rafmacalaba/datause-encoder-data huggingface dataset:
household model’s advantage, in terms of precision, is smaller for out-of-sample municipalities than for in-sample municipalities.

# _5.2 Accuracy and coverage rates_

**Error! Reference source not found.** presents predicted-true plots for of the poverty predictions for in-sample predictions (left) and out-of-sample predictions (right) for the household model, the sub-area model, and the area-level model. For each model, it is visually clear that the in-sample predictions are more accurate than the out-of-sample predictions. Across models, the householdlevel model is discernably more accurate in-sample, as the estimates are generally closer to the dotted line than in the sub-area model. Out-of-sample, the differences are more difficult to distinguish.

Table 3 displays summary measures of the accuracy and coverage rates of the estimates from the different models. The second-to-last row shows that the 2010 census-based estimates are the most accurate, especially out of sample, with estimated correlations above 0.9.<sup>18</sup> The household model improves on the accuracy of the direct estimates for in-sample municipalities, with the correlation between the estimates and the true estimates rising from 0.80 to 0.86. Consistent with that, the root mean squared deviation falls 25 percent, from 0.126 to 0.094. The sub-area model fares slightly worse than the estimates from the household-level model with respect to both accuracy measures but are an improvement over the direct estimates. The area-level model estimates are the least accurate and do not improve on the direct estimates in terms of correlation but do improve on the direct estimates in terms of RMSD. This may reflect imprecision in the estimated variance of municipal poverty derived from the sample, which are an input into the area-level model.

Out of sample, the predictive performance of all estimators suffers from the lack of sample data on which to condition the estimated random area effects.<sup>19</sup> The out-of-sample correlation is only 0.70 for the household-level and sub-area level model, and 0.66 for the area-level model. Similarly, root mean squared deviation of the household-level model is nearly twice as high out of sample,

> 18 However, derivations of the 2010 poverty estimates are used as predictors in the official 2015 poverty estimates. As such, part of the higher correlation might be mechanical.

> 19 The lower predictive performance out of sample is not due to overfitting the model, since the plug-in lasso selects more parsimonious models than cross-validation.

23
Studies of the United States are able to model direct occupational flows between industries (e.g., Ellison et al. 2010, Glaeser and Kerr 2009). We unfortunately lack such data for India. We instead take a very simple approach. Greenstone et al. (2010) calculate from the Current Population Survey the rate at which workers move between industries in the United States. Using their measure of labor similarity for two industries, we define Labordi=∑k=1,...,I Edk/Ed∙Mobilityi←k. This metric is a weighted average of the labor similarity of industries to the focal industry i, with the weights being each industry‘s share of employment in the local district. The metric is again by construction mostly orthogonal to city size.

These metrics condense large and diverse industrial structures for cities into manageable statistics of local industrial conditions. The metrics do have limitations, though. First, we do not capture potential interactions that exist beyond the local district, but factor and product markets can be wider than a district (e.g., Rosenthal and Strange 2001, Kerr and Kominers 2010). Second, the metrics do not consider final consumers. In unconditional estimates, we separately model city populations. Third, the metrics do not measure differences across districts in worker or input quality except to the extent that they are reflected in industry compositions or observable features like education levels. Finally, these metrics can suffer from omitted variable biases should another district-industry factor jointly determine both incumbent structures and entry rates. We will use changes in industrial conditions from 1989 to 2005 to partially address this concern.<sup>12</sup>

# **Unconditional Estimations of Spatial Entrepreneurship**

We characterize entry through a series of linear regressions with the above determinants as explanatory variables: ln(Entrydi) = ηi + β∙Xd + γ∙ln(Incumbent Employmentdi) + γI∙Inputdi + γO∙Outputdi + γL∙Labordi + γC∙Chinitzdi + εdi.

12 There are several factors that we do not consider in this study: natural cost advantages (e.g., Ellison and Glaeser 1999), local industrial diversity (Jacobs 1970), and entrepreneurial culture (Hofstede 2001, Florida 2005, Falck et al. 2011). Perhaps most important, we also do not model Marshall‘s (1920) third agglomeration rationale of knowledge spillovers because the data used to typically measure knowledge spillovers (e.g., local patent citations) are not available consistently at the level of detail that we require in India. It should be noted that the customer/supplier and labor pooling rationales both overlap with knowledge flows to some extent. For example, Porter (1990) emphasizes that proximity to customers and suppliers can enhance innovation by increasing knowledge flows about which products are working and what new products are desired. Likewise, occupational sharing is often associated with the knowledge and skills of the workers. To some degree, knowledge flows will be included in these estimates.

