rafmacalaba/datause-encoder-data huggingface dataset:
# **Table 19: Cross-country correlations between measures of deposit account penetration, loan penetration, and demographic penetration of branches and country characteristics**

This table summarizes the pairwise correlations between measures of deposit account penetration, loan penetration, and demographic penetration of branches and different country characteristics. Each cell displays the pairwise correlation between the variable at the top of the column and the covariate in the corresponding row. The definition of each variable is shown in Appendix 1. ***= significant at the 1% level; ** = significant at the 5% level; * = significant at the 10% level.

||Accounts per<br>thousand adults<br>in commercial<br>banks (logs)|Loans per<br>thousand adults in<br>commercial banks<br>(logs)|Branches per<br>thousand adults in<br>commercial banks<br>(logs)|Branches per<br>squared km in<br>commercial banks<br>(logs)|
|---|---|---|---|---|
|GDP per capita (log)|0.80***|0.86***|0.77***|0.53***|
|Population Density (log)|0.25**|-0.04|0.08|0.79***|
|Gini|-0.16|0.16|-0.25**|-0.45***|
|Inflation|-0.46***|-0.24**|-0.30***|-0.31***|
|Deposit Insurance|0.46***|0.51***|0.38***|0.30***|
|Electricity consumption|0.74***|0.80***|0.70***|0.44***|
|Phone lines|0.84***|0.85***|0.79***|0.59***|
|Road density|0.53***|0.42***|0.49***|0.82***|
|Absence of violence|0.49***|0.46***|0.53***|0.32***|
|Offshore|0.21**|0.20|0.18**|0.37***|
|Concentration|-0.28**|-0.35***|-0.18*|-0.23**|
|Government bank share|-0.18|-0.19|-0.21*|-0.11|
|Foreign bank share|-0.02|-0.03|-0.03|-0.04|
|KYC requirements|-0.18*|-0.05|-0.24***|-0.15*|
|Basic Accounts (Exc.)|-0.17|-0.03|-0.16|0.07|
|Basic Accounts (Req.)|0.11|0.13|0.18**|0.10|
|Documents required|-0.22|-0.05|-0.07|-0.28*|
|Min. Bal. Open checking|-0.44***|-0.41***|-0.46***|-0.33***|
|Checking annual fee|-0.50***|-0.44**|-0.58***|-0.41***|
|Tax incentives|0.28***|0.25**|0.25***|0.12|
|Operations agents (savings)|0.20*|0.20|0.11|0.11|
|Postal Network|0.35***|0.28**|0.29***|0.31***|
|Contract enforcement|-0.47***|-0.32***|-0.43***|-0.28***|
|Creditor Rights Index|0.37***|0.31**|0.25***|0.28***|
|Creditor Inf. Index|0.48***|0.60***|0.52***|0.32***|
|Places to submit loan|0.51***|0.63***|0.52***|0.25*|
|Minimum loan|-0.32**|-0.34*|-0.36**|-0.14|
|Fee consumer loan|-0.17|0.06|-0.02|-0.18|
|Days process loan|-0.27*|-0.24|-0.21|-0.06|
|Operations agents (credit)|0.17|0.22*|0.14|0.10|
|Branch approval|-0.08|-0.09|-0.28***|-0.08|

53
(EAM from its acronym in Spanish.). EAM is a census of manufacturing firms in Colombia that employ at least 10 workers or have an output value about 73,000 USD.<sup>2</sup> It gathers very detailed information about employment levels and costs, asset levels and investment, output and intermediate materials use for production. Starting in the year 2008 it also collects an ICT module where information about internet use and technology can be found. In the sample, 6,999 (48.3%) of the plants have information for every year and were used as a balanced sample to check the robustness of the results. I also use broadband speed data at the state level kindly provided by the Communications Regulation Commission for the years 2008‐2014. The sample used to estimate total factor productivity was a subsample of these firms and it is described in detail in that section.

# ICT use in the Colombian manufacturing sector

In this section I describe the results about ICT use in Colombian firms from the information collected in EAM special modules for the years 2008 through 2014. The pooled sample summary statistics for the five‐year period are discussed. In this section the analysis is broken down by firm size and I report information collected in the ICT module about the use of computers at the firm, internet access, and the type and speed of internet connections.

