rafmacalaba/datause-encoder-data huggingface dataset:
revision of the International Standard Classification of Occupations (ISCO-68). To make the classification consistent, I first convert the national classification into the ISCO-68. Following, I remap the ISCO-68 occupations to ISCO-88 following the scheme in Ganzeboom and Treiman (1996). Again, I do not find any unusual kinks in the share of occupations as a result of these transformations.

**El Salvador:** The country dollarized its economy on January 1, 2001. Hence, labor income in the preceding survey years, was divided by the exchange rate to convert it to dollars.

**Panama:** From 2011 to 2013 occupations in the national surveys match the major groups of the ISCO-88. For previous years, Panama has its own classification of occupations (NCO-2000). The system follows the ISCO-88 taxonomy and it is straightforward to construct correspondence between the two.  The NCO2000 classification was used from 2001 to 2010. Before 2001, a different classification was in use (NCO90). The working manual from the National Institute of Statistics and Census (1999) provides the correspondence tables between the NCO-1990 and the NCO-2000 taxonomies. Using these, I harmonize the occupations across the survey years.

# **A2. Calibration details**

**Parameters invariant over time:** There are seven parameters in the model that are invariant over time. Barring the elasticity of substitution across occupation-specific labor inputs , all time-invariant parameters are calibrated for each country separately. For the baseline results, I assume and report the responsiveness of the results by considering other values of . σσ σσ= 4 **a.   Preference parameter :** The parameter captures the trade-off between consumption and time σσ dedicated in accumulating human capital. To calibrate the parameter, I match the Mincerian returns to ββ schooling. The first step is to collect for year using the following regression ������� ii ln�wψψ i tt i i where variables under bars represent the average for a group ggee ii�= ααii + ψψii ss̅ ii + DDii in any occupation + εε ii and are group

As a worker’s lifetime endowment of time is 1 spread over 25 years of that in the data, the model 1 equivalent of 1 year is = 0.04 . The average estimates of across all years serves as the time-invariant 25 measure used in the quantitative analysis. ii ββ 37
regional building inventories such as the AIR Tropical Cyclone Model for the Caribbean.<sup>15</sup> The resulting construction, occupancy mix, and height distribution of the building stock is a fundamental determinant of the region’s vulnerability and the reason why many Caribbean islands have suffered deep impacts from tropical storms and hurricanes in the last 20 years.

**Figure 2. Tropical storms and hurricanes in the Caribbean, by country and category (2000–12)**

<!-- Start of picture text --> Cuba 11 11 9 9 8 6<br>Bahamas, The 11 8 8 7 8 6<br>Haiti 10 10 10 10 4 3<br>Dominican Republic 10 10 8 7 5 2<br>Bonaire, St. Eustatius and Saba 12 6 2 3 5 3<br>Puerto Rico 8 6 6 6 3 1<br>St. Vincent and the Grenadines 11 6 5 3 2 1<br>Jamaica 6 4 4 5 4 5<br>Virgin Islands, U.S. 6 5 5 5 3 2<br>British Virgin Islands 6 6 5 3 3 2<br>St. Kitts and Nevis 8 7 3 3 2<br>Cat. 0<br>Trinidad and Tobago 13 3 1 2 2 1<br> Cat. 1<br>Guadeloupe 13 6 2 1<br>Grenada 10 4 2 2 2 2 Cat. 2<br>Barbados 12 5 3 11 Cat. 3<br>Antigua and Barbuda 8 5 4 2 2 1 Cat. 4<br>St. Lucia 11 3 1 2 2 2 Cat. 5<br>Cayman Islands 7 2 2 3 3 4<br>St. Martin 5 6 5 11 2<br>Bermuda 3 4 3 2 4 4<br>Sint Maarten 5 5 5 11 2<br>Montserrat 9 7 3<br>Anguilla 5 4 4 3 1 2<br>Turks and Caicos Islands 8 1 2 2 2 3<br>Dominica 13 2 2 1<br>Martinique 11 2 1111<br>Aruba 10 1111 2<br>0 10 20 30 40 50 60<br><!-- End of picture text --> _Source:_ Authors’ calculations based on data from The National Hurricane Center's North Atlantic hurricane database reanalysis project (HURDAT2) created by the Hurricane Research Division, and NOAA's Atlantic Oceanographic and Meteorological Laboratory.

