rafmacalaba/datause-encoder-data huggingface dataset:
in calculating channel position-level land statistics. On the other hand, including these nonfarm households could have an important scaling function. For example, a community of 100 households each owning 100 acres is likely to have more influence than a community consisting of just a single 100-acre farm surrounded by 99 non-farm households; yet, mean landholdings _across farm households_ is identical in these two communities, whereas mean landholdings _across all households_ is indeed higher in the presumptively more powerful one. If the second community, instead, consisted of 100 farms of 100 acres and 100 non-farm households, the mean across all households would imply that the second is less powerful than the first when it is, in fact, equally powerful. In this case, using the means across farm households would (correctly) imply communities with identical lobbying influence (see Appendix Table B.2 for a visual guide to these examples). In short, for our purposes, there is no unambiguously valid choice of population over which to compute land ownership statistics. Prudence, therefore, dictates using both approaches.

Because the four rounds of sample census data are not everywhere dense in villages, we are not able to match _both_ head and tail _mouzas_ for every channel. Our analysis of power asymmetry is, thus, based on fewer FOs than were present in the baseline samples. For the 60 km spatial matching sample, the number of FOs covered falls from 349 to 247, when land statistics are taken over only farm households, and to 252, when land statistics are taken over all census households.<sup>24</sup> There is also a modest (positive) correlation between land ownership statistics at head and tail of the same channel (see Appendix Figure B.2), which is why these variables must be included together in the regressions.

> 24Of the 247 FOs represented in the former case, 15% are in Bahawalnagar Circle, 29% are in Derajat, 17% are in LBDC, 21% are in LCC East, and 17% are in LCC West. The corresponding breakdown across all 396 FOs in Punjab is 17%, 30%, 13%, 21%, and 18%. In line with this similarity in composition, main treatment effects are very close to those in Table 3 when estimated on the smaller samples of channels with land data (results available upon request).

30
Blackburn, K., N. Bose, and S. Capasso. 2012. “Tax Evasion, the Underground Economy and Financial Development.” _Journal of Economic Behavior and Organization_ 83(2): 243–253.

- Bouvatier, V., and A. Delatte. 2015. "Waves of International Banking Integration: A Tale of Regional Differences," European Economic Review 80(C): 354-373.

- Capasso, S., and T. Jappelli. 2013. “Financial Development and the Underground Economy.” _Journal of Development Economics_ 101 (March): 167-178.

- Capasso, S., F. Ohnsorge, and S. Yu. 2022. “Financial Development and Informality: A Literature Review.” The Manchester School.

- Catão, L., C. Pages, and M. R. Rosales. 2009. “Financial Dependence, Formal Credit, and Informal Jobs: New Evidence from Brazilian Household Data,” IDB Working Paper 118, Inter-American Development Bank, Washington, DC.

- Chodorow-Reich, G., O. Darmouni, S. Luck, and M. Plosser. Forthcoming. “Bank Liquidity Provision Across the Firm Size Distribution.” _Journal of Financial Economics_ .

- Č ihák, M., A. Demirgü č -Kunt, E. Feyen, and R. Levine. 2013. “Financial Development in 205 economies, 1960 to 2010." National Bureau of Economic Research, No. w18946.

- Claessens, S., M. A. Kose, and M. E. Terrones. 2012. “How Do Business and Financial Cycles Interact?” _Journal of International Economics_ 87(1): 178-190.

- Csonto, B., A. Guerson, A. Myrvoda, E. Seworder, and S. Munoz. 2018. “Bank Network Analysis in the ECCU” IMF Working Papers 18/162, International Monetary Fund, Washington, DC.

- Davidson, R., and J. MacKinnon. 2006. “The Power of Bootstrap and Asymptotic Tests.” –

- _Journal of Econometrics_ 133(2): 421 41.

- Dabla-Norris, E., M. Gradstein, and G. Inchauste. 2008. “What Causes Firms to Hide Output?” _Journal of Development Economics_ 85 (1-2): 1-27.

- De Haan, R. Pleninger, J. Sturm. 2021. “Does Financial Development Reduce the Poverty Gap?” Social Indicators Research 161: 1-27.

- Elgin, C., and O. Öztunal ı . 2012. “Shadow Economies around the World: Model-Based Estimates.” Working Papers 2012/05, Bogazici University.

- Elgin, C., and B. Uras. 2013. “Is Informality A Barrier to Financial Development?”