**Table 1 Distribution of firms by total employment size**

||Full samplep|eriod||
|---|---|---|---|
|Size|Frequency<br>Pe|rcent<br>Cu|m.|
|[0,50]|91,861|72.07|72.07|
|(50,200]|22,776|17.87|89.94|
|(200,1000]|10,382|8.15|98.09|
|>1000|2,438|1.91|100|
|Total|127,457|100||
||Year 200|8||
|Size|Frequency<br>Pe|rcent<br>Cu|m.|
|[0,50]|6,708|69.21|69.21|
|(50,200]|1,884|19.44|88.65|
|(200,1000]|862|8.89|97.54|
|>1000|238|2.46|100|
|Total|9,692|100||

Source: EAM‐DANE 2008‐2014. Author’s calculations.

The top panel in Table 1 shows that 72% of firms in the sample hired up to 50 workers and are considered by most definitions small firms; 18% of firms hired between 50 and 200, 8% between 200 and 1,000 and about 2% of firms hired more than 1,000 workers between 2008 and 2014. It is clear that most firms in the sample are small units of production, and the reader should take this into account when interpreting the results. The bottom panel restricts the sample to the initial year, 2008, where ICT information started to be collected. The main takeaway is the stability in the distribution of firm sizes during the period of analysis.

2 137.2 million COP in the year 2013 and exchange rate of 1,867 COP per USD, which is the average exchange rate for that year.

5
|Congo|30|Argentina|86|MED|
|---|---|---|---|---|
|Brazil|187|Kazakhstan|88|LO|
|Turkey|72|Chile|100|LO|
|Albania|67|Australia'|115|MED|
|China- 1994|46-69|United Kingdom'|184|MED|
|China-1998|94|Netherlandse|188|MED|
|Ukraine|141|Denmark'|189|MED|
|||Switzerland|189|MED|
|United States|113|Sweden|210|HI|
|Japan|162|Hungary|213|HI|
|Germany|157|Uruguay|214|HI|
|France|216|Poland|220|HI|
|Italy|242|Croatia|350|HI|
|Canada|121||||

Note:

- a. IPD is the present value of the accrued rights of pensioners and workers, under old system.

- b. _Sources:_ Kane and Palacios, Finance and Development, June 1996, p. 38. Robert Palacios for Albania, Cheikh Kane for Burkina Faso, Congo, Mali;

- Paul Van der Noord and Richard Herd, Pension Liabilities in the Seven Major Economies, OECD 1993 for OECD Countries. World Bank 1997 for China-1994; Dorfman and Sin 2000 for China-1998 estimates.

- c. Estimates by James based on current public expenditure. Details available upon request. Hungary, Peru and Uruguay are taken from Kane and Palacios, Finance and Development, June 1996, p. 38.

- d. LO: minimum pension guarantee, financed out of general revenues (except Bolivia).

- e. MED: flat (or compressed) public pillar and means-testing, usually financed out of general revenues.

- HI: large eamings-related public pillar, financed out of payroll tax.

- f. Old system featured universal benefits financed out of general revenues, not out of earmarked payroll taxes. Hence, the IPD owed as repayment of past contributions is less applicable.

There are three principle ways to finance transition: cutting the size of IPD, finding special assets, and using taxation or issuing debt.

_Reduce the value of IPD:_

-

- Downsize the old system by reducing benefits, raising retirement age,

- Recognize past service cautiously through a recognition bond to be cashed upon retirement. The face value of the bond can be reduced if workers have confidence in the new system and young workers will be better off even without compensation for their past contributions. Mexico gives workers the right to return to the old system at the time of their retirement, if this would improve their welfare.

- Make only a partial switch to the new defined contribution system. Keep some workers in the old system, thus reducing the financing gap.

- Retain a PAYGO public pillar in the new system.

_Find special revenue sources:_

- Use social security surplus, if there is a pre-existing reserve in the social security system. The U.S. social security trust fund can be used in this way.

