rafmacalaba/datause-encoder-data huggingface dataset:
Most of the documented research on marriage payments has examined India, where the widespread dowry phenomenon has become associated with the rise in dowry prices, with important implications for the welfare of women (Rao 1993; Edlund 1999). Although this practice was prohibited as early as 1961, it is impossible to eliminate and has spread and increased.

In a study of rural South India, Srinivasan (2005) provides strong evidence in favor of a preference for sons over daughters. This nonpreference for daughters is justified by the large amounts of dowry a woman’s must pay to the groom’s family. However, women in this study seem to be in favor of this practice, which is primarily considered a way to create bonds among families and to provide protection and not in connection with growing evidence of dowry-related violence. Pande and Astone (2007) use data from the National Family Health Survey of India for the 1992–1993 period to show that dowry is one of the determinants of son preference in rural India.

Comparatively little research has explored marriage transfers in the rest of south Asia, though several studies point to dowry payments now occurring in Bangladesh, Pakistan, and Sri Lanka. Anderson (2000) analyzes the causes of dowry in contemporary Pakistan. He documents three possible explanations for the use of such a payment: a transfer to the groom’s family to acquire a high-quality groom, a compensation payment to the groom for receiving the bride (who is considered a liability for the family because she does not contribute to the household income), and a premortem inheritance to the daughter, which is preserved throughout marriage. The empirical results support the groom-price explanation for urban areas, whereas dowry is considered to be inheritance in rural areas.

An article by Nasrin (2011) examines contemporary dowry payments in Bangladesh and considers dowry to be an asymmetry in gender equity that favors men. Her study of three villages shows that girls are often considered a burden for the family because of the cost of dowries. Parents seem to be willing to place their daughters into early marriages because of the low level of marriage transfers.

Substantial research by anthropologists has aimed to distinguish between societies in which the burden of marriage payments falls primarily on the groom’s family and brideprices are paid and those in which the bulk of the transfer comes from the bride’s family and dowries are paid. Brideprice-paying societies are relatively 16
whether the simple difference or the absolute difference between the two scales was used as the dependent variable.

Divergent responses might be particularly problematic for the accurate measurement of women’s agency across contexts if the relationship documented above - between income group classification and response discrepancy - were different for men and women. We calculate the absolute difference ǀ(A)-(B)ǀ in scale responses, henceforth referred to as the scale response discrepancy, and conduct a KolmogorovSmirnov test of the hypothesis that there is no difference in the distributions of men’s and women’s scale response discrepancy. This hypothesis can be rejected for upper-middle-income countries (p<0.00), cannot be rejected for lower-middle-income (p=0.22) and low-income (p=0.50) countries, and can only be rejected at the at the 10-percent significance level for high-income countries (p=0.09).

Conducting the same test by geographical region, using the World Development Indicators classification, we find that the hypothesis can be rejected at the 1-percent level in Europe & Central Asia (p<0.00) and at the 5-percent level in East Asia & Pacific (p=0.04), Middle East & North Africa (p=0.02), Latin America & Caribbean (p=0.02) and South Asia (p=0.02). In Sub-Saharan Africa (p=0.14) and in North America (p=0.93), the hypothesis cannot be rejected. The distribution of the scale response discrepancy, separately by income group classification and gender, is shown below in Figure 3. The first and third quartiles of the distribution are at the edges of the box, the median is indicated with a vertical line in the interior of the box (or left box edge in cases where the first quartile and median overlap), and the 10th and 90th percentiles are at the ends of the whiskers.

