rafmacalaba/datause-encoder-data huggingface dataset:
established for managing liquidity risk; (iii) oversee the implementation and maintenance of management information and other systems that identify, measure, monitor, and control the bank's liquidity risk; and (iv) establish effective internal controls over the liquidity risk management process.

# **G.  Stages of Funding Crisis**

34. Anticipating as soon as signs of deterioration of liquidity appear, management must take immediate remedial action under certain constraints. However, managers are also subject to the requirements of their objective function, which is to maximize the bank’s return to shareholders (within certain risk tolerance). For the latter reason, the initial actions to restore liquidity (Stages 1 and 2 in Chart 7, must attempt to minimize the negative impact on earnings. From Stage 3 onwards, the bank’s management is under increasing pressure to take actions which privilege liquidity acquisition over short-term return considerations. Stage 4 indicates that desperate management measures are required as liquidity reserves can dry up very fast, particularly if there is distrust in the bank. Possible supervisory corrective actions would be triggered and if the bank’s franchise is still valuable and the bank can be rescued, a supervisory approved plan should be put in place, including some LOLR liquidity support (if the bank still has franchise value and high quality collateral).

**Chart 7:**

Stages of a Funding Crisis <!-- Start of picture text --> Liquid<br>Funds<br>Stage 1: No Problem Stage 2: Minor Impairment Stage 3: Mounting Troubles Stage 4: Fall Off the Cliff •Too late to enhance<br>Ordinary course of business. •The full extent of the problems may be hidden More or more events cause reductions in confidence liquidity•If you built up in Stage 2<br>Normal funding of •Trigger more frequent reporting •Too late to add new term & moved fast in Stage 3<br>liquidity gaps •Good relationship management funding you have a big parachute–<br>and communications can help you will fall slowly & have<br>•Need to implement asset time to cure underlying<br>•Stock liquidity can be built sales problems or sell<br>•Liability terms can be •Need to trigger other •If you built a small<br>lengthened contingent plans parachute, you will fall<br>•New term funding sources can fast. Bye ...<br>be added.<br><!-- End of picture text --> Source: Matz, Leonard, ‘Liquidity Risk--New Lessons and Old Lessons’, _Sungard, Bancwire Risk Monthly_ , September 2007.
**Table 3a AVE protection that U.S. goods exports face by destinations, 2007** <u>(percent)</u>

||India|US|NAFTA|EU27|LAC|Japan|China|MENA|ASEAN|SAFTA|SSA|ROW|Total|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Agriculture|26.5|n.a.|3.3|3.7|10.2|13.3|11.3|5.3|5.1|5.7|5.1|67.8|16.4|
|Processed agriculture|54.1|n.a.|11.0|14.6|13.0|30.0|10.5|17.6|9.3|17.0|17.2|20.1|15.7|
|Coal, oil, gas and other mining|13.1|n.a.|0.0|0.0|1.6|0.0|0.7|1.1|1.7|5.4|3.4|0.5|0.3|
|Textiles and apparel|15.0|n.a.|0.0|7.6|8.1|7.0|8.5|5.4|8.1|9.3|19.2|6.9|4.6|
|Other light manufacturing|14.8|n.a.|0.0|2.2|13.4|3.8|4.0|6.1|5.2|15.5|15.5|3.8|2.7|
|Paper and mineral products|13.7|n.a.|0.0|1.0|7.8|0.2|2.6|4.6|4.4|10.4|8.0|2.1|1.8|
|Chemical, rubber and plastic products|10.8|n.a.|0.0|2.3|7.4|1.5|142.5|4.4|3.5|8.7|5.3|3.2|11.9|
|Iron, steel and metal products|16.8|n.a.|0.0|1.6|9.8|0.9|4.2|3.8|3.0|10.0|8.8|1.0|1.8|
|Petroleum and coke products|15.0|n.a.|0.0|1.9|5.0|0.2|6.0|2.6|1.8|13.9|7.3|2.3|2.4|
|Transport equipment|4.9|n.a.|0.0|3.3|8.3|0.0|6.8|3.0|3.0|7.7|9.4|4.0|2.4|
|Electronics and machinery|9.7|n.a.|0.0|1.1|6.8|0.1|3.3|3.8|0.9|8.6|4.3|2.3|2.0|
|Other manufacturing|15.0|n.a.|0.0|1.2|14.3|1.7|13.0|2.4|78.9|10.8|15.4|5.1|5.7|
|Total|9.9|n.a.|0.7|2.3|7.6|5.0|29.8|4.3|3.3|8.5|7.2|7.9|5.3|

