rafmacalaba/datause-encoder-data huggingface dataset:
# **References**

- Alkire, Sabina and James Foster. (2011). "Counting and multipillaral poverty measurement." _Journal of Public Economics,_ 95(7): 476-487.

- Atkinson, Anthony B. (2003). "Multipillaral deprivation: contrasting social welfare and counting approaches." _Journal of Economic Inequality,_ 1(1): 51-65.

- Beegle, K., Christiaensen, L., Dabalen, A., & Gaddis, I. (2016). _Poverty in a Rising Africa_ . Washington DC: The World Bank.

- Cameron, Grant, Hai-Anh Dang, Mustafa Dinc, James Foster, and Michael Lokshin. (2021). “Measuring the Statistical Capacity of Nations”. _Oxford Bulletin of Economics and Statistics_ . Doi: <u><mark>https://doi.org/10.1111/obes.12421</mark></u>

- Chakravarty, Satya R. and Conchita D'Ambrosio. (2006). “The Measurement of Social Exclusion”. _Review of Income and wealth_ , 52(3): 377-398.

- <mark>Conrad, Cathy C., and Krista G. Hilchey. (2011). "A review of citizen science and communitybased environmental monitoring: issues and opportunities."</mark> _<mark>Environmental Monitoring and Assessment</mark>_ <mark>, 176(1): 273-291.</mark>

- Dang, Hai-Anh and Umar Serajuddin. (2020). "Tracking the Sustainable Development Goals: Emerging Measurement Challenges and Further Reflections". _World Development_ , 127: 104570.

- Dang, Hai‐Anh, Dean Jolliffe, and Calogero Carletto. (2019). “Data Gaps, Data Incomparability, and Data Imputation: A Review of Poverty Measurement Methods for Data‐Scarce Environments.” _Journal of Economic Surveys_ , 33(3): 757-797.

- <mark>Dang, Hai-Anh; Dinc, Mustafa; Diaz, Juderica; Maeda, Hiroko; Pullinger, John; Serajuddin, Umar; Stacy, Brian; Wolde, Dereje.  (2021).</mark> _<mark>Measuring the Statistical Performance of Countries: An Overview of Updates to the World Bank Statistical Capacity Index.</mark>_ <mark>World Bank, Washington, DC.</mark>

- Devarajan, Shantayanan. (2013). "Africa's statistical tragedy." _Review of Income and Wealth_ , 59: S9-S15.

- Fantom, N. and N. Watanabe (2008). “Improving the World Bank’s Database of Statistical Capacity”. _African Statistical Newsletter_ , 2(2): 21-22.

Food and Agriculture Organization of the United Nations, Asia and Pacific Commission on Agricultural Statistics (2012). _Report on Initial Country Assessments_ . <u>http://www.fao.org/fileadmin/templates/ess/ess_test_folder/Workshops_Events/APCAS_24/ Paper_after/APCAS-12-7-__Report_Country_Assessment.pdf.</u> 21
now in developing countries, researchers working with survey data should strongly consider implementing multi-way cluster robust standard errors.

Finally, there are two theoretically simple ways to reduce the design effects caused by the clustering of responses by interviewers. One is to simply expand the sample size using an expected interviewer design effect while keeping the number of respondents per interviewer constant. This approach is what is used to account for area cluster design effects. Another approach is to reduce the number of respondents per interviewer. In this analysis, all things being equal, a doubling of the number of interviewers from 20 to 40 would result in a reduction in the average design effect from 7.2 to 4. In both cases however there are limits to how many interviewers can be trained and managed without effects on data quality. Additionally, there may be limits to the number of high-quality interviewers available to work on a given survey. Nevertheless, survey implementors should increase the number of interviewers to the maximum number that can all be trained and managed well.

This paper provides some tentative evidence that interviewers with college degrees or higher, those with local knowledge of the survey area and those with experience conducting surveys, produce data with lower interviewer design effects. Future research could address interviewer characteristics associated with reduced interviewer design effects more robustly. Since the cost of interviewing interviewers during a survey is minimal, interviews of interviewers should become a common practice in survey implementation. Such practice would allow for meta studies to identify the relative importance of different interviewer characteristics for data quality.

