rafmacalaba/datause-encoder-data huggingface dataset:
21 CTC's rates of return on networth approached or exceeded 10 percent during the 1960s and 1980s, except in 1982 (figure 10). Under public ownership in the 1970s, CTC's rates of return were even negative early in the decade. As for ENTEL, recent data for the 1980s indicate that the company has had rates of return far higher than any reasonable return on investment in teleconmnunications. This windfall profit is due largely to revenue from international incoming calls.

_Overall change in welfare._ Consumers can be said to be better off the larger the network, the better the quality of service, and the lower the price, and producers to be better off the higher the tariffs and the greater the return on investment. Competition would be expected to reduce returns for competitors, and expansion of the network would be expected to benefit providers of complementary services. An assessment of the welfare impact of regulatory reforms must sum up these effects to reach a bottom line conclusion.

This kind of analysis has been perforned for CTC: the social net present value of CTC under private operation was subtracted from its simulated value under continued public ownership (Galal and others, forthcoming). The analysis concluded that privatization and regulation in the 1980s improved the welfare of Chileans, as well as foreigners (table 3). Consumers were the biggest winners, but shareholders, government, and interestingly, ENTEL (which benefitted from CTC's expansion) also gained.

**Table 3. Welfare effects of privatizing CTC** (1988 net present values; billions of pesos)

|CTC||Net gains|
|---|---|---|
|Under private<br>operation|Under public<br>operation|from<br>privatization|
|Government<br>97|64|33|
|Domestic shareholders<br>19|11|8|
|Consumers<br>1104|588|516|
|Competitors<br>43|27|16|
|Total domestic welfare<br>1263|690|574|
|Foreign Shareholders<br>39|0|39|
|Total world welfare<br>1301|689|612|

Source: Galal and others (forthcoming 1994).
|Food groups, monthly pe<br>|r adult equiv|alent consum<br>|ption, real||||||
|---|---|---|---|---|---|---|---|---|
|Bread and cereals|6,232|13,653|119%|11,851|11,431|16,070|41%|36%|
|Meat|1,869|2,701|45%|7,297|7,120|9,650|36%|32%|
|Fish|1,284|2,418|88%|2,295|2,502|3,396|36%|48%|
|Milk, cheese and eggs|458|1,165|154%|2,929|3,031|4,828|59%|65%|
|Oils and fats|900|1,363|51%|1,396|1,629|1,843|13%|32%|
|Fruit|890|978|10%|4,239|4,465|5,715|28%|35%|
|Vegetables|3,423|7,143|109%|5,238|5,474|7,464|36%|43%|
|Sugar, jam, honey &<br>chocolate|967|991|2%|1,821|1,991|2,294|15%|26%|
|Food products n.e.c.|169|257|52%|273|260|257|‐1%|‐6%|
|Coffee, tea and cocoa|193|151|‐22%|350|360|402|12%|15%|
|Water, soft drinks,|346|222|‐36%|2069|2356|3358|43%|62%|
|juices, etc.||||,|,|,|||
|<br>Food groups, monthly pe|r adult equiv|alent consum|ption, real v|alues in pric|es of the bas|e year (CPI)|||
|Bread and cereals|10,333|13,653|32%|14,185|11,431|12,780|12%|‐10%|
|Meat|3,099|2,701|‐13%|8,734|7,120|7,675|8%|‐12%|
|Fish|2,129|2,418|14%|2,747|2,502|2,700|8%|‐2%|
|Milk, cheese and eggs|759|1,165|53%|3,506|3,031|3,839|27%|10%|
|Oils and fats|1,492|1,363|‐9%|1,671|1,629|1,466|‐10%|‐12%|
|Fruit|1,476|978|‐34%|5,074|4,465|4,545|2%|‐10%|
|Vegetables|5,675|7,143|26%|6,269|5,474|5,936|8%|‐5%|
|Sugar, jam, honey &<br>chocolate|1,603|991|‐38%|2,180|1,991|1,824|‐8%|‐16%|
|Food products n.e.c.|280|257|‐8%|327|260|205|‐21%|‐37%|
|Coffee, tea and cocoa|320|151|‐53%|419|360|320|‐11%|‐24%|
|Water, soft drinks,<br>juices, etc.|574|222|‐61%|2,477|2,356|2,670|13%|8%|

Figure 9 presents the shares of food groups in total food consumption. It shows that Tanzanians tend to consume bread and cereals the most, which make up about one‐third of food consumption. This is consistent across the HBS and NPS surveys. The second most consumed food group is vegetables for the HBS and meat for the NPS.

