rafmacalaba/data-use-mentions-extended huggingface dataset:
and indirectly inspected firms to this weighted sample of control firms, in terms of the characteristics available from the listing survey. Although this offers far fewer variables for checking balance than is possible for the communication and free cost treatments, the results are still reassuring, with only one out of ten coefficients being significant at the 10 percent level.

# **4.5. Follow-up Surveys**

A very short phone survey of firms selected for the communication and free cost treatments was carried out between April 10 and 18, 2012 by Sensus. The purpose of this survey was to follow-up with the firms that had received the communication or free cost content and see whether they had started the process of formalization, their intent to formalize, and whether the information in the brochure had been useful. It was also intended to serve as a last prompt to use the information and free cost offer. This survey was not given to firms that Sensus had not made the treatment offers to, and so was fielded to 464 firms, of which 367 responded (79 percent). In this survey 86 percent of the firms said 14
4: Proportion of respondents who agree that the response was justified in the punishment survey. Standard errors have been clustered by village. Of the 241 respondents, 38 were not able to answer for vignette V4, one was not able to answer for V1, and one was unable to answer for V3. Among the men who did respond for V4, 99 were low-caste and 104 were high-caste.

The caste difference in the proportion who would respond with aggression is in every case statistically significant ( _p <_ 0 _._ 01). A few examples of aggressive responses are:

- “I would do the same as Mahesh because I lost my honor.”

- “I would do tit for tat, otherwise people will think I am weak.”

- “I would do the same, it is wrong to cause a loss.”

- “I would do the same as Mahesh because a mistake is a mistake.”

For the low-caste respondents, non-aggression was much more common. 78 percent of responses for V1-V3 and 63 percent of responses for V4 indicated a non-aggressive approach, and suggested intent to solve the problem, forgive, reconcile, or let things be. Examples of non-aggressive responses were:

- “I would talk and find an agreement.”

- “I would deal with it peacefully.”

- “I would do nothing, tolerate it.”

- “I would forgive.”

18
# **<mark>Figure 2</mark>**

<!-- Start of picture text --> Contribution of the Cyclical Component of each Social Indicator to its Total Variance,<br>World Sample<br>80<br>Components of the HDI<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>Unemployment Rate Monetary Poverty HDI Income Education Life Expectancy<br>Share of the Cyclical Component to Total Variance (as percentage)<br><!-- End of picture text --> Notes: Poverty line of 5.50 dollars per person a day (2011 PPP). The figures reported correspond to the median value across countries. Black lines denote two standard error intervals (weighted by real GDP). Estimations of the contribution of the cyclical component based on a univariate model.

Sources: Authors’ estimations based on PovcalNet (March 2019), SEDLAC (CEDLAS and World Bank), Human Development Report Office (United Nations), and WEO (October 2018).

18
living in the same place of their upbringing.<sup>8</sup> For each subgroup _d_ of children (0-15 years old)— classified according to their gender, urban/rural zone, and subnational region of residence—we calculate the share of the population with access to each opportunity in the 1980s using census data and compare it to the corresponding level of household income in the most recent household survey. The following quadratic form summarizes the estimation: 2 (1) dd dd,jj dd,jj YY = αα+ ΚSS + ΡSS In this case, is the share of the population in subgroup _d_ with access to opportunity _j_ in to dd,jj the oldest census available for each country. For most countries in Latin America, the oldest census SS available is from the decade of the 1980s. , in contrast, is the mean income of subgroup _d_ in the dd most recent household survey available. Finally, YY Κ and Ρ are the estimators of the regression.

Given that household surveys are not designed to provide estimates for such levels of demographic and geographical disaggregation, it is necessary to borrow strength from large auxiliary data—like censuses—and take advantage of their level of precision. This information is then combined with data from the household surveys to implement an econometric model that yields reliable small area estimates (Rao and Molina, 2015). The small area estimation 8 The assumption of no migration is in fact conservative for the purpose of this paper. In most of the cases, people moved to a better place than the current one. If that is the case, people who were born in places with low access to opportunities would move, in general, to places with better access to such services, so that their final current level of income is similar to the level of income of those who were born in the better-off places.

19
that the World Bank may face in preparing and supervising projects. With reference to the latter, the practical utility of the DeCODE framework has already been demonstrated within country-<sup>1</sup> and sector-specific challenges.<sup>2</sup> This paper contributes to our understanding of the impact these challenges have relative to project outcomes and World Bank performance overall.

