Introduction

 

           In this exercise we use the NSSO (55^th round) data to measure the share of informal workers in different sectors of the economy. The procedure is by identifying the sectors as formal and informal on the basis of the manufacturing activities carried out on an industrial classification basis. Such a scrutiny of the data helps in understanding the contribution of the informal sector in the Indian economy, and its role in employment creation, production and income generation. As in other developing countries the concept of informal economy is not straightforward.  Informality is a complex phenomenon, making it very difficult to quantify this in an economy and thus build a systematic data set.

 

The paper draws heavily from the study conducted by NCAER under an IDRC funded project (Sinha, Siddiqui, Munjal and Subudhi, 2003). We have traced the formal and informal sector interrelationship through a Social Accounting Matrix (SAM) framework.

 

            With wide range of activities under the sphere of informal activities, the parameters may be quite varied to describe the informal sector.  So we need to first define how we perceive the informal sector. To understand the macro aspects of the informal sector we consider three perspectives through which the informal sector is defined: (a) small-scale production, which is identified as unregistered part of a sector in the National Statistics; (b) informal ‘factor ownership’: workers involved in casual work and own-account workers and (c) households: having members involved in informal activities.

 

            We measure the size and characteristics of the informal economy by analysing various published and raw data collected by the central statistical office. Such data analysis helps in improvement of economic statistics and national accounts as a base for macro economic analysis, planning and policy formulation and also in understanding the contribution of the informal sector on the many aspects of the economy.

 

            One of the objectives of this study is to build the informal sector data into a framework that helps in studying the impact policy changes on this sector. The role of the informal sector in employment, production and income generation can be analysed through a framework that interlinks the different agents in the economy. The macro policies impact the informal sector and the informal sector affect production, employment and income distribution of other sectors as well. So understanding the interlinkages of the informal sector with other sectors of the economy is important to trace the impact of macro policy changes on this sector and the people involved in this sector.

           Social Accounting Matrix

 

            A growing dissatisfaction with the system of national accounting, which mainly emphasised on growth without any regard for distributional aspects led to the development of Social Accounting Matrices (SAMs) during the 70s.  The pioneer in building a systematic social system was Richard Stone as an extension of input-output data and analysis (see Stone, 1970, 1971, 1981, 1985 and 1986). Important distinctions that can be made in the SAMs are that between production activities and commodity accounts. Such distinctions would also enable analysing the income generating role of  the different types of production activities that produce the same Type of commodity, like the formal and informal dichotomy. A SAM would show how sectoral value added would accrue to various factors of production and their institutional owners, and how these incomes are spent. Khan and Thorbecke (1988) have considered the utility of a SAM as a useful conceptual framework. This framework can show that interrelationship between (a) structure of production (b) distribution of value added generated by production activities and yielding factoral income distribution and (c) the income distribution by socio-economic groups and corresponding consumption and saving behaviour of these groups.  In their work where they have identified formal and informal production process, the factors of production have been broken down according to such criteria as labour skills or occupation of the worker, paid unpaid (self employed), location (urban, rural) and informal and formal capital income.

 

            The transformation from value added from various kinds of production activities to the factoral income distribution and corresponding consumption is a major contribution of this work in building a consistency framework.  In their SAM framework they have described three endogenous accounts i.e., factors, institutions and production activities and three exogenous accounts, government, capital and rest of the world.  The interest was to study the impact on incomes of factors, households and production.

