back on the rails: competition and productivity in state...

Back on the Rails: Competition and Productivity inState-owned Industry

By Sanghamitra Das, Kala Krishna, Sergey Lychagin, and RohiniSomanathan∗

We use a proprietary data set on the floor-level operations at thelargest rail mill in India to study the response of productivity tothe threat of entry. Output per active shift increased by 28 per-cent over three years with minimal changes in physical capital andemployment. By combining data on the timing of various trainingprograms in the mill with shift-level variation in worker composi-tion, we are able to attribute over half of the higher productivityto training specifically targeted towards improving rail output. Ourwork suggests high returns to knowledge-enhancing investment inemerging economies.JEL: D24, J24, L23, L61, M53.Keywords: Total Factor Productivity (TFP), Plant level data,Competitiveness and trade.

Productivity differences feature prominently in explanations of unequal eco-nomic performance: Hall and Jones (1999) attribute most of the variation inoutput per worker across countries to differences in total factor productivity(TFP). The dispersion in productivity is also used to measure the extent to whichfirms operate inside their production frontier. Syverson (2004) estimates within-industry distributions of TFP in the United States and finds that firms in the90th percentile are twice as productive as those in the 10th percentile. These ra-tios are especially large for emerging economies. Based on plant-level data fromIndia and China, Hsieh and Klenow (2009) argue that a reallocation of resourcesthat would bring the dispersion of productivity down to U.S. levels could raiseoutput by as much as 50 percent.

In much of the literature, TFP is defined as a residual, while its response topolicy is evaluated through correlations with a handful of observed variables. A

∗ Krishna: Pennsylvania State University, NBER and CESIfo, 523 Kern, University Park, PA, USA,[email protected]. Lychagin: Department of Economics, Central European University, Nador 11, Budapest,Hungary, [email protected]. Somanathan: Department of Economics, Delhi School of Economics, Delhi110007, India, [email protected]. Sanghamitra Das passed away unexpectedly in December 2008 andis greatly missed. We are especially grateful to the Steel Authority of India (SAIL) and to the Ministryof Railways for access to data and answers to many of our queries along the way. Our special thanks toArvind Pande, A.V. Kamlakar, Arti Luniya and G. Upadhyaya who were all at SAIL when we startedour project and to Naveen Chopra from the Railways. We thank George Alessandria, Ana Cecilia Fieler,Morita Hodaka, Michael Keane, James A. Schmitz, E. Somanathan and three anonymous referees for veryuseful comments. The Planning and Policy Research Unit at the Indian Statistical Institute providedfinancial support. We also thank the Human Capital Foundation (www.hcfoundation.ru), and especiallyAndrey P. Vavilov, for support of the Center for the Study of Auctions, Procurements, and CompetitionPolicy (CAPCP, http://capcp.psu.edu/) at Penn State University.

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recent alternative approach focuses on the production processes of firms operatingwithin narrowly defined markets to identify specific institutions and incentivesthat engender productivity change. Schmitz (2005), for example, shows howiron-ore mines in North America increased capacity utilization through changesin labor contracts when they were faced with competition from Brazilian imports.Ichniowski, Shaw and Prennushi (1997) use longitudinal data on productivity andhuman resource management from 36 steel finishing lines across the United States,linking innovative employment practices to increased uptime. Bloom et al. (2010)find management consulting services provided to a random sample of textile firmsin India resulted in lower defect rates.

Our paper is very much a part of this emerging literature. We study produc-tivity change at the Bhilai Rail and Structural Mill in the early 2000s. The millis owned by the Steel Authority of India (SAIL), a large public sector companywhich had been the sole supplier of rails to the Indian Railways since its inceptionin 1960. There was an implicit agreement that this would continue as long as itwas able to meet orders and maintain quality standards. Under the newly lib-eralized policy regime of the 1990s, the railways embarked on an ambitious planof track replacements and network expansion that increased Railways’ potentialdemand of rails from SAIL. However, in the late 1990s there was a series of rail-way accidents, which turned out to be due to the poor quality of rails. These twofactors resulted in questions regarding SAIL’s ability to perform as needed. Therailways began considering alternative suppliers and in 1999 an Indian conglom-erate planned to set up a rail mill in close geographical proximity to the SAILplant.

The highly paid and historically secure jobs at the mill were threatened bythese events. The management believed that entry could be averted if the largerorders and new quality standards of the Railways were able to be met by SAIL.A variety of programs were initiated to motivate and train workers and controlabsenteeism. Between 2000-2003, the supply of rails almost doubled. This wasachieved in part through a shift in the composition of output. The mill movedaway from other products, collectively called structurals, towards rails. There wasalso a substantial increase in the utilization of capacity and in worker productivity.Total shifts worked went up by 15 percent, shifts producing rails went up by 83percent and the average output per rail shift increased by 28 percent. The numberof defects were cut in half and delays caused by employee errors went down by 43percent. All of this occurred without any substantial investments in the plant orchanges in hiring practices.

We use data from over 3,000 production shifts during this period to understandthe mechanisms underlying this surge in productivity. For each shift we use twotypes of logs. The first of these records inputs, outputs and all production delaysalong with their precise causes. The second lists all employees present on themill floor during the shift. We combine these shift-level data with backgroundinformation on employee demographics, designations and training. This allows

VOL. VOL NO. ISSUE BACK ON THE RAILS 3

us to generate stocks of different types of labor and training for each shift. Thecomposition of the labor on the factory floor changes across shifts as employeestake planned and unplanned days off. This generates the variation in labor andtraining that we exploit in estimating the determinants of productivity. Thefactory floor data that we use, though restricted to a particular plant, are moredetailed than any used in previous work.

We find that the most systematic influence on output per worker was trainingspecifically targeted at rails. One measure of output for each shift is the numberof rectangular blocks of steel called blooms that are shaped into rails. We findthat a little over half of the 46 additional blooms processed per shift between thebeginning and end of our period can be attributed to a training program termedAcceptance of rails, which was administered in the summer of 2001. The principalmechanism through which productivity improvements are realized is a declinein the probability of delays caused by employee mistakes and malfunctioningmachinery.

The rest of the paper is organized as follows. Section I provides some back-ground on the SAIL plant and documents the emergence of competition in themarket for rails in the late 1990s. Section II describes the construction of ourdata set and summarizes changes in inputs and outputs over our study period.Section III presents reduced-form estimates of the determinants of output perworking shift and examines their robustness to alternative specifications. SectionIV focuses on the mechanisms through which higher productivity was achievedby separately estimating the probabilities of different classes of delays, their du-rations and rates of production per unit of operating. Section V summarizes andconcludes.

I. The Institutional Setting

The Bhilai Rail and Structural Mill

The rail mill we study was commissioned in 1960 as part of a large integratedsteel plant owned by SAIL and built with Soviet cooperation. The SAIL plantis one of three public sector plants built around this time to promote growth inbasic industry. It is located in the town of Bhilai in central India, in proximityto the mineral deposits needed for steel production. The plant has successfullytransformed Bhilai and ninety six of its surrounding villages. Local residents weregiven preference in employment and, over the years, sons often joined their fathersas employees in the plant.

Regular jobs at the plant come with excellent fringe benefits including schooling,health care, housing and paid leave. Bhilai now has 48 schools, 10 health centersand 5 hospitals. These amenities are especially valued because the rest of thisregion continues to be poor and remote. Workers here, in comparison with otherindustrial workers in India, have been referred to as the aristocracy of labor (Parry1999). Although there is a strong correlation between seniority and pay and a


very weak one between pay and performance, SAIL is widely regarded as a verysuccessful firm.

Rails are produced in the Rail and Structural Mill (RSM) which also producesseveral types of beams and other products in addition to rails. These are col-lectively called structurals and are used in major infrastructural projects or asintermediate inputs in the production of heavy machinery. For both rails andstructurals, production follows a similar process and takes place continuously inthree eight-hour shifts. Steel blooms enter a furnace, emerge onto stands on whichthey are shaped or rolled, then cut into the required sizes before being cooled andtested. Teams of workers contribute at different stages. A total of 330 workersare employed at the mill during the period we study. On joining the mill, eachworker is assigned a personal number and a brigade or work team. We used theseto identify the set of workers in each shift.

The mill has only one production line and, since machinery needs to be adjustedfor each product, most shifts are devoted to either rails or structurals and usuallyto a narrowly defined product within these categories. This is a useful aspect ofour data; some physical investment and training programs were targeted specifi-cally at rails and we are able to identify their effects by looking at the differencesin the output of rails relative to structurals. This strategy is discussed further inSection III.

