Sunday, 21 November 2010
Causes of Industrial sickness:
Causes of Industrial sickness:
Identification of the most proximate causes of industrial sickness is one of the prime objectives of the present study. In fact, policy to readers the problems of industrial sickness can not be formul4ated in the absence of proper understanding of the factors which precitate such a situation.
The chapter starts with a discussion of the symptoms of industrial sickness. The analytical framework used distinguishes the causes of industrial sickness between “external and “internal” factors. Further, attempts have been made to identify the causes emerging at various phases of the production cycle, particularly constraints in project implementation, constraints in production, and constraints of personnel. Through statistical test some hypotheses the factors which underline the sickness phenomenon. Finally, an econometric test has been used to establish the relatives’ impotence of the proximate causes of industrial sickness.
Symptoms and Factors:
Symptoms:
As mentioned earlier, in order to understand the causes of industrial sickness, it is important to follow its symptoms. Table 6.1 presents the perception of the owners of the sick enterprises regarding enterprise level symptoms of industrial morbidity.
It transpires from Table 6.1 that fall in capacity utilization leading to decline in production in the single most important (41%) “first” symptom of industrial sickness. In fact, the same factor figures very high, ie. Second (17%) in the ranking of “second” symptom. Moreover, the second most important (15%) “first” symptom,viz no production for a long time is an extreme situation of capacity utilization.
Table 6.1
Symptoms of Industrial Sickness
| Symptoms | First Factors | Second Factors | ||||
| | Number | % | Rank | Number | % | Rank |
| Failure in Loan Repayment | 19 | 10% | 3 | 25 | 20% | 1 |
| Low sales Revenue /Profit(than before) | 16 | 8% | 4 | 17 | 13% | 3 |
| Higher Cost of Production | 14 | 7% | 5 | 15 | 12% | 4 |
| Doing Sub-Contract Jobs Only | 8 | 4% | 8 | 7 | 6% | 7 |
| Low capacity Utilisation /Low Production | 77 | 41% | 1 | 21 | 17% | 2 |
| Unsold Output/Low Demand | 12 | 6% | 6 | 11 | 9% | 6 |
| Asset Depletion | 4 | 2% | 9 | 2 | 2% | 8 |
| No Production for Long time | 28 | 15% | 2 | 13 | 10% | 5 |
| Others | 11 | 6% | 7 | 15 | 12% | 4 |
| Total | 189 | 100% | --- | 126 | 100% | -- |
Source: BIDS Sick Industries Study, 1998
Apart from low capacity utilization, decline in production, cessasion of production, the another factor which figures prominently as distinctive symptom of industrial sickness seems to be failure in loan repayment. Inability in debt servicing is the third most important (10%) “first” symptom and second most important (20%) “Second” order symptom of industrial sickness.
The other symptoms which suggest that an enterprise is in trouble include the following falling sales revenue (profit),increase in stock of unsold goods, rise in production cost, resorting to sub-contract jobs, asset depletion, etc.
Internal Factors:
Table 6.2 presents the internal factors causing industrial sickness in terms of their relative importance. Among the factors internal to the enterprise which cause industrial sickness, the entrepreneurs have singled out use of obsolete technology as the most important one (23%) in the first order. The issue of technology also figures prominently (ranked third with 12% responses a s a second order internal factor.
Appointment of inappropriate personnel and lack of working capital have been identified as the second (15%) and third (13%) most important factors (in the first set), leading to poor performance of the unit.
On the other hand, poor product quality (18%) has been cited as the second most important (18%) factors in the second order. In this group, in the third place (12%) two factors namely poor amount of working capital and poor feasibility study jointly.