17
Figure 8 presents data on the absolute central government budget appropriations or outlays for R&D for environmental technologies and for clean energy technologies (a subset of environmental technologies) for 33 countries including 8 developing countries (on the right-hand side, in millions of US$ in PPP terms). The fact that no developing country is in the top 10 absolute funders of energy and environment R&D reflects data availability (China, for instance, is not included presumably due to data comparability issues), but is also consistent with the more limited technological capabilities for formal R&D in most developing countries; the only developing countries included among the top 20 funders for which comparable data were available are the Russian Federation (11), Mexico (12), the Czech Republic (19) and Poland (20).<sup>21</sup> <!-- Start of picture text --> Figure 8. Government R&D budgets for energy and the environment, 2010<br>A. As a percentage of the total government R&D B. Total budget on Environment + Energy,<br>budget Millions USD PPP<br>NZL (2008)<br>JPN<br>JPN<br>USA (2009)<br>KOR<br>DEU<br>AUS<br>KOR<br>CAN (2008)<br>FRA (2008)<br>MEX (2006)<br>ESP (2009)<br>FIN<br>ITA<br>ESP (2009)<br>CAN (2008)<br>FRA (2008)<br>GBR (2009)<br>EST (2009) AUS<br>DNK RUS (2009)<br>DEU MEX (2006)<br>ITA FIN<br>NOR SWE (2009)<br>CZE DNK<br>SVN (2009) NOR<br>SWE (2009) NLD<br>POL (2008) PRT<br>IRL (2009) Energy CZE<br>SVK POL (2008)<br>PRT BEL (2009)<br>LUX Environment AUT<br>GRC (2008) NZL (2008)<br>BEL (2009) IRL (2009)<br>AUT GRC (2008)<br>GBR (2009) HUN<br>HUN CHE (2008)<br>NLD SVN (2009)<br>ISL (2009) SVK<br>USA (2009) ISR<br>RUS (2009) EST (2009)<br>ISR LUX<br>CHE (2008) ISL (2009)<br>0 1000 2000 3000 4000<br>0% 5% 10%<br>Source: OECD, Research and Development Database, May 2011. Note: Government budget appropriations or<br>outlays for R&D measures the funds committed by the federal/central government for R&D. It can be broken<br>down by various socio-economic objectives, defined on the basis of the primary purpose of the funder, including<br>control and care for the environment as well as energy.<br><!-- End of picture text --> 21 To the extent that the figures in Box 4 are comparable, Brazil with green R&D expenditures of $300 million would be between the Russian Federation and Mexico.

18
mostly in Africa and Asia (see Table 6).<sup>64</sup> There are fewer variations among CEO-based models and board-based models. The main organizational design difference observed within CEO-based models is the appointment of the CEO.

# **TABLE 6.  GOVERNANCE STRUCTURE**

||GOVERNANCE<br>MODEL|COMPOSITION ANDAPPOINTMENT|
|---|---|---|
|**KRA**|Board of Directors|•<br>Board consists of chairman (appointed by president), the<br>CG, the Permanent Sec. (MOF), the Attorney General,<br>and six others by virtue of their knowledge and<br>experience (appointed by the minister), excluding public<br>officers<br>•<br>Minister appoints CG on recommendation of Board<br>•<br>CG, PS, and AG are ex officio members and have no right<br>to vote<br>•<br>Minister may terminate board members’ appointment for<br>sufficient cause as specified in the Gazette<br>•<br>Minister may terminate CG’s appointment for sufficient<br>cause|
|**SARS**|Advisory Board|•<br>Board consists of up to eight persons (appointed by<br>minister of finance), the commissioner, and up to two<br>senior SARS employees (named by commissioner)<br>•<br>Minister of finance appoints commissioner after<br>consulting with cabinet and advisory board|
|**URA**|Board of Directors|•<br>Board consists of chairperson (appointed by the minister),<br>one representative of the MOF, one representative of the<br>ministry of trade and industry, one representative of the<br>Uganda Manufacturers Association, the CG, and two<br>others by virtue of their knowledge and experience in<br>taxation (appointed by the minister), excluding public<br>officers<br>•<br>Ministerappoints CG on recommendationof Board|
|||_Latin America_|
|**SAT**|Board of Directors|•<br>Minister appoints two members (public only) and SAT<br>president appoints two high level SAT employees to<br>board|
|||•<br>President of republic appoints SAT president|
|**SENIAT**|Superintendent|•<br>Minister appoints superintendent and adjunct<br>superintendent|
|||•<br>Minister may remove superintendent|
|**SUNAT**|Superintendent|•<br>President of republic appoints and removes<br>superintendentonsuggestionof ministerof finance|

