rafmacalaba/datause-encoder-data huggingface dataset:
Financial Regulation and Development Table 1: Regulation and Banks: Quarter Variation (Million Real Birr)

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|Variables|Deposits|Lending|NBE bills|Safe assets|
|||Small Banks|||
|Quarter 2|50.29|63.17|8.848|209.1|
||(91.82)|(158.0)|(31.11)|(155.4)|
|Quarter 3|80.21|73.17|10.64|51.73|
||(78.86)|(138.5)|(29.95)|(87.00)|
|Quarter 4|180.4**|55.65|17.67|35.45|
||(78.86)|(144.6)|(28.34)|(92.50)|
||Small|Banks and Post-Policy|||
|Quarter 5|266.0***|73.49|126.6***|159.8***|
||(60.12)|(57.68)|(26.09)|(36.28)|
|Quarter 6|362.8***|145.3|164.2***|417.2**|
||(90.74)|(140.5)|(26.47)|(177.3)|
|Quarter 7|397.9***|182.1|206.7***|231.3**|
||(81.63)|(132.7)|(28.76)|(91.24)|
|Quarter 8|468.6***|215.3|239.6***|122.8|
||(78.46)|(136.2)|(26.85)|(99.16)|
|Quarter 9|597.3***|255.2***|277.7***|260.9***|
||(58.59)|(55.18)|(28.03)|(39.62)|
|Quarter 10|696.4***|311.8**|291.7***|559.0***|
||(86.38)|(148.8)|(26.78)|(192.1)|
|Quarter 11|730.2***|334.4**|321.3***|350.6***|
||(82.00)|(140.5)|(28.73)|(86.66)|
|Quarter 12|935.6***|375.6**|360.1***|358.0***|
||(82.30)|(145.6)|(27.91)|(95.71)|
|Quarter 13|1,085***|470.5***|393.2***|356.8***|
||(75.50)|(65.43)|(26.90)|(40.97)|
|||Big Banks|||
|Big Bank _×_ Quarter 2|149.3|_−_145.4|6.589|_−_328.3|
||(214.4)|(225.6)|(157.5)|(232.8)|
|Big Bank _×_ Quarter 3|311.2|49.33|14.12|_−_48.10|
||(212.6)|(209.5)|(148.0)|(158.6)|
|Big Bank _×_ Quarter 4|130.0|47.93|16.18|_−_8.906|
||(228.8)|(209.4)|(145.6)|(158.8)|

21
# **Table 7 Simulation of Transit Overheads from Lomé (Togo) to Ouagadougou (Burkina Faso)**

|Reference a 30 MFCFA 40’ container, January<br>2006.|ValueFCFA|% transportation|
|---|---|---|
|**Baseline transportation fees**|**_1,100,000_**|100%|
|**Facilitation payment at road blocks**|**_100,000_**|9%|
|**Other facilitation payments for transit**<br>**(estimated)**|**_200,000_**|18%|
|**Official transit fees in Togo**|**_111,000_**|10%|
|_Freight bureau_|10,000|1%|
|_Transit Terminal (Chamber of Commerce)_|10,000|1%|
|_Convoy fees (customs)_|10,000|1%|
|_Shippers 'council waybill_|10,000|1%|
|_Transit Carnet Togo (Chamber of Commerce)_|6,000|1%|
|_Transit Bond in Togo (0.25%)_|75,000|7%|
|**Official transit fees in Burkina Faso**|**_119,000_**|11%|
|_Transit Carnet Burkina (Chamber of Commerce)_|6,000|1%|
|<br>_Transit Bond in Burkina_|75,000|7%|
|_Shippers' council waybill_|2,500|0%|
|_customs IT fee at border_|5,000|0%|
|_Convoy fees (customs)_|5,000|0%|
|_Warehousing fee in Ouagadougou (Chamber of_|||
|_Commerce)_|31,500|3%|
|**Freight forwarder's fees, including****_:_**|**_170,000_**|15%|
|_Togo (initiation of transit)_|_75,000_|_7%_|
|_Border_|_20,000_|_2%_|
|_Ouagadougou (termination of transit)_|_75,000_|_7%_|
|**Mandatory insurance on transit goods (min**|||
|**0.3%)**|**_90,000_**|8%|
|**Total transit logistics overheads**|**790,000**|72%|
|Facilitating payments|300,000|27%|
|<br>Avoidable public procedures.|156,250|14%|
|<br>Avoidable private services|175,000|16%|
|**_Total avoidable costs_**|**_631,250_**|**_57%_**|
|**_Total non-avoidable costs (bonds, forwarders fess_**|||
|**_at half current level)_**|**_158,750_**|**_14%_**|