 

II.1.      Input-Output Sectoral Reclassification

 

The 115-sector input-output table provided by the CSO for the year 1993-94, has been aggregated to 11 sectors with their formal informal dimensions. The formal informal identification of the sectors is done by scrutinising the nature of work involved in them. While deciding on the sectors, it is to be kept in mind that the sectors are quite heterogeneous with respect to each other. The sectors are as follows:

 

1.      Agriculture (Informal)

2.      Agro-processing (Formal)

3.      Agro-processing (Informal)

4.      Readymade Garments (Formal)

5.      Readymade Garments (Informal)

6.      Other Manufacturing Goods (Formal)

7.      Other Manufacturing Goods (Informal)

8.      Construction (Informal)

9.      Other Services (Formal)

10. Other Services (Informal)

11. Government Services (Formal)

 

The share of informality has been a concern while formulating our sectoral groupings. To break up the intermediate flow into formal and informal parts, two aspects are considered.  One is the output break up and the other the input break up.  The production output flow is split into formal and informal part by considering the share of output that is produced formally using the Enterprise Surveys, ASI and NAS information.  At present the input flows are broken up using output share of formal and informal parts of a sector.  Such break ups may not be very accurate, but there is a data constraint in formulating better breakups.

 

While constructing the 1999-00 SAM from 1993-94 input-output table, the coefficients are updated using relative price information to arrive at 1999-00 values.  The value added and outputs for the sectors are further adjusted into formal and informal parts for 1999-00, using the GDP and NDP information available for the year from the NAS. It should be noted here that value added and outputs are not directly available for all our sectors from NAS. The sectors for which they are directly available are - Agriculture, Construction Services and Public Administration and defence. For manufactured goods, NAS gives unadjusted value added from both registered and unregistered sectors. Total adjusted value added for the whole sector is also given. Hence computing adjusted value added for all the sectors and combining registered and unregistered, we get required value added for individual items that comprise of manufactured goods. To get value of output for sectors for which these are not available from NAS, we use value added output ratio of 1993-94 I-O table and apply it on value added information. Using these ratios we get value added and outputs for all the 11 sectors of our study. From NAS, we get registered and unregistered value added for all the sectors.  The sectors are finally broken into formal and informal components using the shares of registered and unregistered in total value added.

 

II.2.   Workers

                                                                                                                

To get the worker information, we have used data from surveys conducted by National Sample Survey Organisation (NSSO) on 'Employment-Unemployment' and 'Informal Non-Agricultural Enterprises' during the 55^th Round for the year 1999-2000. Also we have used NSSO survey on 'Consumer Expenditure' to get the expenditure pattern of each type of household. 'Employment-Unemployment' and 'Consumer Expenditure' are household surveys conducted both in rural and urban areas. In all, 97986 rural and 67258 urban households were surveyed. All the households gave information on work status of each of its member. So the survey gives information of 819013 persons - of which 62 % are from rural areas and 38 % from urban areas. Employment details of all these people are collected, which includes, their nature of work, the industry to which they belong (to be entered according to NIC-98 classification at 5-digit level), the wages they earn, number of persons hired by the industry to which they belong and whether the industry uses electricity for manufacturing. The 'work status' describes the nature of work of the household members. 

 

The distribution of workers by each of the 11 sectors into different types of factors of production is computed by using the data from the Employment-Unemployment Survey (Round 55) of the NSSO.  In this survey, each household member is asked about his/her status of work. We process the information and distinguish the following 12 factors of production.

 

1.      Labour Casual - Female

2.      Labour Casual - Male

3.      Labour HB - Female

4.      Labour HB - Male

5.      Labour Regular - Female

6.      Labour Regular - Male

7.      OAW - Female

8.      OAW - Male

9.      OAW HB - Female

10. OAW HB - Male

11. Employer - Female

12. Employer - Male

 

Casual labour, male or female, are informal workers as they get the wage returns according to the terms of daily or periodic work contract, both in formal and informal sectors. Regular wage earners are formal workers as they get returns on regular basis and employers are formal capital owners. The Employers employ either 10 or more workers with the electricity or employ 20 or more workers without the electricity. Rest of the employers are called Own Account Workers. All these employment details were used in working out the number of workers employed in each of our sectors. For this exercise, we mapped the industry codes that are entered at 5-digit level with our sectors. So each of our sectors became an aggregation of number of industry codes.  We followed the assumption here that there are no formal workers and capital owners in informal sectors. So, whatever formal workers/capitalists we got in informal part of the sector, we clubbed them with informal workers of that sector.  It should be noted here that activity involved in each industry code behaves differently in rural and urban areas and hence was carefully scrutinised to conclude whether it can be called formal or informal. This gave us different formal informal sectoral aggregation for rural and urban areas. Formal industry codes aggregate to form the formal part of a sector. Same holds true for the informal part.

