rafmacalaba/datause-encoder-data huggingface dataset:
# **Table A10: First stage: Log Refugees at 50km instrumented by distance-based IV with decay function represented (i.e. IV generated using 1)mean border distance,** **<u>2)square of mean border distance and 3) square root of mean border distance)</u>**

|||||||Log of Refu|gees (50km)||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|
|Log of Distance IV (mean boarder distance)|9.24792***|9.24997***|8.71514***|8.68000***|||||||||
||(2.10281)|(2.10376)|(2.27960)|(2.30744)|||||||||
|Log of Distance IV (square of mean border<br>distance)|||||5.85976***|5.86074***|5.57445***|5.58123***|||||
||||||(1.37952)|(1.38019)|(1.51271)|(1.53770)|||||
|Log of Distance IV (square-root of mean border<br>distance)|||||||||15.01496***|15.02006***|14.16263***|14.08865***|
||||||||||(3.70476)|(3.70665)|(3.92898)|(3.96684)|
|HH head age||-0.00264|-0.00001|-0.00347||-0.00221|0.00062|-0.00114||-0.00291|-0.00033|-0.00403|
|||(0.00363)|(0.00323)|(0.01550)||(0.00363)|(0.00323)|(0.01584)||(0.00369)|(0.00330)|(0.01589)|
|Age squared||0.00003|-0.00000|0.00002||0.00002|-0.00001|-0.00002||0.00003|0.00000|0.00003|
|||(0.00003)|(0.00003)|(0.00016)||(0.00003)|(0.00003)|(0.00017)||(0.00003)|(0.00003)|(0.00017)|
|HH head sex (male)||-0.00493|-0.00571|0.00350||-0.00431|-0.00663|0.01889||-0.00574|-0.00520|-0.01100|
|||(0.01102)|(0.01611)|(0.08026)||(0.01107)|(0.01623)|(0.07668)||(0.01129)|(0.01669)|(0.08532)|
|Household size|||0.00070||||0.00058||||0.00083||
||||(0.00160)||||(0.00154)||||(0.00168)||
|Married monogamously|||-0.10517||||-0.10349||||-0.10746||
||||(0.07852)||||(0.08116)||||(0.07647)||
|Married polygamously|||-0.11545||||-0.11462||||-0.11665||
||||(0.07481)||||(0.07740)||||(0.07256)||
|Separated (divorced/widowed)|||-0.07393||||-0.07242||||-0.07481||
||||(0.07726)||||(0.07983)||||(0.07513)||
|Completed primary sch|||0.00050||||-0.00205||||0.00139||
||||(0.01293)||||(0.01303)||||(0.01301)||
|Commercial farming|||0.00052||||-0.00378||||0.00410||
||||(0.00885)||||(0.01001)||||(0.00845)||
|Wage employment|||-0.01362||||-0.01414||||-0.01457||
||||(0.02168)||||(0.02209)||||(0.02199)||
|Non-agricultural self-employment|||0.00461||||0.00545||||0.00478||

11
purpose of the present paper, in 2005 the module was added to the final survey of treatment and comparison samples in the SWP evaluation to elicit perceptions of how welfare had changed over time since the project began. The module asked respondents to assess whether various aspects of their lives had improved over the preceding 10 years. These involved a long list of aspects of well-being and in each case the respondent was asked whether this item had improved or not over the last 10 years, on a 5-point scale, ―much worse,‖ ―slightly worse,‖ ―no different,‖ ―slightly better‖ and ―much better‖. Matching questions were asked about perceived current standards of living. The sample was restricted to adults who were at least 28 years of age at the time of the interview.

In measuring impacts for such a program one should allow for bias arising from how differences in initial characteristics influence subsequent trajectories; this is known to be important in poor-area development projects.<sup>14</sup> Chen et al. (2009) used propensity score (PS) weighting and trimming for this purpose. The method proposed by Hirano, Imbens and Ridder (2003) was used for PS weighting.<sup>15</sup> This allows for heterogeneity in the (observable) baseline characteristics that may be correlated with subsequent changes over time and so bias the DD results. The samples were also trimmed to assure sufficient overlap in propensity scores.<sup>16</sup> Of course, these adjustments for bias require the baseline data. Only the results without PS weighting and trimming would be feasible in a single survey round post-intervention.

