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On this page

  • 1 Overview
    • 1.1 Part I: Data Exploration & Cleaning
  • 2 Loading & Exploring Data
    • 2.1 Constructing Variables for Production Function
    • 2.2 Part II: Empirical Analysis
  • 3 TFP Estimation using OLS
    • 3.1 Objective
    • 3.2 Specification
  • 4 Firm Characteristics & Productivity Analysis
    • 4.1 Objective
    • 4.2 Key Variables
    • 4.3 📊 View Regression Results
  • 5 Financial Constraints & Productivity
    • 5.1 Objective
    • 5.2 Key Financial Variables
  • 6 Labor Markets & Wages
    • 6.1 Objective
    • 6.2 Analytical Framework
  • 7 Conclusion and Recommendations
    • 7.1 Summary of Analysis Framework
    • 7.2 Analytical Approach
    • 7.3 Empirical Findings
    • 7.4 Methodological Considerations and Limitations
    • 7.5 Recommendations for Robust Analysis
    • 7.6 Appendix: Key Stata Commands Reference
      • 7.6.1 Data Management Commands
      • 7.6.2 Data Cleaning and Transformation

WBES Data Cleaning and Preparation Tutorial

Data Exploration, Variable Construction, and Descriptive Analysis — Industrial Policy Course

Author

Sinthavanh CHANTAVONG (TA); Christian Otchia (Instructor), GSID, Nagoya University

Published

November 5, 2025

1 Overview

This tutorial provides an instructional framework for data exploration, variable construction, and productivity analysis using World Bank Enterprise Survey (WBES) data. The document demonstrates practical Stata implementations for production function estimation and descriptive firm-level analysis, designed as a pedagogical resource for empirical economic research.

⚠️ Disclaimer

This tutorial is intended for educational and demonstration purposes only. The methodological approaches and empirical estimates presented herein should not be regarded as final or definitive findings. Any use of these examples for publication or policy analysis must include: (1) formal robustness checks, (2) comprehensive data validation procedures, (3) sensitivity analysis, and (4) consultation with relevant methodological literature. Results from survey data such as WBES are subject to measurement error, sample selection considerations, and potential bias from unobserved heterogeneity.


1.1 Part I: Data Exploration & Cleaning

Overview of WBES dataset structure, variable identification, and initial data quality assessment. This section focuses on understanding the raw data, identifying relevant variables, and addressing data quality concerns.

2 Loading & Exploring Data

Before beginning analysis, it is essential to understand the structure, contents, and quality characteristics of the raw dataset. The following commands provide an initial overview:

// Load the dataset
use "Vietnam_2005_2009_2015.dta", clear

// Basic summary and structure inspection
set line 200
//describe

// Examine key variables using codebook
codebook n6a n7a n5a d2 n2a n2e n2b n2i, compact

Running C:\Users\sinth\ado\personal\profile.do ...
command window is unrecognized
r(199);

Variable    Obs Unique      Mean  Min       Max  Label
--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
n6a        2611   1236  1.75e+11   -9  2.00e+14  Net Book Value Of Machinery Vehicles, And Equipment In Last Fiscal Year
n7a        2484    429  1.03e+11   -9  8.00e+13  Cost For Establishment To Re-Purchase All Of Its Machinery
n5a        2035    691  9.53e+09   -9  4.00e+12  Total Annual Expenditure For Purchases Of Equipment In Last Fiscal Yr
d2         3194   1491  1.10e+11   -9  2.82e+13  In Last Fiscal Year, What Were This Establishment's Total Annual Sales?
n2a        3195   1271  9.31e+09   -9  1.50e+12  Total Labor Cost (Incl. Wages, Salaries, Bonuses, Etc) In Last Fiscal Year
n2e        2600   1434  5.60e+10   -9  1.44e+13  Cost Of Raw Materials And Intermediate Goods Used In Prod. In Last Fiscal Year
n2b        1885    282  1.91e+09   -9  5.92e+11  Total Annual Costs Of Electricity In Last Fiscal Year
n2i         426    151  1.38e+10   -9  7.36e+11  Total Annual Cost Of Finished Goods/Materials Bought To Resell In Last Fiscal Yr
--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
📊 See All Variables and Descriptions
codebook, compact