_(Unit CFA Francs 1 Euro= 655.957 FCFA)_ _Source: Authors’ Calculations. The simulation excludes the cost of final clearance_ 37
**Table 5: Questions about Subjective Perceptions about Corruption**

||N|N<br>Reticent|Coefficient<br>Reticence<br>Dummy|t‐statistic|Uncond.<br>Mean|
|---|---|---|---|---|---|
|**Do you think that corruption**<br>**presents any obstacle to the**<br>**current operations ofyour**<br>**establishment**<br>**?**<br>(scale 0‐4; 0: no obstacle;<br>4: very severe obstacle)|4641|610|‐0.0965|(‐1.62)|1.79|
|**Indicate if corruption constitutes**<br>**the most, second most, or third**<br>**most serious obstacle [to the**<br>**current operations of your**<br>**establishment].**<br>**(Respondent provided with a list**<br>**of 20 possibilities)**<br>(0=corruption not named;<br>1: respondent names corruption as<br>on of top three obstacles)|4641|610|_0.0432***_|_(2.73)_|0.139|

t statistics in parentheses

- p<0.10, ** p<0.05. *** p<0.01

21
assets into each individual-level interview.<sup>6</sup> Across all LSMS+ supported surveys, information on individual asset ownership and valuation was entirely self-reported.

The LSMS+ assets modules also examine nuances of ownership that are not well-covered in surveys on wealth. This includes differentiating between respondents’ exclusive versus joint reported ownership of each asset in a given asset class.<sup>7</sup> In the case of joint ownership, respondents were asked to identify who shared the asset within the household (through those individuals’ unique household roster identifiers), as well as the number of men and women outside the household who shared ownership of the asset. For residential and non-residential land, different ownership and rights constructs were covered as well. For residential and non-residential land, for example, individuals were asked about overall reported ownership, economic ownership (being able to control money over the proceeds from hypothetical land sales), as well as documented ownership/whether the respondent’s name was on a title or certificate to the land. Whether landowning respondents had rights to sell, bequeath, rent, use as collateral, and invest were also asked. Additional questions were asked on whether these nuances of ownership and rights were held jointly or exclusively. This level of detail highlights important patterns of ownership and decision-making that can inform policy efforts to expand access to financial services, land, and property rights in general. Our results also show, under the individual-level survey approach, a substantial share of agreement among 6 Time and budget constraints can hamper interviewing all adult household members, leading to the risk of nonresponse. Among the LSMS+ surveys that have been completed so far however; response rates have been relatively high among eligible adults. In Malawi, 82 percent of all eligible adults were successfully interviewed; in Tanzania, Ethiopia and Cambodia, these shares were 80, 96, and 92 percent, respectively (see Hasanbasri et al., 2021c, 2021d for more details).

7 The one exception was for consumer durables in Cambodia (computers, as well as motorized and non-motorized vehicles), where respondents were only asked about ownership overall, and not asked to identify joint owners. This stems from the protocol and questionnaire used in MEXA, also in line with the sample durable module in the UN-EDGE guidelines.

