rafmacalaba/datause-encoder-data huggingface dataset:
The foreign and gfovernmn sctors are the pushdin:z setr x As explained, a key question is whether the foreign surplus may be regarded as exogenous to the business and household surpluses. Regression equations explaining household and business surpluses respectively by foreign or42 foreign plus government surplus (FG) identified a strong negative correlation in the case of the business sector, and a weaker negative correlation in the case of the household sector. (For example equations 3 and 4).

These estimates were obtained by ordinary least squares. One way of gauging the degree to which FG is exogenous is to re-estimate the equation by an instrumental variables technique (such as 2SLS) and assess the difference between the two estimates. Equations 5 and 6 use an instrument list43 with this in mind. The finding is that the coefficient of the sum of foreign and government surpluses does not change very dramatically when moving from OLS to 2SLS, especially when a reduced data set omitting three countries which experience outlying behavior is used (equations 3' to 6')44. To the extent that there is bias, it is in the sense of exaggerating the impact of foreign and government surplus on the household sector. With the full sample, after correcting for simultaneous equations bias, the estimated impact of the sum of foreign and government surplus on 42 The similarity of the coefficients on the G and F terms in the estimated equations suggests that the business and household sectors both respond to a financing shock to a degree which is independent of the source of the shock. This was verified by imPosing the restriction that the coefficient on each of F and G be the same. This restriction satisfied the appropriate F-test. 43 GNP per capita, share of exports in GNP, inflation, population, real interest differential vis-a-vis the US$ and GNP growth rate. 44 Observations for Malaysia, the Philippines and Portugal are outliers in equations (3) and (4). This suggested examining results for a reduced data set omitting these countries. The exclusion of outliers is always controversial; in this case the uncertain quality of some of the data, and the certainty that the equations are underspecified seems to argue in favor of erring on the side of exclusion.

40
**Figure 6: Change in GDP and change in informality, annualized**

<!-- Start of picture text --> Panel A: Total informality Panel B: Low-educated Self<br>employed<br>6.0% 5.0%<br>LBR<br>4.0%<br>4.0% UGA<br>3.0%<br>2.0%<br>2.0%<br>ZMB CMR<br>0.0% CMR 1.0% ZMB<br>-2.0% 0.0% 2.0% 4.0%GHA 6.0%<br>-2.0% 0.0%<br>-2.0% 0.0% 2.0% 4.0% 6.0%<br>-1.0%<br>-4.0% GHA UGA<br>LBR -2.0%<br>-6.0% Annualized change in GDP Annualized change in GDP<br>Panel C: Informal wage Panel D: Unpaid<br>6.0% employees 30.0% workers<br>GHA UGA<br>4.0%<br>UGA 20.0%<br>2.0%<br>CMR<br>ZMB<br>0.0% 10.0%<br>-2.0% 0.0% 2.0% 4.0% 6.0%<br>-2.0%<br>0.0%<br>-4.0% ZM B<br>-2.0% 0.0% 2.0% 4.0%GHA 6.0%<br>-6.0% -10.0% CMR<br>-8.0%<br>-10.0% LBR -20.0%<br>LBR<br>-12.0%<br>Annualized change in GDP -30.0% Annualized change in GDP<br>informality<br>for LE-SE<br>Annualized change in total<br>Annualized change in informality<br>wage employees<br>Annualized change in<br>Annualized change in informal informalily for unpaid workers<br><!-- End of picture text --> Source: Own elaboration based on GMD data (y-axis) and WDI (x-axis).

In summary, the urban informal sector is large and persistent in Sub-Saharan Africa. The formal private sector employs approximately 18 percent of urban workers while the informal sector is the source of more than half of all jobs. It is dominated by low-educated self-employed workers, as well as informal sector employees. Short-term trends suggest that urban labor markets do not uniformly move toward formality in the short run, though there may be a shift toward more organized employment types as people move away from unpaid labor into other types of informal work. Economic growth does not seem to necessarily lead to a decline in informality in the short-run.