**Table 3b AVE protection that Indian goods exports face by destinations, 2007** <u>(percent)</u>

||India|US|NAFTA|EU27|LAC|Japan|China|MENA|ASEAN|SAFTA|SSA|ROW|Total|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Agriculture|n.a.|1.0|3.3|3.0|11.0|3.2|36.8|6.8|8.4|11.2|10.5|27.6|12.6|
|Processed agriculture|n.a.|2.2|5.7|10.9|11.5|3.2|9.7|7.0|6.6|10.9|14.6|11.6|8.6|
|Coal, oil, gas and other mining|n.a.|0.0|4.2|0.0|2.5|0.2|0.1|2.4|2.5|8.3|5.2|0.2|0.3|
|Textiles and apparel|n.a.|9.1|16.9|7.9|13.6|5.4|5.3|9.5|9.2|8.8|18.3|7.4|9.2|
|Other light manufacturing|n.a.|3.8|8.6|2.2|11.6|12.8|5.9|8.8|4.4|14.6|17.5|4.3|3.8|
|Paper and mineral products|n.a.|1.5|5.2|0.5|8.8|0.0|4.5|5.9|6.6|15.2|12.7|3.8|4.5|
|Chemical, rubber and plastic products|n.a.|0.7|3.6|0.5|7.0|0.1|5.5|6.3|2.7|7.8|6.8|3.4|3.6|
|Iron, steel and metal products|n.a.|0.1|4.4|0.3|8.1|0.9|3.2|3.2|3.9|6.1|13.2|2.2|3.0|
|Petroleum and coke products|n.a.|0.7|7.1|0.0|0.8|0.5|5.8|7.6|3.0|13.7|7.8|3.2|4.7|
|Transport equipment|n.a.|0.0|24.0|2.4|16.4|0.0|8.3|7.9|17.1|14.8|7.7|4.6|7.1|
|Electronics and machinery|n.a.|0.2|3.6|0.1|8.7|0.0|4.3|5.2|2.9|6.9|5.8|3.0|2.6|
|Other manufacturing|n.a.|0.0|1.1|0.7|7.1|0.2|2.9|4.0|0.6|14.5|17.1|0.5|1.6|
|Total|n.a.|2.7|8.9|3.0|6.5|1.2|3.9|5.6|4.5|10.9|10.1|4.8|4.8|

_Source_ : GTAP 8 Database. Notes: n.a. = not applicable.

12
wages are significantly higher compared to those of unprotected informal workers. In the informal sector, wages are more aligned (Table 5.2).

Table 5.2: Average Annual Earnings for Permanent and Temporary <u>Workers in Disperse Rural Areas of Argentina, 2003 (AR$)</u>

||Permanent|Temporary|Formal|Informal|
|---|---|---|---|---|
||Tota|l Sample|||
|Self-employed|4,325.2|1,441.7|7,895.1|2,602.5|
|Wageworker|3,811.4|1,507.7|4,122.8|2,061.8|
|Source: Own calcul|ation based on RH|S 2003.|||

Male wages are highest in industry, followed by the public administration and agriculture (Table 5.3). Services pay the lowest wages both to the male and female workers. The highest female wages are paid in the public administration followed by agriculture. Table 5.3: Average Salary for Wageworkers and Self-employed by Gender Disperse Rural Areas <u>of Argentina, 2003 (AR$)</u>

||Male<br>Wageworkers|<br>Self-<br>employed|Fema<br>Wageworkers|le<br>Self-<br>employed|
|---|---|---|---|---|
|Agriculture &<br>Livestock|3,018.8|3,283.3|3,742.3|1,174.932|
|Industry|3,650.4|1,335.8|1,513.9|4,834.8|
|Services &|1,729.5|4,295.5|1,475.6|1,729.5|
|Commerce|||||
|Public|3,483.9|NA|4,037.6|NA|
|Administration<br>Other|2,258.2|1,649.6|2,400.0|600|

Notes: Exchange rate US$=3 Argentinean pesos (approximation). Source: Own calculation based on RHS 2003.