In estimating the probits for whether a village was selected for SWP, covariates were chosen to reflect the selection criteria used by the project staff as well as the research team’s priors on how other factors (such as remoteness and village ethnicity) may have influenced SWP placement. Chen et al. (2009) discuss the results in greater detail. They found that project villages tended to be in more hilly/mountainous areas, less well endowed with infrastructure, with lower mean income and consumption in the baseline. In most respects, the SWP villages tended to be poorer than other villages within the project counties. Using the propensity scores

> office). The module was further tested and refined by staff of the national and provincial statistics offices in each of the three study provinces.

> 14

> Jalan and Ravallion (1998) provide evidence using regional growth models for rural China.

> 15 For details see Chen et al., (2009).

> 16

> For their ―trimmed sample‖ Chen et al. (2009) chose the PS interval (0.1, 0.9), corresponding to the efficiency bounds recommended by Crump et al. (2006) for estimating average treatment effects with minimum variance.

9
9

# TABLE 1

# TRANSFERS TO OTHER LEVELS OF GOVERNMENT IN SELECTED COUNTRIES, 1974 AND 1984

||Trans<br>Exp|fers as<br>enditu<br>Gov|Percent of Total<br>res<br>of Gganting<br>ernment|GDP|
|---|---|---|---|---|
|Country|1974|1984|Inorease (+) or<br>Decrease(-)|Buoyancy<br>Coefficients|
|Argentina|10.5|6.6||1.09|
|Bolivia|5.7<br>|1.3<br>||0.74<br>|
|Botswana|6.9|8.4|+|1.49|
|Brazil|13.7<br>|11.1|-|0.98|
|Chile|9.5|_3.0_|-|0.30|
|Indonesia|18.6<br>|17.8<br>|-|1.32|
|Kenya|7.0|3.2|_|0.27|
|Korea|24.0|28.7|+|1.57|
|Malawi|6.5|6.5|o|1.37|
|Mauritius|3.8|4.1|+|1.96<br>|
|Pakistan|8.3|<br>9.8|+|1.52|
|Philippines|<br>5.6|5.3||0.73|
|Thailand|13.3|3.5||0.16|
|Uruguay|0.8|1.7|+|2.03|
|Venezuela|12.8|13.2|+|1.37|
|Median|7.0|5.3|||

aCentral government transfers only; data exclude shared taxes.

bComputed as percentage change in t;ansfers between 19741984 relative to percentage change in GDP.

C1 975 data.

_OURCE: International Monetary Fund, Government Finance Statistics Yearbook, 1984 and 1986 (Washington, DC: IMF, 1984 and 1986).
# Measuring the Economic Gain of Investing in Girls: The Girl Effect Dividend<sup>1</sup>

_Jad Chaaban_<sup>_2_</sup> _Wendy Cunningham_<sup>_3_</sup> JEL Classification: I0, J16, O4 Key Words: Gender, Economic Growth, Health and Education

> 1 We would like to thank Nithya Gopu, Ilana Finley, Lynn Renken, Sir Michael Marmot, Ruth Levine Miriam Temin, Shubha Chakravarty, Nistha Sinha, Maroula Haddad, and Francesca Montini for their comments and suggestions. All views expressed are those of the authors and do not necessarily reflect the opinions of their institutions.

> 2 American University of Beirut.

> 3 The World Bank.
3 between 5 and 8 percent and those simple locally-produced improvements in traditional postharvest technology systems, are often already cost effective. Is this time different?

To begin, the global FAO estimates quoted above hide a large degree of heterogeneity with estimates varying substantially depending on the metric, crop and stages in the chain considered. When expressed in terms of calories for example, as opposed to weight, the overall food loss in SubSaharan Africa (SSA) is estimated at 23 percent (Lipinski et al., 2013), as opposed to 37 percent in weight (FAO, 2011). And instead of 37 percent for all foods, the PHL for cereals alone is estimated at 20.5 percent (again in weight, FAO, 2011). The latter discrepancy follows from the fact that both roots and tubers (R&T) and fruits and vegetables (F&V) have a much higher water content than cereals<sup>3</sup> , while the similarity in the PHL estimates for food (in calories) and cereals (in weight) reflects the fact that cereals still constitute the major share of SSA’s caloric intake (around 40%, FAOSTAT).

