rafmacalaba/datause-encoder-data huggingface dataset:
collected in the state of Pernambuco, Brazil, in order to better understand actual and expected behaviors related to domestic violence among different social groups, as well as the knowledge of Maria da Penha law and infrastructure supporting it, including DEAMs<sup>7</sup> . The qualitative study included in-depth interviews in four cities with women victims of violence, focus groups and questionnaires with general population (men and women), structured interviews with key informants (service providers, judicial professionals, administrative staff –referred to as ‘service providers’ from now on). The findings from this qualitative work are predominantly used to explain heterogeneity in impacts from the quantitative analysis. The entire qualitative report can be found at Mueller et al. 2012.

# **4. Empirical Strategy**

To identify the effects of DEAMs on the homicide rate of women aged 15 to 49, we apply a difference-in-difference approach, taking advantage of the variation in the timing of establishment of DEAMs across various municipalities. We use the following specification: where ��� is the outcome variable - homicide rate per 100,000 women aged 15 to 49 due to aggression in municipality _m_ in year _t_ . We also explore changes in the homicide rate of men in the same age group for two reasons. First, regressions on male homicide rates serve as placebo tests: we would not expect the male homicide rate to change with the women’s police stations targeted toward women’s safety. If the male homicide rate was also falling, we might assume that overall crime was changing due to increased police presence rather than domestic violence was going down due to greater attention to women. Second, regressions on male homicide rate allow us to explore the possibility of increase in male homicide if as a result of substituting women police officers for male police officers by municipalities. Fixed effect regressions<sup>8</sup> with controls for municipal spending reveal that establishment of DEAMs is not associated with increase in any of the various police spending variables listed in the summary statistics in Table 1, suggesting that substitution is plausible.

��� is a dummy, equal to 1 if there was a DEAM in municipality _m_ in year _t_ . �� and �� are municipality and year fixed effects. ��� is an extensive set of time variant municipal controls.

> 7 This work was supported through the World Bank Policy and Social Impact Analysis grant.

> 8 Available from the authors upon request.

13
and Blundell and Bond [32]). The main difference between the two methods is in the choice of the endogenous variables and corresponding instrumental variables. As reported by Sung et al. [33], the system GMM estimator is theoretically more efficient than the difference GMM estimator because it allows for a richer set of instruments. The difference GMM estimator may also suffer from weak finite sample bias when the cross-section dimension (N) is large enough. In our sample, the cross-section dimension (N=110) and time dimension (T=8). In this paper we adopt the system GMM estimator.

The efficiency of the system GMM depends on the following two specification tests: (i) the difference-in-Hansen test for too many instruments; and (ii) the Arellano and Bond test for second-order autocorrelation (i.e., AR(2) test). When employing our data, the analysis suggests that there is no instruments proliferation or we do not have too many instruments.

# **2.3. Data**

We use a balanced panel data set for 110 countries, with eight data points covering 1980-2015 corresponding to 1980, 1985, 1990, 1995, 2000, 2005, 2010, and 2015. Prior to the Kyoto Protocol adopted in 1997, CO2 emissions where not much of a concern. However, mounting concerns to the environment and the need to limit CO2 emissions ignited worldwide concerns, (e.g., the establishment of the UNFCCC and the 2015 Paris Climate Change Conference). To understand how changes in awareness of climate change impacts CO2, the analysis not only investigates the whole period of analysis, but also looks at two subsamples: early (1980-1990) and late (2005-2015) periods.

The 110 countries are classified into four groups based on their per capita gross national income (GNI) calculated using the World Bank Atlas method 2016 [34]: lowincome countries (LI countries, less than $1,005), lower-middle-income countries (LMI countries, $1,006-$3,955), upper-middle-income countries (UMI countries, $3,956$12,235), and high-income countries (HI countries, more than $12,236). The lowincome subpanel in this study consists of data for 10 countries, while the lower-middle8
First, as explained below, we construct the indices for return and risk in a way such that each observation represents the value for a country/year relative to the corresponding mean for the whole world in all time periods. Second, we allow for time-specific effects in the estimated regression; this is equivalent to a regression in which each variable enters as deviations with respect to the cross-sectional mean in a particular year.