31
**Exhibit 5. Earnings shocks and debt “at risk” according to the ICR**

_Fraction of total corporate debt, %_ <!-- Start of picture text --> Debt at Risk (ICR)<br>60<br>51.2<br>50<br>40 37.0<br>31.7<br>30<br>21.8<br>19.5<br>20 16.0<br>10<br>0<br>no shock 10% 20% 30% 40% 50%<br>Shock to EBIT, %<br>Note: Sample only includes firms with non-missing ICR and QR.<br>Exhibit 6. Receivables shocks and debt “at risk” according to the QR<br>Fraction of total corporate debt, %<br>Debt at Risk (QR)<br>100 89.7 91.8<br>90 81.3 84.7<br>80 71.7<br>70<br>60.3<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>no shock 10% 20% 30% 40% 50%<br>Shock to receivables, %<br>Debt at Risk, % of total debt<br>Debt at Risk, % of total debt<br><!-- End of picture text --> Note: Sample only includes firms with non-missing ICR and QR.

**Exhibit 6. Receivables shocks and debt “at risk” according to the QR** _Fraction of total corporate debt, %_

Note: Sample only includes firms with non-missing ICR and QR.

**Prior to the pandemic, 63.9 percent of corporate debt across all countries in our sample was “at risk” according to** **_either_ the QR** **_or_ the ICR (** D 1 **) of which 12.4 percentage points was “at risk” according to both (** D 2 **).** Exhibit 7 presents D 1 (“vulnerable”) and D 2 (“very DD vulnerable”) as a function of the magnitudes of simultaneous earnings shocks and receivables shocks. The D 1 is driven by QR and shows the steepest growth as receivables shocks increase DD DD DD when earnings shocks remain relatively low, and vice versa. A 30-30 percent earnings-receivables DD

12
**Figure 4.2:  Growth of Commercial Bank Lending Against Personal and Household Access to Loans**

**Figure 4.3:  Households that Took Out Loans 2000/01-2007**

<!-- Start of picture text --> Commercial Bank Lending to Personal 2001 - 2008 Households that took Out Loans<br>9,000.00 30.0%<br>6<br>8,000.00<br>25.0%<br>7,000.00 5<br>6,000.00 20.0%<br>4<br>Urban<br>5,000.00<br>4,000.00 15.0% 3 y = 2.1x - 1.5 Rural<br>3,000.00 10.0% 2 Tanzania<br>2,000.00 Linear (Tanzania)<br>5.0% 1<br>1,000.00<br>- 2.13 0.0% 0<br>00/01 2007<br>% Households<br>In Tshs 10 bn (2001 Prices)<br>% of Total Private Sector Lending<br>2001 2002 2003 2004 2005 2006 2007 2008<br><!-- End of picture text --> _Source: Bank of Tanzania, 2009 Source: Household Budget Survey, 2007_ 47. The ILFS 2006 data corroborate the finding that access to finance by HEs (especially those in the rural areas) are severely limited. Only 7 percent of operators with HEs as their main activity reported having received credit from anyone – bank or relative - in the last 12 months, against 13 percent of microenterprise owners. Only 9 percent of all households had access to formal credit, most of whom are urban households.

