rafmacalaba/data-use-mentions-extended huggingface dataset:
reporting changes in response to transitory deviations from trend. This concern is also mitigated by the fact that reporting in the DHS is not observable by husbands, due to measures taken by the DHS surveyors to ensure privacy and confidentiality, described above.<sup>17</sup> Anderberg et al. (2016) use data from a crime survey which, in principle, has the same issues as our data. Other studies have used more institutionalized data which have different limitations. For instance, Aizer (2010) uses hospitalization data, but only severe cases of IPV are hospitalized. Mani et al. (2012) use police reports, but many women are reluctant to report IPV to the police for fear that their husbands will either exercise backlash or be arrested and taken away (e.g. Iyenger 2009; Amaral et al. 2019).

The estimation sample includes 31 and 26 countries, respectively, for which physical and sexual violence data from the DHS are available for 2005-2016 (see Table S1.1).<sup>18</sup> Estimates are reported for each of physical and sexual violence and their sum. The study does not use emotional abuse as it is a more subjective measure of abuse. There is substantial variation in the incidence of physical and sexual violence across countries; see Figures 1(a) and 1(b).<sup>19</sup>

# Unemployment rate shocks

Data on male and female unemployment rates are obtained from the World Development Indicators (WDI), sourced from the ILOSTAT database.<sup>20</sup> Although violence measures are only available for

> 17 Only 0.94% of women in the sample were selected for the domestic module but not interviewed due to lack of guaranteed privacy.

> 18 The sample size for sexual violence is smaller, because in five countries only one of the questions on sexual abuse is included, so that the measure of sexual violence in these countries is not comparable to the rest
**Figure 1: Distribution of COVID-19 Deaths by Age Group for Each Country**

<!-- Start of picture text --> Afghanistan<br>Pakistan<br>Philippines<br>South Africa<br>Colombia<br>Peru<br>Brazil<br>Mexico<br>Malaysia<br>Argentina<br>Chile<br>Hungary<br>Portugal<br>Spain<br>Korea, Rep.<br>Italy<br>Japan<br>France<br>Canada<br>Germany<br>Netherlands<br>Sweden<br>Australia<br>Denmark<br>Norway<br>Switzerland<br>0 10% 20% 30% 40% 50% 60% 70% 80% 90% 100<br>Under 50 50-59 60-69 70-79 80+<br><!-- End of picture text --> Note: See Appendix for data sources. Countries are shown ranked by 2018 GDP pc in current USD from WDI.

**Figure 2: Hypothetical COVID-19 Deaths by Age Group, Using Country Age-Specific COVID-19 Mortality Rates and the Mean High-Income Population Age Distribution**

<!-- Start of picture text --> Afghanistan<br>Pakistan<br>Philippines<br>South Africa<br>Colombia<br>Peru<br>Brazil<br>Mexico<br>Malaysia<br>Argentina<br>Chile<br>Hungary<br>Portugal<br>Spain<br>Republic of Korea<br>Italy<br>Japan<br>France<br>Canada<br>Germany<br>Netherlands<br>Sweden<br>Australia<br>Denmark<br>Norway<br>Switzerland<br>0 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%<br>Under 50 50-59 60-69 70-79 80+<br><!-
urbanization, even though it has been declared as a census town<sup>2</sup> ), which will permit investigation into the nature of classification, their proximity to each other permits a closer look at the regulatory issues relating to rural administration. While both Bishnugarh and Cherra belong to Bishnugarh Panchayat Samiti, they are in different Gram panchayats – Bishnugarh is in Bishnugarh Gram panchayat with two other villages, while Cherra is in Cherra Gram Panchayat, with one other village. We plot these two CTs in a manner similar to Erein. As seen in Figure 2, both Bishnugarh and Cherra are ‘average’ in terms of their development index. It should also be noted that Jharkhand has a number

> 2 This is because there is a predictive component to the classification of rural and urban areas. Since CTs are identified prior to the census operation, information from the last census is used for such identification. For CTs in 2011, all villages having a population of 4,000 or more at the 2001 census are taken up for examination with the assumption that such settlements are expected to cross 5,000 population by the time of the 2011 census. For such units the density and the male main workforce composition of the population are examined to see whether these qualify to be categorized as CT.

