rafmacalaba/datause-encoder-data huggingface dataset:
is highly underreported. Incidences of violence are estimated to be higher in urban households compared to rural ones, but higher among the poor in both urban and rural areas.

In terms of underreporting, females tended to underreport relative to males. Underreporting tends to be higher among urban households. Underreporting also tends to be highest for the professionally educated males and females, and thus shows a tendency to rise with education. In terms of religion, Hindus record higher rates of underreporting compared to other religious groups in the study. Among all the religious groups, women tend to underreport more than men. Furthermore, Hindu women tend to underreport more than Muslim and Christian women. There are no clear patterns of underreporting or over-reporting of physical harassment in public/private buses.

Overall, list randomization estimates higher rates of domestic violence as compared to direct questioning methods and provides a strong benchmark for measuring the incidence of domestic violence as well as physical harassment on public and private buses. However, it should be noted that Kerala has relatively lower rates of these types of violence compared to most states, particularly states located in north and central India. It is possible, or even likely, that the rates are heavily underreported for these states as well. Future studies therefore should further explore the misreporting of gender-based violence in the states of central and northern India.

The levels of incidence of domestic violence and physical harassment in public/private buses that are presented in this study can be viewed as a lower bound of these rates. This is because the incidence that is measured through this study is at the household level and not at the individual level. In cases where there are more than one woman facing domestic violence or physical harassment in public/private buses in a household, the rate of incidence at the individual level can be higher.

Apart from this difference in measurement at the household level, the prevalence of domestic violence as calculated by this study is about the same as that calculated by the India National Family Health Survey (NFHS) of 2005-06, where the incidence is measured at the individual level. However, 18
**Table 13: Household Characteristics by Enterprise Ownership**

||**Household**<br>|**owning**<br>|**Household**<br>|**s without**<br>|
|---|---|---|---|---|
||**enterp**|**ise**|**enterp**|**rise**|
||**Mean**|**SD**|**Mean**|**Sd**|
|**_Characteristics of the household head_**|||||
|Age of the Head|42.41|13.32|44.07|16.02|
|Gender (1=male)|0.59||0.80||
|Years of schooling|1.56|2.64|1.28|2.78|
|Married|||||
||0.77||0.59||
|Divorced or separated|0.07||0.25||
|Widowed|||||
||0.12||0.13||
|**_Household Composition_**|||||
|Household size|4.28|2.24|4.58|2.11|
|Adult men|1.20|1.03|1.42|1.04|
|Adult women|1.50|0.86|1.40|0.82|
|Children (<5 years)|0.56|0.71|0.71|0.76|
|Elderly (>65 years)|0.11|0.33|0.15|0.38|
|**_Distances_**|||||
|Distance to all weather road|2.09|1.27|2.417|1.09|
|Distance to nearest market|1.52|0.98|2.12|0.74|
|Distance to nearest financial centre|2.57|1.30|2.82|0.89|
|**_Access to Credit_**|||||
|Financial centre in the village|0.09||0.13||
|Collateral not required|0.34||0.31||
|Land can be used as collateral|0.13||0.13||
|Co-signer not required|0.19||0.20||
|**_Urbanity_**|||||
|Remote location?|0.45||0.75||
|Semi-remote location?|0.24||0.11||
|Semi-urban location?|0.09||0.10||
|Location in rural town?|0.22||0.04||
|**_Plough Ownership_**|||||
|Owns plow|.41|.49|.78|.41|
|**_Expenditure_**|||||
|Log annual expenditure per adult|7.31|0.59|7.28|0.59|
|Observations|||||
||625||2,284||

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<!-- Start of picture text --> NA NA<br>jt jt<br>NA NA Jt,t-1<br>jt-1 jt-1<br><!-- End of picture text -->

> ota — jt,t-1 = ~~=~~ <u>5</u> ((LABSHj,jt + LABSHjjt-) ~~t-1)(——-GP Hej~~ + [1 — ~~+~~ <u>2</u> (LABSHjejt + LABSHje-1)|Jt-1 ~~(5)~~ <u>RKMA,</u>

NA NA NA NA NA LABSH: The share of labor income of employees and self — employed workers in GDP
_Table 4: Effects of changes in BMI-based mortality risk on changes in income_

||(1)<br>Change in total<br>income|(2)<br>Change in<br>unearned income|(3)<br>Change in<br>earned income|
|---|---|---|---|
|Change in average BMI-based mortality risk among|-0.599|0.761|-1.026|
|household members aged 18+ in 1992/93|(1.91)|(1.14)|(2.61)|

Note: t-statistics in parentheses. Regressions include all covariates listed in Table 2 and commune fixed effects, which were jointly significant.