48. Of the HEs who received credit, 48 percent obtained it from relatives, compared to 22 percent of microenterprises. While borrowing from relatives is much more common in rural and secondary urban areas, it is less so in Dar es Salaam, where only 28 percent of HE operators reported it as a source of credit over the last year. For another 30 percent of HEs in Dar es Salaam, credit sources are business associations, NGOs, or donor projects, a much higher proportion than in rural or secondary urban areas. Credit from banks and other financial institutions is considerably lower among HEs, even in Dar es Salaam, than among microenterprises Table 4.4).<sup>14</sup>

**Table ‎4.4:  Credit Sources Among HE Operators and the Microenterprise Operators in the Informal Sector, 2006 (percent)**

||||_HEs_<br>||_M_<br>|_icroenterprises_<br>|
|---|---|---|---|---|---|---|
||Rural|Secondary|Urban|Dares Salaa|m Total|Total|
|Relative or friend|55.6|49.1||27.7|48.2|21.8|
|Rotating savings & credit group (UPATU)|11.3|4.8||9.1|8.6|5.4|
|Savings and credit co-operative (SACCO)|14.1|13.3||15.6|14.1|16.6|
|Business association, NGO, donor project|9.8|13.2||29.2|14.6|11.9|
|Bank or financial institution|2.2|7.4||10.2|5.6|18.9|
|Other sources|6.9|12.1||8.3|9.0|25.3|
|Total|100|100||100|100|100|

_Source: Calculations based on the ILFS 2006 data_ 14 The sample size of non-HEs in the informal sector does not allow for a meaningful disaggregation by area of residence.

24
omitted. By this method, 11% of the 50,115 observations for export unit values were excluded and 8% of the import unit values. The resulting cleaned data set had on average about 294 export and 432 import price observations per country.<sup>13</sup> For measuring the expenditure price level, we used the PPP’s for three categories of final goods (private consumption, government consumption and investment) provided by the latest version of the PWT, so M = 3.<sup>14</sup> Denoting these aggregate prices from PWT by PPPij, we compute the “expenditure price levels” for each country, defined as: where Ej denotes the local currency price of US$ in each country j. Unlike the PPP’s, the price levels are unit free, and indicate how the US$ prices in each country compare to the reference prices, also in dollars. In column (1) of Tables 1 and 2, we report the expenditure price levels.

We next combine the three categories of final goods with i = 4,...,N+3 categories of export and import unit-values, and compute the extended-GK system in (12)-(15). In column (2) we report the relative output price level PLoj , computed as PPPjo / E j . The normalization used in column (2) is identical to that in column (1), i.e. the value of real GDP or real je GDPjo summed over all 151 countries equals the summed nominal value of GDP in US$. The difference between the output price levels PPP and expenditure price levels jo PPPje , and hence between real GDP measured either from the output or the expenditure side, varies highly across countries, as shown in column (9). In Table 1 we report the 51 countries with real GDPje > real GDPjo , and in Table 2 the 100 countries with real GDPje < real GDPjo ; the sample is also divided this way for the summary statistics reported in Table 3.

> 12  These regression results are available on request.

> 13 We also experimented with a looser criteria, omitting only unit values greater than 10 or less than one-tenth. In this case, the percentages omitted unit values drops to 5.4% for exports and 3.2% for imports, but the overall results are similar to those reported here.

> 14  An alternative to using just three categories of final goods (private consumption, government consumption and investment) is to use the benchmark ICP data for 1996, which gives 30 categories of final goods. However, that benchmark data is only available for about 50 countries (for which we also have trade data). By collapsing the 30 final goods into three aggregates, and using those PPP’s from PWT, we instead obtain data on 151 countries.

15
# **Box 4: An illustrative econometric estimation of the components in the nominal lending interest rate**

The decomposition of the nominal lending interest rate is calculated using a panel data regression model with country fixed effects and first-order error structure to adjust for serial correlation. We use the method of Baltagi and Wu (1999) to estimate coefficients. The Baltagi and Wu (1999) method uses feasible generalized least squares (FGLS). We use annual data between 2005 and 2017.

**Independent variables:** the basic model has the following RHS variables:

- <u>Macro-fiscal conditions: inflation rate, inflation volatility (rollover standard deviation of</u> inflation rate for a window of last 12 months), public debt (% GDP), savings rate (% GDP), GDP per capita, and policy interest rate. Sources: IMF-IFS, IMF Global Debt Database, World Bank World Development Indicators.

- <u>Banking sector characteristics: overhead cost (% of total assets), non-performing loans (% of</u> total gross loans), non-interest income (% total income), bank asset concentration for three major banks (% total banking system assets), and private credit (% GDP). Source: World Bank Finstats 2019.

