rafmacalaba/data-use-mentions-extended huggingface dataset:
Table A.5: Physical Intimate Partner Violence: Reduced-form Estimates

||F|ull Sampl|e|Rural|Urban|
|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|
|Fraction of female leaders in close elections|0.316**|0.323**|0.358**|0.484***|-0.018|
||(0.161)|(0.155)|(0.157)|(0.185)|(0.216)|
|Observations|65,292|65,292|62,122|43,981|18,141|
|Outcome mean|0.000|0.000|0.006|0.031|-0.052|
|State fxed efects|x|x|x|x|x|
|District characteristics||x|x|x|x|
|Individual characteristics|||x|x|x|

_Notes:_ Data are from the 2015-16 National Family Health Survey (NFHS) of India. The table presents the reduced-form regression results. Columns (1)-(3) include all women in the regressions, while columns (4) and (5) restrict the samples to those living in rural and urban areas, respectively. We define close elections as those in which the victory margin is less that 3 percent of votes. All regressions include state fixed effects and control for the share of seats in the district that had close elections between women and men. Individual controls include age, years of schooling, and indicator variables for living in a rural area, religion, and caste. District-level controls include the share of female population, the share of urban population, and the share of SC-ST population
|
|Totald<br>2 1 9 <sup>, </sup>8 1 9 <sup>.5ab</sup>|1 68 1 0 0 <sup>.</sup>4 <sup>ac</sup>|232,077.4|138.1|

a) excludes indirect taxes and subsidies, as taken from the detailed national accounts document Sistema de Cuentas Nacionales de Mexico, 1981: 3,545.1 million pesos for alcoholic beverages, 2,598.0 million pesos for tobacco, and 4,836.2 million pesos for petroleum refining.

b) includes 7,148.2 million pesos (excluding indirect taxes and subsidies) for petroleum refining, which are not shown in the census, but taken from the detailed national accounts document Sistema de Cuentas Nacionales de Mexico, 1981.

c) includes 4,545.1 million pesos (excluding indirect taxes and subsidies) for petroleum refining, which are not shown in the census, but taken from the detailed national accounts document Sistema de Cuentas Nacionales de Mexico, 1981.

d) excludes activities of head offices and auxiliaries with payrolls of 5,816 million pesos.

Sources: Column (1) and (2) (except figures mentioned under footnotes a) to c)) calculated from Resumen General (see text); column (3) from Sistema de Cuentas Nacionales de Mexico.
As  would  be  predicted  by  new  heterogeneous  firm  models  of  international  trade,  Brazil’s trade  liberalization  in  the  early  1990s  was  associated  with  significant  firm-­‐level  entry  into exporting.  The  share  of  exporting  firms  increased  over  50%—from  only  8.5%  in  1990  to 12.9%  in  1994  before  leveling  off.

**Tariffs** In   our   analysis   of   Brazil’s   trade   policy,   we   concentrate   on   two   trade   protection measures:   the   final   goods   output   tariff   and   the   effective   rate   of   protection   (ERP).   The effective   rate   of   protection   allows   us   to   incorporate   changes   in   tariffs   placed   on   inputs   into   a firm’s   production   process.   Our   data   on   final   goods   tariffs   are   from   Muendler   (2003),   who reports   monthly   nominal   final   goods   output   tariffs   at   the _Nível   80_ Brazilian   industrial classification   level.   We   access   monthly   ERP   at   the _Nível   80_ industrial   classification   level from   Kume,   Piani,   and   Souza   (2003).<sup>15</sup> We   match   the   December   tariffs   from   1990   to   1998 with   our   annual   RAIS   worker   data   by   the   2-­‐digit   IBGE   subsector<sup>16</sup> to   identify   workers   and firms   in   industries   with   differential   rates   of   protection   and   liberalization   experiences.

