rafmacalaba/datause-encoder-data huggingface dataset:
education, which may lead to biased estimates of causal relationships between violence and other factors. When such surveys are available, most are cross-sectional and are a limited tool to assess longitudinal analyses of economic and behavioral change.

Researchers may use administrative data to deal with some of these concerns. Police records have been used occasionally (for example, Mele 2009), but few victims report to the police. In Italy, only 7 percent of cases of IPV were reported to the police (UNSD 2014). Thus, health data, such as hospitalizations and homicides, can be preferred proxies for IPV. After all, globally, almost half of the homicides against women in 2012 (47 percent) were carried out by intimate partners or family members, in contrast to less than 6 percent of homicides against men (UNODC 2011), and it is nearly impossible to underreport a death. Aizer (2010) uses hospitalizations by assault in California, and, in the context of Brazil, Perova and Reynolds (2017) use data on female homicides as a proxy for IPV. Limitations remain, however, in that only the severe physical IPV is measured because the administrative data do not provide information on emotional and economic IPV or instances of IPV that do not require medical treatment.

**Background: Empirical Evidence**

There is an extensive literature on the relationship between women’s income and IPV. Depending on the theoretical framework adopted, the relationship is hypothesized to be either negative or positive. The Nash bargaining framework suggests that higher income is likely to trigger a reduction in IPV (Manser and Brown 1980). A reduction in stress because of a relaxation of the budget constraint regardless of who generates more income may also reduce violence (Buller et al. 2018). Models of asymmetric information (Bloch and Rao 2002) or models that explicitly assign value to traditional gender identity (for instance, Akerlof and Kranton 2000) suggest that an increase in women’s income may be associated with a higher risk of violence. Similarly, the psychological literature suggests the possibility of the use of violence as a tool to assert male dominance in backlash theories (Buller et al. 2018; Macmillan and Gartner 1999).

The conclusions in the empirical literature vary. A number of studies examine specific programs rather than natural labor market events. Buller et al. (2018) provide a thorough review of the literature on cash transfers and IPV in lower- and middle-income countries. They find that, in most settings, cash transfers are associated with reductions in IPV, suggesting that a Nash bargaining framework or a decline in violence because of reduced stress from economic uncertainly in the household may provide more accurate models of intrahousehold dynamics in most settings relative to theories of violence as an 4
(or better), a tile or cement floor, electric lighting, piped water, a flush toilet and whether it owns a radio, owns a phone and owns an iron.

Our asset index combines housing and other assets with amenities. Yet, amenities (such as contentment with local public services and area security) often explain more of people’s intention to migrate than satisfaction with their personal assets and living standards (Dustmann and Okatenko, 2014). To help distinguish between the effect of these two types of wealth (private/public) on attracting migrants, we also create a separate asset index based on variables that relate purely to housing and other asset wealth (number of bedrooms per person, type of roof, wall, or floor and ownership of radio, iron and phone), as well as an amenities index based purely on the subset of variables relating to amenities (availability of electric lighting, piped water and flush toilet).

Housing, utilities and assets outcomes improve as one moves from villages over towns to cities, in line with what Gollin et al. (2021) document for much of the developing world (Table 3). This is reflected in the constructed wealth index which is much higher in towns than in rural areas and higher still in cities compared to towns (see also Figure 2). We also see that, with respect to the constructed wealth index, the 10th percentile in cities already outperforms the 90th percentile in rural areas. More broadly, the town distribution of wealth mostly dominates this of rural areas, while the city wealth distribution dominates both (Figure 3).

Furthermore, urban households are smaller, younger and their members have more years of formal education. City households are also slightly more educated than town households (0.6 more years of formal education on average – maximum years of education in the household is 8.64 years on average in the city versus 8.01 in towns). The census asks whether the household experienced any death among its members in the 12 months preceding the interview, followed by a question on the sex and age of the deceased. Somewhat fewer urban households were bereaved and in cities the age of the deceased is higher, although the size effects are not large. Very few rural households have a non-seasonally employed member, but respectively 24% and 38% of town and city households do. Finally, while only 9% of households have a member working outside of the agricultural sector in rural areas, 54% and 74% do so in towns and cities.

