rafmacalaba/datause-encoder-data huggingface dataset:
1 Psacharopoulos, G., Patrinos, H.A., Collis, V., and Vegas, E. 2020. The COVID-19 cost of school closures. Brookings Institution Blog series Education plus development. <u>https://www.brookings.edu/blog/education-plus-development/2020/04/29/the-covid-19-cost-ofschool-closures/; Psacharopoulos, G., Patrinos, H.A., Collis, V., and Vegas, E. 2020. The COVID-</u> 19 cost of school closures. World Bank blog series Education for Global Development <u>https://blogs.worldbank.org/education/covid-19-cost-school-closures.</u> 2 Ichino, A., and Winter-Ebmer, R. 2004. The Long-Run Educational Cost of World War II. Journal of Labor Economics 22(1): 57-86 <u>https://econpapers.repec.org/RePEc:ucp:jlabec:v:22:y:2004:i:1:p:57-86.</u> 3 Van Lancker, W., and Parolin, Z. 2020. COVID-19, school closures, and child poverty: a social crisis in the making. The Lancet Public Health 1(5): E243-244 <u>https://www.thelancet.com/journals/lanpub/article/PIIS2468-2667(20)30084-0/fulltext.</u> 4 Almond, D. 2006. Is the 1918 influenza pandemic over? long-term effects of in utero influenza exposure in the post-1940 U.S. population. Journal of Political Economy 114(4): 672-712: = <u>https://www.jstor.org/stable/10.1086/507154?seq 1#metadata_info_tab_contents; Correia, S.,</u> Luck, S., and Verner, E. 2020. Pandemics Depress the Economy, Public Health Interventions Do Not: Evidence from the 1918 Flu. Available at SSRN: <u>https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3561560.</u> 5 Almond, D. 2006. Is the 1918 influenza pandemic over? long-term effects of in utero influenza exposure in the post-1940 U.S. population. Journal of Political Economy 114(4): 672-712: <u>https://www.jstor.org/stable/10.1086/507154?seq=1#metadata_info_tab_contents; Schwandt, H.</u> 2017. The Lasting Legacy of Seasonal Influenza: In-Utero Exposure and Labor Market Outcomes. IZA DP No. 10589: https://www.iza.org/publications/dp/10589/the-lasting-legacy-of- <u>seasonal-influenza-in-utero-exposure-and-labor-market-outcomes.</u> 6 Cockx, B. 2016. Do youths graduating in a recession incur permanent losses? IZA World of Labor, 281 doi: 10.15185/izawol.281

> 7 <mark>Altonji, J.G., Kahn, L.B., and Speer, J.D. 2016. Cashier or Consultant? Entry Labor Market Conditions, Field of Study, and Career Success. Journal of Labor Economics 34(S1) (Part 2, January): S361-S401 https://doi.org/10.1086/682938.</mark>

> 8 Jonas, O.B. 2013. Pandemic Risk. Washington, DC: The World Bank: <u>https://www.worldbank.org/content/dam/Worldbank/document/HDN/Health/WDR14_bp_Pande mic_Risk_Jonas.pdf; Burns, A., Mensbrugghe D., and Timmer, H. 2006. Evaluating the economic</u> consequences of avian influenza. Washington, DC: The World Bank: <u>http://documents.worldbank.org/curated/en/977141468158986545/Evaluating-the-economicconsequences-of-avian-influenza</u>