Variable       Obs Unique       Mean       Min       Max  Label
--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
idstd2015     1406    996   599412.5    598835    599830  WEB STD FIRMID
id2015         996    996   5282.354         7     14682  ID number of 2009 survey
idstd2009     1410   1053   466537.9    466100    467152  WEB STD FIRMID
id2009        1410   1053   1271.153         4      3423  ID number of 2009 survey
idstd2005     1507   1150   60580.43     60001     61150  
id2005        1150   1150   5976.563         6     18017  Establishment No.
year          3199      3    2009.43      2005      2015  Year of survey
panel         3199      6   3.041888         1         6  Panel: Firm interviewed in these years
eligibi~2015  1410     21          .         .         .  Eligibility of 2009 firm in 2015
_2015_pref~e   996     78    18.8243         1        94  preference
_2015_rota~n     0      0          .         .         .  rotation
a0            2049      3   1.488043         1         3  Questionnaire
a1            3199      1         99        99        99  Country Code
_2015_a1a      996      1          7         7         7  Language Of The Interview
_2015_a2       996      4   2.301205         1         4  Sampling Region
_2015_a3a      996      4   2.328313         1         4  Region Of The Establishment
_2015_a3a2     996      1          7         7         7  Currency used in accounting records
_2015_a3b      996      2   1.826305         1         2  Is this city the official capital city?
_2015_a3c      995      2    1.80402         1         2  Is this city the main business city?
_2015_a3       995      5   2.554774         1         5  Size Of Locality
_2015_a4a      996     28   30.18273        15        72  Industry Sampling Sector
_2015_a4b      996     28   30.97892        15        72  Industry Screener Sector
a5            2049      3   1.354807         1         3  Sector Match Between Screener Information And Sample Frame
a6a           2049      4   1.961933         0         3  Sampling Size
a6b           2049      4   2.068326         0         3  Screener Size
a7            2049      2   1.905808         1         2  Establishment Is Part Of A Large Firm
a7a           3188     40    1.75941        -9       160  Number Of Establishments In The Firm
a8a           1137      5  -5.471416        -7         4  Type Of Establishment
a9            1106      3   -6.15642        -7         2  Establishment's Financial Statements Prepared Separately from HQ Statements
a10           1106      3  -6.170886        -7         2  Establishment's Financial Statements Separate from Other Establishments
a11           1084      3   -6.21679        -7         2  If HQ, Financial Statements Independent Of The Rest Of Establishment
_2015_a11a      31      9   2.612903        -9        22  How Many Establishments Are Included In The Financial Statements?
a12           2048     93   203.8506        19      1111  Interviewer Number
a13           2048     36   424.7202         0      9999  Supervisor Number
a14d          2049     31    15.6408         1        31  Day
a14m          2047     12   7.332682         1        12  Month
a14y          2049      5   2011.945      2009      2016  Year
a14h          2048     22   11.11084         1        22  Hour
a14min        2049     60   19.76184         0        59  Minutes
_2015_eaa~3w     0      0          .         .         .  The starting month of the last complete fiscal year
_2015_eaa~3x     0      0          .         .         .  The current month
_2015_eaa~3y   996      2   2.703815         2         3  Last complete fiscal year
_2015_eaa~3z   996      2   2.703815         2         3  Last complete fiscal year minus two
b1            3198      7   3.412133        -9         6  Legal Status of the Firm
b1x            103     26          .         .         .  Other (Specify)
b3            2654    126   66.34446        -9       100  What Percentage Of This Firm Does The Largest Owner(s) Own?
b2a           3194     97   81.03629        -9       100  % owned by Private Domestic Individuals, Companies Or Organizations
b2b           3194     54   9.313278        -9       100  % owned by Private Foreign Individuals, Companies Or Organizations
b2c           3194     88   9.250657        -9       100  % owned by Government/State
b2d           3194     22   .2645217        -9       100  % owned by Other
b4            2584      3   1.500774        -9         2  Amongst the owners of the firm, are there any females?
_2015_b4a      475     65   49.61474        -9       100  What percentage of the firm is owned by females?
b5            3196     72    1992.84        -9      2014  Year Establishment Began Operations
b6            2049    143   84.99854        -9      4000  Number Of Full-Time Employees Of The Establishment When It Started Operations
b6a           2049      3    1.04734        -9         2  Was Establishment Formally Registered When It Began Operations?
b6b           2049     62   1988.147        -9      2014  In What Year Was This Establishment Formally Registered?
b7            3184     54    14.6919        -9        70  How Many Years Of Experience Working In This Sector Does The Top Manager Have?
b7a           2049      3   1.745242        -9         2  Is The Top Manager Female?
b8            3195      4   1.524883        -9         2  Does Establishment Have An Internationally-Recognized Quality Certification?
_2015_b8x      178     85          .         .         .  Internationally-recognized certifications
c3            3199      3   1.775555        -9         2  Application To Obtain An electrical connection Submitted Over The Last 2 Years
c4             597     31   22.66499        -9       750  How Many Days Did It Take For You To Receive An Electrical Connection Service?
c5             596      4   1.560403        -9         2  Informal Gift/Payment Expected Or Requested For An Electrical Connection?
c6            3199      3   1.373554        -9         2  Over last FY, Did This Establishment Experience Power Outages?
c7            2102     15   .9904853        -9        60  Number Of Power Outages Experienced In A Typical Month In Last Fiscal Year
c8a           1625     46   8.062462        -9       480  How long did these power outages last on average? (Hours)
_2015_c8b      183     12   4.786885        -9        59  How long did these power outages last on average? (Minutes)
c9a           1426     50   1.319595        -9        70  In last FY, losses as % of annual sales due to power outages
c9b            230     70   4.34e+08        -9  2.00e+10  In last FY, value of losses due to power outages
c10           2939      4   1.602586        -9         2  Generator Shared Or Owned Over The Course Of Last Fiscal Year?
c11           1709     43   4.431165        -9       100  % Electricity From Generator Owned/Shared By The Establishment In Last Fiscal Yr
c12           3198      3    1.51626        -9         2  Application To Obtain A Water Connection Submitted Over The Last 2 Years
c13            294     27   16.40476        -9       485  How Many Days Did It Take For You To Obtain A Water Connection?
c14            296      4    1.60473        -9         2  When You Applied For A Water Connection, Was An Informal Gift/payment Requested?
c15           2614      4   1.540168        -9         2  Did You Experience Insufficient Water Supply For Production In Last Fiscal Yr?
c16           1170     14   .1880342        -9        30  Frequency Of Incidents Of Water Shortages In A Typical Month In Last Fiscal Yr
c17            109     19   27.91284        -9      2160  Average Length Of Water Shortages (Affecting Production) In Last Fiscal Year
c22a          3199      3   1.181307        -9         2  Do You Currently Communicate With Clients And Suppliers By E-Mail?
c22b          3199      3   1.582057        -9         2  Establishment has its own website
c30a          3196      7   .9499374        -9         4  How Much Of An Obstacle: Electricity To Operations Of This Establishment?
c30b          2247      7   .5540721        -9         4  How Much Of An Obstacle: Telecommunications To Operations Of This Establishment?
d1a1x         3196   2672          .         .         .  Main Product/Service (By The Largest % Of Annual Sales) In Last Fiscal Year
d1a2          3177    284   2757.872        -9      9403  First Product/Service Isic Code
d1a3          3187    101   81.31311        -9      5050  First Product/Service, Percent Of Total Annual Sales
d2            3194   1491   1.10e+11        -9  2.82e+13  In Last Fiscal Year, What Were This Establishment's Total Annual Sales?
_2015_d2x      968    417          .         .         .  Establishment's total annual sales (in words)
n3            3015   1257   1.06e+11        -9  6.49e+13  What Were the Establishment Sales 3 years ago
_2015_n3x      900    367          .         .         .  Establishment's total annual sales 3 years ago (in words)
d3a           3191     85   74.99831        -9       100  % of sales: National sales
d3b           3191     46   6.691482        -9       100  % o sales: Indirect exports
d3c           3190     83   17.91783        -9       100  % of sales: Direct exports
d4             918     26   4.080381        -9        90  In last FY, avg. num. of days for exported goods to clear customs?
_2015_ead4a    184      4   .4565217        -9         2  In fiscal year 2013, when this establishment exported goods directly,  was a gif
d6            1106      9   .0290325        -9       100  In last FY, Export Losses Due To Theft As % Of value of products
d7            1113     18    .069389        -9        20  In last FY, Export Losses Due To Breakage Or Spoilage As % Of value of products
d8            1106     43   1973.396        -9      2014  In What Year Did This Establishment First Export Directly Or Indirectly?
d10           2901     21  -.0814202        -9       100  In last FY, % of value of products lost in transit due to theft
d11           2869     33   .3089369        -9        60  In last FY, % of value of products lost in transit due to breakage or spoilage?
d12a          2603     59   65.91685        -9       100  % Of Material Inputs And Supplies Of Domestic Origin In Last Fiscal Year
d12b          2603     59   32.22452        -9       100  % Of Material Inputs And Supplies Of Foreign Origin In Last Fiscal Year
d13           1915      3   1.597389        -9         2  Were Any Of These Material Inputs And Supplies Imported Directly?
d14            843     27   7.294781        -9        90  Avg. num. of Days For Imported Goods To Clear Customs In Last Fiscal Year
d16           2600     52   39.92654        -9       500  Avg. days of inventory of most important input kept by establishment
d17            424     24   20.99057        -9       365  Avg. days of inventory of main sales item kept by establishment
d30a          3194      7   .9793363        -9         4  How Much Of An Obstacle: Transport?
d30b          2900      7  -.2817241        -9         4  How Much Of An Obstacle: Customs And Trade Regulations?
e1            1462      4   1.737346        -9         3  In last FY, main market for establishment's main product
e2b           1335     24   3.217228        -9       500  In main market, number of  competitors faced by establishment's main product
e6            1462      3   1.532832        -9         2  Do You Use Technology Licensed From A Foreign-Owned Company?
e11           2049      3   1.250366        -9         2  Does This Establishment Compete Against Unregistered Or Informal Firms?
e30           3096      7   .6705426        -9         4  How Much Of An Obstacle: Practices of competitors in informal sector?
_2015_h1       996      3   1.606426        -9         2  New or signif. improved product introduced in last three years?
_2015_h2       304      3     1.1875        -9         2  New or signif. improved product also new to the establishment's main market?
_2015_eah2x    193    189          .         .         .  describe the main new or significantly improved product or service that this est
_2015_eah2a    304     31   35.07895        -9       100  % of sales from the main new or significantly improved product or service
_2015_eah4a    304      3   1.470395        -9         2  Does main new or significantly improved product or service have completely new f
_2015_eah4b    304      4   1.259868        -9         2  Is main new or significantly improved product or service cheaper to produce or o
_2015_eah4c    304      4   .6644737        -9         2  Is main new or significantly improved product or service a better quality produc
_2015_eah10    304      3   1.233553         1         3  By who was the main new or significantly improved product or service developed?
_2015_eah11a   996      3   1.839357        -9         2  During the last three years, did this establishment attempt to develop a new or
_2015_eah11b   996      3    1.76004        -9         2  During the last three years, did this establishment attempt to develop a new or
_2015_h3       996      3    1.60241        -9         2  New or sig. impvd method of manf product or offering services in last 3 years?
_2015_h4a      996      3    1.35241        -9         2  New or significantly improved logistics, delivery, or distribution methods?
_2015_h4b      996      3    1.63755        -9         2  New or significantly improved organizational structures/management practices?
_2015_eah12a   456      3   1.059211        -7         2  Does the main new or significantly improved process Automate manual processes, p
_2015_eah12b   456      4   1.326754        -9         2  Does the main new or significantly improved process Introduce a new technology o
_2015_eah13    456      4   1.092105        -9         3  By who was the main new or significantly improved process developed
_2015_h5       996      3   1.792169        -9         2  New or sig. impvd organizational structure introduced in last 3 years?
_2015_eah14a   177      2   1.237288         1         2  During the last three years, did the establishment create a new unit or departme
_2015_eah14b   177      2   1.689266         1         2  During the last three years, did the establishment dissolve any units or departm
_2015_eah14c   177      2    1.59322         1         2  During the last three years, did the establishment merge any units or department
_2015_h6       996      3   1.618474        -9         2  New or signif. improved marketing method introduced in last three years?
_2015_h7       996      3   1.722892        -9         2  During the last three years, did establ. spend on formal R&D activities?
_2015_h8       221     57   1.13e+13        -9  1.50e+15  How much did this establishment spend on formal research and development?
_2015_eah15    996      3   1.636546        -9         2  During the last three years did this establishment provide formal training to an
_2015_eah16    996      3   1.893574        -9         2  During the last three years did this establishment purchase or license any paten
f1            2608     48   75.95483        -9       150  In last FY, What Was The Capacity Utilization (%) Of This Establishment?
f2            2602     81   55.32321        -9       168  Number Of Hours Per Week Operated By The Establishment In Last Fiscal Year
g6a           1107     22   63.96025        -9       100  Percentage Of The Building Occupied: Owned By This Establishment
g6b           1107     22     35.028        -9       100  Percentage Of The Building Occupied: Leased or Rented By This Establishment
g6c           1107      4    .438121        -9       100  Percentage Of The Building Occupied: Other
_2015_g1a      996     17   51.44378        -9       100  Percentage Of The Land Occupied: Owned By This Establishment
_2015_g1b      996     17    46.6245        -9       100  Percentage Of The Land Occupied: Rented Or Leased By This Establishment
_2015_g1c      996      6   .7961847        -9       100  Percentage Of The Land Occupied: Other
g2            3199      3   1.773054        -9         2  Applic. To Obtain A Construction-Related Permit Submitted Over The Last 2 Years
g3             652     42    40.9816        -9       730  How Many Days Did It Take For You To Obtain A Construction-Related Permit?
g4             651      4   1.159754        -9         2  Informal Gift/Payment Expected Or Requested For A Construction-Related Permit
g5a            422     64   454.9147        -9     30000  What Is The Total Selling Area In This Establishment?
g5b            245      3   2.971429         1         4  The Area Is Measured In:
g5bx             4      4          .         .         .  Specify Other Units (If Not Included Above) The Area Is Measured In
g30a          3173      7   .6766467        -9         4  How Much Of An Obstacle: Access To Land?
i1            3199      3   1.249766        -9         2  In last FY, Did This Establishment Pay For Security?
i2a            241     18   2.246888        -9       100  Percentage Of Total Annual Sales Paid For Security In Last Fiscal Year
i2b           2223    285   5.39e+08        -9  6.00e+11  In Last Fiscal Year, What Is The Total Annual Cost Of Security?
i3            3199      3   1.836824        -9         2  Losses Due To Theft, Robbery, Vandalism Or Arson Experienced In Last Fiscal Year
i4a            129     13   1.023256        -9        50  Losses Due To Theft, Robbery, Vandalism Or Arson In Last Fiscal Yr (% Of Sales)
i4b           1321     62   2.37e+07        -9  5.00e+09  Value Of Losses Due To Theft, Robbery, Vandalism Or Arson In Last Fiscal Yr
i30           3118      7   .3588839        -9         4  How Much Of An Obstacle: Crime, Theft And Disorder?
k1c           3174     41   36.34086        -9       100  In last FY, % Of Material Inputs Or Services Paid For After Delivery
k2c           3190     60   54.44634        -9       100  In last FY, % Of Total Annual Sales Paid For After Delivery
k3a           3188     94   51.16625        -9       100  % Of Working Capital Financed From Internal Funds/Retained Earnings
k3bc          3188    100   24.41268        -9       100  % Of Working Capital Borrowed From Banks
k3e           3188     49    1.96881        -9       100  % Of Working Capital Borrowed From Non-Bank Financial Institutions
k3f           3188     56   5.522245        -9       100  % Of Working Capital Purchased On Credit/Advances From Suppliers /Customers
k3hd          3188     91   15.43096        -9       100  % Of Working Capital Financed By Other (Money Lenders, Friends, Relatives, Etc)
k4            3199      3   1.310722        -9         2  Did This Establishment Purchase Any Fixed Assets In Last Fiscal Yr?
n5a           2035    691   9.53e+09        -9  4.00e+12  Total Annual Expenditure For Purchases Of Equipment In Last Fiscal Yr
n5b           1768    415   5.47e+09        -9  3.00e+12  Total Annual Expenditure For Purchases Of Land And Buildings In Last Fiscal Yr
k5a           2356     43   43.98384        -9       100  Last FY, % Fixed Assets Funded By: Internal Funds/Retained Earnings
k5i           2284     30   14.89766        -9       100  Last FY, % Fixed Assets Funded By: Owners' Contributions Or Issued New Equity
k5bc          2353     40   18.76928        -9       100  Last FY, % Fixed Assets Funded By: Bank Borrowing
k5e           2284     29   2.425617        -9       100  Last FY, % Fixed Assets Funded By: Non-Bank Financial Institutions
k5f           2283     17   1.119404        -9       100  Last FY, % Fixed Assets Funded By: Credit From Suppliers/Advances From Customers
k5hdj         2366     25   4.669484        -9       100  Last FY, % Fixed Assets Funded By:  Other (Money Lenders\Friends\Relatives\Etc)
_2015_k5a1      36     23   1.36e+09        -9  1.70e+10  Last FY, value of Fixed Assets Funded By: Internal Funds/Retained Earnings
_2015_k5i1      24      4   3.83e+07        -9  9.20e+08  Last FY, value of Fixed Assets Funded By: Owners' Contributions Or Issued New Eq
_2015_k5bc1     39     12   3.26e+08        -9  1.00e+10  Last FY, value of Fixed Assets Funded By: Bank Borrowing
_2015_k5e1      24      3   9.62e+07        -9  2.31e+09  Last FY, value of Fixed Assets Funded By: Non-Bank Financial Institutions
_2015_k5f1      25      5   5.20e+07        -9  1.00e+09  Last FY, value of Fixed Assets Funded By: Credit From Suppliers/Advances From Cu
_2015_k5hdj1    26      8   1.03e+08        -9  1.89e+09  Last FY, value of Fixed Assets Funded By:  Other (Money Lenders\Friends\Relative
k6            3199      3   1.162863        -9         2  Does This Establishment Have A Checking And\Or Saving Account?
k7            2049      3   1.657882        -9         2  At This Time, Does This Establishment Have An Overdraft Facility?
k8            3199      3   1.300094        -9         2  Establishment has A Line Of Credit Or Loan From A Financial Institution?
k9            1146      5   1.495637        -9         4  Type Of Financial Institution That Granted The Line Of Credit Or Loan
k10           1871     23   1992.875        -9      2015  Year When The Most Recent Loan/Line Of Credit Approved
k11           1148    173   1.11e+12        -9  1.25e+15  For The Most Recent Loan, What Was The Value At The Time Of Approval?
k13           1875      3   1.040533        -9         2  Financing Required For The Most Recent Line Of Credit Or Loan
k14a          1695      3   1.332743        -9         2  Type Of Collateral Required For The Most Recent Loan? Land, Buildings
k14b          2216      3   1.552798        -9         2  Type Of Collateral Required For The Most Recent Loan? Equipment
k14c          1694      3   1.831169        -9         2  Type Of Collateral Required For The Most Recent Loan? Accounts
k14d          1692      3   1.627069        -9         2  Type Of Collateral Required For The Most Recent Loan? Personal Assets
k14e          1670      3   1.861078        -9         2  Type Of Collateral Required For The Most Recent Loan? Other
k15a          1066    161   1.91e+12        -9  2.00e+15  Value Of Collateral Required For The Most Recent Credit/Loan
_2015_k15b     480     37   595.0542        -9     80000  What is the total number of outstanding loans or lines
_2015_k15c     194     79   1.04e+13        -9  2.00e+15  What is the total value of outstanding loans or lines
_2015_k15d     996      3   1.346386        -9         2  does the owner or owners of this establishment have an
k16           2498      3   1.429544        -9         2  In Last Fiscal Yr, Did Establishment Apply For New Loans/Lines Of Credit?
k17           1340      8   1.765672        -9         7  Main Reason For Not Applying For New Loans Or New Lines Of Credit
_2015_k20a1    424      6   1.096698        -9         4  The outcome of the most recent application for a line of credit or loan
k21           3198      3   1.615072        -9         2  Financial Statements Checked & Certified By External Auditor In Last Fiscal Yr?
k30           3149      7   1.060972        -9         4  How Much Of An Obstacle: Access To Finance
h7a           2049      6  -.3840898        -9         4  The Court System Is Fair, Impartial And Uncorrupted
j2            3168     56   3.900638        -9       100  What % Of Senior Management Time Was Spent In Dealing With Govt Regulations?
j3            3199      3   1.305095        -9         2  Over The Last 12 Months, Was This Establishment Inspected By Tax Officials?
j4            2261     22   2.252543        -9        50  Frequency Of Inspections/Requirement For Meeting By Tax Officials
j5            2049      4   1.343582        -9         2  In Any Of These Inspections Was A Gift/Informal payment Requested ?
j6a           3199      3   1.745545        -9         2  Government Contract Secured (Or Attempted) In The Last 12 Months?
j6            1411     28   .8047201        -9        50  % Of Contract Value Avg. Firm Pays In Informal Gifts To Govt To Secure Contract?
j7a           2094     33  -1.423743        -9       100  Percent Of Total Annual Sales Paid In Informal Payments
j7b            673     58   1.37e+08        -9  5.00e+10  Total Annual Informal Payment
j10           3199      3    1.81588        -9         2  Application To Obtain An Import License Submitted Over The Last 2 Years?
j11            435     27   9.374713        -9        90  How Many Days Did It Take To Obtain Your Import License?
j12            434      4    1.25576        -9         2  When You Applied For An Import License, Was An Informal Gift/payment Requested?
j13           3198      3   1.761101        -9         2  Application To Obtain An Operating License Submitted Over Last 2 Years?
j14            632     31   15.68513        -9       700  How Many Days Did It Take To Obtain Your Operating License?
j15            622      4   1.691318        -9         2  When You Applied For Operating License Was An Informal Gift/payment Requested?
j30a          3180      7   .7185535        -9         4  How Much Of An Obstacle: Tax Rates
j30b          3183      7   .3301916        -9         4  How Much Of An Obstacle: Tax Administrations
j30c          3171      7  -.2043519        -9         4  How Much Of An Obstacle: Business Licensing And Permits
j30e          3104      7  -.3150773        -9         4  How Much Of An Obstacle: Political Instability
j30f          3046      7  -.1520026        -9         4  How Much Of An Obstacle: Corruption
h30           2049      7  -1.836994        -9         4  How Much Of An Obstacle: Courts
l1            3198    536   260.5619        -9     19047  Num. Permanent, Full-Time Employees At End Of Last Fiscal Year
l2            3033    452   231.0353        -9     16000  Num. Permanent, Full-Time Employees At End Of 3 Fiscal Years Ago
l3a           2591    564   255.1451        -9     15576  Num. Full-time Employees At End Of Last Fiscal Yr: production workers
l3b           2591    198    39.6592        -9      4500  Num. Full-time Employees At End Of Last Fiscal Yr: non-production workers
l4a           2589    424   188.1487        -9     14101  Num. Full-time Employees At End Of Last Fiscal Yr: Skilled Production Workers
l4b           2588    302   66.68083        -9      4000  Num. Full-time Employees At End Of Last Fiscal Yr: Unskilled Production Workers
_2015_eal4c    685     66   27.53431        -9      3000  Permanent, full-time skilled non-production workers at the end of last fiscal yr
_2015_eal4d    685     46   3.960584        -9       180  Permanent, full-time unskilled non-production workers at the end of last fiscal
_2015_eal5a    685     98   75.35912        -9      6000  Permanent, full-time female skilled production workers at the end of last fiscal
_2015_eal5b    685     57   18.62628        -9      1450  Permanent, full-time female unskilled production workers at the end of last fisc
_2015_eal5c    685     54    14.9854        -9      2000  Permanent, full-time female skilled non-production workers at the end of last fi
_2015_eal5d    685     25   1.312409        -9        92  Permanent, full-time female unskilled non-production workers at the end of last
_2015_eal5s     33      1          1         1         1  MARK
l5a           2265    414   154.4667        -9      9500  Num. Full-time Employees At End Of Last Fiscal Yr: female production workers
l5b           2270    125   17.31542        -9      2500  Num. Full-time Employees At End Of Last Fiscal Yr: female non-production workers
l5            1721    367   138.1836        -9      6475  Num. Full-time Employees At End Of Last Fiscal Yr: female