# **References**

Brunton-Smith, Ian, P. Sturgis & G. Leckie. 2016. Detecting and understanding interviewer effects on survey data by using a cross-classified mixed effects location–scale model. _Journal of the Royal Statistical Society – Series A, Statistics in Society,_ 18(2): 551-568.

Caeyers, B., Chalmers, N. & De Weerdt, J. 2012. Improving consumption measurement and other survey data through CAPI: Evidence from a randomized experiment. _Journal of Development Economics,_ 98: 19-33.

Cameron, A.C., J. Gelbach and D. Miller. 2011. Robust Inference with Multiway Clustering. _Journal of Business & Economic Statistics,_ 29(2): 238-249.

Haber, Noah. P.J. Robyn, S. Hamadou, G. Yama, H Hien, D. Louvouezo & G. Fink. 2018. Surveyor Gender Modifies Average Survey Responses: Evidence from Household Surveys in Four Sub-Saharan African Countries. _arXiv_ , 1810.01981.

Kish, Leslie. 1962. Studies of Interviewer Variance for Attitudinal Variables. _Journal of the American Statistical Association,_ 57: 92–115.

Lupu, Noam & K. Michelitch. 2018. Advances in Survey Methods for the Developing World. _Annual Review of Political Science,_ 21: 195-214.

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Rapid progress in industrialization of primarily heavy and chemical industries and the progressive modernization of the industrial structure led to the emergence of the Japan’s full-scale modern industrial zones in four areas (Keihin, Hanshin, Chukyo and Kitakyushu), and the concentration of the population in metropolitan areas (Tokyo, Kanagawa, Aichi, Osaka, Hyogo and Fukuoka). For example, when looking at the changes in the industrial production output by region from 1909 to 1935, according to the Industrial Statistics of Japan, the rate of production increased steadily from 54% to 62% in the four industrial zones but that in the Tohoku region (six prefectures) was consistently less than mere 3% in all years studied (Ikeguchi 1967). In addition, the poor harvest of 1913 caused by spells of intense cold, following the lean years of 1902 and 1905, afflicted Tohoku (Iwamoto 1994).

At that time, the population of Sendai City was relatively stable, largely comprising government officers, school teachers, company employees, students, military personnel and self-employed individuals, etc., and “it exhibited much of the characteristics indicative of a city with such specific functions as public services and self-employed businesses” (CHCCs 1954a). This characteristics could be found in the statistics of enrolled students at Sendai Higher Technical School, which was established in 1906 under the Ministry of Education; in 1925, 43.5% of entering students were natives of the Miyagi prefecture, but only 8.3% of graduates found employment in Miyagi and 20.3% were employed in Tokyo, mostly as government engineers rather than engineers of private 17
losses and loans more likely to cover business losses. For the typical SME owner, grants covered almost one-quarter of housing losses, but less than 2 percent of business losses. Loans covered an additional 13.7 percent of business losses for the SME owners.

In Sri Lanka, cash aid came primarily through four programs. First, the Sri Lankan government paid surviving family members 15,000 Sri Lankan Rupees (LKR, about $150) for each person killed by the tsunami, to offset funeral expenses. Second, the government and the World Bank provided four grants of 5,000 LKR (about US $50) to 220,000 households suffering direct damage from the tsunami.<sup>7</sup> Third, the government provided aid to rebuild houses destroyed by the tsunami. Finally, the government and numerous NGOs sponsored cash-for-work programs, which typically paid workers around 300-350 LKR ($3.00-$3.50) per day to participate in cleanup and rebuilding activities. So while much of the aid came in-kind, there was a significant amount of cash aid provided.

Our April 2005 microenterprise survey indicates that the households of owners received an average of $115 in aid during the first three months of the tsunami, of which $101 came from government programs, $8 from NGOs and $6 from other sources. Table 1 shows that by July 2005, 94 percent of those reporting damage had receive some form of aid. However, the mean aid received ($332 among those receiving something) was less than 10 percent of the reported damage.<sup>8</sup> The grants reported by microenterprise owners cover only a small portion of the losses they incurred. Loans were much less common. Less then 4 percent of microenterprise owners reported receiving loans, and these covered less than 1 percent of losses in aggregate. The data on Table 1 are self reported, and we should be concerned that the reported losses are exaggerated and the reported aid

> 7 According to Jayasuriya et al (2005), the number of beneficiaries was reduced from over 800,000 to around 200,000 after the first two payments. The smaller number represents only around 50,000 households.