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Policy Research Working Paper 10464

# **Abstract**

Data from 2,251 small and medium-size farms for 2021 and 2022 show that area reductions in response to the Russian invasion of Ukraine remained limited. However, worsening terms of trade reduced farm profitability, implying that 46 percent of farms had a negative cash flow and 54 percent (67 percent in the 50–120 hectare group) were credit constrained in 2022, implying that longer term effects may be more adverse. Total factor productivity varies significantly across size groups but is not significantly different between formal and informal farms in the same size group. This suggests that limited transferability of land use rights that are disproportionately used by smaller farms may be one reason for low productivity. Improving transferability of land, digital access to markets, and mortgage lending could thus trigger investment and growth in higher value products by small and medium-size farms to solidify Ukraine’s comparative advantage in agriculture and improve rural living conditions in the context of reconstruction.

This paper is a product of the Development Research Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at kdeininger@worldbank.org. or dali1@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
|TV at Home|0.957<br>_(0.202)_|0.945<br>_(0.229)_|0.012|9,025|
|---|---|---|---|---|
|PC at Home|0.962|0.954|0.009|9,025|
||_(0.190)_|_(0.210)_|||
|Car at Home|0.869|0.873|-0.004|9,025|
||_(0.337)_|_(0.333)_|||
|Family Goes on Vaca-|||||
|tion|0.715|0.687|0.029|9,025|
||_(0.451)_|_(0.464)_|||
|Mother Lives at Home|0.976|0.967|0.008**|9,025|
||_(0.154)_|_(0.178)_|||
|Father Lives at Home|0.951|0.942|0.010*|9,025|
|Mother College Edu-|_(0.215)_|_(0.235)_|||
|cated|0.310|0.303|0.006|9,025|
||_(0.462)_|_(0.460)_|||
|Father College Educated|0.296|0.285|0.010|9,025|
||_(0.456)_|_(0.452)_|||

* p < 0.05, ** p < 0.01, *** p < 0.001 _Notes:_ Standard deviations in parentheses. T-tests with robust standard errors clustered at the school level. All figures are rounder to three decimal places.

_Source:_ Own survey data, administrative data, school year 2015/2016, own calculations.

_Table A2.2: Impacts on Deliberate Practice Beliefs and Grit, Balanced Sample (Z-Scores)_

|Deliberate Practice Beliefs<br>(1)|Grit<br>(2)|Grit: Effort<br>(3)|Grit: Interest<br>(4)|
|---|---|---|---|
|Treatment 1<br>0.162***|-0.064***|0.051**|-0.127***|
|“Student Self-Learning”<br>_(0.019)_|_(0.020)_|_(0.021)_|_(0.019)_|
|Treatment 2<br>0.236***|-0.032|0.062***|-0.094***|
|“Teacher Delivery”<br>_(0.018)_|_(0.022)_|_(0.019)_|_(0.022)_|
|N<br>18,718|18,718|18,718|18,718|
|N Control<br>7,286|7,286|7,286|7,286|
|N Treatment 1<br>5,424|5,424|5,424|5,424|
|N Treatment 2<br>6,008|6,008|6,008|6,008|
|_R2_<br>_0.324_|_0.344_|_0.335_|_0.238_|
|* p < 0.05, ** p < 0.|01, *** p < 0.001|||

_Notes:_ All regressions control for pre-treatment outcomes, demographic controls (including dummies for age, gender, academic year, and ethnicity), and municipality fixed effects. Robust standard errors are clustered at the school level in parentheses. All figures are rounded to three decimal places.

_Source:_ Own survey data, school year 2015/2016, own calculations.

34
# **Does it pay firms to register for taxes? The impact of formality on firm profitability**

David McKenzie, _World Bank, BREAD and IZA_<sup>_#_</sup> Yaye Seynabou Sakho, _World Bank_ _JEL Classification Codes:_ O17; O12; D21. _Keywords :_ Informal Sector ; Entrepreneurial Ability ; Tax Registration.