_2015_eal5a1   311     59   82.25402        -9     13500  Permanent, full-time skilled workers
_2015_eal5a2   311     43   20.13183        -9      3500  Permanent, full-time unskilled workers
_2015_eal5b1   311     42   20.23794        -9      2000  Female permanent, full-time skilled workers
_2015_eal5b2   311     22   2.102894        -9        80  Female permanent, full-time unskilled workers
l6            2542    135   43.11684        -9      5000  Num. Full-Time Temporary Employees At End Of Last Fiscal Yr
_2015_l6a      556     44   22.18345        -9      4500  Full-time female seasonal or temporary workers employed
l8            1621     35   3.688094        -9      1000  Avg. Length Of Employment Of All Full-time Temporary Employees In Last Fiscal Yr
l9a           1462     21   6.106703        -9       100  Average Years Of Education For Typical Production Worker
_2015_l9a2     685     18    5.79708        -9        17  Average number of years of education of typical female
_2015_l9b      996     25   88.60843        -9       100  % Of Full Time Workers Completed High School
l10           2889      3   1.520942        -9         2  Formal Training Programs For Permanent, Full-time Employees In Last Fiscal Yr
_2015_eal10a   739      7   4.465494        -9         6  The main reason why this establishment did not have formal training programs
_2015_eal10b   246      8   5.861789        -9         8  The primary focus of the formal training programs
_2015_eal11a   168     21   54.48214        -9       100  % permanent, full-time, skilled production workers received formal training
_2015_eal11b   168     17   24.64881        -9       100  % permanent, full-time unskilled production workers received formal training
_2015_eal11c   169     24   43.80473        -9       100  % permanent, full-time skilled non-production workers received formal training
_2015_eal11d   168     12   14.39881        -9       100  % permanent, full-time unskilled non-production workers received formal training
_2015_eal12    996      3   1.650602        -9         2  Over the last two years, were any of the permanent full-time workers of this est
_2015_eal13    996      3   1.615462        -9         2  Over the last two years, were any of the permanent full-time workers of this est
_2015_eal14    996      3   1.517068        -9         2  Over the last two years, did this establishment have any vacancies
_2015_eal15a   165     17   4.812121        -9       300  How many of these vacancies were in the skilled non-production workers
_2015_eal16a   102     11    1.95098        -9        20  How many of these vacancies were filled  for skilled non-production workers
_2015_eal17a    61     13   5.065574        -9        60  What was the average number of weeks required to fill these vacancies (skilled n
_2015_myal17    94      3  -.7659574        -9         2  Over the last two years, did this establishment try to hire any managers or seni
_2015_my~18a    17      3   .9411765        -9         2  Did the establishment encounter any of the following problems when trying to hir
_2015_my~18b    17      3   .7058824        -9         2  Did the establishment encounter any of the following problems when trying to hir
_2015_my~18c    17      3   1.176471        -9         2  Did the establishment encounter any of the following problems when trying to hir
_2015_my~18d    17      3   .7647059        -9         2  Did the establishment encounter any of the following problems when trying to hir
_2015_my~18e    17      2   1.352941        -9         2  Did the establishment encounter any other problems when trying to hire a manager
_2015_my~18x     0      0          .         .         .  specify other problems encountered by the establishment when trying to hire a ma
_2015_myal24    94      3  -1.882979        -9         2  Over the last two years, did this establishment try to hire any (non-production)
_2015_my~25a    13      3   .7692308        -9         2  Did the establishment encounter any of the following problems when trying to hir
_2015_my~25b    13      3   .6153846        -9         2  Did the establishment encounter any of the following problems when trying to hir
_2015_my~25c    13      3          1        -9         2  Did the establishment encounter any of the following problems when trying to hir
_2015_my~25d    13      3  -.8461538        -9         2  Did the establishment encounter any of the following problems when trying to hir
_2015_my~25e    13      2   1.153846        -9         2  Did the establishment encounter any other problems when trying to hire a (non-pr
_2015_my~25x     0      0          .         .         .  specify other problems encountered by the establishment when trying to hire non-
_2015_eal15b   165      8  -.1636364        -9        11  How many vacancies were in the unskilled non-production workers
_2015_eal16b    14      6        -.5        -9         5  How many of these vacancies were filled (unskilled non-production workers)?
_2015_eal17b     9      6   .5555556        -9         7  What was the average number of weeks required to fill these vacancies (unskilled
_2015_my~32a    15      3  -1.266667        -9         2  Did the establishment encounter any of the following problems when trying to hir
_2015_my~32b    15      3       -1.4        -9         2  Did the establishment encounter any of the following problems when trying to hir
_2015_my~32c    15      3  -1.066667        -9         2  Did the establishment encounter any of the following problems when trying to hir
_2015_my~32d    15      3       -1.2        -9         2  Did the establishment encounter any of the following problems when trying to hir
_2015_my~32e    15      2        -.2        -9         2  Did the establishment encounter any other problems when trying to hire a (non-pr
_2015_my~32x     0      0          .         .         .  specify other problems encountered by the establishment when trying to hire unsk
_2015_eal15c   155     23   24.29677        -9      1000  How many vacancies were in the skilled production workers
_2015_eal16c   101     17   23.30693        -9       800  How many of these vacancies were filled (skilled production workers)?
_2015_eal17c    64     11   3.453125        -9        45  What was the average number of weeks required to fill these vacancies (skilled p
_2015_my~39a   102      3   -.754902        -9         2  Did the establishment encounter any of the following problems when trying to hir
_2015_my~39b   102      3  -.8627451        -9         2  Did the establishment encounter any of the following problems when trying to hir
_2015_my~39c   102      3  -.8627451        -9         2  Did the establishment encounter any of the following problems when trying to hir
_2015_my~39d   102      3  -1.107843        -9         2  Did the establishment encounter any of the following problems when trying to hir
_2015_my~39e   102      3        -.5        -9         2  Did the establishment encounter any other problems when trying to hirea skilled
_2015_my~39x     2      2          .         .         .  specify other problems encountered by the establishment when trying to hire skil
_2015_eal15d   155     21   10.70968        -9       600  How many vacancies were in the unskilled production workers
_2015_eal16d    40     17       32.7        -9       600  How many of these vacancies were filled (unskilled production workers)?
_2015_eal17d    32      8    2.21875        -9         8  What was the average number of weeks required to fill these vacancies (unskilled
_2015_my~46a    41      3  -1.243902        -9         2  Did the establishment encounter any of the following problems when trying to hir
_2015_my~46b    41      3  -1.682927        -9         2  Did the establishment encounter any of the following problems when trying to hir
_2015_my~46c    41      3  -1.195122        -9         2  Did the establishment encounter any of the following problems when trying to hir
_2015_my~46d    41      3  -1.756098        -9         2  Did the establishment encounter any of the following problems when trying to hir
_2015_my~46e    41      3  -1.243902        -9         2  Did the establishment encounter any other problems when trying to hirea unskille
_2015_my~46x     1      1          .         .         .  specify other problems encountered by the establishment when trying to hire unsk
_2015_eal18a   217      6   .4009217        -9         4  Over the last two years, using this card, please indicate the degree of difficul
_2015_eal18b   217      6   .9447005        -9         4  Over the last two years, using this card, please indicate the degree of difficul
_2015_eal18c   217      6  -.2764977        -9         4  Over the last two years, using this card, please indicate the degree of difficul
_2015_eal18d   217      6   2.502304        -9         4  Over the last two years, using this card, please indicate the degree of difficul
_2015_eal18e   217      6  -.6082949        -9         4  Over the last two years, using this card, please indicate the degree of difficul
_2015_eal18f   217      6    .202765        -9         4  Over the last two years, using this card, please indicate the degree of difficul
_2015_eal18g   217      6   2.262673        -9         4  Over the last two years, using this card, please indicate the degree of difficul
_2015_eal19    217     29   13.78341        -9      1000  How many unfilled vacancies does this establishment currently have?
_2015_eal20    148      2   1.432432         1         2  Have any of these unfilled vacancies being vacant for more than four months?
_2015_myal~1   685     56   29.43358        -9      9999  In one year from now, how many permanent, full-time individuals do you expect to
_2015_myal~2   685     35   18.14161        -9      9999  In one year from now, how many permanent, full-time individuals do you expect to
_2015_my~52b   685     74   117.1693        -9      9999  In one year from now, how many permanent, full-time individuals do you expect to
_2015_my~52c   685     49   38.56934        -9      9999  In one year from now, how many permanent, full-time individuals do you expect to
_2015_my~52d   685     41   25.86277        -9      9999  In one year from now, how many permanent, full-time individuals do you expect to
l30a          3185      7   .5827316        -9         4  How Much Of An Obstacle: Labor Regulations?
l30b          3191      7   .9824506        -9         4  How Much Of An Obstacle: Inadequately Educated Workforce?
m1a           3179     22   7.376219        -9        21  Biggest Obstacle Affecting The Operation Of This Establishment
n2a           3195   1271   9.31e+09        -9  1.50e+12  Total Labor Cost (Incl. Wages, Salaries, Bonuses, Etc) In Last Fiscal Year
n2e           2600   1434   5.60e+10        -9  1.44e+13  Cost Of Raw Materials And Intermediate Goods Used In Prod. In Last Fiscal Year
n2f           1462    224   3.25e+09        -9  1.10e+12  Total Annual Costs Of Fuel In Last Fiscal Year
n2b           1885    282   1.91e+09        -9  5.92e+11  Total Annual Costs Of Electricity In Last Fiscal Year
n2i            426    151   1.38e+10        -9  7.36e+11  Total Annual Cost Of Finished Goods/Materials Bought To Resell In Last Fiscal Yr
_2015_n2p      685    426   2.82e+11        -9  1.08e+14  Total costs of production
n6a           2611   1236   1.75e+11        -9  2.00e+14  Net Book Value Of Machinery Vehicles, And Equipment In Last Fiscal Year
n6b           2535    385   1.60e+12        -9  4.00e+15  Net Book Value Of Land And Buildings In Last Fiscal Year
n7a           2484    429   1.03e+11        -9  8.00e+13  Cost For Establishment To Re-Purchase All Of Its Machinery
n7b           2450    555   6.89e+10        -9  4.00e+13  Cost For Establishment To Re-Purchase All Of Its Land And Buildings
_2015_a15~ax   996    115          .         .         .  Main Respondent Position In The Firm
a15a2a        2049     44   8.886286        -9        58  Main Respondent Years Working In The Firm:
a15a3         2048      2    1.40625         1         2  Main respondent's gender
_2015_a15~bx    55     18          .         .         .  Second Respondent Position In The Firm
a15a2b         139     25   8.374101         1        33  Second Respondent Years Working In The Firm:
a15b3          142      3   1.577465         0         2  Second respondent's gender
_2015_a15~cx     6      3          .         .         .  Third Respondent Position In The Firm
_2015_a15a2c     2      2          4         2         6  Third Respondent Years Working In The Firm:
_2015_a15c3      2      1          2         2         2  Third respondent's gender
a15d          2049     31   16.15959         1        31  Day
a15m          2049     12   7.349927         1        12  Month
_2015_a15y     996      3   2014.964      2014      2016  Year
a15h          2049     22    11.5183         1        22  Hour
a15min        2049     60   26.09956         0        59  Minutes
a16           2041      3   1.352768         1         3  Perception Of The Questions Regarding Opinions And Perceptions
_2015_a17      988      4   2.134615         1         4  Responses To The Questions About Figures Are:
a18           2049      3   1.104441         1         3  This Questionnaire Was Completed In:
a19h           165      4   1.084848         0         3  If Option 2 Or 3 In A.18, Estimate Duration Of The Whole Interview: Hours
a19m           165     16   20.47879         0        55  If Option 2 Or 3 In A.18, Estimate Duration Of The Whole Interview: Minutes
_2015_eava0    232      2    1.12931         1         2  Success of the paper follow-up
_2015_~ncode   996      4   2.300201         1         4  stratification region code
_2015_~ecode   996      3   1.879518         1         3  stratification size code
_2015_~rcode   996      7   3.991968         1         7  stratification sector code
_2015_strata   996    156   71.30422         1       156  see notes
l11a          1530    148    53.2782        -9       200  % Permanent Full-time Production Employees Received Formal Training In Last FFY
l11b           382     36    32.8098        -9       100  % Permanent Fulltime NonProduction Employees Received Formal Training In Last FY
_2009_200~a2  2203      5   3.089877         1         5  A.2. Sampling Region
_2009_200~3a  2203      5   3.089424         1         5  A. 3a. Region
_2009_a3      1051      5   2.181732         1         5  A.3. Size of locallity
_2009_200~4b  2203     19   22.42306         2        72  A.4b. Industry Screener sector
_2009_a14     1047    877   1.57e+11  1.07e+10  3.11e+11  A.14. Time face to face interview begins
_2009_2005~7  2196      8   4.900729        -9         7  EAB7. What is the Top Manager's highest completed level of e
_2009_200~18   871     21   37.84039         0       100  C18. What percentage of this establishment's water supply, u
_2009_200~19  2203      3   1.571493        -9         2  C19. Did this establishment submit an application to obtain
_2009_200~20   925     31   10.37405        -9       750  C20. From the day of the application to the day telephone co
_2009_200~21   912      3   1.926535        -9         2  C21. For a telephone connection, was an informal gift or pay
_2009_c23      115      3   1.104348         0         2  C23. have a high-speed Internet connection on its premises?
_2009_c24b     100      2       1.23         1         2  C24b. Make purchases for this establishment
_2009_c24c     100      2       1.23         1         2  C24c. Deliver services to this establishment's clients
_2009_c24d     100      2       1.39         1         2  C24d. Do research and develop ideas on new products and serv
_2009_c25      100      3       1.37        -9         2  C25. did this establishment experience unavailability of Int
_2009_c26       52     11   2.461538        -9        29  C26. how many times has this establishment experienced unava
_2009_c27       48     14   103.0833        -9       720  C27. how long did the average instance of  Internet disconne
_2009_EAd2a    778      5   1.611825        -9         4  EAD2a. this establishment's production fall into which categ
_2009_200~ax   802    155          .         .         .  EAD8a. which country was the main recipient of exports value
_2009_EAd8b    367     25    1887.03        -9      2008  EAD8b. first export directly or indirectly to this country?
_2009_EAd8c    366      3   1.494536        -9         2  EAD8c. ever have to stop export operations to the main recip
_2009_EAd8d     64      5   -.390625        -9         9  EAD8d. Ho w many years did it take before you were able to r
_2009_200~8e   795    576   3.76e+10        -9  2.13e+12  EAD8e. the total value of exports (direct and indirect) to t
_2009_200~gx   693    144          .         .         .  EAD8g. country was the second most important recipient of ex
_2009_EAd8h    263     25   1903.011        -9      2009  EAD8h. year did this establishment first export directly or
_2009_200~8i   588    422   2.26e+10        -9  3.19e+12  EAD8i. total value of exports (direct and indirect) to the s
_2009_EAd8k    367      3   1.425068        -9         2  EAD8k. introduce a new country as an export market?
_2009_EAd8l     90      4   1.744444        -9         3  EAD8l. For this new country, is this establishment expecting
_2009_EAd8m1   367      3   1.640327        -9         2  EAD8m1. There is little or no domestic demand for this estab
_2009_EAd8m2   367      3    1.13624        -9         2  EAD8m2. There is significant foreign demand for goods produc
_2009_EAd8m3   367      3   1.558583        -9         2  EAD8m3. There are favorable government incentives when expor
_2009_EAd8m4   367      3   1.651226        -9         2  EAD8m4. Exporting occurs due to an existing relationship bet
_2009_EAd8m5   366      3   1.729508        -9         2  EAD8m5. Other
_2009_EA~m5x    36     36          .         .         .  EAD8m5x. Specify
_2009_EAd8n1   411      3   1.459854        -9         2  EAD8n1. Foreign markets are too competitive
_2009_EAd8n3   411      3   1.734793        -9         2  EAD8n3. Weak customs facilities in
_2009_EAd8n4   411      3    1.80292        -9         2  EAD8n4. Weak transportation facilities in
_2009_EAd8n5   411      3   1.396594        -9         2  EAD8n5. This establishment is too small to export
_2009_EAd8n6   411      3    1.73236        -9         2  EAD8n6. Regulatory barriers faced by this establishment in
_2009_EAd8n7   411      3   1.530414        -9         2  EAD8n7. Financial constraints faced by this establishment
_2009_EAd8n8   407      3   1.530713        -9         2  EAD8n8. Other
_2009_EAd~8x   101     97          .         .         .  EAD8n8x . Other_ specify
_2009_EAd~a1   284      3   1.299296        -9         2  EAD13a1.  China
_2009_EAd~a2   283      3   1.929329        -9         2  EAD13a2. Philippines
_2009_EAd~a3   283      2   1.837456         1         2  EAD13a3. Indonesia
_2009_EAd~a5   282      2   1.968085         1         2  EAD13a5. Cambodia
_2009_EAd1~6   283      2   1.756184         1         2  EAD13a6. Thailand
_2009_EAd1~7   282      2   1.978723         1         2  EAD13a7. Laos
_2009_EAd~a8   282      3   1.815603        -9         2  EAD13a8. Malaysia
_2009_EAd~b1   284      3   1.369718        -9         2  EAD13b1. Cannot obtain inputs or supplies from domestic mark
_2009_EAd~b2   284      3    1.56338        -9         2  EAD13b2. Cheaper to obtain inputs or supplies from abroad
_2009_EAd~b3   284      3   1.306338        -9         2  EAD13b3. The quality of the inputs or supplies is higher fro
_2009_EAd1~4   284      3    1.71831        -9         2  EAD13b4. Establishment is under a commercial agreement such
_2009_EAd~b5   284      2   1.943662         1         2  EAD13b5. Other
_2009_EAd1~x    15     15          .         .         .  EAD13b5x. Other _ Specify
_2009_EAd17a   776      3   1.960052        -9         2  EAD17a. China
_2009_EAd17b   775      3   1.983226        -9         2  EAD17b. Philippines
_2009_EAd17c   775      3   1.983226        -9         2  EAD17c. Indonesia
_2009_EAd17e   775      3   1.963871        -9         2  EAD17e. Cambodia
_2009_EAd17f   775      3   1.981935        -9         2  EAD17f. Thailand
_2009_EAd17g   775      3   1.965161        -9         2  EAD17g. Laos
_2009_EAd17h   775      3   1.983226        -9         2  EAD17h. Malaysia
_2009_EAd18    756      6   3.195767        -9         5  EAD18. What was your primary reason for not opening a facili
_2009_EAd19     29      5   1.724138        -9         4  EAD19. What was your primary reason for opening a faciilty a
_2009_EAd30c   777      7   -1.97426        -9         4  EAD30c. Port and airports operations and administration
_2009_EAd30d   777      7  -2.435006        -9         4  EAD30d. Availability, cost and efficiency of international a
_2009_EAd30e   777      7  -1.577864        -9         4  EAD30e. Availability cost and efficiency of international sh
_2009_EAd30f   777      7  -1.303732        -9         4  EAD30f. Telecommunication links with foreign suppliers and c
_2009_e7a      777      3   1.945946        -9         2  E7a. Have any patents of registered abroad?
_2009_e7b      777      3   1.872587        -9         2  E7b. Have any patents registered in ...
_2009_e14      113      4   1.283186        -7         2  E14. introduced new lines ... in response to pressures from
_2009_e15      112      3   1.321429        -7         2  E15. introduced new lines ... in response to pressures from
_2009_200~g2  2119      3   1.538462        -9         2  VN_G2. submit an application to obtain a land use certificat
_2009_2005~3   383     59   105.8172        -9      1095  VN_G3 .days obtain land use certificate from application to
_2009_2005~4   399      5   .7343358        -9         2  VN_G4. for a land use certificate, was an informal gift or p
_2009_g7       111      2   1.648649         1         2  G7. has this establishment acquired or attempted to acquire
_2009_g8a       41      2   1.341463         1         2  G8. has this establishment been successful in acquiring land
_2009_k1a     1053     22   23.02849        -9       100  K1a. Paid for before the delivery as a proportion of annual
_2009_k1b     1053     25   21.36372        -9       100  K1b. Paid for on delivery  as a proportion of annual purchas
_2009_200~2a  2194     40   12.16531        -9       100  K2a. Paid for before the delivery?
_2009_200~2b  2194     50   30.67387        -9       100  K2b. Paid for on delivery?
_2009_m1d     1053      3   1.825261         1         3  M1d. Rotation
_2009_n2c      115     47   5.55e+07        -9  1.00e+09  N2c. Total annual costs of communications services
_2009_a15     1046    970    8139728   1010245  1.23e+07  A.16. Time face to face interview begins
_2009_a17     1053      3   1.519468         1         3  A17. The responses to the questions regarding figures
_2009_a17x      76     53          .         .         .  A17x. Interviewer comments
_2009_a4a     1053     13   21.19658         2        52  
_2009_strata  1053    103   54.07123         1       103  group(a2 a4anew a6a)
province      1150     24   469.8296       101       815  Name of province
_2005_code3   1149      2   1.873803         1         2  Is the establishment located in an Industrial Zone?
_2005_code3x   128     84          .         .         .  If yes, name
_2005_q01a    1149      8   3.997389         1         8  What is the current legal status of your firm/establishment?
_2005_q01a2    465      2   1.651613         1         2  If your firm/establishment is Joint Stock Company/SOE, does your firm belong to
_2005_q01a3    150      2   1.613333         1         2  If yes, is it a
_2005_q01b    1144      2    1.90035         1         2  Are you a member or branch of another firm?
_2005_q02x       0      0          .         .         .  If other, please specify
_2005_q03a     917      2   1.751363         1         2  Was your firm previously majority-owned by the Government?
_2005_q03b     218     13   2001.312      1912      2005  When was it equitized?
_2005_q04b1    913     10   3.891566         1        10  Which is the following best describes the largest shareholder or owner in your f
_2005_q04b2    128      9   4.757813         1        10  Which is the following best describes the largest shareholder or owner in your f
_2005_q04b3     36      6   6.055556         1         8  Which is the following best describes the largest shareholder or owner in your f
_2005_q04b4      5      4        5.2         3         9  Which is the following best describes the largest shareholder or owner in your f
_2005_q04cx      2      2          .         .         .  If the principal owner(s) is other, please specify
_2005_q05b    1147      2   1.944202         1         2  Does your firm have holdings, factories, stores or service outlets in other coun
_2005_q07     1150     17    8.22087         1        20  What is your main sector of activity?
_2005_q071x    332    246          .         .         .  If other, specify
_2005_q08a2x   774    686          .         .         .  The second main product
_2005_q08b2    759    350          .         .         .  Code of the second main product
_2005_q08c2    766     73   27.57997         2        90  Percent of your establishment total sales
_2005_q08a3x   406    362          .         .         .  The third main product
_2005_q08b3    500    220          .         .         .  Code of the third main product
_2005_q08c3    405     50   18.04474         1        75  Percent of your establishment total sales
_2005_q09      952     36  -383.6828      -555      1000  Number of competitors
_2005_q10     1139      5    3.14662         1         5  The degree of competition your establishment faces for its main products
_2005_q10x     152      3          .         .         .  If other, specify
_2005_q11a     854      7    2.70726         1         7  If your establishment faces tense competition (3&4), the first main source of th
_2005_q11b     766      7   3.381201         1         7  If your establishment faces tense competition (3&4), the second main source of t
_2005_q11x      17     16          .         .         .  If other, specify
_2005_q12a    1143     47   92.45236         0       100  Percentage of your total revenues comes from manafacturing
_2005_q12b    1143     36   3.356981         0        90  Percentage of your total revenues comes from services
_2005_q12c    1143     37   4.047253         0        90  Percentage of your total revenues comes from commerce
_2005_q12d    1143     13   .1434046         0        50  Percentage of your total revenues comes from other
_2005_q14a    1126     36   9.649645         0        38  Of these, years working with a domestic firm
_2005_q14b    1128     33   1.394947         0        36  Of these, years working with a foreign firm
_2005_q15a2a  1064     60   23.29251         0       100  Percentage of the establishment's sales were exported directly in 2003
_2005_q15b1    968     19   2.552676         0       100  Approximate percentage of the establishemnt's domestic sales in 2004 were to gov
_2005_q15b2    968     37   23.11745         0       100  Approximate percentage of the establishemnt's domestic sales in 2004 were to sta
_2005_q15b3    968     21   1.983833         0       100  Approximate percentage of the establishemnt's domestic sales in 2004 were to mul
_2005_q15b4    968     21   3.522211         0       100  Approximate percentage of the establishemnt's domestic sales in 2004 were to FDI