The Emergence of Competition

Between 1960 and the late 1990s, the RSM had an exclusive relationship withthe Indian Railways. The railways bought rails only from SAIL and since therewere very few other buyers for rails, the mill responded to fluctuations in ordersfrom the railways by adjusting the number of shifts devoted to producing struc-turals. In 1997, the Ninth Five Year Plan for the Indian economy acknowledgedinfrastructure as a limiting factor for growth and laid down ambitious targets fornew rail lines and track replacements. There was a four-fold increase in physi-cal targets between 1997 and 2002 and Railways projected that they would need450,000 metric tons of rails for the 1998-1999 fiscal year.1

The new targets were below the notional capacity of the mill at Bhilai butthey were almost 30 percent higher than the annual supplies of rails at the time,which the RSM had difficulty supplying to begin with. The Ministry of Railwaysquestioned the mill’s ability to augment supplies while maintaining quality. In1997, and again in 1998 it invited domestic and international firms to set uprail facilities to provide the additional 100,000 tons. A total of twenty five firmsparticipated in preliminary meetings but none were willing to invest without aformal long-term purchase commitment and the railways were reluctant to enter

1These plans for expansion are documented in the Ninth Five Year Plan, Annexure 7.1.6 (Governmentof India, Planning Commission 1997)


into any agreement of this kind.2

The reputation of the mill suffered a major setback in late 1998 after a trainaccident killed 210 people. A committee appointed to investigate the accidentattributed it to fractured rails. Orders from the mill were temporarily suspended.Greater rail safety required lowering the hydrogen content of rails to make themless brittle and using longer rails so that tracks would have fewer joins. In Novem-ber 1999, an Indian conglomerate, Jindal Steel and Power Limited (JSPL), wroteto the railways announcing its decision to set up a new rail mill with the capacityto produce high quality long rails.3

Uncertainty within both SAIL and JSPL continued as the railways deliberatedon their future course of action. A loss of SAIL’s market share would have im-plications for the financial stability of the whole company: rails were far moreprofitable than other steel products and rail demand was projected to expandsteadily while the market for other steel products was uncertain. The industrialliberalization policies of the early nineties had already brought substantial privateinvestment into mid-sized steel plants. For SAIL this meant increased competi-tion for its other steel products, reduced prices and profits and almost no internalfunds that could be used for modernizing the rail mill.

In April 2001, JSPL wrote to the railways once again announcing that theyexpected to start production within the year and asked for an assurance thattheir rails would be purchased:4

While ushering this new era of rail safety and opening a new chapter inrail renewals, we are looking for an assurance from the Indian Railwaysthat once our rails are offered, we will be given equal opportunity tosupply rails, if our quality is superior and price is competitive.

Indian Railways wrote back two months later saying that they would considerthis proposal. However in October 2001, there was a reversal in this stand afterthe Ministry of Steel, which held most of the stock in SAIL, held meetings with theMinister of Railways and SAIL management. The Minister of Steel committed tofinancially supporting long-run investments by SAIL in order to help the companymeet the needs of the Railways. In December of the same year, the Ministry of

2This chronology of events in this section was constructed based on interviews and written commu-nication with officials in the Railways and current and former managers in SAIL.

3The following is an example of the reports in the press at that time:

Purchases from SAIL were stopped for a brief period of four months following the acci-dent in Khanna, when the quality of rail was questioned by the Railway Safety Committee.However, purchases were later resumed in April 1999. (. . . )

Jindal Steel and Power plans to break SAIL’s hold over the huge orders by manufac-turing rail for the domestic market from the next year. The company will manufacture78 metre long rails, by acquiring and relocating a rail and structural mill in South Africa,near Raigarh in MP.

(Indian Express, June 9, 2000: “Railways to procure Rs 400 cr worth rails from Bhilai Steel” by JyotiMukul)

4Excerpt from a letter dated April 18, 2001 from the Managing Director, JSPL to the Railway Board.


Railways wrote to JSPL informing them that it was committed to continuing railprocurements entirely from SAIL; the Minister of Railways made the followingannouncement:

In the meetings held on 6th October, 2001 and 23rd October, 2001,Ministry of Steel have confirmed to meet the demand of Indian Rail-ways of 650,000 metric tons of rails per year. (. . . ) Railways shouldcontinue to follow the policy of procuring rails from SAIL.

A resolution to the controversy finally came in February 2003. A Memorandumof Understanding was signed by the Ministry of Railways and SAIL “for themutual benefit of both the organizations”. The Railways committed to buyingrails from SAIL provided the latter met the delivery schedule that was agreedupon by both parties.5

Given this series of events, it is understandable that the management and work-ers at the RSM faced intense pressure over the 1999-2003 period. In the absenceof significant quantity and quality improvements, SAIL’s share in the marketfor rails was severely threatened. Their jobs, while secure as long as the plantsurvived, were in jeopardy if it did not. They also knew that, at least in theshort run, improved performance would have to come about without any majorinvestments in the plant or changes in the labor force.

Several new training programs were designed. Training had always been im-portant in Bhilai because incoming workers had to be taught specialized skillsrelated to their particular job within the plant. In addition to the training pro-grams aimed at obtaining international certification for the plant, specializedtraining for rail mill workers was also introduced when management perceived athreat to SAIL’s market share for rails6.

The response of rail output to the above changes was impressive. Averageoutput per shift increased by 46 blooms or 28 percent between the first quarter of2000 and 2003. Some of this increase was brought about through improvementsin departments outside the rail mill. For example, production targets for railblooms in the Steel Melting Shop went up as a result of training; blooms form theprimary non-labor input in rail production.7 Not surprisingly, over our period,we find fewer delays caused by the inadequate or poor quality steel. However,a good part of the increase in output came from changes within the RSM thatraised productivity. The remainder of the paper focuses on this.

5Clause (ii) of the Memorandum reads: “The Railways commit to buy from SAIL its total requirementof long rails/long rail panels, as also the balance of its normal requirements in other lengths like 13m,26m, etc. subject to annual review within the overall policies of Govt. of India.”

6The changing role of training and details of particular programs before our period of interest arediscussed extensively in Upadhyaya (2011).

7Upadhyaya (2011), p.125.


II. Our Data

The unit of observation in our analysis is an eight-hour shift. The core of ourdata set comes from two types of logs kept by the plant for each shift during theperiod Jan 1, 2000 to March 31, 2003. The first of these is the delay report. Thisrecords inputs, outputs and all delay episodes during the shift. For shifts withmultiple products it gives us the minutes of uptime spent on each product, and foreach delay, its cause and duration. Also listed are the executives supervising theshift and the work brigade on duty. The second log we use is the daily presenteereport (DPR) which lists all workers on the floor during a shift. For those inthe same brigade who are absent, it usually notes the reason for their absenceand the type of leave that they are granted. We supplement the shift-level logsdescribed above with administrative data on worker designations, demographicsand the dates of all formal training undertaken by employees of the mill.

The technological process is organized around ten groups of workers. Sevenof them man the stations along the production line. The other three groups(Executives, Crane Operators and Technicians) participate at multiple stages inthe process. Since groups perform different tasks, we treat them as separate typesof labor and construct ten labor variables from the shift-wise numbers in each ofthese groups. A schematic representation of the production process and a briefjob description of each group is described in Section A.A1 of the Appendix.

There are a total of 3,558 possible shifts in the 39 month period covered byour data set.8 The delay report from which we obtain data on output and delayswas only available to us in the form of paper slips, one for each shift. Some ofslips these were illegible and a few were missing. In addition, some shifts weredropped because there were devoted entirely to maintenance. These factors andsome data cleaning (described in Section A.A2) leave us with 3,121 shifts or 88percent of the 3,558 possible shifts in our period.

