rafmacalaba/datause-encoder-data huggingface dataset:
Our field experiments involve the 50 villages in Gujarat where rainfall insurance was offered in 2007. Twenty of these villages had not previously been exposed to the product, while in the remaining 30 villages SEWA had marketed insurance to households in 2006. We use different field experiments for these two groups of villages. For villages with no prior exposure to insurance, SEWA used portable _video players_ to deliver a 90-second marketing message directly to household-decision makers.<sup>14</sup> Each treated household was randomly assigned one of eight different videos. For villages where insurance had been offered in 2006, SEWA instead distributed _flyers_ to households, containing one of six randomly assigned messages.

These treatments were delivered to a cross-section of households in each village, including all the households which participated in the 2006 survey. Each treated household received a non-transferable coupon bearing their name and address, to be presented for a discount when insurance was purchased. The coupon serial number indicated which marketing message the household received. The size of this discount was randomized in the 20 villages receiving video treatments: 40% of households receive Rs. 5, 40% receive Rs. 15, and 20% receive Rs. 30. This randomization allows us to estimate the price elasticity of rainfall insurance demand. In the 30 villages receiving flyer treatments, the discount was always fixed at Rs. 5.

_Gujarat: Details of video and flyer messages. –_ In the video experiments, we randomize the message viewed by the household along four dimensions. One experiment tests the sensitivity of demand to the prominence of the trusted SEWA brand. The other three treatments test the sensitivity of demand to framing effects. A full description of the combinations of treatments used is presented in the Online Appendix B.<sup>15</sup> Basic features are as follows:

- SEWA Brand (Yes or No): SEWA has worked for many years in the study villages, while IFFCO-TOKIO is almost unknown. In the “Strong SEWA brand” treatment, videos clearly indicate the product is offered by SEWA. Alternatively, SEWA is not mentioned.

- Peer vs. Authority Figure: Farmers may weigh information sources differentially when learning about insurance. In the “Peer” treatment, a product endorsement is delivered by a local farmer. In the “Authority” treatment, a teacher delivers the endorsement.

> 14 The use of video players allows SEWA to explain the product to the households in a consistent manner. It allows for a more careful experimental treatment, as the individual conducting the marketing is not solely responsible for delivering the experimental message.

> 15 For households that were part of our 2006 household survey, four videos are used (A-D in Online Appendix B Table 2). For this group, the SEWA brand is included in all videos. For households that receive a video marketing treatment but were not part of the original survey, one of the eight different videos is randomly assigned, four of which include the SEWA brand.

12
Because establishment-level data are collected by the Census Bureau under the authority of Title 13 of the US Code, the Bureau prohibits the release of information that could be used to identify or closely approximate the data for an individual establishment or enterprise. Consequently, only a limited number of researchers working as Special Sworn Employees (SSEs) and Census Bureau staff have direct access to the LRD.

## 3.1. The Conceptual Goal

Access to the emissions, risk and economic data described above presents a unique opportunity to develop a comprehensive picture of the environmental and human health risks associated with industrial development. The US EPA's databases and the LRD contain samples of facility-level information of an unmatched size and detail, enabling a reasonable estimate to be made of the pollution associated with any given level of activity, in any specified industrial sector. Conceptually, such estimates can be presented as an index of "pollution intensity", expressed as a ratio of pollution per unit of manufacturing activity:

# pollutant output intensity = _pollutant output

# total manufacturing activity

Initially, this project focused on the generation of all-media toxic pollution intensity indices from the data 11
The second column shows that while the microfinance banks made up just 10 percent of the institutions in the sample, they are relatively large, accounting for over half of all the assets of the institutions in the sample (converted into purchasing power equivalents to yield $25.3 billion in total assets). Nongovernmental organizations, in contrast, make up 45 percent of the institutions but can claim just 21 percent of the total assets. For all institutions, the loan portfolio is their most important asset; the result implies that banks lend in much higher volume than others.

Nongovernmental organizations, though, reach more borrowers in total. The third column shows that nongovernmental organizations can claim about one-half of the 18 million customers in this data set, with banks claiming one-quarter. Donors at large aid agencies have pushed hard to encourage the commercialization of microfinance, but the evidence here suggests that nonprofit microfinance agencies still matter in a big way. That impression is reinforced in the data of Gonzalez and Rosenberg (2006), which shows that nongovernmental organizations served one-quarter of the 94 million borrowers seen in 2004, with self-help groups serving another 29 percent. (Self-help groups are a variant of microfinance commonly seen in India and are typically organized by nongovernmental organizations linked to banks.) Microfinance banks and licensed non-bank financial institutions served just 17 percent of all borrowers. Government institutions—often inefficient and substantially-subsidized--over-shadowed the banks by serving 30 percent of all coverage. In terms of borrowers, the greatest scale of outreach at this juncture is thus not from commercial institutions but from others. Trends in outreach will likely shift toward private sector banks as they grow and spread, but today nongovernmental organizations and other non-profits maintain a large and distinct niche.