26
**_Table 4: Vehicle speed by road type and slope_**

||||**_Vehicle S_**<br>|**_peed, by Slope (%)_**<br>||
|---|---|---|---|---|---|
|**Class**|**Road Type **|**0‐10%: Plain**|**10‐25%: Rolling**|**25‐60%: Mountainous**|**>60%: Steep**|
|1|SRN|50 km/h|40 km/h|30 km/h|20 km/h|
|2|DRCN|40 km/h|30 km/h|20 km/h|15 km/h|
|3|SUR, UR,<br>unpaved DRCNs,<br>Far Western SRNs|20 km/h|15 km/h|10 km/h|7.5 km/h|
|4|VR, OSM paths,<br>unpaved DRCNs (Far<br>West),>2400m|4 km/h|2.6 km/h|1.7 km/h|0.85 km/h|

## Monsoon modifications

Heavy rains during the monsoon season drastically impact vehicle travel times on Nepal’s poorly built and maintained roads. Travel times can increase greatly depending on the surface material and maintenance quality of the road; some areas become completely inaccessible by vehicles. Therefore, we have calculated a separate monsoon season Cost Time layer by modifying the above travel times according to the road surface and condition. Walking paths are less affected by monsoon rains and are modified accordingly. Where surface conditions are not specified a uniform modifier of 50% has been applied to all vehicular roads and 75% to all paths.

**_Table 5: Monsoon speed modifications_**

|**Reported road**<br>|**_% Chan_**<br>**Asphalt & Surface**<br>|**_ge in Travel Time(_**<br>|**_km/h), Monsoon Sea_**<br>|**_son_**<br>|
|---|---|---|---|---|
|**condition**|**Treatment**|**Gravel Road**|**Earthen Road**|**Walking Path**|
|VeryGood|100%|90%|50%|90%|
|Good|100%|70%|40%|85%|
|Fair|75%|50%|30%|75%|
|Poor|50%|40%|20%|75%|
|VeryPoor|40%|30%|15%|75%|

Because Nepal’s poorly built roads erode quickly, maintaining accurate and up to date road condition data is a Sisyphean task. Road conditions change from year to year and the Department of Roads data collection apparatuses cannot keep up, presenting an unavoidable source of error for the monsoon model. Despite this, we elected to continue using the surface ratings provided by the Measuring Rural Access team because the road network data they collected is reasonably recent (2015) and by far the most complete data set available within Nepal. Moreover, IRI ratings may not capture the precise state of the road in a given moment, but they do suggest the priority given by local and regional governments to maintaining a particular road. Therefore, for modeling purposes they are sufficient to indicate generic conditions, with some acceptance of error.

# Calculation for off-road travel

Land cover type is a classifying factor for off‐road, off‐path walking speeds. Initial land cover data was taken from ICIMOD and additional water bodies from the OSM river network data were rasterized and merged into this data set to produce a final land cover raster. The average walking speed from the household survey (distance to the closest road / time to the closest road) was around 4.3 km per hour. We determined the walking speed over a flat surface for each landcover category with reference to similar studies such as Nelson (2000) and Black (2004) (Table 6).
<!-- Start of picture text --> _ Protein mat m Carbohyorate<br>H 160%<br>PyFa== 140% 144%<br>2E 120% 120% 219% 116%<br>g2 103% 101%<br>g 89% 91% 90%<br>Z<br>PaS% 80%7 em 73%<br>H ;<br>E<br>z2<br>E 40%<br>8<br>e 20%<br>2 0%<br>‘Actual Consumption 2000 ‘Actual Consumption 2016 F8DG-scaled consumption FAT-Lancet-scaled consumption<br><!-- End of picture text --> <!-- Start of picture text --> came Zn VILA BLVt.Bo Folate<br>BE 140%<br>Pa3=g 120% 130%<br>4a<br>€H 3%<br>€ 81% 180% 80%<br>& 80%<br>rs£i ; 649660% 62% 65% en 62%4<br>Z 60% som 5596<br>8 42% 41% 44%<br>zg5 40% 38%<br>é 33% 35%)<br>&<br>S3 20%.<br>2%<br>& ‘Actual Consumption 2000 Actual Consumption 2016 FBDG-scaled consumption EAT-Lancet-scaled consumption<br><!-- End of picture text -->
**Table 5. Discrete time hazard analysis of first‐migration for both men and women separately including a model for baseline social network measures; a model for gender‐specific male and female family and community networks; and a model with interactions with post‐1996 dummy,  Albania LSMS 2005 (control variables not shown)**