- Use Asset-debt swaps. If public enterprises are being privatized, some of the proceeds can be used to pay off the pension debt. Bolivia, Peru and Poland, have used this strategy. Bolivia used proceeds from the

12
type than farmers in other AEZs. For example, in a dry savannah, we expect that farmers are more likely to choose farm types with some livestock, either mixed farms or livestock only farms due to a favorable climate condition to livestock (Seo and Mendelsohn 2008). Differentiating Equation (5) with respect to a climate variable _Zl_ yields: The marginal impact of each climate variable on the probability of each farm type depends upon the climate in each AEZ. It therefore follows that farms in each AEZ will react differently to climate change even though the parameters of the model are not specific to an AEZ.

# **3. Description of Data**

The paper uses a typology of Agro-Ecological Zones developed by the Food and Agriculture Organization as a mechanism to classify the crop potential of land (FAO 1978). The AEZs are defined using the length of the growing season. The length of the growing season, in turn, is defined as the period where precipitation and stored soil moisture is greater than half of the evapotranspiration during this period. The longer the growing season, the more crops can be planted (multiple seasons) and the higher are the yields (Fischer and van Velthuizen 1996; Vortman et al. 1999). FAO has classified land throughout Africa using this AEZ concept.

The economic data for this study were collected by the national teams in ten countries across Africa. South Africa and Zambia were sampled from Southern Africa, Kenya and Ethiopia were from East Africa, Senegal, Niger, Burkina Faso, Ghana, and Cameroon were from West Africa, and Egypt was chosen from North Africa. Data from Zimbabwe could not be used due to the political turmoil in that country during the survey period. In each country, districts were selected so that they represent a broad range of climates in that country. In each selected district, a survey was conducted of randomly selected farms. The sampling was clustered in villages to reduce sampling costs (Dinar et al. 2008). Once the surveys were cleaned, a total of 8500 observations remained.

The economic surveys were then matched with climate data. The temperature data come 6
- Education: a) Children and teenagers between 6 and 15 must be enrolled in school and attend classes at least 85% of the time; b) Students between 16 and 17 must attend school with a frequency of at least 75%.

In cases where the households have children and adolescents at risk of or removed from child labor by the Program to Eradicate Child Labor (PETI), they must attend a special service provided by PETI and obtain the minimum frequency of 85% on a monthly basis. In the event of non-compliance, additional services are provided to these families to support them to meet the requirements; however, in the case of continued non-compliance, they may have the benefit of the Bolsa Fam ́ılia suspended or canceled.

Table 2 shows the number of BFP beneficiaries according to regions in Brazil. The number of beneficiaries increased from 6.57 million households in 2004 to 13.35 million households in 2011. The average number of members in households that received the program between 2008 and 2009 was 4.65 (median 4.00), and the average number of children and teenagers in these households was 2.25 (median 2.00).<sup>52</sup> This means that in 2009, approximately 57.5 million individuals, including almost 27.8 million children benefited from the transfers.

|**Region**|**2004**|**2005**|**2006**|**2007**|**2008**|**2009**|**2010**|**2011**|
|---|---|---|---|---|---|---|---|---|
|North|528|698|1,024|1,082|1,076|1,286|1,348|1,477|
|Northeast|3,320|4,246|5,443|5,574|5,445|6,208|6,455|6,826|
|Southeast|1,731|2,325|2,876|2,848|2,637|3,105|3,186|3,296|
|South|701|987|1,027|956|854|1,096|1,064|1,036|
|Center-West|292|445|597|584|546|677|725|718|
|Total|6,572|8,700|10,966|11,043|10,558|12,371|12,778|13,352|

Table 2: Number of BFP beneficiaries by region (in thousand of households) Source: Ministry of Social Development (2012). The number of beneficiaries and the amount of transfer by state are available in section 8.1 of the appendix.

Table 3 shows that the amount of transfer to the households grew from R$ 834 million in 2004 (in values of 2011) to R$ 1.6 billion in 2011. This increase of 92% in real terms is mainly explained by the rise in the number of beneficiaries (103% in this period).

