 9 0.86 9 Printing and reproduction of recorded media 5 0.48 10 Other manufacturing 3 0.29 11 Manufacture of beverages 1 0.1 12 Manufacture of chemicals and chemical products 1 0.1 13 Manufacture of basic metals 1 0.1 Total 1,044 100 Source: Author’s computation From this survey, enterprises were provided with a list of constraints they experience while running their business and further they were asked to choose 14 Results the main constraint they had experienced in the last one year. The obstacles included were: lack of collateral for credit, interference from authorities, licenses, taxes, other government regulations, lack of markets, poor roads/transport, local competition, foreign competition, lack of skilled manpower, shortage of raw materials or stock, power interruption, inaccessibility to electricity, poor access to water supply, poor security and lack of space. Lack of markets was the top constraint, pointed out by 19.7 per cent of firms surveyed. This was followed by licenses at 14.56 per cent, local competition (12.09%) and power interruption (8.15%). As seen in the figure below, lack of collateral was also cited as a major constraint faced by businesses (6.57%). 6.4 per cent reported poor roads/transport as a stumbling block to their businesses. Insecurity was another challenge that businesses reported, with 3.63 per cent of the businesses reporting this as the top constraint. Also, shortage of raw material was reported to be a main problem experienced by businesses (5.47%). Inaccessibility to electricity and poor access to water supply was also a severe stumbling block for 1.68 per cent and 1.21 per cent of firms, respectively. Another main constraint experienced by businesses was lack of skilled manpower (1.05%). Figure 4.1: Main constraint experienced by enterprises in the last one year Lack of markets 19.65 Licenses 14.56 Local competition 12.09 Power interruption 8.15 Lack of collateral for credit 6.57 poor roads/transport 6.04 Shortage of raw materials or stock 5.47 poor security 3.63 Inaccessibility to electricity 1.68 Poor access to water supply 1.21 Lack of skilled manpower 1.05 lack of space 1 Interference from authorities 0.84 Other government Regulations 0.63 Foreign Competition 0.47 Taxes 0.32 0 5 10 15 20 25 percent 15 4.1 Descriptive statistics of variables used in the regression model In the manufacturing sector, 30 percent of enterprises that were operating in the manufacturing fabricated materials had access to credit. The figure for those in the food product was 32 percent. Those businesses that weremanufacturing wearing apparel 35 percent had access to credit. This figure compared closely to enterprises that were manufacture offurniture whose 37percent of the businesses had access to credit. Businesses that were manufacturing wood & wood products had a higher access to credit that these other subsectors (43percent). The descriptive statistics are presented in Tables 4.2 to 4.4. Access to water was generally low in all subsectors as compared to credit. Only four percent of the businesses that were manufacturing fabricated materials had access to water. In the food products subsector, only one percent of the enterprises had access to water which is a crucial asset in the food industry. Three percent of businesses in the furniture subsector had access to water and two percent of the enterprises manufacturing wearing apparel had access to this utility. 19 constraint Effect of business environment on productivity of informal manufacturing enterprises in Kenya About 1 per cent of the businesses identified lack of space as a key obstacle. The other hindrances reported were interference from government authorities (0.84%), other government regulations (0.63%), foreign competition (0.47%) and taxes (0.32%). 4.1 Descriptive Statistics of Variables used in the Regression Model In the manufacturing sector, 30 per cent of enterprises that were operating in the manufacturing fabricated materials had access to credit. The figure for those in the food product was 32 per cent. Those businesses that were manufacturing wearing apparel had 35 per cent having access to credit. This figure compared closely to enterprises that were manufacture of furniture whose 37 per cent of the businesses had access to credit. Businesses that were manufacturing wood and wood products had a higher access to credit than these other sub-sectors (43%). The descriptive statistics are presented in Tables 4.2 to 4.4. Access to water was generally low in all sub-sectors as compared to credit. Only 4 per cent of the businesses that were manufacturing fabricated materials had access to water. In the food products sub-sector, only 1 per cent of the enterprises had access to water, which is a crucial asset in the food industry. Three (3) per