- <u>Business environment: credit bureau coverage (% of adults), time to resolve insolvency (years).</u> and rule of law index. Sources: World Bank Doing Business, and Governance Indicators (Kaufmann et al., 2010).

**Decomposition of interest rate (level):** Once the coefficients are estimated, we attribute each estimated coefficient to a value of each driver. For instance, to decompose the average lending interest rate in 2017, we calculate the average value of each driver for that year across 64 countries and multiply them to the corresponding coefficient. Later, the results of that multiplication are added based on the three main driving groups (i.e., macro-fiscal conditions, banking sector characteristics, and business environment). We put together country-fixed effects and the regression estimated constant as part not explained by the drivers. These calculations determine the components of the lending interest rate (level).

**Decomposition of interest rate (changes)** : We calculate the difference between the average values of the drivers in 2017 vs. the average value of drivers in 2007. Later, we multiply that difference by the corresponding estimated coefficient and add the results using the three driving groups. The final result is the change in the interest rate attributed to changes in a particular driving group between 2007 and 2017.

**Regional decompositions:** We follow the same methodology but adjusting with country fixed effects associated with the countries that belong to the same region.

**Caveats** : The decomposition is based on a panel regression using data between 2005 and 2017. Consequently, non-linear relationships between variables, or regime structure changes are not captured by the model. The estimations use information at country level data so, some bank-level regressors are not included due to no existence of similar variable as a country-level information.

19
|Female Ownership Y:1 N:0|6.148***|5.943***|5.905***|5.880***|5.890***|
|---|---|---|---|---|---|
||(0.616)|(0.652)|(0.652)|(0.653)|(0.653)|
|Skills Obstacle (0-4)|0.600***|0.662**|0.625**|0.620**|0.622**|
||(0.227)|(0.269)|(0.270)|(0.269)|(0.269)|
|How Much Of An Obstacle:|-0.037|0.020|-0.010|-0.004|-0.012|
|Transport?||||||
||(0.193)|(0.209)|(0.209)|(0.209)|(0.209)|
|Firm Purchased Fixed Assets Y:1|-0.940*|-1.076*|-1.042*|-1.017*|-0.999*|
|N:0||||||
||(0.525)|(0.558)|(0.557)|(0.558)|(0.558)|
|Power Outages (days)||-0.168**|-0.151**|-0.154**|-0.152**|
|||(0.069)|(0.067)|(0.067)|(0.068)|
|Courts Obstacle (cell average)||-0.075|-0.644|-0.531|-0.558|
|||(0.765)|(0.740)|(0.737)|(0.739)|
|Crime Losses Y:1 N:0||-0.633|-0.688|-0.693|-0.703|
|||(0.670)|(0.671)|(0.671)|(0.672)|
|Firm Inspected Y:1 N:0||-0.759|-0.811|-0.809|-0.780|
|||(0.602)|(0.603)|(0.604)|(0.604)|
|How Much Of An Obstacle: Tax||-0.216|-0.257|-0.253|-0.269|
|Rates||||||
|||(0.230)|(0.229)|(0.228)|(0.228)|
|How Much Of An Obstacle: Labor||0.018|0.034|0.053|0.057|
|Regulations?||||||
|||(0.301)|(0.301)|(0.300)|(0.300)|
|Population (logs)|||-35.761***|-35.262***|-35.328***|
||||(10.819)|(10.743)|(10.887)|
|Urbanization (%)|||1.857***|1.882***|1.840***|
||||(0.399)|(0.401)|(0.391)|
|WBL Index|||17.226**|17.122**|28.064**|
||||(8.478)|(8.398)|(11.187)|

77
# C. Predicting Road Disruptions

By combining the information on the likely impacts of hazard events on road segments of a particular surface type and gradient level, the accumulated disruption of the East Road over 30 years was estimated for each road scenario. These disruptions are detailed in Table 4.

