rafmacalaba/data-use-mentions-extended huggingface dataset:
data on “raider” attacks from the Center for Public Procedures “Business Against Corruption”. The latter also allows us to make an important empirical contribution to the literatures on state-sponsored violence and property rights. Because raider attacks in Russia are typically tied to real violations of the law, which are then used by the state to expropriate assets in support of private interests; it is difficult to disentangle state-abetted violence from typical law enforcement practices. Existing studies have consequently been limited to surveys of the perceptions of firm managers and qualitative work. Our data from “Business Against Corruption” consists of actual reported instances of violence against firms subjected to multiple reviews to insure it reflects real stateabetted violence against firms (see Section IV). As such, we are among the first to study state-abetted violence and investment in Russia using objective data.

Substantively, our paper can provide important guidance to policy makers looking to improve their area’s business climate and investment levels. Limited time, resources, and political capital often mean that politicians face real trade-offs when making regulatory reforms. This paper intends to provide valuable insights into the importance of lowering overall regulatory barriers versus improving the consistency of regulatory enforcement that should make this trade-off easier to navigate.

We should note at the outset that this paper is an exploratory exercise. Our goal is not necessarily to produce strong causal claims, but to validate whether there is a relationship between our proxies for uncertainty in regulatory enforcement – variation in corruption and inconsistency in property rights enforcement – and investment behavior. We leave a more causally rigorous empirical design for future iterations of this paper. Nonetheless, our results do indicate strong (and negative) correlations between these proxies of uncertainty in regulatory enforcement and the willingness of firms to invest in Russia. Despite our inability to argue strongly for causation, we believe that these correlations are interesting in and of themselves given the relative paucity of tests of the effect of uncertainty in informal practices related to regulation on investment. To our knowledge only Malesky and Samphantharak (2008) and
**_Electricity Sector Reform in Developing Countries, Jamasb, Mota, Newbery, Pollitt_**

proposed are rather limited, there are no time or country effects, no controls for macroeconomic reform or the size of the stock market, and not much is said about the error structure. In addition, the main explanatory variables are dummies.<sup>4</sup> At this stage we can draw some conclusions from this brief review of econometric studies. First, at the current state of reforms in the electricity sector one should be able to work with panel datasets rather than simple cross-section models, preferably containing pre- and post-reform data. As observed in Fink, Mattoo, and Rathindran (2002), estimating a model containing time-series cross-section data usually implies a complicated error structure. Problems such as serial and/or contemporaneous correlation and heteroskedasticity should be addressed in the estimation procedure. Ignoring presence of serial correlation results in consistent but inefficient estimators. Similarly, assuming homoskedastic disturbances when heteroskedasticity is present will still result in consistent estimators, but correction for heteroskedasticity makes the estimation far more efficient (Baltagi 1995). Dealing with contemporaneous correlation across panels is a more complicated matter, considering the availability of data. The appropriate estimation that incorporates contemporaneous correlation would require as many time series as there are panels (in order to satisfy matrix invertibility conditions), which is usually not possible. For example, a dataset with panel of 30 countries would also require data for 30 years.

Second, especially when dealing with a varied sample of (developed and developing) countries and a larger time series, country and time effects should be included in the model. These should be included in the form of fixed effects when we are focusing on a specific set of countries. When we are drawing individual countries randomly from a large population, the use of a random effects specification is appropriate. To strengthen our choice we should perform a specification test (Hausman’s test).

Finally, further empirical research should concentrate efforts in targeting
as in group (ii) (with information campaign). In treated villages, households received cash conditional only on attending the information campaign promoting good health practices and schooling.

In a context that might be similar to the situation many countries might confront when the COVID-19 pandemic eases and finally ends, school enrollment increased nationwide and rapidly in Guinea in the aftermath of Ebola. We find that it increased significantly more in treated villages. From a low baseline of around 40 percent of primary-school age enrollment,<sup>4</sup> treated villages increased their school enrollment by more than 11 percentage points compared to control villages. But this massive increase in school enrollment did not lead to impacts on learning outcomes as measured by mathematics, French and literacy.

This result is in line with the previous literature on cash transfers, which is large and has been reviewed extensively (Fiszbein and Schady 2009; Snilstveit et al. 2015, Ganimian and Murnane, 2016; Glewwe and Muralidharan, 2016 and Molina Millán et al., 2019). The overall conclusion

> 3 In our data, the main reasons given for not sending children to school are 35 percent because of its cost and another 13 percent because of the need for the child to work (at home, field or in town).