Table 6.2
Internal Factors of Industrial Sickness
| Factors | First Factors | Second Factors | ||||
| | Number | % | Rank | Number | % | Rank |
| Lack of Experience | 5 | 5% | 6 | --- | --- | -- |
| Poor Management | 8 | 9% | 5 | 1 | 3% | 6 |
| Wrong feasibility / Uneconomic Plant size | 5 | 5% | 6 | 4 | 12% | 3 |
| Lack of working Capital | 12 | 13% | 3 | 4 | 12% | 3 |
| Non-cooperation of the employee | 1 | 1% | 8 | 1 | 3% | 6 |
| Obsolete technology | 21 | 23% | 1 | 4 | 12% | 3 |
| Faulty employee appointment | 14 | 15% | 2 | 3 | 9% | 4 |
| Non-cooperation among owners | 5 | 5% | 6 | -- | --- | -- |
| Marketing Problem | 10 | 11% | 4 | 9 | 26% | 1 |
| Dependence on single financial source/institute | 1 | 1% | 8 | - | -- | -- |
| Irregular wage payment | 1 | 1% | 8 | -- | -- | -- |
| High interest bearing advance from agents | 1 | 1% | 8 | -- | -- | --- |
| Poor product quality | 5 | 5% | 6 | 6 | 18% | 2 |
| Others | 4 | 4% | 7 | 2 | 6% | 5 |
| Total | 93 | 100% | --- | 34 | 100% | --- |
Source: BIDS Sick Industries Study, 1998
Following are some of the other factors which have gone wrong but were within the control of the entrepreneurs of the sick units: marketing problem, lack of experience of the owners, non- cooperation among the owners, etc. One would presume that these are the routine problems which an entrepreneur has to face to run his /her unit profitably
External Causes:
Among the external factors, lack of working capital has been mentioned as the single most important cause in first (35%) and second a (24%) order respectively (see Table 6.3). Table 6.3 further shows that the nature calamity and trade liberalization, according to the entrepreneurs are the second (13%0 and third (9%) most important factors respectively among the first set of factors.
Among he second set of external factors, poor infrastructural services (including utilities) and political unrest have been mentioned in the second (14%) and third (13%) places respectively.
Table 6.3
External Causes of Industrial sickness
| First external Factors of sickness | First Factors | Second Factors | ||||
| | Number | % | Rank | Number | % | Rank |
| Lack of working Capital | 70 | 35% | 1 | 45 | 24 | 1 |
| Political Unrest | 10 | 5% | 6 | 24 | 13 | 3 |
| Smuggling | 6 | 3% | 8 | 18 | 10 | 4 |
| Trade liberalization | 18 | 9% | 3 | 11 | 6 | 7 |
| Poor infrastructure / Utilities | 14 | 7% | 5 | 26 | 14 | 2 |
| Global price fluctuate | 7 | 3.5% | 7 | 5 | 3 | 10 |
| Problems in disbursement of project loan (already sanctioned) | 15 | 7.5% | 4 | 10 | 5 | 8 |
| Bank control over machinery purchase | 2 | 1% | 11 | 1 | 5 | -- |
| Natural calamities | 27 | 13% | 2 | 15 | 8 | 5 |
| Duty on raw materials /customs problems | 5 | 2.5% | 9 | 4 | 2 | 11 |
| Non-availability of raw materials | 3 | 1.4% | 10 | 5 | 3 | 10 |
| Lack of modern technology | 1 | 0.5% | 12 | -- | -- | -- |
| Long project implementation period | 1 | 0.5% | 12 | -- | -- | -- |
| Lack of demand for the product | 3 | 1.5% | 10 | 6 | 3 | 9 |
| Closure of BTMC weaving mills | 1 | 0.5% | ---11 | -- | -- | -- |
| High interest rate on bank loan | 1 | 0.5% | 11 | 2 | 1 | 12 |
| Others | 18 | 9% | 3 | 14 | 7 | 6 |
| Total | 202 | 100% | -- | 188 | 100 | --- |
Source: BIDS Sick Industries Study, 1998
6.2 Constraints Faced in Production Cycle
Implementation
It is usually argued that an enterprise falls sick not due to what happens during production, but due to factors affecting its implementation. Table 6.4 shows that the proportion of sick units (66%) who got prepared a feasibility study was higher than the non-sick counterparts (54%). This is because the incidence of institutional financing is higher among sick units, and institutional financing demands preparation of feasibility study. The table further shows that whatever may be the extent of preparation of the feasibility studies among the non-sick, entrepreneurs, they seem to have better quality study as 96% of them responded in affirmative in comparison to that of 58% among the owners of sick units. In this respect one can refer to Table 6.5 which shows the breakdown of these feasibility studies by source Incidentally, while an overwhelming proportion of the appraisals for the units which turned sick was done by the financing banks (46%), while in case of non-sick units by the entrepreneurs themselves (more than 69%).