8
# **Table 3: Empowerment regressions with government-defined caste categories**

|**Panel (a): Bihar**|(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
|---|---|---|---|---|---|---|---|---|---|
||||Inp|ut|||Mobi<br>Health|lity||
||Employed|Durables|Tuition|Livelihoods|Politics|Store|Centre|Friend|Bank|
|ST|-0.0342|-0.0491|-0.0434|-0.0317|-0.105*|0.0252|-0.0753|0.00463|0.0460|
||(-0.88)|(-1.37)|(-1.08)|(-0.80)|(-2.37)|(0.55)|(-1.92)|(0.24)|(0.98)|
|OBC|-0.0917***|0.00301|0.0226*|-0.0942***|-0.00350|-0.117***|-0.0239**|0.00192|-0.00377|
||(-8.32)|(0.35)|(2.10)|(-7.09)|(-0.28)|(-8.44)|(-2.78)|(0.42)|(-0.28)|
|EBC|-0.0808***|-0.00748|0.0167|-0.0916***|0.0148|-0.0409*|-0.00227|-0.00809|0.0210|
||(-4.67)|(-0.58)|(1.05)|(-4.43)|(0.77)|(-2.09)|(-0.20)|(-0.97)|(0.97)|
|Muslim|-0.191***|-0.000158|-0.00925|-0.134***|-0.0332|-0.118***|-0.000230|-0.0129|-0.0220|
||(-8.72)|(-0.01)|(-0.44)|(-4.97)|(-1.38)|(-4.73)|(-0.02)|(-1.10)|(-0.91)|
|General|-0.271***|-0.0113|-0.00355|-0.185***|-0.0192|-0.271***|0.00219|-0.0210|-0.0501|
||(-12.01)|(-0.80)|(-0.18)|(-6.59)|(-0.86)|(-9.52)|(0.16)|(-1.82)|(-1.85)|
|Some Schooling|-0.0936***|0.0198*|0.0364***|-0.0035|0.0256*|-0.0598***|0.0052|0.0084|0.115***|
||(-8.62)|(2.38)|(3.49)|(-0.27)|(2.13)|(-4.49)|(0.67)|(1.79)|(8.85)|
|Female headed household|0.0507***|0.0193*|0.0338**|0.0596***|0.0272*|0.0462***|-0.0145|-0.0084|0.0158|
||(4.98)|(2.34)|(3.03)|(5.13)|(2.21)|(3.85)|(-1.77)|(-1.51)|(1.26)|
|Per capita expenditure|-0.156**|-0.0684|0.0299|-0.106|0.122*|-0.0328|-0.0151|0.0335|0.225***|
||(-3.00)|(-1.77)|(0.54)|(-1.70)|(2.00)|(-0.53)|(-0.42)|(1.31)|(3.34)|
|Per capita expenditure||||||||||
|squared|0.0237|0.0223|-0.0440|0.0417|-0.0697*|-0.0343|0.00265|-0.0170|-0.0573|
||(0.82)|(1.14)|(-1.45)|(1.24)|(-2.11)|(-1.04)|(0.15)|(-1.32)|(-1.51)|
|Land|-0.0112***|0.0007|0.004*|-0.005|0.001|-0.0163***|-0.0022|0.0023*|0.01**|
||(-4.84)|(0.38)|(1.98)|(-1.45)|(0.38)|(-4.41)|(-0.91)|(2.45)|(2.93)|
|Observations|12584|8637|8637|8637|8637|8637|8637|8637|8637|
|Adjusted R-squared|0.302|0.087|0.122|0.118|0.135|0.205|0.090|0.024|0.086|

Notes: (1) Source: Author's calculations based on data collected by Social Observatory, World Bank and Government of Bihar. (2) SC is the omitted caste group. (3) Each column represents a separate regression wherein an `empowerment indicator’ is regressed on variables that are reported as well as additional controls: age, age squared, marital status of the woman, age at marriage, and a dummy variable for some schooling of the female respondent; a dummy variable for whether it is female-headed household, per-capital expenditure, per-capita expenditure squared, landholdings, education of the household head, number of members in the household and panchayat level fixed-effects. The female employment is run for all female adults in the sample (individual level). (4) We report robust standard errors in the brackets. (5) We report the level of significance: * p value < .05 , ** p value < .01 and *** p value < .001.

27
wage premium, and there are significant concerns about worker health and the safety of working conditions (Blattman and Dercon 2016).

# **b) Service-Led Development without a Manufacturing Base**

There is the question of whether service sectors with productivity-enhancing characteristics “need” a manufacturing core to develop. High-productivity services such as transportation and communications, wholesale and retail trade, and professional, scientific, and technical services serve consumers directly but are also linked to other sectors. Therefore, to the extent that final demand contributes substantially to the growth of a given services subsector, opportunities can be created independent of a country’s manufacturing base.