> # Corresponding author: <u>dmckenzie@worldbank.org. We thank Bill Maloney, Pablo Fajnzylber and</u> participants in the Finance and Private Sector Development research seminar at the World Bank for helpful comments. The survey used in this paper was financed by a World Bank ESW on policies to increase the formality and productivity of firms. All views expressed in the paper are those of the authors, and not of the World Bank, its executive directors, or the countries which they represent.
KAMPUCHEA, **DEM.** 2. Projections - **NRR-1** by 2015

|Yewr|Population|00-<br>ratio|Ptriod|to*o<br>**rote**|cm|CDR|TF4|**ma"**<br>**i**|8<br>**Fmlte**|lo|X1000|
|---|---|---|---|---|---|---|---|---|---|---|---|
|1990|<br>8243|60.7|1990-94|2.10|34.9|13.9|4.20|50.1|53.4|108|156|
|199S|<br>9156|80.7|1995-99|1.55|27.0|11.5|3.69|53.2|56.8|89|127|
|2000|9893|69.8|2000-04|1.25|22.5|10.0|3.20|56.2|60.1|74|103|
|2005|10529|57.6|2005-09|<br>1.19|21.7|9.7|2.77|57.9|61.9|67|92|
|2010|11177|49.5|2010-14|1.14|20.9|9.5|2.40|59.6|63.8|60|81|
|2015<br>|11832|47.5<br>|2014-19|1.10|20.4|9.4|2.27|61.3|65.8|53|70|
|2020<br>|12501<br>|49.0<br>|2020-24<br>|0.98<br>|19.0<br>|9.2<br>|2.23<br>|63.2<br>|67.9|46<br>|59<br>|
|2025|13132<br>|50.1<br>|2025-49<br>|0.67<br>|15.9<br>|9.2<br>|2.14<br>|67.8|73.0<br>|28<br>|35<br>|
|2050<br>|<br>15536<br>|47.4<br>|2050-74<br>|0.37<br>|13.7<br>|10.0<br>|2.09<br>|73.4<br>|79.1<br>|10<br>|12<br>|
|2075<br>|17045<br>|59.4<br>|2073-W<br>|0.20<br>|12.8<br>|10.8<br>|2.07<br>|77.2<br>|83.2<br>|6<br>|7<br>|
|2100|17934|66.7|2100-24<br>|0.13|12.2|10.9|2.06|80.2|86.5|3|4|
|2125<br>2150|18547<br>18857|70.0<br>72.1|212S-49|0.07|11.9|11.3|2.06|81.3|87.8|2|3|

# 3. Age structure (percent): 1990 and 2025

<!-- Start of picture text --> 70-74<br>65-69<br>430-654<br>55-59<br>50-54<br>45-49<br>40-44<br>35-39<br>R S5 R gm30-34<br>25-29<br>20-24<br>15-19<br>10-14<br>5-9<br>0-4<br>lO 8 6 4 2 0 2 4 6 8 10<br><!-- End of picture text --> <!-- Start of picture text --> 10 8 6 4 2 0 2 4 6 8 10<br><!-- End of picture text -->

# 4. Comparison of Country with Region and Income Group, 1985-90

<!-- Start of picture text --> TFR eO<br><!-- End of picture text --> Growth rate Coun~try Region Income group <!-- Start of picture text --> Country Region Income group Coun~try Region Income grou.p<br><!-- End of picture text --> 63
<!-- Start of picture text --> 1<br>.9<br>.8<br>.7<br>.6<br>.5<br>.4<br>.3<br>12 13 14 15 16 17 18<br>age<br> all<br>1<br>.9<br>.8<br>.7<br>.6<br>.5<br>.4<br>.3<br>12 13 14 15 16 17 18<br>age<br> boys<br>1<br>.9<br>.8<br>.7<br>.6<br>.5<br>.4<br>.3<br>12 13 14 15 16 17 18<br>age<br> girls<br>%<br>attending school<br>%<br>attending school<br>%<br>attending school<br><!-- End of picture text --> Figure 1 - School attendance, by age and gender. Source: Own calculations based on World Bank, Fortaleza Survey 2003.

8
Each point corresponds to the average savings rate and Income level for a given country during a 10-year period (only non-overlapping perioda were used; the Figure has the same features If we take averages over a different time horizon).<sup>The</sup> solid line in this Flgure corresponds an estimate<sup>of the</sup> hyperbola described in (46). Figure 6 presents the same<sup>evidence</sup> but concentrating on countries with incomes less than 15000 1987 US dollars. The dotted line in Figure 7 depicts an<sup>estimate of</sup> (46) while the solid line was constructed by ordering<sup>the</sup> countries by income, dividing them into 10 classes and computing the simple average of the savings rate for these various<sup>classes.</sup> This set of Figures shows a relation between<sup>the rate of savings</sup> and the level of Income that accords remarkably<sup>with the</sup> predictions of equation (46).