9
# **A. Appendix: Tables**

## **Table 1 Variable**

|Variable|Defnition|Source|
|---|---|---|
|1_−R_2|Price informativeness, defned as one minus _R_<sup>2 </sup>from<br>regressing a frm’s daily returns on market and industry<br>indices over year t|CRSP|
|_SIZE_|Logarithm of the book value of assets|Compustat|
|_INSIDER_|Ratio of frm’s shares traded by insiders in a given year<br>to the total number of shares traded (in percentage terms).<br>Insider traders refers to open market stock transactions by<br>the top fve executives (CEO, CFO, COO, President and<br>Chairman of the board)|Thompson Financial<br>Insider Trading<br>Database and CRSP|
|_INST_|Percentage of shares held by institutional investors|Thompson Financial|
|_ANALY ST_|Number of analysts issuing forecasts for each stock|IBES|
|_DIS_|Analyst forecast dispersion, defned as the natural logarithm<br>of the standard deviation of analysts’ forecasts of earnings<br>per share, times the number of shares, scaled by the book<br>value of total assets as in Gilchrist et al. (2005)|IBES and Compustat|
|_CF_|Cash fow, defned as net income before extraordinary items +<br>depreciation and amortization expenses + R&D expenses<br>scaled by assets|Compustat|
|_Q_|Average Q, defned as [Book value of assets - book value<br>of equity + market value of equity]/book value of assets|Compustat|
|_I_|Investment rate, defned as the ratio of capital expenditures<br>to lagged fxed assets|Compustat|

## **Table 2**

## **Summary statistics**

This table reports the summary statistics of the main variables used in the analysis. For each variable I present its mean, standard deviation, 5<sup>_th_</sup> , 25<sup>_th_</sup> , 50<sup>_th_</sup> , 75<sup>_th_</sup> and 95<sup>_th_</sup> percentile. All variables are defined in Table 1. The sample period is from 1990 to 2010.

|Variable|Number<br>of observations|Mean|St.Dev.|5%|25%|50%|75%|95%|
|---|---|---|---|---|---|---|---|---|
|1_−R_2|34819|0.82|0.18|0.43|0.72|0.89|0.97|1.00|
|_SIZE_|34819|7.67|2.61|3.70|5.80|7.30|9.37|12.04|
|_INSIDER_|34819|6.89|12.69|0.01|0.08|0.43|3.16|34.04|
|_INST_|34819|0.52|0.26|0.09|0.31|0.53|0.73|0.93|
|_DIS_|34819|-3.05|1.12|-4.89|-3.78|-3.07|-2.34|-1.15|
|_CF_|34819|1627|2502|-5.37|17.02|141|5706|7805|
|_Q_|35051|2.81|2.16|0.93|1.14|1.63|6.31|8.31|
|_I_|24155|0.29|0.42|0.49|0.11|0.19|0.34|0.79|

26
15 be quite a complex task. Therefore the analysis is based on data in nominal terms. Furthermore, as new investments and liquidations are likely to be influenced quite differently by the investment policy of a country gross flow data would be desirable. However, all these data requirements cannot be met in practice. (Properly deflated data would have been preferable, of course.) The most broadly based regional as well as sectoral data set is the stock statistic, which covers time series for about 70 countries for the period 1976 to 1987 (source: Deutsche Bundesbank). The countries covered are either major holders or holders of several small amounts of German FDI. If a country is not listed or its numbers are not shown, it can, however, not be concluded that its German FDI stock is close to zero; it is rather a holder of one or two investments that are not shown due to confidentiality.

The valuation problem raised above gets even worse when "net outflows" are calculated from an increase in stocks of primary and secondary FDI, because capital gains and losses due to valuation changes and exchange rate variations are included. Valuation changes due to exchange rate movements are a particularly severe problem in German data. The stock values are usually historical book values converted into German mark at the end of each year. Thus converting the stocks into the currency of the host country at end-of-period exchange rates, taking first differences and converting those back to German mark at averageof-period exchange rates should lead to a better approximation to the flow data of the balance of payments. Indeed the correlation between the increase in stocks and flows is higher when the increase in stocks is measured in the way described above. There are, however, a few major recipient countries for which the above described adjustment is false, namely those which suffer from high inflation rates. In those countries book values are usually adjusted for inflation. Thus
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Why are the effects of vocational training much more positive for men than they are for women? Is it that the nature of the training, and the way men and women experienced the program was very different? To investigate this further, we analyze:

- (a) The details of the training program and the experience as reported by male and female trainees in the treatment group, and

- (b) Summary statistics on the baseline conditions faced, to identify whether females were differentially constrained based on their domestic situation.