> 4 The baseline survey was conducted during the Ebola crisis, but the question on enrollment refers to the situation just before the Ebola crisis.

2
More women are getting education and are more involved in their employment decisions. Thus, in this context, a collective model is also plausible and the household human development investment is a function of women’s decision making power.

One essential element of examining the association between women’s decision making power and human development investment is how to measure decision-making power. In the economic literature, decision-making and bargaining power are measured by the relative income of the male and female heads of household or by the ratio of number of school years completed by female to male head of household. The underlying assumption is that women who bring more income to households or women with a higher level of education are more likely to have greater bargaining power at home. However, these measures are often endogenous because income and education are major determinants of the budget share. On the other hand, the social science literature takes a much broader view on measurement of decision-making power; for example, Adato and others (2003) suggested that each member’s bargaining power is based on four factors: control over resources, influence over the bargaining process, interpersonal networks, and basic attitudinal attributes.

One contribution of this study is the ability to use a set of more direct measures of decisionmaking power from the Pakistan Social and Living Standard Measurement Survey, which asks directly who in the household makes decisions on key issues, such as women’s education and employment, the use of birth control, the number of children, and expenditures. These questions capture factors embedded in the bargaining process that income or education alone could not capture.

3
||**Pooled**<br>**base**|**Pooled**|**2012-2018**<br>**Base**|**2012-2018**<br>**Base**|
|---|---|---|---|---|
||**informal**|**base formal**|**informal**|**formal**|
|**Age group (<30 omit.)**|||||
|30-49|1.120|1.140|0.985|1.963|
||(0.368)|(0.446)|(0.505)|(1.756)|
|50+|1.106|0.775|2.063|2.646|
||(0.434)|(0.360)|(1.480)|(2.703)|
|**N**|788|675|295|190|
|**Pseudo R-squared**|0.193|0.153|0.279|0.192|

# Source: Author’s calculations based on ELMPS 1998-2018

Notes: *p<0.05; **p<0.01; ***p<0.001. Blank cells indicate dropped perfect predictors

# **4.5.4. Formality and growth**

We turn now to our last dynamic outcome, growth (an increase in the number of workers) among surviving firms (Figure 20). Formal firms are slightly more likely to grow than informal firms, although the gap in growth has narrowed over time. In 1998-2006, while 29% of informal firms grew, 35% of formal firms did so. However, in 2012-2018, while 19% of informal firms grew 21
in the burden of care. The country has smaller wage gaps than the OECD average, but this gap is associated with the unexplained component of the observables and is greater at the extremes of the income and education distributions.

# **3. Data**

# **Sources and definitions**

We use the Great Integrated Household Survey (GEIH), a probabilistic sampling survey that gathers data on individuals' employment status, including their occupation, income, access to social security, and job search activities, as well as their demographic characteristics such as gender, age, education level, and sources of income. The GEIH is nationally representative and uses a self-weighted sampling method stratified by unequal clusters. The survey's primary aim is to address the factors that affect labor participation and estimate gender-based labor gaps. Additionally, the survey includes information at the regional, departmental, and department capital levels.

The GEIH's sample frame was updated in 2022 with information from the 2018 National Population and Housing Census (CNPV), resulting in a structural change in trend series compared to the previous sample frame collected until December 2021. Because our work requires comparing results over time since 2015 and using information from the Mission for the Splicing of the Employment, Poverty, and Inequality Series (MESEP) as a source for labor income variables, we use the 2005 frame GEIH data between 2015 and 2021.<sup>5</sup> The labor income variable comprises five categories of income that vary depending on the type of employment relationship, including First Activity Monetary Income (IMPA) for wage earners and self-employed, Income in Kind (IE) for wage earners only, Second Activity Income (ISA) for wage earners, self-employed, and family workers, Monetary Income of the Unemployed and Inactive (IMDI) for the unemployed and inactive, and Income from other sources (IOF) available to everyone. From 2009 onwards, the survey covers 437 municipalities and annually visits an average of 248,000 households concentrated in 22
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# <u>3.1 Background about COVID-19 and vaccination rates in PNG</u>

PNG is the second largest fragile state in East Asia and the Pacific (after Myanmar) with a population of almost 10 million people, over a third of which live below the extreme poverty line (World Bank, 2021). The country is diverse, with over 800 languages spoken, and large pockets of the population are geographically isolated due to mountainous terrain and remote islands (Filer and Wood, 2021). On average, adults have completed less than five years of schooling, which is one of the reasons that PNG ranks in the bottom 20 percent of countries globally in terms of the United Nations Human Development Index (United Nations Development Programme, 2020).<sup>3</sup> Provision of public services, such as access to basic health care facilities, is poor (Howes et al., 2014). PNG also has among the lowest vaccination rates in the world for a range of diseases, which contributed to a polio outbreak in the country in 2018 (Howes and Mambon, 2021; Morgan et al., 2020). In addition, vaccination rates have fallen considerably in recent years (Howes and Mambon, 2021).

