rafmacalaba/data-use-mentions-extended huggingface dataset:
The recovery time estimation requires assessing the state of existing assets, evaluating possible damages, and estimating the construction works capacity and availability of financial and human resources. In addition, damage assessments and other estimations can be used to calculate the workforce needed in the event of a disaster. Furthermore, the gap between the workforce needed to recover functionality and actual available personnel provides essential inputs to estimate the time for recovery. If damage assessments are not available or not possible to estimate, proxies using historical data may provide an estimate of the likely resource demands and time required for recovery. Any of these cases will require empirical data obtained through expert interviews and by examining the input requirements for recovery in similar historical cases.

The time for recovery ( TfR ) associated with the various dimensions of functionality (i.e., lifesaving, relief, and economic) may be different. For lifesaving, functionality is reestablished with a minimal level of restored access (e.g. if one lane of a highway clears for passage of emergency vehicles). However, pre-event traffic demand cannot be restored to a functional level until the disrupted road is open to the public again. The time for recovery in terms of the economic dimension of functionality is often longer than that of lifesaving and relief.

In Japan, for example, a post-tsunami road clearance plan is currently under development that specifies required recovery times. The plan focuses on highways threatened by a potential large-scale tsunami resulting from an expected Nankai Trough Earthquake with 9.0 SI. At Wakayama Prefecture, the plan requires the clearance of at least one lane of the connecting highways leading to the coastal areas from inland highways within 24 hours after a tsunami. Within 48 hours, important places (e.g., city halls) in coastal areas are to be connected with inland highways by clearing at least one lane of coastal highway. All coastal highways required to provide life-saving services, conduct search and rescue activities, and deliver emergency relief must be cleared within 72 hours. To implement this
(1999) and Frank and Hummer (2002) choose to treat migration and remittances as exogenous, and as a consequence, it is likely that their estimated effects of migration are contaminated by selection bias. This paper uses a broad representative survey on both health measures studied by these authors, and explicitly deals with the endogeneity of migration. In addition we also consider the impact of migration on preventative health care. Previous research has found an important role for migration networks in providing information about the border crossing process (Espinosa and Massey, 1997) and about labor market opportunities in the U.S. (Munshi, 2003). Our paper provides a first investigation of the channels through which migration affects child health, and shows that the migration process can also result in improved health knowledge.

The remainder of the paper is structured as follows: Section 2 describes the ENADID data to be used in this study; Section 3 outlines the possible selection effects arising from migration and the instrumental variables method to be used; Section 4 estimates the impact of migration on fertility; Section 5 provides the main results of the effects of migration on infant mortality, birthweights, and preventative care; Section 6 investigates the channels through which migration impacts health outcomes and Section 7 concludes. An appendix provides further details on the construction of our measure of health knowledge.

# **2. Data**

This paper uses data from the 1997 Encuesta Nacional de Dinámica Demográfica (ENADID) (National Survey of Demographic Dynamics) conducted by Mexico’s national statistical agency, the Instituto Nacional de Estadística, Geografía e Informática (INEGI) in the last quarter of 1997.<sup>6</sup> The ENADID is a nationally representative demographic survey providing information about fertility and contraceptive practices, mortality, and migration. As will be detailed below, our analysis uses existing migration networks within each community in order to control for the non-random selection of migrants. The theoretical foundations of our work apply most directly to migration networks in rural communities, so
Review of Economics and Statistics_ , 88(4): 641–658.

- Shingal, A. 2015. “Econometric Analyses of Home Bias in Government Procurement,” _Review of International Economics_ , 23: 188–219.

- Shingal, A. and V. Ereshchenko. 2020. Public procurement, in A. Mattoo, N. Rocha and M. Ruta (eds.), _Handbook of Deep Trade Agreements_ . Washington, DC: World Bank.

- UNCITRAL. 2014. _Model Law on Public Procurement_ . Vienna: UNCITRAL Secretariat.