> 15 For more information, see <u>https://www.verisk.com/press-releases/2020/july/air-worldwide-releases-updatedtropical-cyclone-and-earthquake-models-for-the-caribbean/</u>

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WAYS OUT OF POVERTY **DIFFUSING BEST PRACTICES AND CREATING CAPABILITIES**

## **TABLE OF CONTENTS**

|List|ofBox|es.......<br>..<br>4|
|---|---|---|
|I.|EXE|CUTIVESUMMARY<br>..|
|II.<br>|HOP|EANDANGER<br>..<br>3|
|Il.|BAS|ICWAYSTOPROMOTEPRO-POORGROWTH<br>**_.._**<br>**_5_**|
|IV.|DRIV|ERSOFGROWTHANDJOBCREATION<br>.<br>.7|
||A.|Accumulationofcapitaland ideas<br>.7|
||B.|ImpedimentsandTraps.10|
||C.|Thekeytodevelopment:Capableinstitutions.11|
||D.|Povertyreduction:Thediffusionofbestpractice and thecreationof<br>capability.16|
|V.|COR|EINSTITUTIONS:MARKETS,FIRMSANDGOVERNMENTS<br>.<br>.18|
||A.|Experimentsandroutines-freedomtoenter andtofail.19|
||B.|Competition-themechanismtoselect themost productivefirms.<br>19|
|VI.|Mar|ketfailure andthepoliticsofchange<br>..<br>26|
|REFE|RENCE|S<br>.<br>.<br>.<br>32|

# **List of Figures**

- Figure 1: Share of world population living on less than 1 US$ per day

- Figure 2: Growth achieves more than redistribution

- Figure 3: What do people think their country needs most to get ahead - more productivity or more redistribution?

Figure 4: Intelligence is scare - natural resources abundant

> Figure 5: Factor accumulation accounts for only a fraction of income differences

- Figure 6: The difference in output per worker is due to differences in total factor productivity

> Figure 7: Location of new product innovation in the United States

Figure 8: Exporters spend more on training and acquiring foreign technology

> Figure 9: Origin of innovation in the United States Figure 10: Frequencies of misconduct of workers in an Italian firm, by region of birth

Figure 11: Distribution of employment by firm size Figure 12: India - Current and potential labor productivity Figure 13: Plant turnover and market share of entrants Figure 14: Decomposition of multifactor productivity growth in manufacturing Figure 15: The effect of industrial policy on total factor productivity growth in the manufacturing sector and on aggregate growth
these cycles, as well as their apparent relationship with financial liberalization and the surge in capital inflows, varied from country to country. For example, in Malaysia, the Philippines and Thailand, bank and non-bank credit to the private sector began growing at higher rates and on a sustained basis following the surge in capital inflows. This high growth rate strained banks in their capacity to properly screen and assess risk of borrowers and projects. In Korea and Indonesia, in contrast, the growth in bank and non-bank credit to the private sector was lower during the inflow period than in the years prior to the surge in fore:ign capital (Figure III. 10).

# **Figure I11.10**

<!-- Start of picture text --> Credit growth as a ratio of GDP growth (annual averages)<br>Opre-inflow period *inflow period<br>6 ..- ---- . -.<br>.3<br>2<br>-----<br> --- n- I --------<br><!-- End of picture text --> _Source: IMF International Financial Statistics. Rates of growth are calculated on an annual basis and in real terms._ The rising fragility was not detected during the lending booms as the growth in banks' loan portfolios was accompanied by rising measured profits. Figure III. 11 shows that in countries in which credit growth was high-except the Philippines-there was an increase in the profitability of the banking sector, consistently across all indicators. Conversely, in countries where the lending boom was smaller-in absolute terms or proportional to GDP-profitability tended to show a small increase, or even a decrease depending on the profitability indicator used.