# **5. Profile of Urban Workers**

Given the persistence of urban informal employment, it is useful to characterize urban workers within employment type. We use unconditional statistical comparisons as well as estimate conditional correlations (Tables 2 and 3) as described in the methodology section above. We consider their demographic characteristics including gender, age, education level, and poverty levels. We also consider their characteristics as workers, namely the 19
following the IRS intervention.

# **A.3 Change in number of villages in each sub-zone**

The number of villages by sub-zone was different from List 1 to List 4, as shown in Table B2. This can be explained by the fact that, in recent years, the boundaries of certain subzones were changed, so that some villages were allocated to a new adjacent sub-zone. The number of treatment villages was finalised when List 2 was drafted for the spraying teams. The total was reduced from 58 to 57. Although, in 6 of the 13 surveyed sub-zones, the number of treatment villages was left unchanged. Column 5 of Table B2 shows that the largest disparities with respect to List 1 appear in sub-zone Haykota (where 3 extra villages were treated) and in sub-zone Mensura (where 3 villages less were treated). In the other sub-zones, the number of treated villages differs from the original figure by at most 1. The number of treatment villages, both in total and by sub-zone, was not changed in the subsequent lists. The number of control villages was left unchanged at 58, from List 1 through List 4. However, column 10 of Table B2 shows that the allocation of control villages across sub-zones changed significantly: in the case of sub-zone Akurdet, it was increased by 3, while it was decreased by 3 in sub-zone Haykota<sup>4</sup> . The problem is less severe in the other sub-zones, in 5 of which the number of controls was left untouched.

# **A.4 Altered village names**

We therefore investigate the characteristics of altered village names and how these might have affected selection into treatment. In Tables B3 and B4 we investigate the presence of any systematic differences between villages whose names were not changed during the operations of the RCT and those villages which instead were changed<sup>5</sup> . Column 1 is analogous to the randomisation checks presented in the paper, while in Column 2 we check if villages with the same name in Lists 1 and 4 differ systematically from those

> 4Notice that in sub-zone Haykota the problem is severe for both treatment villages (+3) and control villages (-3).

> 5Notice that we compare villages with altered name or treatment allocation, to all other villages in Gash Barka.

4
tempting to conclude that Chile has an innovation shortfall until one notices that Chile also has low investment rates in human and physical capital, as reflected in a composite capital-output ratio of 50% of the U.S. level.<sup>4</sup> Perhaps whatever leads Chile to have low investment rates in human and physical capital may also explain its low R&D investment rate. Figure 1 extends this analysis to 48 countries for which we have the required data for 1995. It shows that there is a positive relationship between R&D investment as a share of GDP and the composite capital-output ratio. Again, the conclusion is that – at least for some countries – low R&D investment rates could be just part of a more general accumulation problem.

The challenge that emerges, then, is to find a way to identify true innovation shortfalls and separate them from cases of low overall accumulation (or “accumulation problems”).<sup>5</sup> For this purpose we propose the use of a model developed and calibrated by Klenow and Rodríguez-Clare (2004) that captures the interactions among accumulation of different types of capital, including “knowledge capital,” and allows for both barriers to general accumulation and barriers that are specific to the accumulation of knowledge. We explore the model’s implications for Latin American countries and find some countries that seem to suffer from true innovation shortfalls. It is important to emphasize, however, that the results from this analysis should be seen as extremely preliminary, as the exercise suffers both from large measurement error intrinsic to the use of international datasets such as those of Barro and Lee (2000) and the Penn World Tables 6.1, as well as model misspecification for some countries. To explore these issues, we consider again the case of Chile and show that some particular adjustments to the model are necessary for a more appropriate analysis. After such adjustments, the conclusion that emerges is that

> http://www.gobiernodechile.cl/21mayo2004/indice_discursos.asp).

> 4 The composite capita-output ratio includes both human and physical capital, and is given by _k_ ≡ _h_ ( _K_ / _Y_ ) α /(1 −α ) , where _h_ is human capital per worker (measured as _h_ = _e_ γ _s_ , where γ is the Mincer coefficient and _s_ is the average years of schooling of the adult population), _K_ is the stock of capital, _Y_ is total output, and α is the share of capital in output (see below for a formal derivation).