# **3. DATA**

# **3.1 NFA** and Wealth

The cornerstone of our data is a set of wealth, foreign asset and foreign liability stocks for a large group of industrial and developing countries spanning the period from the 1960s to the present. Construction of this data set is documented in Kraay _et al._ (2000), so for the sake of brevity here we limit our remarks to a few key issues. The total wealth of country i's residents at time t is defined as _W,_ **_=_** _NFAil +K,_ **_+Gi_** where **NFA** denotes the country's net foreign assets, K is the capital stock, and **G** denotes the Central Bank's gold holdings." In turn, net foreign assets are defined as

**_NFA,, = E,,f, -_ E,1,** **_+_** _Lif.,_ **_- Lft., (8)_**

> where Eifdenotes local residents' holdings of capital abroad, **_Ef_** denotes domestic capital owned

> **by** foreigners, **Liy** are loans issued **by** domestic residents to foreigners (inclusive of foreign

> currency reserves held **by** the domestic Central Bank) and **_Lf,_** are loans from foreigners to domestic residents. **All** quantities are measured in **1995 US** dollars at PPP.

The various wealth components shown above are constructed in two steps. First, we use the limited available information on stocks of these assets to determine an initial value. The second step involves the use of flow data and estimates of changes in the value of these assets to

> ' Thus, we abstract from other components of wealth such as natural resources and human capital.

11
The health care system in rural Burkina Faso is relatively underdeveloped. In the 75 survey villages, not every village has a health clinic, but the median distance to the nearest health clinic is only two kilometers (average distance is 2.85 kilometers). This is below the Burkina Faso national average distance to a basic health care center, which is 7.5 kilometers (Ministry of Health 2007). In addition, there are no statistically significant differences across treatment and control groups in the average distance to a health clinic.

Access to basic health care remains poor in Burkina Faso. In 2007, only 36 percent of the population had access to basic health care (Ministry of Heath 2010). The available data also suggest that the health system is under-staffed. In 2009, the ratios of total population to health care professionals indicate there are 22,522 people per physician, 94,564 per pharmacist, and 2,892 people per nurse (Ministry of Health 2010). The poor access to health services translates into lower utilization of health care, especially preventive health care. In rural Burkina Faso, it is estimated that only 60 percent of baby deliveries are attended by a trained health care provider, with large variations across regions (INSD/ORC Macro 2010). Noticeable exceptions include immunization coverage (more than 80 percent of children in rural areas) and utilization of prenatal care (around 94 percent of pregnant women in rural areas).

In Table 2, we use baseline data to test the balance of the randomization experiment. We first present the mean of the specific variable measured at the baseline for the control group and each of the four treatment arms. In the subsequent columns, we present p-values from a Wald test comparing the treatment arm with control group. Finally, in the last column (under the control group heading), we present the p-value for an F-test of the joint test that the means of the five groups are equal. Results show balance across characteristics for children, parents, and measures of supply-side health clinic quality. The main dependent variable used in this paper, 9
38 percent increase in gas price would increase poverty incidence to 24.1 percent. Thus, an additional 0.6 percent of Armenian households would be classified as poor with the April 2010 gas price hike alone.

# **_What is the cost of protecting the poor and/or all affected households?_**

The aggregate cost to the households of the gas price increase is about 0.73 percent of 2009 GDP (or about 22.5 billion drams). Table 8 presents the aggregate increase in expenditures on gas estimated to account for the increase in the gas price. The estimates are the upper bound since our simple model assumes consumption levels are unchanged (no substitution). More importantly, the calculations provide a rough cost of protecting the poor or any share of the affected households to ensure that they are as better off as they were before the gas price increase. For example, protecting the energy consumption of households from the two poorest quintiles requires energy subsidies of about 0.18 percent of GDP (or about 5.8 billion drams). However, these calculations assume perfect targeting by the subsidy program of the intended beneficiaries. In practice, it is impossible to achieve perfect targeting.