Table 5 reports the estimates of eqn (2) which adds health insurance, the poverty dummy and their interactions with BMI changes. The interactions are insignificant—there appears to be no significant differences between the poor and non-poor or between the insured and uninsured in the effects of BMI changes on changes in either unearned and earned income.

_Table 5: Differential effects of BMI changes on income by insurance status and income group_

||(1)<br>Change in<br>total income|(2)<br>Change in<br>unearned income|(3)<br>Change in<br>earned income|
|---|---|---|---|
|Health insurance|0.223|0.324|0.147|
||(6.95)|(4.05)|(3.87)|
|Change in average BMI among household|0.653|-1.066|0.803|
|members aged 18+ in 1992/93|(2.41)|(1.51)|(2.45)|
|BMI x Health insurance|0.201|0.276|0.577|
||(0.48)|(0.27)|(1.16)|
|Poor 1992/93|0.851|0.499|0.832|
||(30.16)|(6.96)|(24.82)|
|BMI x Poor 1992/93|0.009|0.740|0.420|
||(0.02)|(0.77)|(0.90)|
|Intercept|-0.155|-0.073|-0.150|
||(0.99)|(0.18)|(0.79)|

Note: t-statistics in parentheses. Regressions include all covariates listed in Table 2 and commune fixed effects, which were jointly significant.

# <u>BMI changes and changes in medical spending</u>

Beyond their impact on income, adverse health shocks are likely to necessitate increases in medical spending. Column 1 of Table 6 shows that adverse health events are associated with increases in medical spending. The size of the coefficient is similar in absolute terms to the size of the income coefficient. This contrasts with the results for Indonesia presented in Gertler and Gruber, where the income effect was much larger in absolute size than the medical spending effect. This may well reflect the different settings. In Vietnam, 80% of health spending is paid out of pocket, compared to 47% in Indonesia (World_Health_Organization 2000; World_Bank, SIDA et al. 2001). Furthermore, in the present sample in 1997, medical outlays accounted for 6% of non-medical consumption, which is substantially larger than the 1% figure reported by Gertler and Gruber.

8
India, Nepal, Pakistan, Romania, Sri Lanka, Tunisia and Turkey return higher than predicted routine manual skill intensity values.

# _Timeline Analysis_

The cross-section analysis above presents the current structure of the skill content in the selected countries at one point in time but it does not allow for tracking dynamic developments. To assess such evolution, we conduct a time comparison of skill intensities for all those countries for which we were able to access appropriate household surveys at different times.<sup>14</sup> We derive skill score time trends for Brazil, Chile, Costa Rica, India, Poland, Sri Lanka, Turkey, and USA. The metric employed for the comparisons are the changes in the relative position of the median score in each of the five skill category between the base and the final year.<sup>15</sup> The numeric results for these eight countries are presented in Annex Table 3.

Figure 3: Time trends in skills utilization <!-- Start of picture text --> 51.6 Panel (a): Non Routine Cognitive Analytical<br>Costa Rica<br>51.2<br>Chile<br>USA<br>Poland<br>50.8<br>Brazil<br>Sri Lanka<br>50.4<br>Turkey<br>India<br>50.0<br>49.6<br>Base Final<br><!-- End of picture text -->

> 14 Many countries changed the way they classify occupations which limits the number of countries in our timeline analysis.

> 15 The methodology identifies the skill content of the median individual in the final years, and identifies in the base year the individual with that skill distribution. Note that there is a median individual per each skill.

**16**
Figure 18: Prospects of upward mobility, by urban/rural areas and capital city vs. rest of the country <!-- Start of picture text --> (a) Urban/rural (b) Capital city/rest of the country<br><!-- End of picture text --> _Source_ : Tiwari et al (2017) Note: The bars represent estimated means and the “whiskers” are the 95 percent confidence intervals.

Next, we turn to how the prospects for upward mobility vary by characteristics across the three countries. In all three countries, available poverty analyses suggest substantial spatial disparities in welfare outcomes and particularly large gaps in poverty rates between urban and rural areas within the country. For upward mobility, however, the pattern across space is not as clear cut. For Armenia, the point estimates suggest a slightly higher mobility for urban areas and the capital city, particularly with respect to the rest of the country. However, given the substantial overlaps between the 95 percent confidence intervals for the two estimates, it is difficult to argue that the difference is statistically distinguishable from zero. In contrast, for Azerbaijan, urban areas and the capital region clearly have witnessed higher mobility than rural and other regions of the country. Georgia, on the other hand, appears to have had exactly the opposite pattern: rural areas and regions other than the capital city have witnessed higher upward mobility, suggesting some degree of catch-up in educational attainments in regions that are likely to have had lower attainments to start with.