Loss estimates also differ substantially depending on the stage in the farm-to-fork chain considered. In particular, FAO (2011) considers five stages in quantifying the ratio of PHL to total production, i.e. losses 1) during harvesting such as from mechanical damage and/or spillage, 2) during postharvest handling, such as drying, winnowing, and storage (insect pests, rodents, rotting), 3) during processing, 4) during distribution and marketing (wholesale, supermarkets, retail and wet markets), and 5) during consumption (i.e. good quality food fit for consumption being discarded).<sup>4</sup> In SSA, most PHL happens during harvesting and post-harvest handling and storage, with for example 68 percent of the 20.5 percent cereal PHL estimated to occur during harvesting on the field (6 percentage points) and postharvest handling and storage (8 percentage points).<sup>5</sup> The latter estimates are also closer to the ones advanced by Lipton. They also highlight the discrepancy between the broad PHL concepts and metrics commonly used to motivate PHL

> 3 The energy of roots and tubers is about one-third of that of an equivalent weight of grain, such as rice or wheat (FAO, 1990). Consequently, even though the weight losses post-harvest for roots and tubers and fruits and vegetables are substantially higher than those of cereals (54 and 66 percent respectively compared with 20.5 percent for cereals), equivalent weight losses post-harvest still lead to much lower losses in calories.

> 4 Losses during the first 4 stages are often labelled “food loss”, losses during the consumption stage “food waste”.

> 5 In the developed world on the other hand, PHL is concentrated in the latter stages of the chain—during distribution (especially retail) and consumption (Lipinski et al., 2013).
# TABLE V

# HUMAN CAPITAL AND ECONOMIC DEVELOPMENT – COMPARING MEASURES

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|---|---|---|---|---|---|---|---|---|
|Human Capital - Learning|0.065***||||0.047**|0.049**|0.055**|0.050**|
||(0.019)||||(0.023)|(0.022)|(0.023)|(0.023)|
|Human Capital - Penn World Tables||0.033***|||0.019|||0.021|
|||(0.011)|||(0.012)|||(0.036)|
|Human Capital - Schooling|||0.016***|||0.009||0.012|
||||(0.006)|||(0.006)||(0.020)|
|Human Capital - HDI Education Index||||0.020**<br>(0.008)|||0.008<br>(0.009)|-0.022<br>(0.022)|
|Observations|107|107|107|107|107|107|107|107|
|R-squared|0.275|0.261|0.255|0.240|0.291|0.290|0.281|0.298|

_Notes:_ Dependent variable: annual growth rates averaged across 2000-2010. Human Capital - Schooling refers to estimates in 2000, the beginning of the time period. Human Capital – Penn World Tables refers to the measure of human capital in the Penn World Tables. Human Capital – Learning refers to the measure of human capital in this database from 2000 onwards. Human Capital - HDI Education Index refers to the measure of education included in the HDI in the year 2000. Results exclude countries in civil war, inflation crises and with rents from natural resources above 15 percent. All independent variables are transformed to log units to derive comparable elasticities. We control for initial GDP per capita refers to levels at the beginning of the period in the year 2000 in all specifications following standard practice in the growth literature.

_Source:_ Schooling data are from Barro-Lee (2013). HDI data are from the Human Development Index Education Index. GDP and human capital data are from PWT 9.0. Learning estimates are from our database.

22
Ministère du Développement Régional et de la Planification. 2012c. Indicateur de développement régional: Pointer les difficultés pour orienter les efforts et suivre le progrès: Gouvernorat de Kasserine. Tunis: Institut Tunisien de la Compétitivité et des Études Quantitatives.

Ministère du Développement Régional et de la Planification. 2012d. _Gouvernorat de Médenine en Chiffres 2011_ . Tunis: Office de Développement du Sud.

Raballand, Gaël, Thomas Cantens, and Guillermo Arenas. 2013. “Mirror Trade Statistics: A Tool to Help Identify Customs Fraud.” In _Reform by Numbers: Measurement Applied to Customs and Tax Administrations in Developing Countries_ , edited by Thomas Cantens, Robert Ireland, and Gaël Raballand, 103–20. Washington, DC: World Bank.

