rafmacalaba/data-use-mentions-extended huggingface dataset:
|Armenia|No|||1|||
|---|---|---|---|---|---|---|
|Benin|No||||||
|Bhutan|No||||1||
|Burundi|No||2||||
|Cameroon|No|4|2|1|1||
|Colombia|No||||||
|Gambia|No|||1|1||
|Ghana|Yes|3|4|2|1|X|
|Kenya|Yes|4|4|1|||
|Kyrgyz Republic|No|1|1||||
|Lesotho|No|2|||||
|Mauritius|No||||||
|Mongolia|No||||||
|Nicaragua|No||||||
|Pakistan|No||||||
|Sierra Leone|No|2|2||1||
|South Africa|No|1|1||||

# **3. Transport-related questions in non-LSMS surveys**

The review also examines transport-related questions in other types of household surveys, including the household surveys captured by International Household Survey Network (IHSN), Gender Data Navigator<sup>6</sup> , and the Afrobarometer surveys.

## **3.1. Non-LSMS Household Surveys**

Besides LSMS surveys, transport information is also collected by a wide variety of household surveys, according to IHSN, a tool for screening the content of 2,087 surveys and censuses in lowand middle-income countries as of 2014. Table 6 lists the numbers of surveys conducted between 2010 and 2014 that contain some detailed information on household transport characteristics and ownership of vehicles, where many surveys are Household Income and Expenditure Surveys, Household Budget Surveys, and Demographic Health Surveys. These
14 -

-

or treating EC member countries effectively as one country, is a debatable point, as individual EC members had differnt trade measures in the data period. Third, averaging of the "transport costs" as obtained from the IMP data with air cargo rates was a duplication; the IMF data already included air cargo transport costs. Fourth, another possible issue is the purchasing power adjustment made to the GNP data. Although there may be some justification for this adjustment, the implications of this adjustment for the interpretation of results are not so clear (see para. 66, p. 32).

# IV. Balassa (1986b, 1988)

23. Using essentially the same approach as Bergsten and Cline (1985), Balassa (1986b, 1988) reached an opposite conclusion. Balassa found that Japan was an "outlier" on the downside among industrial countries. In other words, when Japan dummies were added to the equations, regression results improved significantly and the negative coefficients of the dummies were statistically significant.

24. Balassa (1986b, 1988) included 18 industrial countries in his sample. Instead of including the EC as a unit as had been done by Bergsten and Cline (1985), Balassa included the EC members as individual countries but used an Ec dummy in each re2ression equation. He also used a dummy for EPTA. In his selection of countries, Balassa decided to include only countries which had per capita incomes of $2,200 or higher and a share of manufactured goods in total exports of at least 20 percent in 1973. Thuc, New Zealand, high income oil-exporting countries and all developing countries were excluded.
central government exercised ownership and control (OECD, 2017), which differs from the criteria defined within the Product Market Regulation Indicator.

> 11 The WB SOE policy tracker revealed that during the COVID-19 pandemic firms with as low as 10% could indeed receive significant support visà-vis their full privately-owned competitors. For instance, Lufthansa with 14% participation of the government received one of the largest programs in the aviation industry with a loan for over € 9 billion.

> 12 In Poland, for instance, some municipal enterprises are registered as “municipal budget entities” ( _samorządowy zakład budżetowy_ ). These entities are legally separate from the local governments that control them, but they are not separate legal persons. In this case, the entities are not considered as SOEs following the definitions here.

> 13 It refers to the ability of the company to generate revenues and profits itself, but it does not limit the analysis to those actually reporting profits.

6
- van der Ploeg, F., Rezai, A. (2021): Optimal carbon pricing in general equilibrium: Temperature caps and stranded assets in an extended annual DSGE model, _Journal of Environmental Economics and Management_ , 110, p. 102522.

- Van der Werf, E. (2008): Production functions for climate policy modeling: An empirical analysis, _Energy economics_ , 30(6), pp. 2964–2979.

- Varga, J., Roeger, W., et al. (2022): E-QUEST: A multisector dynamic general equilibrium model with energy and a model-based assessment to reach the EU climate targets, _Economic Modelling_ , p. 105911.