Graph 1 shows the share of total BFP transfers to GDP<sup>53</sup> (left side) and the share of treated households by region (right side). Besides illustrating the relevance of BFP, graph 1 also provides information on the regional heterogeneity in Brazil, which is complemented by graph 2 that shows differences among regions in terms of population distribution, GDP and GDP per capita.

> 52According to the POF 2008-2009.

> 53The period up to 2009 is constrained by the information available for regional GDP.

20
jective measure is based on the self-reported difficulty of financing for the operation and growth of the business. In 2002 and 2005 respondents are separately asked to assess the degree of difficulty in access to (“e.g., collateral required or financing not available from banks”) and cost of financing (“e.g., interest rates and charges”), ranging from 1 (“No obstacle”) to 4 (“Major obstacle”). In 2009 and 2013, these two questions are merged into one and respondents are asked about the concerns due to access to financing. Also the scale of the importance of the constraint is different and varies from 1 (“No obstacle”) to 5 (“Very severe obstacle”). We harmonize the scales across years and construct the variable as follows: The subjective and objective measures of being capital constrained are significantly correlated. This evidence is consistent with Hallward-Driemeier and Aterido (2009) and Gorodnichenko and Schnitzer (2010) that relate subjective financing constraints to the amount of credit issued to the private sector, expressed as a percentage of GDP.

However, the objective measure of credit constraint may suffer from endogeneity bias. Firms may be credit unconstrained because banks extend them credit (supply-side channel) or because they do not demand credit as demand falls (demand-side channel). To disentangle these two channels we propose two instruments for the objective measure: the share of domestic credit provided by the banking sector (as a percentage of GDP), and the share of foreign banks out of the total number of banks in each country at time _t_ and _t −_ 1. Both instruments capture the health of the financial market and are correlated to whether or not a firm may receive credit but exogenous to the firm’s export decisions.

Credit constraints may have an important impact on firm exports in ECA countries, especially given the large presence of foreign banks in the region. Based on data from Claessens and van Horen (2012), Figure 2 shows that the ECA region stands out in terms of presence of foreign banks. As European banks are the largest sources of FDI in the banking sector of ECA countries, this initial wave of transmission of credit shocks produced immediate effects on the credit markets of the countries we study.<sup>7</sup> There is indeed growing evidence that banks transmit credit shocks

> 6Alternative possible answers to question (1) are “Application procedures for loans or lines of credit are complex”, “Interest rates are not favorable”, “Collateral requirements are too high”, “Size of loan and maturity are insufficient”, “It is necessary to make informal payments to get bank loans”, “Did not think it would be approved”.

> 7Giannetti and Laeven (2012) show that the collapse of the global market for syndicated loans during financial crisis can in part be explained by a flight home effect whereby lenders rebalance their loan portfolios in favor of domestic

7
- 26 -

- (iii) Normalizing household consumption by the number of persons makes no allowance for the likely variation in consumption needs between different persons, according to (for example) age or gender. There is a large literature on the construction of equivalence scales which allow the normalization to take account of such differences in household composition, though it remains that there is no single ideal method of dealing with this

- issue.<sup>21</sup> Future work on measuring poverty in Indonesia might fruitfully address this issue, but for present purposes we shall follow past practice.

- (iv) The use of consumption rather than income is generally desirable in th's setting, since incomes fluctuate more and households can save. It can be argued that consumption expenditures for a single date will provide a better measure of a household's standard of living than current income. However, it is also of interest to examine the effects on incomes, as this is where many of the immediate consequences of macroeconomic adjustment are felt.<sup>22</sup>