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###

## **Use of accounts to receive payments**

Adults using a formal account in the past year to receive payments (%) <!-- Start of picture text --> FROM WORK 50<br>OR SELLING GOODS<br>42<br>FROM THE<br>GOVERNMENT<br>27<br>20<br>17<br>10 10 11<br>6 7 6 6<br>3 3<br>MIDDLE EAST SOUTH SUB-SAHARAN EAST ASIA LATIN AMERICA EUROPE & HIGH-INCOME<br>& NORTH AFRICA ASIA AFRICA & PACIFIC & CARIBBEAN CENTRAL ASIA ECONOMIES<br>Source: Demirguc-Kunt and Klapper 2012.<br><!-- End of picture text -->

###

## **Use of accounts for family remittances**

Adults using a formal account in the past year to transfer money to or from relatives living elsewhere (%) <!-- Start of picture text --> 18<br>TO RECEIVE MONEY<br>TO SEND MONEY<br>13<br>9 9<br>7<br>6<br>5<br>4<br>3 3 3<br>2 2 2<br>SOUTH MIDDLE EAST LATIN AMERICA EUROPE & SUB-SAHARAN EAST ASIA HIGH-INCOME<br>ASIA & NORTH AFRICA & CARIBBEAN CENTRAL ASIA AFRICA & PACIFIC ECONOMIES<br>Source: Demirguc-Kunt and Klapper 2012.<br><!-- End of picture text -->

# SENDING OR RECEIVING REMITTANCES

In 2011 remittance payments of more than $350 billion were sent around the world.<sup>23</sup> While sending financial support to—or receiving it from—relatives living elsewhere often does not require having an account, accounts do frequently help facilitate this worldwide transfer of wealth. In Sub-Saharan Africa, where a comparatively large share of account holders report using their account to save, a primary (and not unrelated) use of accounts also appears to be the receipt of remittances. Indeed, Sub-Saharan Africa has the largest share of account holders reporting the use of their accounts to send or receive family remittances. Some 38 percent of adults with a formal account (and 9 percent of all adults) report having used their account to receive remittances in the past 12 months (figure 1.20). Use of a formal account to receive remittances is particularly common among account holders in several countries of southern Africa, including Botswana, Lesotho, and Swaziland. The steady receipt of remittances has been shown to ease access to credit in some cases, because banks view regular remittance payments as a reliable source of income.<sup>24</sup>

**25**

MEASURING FINANCIAL INCLUSION
Annex Table 9: The Asset Transfer Process

|**Country**<br>**Example**|**Criteria for asset transfer and**<br>**transfer price**|**Process of Asset Transfer**|**Amount and Sectoral Breakdown of Assets transferred to AMC**|
|---|---|---|---|
|Ghana<br>(82-89)||*<br>Following the classification, NPART assigned a<br>recovery estimate, in percentage and amount to<br>each individual account.<br>*<br>As part of normal work-out activities, NPART's<br>staff performed this function as they received<br>extensive training from the expatriate experts on<br>loan workout, asset appraisal, property inspections,<br>etc.. However, its role in corporate restructuring<br>was limited.||
|Sweden<br>(91 94)|**Securum/Retriva:**<br>*<br>Criteria for Transfer: Mainly<br>size and complexity of loan:<br>only loans over SEK 15 million<br>were transferred, and they<br>typically consisted of<br>corporations with operations in<br>several countries or<br>complicated structures in terns<br>of subsidiaries.<br>*<br>Transfer Price:NPLs were<br>transferred at book value.<br>*<br>Private AMCs: NPLs<br>transferred at market value,<br>consisting of loans of parent<br>bank.|**Securum/Retriva:**<br>*<br>Before assets were transferred to AMCs, they had<br>to go through valuation process to assess true<br> <br>market value.<br>*<br>One-off process, non-performing assets of<br>Nordbanken & Gota Bank transferred to Securum<br>and Retriva, respectively.|Amount of total asset transferred as percentage of total banking system assets/banking<br>system loans and percentage of GDP:<br>**Securum:**<br>Grum:<br>*<br>Gross value:SEK 67 billion, 4.4% of total banking assets (4.6% of GDP). After write-<br>down: SEK 50 billion 3.3% of total banking assets ( 3.4% of GDP).<br>**Retriva:**<br>*<br>Gross value SEK 45 billion or 3.0.% of banking assets (about 3.1% of GDP). After<br>write-down, SEK 19.5 billion or 1.3%% of total banking assets (1.4% of GDP).<br>Sectoral Breakdown of loans transferred to AMC<br>Securum:<br>*<br>Mostly real estate loans.<br>*<br>Loans: 91.1%. Share portfolio: 6.2%. Real Estate: 2.7%.<br>**Retriva:**<br>*****<br>Loans: 86.2%. Real Estate: 12.3%. Share portfolio:**1.6%.**|
||||Were any loans**left**in banks and if so amount and type (i. e. loans deemed unviable,<br>corporate loans etc)<br>**Securum/Retriva:**<br>*<br>All NPLs with assessed value under SEK 15 million, or of low level of complexity<br>(i.e. mortgages, consumer credits) were not transferred to AMCs.<br>*****<br>Banks were**left**with a ratio of bad loans similar to the one prevailing in market.|
# **Which teachers are most absent?**