- Vitek, F., Gaspar, V., Adrian, T. (2022): A Medium-Scale DSGE Model for the Integrated Policy Framework, IMF Working Papers 2022/015, International Monetary Fund.

- Voyvoda, E., Yeldan, A. E. (2015): Low Carbon Development Pathways and Priorities for Turkey, Tech. rep., Istanbul Policy Center.

- Way, R., Ives, M. C., Mealy, P., Farmer, J. D. (2022): Empirically grounded technology forecasts and the energy transition, _Joule_ , 6(9), pp. 2057–2082.

- World Bank (2022): T ̈urkiye Country Climate and Development Report, CCDR Series, World Bank Group.

- World Bank (2023): World Development Indicators, data retrieved from World Development Indicators, `https://databank.worldbank.org/source/ world-development-indicators` .

34
(multiplied by 100).

# _Robustness check: The role of the agricultural sector_

One of the limitations of the STEP surveys is that they cover urban areas only, which leads to an under-representation of the agricultural sector. This could introduce a bias to our estimates if most of the changes in the task content of jobs is driven by a transition out of agriculture. To assess to what extent the exclusion of agriculture is driving the results, we re-calculate the O*NET-based indexes including the agricultural sector and occupations in the I2D2 data set. We then estimate the cross-country equations using these indexes. As seen in Table 8 and Table 9, the results are very similar for both samples, as the magnitudes of the main estimated coefficients are very close. Thereby, the main findings do not seem to be driven by the exclusion of the agricultural sector.

26
<!-- Start of picture text --> World Bank projects to isolate exogenous changes in public expenditures<br>from those endogenously responding to the state of the business cycle..<br>Romer and Romer (2010) also adopt a narrative approach and document<br>the time of legislative changes to the U.S. tax code in an attempt to identify<br>tax policy shocks and classify whether tax changes were anticipated and<br>whether they were explicitly aimed at addressing business cycle conditions.<br>The second approach is the Structural Vector Autoregression (SVAR)<br>methodology introduced by Blanchard and Perotti (2002). They identify fis-<br>cal shocks using information about fiscal institutions. On the expenditure<br>side, their identifying assumption is that discretionary government expendi-<br>ture requires at least one quarter to respond to business cycle conditions.<br>For taxes, they use the output elasticities of tax revenues, estimated from<br>information about tax codes, to differentiate between the endogenous reac-<br>tions of tax revenues to output fluctuations and unanticipated shocks to tax<br>revenues.<br>There has been much debate as to the relative merits of these two ap-<br>proaches. Perotti (2004) and Ramey (2009) have extensive comparisons of<br>the SVAR and narrative approaches to identifying government expenditure<br>shocks. Ilzetzki, Mendoza and Végh (2010) discuss extending the two ap-<br>proaches to countries outside the OECD. Favero and Giavazzi (2010) and<br>Perotti (2011) compare and attempt to reconcile the very different results<br>arising from Romer and Romer’s (2010) and the SVAR approaches to iden-<br>tifying tax policy shocks. Our study does not innovate on identification of<br>fiscal shocks; we rather take off the shelf the Blanchard and Perotti (2002)
a simple OLS method while the backward elimination starts with all the candidate variables and then deletes each variable that falls below a p-value threshold. We use the backward elimination process while setting the p-value threshold to 0.05. This is chosen over the forward elimination approach because forward elimination depends on the order in which variables are chosen.<sup>11</sup> T Having chosen the set of candidate explanatory variables xch, equation 1 is originally estimated using ordinary least squares. The regressions are weighted using the sampling weights within the surveys. To allow for the possibility of intra-cluster correlations of household expenditures, the random disturbance term is defined as follows: T where η and ε are assumed independent, uncorrelated with xch and as having different data generating processes. These two components of the error term are assumed to have mean zero and 2 2 variances ση and σε,c, which indicates that the latter is permitted to be heteroskedastic and vary across households in a given cluster, while the former is assumed to be a constant. Clusters are defined as districts, the lowest level of spatial disaggregation that can be matched between CES 2011/12 and SCS Health 2017/18.<sup>12</sup> Our approach allows for the possibility of normal or nonnormal heteroskedastic error terms. The variance-covariance matrix of the error term is computed using the methods described in Nguyen et al. (2018).

