rafmacalaba/datause-encoder-data huggingface dataset:
# **References**

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38
Our analysis is based on cross-sectional household-level data taken from the 2010 wave of the _Life in Transition Survey_ . The 2010 LiTS survey covers eleven of the 22 countries in Europe and Central Asia which experienced a banking crisis between 2008 and 2011. Among these countries are five highincome countries from Western Europe and six middle-income countries from Eastern Europe / Central Asia. In each of these countries the survey covered between 900 and 1,583 households. We assess the impact of the crises on households using available indicators on changes in household income, consumption, education expenditures and health expenditures during 2008/2009. The advantage of the LiTS data is that it provides us with comparable data on the impact of the recent crises for a representative sample of households in a large number of countries. However, the crosssectional nature of the data limits the extent to which the correlations observed in the data may be interpreted as causal relationships. Thus, the results presented in the paper should be interpreted with caution.

In line with studies of financial crises in the 1990s our analysis shows that the impact of the recent banking crises on households occurred mainly through the labor market channel, and mainly through reduced wage income as opposed to job losses. Income reductions were more significant in the middle-income countries of Eastern Europe than in the high-income countries of Western Europe, leading to a more significant reduction in basic consumption. In Eastern Europe, urban and highincome households were more likely to experience income shocks, but seem to have smoothed these out through a reduction in non-essential consumption. In the West, rural households seem to have been hit harder by the crises than urban households.

We contribute to the literature by examining potential macro-level, meso-level and micro-level mitigators of the impact of banking crises on households. At the macro level, our analysis suggests that a flexible monetary regime may have mitigated the impact of banking crises on households: Households which were hit by an income shock were less likely to cut back on consumption in those countries which had an independent monetary policy and a floating currency than in countries which are part of a currency union or have pegged currencies. At the meso level, we find that foreign bank ownership does not seem to have amplified the crisis impact on households. In line with evidence from previous crises our analysis suggests that at the micro level, the diversification of household income, access to informal and formal credit as well as the build-up of a stock of non-financial assets are important stabilizers of household specific income shocks. The importance of income diversification and non-financial assets is similar in high-income and middle-income economies, while access to informal credit is an important stabilizer only in middle-income countries.

5
# **Figure 4: Banking sector risks in EMDEs**

- A. NPLs (% Total gross loans) 2000‐2017

<!-- Start of picture text --> 25 25 pctl<br>median<br>20<br>75 pctl<br>15<br>10<br>5<br>0<br>C. Probability of default of financial institutions (%)<br>2000 2017<br>8<br>25 pctl<br>7 median<br>6 75 pctl<br>5<br>4<br>3<br>2<br>1<br>0<br>E. Banks’ claims on total government and public<br>non‐financial entities (% of GDP) 2000‐2017<br>16 25 pctl<br>14 median<br>12 75 pctl<br>10<br>8<br>6<br>4<br>2<br>0<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017<br><!-- End of picture text -->

- B. NPLs (% Total gross loans) 2017 regions

<!-- Start of picture text --> 18<br>25 pctl<br>16 median<br>14 75 pctl<br>12<br>10<br>8<br>6<br>4<br>2<br>0<br>AFR EAP ECA LAC MNA SAR<br><!-- End of picture text -->

- D. Probability of default of financial institutions (%) 2017 regions

<!-- Start of picture text --> 3.5 25 pctl<br>median<br>3<br>75 pctl<br>2.5<br>2<br>1.5<br>1<br>0.5<br>0<br>AFR EAP ECA LAC MENA SAR<br><!-- End of picture text -->

- F. Banks’ claims on total government and public non‐financial entities (% of GDP) 2017

<!-- Start of picture text --> 40<br>25 pctl<br>35 median<br>75 pctl<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>AFR EAP ECA LAC MNA SAR<br><!-- End of picture text --> Note: pctl means percentile. Sources: NPLs / Total Loans, FSI IMF; Probability of default, National University of Singapore – Credit Research Initiative; Bank sovereign exposure / Bank assets Fitch Connect; Claims data IFS IMF.

46
Diluting the market share of the monopoly in ICT from 100 percent to 45 percent is expected to reduce consumer prices by 25.3 percent (table 2). In addition, the results of implementing the simulation in multiple steps suggest that the welfare impact of reducing the monopoly’s market share would increase at a declining rate as new telecommunication providers enter the market (figure 2).