Overall, males and females report similar experiences during training. Male and female trainees are of similar age, the training programs were of similar length on average, they received similar-sized stipends from the implementing organization, and the MC attendance and mentorship/encouragement were all comparable. However, boys are significantly less likely to have missed any days of training, and they are slightly (10%) less likely to drop out, although the latter difference is not statistically significant. These suggest - as the drop-out and attrition analysis did before – that women are participating in training in a more constrained environment. These slight gender differences then translate into better “real” experiences for boys: (a) MCs are significantly more likely (by 9 percentage points, or 20%) to help boys with food and money during the training period, (b) boys are accordingly 4 percentage points (8%) less likely to report that the (same sized) stipend is insufficient to meet their needs, and (c) boys are significantly more likely to receive paid work from the MCs after the completion of training. Paid employment is a rare outcome, and the 2.8 percentage points greater likelihood of boys receiving that offer from an MC represents a large (233%) increase over girls.

In terms of the differential conditions faced by women at baseline before the training is implemented, comparison of summary statistics indicate that women live in households with fewer adults and more dependent children. Women report spending almost twice as much time as men on household and agricultural chores. Men, on the other hand, are older, more likely to be the head of household, and less likely to still be living with at least one parent. They are more likely to have completed secondary school, and they spend more time in paid labor. While both male and female youth of Malawi are burdened with a great deal of family responsibility at a young age, the fact that men’s responsibilities appear to be more financial in nature, and more likely to carry market returns, may imply that they have the chance to develop skills outside the home that allow them to make better use of the training.

18
Table 1b: Establishment size distribution for services

||India 2001|India 2006|US 1997|
|---|---|---|---|
|A. Establishment count distribution|2001|2006||
|Tiny (1-5 employees)|0.953|0.956|0.649|
|Other OAE (6+ employees)|0.002|0.001||
|Micro (6-19 employees)|0.041|0.039|0.241|
|Small (20-39 employees)|0.003|0.002|0.054|
|Medium (40-99 employees)|0.000|0.001|0.034|
|Large (100-499 employees)|0.000|0.000|0.020|
|Mega (500+ employees)|0.000|0.000|0.003|
|Total unorganized share|0.955|0.957|0.649|
|Total organized share|0.045|0.043|0.352|
|B. Employment weighted distribution||||
|Tiny (1-5 employees)|0.748|0.727|0.090|
|Other OAE (6+ employees)|0.014|0.007||
|Micro (6-19 employees)|0.172|0.170|0.163|
|Small (20-39 employees)|0.039|0.033|0.099|
|Medium (40-99 employees)|0.015|0.022|0.140|
|Large (100-499 employees)|0.011|0.013|0.258|
|Mega (500+ employees)|0.001|0.028|0.251|
|Total unorganized share|0.761|0.733|0.090|
|Total organized share|0.239|0.267|0.911|

Notes: See Table 1a. U.S. descriptive statistics taken from 1997 Census of Services. "Ownaccount enterprises" (OAE) are firms that do not employ any hired worker on a regular basis.
33

# CHAPTER IV EMMD FT;S;g

Global food production growth outpaced the growth in world population over the past three decades. Yet, vast population groups throughout the developing world, continue to suffer from problems of hunger and malnutrition. _Aun_ overwhelming majority of the at-risk groups are concentrated in Asia and Africa. Two sets of indicators, namely indicators of undernutrition and malnutrition, are used to identify and monitor at-risk population groups. The firL.: relates to deficiency of caloric intake; and the latter relates to deficiency of one or more essential nutrients in the daily diet.29