The management at the mill classifies all delays into four classes: Outside, Fin-ishing, Planned and Avoidable. Outside delays are those that occur due to eventsoutside the control of the managers and workers in the mill. These may be unan-ticipated, such as those caused by gas shortages or electrical faults, or anticipatedbut unavoidable as in the case of electricity rationing or an inadequate supply ofrail steel. Finishing delays reflect downstream constraints that slow down pro-duction in the mill. The most common reason for such a delay is the cooling bedor the shipping bay being full and unable to accept rails from the production area.Planned delays are used for scheduled maintenance or adjustments of equipment.Avoidable delays are due to mechanical problems often caused by worker neglect.From the description of each delay, we know the stage of the production processat which it occurs, but have no reference to the group of workers that may be at

8Unfortunately, our data do not cover the time period before 1999, when the RSM’s status as asole supplier of rails was not seriously challenged. We dropped all observations from 1999; in this year,the RSM was operating below its capacity due to the sudden cancellation of orders after the accidentdescribed in the previous section.


fault.The richness of our shift-level data is captured in the following example: On

August 29th, 2000, the second shift (2 p.m. to 10 p.m.) was devoted entirelyto producing rails weighing 60 kilograms per meter. Brigade 3 was on the floor,229 steel blooms were rolled into rails and one was cobbled or twisted and had tobe removed from the production belt. There were 3 delay episodes totaling 55minutes. The first two were planned delays and were used to adjust machinery.The third resulted from a crane malfunctioning. There were 65 workers on thefactory floor. A total of 48 workers were absent on that day. For 34 of theseworkers, it was their weekly day off, 1 was in a training program and the rest wereon leave for personal reasons. Some of these personal absences were unanticipated.

We combine these shift-level data with personnel data on employees and train-ing records. Personnel files record employee birthplace, education, seniority, casteand designation. Parry (1999) has suggested that workers born in the local areashow some solidarity and sometimes unite against those migrating in from theoutside. There is also some tension between the Scheduled Castes and Sched-uled Tribes (SCST) and other castes. The former are eligible for state-mandatedaffirmative action and SAIL, being a public sector enterprise, follows these poli-cies strictly.9 To examine whether diversity could affect output through limitedcooperation among workers, we construct two indices of diversity based on thebirthplace and caste of workers in each shift.

Ilocal = min(Slocal, 1− Slocal),Icaste = min(Scaste, 1− Scaste)

where Slocal and Scaste are the shares of workers from the local area and fromthe SCST category respectively.

Our most important set of explanatory variables are constructed from trainingrecords. Although there were a large number of programs, many of them lastedonly a couple of days and involved few employees. Training records include a briefdescription of each program, start and end dates and a list of employees trained.By combining the dates of training and the employees trained with attendancedata from the DPR, we are able to generate training stocks for each shift in ourdata. We aggregate individual stocks of all workers on the attendance sheet forthat shift to construct our nine stocks for every shift.

We classify total training time into the nine categories listed in the third panelof Table 1. This is based on the types of skills being targeted by the program. Alltraining designed to achieve formal certification is labeled quality control train-ing while programs aimed at increasing output is labeled productivity training.We thereby distinguish between programs that were instituted to meet formalrequirements and those specifically designed to improve the functioning of the

9 “In the higher grades there are certainly tensions over a promotion policy which allows ScheduledCaste workers to leap frog others more senior.”Parry (1999), p. 133.


mill. The four largest training programs account for about 65 percent of trainingtime. These are listed in Table A2 of the Appendix.

The training episode that accounted for a bulk of productivity training wastermed the Acceptance of Rails Program. Although most mill workers understoodtheir own jobs very well, they were ignorant of recent changes in the market:higher quality standards, more competition and the possible threat of closure.The program was intended to bridge this gap. Workers were briefed on changesin the world market for steel and on the new demands of the Railways. Theywere told about the threat from JSPL and the restructuring procedures that arefollowed for public sector enterprises like SAIL when they are declared to beunprofitable. It was emphasized that SAIL’s great strength lay in the qualityof its workers and focus groups were created to come up with action plans thatcould meet new targets10. These action plans were then made public on a noticeboard so that all workers could view specific suggestions put forward by eachgroup.11 Since the emphasis of this program was on rail production, the programalso provides us with an identification strategy to estimate the effect of trainingthrough a comparison of the output of rails relative to other products.

Summary statistics for our main variables are reported in Table 1. The numberof shifts devoted to rail production went from 133 in the first quarter of 2000 to243 in the first quarter of 2003. There was also an overall increase in the numberof shifts worked with fewer shutdowns. Average output per shift increased by 46blooms or 28 percent between the first quarter of 2000 and 2003. The rate atwhich blooms were rolled per unit of uptime went up by roughly 17 percent. Thenumber of defective blooms was halved and the number of interruptions causedby avoidable delays went from 1.4 per shift to 0.8.

By summing across the different worker designations, we get the total number ofworkers on the floor. This was about 66 workers in both quarters.12 Conditionalon the occurrence of a delay, the average downtime did not change much betweenthe two quarters for avoidable and planned delays, while it increased somewhatfor the other two delay classes. Multiplying average delays by the downtime pershift, we find that the total minutes lost went down by about 20 minutes per shiftbetween these two quarters. The time pattern of delays and their durations isfurther discussed in the Appendix and depicted in Figure A2.

III. Explaining Total Output

We begin with a reduced form model of total output as a function of inputsin order to isolate the shift-level characteristics that are systematically associ-

10A former junior manager at the RSM attributed “the democratization of knowledge” as a majorfactor in improving productivity. This referred to key technical knowledge being in the hands of manyoperatives rather than restricted to a few.

11A description of the structure of this program was provided to us by one of the mill managers whowas also faculty in the training program.

12 In total about 330 different workers were employed by the RSM over this period. Turnover anddays off account for the difference in this number and the number on the floor across three shifts.


Table 1—Summary statistics

2000 Q1 2003 Q1Sample size (# of shifts) 3,121 225 259Shifts producing rails 2,609 133 243Shifts producing structurals 512 92 16

Mean Std. dev. 2000 Q1 2003 Q1Output, blooms/shift 181.78 52.54 164.54 211.06Defective output, blooms/shift 1.64 1.68 2.64 1.31Rolling rate, blooms/min. 0.57 0.07 0.51 0.60

Delays per shift (#)Avoidable 1.30 1.43 1.36 0.83Finishing 0.22 0.43 0.07 0.10Outside 0.76 0.92 0.86 0.51Planned 1.85 1.21 1.80 2.10

Downtime per delay (minutes)Avoidable 29.60 38.70 30.15 28.58Finishing 25.07 25.73 20.67 26.20Outside 62.61 76.08 50.26 61.02Planned 35.65 31.70 35.69 34.63

Labor input: Number of workers (per shift)Control Men 5.37 1.61 5.22 6.03Coggers 4.77 1.24 4.27 4.44Crane Operators 4.87 1.08 5.16 5.49Executives 1.92 0.33 1.60 2.08Furnace Maintenance 6.19 1.30 6.44 5.97Ground Staff 18.83 2.20 18.97 19.68SCM Team 12.33 1.96 12.03 12.40Services 5.81 1.27 5.67 6.04Saw Spell 4.65 1.34 5.00 4.44Technicians 1.20 0.73 1.22 1.04

Training stocks of workers on the floor (days, per shift)Productivity 62.80 54.44 0.00 109.55Cost Reduction 23.68 11.20 18.52 32.82Motivational 20.91 34.34 3.60 118.76Safety 20.10 9.02 7.49 28.70Quality Control 19.16 18.57 4.19 41.97Environmental 14.29 19.54 0.00 44.15IT 6.45 4.83 1.71 10.78Job Instruction 3.21 2.67 0.00 5.92Other 21.23 12.25 13.65 35.41

Diversity indices (per shift)SCST vs. Other Castes 0.34 0.03 0.34 0.35Locals vs. Migrants 0.41 0.05 0.41 0.42


ated with the increase in productivity. In the next section we look more closelyat how these sources differentially affect delay probabilities, their duration andproduction rates.

A. The Basic Specifications

We estimate a shift-level output equation:

Ys = βXs + εs

where Ys refers to the number of blooms rolled during shift s and Xs is a vectorof explanatory variables for the shift. The shift-wise characteristics Xs includethe number of workers disaggregated by their designation, diversity indices basedon hometown and caste, and training stocks by nine training categories. In everyspecification of our model we control for the type of output being produced, asheavier products are typically slower to roll. We also control for the time of day,as morning shifts are usually less productive, and for the brigade-specific fixedunobservables. In addition we control for an instance where there was a changein equipment, and allow it to affect products differentially13.