Given the structure of the errors in equation 2, an OLS estimation of model 1 would underestimate uncertainty. Therefore, in the second step, the model is re-estimated using Generalized Least Squares (GLS) to control for the heterogeneity in the cluster specific errors, so: Using a Monte Carlo approach, 100 samples from the training data are drawn to obtain 100 values of the coefficients β̂GLS and of the error components η̂C and ε̂ch (the latter based on assumptions about their distribution and estimates of their variances σ̂η2 and σ̂ε,c2 from previous stages).
Furthermore, the estimates of distortions themselves are biased if they are inferred using a model with no price-effects.

# **References**

Balassa, Bela. 1964. The Purchasing-Power Parity Doctrine: A Reappraisal. Journal of Political Economy, 72(6), 584–596.

Comin, Diego A, Lashkari, Danial, & Mestieri, Marti. 2018. Structural Change with Long-Run Income and Price Effects. Working Paper: Dartmouth College, Yale University & Northwestern University.

Duarte, Margarida, & Restuccia, Diego. 2010. The Role of the Structural Transformation in Aggregate Productivity. Quarterly Journal of Economics, 125(1), 129–173.

Fadinger, Harald, Ghiglino, Christian, & Teteryatnikova, Mariya. 2018. Income Differences and Input-Output Structure. Working Paper: University of Mannheim, University of London and European University Institute.

Feenstra, Robert C., Inklaar, Robert, & Timmer, Marcel P. 2015. The Next Generation of the Penn World Table. American Economic Review, 105(10), 3150–3182.

Greene, William H. 2011. Econometric Analysis. Seventh Edition, Upper Saddle River, NJ: Prentice Hall.

Hirschman, Albert O. 1958. The Strategy of Economic Development. New Haven, CT: Yale University Press.

Inklaar, Robert, & Timmer, Marcel P. 2014. The Relative Price of Services. Review of Income and Wealth, 60(4), 727–746.

ISIC Rev. 3.1. 2002. International Standard Industrial Classification of All Economic Activities. Statistics Division, United Nations Statistical Commission, New York.

Jones, Charles. 2011. Intermediate Goods and Weak Links in the Theory of Economic Development. American Economic Journal: Macroeconomics, 3(2), 1–28.

Leontief, Wassily W. 1936. Quantitative Input and Output Relations in the Economic Systems of the United States. Review of Economic Statistics, 105–125.

OECD. 2017a. Final Consumption Expenditure of Households (Table 5)
selected from the e-health database were individuals between the age of 35 and 68 years old, and who had not been screened for diabetes or hypertension in the last 12 months. From this sampling list, individuals were randomly assigned to the 5 study arms of the study.

After the randomization, potential study participants were contacted in two phases. The first phase, conducted by phone, was the eligibility verification. The second phase, conducted face-toface, was the informed consent. These two phases took place between June and September 2019 for the 4 intervention groups and between January 10 and February 20, 2020, for the control group.

For the eligibility verification, the fieldworkers contacted by phone all 6,934 individuals to verify whether they could be reached, were available and eligible for the study. The results of the verification indicated that: 1,966 (28.4 percent) were out of reach, had the wrong contact information or could not be located, 1,893 (27.3 percent) were temporarily out of their residence, 386 (5.6 percent) had already been screened at one of the selected PHC facilities during the 11
where _dij_ is the Euclidean distance between the geographic centers of prefectures _i_ and _j_ , _diport_ is the Euclidean distance between the center of region _i_ and that of the nearest prefecture which has a major international port, and _area_ is a prefecture's land area (in km<sup>2</sup> ). The data on land areas is taken from _Regional Economic Statistical Yearbook 2008_ . Eight major international ports are considered: Shanghai, Ningbo, Tianjin, Guangzhou, Qingdao City, Dalian City, Qinhuangdao City and Shenzhen City. These correspond to the eight Chinese ports which had the largest throughput (measured in million tonnes of traffic) in ISL (2006). Pre-multiplication of a variable by **W** * **W** represents the second-order spatial lag of that variable.