Table 6.4
Availability and Quality of Feasibility study
| Issue | Sick | Non-Sick | ||||
| | Yes | No | Total | Yes | No | Total |
| Whether had feasibility study | 134 (66%) | 68 (34%) | 202 (100%) | 49 (54%) | 41 (46%) | 90 (100%) |
| Whether feasibility study was good | 76 (58%) | 56 (42%) | 132 (100%) | 46 (96%) | 2 (4%) | 48 (100%) |
The other factor which may be noted is that the potentially sick units experienced more cost over runs (59%) in comparison to their counterparts (41%) during implementation of the projects. Moreover, the extent of cost overrun was also much higher among the sick units. For instance, while 16% of the prospective sick units incurred more than 20% cost overrun, the matching figure was 10% for non-sick units.
Table 6.5
Sources of Projects Appraisal
| Source | Sick | Non-sick | ||
| | Number | Rank | Number | Rank |
| Financing bank | 61 | 1 | 08 | 2 |
| Consultant | 32 | 2 | 06 | 3 |
| Self | 15 | 4 | 34 | 1 |
| BSCIC | 18 | 3 | 01 | 4 |
| BSCIC & Others | 04 | 5 | -- | -- |
| Agri. Ministry | 01 | 7 | -- | -- |
| Others | 02 | 6 | -- | -- |
| Total | 133 | -- | 49 | -- |
Source: BIDS Sick Industries Study, 1998
Table 6.6
Extent of Cost Overrun
| Extent | Sick | Non-Sick |
| 0% | 83 (41%) | 53 (59%) |
| 1%-10% | 47 (23%) | 28 (31%) |
| 11%-20% | 41 (20%) | -- |
| 20%+ | 21 (16%) | 9 (10%) |
| Total | 202 (100%) | 90 (100%) |
Source: BIDS Sick Industries Study, 1998
Production:
A large group of enterprises fall sick because of the constraints they face in the process of production Table 6.7 presents information on set of such constraints as faced by both sick and non-sick units.
Table 6.7
Advertises Faced in the Production Process
| Issue | Sick | Non-Sick | ||||
| | Yes | No | Total | Yes | No | Total |
| Change of Ownership | 43 (21.8) | 154 (78.2) | 197 (100.0) | 12 (13.5) | 77 (86.5) | 89 (100.0) |
| Wage/Salary increased at a faster rate than the output price | 44 (21.8) | 158 (78.2) | 202 (100.0) | 9 (10.0) | 81 (90.0) | 90 (100.0) |
| Problem in availability of raw material | 69 (34.2) | 133 (65.8) | 202 (100.0) | 15 (16.7) | 75 (83.5) | 90 (100.0) |
| Output price went down | 103 (51.5) | 97 (48.5) | 200 (100.0) | 15 (16.7) | 75 (83.3) | 90 (100.0) |
| Price of raw material increased faster than output price | 100 (49.5) | 102 (50.5) | 202 (100.0) | 15 (16.7) | 75 (83.3) | 90 (100.0) |
| Storage Problem of input | 12 (6.0) | 188 (94.0) | 200 (100.0) | 7 (8.0) | 83 (92.0) | 90 (100.0) |
| Storage Problem of output | 14 (7.0) | 186 (93.0) | 200 (100.0) | 9 (10.0) | 81 (90.0) | 90 (100.0) |
| Production interruption by political unrest | 136 (67.0) | 65 (33.0) | 201 (100.0) | 65 (72.0) | 25 (28.0) | 90 (100.0) |
Note: Figures within parentheses indicate percent
Source: BIDS Sick Industries Study, 1998
Table 6.4
Availability of Transport Facility
| Item | Sick | Non-Sick | ||||||
| | Very Good | Fairly Good | Not Good | Total | Very Good | Fairly Good | Not Good | Total |
| For Input | 62 (31%) | 122 (60%) | 18 (9%) | 202 (100) | 27 (30%) | 60 (67%) | 3 (3%) | 90 (100%) |
| For Output | 58 (29%) | 127 (63%) | 17 (8%) | 202 (30%) | 27 30%) | 60 (67%) | 3 (3%) | 90 (100%) |
Source: BIDS Sick Industries Study, 1998
The table shows that the ownership has changed more frequently in the sick units (21.8%) in comparison to its counterparts (13.5%). Wage and salary bill has increased at a faster rate in the sick units (21.8%) than in the controls (10.0%). Incidence of problem with availability of raw materials was also higher amongst the sick units (34.2%) as against non-sick units (16.7%). The share of units where the output price went down was also higher in the sick units (51.5%) in comparison to non-sick units (42.2%). Consequently, sick units in larger proportion (49.5%) in comparison to non-sick units (16 7%) experienced price of raw materials going up at rate faster than that of the output.