Second, the identification of delivery challenges provides a way to capture variation in project performance across a broad set of logged project case histories. By drawing on implementation completion reports, we strive to aggregate and analyze the impact of obstacles that are organically defined by those who supervise disbursement projects in the first place. Put together, this allows for a holistic analysis of success and failure across the project’s lifespan. By using a cross-cutting measure of delivery challenges, this paper identifies the most likely issues projects face, as well as the magnitude of effects posed by said challenges. Matching these factors to the sequence in which delivery challenges are likely to be expected, we develop a typology for structured and focused analysis. This is especially helpful in addressing issues related to model selection and diagnosis, as it provides a means of simultaneously assessing the various dimensions along which projects have historically under-performed.

The data and methods proposed above serve to generate standardized coefficients for the most impactful challenges faced in the implementation of World Bank projects. Though some of the measures are comparatively less informative, the examination of the full corpus of delivery challenges allows us to weigh the relative impact of each. The paper is organized as follows. Section 2 offers an overview of the relevant literature on project effectiveness. Section 3 summarizes the conceptual framework, including a description of the delivery challenge taxonomy, the outcome measures tested, and the methodology. Section 4 presents the results of the analysis, Section 5 presents findings on World Bank performance and outcomes, and Section 6 concludes.

> 1Link: How to Overcome Delivery Gaps: Some Examples from Korea

> 2Link: Addressing Delivery Challenges
Fiji, for example, Rewa Province (which captures the urban area of Suva, and had a population of around 101,000 at the time of the 2007 census) has a remoteness estimate (population-weighted average distance to all provinces) of 82km and an internal dispersion of 5km, while Rotuma Province (population 2,000) has a remoteness estimate of 318km and internal dispersion of 130km. Similarly, in Tonga, Tongatapu Province (which captures Nuku’alofa and had an estimated population of around 75,000 in 2016) has a remoteness estimate of 65km with internal dispersion of 9km, while Niuas Province (population 1,200) has a remoteness estimate of 569km with internal dispersion of 100km. The results hint at the extent to which agglomeration opportunities on the one hand, and service delivery challenges on the other, might vary across provinces within any given country.

> 14 See Appendix C for further details on the selection of regions and hubs.

31
considering only the first stage of the design. In step 2, model parameters are newly generated in each simulation. Finally, in step 3, location effects are generated from their conditional distribution given the sample residuals (determined by the nested error model under normality), and household-specific errors are generated from their distribution under the assumed nested error model with normality.

Even if the bootstrap procedure is not the same, standard errors are obtained similarly as in the traditional ELL approach and are thus different from MSEs. Note that the traditional ELL approach was initially based on the multiple imputation literature. According to Rubin (1996, and noted in StataCorp, 2019), the objective of multiple imputation is not to re-create the missing data as closely as possible, but to handle it in a manner that allows for statistical inference.

# **5 Comparison of CB-EB, traditional ELL and EB methods**

Because EB prediction uses the survey data to estimate the location effect, its benefits are evident only in locations present both in the census and the survey. In the traditional ELL approach, the location effect was originally at the cluster level and, in most surveys from developing countries where the World Bank focuses its efforts, the sampled clusters represent a small percentage of all the clusters in the country. This may also be the case for locations above clusters. For example, in a recently completed exercise in Moldova, the survey only contained 129 comunas out of a total of 901. Consequently, only a small share of these comunas benefit from EB prediction (Corral and Cojocaru, 2019). Nevertheless, differences also arise due to the computational procedures used to obtain the point estimates and their corresponding measures of noise.

12
in the OECD sample. “Peace” is a dummy that takes a value of 1 in country _i_ during year _t_ there is no civil war, defined as violence above an intensity of 0.008 battle-related deaths per 1000 inhabitants. “Persistence of peace” is the likelihood, calculated at the country level that a year of peace is followed by another year of peace. “Persistence of conflict” is the likelihood that a year of conflict is followed by another year of conflict. “First five years of peace” is a dummy that indicates if the end of the most recent conflict has been less than five years ago. “Fragile peace” is a dummy that takes a value of 1 if a year is peaceful, within five years of the last conflict and reverts back to conflict within the next five years.

