rafmacalaba/datause-encoder-data huggingface dataset:
-23-

constructed as a difference between the recent (third round) diarrhea or illness incidence and the average incidence over six survey rounds. The variable is further discussed below.

Morbidity among the sample children is high. Nearly 45 percent of the samples had either episodes of diarrhea or had some illness in the two weeks preceding the third visit. The survey indicates<sup>that 16</sup> percent of children experience **d** iarrhea in the two weeks preceding the third visit, lasting 0.72 days on average (standar **d** eviation of 2.03). Over the six visits, the average number of days with diarrhea per twoweek period was 0.93 indicating that incidence in other rounds were higher. **Tt** maybe noted that the third round survey<sup>was conducted</sup> during the coolest season, after the monsoon<sup>rains. Illness other than</sup> diarrhea reported for these children averaged 1.38 days<sup>per two-week</sup> period, affecting 36 percent of all the children and is the basis for the illness variable used in the model.

Child feeding and weaning practices directly affect child nutrition (Habicht et al. 1976). Apart from the issue related<sup>to the effects of</sup> the duration and quantity of mother's milk, the problem is intertwined with the more general problem of<sup>hygiene and sanitary environment</sup> affecting the use of non-breast milk. This is particularly relevant where the availability of potable water in the village is not secure. Sixty five percent of the children were exclusively breast-fed, while infant formula feeding is not prevalent. From the age of six months, mothers generally start giving buffalo milk, usually diluted in equal amount of water to infants, as supplements to mother's milk. This
Furthermore, the estimates of distortions themselves are biased if they are inferred using a model with no price-effects.

# **References**

Balassa, Bela. 1964. The Purchasing-Power Parity Doctrine: A Reappraisal. Journal of Political Economy, 72(6), 584–596.

Comin, Diego A, Lashkari, Danial, & Mestieri, Marti. 2018. Structural Change with Long-Run Income and Price Effects. Working Paper: Dartmouth College, Yale University & Northwestern University.

Duarte, Margarida, & Restuccia, Diego. 2010. The Role of the Structural Transformation in Aggregate Productivity. Quarterly Journal of Economics, 125(1), 129–173.

Fadinger, Harald, Ghiglino, Christian, & Teteryatnikova, Mariya. 2018. Income Differences and Input-Output Structure. Working Paper: University of Mannheim, University of London and European University Institute.

Feenstra, Robert C., Inklaar, Robert, & Timmer, Marcel P. 2015. The Next Generation of the Penn World Table. American Economic Review, 105(10), 3150–3182.

Greene, William H. 2011. Econometric Analysis. Seventh Edition, Upper Saddle River, NJ: Prentice Hall.

Hirschman, Albert O. 1958. The Strategy of Economic Development. New Haven, CT: Yale University Press.

Inklaar, Robert, & Timmer, Marcel P. 2014. The Relative Price of Services. Review of Income and Wealth, 60(4), 727–746.

ISIC Rev. 3.1. 2002. International Standard Industrial Classification of All Economic Activities. Statistics Division, United Nations Statistical Commission, New York.

Jones, Charles. 2011. Intermediate Goods and Weak Links in the Theory of Economic Development. American Economic Journal: Macroeconomics, 3(2), 1–28.

Leontief, Wassily W. 1936. Quantitative Input and Output Relations in the Economic Systems of the United States. Review of Economic Statistics, 105–125.

OECD. 2017a. Final Consumption Expenditure of Households (Table 5), National Accounts. OECD Publishing, Paris.

OECD. 2017b. Input-Output Tables. OECD Publishing, Paris.

6
23 We were able to calculate the cost of ATP2 extension provision from the financial reports of the 5 private companies for the period 8/95-8/96. The share of the major cost components in the extension provision cost are presented in Figure 9.

Figure 9: Share of major components in extension provision cost of 5 ATP2 companies <!-- Start of picture text --> 1 Misc, includ. rent,<br>95.00 -<br>90.00 - 0 Office Supply and Utilities<br>85.00<br>-i .. _ _ O Administration<br>U 80.00- _<br>_ 0 Training of Producers<br>75.00<br>70.00 0 Transportation<br>65.00<br>0 Salaries, per diem, and Social<br>60.00 -_ _ II _ + _ . _<br>1 2 3 4 5<br>Comnpany<br><!-- End of picture text --> As can be seen from Figure 9, salary is the main component in the cost of extension provision, varying greatly among the companies. Transportation cost is also a relatively important cost component that varies among the companies, mainly because of location issues. On the average, 72% of the costs was spent on salaries, 10% on transportation, 3% on producer training activities, and the rest (15%), was spent on administration, utility and miscellaneous costs.