In contrast, as Table 6.7 further shows that the sick units had lesser problems with storage of inputs and outputs. At the same time, these units were less disrupted by political unrest. These findings do not come as a surprise as a large majority of the sick units are not in operation; as such these problems are not affecting them any more.
However, in terms of transport availability, one would observe from Table 6.8 that the sick units were located at relatively disadvantaged locations concerning both input and output deliveries.
Personnel and Labor Relations
A lack of adequate workforce may render an enterprise sick Table 6.9 shows that proportion of sick units (22%) facing lack of personnel is double than that of the non-sick units (11%). However, in both set of enterprises, the owners encountered most difficulty in hiring high quality production workers. Although, up to a point, the sick units have had difficulty in mobilizing a proper set of managerial staff. Absence of a healthy labour relation at the firm-level affects the performance of an enterprise. Given the major features of labor relation, it is often considered that presence of organized labour movement is a hindrance to efficient management of enterprise. Thus, one observes that the share of sick units have registered trade unions is higher (12%) than that of in the non-sick units (6%). More importantly, while more than 14% of the sick ceased production due to labour unrest, not a single non-sick enterprise had to close down for that reason.
Table 6.9
Workforce Related Problems
| Production Constraints | Sick | Non-Sick | ||||
| Yes | No | Total | Yes | No | Total | |
| Whether faced any lack of personnel | 46 (22%) | 156 (78%) | 202 (100%) | 10 (11%) | 80 (89%) | 90 (100%) |
| Whether have any registered trade union | 25 (12%) | 177 (88%) | 202 (100%) | 5 (6%) | 85 (94%) | 90 (100%) |
| Whether ever closed down for labour unrest | 29 (14%) | 173 (86%) | 202 (100%) | -- | 90 (100%) | 90 (100%) |
Source: BIDS Sick Industries Study, 1998
Table 6.10
Type of Personnel Lacking
| Type of Personnel | | ||||
| Sick | Non-Sick | Total | |||
| | N (0%) | Rank | | N (0%) | Rank |
| Managerial staff | 1 (2%) | 3 | - | 1 (2%) | 3 |
| Factory/production workers | 32 (70%) | 1 | 10 (100%) | 42 (75%) | 1 |
| Both type of workers | 13 (28%) | 2 | -- | 13 (23%) | 2 |
| Total | 46 (100%) | -- | 10 (100%) | 56 (100%) | -- |
Source: BIDS Sick Industries Study, 1998
6.3 Test of Hypotheses
The enterprise survey yields Lime indicative variables which have their own importance in terms of judging an enterprise for its status and performance along with policy implications at macro level. We have selected a set of indicative variables, as shown in Table 6.11, to test whether they have significant variation in probability sense between "sick and "non-sick" enterprises. Accordingly, we have computed the respective mean of the indicative variables for the category of" sick" and "non-sick" enterprises and t-test is carried out for the hypotheses equality of means of%) the indicative variables across "sick" and "non-sick". It appears from Table 6.11 that indicative variables (a) interest payment output ratio and (b) fixed capital per employee are not significantly different at 10% level for "sick and "non-sick category of enterprises. Three variables, as shown in Table 6.11, are highly significant at 1% level viz. output/ labour ratio, rate of capacity utilization and share of equity in start-up capital. The rest of the variables are significant at different levels. So, we can conclude from the test as shown in Table 6.11 that there is a significant difference en "sick" and "non-sick" enterprises.