|Table 4.|Rain event|disruptions|byscenario|Scenario 0. S|tatusQuo(SQ)|||
|---|---|---|---|---|---|---|---|
|Surface|Sloe|Length|Disruption|Disruption|Frequency|Disruption, 30 years|Total disruption 30 years|
||p|(km)|(%)|(km)|(years)|(km)|(km)|
|GR|<6%|22.4|2.5%|0.6|30|0.6|0.6|
|GR|6%-9%|7.2|5%|0.4|10|1.1|18|
|GR|6%-9%|7.2|10%|0.7|30|0.7|.|
|GR|9%-15%|7.3|10%|0.7|3|7.3||
|GR|9%-15%|7.3|15%|1.1|10|3.3|12.1|
|GR|9%-15%|7.3|20%|1.5|30|1.5||
|GR|>15%|4.7|20%|0.9|3|9.3||
|GR|>15%|4.7|30%|1.4|10|4.2|15.4|
|GR|>15%|4.7|40%|1.9|30|1.9||
|Total||41.5||||29.8|29.8|
|||||Scenario 1. Med<br>|ium Design(MD)<br>|||
|Surface|Slope|Length<br>(km)|Disruption<br>(%)|Disruption<br>(km)|Frequency<br>(years)|Disruption, 30 years|Total disruption, 30 years|
|GR|<6%|22.4|2.5%|0.6|30|0.6|0.6|
|GR|6%-9%|7.2|5%|0.4|10|1.1||
|GR|6%-9%|7.2|10%|0.7|30|0.7|1.8|
|BT|9%-15%|7.3|5%|0.4|10|1.2|19|
|BT|9%-15%|7.3|10%|0.7|30|0.7|.|
|CC|>15%|4.7|10%|0.5|30|0.5|0.5|
|Total||41.5||||4.8|4.8|
|||||Scenario 2. Hi<br>|gh Design(HD)<br>|||
|Surface|Slope|Length<br>(km)|Disruption<br>(%)|Disruption<br>(km)|Frequency<br>(years)|Disruption, 30 years|Total disruption, 30 years|
|GR|<6%|22.4|2.5%|0.6|30|0.6|0.6|
|BT|6%-9%|7.2|2.5%|0.2|30|0.2|0.2|
|BT|9%-15%|7.3|5%|0.4|10|1.2|19|
|BT|9%-15%|7.3|10%|0.7|30|0.7|.|
|CC|>15%|4.7|10%|0.5|30|0.5|0.5|
|Total||415||||31|31|
|||.<br>|Sc<br>|enario 3. Steep,Lo<br>|ngSlopes Design(S<br>|.<br>LS)|.|
|Surface|Slope|Length<br>(km)|Disruption<br>(%)|Disruption<br>(km)|Frequency<br>(years)|Disruption, 30 years|Total disruption, 30 years|
|GR|<6%|17.1|2.5%|0.4|30|0.4|04|
|BT|<6%|5.3|N/A|N/A|N/A|N/A|.|
|GR|6%-9%|3.5|3%|0.1|10|0.3||
|GR|6%-9%|3.5|5%|0.2|30|0.2|0.5|
|BT|6%-9%|3.7|2.5%|0.1|30|0.1||
|GR|9%-15%|3.0|5%|0.2|3|1.5||
|GR|9%-15%|3.0|7.5%|0.2|10|0.7||
|GR|9%-15%|3.0|10%|0.3|30|0.3|3.6|
|BT|9%-15%|4.3|5%|0.2|10|0.6||
|BT|9%-15%|4.3|10%|0.4|30|0.4||
|GR|>15%|1.8|10%|0.2|3|1.8||
|GR|>15%|1.8|15%|0.3|10|0.8||
|GR|>15%|1.8|20%|0.4|30|0.4||
|BT|>15%|2.9|2.5%|0.1|3|0.7|5.1|
|BT|>15%|2.9|10%|0.3|10|0.9||
|BT|>15%|29|20%|06|30|06||
|<br>Total||.<br>41.5||.||.<br>9.6|9.6|

18
- Lam, D., “Generating Extreme Inequality: Schooling, Earnings, and Intergenerational Transmission of Human Capital in South Africa and Brazil,” Population Studies Center, University of Michigan, Report 99-439, 1999.

- Litchfield, J.,  “Introductory Text on Inequality Measurement,” World Bank Poverty Reduction and Economic Management Group Internet Site, 1999.