A range of professional, scientific and technical services – including software services, business process outsourcing and other information technology services, accounting, legal services, education and health care – are increasingly “stand-alone” whereby transactions takes place directly between a service provider and the final consumer. Numerous LMICs have sought to diversify their export baskets through offshore professional services. Many countries began with BPO services, such as contact and call centers, which laid the foundation for higher-value services such as finance and accounting. India was at the forefront of diversifying into these operations (Nayyar 2012), where final demand and (net) exports, respectively, accounted for about 90 percent and two-thirds of the value-added growth in professional, scientific and technical services (figure 4). Other countries that have successfully entered the market are Costa Rica and the Philippines (Bamber et al. 2017). Medical tourism is also on the rise, including in Sub-Saharan African countries, where many hospitals are treating foreign patients (Dihel and Goswami 2016).

**Figure 4: Decomposition of value added growth in professional, scientific and technical services, 2000-2014**

<!-- Start of picture text --> Intermediate Demand Consumption Investment Net Export Annual Average Growth of Services VA<br>180.00% 60.00%<br>150.00%<br>120.00%<br>90.00% 30.00%<br>60.00%<br>30.00%<br>0.00% 0.00%<br>‐30.00%<br>‐60.00%<br>‐90.00% ‐30.00%<br>CHN IND BRA MEX USA<br>Country, Sorted by the Growth Rate of Countries<br>Sub‐Sectors<br>Added of Services<br>Value Added of Services Growth<br>Share of Components' contribution to Annual Average Growth Rate of Value<br><!-- End of picture text --> Source: Nayyar, Cruz and Zhu (2018) In addition, there is a range of professional services that are either embedded in goods and often bundled together in a single product—including apps for personal electronic devices, after-sales 13
|**Number**|**Question**|**Response**|**Skip**|**Notes**|
|---|---|---|---|---|
|7.<br>|<sup>Does this facility report cases/events according to the</sup><br>National Early Warning and Reporting System?||Yes=1<br>No=2<br>Don’t know=−96|JEE- Real-time Surveillance|
|8.|Has this facility established mechanisms for real-time<br>surveillance information exchange with other facilities and<br>local authorities?||Yes=1<br>No=2<br>Don’t know=−96||
|9.<br>|<sup>In the case of an emergency, are essential medicines</sup><br>stockpiled in this facility?||Yes=1<br>No=2<br>Don’t know=−96||
|10.|<sup>In the case of an emergency, are essential medicines</sup><br>accessible from medical buffer stores?||Yes=1<br>No=2<br>Don’t know=−96||
|11.|In the case of an emergency, does this facility havebackup<br>transportation and referral systemsin place for continuity of<br>essential health services?||Yes=1<br>No=2<br>Don’t know=−96||
|12.|In the case of an emergency, does this facility have access to<br>backup human resources(health professional staff and other<br>staff) for continuity of essential health services?||Yes=1<br>No=2<br>Don’t know=−96||
|13.|In the case of an emergency, does this facility havebackup<br>equipment(such as communication equipment, fuel, and<br>generator) in place for continuity of essential health services?||Yes=1<br>No=2<br>Don’t know=−96||
|14.|In the case of an emergency, has this facility established a<br>contingency plan to ensure temporary housing to health<br>professional staff, other staff and their families?||Yes=1<br>No=2<br>Don’t know=−96||
|15.|Does this facility separate triage and waiting areas?||Yes=1<br>No=2<br>Don’t know=−96||

34
<!-- Start of picture text --> Table VIII<br>State Roads in Big and Small Statesa<br>[t-statistics in parenthesis]<br>[1] [2] l<br>Independent Variables:<br>Election Year*Big 1637.02 1825.07<br>[1.861 [1.93]<br>Election Year*Small 30.00 233.79<br>[0.04] [0.261<br>State domestic product [SDP] 1.01<br>[0.46]<br>Share of agriculture in SDP -5567.94<br>[-1.26]<br>Proportion of rural population -34203.37<br>[-0.97]<br>Number of Observations 330 308<br>R-sq 0.2002 0.2254<br>a. Big=1 for the 5 largest states, and Small=1 for the 9 smaller states.<br>Regressions include state and year effects.<br><!-- End of picture text --> 49
# **5. Results Comparing the Different Methods**

# **5.1 Comparison of Samples and Estimates of Migration and Remittance Receipt**

We expect that the snowball and intercept surveys will oversample individuals which are more connected to Japan and to the Nikkei community in Brazil. This should be especially the case for the seed households in the snowball survey, who are all members of Nikkei associations. As discussed above, weighting the intercept survey households helps correct for the oversampling of individuals who attend more community events and locations, and therefore should bring the intercept survey results closer to the stratified survey. We therefore wish to test the following hypotheses: _H1:_ The intercept and snowball households sampled will be more closely connected to the Nikkei community than randomly sampled Nikkei households.