|||**Male**<br>|||**Female**<br>||
|---|---|---|---|---|---|---|
|**First Migration**|**OR**|**SE(b)**|**P>|z|**|**OR**|**SE(b)**|**P>|z|**|
||**Baseline So**<br>|**cial Networ**<br>|**k Effects**<br>||||
|**Family/Friends in 1990**|0.802|0.156|0.257|0.925|0.305|0.813|
|**Relatives in 1990**|1.139|0.119|0.211|1.072|0.172|0.665|
|**Family Network**|1.287|0.047|0.000|1.285|0.045|0.000|
|**Community Network**|1.408|0.471|0.306|2.710|1.192|0.023|
|**How M**<br>|**ale and Fema**<br>|**le Social Net**<br>|**work Effect**<br>|**s Vary**<br>|||
|**Family/Friends in 1990**|0.826|0.161|0.326|0.932|0.305|0.830|
|**Relatives in 1990**|1.143|0.119|0.201|1.074|0.173|0.656|
|**Family Male Network**|1.312|0.056|0.000|1.199|0.058|0.000|
|**Family Female Network**|1.216|0.091|0.009|1.449|0.093|0.000|
|**Community Male Network**|1.708|0.278|0.001|1.575|0.308|0.020|
|**Community Female Network**|0.203|0.112|0.004|1.336|0.641|0.546|
|**How**<br>|**Social Networ**<br>|**k Effects Ch**<br>|**ange Over**<br>|**Time**<br>|||
|**Family Male Network**|1.560|0.145|0.000|1.353|0.165|0.013|
|**Family Male Network x Epoch**|0.812|0.077|0.027|0.872|0.115|0.299|
|**Family Female Network**|1.582|0.276|0.009|2.199|0.335|0.000|
|**Family Female Network x Epoch**|0.751|0.134|0.108|0.628|0.104|0.005|
|**Community Male Network**|3.806|1.828|0.005|0.447|0.475|0.449|
|**Comm Male Network x Epoch**|0.413|0.208|0.078|3.634|3.912|0.231|
|**Community Female Network**|0.687|1.359|0.849|1517.122|4420.074|0.012|
|**Comm Female Network x Epoch**|0.319|0.643|0.571|0.001|0.003|0.016|
|**Number of Cases**||**27028**|||**36134**||

27
We have used _CorruptionObstacle_ as a summary measure of perception of corruption. Our confidence on the results would be strengthened if this measure is supported by more detailed measures related to corruption or government expropriation. Indeed, if auditing leads to more exposure to government expropriation, we are likely to see more specific harassments in various areas, such as applying for licenses and permits and dealing with tax collectors. The harassment in these areas, fortunately, is covered in WBES. We thus construct two alternative firm-level corruption obstacle indicators: the firm perceives moderate-or-above obstacles in the areas of licenses and permits ( _LicenseDummy_ ) and tax collections ( _TaxRateDummy_ ). We also construct the third measure, _CorruptionIndex_ , equal to the sum of _LicenseDummy_ and _TaxRateDummy._ The OLS results in Table 8 show that _Audit_ is significantly related to higher levels of licenserelated obstacles and to _CorruptionIndex_ . The 2SLS results show that after dealing with endogeneity, _Audit_ significantly increases the level of license and tax-related obstacles. _Audit_ is also significantly positively related to _CorruptionIndex_ . These results lend further support for our hypothesis that information disclosure leads to higher level of corruption obstacles.

# **V. Audit and Firm Growth**

# **A. OLS Regressions**

We have shown that financial disclosure by firms through auditing entails both benefits (i.e., better access to finance) and costs (i.e., exposure to “grabbing hands”). What is the net effect of auditing? We shed light on this question by examining the effect of auditing on firm development, measured by sales growth, which can be interpreted as (roughly) the net effect of auditing. To this end, we estimate the following equation:

> _SalesGrowthi,j = α + β1 Auditi,j + β2 FirmSizei,j + β3 FirmAgei,j + β4 Experiencei,j + β5 Governmenti,j + β6 Foreigni,j_