36
for constructed indices capturing consumption, economic self-sufficiency/maintaining employment, and optimism and perception of socioeconomic status although measures of stress and depression increased as well.

There is also evidence on the impact of microcredit using natural experiments on the expansion of credit. Using the natural experiment on bank expansion in India, research has documented a 1.52 percent decrease in rural poverty for every 1 percentage point increase in the share of credit disbursed (Burgess and Pande 2005), although these findings have been contested (Panagariya 2006 and Kochar 2011). Another study uses Thailand’s Million Baht Village Fund Program to assess the impact of microcredit (Kaboski and Townsend, 2011 and 2012). As part of the program, every village in Thailand was given 1 million Baht (about $24,000) to start a village bank that made loans available to villagers. Exploiting the fact that all villages receiving the same amount of money regardless of population size led to large variation in how much credit availability increased across villages, the study found that income and consumption increased and assets decreased initially, but then return to trend within a couple of years. Given the initial increase in income and consumption, the study surprisingly found no effect on new business creation.

# **3.4** **<u>Insurance</u>**

Insurance products can be a critical tool in managing financial risks related to large, unexpected expenses such as those stemming from sudden illness, crop failures, natural disasters, or income loss due to the death of a wage earner (for an overview see Karlan and Morduch, 2010).<sup>8</sup> While many people use savings and credit to manage financial risks and may informally share risks within their family or community, formal insurance offers additional benefits. Formal insurance products can pool risk over a much larger population, which affords households broader coverage than they would have if they relied on their own savings, credit, or community. This is especially true for adults in low-income households with limited assets. Moreover, formal insurance products can provide protection from common risks faced by individuals in the same

> 8 Unfortunately, the 2014 Global Findex database does not collect data on the use of insurance and, to the best of our knowledge, no similar data exists for the use of insurance.

17
Gibbons, R. (1992), _A primer on game theory_ . New York: Harvester Wheatsheaf.

Khemani, S. (2010), Political economy of infrastructure spending in India. World Bank Policy Research Paper WPS 5423.

Kreps, D. and Wilson, R. (1982), Reputation and imperfect information. _Journal of Economic Theory_ , 27, 253-279.

Milgrom, P. and Roberts, J. (1982), Predation, reputation and entry deterrence. _Journal of Economic Theory_ , 27, 280-312.

Nauges, C. and Strand, J. (2007), Estimation of non-tap water demand in Central American cities. _Resource and Energy Economics_ , 29, 2007, 165-182.

Nauges, C. and Strand, J. and Walker, I. (2009), The value of water connections in Central American cities: A revealed preference study. _Environment and Development Economics_ , 14, pp 349-370.

Nelson, R. R. (1956), A theory of low-level equilibrium trap in underdeveloped economies. _American Economic Review_ , 46, 894-908.

Singh, B., Ramasbubban, R., Bhatia, R., Briscoe, J., Griffin, C. C. and Kim, C. (1993), Rural water supply in Kerala, India: How to emerge from a low-level equilibrium trap. _Water Resources Research_ , 29, 1931-1942.

Spence, A. M. (1974), _Market signaling. Information transfer in hiring and related screening processes_ . Cambridge, MA: Harvard University Press.

Strand, J. (2000), A political economy analysis of water pricing in Honduras’s capital, Tegucigalpa. In Dinar (2000), pp. 237-258.

Strand, J. and Walker, I. (2005), Water markets and demand in Central American cities. _Environment and Development Economics_ , 10 no 3, 313-335.

Walker, I. et.al. (1999), Reform efforts in the Honduran water sector. In W. Savedoff and P. Spiller (eds.): _Spilled Water_ . IADB – Latin American Research Network, pp. 35-88.