**Table 2. Price Changes after Diluting the Market Share of the ICT Monopoly**

|MONOPOLY MARKET<br><br>|STEP 1<br><br>|COMPETITIVE MARKET<br><br>|
|---|---|---|
|MMMsMM(%) = 11<br>η= −1. 5<br>|MPMuMuhMPu(%) = 45<br>η= −0.81<br>|MPMuMuhMPu(%) = 0<br>η<sup>∗</sup>= −0.25<br>|
|<br>p1= 3. 1<br>_Source:_Elaboration based on data derive<br>_Note:_The elasticity is interpreted not on|<br>p1 = 2.24<br>d from the WELCOM Stata Tool and t<br>ly as the sensitivity of current consum|p0 = 1.0<br>he 2015/16 HCES; see CSA 2018.<br>ers of the service, but also as the expected|

_Source:_ Elaboration based on data derived from the WELCOM Stata Tool and the 2015/16 HCES; see CSA 2018. _Note:_ The elasticity is interpreted not only as the sensitivity of current consumers of the service, but also as the expected response to the demand of both current and new users. The changes in the value of the elasticity occur because of the simulation of a large price drop for the item of interest, resulting in a larger magnitude in the quantity demanded and a decline in the absolute value of elasticity.

All current users of ICT services across the consumption distribution are expected to benefit from greater competition in the telecommunication sector, but the gains will disproportionately benefit households in the top consumption deciles because they adopt ICTs at higher rates and spend more on the services, both in absolute and relative terms. In contrast, the effects on poverty reduction among current consumers are expected to be modest because most households at the bottom of the consumption distribution and around the poverty line exhibit low ICT adoption and low ICT expenditures.

**Figure 2. The Market Share of the Telecommunication Monopoly and Consumer Prices**

<!-- Start of picture text --> 0 20 40 60 80 100<br>Market power (market size in %)<br>3<br>2.5<br>2<br>Price (competitive market = 1) 1.5<br>1<br><!-- End of picture text --> _Source:_ Elaboration based on data derived from the WELCOM Stata Tool.

The results indicate that, based on simulating a decline in Ethio Telecom’s market share in mobile services from 100 percent to 45 percent and a price elasticity of demand of −1.50, a shift to a more competitive market would induce a 0.22 percentage point reduction in poverty among current users 11
of the obstacles some Indians face in raising capital for their software startups they actively raised venture capital from U.S. investment firms and organized conferences in the U.S. to heighten the awareness of the potential of India’s software industry<sup>31</sup> . A group of NRIs in the U.S. also founded TIE (The IndUS Entrepreneurs), a mentoring group for technology entrepreneurs in India to network with their counterparts in the U.S. and learn from their experiences<sup>32</sup> . Finally, NRIs were actively involved in lobbying efforts urging the Government of India to revamp its telecommunication policies and other regulations that have impeded the growth of the Indian software industry<sup>33</sup> .

Foreign direct investment accounted for a large percentage of early investment in the sector. For example, in 1996, foreign companies accounted for 70% of the investment in software development in Bangalore.<sup>34</sup> And this contribution understates the true impact. Texas Instruments (TI), the first foreign firm to establish an offshore software facility in Bangalore in 1984 after IBM’s departure, augmented Bangalore’s inadequate land-based telecommunication infrastructure by investing in its own satellite communications network, in conjunction with Videsh Sanchar Nigam (VSNL), the government’s overseas communication agency<sup>35</sup> . Some of TI’s lines were later leased to other software firms, enabling them to expand their India-based operations instead of relying solely on onsite services abroad. Until the government built software technology parks in the 1990s linked to earth stations and other telecommunications infrastructure, TI’s satellite network remained an important driving force behind the offshore development of software exports.

Once U.S. based firms had become interested in India, Bangalore’s reputation for technical excellence and its abundant supply of IT graduates from its 3 universities, 14 engineering colleges and 47 polytechnic schools<sup>36</sup> made it a natural choice for foreign companies to locate their software firms.

The foreign role has been of major importance as it provided much of the infrastructure and international knowledge that allowed Indian firms to exploit

> 31 Kripalani (2000).

> 32 Biers and Dhume (2000).

> 33 Kirpalani (2000).

> 34 The Economist, 1996.

> 35 The telecommunications industry in India is state-controlled, hence the need for TI to procure the services of VSNL instead of a private firm.

39
Income from labor and capital accrue to the representative household. Households’ total disposable income (the sum of factors’ incomes and remittances<sup>16</sup> minus direct taxes and social contributions) is allocated to consumption and savings using the Extended Linear Expenditure System (ELES) specification. This specification allows for the existence of incompressible per capita levels of consumption (e.g. basic education for the youth, health education for the elderly) of the different products. In turn, dependency ratios influence households’ savings rate: the higher the former, the lower the latter.