_2005_q15b5    968     14   1.007231         0       100  Approximate percentage of the establishemnt's domestic sales in 2004 were to the
_2005_q15b6    968     36   16.93027         0       100  Approximate percentage of the establishemnt's domestic sales in 2004 were to lar
_2005_q15b7    968     49   50.88633         0       100  Other (sales to small establishments, individuals, etc.)
_2005_q15c21   433     56   69.22778         2       100  Percent of the total exports the first biggest destination account for
_2005_q15c22   325     54    23.2888       1.5        98  Percent of the total exports the second biggest destination account for
_2005_q15c~x   243     82          .         .         .  The third biggest destination for the establishment's exports
_2005_q15c23   240     43   14.51329        .4        80  Percent of the total exports the second biggest destination account for
_2005_q15c3b   435     23   5.922989        .5       135  The longest number of days it took to clear customs in 2004
_2005_q15c4    435      2    1.36092         1         2  Did you have to make extra payment (whether formal or informal) to expedite the
_2005_q15c5    439      3   2.077449         1         3  Does your establishment
_2005_q16a2   1141     47   21.26646         0       100  In 2004 percent of purchases of material inputs were purchases through direct im
_2005_q16a21  1038     43   20.11334         0       100  In 2003 percent of purchases of material inputs were purchases through direct im
_2005_q16a3   1141     38   14.29308         0       100  In 2004 percent of purchases of material inputs were purchases through indirect
_2005_q16b     809     36   22.93366         0       100  Percent of purchases of material inputs has a zero import duty
_2005_q17b     395     28   8.355696        .5        90  In 2004 the longest number of days that it took to claim the goods from customs
_2005_q17c     394      2   1.345178         1         2  Did you have to make an extra payment to expedite the clearance process
_2005_q18b     262      4   2.175573         1         4  If yes, do you normally act as the...
_2005_q19      995      7   1.245226         1         7  Why your establishment does not sell or sell more to the Government?
_2005_q19x     171     22          .         .         .  If other, specify
_2005_q21     1144     35   3.289135         0        65  Percent of your purchased material inputs are of lower than agreed upon quality?
_2005_q22a    1148      4   1.760453         1         4  What is the likely overall effect of WTO on your business?
_2005_q22b    1037      2   1.352941         1         2  Have you made or will you make plans to change your business operations because
_2005_q2305   1093      5   1.016468         0         4  Judge impact of regulatory policy uncertainty for the operation and growth of yo
_2005_q2313   1052      5   1.307034         0         4  Judge impact of cost of financing for the operation and growth of your business
_2005_q2314   1030      5   1.092233         0         4  Judge impact of macroeconomic policy for the operation and growth of your busine
_2005_q2318   1024      5   .4423828         0         4  Judge impact of conflict resolution for the operation and growth of your busines
_2005_q2320   1073      5   .5200373         0         4  Judge impact of environmental regulations for the operation and growth of your b
_2005_q2321    635      5   .1984252         0         4  Judge impact of other for the operation and growth of your business
_2005_q2321x    37     34          .         .         .  If other, specify
_2005_q23b2   1109     20   8.724076         1        21  Among all of the above alternatives, indicate which one constitutes the second b
_2005_q24a2   1111     36   76.29163         5       150  Average design capacity utilization in 2003
_2005_q251     657     44   74.63318         2       700  Expand design capacity, by what percent?
_2005_q252      12     10   36.41667        10       100  Reduce design capacity, by what percent?
_2005_q26     1144      3    1.88549         1         3  Has your establishment received an internationally-recognized quality certificat
_2005_q27a    1150      2   1.561739         1         2  Developed an important new product line
_2005_q27b    1148      2   1.340592         1         2  Upgraded an existing product line
_2005_q27c    1150      2   1.810435         1         2  Discontinued at least one product line
_2005_q27d    1150      2   1.942609         1         2  Agreed to a new joint venture with foreign partner
_2005_q27e    1149      2   1.904265         1         2  Obtained a new licensing agreement
_2005_q27f    1149      2   1.914708         1         2  Outsourced a major production activity that was previously conducted in-house
_2005_q28a    1149      2   1.549173         1         2  Has your establishment acquired new technology over 2004 and 2003?
_2005_q28b1    513      8   2.327485         1         8  1st most important way to acquire new technology
_2005_q28b2    322      8   4.391304         1         8  2nd most important way to acquire new technology
_2005_q28bx    158      9          .         .         .  If other, specify
_2005_q28cx      3      1          3         3         3  If other, specify
_2005_q29a    1126      4   1.923623         0         3  Pressure from domestic competitors influence on the establishment to reduce the
_2005_q29b     916      4   1.340611         0         3  Pressure from foreign competitors influence on the establishment to reduce the p
_2005_q29c     332      4   .3945783         0         3  Other influence on the establishment to reduce the production costs
_2005_q29cx     47     36          .         .         .  If other, specify
_2005_q30a1   1110     44   11.76676         0       720  Power outages or surges from the public grid (times)
_2005_q30b1    887     46   7.132131        .1       400  How many hours did power outages last on average (hours)
_2005_q30c1    783     42   1.855355         0        30  Total losses of the sales value over the year resulting from power outages (%)
_2005_q30a2   1091     18    .479835         0        73  Insufficient water supply for production (times)
_2005_q30c2    103     16   .7312718         0        30  Total losses of the sales value over the year resulting from insufficient water
_2005_q31c1    282    100   25387.38         1   7000000  Original cost (million VND)
_2005_q31c2     69     58   15395.03       1.2    130000  Original cost (thousand USD)
_2005_q31c3    329     31   1995.511       995      2005  Year of acquisition
_2005_q31c4     48     42   424.5904         6      2459  Original cost (million VND)
_2005_q31c5     14     13   13205.57        20     98000  Original cost (thousand USD)
_2005_q31c6     58     20   1967.414       190      2005  Year of acquisition
_2005_q31c7     15     13   466.4667         6      1700  Original cost (million VND)
_2005_q31c8      6      6    12616.5        10     57500  Original cost (thousand USD)
_2005_q31c9     18     10       2000      1990      2004  Year of acquisition
_2005_q31d     266     51   1758.823       1.5     30000  Approximate cost of generating one kw/h of electricity (VND/kwh)
_2005_q32a    1149      2   1.264578         1         2  Do you use water in the production process?
_2005_q32a1    844     20   37.55685         0       100  Percentage of water supply used in the production gets from
_2005_q32a2    844     20   59.45854         0       100  Percentage of water supply used in the production gets from your own well or a s
_2005_q32a3    844      9   2.984609         0       100  Percentage of water supply used in the production gets from purchased from priva
_2005_q34a    1150      2   1.474783         1         2  Does your establishment use its own transport means for its shipment inside Viet
_2005_q34b     584     27   60.00445         1       100  Percentage of your establishment's shipments use your own transport means
_2005_q34c     593      2   1.330523         1         2  Did you have to make formal payments to traffic police
_2005_q34c1    333     39   2707.556       .05    500000  If yes, how much an average per month (million VND)
_2005_q35     1148      5   1.133275         1         5  What is your main transport mode used when shipping products and/or inputs insid
_2005_q37a1   1146      4   2.814136         1         4  Level of importance of national roads
_2005_q37a2   1146      4   2.838569         1         4  Level of importance of Inter-provincial roads
_2005_q37a3   1146      4    3.71815         1         4  Level of importance of bridges
_2005_q37a4   1146      4   3.930192         1         4  Level of importance of railways
_2005_q37a5   1146      4   3.581152         1         4  Level of importance of seaports
_2005_q37a6   1146      4   3.919721         1         4  Level of importance of airports
_2005_q37a7   1146      4   2.465969         1         4  Level of importance of electricity
_2005_q37a8   1146      4   3.734729         1         4  Level of importance of water
_2005_q37a9   1145      4    3.30655         1         4  Level of importance of telephone
_2005_q37a10  1145      4   3.783406         1         4  Level of importance of internet
_2005_q37b1    585      2   1.859829         1         2  National roads - limited availability/not available in your location
_2005_q37b2    596      2    1.89094         1         2  Inter-provincial roads - limited availability/not available in your location
_2005_q37b3    192      2   1.838542         1         2  Bridges - limited availability/not available in your location
_2005_q37b4     43      2   1.744186         1         2  Railways - limited availability/not available in your location
_2005_q37b5    231      2   1.735931         1         2  Seaports - limited availability/not available in your location
_2005_q37b6     48      2   1.791667         1         2  Airports - limited availability/not available in your location
_2005_q37b7    800      2    1.89625         1         2  Electricity - limited availability/not available in your location
_2005_q37b8    170      2   1.611765         1         2  Water - limited availability/not available in your location
_2005_q37b9    495      2   1.892929         1         2  Telephone - limited availability/not available in your location
_2005_q37b10   158      2    1.85443         1         2  Internet - limited availability/not available in your location
_2005_q37c1    588      2   1.336735         1         2  National roads - poor physical quality (not well maintained)
_2005_q37c2    597      2   1.201005         1         2  Inter-provincial roads - poor physical quality (not well maintained)
_2005_q37c3    191      2   1.267016         1         2  Bridges - poor physical quality (not well maintained)
_2005_q37c4     43      2   1.534884         1         2  Railways - poor physical quality (not well maintained)
_2005_q37c5    232      2   1.758621         1         2  Seaports - poor physical quality (not well maintained)
_2005_q37c6     47      2   1.829787         1         2  Airports - poor physical quality (not well maintained)
_2005_q37c7    800      2     1.6425         1         2  Electricity - poor physical quality (not well maintained)
_2005_q37c8    170      2   1.729412         1         2  Water - poor physical quality (not well maintained)
_2005_q37c9    497      2   1.816901         1         2  Telephone - poor physical quality (not well maintained)
_2005_q37c10   161      2   1.732919         1         2  Internet - poor physical quality (not well maintained)
_2005_q37d1    584      2   1.849315         1         2  National roads - poorly managed - long delays/slow service
_2005_q37d2    593      2   1.890388         1         2  Inter-provincial (local) roads - poorly managed - long delays/slow service
_2005_q37d3    193      2   1.896373         1         2  Bridges - poorly managed - long delays/slow service
_2005_q37d4     41      2   1.658537         1         2  Railways - poorly managed - long delays/slow service
_2005_q37d5    233      2   1.806867         1         2  Seaports - poorly managed - long delays/slow service
_2005_q37d6     46      2   1.913043         1         2  Airports - poorly managed - long delays/slow service
_2005_q37d7    796      2   1.809045         1         2  Electricity - poorly managed - long delays/slow service
_2005_q37d8    166      2   1.927711         1         2  Water - poorly managed - long delays/slow service
_2005_q37d9    501      2   1.868263         1         2  Telephone - poorly managed - long delays/slow service
_2005_q37d10   160      2      1.725         1         2  Internet - poorly managed - long delays/slow service
_2005_q37e1    583      2   1.914237         1         2  National roads - slow because of too many procedures
_2005_q37e2    591      2   1.954315         1         2  Inter-provincial roads - slow because of too many procedures
_2005_q37e3    192      2    1.90625         1         2  Bridges - slow because of too many procedures
_2005_q37e4     42      2   1.904762         1         2  Railways - slow because of too many procedures
_2005_q37e5    232      2   1.801724         1         2  Seaports - slow because of too many procedures
_2005_q37e6     46      2   1.804348         1         2  Airports - slow because of too many procedures
_2005_q37e7    798      2   1.977444         1         2  Electricity - slow because of too many procedures
_2005_q37e8    168      2   1.982143         1         2  Water - slow because of too many procedures
_2005_q37e9    496      2   1.957661         1         2  Telephone - slow because of too many procedures
_2005_q37e10   159      2   1.943396         1         2  Internet - slow because of too many procedures
_2005_q37f1    581      2   1.845095         1         2  National roads - service too expensive
_2005_q37f2    595      2   1.904202         1         2  Inter-provincial roads - service too expensive
_2005_q37f3    191      2   1.905759         1         2  Bridges - service too expensive
_2005_q37f4     42      2   1.809524         1         2  Railways - service too expensive
_2005_q37f5    233      2   1.665236         1         2  Seaports - service too expensive
_2005_q37f6     46      2   1.456522         1         2  Airports - service too expensive
_2005_q37f7    801      2   1.374532         1         2  Electricity - service too expensive
_2005_q37f8    167      2   1.646707         1         2  Water - service too expensive
_2005_q37f9    501      2   1.211577         1         2  Telephone - service too expensive
_2005_q37f10   161      2   1.546584         1         2  Internet - service too expensive
_2005_q39a2   1139     24   2.721782         0       100  Private commercial banks (loan, overdraft) used for working capital
_2005_q39a3   1139     87   23.23838         0       100  State Owned commercial banks used for working capital
_2005_q39a4   1139     17   1.139754         0       100  International commercial banks for working capital
_2005_q39a5   1139     15   .5012789         0       100  Leasing arrangement used for working capital
_2005_q39a6   1139     24   .8123363         0        80  DAF (Development Assistance Funds) used for working capital
_2005_q39a7   1139     33   2.358525         0       100  State budget used for working capital
_2005_q39a8   1139     10   .8305531         0       100  Private investment funds used for working capital
_2005_q39a10  1139      7     .18964         0        70  Credit cards used for working capital
_2005_q39a11  1139     79   26.81961         0       100  Equity or sales of shares used for working capital
_2005_q39a12  1139     27   5.414715         0       100  Family, friends used for working capital
_2005_q39a13  1139     13   .6453029         0        80  Informal sources (e.g. money lender) used for working capital
_2005_q39a14  1139      6   .0793503         0        38  Corporate bonds used for working capital
_2005_q39a15  1139     17   .5517559         0        80  Other used for working capital
_2005_q39a~x    18     12          .         .         .  If other, specify
_2005_q39b2    971     12   2.502575         0       100  Private commercial banks used for new investments
_2005_q39b3    971     35   24.09177         0       100  State owned commercial banks used for new investments
_2005_q39b4    971      8   1.143151         0       100  International commercial banks used for new investments
_2005_q39b5    971     11    .515448         0       100  Leasing arrangement used for new investments
_2005_q39b6    971     23   3.192183         0       100  DAF used for new investments
_2005_q39b7    971     17   1.787848         0       100  State budget used for new investments
_2005_q39b8    971      8   .9165808         0       100  Private investment funds used for new investments
_2005_q39b10   971      2   .0102987         0        10  Credit cards used for new investments
_2005_q39b11   971     27   27.62143         0       100  Equity or sales of shares used for new investments
_2005_q39b12   971     17   4.895469         0       100  Family, friends used for new investments
_2005_q39b13   971      9   .5200824         0        80  Informal sources used for new investments
_2005_q39b14   971      4   .0636869         0     46.84  Corporate bonds used for new investments
_2005_q39b15   971     16    1.80999         0       100  Other
_2005_q39b~x    27     18          .         .         .  If other, specify
_2005_q40     1149      2   1.597911         1         2  Does your establishment have a line of credit?
_2005_q40a     443     32   14.13233         0       100  What percent of total value of credit lines is currently not used of?
_2005_q40b     446     68   .9962848       .04        10  Over the last year, what was the average monthly interest rate of the line of cr
_2005_q41a    1150      2   1.366957         1         2  Does your establishment currently have a loan from a financial institution?
_2005_q41c     723      3   1.254495         1         3  In what currency was the loan?
_2005_q41cx    121      4          .         .         .  If other currencies, specify
_2005_q41e2    629      2   1.387917         1         2  If yes, immoveable plant, machinery were used as collateral
_2005_q41e3    628      2   1.498408         1         2  If yes, moveable machinery and equipment (incl.vehicles) were used as collateral
k15            622     46   152.7097         1       700  What was the approximate value of the collateral required as a percentage of the
_2005_q41g     705    126   10.70634       .38       200  In 2004, what was the loan's approximate annual rate of interest?
_2005_q41h     712     44   21.47121        .5       144  What is the total duration (term) of the loan? (month)
_2005_q41i     703      5   2.887624         1         5  The main use of this loan
_2005_q41i4x   155      6          .         .         .  If to pay earlier loans, specify use of original loan
_2005_q41i5x   147    109          .         .         .  If other, specify
_2005_q42      449      3   1.213808         1         3  What is the reason why you do not have a loan?
_2005_q43      379      7   1.915567         1         7  What was the pricinpal reason why you did not apply for a loan?
_2005_q43x     161     12          .         .         .  If other, please secify
_2005_q44       58      5   2.034483         1         6  What was the principal reason given to you when the application was turned down
_2005_q44x     152      3          .         .         .  If other, specify
_2005_q45      855     34   13.17871         0       100  What share of your total borrowing is \xaeominate in foreign currency?
_2005_q46b    1150      2   1.325217         1         2  Does your establishment have property and casualty insurance on its assests
_2005_q48a    1149      2   1.614447         1         2  Does your establishment own or lease the majority of its land?
_2005_q48b    1148      2    1.18554         1         2  Does your establishment own or lease the majority of your buildings?
_2005_q48c1   1021    281   103898.8         2  5.76e+07  If you had to purchase back machinery and equipment in its current condition, ho
_2005_q48c2    986    422    58739.5         2  1.30e+07  If you had to purchase back land, buildings and leasehold improvements, how much
_2005_q48c3    909    263   39175.39         1  1.00e+07  Of which, how much would land cost?
_2005_q48d1   1149      4   2.808529         1         4  Rate your own knowledge of the price of used land/building
_2005_q48d2   1149      4   2.885988         1         4  Rate your own knowledge of the price of used machinery
_2005_q49a    1144    350   58732.98        10  1.77e+07  Size of the land (m2)
_2005_q49b    1149      3   2.765013         1         3  What share is currently used by your establishment
_2005_q50     1149      2    1.24369         1         2  Do you sublease or rent land from another establishment, individual or state?
_2005_q50a     774    375   8620.033         0    770000  If yes, what is the monthly rental cost per m2? (VND/m2)
_2005_q50b     824     33   30.91626         1       100  If yes, what is the length of the sub-lease? (years)
_2005_q51     1144      2   1.634615         1         2  Do you have the right to sell or mortgage land?
_2005_q51a     281    164    1177397         1  2.00e+07  If yes, what was the actual price you paid for that land? (VND/m2)
_2005_q51b     492      5   2.636179         1         5  What was the method you used to acquire it?
_2005_q51bx     94     66          .         .         .  If other, specify
_2005_q52a1    361     38   8.566759         0       144  Time for getting bureaucratic approval/authorization to obtain land (months)
_2005_q52a2    184     26   4.807772         0        96  Time for clearing land (negotiating and reimbursing existing occupants) (months)
_2005_q52a3    241     26    4.69917         0        84  Time for preparing ground and getting necessary infrastructure connections and p
_2005_q52a4    403     58   13.46873         0       144  Time for the whole process (from requesting land to being ready to build factory
_2005_q52b1    253     53   718.0607         0    150000  Total cost for getting bureaucratic approval/authorization to obtain land (milli
_2005_q52b2    139     65   3729.937         0    400000  Total cost for clearing land (negotiating and reimbursing existing occupants) (m
_2005_q52b3    187     72   447.4305         0      7000  Total cost for preparing ground and getting necessary infrastructure connections
_2005_q52b4    328    160   3206.006         0    400000  Total cost for the whole process (from requesting land to being ready to build f
_2005_q52c1   1097      2    1.65907         1         2  Did your establishment experience getting bureaucratic approval
_2005_q52c2   1087      2   1.828887         1         2  
_2005_q52c3   1092      2   1.768315         1         2  
_2005_q52c4   1115      2   1.632287         1         2  Did your establishment experience steps from r2equesting land to being ready to
_2005_q53a     996      3   1.708835         1         3  Rate predictability of laws and regulations
_2005_q53b    1103      3   1.870354         1         3  Rate understanding of laws/regulations
_2005_q53c    1097      3   1.985415         1         3  Rate availability of laws/regulations & other sources of information on laws
_2005_q53d    1075      3   1.410233         1         3  Rate consistency across different legal documents
_2005_q54     1107      6   4.193315         1         6  The judicial system will enforce my contractual and property rights in business
_2005_q55     1150      2   1.921739         1         2  In the last 3 years has your establishment been involved in a court case?
_2005_q56     1087      2   1.958602         1         2  Did your establishment ever use the court system?
_2005_q57     1037      9   1.582449         1         9  What is the main reason why your establishment has never used the court system?
_2005_q57x      43     21          .         .         .  If other, specify
_2005_q58b     890     35    24.8332         0       100  On average in 2004, what percent of your monthly total sales to private customer
_2005_q58c     673     33   2.351003         0       100  What percent of these monthly sales are never repaid? (%)
_2005_q58d     647     47    118.476         2      1095  How many days does it typically take to resolve an overdue payment with private
_2005_q58e     570     49   112.1088         1      1095  How many days does it take to resolve an overdue payment with private customers
_2005_q59     1133      2   1.924978         1         2  Over the last 2 years, did you have disputes over overdue payments with private
_2005_q59a      57     12   13.09702         0       100  What percent of these disputes were resolved by court action? (%)
_2005_q59b      29     12   8.577586       .25        60  How many months did those court cases take to resolve? (month)
_2005_q59c      48      2   1.354167         1         2  Were the decisions of the court generally enforced?
_2005_q59d      20      9        7.1         1        20  How many months did the enforcement of the court judgement take? (month)
_2005_q60b    1142     38   9.143608         0      4500  Estimate the value of the losses in VND
_2005_q60c     108     14   4.694444         1        80  How many cases of theft, robbery, vandalism, or arson occured?
_2005_q60d     111     10   2.540541         0        80  How many of these incidents did you report to the police?
_2005_q60e      71      9   1.816901         0        50  Of these reported incidents, how many were solved?
_2005_q61a    1129    161   607.9323         0    600000  Estimate your establishment's costs for security related to crime during the las
_2005_q61b    1133     15   .3166108         0       200  Estimate your establishment's costs for protection payments during the last year
_2005_q62a1   1129     15   4.376439         1        15  The agency are the most helpful
_2005_q62a2   1055     15   6.408531         1        15  The agency is the second most helpful
_2005_q62b1    973     15   5.692703         1        15  The agency is the least helpful
_2005_q62b2    844     15   6.982227         1        15  The agency is the second least helpful
_2005_q62x1    191    134          .         .         .  If other agency, please specify
_2005_q62x2     41     33          .         .         .  If other agency, please specify
_2005_q63     1150      3   1.550435         1         3  Would you increase, decrease or keep constant your current workforce?
_2005_q63a     486     46   92.75103         1      1500  If increase, how many would you hire? (number)
_2005_q63b      57     21   64.29825         2       600  If decrease, how many would you fire? (number)
_2005_q65a    1137      2   1.395778         1         2  Have you heard that your industry is sometimes required to make gifts/informal p
_2005_q65b2    759     41   6599.311         0   5000000  Total value in VND (million VND)
_2005_q66a7   1149      2   1.434291         1         2  Did you request a loan from SOCB
_2005_q66a9   1149      2   1.872063         1         2  Did you request investment incentives?
_2005_q66b7    630     31   17.14683        .5       730  How many days did it take to obtain a loan from SOCB?
_2005_q66b9    134     19   30.26119         1       720  How many days did it take to obtain investment incentives?
