rafmacalaba/data-use-mentions-extended huggingface dataset:
>(0.0557)|0.6***<br>(0.0624)|0.603***<br>(0.0636)|0.602***<br>(0.0656)|0.606***<br>(0.0678)|
|Constant|0.225***<br>(0.0723)|0.148*<br>(0.0797)|0.144*<br>(0.0819)|0.145*<br>(0.0854)|0.138<br>(0.0901)|
|Observations|805|780|769|760|749|
|R-squared|0.294|0.293|0.291|0.282|0.277|

Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 Notes: Observed daily consumption is calculated as <u>f e e e p</u> based on the billing data set. The daily energy ) (ssccii gg f ggmmggcciimmiicci e ccggnnsscciiggdd cc e mm hggccssgghggmmdd ggooggmm m mm cciim ss iinn ggnngg eggm pggmmiiggdd ) <u>e e e</u> (nncciiccggmm ggff ddmmeess ccggiibbgservices demand based on survey data is calculated asnn ffiimmssii mmnndd m ssii cciim mmggccggiiooggdd ccee iihgg hggccssgghggmmdd ggooggmm iihgg eeggm 365 ~~.~~ mmn ccm ggnnggm e ssggmmooiiccggss ddggiimmnnddggdd cc e iihgg hggccssgghggmmdd ccmmssggdd ggnn ssccmmoogg e ddmmiimm Model (1) comprises of the entire sample of 805 households, whereas models (2), (3), (4) and (5) correspond to trimmed samples, comprising of households with monthly consumption above 14.9, 16.5, 18.2 and 20.1 units in the billing data respectively.

47
**Table A3. Calculations of the learning for EU countries not participating in PIRLS 2021**

|Country|UNESCO weeks with<br>full closures|Expected average<br>learning loss for<br>weeks with full<br>closures|Expected learning<br>loss for students at<br>the first achievement<br>decile|Expected learning<br>loss for students at<br>the ninth achievement<br>decile|
|---|---|---|---|---|
|Estonia|15|-17.50|-22.57|-12.62|
|Greece|18|-20.92|-25.51|-16.91|
|Romania|22|-25.48|-29.43|-22.63|
|Luxembourg|9|-10.66|-16.69|-4.04|

Note: Calculations using UNESCO data and estimates for the average loss in EU countries per week of school closures from Table 1, and quantile regression estimates.

19
In the case of Rio de Janeiro, fare readjustments are set by different economic indices, determined in the concession contracts for the operation of each mode. The impact of fuel price increases on fare increases, therefore, is based on the share of fuel price changes to the composition of each one of these indices. These increases also affect the fares of services that are not directly reliant on fossil fuels to operate (Rail, Metro and Light Rail), albeit at a lower rate than services that are petroleum dependent. Table 1 summarizes the indices that control fare readjustments and the fuel price share on each one of them for each transit service considered in this study.

_Table 1: Impact of fuel price increases on transit service fare in Rio de Janeiro due to fuel cost increases_

|**Service**|**Base Fare**<br>**BRL**|**Range**<br>**PPP $ (2018)**|**Share of fare**<br>**price increase**<br>**attributed to fuel**<br>**price change **|**Explanation**|
|---|---|---|---|---|
|Bus|4.05|1.84|21.00%|Based on a readjustment formula defined as part of the bus<br>system bidding.<sup>9</sup>|
|Ferry|6.5|2.95|6.01%|Ferry price increase is set by the IPCA (Broad Consumer Price<br>Index,in Portuguese).<sup>10</sup>|
|Rail,<br>Metro,<br>Light<br>Rail|3.8 – 5.8|1.73-2.64|6.05%|These are adjusted based on the IGP-M (General
Table 2: Summary Statistics for the Survey

||N|Survey<br>%|Po<br>N|pulation<br>%|% of<br>population|
|---|---|---|---|---|---|
|ICT|126|23.6%|894|0.05%|14.1%|
|Manufacturing|87|16.3%|15,110|14.21%|0.57%|
|Retail Trade|202|37.9%|54,618|51.36%|0.37%|
|Other Industries|118|22.1%|51,091|48.04%|0.23%|
|Total|533|100%|106,338|100%|0.50%|

_Notes:_ the population refers to the number of establishments according to the 2017 census. The last column is the number of interviews in the survey sample divided the number of establishments in the population. _Sources:_ PCBS, authors’ survey.

# **3.2 Other Data**

Jointly with the survey, auxiliary data is used in this paper.