The last two columns in Table 1 show that non-profits also serve more women than banks, and they use more subsidies. While nongovernmental organizations serve half of all borrowers in the sample, they serve three-quarters of the female borrowers. Banks, in contrast, serve a quarter of all borrowers but just 6 percent of the female borrowers. (Note that only 290 of the 346 institutions report on their coverage of women, and nongovernmental organizations are more likely to report, which is telling in itself.) The final column of Table 2 shows the 10
WPS3463

# **Employment Regulations through the eyes of employers Do they matter and how do firms respond to them?**

Gaëlle Pierre and Stefano Scarpetta<sup>*</sup> World Bank, 1818 H Street NW, Washington, DC 20433

## _Abstract_

_In this paper, we present evidence on how employers perceive labor regulations and react when these are perceived to constrain the operation of their firm. The paper draws from harmonized surveys of (up to) 17,000 firms around the world and compares employers’ responses with actual labor legislation. We find that employers’ concerns about labor regulations are closely matched by the relative stringency of de jure labor laws. Countries that have, from an international perspective, tight labor regulations tend to have higher proportions of employers reporting these regulations as severe constraints. But not all firms are affected in the same way by onerous labor regulations. Medium-size firms are those whose business and prospects for growth are most negatively affected. Similarly, innovating firms are disproportionally affected by tight labor regulations. There is also clear evidence in the data that firms facing tight regulations invest more in training and make greater use of temporary employment. Small firms mainly rely on temporary employment, while medium and large firms, as well as innovating firms, tend to rely more on on-the-job training if labor regulations make hiring and firing very costly._ _Keywords_ : employment protection indices, firms surveys, training, temporary employment _JEL classification:_ J23, J65, K31

> * Corresponding author: Stefano Scarpetta (mailto:Sscarpetta@worldbank.org ; Tel: +1 202 458 1119.
savings when only the family member is trained, and small and insignificant impacts on the knowledge, behaviors, and outcomes of family members when only their migrant worker is trained.

This paper relates to two main literatures. First, there is a nascent literature that has examined the impacts of financial education in developing countries. Examples include Cole et al. (2011) who look at savings account take-up in Indonesia, Hastings and Tejeda-Ashton (2008) who examine selection of retirement funds in a hypothetical scenario, and Gine et al. (2011) and Cai (2012) who both examine the decision of farmers to purchase weather insurance, and the interaction of financial education with social networks. Two parallel and complementary studies also look at financial literacy and remittances. Gibson et al (2012) study the impact of teaching migrants in New Zealand and Australia about the different methods available for remitting and the costs of each, finding an increase in financial knowledge and information seeking behavior, reduced risk of switching to costlier remittance products, but no change in either the frequency or level of remittances. Seshan and Yang (2012) provide a savings-oriented financial literacy workshop to Indian male migrant workers in Qatar. They find some changes in financial practices in their full sample, but no significant impacts on savings levels or remittances. However, when they split their sample by baseline savings levels, training raises the migrant’s savings levels in the low baseline savings group. Our work complements and builds on these studies, by focusing on outcomes for household members left behind in the developing country, implementing multiple surveys for more statistical power, and by examining how the impacts of the course vary with who is trained.

Secondly, our paper contributes to a new experimental literature on policies to enhance the development impact of migration, which are summarized in McKenzie (2012). Recently Ashraf et al. (2011) and Chin (2011) have both found increases in savings among migrants in response to direct efforts to provide greater access to savings accounts, which complements our finding that families are saving less than may be optimum for them. Our paper shows that offering financial education to migrants and their families offers another way to get more savings out of the same levels of remittances.

The remainder of the paper is structured as follows: Section 2 provides the Indonesian context, the sample used in our experiment, the content of the financial literacy training program, and the experimental design. Section 3 provides our results on the impacts of the financial literacy program on the knowledge, financial behaviors, and remittance and savings outcomes for migrant families. Finally, Section 4 concludes.

4
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enterprises we need to control for the fact that some MFIs use a flat rate method to calculate interest rate while some use a declining balance method.