 

In our study, we captured the information on home-based workers too. Home based workers - both labour and own account workers - form some proportion of informal workforce. To get this proportion, we used data collected by NSSO in their survey for 'Informal Non-Agricultural Enterprises'. In total, 197649 numbers of such enterprises were surveyed. These enterprises are classified as home-based or otherwise depending on the location of the enterprise. In percentage terms, of the total informal enterprises, 32.1% are home-based and rest are non-home-based. The workers employed in home-based enterprises are called home-based workers and those employed in non-home-based enterprises are called non-home-based workers or other informal workers. Each worker's status of work was also recorded depending on which, we called them casual labourers or own account workers. Using these data, we got the proportion of home-based workers in total workers employed in informal sectors. Again the mapping of industry codes, given in 5-digit, with our set of sectors was carried out using the same aggregation scheme as done before. We have also distinguished workers as male or female and hence we obtained following proportions:

 

Table II.1.  Proportion of home-based workers in total workers for

informal non-agricultural sectors

 

	Casual Worker		Own Account Worker		Total
	Male HB (as % of total male Casual workers)	Female HB (as % of total female Casual workers)	Male HB (as % of total male OAW)	Female HB (as % of total female OAW)	Casual Worker	Own Account Worker
RURAL AREA
Agroprocessing	16.7	31.3	46.9	77.7	28.35%	71.65%
Other Manufacturing	18.7	25.0	54.8	84.5	30.90%	69.10%
Readymade Garments	14.5	18.2	43.4	83.7	23.56%	76.44%
Construction	0.0	0.0	0.0	0.0	28.98%	71.02%
Other Services	16.3	14.0	27.9	46.6	22.17%	77.83%
URBAN AREA
Agroprocessing	25.0	37.0	35.8	74.7	40.46%	59.54%
Other Manufacturing	21.4	37.5	31.6	72.4	47.81%	52.19%
Readymade Garments	30.4	36.8	35.2	76.5	45.05%	54.95%
Construction	0.0	0.0	0.0	0.0	43.28%	56.72%
Other Services	12.8	20.0	18.2	33.3	39.56%	60.44%

 

Applying these proportions on numbers of both rural and urban casual and own account workers that we obtained from 'Employment-Unemployment' survey data, we get home-based and other workers for all the informal sectors. Also combining male and female workers, we get following results:

 

 

 

 

 

 

 

 

 

 

Table II.2.  Percentage Distribution of  Factor Owners In Sectors for all workers

 

 

The following table gives the distribution of female and male workforce across sectors.

 

Table II.3.  Percentage Share of Workers by Sectors

 

		Female Workers across sectors	Male Workers across sectors
Agriculture	I	72.51	52.38
Agro Processing	F	1.16	0.72
Agro Processing	I	3.24	1.34
Readymade Garments	F	0.29	0.35
Readymade Garments	I	0.19	0.24
Other manufacturing goods	F	3.15	6.91
Other manufacturing goods	I	3.69	4.76
Construction	I	1.16	4.30
Other Services	F	8.62	14.51
Other Services	I	4.98	11.30
Government Services	F	1.02	3.17
Total		100.00	100.00

 

Apart from the worker information, average wages earned by each type of labour and for each sector are also obtained from NSSO data. We use this worker and wages information to get the value added distribution of our factors of production across sectors. We distribute the NAS value added for each sector according to the factor ownership.  The NAS provides sectoral information with respect to a broad division of registered and unregistered parts of value added.  Labour and capital earnings from NAS are then distributed according to NSSO share of each type of labour and capital ownership and wage and capital earning rates both for male and female. NSSO shares have been used to get an estimate of value added distribution by casual labour, regular labour, home-based labour, own account workers, own account home-based workers and employers by gender.