**Maps and Geographic unit.** The primary geographic unit is the locality or town. The PCBS defined locality as _“a permanently inhabited place, which has an independent municipal administration, or a permanently inhabited, separated place, not included within the formal boundaries of another locality.”_<sup>21</sup> The West Bank is partitioned in 443 villages as of 2007. The shapefiles on the location of the Palestinian buildups and other spatial information has been kindly provided by UNOCHA.

**Population and Establishment Census.** Access to the Establishment Census has been negotiated by the author during several visits to the West Bank. The two Census datasets contain data on population, employment
and Forest Meteorology, 148_ (6-7).

- BOURDET, Y. (2004). “A tale of Three Countries – Structure, Reform and Performance of the Cotton Sector in Mali, Burkina Faso and Benin.” Sida Country Economic Report 2004:2.

- BRAMBILLA, I. and G. PORTO. (Forthcoming). “Market Structure, Outgrower Contracts and Farm Output. Evidence from Cotton Reforms in Zambia.” Forthcoming in _Oxford Economic Papers_ .

- UNIVERSITY OF EAST ANGLIA CLIMATIC RESEARCH UNIT Database. <u>http://www.cru.uea.ac.uk/</u> (accessed May, 2011).

- DELPEUCH, C and A LEBLOIS (2011). “Sub-Saharan African Cotton Policies in Retrospect.” GEMCIRED Working Paper, Sciences Po, Paris

- DELPEUCH, C., A. VANDEPLAS and J. SWINNEN (2010). “Revisiting the „Cotton Problem‟: A Comparative Analysis of Cotton Reforms in Sub-Saharan Africa.” Paper prepared for _rd th_

- presentation at _3 AAAE & 48 A.E.A.S.A Conference_ (Cape Town, September 19-23).

- DORWARD, A., J. KYDD AND C. POULTON (2004). “Market and Coordination Failures in Poor Rural Economies: Policy Implications for Agricultural and Rural Development.” Presented the AAAE Conference Shaping the future of African Agriculture for Development : The Role of Social Scientists, 6 to 8 December, Nairobi.

- FAOSTAT Database (2011). United Nations Food and Agriculture Organization, Rome. <u>http://faostat.fao.org/ (accessed May, 2011).</u>

- FONTAINE, B., and S. JANICOT. 1996. “Sea surface temperature fields associated with West African rainfall anomaly types.” _Journal of Climate,_ 9 (11), pp. 2935-2940.

- HESTON, A. R. SUMMERS and B. ATEN (2010). Penn World Table Version
countries. The concept of citizenship must only be used for Germany, Italy, as well as for Greece (European Council data are based on the concept of citizenship), Japan and Korea.

Data available in 1990. The methodology is similar for 1990. To allow comparisons between 1990 and 2000, we consider the same number of receiving countries in 1990 (even if Hungary, Poland, ex-Czechoslovakia, Korea and Mexico were not members of the OECD). Our counterfactual 1990 OECD zone includes 29 countries (Slovakia and the Czech Republic were uni...ed). We estimate the total stock of adult immigrants in the OECD area at 39.844 million. Mexico is still the only country for which data on age, country of birth and educational attainment are missing (0.341 million immigrants). Data on immigrants’ age and educational attainment are not available in ex-Czechoslovakia, Hungary, Poland, Italy, Japan, Iceland, Korea and Greece (2.641 million immigrants). However, combining European Council data and the European Labor Force Survey provides a good approximation for Greece and Italy (0.646 million adult immigrants). In the other 20 countries (36.862 million adult immigrants), Census or register data are (or will be) available. Adding Censuses, Italian and Greek surveys, we capture 37.508 million immigrants, i.e. 94.1% of the total. About 2.1 million of those immigrants did not report their country of origin. We have accurate information on educational attainment and origin for 35.385 million immigrants, i.e. 88.8% of the OECD adult immigration stock (to be compared with 38.5% in the US Census).

In the 22 countries (20 Censuses/Registers, Italy and Greece) where data on 12
information: From the largest to the smallest commercial partner (buyers or sellers), specify town, district, and average shipping time. This question allows us to compute the probability that a firm in location _i_ has a client or seller in location _n_ . We explicitly asked ICT firms if they were selling physical goods as output. 75% of them replied affirmatively.

We model trade costs as a function of distance. We assume that bilateral trade costs are a constant elasticity function of distance and a stochastic error, _τni_<sup>_j_=time</sup><sup>_t_</sup> _ni_<sup>_j_where</sup><sup>_tj_isa</sup> parameter to be estimated.