Comparison of the cost share between ATPI and ATP2 provides some interesting results. Salary cost and expenditures on producer training are strikingly similar, around 70% and 5%, respectively. Transportation cost share in ATPI is doubled compared with ATP2 (20% and 10% respectively), and administration cost are tripled in ATP2 compared with ATP I (I 5% and 5%, respectively). These two differences can be explained on the ground that (1) private companies in ATP2, unlike public agencies in ATP 1, have more flexibility in selecting their clientele, also based on location, so their transportation costs are minimized, and (2) the administration costs in private
this cross-sectional study does not aim to estimate the causal effect of slum declaration on tenure security or housing outcomes. Estimating the causal effect of slum declaration as the value reflected in housing rent would result in an underestimation if I statistically control the current housing conditions and the accessibility to basic services. This is because these variables have already been affected by slum declaration. Nevertheless, this study offers enlightening insight as to how slum dwellers evaluate the current benefit of slum declaration for their tenure security.

# **4 Methodology**

## **Data**

This paper relies on household surveys collected in the city of Pune in 2013 by the author. Based on a two-stage random sampling scheme, 56 slum settlements are randomly chosen out of the 477 slums listed in MASHAL (2011). Black dots in Figure 1 indicate the surveyed slums. From each of the selected slums, 10 households are randomly selected as respondents for the survey, amounting to 562 total respondents.<sup>6</sup> The survey includes questions about a variety of household and housing characteristics. Surveyors visit respondents, read aloud the questionnaire in either Hindi or Marathi, a local language spoken in Maharashtra, and write down answers on behalf of them. The locations of surveyed households are recoded in longitude and latitude by referencing the geographic information system (GIS) maps in MASHAL (2011) and Google Earth satellite images. In addition, I retrieve slum-level information from MASHAL (2011) and combine this with the survey data.

Table 1 reports summary statistics for housing, household, and slum and locational characteristics.<sup>7</sup> While 72 respondents currently pay monthly rent for their housing, the other households pay no rent. The survey asks the latter group of households to figure out the amount of money other people would pay for the monthly rent. Recovering imputed rent in such a manner is a common practice in hedonic literature (Malpezzi, 2003). Among the total of 562 respondents, 84 households do not report the amount of rent. The influence of omitting those households who do not report rent is assessed as a robustness check against a sample selection bias at the end of Section 5. The average of the reported monthly rent is 3,128 rupees, which is worth one-third of the average monthly household expenditure (9,868 rupees).<sup>8</sup> (Insert Table 1 here) 11
Figure A.2: Corporate Tax Expenditures as a Share of GDP <!-- Start of picture text --> (a) Administrative micro-data (b) Global CIT Expenditures<br><!-- End of picture text --> <!-- Start of picture text --> Our Estimates<br>CRI Government Estimates<br>HND<br>SWZ<br>RWA<br>DOM<br>GTM MEX<br>SEN<br>UGA<br>ECU MNE<br>ETH ALB<br>6 7 8 9 10 11 6 7 8 9 10 11<br>GDP per capita (Log) GDP per capita (Log)<br>(c) In-sample Comparison: Our Estimates vs. Gov-<br>ernment Estimates<br>CRI<br>HND<br>SWZ<br>RWA<br>DOM<br>MEX GTM<br>SEN<br>UGA<br>ECU<br>ALB<br>0 .5 1 1.5 2<br>CIT Expenditures: Gov. Estimates(% of GDP)<br>2 4<br>1.5 3<br>1 2<br>.5 1<br>CIT expenditures (% of GDP) CIT expenditures (% of GDP)<br>0 0<br>2<br>1.5<br>1<br>.5<br>0<br>CIT Expenditures: Our Estimates(% of GDP)<br><!-- End of picture text --> Note: This figure shows estimates for the size of corporate income tax expenditures as a share of countries’ GDP. Panel (a) shows our estimates for the 13 sample countries, ranked by per capita log GDP (2015 constant USD from the World Bank). Panel (b) adds estimates of CIT expenditures from 66 additional countries, available in the Global Tax Expenditure Dataset (GTED), for a total sample size of 79 countries. The new estimates come from countries’ official tax expenditure reports, which based on international standards should be completed yearly. In practice, developing countries do not systematically produce tax expenditure reports, and when they do, the methods vary significantly depending on each country’s definition and statistical capacity. Panel (c) shows the correlation between our estimates of tax expenditures, and those from official tax expenditure reports, for the 11 sample countries where both are available. The dotted line is the 45-degree line. For sample countries, we adjust the GTED tax expenditure numbers when further details are available in the country’s tax expenditure report, to include all relevant expenditures. To compute aggregate tax expenditures from the microdata, we first use the difference between the top statutory tax rate and the firm-specific ETRs multiplied by their net profits to compute firm-level tax expenditures. We then aggregate these tax expenditures across firms, within country, and divide it by the sum of corporate tax liabilities, yielding a ratio of foregone CIT revenue to actual tax revenue. We multiply this ratio by the CIT collection as a share of GDP (using tax revenue data from Bachas et al. 2022) to obtain total tax expenditure as a share of GDP. This figure is discussed in Section 3.1.