Figure  2.1  displays  both  the  mean  and  median  values  of  the  effective  rate  of  protection  in the  manufacturing  sector  during  the  1990  to  1998  period.  The  early  1990s  experienced sharp  declines  in  the  effective  rate  of  protection.  Mean  rates  fell  from  approximately  60 percent  to  20  percent,  while  median  rates  fell  from  40  percent  to  20  percent  in  a  half-­‐ decade.  The  slight  aforementioned  protectionist  response  to  the  appreciation  of  the _real_ is also  evident.  Most  strongly  in  the  early  part  of  the  decade,  median  ERP  was  smaller  than average  ERP,  suggesting  that  the  distribution  of  the  effective  rate  of  protection  is  skewed  to the  right.  Over  time,  as  the  sectoral  variation  narrows,  the  mean  ERP  and  median  ERP converge.

This  substantial  cross-­‐industry  variation  in  both  levels  and  changes  in  the
that are more readily developed when it comes to the later empirics.

From (1), the necessary condition<sup>14</sup> on the multinational’s choice of investment in the

> 13See for example Becker and Riedel (2012), using firm level data for EU firms, and Desai, Foley and Hines (2009) for the United States. Note too that, being rooted directly in commercial relations between affiliates, the complementariness in mind here differs from that which can arise indirectly as tax changes act on the cost of capital by affecting the gains from ‘paper’ profit shifting—that is the mechanism at work in Su ́arez Serrato (2019), for example, and is addressed in Section 5 below.

> 14It is assumed that Π is concave over non-negative **_K_** _≡{K_ 1 _, ..., Kh}_ .

7
growth is defined as that between period _t_ -1 and _t_ , ln _yvt_<sup>−ln</sup><sup>_y_</sup> _vt_ −1<sup>. Note that in keeping with the</sup> focus on household growth, we examine the average household growth rate, not the growth in average incomes. Our empirical objective is to determine whether our estimate of α _v_ is robust to controlling for village “fixed effects.” Implementation requires addressing two immediate concerns: constructing a village-panel data set, and using an estimator that accounts for the potentially dynamic error structure implicit in Equation (4). To construct the village-panel we use the balanced panel of households to construct the growth-rate and mean log income variables for each period, symmetric with the household regressions, and the larger non-attritted samples to construct the key village covariates like village

> 29 See, for example, Forbes (2000) and Banerjee and Duflo (2003) for a discussion of the merits and pitfalls associated with the panel-data (“fixed effects”) approach in cross-country data. 30 See Kuznets (1955). Ravallion and Chen (2007) explore precisely this question as they investigate linkages between growth rates and various measures of poverty and inequality using nationally representative NBS data. Panel analyses using state-level data from the US also finds evidence of a Kuznets process leading to a positive association between inequality and growth (Frank, 2009).

_Inequality and Growth in Rural China_ , Page 19
_5_ population growth when auto ownership levels have been high have lower densities than older cities. Although average densities vary significantly across high income cities, the trends in urban population density in high income cities are similar: population densities are falling in virtually all high income cities (Ingram, 1997). However, because buildings and lot sizes are durable, population density adjusts slowly and historical development patterns persist.

**Figure 1. Per Capita Income and Population Density, 37 World Cities**

<!-- Start of picture text --> 100,000<br>17< Bandung Guangzhou HK A<br>A A Seoul A<br>10,000 Jakaan Lod A Osaka<br>Surabaya Manilagkk Singapore<br>. 1,000<br>Perth Phoenix<br>A Actual<br>| Equation<br>100<br>100 1000 10000 100000<br>Per capita GNP (in 1987 US$)<br><!-- End of picture text -->

The effect of historical paths of development on urban densities is represented by a new variable, the historical spatial endowment index, that measures the variation in per capita land area across cities that is not explained by income and population. Using the urban data set, per capita land area (the inverse of population density) is the dependent variable in the regression shown below (standard errors are in parentheses under the coefficient estimates). It is a function of population (P) and per capita income (Y).