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The dependent variable ( _ethical_orientation_ ) is a binary variable that we define in different ways to capture each ESG ethical dimension ( _mon_emi, training_ , and _top_man_fem_ , respectively). Specifically, we capture the environmental dimension with a binary variable that takes the value of one if the firm has monitored its CO2 emissions over the past three years ( _“mon_emi”_ ), and zero otherwise.<sup>8</sup> The social dimension is captured by a binary variable that takes the value of one if the firm offered formal training programs for permanent, full-time employees (“ _training_ ”) in the last fiscal year, and zero otherwise. The governance dimension is captured by a binary variable that takes the value of one if the firm has female top managers (“ _top_man_fem_ ”), and zero otherwise. The main variable of interest is the interaction between digital orientation and technological orientation (digital orientationict ∗ tech orientationict).

_X_ is a vector of control variables including firm size (“ _large_ ”), the presence of a line of credit or loan from a financial institution (“ _fin_ins_ ”), the real annual sales growth in percent (“ _sal_gro_ ”), the age of a firm (“ _age_ ”) and the logarithm of GDP per capita (“ _log (GDPpercapita)_ ”). We use median sampling weights in all estimates to allow inference to the entire population.<sup>9</sup> The model is saturated by adding countryand year-fixed effects to capture country- and time-invariant unobservable factors. Industry fixed effects alone are not included because the variable capturing technology intensity is computed at the industry level. However, in some

> 8 CO2 emissions monitoring data is only available for the years 2020-2023 for 49 countries.

> 9 For an in-depth explanation, see the ES sampling note available at the following link: <u>https://www.enterprisesurveys.org/en/methodology</u> (last accessed: July 2024). In line with the note recommendation, we use median weights in all estimates, that is weights that account for eligibility of inclusion in the survey.

11
**Table 11**

**Indicators of Debt Burdens and Debt Payment Difficulties**

|**Families ranked**<br>**by income**|**Percentage with**<br>**ratio of debt payments**<br>**to family income above**<br>**40 percent**|**Percentage with a**<br>**debt payment late**<br>**60 days or more in**<br>**previous year**|
|---|---|---|
|Lowest 20%|27.0|13.4|
|20-39.9%|16.0|11.7|
|40-59.9%|11.7|7.9|
|60-79.9%|5.6|4.0|
|80-89.9%|3.5|2.6|
|90-100%|2.0|1.3|

Source: 2001 Survey of Consumer Finances (Aizcorbe et al, 2003) The strong link between household income and indicators of credit risk should not be surprising. After all, since many lower-income households have no financial savings, any economic disruption, such as a health crisis in the family, a job termination, etc, can force a family to put off non-essential expenditures, including debt service obligations. In addition, there is a strong correlation between household education and income. If education is linked to money management skills or to an awareness of the benefits of a good credit record, this may also partly explain the correlation between household income and indicators of credit risk.

An impaired credit record does not necessarily shut one off from credit. Mainstream lenders will extend loans to people with somewhat impaired credit histories or with fairly heavy debt burdens, but they commonly increase the interest rate on the loans to reflect the increased risk. If an individual has a more seriously impaired credit history or heavier debt burden than mainstream lenders will accept, the individual can often borrow from “subprime” lenders, many of which are subsidiaries of mainstream prime lenders. Subprime lending is generally a separate business from prime lending because the business practices of the subprime lenders can differ substantially from those of the prime. Subprime lending frequently involves much more person-to-person contact and much faster responses when a borrower falls behind on a scheduled payment. Individuals with seriously impaired credit histories or very heavy debt burdens can even lose access to the subprime market. At this point, if they need credit, they can turn to pawnshops, payday lenders, or rent-toown operations that do not pull traditional credit reports.<sup>9</sup> A pawnshop will lend to anyone since the loan is based only on the value of the collateral that the customer leaves in the possession of the pawnbroker. A payday lender will lend to almost anyone who has a checking account in good standing, recent steady employment, and who has not failed to honor a previous payday loan. Rent-to-own stores will also implicitly provide credit for the purchase of furniture and other consumer goods because the stores retain legal ownership of the goods, making repossession in the case of missed payments much simpler.

> 9 One might call such institutions, “sub-subprime” lenders.

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18 (15%).2 Most quotas set in he ITAs with the Visegrid counies are the same in of their past export performance.? For iustance, the Polish quota for polyetylene is the s-me as that for the FCSK and Hungary, while its exports to the EC in 1991-were a small fraction of other CEE-3 exporls (see Table 6). TIis lack of a link between tarff quotas and past export performance is especially manifest in the cas of cars. The tariffquota of US$ 91 million set for motor vehicle imports (CN.87032110 through 8703909) from the FCSK (ECU 80.483 million as compared with ECU 125 million for ie other two ccmmtries) is about one third lower than that for -Hungary, even though in 1990 FCSK inports it the EC were much larger than Hungrian ones (US$ 84 millon as compared with US$ 2 million)?' It is wort nodng hat actual imports from dte FCSK were significanly larger than quotas for all products listed in Table 6 (especially for wire of iron or non-alloy steel whose. share of quota was only 6 percen). However, this did not prevent FCSK producew from increasing exrs of these products in 1992-the only excption hi the sample was polyethylene whose inports i the EC declid during te first year of the [TA.