Table 6.11
Test of Significance of some Indicative Variables
| Indicative Variable | Sick | Non- Sick | t | Comments | ||
| Ni | Xi | Ni | Xi | |||
| Output-Labour Ratio in peak Years (Tk.) | 193 | 295125 | 90 | 855145 | -1.87 | Significant at 10% level |
| Output-Labour Ratio in Current Years (Tk.) | 192 | 183161 | 90 | 413460 | -4.32** | Highly Significant at 1% level |
| Interest Payment-Output ratio in Peak Year in percent | 116 | 11.72 | 46 | 9.14 | 0.37 | Not significant even at 10% level |
| Liability –Assets ratio in percent | 202 | 4.35 | 90 | 0.84 | 2.49* | Significant at 2% level |
| Capacity Utilization in percent | 129 | 38.55 | 89 | 67.34 | -10.70* | Highly Significant at 1% level |
| Fixed Capital Per Employee(Tk) | 202 | 352169 | 90 | 196884 | 1.61 | Not significant even at 10% level |
| Start Up Equity in percent | 202 | 61.03 | 90 | 80.62 | -6.72 | Highly Significant at 1% level |
Source: BIDS Sick Industries Study, 1998
Finally, in order to identify the most important factors which underlie the industrial sickness syndrome, an econometric test was undertaken. Care was taken to isolate the input from outcome indicators. Logit estimates of a number of variables were prepared with different combinations of the intervening variables. Table 6.12 presents the logit estimates which have yielded a good fit. While overall predictability count is 87%' predictability count for 'sick' enterprise is 91%.
Table 6.12
Logit Estimates
| Variables | ß | t-ratio | Comments |
| Start up equity (%) | -0.0199 | 3.37 | |
| Dummy for trade liberalisation | 0.7689 | 1.95 | Significant at 5% level |
| Percentage of cost overrun | 0.0351 | 1.92 | Significant at 10% level |
| Implementation period (no. of month) | 0.1839 | 3.08 | Significant at 5% level |
| Change of ownership | 0.8037 | 1.60 | Significant at 10% level |
| Received adequate working capital from bank | -2.6737 | 7.17 | Significant at 1% level |
| Audit | 0.4375 | 1.11 | Close to 10% significance level |
| Ever closed down due to labore unrest | 8.4186 | 0.52 | Significant at 1% level |
| Constant | 1.1837 | 1.37 | ---- |
Source: Computed from BIDS Sick Industries Study, 1998
The dependent variable is the type of enterprise. The dependent variable shows whether the enterprise is 'sick' or 'not sick' (TYPE-ENT = 1 and 0 respectively). The results show that almost all the variables are significant i.e. all the variables significantly, explain the symptom of sickness. Two of the variables have negative relation with the dependant variable. If 'start up equity' and 'receive of adequate working capital from the bank' are high, the (log) odds of being a sick enterprise are low. For a one- percent increase in start up equity the (log) odds of being a sick enterprise decreases .02 percent. Also for a one percent increases in the incident of receiving adequate working capital & from the bank lowers the (log) odds of being a sick enterprise by 2.7%. For a one percent change in the incidence of 'liberalisation', 'percentage cost overrun', 'length of implementation period' and the incidence of 'change of ownership', the (log) odds of being sick increases by 0.76%, 0.04%' 0.18% and 0.8% respectively. There is very high positive relation between the incidence of 'closure for labour unrest' and the (log) odds of being a 'sick' enterprise. For a one percent change in the incidence of 'closure for labour unrest', the (log) odds of being a sick enterprise changes 8.4%. The above analysis shows that low percentage of start up equity lack of receiving adequate working capital from the bank, long period of project implementation, labour unrest, change of ownership, higher percentage of cost overrun low percentage of start up equity, and the incidence of trade liberalisation are the main reasons for converting a new enterprise to a 'sick' one.
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