- Vecchi, V., N. Cusumano and E. Boyer. 2020. Medical Supply Acquisition in Italy and the United States in the Era of COVID-19: The Case for Strategic Procurement and Public–Private Partnerships, _American Review of Public Administration._ At: <u><mark>https://doi.org/10.1177/0275074020942061</mark></u>

- World Bank. 2017. _Benchmarking Public Procurement: Assessing Public Procurement Regulatory Systems in 180 Economies_ . Washington, DC: World Bank.

- World Bank. 2020. Doing Business Database, Contracting with the Government Data. Washington, DC: World Bank. <u>https://www.doingbusiness.org/en/data/exploretopics/contracting-with-the-government</u>

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Policy Research Working Paper 11062

# **Abstract**

This paper presents the results from a novel field experiment that examined the impact of in- creasing the presence of revenue authority officers on tax compliance and tax morale among small and medium-size enterprises in a lower-income country. The experiment was embedded in the implementation of a representative, face-to-face survey of SMEs across mainland Tanzania. An independent survey firm was accompanied by Tanzania Revenue Authority officers, who observed the interviews in a randomly selected set of urban and peri-urban wards. This translated into a temporary increase in the presence of tax officers throughout parts of the country. The findings indicate that an increase in tax officer presence did not have a significant overall impact on tax compliance and tax morale among SMEs, as measured using a combination of administrative and survey data. However, there were short-term increases in compliance in the largest city and sustained increases in tax morale in the rest of the country. A follow-up survey suggests that these results were likely driven by an increase in the perceived credibility of enforcement rather than meaningful increases in perceptions of facilitation and trust.

This paper is a product of the Development Impact Group, Development Economics and the Poverty and Equity Global Department. 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 jkarver@worldbank.org. A verified reproducibility package for this paper is available at http://reproducibility.worldbank.org, click **here** for direct access.

<!-- Start of picture text --> TRANSPARENT<br>ANALYSIS<br>G<br>R E<br>N<br>S<br>I P<br>Y E<br>K A<br>C A<br>R P<br>
|0.50|0|1|0.642|0.552|(0.275)|
|Reasons to not complete the survey|||||||||
|Refusal to response (%)|294|0.67|0.47|0|1|0.686|0.619|(0.275)|
|Connectivity problems (%)|294|0.33|0.47|0|1|0.314|0.381|(0.275)|
|_Panel B: Sensitive information measures using data from..._|||||||||
|Knowledge incorporation survey|||||||||
|Stress high (%)|305|0.43|0.50|0|1|0.584|0.370|(0.001)***|
|Physical punishment is effective (%)|305|0.54|0.50|0|1|0.539|0.546|(0.912)|
|Baseline survey|||||||||
|Stress high (%)|358|0.31|0.46|0|1|0.295|0.324|(0.558)|
|Physical punishment is effective (%)|358|0.48|0.50|0|1|0.500|0.456|(0.407)|
|Follow - up survey|||||||||
|Stress high (%)|265|0.17|0.38|0|1|0.202|0.147|(0.243)|
|Physical punishment is effective (%)|265|0.37|0.48|0|1|0.341|0.390
**Figure 5. Population out of the labor force, by** **<u>gender, age, and locality</u>**

<!-- Start of picture text --> 100%<br><!-- End of picture text --> <!-- Start of picture text --> 80%<br>60%<br>40%<br>20%<br>0%<br>Male Female Male Female Male Female Male Female Male Female Male Female Overall<br>15-24 25-34 35-44 45-54 55-64 Share in LF Population<br>Sudan Urban Rural<br><!-- End of picture text --> _Source:_ World Bank staff calculations based on NHBPS 2014/15. Note: LF means “Labor Force” Being a housewife and full-time student are the main reasons why women and youth are out of the labor force, respectively. More than two-thirds (70 percent) of women reported being out of the labor force because they were either a housewife or homemaker , compared to only 2 percent for men. Among youth, 64 percent reported the reason as being full-time students. Nearly half of the household heads were in the labor force, but only 52 percent were employed (Figure 6).