The Government of Armenia used the Family Benefit (FB) program targeting scheme to provide compensation against the gas price hike. It introduced a lifeline tariffs for poor gas consumers who are beneficiaries of the FB program.<sup>9</sup> Under this arrangement, poor households pay AMD 100/m3 instead of regular AMD132 on the first 300 m3 of gas consumed during the year. This benefit became effective on April 1, 2011. Based on the FB database, the total cost of the lifeline tariffs for one year was about 1.056 billion drams (about 0.03% of GDP). For comparison, assuming the FB targeting mechanism was used and the poorest two quintiles were targeted, the fiscal cost of full compensation would have been about 0.22 percent of GDP (or about 6.9 billion drams).

> 9 FB, Armenia’s main social safety net program, achieves a targeting performance of about 61 percent, i.e., about 61 percent of the program resources reach the poorest quintile and about 80 percent reach the poorest two quintiles. However, its coverage of the poor is low at about 33%.

17
- How serious do you consider poor water quality, air quality, sewage and sanitation to be here in your own community? (environment)

- Taking all things together would you say you are [happy, unhappy]? (happiness)

On the opposite, the following questions (governance outcomes) are assumed to be exposed to bias effects:

- How much confidence do you have in government? (trust: executive branch)

- How much confidence do you have in parliament? (trust: legislative branch)

- How much confidence do you have in press? (trust: press)

- How much confidence do you have in television? (trust: television)

- How much confidence do you have in courts? (trust: courts)

# 5.2.1 Testing for the intimidation, indoctrination and ”critical citizenship” effects

We use 2 types of estimation procedures to extract γi, ηi and μi - effects of intimidation, indoctrination and ”critical citizenship” in a country i. First, we test for indoctrination (ηi) on an individual level, since there can hardly be any proxy for biasedness of mass-media (indoctrination) on a country-level. On a contrary, it is hard to come up with the proxies for personal intimidation and ”critical citizenship” (this effect was in fact defined only for countries as a whole). That is why we use country-level regressions to identify these effects.

As the proxy for indoctrination we take the frequency, with which an individual exposes her- or himself to media - TV and press. Specifically, we use questions ”Did you watch TV during the last week?” and ”Did you read newspapers last week?” from the World Values Survey. The more people watch TV or read newspaper the more they are exposed to possible indoctrination (or excessive criticism of massmedia). The exact estimation model then becomes: where tvij, pressij are the dummies for watching TV and reading newspapers last week (as it was posed in the questions of the survey), demogrij is a set of individual demographic variables (we take respondent’s education, income, age, marital status, political activism - participation in demonstrations, boycotts, signing petitions).

We report the results in the Table 3. The main conclusion from it is that even though developing countries, especially those in Middle East and East Asia, seem to be indoctrinated, the mass media bias is also present in many developed countries - Japan, Sweden, Switzerland, USA, France. This might be the outcome not of state monopoly (or dictate) on mass media, but of too optimistic or patriotic news coverage in these countries. The magnitude of the indoctrination effect ranges from 0.02 (except for Ukraine and Rwanda, where those who watch TV are actually more 20
# **Figure 3: Migrant Workers of Japanese Origin**

<!-- Start of picture text --> 300,000<br>250,000<br>200,000<br>150,000<br>100,000<br>50,000<br>0<br>1988 1989 1990 1991 1996 1997 1998 1999 2000 2001 2002 2003 2004<br><!-- End of picture text --> Source: Japanese Ministry of Justice.

45
y kkpmigrant —~ pmigrant“China <!-- Start of picture text --> pmigrant pmigrant pnon-migrant<br>migrant<br>“chinai ( China“china } ( i “china }<br>( pmigr “chinaChin a nt ) ( pnon-migrant i “china )<br>( pmigr “chinaChin a nt ) ( pmon-migrant i “china )<br><!-- End of picture text -->

# <u>pmigrant</u>

pmigrant China
28

- ———. 2012b. “Tanzania Low Cost Rural Electrification Results Report.” Draft report, Washington, DC.

- O’Sullivan, Kyran, and Douglas Barnes. 2006. “Energy Policies and Multitopic Household Surveys.” World Bank Working Paper No. 90, Washington, DC.

- UN (United Nations). 2011. “International Year of Sustainable Energy for All.” Res. 65/151 United Nations, New York.