11
|**Table 6: Impacts on Knowledge about Formalization and Inspec**|**tions**|||||**Inspecto**|**r vs Control Bl**|**ocks**|
|---|---|---|---|---|---|---|---|---|
|||**Communi**|**ation vs Contr**|**ol Blocks**|||Inspector|Indirectly|
||Sample<br>Size|Control<br>Mean|Free Cost<br>Difference|Communication<br>Difference|Sample<br>Size|Control<br>Mean|Assigned<br>Difference|Inspected<br>Difference|
|_Information and Knowledge_|||||||||
|Claims to know procedures for formalizing|817|0.59|0.00415|0.0303|1,393|0.62|-0.00871|0.0458|
||||(0.0432)|(0.0380)|||(0.0349)|(0.0318)|
|Knows cost of registration is between R$200 and R$400|817|0.03|-0.0111|0.00734|1,393|0.03|0.00170|-0.0197*|
||||(0.0117)|(0.0159)|||(0.0130)|(0.0103)|
|Knows tax rate if registered is between 4% and 8%|817|0.04|0.0213|0.0120|1,393|0.04|0.0272*|0.00866|
||||(0.0201)|(0.0184)|||(0.0164)|(0.0150)|
|Claims to use an accountant|817|0.27|0.116**|0.108**|1,393|0.48|0.0658*|-0.00847|
||||(0.0470)|(0.0470)|||(0.0348)|(0.0391)|
|Knows cost of an accountant is R$200-R$400/month|817|0.10|**0.118*****|0.0406|1,393|0.18|**0.0907*****|0.0562*|
||||(0.0312)|(0.0300)|||(0.0312)|(0.0307)|
|_Inspections_|||||||||
|Municipal inspector visit in past year|817|0.42|0.0346|0.0709|1,393|0.47|**0.135*****|0.0465|
||||(0.0467)|(0.0438)|||(0.0367)|(0.0392)|
|Received information on how to formalize from any inspector|817|0.14|-0.0404|-0.00387|1,393|0.12|0.0517*|0.00272|
||||(0.0262)|(0.0318)|||(0.0275)|(0.0267)|
|Reports a neighboring firm inspected in past year|817|0.19|0.00429|-0.0231|1,393|0.19|0.000869|0.0355|
||||(0.0370)|(0.0372)|||(0.0295)|(0.0326)|
|Was notified or fined by an inspector in past year|817|0.10|-0.0223|-0.0292|1,393|0.10|0.0324|0.0306|
|Notes: Standard errors in parentheses, clustered at the block le<br>respectively, after controlling for randomization strata. Samplin<br>Coefficients in**bold**remain significant applying the Benjamini-|vel. *, *<br>g weigh<br>Hochber|*, and *** i<br>ts are used<br>g (1995) pro|(0.0264)<br>ndicate statisti<br>for the Inspec<br>cedure within|(0.0257)<br>cally different from<br>tor vs Control blocks<br>a family of outcome|control me<br>comparis<br>s to contr|an at the<br>ons.<br>ol false dis|(0.0248)<br>10, 5 and 1 per<br>coveries.|(0.0245)<br>cent levels|

38
**Figure 2: Percentage and number of new female and male sole proprietors in 2016**

<!-- Start of picture text --> 100<br>90<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>New female sole proprietors New male sole proprietors<br>Source: Entrepreneurship Database.<br>Note: The labels show the absolute numbers of new female- and male-owned sole proprietorships registered in 2016.<br>Gender gaps in female business entry and ownership are associated with substantial losses to national<br>income and economic development at both the national and regional levels. While the data show that female<br>entrepreneurs choose to start their firms as sole proprietorships more often than LLCs, there was a<br>significant gender gap between female and male entrepreneurs in 2016. The average proportion of new<br>female LLC owners and sole proprietors in three income groups are shown in figure 3. Low-income<br>economies are characterized by the largest gaps in female business ownership at the level of both LLC<br>owners and sole proprietors.<br>Figure 3: Average income-group percentages of new female LLC owners and sole proprietors in<br>2016<br>40<br>35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>Low income Middle income High income<br>New female LLC owners New female sole proprietors<br>13,701 132,465 1,060 3,888 17,988 168,252 1,716 12,217 219,709 113,622 1,478 21,971 285,078 185,556 17,200 73,478 51,840 152,133 19,367 8,650 1,373 8,934 101,717 19,952 7,470 4,861 17,115 2,582 1,134 30,677 2,854 7,769<br>73,504 39,960 5,809<br>New sole proprietors, by gender (%) 21,143 149,760 1,056 3,450 15,277 136,492 1,377 8,553 143,464 67,686 860 12,223 156,216 101,332 9,161 37,188 25,655 73,243 8,946 3,886 592 3,304 36,234 6,977 2,505 1,574 5,401 753 244 5,387 492 937 4,212 2,213 42<br>Austria Philippines Estonia Jamaica Tajikistan Malaysia Brunei Darussalam Romania France Netherlands Rwanda Belarus Germany Poland Taiwan, China Nigeria Croatia Italy Chile Georgia Suriname Mauritius Azerbaijan Senegal Zambia Kosovo Oman Mexico Jordan United Arab Emirates Mauritania Nepal Saudi Arabia Turkey Afghanistan<br>Average share of new female LLC owners and sole proprietors (%)<br><!-- End of picture text --> _Note:_ The labels show the absolute numbers of new female- and male-owned sole proprietorships registered in 2016.