31
**Table 6: Use of fixed GDP weights in calculating relative ratings**

||Pre-Crisis<br>(1)|Post-Crisis<br>(2)|
|---|---|---|
|L.Net Inflows|0.094**|0.105***|
||(0.039)|(0.038)|
|Relative rating|0.169*<br>(0.087)|0.178**<br>(0.080)|
|Growth diff.|0.198**<br>(0.099)|0.263***<br>(0.087)|
|Int. rate diff.|0.03|0.451***|
||(0.117)|(0.118)|
|VIX|-0.118***|0.055|
||(0.041)|(0.040)|
|Country F.E.|Y|Y|
|Obs.|780|859|
|Countries|26|26|
|Adj. R-squared|0.295|0.238|

Notes: All regressions have net capital inflows as the dependent variable and are estimated using a dynamic panel data model with a lagged dependent variable. The sample includes unbalanced quarterly panel data for 26 emerging and frontier market economies for the 1998-2017 period. The pre-crisis period includes Q1 1998-Q2 2008 while the post-crisis period includes Q2 2009-Q4 2017. Heteroskedasticity-consistent robust standard errors are presented in parenthesis. * indicates significance at 10 percent, ** at 5 percent, and *** at 1 percent.

**Table 7: Robustness of baseline results to the use of gross capital inflows**

||**Abs**<br>|**olute rating**<br>|**Rela**<br>|**tive to peers**<br>|
|---|---|---|---|---|
|Gross Inflows|Pre-Crisis<br>(1)|Post-Crisis<br>(2)|Pre-Crisis<br>(3)|Post-Crisis<br>(4)|
|L.Net Inflows|0.143***|-0.008|0.191***|-0.011|
||(0.039)|(0.038)|(0.040)|(0.038)|
|S&P rating|0.510***<br>(0.086)|0.370***<br>(0.097)|||
|Relative rating|||0.044<br>(0.089)|0.454***<br>(0.093)|
|Growth diff.|-0.027|0.188*|0.062|0.178*|
||(0.106)|(0.101)|(0.105)|(0.101)|
|Interest rate diff.|0.100|0.167|0.020|0.217|
||(0.125)|(0.135)|(0.124)|(0.135)|
|VIX|-0.203***|0.027|-0.274***|0.010|
||(0.045)|(0.046)|(0.045)|(0.045)|
|Country fixed effects|Y|Y|Y|Y|
|No. of obs.|780|859|780|859|
|No. of countries|26|26|26|26|
|Adj. R-squared|0.443|0.161|0.414|0.171|

Notes: All regressions have gross capital inflows as the dependent variable and are estimated using a dynamic panel data model with a lagged dependent variable. The sample includes unbalanced quarterly panel data for 26 emerging and frontier market economies for the 1998-2017 period. The pre-crisis period includes Q1 1998-Q2 2008 while the post-crisis period includes Q2 2009-Q4 2017. Heteroskedasticity-consistent robust standard errors are presented in parenthesis. * indicates significance at 10 percent, ** at 5 percent, and *** at 1 percent.