To design policy to improve school quality, it is important to know the pattern of absences: who is most absent, and when? As Chaudhury _et al_ (2006) point out, if a small number of teachers account for most of the absences, then governments that want to tackle the problem need to identify those missing teachers and tailor its policies accordingly. If it turns out that the missing teachers are “ghosts”, in the sense that they either do not exist or are on the rolls merely to draw a salary, then the solution will likely involve culling them from the system. If the absent teachers are missing school not because of malfeasance but because of extended illness, as could happen in countries hit hardest by the HIV/AIDS crisis, then school systems may need to focus their efforts on making sure that classes are quickly covered by other teachers.

This type of analysis has to be done on a country‐by‐country basis; like the levels of absence, the reasons for it vary across countries. Without tracking each teacher’s attendance very frequently – on a weekly basis, say – it is difficult to be sure of that teacher’s underlying propensity for absence. But by looking at the overall distribution of absences across a handful of visits to the same schools over weeks or months, it is possible to infer how often teachers are typically absent.

The evidence from the multi‐country study in 2002‐03 suggests that it is typically not just a small group that is responsible for absence. In all but one of the six countries analyzed by Chaudhury _et al_ (2006), frequent absence appears to be a system‐wide problem, with many teachers being absent at higher rates than would typically be expected in a developed‐country school system.<sup>9</sup> This implies that the reasons for absence are also likely to be systemic in nature – such as system‐wide failures in accountability, low levels of pay, poor housing and transportation for teachers, or simply low expectations of teacher performance across the board. Later in this section, we discuss which of these systemic explanations is most likely to be valid; but as a preface, it is worth noting that there is little evidence that low base pay levels are the culprit.

Another way of answering the “who is absent?” question is to compare absence rates for groups of teachers with different characteristics. The evidence shows that these individual characteristics typically do not have a great deal of explanatory power. In multivariate analysis in the six‐country study, characteristics such as

> 9 An exception is Ecuador, where it appeared in 2002‐03 that a small minority of teachers may have accounted for a large share of the absence (Rogers, Lopez Calix, Chaudhury, Hammer, Cordoba, Kremer, and Muralidharan, 2004).
an additional control, we control for the distance of each city (and hence economic sector) from the power stations. We calculate the distance of each city from each power station using the Haversine formula (Robusto (1957)),<sup>1</sup> where _dj_ is the distance of each city to a reference location containing a coal-fired power station ( _r_ ), using the longitude ( _φ_ ) and latitude ( _λ_ ) as inputs. We then use the nearest power station of each city as the control variable. There is evidence that neighborhoods in close proximity to power stations are especially sensitive to pollution (Zhang et al. (2022)).

Because of the potential endogeneity issues raised previously, we utilize the predicted value of pollution by using the building of coal-fired stations as an instrument; that is the variable _Dj,k,t_ takes the value of either 0 or 1, therefore, the estimated pollution variable is a dichotomous variable of 0 or a real number below 1, _γ_ ˆ _j,k,t ∈_ (0 _,_ 1]. Therefore, equation 2 can be considered as a difference in difference regression. The coefficient _φ_ measures the relative marginal effects of pollution in cities containing a coal-fired station relative to cities with no coal-fired stations within the same province or region. In addition, _POL_ ˆ _j,k,t_ captures the intensity of pollution on affected cities by coal-fired plants. Specifically, _γ_ ˆ _j,k,t_ is larger for cities with higher pollution levels, introducing an extra distinction within cities with coal-fired plants.

# **5 Data**

Here we describe the data availability for pollution and labor productivity at the city level. Chile is administratively divided into 15 regions, with 50 provinces and 346 cities, called ”comunas”. Meanwhile, the sectoral information is provided following ISIC rev.4 standards.

