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Field
Last Published
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Before
April 26, 2024 01:30 AM
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After
May 01, 2024 02:56 AM
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Field
Primary Outcomes (Explanation)
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Before
We will construct the worker type variable in two ways
1) Workers who work on fixed work contracts will be classified as high types as these contracts are highly valued
2) Workers who earned earned higher average income in the days before the survey will be classified as high type
Firms will be classified as large if they have higher number of construction sites/labour working under them. Firms which are managed by former masons might be considered as small.
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After
We will construct the worker type variable in two ways and analyse how high workers differ from low-type workers in taking up contracts which are back-loaded (we hypothesize that high type workers are less likely to take up contracts which have wage theft risks).
1) Workers who work on fixed work contracts will be classified as high types as these contracts are highly valued
2) Workers who earned earned higher average income in the days before the survey will be classified as high type
Firms will be classified as large if they have higher number of construction sites/labour working under them. Firms which are managed by former masons might be considered as small.
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Field
Planned Number of Clusters
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Before
We will cluster at the individual level for the firms as we observe them hiring more than one workers.
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After
We will cluster at the individual level for the firms as we observe them hiring more than one workers, and because we offer them more than one contracts to hire workers on.
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Field
Planned Number of Observations
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Before
Workers: Approximately 2000.
25% of workers who accept contracts will be provided jobs.
Firms: Up to 800
There is a possibility that we might not be able to reach 2000 workers. Similarly, number of firms may be below 800.
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After
Workers: Approximately 2000.
Each worker has a 25% probability of being given a job (for each contract they are offered).
Firms: Up to 800
There is a possibility that we might not be able to reach 2000 workers. Similarly, number of firms may be below 800.
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Field
Sample size (or number of clusters) by treatment arms
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Before
Workers
For the first 600-800 workers we will use the following sample size
1) Daily payment*uninsured* 3 day contracts: 15%
2) Steep Back-loaded* uninsured * 3 day contracts: 15%
3) Smooth Back-loaded* uninsured * 3 day contracts: 25%
4) Steep Back-loaded* insured * 3 day contracts: 20%
5) Smooth Back-loaded* insured * 3 day contracts: 20%
6) Daily payment*insured* 3 day contracts: 5%
For the next 1200-1400 sample we will divide the sample between the 3 and 7 day contracts such as the overall sample has ratio 60:40. The break up of 3 day contracts will be the same as above and the breakup of 7 day contract sample is below:
1) Daily payment*uninsured* 7 day contracts: 15%
2) Steep Back-loaded* uninsured * 7 day contracts: 15%
3) Smooth Back-loaded* uninsured * 7 day contracts: 25%
4) Steep Back-loaded* insured * 7 day contracts: 20%
5) Smooth Back-loaded* insured * 7 day contracts: 20%
6) Daily payment*insured* 7 day contracts: 5%
Firms:
For the first 60-100 firms the sample will be divided as:
1) Daily wage*no credit * no guarantor: 15%
2) Steep Back-loaded * no credit * no guarantor: 15%
3) Daily wage*credit * no guarantor: 15%
4) Steep Back-loaded * credit * no guarantor: 15%
5) Daily wage*no credit * guarantor: 15%
6) Steep Back-loaded * no credit * guarantor: 15%
7) Daily wage*credit * guarantor: 5%
8) Steep Back-loaded * credit * guarantor: 5%
In the remaining sample of firms we will add smooth back-loaded contract as an additional treatment. We might cross-randomize this sample with the type of worker (low or high) type.
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After
Workers
For the first 300-500 workers we will use the following sample size
1) Daily payment*uninsured* 3 day contracts: 15%
2) Steep Back-loaded* uninsured * 3 day contracts: 15%
3) Smooth Back-loaded* uninsured * 3 day contracts: 20%
4) Steep Back-loaded* insured * 3 day contracts: 20%
5) Smooth Back-loaded* insured * 3 day contracts: 20%
6) Daily payment*insured* 3 day contracts: 10%
For the remaining 1300-1500 sample we will offer each worker two contracts, one of which will be a 3 day contract and other will be a 7 day contract. The break up of 3 day contracts will be the same as above and the breakup of 7 day contract sample is below:
1) Daily payment*uninsured* 7 day contracts: 15%
2) Steep Back-loaded* uninsured * 7 day contracts: 15%
3) Smooth Back-loaded* uninsured * 7 day contracts: 20%
4) Steep Back-loaded* insured * 7 day contracts: 20%
5) Smooth Back-loaded* insured * 7 day contracts: 20%
6) Daily payment*insured* 7 day contracts: 10%
Firms:
Each firm will be offered 12-16 different contracts, one of which will be randomly chosen and implemented.