> _+ β7 Exporter i,j   + β8 Competei,j + β9 CorruptionControli,j+ β10 Privi,j + θ' Macro Controlsj + εi,j_ (5)

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The random forests method we use works as a combination of individual classification trees. Each of these trees focuses only on a subset of the explanatory variables to develop its nodes. That way, the classification trees are not correlated and offer different information on the underlying data generation process (Breiman 2001a; James and others 2013). Implementation of the random forests method is based on the package provided by Liaw and Wiener (2002) for the R program. As before, to compute prediction accuracy we split the sample between a training set and a validation set and repeat the exercise 10,000 times to estimate the mean and the confidence interval of the relevant metrics.

The benchmark specification under the logit regression framework performs as well as the random forests method, and the results from the two methods are highly correlated. The share of out‐of‐sample units correctly predicted when using the random forests method is 85.2 percent, or only 1 percentage point higher than when using the benchmark specification. This difference is less than the estimated standard error, which is around 1.4 percent under both the benchmark probabilistic model and the random forests method.

# **5. The rate and speed of urbanization**

The probabilistic model built from the sample of 1,277 units using subjective assessments can be used to predict the urban status of all 564,052 units in the universe of India in 2011. The key indicators for each unit are plugged into the model to compute the unit’s likelihood of being urban. If this likelihood is above 0.5, the unit is deemed urban; otherwise, it is considered rural. Combining indicators from the ground and from outer space, this procedure yields an urbanization rate that is almost identical to that in the 2011 Census of India. However, this similarity hides important discrepancies across administrative types, especially in the midrange between villages and municipal corporations. The exercise also shows that relying exclusively on one type of indicators, such as nighttime light, can yield potentially misleading results for India’s rate and speed of urbanization.

## _The urbanization rate in 2011_

According to the 2011 Census of India, the share of the population living in urban areas was 31.2 percent. But this figure is computed for a set of units broader than our universe because we excluded small states and union territories as well as villages for which information was missing. If only the 564,052 units considered in our analyses were retained, the 2011 Census of India would yield an urbanization rate of 31.6 percent. This is almost identical to the 31.4 percent rate predicted by the benchmark specification of the probabilistic model (figure 7).

Predictions using other specifications vary, but they also fall within a close range of the official estimate by the 2011 Census of India. The urbanization rate varies between 27.0 percent using built‐up share as the only key indicator and 33.9 percent using only population size. The predicted urbanization rate is similar31.8 percent when using the random forests method.

22
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Policy Research Working Paper 9610

# **Abstract**

Several studies in the literature have adopted attitude or perception-based survey questions to evaluate the business environment and its effect on firms. The Enterprise Surveys of the World Bank are not an exception. In the case of the Enterprise Surveys, these questions involve rating an element of the business environment at the end of each section of the survey instrument. Such questions are often used but sometimes are inconsistent with responses elicited on the experience of the firm over a specific timeframe—experience-based questions. The literature is mixed as to whether perception-based questions are susceptible to anchoring or context effects. In this study, an experiment is set up to explore whether perceptions of the business environment are stable or vulnerable to the ordering of questions in the Enterprise Surveys questionnaire. The experiment entails randomizing the placement order of the perception-based questions at the end of a section or at the beginning of the survey. Significant question-order effects are uncovered only for perceptions of corruption and business licensing and permits but not the other elements, after accounting for a variety of factors. The study recommends that analysis in these two areas should go beyond perception-based questions and verify their findings with experience-based questions.

This paper is a product of the Global Indicators Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at aislam@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
TSC implementation was coordinated within Blocks, which are one administrative level above individual communities. Block TSC offices coordinated latrine mason training, CLTS and social mobilization activities, and community outreach efforts. Block TSC offices also maintained official records of funds released to communities by the district, expenditures under the TSC, and toilet construction progress.

WSP’s Scaling Up Rural Sanitation program in India supported the TSC program in two states: Himachal Pradesh and Madhya Pradesh. WSP supported local authorities in the creation of an enabling environment to facilitate TSC activities, the development of local capacities at the district level and the use of monitoring information to assess progress towards TSC goals.