## **5.1 Pollution data**

Data for particulate matter (PM2.5) concentrations was sourced from van Donkelaar et al. (2016). This data contains annual global surface concentrations (micrograms per cubic meter) of mineral dust and sea-salt filtered fine particulate matter of 2.5 micrometers or smaller in diameter. This annual data set is gridded at 0.01 degrees, which allows us to connect pollution concentration levels for each city.

Chilean pollution data from the Ministry of Environment<sup>2</sup> is compared to the gridded data. For the majority of the cities the correlation between official and satellite data over time is high. Figure 3 presents the correlation for selected cities.

> 1We could easily use Euclidean distance, but this is a straight line distance measure, where as the Haversine distance measure accounts for great-circle distances, typically applicable for the earth’s geometry.

> 2 `httpss://sinca.mma.gob.cl/`

7
<!-- Start of picture text --> Top 10%<br>Bg<br>ab<br>2 ONG ee OO ane<br>Bo, Top 5%<br>os<br>iS<br>is)<br>Oo<br>=<br>a7<br>s Top 1%<br>»<br>Gy<br>is)<br>oN<br>gHy Top 0.1%<br>io)<br>4<br>°<br>2000 2005 2010 2015 2020<br>Year<br><!-- End of picture text -->
Growth rates have varied considerably across countries and regions (Table 2). Most Anglo-American countries, where mutual funds were already well developed in the early 1990s, registered growth rates of between 20 and 30 per cent per year. In Europe, some countries, such as Greece and Italy, experienced very rapid growth, while others, most notably France, recorded low growth. Among middle-income countries, Morocco, Argentina, Hungary and Tunisia achieved very high growth from low starting points. Except for Hungary, where mutual funds are increasingly integrated into the European market, the experience of mutual funds in the other three countries has suffered in later years.

Setting Luxembourg aside as a very special case, the United States is in a class of *its own in the development of mutual funds. Following their spectacular growth in the 1990s mutual fund assets rose from the equivalent of 26 percent of GDP in 1992 to 65% in 1998 (Table 3). Only in Hong Kong (China) do mutual funds come anywhere near this level, having grown from 16 percent of GDP in 1992 to 52 percent in 1998.6 In Canada and several European countries (Spain, France, Italy and Austria) mutual fund assets correspond to between 30 and 40 percent of GDP. Several European countries, including Belgium, Greece, the Netherlands, Portugal, Switzerland, Sweden and the United Kingdom, have mutual fund assets ranging between 20 and 30 percent of GDP.

In continental Europe, Denmark, Finland, Germany and Norway continue to have relatively underdeveloped mutual fund sectors. In the case of Germany, this is partly offset by two factors: the strong presence of closed-end funds, which are not included in the above statistics; and the use of mutual funds based in Luxembourg by many German investors, mostly for tax reasons. The effect of Luxembourg on the size of German mutual funds is likely to be much greater than on mutual funds in other European countries because of the prominent role played by leading German banks in the Luxembourg market.

> **6** It should be noted, however, that the data on Hong Kong mutual funds represent an estimate of the component that relates to domestic holdings. By far the largest component of the market is represented by holdings of nonresidents.

15
example of this evidence is depicted in Figure 7.1, which plots relative student performance under the four conditions resulting from the presence and absence of central exams and school autonomy over teacher salaries, after controlling for dozens of student, family, and school background factors. The figure shows that school autonomy regarding teacher salaries is negatively associated with student performance in systems without central exams. In systems with central exams, student performance is generally higher than in systems without central exams, reflecting the increased accountability. In addition, the effect of school autonomy is turned completely around in systems with central exams: Salary autonomy of schools has positive effects on student performance in central-exam systems. This pattern of results has been found in TIMSS, TIMSS-Repeat, and PISA, and similar cases where external exams turn a negative autonomy effect around into a positive effect have been found for other decision making areas such as school autonomy in determining course content and teacher influence on resource funding.

**Figure 7.1: Accountability, Autonomy, and Student Performance across Countries**

Performance difference between the four categories relative to the lowest category, after controlling for numerous additional impact factors.

<!-- Start of picture text --> 76.2<br>80<br>Math performance<br>in TIMSS/TIMSS-R<br>test scores (relative 70<br>to lowest category) 55.5<br>60<br>50<br>40<br>30<br>20<br>10 23.7 Yes<br>0 Central<br>0.0 No exams<br>No<br>School autonomy<br>over teacher salaries Yes<br><!-- End of picture text --> Source: Wößmann (2005c).

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