# _Additional parameters used in the numerical solution of the theoretical model:_

As outlined in section 3.2, to obtain a numerical solution for the system of equations [3] – [7], which describe the short-run equilibrium of our theoretical model, we also require measures for the parameters  ,  ,  . For 2007, the measures for  and  , which correspond to the regional shares of the national urban and rural workforces respectively, are constructed using data on "urban employed persons" and "total employed persons" from the _Regional Economic Statistical Yearbook 2008_ . For  , rural employment is calculated as total employed persons minus urban employed persons<sup>35</sup> According to the _China Statistical Yearbook 2008_ , employed persons refer to " _people aged 16 and over who are engaged in gainful employment and thus receive remuneration payment or earn business income_ ".  , the national share of urban workers in total employment, is measured using the same data.

The data on "total employed persons" is also used in the calculation of GDP per worker for 2007, which is used to help assess the fit of the "after" equilibrium solution. GDP per capita for
using macro data point to an underestimation over time in incomes and consumption expenditures. As a result, household vulnerability in the financial margin threshold calculations would be biased upwards as subsistence consumption levels are imposed on a sample of underestimated incomes. To a lesser degree, this upward bias is also present when arbitrary thresholds are used to tag household as vulnerable. But the problems with the HBS represent a bias only for the initial level of vulnerability; this is less of a concern once, as is the case in this paper, the focus is on changes in vulnerability as a result of economic shocks among households with debt.

The characteristics of indebted and vulnerable household groups are presented in Appendix Table. The table looks at mean values of several household-level indicators while distinguishing according to the vulnerability status of households—vulnerable versus non-vulnerable. Figure 4 already suggests that household vulnerability is more evenly distributed across income quintiles than is the case when the financial margin threshold is used to tag households as vulnerable. With the arbitrary threshold, where the distinction between vulnerable and non-vulnerable (i.e., simply indebted) households is less stark, _t_ -tests suggest that only the mean values on household income (and its sources) and savings—and marginally the household responses on living standards—are not equivalent across vulnerability status. Characteristics on head of household and on the possession of durable goods do not show a statistically significant difference between vulnerable and non-vulnerable households. By contrast, with the financial margin threshold, almost all the mean value indicators of vulnerable households are statistically lower than those of 8
. Production potential in the “bread baskets” of Eastern Europe and Central Asia. Global Food Security 14, 38-53 Vannoppen, A., Gobin, A., 2021. Estimating Farm Wheat Yields from NDVI and Meteorological Data. Agronomy 11, 946 von Cramon-Taubadel, S., 2022. Russia’s Invasion of Ukraine – Implications for Grain Markets and Food Security. German Journal of Agricultural Economics 71, 1-13 Waldner, F., Bellemans, N., Hochman, Z., Newby, T., de Abelleyra, D., Verón, S.R., Bartalev, S., Lavreniuk, M., Kussul, N., Maire, G.L., Simoes, M., Skakun, S., Defourny, P., 2019. Roadside collection of training data for cropland mapping is viable when environmental and management gradients are surveyed. International Journal of Applied Earth Observation and Geoinformation 80, 82-93 Witmer, F.D.W., 2015. Remote sensing of violent conflict: eyes from above. International Journal of Remote Sensing 36, 23262352 Wouters, L., Couasnon, A., de Ruiter, M.C., van den Homberg, M.J.C., Teklesadik, A., de Moel, H., 2021. Improving flood damage assessments in data-scarce areas by retrieval of building characteristics through UAV image segmentation and machine learning – a case study of the 2019 floods in southern Malawi. Nat. Hazards Earth Syst. Sci. 21, 3199-3218 Zadorozhna, O., 2020. Clientelism and Land Market Outcomes in Ukraine. Eastern European Economics 58, 478-496 28
Policy Research Working Paper 8976