> 5 In part, this paper is inspired by the recent contribution by Hausmann, Rodrik and Velasco (2005), titled “Growth Diagnostics,” in which they argue that different countries may be affected by different binding constraints to growth, and that the goal of development economics should be to identify those constraints. A significant difference in our approach compared to theirs is that whereas they argue that one should look at shadow prices, we instead look at quantities but use theory to infer the corresponding shadow prices.

3
# **Table 4: The Impact of Survey Mode on Weekly Labor Market Reporting**

<mark>The impact of survey mode on labor market reporting Phone weekly vs in person weekly</mark> <u><mark>(omitted category)</mark></u>

|Phone wee|klyvs inper|son weekly (|omitted catego<br>Days-|ry)|Hours-|
|---|---|---|---|---|---|
||Working|Days|conditional<br>on work|Hours|conditional<br>on work|
|Panel A: Main effects||||||
||(1)|(2)|(3)|(4)|(5)|
|Phone weekly|-0.08***|-0.57***|-0.15|-6.86***|-3.61|
||(0.03)|(0.18)|(0.10)|(2.49)|(2.38)|
||[0.01]|[0.01]|[0.13]|[0.01]|[0.13]|
|Mean in person weekly|0.86|4.60|5.37|41.34|48.31|
|N|3,951|3,951|3,190|3,951|3,190|
|Adjusted R2|0.029|0.033|0.004|0.025|0.004|
|Panel B: Heterogeneity by self-em|ployment st|atus||||
||(6)|(7)|(8)|(9)|(10)|
|Phone weekly|-0.01|-0.09|-0.02|-2.51|-2.22|
||(0.04)|(0.27)|(0.14)|(3.47)|(3.24)|
||[0.87]|[0.87]|[0.90]|[0.74]|[0.74]|
|Self-employed|0.04|0.29|0.11|0.55|-1.49|
||(0.04)|(0.26)|(0.15)|(3.31)|(3.08)|
||[0.74]|[0.74]|[0.74]|[0.90]|[0.86]|
|Phone weekly*Self-employed|-0.12**|-0.83**|-0.23|-7.99*|-3.21|
||(0.05)|(0.35)|(0.20)|(4.35)|(4.03)|
||[0.16]|[0.16]|[0.74]|[0.34]|[0.74]|
|Mean wage employed in-person||||||
|weekly|0.83|4.41|5.30|41.00|49.23|
|Phone effect self-employed|-0.14|-0.92|-0.24|-10.51|-5.43|
|p|0.02|0.02|0.25|0.07|0.43|
|Number of observations|3951|3951|3190|3951|3190|
|Adjusted R2|0.035|0.040|0.006|0.033|0.009|
|Note: *** p<0.01, ** p<0.05, * p<<br>parentheses below the coefficient<br>in square brackets. The omitted c<br>completed weekly in person inter<br>corresponding to 1/number of we<br>include week of interview dummi|0.10, standa<br>. Multiple te<br>ategory are i<br>views. Regre<br>eks for whic<br>es.|rd errors cl<br>sting adjust<br>ndividuals w<br>ssions are a<br>h we have d|ustered by hous<br>ed FDR-adjuste<br>ho were worki<br>ttrition weighte<br>ata for each ind|ehold are p<br>d q-values a<br>ng at baselin<br>d (with weig<br>ividual). All|resented in<br>re presented<br>e and<br>hts<br>regressions|