- Webb, Michael, and David Pearce. 1985. “Economic Benefits of Electricity Supply.” Energy Department Paper No. 25, World Bank, Washington, DC.

- World Bank. 1996. _Rural Energy and Development: Improving Energy Supplies for Two Billion People._ Development in Practice, Washington, DC: World Bank.

- ———. 2000a. “Mini-Grid Design Manual.” Technical Paper 007, Energy Sector Management Assistance Program (ESMAP), World Bank, Washington, DC.

- ———. 2000b. _Reducing the Cost of Grid Extension for Rural Electrification._ Report No. 227/00, Energy Sector Management Assistance Program (ESMAP), World Bank, Washington, DC.

- ———. 2001. _Peri-Urban Electricity Consumers—A Forgotten but Important Group: What Can We Do to Electrify Them?_ Report No. 249/01, Energy Sector Management Assistance Program (ESMAP), World Bank, Washington, DC.

- ———. 2002. _Rural Electrification and Development in the Philippines: Measuring the Social and Economic Benefits._ Report, Energy Sector Management Assistance Program (ESMAP), World Bank, Washington, DC.

- ———. 2004. _The Impact of Energy on Women’s Lives in Rural India_ . Report No 215/05, Energy Sector Management Assistance Program (ESMAP), World Bank, Washington, DC.

- ———. 2005. “Output-Based Aid: Supporting Infrastructure Delivery through Explicit and Performance-Based Subsidies.” Working Paper Series Paper No. 4, Global Partnership for Output-Based Aid (GPOBA), Washington, DC.

- ———. 2006. “Sub-Saharan Africa: Introducing Low-Cost Methods in Electricity Distribution Networks.” Technical Paper 104/06, Energy Sector Management Assistance Program (ESMAP), World Bank, Washington, DC.

- ———. 2007. _Making Infrastructure Projects Responsive to the Needs of Women in Rural and Remote Areas: Access to Rural Electrification in Lao PDR_ . Washington, DC: World Bank.

- ———. 2008a. _Shining a Light on Women’s Productivity in Lao PDR_ : _Gender Equality as Smart Economics._ World Bank: Washington, DC.

- ———. 2008b. “Zambia: A Proposed Grant to the Republic of Zambia for an Increased Access to Electricity Services Project.” Project Appraisal Document, World Bank, Washington, DC.

- ———. 2009a. “Côte d'Ivoire: A Proposed Grant to the Republic of Côte d’Ivoire for an Urgent Electricity Rehabilitation Project _._ ” Project Appraisal Document, World Bank, Washington, DC.

- ———. 2009b. “Getting Electricity—A Pilot Study by the Doing Business Project.” <u>http://www.doingbusiness.org/features/GettingElectricity-project.aspx</u> .
though the effect is not significant, the latter tends to decrease with the banking sector’s development. Our results also show that exchange rate risk has adverse effects on developing countries’ energy projects attractiveness to private investors as a high exchange rate risk appears to reduce private investment. These findings therefore confirm our previous results from Table 14 as to the importance of capital markets’ development and exchange rate risk for the private sector’s investment decisions, and support the conclusion that the positive effects of financial development are driven by the domestic capital markets’ development. Furthermore, parameter estimates emphasize that investors are more willing to contribute to energy projects’ financing when there exists an independent regulator. Therefore, the domestic energy sector’s regulatory framework also matters for private investors.

**Table 15**

|SYS-GM|M parameter estimates||
|---|---|---|
|Variable|Coefficient|Std error|
|_lag(privinvt)_|.6151758***|.0482015|
|_Bsdev_|-44.05866|51.64461|
|_Cmdev_|185.4939***|66.0554|
|_exchraterisk_|17.32381***|7.450965|
|_Indepreg_|299.4898*|221.1928|
|Obs.|306||
|Fisher test|F(5, 28) = 91.01***||
|Arellano Bond test - Order 1|-2.44***||
|Arellano Bond test - Order 2|-0.05||
|Hansen-J|chi2(29) = 25.19||
|Dif-Hansen|chi2(15)= 11.28||