We illustrate this channel by estimating an actual risk model for the critical period of the first five years after conflict and bringing this model to the FDI data. The technique we deploy is akin to a two stage least squares IV estimation. We first estimate a risk model and then use the fitted values of this model to explain investment. However, the aim here is not to provide identification but to illustrate the plausibility of the idea that multinationals build expectations based on observables and are influenced by these in their decision to invest. The model we use is based on two variables highlighted in this report: “political exclusion” and “refugees”. In Table 10 column (1) we use the share of the population that is excluded from political power, the share of the population that is discriminated, the number of refugees per capita and a dummy that indicates the absence of refugees to predict fragility. In this regression we focus on all years that were five years after conflict and use our explanatory variables to distinguish the years that are followed by a relapse to conflict from those that do not. All variables have the expected sign and are significantly correlated with conflict relapse. The fitted values
characteristics and no clear comparison group, such as natural gas pipelines and commercial banks, or industries with rapid technological change over the study period, such as wired and wireless telecommunications. There are case studies of interventions in some of these industries (Casta ̃neda Sabido and Elbittar, 2013; Castellanos, Del Angel<sup> ́</sup> and Garza-Garc ́ıa, 2016).

We cover a greater subset of investigated industries than several previous studies of antitrust enforcement, which faced the similar challenge of matching relevant markets to plausibly similar controls. In his analysis of the effects of United States antitrust indictments, Sproul (1993) identifies relevant market control prices for 25 of “roughly 400” price-fixing indictments under the Sherman Act. In his analysis of United States antitrust decisions regarding horizontal mergers, Kwoka (2014) identifies 49 transactions for which rigorous retrospective studies of their effects are available. These transactions span the period 19762009, and during the period 1996-2011 alone, the FTC investigated 264 horizontal mergers.

**The Economic Census** Having defined treatment and control industries in each case, we construct a series of economic variables for these industries using the establishmentlevel micro-data of the quinquennial Economic Censuses covering the calendar years 1993 to 2018. These censuses include every establishment in Mexico in every sector except agriculture, forestry, and hunting, and survey all physical establishments, including sole proprietorships and firms with so-called informal workers that do not contribute to the national social security scheme. The censuses are carried out by Mexico’s national statistics body INEGI ( _Instituto Nacional de Estad ́ıstica, Geograf ́ıa e Inform ́atica_ ), which provided us with access to the establishment-level microdata that we use to construct key variables: sales, employment, wages, operating profit margin, and total factor productivity (TFP). Sales and employment are measured as totals for each industry and year. Wages are measured as total remuneration to employees divided by total employment for each industry and year. The operating profit margin is measured as value-added minus
a matter of interest on its own. Health is an important aspect of human welfare, and health inequality is relevant to the equity of health, a broader notion (Sen, 2002). Given that child health indicators are important in predicting the future social standing of the child (Nystrom Peck, 1992; Montgomery et al., 1996), the existence of child health inequality in itself is indeed a concern.

In what follows, we shall focus on the decomposition analysis of inequality indicators. The analysis we carry out is different from most other studies on health inequality decomposition in at least three respects. First, despite the growing literature on health inequality, there have been relatively few studies carried out at a spatially disaggregated level, especially in developing countries. The methodology we developed in this study allows us to analyze health inequality at a level of small geographic areas even in the absence of a large number of observations of health indicators collected at that level.

Second, while the majority of health inequality literature uses mortality data, we use the standardized height and weight. Because death is a rare event, it may not be the best indicator at a very disaggregated level. Also, the probability of death is influenced by the individual’s experiences over the course of his/her life. This makes it difficult to compare populations with different demographic groups. Standardized height and weight for children have some advantages here. Moreover, anthropometric indicators are likely to be more reliable than mortality data in countries like Cambodia where vital records are not fully maintained. Pradhan et al. (2003) discusses additional advantages of the standardized height measure.<sup>13</sup> Third, we emphasize direct policy implications of inequality for targeting. Geographic targeting is meaningful only when there exists spatial heterogeneity in malnutrition or consumption. Hence, the between-location component of overall inequality is relevant to potential gains from geographic targeting. In many of the health inequality studies, the focus

> 13Pradhan et al. (2003) prefer height to
private sector provided by commercial banks and, if data are available, by other financial corporations. Median debt, based on 148 EMDEs and 29 LICs.

A.B. Non-LIC EMDEs= Emerging market and developing economies excluding LICs; LICs= Low-income countries.

B. Orange lines indicate interquartile ranges of Herfindahl-Hirschmann concentration index, which measures the degree of product concentration, with values closer to 100 indicating a country’s exports are highly concentrated in a few products.