24
# **Table 12: Inequality decomposition implied by Table 6**

||Sha|re of inequalit|y attributable to|each source|(%):|
|---|---|---|---|---|---|
||All|Rural|Urban|Men|Women|
|Male|0.17|0.00|1.56|--|--|
|Age|0.82|0.90|1.82|-1.50|-5.67|
|Age squared|-1.45|-1.09|-1.74|4.14|5.54|
|Muslim|-0.06|-0.21|0.32|-0.09|-0.08|
|Serere|1.39|9.16|-0.06|1.97|0.97|
|Poular|0.54|0.50|0.16|1.91|0.05|
|Diola|-0.06|1.41|-0.03|-0.04|-0.15|
|Mandingue|0.11|-0.04|0.41|0.13|0.06|
|<br>Sarakole|0.00|0.18|0.02|-0.05|0.02|
|Mandiaque|-0.07|0.32|0.05|-0.04|-0.01|
|Other ethnicity|0.02|0.07|-0.01|0.31|0.03|
|<br>Brothers same father|0.06|-0.05|0.27|0.16|0.00|
|Brothers same parents|0.14|0.35|0.06|0.66|0.01|
|Sisters same father|-0.04|-0.08|0.03|0.04|0.03|
|Sisters same parents|0.36|0.69|0.26|0.24|0.70|
|<br>Brothers same mother|0.13|0.02|0.43|0.07|0.24|
|Sisters same mother|0.03|-0.02|0.00|-0.07|0.09|
|First same gender|0.00|-0.18|-0.08|-0.07|-0.01|
|First of siblings|-0.04|0.51|0.53|0.00|0.06|
|<br>First born is male|0.21|1.00|0.13|0.05|-0.09|
|Father dead recently|0.02|0.03|0.10|0.00|0.01|
|Father in farming|-1.82|-0.21|-1.03|-1.70|-1.46|
|Mother in farming|0.16|-0.40|0.67|1.82|-1.21|
|<br>Father in non-farm|5.75|3.22|4.21|7.46|4.80|
|Mother in non-farm|-0.40|0.79|0.38|-0.51|-0.40|
|Father‘s schooling|0.34|-0.25|1.20|0.87|0.02|
|Mother‘s schooling|1.07|0.38|2.43|0.44|1.85|
|<br>Father rural|-2.53|-0.22|-0.27|-3.42|-2.10|
|Mother rural|3.46|0.30|0.87|2.71|4.33|
|Log hh size|3.00|1.11|8.46|3.07|2.80|
|Log cell size|6.39|27.57|7.62|4.85|3.44|
|<br>Share of cell aged<5|3.62|7.14|4.21|2.93|3.20|
|<br>Share of cell adults|10.63|11.15|18.11|5.69|9.02|
|Has formal schooling|9.21|3.75|10.15|7.04|10.88|
|<br>Fostered|0.91|0.72|1.14|1.41|0.22|
|Fostered young|-0.06|0.00|-0.08|-0.42|0.55|
|<br>Age at first marriage|0.99|4.28|-0.42|-0.47|2.64|
|<br>Inherited land|0.25|0.31|0.28|0.90|-0.03|
|Inherited house|0.07|-0.76|0.41|-0.01|0.10|
|Other inheritance|0.08|1.44|0.36|-0.49|0.01|
|Rural|23.80|--|na|23.46|27.57|
|Total share explained|50.40|30.10|39.40|47.90|52.90|

Note: All sources do not add to the total share explained due to the omission of the share of inequality due to department of residence.

38
Figure 4. The Global Progression of Severity: Daily Severity from COVID-19 and Proportionate Mortality by Top Cause <!-- Start of picture text --> 6<br>3<br>5<br>4<br>5<br>3<br>2<br>1<br>0<br>Mar 1 Apr 1 May 1<br>Daily severity ratio (percent)<br><!-- End of picture text --> Source: Johns Hopkins University; World Health Organization. Note: The daily severity ratio is the daily equivalent of the cumulative ratio. Horizontal line shows nth cause of death above or around the maximum daily severity ratio. Cut-off date for all data is May 23.

The pandemic’s severity seems to be lower in countries with large populations (Figure 3.B). The data are choppy, but larger countries tend to have lower numbers. Among the 12 with 100+ million people, China and India (not shown in the figure for presentational reasons) have severity ratios of 0.01 and 0.02 percent whereas others in this category are well below 1 percent except for the United States (15 percent), Brazil (9 percent) and Mexico (6 percent). The low numbers in large countries may reflect a strong regional concentration of the pandemic at the subnational level—compare the severity of the pandemic for China as whole versus the province of Hubei (Fenz and Kharas, 2020). Among countries with smaller populations, the severity ratios appear to be much more spread out.