> 8 The enterprises appear to have reported grants provided by our project itself, as described below, so these amounts likely overstate the aid received by typical microenterprises. Those receiving our grants reported cash aid averaging just over $100 more than those not receiving our grants.

9
# **References**

- AHLFELDT, G. M. (2013): “If we build it, will they pay? Predicting property price effects of transport innovations,” _Environment and Planning A_ , 45, 1977–1994.

-  ́ALVAREZ, J. A. C. AND B. P. RESOSUDARMO (2019): “The cost of floods in developing countries<sup>_′_</sup> megacities: a hedonic price analysis of the Jakarta housing market, Indonesia,” _Environmental Economics and Policy Studies_ , 21, 555–577.

- BARTLETT, S. (2008): “Climate change and urban children: impacts and implications for adaptation in low-and middle-income countries,” _Environment and Urbanization_ , 20, 501–519.

- BARZIN, S., P. AVNER, J. RENTSCHLER, AND N. O<sup>_′_</sup> CLERYA (2022): “Where Are All the Jobs? A Machine Learning Approach for High Resolution Urban Employment Prediction in Developing Countries,” _World Bank_ , Washington, DC, Policy Research working paper, no. WPS 9979.

- BELTR  ́AN, A., D. MADDISON, AND R. J. ELLIOTT (2018): “Is flood risk capitalised into property values?” _Ecological Economics_ , 146, 668–685.

- BIN, O. AND J. B. KRUSE (2006): “Real estate market response to coastal flood hazards,” _Natural Hazards Review_ , 7, 137–144.

- BIN, O. AND S. POLASKY (2004): “Effects of flood hazards on property values: evidence before and after Hurricane Floyd,” _Land Economics_ , 80, 490–500.

- BISHOP, K. C., N. V. KUMINOFF, H. S. BANZHAF, K. J. BOYLE, K. VON GRAVENITZ, J. C. POPE, V. K. SMITH, AND C. D. TIMMINS (2020): “Best practices for using hedonic property value models to measure willingness to pay for environmental quality,” _Review of Environmental Economics and Policy_ , 14, 260–281.

- BRUECKNER, J. K. (2011): “Lectures on urban economics,” _MIT press_ .

- CASTELLS-QUINTANA, D. AND H. WENBAN-SMITH (2020): “Population Dynamics, Urbanisation without Growth, and the Rise of Megacities,” _The Journal of Development Studies_ , 56, 1663–1682.

- ERMAN, A., E. MOTTE, R. GOYAL, A. ASARE, S. TAKAMATSU, X. CHEN, S. MALGIOGLIO, A. SKINNER, N. YOSHIDA, AND S. HALLEGATTE (2020): “The Road to Recovery the Role of Poverty in the Exposure, Vulnerability and Resilience to Floods in Accra,” _EconDisCliCha_ , 171–193.

- ERMAN, A., M. TARIVERDI, M. OBOLENSKY, X. CHEN, R. C. VINCENT, S. MALGIOGLIO, J. RENTSCHLER, S. HALLEGATTE, AND N. YOSHIDA (2019): “Wading out the storm: The role of poverty in exposure, vulnerability and resilience to floods in Dar Es Salaam,” _The World Bank_ , Washington, DC, Policy Research working paper, no. WPS 8976.

- FAY, M. AND C. OPAL (1999): “Urbanization without growth: a not-so-uncommon phenomenon,” _The World Bank_ , Washington, DC, Policy Research working paper, no. WPS 2412.

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invaluable insights on dimensions of governance.<sup>11</sup> Here we note that these types of data may also be thought of as imperfect proxies for the broader concepts of governance that that they measure, and therefore also are subject to margins of error. In addition, as a practical matter, such measures of corruption or other dimensions of governance most often are generated only for a specific country at a single point in time, and therefore cannot be included in a regularly updated cross-country dataset like the WGI.