**Figure 6. Reasons for being out of the labor force**

<!-- Start of picture text --> 100%<br>Income recipient<br>80%<br>24%<br>2% Pensioner/retired<br>36.6%<br>60% 70% Disabled/too sick<br>40% 79% 93% 89% Too old<br>73%<br>64%<br>60%<br>48.5% No hope to find job<br>20%<br>21% Full time<br>9%<br>0
– indirectly varied somewhat in size, from about 5,500 to 9,000 according to the 2001 Census. Assuming an average household size of 5.5, and allowing for population growth in the interim, the typical catchment area contains about 1,400 households. Deflate this to 1,200 to allow for the fact that as many as one half enjoy only an indirect connection. To arrive at an estimate of the benefit per household, I employ a related estimate from Khandker _et al_ .’s (2009) study of rural roads in Bangladesh, namely, that the ‘commercial’ benefit is 11 percent of per capita household consumption. In 2001, the average household income in the 30-village sample referred to above was Rs 16,600 (van Dillen, 2008). Since average savings rates in these communities are surely low, set average household consumption at Rs 15,000, thereby yielding a corresponding commercial benefit of Rs 1,650 a year, or about Rs 2,250 in 2008 prices. Scaling up by the effective number of households in the catchment, we obtain an aggregate annual benefit in the commercial sphere alone of Rs 2.70 million. At _ρ_ = 5.5 percent, the present value thereof is Rs 38.70 million, which handsomely exceeds the present value of the investment costs in the range of Rs 22.9 – 34.4 million. At _ρ_ = 0 _._ 10 percent, the present value of the ‘commercial’ benefits is Rs 25.30 million, which lies in the middle of the range of _PV_<sup>_∗_</sup> ( _I_ ; _ρ_ = 0 _._ 10), namely, Rs 20.5 – 30.7 million. With the benefits in the spheres of education and health still to come into the reckoning, there is every prospect that all these roads will indeed pass at the test rate of 10 percent, even if the flow of benefits does not grow
widely used over the past few years for fiscal incidence analysis in both developing and developed countries.

The application of this fiscal incidence framework requires both microdata and macrodata. The microdata come from the second wave of the National Survey on Household Living Conditions and Agriculture<sup>6</sup> (ECVMA), and the state budget and national account data provide the macrodata. The ECVMA is a survey analogous to the Living Standards Measurement Study-Integrated Survey on Agriculture (LSMS-ISA), which is a program of the World Bank Group designed to collect nationally representative data at the household and individual levels on a variety of social indicators, such as consumption and agriculture. The LSMS-ISA is a survey conducted every three years. The second wave of the panel survey in Niger was implemented in 2014, covering both urban and rural areas in all regions of the country. The ECVMA sample was chosen through a random two-stage process. In the first stage, a number of enumeration areas were selected with probability proportional to size, using the 2001 General Census of Population and Housing as the base for the sample. In the second stage, 12 (urban) or 18 (rural) households were selected, with equal probability in each enumeration area.

The state budget and national accounts data come from Niger’s BOOST,<sup>7</sup> and the Statistics Book of Niger’s National Institute of Statistics. Government social spending in education and health come from BOOST, which is a World Bank Group project to enhance budget analysis across the globe by improving access to government expenditure data and linking spending to outputs and outcomes to the extent of possibilities. Data on public taxes come from the Statistics Book. The fiscal system analyzed here includes both direct interventions (direct taxes and direct transfers); and indirect interventions, such as value-added tax (VAT), excise tax, and other indirect taxes; and the monetized value of public education and health services. Additional details on the application of the CEQ in Niger can be found
Table 2: Transition from Non-Statutory to Statutory Right (Residential, Probit)

|Dependent variable: Transi|tion NS to|S|||
|---|---|---|---|---|
|Distance to CBD (km)|-0.041<sup>_∗∗∗_</sup>|-0.047<sup>_∗∗∗_</sup>|-0.056<sup>_∗∗∗_</sup>||
||(0.010)|(0.011)|(0.014)||
|Distance to road (km)||-0.080<sup>_∗∗∗_</sup><br>(0.019)|-0.081<sup>_∗∗∗_</sup><br>(0.021)||
|Log(area) (m2)|||0.25<sup>_∗∗_</sup><br>(0.080)|0.23<sup>_∗∗_</sup><br>(0.071)|
|Water dummy|||-0.41<br>(0.44)||
|Electricity dummy|||1.09<br>(0.98)||
|South bank dummy|||-0.42<sup>_∗∗_</sup><br>(0.15)||
|Log(land price) (CFA/m2)|||0.039<br>(0.071)|0.13<sup>_∗_</sup><br>(0.056)|
|Observations|777|777|777|777|