Table 1 shows that the relation between<sup>the inverse of the</sup> level of Income and the rate of savings holds up<sup>when we move</sup> from a bivariate comparison to a multivariate analysis. The data used in these regression is that of the<sup>Barro-Wolf data set</sup> extended to include World Bank measures of the<sup>savings rate and</sup> of the share of exports In GDP. Standard errors, reported<sup>in</sup> parenthesis, are based on White's (1980) heteroskedasticity-consistent covariance matrix. The first regression describes the relation between the savings<sup>rate and</sup> the inverse of the level of income 1960 (INVGDP60), depicted in Figures S through 7. The second regression includes the variables in the Barro-Wolf data set that<sup>are not highly</sup> correlated with the initial income level. These variables are the share of government consumption expenditures<sup>in GDP (GOV),</sup> number of revolutions per year (REVOL), number of assassinations per million population per year (ASSASS)<sup>and the magnitude of the</sup> deviation of the Investment deflator from the<sup>sample average</sup> (PPI60DEV). In this regression the coefficient on<sup>INVGDP60</sup> continues to be negative and significant. The same result<sup>holds</sup> when we Include, in the third regression, the share of exports In GDP (XGDP) **.** The motivation to Include this variable is Levine and Renelt's (1990) finding that this is the only variable that 34
# **Figure 10: Hirschman-Herfindahl Index by SITC Rev.1, 1970/71-2007/08**

<!-- Start of picture text --> Leather & Rubber<br>Leather Manuf.of leather or of Fur skins, tanned or Materials of rubber Articles of rubber,nes<br>artif.or rec dressed, inclu<br>HHI 70‐71 HHI 80‐81 HHI 90‐91 HHI 00‐01 HHI 05‐06 HHI 07‐08<br>2,600<br>2,114<br>1,201 1,179<br>696 619 673 809 828 821 823 633 551 663 795 902 757 906 967 1,030 888 851 656 618 932 908 802 660 558 549<br><!-- End of picture text --> <!-- Start of picture text --> Textiles<br>Textile yarn and Cotton Text fabrics Tulle, lace, Special textile Made‐up Floor coverings,<br>thread fabrics,woven woven ex embroidery, fabrics and articles,wholly or tapestries, etc.<br>ex.narrow or s narrow, spec, ribbons, t related chiefly<br>HHI 70‐71 HHI 80‐81 HHI 90‐91 HHI 00‐01 HHI 05‐06 HHI 07‐08<br>Iron & Steel<br>Pig iron, Ingots & other Iron and steel bars, Universals,plates Hoop and strip of Rails & rlwy track Iron and steel wire,<br>spiegeleisen, primary forms of rods, angles,sha and sheets of iro iron or steel constr mat. of i excluding wire<br>sponge iron iro<br>HHI 70‐71 HHI 80‐81 HHI 90‐91 HHI 00‐01 HHI 05‐06 HHI 07‐08<br>1,815<br>1,652<br>1,174<br>825 608 536 464 547 611 531 555 838 633 914 885 945 821 754 714 828 842 955 741 712 637 726 768 819 735 718 687 615 599 477 537 644 930 803 969 822 850 862<br>4,709<br>2,065<br>612 564 545 542 560 559 1,015 839 616 583 518 529 1,137 852 590 454 445 508 1,368 1,141 906 607 523 524 1,363 1,502 983 1,207 839 718 642 625 1,401 965 789 511 485 535<br><!-- End of picture text --> 20
SIERRA LEONE‘S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE 2001, and when Ghana Airways stopped services, Kenyan Airways and Slok Air (originally from Nigeria but now from the Gambia) quickly replaced and augmented the capacity that had disappeared. Because nine carriers provided a relatively evenly balanced set of seat capacities, the Herfindahl index shows the sector to be somewhat more competitive (table 7). This may be the result of there being no domestic carrier; due to the size of the country all scheduled services are international and intercontinental.

**Figure 12. Evolution of capacity and city pairs in Sierra Leone**

<!-- Start of picture text --> a. Capacity b. City pairs<br>450,000 12<br>400,000<br>10<br>350,000<br>300,000 8<br>250,000<br>6<br>200,000<br>150,000 4<br>100,000<br>2<br>50,000<br>- 0<br>2001 2004 2007 2009 (Est) 2001 2004 2007 2009 (Est)<br>Total Total<br>International International<br>Intercontinental<br>Intercontinental excluding flights between NA and SSA<br>City Pairs<br>Number of seats<br><!-- End of picture text --> Source: Bofinger 2008. Derived from AICD national database downloadable from http://www.infrastructureafrica.org/aicd/tools/data. Note: As reported to international reservation systems.