Second, the data source must provide original primary data produced using a credible structured methodology, rather than simply be a compilation of existing data. For example, we do not use the Transparency International Corruption Perceptions Index as a data source for the WGI Control of Corruption Indicator because the Corruption Perceptions Index is itself a compilation of existing data sources and does not involve new data collection. Third, the data sources must cover multiple countries, so that cross-country comparisons are possible. For example, a survey of public officials carried out in just one country can be extremely valuable for diagnosing governance constraints across institutions within a country. However, if this survey is not also carried out with the same questions and methodology in other countries, it cannot be used in the WGI which is designed to inform cross-country comparisons. Fourth and finally, the data sources must be regularly updated – ideally annually – although we do use several data sources that are updated only once every two or every three years, in which case we use the most recently available data until a new update becomes available.<sup>12</sup> This requirement is needed for the data to be able to inform over-time comparisons of the aggregate indicators.

The number of data sources included in the WGI has increased over time. The WGI data for 1996 are based on just 12 data sources, while the data for 2023 are based on 30 data sources (see Table 1). This reflects a sharp increase over the past 25 years in the number of organizations producing original primary data relevant for measuring various dimensions of governance. Over the entire period from 1996 to 2023, a total of 35 data sources have been used in the WGI. The difference between this figure and the 30 data sources used in 2023 reflects the fact that five data sources have been discontinued from the WGI over the years, either because the data source itself no longer is produced

> 11 See for example Golden and Picci (2005), Best et. al. (2015), Freund et. al. (2017), Chalenard et. al (2023) and Best et. al. (forthcoming).  For an overview of different types of governance data and their pros and cons, see Kaufmann and Kraay (2008).

> 12 In a few exceptional cases, we have extended this to four years while waiting for a new round of a data source to become available.  See www.govindicators.org of a complete description of the mapping of data sources to calendar years in the WGI.

10
Futures Exchange (SAFEX), suggesting high and excess seasonal sensitivity for the dominant staple in these countries. The seasonal gaps are lower (15 to 25 percent) for other staple cereals, consistent with their greater integration in the international markets (rice) and better storability (millet and sorghum). Juxtaposition of the seasonal evolution of the weighted staple price index against the monthly fluctuations in food and non-food consumption, further suggests that household food consumption closely tracks staple prices in each of the three countries, decreasing when staple prices increase; increasing when they decline. Seasonality continues to permeate African food markets and much of their livelihoods, challenging households to smooth their consumption.

The paper proceeds by discussing how best to measure seasonality, conceptually as well as empirically (section 2). Contextual information about the food price data and markets is provided in section 3, together with a review of the consumption measures. The price seasonality measures are then presented in Section 4, including some robustness checks. Section 5 reviews the insights obtained from estimating intra-annual variation in consumption across different settings in the three study countries and juxtaposing this with the seasonal evolution of staple prices. Section 6 concludes.

# **2 Measuring seasonality – concepts and methods**

Seasonality refers to certain regular intra-annual movements of a variable of interest. This could be a price or consumption (the focal variables here), but it could also concern labor supply patterns, children’s or adult health status, fertility behavior, etc. To capture the degree of seasonality, different measures have been proposed, each focusing on different aspects of the intraannual flow. The development economics literature has typically used a _year-specific_ seasonal gap measure, defined as the range between the price immediately prior to the arrival of the new harvest and this prevailing once the harvest is fully in (see for instance Dostie, Haggblade, and Randriamamonjy, 2002; or Orr, Mwale and Saiti-Chitsonga, 2009). This measure works well for crops where there is a single annual harvest. It is less useful in more complex environments. Year-specific 5
**Table 4: Impact of Minas Facil Expresso Introduction on Municipal Tax Receipts Dependent Variable:** Total Business Taxes Received in the Municipality in a Month

||Full<br>Sample|36-month<br>Common <br>Support|36-month<br> Nearest<br> Neighbor|Planned<br>Implementation<br>Candidates|
|---|---|---|---|---|
|_Panel A: Difference-in-Differences_|||||
|Treatment Effect|32,754***|27,886***|5,174|-7,564|
||(5,383)|(4,919)|(5,226)|(5,907)|
|_Panel B: Fixed Effects_|||||
|Treatment Effect|21,586***|28,603***|7,558|-7,152|
||(7,667)|(5,031)|(22,382)|(6,122)|
|Observations|47,676|41,644|5,046|4,234|
||853|718|84|73|
|Treatment Mean Sept 2010-Aug2011|48367|48367|48367|48367|

Notes: Panel A controls for the mean number of formalizations in the municipality in each of 2008, 2009 and 2010.