21
|**270750**|Other mixtures of aromatic hydrocarbons, 65% by volume<br>or more (including losses) are distilled at 250 ° C according<br>to ASTM D 86 method|tonne|4560 MDL|
|---|---|---|---|
|**270900100**|Naturalgas condensate|tonne|4560 MDL|
|**271012110–**<br>**271019290**|Light and medium distillates|tonne|4560 MDL|
|**271019310–**<br>|Gasoil, including diesel, and other heating fuel|tonne|1926 MDL|
|**271019480**||||
|**271019510**|Liquid fuel for specificprocessing|tonne|409 MDL|
|**271019620**<br>**–**|Liquid fuel with sulfur content|tonne|409 MDL|
|**271019680**||||
|**271020110–**|Gasoil|tonne|1926 MDL|
|**271020190**||||
|**271020310–**<br>**271020390**|Liquid fuel|tonne|409 MDL|
|**271112**|Propane|tonne|2875 MDL|
|**271113**|Bhutan|tonne|2875 MDL|
|**271114000**|Ethylene, propylene,butylene and butadiene|tonne|2875 MDL|
|**271119000**|Other liquifiedgases|tonne|2875 MDL|
|**280430000**|Nitrogen|tonne|3006 MDL|
|**280440000**|Oxygen|tonne|3322 MDL|
|**290110000**|Acyclic saturated hydrocarbons|tonne|4560 MDL|
|**искл.290124000**|Buta-1,3-diene|tonne|4560 MDL|
|**290129000**|Other acyclic unsaturated hydrocarbons|tonne|4560 MDL|
|**290211000**|Cyclohexane|tonne|4560 MDL|
|**290219000**|Other cycloalkanes,cycloalkenes and cycloterpenes|tonne|4560 MDL|
|**искл.290220000**|Benzene for use as a fuel or heatingmaterial|tonne|4560 MDL|
|**290230000**|Toluene|tonne|4560 MDL|
|**290244000**|Mixtures of xylene isomers|tonne|4560 MDL|
|**290290000**|Other cyclic hydrocarbons|tonne|4560 MDL|
|**290511000–**<br>**290513000**|Saturated monoalcohols (methanol, propanol, butan-1-ol)|tonne|4560 MDL|
|**290514**|Other butanols|tonne|4560 MDL|
|**290516**|Octanol(Octyl alcohol)and its isomers|tonne|4560 MDL|
|**искл.290519000**|Pentanol(amyl alcohol)|tonne|4560 MDL|
|**2909**|Ordinary<br>ethers,<br>etheric<br>spirits,<br>etherphenols,<br>etherspirtophenols, peroxides of alcohols of ordinary ethers<br>and ketones (of a certain or indeterminate chemical<br>composition) and their halogenated, sulphonated, nitrated<br>or nitrosated derivatives|tonne|4560 MDL|
|**330300**|Perfumes|value in MDL|30%|
|**381400900**|Other organic solvents and thinners are complex, not<br>elsewhere specified or included; prefabricated paint and<br>varnish removers|tonne|4560 MDL|
|**381700500**|Alkylbenzene linear|tonne|4560 MDL|
|**381700800**|Other alkylbenzenes|tonne|4560 MDL|
|**430310**|Garments of mink,arctic fox,fox,sable|value in MDL|25%|
|**711311000**|Jewelry and parts thereof of silver, whether or not<br>electroplated,clad or not clad with otherprecious metals|gram|2.59 MDL|
|**711319000**|Jewelry and parts thereof from other precious metals,<br>whether or not electroplated, whether or not clad with<br>precious metals|gram|39.27 MDL|
|**711320000**|Jewelry and parts thereof of base metal clad with precious<br>metals|gram|39.27 MDL|

_Source: Fiscal Code of the Republic of Moldova._ 37
Financial providers face a trade-off between broadening their agent network and effectively training and monitoring their agents (Agent Network Accelerator 2016), leading to growing concerns among regulators about the quality of the service delivered. Low barriers to entry for new agents lead to networks of agents with lower levels of training and accountability when compared to branch staff. Table 9.1 reproduces parts of Appendix Table OA2 from Buri et al. (2018a) and compares the characteristics of BSN banking agents, mobile money agents and branch tellers in the study areas. Mobile money agents are the least educated with 11.7 years, followed by banking agents with 14 years and branch staff with 16.4 years of education. Perhaps as a result, mobile money agents also have lower household income (487.4 USD) than banking agents (767.5 USD) or branch staff (838 USD). BSN branch staff also report higher levels of financial knowledge compared to either banking agents or mobile money agents in the area. It thus appears that agents may not have the same level of training and accountability as traditional financial services staff.

In rural areas, low population density and low transaction volumes cannot support multiple agents located walking distance from each other, and as a result, consumers have limited payment, credit, and savings choices, and local mobile money agents and bank branches may enjoy local monopolies. This market structure has led to policy interventions such as relaxing or outright banning exclusivity clauses in agent-mobile money operator contracts in Kenya and Uganda. Additionally, financial services and especially mobile money tend to be dominated by one or two large firms per country. Even in Kenya and Tanzania, where digital credit startups have proliferated in recent years, the markets are still dominated by just two and three top firms, respectively (Izaguirre, Kaffenberger, and Mazer 2018). This suggests that shared agent networks may create moral hazard for training and responsibility, and they seem to correlate with a worse customer experience at the agent (Agent Network Accelerator 2016b).