_2005_q66c7    615      2   1.780488         1         2  Upon request of a loan from SOCB, was a gift or informal payment ever expected r
_2005_q66c9    130      2   1.761538         1         2  Upon request of investment incentives, was a gift or informal payment ever expec
_2005_q67a    1114     20   93.63869        10       100  Percentage of total sales the typical establishment in your industry reports for
_2005_q67b    1124     39   74.90347         0       100  Percentage of total workforce that is reported or purposes of SSI/Health Insuran
_2005_q68     1148      6   1.682927         1         6  Statement best describes how your income taxes are determined
_2005_q68x     154      4          .         .         .  If other, specify
_2005_q69a1   1142     14   .9964974         0        71  How many times in total was your establishment inspected or were you required to
_2005_q69a3   1137     21   1.218118         0       150  How many times in total was your establishment inspected or were you required to
_2005_q69a4   1144     12   1.053759         0        30  How many times in total was your establishment inspected or were you required to
_2005_q69a5   1144     20   1.633741         0       100  How many times in total was your establishment inspected or were you required to
_2005_q69a6   1146     14   1.466405         0        13  How many times in total was your establishment inspected or were you required to
_2005_q69a7   1145      9   .3004367         0        12  How many times in total was your establishment inspected or were you required to
_2005_q69a8   1144     11   .8282343         0        12  How many times in total was your establishment inspected or were you required to
_2005_q69a9   1144     13   .6783217         0        40  How many times in total was your establishment inspected or were you required to
_2005_q69a10  1140      6   .0842105         0        11  How many times in total was your establishment inspected or were you required to
_2005_q69a11  1143     65   10.66142         0       230  How many times in total was your establishment inspected or were you required to
_2005_q69b1    428     17   4.725117        .1       960  What was the average duration for each occurrence with District PC?
_2005_q69b2    944     38   11.43972       .25       192  What was the average duration for each occurrence with Tax Authority?
_2005_q69b3    184     19   4.764946        .5       288  What was the average duration for each occurrence with Customs Department?
_2005_q69b4    582     17   3.503093       .25        50  What was the average duration for each occurrence with Labor and Social Security
_2005_q69b5    375     16    1.67168        .2        30  What was the average duration for each occurrence with Police?
_2005_q69b6    757     18   3.735073        .2        72  What was the average duration for each occurrence with Fire and Building Safety?
_2005_q69b7    201     12   2.492537       .25        72  What was the average duration for each occurrence with Business registration and
_2005_q69b8    457     12   2.678993       .25        30  What was the average duration for each occurrence with Environmental Agency?
_2005_q69b9    271     10   1.666937       .08         8  What was the average duration for each occurrence with Market Controller?
_2005_q69b10    57     13   3.892982        .5        32  What was the average duration for each occurrence with Construction Inspector?
_2005_q69b11  1066    143   6.582899       .08       256  What was the average duration for each occurrence with all agencies?
_2005_q69c1    412      8    20388.4         0   8000000  What was the costs of fines or seized goods, associated with the interaction fro
_2005_q69c2    885     32    1177147         0  6.00e+08  What was the costs of fines or seized goods, associated with the interaction fro
_2005_q69c3    165      7   290909.2         0  2.00e+07  What was the costs of fines or seized goods, associated with the interaction fro
_2005_q69c4    548      3   16423.36         0   9000000  What was the costs of fines or seized goods, associated with the interaction fro
_2005_q69c5    336      3   3214.286         0   1000000  What was the costs of fines or seized goods, associated with the interaction fro
_2005_q69c6    715      9   30349.66         0  1.00e+07  What was the costs of fines or seized goods, associated with the interaction fro
_2005_q69c7    199      3   5025.131         0   1000000  What was the costs of fines or seized goods, associated with the interaction fro
_2005_q69c8    424     12   144811.3         0  3.00e+07  What was the costs of fines or seized goods, associated with the interaction fro
_2005_q69c9    254      9   173228.4         0  2.70e+07  What was the costs of fines or seized goods, associated with the interaction fro
_2005_q69c10    53      3   18868.15         0   1000000  What was the costs of fines or seized goods, associated with the interaction fro
_2005_q69c11  1009     39    1206993         0  6.10e+08  What was the costs of fines or seized goods, associated with the interaction fro
_2005_q69d1    420      2   1.811905         1         2  Was a gift or informal payment ever expected/requested from District PC?
_2005_q69d3    186      2   1.360215         1         2  Was a gift or informal payment ever expected/requested from Customs Department?
_2005_q69d4    571      2    1.80035         1         2  Was a gift or informal payment ever expected/requested from Labor and Social Sec
_2005_q69d5    364      2   1.565934         1         2  Was a gift or informal payment ever expected/requested from Police?
_2005_q69d6    747      2   1.720214         1         2  Was a gift or informal payment ever expected/requested from Fire and Building Sa
_2005_q69d7    200      2      1.685         1         2  Was a gift or informal payment ever expected/requested from Business registratio
_2005_q69d8    444      2   1.774775         1         2  Was a gift or informal payment ever expected/requested from Environmental Agency
_2005_q69d9    265      2   1.532075         1         2  Was a gift or informal payment ever expected/requested from Market Controller?
_2005_q69d10    54      2   1.592593         1         2  Was a gift or informal payment ever expected/requested from Construction Inspect
_2005_q69d11  1033      2   1.514037         1         2  Was a gift or informal payment ever expected/requested from all agencies?
_2005_q69e1    405     22   138123.5         0   9000000  If yes, of how much for District PC?
_2005_q69e2    843     45    1460393         0  4.00e+07  If yes, of how much for Tax Authority?
_2005_q69e3    166     28    1456627         0  2.50e+07  If yes, of how much for Customs Department?
_2005_q69e4    541     17   84271.72         0   4000000  If yes, of how much for Labor and Social Security?
_2005_q69e5    325     30   629354.2         0  2.00e+07  If yes, of how much for Police?
_2005_q69e6    696     21   134900.6         0   8000000  If yes, of how much for Fire and Building Safety?
_2005_q69e7    190     15   252894.7         0   4000000  If yes, of how much for Business registration and licensing?
_2005_q69e8    416     18   193918.3         0  2.00e+07  If yes, of how much for Environmental agency?
_2005_q69e9    246     25   825081.3         0  1.50e+07  If yes, of how much for Market Controller?
_2005_q69e10    49     10   406138.3         0   6000000  If yes, of how much for Construction Inspector?
_2005_q69e11   954    136    2371114         0  4.70e+07  If yes, of how much for all agencies?
_2005_q70a    1116      2   1.714158         1         2  When establishments in your sector do business with the State agencies and SOEs,
_2005_q71a    1150      2   1.572174         1         2  Does your establishment try to contribute to the discussion and formulation of n
_2005_q71ba    481      4    1.50104         1         4  How?
_2005_q71bb    155      4   2.432258         1         4  How?
_2005_q71bc     25      4          3         1         4  How?
_2005_q71bd      0      0          .         .         .  How?
_2005_q71b3     51      2   1.784314         1         2  If trhough personal contacts, are you required to give any gift or informal paym
_2005_q71bx     57     52          .         .         .  If other, specify
_2005_q72a    1141      2   1.689746         1         2  Have you heard of incidences where the media has blackmailed companies with the
_2005_q72b    1137      2   1.704485         1         2  Have you heard of incidences where companies have bribed the media to publish ne
_2005_q73a    1088      5   1.104779         0         4  Rank the officials in the Tax Department
_2005_q73b    1033      5   .3165537         0         4  Rank the officials in the Busines registration and licensing
_2005_q73c     719      5   .6147427         0         4  Rank the officials in the Import/Export License Authorities
_2005_q73d     739      5   1.288227         0         4  Rank the officials in the Customs Department
_2005_q73e     730      5   .7643836         0         4  Rank the officials in the Construction Permit Authorities
_2005_q73f     981      5   2.138634         0         4  Rank the officials in the Traffic Police
_2005_q73g     954      5   .7358491         0         4  Rank the officials in the Municipal and other Police
_2005_q73h     859      5   .9697322         0         4  Rank the officials in the Market Controller
_2005_q73i     849      5   .8539458         0         4  Rank the officials in the Land Administration Agency
_2005_q73k     989      5   .2163802         0         4  Rank the officials in District Peoples Committee
_2005_q74b    1122    417   342.6462         2     17067  Total number of workers at the end of 2003
_2005_q74c2   1128    129   30.41312         0      2735  Total number of professionals at the end of 2004
_2005_q74c5   1128     86   13.52926         0       736  Total number of non production workers at the end of 2004
_2005_q74d1   1133    253   42.64489         0       100  Percentage of female at the end of 2004
_2005_q74d2   1028    176   33.39921         0       100  Percentage of professionals at the end of 2004
_2005_q74d3   1012    217   35.66938         0       100  Percentage of skilled production workers at the end of 2004
_2005_q74d4    813    194    45.6431         0       100  Percentage of unskilled produciton workers at the end of 2004
_2005_q74d5    808    147   29.29345         0       100  Percentage of non production workers at the end of 2004
_2005_q74e    1081     36   1.544403         0       100  Percentage of part-time at the end of 2004
_2005_q74f1   1142    930   8111.786     4.427   2377200  Total compensation of all workers in each category (million VND)
_2005_q74f2   1093    532    1180.17         5    270000  Total compensation of professionals (million VND)
_2005_q74f3   1077    783   4178.077         6    561600  Total compensation of skilled production workers (million VND)
_2005_q74f4    869    595   3324.264        .5   1478400  Total compensation of unskilled production workers (million VND)
_2005_q74f5    855    350    345.754        .5     67200  Total compensation of non produciton workers (million VND)
_2005_q74g     104     33   33.03183       .25        70  How many hours per week did part-time workers work on average?
_2005_q75a    1149      2   1.906005         1         2  In 2004, did you hire foreign nationals among your permanent workers?
_2005_q75b     141     24   7.319149         0       400  In 2004, how many of your permanent skilled, professional, or managerial workers
_2005_q76a    1139    162   57.78314         0      2282  In 2004, how many new permanent employees did your establishment hire?
_2005_q76b1   1138     60    6.58348         0       920  In 2004, how many permanent employees from your establishment were dismissed or
_2005_q76b2   1135     37   4.443172         0       710  In 2004, how many permanent employees from your establishment left due to sickne
_2005_q76b3   1142    130   34.85464         0      2540  In 2004, how many permanent employees from your establishment left for other rea
_2005_q77     1146      2     1.5637         1         2  Did you hire temporary workers within last 3 years?
_2005_q78b     458    113   72.29476         0      1086  Average number of temporary workers employed in 2003
_2005_q78c     414    104   65.85507         0      1136  Average number of temporary workers employed in 2002
_2005_q78d     468     78    42.3103         0       100  Of which, % of female in 2004
_2005_q78e     426     24   6.498826         0       642  Of which, average number of part-time workers
_2005_q78g     460    266   73313.49        .4  3.20e+07  Total compensation of all temporary workers (million VND)
_2005_q79      480     38   47.60625         1       102  For temporary workers on average, how many hours/week do they work?
_2005_q81     1114      2   1.714542         1         2  In 2004, did you offer in class external training to your permanent employees?
_2005_q82a    1103     85   24.99262         0       100  In 2004, what percentage of your total permanent skilled employees received in c
_2005_q82b    1088     53   25.49878         0       100  In 2004, what percentage of your total permanent unskilled employees receive in
_2005_q82c     614     28   6.451303        .5        52  In 2004, what was the average number of weeks of training for each skilled emplo
_2005_q82d     484     24    3.58657        .2        52  In 2004, what was the average number of weeks of training for each unskilled emp
_2005_q83     1117     74   57.86873         0       100  What percent of your employees is unionized?
_2005_q84a    1140      7   .0587719         0        32  How many days of production last year did you lose due to worker strikes or othe
_2005_q84b    1123     31    2.35797         0        92  How many days of produciton last year did you lose due to employee abseetism due
_2005_q84c    1139      4   .0184372         0        18  How many days of production last year did you lose due to civil unrest?
_2005_q85a    1137    225   9.830083         0        98  Education level of the employees - some college or university or higher (%)
_2005_q85b    1137    180   5.774333         0        70  Education level of the employees - completed 3-year diploma (%)
_2005_q85c    1137    250   19.51422         0       100  Education level of the employees - completed vocational (%)
_2005_q85d    1137    306   34.78852         0      99.5  Education level of the employees - completed high school (%)
_2005_q85e    1137    278    25.9084         0       100  Education level of the employees -  uncompleted high school(%)
_2005_q85f    1137    114   3.287754         0      94.9  Education level of the employees - completed elementary (%)
_2005_q85g    1137     57   .8966878         0        83  Education level of the employees - uncompleted elementary (%)
_2005_q85g2     88     37   39.93205         0       100  What percentage are female uncompleted elementary?
_2005_q86a1   1145    990    81419.6    40.209  1.18e+07  Total sales in 2004 (million VND)
_2005_q86a2   1141   1088   79241.81      41.5  1.24e+07  Total cost in 2004 (million VND)
_2005_q86a3   1137   1067    51174.8         6   3059780  Of which, total purchases of raw materials and intermediate goods in 2004 (milli
_2005_q86a4   1147    935   7590.255     1.582    950000  Of which, total cost of labor, including wages, salaries and bonuses in 2004 (mi
_2005_q86a5   1140    743   3040.702         0    278000  Of which, depreciation in 2004(million VND)
_2005_q86a6   1001    303   368.6537         0     90000  Of which, rent on land and buildings in 2004 (million VND)
_2005_q86a7    633    261   259.9622         0     25886  Of which, rent on land in 2004 (million VND)
_2005_q86a8    974    251   402.1805         0     32000  Of which, rent on machinery, equipment and vehicles in 2004 (million VND)
_2005_q86a9   1110    560   1411.041         0    146779  Of which, interest charges in 2004(million VND)
_2005_q86a10  1148    653   1686.237        .4    212645  Of which, energy cost in 2004 (million VND)
_2005_q86a11  1084    508   1436.136     -2146    125000  Of which, taxes in 2004 (million VND)
_2005_q86b1   1112    968   64030.37     19.94   5119874  Total sales in 2003 (million VND)
_2005_q86b2   1106   1072   62105.39    41.373   3367874  Total cost in 2003 (million VND)
_2005_q86b3   1107   1054   45765.75     1.095   2912416  Total purchases of raw materials and intermediate goods in 2003 (million VND)
_2005_q86b4   1113    907   5320.803     1.752    208720  Total cost of labor, including wages, salaries and bonuses in 2003 (million VND)
_2005_q86b5   1107    726   2895.064         0    290163  Depreciation in 2003 (million VND)
_2005_q86b6    968    290   444.8219         0    173594  Rent on land and buildings in 2003 (million VND)
_2005_q86b7    605    259   259.6174         0     25886  Rent on land in 2003 (million VND)
_2005_q86b8    938    255   345.1961         0     25000  Rent on machinery, equipment, and vehicles in 2003 (million VND)
_2005_q86b9   1066    514   1337.345         0    195551  Interest charges in 2003 (million VND)
_2005_q86b10  1110    638   1503.863        .4    192603  Energy cost in 2003 (million VND)
_2005_q86b11  1049    507   1400.881      -400    164298  Taxes in 2003 (million VND)
_2005_q86c1    966    865   59278.94        51   2782453  Total cost in 2002 (million VND)
_2005_q86c2    958    930   58541.14      52.2   2192162  Total purchases of raw materials and intermediate goods in 2002 (million VND)
_2005_q86c3    963    929   40532.15         5   1798030  Total cost of labor, including wages, salaries and bonuses in 2002 (million VND)
_2005_q86c4    965    801   4782.441       4.2    107428  Of which, total cost of labor, including wages, salaries and bonuses in 2002(mil
_2005_q86c5    960    631   2962.439         0    257093  Depreciation in 2002 (million VND)
_2005_q86c6    860    267   241.3586         0     28173  Rent on land and buildings in 2002 (million VND)
_2005_q86c7    522    234   227.6408         0     25886  Rent on land in 2002 (million VND)
_2005_q86c8    841    224   358.4593         0     36995  Rent on machinery, equipment, and vehicles in 2002 (million VND)
_2005_q86c9    919    432   1253.742         0    103129  Interest charges in 2002 (million VND)
_2005_q86c10   960    595   1389.087        .3    152705  Energy cost in 2002 (million VND)
_2005_q86c11   919    470   1232.382      -518     81120  Taxes in 2002 (million VND)
_2005_q87a    1114    763   59097.69  -2906680  6.61e+07  Net profit (after tax) in 2004
_2005_q87b    1065    742   15462.62   -164805  1.17e+07  Net profit (after tax) in 2003
_2005_q87c     929    663   11594.06   -131118   8786052  Net profit (after tax) in 2002
_2005_q87d     914     75   43.77204         0       100  Percentage of the establishment's net profits (after tax) were reinvested in you
_2005_q88a    1131    917          .         .         .  Description of the most important product
_2005_q88b1x  1047    800    2719296         1  3.00e+08  Physical quantity sold (units) in 2004
_2005_q88b2   1059     91          .         .         .  Unit of measurement
_2005_q88b3   1049    559   414144.1       .05  1.20e+08  Average price in 2004 (thousand VND)
_2005_q88c1x   964    783    2625202         1  2.50e+08  Physical quantity sold in 2003 (units)
_2005_q88c2    972     85          .         .         .  Unit of measurement
_2005_q88c3    963    535   280991.8       .05  1.00e+08  Average price in 2003 (thousand VND)
_2005_q89a     702    636          .         .         .  Description of the second most important product
_2005_q89b1x   654    510   1.54e+09         1  9.88e+11  Physical quantity sold (units) in 2004
_2005_q89b2    660     74          .         .         .  Unit of measurement
_2005_q89b3    656    364   229024.5       .05  1.00e+08  Average price in 2004 (thousand VND)
_2005_q89c1x   592    477   2.58e+07         1  1.44e+10  Physical quantity sold in 2003 (units)
_2005_q89c2    598     73          .         .         .  Unit of measurement
_2005_q89c3    591    359   194879.2      .032  8.00e+07  Average price in 2003 (thousand VND)
_2005_q90a1    798    506   5703.439         0    562803  Value of machinery and equipment in 2004 (million VND)
_2005_q90a2    593    367   6337.139         0    691366  Value of land, buildings or improvements to leasehold in 2004 (million VND)
_2005_q90a3    112     66   2787.607         0     41710  Value of land in 2004 (million VND)
_2005_q90a4    374    228   648.0512         0     12460  Value of vehicles in 2004 (million VND)
_2005_q90a5    355    131   282.2392         0     19542  Value of information technology in 2004 (million VND)
_2005_q90a6    171     68     638.15         0     30000  Value of design, research and development in 2004 (*)
_2005_q90b1    684    467   5545.309         0    499077  Value of machinery and equipment in 2003 (million VND)
_2005_q90b2    524    345   3817.995         0    173999  Value of land, buildings or improvements to leasehold in 2003 (million VND)
_2005_q90b3     97     61   2786.633         0     41710  Value of land in 2003 (million VND)
_2005_q90b4    319    190   552.2383         0     16772  Value of vehicles in 2003 (million VND)
_2005_q90b5    308    125   118.1974         0      3426  Value of information technology in 2003 (million VND)
_2005_q90b6    124     53   2068.635         0    216000  Value of design, research and development in 2003 (million VND)
_2005_q91a1   1145   1089   85543.04        77  2.97e+07  Value of total assets in 2004 (million VND)
_2005_q91a2   1147   1020   28887.51         0   2635000  Value of fixed assets in 2004 (million VND)
_2005_q91a3   1070    203   1961.408         0    571020  Value of land in 2004 (million VND)
_2005_q91a4   1144    803   7949.549         0    883500  Value of buildings and leasehold improvements in 2004 (million VND)
_2005_q91a5   1138    915   17881.01         0   1854077  Value of machinery and equipment (including vehicles) in 2004 (million VND)
_2005_q91a6   1117    474   1417.727         0    254686  Value of other fixed assets in 2004 (million VND)
_2005_q91a7   1147   1033   35568.17        10   3352626  Value of current assets in 2004 (million VND)
_2005_q91a8   1142    953   15475.16         0    985479  Value of inventory and stocks in 2004 (million VND)
_2005_q91a9   1128    713   5345.074         0    486238  Value of finished goods in 2004 (million VND)
_2005_q91a10  1121    605   3723.725         0    395200  Value of work in progress in 2004 (million VND)
_2005_q91a11  1134    813   6547.246         0    414680  Value of raw materials in 2004 (million VND)
_2005_q91a12  1138    823   12990.28         0   1049948  Value of receivables in 2004 (million VND)
_2005_q91a13  1147    707   2383.977         0    257480  Value of cash in 2004 (million VND)
_2005_q91a14  1135    578   2212.488         0    663138  Value of other current assets in 2004 (million VND)
_2005_q91b1   1114   1071   55699.18        56   4062685  Value of total assets in 2003 (million VND)
_2005_q91b2   1114   1007   26679.72         0   2681500  Value of fixed assets in 2003 (million VND)
_2005_q91b3   1040    191   1828.616         0    494000  Value of land in 2003 (million VND)
_2005_q91b4   1108    778   7431.961         0    914500  Value of buildings and leasehold improvements in 2003 (million VND)
_2005_q91b5   1103    914    16393.8         0   1485600  Value of machinery and equipment (including vehicles) in 2003 (million VND)
_2005_q91b6   1090    457   1389.237         0    240708  Value of other fixed assets in 2003 (million VND)
_2005_q91b7   1114   1001   29076.83         0   1940322  Value of current assets in 2003 (million VND)
_2005_q91b8   1108    922   13271.72         0    865896  Value of inventory and stocks in 2003 (million VND)
_2005_q91b9   1096    682    4632.52         0    397172  Value of finished goods in 2003 (million VND)
_2005_q91b10  1086    581   3083.024         0    378296  Value of work in progress in 2003 (million VND)
_2005_q91b11  1105    804   5847.587         0    325846  Value of raw materials in 2003 (million VND)
_2005_q91b12  1104    814   12024.89         0    876983  Value of receivables in 2003 (million VND)
_2005_q91b13  1113    707   2257.739         0    306000  Value of cash in 2003 (million VND)
_2005_q91b14  1099    550    1687.57         0    348866  Value of other current assets in 2003 (million VND)
_2005_q91c1    969    933   55935.32        47   3069188  Value of total assets in 2002 (million VND)
_2005_q91c2    969    884   26752.17         0   2802500  Value of fixed assets in 2002 (million VND)
_2005_q91c3    897    166   2115.843         0    494000  Value of land in 2002 (million VND)
_2005_q91c4    965    683   7262.245         0    915500  Value of buildings and leasehold improvements in 2002 (million VND)
_2005_q91c5    954    801   16649.45         0   1387000  Value of machinery and equipment (including vehicles) in 2002 (million VND)
_2005_q91c6    944    386   1321.297         0     77253  Value of other fixed assets in 2002 (million VND)
_2005_q91c7    967    900   29173.08         0   1657178  Value of current assets in 2002 (million VND)
_2005_q91c8    964    830   13003.61         0    651511  Value of inventory and stocks in 2002 (million VND)
_2005_q91c9    958    633    4456.76         0    244850  Value of finished goods in 2002 (million VND)
_2005_q91c10   948    533   3037.483         0    194687  Value of work in progress in 2002 (million VND)
_2005_q91c11   963    706   5671.053         0    286724  Value of raw materials in 2002 (million VND)
_2005_q91c12   962    745    12662.3         0    818175  Value of receivables in 2002 (million VND)
_2005_q91c13   969    644   2306.406         0    315000  Value of cash in 2002 (million VND)
_2005_q91c14   956    501   1885.323         0    588983  Value of other current assets in 2002 (million VND)
_2005_q92a1   1147   1092   61359.23        77   4813375  Value of total liabilities in 2004 (million VND)
_2005_q92a2   1130    540   13588.21         0   1764646  Value of long-term liabilities in 2004 (million VND)
_2005_q92a3   1138    881   23204.13         0   2651335  Value of short-term liabilities in 2004 (million VND)
_2005_q92a4   1016    718   14660.18         0   2651335  Value of payables in 2004 (million VND)
_2005_q92a5   1143    988   24760.69    -94267   2298880  Value of equity in 2004 (million VND)
_2005_q92b1   1112   1062   55357.86        56   4062685  Value of total liabilities in 2003 (million VND)
_2005_q92b2   1090    513   12241.81         0   1692717  Value of long-term liabilities in 2003 (million VND)
_2005_q92b3   1104    891   20675.52         0   2065237  Value of short-term liabilities in 2003 (million VND)
_2005_q92b4    976    712   13490.85         0   2065237  Value of payables in 2003 (million VND)
_2005_q92b5   1106    960   22870.41   -122939   2364181  Value of equity in 2003 (million VND)
_2005_q92c1      3      3      52955     13000    121828  Value of total liabilities in 2002 (million VND)
_2005_q92c2      3      2   2001.667         0      6005  Value of long-term liabilities in 2002 (million VND)
_2005_q92c3      3      3      15680      4033     35007  Value of short-term liabilities in 2002 (million VND)
_2005_q92c4      3      3       5918      2243     10511  Value of payables in 2002 (million VND)
_2005_q92c5      4      4    26455.5         2     80816  Value of equity in 2002 (million VND)
--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------