This estimated equation is used to calculate the predicted amount of per capita land area for each of the cities included in the sample. The historical spatial endowment index is defined as the log of each city's actual per capita land area divided by its predicted per capita land area, and it is used in place of population density in the urban regressions in Appendix Tables 5 and 7..

The historical spatial endowment index measures the residual from the above equation and
manager is also motivated to improve management practices and take other steps to enhance productivity.

Our third hypothesis is that PCs will improve performance if they elicit both the government's and the firms' commitment. When managers are not committed--perhaps because they expect to use their information advantage to bargain down the targets ex post --then ex ante they will exert only enough effort to fulfill the anticipated bargaineddown targets. In this case a PC will fail to improve the manager's incentives, and fail to turn around the firm's performance. Alternatively, when government is not committed, it will fail to enforce the contract and/or will renege on paying the promised incentives. Since government is both a signatory and the enforcer of the contract, it is especially important that its commitment be credible. If the manager and employees do not believe that the government will honor the contract and pay the incentive if they meet their targets, they will hesitate to put in more effort for fear of ex post expropriation. Unfortunately our data do not permit us to test this hypothesis by assessing measures of commitment such as contract enforcement, reputation, or the like. Instead we proxy commitment by the length of the contract (longer contracts signal greater commitment by government) and by bonding (committed managers will agree to post bond). We expect that longer contracts and the use of bonds will be associated with greater improvements in performance.

# **IV. Effects of PC Participation and Provisions**

We examined how PC participation and provisions affected the productivity of SOEs using a panel data set consisting of 769 firms from 1980 to 1989 located in four 12
to construct exogenous benchmark/reference prices to which we will compare the unit prices of the items included the import declarations in the Madagascar customs data (as will be described below).

- **Delegated randomization of inspector assignment and IT manipulation** On November 18, 2017 the assignment of inspectors to declarations was delegated to GasyNet. By comparing daily their list of declarations (that their system randomly assigned to some inspector) to the list of declarations that cleared customs from the customs administration, GasyNet was able to identify declarations that were withheld from the delegated randomization - as will be discussed in Section 9. They provided us with the list of withheld declarations.

- **Human resources data** Information on inspectors’ gender, education, age, and date of entry into work for the customs administration were provided by the Human Resources Department ( _Direction des ressources et de la formation_ ).

- **Inspectors’ survey** In 2017 we conducted a nationwide survey of inspectors which contained questions on job and pay satisfaction, corruption, ethics, fairness, and interactions with brokers and importers.

Madagascar’s raw customs data covers all formal import transactions made under several regimes: final imports for consumption (imports for home use), re-imports, temporary admissions, inward processing, warehouse, and other. Our analysis focuses on import declarations subject to taxation and to a physical or a documentary control by customs inspectors in Toamasina.<sup>15</sup> This implies focusing only on imports for home use and re-imports and excluding declarations from importers that are members of the “ _Procédure Accélérée de Dédouanement_ ” (PAD), a trusted trader program that allows member firms to benefit from expedited clearance procedures with minimal controls at the border. To minimize the risk of identifying as likely to be suspect of corruption declarations that are not, we remove from the sample (i) declarations registered by brokers that do not interact frequently with customs (i.e., brokers that register less than 50 declarations per
**_Data_ .** Using annual data, the sample includes 155 EMDEs for 1970-2018. This includes 36 EMDEs that are energy exporting (oil, gas, or coal), defined as in Appendix 1, and 120 other EMDEs. Data on output, investment, consumption, and productivity are drawn from the World Bank _World Development Indicators_ . The exchange rate classification follows the IMF’s _Annual Report on Exchange Arrangements and Restrictions_ . High (low) public debt is above (below) 70 percent of GDP for high-income EMDEs and 30 percent of GDP for upper-middle-income, lowermiddle-income, and low-income EMDEs.