 

II.3.     Value Added Generation

 

The factor ownership and the earnings by gender as depicted in the following tables reflect the fact that women workers generally earn less compared to their male counterparts in all sectors except as own account workers in agriculture and employers in other manufacturing formal sector. However, as we do not robust gender specific rates of returns these numbers are not as such very representative. Both male casual labourers and regular labourers earn substantially more than female counterparts.

 

Table II.4.  Percentage distribution Of Factor Owners in Industry sectors

 

Sector	Agri.	Agro Process ing	Agro Process ing	Ready made Garments	Ready made Garments	Other manufactur-ing goods	Other manufactur-ing goods	Cons- truc-tion	Other Ser-vices	Other Services	Govt. Services S	Total
Factors of Production	I	F	I	F	I	F	I	I	F	I	F
Lab Cas – Female	28.85	14.55	17.36	4.91	6.40	7.87	12.19	8.76	2.63	9.91	0.46	19.76
Lab Cas – Male	39.80	16.07	23.89	20.76	25.73	29.02	35.24	70.35	8.46	45.24	2.54	34.93
Lab HB – Female	0.00	0.00	8.65	0.00	1.53	0.00	5.17	0.00	0.00	1.76	0.00	0.56
Lab HB – Male	0.00	0.00	6.74	0.00	4.43	0.00	9.86	0.00	0.00	8.94	0.00	1.44
Lab Reg – Female	0.00	7.90	0.00	10.25	0.00	3.11	0.00	0.00	11.14	0.00	10.00	1.99
Lab Reg – Male	0.00	29.59	0.00	28.68	0.00	40.60	0.00	0.00	40.21	0.00	87.00	10.20
OAW – Female	4.65	14.41	4.64	7.64	2.41	3.23	0.78	0.15	4.01	1.16	0.00	3.79
OAW – Male	26.69	17.19	12.36	26.54	26.75	15.86	16.35	20.74	33.46	23.51	0.00	24.92
OAW - HB – Female	0.00	0.00	16.12	0.00	12.27	0.00	3.83	0.00	0.00	0.99	0.00	0.59
OAW - HB – Male	0.00	0.00	10.24	0.00	20.49	0.00	16.58	0.00	0.00	8.49	0.00	1.79
Employer – Female	0.00	0.00	0.00	0.00	0.00	0.01	0.00	0.00	0.00	0.00	0.00	0.00
Employer – Male	0.00	0.30	0.00	1.22	0. 00	0.29	0.00	0.00	0.09	0.00	0.00	0.04
Total	100.00	100.00	100.00	100.00	100.00	100.00	100.00	100.00	100.00	100.00	100.00	100.00

 

 

 

 

 

 

 

 

Table II.5.  Percentage Distribution of Factor Earnings in Industry sectors

 