_H2:_ weighting the intercept survey will bring the sample closer to the random sample.

_H3:_ the intercept and snowball samples will over-sample _issei and nissei_ (first- and second-generation Nikkei) who will be more strongly connected to Japan, and undersample _sansei_ and _yonsei_ (third- and fourth generation Nikkei), who are likely to be more integrated into Brazil and less likely to attend community events or belong to community associations.

_H4:_ the snowball and intercept surveys will overstate the proportion of households with migrant experience, due to oversampling households with more links to Japan.

_H5:_ Refusal rates for questions about remittances will be higher for the intercept survey, since they take place in a public location.

Table 11 compares characteristics of the households surveyed using the different survey methods. Comparing the different samples, we see strong evidence of the first hypothesis. Household members in the intercept and snowball samples are much more likely to read Nikkei newspapers, books and newsletters, listen to Nikkei/Japanese radio, watch Nikkei TV programs and visit Japanese/Nikkei websites than randomly chosen Nikkei households in the stratified sample. For example, 45 percent of households in the snowball sample have a member who reads Japanese/Nikkei newspapers, compared to 28 percent in the weighted intercept survey in São Paulo, and 13 percent in the São Paulo 30

-  -
ing accessibility, which is ultimately relevant to welfare.<sup>7</sup> In our companion paper (Akbar, Couture, Duranton, and Storeygard, 2018), we rely on the mobility (speed) index developed here as key component of an analogous accessibility (travel time) index. The other key component of accessibility is a proximity (distance to destinations) index, which also builds on the approach that we develop here.

Our investigation raises three challenges. The first is methodological. We propose a new approach to measure various forms of mobility from trip information, and to decompose them into uncongested mobility and delays caused by congestion. The second is a travel data challenge. There is no comprehensive source of data about urban transportation in Indian cities. Our approach is to collect data on predicted travel time from a popular website, Google Maps (gm).<sup>8</sup> For each city, we designed a sample of trips and sampled each trip at different times on different days. Our main worry is that these counterfactual trips may not be representative of the actual travel conditions faced by city residents. To address this worry, we use four different trip design strategies. These strategies aim to replicate some characteristics of actual trips taken by urban households in other countries. We show that our city mobility indices vary little by sampling strategies, type of trip destinations, origin and direction of travel, or time of day. Finally, we face the challenge of consistently defining and measuring the cities in which we measure counterfactual trips. To answer this challenge, we rely on a wide variety of sources including the census of India, OpenStreetMap, and satellite imagery.

# **2. Data collection**

In this section we provide an overview of our data. Further details are available in Appendix A.

## **_2.1 City sample_**

United Nations (2015) reports the names and locations of 166 cities in India that reached a population of 300,000 by 2014. Following Harari (2016) and Ch, Martin, and Vargas (2017),

> 7Formal welfare measures of accessibility were pioneered by Ben-Akiva and Lerman (1985) but their data requirements made it hard to implement them empirically. See Couture (2014) for recent developments and Duranton and Guerra (2016), Venter (2016), or Quinet (2017) for reviews on the topic.

> 8 `https://en.wikipedia.org/wiki/Google_Maps` . Accessed, January 23, 2017. A number of new studies, which we discuss later in the paper, also use Google Maps to measure traffic in a developing city, notably Kreindler (2016), Hanna, Kreindler, and Olken (2017), and Akbar and Duranton (2018).

3
WPS4261

**CLIMATE CHANGE IMPACTS ON ANIMAL HUSBANDRY IN AFRICA: A RICARDIAN ANALYSIS**<sup>**1**</sup>

**Sungno Niggol Seo**<sup>**2**</sup> **and Robert Mendelsohn**<sup>**3**</sup>

World Bank Policy Research Working Paper 4261, June 2007 _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 view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._

> 1 An earlier version of this Working Paper was published as CEEPA Discussion Paper number 9.

2 University of Aberdeen Business School, United Kingdom, e-mail: niggol.seo@abdn.ac.uk.

3School of Forestry and Environmental Studies, Yale University, 230 Prospect Street, New Haven, CT 06511, USA, e-mail: niggol.seo@yale.edu and robert.mendelsohn@yale.edu Tel: 203-432-5128.