# **Challenges**

Airport accessibility needs to be improved. Sierra Leone‘s main airport at Freetown is an island, requiring passengers to take ferries to reach the mainland.

Aging aircraft and air safety also pose a concern. The average age of aircraft serving Sierra Leone is high; close to half of the total aircraft are obsolete. The aircraft age may be related to the carrier playing the largest role: Slok Air. As Kenyan and other carriers add capacity, the ratio may improve. But safety oversight, according to the last International Civil Aviation Organization audit, is in need of much improvement on all levels.

27
costly practices. For instance, the number of litigious re-routings from the yellow channel (documents control) to the red channel (physical inspection) declined tremendously.

# _d.   The Neglected Soft Power of Competition_

While evidence accumulated on the strong effect of infrastructure on trade costs, whether the right policy response was a “big push” in infrastructure investment was questioned by Teravalinthorn and Raballand (2008). This preoccupation reflects a new awareness of the importance of the ‘logistics markets’ inspired by Chile’s deregulation of its shipping industry, which until 1979 had been regulated by a cargo reservation system dating back from the 1950s.

Since maritime transport still accounts for 80% of world transport, it is important to explore how generalizable the Chilean experience is. Fink et al. (2002) and Clark et al (2004) explored the impact of efficiency on shipping costs to the US. Relying on efficiency estimates of ports drawn from interviews, Fink et al. regressed freight-rate data for U.S. seaborne imports on the existence of maritime cartels (so-called “shipping conferences”) as well as various restrictive regulations applying to shipping (cargo reservation schemes) and port operations. Their cross-section estimates suggest that cartels pushed up freight rates by about a third but the evidence on policy restrictions was inconclusive.

Drawing on reliable US data on bilateral import charges at the HS-6 level over the period 19912003, Blonigen and Wilson (2008) regressed for each product import charges on all relevant characteristics except changes in product composition. After controlling for all other factors affecting charges, their port fixed effects provided an efficiency ranking of US and foreign ports. Overall, they estimated that a 10 percent increase in port efficiency increased trade between a 13
This paper uses daily returns of stocks listed on the NYSE, AMEX and NASDAQ with share code 10 and 11 from July 1963 to December 2017 to compute downside betas. The data are obtained from the Center for Research in Security Prices (CRSP). Stocks with prices less than five dollars are excluded. In order to ensure enough observations, stocks must have at least 100 observations in the year that downside betas are constructed. All returns reported in this paper are excess monthly returns, where risk-free returns are approximated by the one-month Treasury Bill rate provided by Ibbotson Associates. The proxy for the market return is the value-weighted return of all stocks included in CRSP.

Firm characteristics are obtained from COMPUSTAT. Size is computed as the log of total assets. For computing liquidity beta, this paper obtains Pástor and Stambaugh liquidity risk factor from Professor Luboš Pástor’s website. To compute risk-adjusted returns, this paper also acquires the Fama-French three factors, five factors and the momentum factor from Professor Ken French’s website.

and respectively.

Panel A in Table 1 reports summary statistics of various betas, i.e., CAPM beta, usual downside beta " _βed_<sup>_−_",ES-impliedbeta"</sup><sup>_β_</sup> _es_<sup>_−_",relativeusualdownsidebeta"Relative</sup><sup>_β_</sup> _ed_<sup>_−_"andrelativeES-</sup> implied downside beta "Relative _βes_<sup>_−_".Columns "Mean", "SD", "Q1", "Median", and "Q3" present</sup> their mean, standard deviation, first quartile, median, and third quartile, respectively. The CAPM beta on average is less than 1, suggesting that the sample in this paper overall is less correlated with the market and the majority may be small firms. The usual downside beta and the ES-implied downside beta are larger and more volatile than the CAPM beta. Subtracting the CAPM beta, the relative usual downside beta is centered around 0. In contrast, the relative ES-implied beta is still significantly positive. Remember that the ES-implied beta should equal the CAPM beta if returns follow normal distribution. Therefore, the summary statistics of the relative ES-implied beta imply that stock returns diverge from normal distribution.

Panel B in Table 1 reports correlations among betas and their correlations with stock returns "Return", Size, book-to-market ratio "BM", coskewness, cokurtosis, and liquidity beta _βL_ . Considering that a high downside beta always implies a high CAPM beta, it is not surprising to see a high correlation of 0.8 between the CAPM beta and the usual downside beta and a correlation 9