Equation 10 can be rewritten as The probability of a firm with productivity _z_ operating in sector _j_ , location _i_ , having trading partners in location _n_ is a function of origin-specific factors, _ζi_ , destination-specific factors, _ζn_ , and the distance between the origin and the destination. _ezjni_ is an error term.

To estimate this elasticity, we use the answers from the survey on the location of trading partners. Observations are the firms in the manufacturing sector with at least two trading partners outside of the town or village where their establishment is located.

Results are reported in table 11. The table reports results for a weighted and an unweighted regressions. Standard errors have been clustered at the firm level. Origin and destination fixed effects are included but not shown in the table. In order to assess the explanatory power of the travel time, we report pseudo- _R_<sup>2</sup> for two competing models, one 26
correlates--indicators such as women's average education level, household characteristics, and place of residence on which inforrnation had been gathered in the 1993 census. In the second stage, the coefficients from these departmental regressions were applied to district-level data from the 1993 census to estimate district-level infant mortality rates (INEI 1997).

# _The FONCODES index_

Peru has a long history of developing "poverty maps" based on composite indices of unmet basic needs. The first of these maps was constructed by Webb with information from the 1961 population census (Webb 1977). This poverty map was updated with new censuses conducted in 1972, 1981, and 1993 (Amat y Le6n n.d.; Banco Central de Reserva 1981; INEI 1994). FONCODES, in turn, has developed composite poverty maps since its creation in 1991, often with technical assistance from the Inter-American Development Bank, the World Bank, and the German development agency GTZ.

The current district-level poverty map was developed by FONCODES and the Ministry of the Presidency. It is based on eight indicators--the rate of chronic malnutrition, illiteracy, schoolaged children not in school, overcrowded housing, inadequate roofing, and the proportion of the population without access to water, sewerage, and electricity. All of these indicators except the rate of chronic malnutrition were estimated with data from the 1993 population census. The rate of chronic malnutrition was estimated from a census of height and weight among school-aged children, also conducted in 1993 (FONCODES 1995 and 1996).

Composite indices invariably involve some arbitrary weighting of individual indicators. The 1996 index standardized each indicator by dividing it by its minimum value, multiplied the rate of chronic malnutrition by seven, and then added all of the individual indicators' For ease of interpretation, FONCODES then standardized the index by dividing all values by the lowest value. The resulting index ranges from a value of 1 to 36.38.

# _Imputed poverty and income_

In Peru, there are no survey-based estimates of income or expenditures at a level
and their employees, and show that larger firms are more compliant since collusion costs increase with firm size. Hsieh and Olken (2014) similarly argue that the effective burden of taxation in developing countries falls more heavily on larger firms. Empirically, Kumler et al. (2015) find that compliance indeed increases with firm size in Mexico. Naritomi (2019) provides evidence suggesting that larger retail stores have more customers and are therefore less able to sustain evasion. Both Bronnenberg and Ellickson (2015) and Lagakos (2016) categorize retailers as either ‘traditional’ (small, labor intensive retailers - our categories 1 to 3) or modern (large, capital intensive retailers - our categories 4 and 5), and argue that traditional retailers are mostly informal.<sup>10</sup> We provide direct evidence that formality and firm size are positively correlated. We use data on formality status and firm size available in the retail firm censuses of four countries in our core sample (Cameroon, Mexico, Peru and Rwanda). Panel A in Figure 1 shows the share of formal firms as a function of log employment in each country.<sup>11</sup> In all countries, retailers with a few employees are overwhelmingly informal, but more than 80% of retailers with 20 or more employees are formal. In Mexico, the census classifies retailers in categories which are similar to our broad place of purchase categories. This enables us to go one step further and directly observe retailer size and formality status. Figure 1 shows for our categories (1) to (5) the log median number of employees (Panel A) and the share of firms paying value-added taxes on their sales (Panel C). We observe that non-brick-and-mortar stores and convenience stores are small and rarely formal, whereas nearly all large stores are formal.

Given this evidence, our baseline formality assignment assigns categories (1) to (3) to the informal sector, and categories (4) and (5) to
public infrastructure investment. Public investment in infrastructure<sup>9</sup> amounts to 2.8% of GDP made up of central government investment (0.6% of GDP) and decentralized government infrastructure outlays (2.2% of GDP). The model economy also matches the average level and composition of imports. Total imports amount to 18.5% of GDP. This is composed of import of consumption goods (3.7% of GDP), capital goods (6.1% of GDP) and intermediate goods (8.7% of GDP).