# The data

Undernutrition. Based on caloric intake data by country from BESD, this paper used index of undernutrition for developing countries, to e..smine regional trends in nutritional status during the period 1965-85. The index is calculated as the ratio of the shortfall/or surplus in per capita caloric consumption to per capita caloric requirements. FAO periodically publishes average caloric requirement norms by country for a reference man or woman. The caloric requirement norms for 1977, the latest available figures in BESD, 29Undernutrition is defined in terms of adequacy of caloric intake which, continued over a long period, results in either loss of normal body weight or reduction in physical activity or both. Malnutrition is distinct from undernutrition, in that malnutrition is also measured in terms of the deficiency in diet of one or more essential nutrients.
**Table 3.1 Access to finance by size, 1996, 1998, 2000**

|**Size**|**1996**|**1998**|**2000**|
|---|---|---|---|
|Micro|41.7|42.1|39.4|
|Small|66.5|63.6|61.8|
|Medium|77.5|73.8|72.1|
|Large|84.3|79.6|78.3|

Source: I&G (2000). Based on data from the Súper Intendencia de Bancos e Instituciones Financieras.

# **THE GOVERNMENT AND ACCESS TO CREDIT: FOGAPE**

On a continuum of government participation in the economy, the Chilean Government is among the most active. While some countries offer an equally complete menu of programs, Chile’s financial programs for SMEs are more operationally oriented, and less oriented toward structural changes to improve productivity. The Chilean portfolio of programs also lacks a philosophical underpinning and internal consistency, compared to approaches in Australia, Ireland, and Scotland (Table 3.2).

Among Chile’s business support programs, the Guarantee Fund for Small Entrepreneurs ( _Fondo de Garantía para Pequeños Empresarios,_ FOGAPE) facilitates access to credit for smaller firms, especially new firms or those with insufficient or no guarantees; it also promote long-term credit. BancoEstado, a commercially oriented government retail bank—and a pioneer in commercial micro business and SME lending—manages this fund. FOGAPE’s value added is in extending the frontier of lending to SMEs _._ More than 80 percent of FOGAPE loans have maturities of less than three years. These credits can be renewed up to a maximum of 10 years. In 2000, the average loan guaranteed was only 320 UF (about US$7500). That year, legal and regulatory adjustments expanded the program and provided for annual guarantees of US$220 million, covering credits up to US$300 million. This was in line with the needs of small entrepreneurs, since most funds are for working capital.<sup>36</sup> FOGAPE offers a streamlined bidding process and general selection criteria for clients. This avoids a more traditional system of overlapping, time-consuming individual client evaluations by the financial institution and the guarantee facility, and results in lower transaction costs. FOGAPE also offers a modest incentive for longer-term loans. Banks use this facility to hedge their SME operations at low cost. Since there is no minimum loan amount, banks with specialized SME platforms are more likely to turn to this program for loan coverage. The ultimate measure of FOGAPE’s success will be an increased SME portfolio among commercial banks.

> 36 See: <u>http://www.prochile.cl/servicios/exportar_paso_a_paso/incentivos5.php.</u>

20
**Table 12. Trade Partners Likely to See the Largest Increases in Exports to Belarus**

|**Partner**|**Change in Import Value**<br>**(US$, millions)**|**Percentage of Total 2012**<br>**Belarus Imports**<br>**from Partner**|**Percentage of Increase in**<br>**Belarus Import Value**<br>**Explained by Increase in**<br>**Imports from Partner**|
|---|---|---|---|
|EU-27*|127.1|1.4|79.1|
|_Of which:_||||
|Poland|40.1|3.0|24.9|
|Germany|30.5|1.1|19.0|
|Lithuania|20.1|5.4|12.5|
|Czech Republic|5.8|1.3|3.6|
|Netherlands|5.1|1.1|3.2|
|Belgium|4.1|1.3|2.6|
|Hungary|3.7|2.7|2.3|
|Latvia|3.5|2.4|2.2|
|France|3.0|0.7|1.9|
|Slovak Republic|2.4|1.8|1.5|
|China|16.4|0.7|10.2|
|Turkey|6.0|1.8|3.8|
|HongKong, China|2.3|164.8|1.4|
|Brazil|2.3|1.5|1.4|
|Serbia|1.9|2.6|1.2|

*No data on trade with Belarus were available for Austria, Denmark, and Italy. _Source:_ World Bank.

> RUSSIA’S TRADE POLICY CHALLANGES – THE WORLD BANK GROUP 39
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