Our results are reported in Table 2. The first column presents the preliminaryestimates. The two training categories that have positive coefficients and arestatistically significant are productivity and motivational training. Safety trainingis marginally significant but with a negative sign. More executives are associatedwith higher output. Surprisingly, the numbers of non-executive workers on thefloor do not seem to be systematically correlated with output. Labor input maybe endogenous; we address this issue using an instrumental variable strategydescribed towards the end of this section. The coefficients on the diversity indicesare large in magnitude, but not tightly estimated. Moreover, since the change inthe diversity is very small (0.01 for both indices between Q1 2000 and Q1 2003),its estimated impact on output is less than 0.5 blooms per shift, a tiny fractionof the observed output change.

One concern with the above estimates is that, by construction, training stocksare weakly increasing and the training coefficient may therefore, at least partly,capture the effect of unobserved output trends. Most training programs are ad-ministered to groups of workers so the stocks of training in work shifts changesharply over time. If the trend in unobservables varies smoothly, its spuriouseffect on the training coefficients should be greatly reduced with the introductionof quarter fixed effects in Column 2. We find that the estimated effects of bothproductivity and motivational training stocks remain almost the same under thisspecification, although the latter estimate becomes less precise. On the other

13In fiscal year of 2001–2002 there are reports of investment for a descaling unit. On September 4th,2002 we observe a new delay cause appearing in the data; it is listed as “jamming at new descaling unit”.For this reason, we infer that the new descaling unit came online roughly at this time.


Table 2—Determinants of output per shift

Dependent variable: blooms rolled per shift (1) (2) (3) (4)Labor input: Number of workers

Control men -0.99 -0.87 -0.86 0.24Coggers -1.13 -0.93 -0.94 1.10Crane Operators -1.91∗ -1.64 -1.58 -2.42Executives 6.82∗ 7.20∗ 7.29∗ 6.95∗

Furnace Maintenance 1.33 1.39 1.42 1.89Ground Staff -0.34 -0.13 -0.10 0.06SCM Team -0.22 -0.01 -0.03 1.07Services 0.23 0.09 -0.06 -1.28Saw Spell -1.02 -1.16 -1.09 -0.57Technicians 0.12 0.51 0.41 0.06

Training stocks (days)Productivity training 0.27∗∗ 0.27∗∗ 0.07 0.02Productivity X Rail Shift 0.25∗∗ 0.25∗∗

Cost Reduction 0.07 0.08 0.06 -0.02Motivational 0.21∗∗ 0.24∗ 0.22∗ 0.23∗

Safety -0.44∗ -0.45 -0.40 -0.44Quality Control 0.21 0.19 0.18 0.14Environmental -0.09 -0.25 -0.28 -0.32IT -0.46 -0.41 -0.42 -0.29Job Instruction 0.23 0.02 0.07 0.09Other 0.06 0.09 0.06 0.05

Diversity indicesSCST vs. Other Castes 42.17 39.14 39.86 42.30Locals vs. Migrants -47.77 -45.42 -42.63 -32.75

Joint tests, p-valuesCoefficients on labor inputs = 0 0.04 0.11 0.11 0.18Coefficients on non-prod. training = 0 0.00 0.14 0.17 0.14Quarter fixed effects No Yes Yes YesObservations (shifts) 3,121 3,121 3,121 3,121

Note: Significance levels: ∗5 percent, ∗∗1 percent (based on heteroskedasticity-robust standard errors).Unreported controls: dummies for brigades, products, time of day, new equipment dummy interactedwith rail shift dummy. Columns 1-3: OLS. Column 4: IV; endogenous variables: non-executive labor;instruments: extraordinary absence, interactions of brigade, quarter, shift, and day of week fixed effects.


hand, safety training, which was marginally significant in Column 1, becomesinsignificant.

An important strategy for identifying the effect of training is to exploit the factthat such training focused primarily on rails, as opposed to structurals. We wouldexpect time-varying unobservables to affect all types of output whereas the maininfluence of such training should be seen in rail production. In Column 3 we addthe interaction of a rail shift dummy and the stock of productivity training toour regression, thereby allowing productivity training to have differential effectson rails and structurals. The results in Column 3 confirm that the effect of suchtraining is coming from rails and not structurals, and that the size of the effect isapproximately the same as in specifications (1)-(2). Figure 1 depicts this result ina more informal way by plotting the average output of rail and structural shifts,indicating the time period when most of the productivity training took place.14

The increase in rail output and the absence of such an increase in structurals isevident. In Table 2 we also report the p-values of the F-tests on the collectivecontribution of labor of all designations and of all training other than productivitytraining. We are unable to reject the null hypotheses of no contribution. Thecoefficients on executives and motivational training are very similar in magnitudeto the previous specification in Column 2 and remain statistically significant.

Pro

duct

ivity

trai

ning

140

160

180

200

220

Out

put p

er s

hift,

blo

oms

Jan 2000 Jan 2001 Jan 2002 Jan 2003Date

Rail shifts Structural shifts95% confidence band

Figure 1. Effect of productivity training on output per shift

Note: The figure is obtained by using kernel-weighted smoothing of shift-level observations. The band-width equals 66 days and was chosen automatically in Stata according to the “rule of thumb”.

14In Figure 1 we control for the type of structurals produced in order to eliminate any effects due tochanges in the composition of structurals as these are very diverse. This is done by first estimating anOLS regression of blooms rolled on indicator variables for each product (whether rails or structurals). Foreach shift we then adjust the output mix by subtracting the fixed effect of the product actually producedin the shift and adding the fixed effect of the base product. The adjusted output is then plotted in Figure1.


Next, we compare the benefits of productivity training with its costs. We findthat an extra day’s productivity training for the brigade raises the output by0.25 blooms per shift. During the episode in which productivity training wasconcentrated (June-July 2001), an average worker received 1.6 days of training.As there are roughly 66 workers on the floor in a rail shift, a total stock of traininggrew by 106 days for an average working brigade. This translates into an outputincrease of 26 blooms per shift. Comparing this number to the increase in railoutput that took place during the summer of 2001 (depicted in Figure 1), it isapparent that the productivity training played a major role. As it happened, thistraining episode coincided with the shutting down of the plant for maintenanceand machinery replacement thereby effectively reducing the opportunity cost,work time lost to training, to zero. However, even stopping the mill for theduration of this training program would result in negligible costs relative to theproductivity gains. The amount of training administered could be completed intwo days (six shifts), leading to approximately 160× 6 = 960 blooms in forfeitedoutput. Given the productivity improvement of 26 blooms per shift, it would takeonly 37 shifts of rail production (less than two weeks) for this investment to payoff. While we do not have figures for any other costs associated with the training,conversations with management suggest that these were not large.

Taken together, our explanatory variables account for most of the output change.The coefficient on executive labor is significant and there are roughly 0.5 moreexecutives on the floor by the first quarter of 2003, which raises the output byabout 3.5 blooms per shift.15 The productivity training raises output by about26 blooms per shift. Thus, we have about 46− 29.5 = 16.5 blooms per shift thatcome from the other inputs. Quarterly fixed effects account for roughly 11 bloomsper shift in this residual output increase.

A careful reader may at this point wish to better understand the nature ofthe identifying variation. In Section A.A4 of the Appendix we present evidencesuggesting that the identification comes from the variation during the period ofJune–July 2001 when the bulk of productivity training took place and when thevariation in training was the greatest.

B. Specification Checks

In this subsection we report the results from a number of exercises used tocheck the results from the model specified above and reported in Table 2. All ofthese reinforce our confidence in the robustness of the productivity training effectoutlined above.

Endogeneity of Labor. — If managers expect breakdowns, they may ask em-ployees to work overtime or postpone earned leave. As a result, the estimated

15This comes from the product of the coefficient on executive labor and the difference in the numbersof executives between Q1 2000 and Q1 2003 as reported in Table 1.


effect of labor would be biased downwards and may even become negative. Weexplore this possibility by using unanticipated worker absence as an instrumentfor labor. Every time an employee is not on the floor with his brigade, the reasonfor his absence is recorded. This absence is often predictable; a worker may havea weekly day-off or have applied for paid leave. However, workers quite oftenskip shifts due to medical emergencies, injuries at work, or other extraordinaryevents. The total number of employees of each kind who do not show up at workat a given shift due to these unanticipated events is clearly a good instrumentfor labor. The first stage regressions show that the absence of each type of laboris indeed highly significant and that the instruments are valid. Estimates fromthe second stage are reported in Column 4 of Table 2. The similarity of thecoefficients in Columns 3 and 4 suggest that labor endogeneity is not a majorissue.16

Attention Diversion. — An alternative explanation for the significance of thecoefficient on the interaction of productivity training and rails is that around thetime productivity training was administered, attention was focused on rails at theexpense of structurals. For example, it could be that the relatively better workerswere assigned to rails. To explore this possibility, we test whether, for eachworker, the number of structural shifts on the worker’s time sheet is systematicallydifferent from what it would be under a random assignment of workers to shifts.