Gender gaps in female business entry and ownership are associated with substantial losses to national income and economic development at both the national and regional levels. While the data show that female entrepreneurs choose to start their firms as sole proprietorships more often than LLCs, there was a significant gender gap between female and male entrepreneurs in 2016. The average proportion of new female LLC owners and sole proprietors in three income groups are shown in figure 3. Low-income economies are characterized by the largest gaps in female business ownership at the level of both LLC owners and sole proprietors.

**Figure 3: Average income-group percentages of new female LLC owners and sole proprietors in 2016**

_Source:_ Entrepreneurship Database.

8
8 where X is variable economic profit (i.e., the total return to the fixed factors), all of the variables are as previously defined, and a is a vector of parameters of the profit function. The consequences of these three types of technical change are considered below using a second-order Taylor-series expansion to approximate any - .itrary functional form for the revenue function. For simplicity and concreteness, a quadratic profit function is specified.

# _Direct Incorporation of Technical Change Variables_

The first specification of technical change is well known from the empirica **l** iterature on the estimation of flexible functional forms (see Binswanger). Under this approach, the technical change variable(s) enter the profit function in the same way as a quasi-fixed factor would, except that being "public" goods they receive a zero factor return at the level of the firm. In this case, the technical change can be thought of as an increase in the supply of nonrival goods which are provided free to individual producers. Using the quadratic profit %unction to illustrate this approach yields:

# (3)

**_s=_** _a+c*'P+ *P'AP_

where P = _[p' w' v'_ **_I''_** is an n x 1 vector of output prices, input prices, fixed factors, and technology variables, ao is an intercept parameter, **ae'** = **cvl, ...** , _atJ_ is a 1 x n vector of parameters, and A is an n x n matrix of parameters, av,,. Typically, only a small number of indexes of technical change **_(Td_** will be required, and a popular specification involves the use of only one such variable, the time index. Give-n the choice of a qjadratic form in equation (3), these variables will enter the profit function quadratically.
- Non-precarious areas. These are neighborhoods where homes are self-constructed, constructed by stages, or constructed with the intervention of a contractor. They are also neighborhoods built entirely by the state for social housing in accordance with urban standards (drainage system, access to water and electricity, tracing roads, and so on) and habitat.

Each EA is then classified as either precarious or non-precarious by the knowledge of local experts based on the definition above (Figure 1).

The sample was drawn from the following seven strata: (a) low-density non-precarious zone, (b) medium-density non-precarious zone, (c) high-density non-precarious zone, (d) low-density precarious zone, (e) medium-density precarious zone, (f) high-density precarious zone, and (g) rural area.

While the Kinshasa Survey is prepared with an innovative approach, the use of the survey requires caution against its data quality and comparability. It is extremely challenging to design a sampling survey to collect information about households and individuals that represents Kinshasa, due to the lack of a recent population census. The sampling of the Kinshasa Survey, which is the primary data for this study, was prepared by drawing on satellite imagery. In addition, unlike the 1-2-3 household survey of 2012, the stratification of the 2018 survey explicitly captures precarious areas. Despite such an innovative sampling approach, the Kinshasa Survey has concerns about the data quality because of a problem in the data collection process (see Batana et al. 2021a for detailed descriptions). Considering all these factors, this study does not report any absolute number of population based on the survey and does not make a direct comparison between the Kinshasa Survey and the 1-2-3 household survey.

The analysis of job accessibility draws on the 2017 JICA Commuter Survey, which was conducted in 2017 with the aims of informing the Urban Transport Master Plan toward 2030 and identifying priority projects. The study area of the survey mainly covers urban parts of Kinshasa Province and spans over 1,450 km<sup>2</sup> (out of the total area of Kinshasa covering 9,985 km<sup>2</sup> ). For the survey, 8,000 households were sampled and randomly selected based on a satellite image of the study area. Surveyors were dispatched to visit the selected households and conduct face-to-face interviews with their representatives to answer the survey questionnaires. The questionnaire was designed to collect socioeconomic and commuting information of each household member.

4
26 in such countries due to the consumption of resources by the tall sector, the _non-tall nonslum_ sector must be disproportionately affected. However, these results should be taken with caution given the lack of global data on supply and crowding in this sector.

# **6. Mechanisms: Urban-Biased Policies and Consumption Cities**

Is the “rise of consumption cities” in some countries driven by their capital/largest city? In resource-rich countries, there is evidence that governments disproportionately invest resources in their capital/largest city (Bates, 1981; World Bank, 2020). Yet, industrialization/FIRE-ization may also lead to the disproportionate growth of a country’s largest city if it requires resources found there (e.g., skilled labor or an international airport).<sup>35</sup> In addition, resource exports cause the growth of small- and medium-sized mining towns or agro-towns (Jedwab, 2013), thus decreasing urban primacy. Therefore, _a priori_ it is unclear whether urban primacy is more prevalent among MFGSERV or NRXGDP countries.