Note: Standard errors in parentheses.
the EBP model is preferred in many applications is its transparency; a nested-error regression model allows for a straightforward estimation of linear coefficients with random effects specified at the target area level. A simple table of coefficients indicates exactly how each variable is related to the measure of household welfare. EBP estimates are also “design consistent,” in the sense that the estimates converge to the population value as the sample becomes large. On the other hand, machine learning methods, while possibly generating more accurate predictions, suffer from a lack of parsimony and transparency (Efron, 2020). While we employ Shapley decompositions to shed light on which predictors have large impacts on predicted outcomes, it is not straightforward to understand the relationship between the set of predictors and the prediction. In addition, much of the formal statistical theory related to measuring the uncertainty associated with predictions from tree-based machine learning is new (Athey et al., 2019). To the best of our knowledge, this is the first paper to show rigorously that a two-stage, residual bootstrap can estimate confidence intervals at least as accurate as those currently used for EBP predictions, when evaluated against unit-level census data as ground truth. In cases where economists and statisticians are willing to sacrifice parsimony and transparency to achieve more accurate predictions and the sample data are sufficiently rich to train accurate machine learning models, the availability of a simple and accurate bootstrap method for estimating uncertainty surmounts a crucial barrier to the use of tree-based machine

> 3For example, most of the predictors we use in this paper are (freely) available through Google Earth Engine.

6
To investigate the importance of the quality of economic environment in a given exporting ACP country an _indicator of economic freedom_ was used ( _Freedomindex_ ). The index was obtained from the Freedom House "Freedom in the World Countries" database.<sup>13</sup> This is a database containing an annual comparative assessment of the state of political rights and civil liberties in 192 countries and 18 related and disputed territories. The lower is the index the more economic freedom the country has. We expect that countries with greater economic freedom are more open and more likely to trade.

To capture _historical linkages_ between trading partners two zero-one type dummy variables were included in the regression. _FrenchExcolonyij_ and _NonFrenchExcolonyij_ take the value of 1 if the exporting country (i) was a colony of France or other partner country(j). Colonial links often reflect not only historical ties but also that the traders of the two partner countries can speak the same language. If a country was an ex-colony of its trading partner trade between the two countries would probably necessitate lower transaction costs thus more trade. A separate dummy is included for non-French ex-colonies and French excolonies because we expect that there might be differences in the intensities of the trade links for French ex-colonies.<sup>14</sup> In many aspects, such as size of the economy or the level of development, South Africa differs from most of the other countries in our sample. In order to avoid having specificities of South Africa drive our results, a dummy taking the value 1 if the exporting country is South Africa is included in the regressions. _DUMijk_ are a set of _k_ dummy variables for agriculture, textiles, clothing, footwear, machinery and mineral products.<sup>15</sup> _Quotak_ is a dummy which takes the value 1 if the product was eligible for quota preferences and zero otherwise. The dummy for quota is included in the regression because it is likely that the circumstances for products entering under a preferential quota are different than for products entering
of the coe¢ cients, but interaction terms are not signi...cant, possibly because the sample is smaller.