The structure of central government tax revenue is similar to the one observed in Colombia. Total tax revenue in the model amounts to 14.1% of GDP, which is very close to what is observed in the data. This percentage includes consumption taxes (7.4% of GDP), labor income taxes (0.6% of GDP) and capital income taxes (6.1% of GDP). The split in the income taxes between labor and capital income taxes is based on information from DIAN on tax withholding figures organized by type of taxpayer. The model’s gross debt-GDP ratio of the central government is set at 54%, which corresponds to the observed ratio by year-end 2003 (source: CONFIS). The net debt-GDP ratio of the rest of the public sector is set at 0% of GDP. This is roughly consistent with CONFIS data estimating the level of the combined public sector (‘non-financial public sector’) gross debt to be 68% of GDP and a combined net debt (excluding holdings of central government bonds in the portfolio of the rest of the public sector) to be close to 54% of GDP. The model’s external debt-GDP ratio is set at 50% of GDP (source: Banco de la República).

The interest rate charged to private and public borrowers is the sum of two components: •• an exogenously given world interest rate
factors including labor has tended to decrease over time, but increased during the 200809 global crisis (Stehrer et al., 2012). Notably, however, the direct LVAX share continued to decline even between 2007 and 2011, implying that indirect labor value added became relatively more important to the labor content contained in countries’ gross exports during the crisis period.

**Figure 1. Direct and total LVAX share**

<!-- Start of picture text --> 0.50<br>0.45<br>0.40<br>0.35<br>0.30<br>0.25<br>0.20<br>0.15<br>0.10<br>1995 1997 2001 2004 2007 2011<br>Direct Total<br><!-- End of picture text --> Note: Based on export-weighted data for 31 countries that are present in the database every year since 1995, including: China, India, Sri Lanka, Vietnam, Argentina, Brazil, Chile, Colombia, Indonesia, the Republic of Korea, Morocco, Mexico, Malaysia, Philippines, Thailand, Turkey, Uruguay, República Bolivariana de Venezuela, Australia, Canada, Denmark, Finland, Germany, Hong Kong SAR, China, Japan, New Zealand, Singapore, Sweden, Taiwan, China, UK and USA.

The decline in the share of labor value added in exports in Figure 1 does not reflect a general decline in the share of labor value added in output. Figure 2 shows that the global share of labor value added in the value 9
farmers. This is very common in the development of individual plans. For example, under the U.S. crop insurance program, twenty years of the loss cost experience at the county level is used to obtain a measure of the typical level of risk for farmers in the county.<sup>4</sup> Rates at the individual level are adjusted in accordance with how the individual farm’s average yield compares to the county average.

Other approaches have been used to combine individual farm yield data with aggregate data. The developers of the U.S. Income Protection (IP) program, a revenue insurance scheme, combine county yield data with individual farm yields to make use of the longer county series and thus improve the amount of yield information used in developing the IP contracts. In the case of the IP program, an attempt is made to distinguish yield variability specific to the farm from yield variability experienced by the entire county. Under provisions of the U.S. federal crop insurance program, farmers lacking a yield history are assigned a yield that is based upon the county average yield (in

> 3 A conceptual discussion on the role of information in insurance contract design is provided in the Appendix.

> 4 Loss cost is given by the ratio of indemnities to total liability and represents a measure of the expected loss rate which, in turn, is a measure of the actuarially fair premium rate. The ratemaking procedures are discussed in Section 6.

5
use of ancillary data from the second round of the Methodological Experiment on Measuring Maize Productivity, Soil Fertility and Variety (MAPS). These data contain full crop-cuts (considered benchmark estimates) for maize of 211 entire plots. The data set has two main limitations for our analysis: the data collection was so swift that the recall length is short with little variation between 0 and 4 months, and, at 211 plots, the sample size is small. The full crop cuts are used to quantify the error in farmer-reported maize harvest and relate it to the length of the recall period. On those plots, the mean farmer-reported harvest is 159 kg (median: 70 kg), while the mean crop cut estimate is 113.5 kg (median: 53.45 kg; Table 5).

For farm labor inputs, the headline outcome is total person-days of labor used per plot during the reference agricultural season. The variable is constructed by summing person-days worked by all household members and hired workers in all activities related to crop production, which are recorded separately for each plot. Family labor makes up the lion’s share of total labor input in all three data sets, accounting for between 91 percent in Tanzania NPS 2012/13, 92 percent in Malawi IHPS 2016/17, and 96 percent in IHS4 (cross section). Total labor input is somewhat higher in Tanzania at 70 person-days than in Malawi with 64 and 62 person-days per plot in the cross section and the panel, respectively. However, given larger plot size in Tanzania, labor intensity (labor per area) is lower there than in the two Malawi samples (Figure 2, row 2). Twenty-six percent of plots in Tanzania NPS 2012/13 use hired labor, compared to 13 percent in Malawi IHS4 and 29 percent in IHPS 2016/17 (Table 2). Once again, the data exhibit an inverse relationship between labor intensity and plot size (Figure 2, row 2).