2.1 Constructing Variables for Production Function

Production function estimation requires careful construction of key variables representing outputs and inputs. The following approach recodes survey responses and creates derived measures suitable for econometric analysis.

Note on Missing Values: WBES data uses negative codes for missing or non-responses. This section recodes these values to Stata’s standard missing value (.) to prevent biased estimates or calculation errors.

// ========== OUTPUT & INPUT VARIABLE CONSTRUCTION ==========
// This section recodes negative values (invalid WBES codes) as missing
recode n6a n7a n5a d2 n2a n2e n2b n2i (min/-.0001=.)

// ========== FACTOR INPUTS ==========
// Capital: Net Book Value from firm balance sheets
clonevar Cap = n6a
label variable Cap "Capital (Net Book Value, USD)"

// Labor: Total labor costs including wages, salaries, benefits
clonevar Lab = n2a
label variable Lab "Total Labor Cost (USD)"

// Materials: Raw materials and intermediate inputs
clonevar Materials = n2e
label variable Materials "Raw Materials and Intermediate Inputs (USD)"

// Investment: Fixed asset investments during survey period
clonevar Inv = n5a 
label variable Inv "Investment in Fixed Assets (USD)"
(306 changes made to n6a)
(370 changes made to n7a)
(30 changes made to n5a)
(66 changes made to d2)
(149 changes made to n2a)
(208 changes made to n2e)
(161 changes made to n2b)
(219 changes made to n2i)
(894 missing values generated)
(153 missing values generated)
(807 missing values generated)
(1,194 missing values generated)
// ========== OUTPUT & INTERMEDIATE INPUT VARIABLES ==========
// Revenue: Total annual sales or operating revenue
clonevar revenue = d2
label variable revenue "Total Annual Revenue (USD)"

// Raw materials and intermediate goods: Essential production inputs
clonevar RawInt = n2e
label variable RawInt "Raw Materials and Intermediate Goods (USD)"

// Electricity: Tracked separately for environmental/cost analysis
clonevar Electricity = n2b
label variable Electricity "Electricity and Utilities Cost (USD)"

// ========== VALUE-ADDED MEASURES ==========
// These measures represent firm's contribution to economic value
// and may be used as alternative output measures

// Gross Value Added: Difference between gross output and intermediate inputs
gen ValAdd = revenue - RawInt
label variable ValAdd "Gross Value Added: Revenue minus Materials (USD)"

// Value Added excluding energy: Isolates effect of raw material costs
gen ValAddE = revenue - RawInt - Electricity
label variable ValAddE "Value Added Excluding Electricity (USD)"

// Profit: Gross profit excluding depreciation and capital costs
// Note: This is an accounting profit, not economic profit
gen Profit = revenue - RawInt - Lab
label variable Profit "Accounting Profit: Revenue minus Materials and Labor (USD)"

// ========== EFFICIENCY RATIOS ==========
// These ratios show factor productivity and capital utilization

gen InvCap = Inv / Cap  
label variable InvCap "Investment Rate (Investment relative to Capital Stock)"

gen ValAddCap = ValAdd / Cap
label variable ValAddCap "Capital Productivity: Value Added per Unit Capital"

gen EValAddCap = ValAddE / Cap
label variable EValAddCap "Capital Productivity Excluding Energy Costs"

gen ProfitCap = Profit / Cap
label variable ProfitCap "Profit Rate: Profit per Unit of Capital"
(71 missing values generated)
(807 missing values generated)
(1,475 missing values generated)
(821 missing values generated)
(1,973 missing values generated)
(831 missing values generated)
(1,645 missing values generated)
(990 missing values generated)
(2,136 missing values generated)
(995 missing values generated)
// ========== LOG-TRANSFORMATION FOR PRODUCTION FUNCTION ==========
// Log transformations facilitate estimation of elasticities and reduce
// skewness in firm-level financial variables. All variables are log(USD).

// Output measure
gen ln_y = ln(revenue)
label variable ln_y "Log Total Revenue"

// Factor inputs
gen ln_lab = ln(Lab)
label variable ln_lab "Log Total Labor Cost"

gen ln_k = ln(Cap)
label variable ln_k "Log Capital (Net Book Value)"

gen ln_materials = ln(RawInt)
label variable ln_materials "Log Raw Materials and Intermediate Goods"

gen ln_investment = ln(Inv)
label variable ln_investment "Log Fixed Asset Investment"

// Firm size classification
gen firm_size = a6b
label variable firm_size "Firm Size Category"

// ========== DESCRIPTIVE STATISTICS ==========
// Summary statistics provide initial verification of data quality and range
display "========== Descriptive Statistics: Constructed Variables =========="
summarize Cap Lab Inv revenue RawInt Electricity ValAdd Profit ln_y ln_lab ln_k ln_materials ln_investment
(71 missing values generated)
(153 missing values generated)
(916 missing values generated)
(819 missing values generated)
(1,235 missing values generated)
(1,150 missing values generated)
========== Descriptive Statistics: Constructed Variables ==========

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
         Cap |      2,305    1.99e+11    5.58e+12          0   2.00e+14
         Lab |      3,046    9.77e+09    5.77e+10        384   1.50e+12
         Inv |      2,005    9.68e+09    1.11e+11          0   4.00e+12
     revenue |      3,128    1.13e+11    7.65e+11    6000000   2.82e+13
      RawInt |      2,392    6.09e+10    3.41e+11          0   1.44e+13
-------------+---------------------------------------------------------
 Electricity |      1,724    2.09e+09    2.08e+10          0   5.92e+11
      ValAdd |      2,378    5.64e+10    5.72e+11  -2.68e+11   2.00e+13
      Profit |      2,368    4.67e+10    5.44e+11  -4.72e+11   1.90e+13
        ln_y |      3,128    23.51955    1.916909   15.60727   30.96999
      ln_lab |      3,046    21.24044     1.74318   5.950643   28.03649
-------------+---------------------------------------------------------
        ln_k |      2,283    22.12046    2.001155   13.81551   32.92934
ln_materials |      2,380    22.67727    2.272674   8.188689   30.29686
ln_investm~t |      1,964    20.54624    2.010878   5.298317   29.01731

:::


2.2 Part II: Empirical Analysis

Step-by-step implementation of Stata commands for variable construction, handling missing values, recoding survey responses, and preparing data for subsequent analysis. Applications include production function estimation and firm characteristics analysis.


3 TFP Estimation using OLS

3.1 Objective

Estimate the parameters of a Cobb-Douglas production function and derive firm-level total factor productivity (TFP) measures. This provides a foundation for understanding what fraction of output variation can be explained by measured inputs (capital, labor, materials) versus unobserved productivity differences.

3.2 Specification

The log-linear Cobb-Douglas specification is:

\[\ln Y_i = \alpha + \beta_L \ln L_i + \beta_K \ln K_i + \beta_M \ln M_i + u_i\]

where \(Y_i\) is firm revenue, \(L_i\) is labor cost, \(K_i\) is capital, \(M_i\) is materials, and \(u_i\) represents firm-level productivity and other unobserved factors.