Sector	Agri cul-ture	Agro Process ing	Agro Processing	Ready made Gar-ments	Ready made Garments	Other manufacturing goods	Other manufacturing goods	Construction	Other Services	Other Ser-vices	Govt. Services	Total
Factors of Production	I	F	I	F	I	F	I	I	F	I	F
Lab Cas – Female	15.92	4.47	8.20	1.67	4.92	1.70	4.31	5.34	0.72	2.60	0.00	5.66
Lab Cas – Male	34.08	7.68	18.77	9.05	24.14	10.07	23.20	67.09	4.06	26.82	0.00	21.39
Lab HB – Female	0.00	0.00	2.04	0.00	0.55	0.00	0.91	0.00	0.00	0.23	0.00	0.09
Lab HB – Male	0.00	0.00	2.65	0.00	2.11	0.00	3.25	0.00	0.00	2.65	0.00	0.54
Lab Reg – Female	0.00	2.64	0.00	4.26	0.00	0.83	0.00	0.00	4.86	0.00	0.00	3.44
Lab Reg – Male	0.00	16.89	0.00	16.68	0.00	19.07	0.00	0.00	22.66	0.00	0.00	17.57
OAW – Female	15.00	30.87	7.31	14.75	2.66	11.39	1.42	0.20	7.23	2.30	0.00	7.46
OAW – Male	35.00	36.82	19.48	51.23	29.53	55.88	29.76	27.37	60.31	46.61	0.00	39.11
OAW - HB – Female	0.00	0.00	25.40	0.00	13.52	0.00	6.97	0.00	0.00	1.96	0.00	0.79
OAW - HB – Male	0.00	0.00	16.13	0.00	22.58	0.00	30.18	0.00	0.00	16.83	0.00	3.80
Employer – Female	0.00	0.00	0.00	0.00	0.00	0.04	0.00	0.00	0.00	0.00	0.00	0.00
Employer – Male	0.00	0.64	0.00	2.36	0.00	1.02	0.00	0.00	0.16	0.00	0.00	0.15
Total	100.0	100.0	100.0	100.00	100.0	100.00	100.0	100.0	100.0	100.00	0.00	100.00

 

 

II.4.      Household Characteristics:-

 

The activity of each member within a household is obtained from the NSSO's 55^th round survey (NSSO, 1999-00). We have used only labour force activities for building the SAM as a base data set for a CGE model (Sinha, Siddiqui, Munjal and Subudhi, 2003).

 

            We distinguish households first as rural or urban households. Next these two types of households are classified as formal and informal by examining the NIC and the “Type” codes of households as formal-informal. The “Type” code formulated on the basis of composite household information, and the household NIC codes are provided by NSSO. Households in the urban region are classified as formal with “Type” codes as employers and regular wage earners. In rural regions, exceptions are households with NIC agriculture; even with formal "Type" code, such households are classified as informal. Finally, households are classified into different income levels, i.e., poor and non-poor within each broad category. The households are then scrutinised to identify the different types of workers and factor owners distinguished by gender that make up the household.

 

The National Sample Survey Organisation (NSSO) survey assigns a five-digit industry code based on the National Industrial Classification (NIC) 1998 to each surveyed household and the status code to each household member based on the nature of their principal activity. Each 5-digit industry code is mapped with our sectors. And the formal-informal identification of the sectors is done after scrutinising these activities and by using qualitative judgement. This exercise is done separately for the rural and urban regions, as there are certain activities, which are informal in rural sector but formal in, urban. The survey also assigns a 'Type' code giving information on the characteristics of the surveyed households. These type codes are different for rural and urban areas. A household is given certain type code depending on the economic activity of the members of the household during the 365 days preceding the date of survey from which the major income of the household was generated.

 

 

 

 

 

NSSO Household Type Code Description
For Rural Areas	For Urban Areas
1. Self employed in non-agriculture	6. Self Employed
2. Agricultural Labour	7. Regular Wage/Salary Earner
3. Other labour	8. Casual Labour
4. Self Employed in agriculture	9. Others
5. Others

 

Using these type codes and the status codes, we assign our own Household Type codes to each household. Our classification of households for rural and urban areas is as follows.

 

 

Our Household Type Code Description
For Rural Areas	For Urban Areas
1. Non-agriculturist - Poor - Formal	7. Poor – Formal
2. Non-agriculturist - Non Poor - Formal	8. Non Poor – Formal
3. Agriculturist - Poor - Informal	9. Poor – Informal
4. Agriculturist - Non Poor - Informal	10. Non Poor – Informal
5. Non-agriculturist - Poor - Informal
6. Non-agriculturist - Non Poor - Informal

 

For Rural Areas, NSSO Household Type codes 1, 2 and 3 are all called Informal Agriculturists in our type of classification. These household types are then split into poor and non-poor using the Planning Commission Poverty lines. According to the Planning Commission criteria for rural sector, those earning less than Rs. 327.56 are poor and for the urban sector, those earning less than an average of Rs. 454.11 per month fall below poverty line. The Type code 'Self employed in Non agriculture' is further studied and broken into formal and informal part. We adopt the CSO definition and assume that self-employed are actually formal employers who employ either 10 or more workers with the aid of power or 20 or more workers without the aid of power. Rest of the self-employed are informal employers. These household types are further split into poor and non-poor categories using poverty lines as mentioned earlier.