For an analysis of recall length, the
,715|0.009|<br>Incipient Agriculture|1,831|
|Vilela|519|0.001|Hunter-gatherers|1,891|
|Wich ́ı|50,419|0.127|Hunter-gatherers|1,881|
|Other|5,301|0.013|-|.|
|Total Natives|955,032|2.407|-|.|

Source: REDATAM INDEC Census Argentina 2010 for columns (1) and (2), see text for columns (3) and (4).

There is geographical variation in the distribution of Natives across provinces as shown by Figure 1. The province with the greatest prevalence of Natives is Chubut, with 8.7 per-

> 5 It is important to note that 149,000 respondents self-identified as Afro-Argentine (“afrodescendientes”), which corresponds to 0.4 percent of the population. While the main focus of this paper is on the native population, we will also provide an analysis of their outcomes as a point of comparison.

4
|(191)|(255)|(255)|(383)|(383)|
||2,500|3,500|7,500|7,500|15,000|15,000|
|Familyworker|(32)|(45)|(96)|(96)|(191)|(191)|

_Source_ : Authors’ analysis based on survey data.

_Note_ : Summary statistics for individuals in survey. Results are shown with and without sampling weights. In Panel (c), we only include wage workers who were able to provide an answer. In Panel (d), if respondents did not provide an exact monthly income, but did indicate the bin into which their income fell, we used the median value from the bin for their income.

43
<br>2<br>-3<br>1<br>-8<br>0<br>2010 2012 2014 2010 2012 2014 2010 2012 2014 2010 2012 2014<br>2010Q1 2010Q3 2011Q1 2011Q3 2012Q1 2012Q3 2013Q1 2013Q3 2014Q1 2014Q3 2015Q1 2015Q3 2016Q1 2016Q3<br>Mongolia Indonesia Malaysia China Vietnam Philippines Thailand<br>LAO KHM VNM MMR MYS THA IDN PHL MNG PNG PLW<br>2011 2012 2013 2014 2015 2011 2012 2013 2014 2015<br><!-- End of picture text --> Sources: Haver Analytics; International Monetary Fund; United Nations Conference on Trade and Development; World Bank, World Development Indicators, World Bank.

A. GDP-weighted averages.

C. Investment-weighted averages. Commodity exporters include Indonesia, Malaysia, Myanmar, and Papua New Guinea. Commodity importers include Cambodia, the Philippines, Thailand, and Vietnam. An increase denotes an improvement in terms-of-trade.

D. Policy rates are the average of end-of-period data.

E. FDI inflows. Weighted averages.

F. For difference from 1990-2014 average, positive values indicate improvement of FDI inflows. LAO = Lao, PDR, KHM = Cambodia, VNM = Vietnam, MMR = Myanmar, MYS = Malaysia, THA = Thailand, IDN = Indonesia, PHL = Philippines, MNG = Mongolia, PNG = Papua New Guinea, PLW = Palau.

11
**Figure B.2: Exports of the Bangladeshi ready-made garment (RMG) sector**

<!-- Start of picture text --> 1980 1985 1990 1995 2000 2005 2010 2015 2020<br>Year<br>Exports of RMG in billion USD (left axis)<br>% of RMG exports to total exports (right axis)<br>Data source: BGMEA Trade Statistics 2021<br>Note that data is on a fiscal year basis, i.e. the data point for 2019 captures July 2019 to June 2020.<br>40<br>80<br>30<br>60<br>20<br>40<br>10 20<br>0 0<br><!-- End of picture text -->

**Figure B.3: Timeline**

<!-- Start of picture text --> Promotion programme in factory<br>Baseline in factory Follow-up in factory<br>(only selected operators)<br>Phone surveys<br>Start work as<br>Nomination<br>supervisor Delayed<br>period and<br>training<br>selection<br>Training<br>Month -2 0 2 4 6 8 10 12<br>Wave 1 household survey Wave 2 household survey<br>Surveys for promotion analysis<br>(selected and nominated operators) Biweekly phone surveys<br>Household surveys<br>Surveys for exposure analysis<br>(exposed and non-exposed operators)<br><!-- End of picture text --> 47