8
have a relationship with a state-owned bank or government-owned bank are more likely to be credit constrained, to innovate, or to engage in environmentally friendly activities. While we do find that credit constrained firms are more likely to borrow from government-owned banks, and that this is especially the case in countries with good institutions, we do not find any evidence that government owned banks target innovative firms or “green” firms.

Although WBES does not contain detailed balance sheet and income statement data, it does include information on firm characteristics, such as ownership, size, and employment. In the first part of our analysis, we use some of these characteristics as control variables, in the second part we use employment growth as the dependent variable. We start by showing that, in terms of employment growth, firms that borrow from government-owned banks do not differ from firms that borrow from private banks. Next, we show that in firms that borrow from government-owned banks employment reacts less to business cycle conditions than in firms that borrow from private banks. This result is in line with bank-level evidence that lending by government-owned banks is less procyclical than lending by private banks (see, among others, Micco and Panizza, 2006). Next, we study the role of credit constraints and show that having a relationship with a government-owned bank is particularly useful for credit constrained firms. Specifically, we find that employment is much more stable in credit constrained firms that have a relationship with a governmentowned bank with respect to credit constrained firms that borrow from a private bank. These results are robust to controlling for country-year fixed effects that absorb all possible country specific shocks and are also robust to controlling for firm fixed effects.

The rest of the paper proceeds as follows. Section 2 describes our data with special focus on the matching of WBES data with bank-level data and on the construction of three latent variables aimed at measuring the presence of credit constraints, innovation, and green activities. Sections 3 and 4 describe our empirical strategy and present regression results. Section 5
_Absenteeism, stock-outs, informal payments and Quantitative Service Delivery Surveys (QSDS)_ Absenteeism, stock-out rates and informal payments can be measured by studies that rely on detailed facility surveys. The Quantitative Service Delivery Survey (QSDS) represents such an approach. A QSDS examines the performance of service providers by collecting data on finances, inputs, outputs, pricing, quality, and supervision from a representative sample of facilities, often combined with interviews of staff and patients.

A QSDS provides a more thorough picture of how money and resources are utilized at the service provision level than do most PETS. The QSDS approach permitted Chaudhury and Hammer (2004) to estimate absenteeism through random visits of health care facilities in multiple locations in Bangladesh over a period of months. To the extent that local health care provision is similar across countries, the measures of absenteeism, stock-outs and informal payments from the QSDS could provide reasonably comparable estimates of governance performance.

The cost of implementing a combined PETS and QSDS is approximately US$200,000.<sup>11</sup> This figure includes a survey – applied in a random representative sample of health facilities – along with a review of documentation and interviews to track the flow of funds through the public health care system.

_Issues in measuring health care worker absenteeism_ Accurate measurement of absenteeism that is comparable across countries requires attention to a number of factors. The basic facility sampling strategy should assure that the survey is representative of the chosen type of facility (e.g. all public primary health care facilities). Unlike normal facility surveys, gathering information on absenteeism also requires that the timing of the facility visit be unannounced. Finally, appropriate information has to be gathered to distinguish excused from unexcused absences. As a governance performance measure, only unexcused absences are of interest. This means it is necessary to gather enough information about facilities to know which staff members are actually supposed to be present and determine, for those who are not available, whether they have legitimate reasons for being absent – such
**|-0.0137*|-0.0163**|-0.0132*|0.0803|0.0785|0.0882*|0.0820*|0.0511|0.0529|-0.0156**|-0.0152**|
|_(% end of period)_|(0.00695)|(0.00700)|(0.00692)|(0.00692)|(0.0506)|(0.0593)|(0.0474)|(0.0487)|(0.0606)|(0.0489)|(0.00699)|(0.00721)|
|<br>Current Account|0.192***|0.216***|0.205***|0.190***|0.137***|0.166**|0.109**|0.118**|0.134*|0.119**|0.210***|0.190***|
|Balance_(% GDP)_|(0.0495)|(0.0579)|(0.0476)|(0.0493)|(0.0501)|(0.0764)|(0.0454)|(0.0486)|(0.0750)|(0.0520)|(0.0582)|(0.0475)|
|_Policy Response_|||||||||||||
|Government Response (GR)<br>_(Oxford tracker)_|-0.0526<br>(00316)||||||||||||
|<br>Stringency Index|.|-0.0505*|-0.0502*|
in the literature.<sup>12</sup> We perform the same regression with the lag variable and find a similar positive relationship with Internet and trade. The results are presented in the appendix.