Table 18 shows the effective interest rate for 25 MFIs that borrow from the country’s premier wholesale microcredit agency (PKSF) to make the microloans available to their borrowers. The calculation of effective interest rate takes into account all fees and additional charges paid by the borrower. The average effective interest rate charged to microenterprises is 26.65 percent if they repay on a monthly basis, while it is 31.59 percent if they pay on a weekly basis. The average effective interest rate charged for rural microcredit programs in general is 32.05 percent.

The average rate of return earned by Bangladeshi microenterprises in our sample falls in the range of 40 to 50 percent, depending on the sector of activity of the microenterprise. Therefore, although MFIs charge higher interest rates than commercial banks, expansion of lending by these institutions to microenterprises could serve to eradicate credit constraints faced by microentrepreneurs. However, we also have to keep in mind that effective interest rates vary quite significantly by the size of MFIs and their sources of funding. For example, those who borrow from PKSF and obtain donations from foreign organizations can afford to charge low effective interest rates (see Table 19). Thus, to ensure that the MFIs remain a sustainable source of borrowing for microenterprises, efforts need to be made to keep the average effective interest rates lower than the average rate of return for these enterprises.<sup>16</sup>

> 16 Using the nationally representative household survey data over 10 years, we attempted to estimate the average rates of returns to microenterprises and found that the rates of returns are not necessarily lower than the average interest rates charged by financial institutions, including MFIs.  However, the rates of interest charged by MFIs are yet found high over this period, as many argued.  Realizing this, in 2010, the country's Microcredit Regulatory Authority (MRA) set a ceiling on interest rates at 27 percent which has forced some MFIs (charging higher rates) to reduce their interest rates below 27 percent.  A recent study from Institute of Microfinance (InM) analyzed the cost of MFIS and found that given 7% cost of fund, the average break-even rate of MFIs is 23-24 percent and thus there is still room for lowering the ceiling interest rate further (Faruqee and Khalily, 2011).  Also the discrepancy between average cost of borrowing and average rate of return may be wider (meaning lower return against higher cost of capital) for certain activities, although on an average we found returns are higher than the average interest charged by financial institutions including MFIs.

25
To address this issue, and bring relativist considerations into poverty measurement in a scientifically credible way, one first needs to say how “welfare” is to be measured. The tradition in economics has been to try to infer welfare from demand behavior. This approach has met some severe identification problems, which render it impossible to infer utility from observed consumer demand behavior amongst heterogeneous households without some rather strong assumptions; see, for example, Pollak (1991) and Browning (1993). Even using high quality surveys (in which an income aggregate is built up from many detailed questions), current incomes are unlikely to reflect well either past or expected future incomes, which are likely to matter to current welfare. Expenditure on current consumption may do a better job in this respect, but will still be an imperfect welfare indicator given that inter-temporal markets do not work perfectly. There are also uncertainties about how best to normalize for heterogeneity in consumption needs, such as stemming from demographic differences between households.

In this light, Sen’s advocacy of a capabilities foundation for poverty measurement can be seen as a means of addressing the identification problem, by broadening the information base for assessing welfare. One strand of the poverty measurement literature has used approaches that anchor poverty lines in the income space to attainments of certain basic functionings, notably being sufficiently well-nourished to maintain good health and normative activity levels.<sup>12</sup> Here too the measurement problems are far from trivial, such as in measuring nutritional attainments and setting normative activity levels. There is some evidence that, in practice, these methods generate poverty lines with similar properties to relative poverty lines, in which the poverty line is roughly proportional to mean income of the area of residence (Ravallion and Bidani, 1994).

However, there is a risk of anchoring poverty lines to a partial sub-set of the capabilities that matter to welfare. Nutritional requirements are an example. The high income elasticity that is found for poverty lines anchored to attaining nutritional requirements alone can arise from factors that one might well deem to be irrelevant to welfare. For example, tastes often shift to more expensive sources of food energy as real incomes rise with urbanization; shifts in relative prices can also play an important role in this change in consumer behavior as economies develop. The resulting poverty comparisons can then be welfare-inconsistent (Ravallion, 1994; 1998).

A potentially important source of information for identifying a more comprehensive welfare metric for anchoring poverty lines is subjective assessments of well-being. In one

> 12 A critical review of these approaches can be found in Ravallion (1994).