WiftIo a detailed analysis of export capacity for each tariff quota/celing group, which goes beyond the sope of this paper, no firm judgment n be passed on the extent of EC concessions. Somen isgb can be derived from the sample of products presented in Table 6, however. This sample, accounting for 30-35 percent of the w alue of quotas and for 18 t0 29 percent of troik exports of these produts to the BC in 1992, is quite large and, terefore, vepfor this group? The duty-free compone vares 24 This **should** not suggeast that **te** rrAs do not contain siifict ocessions. in fact, market access for thes products will move to duty-fee trade over a five-year period.

_2s_ Examples of _different tariff_ quotas or **ceilings** **_interaa_** indclude: the FCSK has lower **quotas** for acids **and salts** (CN. 2981400)-ECU 210,000 as compared with ECU 368,000 for Hunar and Poland), for **leather** clothing and apparel accessories (CN.4203100, 42032100, 42032991, 42032999, 42033000,42034000-ECU 4.3 million as compared with ECU 6.6 miion). Tbe FCSK and Hungay have smaller quota for fibrbords (CN.4411-ECU 4 million vsus ECU 7 million for Poland). The quotas for Bulgaria and Romania are an avere around 5 percent higher than for the CEB-3.

_2_ On **the odter** band, it could have been purely acdental that the Polish quota _(the_ same _as_ the Hungarian) was close to the value of its imports into the EC in 1988 (US$ 145 miion) and 1989 (USS 128 million).

Latecomers-Bulgaria **and** Ronmania-=a not included, simply because **dir** respcive Agrmnt **wit** the **Co is** went into force a year **lat.**
Athey, S., & Wager, S. (2021). Policy learning with observational data. _Econometrica_ , _89_ (1), 133– 161. https://doi.org/https://doi.org/10.3982/ECTA15732 Becker, G. S. (1968). Crime and punishment: An economic approach. _Journal of Political Economy_ , _76_ (2), 169–217. Retrieved January 22, 2024, from http: //www. jstor. org /stable/ 1830482 Belloni, A., Chernozhukov, V., Fernández-Val, I., & Hansen, C. (2017). Program evaluation and causal inference with high-dimensional data. _Econometrica_ , _85_ (1), 233–298. https://doi. org/https://doi.org/10.3982/ECTA12723 Belloni, A., Chernozhukov, V., & Hansen, C. (2013). Inference on Treatment Effects after Selection among High-Dimensional Controls†. _The Review of Economic Studies_ , _81_ (2), 608–650. https://doi.org/10.1093/restud/rdt044 Besley, T., Jensen, A., & Persson, T. (2023). Norms, enforcement, and tax evasion. _Review of Economics and Statistics_ , _105_ (4), 998–1007.

Bhargava, S., & Manoli, D. (2015). Psychological frictions and the incomplete take-up of so- cial benefits: Evidence from an irs field experiment. _American Economic Review_ , _105_ (11), 3489–3529.

Bishop, C. M. (2006). _Pattern Recognition and Machine Learning_ (M. Jordan, J. Kleinberg, & B. Schölkopf, Eds.; Vol. 4). Springer. https://doi.org/10.1117/1.2819119 Boning, W. C., Hendren, N., Sprung-Keyser, B., & Stuart, E. (2023). _A welfare analysis of tax audits across the income distribution_ .

Caron, A., Baio, G., & Manolopoulou, I. (2022). Estimating Individual Treatment Effects using Non-Parametric Regression Models: a Review. _Journal of the Royal Statistical Society Series A: Statistics in Society_ , _185_ (3), 1115–1149. https://doi.org/10.1111/rssa.12824

- CBO. (2020). Trends in the internal revenue service’s funding and enforcement. https://doi.org/ https://www.cbo.gov/publication/56422

- Chernozhukov, V., Chetverikov, D., Demirer, M., Duflo, E., Hansen, C., Newey, W., & Robins, J. (2018). Double/debiased machine learning for treatment and structural parameters. _The Econometrics Journal_ , _21_ (1), C1–C68. https://doi.org/10.1111/ectj.12097

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