- (v) The SUSENAS aims to provide a random sample of the entire population. However, it is probably difficult to survey certain types of households, and a disproportionate number of these may be poor; individuals without fixed abode are an obvious example. Though we do not have any evidence to support the view, it is not implausible that the itinerant urban poor in large cities such as Jakarta are not well represented in the SUSENAS. If there has been an increase in the numbers of such persons during the adjustment period then this will bias downward our
12 Figure 3: Participation in ATP1 in the first two years, by agroclimatic seasons <!-- Start of picture text --> Participation in ATPI<br>180 6000<br>160 - W--U- Groups 5000<br>140 Producers<br>120 4000 i<br>o U ~~~~~~~~~~~~3000<br>802<br>60 2000<br>40119 7et 10<br>O -I -O<br>Pilot Primera Postrera Primaera<br>96 96 97 (est)<br><!-- End of picture text --> Table 3: Producers' agreed upon, and actual payments, and payment rates for ATPl services

|**Region**|||**Payments**|**(Cordobas**|**)**||
|---|---|---|---|---|---|---|
|||_Postrera_19<br>bv 6/30/96|95<br>|_Prim_|_era + Postr_<br>by 1/30/|_era_1996<br>97|
||Agreed<br>upon|<br>Actual<br>payment|% paid|Agreed<br>upon|<br>Actual<br>paymen|<br>% paid|
|Al|3258|1457|44.7|23260|12170|52.3|
|A2|2056|1594|77.5|18742|11561|61.0|
|B3|3970|2820|71.0|34108|27675|81.0|
|B5|1410|1410|100.0|5225|1908|36.0|
|C6|1061|158|14.9|N/A|N/A|N/A|
|Total|11755|7439|63.3|81335|53314|65|

Source: for _Postrera_ 1995: Keynan et al., (1997); for _Primera_ and _Postrera_ 1996: Garcia, (1997a).

Note: N/A= Not available.
|**Table6.2DaysLostPerCase**in**Mexico**<br>(unlessstated otherwise)|**CityfortheGe**|**neralPopulation**|
|---|---|---|
|_Endpoints_|_Dayslost'_|_Source_|
|_Hospitaladmission_|||
|<br>Respiratory|8|ExternE(1999,2000)|
|Cardiocerebrovascular|45|ExternE(1999,2000)|
|Congestiveheartfailure(elderly)|7|<br>ExternE(1999,2000)|
|_Emergencyroomvisits_|||
|<br>Respiratory|5|ExternE(1999,2000)|
|_Restrictedactivitydays_|||
|<br>Total(adultsandchildren)|ob||
|<br>Work-lossday|I||
|<br>_Minorrestrictedactivitydays_|||
|<br>Total(adults)|0|Assumed|
|_Effectsinasthmatics_|||
|Asthmaattacks|1|ExtemE(1999,2000)|
|Coughwithoutphlegm(children)|1|<br>Assumed|
|<br>Coughwith phlegm(children)|1|Assumed|
|<br>Somerespiratorysymptoms(children)|1|Assumed|
|<br>_Chronicmorbidity_|||
|Chronicbronchitis, additionalcases|7|ExtrapolatedfromERISCAC|
|Chronic cough,prevalence(children)|7|ExtrapolatedfromERISCAC|

a. Including recovery days at home.

b. The loss-of-productivity part is accounted for by the work-loss-day part of RAD.

c. See Cesar and others (2000).

Second, loss of productivity occurs during environmental contingencies (ECs) or alerts. As discussed in section 2.2 these alerts lead to temporary closures in production infrastructure to avoid further air pollution. Here the loss of productivity is measured by estimating the difference in gross national product (GNP) with and without an environmental alerts. A distinction has been made between production losses in the industry and the transport sectors.

# **_Cost of Illness_**

The cost of illness for the different morbidity endpoints is quantified in terms of direct costs for treatment of an illness. These costs are dependent on the social security system. In Mexico the most common health systems are the public health insurance system for uninsured people _(Poblacion Abierta),_ the public health system for low-income employed people (IMSS), and the private health insurance system _(Privado)._ Hemandez-Avila and others (1995) conducted a COI study for Mexico by including the costs of consultations, laboratory tests, and medication. The inflation-corrected numbers they obtained are presented in table 6.3.