A more robust method to address the endogeneity issue is to find an instrumental variable for the Internet adoption. We use the price of an entry-level broadband subscription as an instrument for Internet adoption. The price of broadband access affects the share of people that uses an Internet connection. Conditional on the Internet infrastructure, lower prices will result in a higher number of Internet users in a country. The price of a broadband subscription fulfils the exclusion restriction as it affects the adoption and use of Internet within the country but does not affect the level of exports. It is also unlikely that the price of broadband subscription will affect trade directly or through indirect channels other than the actual use of the Internet. This instrument is correlated with Internet adoption and the results of the first stage suggest that it is a relevant instrument.<sup>13</sup> The price data covers the period 2008-2013 and comes from various years of the ITU “Measuring the Internet Society” reports.<sup>14</sup> In the report, an ICT price basket is constructed to provide information on the cost and affordability of fixed telephones, mobile cellular and fixed broadband. In the analysis, we will use the price of fixed broadband in purchasing power parity (PPP) terms.<sup>15</sup> We present the results of the regressions in Table 6 with country-pair and year fixed effects for total trade, average trade and the number of products. The results of the OLS regressions are presented for comparison as the time period of the data used is different from Table 3 due to the data availability of the broadband subscription prices.

The two-stage least squares (2SLS) regressions confirm the results in the previous table. Internet adoption in the exporter increases bilateral exports, intensive margin and extensive margin. The 2SLS
Policy Research Working Paper 10043

# **Abstract**

Using spatial fixed effects and time-varying controls, this paper draws on complete registry data for 1981–2019, supplemented by satellite imagery, to analyze impacts of urban land titling for some 40,000 grid cells in Lesotho. Beyond confirming the short-term impacts on female co-ownership and investment, previously reported, the paper documents medium-term impacts on land sale and mortgage market activity and women’s participation in these markets. Although titling was instrumental in ensuring the effectiveness of an earlier legal reform that allowed women to be co-owners of land, the credit and land market effects are due not to titling but to changes in policy to reduce the transaction cost of registering land that took effect just before titling started. Downward shifts in the time required to register transactions support this interpretation. The paper concludes by discussing what the evidence implies for design and evaluation of property registration programs.

This paper is a product of the Development Research Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at kdeininger@worldbank.org and dali1@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
developed/drawn on by losers from reform (e.g., unemployment payments plus training grants to build up new skills so displaced workers can earn the same wage as before), and perhaps increased costs of crime in so far as its incidence rises with transitional unemployment. Those one -off costs, which need to be weighed against the non-stop flow of economic benefits from reform, tend to be smaller, the longer the phase-in period or smaller the tariff or subsidy cut per year (Furusawa and Lai 1999).<sup>22</sup> They also appear to be small relative to the benefits from reform. An early study by Magee (1972) for the United States estimated the cost of job changes including temporary unemployment to be no more than one-eighth of the initial benefits from tariff and quota elimination. Even assuming that transition took as many as five years, he estimated a benefit/cost ratio of 25. A subsequent study which examined a 50 percent cut in US tariffs (but not quotas) came up with a similar benefit/cost estimate (Baldwin, Mutti and Richardson 1980). In more recent debates about trade and labor, analysts have not found a significant link between import expansion and increased unemployment (see Greenaway and Nelson 2002).