_Source: ARA laws, acts, and decrees._

> 64 The Mexican and Guatemalan cases are the only Latin American ARAs that use the board of directors model, while the South African case is the only one in Africa that uses an advisory board instead of a board of directors.

50
**Table 5. Economic Outcomes RDD Results by Outcome and Round**

||**Income**|**Log Income**|**Wealth**|**Log Wealth**|**Consumption**|**Log Consumption**|
|---|---|---|---|---|---|---|
|**2008**|||||||
|50% bandwidth|2,526***|0.337*|10,259|0.125|1,529***|0.383***|
|100% bandwidth|1,347**|0.383***|3,511|0.068|556|0.283***|
|200% bandwidth|519|0.229*|106|-0.061|524|0.129|
|**2011**|||||||
|50% bandwidth|2,189|0.182|9,243|-0.257|2,190**|0.439**|
|100% bandwidth|1,488|0.281|13,220|0.727|1,730**|0.294*|
|200% bandwidth|882|0.170|5,930|0.608|1,002*|0.239*|
|**2013**|||||||
|50% bandwidth|597|0.561**|30,816|0.395|1,740**|0.345|
|100% bandwidth|618|0.278|13,900|0.185|702|0.107|
|200% bandwidth|754|0.322|3,718|-0.058|276|0.056|

_Source:_ Authors’ calculations based on ONDH impact evaluation panel survey. _Note:_ *p < 0.1; **p < 0.05; ***p < 0.01. Standard errors clustered on commune level.

20
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When the Rivers Run Dry: Liquidity and the Use of Wholesale Funds in the Transmission of the U.S. Subprime Crisis<sup>*</sup> Claudio Raddatz _The World Bank_

**JEL Codes:** F3, G21.

# **Keywords:** Financial crisis, contagion, wholesale funds market, event study

*Development Economics Research Group; Macroeconomics and Growth Unit. E- mail:craddatz@worldbank.org. I thank Alfonso Astudillo for superb research assistance and the World Bank Grant for Research on the Financial Crisis for financial support. Comments from Ari Aisen and seminar participants at the Center for Applied Economics of the University of Chile and the Central Bank of Chile are gratefully acknowledged. All views expressed in this paper are the author’s only and do not necessarily represent those of the World Bank, its Executive Directors, or the countries they represent.
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the distribution (at 10<sup>th</sup> and 25<sup>th</sup> quantiles), four indicators come out statistically significant. Comparing the magnitude of the coefficients, the results from OLS and UQR from Model 1 suggest that food consumption score has the highest association with both of the nutritional outcomes followed by per capita expenditure.