More specifically, denote by N the total number of shifts in the sample and byNs and Nr the structural and rail shifts respectively17. Suppose a worker worksa total of ni shifts and nis structural shifts. We can compute the probabilitythat the worker is assigned to nis structural shifts under a random assignment ofworkers to shifts:

Pr{nis|ni, N,Ns} =

(Ns

nis

)(Nr

ni−nis

)(Nni

)We use this probability to construct confidence intervals for nis under the inde-pendent assignment hypothesis. We cannot reject the independent assignmenthypothesis for any of the workers in the post training period (August – December2001).

We also ask whether the increase in rail output could have come from manage-rial attention being diverted from structurals to rails. We see from the outputregressions that shifts with more managers produce more output. Do rail shiftshave more managers on the floor in the period following August 1, 2001, whenthe productivity training was completed? We test for this in Table 3. We findmore executives on the floor for rail shifts throughout the period but the increase

16We do not have data on the reason for absences for executives and are therefore unable to constructa separate instrument for them. Their numbers are treated as exogenous.

17In the period immediately following the main episode of productivity training (August–December2001), N = 409, Ns = 61, and ni = 101.3.


Table 3—Managerial attention to rail and structural shifts

Dependent variable: # of executivesPost training 0.17∗∗

Post training X Rail shift -0.05Rail Shift 0.05∗

Const 1.81∗∗

Observations (shifts) 3,121Note: Significance levels: ∗5 percent, ∗∗1 percent (based on heteroskedasticity-robust standard errors).Post training: equals one after the productivity training episode. Rail shift: rail shift dummy. Theestimates suggest that the productivity training did not coincide with any disproportionate increase inthe presence of managers in shifts producing rails.

in the number of executives after the productivity training is not higher for railsthan for structurals.

Motivational Training. — While the effect of productivity training is large andsystematic in all our specifications, our results on motivational training do notappear to be as robust. We re-run the regression in Column 3 of Table 2 usingyearly, monthly, and bi-weekly time fixed effects. These results are reported in Ta-ble 4. While the coefficients on productivity training interacted with rails remainsignificant in all specifications, those for motivational training do not. As de-scribed below, we also find that the significance of motivational training vanisheswhen we cluster standard errors by week to allow for possible autocorrelation.

Placebo Tests Using Other Training Types. — If structural shifts are a goodcontrol group for rails then the types of training not specifically targeted to railsshould affect both rails and structurals in the same way. By using interactionsof other types of training with a rail dummy, we can perform a series of placebotests of our model. In Table 5, we report coefficients from estimating the regres-sion in Column 3 of Table 2 adding other kinds of training interacted with therail shift dummy one at a time. We find only the interaction with motivationaltraining to be significant at the 5 percent level. Moreover, even this coefficientbecomes insignificant when we use bi-weekly, monthly, or yearly dummies insteadof quarterly ones. Through all of these specifications the coefficient on produc-tivity training interacted with rails remain stable and significant at the 1 percentlevel.

Flexible Error Structures. — In all of the above exercises, we used robuststandard errors to calculate t-statistics. Here, we perform some simple checksto see how sensitive the results are to using less restrictive assumptions aboutthe error term. We estimate the same equation as in Column 3 of Table 2 and


Table 4—Motivational training, sensitivity analysis

Dependent variable: blooms rolled (1) (2) (3) (4)Labor input: Number of workers

Control men -0.91 -0.86 -0.46 -0.50Coggers -1.05 -0.94 -0.61 -0.51Crane Operators -1.96∗ -1.58 -1.28 -1.84Executives 7.55∗∗ 7.29∗ 6.91∗ 6.57∗

Furnace Maintenance 1.24 1.42 1.53 1.47Ground Staff -0.17 -0.10 0.25 0.04SCM Team -0.26 -0.03 0.28 0.39Services -0.10 -0.06 -0.02 -0.10Saw Spell -1.21 -1.09 -0.82 -0.72Technicians 0.46 0.41 0.97 0.40

Training stocks (days)Productivity training 0.07 0.07 -0.10 -0.15Productivity X Rail Shift 0.25∗∗ 0.25∗∗ 0.26∗∗ 0.26∗∗

Cost Reduction 0.01 0.06 0.11 0.11Motivational 0.25∗∗ 0.22∗ 0.19 0.17Safety -0.29 -0.40 -0.50∗ -0.58∗

Quality Control 0.21 0.18 -0.04 -0.07Environmental -0.14 -0.28 -0.11 -0.02IT -0.31 -0.42 -0.29 -0.25Job Instruction 0.40 0.07 -0.12 -0.13Other 0.03 0.06 0.02 0.06

Diversity indicesSCST vs. Other Castes 39.40 39.86 39.10 39.39Locals vs. Migrants -46.51 -42.63 -58.17∗ -57.64∗

Time fixed effects Year Quarter Month 2 weeksObservations 3,121 3,121 3,121 3,121

Note: OLS estimates of the output per shift regression. Significance levels: ∗5 percent, ∗∗1 percent (basedon heteroskedasticity-robust standard errors). Unreported controls: dummies for brigades, products, timeof day, new equipment dummy interacted with rail shift dummy. Columns 1–4 demonstrate sensitivityof the motivational training coefficient on the type of time dummies used.


Table 5—Placebo test

Dependent variable: Time fixed effectsblooms rolled per shift yearly quarterly monthly bi-weeklyCost Reduction X Rail Shift -0.12 -0.10 -0.07 -0.08Motivational X Rail Shift 0.42 0.45∗ 0.42 0.23Safety X Rail Shift 0.27 0.34 0.38 0.31Quality Control X Rail Shift 0.30 0.33 0.30 0.21Environmental X Rail Shift 0.24 0.27 0.23 0.17IT X Rail Shift 0.15 0.15 -0.03 -0.33Job Instruction X Rail Shift 0.12 0.49 0.09 -0.59Other X Rail Shift 0.32 0.33 0.36 0.33

Note: Each cell corresponds to a separate regression of output on the same set of explanatory variablesas in Column (3) of Table 2 plus a stock of training of a given type interacted with the rail shift dummy.Only the coefficient on this interaction is reported. Rows vary in the type of training, columns vary in thetype of time fixed effects. These estimates demonstrate that only productivity training had significantlydifferential effect on output of rails and structurals. This is consistent with the content of training, asonly the productivity training program was specifically targeted at rails. Significance levels: ∗5 percent,∗∗1 percent (based on heteroskedasticity-robust standard errors).

cluster standard errors by week. This allows errors to be autocorrelated. Asshown in Table 6, this has a small effect on the t-statistics of interest except formotivational training, which is no longer significant.

Learning by Doing. — Could learning by doing be responsible for the produc-tivity improvement seen? The plant is clearly switching to making more rails.Learning by doing tends to have diminishing returns, and given that the planthas been making rails since 1960s, learning by doing would have occurred muchearlier. This is not say that continuous improvement on the floor did not occur.Our argument is that training may well have facilitated this.