# **Progression over Time**

To examine the progression of severity over time, we switch from cumulative to daily measures. The daily numbers are calculated by comparing COVID-related deaths and baseline deaths for all or top causes with respect to the period of one single day. The discrete daily data shows significant noise, but they allow us to examine patterns related to the speed and sustained strength of the progression of the pandemic at the country level. The raw data also allows us to detect sudden adjustments due to the statistical methods and other one-off country-specific factors.

The global numbers in Figure 4 illustrate the steady rise in daily severity, followed by a significant tapering in recent weeks. Around end-March, COVID-related deaths exceeded the total number of daily deaths from Alzheimer’s – the top fifth cause of death globally. The daily additions continued to rise through the middle of April when they started to exceed the global third cause of death, which is chronic obstructive pulmonary disease.

10
<!-- Start of picture text --> South es|<br> Asia eea || |<br>OECD nT<br>Middle East re ee ee gw Ar transport (higher is<br>Latin America and Caribbean pel | slobal O Inland waterways (higher<br>Europe ee cone is better)<br> and CentralEast Asia ——rEee ee average<br>ASia Minnis<br>a<br>.——<br>[]<br>0.000 0.200 0400 0.600 0.800 1.000<br>Ct<br>South Asia [ee | | ||<br>OECD iene<br>Middle East es |_| m Hidden import barriers<br>SS a es a (higher is few er barriers)<br>LatinAmerica and Caribbean Xx» OBribery (higher is few er)<br>Europe and Central Asia aSSS gichalas<br>East EEee ee<br>Asia Ginn]<br>a<br>Africa A<br>Cia<br>0.000 0.200 0.400 0.600 0.800 1.000<br>Ct<br><!-- End of picture text -->
## Appendix 6: Draft pandemic preparedness and response survey module for SDI

This novel module has been developed by pandemic preparedness experts and an earlier version was piloted in Bhutan (in preparation for data collection for a health SDI in the coming year). Going forward, this information will be collected as part of standard health SDI surveys. The SDI Team welcomes feedback on this module (please send comments to Kathryn Andrews kandrews@worldbank.org).

# **_Bhutan Health Service Delivery Indicators Survey 2020_**

### **Emergency Preparedness Module**

#### **_EMERGENCY PREPAREDNESS AND RESPONSE SERVICES_**

|**Enumerator reads to respondent:**Next, we have some questions about this facility’s emergency preparedness and response services.|
|---|

|**Number**|<br>**Question**|<br>**Response**|<br>**Skip**|<br>**Notes**|
|---|---|---|---|---|
|1.|Does this facility have a ‘Health Emergency and Disaster<br>Contingency Plan’ and/or ‘Public Health Emergency<br>Contingency Plan’?<br>_(verify)_||Yes=1<br>No=2<br>Don’t know=−96|In Bhutan, if the facility has a plan, it<br>will automatically have all of the gov-<br>required elements, so no need to ask<br>about the components of the plan|
|2.<br>|Has this facility allocated funds earmarked for emergency?||Yes=1<br>No=2<br>Don’t know=−96|Preparedness|
|3.<br>|Has this facility identified a surveillance focal point?||Yes=1<br>No=2<br>Don’t know=−96|JEE- Real-time Surveillance|
|4.<br>|<sup>In the case of an emergency, has this facility identified a risk</sup><br>communication focal point?||Yes=1<br>No=2<br>Don’t know=−96|JEE-risk communication (emergency<br>response)|
|5.|In the case of an emergency, has this facility defined<br>protocols to communicate with other health facilities, affected<br>communities and public?||Yes=1<br>No=2<br>Don’t know=−96|JEE-risk communication|
|6.|In the case of an emergency, has this facility defined reporting<br>protocols and relevant contacts for national and sub-national<br>reporting?||Yes=1<br>No=2<br>Don’t know=−96|JEE-reporting (emergency response)|