**Cross-Sectional Regressions: Country-Level.** We use model (8) to study the correlations between the primacy rate – the share of the country’s urban population that lives in the largest city (source: World Bank (2021)) – in 2020 and the variables of interest: MFGSERV (2020), NRXGDP (mean 1960-2020) and DEINDU (change 1980-2020). We control for initial conditions, i.e. primacy, MFGSERV, and NRXGDP in 1960, thus capturing _longdifference_ correlations. We control for the urban share in 2020, add the controls for area, population, small islands and urban definitions, and use populations in 2020 as weights.

As seen in Col. (1) of Table 8, only slightly higher primacy rates are observed in NRXGDP and DEINDU countries relative to MFGSERV countries. The differences are not significant.<sup>36</sup> In Col. (2), we decompose NRXGDP into the export-to-GDP ratios of fuel & mining (FMXGDP) and agriculture (AGXGDP). The results do not show particularly high primacy rates in FMXGDP countries, but they show less primacy in AGXGDP countries. Yet, the high standard errors suggest that it is not the case for all agricultural exports.

**Panel Regressions: Country-Level.** We use the same 10-year panel regression as before

> 35In the longer run, as the cost of space increases and more stringent environmental regulations are adopted in larger cities, industrial activities move to small and medium-sized cities, thus reducing primacy.

> 36While some resource-rich countries such as Angola, Cˆote d’Ivoire, and Malaysia have high urban primacy rates (30%-40%), resource-poor countries such as Bangladesh, Japan and Thailand have similarly high rates. Argentina, the Arab Republic of Egypt and Peru – countries with intermediary levels of resource richness – also have high rates.
# **Reference:**

Congqing University. 2007. “WTP Survey and Study for Water Tariff Reform in Western Chongqing.” Washington, D.C. World Bank.

Ehemann, R.W., J.M. Duke, and J. Mackenzie. 2001. “A Spatial Analysis of the Distributional Effects of Water Quality Management.” Newark, DE. University of Delaware.

Fankhauser, S. and S. Tepic. 2007. :Can Poor Consumers Pay for Energy and Water? An Affordability Analysis for Transition Countries.” _Energy Policy._ 35:1038–1049.

Garcia, S., and A. Reynaud. 2004. “Estimating the Benefits of Efficient Water Pricing in France.” _Resource and Energy Economics._ 26: 1–25.

Grafton R.Q., and T. Kompas. 2007. “Pricing Sydney Water. _The Australian Journal of Agricultural and Resource Economics._ 51:227–241.

Griffin, C.C., J. Briscoe, B. Singh, R. Ramasubban, and R. Bhatia. 1995. “Contingent Valuation and Actual Behavior: Predicting Connections to New Water Systems in the State of Kerala, India.” World Bank Working Paper. Washington, D.C. World Bank.

Griffin, R. 2006. _Water Resource Economics: The Analysis of Scarcity, Policies, and Projects_ . Cambridge, MA: The MIT Press.

HWCC.net. 2006. “Increasing Block Water Pricing to Solve Water Conservation Problems.” Available online at: http://www.hwcc.com.cn/newsdisplay/newsdisplay.asp?Id=144324 Johansson, R. 2000. “Pricing Irrigation Water—A Literature Survey.” World Bank Working Paper. Washington, D.C. World Bank.

Mitchell, R. and R. Carson. 1989. _Using Surveys to Value Public Goods_ . Resources for the Future..Washington, D.C.

Nature. 2008. “Water under Pressure,” Special Issues. _Nature_ 452, 269 (2008).

NSB (National Statistics Bureau). 2006. _China National Statistics Yearbook_ . Beijing, China.

OECD (Organization for Economic Co-operation and Development). 2003a. _Social Issues in the Provision and Pricing of Water Services_ . Paris: OECD.

OECD (Organization for Economic Co-operation and Development). 2003b. _Distribution of Costs and Environmental Impacts of Water Services in OECD States: Affordability Measurement and Policies_ . Paris: OECD.