Alternative Definitions of Training Stocks. — Our main specification usesthe aggregate stock of training for workers in a shift. In order to check if our resultsare sensitive to this specification choice, we experiment with four alternative typestraining variables. In the first two specifications, the training stock is allowed todepreciate at an annualized rate of 10 and 20 percent respectively. In our thirdspecification, we impose decreasing returns to training by using the square rootof days of training received by the worker and computing total training stock asthe sum of these squares. Our fourth specification defines training as the non-depreciating stock divided by the number of workers on the floor in order toavoid a possibly spurious correlation between training and output through thebrigade’s size. We re-estimate the equation from Column 3 of Table 2 using eachof these definitions of training and find that the estimates are almost identical tothe original numbers. In particular, the estimated effect of productivity training


Table 6—Output equation, t-statistics based on the robust and clustered errors

Dependent variable: blooms rolled (1) (2) (3)Labor input: Number of workers

Control men -0.86 (-1.08) (-1.05)Coggers -0.94 (-1.04) (-1.10)Crane Operators -1.58 (-1.60) (-1.62)Executives 7.29 (2.52)∗ (2.49)∗

Furnace Maintenance 1.42 (1.83) (1.84)Ground Staff -0.10 (-0.20) (-0.21)SCM Team -0.03 (-0.05) (-0.05)Services -0.06 (-0.07) (-0.07)Saw Spell -1.09 (-1.16) (-1.11)Technicians 0.41 (0.26) (0.25)

Training stocks (days)Productivity training 0.07 (0.61) (0.66)Productivity X Rail Shift 0.25 (4.19)∗∗ (3.63)∗∗

Cost Reduction 0.06 (0.28) (0.35)Motivational 0.22 (2.01)∗ (1.44)Safety -0.40 (-1.69) (-1.71)Quality Control 0.18 (1.21) (1.50)Environmental -0.28 (-1.53) (-1.49)IT -0.42 (-1.26) (-1.27)Job Instruction 0.07 (0.10) (0.11)Other 0.06 (0.34) (0.41)

Diversity indicesSCST vs. Other Castes 39.86 (1.26) (1.31)Locals vs. Migrants -42.63 (-1.50) (-1.70)Observations 3,121

Note: OLS estimates of the output per shift regression. Significance levels: ∗5 percent, ∗∗1 percent.Unreported controls: dummies for brigades, products, time of day, new equipment dummy interacted withrail shift dummy. Column 1: Coefficient estimates. Column 2: t-statistics based on heteroskedasticity-robust errors. Column 3: t-statistics based on errors clustered by week.


varies between 0.26 and 0.27 (compare to 0.25, the same estimate in Table 2) andis significant at the 1 percent level.

To summarize, our results suggest that there is at best a weak link betweenlabor and output. With the exception of executive workers, the number of work-ers on the floor is not consistently associated with higher output. One possibleexplanation of this result is that the RSM is overstaffed, which is in line withpopular beliefs about public sector enterprises in India. Allowing for endogeneityof workers on the floor does not affect the estimates. Diversity indices seem tohave had little systematic effect on output in our data. Training seems to matterand, in particular, productivity training has large and durable effects.

IV. Explaining Delays and Rates

Our results in the previous section suggest that productivity training might bea key factor in the improvement in output per shift. We now look more closely atthe mechanics of this increase in terms of changes in the probability and durationof specific types of delays, and the rates at which blooms were rolled while theplant was operating.

By definition, output is the product of the rolling rate and uptime, with uptimebeing the total shift time of 480 minutes less time lost in delays of various types.We decompose downtime into avoidable, outside, finishing, and planned delays.Many shifts have no delays in some of these categories. To account for this feature,we estimate a logit model for the probability of each type of delay and a separatelinear regression for the delay’s duration conditional on its occurrence.

The results are summarized in Table 7. To facilitate their interpretation, werescaled estimates so that they represent marginal effects of explanatory variableson output. Thus the coefficient on productivity training of 0.05 in the last rowof Column 1 means that an extra day of such training to a particular worker onthe floor reduces the probability of avoidable delays in a way that results in 0.05additional blooms rolled in that shift.18

Columns 1, 3, 5, and 7 of Table 7 report the estimates from the logit equationsfor each of the four delay types. Columns 2, 4, 6, and 8 report the resultsfor delay durations and Column 9 presents the estimates from a linear modelwith the rolling rate as the dependent variable. To see which estimates remainstable regardless of the choice of temporal controls (bi-weekly, monthly, or yearlydummies instead of the quarterly ones used in Table 7), we italicize the estimatesthat do not change sign and stay significant at the 5 percent level in all fourvariants. Similarly, we show in bold estimates that do not change sign and remainsignificant at the 1 percent level.

The estimates presented in Table 7 confirm our main result from the previoussection: productivity training significantly increases output. It does so throughits effects on the probabilities of avoidable, outside and planned delays rather

18The derivation of these marginal effects is described in Section A.A3 of the Appendix.

VOL. VOL NO. ISSUE BACK ON THE RAILS 21Table7—

Componentsofoutput

Pa

Da

Po

Do

Pp

Dp

Pf

Df

RL

ab

or

inp

ut:

Nu

mb

er

of

work

ers

Con

trol

Men

-0.5

7∗∗

0.30

0.05

0.54

-0.4

3-0

.24

0.02

0.02

-0.2

9C

ogge

rs0.2

70.

12-0

.15

-0.5

40.

00-0

.61∗

0.03

0.03

-0.1

7C

ran

eO

per

ators

-0.0

8-0

.70∗

-0.1

4-0

.71

0.07

0.14

0.03

0.03

-0.0

9E

xec

uti

ves

1.50

∗0.

641.

31-0

.70

2.11

0.92

0.29

0.29

-0.5

1F

urn

ace

-0.0

4-0

.04

0.47

0.52

0.27

0.39

-0.0

4-0

.04

0.28

Gro

un

dS

taff

-0.0

8-0

.48∗∗

0.02

-0.2

10.

20-0

.01

0.03

0.03

0.20

SC

Mte

am

-0.0

40.

220.

140.

04-0

.29

-0.1

1-0

.01

-0.0

1-0

.05

Ser

vic

es-0

.42

-0.3

80.

310.

23-0

.39

0.21

0.02

0.02

0.14

Saw

Sp

ell

-0.4

3-0

.36

-0.3

70.

26-0

.29

0.01

0.03

0.03

-0.0

9T

ech

nic

ian

s-0

.40

-0.1

3-0

.03

0.10

0.69

-0.2

90.

030.

030.

39T

rain

ing

stock

s(d

ays)

Pro

du

ctiv

ity

-0.0

30.

040.

10∗

0.08

-0.0

9∗0.

06-0

.00

-0.0

0-0

.04

Pro

du

ctiv

ity

XR

ail

Sh

ift

0.0

5∗∗

-0.0

00.0

6∗∗

0.01

0.0

8∗∗

-0.0

40.

000.

000.0

7∗∗

Cost

Red

uct

ion

-0.1

20.

060.

010.

16-0

.06

0.13

-0.0

2-0

.02

-0.0

4M

oti

vati

on

al0.0

9∗∗

0.10

-0.0

1-0

.12∗

0.04

0.05

0.02

∗∗0.

020.

03S

afet

y0.0

2-0

.04

0.03

-0.1

90.

10-0

.21∗

0.01

0.01

0.00

Qu

ali

tyC

ontr

ol0.1

0∗0.

07-0

.09

-0.0

50.

04-0

.01

0.01

0.01

0.0

6∗

Envir

onm

enta

l-0

.04

-0.1

5-0

.04

-0.0

6-0

.02

0.07

0.00

0.00

-0.0

8∗

IT0.0

40.

01-0

.07

-0.2

50.

020.

05-0

.06∗

-0.0

6-0

.00

Job

Inst

ruct

ion

0.0

00.

04-0

.33

0.13

0.07

-0.2

1-0

.01

-0.0

10.

01O

ther

0.1

6∗∗

0.12

-0.0

4-0

.08

0.01

-0.1

6∗

0.01

0.01

0.01

Div

ers

ity

ind

ices

SC

ST

vs.

Oth

erC

ast

es11

.95

9.13

-4.1

712

.19

4.20

18.7

0-0

.66

-0.6

6-9

.43

Loca

lsvs.

Mig

rants

-7.8

2-1

.40

-7.0

2-2

.32

-15.

08-5

.81

-1.3

0-1

.30

-6.4

7U

nit

ofob

serv

atio

nb

loom

del

ayb

loom

del

ayblo

omdel

ayb

loom

del

aysh

ift

Ob

serv

atio

ns

572

,446

4,04

257

2,44

62,

368

572,

446

5,78

357

2,44

667

23,

121

Note:

Sig

nifi

can

cele

vel

s:∗5

per

cent,

∗∗1

per

cent

(base

don

het

erosk

edast

icit

y-r

ob

ust

erro

rs).

Px

den

ote

sa

logit

mod

elfo

rth

ep

rob

ab

ilit

yof

aty

pex

del

ay.

Dx

den

ote

sa

lin

ear

regre

ssio

nof

the

del

ay’s

du

rati

on

con

dit

ion

al

on

occ

urr

ence

.R

—lin

ear

regre

ssio

nof

the

rollin

gra

te.