For developing countries also the evidence seems to suggest low costs of adjustment, not least because trade reform typically causes a growth spurt (Krueger 1983). In a study of 13 liberalization efforts for nine developing countries, Michaely et al. (1991) found only one example where employment was not higher within a year. A survey of 18 Latin American countries for the period 1970 to 1996, by Marquez and Pages (1998), found some increases in short-term unemployment, but mainly in countries where the real exchange rate appreciated as a result of capital inflows that had accompanied the reforms. That small short-term

> 22 The adjustment required also tends to be small when compared with the changes due to exchange rate fluctuations, technological improvements, preference
tantly, they could also improve inter-personal comparisons across men. Such advantage is easier to grasp when thinking of another type of individual level data that depends less on household level circumstances. This is arguably the case of (self reported) disabilities. The extreme disability of one senior individual may drastically affect her well-being, but it needs not necessarily have the same impact on other household members. If such disability is accounted at the household level as a moderate deprivation, this leads to many identification mistakes. For instance, the (“extremely”) disabled person may not be identified as poor if her household does not face other types of deprivations. Alternatively, her relatives may wrongly be identified as poor if the household’s moderate disability deprivation pushes the household beyond the identification threshold. Interestingly, accounting at individual level for deprivations that affect one category of individual (e.g., seniors) may improve well-being comparisons across another category of individuals (say children).

# **3.6 Beyond ad-hoc weights**

One key data constraint is that non-monetary data typically do not contain information on the relevant preference u. As a result, selecting appropriate values for the weights of a multidimensional indicator is challenging for practitioners. This explains the popularity of the “equal-weights” procedure, which allows accounting for non-monetary data without requiring any information on u.<sup>60</sup> The main risk is that equal weights deviate too much from the weights that would best reflect preference u (Ravallion, 2012).<sup>61</sup> Another risk is that the “equal-weights” procedure decreases the perceived legitimacy of multidimensional poverty measures because of this obviously ad-hoc procedure.

Improving on this issue requires some information on the relevant preference u. This information can potentially be taken from different sources (Decancq and Lugo, 2013). Subjective well-being data may be one source of information on u, as suggested by Decancq et al. (2015) or by in Decancq et al. (2019)
is less easy to consider these simply as adjustments to the NiJES results reported above. The formal/self-employed differential is slightly, although not significantly higher (11,5 vs 9,4) and the contract/self-employed differential is also broadly similar (10,-i vs 7, 8) with a substtial but insignificant difference in the median. The differential from informal/ salaried employment issubstantially higher (33,25 vs 19,16) and significant different.

7
time. In this case, after the model parameters of the financial variables are computed internally, the DM can use the manual estimation option to produce financial scenarios based on his/her future views or current market based data, considering that estimations based on historical data are not ―desired‖ reflectors of the future state of the economy. Forward looking scenarios can be generated based on medium-term program targets of the government, central bank inflation targets, or market expectations such as forward rates and implied option volatilities. For example, in exchange rate scenario generation, one can adjust the drift term in line with rate of increase implied by forward rates. Once the ―estimated‖ or ―user calibrated‖ parameters are determined for all financial variables, these are used to create final simulation paths.

The simulation algorithms then produce the summary statistics (mean, 70<sup>th</sup> percentile, etc.) regarding the distributions of financial variables. The DM can also interfere at this stage if the summary statistics of these simulated paths reveal that the estimations based on historical data deviates significantly from his/her market expectations. The DM could then calibrate the model parameters by ―trial-error‖ method until he/she reaches the desired distributions.

Even though, the ―manual‖ option provides additional flexibility, in the implementation of TDSM, scenario generation based on automatic model estimation is considered as the default case. In practice, no user calibration is performed without taking into account the scenario trees generated by using the selected financial models described in section 4.2.1.