cent of businesses in the furniture sub-sector had access to water and two percent of the enterprises manufacturing wearing apparel had access to this utility. Businesses that were manufacturing wood and wood products also had low access to water, with only three (3) per cent having reported they have access to water. In the five sub-sectors, access to electricity was generally high. Enterprises in the manufacture of fabricated materials, food product, furniture,wearing apparel and wood and wood products sub-sector had access to electricity at 98, 56, 77, 75 and 80 per cent, respectively. Access to computer was very low in all the sub-sectors (less than 1%). Descriptive statistics also show that, on average, businesses in the five sub-sectors have been operating, on average, for between 9 to 11 years and most of these businesses were sole proprietorship. Enterprises in the fabricated material sub- sector and furniture train their employees more as compared to food, wearing apparel and wood and wood product sub-sector. Specifically, those businesses were manufacturing fabricated materials at 12 per cent of their employees while those in furniture sub-sector had 10 per cent reporting to have trained their employees. Most businesses in these five sub-sectors sell their products to SMEs (more than 90%) compared to individual consumers. On average, businesses were spending low on research and development (less than Ksh 500 per month) and some businesses reported as low as zero expenditure on research and development. 16 Results Table 4.2: Descriptive statistics for enterprises in the manufacture of fabricated materials and manufacture of food product sub-sectors Manufacture of fabricated Manufacture of food products materials Variable Obs Mean Std. Min Max Obs Mean Std. Min Max Dev. Dev. Business environment Access to credit 157 0.31 0.46 0 1 203 0.33 0.47 0 1 Access to water 157 0.04 0.21 0 1 203 0.01 0.12 0 1 Access to electricity 157 0.99 0.11 0 1 203 0.56 0.50 0 1 Access to computer 157 0.01 0.08 0 1 203 0.00 0.07 0 1 State of roads 157 1.00 0.77 0 2 203 1.31 0.74 0 2 Training/skills 157 0.12 0.33 0 1 203 0.02 0.16 0 1 Entrepreneur characteristics Education of owner 157 1.60 0.98 0 3 203 1.62 0.95 0 3 Gender 157 0.98 0.14 0 1 203 0.67 0.47 0 1 Firm characteristic Age of business 157 9.18 6.12 1 34 203 9.05 7.39 1 49 Market outlet 157 0.90 0.30 0 1 203 0.94 0.25 0 1 Research & development 156 106.54 703.97 0 6120 203 271.03 1,836.39 0 15,000 Business size 157 0.04 0.22 0 2 203 0.03 0.20 0 2 Ownership structure 157 0.93 0.26 0 1 203 0.96 0.20 0 1 Source: Author’s calculation Table 4.3: Descriptive statistics for enterprises in the manufacture of furniture and wearing apparel Manufacture of furniture Manufacture of wearing apparel Std. Std. Variable Obs Mean Dev. Min Max Obs Mean Dev. Min Max Business environment Access to credit 206 0.38 0.49 0 1 370 0.35 0.48 0 1 Access to water 206 0.04 0.19 0 1 370 0.02 0.15 0 1 Access to electricity 206 0.77 0.42 0 1 370 0.75 0.43 0 1 Access to computer 206 0.00 0.00 0 0 370 0.01 0.07 0 1 State of roads 206 1.01 0.77 0 2 370 0.98 0.82 0 2 Training/skills 206 0.10 0.30 0 1 370 0.04 0.20 0 1 17 Effect of business environment on productivity of informal manufacturing enterprises in Kenya Entrepreneur characteristics Education of owner 206 1.43 0.89 0 3 370 1.42 0.86 0 3 Gender 206 0.99 0.12 0 1 370 0.32 0.47 0 1 Firm characteristics Market outlet 206 0.92 0.28 0 1 370 0.94 0.24 0 1 Research & development 205 125.12 986.66 0 10500 370 30.61 209.56 0 3000 Business size 206 0.01 0.12 0 1 370 0.01 0.13 0 2 Age of business 206 11.41 8.26 2 41 370 10.63 8.33 1 71 Ownership structure 206 0.91 0.28 0 1 370 0.92 0.27 0 1 Source: Author’s calculation Table 4.4: Descriptive statistics for enterprises in the manufacture of wood products sub-sector Manufacture of Wood and wood products Variable Obs Mean Std. Dev. Min Max Business environment Access to credit 62 0.4355 0.4999 0 1 Access to water 62 0.0323 0.1781 0 1 Access to electricity 62 0.8065 0.3983 0 1 Access to computer 62 0.0161 0.1270 0 1 State of roads 62 0.7419 0.8285 0 2 Training /skills 62 0.0484 0.2163 0 1 Entrepreneur characteristics Education of owner 62 1.5806 0.8971 0 3 Gender 62 0.9355 0.2477 0 1 Firm characteristics Age of business 62 10.3065 5.9934 2 26 Market outlet 62 0.9839 0.1270 0 1 Research&development 61 210.2459 823.2732 0 6000 Ownership structure 62 0.9355 0.2477 0 1 Business size 62 0.0806 0.3289 0 2 Source: Author’s calculation 18 Results 4.2 Results from Cobb-Douglas Production Function Table 4.5 shows that all the coefficients of labour, capital and intermediate inputs are positive and significant, with the exception of the capital coefficient in the food products industry, which is negative and weakly significant. Felipe and