# **Abstract**

Dar es Salaam is frequently affected by severe flooding causing destruction and impeding daily life of its 4.5 million inhabitants. The focus of this paper is on the role of poverty in the impact of floods on households, focusing on both direct (damage to or loss of assets or property) and indirect (losses involving health, infrastructure, labor, and education) impacts using household survey data. Poorer households are more likely to be affected by floods; directly affected households are more likely female-headed and have more insecure tenure arrangements; and indirectly affected households tend to have access to poorer quality infrastructure. Focusing on the floods of April 2018, affected households suffered losses of 23 percent of annual income on average. Surprisingly, poorer households are not over-represented among the households that lost the most - even in relation to their income, possibly because 77 percent of total losses were due to asset losses, with richer households having more valuable assets. Although indirect losses were relatively small, they had significant well-being effects for the affected households. It is estimated that households’ losses due to the April 2018 flood reached more than US$100 million, representing between 2–4 percent of the gross domestic product of Dar es Salaam. Furthermore, poorer households were less likely to recover from flood exposure. The report finds that access to finance play an important role in recovery for households.

This paper is a product of the Global Facility of Disaster Reduction and Recovery. 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 aerman@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
in education or other measures of skill). There is no distinction between formal and informal fi rms, but only between formal and informal workers (our assumption is that all fi rms are fully registered, although part or all of their workers may not be legally registered).

We consider informal workers to be unregulated workers who coexist with formal workers in a single labor market, as opposed to assuming segmented labor markets.<sup>13</sup> However,

> 12In the fi rm level data we use in the paper the number of inspections in a fi rm in 2001 varies between zero and 60. The median number of annual labor inspections is 1 and the mean is 1.33.

> 13Maloney (2004) (and others) argue for a uni fi ed labor market approach as opposed to a dual view of the labor market.

8
# **References**

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- Aghion, P., Bergeaud, A., Boppart, T., Klenow, P. J. & Li, H. (2019), A theory of falling growth and rising rents, Technical report, National Bureau of Economic Research.

- Alam, A., Casere, Paloma, A. C. & Udompsaph, C. (2008), Unleashing prosperity: Productivity growth in eastern europe and the former soviet union, Staff Report 45627, World Bank.

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- Bond, S., Hashemi, A., Kaplan, G. & Zoch, P. (2020), Some unpleasant markup arithmetic: Production function elasticities and their estimation from production data, NBER Working Paper No. 27002, National Bureau of Economic Research.

- Brynjolfsson, E. & Hit, L. (2010), ‘Beyond computation: Information technology, organizational transformation and business performance’, _Journal of Economic Perspectives_ **4** , 23–48.

- Byrne, D. & Corrado, C. (2017), ‘ICT prices and ICT services: What do they tell us about productivity and technology?’, _Finance and Economics_ **2017/015** . Discussion Paper.

- Campbell, J., Goldfarb, A. & Tucker, C. (2015), ‘Privacy regulation and market structure’, _Journal of Economics & Management Strategy_ **24** (
that the restrictions that are the use of the credit-to-GDP ratio are implied by supported by our cross-country panel data. Our view thus is that the use of the credit-to-GDP ratio to determine equilibrium credit, and therefore also to determine excessive credit provision, appears to be inappropriate.

# **4.3. Linking the cross country variation to country-specific characteristics**

As outlined in Section 3.1.3, we use the BMA framework to reduce the large set of 42 potential development indicators to a smaller subset of around 15 _−_ 20 variables. The criterion for inclusion of a given variable in the subset is its posterior inclusion probability (PIP). We use a PIP threshold value of 25% for a variable to be included in the subset. This value may seem low, nonetheless, the purpose here is to perform a first round of _”pruning”_ rather than finding the final model.<sup>30</sup> Our objective is to reduce the set of all potential regressors to a smaller set of highly relevant determinants of the cross-country variation in _β_<sup>_gdp_</sup> and _β_<sup>_de f_</sup> . The Lasso is thus used as a variable selection tool.<sup>31</sup> Note that we follow the same variable selection or reduction procedure to model the cross-country variation in the _β_<sup>ˆ</sup><sup>_gdp_</sup> as well as in the _β_<sup>ˆ</sup><sup>_de f_</sup> and _α_ ˆ coefficients. To avoid unnecessary repetition and to keep the section as short and informative as possible, we only present the BMA and Lasso regression results for the _β_<sup>ˆ</sup><sup>_gdp_</sup> coefficient in this section and provide equivalent results for the _β_<sup>ˆ</sup><sup>_de f_</sup> and _α_