# **4.3 Empirical results from the local projections model**

**_Demand-driven versus supply-driven oil price plunges._** EMDE output evolved differently in demand-driven and supply-driven oil price plunges. In the first year of both supply- and demanddriven oil price plunges, EMDE output fell by about 0.5 and 0.3 percent, respectively (Figure 5). The recovery, however, differed: output recovered after demand-driven oil price plunges and, three years later, had returned to the baseline; after supply-driven oil price plunges, EMDE output did not recover and remained below the baseline three years later.<sup>12</sup>

**_Demand-driven plunges._** The impact on energy exporters and importers were similar for demanddriven plunges. Previous demand-driven oil price plunges were associated with global recessions or slowdowns, which tended to be associated with an initial output decline in EMDEs (0.3 percent) in the year of the plunge that was recouped within three years. Output, investment, and consumption in energy exporters and other EMDEs recovered together with oil prices.

**_Supply-driven plunges._** The impact from supply-driven oil price plunges left a lasting impact on EMDE energy exporters. Supply-driven oil price plunges were associated with initial output losses in energy exporters of somewhat larger magnitude than those associated with demand-driven plunges (0.5 percent in the first year). Almost
model, a state-space model that can be used to construct counterfactuals from observational data in time series settings (Varian, 2014; Brodersen et al., 2015). Applying the BSTS to the traffic data, we compare actual congestion outcomes with an estimate of what _would have been_ observed in the absence of a lockdown and infer the causal impact of the restriction policy on congestion. We focus on Bogotá, New Delhi, New York City, and Paris, four highly congested metropolitan areas that enforced stringent lockdown policies to curb the spread of Covid-19.

# B.1. Measuring Traffic Congestion

Our data set comes from trafficindex.org, a website that uses the Google Maps traffic layers to compile time series observations on traffic congestion. Google Live Traffic Information, the underlying data source, reports high resolution traffic conditions from anonymized users of a smartphone-enabled application. The layers are based on a fourcolor scheme that characterizes the traffic status in real-time on a given road segment. More specifically, the color green indicates relatively free-flow traffic conditions while dark red denotes highly congested traffic. Orange and red, the two intermediate colors, correspond to medium and near-heavy congestion, respectively.

According to the website, the images of each road segment for both the entire city and the city center only were queried every 20 minutes, 24 hours per day, using the Google Maps Application Programming Interface.<sup>5</sup> Then, using the pixel-level information on of these images, the website computes a daily Traffic Congestion Index (TCI) at each level of aggregation (city center or entire city) as:

> 5 We had similar images queried for Paris from Google Maps and compared them to a sample of data sets from the website covering the same period. No major discrepancies between the two series were found.

15
# **1. Introduction**

Programs catering to the financial well-being of the elderly are coming under strain across the world. The depletion of public coffers during the financial crisis has left governments struggling to cope with rising retirement costs triggered by aging populations, low birth rates, and weakened informal safety nets (Ellis, et al., 2014). That this crisis threatens both the elderly and the young is well-established; it also endangers future generations, who will shoulder a disproportionate share of the costs of caring for older generations directly or indirectly (Gratton, 1996; Fridson, 2015).

Despite rising interest in the safety net for elderly people, there is very limited information on both formal and informal voluntary saving for old age. Drawing on novel microeconomic data, this paper offers a detailed descriptive analysis of saving for old age around the world and across geographic regions. It also features an empirical analysis of the determinants of saving for old age, using detailed individual characteristics, macroeconomic characteristics at the country level, and country pension-system characteristics. To our knowledge this is the first study examining saving for old age around the world using detailed global micro-data.