In Madhya Pradesh, the department of rural development was responsible for implementing the TSC. A concurrent program in Madhya Pradesh named _Nirmal Vatika_ (“Clean House” in Hindi) provided additional support in the form of household subsidies and toilet construction materials during the evaluation period. TSC and _Nirmal Vatika_ together provided at least Rs 4200 (US $80) to Below Poverty Line (BPL) households and at least Rs 2000 (US $40) to nonBPL households to support toilet construction. These costs were determined by the government to be adequate to construct a single toilet with proper sub-structure including a slab and water seal, and a superstructure for the toilet. BPL households were expected to complete the superstructure or toilet room (4 walls, a roof and a door) using locally available materials. In addition, community level ODF awards (named _Nirmal Gram Puruskar_ ) are provided for GPs ranging from Rs 50,000 (US $1,000) for GPs with populations ≤1000 to Rs 500,000 (US $10,000) for GPs with populations of 10,000 or more. Awards for achieving ODF status were also given at the Block level (maximum award of Rs 2,000,000 or US $40,000) and at the District level (maximum award of Rs 5,000,000 or US $100,000). In addition to usual government efforts, WSP supported training for triggering activities in the 20 intervention GPs in each district to help build the capacity of local government staff and program volunteers.

# **3.  Evaluation Design and Methodology**

The evaluation used a cluster-randomized, controlled trial to evaluate the impacts of TSC in the Dhar and Khargone districts of MP. The study randomized communities within each district into two groups: an intervention group who received the TSC program and a control group who did not receive TSC activities until after the evaluation. The evaluation conducted household surveys to measure household and child characteristics both before and after TSC implementation.

## **3.1  Sample Size**

The study was originally designed to have 80% power to detect a reduction in diarrhea prevalence among children < 5 years old of 4.5 percentage points assuming 15% prevalence in the control group (or a 30% relative reduction) with a two-sided alpha of 5%. This led to a 6 / 41
figures could be interpreted as better reflecting underlying trends. This should be thought of as a maximal adjustment, given that it neglects the offsetting impact of simultaneous money base increases which could have contributed to the currency movements. The adjusted average figure is lower in the 1990s, and little different from the unadjusted for 2001 to 2004. The adjusted figures suggest a faster underlying rise in dollarization up to 2001, and a subsequent fall back that was less severe. Instead of the 2003 figure being lower than that of 1998, the adjusted data suggest a fall back only to somewhere between the levels of 1999 and 2000.

However, the adjustment does not put into question the evidence that there was a break in the growing trend of dollarization around 2001. Although a mechanical exchange rate cause is ruled out by the calculations here presented, the changing fortunes of the dollar could have played a role here, indirectly through an effect on desired dollarization.

<!-- Start of picture text --> 0.4<br>0.3<br>Mean<br>0.2<br>Median<br>0.1<br>EUR/USD<br>0<br>$ization-weighted<br>-0.1<br>mean<br>-0.2<br>-0.3<br>1990 1993 1996 1999 2002 2005<br>log change in exchange rate<br><!-- End of picture text --> Figure 7. _Annual dollar appreciation against currencies in the sample_ There are other possible causes for a break in the upward trend of dollarization around 2001. We already mentioned the direct effect of the forced conversion (at an unfavorable rate) of dollar deposits in Argentina.<sup>11</sup> This certainly lowered the Argentine percentage but might also have had a demonstration effect on behavior elsewhere: dollar deposits not as attractive now. Did that result in a shift to offshore deposits in lieu?

> 11 Note that measured, on-shore, dollarization is not a comprehensive picture of portfolio preferences. Outlawed in many countries, dollar deposits can show up much less than would be predicted for a country where they are freely permitted.  One only has to look at the sharp drop in Argentine dollar-denominated deposits in 2002. It is not that Argentina’s monetary authorities had suddenly produced a much more attractive product that displaced dollars in the preferred liquid portfolio of Argentine residents.  Far from it; they had just outlawed most holdings of dollar deposits in Argentine accounts.

16