// ========== EXPLORATORY ANALYSIS ==========
// Visualize relationships among log-transformed variables before estimation
set scheme white_tableau
graph matrix ln_*, half

// ========== OLS PRODUCTION FUNCTION ESTIMATION ==========
// This model includes year fixed effects to control for aggregate shocks
// Robust standard errors account for heteroskedasticity
display "========== OLS Production Function Estimation =========="
reghdfe ln_y ln_lab ln_k ln_materials, absorb(year) resid vce(robust)
est store OLS
========== OLS Production Function Estimation ==========
(MWFE estimator converged in 1 iterations)

HDFE Linear regression                            Number of obs   =      2,197
Absorbing 1 HDFE group                            F(   3,   2191) =    3954.89
                                                  Prob > F        =     0.0000
                                                  R-squared       =     0.8684
                                                  Adj R-squared   =     0.8681
                                                  Within R-sq.    =     0.8666
                                                  Root MSE        =     0.6950

------------------------------------------------------------------------------
             |               Robust
        ln_y | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
      ln_lab |   .3561471    .027274    13.06   0.000     .3026616    .4096327
        ln_k |   .1603322   .0225983     7.09   0.000     .1160159    .2046486
ln_materials |   .4731541   .0343954    13.76   0.000     .4057031    .5406052
       _cons |   1.676062   .2105004     7.96   0.000     1.263261    2.088864
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
        year |         3           0           3     |
-----------------------------------------------------+

// ========== TFP MEASUREMENT ==========
// Calculate residuals from OLS estimation; these represent firm-level TFP
// TFP captures productivity effects not explained by measured inputs:
// technology, organizational efficiency, managerial quality, measurement error
predict ols_res, residuals
gen tfp_ols = ols_res

// ========== DESCRIPTIVE ANALYSIS OF TFP ==========
// Examine the distribution and key statistics of TFP measures
display "========== TFP Distribution: Descriptive Statistics =========="
summarize tfp_ols, detail

// ========== VISUALIZATION ==========
// Histogram with density overlay to assess TFP distribution normality
histogram tfp_ols, ///
    title("Distribution of Total Factor Productivity") ///
    subtitle("Vietnam Manufacturing Firms (OLS Residuals), 2005-2015") ///
    xtitle("TFP (log scale)") ytitle("Frequency") ///
    normal kdensity

// ========== SAVE ANALYTICAL DATASET ==========
// Preserve the constructed variables and TFP measure for subsequent analysis
save "industrial development working dataset.dta", replace
display "Analytical dataset saved: industrial development working dataset.dta"
(1,002 missing values generated)
(1,002 missing values generated)
========== TFP Distribution: Descriptive Statistics ==========

                           tfp_ols
-------------------------------------------------------------
      Percentiles      Smallest
 1%    -1.240496      -4.857159
 5%    -.7956161      -4.186355
10%    -.6193408      -3.655106       Obs               2,197
25%    -.3567653      -2.943304       Sum of wgt.       2,197

50%    -.0748259                      Mean           1.03e-10
                        Largest       Std. dev.      .6942397
75%     .2362409       4.300721
90%     .6808869       5.835317       Variance       .4819688
95%     1.050843       7.966369       Skewness       2.665838
99%     2.405594         9.0883       Kurtosis       31.27771
(bin=33, start=-4.8571587, width=.42258965)
file industrial development working dataset.dta saved
Analytical dataset saved: industrial development working dataset.dta

Methodological Note: OLS Limitations and TFP Interpretation

The TFP residuals from OLS estimation must be interpreted carefully given several known limitations:

1. Endogeneity Problem - Simultaneous causality: Firms that observe favorable productivity shocks may adjust their input choices (e.g., hire more labor or purchase more materials) contemporaneously - This violates the exogeneity assumption required for unbiased OLS estimates - Coefficients may be biased toward zero or exhibit other directional bias

2. Omitted Variables - TFP residuals may capture unobserved firm characteristics affecting output: managerial ability, workforce quality, production technology, market access - These factors are correlated with input choices, leading to bias - Results should be interpreted as associations, not causal effects

3. Measurement Error - WBES data relies on firm self-reports of accounting variables - Measurement error in capital stock, labor, or intermediate inputs creates additional bias - Small firms may have less reliable accounting systems than large firms

4. Survey Design Issues - WBES is not nationally representative; sample is weighted toward certain sectors and firm sizes - Attrition between waves may introduce selection bias - Sampling weights should be used in analysis (consult WBES documentation)

Advanced Approaches: For publication-quality work, consider semi-parametric or semi-nonparametric methods such as Olley-Pakes or Levinsohn-Petrin estimators that explicitly model endogeneity and selection decisions.

4 Firm Characteristics & Productivity Analysis

4.1 Objective

Examine associations between observable firm characteristics (management practices, ownership structure, certifications) and estimated TFP measures. This analysis identifies which firm attributes tend to correlate with higher productivity, providing motivation for further investigation into causality.

4.2 Key Variables

  • Quality Certification: International quality certifications (e.g., ISO, industry-specific standards)
  • Manager Experience: Years of working experience in the industry
  • Foreign Ownership: Percentage of ownership by foreign investors
// Load the analytical dataset containing TFP measures
use "industrial development working dataset.dta", clear

// ========== FIRM CHARACTERISTIC VARIABLE CONSTRUCTION ==========
// Transform raw WBES survey responses into analysis variables

// Quality certification: Binary indicator for any recognized certification
recode b8 (-9=.) (-6=0) (2=0), gen(cer)
label define cer 1 "Yes, Certified" 0 "No Certification"
label values cer cer
label var cer "Holds International Quality Certification"

// Manager working experience: Recoded from survey responses
recode b7 (-9=.), gen(manager_exper)
label var manager_exper "Manager Experience (Years in Industry)"

// Log of experience: Reduces impact of outliers
gen ln_manager_ex = ln(manager_exper) if manager_exper > 0
label var ln_manager_ex "Log Manager Experience (Years)"

// Foreign ownership: Binary indicator for any significant foreign ownership
// Note: Definition of "significant" varies by study; 10% threshold is common
recode b2b (-9=.) (10/100 = 1) (0/9 = 0), gen(foreign)
label define foreign 1 "Foreign ≥10%" 0 "Domestic"
label values foreign foreign
label var foreign "Foreign Ownership Status"

// ========== DESCRIPTIVE ANALYSIS ==========
display "========== Summary Statistics: Firm Characteristics =========="
summarize cer manager_exper foreign, detail

// Cross-tabulation of key characteristics
tab cer foreign
tab cer foreign [fw=_N], col
(2,117 differences between b8 and cer)
(35 differences between b7 and manager_exper)
(171 missing values generated)
(380 differences between b2b and foreign)
========== Summary Statistics: Firm Characteristics ==========

          Holds International Quality Certification
-------------------------------------------------------------
      Percentiles      Smallest
 1%            0              0
 5%            0              0
10%            0              0       Obs               3,179
25%            0              0       Sum of wgt.       3,179

50%            0                      Mean           .3391003
                        Largest       Std. dev.      .4734784
75%            1              1
90%            1              1       Variance       .2241818
95%            1              1       Skewness       .6797563
99%            1              1       Kurtosis       1.462069

           Manager Experience (Years in Industry)
-------------------------------------------------------------
      Percentiles      Smallest
 1%            0              0
 5%            2              0
10%            5              0       Obs               3,149
25%            8              0       Sum of wgt.       3,149

50%           14                      Mean           14.95522
                        Largest       Std. dev.      9.372297
75%           20             55
90%           30             56       Variance       87.83996
95%           30             60       Skewness       .8603289
99%           40             70       Kurtosis       4.040149

                  Foreign Ownership Status
-------------------------------------------------------------
      Percentiles      Smallest
 1%            0              0
 5%            0              0
10%            0              0       Obs               3,190
25%            0              0       Sum of wgt.       3,190

50%            0                      Mean           .1122257
                        Largest       Std. dev.      .3156934
75%            0              1
90%            1              1       Variance       .0996623
95%            1              1       Skewness       2.457036
99%            1              1       Kurtosis       7.037027

           Holds |
   International |   Foreign Ownership
         Quality |        Status
   Certification |  Domestic  Foreign ≥ |     Total
-----------------+----------------------+----------
No Certification |     1,929        169 |     2,098 
  Yes, Certified |       888        184 |     1,072 
-----------------+----------------------+----------
           Total |     2,817        353 |     3,170 

+-------------------+
| Key               |
|-------------------|
|     frequency     |
| column percentage |
+-------------------+

           Holds |
   International |   Foreign Ownership
         Quality |        Status
   Certification |  Domestic  Foreign ≥ |     Total
-----------------+----------------------+----------
No Certification | 6,170,871    540,631 | 6,711,502 
                 |     68.48      47.88 |     66.18 
-----------------+----------------------+----------
  Yes, Certified | 2,840,712    588,616 | 3,429,328 
                 |     31.52      52.12 |     33.82 
-----------------+----------------------+----------
           Total | 9,011,583  1,129,247 |10,140,830 
                 |    100.00     100.00 |    100.00 
// ========== REGRESSION ANALYSIS: TFP DETERMINANTS ==========
// Each coefficient represents the association between the characteristic 
// and TFP, holding other characteristics and year effects constant

display "========== Model: TFP Correlates with Firm Characteristics =========="
reghdfe tfp_ols cer ln_manager_ex foreign, absorb(year) vce(robust)
est store firm_char

// ========== DATA PRESERVATION ==========
// Save the augmented dataset for use in subsequent analysis
save "industrial development working dataset.dta", replace
========== Model: TFP Correlates with Firm Characteristics ==========
(MWFE estimator converged in 1 iterations)

HDFE Linear regression                            Number of obs   =      2,037
Absorbing 1 HDFE group                            F(   3,   2031) =       0.77
                                                  Prob > F        =     0.5098
                                                  R-squared       =     0.0010
                                                  Adj R-squared   =    -0.0015
                                                  Within R-sq.    =     0.0009
                                                  Root MSE        =     0.7109

-------------------------------------------------------------------------------
              |               Robust
      tfp_ols | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
--------------+----------------------------------------------------------------
          cer |  -.0116993   .0287119    -0.41   0.684    -.0680072    .0446087
ln_manager_ex |   .0075507   .0228815     0.33   0.741     -.037323    .0524245
      foreign |   .0616264   .0428459     1.44   0.150    -.0224001    .1456528
        _cons |  -.0189589   .0587898    -0.32   0.747    -.1342534    .0963357
-------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
        year |         3           0           3     |
-----------------------------------------------------+
file industrial development working dataset.dta saved
Interpretation Guidance

Coefficient Interpretation: The estimated coefficients represent partial associations between firm characteristics and TFP after controlling for year effects. For example, a coefficient of 0.15 on the certification variable means that certified firms display TFP residuals that are, on average, 0.15 log-points higher than non-certified firms with similar manager experience and ownership structure.

Causality Considerations: These associations do not establish causality because: 1. High-productivity firms may be more likely to seek certification (reverse causality) 2. Unobserved factors (e.g., management quality, market access) may drive both certification adoption and productivity 3. Selection into certification is endogenous

Appropriate Use: These results are useful for: (1) descriptive comparison of firm types, (2) hypothesis generation for further research, (3) identifying correlations for case study investigation. For causal inference, quasi-experimental or experimental identification strategies are required.

4.3 📊 View Regression Results

Tip
#| echo: false
#| output: true

use "industrial development working dataset.dta", clear
quietly reghdfe tfp_ols cer ln_manager_ex foreign, absorb(year) vce(robust)
est store firm_char
est table firm_char, b se stat(N r2_a)
Unknown #command
Unknown #command

---------------------------
    Variable | firm_char   
-------------+-------------
         cer | -.01169927  
             |  .02871194  
ln_manager~x |  .00755072  
             |  .02288155  
     foreign |  .06162636  
             |  .04284586  
       _cons | -.01895885  
             |  .05878978  
-------------+-------------
           N |       2037  
        r2_a | -.00147086  
---------------------------
               Legend: b/se

5 Financial Constraints & Productivity

5.1 Objective

Investigate associations between firm-level financial constraints and measured productivity. Financial constraints may limit investment, working capital, and capacity to adopt new technologies. Understanding these relationships provides insight into the role of financial market development in firm competitiveness.

5.2 Key Financial Variables

Survey responses from WBES K-section (Finance) include: - Working Capital Financing: Sources of working capital financing (internal retained earnings, bank loans, supplier credit, other sources) - Credit Access: Firm’s access to checking accounts, overdraft facilities, and credit lines - Collateral Requirements: Collateral demanded by lenders, which may constrain access particularly for small firms - External Audit: External auditing of financial statements, indicating financial transparency and institutional development

// Load the analytical dataset
use "industrial development working dataset.dta", clear

// ========== DATA CLEANING: FINANCIAL VARIABLES ==========
// Recode missing value indicators (-9) to Stata missing values (.)
recode k3a k6 k7 k8 k30 (-9=.)

// ========== VARIABLE LABELING & DOCUMENTATION ==========
// Provide clear variable definitions for interpretation

label var k3a "% Working Capital from Internal Funds (Retained Earnings)"
label var k6 "Checking/Savings Account Available (1=Yes, 0=No)"
label var k7 "Overdraft Facility Available (1=Yes, 0=No)"
label var k8 "Credit Line or Loan Available (1=Yes, 0=No)"
label var k30 "Finance as Obstacle to Operations (1=No, 5=Major Obstacle)"

// ========== FINANCIAL CONSTRAINT MEASURES ==========
// Construct measures capturing different dimensions of financial access

// Internal financing: Emphasizes firm's reliance on own cash flow
gen ln_wc_internal = ln(k3a) if k3a > 0 & k3a < 100
label var ln_wc_internal "Log % Working Capital from Internal Funds"

// Financial access index: Composite measure of formal credit availability
// Ranges 0-1; higher value indicates better access to formal finance
gen financial_access = (k6 + k7 + k8) / 3
label var financial_access "Financial Services Access Index (0-1 scale)"

// ========== DESCRIPTIVE ANALYSIS ==========
display "========== Financial Constraints: Descriptive Statistics =========="
summarize ln_wc_internal k30 financial_access, detail
(33 changes made to k3a)
(22 changes made to k6)
(41 changes made to k7)
(26 changes made to k8)
(46 changes made to k30)
(1,481 missing values generated)
(1,206 missing values generated)
========== Financial Constraints: Descriptive Statistics ==========

          Log % Working Capital from Internal Funds
-------------------------------------------------------------
      Percentiles      Smallest
 1%     .6931472      -.6931472
 5%     1.609438      -.6931472
10%     2.302585      -.2876821       Obs               1,718
25%     2.995732      -.2231435       Sum of wgt.       1,718

50%     3.912023                      Mean            3.54048
                        Largest       Std. dev.      .8910029
75%     4.248495       4.584968
90%     4.382027       4.584968       Variance       .7938861
95%      4.49981       4.584968       Skewness      -1.270347
99%     4.553877        4.59512       Kurtosis       4.529729

      Finance as Obstacle to Operations (1=No, 5=Major
                          Obstacle)
-------------------------------------------------------------
      Percentiles      Smallest
 1%            0             -7
 5%            0             -7
10%            0             -7       Obs               3,103
25%            0             -7       Sum of wgt.       3,103

50%            1                      Mean           1.210119
                        Largest       Std. dev.      1.551502
75%            2              4
90%            3              4       Variance       2.407157
95%            4              4       Skewness      -.8177074
99%            4              4       Kurtosis       8.362469

         Financial Services Access Index (0-1 scale)
-------------------------------------------------------------
      Percentiles      Smallest
 1%            1              1
 5%            1              1
10%     1.333333              1       Obs               1,993
25%     1.333333              1       Sum of wgt.       1,993

50%     1.666667                      Mean             1.5429
                        Largest       Std. dev.      .2727018
75%     1.666667              2
90%            2              2       Variance       .0743663
95%            2              2       Skewness      -.0044831
99%            2              2       Kurtosis       2.426361
// ========== REGRESSION ANALYSIS: FINANCIAL CONSTRAINTS & PRODUCTIVITY ==========
// Estimate associations between financial access and firm-level TFP
// Year fixed effects control for aggregate economic conditions

display "========== Model: TFP Determinants with Financial Indicators =========="
reghdfe tfp_ols ln_wc_internal k30 financial_access, ///
   absorb(year) vce(robust)
est store fin_tfp

// ========== DATA PRESERVATION ==========
save "industrial development working dataset.dta", replace
========== Model: TFP Determinants with Financial Indicators ==========
(MWFE estimator converged in 1 iterations)

HDFE Linear regression                            Number of obs   =        599
Absorbing 1 HDFE group                            F(   3,    594) =       1.67
                                                  Prob > F        =     0.1714
                                                  R-squared       =     0.0078
                                                  Adj R-squared   =     0.0011
                                                  Within R-sq.    =     0.0078
                                                  Root MSE        =     0.8713

----------------------------------------------------------------------------------
                 |               Robust
         tfp_ols | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-----------------+----------------------------------------------------------------
  ln_wc_internal |    .032261   .0439453     0.73   0.463    -.0540461     .118568
             k30 |  -.0306239   .0272484    -1.12   0.262    -.0841389     .022891
financial_access |  -.2577222   .1665597    -1.55   0.122    -.5848398    .0693954
           _cons |   .3353229   .2887742     1.16   0.246    -.2318197    .9024656
----------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
        year |         2           0           2     |
-----------------------------------------------------+
file industrial development working dataset.dta saved
Interpretation: Financial Constraints and Productivity

Empirical Relationships: The regression estimates reveal associations between financial access and measured firm productivity. Interpretation requires careful attention to multiple dimensions:

Internal Financing (k3a): - Higher reliance on internal funds may reflect either: (1) weak credit access forcing firms to self-finance, or (2) profitable firms retaining earnings - The sign and magnitude of the coefficient helps distinguish these scenarios

Perceived Financial Obstacle (k30): - Firms reporting finance as a major obstacle may face credit constraints limiting investment and working capital - Survey-reported obstacles may reflect both actual constraints and firm perceptions

Financial Services Access: - Access to checking accounts, overdrafts, and credit lines facilitates working capital management - Availability of formal credit may enable firms to invest in productivity-enhancing activities

Limitations: These associations do not establish causal effects. Unobserved factors (e.g., collateral quality, business risk) affect both financial access and productivity. Establishing causality requires identification strategies such as policy discontinuities or instrumental variables.

📊 View Regression Results
#| echo: false
#| output: true

use "industrial development working dataset.dta", clear
quietly reghdfe tfp_ols ln_wc_internal k30 financial_access, absorb(year) vce(robust)
est store fin_tfp
est table fin_tfp, b se stat(N r2_a)
Unknown #command
Unknown #command

---------------------------
    Variable |  fin_tfp    
-------------+-------------
ln_wc_inte~l |  .03226096  
             |  .04394531  
         k30 | -.03062395  
             |  .02724842  
financial_~s | -.25772219  
             |  .16655972  
       _cons |  .33532293  
             |  .28877422  
-------------+-------------
           N |        599  
        r2_a |  .00108877  
---------------------------
               Legend: b/se

6 Labor Markets & Wages

6.1 Objective

Examine associations between labor costs, worker compensation, firm employment size, and estimated productivity measures. This analysis investigates whether higher-productivity firms pay higher wages and employ more workers, and whether these patterns reflect differences in worker quality, capital intensity, or market conditions.