29
evaluated in the last grade of primary education (sixth grade) dropped out in the transition from primary to lower secondary, 18% of the remaining three-quarters (14% overall) left during lower secondary school, and 32% of the students who managed to reach the last year of lower secondary dropped out in the transition to upper secondary. Taken altogether, these figures imply a survival rate from sixth to tenth grade of 42%, meaning that the majority of sixth grade students drop out during this four-year period.<sup>6</sup> This pattern is consistent with the information that emerges from household surveys in Guatemala: for example, Adelman and Székely (2016) find that for the cohort reaching age 18 in 2012-2014, the percentage of youth enrolled in any grade falls from 80% to 47% from age 12 to 16.

While administrative enrollment data have several advantages for the study of school dropout, the records are still in an incipient phase of development and therefore subject to some error. In Guatemala, school principals (and teachers) manually enter information into a web-based information system, creating the possibility of typographical mistakes, incomplete data, and other human errors. Therefore, it is possible that some students who are not listed as enrolled in a particular year are not real dropouts (i.e. our data could overestimate the dropout rate). Some inconsistencies in the educational trajectories of the students have also been found (e.g., students skipping grades from one year to the next). To address this, a new data platform was implemented in 2015, with automatic flags to substantially reduce inconsistencies and improve the quality of data entered. Nevertheless, as we discussed above, when we compare the survival rates estimated with both administrative and household survey data we find similar rates, suggesting that the measurement error of dropout is relatively small in our data set.

# **_3.2 Honduras_**

In Honduras, the _Secretaría de Educación_ (SEDUC) began collecting administrative records in 2013. Student information is reported by teachers in both public and private institutions, who input their rosters into a web-based system. These records gather information for all formal sector students in Honduras and allow the tracking of the same children over time (about 1.5 million).

> 6 As previously mentioned, the dropout rates in the transition from primary to lower secondary education and within lower secondary are based on a sample of students in the official sector, which represents 87% of all the students in sixth grade in 2013.

10
**Figure 2.A. Evolution of International Investment Positions and Share of Active Links: Bank Loans and Deposits**