 

For Urban areas, household type - 'regular wage earners', as in NSSO classification, is classified as formal households and type 'casual labour' as informal households. Moreover,  'self employed' are both formal and informal, and are again broken into these as in the rural sector. These households are then differentiated into poor and non-poor using the Planning Commission criteria as mentioned above.

 

 

Table II.6.  Types of Households by Formal and Informal Categories (%)

 

 

Table II.8.  Distribution of male member activity within formal informal

households (percent)

MALE	Rural Formal Non Agriculturist		Urban Formal		Rural Informal - Agriculturist		Rural Informal - Non Agriculturist		Urban Informal
	Poor	Non Poor	Poor	Non Poor	Poor	Non Poor	Poor	Non Poor	Poor	Non Poor
Casual Labour	8.87	5.23	6.23	2.20	35.50	32.19	11.39	11.31	25.14	18.14
Home Based Labour	0.11	0.06	0.08	0.03	0.44	0.40	0.14	0.14	0.31	0.99
Regular Labour	36.31	47.87	36.01	48.51	1.24	2.22	0.86	1.79	3.34	4.15
Own Account Worker	0.28	1.32	2.85	2.89	11.83	21.58	32.23	38.99	19.35	28.58
Own Account Worker - Home Based	0.02	0.09	0.19	0.19	0.79	1.44	2.15	2.60	1.29	6.58
Employer	0.00	0.65	0.02	0.18	0.00	0.00	0.01	0.06	0.00	0.10
Worker	45.59	55.23	45.37	54.00	49.79	57.82	46.77	54.89	49.42	58.53
Non Worker	54.41	44.77	54.63	46.00	50.21	42.18	53.23	45.11	50.58	41.47
Total	100	100	100	100	100	100	100	100	100	100
Total Percentage by HH category	0.23%	0.83%	1.86%	9.88%	22.16%	38.58%	3.26%	8.06%	5.77%	9.36%

 

 

Table II.9.  Distribution of member activity within all households (percent)
	FEMALE		MALE
Casual Labour	20.8011%		25.1169%
Home Based Labour	0.2627%		0.3801%
Regular Labour	1.7433%		7.8248%
Own Account Worker	3.5242%		19.2865%
Own Account Worker - Home Based	0.7945%		1.7217%
Employer	0.0012%		0.0384%
Worker	27.1270%		54.3684 %
Non Worker	72.8730%		45.6316%
Total	100.0000%		100.0000%

 

The above tables show that  female work force is mostly informal and even in formal rural households females are engaged more as informal workers.

 

 

 

 

II.            The CGE Model

 

We have discussed the data as a base for the modelling structure. We now characterise the economic system generating the data. We can think of a (general equilibrium) model as a systematic and internally consistent description of the behavioural relations, constants and market clearing conditions, which could have generated the SAM. Moreover, the specific form of the model will determine how the system reacts when perturbed.

 

The CGE model developed is of the type discussed in Devarajan, et al (1996), which are widely used trade focussed models for developing countries. In this model we intend to characterize the consequences of changes in trade policy for the distribution of income between the formal and informal factors (Sinha and Adam, 2000) distinguished by gender and across a variety of household types. The distribution of factor income flows, both formal and informal by gender, between household types is treated as parametric, obtained from the baseline data. This model is similar in character to the CGE model developed for India, Sinha and Sangeeta (2000) where factors of production are distinguished six categories divided by gender.

 

III.1 Simulations

 

To analyse the impact of trade policy changes and certain pro-poor domestic policy change we have designed three simulations using the CGE model. The simulations are:

 

Simulation 1: A decrease in tariff across the board in all manufacturing sectors.