Adams (2005) and Wexler and Loecker (2016) attribute this to the measurement errors in the inputs. The coefficient of labour is highest in the manufacture of wearing apparels with a coefficient of 0.58, while the coefficient of capital is highest in the manufacture of fabricated materials with a coefficient of 0.20. The coefficient of intermediate inputs is highest in the manufacture of furniture with a coefficient of 0.52. These results imply that manufacture of wearing apparels is the most labour-intensive; the manufacture of fabricated metals is the most capital- intensive while the manufacture of furniture consumes the largest share of inputs and raw materials (intermediate inputs). With the exception of manufacture of furniture, all the sectors have their largest shares of labour input. This implies that most sectors are labour-intensive Table 4.5: Results of the Cobb-Douglas production function Intermediate No. of Sector Labour Capital inputs firms Wearing apparels 0.58*** (0.04) 0.11*** (0.03) 0.30*** (0.03) 370 Furniture 0.38*** (0.06) 0.10** (0.04) 0.52*** (0.06) 206 Food products 0.63*** (0.07) (0.09)* (0.05) 0.46*** (0.05) 203 0.20*** Fabricated products 0.43*** (0.07) 0.37*** (0.05) 157 (0.06) Wood and wood products except 0.55*** (0.11) 0.19** (0.03) 0.25*** (0.08) 62 furniture Source: Author’s calculation Having estimated the production function, regression analysis was carried out to find out the determinants of TFP in the five manufacturing sub-sectors. The results are presented in Table 4.6. 19 Effect of business environment on productivity of informal manufacturing enterprises in Kenya Table 4.6: Regression results for determinants of productivity in the informal manufacturing sub-sectors Manufacturing Fabricated Food Furniture Wearing Wood and sub-sectors material products apparel wood products Coeff. Coeff. Coeff. Coeff. Coeff. (std. error) (std. error) (std. error) (std. error) (std. error) 1. Business environment indicators Access to water 0.37 -0.46 -0.22 0.79*** 1.11*** (0.29) (0.54) (0.33) (0.25) (0.25) Access to electricity 0.14 0.53*** -0.05 0.18* -0.33 (0.19) (0.15) (0.72) (0.09) (0.26) Access to computer -0.04 1.23 *** 0.00 0.48 2.76*** (0.13) (0.17) (0.30) (0.37) State of roads Fair -0.03 0.15 -0.05 -0.08 0.18 (0.23) (0.17) (0.15) (0.10) (0.20) Bad 0.11 -0.22 -0.23 0.05 0.18 (0.19) (0.16) (0.15) (0.10) (0.27) Training 0.63*** 1.19 0.03 -0.16 1.13 (0.17) (0.90) (0.22) (0.27) (0.94) Access to credit 0.45 0.21 0.11 0.14 -0.26 (0.35) (0.16) (0.25) (0.17) (0.39) 2. Entrepreneur characteristics Education Primary 0.03 -0.06 0.18 -0.24* 0.82** (0.28) (0.79) (0.17) (0.14) (0.33) Secondary -0.05 0.03 0.20 -0.10 0.85*** (0.27) (0.20) (0.20) (0.18) (0.29) Post-secondary 0.22 -0.14 0.18 -0.13 0.88** (0.30) (0.21) (0.19) (0.16) (0.32) Gender -0.08 0.04 -0.02 0.18* -0.06 (0.14) (0.65) (0.10) (0.26) 3. Firm characteristics Age of the business 0.03*** -0.01 0.00 0.01 0.00 (0.01) (0.01) (0.01) (0.01) (0.02) 20 Results Market outlet -0.05 -0.35 0.07 -0.11 -1.98*** (0.22) (0.25) (0.17) (0.20) Research & 0.00 ** 0.00*** 0.00 0.00* 0.00*** development (0.00) (0.00) (0.00) (0.00) (0.00) Business size Small -0.54 0.19 0.46*** -0.37* -0.03 (1.18) (0.14) (0.19) (0.34) Medium -2.19**** -1.09 --- -1.63*** -2.38*** (0.27) (2.10) (0.45) (0.33) Ownership 0.09 -0.12 -0.12 0.12 0.23 (0.34) (0.19) (0.24) (0.20) (0.25) 4. Interaction variables Business size*credit 0.53 0.43 -1.05** 0.00 0.00*** (0.50) (1.08) (0.54) (0.00) (0.00) Business 1.03** -0.67 0.00 0.23 0.00*** size*market (0.51) (1.05) (0.21) (0.00) Business age * credit -0.05 0.03 -0.01 0.00 0.02 (0.03) (0.20) (0.02) (0.01) (0.03) Constant -0.08* 0.38** 0.09* -0.07** 1.17** (0.65) (0.29) (0.78) (0.27) (0.51) Source: Author’s computation Notes: Standard errors are in brackets The coefficient of water was positive and significant in wearing apparel and wood and wood products. This implies that firms with access to piped water had higher productivity than those that did not. Specifically, a firm in the manufacture of wearing apparel sector with access to piped water had a productivity of 0.79 higher than firms in the same sector without access to this facility. However, firms with access to piped water in the manufacture of wood and wood products had a productivity of 1.11 lower than firms in the same sector but without access to piped water. Although this finding is in contradiction to the expectations, it is in line with Augier, Dovis and Gasiorek who found a negative relationship between water shortage and productivity on manufacturing firms in Morocco. The coefficient of access to electricity was positive and significant in the food products and in the wearing apparel sub-sectors. Firms that had access to electricity in the manufacturing food products had a productivity