- Londoño, J. and M. Székely, "Persistent Poverty and Excess Inequality: Latin America, 19701995." Office of the Chief Economist, Working Paper series 375, Inter-American Development Bank, 1997.

- Lunde, T., E. Skoufias, and H. Patrinos, “Indigenous peoples in Latin America: Economic Opportunities and Social Networks,” World Bank Policy Research Working Paper 4227, May 2007.

- MacIsaac, D. and H. Patrinos, “Labor Market Discrimination Against Indigenous People in Peru,” _Journal of Development Studies_ , 32(2), 218-33, December 1995.

- Maloney, W. and J. Mendez, “Measuring the Impact of Minimum Wages: Evidence from Latin America,” in _Law and Employment: Lessons from Latin America and the Caribbean_ , J. Heckman and C. Pages (eds), University of Chicago Press and NBER, 2003.

- Medrano, P., C. Sanhueza and D. Contreras, “Inequality in Latin America: a synthesis of recent research on the levels, trends, effects and determinants of inequality in its different dimensions,” The Inter-Regional Inequality Facility, February 2006.

- Morley, S. 2001. _The Income Distribution Problem in Latin America and the Caribbean._ Santiago: Economic Commission for Latin America and the Caribbean.

- Neumark, D., W. Cunningham, and L. Siga, “The Effects of the Minimum Wage in Brazil on the Distribution of Family Incomes: 1996-2001,” _Journal of Development Economics_ , 80(1), 136-59, June 2006.

- Pagan, J., “Gender Differences in Labor Market Decisions in Rural Guatemala,” _Review of Development Economics_ , 6(3), 428-41, October 2002.

- Patrinos, H., “Differences in Education and Earnings across Ethnic Groups in Guatemala,” _Quarterly Review of Economics and Finance_ , 37(4), 809-21, Fall 1997.

- Patrinos, H., L. Lopez-Calva, and R. Bando, “Child Labor, School Attendance, and Indigenous Households: Evidence from Mexico,” World Bank Policy Research Working Paper 3487, January 2005.

22
and cybersecurity.<sup>5</sup> We attempt to control for the factors from these studies in the regressions presented below.

A smaller literature explores the drivers of DFS growth during the COVID-19 pandemic itself. Based on a household survey in India, those who switched to using digital payments during the lockdown were significantly more likely to be aware of digital payments modes, more highly educated, and with access to smartphones and debit cards (Saroy et al., 2022). These are similar to characteristics of DFS users in studies prior to the pandemic who were found to be richer (Cohen and Rysman, 2013; Fujiki and Nakashami, 2019), better educated (Koulayev et al., 2016), and more aware of digital payments and generally more financially literate (Wyman, 2017).<sup>6</sup> Other drivers of DFS adoption differed across the pre-pandemic and pandemic periods. For example, and perhaps not surprisingly, the increase in digital payments during the pandemic depended on social distancing norms and containment measures (De’ et al., 2020, Alber and Dabour, 2020). While studies that predated the pandemic found that younger respondents were more likely to be DFS users, Saroy et al. (2022) found that during the pandemic middle-aged survey respondents in India were the most likely to have become DFS users, and that many of them had earlier tried but subsequently abandoned digital payments. Similarly, Jonker et al. (2022) found that digital payments increased more sharply during the pandemic for older age groups, which had typically been slower to adopt DFS.

> 5 On approaches to regulating DFS, see Staschen and Meagher (2018), Gutierrez and Singh (2013), and Tarazi and Breloff (2010). Gutierrez and Singh (2013) also provide evidence of a positive association between mobile money usage and aspects of the regulatory regime related to e-contracting, consumer protection, and interoperability, among other factors, for a sample of thirty-five countries. On the benefits of digital identification systems, and biometric identification in particular, in contexts where human capabilities (illiteracy and low levels of financial/digital literacy) constrain financial inclusion, see Muralidharan, Niehaus, and Sukhtankar (2016).

> 6 Individual education and income were also significantly associated with broader measures of financial inclusion in studies that used Findex data (see, e.g., Allen et al., 2016; Deléchat et al., 2018).

5