**Table 9: Height-for-age Z-score (Uganda)**

|||**Model 1**<br>|**- Without C**<br>|**ontrols**<br>||
|---|---|---|---|---|---|
|VARIABLES|OLS|Unc|onditionalqu|antile regres|sion|
|||10th|25th|50th|75th|
|1 – SSEXR|0.071*|0.064|0.126**|0.090**|0.104*|
||(0.041)|(0.068)|(0.050)|(0.045)|(0.053)|
|FCS|0.197***|0.241***|0.229***|0.225***|0.239***|
||(0.038)|(0.067)|(0.048)|(0.042)|(0.050)|
|HDDS|0.085**|0.094|0.115**|0.113**|0.082|
||(0.041)|(0.074)|(0.050)|(0.045)|(0.052)|
|Per capita expenditure|0.139***|0.236***|0.150***|0.118***|0.115**|
||(0.041)|(0.065)|(0.051)|(0.045)|(0.056)|
|Food share|0.029|0.075|0.030|0.019|0.043|
||(0.038)|(0.065)|(0.045)|(0.041)|(0.048)|
|||**Model**|**2 - With Co**|**ntrols**||
|VARIABLES|OLS|Unc|onditionalqu|antile regres|sion|
|||10th|25th|50th|75th|
|1 - SSEXR|0.036|-0.069|0.085|0.066|0.097*|
||(0.045)|(0.077)|(0.055)|(0.048)|(0.056)|
|FCS|0.150***|0.133*|0.179***|0.172***|0.200***|
||(0.042)|(0.076)|(0.055)|(0.048)|(0.057)|
|HDDS|0.021|-0.048|0.055|0.053|0.007|
||(0.049)|(0.089)|(0.058)|(0.051)|(0.060)|
|Per capita expenditure|0.121**|0.177**|0.154**|0.122**|0.094|
||(0.050)|(0.083)|(0.063)|(0.053)|(0.062)|
|Food share|0.005|0.077|0.018|-0.028|0.009|
||(0.041)|(0.070)|(0.048)|(0.043)|(0.050)|

When we control for a broader set of characteristics that could also be correlated with nutritional status in Model 2, only two measures – namely food consumption score and per capita expenditure – remain statistically significant for both height-for-age and weight-for-age. This result remains qualitatively similar to the results from Model 1, with food consumption score having the highest association with both measures of nutritional outcomes followed by per capita expenditure.

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low, but better than for the other informal types, suggesting that although they do not enjoy the benefits associated with a formal wage job, their earnings are such that they can access more goods and services than those working in other informal employment types. More than 85 percent have access to a mobile phone.

Looking within the informal firm sectors, some differences arise: Low-educated self-employed firms are simple retail operations with low profits, job creation, or modern business practices. Most owners are adults, despite a larger share of young owners in comparison to other firm types. Half of them are women with a larger female share among low-productivity firms. Most firms engage in retail trade, operating outside household premises, and 41 percent include unpaid employees. On average, LE-SE have the lowest profits, equal to the monthly poverty line, though some have profits in-line with employers. Access to financial services is very low, with less than one-twentieth of firms having a bank account or using banks or microfinance institutions for their regular operations. More than half report that they do not see the benefit of formalization.

High-educated self-employed enterprises are young and productive retail businesses. Most HE-SE owners are adults and more than half of the sample are women, especially among high-productivity firms. HE-SE are younger than other firms, with 90 percent operating for no more than five years. Their profits are double those of other business types, their profits per worker, a proxy for labor productivity, is higher and more concentrated than the other firm types. They are mostly in retail trade and operate outsides the household premises, operating in less poor neighborhoods. Though using few modern business practices, they are more connected to the financial sector than other firm types. In addition to a not seeing the benefit of formalization, HE-SE are not registered due to lack of information on how to register.

Employers are older firms that managed to grow and create jobs though firm productivity is modest. By definition, the key defining characteristic of employers is that they employ an average of 2.5 paid workers, as compared to no paid workers in other firm types. A majority of employers are low-educated adult men (55.5 percent). They are more active in services or manufacturing than are the self-employed. They have the lowest use of good business practices, such as not mixing business accounts with household expenses, but slighter better use of financial services, including mobile money, than the self-employed. They tend to live in less dense areas that are accessible to other urban neighborhoods. While employers have the highest total revenues and profits, their per capital revenues and profits are on par with LE-SE enterprises.

# **9. Conclusions**

Informality strongly shapes Sub-Saharan Africa’s labor markets. This paper uses data from 26 harmonized household and labor surveys (GMD) and three Informal Enterprises (IES) to sketch a profile of the urban informal sector in Sub-Saharan Africa.

Page **44** of **60**
Table E7: Share of Significant Temperature Coefficients in Ethiopia

||MERRA-2|ERA5|CPC|
|---|---|---|---|
|Mean Daily Temperature|0.67|0.75|0.50|
|Median Daily Temperature|0.67|0.58|0.50|
|Variance of Daily Temperature|0.33|0.58|0.50|
|Skew of Daily Temperature|0.58|0.25|0.67|
|Growing Degree Days (GDD)|0.17|0.08|0.33|
|Deviation in GDD|0.08|0.25|0.33|
|Z-Score of GDD|0.17|0.25|0.33|
|Maximum Daily Temperature|0.33|0.42|0.42|
|Observations|96|96|96|

_Note:_ The table presents the share of statistically significant temperature coefficients in Ethiopia.