Th

ees

tim

ate

sare

rep

ort

edin

the

form

of

marg

inal

effec

tson

the

tota

lou

tpu

tev

alu

ate

dat

the

sam

ple

mea

ns

(see

Ap

pen

dix

A.A

3fo

rd

etails)

.U

nre

port

edco

ntr

ols

:B

rigad

e,qu

art

er,

pro

du

ct,

tim

eof

day

fixed

effec

ts,

new

equ

ipm

ent

dum

my

inte

ract

edw

ith

the

rail

shif

td

um

my.

Sen

siti

vit

ych

ecks:

we

rep

lace

qu

art

erly

fixed

effec

tsw

ith

yea

rly,

month

lyan

db

i-w

eekly

fixed

effec

ts.

Coeffi

cien

tsth

at

do

not

chan

ge

sign

an

dst

ay

sign

ifica

nt

thro

ugh

ou

tall

fou

rsp

ecifi

cati

on

sat

5p

erce

nt

are

italicized,

at

1p

erce

nt

—are

rep

ort

edin

bold

.


than through the duration of these delays. It also has a significant effect onrolling rates. The total effect of an additional day of productivity training for awork shift can be obtained by summing the coefficients on productivity traininginteracted with the rail dummy across the columns in Table 7. This gives us0.23 blooms per day of training when all coefficients are summed up and 0.26blooms per day of training when only the significant coefficients are summed up.Reassuringly, this is about the same size as the 0.25 we obtained in the outputregression above.

The coefficients on motivational training change sign and become insignificantwhen we replace quarterly fixed effects with monthly fixed effects. This is inline with the lack of robustness in the motivational training coefficient that weidentified earlier. This supports our earlier interpretation that the estimatedeffect of motivational training may be picking up some coincidental shock. Othertraining seems to reduce the probability of avoidable delays but raises the lengthof planned delays resulting in no net effect. Quality control training seems toraise the rolling rate at the 5 percent level in all the regressions.

As in the previous section, the link between the number of workers on the floorand the level of output is mixed. Even the coefficient on the number of executiveson the floor is not consistently significant even at the 5 percent level. Moreover,having more ground staff seems to be associated with longer avoidable delays (atthe 5 percent level) when such delays do occur. Shifts with more control menon the floor seem to have more frequent avoidable delays. In sum, there is noevidence that larger teams make fewer mistakes or fix these mistakes faster. Thisfurther supports the anecdotal evidence on overstaffing at the Bhilai Steel Plantgiven in Parry (1999).

Thus, productivity training seems to be the only explanatory variable that issystematically associated with higher output in all our specifications.19 Suchtraining seems to have been crucial in increasing output with the total effect ofthe productivity training program equal to approximately 24 blooms of additionaloutput per shift (or 26 blooms according to the estimates from Section III). Hadmanagement done nothing to train the employees, the growth in output wouldhave been much more modest.

V. Conclusions

We see the contribution of this paper as primarily showing how competitivepressures on a firm resulted in significant productivity improvement. In line withrecent work in industrial organization, we find worker training to be a low cost ap-proach to improving output per worker. Our work also suggests that competitivepressures can operative quite effectively even within state-owned enterprises.

19We could quantify and better illustrate the total effect of such training on output by performing a setof counterfactual simulations where the stocks of such training and other possibly relevant explanatoryvariables are varied and the dynamics of simulated output are compared with observed output. Theseare described in the working paper version of this paper but not here.


We show, albeit in a limited context, that the threat of competition broughtabout efficiency gains in a state-owned enterprise. This mirrors findings by Bridg-man, Gomes and Teixeira (2011) who show that the end of the legally enforcedmonopoly of Brazil’s state-owned gas company, Petrobras, led to a doubling inproductivity without any actual change in market share. The case for privatiza-tion in both Brazil and India is based on the characterization of public enterprisesas bureaucratic organizations providing weak incentives and facing soft budgetconstraints. Our work casts some doubt on this assumption and suggests thatcompetition in these settings may be especially effective in raising productivity.

REFERENCES

Bloom, Nicholas, Aprajit Mahajan, David McKenzie, and JohnRoberts. 2010. “Why do firms in developing countries have low productivity?”American Economic Review: Papers and Proceedings, 100(2): 619–623.

Bridgman, Benjamin, Victor Gomes, and Arilton Teixeira. 2011.“Threatening to Increase Productivity: Evidence from Brazil’s Oil Industry.”World Development, 39(8): 1372–1385.

Government of India, Planning Commission. 1997. “Ninth Five Year Plan.”New Delhi.

Hall, Robert, and Charles Jones. 1999. “Why Do Some Countries Produce SoMuch More Output Per Worker Than Others?” Quarterly Journal of Economics,114(1): 83–116.

Hsieh, Chang-Tai, and Peter Klenow. 2009. “Misallocation and Manufac-turing TFP in China and India.” Quarterly Journal of Economics, 124(4): 1404–1448.

Ichniowski, Casey, Kathryn Shaw, and Giovanna Prennushi. 1997. “TheEffects of Human Resource Management Practices on Productivity: A Study ofSteel Finishing Lines.” The American Economic Review, 87(3): 291–313.

Parry, Jonathan P. 1999. “Lords of Labour: Working and Shirking in Bhilai.”Contributions to Indian Sociology, 33(1-2): 107–140.

Schmitz, Jr., James A. 2005. “What Determines Productivity? Lessons Fromthe Dramatic Recovery of the U.S. and Canadian Iron Ore Industries FollowingTheir Early 1980s Crisis.” Journal of Political Economy, 113(3): 582–625.

Syverson, Chad. 2004. “Product substitutability and productivity dispersion.”Review of Economics and Statistics, 86(2): 534–550.

Upadhyaya, G. 2011. HR Interventions In The Age Of Competitiveness. ReemPublications.


Appendix

A1. Production Process

Figure A1 is a schematic representation of the production process. The maininput is a long rectangular block of steel called a bloom. These are stored in thebloom yard and passed through different sections in a sequential process whichconverts them into rail tracks or heavy structurals (beams, angles, etc.). Theyfirst enter one of four furnaces where they are reheated. They then move througha series of work tables in the mill area where they are shaped. The type of thefinal product is determined by a cross-section template used at this stage. If thetemplate has the shape of an angle, an angle is produced; if the template has theshape of a rail, a rail is produced. The shaped blooms are cut to ordered lengthsin the hot saw area and then stamped and moved to a cooling bed. Defectiveor mis-shapen blooms are referred to as cobbled and are set aside, the rest areclassified as rolled. The mill runs twenty four hours a day, seven days a week withrare shutdowns for service and repairs. Production workers are rotated amongthree eight-hour production shifts.

Figure A1. The production process in the RSM.

Each worker, at any point in time, has a designation based on their job descrip-tion and their seniority. Designations can be usefully divided into a few groups.Some workers are restricted to a particular location in the production processwhile others move around and ensure its smooth operation. In the furnace area,the services team keeps records of blooms, control men move the blooms in and


out of the furnace and the furnace maintenance team, as the name suggests,ensures the furnace is in working order. In the mill area, ground staff are onthe floor of the mill to ensure production flows smoothly. The SCM team is agroup of senior control men and motor operators who along with the coggers sitin pulpits and direct the actual rolling of the rails in this part of the plant. Inthe hot saw area we have the saw spell team. Crane operators man cranes thattransport blooms at various stages of production, technicians are responsible forfixing mechanical problems in the machines, while the executives oversee the op-eration as a whole. There are shift-wise variations in these numbers generated bythe number and types of workers on leave during any particular shift.

Shifts are operated by groups of workers called brigades that remain relativelystable over time. Each worker, at the time of joining the mill is assigned to one ofthese brigades. Brigade membership can be changed based on worker preferencesand decisions of the supervisory and executive staff but these movements areinfrequent. There are more people in a brigade than typically work in a shift,allowing for weekly days off and other types of leave. Brigades are rotated weeklyacross shifts: if a brigade works the morning shift in week one, it is switched towork the afternoon shift for week two and and the night shift for week three.

A2. Data Construction

There are a maximum of 3,558 shifts that cover the period of January 1, 2000to March 31, 2003. Our data cleaning procedure drops 76 shifts with missingdelay data and 126 shifts with missing or zero output; the majority of these shiftsoccur during regular maintenance days, when the entire mill is shut down. Wealso exclude 23 shifts with evident inconsistencies in the data such as those withzero uptime and non-zero output, or abnormally high or low rolling rates.

For each shift in the remaining sample we have a description of the type ofoutput being produced. All products fall into two broad categories: rails andheavy structurals. We eliminate 212 shifts producing exotic products (such ascrossing sleepers) or more than one type of output. The remaining 3,121 shiftsconstitute our main sample and are listed in Table A1 by product type.