Sample sizes are for panels A and B.

Robust standard errors in parentheses, clustered at the municipality level.

*, **, and *** indicate significance at the 10, 5, and 1 percent levels respectively.

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# **2 Existing literature**

The deep Covid-19 induced global recession is putting at risk the gains in poverty reduction made over the past three decades. Understanding the ability of already existing social protection programs to prevent a permanent increase in the levels of poverty is thus of obvious importance. Yet, academic research on their performance during and after large aggregate shocks is largely missing. The existing literature focuses mostly on the ability of cash-transfer programs to mitigate household-level shocks. An early study (de Janvry, Finan, Sadoulet & Vakis 2006) employs data from the randomized pilot of Mexico’s flagship conditional cash-transfer (CCT) program Progresa which also contains information on a variety of self-reported shocks at the household and village levels. This allows for testing the interaction of the randomly assigned cash-transfer with the prevalence of a shock. It finds that in the control group an unemployment or illness shock for the household head, or a natural disaster, has a large and significant negative effect on schooling. These effects are either much smaller or non-detectable in treatment localities. Interestingly, this effect is not the result of higher labor force participation by school-aged children: While some of these shocks increase the probability of a child working, there is no mitigating effect due to Progresa. A related and more recent study (Adhvaryu, Nyshadham, Molina & Tamayo 2018) looks specifically at the longer term consequences of income shocks. Using the same data source, the authors interact the randomized 5
# **Inter-Firm Trade Finance in Times of Crisis**

Anna Maria C. Menichini<sup></sup> Università di Salerno and CSEF

>  I am grateful to Jean-Pierre Chauffour, Thomas Farole, and Leora Klapper for valuable comments and suggestions. I bear sole responsibility for any error. Address for correspondence: Dipartimento di Scienze Economiche e Statistiche, Università degli Studi di Salerno, via Ponte Don Melillo, 84084 Fisciano (SA). Tel.: +39 089 962174.
|**Table 3: Time Path of Treatment Impact on Formalization Rates**<br>**Dependent Variable:**Number of Firms Registeringin the Municipalit|yin a Mont|h<br>36-month|36-month|Planned|
|---|---|---|---|---|
||Full<br>Sample|Common <br>Support|Nearest<br> Neighbor|Implementation<br>Candidates|
|_Panel A: Difference-in-Differences_|||||
|Average impact six months before Minas Facil Expresso introduced|-0.0849|0.0571|0.563|0.799*|
||(0.327)|(0.331)|(0.583)|(0.478)|
|Average impact in month of implementation and month after|-2.330***|-2.171***|-1.793**|-1.588**|
||(0.449)|(0.449)|(0.837)|(0.635)|
|Average impact in months two or more after implementation|-0.787**|-0.703**|-0.741|-0.724|
||(0.312)|(0.323)|(0.777)|(0.679)|
|_Panel B: Fixed-Effects_|||||
|Average impact six months before Minas Facil Expresso introduced|-0.0783|0.0650|0.594|0.850*|
||(0.327)|(0.331)|(0.586)|(0.477)|
|Average impact in month of implementation and month after|-2.322***|-2.161***|-1.739**|-1.496**|
||(0.449)|(0.449)|(0.842)|(0.630)|
|Average impact in months two or more after implementation|-0.731**|-0.641**|-0.612|-0.536|
||(0.303)|(0.313)|(0.782)|(0.658)|
|Sample Size|46,032|23,072|4,704|4,088|
|Number of Municipalities|822|412|84|73|
|Treatment Mean Sept 2010-Aug2011|12.55|12.55|12.55|12.55|
|Notes:<br>Panel A controls for the mean number of formalizations in the munic<br>Robust standard errors in parentheses, clustered at the municipality<br>*, **, and *** indicate significance at the 10, 5, and 1 percent levels r|ipality in e<br>level.<br>espectively|ach of 200<br>.|8, 2009, and|2010.|

19