The 2014 Global Findex data show how and why adults save and borrow and shed light on their financial resilience to unexpected expenses (Demirgüç-Kunt, et al., 2015). In 2014, 56% of adults around the world reported having saved money in the past 12 months. Adults in high-income OECD economies and East Asia and the Pacific were the most likely to have done so, with 71% reporting that they had saved, followed by 60% of adults in SubSaharan Africa. In other regions the figure stands between 30% and 40%. The 2014 Global Findex survey also asked about three specific reasons for saving. While marked differences emerge across regions, almost 25% of adults worldwide reported having saved in the past year for old age. A similar share reported saving for education expenses, while 14% reported saving to start, expand, or operate
Figure 1. The position of different countries’ ventiles in global income distribution <!-- Start of picture text --> France<br>Brazil<br>Sri Lanka<br>Indon-rural<br>0 5 10 15 20<br>country ventile<br>100<br>80<br>60<br>40<br>20<br>percentile of world income distribution<br>0<br><!-- End of picture text --> Source: Calculated from World Income Distribution (WYD) data. Available at <u>http://econ.worldbank.org/projects/inequality.</u> 19
system that defined the kind of citizens that the system would develop: “To raise individuals of the information age who are devoted to the principles and reforms of Atatürk, whose thinking, perception, and problem-solving capabilities have been developed, who are democratic, devoted to freedom, faithful to moral values, open-minded, and aware of their personal duties and responsibilities...” (8th Five Year Development Plan, paragraph 675).

The Basic Education Law (Law No. 4306) passed in August 1997 mandated eight years of compulsory education. This launched an unprecedented expansion of public primary schooling. The eight-year Basic Education Program involved a broad range of actions. As a result, enrollment in basic education increased by over 1.1 million students, raising the gross enrollment ratio from 85.63 percent in 1997 to 96.30 percent in 2002. Enrollment rates for girls, especially in rural areas, made particularly impressive gains. For example, in the nine provinces in the eastern and southeastern regions of Turkey that had the largest gender disparity, female enrollment increased 160 percent. Using a combination of government and private contributions, the Ministry of National Education built 81,500 new primary-education classrooms during the five-year period 1997–2002, increasing classroom supply by 30 percent (World Bank 2005).

The 1997 reform extended compulsory schooling from grade 5 to grade 8. We use it here as a natural policy experiment in the subsequent analysis. This type of policy-related instrument (based on compulsory schooling) is frequently viewed as an ideal instrument. It provides the return to persons who decide to enroll only because of the policy change. This reform affected 24 percent of the wage earners in the 2017 HLFS survey.

7
_Figure 8b: Reason for vaccine hesitancy, countries with multiple responses_ <!-- Start of picture text --> Kenya<br>Uganda<br>Sudan<br>Mali<br>Nigeria<br>Gambia<br>Georgia<br>0 20 40 60 80 100 120 140 160<br>Safety (includes side effects)<br>Efficacy<br>Distrust (of government/pharmaceutical industry/international community)<br>Dislike vaccines in general<br>Prefer natural immunity<br>Lack of knowledge/access<br>Religious<br>Not eligible<br><!-- End of picture text --> Notes: The X axis represents share of vaccine hesitant households. In some cases, the share is larger than 100% because multiple responses were allowed For the construction of harmonized response categories, see Appendix Table 2b.

# **4. Discussion**

# _4a. Principal findings_

Data from the HFPS were used to investigate COVID-19 vaccine hesitancy. On average across 53 countries, one in five adults are hesitant about getting a COVID-19 vaccine. The highest levels were observed in Eastern European and Central Asian countries and the lowest levels in Latin American and Caribbean countries. Among population groups, female respondents, younger adults, and those with less formal education reported higher levels of COVID-19 vaccine hesitancy than their respective counterparts. Between October 2020 and August 2021, there tended to be little change in levels of hesitancy except in Iraq, Malawi, and Uzbekistan, where hesitancy increased. The main self-reported reason for being hesitant was concerns over safety, especially worries about side effects, and to a lesser extent, concerns over vaccine efficacy and dislike of vaccines in general.

# _4b. Strengths and comparisons with other studies_

This study has several advantages over the existing literature. It is based on a very large sample of respondents from national surveys in 53 developing countries, a part of the world that is underrepresented in the literature. Over half of the