For each of the shifts we use, we generate the total stock of training by summingthe number of days of training received by each employee on the shift as of thatdate. The four largest training programs for RSM employees during our studyperiod are given in Table A2.

A3. Marginal Effects on Output per Shift

To facilitate the interpretation of the results obtained in Section IV, it is usefulto express all the estimates in terms of implied output gains. Let the expectedoutput per shift be Y = E[Y ]. Suppose that one is interested in the effect ofproductivity training (denoted as Xpt). To derive the expression for this effect,one has to relate Y to the model’s parameters, β. This is done as follows.


Table A1—Shifts in the main sample, by product type

Product # shifts Product # shiftsRails: Structurals: BeamsR-52 1,428 B-250 89R-60 1,149 B-300 82BS-90 32 B-350 17Structurals: Channels B-400 27CH-250 54 B-450 26CH-300 19 B-500 50CH-400 40 B-600 108

Table A2—The four biggest training programs of RSM employees, Jan 2000-March 2003

Name of program Dates Category Share in totaltraining time,percent

Acceptance of rails program June 2001 –July 2001

Productivity 22

ISO-9000 workshop May 2001,March 2002

Quality control 9

ISO-14001 workshop Jan 2002,July 2002

Environmental 10

Success through empower-ment of people

Oct 2002 –Jan 2003

Motivational 24


Let T be the time needed to roll one bloom, taking into account all delays thatoccur along the way. This is a random variable; its distribution is determined byprobabilities of delays of each type and the respective distributions of downtime.Specifically, the distribution of T is fully determined by the model’s parameters,β and the observables, X.

Denote Px a probability of a delay of type x for a single bloom and Dx a delaytime in minutes if the delay occurs. If R is the rolling rate, then the expectedtime required to role a bloom is the inverse of the rolling rate plus all expecteddelays for a single bloom. Denoting the expected value of T by T , we have

T (X) = R(X, β)−1+Pa(X, β)E[Da|Da > 0,X, β]+

Po(X, β)E[Do|Do > 0,X, β]+

Pp(X, β)E[Dp|Dp > 0,X, β]+

Pf (X, β)E[Df |Df > 0,X, β].

The expected number of blooms rolled in one minute is then 1/T and expectedoutput per shift is

Y = p480

Twhere p is the probability of a non defective bloom. After substituting the aboveexpression for T into the expression for Y , one can derive the marginal effect ofany component of X. In particular, the effect of productivity training equals

dY (X)

dXpt= −480p

T2

dPa

dXptDa︸︷︷︸

Column 1

−480p

T2 Pa

dDa

dXpt︸︷︷︸Column 2

−480p

T2

dPo

dXptDo︸︷︷︸

Column 3

−480p

T2 Po

dDo


−480p

T2

dPp

dXptDp︸︷︷︸

Column 5

−480p

T2 Pp

dDp


−480p

T2

dPf

dXptDf︸︷︷︸

Column 7

−480p

T2 Pf

dDf


+480p

(TR)2︸︷︷︸Column 9

where Dj = E[Dj |Dj > 0], j ∈ (a, f, o, p). The labels in the above equation

refer to columns in Table 7. Note that as dY (X)dXpt

is a function of X, we need to

specify where it is evaluated: the numbers reported in Table 7 are the values ofthe components of this function evaluated at the sample mean of X.


Table A3—Identifying variation in the output regression

Dependent variable: (1) (2) (3) (4) (5) (6)blooms rolled

Labor input: Number of workersControl men -0.86 0.21 -0.91 -0.29 -0.57 0.28Coggers -0.94 -0.01 -2.07 0.26 -0.72 -4.10Crane Operators -1.58 0.28 -1.74 -1.04 -1.37 -2.09Executives 7.29∗ 7.37∗ 6.04 9.97∗ 7.67∗∗ 11.89Furnace 1.42 1.56 1.62 1.41 1.40 0.35Ground Staff -0.10 -0.91 0.88 -0.08 0.21 -1.97SCM Team -0.03 0.28 -0.72 -0.16 0.15 1.46Services -0.06 -1.82 1.06 -0.12 0.29 2.07Saw Spell -1.09 -0.16 -0.78 -1.14 -0.80 1.35Technicians 0.41 -4.02 3.76 -0.48 0.74 -7.49

Training stocks (days)Productivity 0.07 -3.46 0.62 0.23— X Rail Shift 0.25∗∗ 1.68 -0.55 -0.18Post training -10.32 -13.62— X Rail Shift 34.61∗∗ 35.68∗

Cost Reduction 0.06 0.37 -0.13 -0.04 0.07 -0.27Motivational 0.22∗ -0.84 0.26∗ 0.04 0.22∗ 1.59Safety -0.40 -0.69 -0.56 -0.38 -0.53∗ 0.96Quality Control 0.18 0.14 0.17 -0.03 0.25 0.73Environmental -0.28 0.00 -0.12 -0.02 -0.19 0.00IT -0.42 0.48 -0.59 -0.26 -0.38 1.99Job Instruction 0.07 -4.12∗ 0.42 0.13 0.08 0.32Other 0.06 0.37 -0.08 0.22 0.15 0.58

Diversity indicesSCST vs.

Other Castes 39.86 22.05 42.48 39.51 25.44 -42.78Locals vs.

Migrants -42.63 -75.25 -58.57 -71.37 -38.66 -50.59Observations (shifts) 3,121 1,306 1,706 3,121 3,121 450

Note: Columns 1-3 and 5-6: OLS estimates; Column 4: FE estimates. Significance levels: ∗5 percent,∗∗1 percent (based on heteroskedasticity-robust errors). Unreported controls: dummies for brigades,products, time of day, new equipment dummy interacted with rail shift dummy. Column 1: Baselinespecification (Column 3 in Table 2). Column 2: Subsample of shifts after the main productivity trainingprogram. Column 3: Subsample of shifts prior the main productivity training program. Column 4:Baseline specification with daily fixed effects replacing quarter dummies. Column 5: Training stockreplaced by the post productivity training dummy. Column 6: Same as Column 3, but the sample isrestricted to April–September 2001 — the period when most of the productivity training was administeredplus two months before and after.


A4. What Identifies the Effect of Training: Cross-sectional or Time-series Variation?

In order to understand what identifies the effect of productivity training, we runa series of regressions. In Column 1 of Table A3, we reproduce the baseline spec-ification from Column 3 of Table 2. In Columns 2 and 3 we estimate the baselinespecification for the period before and after the main episode. The coefficients onthe interaction of productivity training and rail dummy are insignificant suggest-ing that it is the variation in training over the period of June 5, 2001 – July 21,2001 is identifying this coefficient. Column 4 reinforces this as daily fixed effectsalso wipe out this interaction suggesting that it is not the variation in trainingacross brigades identifying the interaction.

In Column 5, we replace the productivity training stock by a post trainingprogram dummy and find that its interaction with the rail shift dummy comesback. Moreover, its size is roughly 34–35 blooms per shift, which is close to ourearlier estimate of 26 blooms per shift. Finally, Column 6 shows that the estimatefor the post training rail shift interaction remain roughly of the same size whetherwe restrict the sample to the six months in 2001 closest to the training periodagain consistent with the variation during the training episode as identifying thecoefficient.

A5. The Pattern of Delays.

Figure A2 visually illustrates the time pattern of delays and their durationstogether with major training episodes. Delay durations seem relatively insensitiveto training and change little over our study period. It may be that technologicalfactors are the primary determinants of how long it takes for production to beresumed once it is interrupted. The probability of avoidable delays falls sharplyafter the productivity training episode and eventually levels off. The probabilitiesof other types of delays seem to oscillate and show no systematic relationship withtraining episodes.


EQ QP E M

EQ QP E M

EQ QP E M

EQ QP E M

2040

6080

100

2040

6080

100

2040

6080

100

2040

6080

100

01

23

01

23

01

23

01

23

Jan 2000 Jan 2001 Jan 2002 Jan 2003

1. Avoidable

2. Outside

3. Finishing

4. Planned

(E) Environmental training (Q) Quality control training

(M) Motivational training (P) Productivity training

Delays per shift Downtime per delay

Dow

ntim

e pe

r de

lay,

min

Del

ays

per

shift

Figure A2. Downtime dynamics and timing of the major training episodes

back on the rails: competition and productivity in state...

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