<!-- Start of picture text --> Panel A. North-North Panel B. North-South<br>30 100% 4.0 100%<br>90% 90%<br>3.5<br>25<br>80% 80%<br>3.0<br>70% 70%<br>20<br>2.5<br>60% 60%<br>15 50% 2.0 50%<br>40% 40%<br>1.5<br>10<br>30% 30%<br>1.0<br>20% 20%<br>5<br>0.5<br>10% 10%<br>0 0% 0.0 0%<br>Panel C. South-North Panel D. South-South<br>4.0 100% 4.0 100%<br>90% 90%<br>3.5 3.5<br>80% 80%<br>3.0 3.0<br>70% 70%<br>2.5 2.5<br>60% 60%<br>2.0 50% 2.0 50%<br>40% 40%<br>1.5 1.5<br>30% 30%<br>1.0 1.0<br>20% 20%<br>0.5 0.5<br>10% 10%<br>0.0 0% 0.0 0%<br>New links Old links Share of active links<br>USD, trillions USD, trillions<br>Share of active links Share of active links<br>2001 2003 2005 2007 2009 2011 2013 2015 2017 2001 2003 2005 2007 2009 2011 2013 2015 2017<br>USD, trillions USD, trillions<br>Share of active links Share of active links<br>2001 2003 2005 2007 2009 2011 2013 2015 2017 2001 2003 2005 2007 2009 2011 2013 2015 2017<br><!-- End of picture text --> Note: This figure shows the trends in the value of international investment positions and share of active links by block for bank loans and deposits. The value of positions distinguishes old and new links. Old links are those that have a positive value in 2001. New links are those that have a zero or missing value in 2001. By definition, there are no new links in 2001. Values are expressed in trillions of 2011 U.S. dollars (USD). The share of active links is the number of country pairs that have a positive value in each year over the number of country pairs with a positive or zero value in the same year. Country pairs with a missing value are not considered. Bilateral data are aggregated for all countries within a source region to all countries within a destination region.
MALI’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

# Air transport

## **Achievements**

Mali’s air transport market is small, with a capacity of 6 seats per 100 inhabitants, which is low compared to West African neighbors (table 6). Local demand is minimal, even by regional standards, and limited by the availability of functional airports (7 airports). Furthermore, fares are out of reach for most of the population.

More than 90 percent of all passengers are regionally or internationally bound. The market is highly concentrated in two places: the long haul destination to Europe (Paris), which had 110,000 passengers and 4000 tons of freight in 2004; and the Bamako airport, which had approximately 200,000 passengers and 5,700 tons of freight in 2009. The country’s other airports have less than 30,000 passengers per year combined, and only Kayes exceeds 10,000 passengers.

**Table 6. Benchmarking air transport indicators for Mali and selected other countries**

|Country|Mali|Côte d’Ivoire|Ghana|Senegal|Burkina Faso|Nigeria|
|---|---|---|---|---|---|---|
|_Traffic (2007)_|||||||
|Domestic seats (seats per year)|—|—|144,183|130,000|20,245|1,199,572|
|Seats for international travel within<br>Africa (seats per year)|564,455|851,003|909,819|1,260,000|244.721|1,373,745|
|Seats for intercontinental travel<br>(seats per year)|165,776|297,891|832,895|1,230,000|147,095|2,437,702|
|Seats available per capita|0.06|0.06|0.08|0.23|0.03|0.09|
|Herfindahl index – air transport<br>market (%)|11.75|9.75|6.28|11.64|22.89|11.28|
|_Quality_|||||||
|Percent of seat-km in newer aircraft|95.6|90.9|96.8||93.4|71.42|
|Percent of seat-km in medium or<br>smaller aircraft|54|52.3|15.7|39.3||29.6|
|Percent of carriers passing<br>IATA/IOSA audit|0|0|0|50.0|0|28.6|
|FAA/IASA audit status|No audit|Fail|Fail|No audit|No audit|No audit|

Source: Bofinger 2009. Derived from AICD national database downloadable from http://www.infrastructureafrica.org/aicd/tools/data Note: All data as of 2007 based on estimations and computations of scheduled advertised seats, as published by the Seabury Aviation Data Group. This captures 98 percent of worldwide traffic, but a higher percentage of African traffic is not captured by the data. The HerfindahlHirschmann Index is a commonly accepted measure of market concentration. It is calculated by squaring the market share of each firm competing in the market and then summing the resulting numbers. A HHI of 100 indicates the market is a monopoly. The lower the HHI, the more competitive the market is.

Recent investments on several local airports—including refurbishment of Bamako Airport—total over 30 billion FCFA (US$60 million). This investment has been complemented by institutional reforms intended to improve airport security. Mali’s Civil Aviation Authority was recently restructured to have more autonomy to deliver licenses and enforce rules and regulations in compliance with the ICAO’s guidelines on safety and security. Many of these reforms have been implemented as part of regional projects and alliances to guarantee harmonization of air transportation rules across West Africa.