Formal sector workers receive not only higher wages than do the informal workers; they also receive benefits. More than half of the formal sector workers receive some form of benefits. Of the individual benefits, bonuses and paid holidays are the most important; 37 percent of the workers receive this benefit.

Child labor still exists in dispersed rural areas of Argentina, but at much lower extent than other countries in the region. International evidence shows that child workers tend to be poor and complete fewer years of schooling than their nonpoor counterparts. Children should not have to work, but an estimated 250 million children are working worldwide. Data reveal that 4 percent of children in Mendoza and Chaco worked in 2003.

19
difference between treated and control investors is driven by treated investors being significantly more likely to ask prevention-focused questions to _all_ startups – directionally, even more so for those with all-male teams. This provides some evidence in support of H1 and H2, suggesting that if investment organizations systematize inquiry by prompting investors to ask startups about risk and reward, investors will inquire more consistently across all startups, and reduce gender disparities in evaluation outcomes.

As shown in Table 3, regression analysis including startup controls showed similar results. Control investors were less likely to take a startup with female founders through due diligence compared to a startup with an all-male team, while treated investors were equally likely to take a startup through due diligence, regardless of founder gender. Control investors were only 65 percent as likely to increase their score by one unit (i.e., from agree to strongly agree to take the startup through due diligence) for startups with female founders compared to all male teams. However, treated investors were equally likely to do so (0.65 main effect multiplied by the 1.63 interaction).<sup>9</sup> This provides more evidence in support of H1. Regression analysis suggests that systematizing inquiry by prompting investors to ask about risk and reward increased the likelihood that investors would ask a preventionfocused question to all startups (by 265%), but that the likelihood increased less for startups with female founders.

These results provide evidence that simply prompting an investor to ask founders about “potential for growth” and “how this venture will mitigate risks” meaningfully affected the types of questions that investors posed to all startups, but particularly those with male founders. For example, the male founder of a platform startup that used mobile technology to connect handymen with work

> 9 Common to many experiments, we focused our design for this experiment on isolating the effects of gender on investment decisions (score), and the effect of our treatments. As an investment decision, we expect other unobservable preferences – including the weather – to add noise (e.g., Dushnitsky & Sarkar 2022).

20
tradables) adjusts in order to maintain a fixed level of foreign savings (i.e. the external balance is held fixed in foreign currency.

# _3.2.5 Agricultural water use and shadow prices_

As mentioned earlier, the model disaggregates agriculture across a number of crops and WMAs. It also separates field crops into irrigated and rainfed production. Since almost all horticultural production takes place under irrigation, around one-fifth of SA’s agricultural land is irrigated (see Table 6). Amongst field crops, irrigation is most prevalent for higher-value crops, such as cotton, tobacco, sugarcane and fodder, and lowest for maize and oil crops. Irrigated land also produces substantially higher yields, with average irrigated maize yields twice those of rainfed maize. The model is calibrated to capture these differences in production levels and yields across crops and regions (i.e. WMAs).

In order to incorporate irrigation water into the model, it is necessary to identify the productivity effects of water on crop yields. This study extended the approach and results of Hassan and Mungatana (2006) to include additional crops modeled in the SAWSAM and updated their estimates of the value of marginal product (VMP) of water using 2002 market output prices. This approach used experimental research trials’ data from SA’s Agricultural Research Council (ARC, 2000) which measure the amount of water needed to achieve different yield levels for a variety of crops to estimate the following quadratic form water-yield response function: Where is output of crop _i_ per hectare of land (in kilograms) and is the amount of water used to produce this level of output (in millimeters)<sup>19</sup> .