D. To ensure comparability, 2010-16 is model-predicted commodity demand growth.

8
must be both comprehensive and updated. This means that the SDS would need to be expanded to eventually cover all occupations in Vietnam. Plans would also need to be made to periodically review occupations already covered by the SDS. These efforts would require investment in survey administration, analysis of data collected, and dissemination of the results. Complementing the SDS with online job vacancy data could help offset some of the costs of frequently updating the occupational information collected by the SDS. Online vacancy data on skills and tasks could help keep occupational profiles fresh in between rounds of data collection, which could likely take place at greater intervals if complemented with online vacancy data. For example, O*NET uses online job postings to identify tools and technologies, to inform task statement taxonomies, and to identify new job titles to update occupational profiles. Incorporating additional data sources into occupational profiles is also important to ensure that a complete picture is created. Labor Force Survey data, firm surveys, consultations with employers and other labor market stakeholders, and many other sources are useful complements to the SDS and online vacancy data. The SDS is thus part of a labor market information ecosystem with the comparative advantage of producing comprehensive, granular information about occupational skills and tasks.

25
25 only). Second, we used some non-nested tests to select between "competing" models: a spreads only model vs. a flows only one. Appendix 7 presents details of the model selection procedures, but we find no compelling evidence for the more general model, except for the cases of Colombia and Mexico, and probably in Brazil. Future versions of this paper that allow for different specifications should take this into consideration.

# b) _What Accounts for Real Interest Rate Variability ?_

Looking at the real interest rate variance decomposition (Appendix 6), several things are worth noting. First, this set of variables explains reasonably well real interest rate variability in the sample countries. In Argentina, Mexico and Peru, shocks to the external capital markets (external spreads) were the main factor explaining real interest rate variability.

The second most common source of real interest rate variability was the real exchange rate. While in Argentina the importance of this variable quickly fades away, in Mexico, Peru and Brazil its relative weight is stable and of significant magnitude.

In Chile and Colombia the "own" shocks to interest rates are the most important factor explaining interest rate variability. In these two cases, this variable can be considered largely exogenous with respect to the rest of the variables in the model. This finding might be related to the fact that these two countries imposed some type of capital controls during this period.

# _c) Determinants of Real Exchange Rate Variability_

Variance decomposition of real exchange rates (Appendix 6) shows that in most countries, shocks to the external capital markets are the main explanatory variable (besides the own shocks) of real exchange rate variability. This contrasts with the Roldos-Hoffmaister (1997) result according to which domestic shocks are the main drivers of real exchange rate variability.

# VI. **Conclusions**

Based on the country-specific Generalized Variance Decompositions we found that during 1992-1998 output variability in most Latin American countries was associated with domestic factors. These results coincide with other work (Hausman-Gavin (
clear that in Ethiopia (1) there are no land markets; (2) there is a limited supply of rural land; and (3) landholders are not mobile. These conditions have important implications for guiding the assumptions used in the tax incidence analysis in section 3.

The gender productivity gap (Aguilar et. al. 2015; Bezabih et al. 2016; Ghebru and Holden 2015) and gender inequities in agriculture and consumption (Teklu 2005) have implications for the burden of area-based land taxes. In Ethiopia, it is considered inappropriate for women to engage in agricultural work, particularly ploughing with oxen (Ghebru and Holden 2015; Holden et al. 2011; Teklu 2005). When male-labor is not accessible, households without male adults rent out their land through sharecropping arrangements, often with male relatives (Bezabih et al. 2016; Ghebru and Holden 2015; Teklu 2005). However, women prefer to hire male laborers because sharecropping agreements can cost half of the crop (Teklu 2005). Sharecropper productivity is also lower on female-owned than on male-owned land (Ghebru and Holden 2015). Women farmers, the majority of whom are either divorced or widowed, have less access to land, agricultural inputs, and agricultural extension services, and their productivity is lower (Aguilar et al. 2015). Consequently, area-based land tax could constitute an implicit gender bias because women are likely to face a heavier tax burden than men when there is a gender productivity and consumption gap.

# **3. Data and Methodology**

# **3.1 The Ethiopia Socioeconomic Survey (ESS4)**

We draw data from the ESS4 2018/19, which collected general information about, for example, sociodemographic characteristics, asset ownership, and agricultural activities, and also asked about whether and how much households paid in land use fee and agricultural income tax (World Bank 2020a). It also elicited information about the employment of individuals, nonfarm enterprises, and ownership and rights to land and other assets.