Pattanayak, S.K., C. Berg, J.C. Yang, and G.V. Houtven. 2006. “The Use of Willingness to Pay Experiments: Estimating Demand for Piped Water Connections in Sri Lanka.” World Bank Working Paper. Washington, D.C. World Bank.
recommended sequence leading to the delivery of oral antimalarials in the presence of fever that is not classified as very severe febrile disease. The recommendations (reproduced in Appendix Table 1) are to treat presumptively for malaria if (1) in high malaria-risk areas, the patient presents with fever, without any general danger sign or stiff neck and without cough with fast breathing and (2) in low malaria-risk areas, the patient presents with fever, without any general danger sign or stiff neck, and without runny nose, measles, other known cause of fever, no cough with fast breathing. Typically, in areas of high endemicity the recommendation is that all patients with fever or history of fever be treated with antimalarials. As the figure (reproduced from MVARA 1998) in Annex 1 shows, the areas of Sub-Saharan Africa under study here are virtually all in areas which are suitable to stable malaria).<sup>3</sup> In order to be encompassing, this paper uses all episodes of fever as the outcome measure. In some cases results for fever without a cough are presented as well. While there is a difference in the incidence of fever and fever without a cough, the patterns across groups are not substantively different.

Although the use of fever to identify malaria is in line with the current INMCI recommendations, it is certainly not perfect. All fever is not malaria. Brinkmann and Brinkmann (1991), after reviewing a substantial volume of literature, estimate that malaria is responsible for 40 percent of episodes of all fever in Africa. A validation of the IMCI protocol for minimally trained health workers in a high malaria transmission area of Kenya (Perkins and others 1997) found that 96 percent of 1,674 patients presenting with fever were classified as having malaria. A follow-up physicians assessment (based on measurement of temperature, hemoglobin determination, blood smear for malarial parasites and chest X-ray) determined that 456 (27 percent) were unlikely to have malaria, and that as many as 16 percent of cases had "fever requiring referral." Others argue that better algorithms for diagnosing fever could be used (for example Redd and others 1996) or that it is not sufficient to rule out relative alternative treatments (Redd and others 1992) - a limitation that the holistic IMCI approach ainied to remedy.

The DHS rely not on third-party health worker or doctor assessments of health status, but on the report of mothers on the fever episodes of their children. This introduces two additional potential sources of error. Do mothers recognize fever in their children, and is there a systematic bias to which mothers recognize fever-and in particular, is this related to household wealth?

3 Stable malaria describes areas with year-round transmission, which may be low or high intensity. Northern regions of Chad, Mali, and Niger are not suitable to stable malaria but in countries covered by the DHS. Northern Mali is excluded by virtue of DHS sample design, and northern Chad and Niger are excluded in the geographic analysis in this paper. In the multivariate analysis, dummy variables for national subregion will adjust for this.

4
# **Table 4: Robustness Tests for EMDEs: Two-Stage Least Squares**

_Dependent variable: Annual change in government expenditure as a share of the three-year MA GDP_

**Notes** : Standard errors are in parentheses: *** _p_ < 0.01, ** _p_ < 0.05, * _p_ < 0.1. Standard errors are clustered at the country level. In the first stage, commodity revenues are instrumented with the country-specific commodity price index scaled with the average commodity revenues as a share of GDP in 2008–2012. The control variables are the IQ index, liquid liabilities as a share of GDP, and the natural log of per capita gross national income. All controls are added in the equation interacting with changes in the commodity price index scaled with the average commodity revenues as a share of GDP in 2008–2012.

42
# **Table 1 – Household Benefits and Social Welfare**

||**2001**|**2002**|**2003**|**2004**|
|---|---|---|---|---|
||**Aver**|**age consumpti**|**on relative to P**|**L (y)**|
|Obs|6214|6159|6123|6121|
|Popul.|3,630,500|3,623,991|3,553,774|3,603,873|
|Mean|1.044|1.271|1.509|1.534|
|Std. Dev.|0.876|1.005|1.160|1.225|
||**Average bene**|**fits relative to P**|**L for benefits r**|**eceipients (B)**|
|Obs|356|876|1115|1166|
|Popul.|269,759|586,764|731,134|813,745|
|Mean|0.162|0.167|0.164|0.143|
|Std. Dev.|0.281|0.311|0.267|0.195|
||**Aver**|**age benefits rel**|**ative to PL for a**|**ll (B)**|
|Obs|6217|6159|6123|6121|
|Popul.|3,632,407|3,623,991|3,553,774|3,603,873|
|Mean|0.012|0.027|0.034|0.032|
|Std. Dev.|0.087|0.139|0.138|0.110|
|||**Coverage a**|**nd poverty**||
|_Benefits coverage_|_5.7_|_14.2_|_18.2_|_19.0_|
|_Poverty headcount_|_62.6_|_48.5_|_34.8_|_34.1_|

20