Since the start of 2021, the COVID-19 situation in PNG has dramatically deteriorated. The cumulative total of confirmed cases grew from 834 and 9 related deaths on January 1 to 17,775 confirmed cases and 192 COVID-19 related deaths by August 2. Most of these cases were reported between the end of February and start of June (see Figure 2). The actual case numbers are likely to have been much higher due to low overall testing, underreporting and limited contact tracing capacity (World Health Organization, 2021). PNG’s Prime Minister, James Marape, claimed that one in three or four Papua New Guineans may have been infected with COVID-19 in March (McGuirk, 2021). Although the epidemiological curve of confirmed COVID-19 cases began to flatten throughout
In order to estimate poverty trends it is important to make sure that numbers are consistently measured across time (Haughton and Khandker 2014). Firstly, the sampling frame and survey instruments should not change during the period selected for the analysis. As shown in Beegle et al. (2010), changes in recall period and how consumption data are collected may result in significantly different poverty rates. Secondly, the welfare aggregate should be defined consistently across years. For example, excluding health expenditures in the first year and including them in the second will make poverty estimates between these years not comparable. Thirdly, the timing of survey work should be the same across years. This is especially important for the surveys without inter-temporal stratification within a year and with high seasonality in economic activities.

# **3. Expenditure‐based welfare aggregate and its components**

The Household Expenditure and Income Survey (HEIS) has been conducted annually by the Statistical Center of Iran (SCI) since 1963 in rural areas and 1968 in urban areas. The unit record data from 1984 onward are publicly available.<sup>6</sup> The survey is nationally representative and two-stage stratified. Strata information, however, is not publicly available for all years. Households are distributed randomly and evenly throughout the year, making one-twelfth of the sample interviewed each month. However, the month of interview information is publicly available only from 2008 onwards. Sample sizes vary over the years ranging from 5,759 households in 1986 to 39,856 households in 2014.

Gregorian notations for years are used in this report, but the actual survey period is left as it is shown in the HEIS: March to March. For example, year 2010 means the survey period between March 2010 and March 2011.

HEIS includes both demographic and income information but its main focus is on expenditures. Surveys collect expenditure information on more than 1,000 items. The recall period of expenditures for most items is the last month. For durables, some education expenses,
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observed income: Consider the OLS estimator of β based on equation (3): where yi,t−1 = Yi,t−1 − (1/N )<sup>PN</sup> i=1<sup>Yi,t−1.Onecanthenshowunderstandard</sup> assumptions that as the number of observations in the cross-section, N , goes to infinity, The term θOLS is the asymptotic bias and shows that OLS will be inconsistent in general. This inconsistency arises due to the following terms:

- i) E (ui,t, Yi,t−1), the covariance between the current period shock to earnings and last periods measured earnings. The standard concern here is the present of individual fixed effects in the error term ui,t, which will lead to this term being positive. This term will also not be zero if earnings shocks, ui,t are autocorrelated.

- ii) Cov (εi,t, εi,t−1) , the covariance between this period’s and last period’s measurement error terms will be non-zero if measurement errors are autocorrelated. Based on the U.S. validation studies, we would expect this term to be positive.

- iii) Cov ¡Yi,t<sup>∗</sup> −1<sup>, εi,t−1</sup> ¢, the covariance between the measurement error and true earnings. The results of Bound and Krueger (1991) suggest this term

7
minimizes net bias and allows us to distinguish anchoring effects that arise from simply “repeating” the same decision from explicit choices that reveal true WTP. The second measure is to model each binary choice in the MPL with a latent utility model and apply an appropriately scaled random-effects probit estimation to estimate WTP. This approach accommodates inconsistency (MSB) as well as dominated choices, and thus avoids selection biases that arise when dropping or recoding MSB or NSB choices. In addition, it provides identification of average WTP, dispersion of individual WTP, and errors in subjects’ choices, and allows us to explicitly estimate biases that arise from order effects.<sup>8</sup> This ability to learn about both the distribution and mean of individual errors is a key advantage of the MPL method relative to alternative preference elicitation methods.