Table E8: Share of Significant Temperature Coefficients in Malawi

||MERRA-2|ERA5|CPC|
|---|---|---|---|
|Mean Daily Temperature|0.92|0.83|0.67|
|Median Daily Temperature|0.75|0.83|0.83|
|Variance of Daily Temperature|0.50|0.42|0.58|
|Skew of Daily Temperature|0.17|0.25|0.67|
|Growing Degree Days (GDD)|0.83|0.33|0.42|
|Deviation in GDD|0.58|0.42|0.33|
|Z-Score of GDD|0.58|0.58|0.42|
|Maximum Daily Temperature|0.58|0.67|0.67|
|Observations|96|96|96|

_Note:_ The table presents the share of statistically significant temperature coefficients in Malawi.

134
**Figure A.3b: Palm Oil World-Wholesale Margin during Pre-Intervention (Lagged Crude Price)**

<!-- Start of picture text --> Palm:Wholesale-World Price Margin<br>01jul2008 01jan2009 01jul2009 01jan2010 01jul2010 01jan2011<br>mdyr<br>bandwidth = .8<br>50<br>40<br>30<br>20<br>10<br>0<br><!-- End of picture text -->

**Figure A.4a: Wheat World-Wholesale Margins during pre-intervention (current price)**

<!-- Start of picture text --> Palm<br>01jul2008 01jul2009 01jul2010 01jul2011 01jul2012<br>mdyr<br>bandwidth = .8<br>60<br>40<br>20<br>0<br><!-- End of picture text -->
clothing, trade-distorting subsidies in agriculture, allowing relatively easy recourse to contingent protection in manufacturing, requiring trade partners to impose voluntary export restrictions in automobiles and steel, and perhaps most egregiously, pushing through rules on the protection of intellectual property despite the vehement opposition of developing countries.

So, is the current open, rules-based economic system safe? History's lesson is that we can never be sure. We cannot be certain that the enmeshing of interests will be strong enough to sustain the status quo. Nor is there a cast-iron guarantee that the current ideological embrace of markets as the predominant basis for organizing economic relations will survive the vicissitudes of intellectual fashion, the selective and self-serving interpretations of policymakers, and the financial crises that will inevitably shock the modern global economy. There is tail-side risk that interests, ideology, and institutions, both domestic and international, will be inadequate to the task of preserving the current system. And then there is always the unforeseeable and the irrational.

There is, therefore, still the possibility that China could seek to leverage its power in certain ways, including: pursuing industrial and protectionist policies domestically; capturing exclusive access to resources abroad; and obtaining privileged access to markets by entering discriminatory arrangements with selected partners.

The temptation to exercise power need not stem from some sinister motive on the part of China but simply from the fact that increased size confers greater market power. Economic selfinterest then dictates that a country exploit this power to improve its welfare by influencing its terms-of-trade, by increasing the price of its exports and/or reducing the price of its imports (including raw materials). In fact, the GATT/WTO has been seen as primarily a mechanism for large countries reciprocally to forego exercising market power (Bagwell and Staiger, 2004). So, our call for a China-inspired round of trade negotiations is to recognize that the previous bargain struck at the time of China‘s WTO accession, may no longer be an equilibrium (from the Chinese perspective). Hence, a new bargain may need to be struck, which would reflect China‘s increased size.

# _Reneging on existing obligations_

The first concern is that China may not live by the obligations that it has already assumed. Four examples are often presented: China‘s IPR regime, China‘s currency policy, China‘s services trade regime, and China‘s export restrictions, with the first being a case of possible violation of the letter of WTO law, the second a case of violating the spirit of WTO law, the third inhabiting the murky ground in between, and the fourth being inconsistent with China‘s accession commitments.<sup>9</sup> In each case, the tension arises because of the inconsistency between certain national policy goals and its WTO commitments.

> 9 See also WTO (2010) and Stewart (2010).

14