All regressions used Ordinary Least Squares and a number of the production functions produced statistically significant results, some of which are reported in Table 7. These coefficients were then applied to the average yields reported in the 2002 Census of

> 19 The ARC data represent national average results obtained under optimal irrigated crop management conditions. Hence these estimates of the effects of water on yield do not reflect variations of climatic, soil and other production conditions in different WMA’s.

18
# **7.1 Tables**

Table 1: COVID-19 and Electricity Consumption in Rwanda

|||||logof|Consumpti|on|||
|---|---|---|---|---|---|---|---|---|
||A|ll|Resid|ential|Non-Res|idential|Small& Me|dium Industries|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|I (Mar-Dec 2020)|0.026***|0.024***|0.046***|0.044***|-0.090***|-0.095***|0.072***|0.070**|
||(0.001)|(0.001)|(0.001)|(0.001)|(0.002)|(0.002)|(0.027)|(0.027)|
|I (Mar-Dec 2019)|0.016***<br>(0.001)|0.016***<br>(0.001)|0.015***<br>(0.001)|0.014***<br>(0.001)|0.033***<br>(0.002)|0.033***<br>(0.002)|0.050**<br>(0.025)|0.057**<br>(0.024)|
|I (Mar-Dec 2018)|-0.002**|-0.003***|-0.001|-0.002***|0.007***|0.003*|0.131***|0.129***|
||(0.001)|(0.001)|(0.001)|(0.001)|(0.002)|(0.002)|(0.034)|(0.034)|
|DID|0.019***|0.017***|0.039***|0.038***|-0.110***|-0.113***|-0.018|-0.023|
||(0.001)|(0.001)|(0.001)|(0.001)|(0.002)|(0.002)|(0.032)|(0.032)|
|Climate Ctrls|No|Yes|No|Yes|No|Yes|No|Yes|
|Customer FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|District X Year FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|R-squared|0.858|0.858|0.849|0.849|0.856|0.856|0.719|0.720|
|Mean dep. Var|27.552|27.552|20.426|20.426|67.526|67.526|256.741|256.741|
|Obs|21352142|21352142|17097530|17097530|3212773|3212773|13345|13345|

Notes: Dependent variable is the log of monthly electricity consumption. I (Mar-Dec 2020) is an indicator variable equal 1 for the period March 1- December 31 2020. Likewise, I (Mar-Dec 2019) and I (Mar-Dec 2018) are indicator for the same period March 1- December 31 in 2019 and 2018 respectively. The reference group is the period Jan-Feb in 2018, 2019, and 2020. DID estimates are derived from the expression _DID_<sup>_ω_</sup> = _β_<sup>ˆ</sup> 2020<sup>_ω−_</sup><sup><u>1</u></sup> 2 �2019 _k_ =2018<sup>_β_ˆ</sup> _k_<sup>_ω_.Climate Ctrls include the monthly</sup> average temperature and total precipitation. Standard errors clustered at district level in parenthesis. _∗_ Significant at 10 percent level _∗∗_ Significant at 5 percent level _∗∗∗_ Significant at 1 percent level 27
- L43 -

FlVU l.C3 FARM INCOME UNDER FIXED RESOURCES (oftI twof SW CeiIe} <!-- Start of picture text --> 240<br>am<br>300 o<br>30<br>no<br>300<br>no<br>go<br>- 3002~a 2032<br>o 30 + No0 o 30MO~ 0<br><!-- End of picture text -->
# 1. Introduction

In 2013, the per-capita number of broadband internet subscriptions in the US was 0.3. In India, the corresponding number was 0.01. In high income countries, over 75% of households owned a computer; in low and middle income countries, the corresponding number was only 27.6%.<sup>1</sup> These disparities suggest that the proliferation of information and communication technologies (ICT) has been vastly different in rich and poor countries. At the same time, a substantial literature documents that ICT was one of the key drivers of growth in advanced economies (e.g., Colecchia and Schreyer, 2002; Basu, Fernald, Oulton and Srinivasan, 2003; Bloom, Sadun and Van Reenen, 2012). In light of this, the dramatic cross-country differences in ICT abundance across countries may seem alarming.