Once the simulation paths are generated, they are exported to MS Excel platform<sup>13</sup> automatically, where monthly forecasts are converted to quarterly<sup>14</sup> —granularity of the model—data and then these quarterly paths are fed into the cash-flow engine module of TDSM in order to estimate cost and risk profiles of alternative debt strategies.

# **4.3 Cash-Flow Engine**

The cash-flow
|
|Malawi<br>1.5|2424|
|Burundi<br>1.0|1137|
|Niger<br>1.0|1858|
|Sierra Leone<br>1.0|940|
|Source:**World Development Report, World Bank (1999).**||

Our indicator captures the attraction of African countries for FDI when they can rely on everything except for their natural resources and market size. Therefore, it reflects not only policy and political variables but also a series of structural factors such as infrastructure, transport costs and human capital. By indicating the attraction of the FDI business climate for each country, it complements the data collected in investors' surveys and cross-country ranking such as _The Africa Competitiveness Report_ published by the World Economic Forum. One has to keep in mind, however, that our indicator reflects existing rather than potential data/information and, thus, might be a poor predicator of future FDI flows.

_5_
**Figure 16. Export and productivity scenarios: Real per-capita annual consumption growth by household type (%-age point deviation from base)**

<!-- Start of picture text --> 4.0<br>agrpweff<br>3.5<br>indupweff<br>3.0<br>transeff<br>2.5<br>2.0<br>1.5<br>1.0<br>0.5<br>0.0<br>hhd-soci hhd-reti hhd-farm hhd-enter hhd-work hhd-remit total<br>-0.5<br><!-- End of picture text --> Note: For notation, see Table 3.

In Figure 17 we see the results generated by the MAMS poverty module for changes in the national poverty rate, the poverty gap, and the Gini coefficient from _base_ scenario in 2020. We see that although poverty rate and poverty gap<sup>**10**</sup> decrease, the inequality grows marginally in all the scenarios, indicating that these market-based shocks tend to have relatively positive impact on households that initially are somewhat better off. The _indupweff_ scenario, which has the greatest poverty-decreasing potential, is the one leading to the highest inequality increase. Clearly, if more equitable income growth is on the agenda of the Moldovan government, market-driven reforms should be completed by a transfer component to assure a more even diffusion of the benefits. (Two of the following simulations address this issue.)

> **10** The poverty gap index (P1) measures the extent to which individuals fall below the poverty line as a proportion of the poverty line.

27
There are two more time paths that are needed to run the growth simulations – investment share of GDP �� and participation rate ���� . Both these drivers of growth are ~~�~~ �� ~~�~~ expected to play a big role going forward and have been identified as such by the policy makers. For this reason, I consider different scenarios for their time paths that help in identifying their relative importance.

- ��

- **Investment share of GDP: .** Bangladesh has realized continued increase in the ��� ~~�~~

- investment share of GDP during the last 35 years. The investment share of GDP has almost doubled from 1980 to 2015 with most of the increases coming from private investment. Needless to say, capital accumulation has been an important driver of growth in the country. This point is illustrated in the graph below which shows the average GDP per capita growth rate during 2011 – 2015 against the average investment share during the same period for more than 200 countries for which data is available from the World Development Indicators database.<sup>6</sup> The relationship between the two variables is positive and highly significant. However, a more important

<!-- Start of picture text --> Correlation = 0.37***<br>CHN<br>BGD LKA IND<br>NPL<br>PAK<br>0 20 40 60<br>Average Investment Share of GDP, 2011 - 2015 (%)<br>Source: World Development Indicators (***Significant at 1%)<br>10<br>5<br>0<br>-5<br>Average GDP per Capita Growth., 2011 - 2015 (%)<br>-10<br><!-- End of picture text --> question is whether capital accumulation can deliver a sustainable high level of growth going into the future.

Average