We also investigate whether cash and in-kind grants have a di¤erentiated e¤ect on capital retention depending on entrepreneur self-control. Figure 5 presents the evolution of capital stock after treatment, contrasting between cash and in-kind grants for entrepreneurs with and without self-control. The Figure shows that in-kind grants are retained in the ...rm and lead to further accumulation of capital whether the entrepreneur lacks self-control or not –although the e¤ect on capital accumulation is strongest for those with low self-control. In contrast, cash grants have, if anything a negative e¤ect on capital accumulation among low self-control entrepreneurs while the e¤ect is positive and growing for those with high self-control. This con...rms that the reason why in-kind grants have a strong positive e¤ect on pro...t is related to self-control issues and their e¤ect on the accumulation of capital. If we further interact the self-control index with high baseline pro...ts, we ...nd that self-control a¤ects the impact of treatment only for female entrepreneurs with high baseline pro...ts. For those with low initial pro...ts, treatment

> 14 The principal component has mean zero, and ranges from -1.67 to 2.26.

33
|Provincial|.245|.339|.584|
|Privatized|.633|.751|1.385|
|**_1996:_**||||
|Private|1.088|.506|1 .595|
|Provincial|-.026|.425|.399|
|Privatized|.758|.742|1.501|

Data Source: B.C.R.A.

Notes: The privatized sample includes only those banks that had completed their privatization as of March, 1996. Only these cases provided at least six months of post-privatization data on income and costs. The cases include Chaco, Corrientes, Entre Rios, Formosa, La Rioja, Misiones, Rio Negro, and Salta. The private sample includes the ten largest private banks in Argentina as of 1996. All public provincial banks are included in the provincial sample, including pre-privatization observations for the eight banks in the privatized sample. In those years where data were available for only a sub-sample of months for a given bank, the data were annualized. For example. for Salta in 1996, costs and income data were totaled over the six months for which data were available, and then multiplied by two.

17
of residence to estimate the impact of conict intensity, as measured by months of war, on adult height. Using the 2016 NDHS women sample, I nd that conict and, in particular, exposure starting very early in one's growing period, has highly signicant and negative impact on women's nal adult height. Findings are robust across (i) model specications and (ii) measures of conict. In validating the dierence-in-dierences estimation strategy used, I nd no evidence of presence of non-parallel dynamics nor of selective migration and fertility. These results are aligned with the biomedical literature that early childhood conditions are highly signicant in determining nal height  early life stunting increases the risk of being short as an adult (Golden, 1994; Martorell et al., 1994). Conditions in the fetal period and early years after birth are profound in inuencing human biology and long-term health. Responses to lack of adequate nutrition of developing fetus may be coded permanently, which is likely to increase later life health hazards (Barker, 1992). Similarly, early years of life are highly susceptible to environmental inuence on height (Schmidt et al., 1995). Nepalese children growing up during the conict experienced substantial levels of physiological and mental stress, and for many, mostly in rural areas under the control of the Maoists, it was a major nutritional shock and reduced access to healthcare. These are likely mechanisms at work and describe the results presented in the paper.

The sucient time gap between the start of the conict and the time of the NDHS 2016 allows me to explore the impacts of the conict on children of the women who were exposed to conict in their childhood. I nd that the mothers' exposure to conict is detrimental for their children's health. Although imprecise, impacts on children's height-for-age z-scores are negative. Results for children's weight-for-height, weight-for-age and BMI z-scores, on the other hand,
0000|-0.0723|-0.1147|
|Observations|(0.4272<br>3419|(0.4636)<br>3194||||

_Source_ : Own calculations using NUL students’ records data for the academic years 2010, 2011, 2012, 2013, and 2014. _Notes_ : OWM is the overall weighted mean. The treatment group is all students in their first year of university studies. Standard deviations are in parentheses. The reported p-values are for tests of equality of means between the treatment and control groups (independent samples). _Diff_ stands for difference in means by treatment status. ̄Norm-diff means normalized differences between treatment and control means computed as _△_ **X** = _~~√~~_ _<u>XS</u>_ <u>10</u> _−_<sup>2+</sup> _<u>X</u>_<sup> ̄</sup><sup>_S_</sup> <u>01</u><sup>2,where</sup><sup>_S_</sup> _G_<sup>2isthesamplevarianceof</sup><sup>_Xi_in</sup> subsample with treatment _Gi_ = _{_ 0 _,_ 1 _}_ . _G_ = 1 if a student is in second year.

18