We show that both non-systematic errors and systematic biases play a significant role in individual choices, using a WTP elicitation example from South Africa. This finding is in line with the literature; in a survey of MPL studies, 17.1 percent of observations included inconsistencies (Filippin and Crosetto, 2016; Crosetto and Filippin, 2016). Research in populations with low literacy and numeracy may be particularly affected: for example, Dave et al. (2010) show that subjects with low math ability make significantly more inconsistent choices. Moreover, while most researchers do not randomize the order in which MPL questions are asked, others have found significant order effects (e.g. Channa et al., 2021). In our application, the order of choices within MPL is an important source of bias in measured WTP, but the order of options within choice has little effect on reported valuations.

We compare our WTP estimates with those we would have obtained using MPL design and estimation approaches found in the literature, and show important differences. First, WTP estimates obtained from non-randomized MPL cannot differentiate true WTP from existing order bias, and some MPL even create new sources of bias (such as MPL
varies across time as some provinces are well protected for hazard events with low return periods, but would be seriously exposed in rare shocks. Finally, exposure varies across sectors, as some are more subjected to and affected by natural forces than others, and the contributions provinces make to the aggregated economy differ as well. Such complexity in exposure calls for sophisticated policy measures. Fortunately, the Vietnamese government is aware of these risks, and is actively trying to minimize them. An overview of existing policies and recommendations on how these can be extended to deal specifically with the hazards revealed here can be found in the main report, part 3.

> 1 Information from Fathom Global, https://tinyurl.com/sfzgo7z

> 2 Based on data provided by the Vietnam National Disaster Management Authority (VNDMA).

> 3 Data on aquaculture ponds and saline intrusion in the Mekong River Delta where obtained from Southern Institute of Water Resources Research (SWIRR) in Hanoi, Vietnam.

> 4 Information from World Bank Open Data available at https://data.worldbank.org/

> 5 For a more detailed explanation of assessment of the coastal protection system in Vietnam, see the technical background paper by Van Ledden et al. (2020).

> 6 The process can be imagined as laying a raster grid consisting only of squares on top of the pond data in polygon format, which consist of many irregular shapes. Due to the fixed grid structure of the raster, not all information from the irregular polygons can be retained. For each square in the raster grid, the rasterization algorithm decides whether to classify the square as “pond” or “non-pond”. In the first approach, only squares in which a pond is located in the square’s center are classified as “pond”. In the second approach, all squares that contain ponds anywhere are classified as “pond”. Overall, the first approach can be expected to underestimate the total pond area while the second approach is more likely to overestimate
lnXijt = β0 + β1EIAijt + δit + φjt + γij + εijt (6) where Xijt is a vector containing trade flows from country _i_ to _j_ in time _t_ for total aggregate (X) bilateral trade flows as well as intermediates (Int), and other (Oth) goods categories, per the BEC classification breakdown. The category for other goods includes consumption goods, capital goods, and goods not specified elsewhere (Appendix 2). EIAijt is a time-variant binary variable (described in section 2.2) equal to 1 if exporting country _i_ and importing country _j_ have an EIA in force in year _t_ . The EIA variable used in the specifications presented in this section was constructed from the DESTA dataset. The terms δit and φjt are exporter-time and importer-time fixed effects, respectively, that control for time-varying country-specific variables that include both bilateral trade costs and GDPs. Importantly, these controls include both “multilateral resistance” terms Ωit and Pjt described in equation (4). Lastly, γij is a country-pair fixed effect used to capture timeinvariant factors influencing trade flows (and commonly used in gravity equations, such as common language among trading partners, contiguity, common colonizer, etc.).<sup>13</sup> In addition to estimating this equation for disaggregated goods, our baseline specification differs from that in BBF in three key ways. First, whereas BBF estimate their version of equation (3) using five-year differenced data, we choose to estimate our gravity specifications in log-level form as in Baier and Bergstrand (2007). This implies that we are going to be looking at the effect of EIAs on trade volumes, instead of the growth rate of trade flows. Second, our country sample includes 89 additional countries beyond both Baier and Bergstrand (2007) and BBF. Third, whereas BBF examine the impact of agreement depth on trade flows, we use a binary EIA variable in order to keep the discussion focused around the effects of geographic proximity. We do follow the Baier and Bergstrand (