This paper was funded by the GEF and the World Bank. It is part of a larger study on the effect of climate change on agriculture in Africa, managed by the World Bank and coordinated by the Centre for Environmental Economics and Policy in Africa (CEEPA), University of Pretoria, South Africa.
Figure 4: Effect of Fuel Subsidies on Indicator for Fishing in a Month <!-- Start of picture text --> (a) Binding vessels (b) Non-binding vessels<br>1.0 1.0<br>0.5 0.5<br>0.0 0.0<br>-0.2 -0.1 0.0 0.1 0.2 -0.3 -0.2 -0.1 0.0 0.1 0.2 0.3<br>Normalized gross tonnage distance Normalized gross tonnage distance<br>1{fishing hours > 0} 1{fishing hours > 0}<br><!-- End of picture text --> Notes: Points are binned sample means of an indicator for a vessel having positive fishing hours in a month and lines are local linear conditional mean functions. These plots reproduce Columns 1 and 3 of Table 5, with the difference in intercepts between the two regression lines in (a) corresponding to the conventional point estimate in Column 1, and (b) corresponding to Column 3.

ever enters or exits the industry during the sample period, regardless of how often they fish. These regressions use binding vessels only, but they no longer require vessels to have been built before 2016 (because being built on or after 2016 is one of our measures of entry). We no longer cluster standard errors at the vessel-level since there is only one observation per vessel. We instead estimate standard errors with the heteroskedasticity-robust nearest neighbor variance estimator because that is the default method in the rdrobust package (Calonico et al., 2022).

Our first measure of entry is an indicator that equals 1 if the vessel was built on or after 2016 and equals 0 otherwise (Column 5 of Table 5).<sup>24</sup> The coefficient is positive, which

> 24The mean-squared error optimal bandwidth is so small that the regression results in an error. We therefore impose the optimal bandwidth from Column 1 of Table 3 to estimate this regression. We also exclude year built as a control variable in this regression since the dependent variable is a function of year built.

28
This analysis involves four ANC tests in line with Kenyan national guidelines: HIV, Urinalysis, Hemoglobin, and Syphilis. Table 5 displays the results for the number of tests provided at the facility, the variable being ANC test between 0 and 4. Similar to what has been found before, electricity service is positively related to the provision of tests, but in this case, the relationship is significant for both stable and unstable connections. The use of ICT for supply management is also positively correlated with the number of tests provided, which might be explained by the need for inputs to perform the rapid tests (strips, containers, etc.). Farther distance to the nearest road of fair or good quality is negatively related to the number of ANC tests provided for the farthest facilities compared to the ones at the facilities with the closest distance to the nearest fair or good quality toad; this might indicate that farther the facilities, more barriers they face in getting the needed inputs. It is worth noting that better infrastructure might also increase the demand for medical services. This may be the underlying reason for the statistically significant relationships between ANC tests and infrastructure variables such as electricity.

_Table 5. Linear regression for Antenatal Care test availability_

||(1)<br>ANC test|(2)<br>ANC test|(3)<br>ANC test|
|---|---|---|---|
|Stable Electricity vs No Access|0.56***||0.55***|
||(0.09)||(0.09)|
|Unstable Electricity vs No Access|0.49***||0.50***|
||(0.09)||(0.09)|
|Uses ICT for supply chain||0.27**|0.26**|
|||(0.11)|(0.11)|
|Second quantile road distance|0.14|0.14|0.13|
||(0.09)|(0.09)|(0.09)|
|Third quantile road distance|-0.16*|-0.18*|-0.15|
||(0.09)|(0.09)|(0.09)|
|Fourth quantile road distance|-0.25**<br>(0.10)|-0.27***<br>(0.10)|-0.25**<br>(0.10)|
|# Outpatient last 3 months|0.00***|0.00**|0.00**|
||(0.00)|(0.00)|(0.00)|
|2G vs No Access|0.27**<br>|0.26**<br>|0.26**<br>|
||(0.13)|(0.13)|(0.13)|
|3G vs No Access|0.23|0.24|0.22|
||(0.20)|(0.20)|(0.20)|
|Catchment pop.|0.01**|0.01**|0.01**|
||(0.01)|(0.01)|(0.01)|
|Constant|2.45***|2.78***|2.33***|
||(0.36)|(0.35)|(0.36)|
|Observations|2,201|2,193|2,193|
|R-squared|0.34|0.33|0.34|
|Controls|Yes|Yes|Yes|
|Fixed Effects|Yes|Yes|Yes|
|Mean|2.080|2.080|2.080|

Robust standard errors in parentheses *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1 17