Motivated by these observations we address two important questions: first, is ICT capital truly scarce in low income countries relative to other capital? And second, if so, what can explain this? Notice that our emphasis on the ICT stock _relative to other capital_ is important, since poor countries not only have fewer internet subscriptions and PCs but also fewer refrigerators and cars.

We begin our analysis by compiling a new dataset that allows us to measure ICT and nonICT (henceforth NICT) capital stocks for a sample of 70 countries at various levels of economic development.<sup>2</sup> Using these data we document that, in low income countries, the value of ICT capital indeed represents a smaller share of the aggregate capital stock. In fact, the differences are even larger in real terms, as we document that ICT capital goods are relatively more expensive in low income countries. This suggests that the scarcity of ICT in developing countries is likely not explained simply by lower capital-labor ratios.

What else can explain the relationship between income per capita and the abundance of ICT capital? One potential mechanism relates to delayed technology adoption. Due to frictions in learning and adopting new technologies, low income countries may be slower to accumulate ICT capital (e.g., Comin and Hobijn, 2010; Gust and Marquez, 2004). A second explanation relates to

> 1These numbers are based on the World Telecommunication/ICT Indicators database provided by the International Telecommunication Union (ITU). For more information on this datasource see Appendix A.

> 2To the best of our knowledge, our dataset is the most comprehensive account of ICT _stocks_ around the world to this date. See Section 2.1 for a comparison with other international datasets containing information on ICT.

2
**<u>Table 1. Household characteristics by region and time</u>**

||**All**|**North **|**1982**<br>**West**|**East**|**South **|**All**|**North **|**1999**<br>**West**|**East**|**South **|
|---|---|---|---|---|---|---|---|---|---|---|
|**Basic Characteristics**|||||||||||
|Household size|6.97|7.49|7.27|7.33|6.03|6.01|6.71|6.04|6.38|5.16|
|Members < 14 years|2.47|2.70|2.74|2.57|1.94|1.85|2.16|2.01|1.93|1.34|
|...14 – 60 years|4.15|4.37|4.21|4.45|3.77|3.74|4.06|3.64|4.08|3.41|
|Head's age|50.05|51.50|49.68|48.60|49.95|49.30|49.87|48.42|48.31|50.40|
|Head’s education (yrs).|1.92|1.64|1.45|2.10|2.62|4.99|5.43|4.24|6.00|5.01|
|Land owned (ha)<sup>a</sup>|1.47|1.56|2.10|0.78|1.00|0.77|0.65|1.02|0.53|0.70|
|Landless (%)<sup>a</sup>|20.99|19.83|18.06|23.08|24.34|25.29|26.43|23.14|27.57|25.76|
|**Consumption & income**|**(Rs.)**||||||||||
|Per capita income|1514|1874|1448|1313|1383|2707|3099|2180|2172|3310|
|Per capita exp.|1275|1453|1195|1080|1311|1640|1839|1536|1675|1575|
|**Income shares (%)**|||||||||||
|Agricultural production|59.50|63.13|69.48|49.50|49.72|51.40|58.62|54.27|36.63|49.24|
|Wage employment|17.90|9.93|14.35|20.13|27.59|26.76|15.53|27.35|31.29|33.41|
|Salary & self-employ’t|19.59|22.46|15.56|27.66|18.01|18.78|24.23|15.19|30.60|12.34|
|**Asset ownership**|||||||||||
|Value of all assets|15906|23330|14916|9406|13899|61367|76666|65464|35426|56394|
|Finance & off-farm (%)|11.26|7.68|14.94|11.41|9.96|20.67|19.18|26.46|9.86|20.42|
|Farm & livestock (%)|22.33|24.89|26.32|18.83|17.25|18.12|15.20|17.59|16.90|22.02|
|House & durables (%)|66.41|67.43|58.75|69.76|72.79|61.21|65.61|55.95|73.24|57.57|
|**Community characterist**|**ics**||||||||||
|Per capita income Gini.|0.32|0.35|0.24|0.34|0.36|0.37|0.39|0.38|0.37|0.33|
|Per capita land Gini|0.63|0.63|0.60|0.61|0.67|0.67|0.67|0.67|0.65|0.68|
|Emp. guarantee scheme|85.38|92.42|86.51|78.08|81.63||||||
|Access to banks (%)|74.90|69.77|80.99|83.85|67.96|87.93|98.46|83.98|93.93|80.38|
|No of observations|3816|923|1268|520|1105|5932|1430|2035|856|1611|