Figure 9.3 shows that a majority of customers visiting agents in Kenya, Uganda, and Tanzania had experienced at least one problem during the visit (Kantar InterMedia, 2016). A greater proportion of women than men report issues with agents in India and Pakistan, although more precision in estimates is needed to draw firmer conclusions.

A recent publication from the International Finance Corporation based on its work with nine MFIs in Africa indicates that, to become financially sustainable, agents need to drive 12
distributions (sales and employment) when examining the results using quantile regressions. Firms in the 25th and 50th percentiles of the sales distribution experienced a significantly more negative impact on their sales than firms in the 75<sup>th</sup> percentile (Table 3, columns 2, 3, and 4). The magnitudes of these negative effects for the 25<sup>th</sup> and 50<sup>th</sup> percentiles appear comparable. In contrast, larger firms at the 75th percentile of the sales distribution may have faced a much smaller and statistically insignificant sales decline following the invasion. These findings suggest that larger firms may have benefited from absorbing some of the sales declines experienced by firms with small and median sales. In some cases, larger firms may have even gained the entire business previously held by smaller and medium-sized firms.

**Table 3: Effect of Russia’s Invasion of Ukraine on Firms’ Sales in Central Asia**

||(1)<br>**mean**|(2)<br>**25**<sup>**th**</sup>**percentile**|(3)<br>**median**|(4)<br>**75**<sup>**th**</sup>**percentile**|
|---|---|---|---|---|
|Effect of the invasion|-0.289|-0.446***|-0.422**|-0.268|
||(0.221)|(0.145)|(0.206)|(0.265)|
|Manufacturing|0.529*|1.172***|0.843*|0.428|
||(0.294)|(0.358)|(0.508)|(0.271)|
|Retail|0.604**|0.511|1.308**|0.977**|
||(0.285)|(0.324)|(0.523)|(0.409)|
|Other Services|-0.129|0.431|0.475|-0.405*|
||(0.264)|(0.322)|(0.477)|(0.227)|
|Small (5-19)|1.408***|0.945***|1.405***|1.466***|
||(0.145)|(0.143)|(0.125)|(0.178)|
|Medium (20-99)|2.125***|1.833***|2.405***|2.568***|
||(0.160)|(0.121)|(0.178)|(0.181)|
|Large (100+)|4.560***|3.085***|5.844***|4.878***|
||(0.652)|(0.479)|(0.263)|(0.267)|
|Constant|11.784***|10.657***|11.396***|13.020***|
||(0.275)|(0.341)|(0.510)|(0.240)|
|Observations|1149|1149|1149|1149|
|R-squared|0.782|0.505|0.571|0.609|

_Robust standard errors are in parentheses. *** p<.01, ** p<.05, * p<.1_ We also observe that manufacturing and retail firms had larger sales than other firms over the studied period. And that small, medium-sized, and large firms had, progressively according to their size, larger sales than micro firms over the studied period.

The estimation results for the effect of Russia’s invasion of Ukraine on firms' employment are presented in Table 4. Again, we cannot identify any significant average effect of the invasion on employment at Central Asian firms. However, the results differ from those for sales. We do not observe any significant negative effect on firms in the 25th and 50th percentiles of the employment distribution. Interestingly, we find a significant positive effect of the invasion on firms' employment in the 75th percentile of the employment distribution. This finding provides some support to our earlier hypothesis that larger firms may have absorbed some of the business, both in terms of sales and employment, from smaller and medium-sized firms following the invasion shock. The question is whether there is a structural indicator of exposure to the invasion shock that can better identify this differentiated impact suggested by the 8
# Table V.3

# Trends in Bank Loans **to** SMEs, 1986-1988

|__________________________|19|86|198|7|198|8|
|---|---|---|---|---|---|---|
|Type of Loan:|Billion Won|% of Total|Billion Won|% of Total|Billion Won|% of Total|
|Collateral-Backed|80.9|66.0|85.5|62.2|104.1|60.7|
|Officially Guaranteed Loans|13.1|10.7|16.9|12.3|22.4|13.1|
|Other Loans (including those<br>with Guarantees from other<br>than Official institutions)|28.6|23.3|35.2|25.6|44.8|26.1|
|Total Loans|122.7|100.0|137.6|100.0|171.3|100.0|

## Source: Kang and Park 1990

70