of 0.53 higher than those without access to electricity and were in the same sub-sector. Similarly, firms with access to electricity in the wearing apparel sub-sector had a productivity of 0.18 higher than those without access to electricity. This finding was contrary to Giang 21 Effect of business environment on productivity of informal manufacturing enterprises in Kenya et al . (2018) whose study did not find any significant relationship between access to electricity and productivity. However, the current findings were inconsistent with Nguimkeu (2013), whose study established a negative and significant relationship between lack of access to electricity and firm productivity in Cameroon. Firms with access to computers in the food products sub-sector had a productivity of 1.23 higher than similar firms, but without access to computers. Similarly, firms in the manufacture of wood and wood products (except furniture) had a productivity of 2.76 higher than those without access to these services. Surprisingly, Giang et al. (2018) found a negative and significant relationship between access to internet and productivity. The coefficient of training was positive and significant in the manufacture of fabricated materials sub-sector. Firms which had accessed training in the last three years had a higher productivity by 0.63 compared to those in the same sector which had not. This is consistent with Taymaz (2010) who found that higher education, better training and export/institution market improve productivity of a firm. However, Giang et al. (2018) did not find a significant relationship between training and productivity. The coefficient of education was negative but weakly significant in the manufacture of wearing apparel for those with primary education. This category had a productivity of 0.24 lower than those without any education. This can be possibly attributed to more experience for those without any education. However, the coefficient of education was positive and significant across all the three levels of education in wood and wood product sub-sectors. The size of the coefficient increased as education level increased. Specifically, those with primary, secondary and post-secondary education had a productivity of 0.82, 0.85 and 0.88, respectively, higher than those without any education. The coefficient of gender was positive but weakly significant in the manufacture of wearing apparel. This implies that men in this sub-sector had a higher productivity by 0.18 compared to women. This was consistent with Sabarwar and Terrer (2008) who found a gender gap differential of 4.1 per cent, which was statistically significant at five per cent level. Pfeifer and Wagner (2013) found similar results. This finding can be attributed to the fact that male workers have a higher probability of being in work, lower probability of layoff due to longer tenure, more human capital investment, taste-based statistical discrepancy and that they are the main provider to the household income. However, Infante et al. (2014) found that participation of female managers improves productivity of manufacturing firms in North West regions of Italy. Bruhn (2009) found gender productivity differentials in micro and small enterprises and not medium and large enterprises. Wong et al. (2017) failed to find any significant effect of gender on productivity. Gaitan, 22 Results Herera and Pablo (2017) also found a positive relationship between gender and productivity. Taymaz (2010) failed to find any significant relationship between gender and productivity. The coefficient of age was positive and significant only in the manufacture of fabricated materials sub-sector. Increase in firm’s age by one year in this sector was found to increase productivity by 0.03. These results are in contrast with Giang et al. (2018) who found that older firms were less productive than young firms by 11 per cent. Lasagni, Nifo and Vecchione (2015) found the coefficient to be positive and negative in some sectors. The coefficient of market outlet was negative and highly significant in the manufacture of wood and wood products. Firms that sell to individual consumers were found to have a productivity of 1.98 lower than firms that sell to MSMEs. The coefficient of research and development was found to be positive and significant in four of the five sectors. The coefficient was not significant in the furniture sub- sector. However, the coefficient was very small. This indicates that research and development is critical in improving productivity, although firms spend minimal amounts on it. This finding was in line with Blanco and Prieger (2016) who found that research and development expenditure had a positive significant long-run effect on TFP. Other studies that have also found a positive