<u>a Figures given in the 1982 column refer to dynasty endowments.</u> b All values are in 1982 Rs. (1 US $ = 9 Rs.). Values for 1999 have been deflated by state level deflators. _Source:_ Own computation from NCAER ARIS/REDS surveys 19
ages 0-4 to 35-39).

To align our model moments with data moments, we make several adjustments. Our definition of informality is based on establishments’ registration status with SAT and IMSS in the last year of the census. Therefore, when calculating moments related to the plant-size distribution in the model, we classify retiring entrepreneurs according to their operational status: those retiring as informal entrepreneurs are counted as informal plants, and similarly for formal plants. The mean size of informal plants is defined as follows: where _n_<sup>0</sup><sup>_,F_</sup> ( _z, j_ ) represents the labor demand of an informal entrepreneur with initial ability _z_ at age _j_ . In Equation (15), the numerator represents the total labor demand by informal entrepreneurs across all eight generations, while the denominator represents the fraction of informal entrepreneurs in equilibrium.

To maintain consistency with the data, we focus on entrepreneurs who remain in their initial sector (formal or informal) when calculating growth rates. The growth rate of informal plants from age 0-4 to 35-39 is defined as: Hence, _g_<sup>_I_</sup> represents the average growth rate of informal establishments, analogous to our empirical measure.<sup>20</sup> The growth rate for formal entrepreneurs follows a similar definition.

Table 3 shows the calibrated parameters whereas the targeted moments and their calibrated counterparts are shown in Table 4. The model matches the overall mean size and smaller informal mean size very successfully. It also matches the growth rates of informal and formal plants closely. Moreover, the model doesn’t have any problem capturing the large concentration of employment in bigger plants and the large fraction of plants with fewer than 10 workers.

> 20The growth rate measured in the cross-sectional data differs from the average growth rate of individual establishments, both in the data and the model. The growth rate in the cross-section would be <u>�</u> _zz_ 01 _,F,F_<sup>_n_0</sup><sup>_,F_(</sup><sup>_z,_8)</sup><sup>_f_(</sup><sup>_z_)</sup><sup>_dz_</sup> defined as <u>�</u> _zz_ 01 _,F,F_<sup>_n_0</sup><sup>_,F_(</sup><sup>_z,_1)</sup><sup>_f_(</sup><sup>_z_)</sup><sup>_dz−_1.</sup>

18
Policy ReseaRch WoRking PaPeR 4629

# **Abstract**

The Addis Ababa Integrated Housing Development Program aims to tackle the housing shortage and unemployment that prevail in Addis Ababa by deploying and supporting small and medium scale enterprises to construct low-cost housing using technologies novel for Ethiopia. The motivation for such support is predicated on the view that small firms create more jobs per unit of investment by virtue of being more labor intensive and that the jobs so created are concentrated among the low-skilled and hence the poor. To assess whether the program has succeeded in biasing technology adoption in favor of labor and thereby contributed to poverty reduction, the impact of the program on technology usage, labor intensity, and earnings is investigated using a unique matched workers-firms dataset, the Addis Ababa Construction Enterprise Survey. The data are representative of all registered construction firms in Addis and were collected specifically for the purpose of analyzing the impact of the program. The authors find that program firms do not adopt different technologies and are not more labor intensive than non-program firms. There is an earnings premium for program participants, who tend to be relatively well-educated, which is heterogeneous and highest for those at the bottom of the earnings distribution.

This paper—a product of the Poverty Reduction and Economic Management Department, Africa Region—is part of a larger effort in the department to support evidence based policy making. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at cruggeriladerchi@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