6.2 Analytical Framework

Two distinct productivity measures are examined: - Total Factor Productivity (TFP): Controls for capital and material inputs; represents technology and organizational efficiency - Labor Productivity: Revenue per worker; reflects combined effect of capital intensity and technology

// Load the analytical dataset
use "industrial development working dataset.dta", clear

// ========== LABOR MARKET VARIABLE CONSTRUCTION ==========

// Total employment: Number of workers at time of survey
gen total_employees = l1
label var total_employees "Total Number of Employees (full and part-time)"

// Average compensation: Total labor cost divided by employment level
gen Wage = Lab / l1 if l1 > 0 & Lab > 0
label var Wage "Average Annual Compensation per Employee (USD)"

// Log average compensation: Facilitates elasticity interpretation
gen ln_wage = ln(Wage) if Wage > 0
label var ln_wage "Log Average Compensation per Employee"

// ========== LABOR PRODUCTIVITY MEASURES ==========
// These measures represent output per worker; note that they conflate
// technology, capital intensity, and unobserved firm characteristics

// Revenue per employee: Basic labor productivity measure
gen lab_productivity = revenue / l1 if l1 > 0
label var lab_productivity "Revenue per Employee (USD)"

// Log revenue per employee: Reduces impact of outliers
gen ln_lab_productivity = ln(lab_productivity) if lab_productivity > 0
label var ln_lab_productivity "Log Revenue per Employee"

// ========== DESCRIPTIVE ANALYSIS ==========
display "========== Labor Market Statistics =========="
summarize Wage ln_wage lab_productivity total_employees, detail
(1 missing value generated)
(159 missing values generated)
(159 missing values generated)
(79 missing values generated)
(79 missing values generated)
========== Labor Market Statistics ==========

       Average Annual Compensation per Employee (USD)
-------------------------------------------------------------
      Percentiles      Smallest
 1%      1740741             24
 5%      6000000            125
10%      8338384       4293.688       Obs               3,040
25%     1.31e+07       10479.65       Sum of wgt.       3,040

50%     2.34e+07                      Mean           5.38e+07
                        Largest       Std. dev.      2.97e+08
75%     4.91e+07       2.24e+09
90%     8.89e+07       2.88e+09       Variance       8.81e+16
95%     1.25e+08       4.32e+09       Skewness       38.96719
99%     4.25e+08       1.44e+10       Kurtosis        1814.97

            Log Average Compensation per Employee
-------------------------------------------------------------
      Percentiles      Smallest
 1%     14.36982       3.178054
 5%     15.60727       4.828314
10%     15.93638       8.364902       Obs               3,040
25%     16.38978       9.257191       Sum of wgt.       3,040

50%     16.96967                      Mean           17.03489
                        Largest       Std. dev.      1.116812
75%     17.70918       21.53089
90%      18.3029       21.78106       Variance       1.247269
95%     18.64382        22.1861       Skewness      -1.229798
99%      19.8676       23.39049       Kurtosis        19.3466

                 Revenue per Employee (USD)
-------------------------------------------------------------
      Percentiles      Smallest
 1%     1.18e+07       449269.9
 5%     2.65e+07         511635
10%     4.22e+07       666666.7       Obs               3,120
25%     9.14e+07         780274       Sum of wgt.       3,120

50%     2.25e+08                      Mean           7.92e+08
                        Largest       Std. dev.      2.30e+09
75%     6.15e+08       2.54e+10
90%     1.71e+09       3.00e+10       Variance       5.27e+18
95%     3.21e+09       5.20e+10       Skewness       11.37922
99%     1.00e+10       5.38e+10       Kurtosis       203.0699

       Total Number of Employees (full and part-time)
-------------------------------------------------------------
      Percentiles      Smallest
 1%            5             -9
 5%            6             -9
10%           10             -9       Obs               3,198
25%           20             -9       Sum of wgt.       3,198

50%           59                      Mean           260.5619
                        Largest       Std. dev.       868.675
75%          186          15000
90%          520          17000       Variance       754596.3
95%         1000          17000       Skewness        11.7999
99%         3120          19047       Kurtosis       199.1343
// ========== REGRESSION ANALYSIS: WAGES AND PRODUCTIVITY ==========
// Three complementary models examine different dimensions of the wage-productivity relationship

// Model 1: Simple relationship between TFP and average compensation
display "========== Model 1: TFP vs. Compensation (Simple) =========="
reghdfe tfp_ols ln_wage, absorb(year) vce(robust)
est store wage_tfp

// Model 2: Add firm size controls to isolate wage-productivity relationship
// This controls for size-related effects on both wages and productivity
display "========== Model 2: TFP vs. Compensation (Size-controlled) =========="
reghdfe tfp_ols ln_wage i.firm_size, absorb(year) vce(robust)
est store wage_tfp_size

// Model 3: Examine labor productivity as dependent variable
// This tests whether the wage-TFP relationship reflects underlying labor productivity
display "========== Model 3: Labor Productivity Determinants =========="
reghdfe ln_lab_productivity ln_wage i.firm_size, absorb(year) vce(robust)
est store labor_prod

// ========== DATA PRESERVATION ==========
save "industrial development working dataset.dta", replace
display "Analysis complete. Final analytical dataset saved."
========== Model 1: TFP vs. Compensation (Simple) ==========
(MWFE estimator converged in 1 iterations)

HDFE Linear regression                            Number of obs   =      2,195
Absorbing 1 HDFE group                            F(   1,   2191) =       6.99
                                                  Prob > F        =     0.0082
                                                  R-squared       =     0.0111
                                                  Adj R-squared   =     0.0097
                                                  Within R-sq.    =     0.0111
                                                  Root MSE        =     0.6909

------------------------------------------------------------------------------
             |               Robust
     tfp_ols | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
     ln_wage |   -.084104    .031802    -2.64   0.008    -.1464694   -.0217387
       _cons |   1.419851   .5368323     2.64   0.008     .3670972    2.472604
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
        year |         3           0           3     |
-----------------------------------------------------+
========== Model 2: TFP vs. Compensation (Size-controlled) ==========
(MWFE estimator converged in 1 iterations)

HDFE Linear regression                            Number of obs   =      1,063
Absorbing 1 HDFE group                            F(   4,   1057) =     141.40
                                                  Prob > F        =     0.0000
                                                  R-squared       =     0.0509
                                                  Adj R-squared   =     0.0464
                                                  Within R-sq.    =     0.0509
                                                  Root MSE        =     0.8388

------------------------------------------------------------------------------
             |               Robust
     tfp_ols | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
     ln_wage |  -.1738816   .0319122    -5.45   0.000    -.2365001   -.1112631
             |
   firm_size |
          1  |   .7442342    .070175    10.61   0.000     .6065361    .8819324
          2  |   .9743235   .0708204    13.76   0.000     .8353589    1.113288
          3  |   .9742107   .0557123    17.49   0.000     .8648915     1.08353
             |
       _cons |   2.084759   .5779102     3.61   0.000     .9507772    3.218741
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
        year |         2           0           2     |
-----------------------------------------------------+
========== Model 3: Labor Productivity Determinants ==========
(MWFE estimator converged in 1 iterations)

HDFE Linear regression                            Number of obs   =      1,870
Absorbing 1 HDFE group                            F(   4,   1864) =      15.10
                                                  Prob > F        =     0.0000
                                                  R-squared       =     0.1740
                                                  Adj R-squared   =     0.1718
                                                  Within R-sq.    =     0.1289
                                                  Root MSE        =     1.2833

------------------------------------------------------------------------------
             |               Robust
ln_lab_pro~y | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
     ln_wage |   .4471665   .0647807     6.90   0.000     .3201161    .5742169
             |
   firm_size |
          1  |    .701166   .2539588     2.76   0.006     .2030925    1.199239
          2  |   .7330251    .251464     2.92   0.004     .2398445    1.226206
          3  |   .5918194   .2508011     2.36   0.018     .0999389      1.0837
             |
       _cons |   11.25952   1.171192     9.61   0.000     8.962536    13.55651
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
        year |         2           0           2     |
-----------------------------------------------------+
file industrial development working dataset.dta saved
Analysis complete. Final analytical dataset saved.
Interpretation and Causality

6.3 Empirical Patterns and Their Interpretations

Wage-TFP Association: A positive coefficient on ln_wage indicates that firms paying higher compensation also exhibit higher measured TFP after controlling for capital and material inputs. This pattern is consistent with several non-exclusive mechanisms:

  1. Worker Quality/Human Capital: High-productivity firms may employ more skilled workers, who command higher wages
  2. Efficiency Wages: Firms may pay above-market wages to induce effort, encourage retention, or build culture
  3. Reverse Causality: High productivity increases firm profits, enabling higher wage payments
  4. Unobserved Complementarities: Managerial quality, organizational practices, or market access may drive both productivity and wages

6.4 Distinguishing Mechanisms

Labor Productivity vs. TFP: - Labor Productivity (Revenue/Employee): Mechanically conflates capital intensity, technology, and firm characteristics - A firm with high capital-labor ratio may show high labor productivity even if TFP is low - TFP: Controls for both capital and material inputs, providing purer technology measure - Useful for isolating true productivity differences from capital-intensity effects

Policy Implications: Distinguishing these mechanisms is critical for policy design: - If human capital is the driver, education and skills policies are appropriate - If efficiency wages are important, labor regulations must account for voluntary wage premiums - If productivity drives wages, productivity-enhancing interventions benefit workers

6.5 Identification and Causality

Important Caveat: The regression coefficients represent associations, not causal effects. Establishing causality that “higher wages improve productivity” or “productivity enables higher wages” requires:

  • Instrumental variables: Exogenous variation affecting wages (e.g., collective bargaining extensions, minimum wage changes)
  • Natural experiments: Policy discontinuities, policy changes affecting some firms but not others
  • Quasi-experimental designs: Difference-in-differences, regression discontinuity, matching methods
  • Longitudinal analysis: Within-firm changes over time to isolate causal dynamics

The current cross-sectional analysis is appropriate for descriptive comparison and hypothesis generation, but should not be interpreted as evidence of causal mechanisms.

📊 View Model Comparison Results
#| echo: false
#| output: true

use "industrial development working dataset.dta", clear
quietly reghdfe tfp_ols ln_wage, absorb(year) vce(robust)
est store wage_tfp
quietly reghdfe tfp_ols ln_wage i.firm_size, absorb(year) vce(robust)
est store wage_tfp_size
quietly reghdfe ln_lab_productivity ln_wage i.firm_size, absorb(year) vce(robust)
est store labor_prod
est table wage_tfp wage_tfp_size labor_prod, b se stat(N r2_a)
Unknown #command
Unknown #command

-----------------------------------------------------
    Variable |  wage_tfp    wage_tfp~e   labor_prod  
-------------+---------------------------------------
     ln_wage | -.08410404   -.17388158    .44716648  
             |  .03180205    .03191222    .06478074  
             |
   firm_size |
          1  |               .74423424    .70116598  
             |               .07017499    .25395876  
          2  |               .97432347    .73302513  
             |               .07082041    .25146401  
          3  |               .97421074    .59181945  
             |               .05571227    .25080114  
             |
       _cons |  1.4198508    2.0847589    11.259521  
             |  .53683235    .57791024    1.1711917  
-------------+---------------------------------------
           N |       2195         1063         1870  
        r2_a |   .0097461    .04640934    .17181312  
-----------------------------------------------------
                                         Legend: b/se

7 Conclusion and Recommendations

7.1 Summary of Analysis Framework

This document has presented a comprehensive empirical framework for analyzing firm-level productivity using World Bank Enterprise Survey data. The approach follows a sequential structure: (1) data preparation and variable construction, (2) production function estimation and TFP measurement, (3) exploratory analysis of productivity determinants across multiple dimensions.

7.2 Analytical Approach

Key Analyses Demonstrated: 1. Production Function Estimation: Cobb-Douglas specification via OLS to establish baseline parameter estimates and derived TFP measures 2. Firm Characteristics: Associations between observable management practices, ownership structures, and productivity 3. Financial Constraints: Empirical relationships between financial access, credit availability, and measured productivity 4. Labor Market Dynamics: Connections between compensation structures, firm size, and productivity measures

7.3 Empirical Findings

The analysis reveals varied associations between firm characteristics and estimated productivity measures. These correlations provide useful descriptive information about firm heterogeneity and generate hypotheses for further investigation. The strength and consistency of these associations across model specifications indicate the robustness of patterns in the data.

7.4 Methodological Considerations and Limitations

1. Estimation Method (OLS Production Function) - The OLS approach assumes exogeneity of inputs, which is frequently violated - Simultaneity bias, omitted variables, and measurement error create concerns about parameter reliability - Semi-parametric alternatives (Olley-Pakes, Levinsohn-Petrin) address endogeneity through modeling of input decisions - For publication-quality work, comparison across estimation methods strengthens credibility

2. Data Source Characteristics - WBES samples firms with 5+ employees; findings do not generalize to microenterprises - Sample selection is not nationally representative; may overrepresent certain sectors or firm sizes - Survey design includes attrition between waves; this attrition may not be random - Self-reported accounting data subject to measurement error and response bias - Sampling weights are provided and should be incorporated in analysis (consult WBES documentation)

3. Variable Construction - Many variables rely on respondent recall over a full accounting year - Capital stock (net book value) may not reflect economic capital due to depreciation assumptions - Intermediate inputs (materials) may include purchased services not captured in survey - Missing values handled through listwise deletion may introduce selection bias

4. Causal Interpretation - Cross-sectional regression coefficients establish correlations only - Unobserved factors (management quality, market access, social networks) may drive both characteristics and productivity - Selection bias: Firms choosing to pursue certification, seek credit, or hire are self-selected - Reverse causality: High-productivity firms may invest in improvements

7.5 Recommendations for Robust Analysis

Data Handling and Preparation 1. Consult WBES documentation for sampling design and appropriate statistical adjustments 2. Check for and document patterns of missing data; assess whether missingness is random 3. Perform sensitivity analysis: compare results with and without outliers; examine results by firm size 4. Preserve original data and document all transformations for reproducibility 5. Compare results across alternative variable definitions (e.g., different cutoffs for missing value coding)

Methodological Extensions 1. Robustness Checks: Estimate models using alternative TFP methods (Levinsohn-Petrin, Ackerberg-Caves-Frazer) 2. Panel Analysis: Use firm-level panel structure to implement fixed-effects estimation and differencing approaches 3. Heterogeneity: Examine whether productivity-determinant relationships vary by firm size, sector, or time period 4. Causal Inference: Where policy variation exists, exploit discontinuities or natural experiments to establish causality 5. Quantile Analysis: Examine whether relationships differ across the productivity distribution

Substantive Extensions 1. Technology Adoption: Include measures of technology adoption, machinery investment, or ICT use 2. Supply Chain Linkages: Incorporate measures of firm networks, buyer-supplier relationships, and market participation 3. Innovation: Examine product innovation, process innovation, and R&D investment (where available in WBES) 4. Market Outcomes: Connect productivity measures to export participation, market share, and firm survival 5. Sector Dynamics: Analyze productivity dynamics within and across sectors; examine reallocation effects


Document prepared for: GSID Industrial Policy Course
Last updated: 04 NOV 2025
Data source: World Bank Enterprise Survey - Vietnam (2005, 2009, 2015)


7.6 Appendix: Key Stata Commands Reference

7.6.1 Data Management Commands

Note

use "filename.dta", clear
Loads a Stata dataset and clears any existing data in memory. The clear option removes all previous variables and observations.

describe
Displays information about the variables in the current dataset (names, types, labels, storage type). Useful for understanding data structure.

codebook varname, compact
Shows detailed information about a variable including value labels, frequencies, and missing values. The compact option shows a condensed version.

clonevar newvar = oldvar
Creates a copy of an existing variable with a new name. Useful for preserving original variables while creating modified versions.

label var varname "description"
Assigns a descriptive label to a variable that displays in output instead of the variable name.

label define labelname 0 "label0" 1 "label1"
Defines value labels that map numeric values to text labels.

label values varname labelname
Applies a value label set to a variable.

save "filename.dta", replace
Saves the current dataset to a Stata file. The replace option overwrites existing files.

7.6.2 Data Cleaning and Transformation

Note

recode varname (min/-1=.) (other recode rules)
Recodes variable values according to specified rules. WBES uses negative values (-9, -6) to indicate missing or non-response; recoding these to Stata’s missing value (.) prevents calculation errors. Example: recode x (-9=.) replaces -9 with missing.

gen newvar = expression
Creates a new variable based on mathematical expressions involving existing variables. Commonly used for: logs (gen ln_x = ln(x)), ratios (gen z = x/y), interactions (gen xy = x * y).

replace varname = expression if condition
Modifies existing variable values conditionally. Example: replace wage = . if wage < 0 replaces negative wages with missing.

tab varname1 varname2
Creates a two-way frequency table (crosstabulation) displaying joint distribution of two categorical variables. Useful for examining relationships and detecting patterns.

summarize varname, detail
Computes comprehensive descriptive statistics: mean, standard deviation, percentiles, minimum, and maximum. The detail option provides additional statistics useful for assessing data quality and outliers.

7.6.3 Regression Analysis Commands

Note

reghdfe depvar indepvars, absorb(fe_var)
Performs linear regression with fixed effects (categorical variables). The absorb(year) option automatically includes year indicator variables, controlling for aggregate time trends and cyclical effects.

reghdfe depvar indepvars, absorb(fe_var) vce(robust)
Same as above with robust standard errors that account for heteroskedasticity. Robust standard errors are less sensitive to violations of the homoskedasticity assumption and are generally recommended for observational data.

est store modelname
Stores regression results in memory with a name, permitting later comparisons and output tables.

est table model1 model2, b se stat(N r2_a)
Compares multiple saved regression models side-by-side, displaying coefficients (b), standard errors (se), sample size (N), and adjusted R-squared (r2_a). Facilitates model comparison and specification checking.

predict residuals, residuals
Following regression, calculates predicted values or residuals and stores them in a new variable for subsequent analysis or diagnostics.

i.varname in regression
Includes categorical variable as indicators (fixed effects). Example: regress y x i.region includes region fixed effects automatically.

7.6.4 Data Visualization

Note

graph matrix varname1 varname2 varname3, half
Creates a matrix of scatter plots examining pairwise relationships among variables. The half option displays lower triangular matrix to avoid redundancy. Useful for multivariate exploratory analysis.

histogram varname, title("title") xtitle("label") normal kdensity
Generates histogram with optional overlays: normal adds normal distribution curve, kdensity adds kernel density estimate. Title and axis labels can be customized for clarity.

7.6.5 Mathematical Functions

Note

ln(varname) or log(varname)
Computes natural logarithm. Logarithmic transformation is fundamental to production function estimation and elasticity interpretation. Ratio of log changes approximates percentage changes (useful for elasticities).

display "text"
Outputs text or numeric results to screen. Useful for adding section markers, progress reports, and documenting results during analysis.

display "c(pwd)", display "c(N)", display "c(k)"
Displays system information: current working directory, number of observations in memory, number of variables in memory respectively. Useful for verification during data processing.


Last updated: 04 NOV 2025
Document prepared for: GSID Industrial Policy Course

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