_32_
Chart 7. Contribution to World Import Growth <!-- Start of picture text --> Percent<br>15-<br>10 .<br>-5<br>-10<br>72 74 76 78 80 82 84 86 88 90 92 94 96<br>= _ World Import Volume Growth<br>------- Growth Attributed to OECD<br>Growth AKtributed to LMICX*<br>Low and Middle Income Countries xduding Eastem Europe and Forner Soviet Union.<br>Source: Intemational Economics Department, World Bank, August 1996.<br><!-- End of picture text --> Another factor in the recent dynamism of world trade has been the new role of the developing countries, especially in East Asia and Latin America. In 1991-93, output growth accelerated in these two regions despite a slowdown in the world aggregate. East Asia's output grew by an average of 8.7 percent a year in 1991-93, accelerating from 7.4 percent average growth in the preceding decade. In Latin America, output grew an average of 3.2 percent a year during 1991-93, accelerating from 1.2 percent average growth in the 1 980s. In the same period, the rate of import growth in the Low and Middle Income Countries excluding Developing Europe, Central Asia, and the Former Soviet Union (LMlCXs) surged to over 10.6 percent a year, from just 2.2 percent in the preceding decade.

Chart 8 shows changing shares in contributions to world import volume growth by income group. The LMIC share of contribution increased from less than 9 percent in 1986-90 to almost 40 percent in 1991-93. Robust trade growth in Hong Kong, Singapore, and Taiwan accounted for most of the surge in import contribution for nonOECD high income countries, which increased from 11 percent in 1986-90 to almost 30 percent in 1991-93. By contrast, OECD's contribution shrank from over 80 percent to 31 percent across the same periods. The Former Soviet Union (FSU) made a negative contribution of more than 14 percent during 1991-93 (2 percent negative contribution 10
-13-

# 301 2 . MEXMIPLE OF PERFOMRMNCE TARGETS

|Operational Perforance Targets<br>1987|1988|1989|1990|1991|1992|
|---|---|---|---|---|---|
|1. FreightTraffic<br>(billion<br>net<br>ton-km)<br>8.0|8.8|9.8|10.1|10.4|10.7|
|2.Locomotive<br>Availability(t)<br>70|73|76|78|79|80|
|3.Locomotive<br>Utilization<br>(%)<br>67|<br>69|<br>70|<br>72|<br>73|<br>74|
|4. LOco. Productivity (million gross ton-km)64|68|76|84|86|88|
|5. Freight Car Availability(0)<br>91|91|<br>92|<br>92|<br>93|<br>93|
|6. Frsight Car Turnaround Time (days)<br>9.0|8.3|7.5|6.8|6.7|6.7|
|7. Average Wagon Loan (net tons)<br>45|<br>45|<br>45|<br>46|<br>46|<br>46|
|S. Maximum Number of Staff (in '000)<br>20.0|20.0|20.0|19.7|19.5|19.2|

|Fi|nancial Performance Targets|1987|1988|1989|1990|1991|1992|1993|
|---|---|---|---|---|---|---|---|---|
|1.|Working Ratio 1/|.93|.94|.77|<br>.75|.73|.71|.69|
|2.|Fteight usuiness<br>WorkingRatio1/|.93|.84|.75|.70|.67|.64|.61|
|3.|Long-Distance Passenger||||||||
||WorkingRatio1/|1.6|1.5|1.4|<br>1.3|1.2|1.1|1.0|
|4.|Metropolitan<br>Train<br>Working||||||||
||Ratio1/|1.4|1.0|1.0|1.0|1.0|1.0|1.0|
|5.|MaxiLmu Normalisation||||||||
||(t Op. Revenue) 2/|12|12|10|8|7|5|3|
|6.|Maximum Borrowings||||||||
||(*mnt. Gen.Funds)3/|340|4/3404|/ 80|<br>45|<br>40|40|40|
|7.|Collection of Accomunt||||||||
||Receivables<br>(Days)|60|60|60|60|60|60|60|

_JJ_ Including normaliuation payments within the stipulated limits.

_ZI_ Normalization payments for long-distance passenger and freight services only, as a percentage of the corresponding total operating revenues, including payments for the long-distance passenger services to reduce to no more than 25* of long-distance passenger operating revenues in 1993. 3J Excluding Bank loan and funds borrowed from the Federal Government for servicing federally guaranteed debt. _J/_ Based on the 1987-1986 combined results.