6
11

# Dynamics

21. In this section of the paper we will investigate the effects of changes in real GDP and export growth on each of the variables. For illustrative purposes, it will be assumed that we are working with an economy that has very inefficient capital and high import elasticities of demand. If you are mathematically inclined, you may want to refer to Appendix A which contains the equations upon which this discussion is based. Please note that the results shown in the following figures are extreme cases chosen for expository purposes: the modeler would not, under ordinary circumstances, expect to see such exaggerated results.

22. We will start the analysis with investment. It was explained earlier that the modeler has two ways of linking investment to GDP: an marginal propensity to invest (Al) or an ICOR (A2). Examples of both can be seen in Figures 1 and 2 below. If the MPI (Al) is used, then the trend in investment merely mimics the trend in GDP. If an ICOR (A2) is used, then investment increases both with the absolute level of GDP and with the rate of growth in GDP. That is why the effect is so much more volatile in Figure 2. (See Appendix A.1) 23. Imports will move in the same direction as, and parallel to the trends of, the variables they are linked to so long as the elasticities of demand are positive. The share of these imports in economic activity, however, will change unless the elasticities are set equal to one. (See Table One.) Figures 3 and 4 are examples of intermediate and capital goods imports during a short burst of GDP growth when the respective elasticities of demand are larger than one and investment is linked to GDP via an ICOR. These will be the standard assumptions in the examples that follow. (See Appendix A.2 and A.3) 24. Consumption and consumption-related imports (Mc) are interdependent. They are also the only aggregates which are linked to the level of exports as well as GDP. Consider consumption first. Positive changes in GDP or imports will add to consumption; increases in investment or exports will take away from it. The total effect of GDP growth is therefore mixed. The final result will be determined by the efficiency of investment, the strength of the import elasticities and the starting import shares. The overall effect, however, will almost always produce a marginal propensity to - consume greater than zero and less than one thus producing the traditional positive relationship between income and consumption. (See Appendix A.4.)
# **Drinking Water Salinity and Infant Mortality in Coastal Bangladesh**

Susmita Dasgupta<sup>1</sup> , Mainul Huq<sup>2</sup> , David Wheeler<sup>3</sup>

**Keywords:** Bangladesh, Coastal Areas, Infant Mortality, Water Salinity

## **JEL Classification: I00, Q54**

- Authors’ names are in alphabetical order.

## **This research was funded by the Knowledge for Change Program.**

We would like to extend our special thanks to Dr. Moqbul Hossain and Dr. Zahirul Huque Khan for the soil salinity and river salinity data in coastal Bangladesh. <mark>We are thankful to</mark> Dr. Abbas Bhuyia, Acting Executive Director, ICDDRB and Dr. Michael Toman, Research Manager, World Bank for their comments and suggestions.

- 1 Lead Environmental Economist, Development Research Group, World Bank

- 2 Consultant, World Bank

- 3 Senior Fellow, World Resources Institute
40<sup>th</sup> percentile having probabilities close to one and those above having probabilities close to zero. We investigate this gap between actual and perfect targeting in the next section.

Next, we identify which of the different methods used for registering households in the UDB led to more progressive inclusion of the poor. Figure 3 shows that the consumption distribution of UDB households registered based on the pre-listing is shifted to the left (poorer) compared to that of UDB households identified through community suggestions (“in UDB, not on pre-listing”). Both of these distributions are poorer compared to households not registered in the UDB. Households that were removed from the survey enumeration pre-listing, and therefore not registered in the UDB, have a consumption distribution that is similar to other households not registered in the UDB. These distributions suggest that, in the SUSETI sample, UDB households are on average poorer than nonUDB households, regardless of the channel through which they have been registered.<sup>14</sup> In Table 1, we provide additional evidence on the socioeconomic differences across households according to their registration channel. Those registered based on the pre-listing have monthly per capita expenditures that are around 15 percent lower on average than those registered through community suggestions. Those registered through the pre-listing also tend to have significantly more family members and children. Their household heads tend to have less schooling and are more likely to be male and working compared to the heads of UDB households registered through community suggestions. Among non-UDB households, those on the pre-listing appear on average poorer, larger, with heads that have two less years of school and are more likely to be male and work. These differences point to the general progressiveness of the pre-listing.

Table 1 also shows that UDB households are more likely than non-UDB households to have previously received social program benefits distributed prior to the implementation of the UDB. For the BLT cash transfer program in 2008, around 55 percent of UDB households report to have been recipients compared to about 25 percent of non-UDB households, and the differences are similar for

> 14 Kolmogorov-Smirnov tests reject equality across all pairwise comparisons of these four distributions.

16