 

Simulation 2: An increase in direct taxes if formal rich households to compensate the decrease in tariff reduction as in simulation 1. This keeps government revenue unchanged.

 

We then present here the findings on the impact of policy changes as noted above on  domestic output and average wages.

 

 

 

The above table shows that there is a contraction in manufacturing and construction sectors as a result of the two simulation exercises. As the manufacturing sectors have to pay high tariffs, opening up accelerates imports in these sectors resulting in domestic contraction in the short run.  On the other hand, construction, which is a non-tradable sector cannot take advantage of exporting at a price advantage even though domestic prices fall as a cascading effect. Hence this sector contracts resulting in massive lay off of casual workers. So the advantage of increased demand for casual workers in other sectors is overridden by contraction in the construction sector.

 

Table III.2  Percentage Change in Real Average wage rate by skill

 

 

As a result, we see that though all wages increase as a result of trade reforms via lowering of prices, the regular wage earners benefit relatively more than the casual workers. Also w see that women workers benefit more than male earners relatively.

 

 

 

 

III.            Concluding Remarks

 

The objective of this work has been to develop a framework that would enable the impact of policy changes on workers and households that are distinguished by gendered members. The basic framework needs a complete mapping of the various sectors, factors of production and households within a consistent macro framework. While developing a SAM for constructing a model that can enable the study of policy changes through very complex relationships that exist in an economy many data issues have been raised here. Mainly interrelationships amongst sectors and other actors in the economy had been captured within a theoretical framework. This is a task that also throws up many concerns as we make our way through the maze of data while measuring the participation of informal workers in different sectors.

 

           The findings show that a large section of the Indian population is involved in informal operations. There are certain sectors, which have more of informal activities than others. Apart from the usual agriculture and livestock related activities we find that activities in textile production, wood and wood products, other manufacturing, manufacture of miscellaneous metal products, construction and combined services also have substantial informal share in production. Share of informal worker in sectors like agriculture, construction, ready made garment products, agro-processing are higher than that of formal workers. Further, the study shows that there is higher number of poorer households within the informal category, which is an expected finding. This study validates some such characteristics of the informal economy.

 

BIBLIOGRAPHY

Bakkar, I. (1994), The Strategic Silence, Gender and Economic Policy, London: Zed Books.

 

Beneria, L. (1995), 'Toward a Greater Integration of Gender in Economics', World Development, vol. 23, no. 11.

 

Carbonetto, Daniel, Jenny Hoyle and Mario Tueros (1985):  “Sector Informal en Lima Metroplitana” Research Progress Report, CEDEP.

 

Carbonetto, D. and M. Ines Carazo de Cabellos (1986): Heterogencidad Technologica y Desarrollo Economico: El Sector Informal, Lima: Instituto Nacional de Planification.

 

Central Statistical Organisation, (2002): National Accounts Statistics, Government of India, New Delhi.

 

Central Statistical Organisation, (2000): "Input-Output Transactions Table, 1993-94", Government of India, New Delhi.

 

Devarajan S., S. Robinson and J.  Lewis (1996): "Getting the Model Right: Applied General Equilibrium Modelling", Cambridge University Press (forthcoming).

 

Duchin, Faye (1998), Structural Economics: Measuring Changes in Technology, Lifestyle, and the Environment, California: Island Press.

 

Elson, D. (1995), 'Gender Awareness in Modeling Structural Adjustment', World Development, vol.23, no. 11.

 

Evers B. (2003): “Broadening the Foundation of Macro-economic Models through a Gender Approach: New Developments” in Macro-Economics Making Gender Matter by Martha Gutierrez (ed). Zed Books, New York.

 

Fontana M. and A.Wood (2000): " Modeling the Effects of Trade on Women at Work and at Home" World Development, Vol. 28. No 7.

 

Gibson, B. and B. Kelley (1994): "A Classical Theory of the Informal Sector", The Manchester School, LXII, No. 1 (March): 81-96.