relationship between R&D and productivity are Doraszelski and Jaumandreu (2013), Leachman and Ray (2014), and Voutsinas and Tsamadias (2014). Small businesses in the manufacture of furniture had a productivity of 0.46 higher than micro businesses. However, small firms in the manufacture of wearing apparel had a productivity of 0.37 lower than micro firms in the same sector. However, for fabricated materials, wearing apparel and wood and wood products, medium businesses had a productivity of 2.19, 1.63 and 2.38, respectively, less than micro businesses. With the exception of the coefficient of small businesses in the furniture businesses, all the other coefficients were negative and significant. This implies that micro businesses have a higher productivity than small and medium businesses. These results are consistent with Satpathy, Chaterjee and Mahakud (2017). Similarly, Williamson (1967), Tornatzky and Fleischer (1990) and Utterback (1994) found that smaller firms have higher productivity due to their efficiency. Diaz and Sanchez (2008) found an inverse relationship between firm size and productivity. However, Eiffert, Gelb and Ramachandran (2005) found that large and exporting firms have higher productivity than small and non-exporting ones. Lee and Tang (2001), Biesebroeck (2005) and Lopez (2015) found a positive relationship between the size of the firm and TFP. They attribute this to learning 23 Effect of business environment on productivity of informal manufacturing enterprises in Kenya by doing effects of the large companies. Lundvall and Battesse (2000) and Biesebroeck (2005) attribute this to the use of better technology by larger firms. Results from interacting the variables Turning to interaction variables, small and medium firms with access to credit in the furniture were found to have lower productivity than small and medium business without access to credit. These results may suggest that most of the firms in the informal sector did not access credit, and those that had access to credit did so at high cost, which is consistent with literature on access to credit in the informal sector. The coefficient of interaction term between business size and credit was positive and significant but very small in the manufacture of wood and wood products. The coefficient of the interaction term between business size and market was positive and significant in the manufacture of fabricated materials and the manufacture of wood and wood products. This indicates that small and medium businesses that sell to individual consumers have a higher productivity than micro businesses that sell to the consumers. This indicates that size matters for productivity, regardless of the market that the firm sells. The coefficients of credit, access to roads, ownership and the interaction between business size and credit were insignificant determinants of productivity in each of the sub-sector. Moreno-Badia and Slootmaekers (2009) and Gasiorek, Davis and Augier (2010) did not find any significant relationship between credit and sectoral productivity. However, Gatti and Love (2008) found a positive relationship between credit and productivity in Bulgaria. Similarly, Essmui et al. (2014) and Giang et al. (2018) found that improved access to finance and productivity are positively related. Gaitan, Herera and Pablo (2017) found that businesses owned by sole proprietors have higher productivity than ones owned by shareholders. 24 5. Summary, Conclusion and Policy Recommendations The informal economy is the lifeblood of many economies today, especially is Sub- Saharan Africa. According to Kenya national statistics, the sector is a key source of employment especially for the young people. Currently, the informal economy has also demonstrated entrepreneurship, flexibility and also supplies local supply chains. The sector is also large and dynamic. This sector has been found to be crucial in terms of job creation compared to the formal sector. However, despite its importance, the sector has been generally proven to be less productive than the formal sector. One of the underlying reasons behind low productivity of the sector is the poor business environment, which directly affects business performance. Example of the business environment factors that have impacted on businesses include lack of collateral for credit, interference from authorities, licenses, taxes, other government regulations, lack of markets, poor roads/transport, local competition, foreign competition, lack of skilled manpower, shortage of raw materials or stock, power interruption, inaccessibility to electricity, poor access to water supply, poor security and lack of space. This study, therefore, sought to understand how these factors affects the productivity of business in the informal