17
# **Table 2: Individual Features**

||Correlation with<br>repayment|t-stat|Number<br>of<br>Features|
|---|---|---|---|
|**Demographics**|||**2**|
|Age|0.073|2.35||
|Female|-0.039|-1.26||
|**Credit Bureau**|||**36**|
|Has a credit bureau record|-0.022|-1.89||
|Summary score (lower is better)|-0.072|-6.15||
|Fraction of debt lost|-0.046|-3.86||
|**Phone usage**|||**5,541**|
|_Categories_|_High performing ex_|_ample feat_|_ure:_|
|Periodicity|-0.163|-5.27|<br>796|
||SMS by day, ratio of<br>frequency to all others|magnitudes|of first fundamental|
|Slope|0.126|4.06|<br>44|
||Slope of daily calls out|||
|Correlation|0.111|3.57|<br>224|
||Correlation in SMS two|months ago a|nd duration today|
|Variance|-0.104|-3.34|<br>4,005|
||Difference between 80<sup>th</sup> <br>days SMS is used|and 50<sup>th</sup>qua|ntile of SMS use on|
|Other|0.100|3.07|<br>542|
||Number of important geo|graphical loc|ation clusters|

<u>Source: authors’ analysis from telecom data.</u> 27
19 Table 6: COMPARISON WITH AW AND ROBERTS (1986)

||Aw and Ro|berts a/|de Melo<br>and Winters (1989)|
|---|---|---|---|
|Rate of||||
|Increases<br>in Price<br>and Quality|Non-Athletic<br>Leather<br>Footwear|<br>Other<br>Footwear|All Footwear<br>Restricted<br>by OMA b/|
|Prices||||
|1978 to 1981|.117|.132|.140<br>(.138)|
|1974-1977 and<br>1981-1982|.021|.058|.037<br>(.036)|
|1974-1977 and<br>1981-1986|--|--|.010<br>(.009)|
|Quality||||
|1978-1981|.055|.018|.023<br>(.014)|
|1974-1977 and<br>1981-1982|.024|.105|.102<br>(.094)|
|1975-1977 and<br>1981-1982|.021|-.011|.054<br>(.033)|
|1974-1977 and<br>1981-1986|--|--|.008<br>(.065)|
|Value Share||||
|1976/1977|.398|.602|1.00|

- a/ Calculations are for calendar years.

- b/ Calculations are for July to June. Calendar-year calculations in parentheses.
similar country-level human capital (as captured by the Global Competitiveness Education System measure or the World Bank’s HLO).

_Access to Finance_ . Compared to domestic firms, foreign-owned firms are less sensitive to local financial development (as captured by the Global Competitiveness indicator, Financial Development). This is consistent with our earlier assumption that foreign-owned firms have better access to finance from their headquarters so that they depend less on the host country’s financial system.

_Infrastructure_ . Relatedly, foreign-owned firms are less hampered by the host country’s electricity quality (as captured by the Global Competitiveness Electricity Quality measure), and less helped by the availability of broadband and internet (as captured by the Global Competitiveness Broadband measure).

_Barriers to entry/exit._ Higher barriers to entry, as captured by a lower value of _Doing Business_ Contruction Permit, are associated with relatively higher _Enterprise Survey’s_ Land Access Obstacles for foreign firms.

Overall, we find foreign-owned firms to be less sensitive to access to finance, electricity quality, and internet quality, but more vulnerable to the lack of physical safety and the difficulty of obtaining construction permits. Interestingly, domestic and foreign-owned firms do not differ much in their responses to corruption and human capital.

# **6. Effects of the business environment on firm performance for micro, small, medium, and large firms**

Firms of various sizes respond differently to the underlying business environment (Beck, Demirgüç-Kunt, and Maksimovic 2005; Beck and Demirgüç-Kunt 2006; Knack and Xu 2017), and countries differ in their distribution of firm size. Thus, how countries and policy makers can adopt size-specific BE policies could have important implications for development (Hsieh and Olken 2014). For instance, very small firms likely face stronger financial constraints to grow out of the status of micro firms (McKenzie 2017). To offer more targeted policy advice, we allow the BE effects on firm performance to depend on firm size. Recognizing that micro and small firms account for the vast majority of firms in developing countries, especially lowincome ones (Hsieh and Olken 2014; Knack and Xu 2017), we classify firms into four categories: micro firms (those with fewer than 20 employees); small firms (those with 21–50 employees); medium firms (those with 51–200 employees); and large firms (those with more 21