As the results from section 7 above suggest that overall economic and financial development levels are both key drivers of private investment when considered jointly, we examine in a final step whether the impact of financial sector development can be attributed to a financial sub-sector in particular, i.e., the banking sector or capital markets. Our empirical results displayed in Table 16 confirm the importance of economic growth and capital markets’ development in developing countries’ attractiveness from private investors’ point of view. Indeed, the variables _growth_ and _cmdev_ positively and significantly affect private investment. These findings therefore provide strong evidence that the _effects of the financial sector’s development on private investments are essentially driven by capital markets’ development_ . When controlling for the regulatory framework in the power sector (Table 17), we find that economic growth and capital markets’ development are still significant in explaining private investment. Our empirical results also highlight that the existence of an independent regulator in the energy sector plays an important role in the attractiveness of 21
14 strengthen infrastructure investment in both economies through equity investments in various sectors; and in 2011 Khazanah National Berhad (Malaysia) and Temesek Holdings (Singapore) set up two strategic ventures to jointly develop infrastructure projects in their respective countries.

# **Box 8**

# **Nigeria Infrastructure Fund**

The Nigeria Infrastructure Fund (NIF) is one of three pools of the recently established Nigeria Investment Authority (NSIA), and the only one that will invest domestically. The NIF holds a 32.5 percent share of the NSIA’s US$1 billion seed capital, the other shares being held by the future generations fund and the stabilization fund. The NSIA’s stated objectives are to “build a savings base for the Nigerian people, enhance the development of Nigerian infrastructure, and provide stabilization support in times of economic stress”.

The NSIA, which commenced operations in October 2012 and made its first investment in September 2013, relies heavily on international partnerships, and this maiden investment consisted of a transfer of $200m to UBS, Credit Suisse and Goldman Sachs, for external management of a fixed income portfolio.

International partnerships are also a key part of the NSIA’s domestic investment policy. The Nigeria Infrastructure Fund will invest in sectors including power, transport, agriculture and health care, and has signed memorandums of understanding with the Africa Finance Corporation and the International Finance Corporation to work together on transactions. For power sector investment, there is an agreement with General Electric, and another one being discussed with Power China.

_Sources: Financial Times, September 16th, 2013 and NSIA website_ SWFs should not seek to duplicate the roles of existing institutions. If a well-managed and skillful national development bank already exists, there would be no need to further fragment domestic investment by adding the SWF at least with regard to domestic investment with a commercial or quasi-commercial return. If inefficient, the development bank should be restructured instead of widening the mandate of the SWF and creating new and potentially competing institutional responsibilities. If no development bank exists, then the SWF’s domestic investment should be subject to a level of oversight at least as rigorous as that of a development bank. This would include clear and publicly disclosed home bias parameters (e.g. permitted mark-down on the rate of return versus the benchmark rate of return on foreign assets), and may include transfers from the budget to compensate domestic investment below quasi-commercial return. The same solution could apply if an inefficient development bank existed but credible restructuring were not an option. However in this case there should be no confusion or overlap between the mandate of the SWF and that of the development bank. Table 3 outlines alternative institutional solutions.

14
# **<u>Distributional Impact Analysis of Past Climate Variability in Rural Indonesia</u>**

1 2 3 Outi Korkeala , David Newhouse , and Mafalda Duarte 1 _University of Sussex, UK_ _2_ _World Bank, Washington DC, USA 3 World Bank, Jakarta, Indonesia_ Key Words: climate variability, agriculture productivity, household consumption, farm profits We thank Shubham Chaudhuri, Boniface Essama-Nssah, Andy Mckay, Shobha Shetty, Emmanuel Skoufias, and Janne Tukiainen, as well as participants of seminars held at the World Bank Jakarta and Sussex University, for helpful comments. We are grateful to colleagues at CEREGE, the NOAA, and Badan Meterologi Klimatologi and Geofisika for sharing rainfall data, Bastian Zaini and Kathleen Beegle for providing kabupaten codes and expenditure aggregates, and John Strauss for sharing IFLS location data. Outi Korkeala thanks the Yrjö Jahnsson Foundation for partially funding this research. The team gratefully acknowledges the overall guidance, supervision, and financial support provided by Vivi Alatas and William Wallace.