33
79 _Table 6.5: Colombia: Welfare Effects ofElectriciy Price Changes, 1983-88_ <!-- Start of picture text --> Price 1984 (Pcsos/kWh) 5.13<br>Price 1988 (Pesos/kWh) 6.95<br>Consumption 1984 (kWh/month) 442<br>Consumption 1988 (kVWhmonth) 392<br>me (Peos/oth) 24000<br>Wdefare effect asuing edticiLy of zeo (percent) -3.35<br>Welfare effict assuming dtsticity of-.32 (percent) -3.16<br>Welfwar effect assuming price lasticity of one (percent) -2.76<br>Noti. Price and eonupionf brEPM onuemome.<br>Lou in amaum mus exed as aprentae of tol incOML<br><!-- End of picture text --> As for tfie subsidy effect of price increases during 1985-88, prices in h residential sector wer still subsidized, altough the level of subsidies varied across dffrent uftlies. Table 6.6 gives te distribution of subsidies to the residential sector for different utilities.

_Table 6.6: Colombia: Electricity Subsidies to the Residential Sector, 1988_

|Utility|Sase 1988<br>(GWh)|Shareof<br>_Sales (l)_|Aveagerate<br>1988; (JSO/Wh)|LRAIC<br>(USOAkWh|Subsid<br>) USS Millio.|
|---|---|---|---|---|---|
|EEEB|2,639|23.8|2.19|7.34|135|
|EEPPM|2,050|18.5|2.32|7.34|103|
|CVC-CALI|1,477|13.3|2.97|7.34<br>|_65_<br>|
|ICEL (group)|3,362|30.3|2.47|7.34|164|
|<br>CORELCA|1,569|14.1|3.40|7.34|60|
|Total|11,097|100|_2.59_|7.34|527|

## _&wmc_ World Bak (1989).

# _Ma conomic Impacts_

Frm 1986 to 1988, increases in consuer prices were relatively high and inflation **peaed at** 30 percent in ihe middlc of 1988, although it declined to 28.1 pecent by the end of the samo yea. Stuies ayz inglatioi **n** C4bmbia show tht the maor fctor responsible for nlation **mt** put bave bem
In column (ii) we use as an alternative entry regulation variable the logarithm of the number of procedures required to set up a business from Djankov et al. (2002).<sup>31</sup> Our results are robust. We find higher entry rates into industries with high entry in the U.S. in countries with fewer entry procedures. The coefficient estimate suggests that the difference in entry rates between retail and pulp in Sweden (that is at the 25<sup>th</sup> percentile in terms of the number of entry procedures) is 0.8 percentage points higher than the difference in entry rates between the same industries in Spain (that is at the 75<sup>th</sup> percentile in terms of the number of entry procedures). In column (iii) we include the monetized value of the entrepreneur’s time to set up a business in the cost of entry. We obtain this variable from Djankov et al. (2002). The results are robust to using this alternative measure of the cost of entry regulation.

Next, we estimate using different samples. In column (iv), we exclude transition countries. Privatization has resulted in the emergence of a large number of private firms in these economies, and we want to make sure our results are not driven by this. Our results are robust to the exclusion of these countries. Our results are also robust to adding back those countries that failed to meet our inclusion criteria (i.e., Iceland, Ireland, Luxembourg and Portugal), and to dropping one country at a time (not reported).

In column (v) we present estimates restricting the sample to firms with over 10 employees. Since many firms that include information on age and sector are missing employment (and financial data), we exclude industries where more than 50% of firms have missing employment data (about 13% of observations). With the caution that employment coverage is limited, we find that the coefficient for the interaction term is negative and statistically significant Next, we try different methods of estimating. In column (vi), we estimate an OLS regression rather than Tobit. In column (vii) we estimate our regression using weighted least squares (WLS) with the logarithm of the number of corporations, by industry and country, as our

> 31 The maximum value of number of entry procedures in the sample is 16, for Italy and Romania.

19