sector, specifically among manufacturing firms. TFP was estimated by use of Cobb-Douglas production function and was used as the dependent variable to estimate the effect of business environment and other firm and entrepreneur characteristics on enterprise productivity using OLS. The study concentrated on five sub-sectors in the manufacturing sector that were well represented in the survey data (fabricated material, food products, furniture, wearing apparel and wood and wood products sub-sector). From the results, business environment variables had a significant effect on productivity of manufacturing firms. Access to water was a significant determinant of productivity especially for those businesses that were manufacturing wearing apparel and wood and wood products. Access to electricity had a positive and significant influence on productivity for those businesses that were engaged in the manufacture of food products and wearing apparels. Firms that had access to electricity and were manufacturing food products had a productivity of 0.53 higher than their counterparts without access to electricity. Similarly, firms with access to electricity in the wearing apparel sub-sector had a productivity of 0.18 higher than those without access to electricity. Access to computer was another important determinant of productivity. Firms with access to computer in the food products sub-sector had a productivity of 1.23 higher those without access to computer in the same sub-sector. Similarly, 25 firms in the manufacture of wood and wood products had a productivity of 2.76 higher than those without access to computer. The coefficient of training was also positive and significant in the manufacture of fabricated materials sub-sector. Firms which had accessed to training had a higher productivity by 0.63 compared to those in the same sector which had not received any training. Other factors that were found to be significant in explaining the productivity of the informal sector were the nature of the ownership of the business, size of the firm, gender of the main decision maker, education of the manager/owner of the enterprise, expenditure on research and development, age of business and market/main customer of the business. These results have an implication on how business environment can have a negative effect indirectly on the performance of businesses in the informal sector. Therefore, this study recommends the following: • The results confirm that business environment have a significant effect on firm productivity. This means that the current emphasis by the government on sparking Kenya’s manufacturing sector to generate more jobs and drive economic growth is unlikely to succeed if they overlook the importance of business environment, which has an influence on performance of businesses. The findings of this study also show the relevance of the current debate on creating an enabling environment for businesses. • Businesses with access to electricity were found to be more productive than those without. This calls for increased distribution of electricity especially for access by informal sector businesses. This will allow many businesses to use the utility and increase their productivity. • Training has an influence on productivity. This emphasizes on the importance of skills in improving productivity in the informal sector. Therefore, there is need for policy makers to come up with a framework for training entrepreneurs in the informal sector and to encourage firms to train their workers. This can be done for example by decentralizing training centres in different regions in the country. • Gender was also proven to be a key determinant of productivity. Male entrepreneurs were more productive in the informal sector than their counterparts. Given that a significant number of workers in the informal sector are women, this finding calls for renewed attention from policy makers to bridge this gender gap in the informal sector. There is need to abolish any social norms that could be limiting the performance of women in the informal sector through the existing and new institutions that focus on gender issues. 26 Summary, conclusion and policy recommendations • Moreover, access to credit has a significant effect on firm productivity. This therefore necessitate for efforts from policy makers to encourage business people to take credit by relaxing some of the minimum requirements for accessing credit, such as collateral. This will strengthen the recent move of launching of Biashara Kenya Fund which aims at easing credit access. • Spending on research and development was found to be positively related with productivity. This calls for business to set aside some amount for research and development. 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