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Field
Additional Keyword(s)
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Before
labor, wage theft, worker rights
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After
labor, wage theft, worker rights, firms
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Field
Intervention (Hidden)
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Before
The study will be conducted in urban areas of Patna, the capital of the state of Bihar, which is one of the poorest in the country. Casual workers in the city, and in other parts of India, often seek work at labor stands. These stands are public places which are spread across the city. Almost all workers seeking work at these stands work in construction or related industries. They arrive at the stand early in the morning and stay there till noon, expecting work, and firms hire them from the stand and take them to the work site. Firms hire workers from these stands. The contracts between the firms and workers range from a single day to several days.
Workers and firms do not know each other. The contractual environment is one of limited commitment and contracts are oral. Thus it is important for the worker to know if the firm is trustworthy and will not renege on its word at the end of work day. Similarly, firms are unaware of the productivity of workers. Ideally, firms and workers would want to engage in a long-term contract as it reduces search costs. However, asymmetric information about each other's type can prevent this from happening. Liquidity constraints on the both the firm and worker side can exacerbate this issue. Additionally, high levels of unemployment can allow firms to exploit workers by asking them to work for longer than they contractually agreed. Hence, workers would want to know ex-ante whether the firm is of the type which tends to exploit workers. This may result in workers not taking up contracts which allow firms to extract excessive labor from them.
HYPOTHESIS
Worker side
H1) Workers are less likely to take up jobs which offer back-loaded contracts.
H2) Offering insurance against wage-theft can increase the take-up of jobs by workers
H3) Workers prefer contracts which protect them from exploitation against excess labor.
H4) Workers prefer short contracts than long contracts and this is driven by risks of wage theft being higher in long contracts.
Firm side
F1) Firms prefer back-loaded contracts to contracts which pay workers daily
F2) Firms prefer longer contracts to short contracts
F3) Provision of credit to firms (which they pay back) leads firms to hire more workers.
Worker-firm match
M1) Workers work for shorter hours in daily wage contracts
M2) Worker absenteeism is higher in daily wage contracts.
To test these hypothesis we recruit workers in several labor stands in Patna. Workers are offered contracts (which are randomized at an individual level) that vary the length, the payment structure and insurance.
We work with firms recruited through snowball sampling. We use a BDM procedure to elicit the preferences of firms for contracts. Each firm is offered to hire workers on 12 different types of contracts, one of which is implemented randomly.
Firms and workers which accept the respective contracts are matched with each other. We will measure productivity of workers after they are matched with the firm.
We list the treatment arms in detail and our hypotheses in comparing these treatment arms in the 'experimental design' section of this filing.
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After
The study will be conducted in urban areas of Patna, the capital of the state of Bihar, which is one of the poorest in the country. Casual workers in the city, and in other parts of India, often seek work at labor stands. These stands are public places which are spread across the city. Almost all workers seeking work at these stands work in construction or related industries. They arrive at the stand early in the morning and stay there till noon, expecting work, and firms hire them from the stand and take them to the work site. Firms hire workers from these stands. The contracts between the firms and workers range from a single day to several days.
Workers and firms do not know each other. The contractual environment is one of limited commitment and contracts are oral. Thus it is important for the worker to know if the firm is trustworthy and will not renege on its word at the end of work day. Similarly, firms are unaware of the productivity of workers. Ideally, firms and workers would want to engage in a long-term contract as it reduces search costs. However, asymmetric information about each other's type can prevent this from happening. Liquidity constraints on the both the firm and worker side can exacerbate this issue. Additionally, high levels of unemployment can allow firms to exploit workers by asking them to work for longer than they contractually agreed. Hence, workers would want to know ex-ante whether the firm is of the type which tends to exploit workers. This may result in workers not taking up contracts which allow firms to extract excessive labor from them.
HYPOTHESIS
Worker side
H1) Workers are less likely to take up jobs which offer back-loaded contracts.
H2) Offering insurance against wage-theft can increase the take-up of jobs by workers
H3) Liquidity constraints prevent workers from taking up back-loaded contracts.
H4) Workers prefer short contracts than long contracts and this is driven by risks of wage theft being higher in long contracts.
Firm side
F1) Firms prefer back-loaded contracts to contracts which pay workers daily.
F2) Firms prefer longer contracts to short contracts
F3) Alleviating firms liquidity constraints leads firms to hire more workers.
Worker-firm match
M1) Workers work for shorter hours in daily wage contracts
M2) Worker absenteeism is higher in daily wage contracts.
To test these hypothesis we recruit workers in several labor stands in Patna. Workers are offered contracts (which are randomized at an individual level) that vary the length, the payment structure and insurance.
We work with firms recruited through snowball sampling. We use a BDM procedure to elicit the preferences of firms for contracts. Each firm is offered to hire workers on 12 to 16 different types of contracts, one of which is implemented randomly.
Firms and workers which accept the respective contracts are matched with each other. We will measure productivity of workers after they are matched with the firm.
We list the treatment arms in detail and our hypotheses in comparing these treatment arms in the 'experimental design' section of this filing.
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