Once their total value is determined (see below), government and investment demands are disaggregated into demands for products and services at the sector level according to fixed coefficient functions.<sup>17</sup> Import demand results from a CES aggregation function of domestic and imported goods. Export supply is symmetrically modeled as a Constant Elasticity of Transformation (CET) function. Producers decide to allocate their output to domestic or foreign markets in response to relative prices. This last rule similarly applies for migration decisions: workers (both skilled and unskilled) arbitrate between the decision to migrate or stay home depending on the differential between real foreign and domestic labor remunerations, with imperfect substitution between the two reflecting preferences and migration costs.

Several macro-economic constraints are introduced in this model: First, the small country assumption holds: thus, imports and exports prices on world markets are exogenous, as is the foreign remuneration of labor for migrants. Capital flows are endogenous and reactive to the remuneration of aggregate capital (human and physical) over its replacement cost, see below, plus some exogenous trends reflecting the growth in world’s savings. Remittances are determined in proportion of the Diaspora’s income (with a growth in remunerations abroad set exogenously), and hence the trade balance (exports minus imports) is also endogenous to insure the equilibrium of

> 16 Thus, remittances are fully fungible with other sources of income, as not assigned to any particular use. 17 Typically, government expenditures are constituted of government services (other services), investment expenditures in physical capital of equipment goods (industry) and construction services (other services), while investment expenditures in human capital only comprise education services. The latter, to deliver its services, employs mostly skilled labor (teachers), whose wage bill constitute half of total production costs in the education sector (and 3/5 of its value added). See the Social Accounting Matrix in Annex 1.

7
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Kaplan, S. and Zingales, L. 1997. “Do investment-cash flow sensitivities provide useful measures of financing constraints?” _Quarterly Journal of Economics_ , 112, 169-215.

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40
30

# Table X

# Growth and Financial Indicators Pooled Cross-Section Time-Series: Three Stage Least Squares

|Dependent<br>Variable|LLY|BANK|PRIVATE|PRIVY|
|---|---|---|---|---|
|GYP|0.035***<br>(0.006)<br>(0.0011|0.036***<br>(0.011)<br>(0.001]|0.014<br>(0.010)<br>(0.1841|0.035***<br>(0.009)<br>(0.001]|
|R2:|0.47|0.39|0.33|0.54|
|GI|0.027***<br>(0.005)<br>(0.001]|0.034***<br>0.009)<br>[0.001]|0.011<br>(0.008)<br>(0.1871|0.032***<br>(0.008)<br>(0.0011|
|R<sup>2</sup>:|0.48|0.54|0.42|0.51|
|_INV_|0.064***<br>(0.018)<br>(0.001]|0.010***<br>(0.031)<br>(0.002]|0.055**<br>(0.026)<br>[0.0441|0.060**<br>(0.028)<br>[0.035]|
|RZ:|0.27|0.18|0.24|0.32|
|EFF|0.030***<br>(0.007)<br>[0.001]|0.035***<br>(0.011)<br>[0.003]|0.005<br>(0.010)<br>(0.660]|0.028**<br>(0.011)<br>(0.012]|
|R<sup>2 </sup>:|0.39<br>(standa<br>(p|0.40<br>rd errors in pare<br>-values in bracke|0.22<br>ntheses)<br>ts)|0.47<br>Observations=169|

* significant at the 0.10 level

** significant at the 0.05 level

*** significant at the 0.01 level

GYP = Real per capita GDP growth rate GK = Real capital stock per capita growth rate INV = Ratio of investment to GDP EFF = GYP - (0.3)*GK LLY = Ratio of liquid liabilities to GDP BANK = Deposit bank domestic credit divided by domestic bank domestic credit plus central bank domestic credit PRIVATE = Ratio of claims on nonfinancial private sector to domestic credit PRIVY = Gross claims on the private sector to GDP Other explanatorv variables: log of initial income, log of initial secondary school enrollment rate, ratio of government expenditures to GDP, inflation rate, and ratio of exports plus imports to GDP.

Instruments: Decade dummy variables, log of initial income, log of initial secondary school enrollment rate, initial ratio of government expenditures to GDP, initial inflation r&te, and initial ratio of exports plus imports to GDP, and the initial value of the financial indicator.
index of economic, social and cultural status in the multi-country analysis of data from the Organisation for Economic Cooperation and Development’s (OECD) Programme for International Student Assessment (PISA) (OECD 2004). Das et al. (2004) use IRT analysis to derive economic status in their study of the determinants of education outcomes in Zambia. They report that the approach yielded similar results to principal components in simulation analysis. Mukherjee (2006) compares an asset index to the latent factor derived from IRT analysis of data from rural India and, once again, finds very small differences in rich-poor gaps in child schooling and in adult literacy.<sup>6</sup> Montgomery and Hewett (2005) use a multiple-indicator multiple-cause (MIMIC) modeling approach in their analysis of the roles of poverty and neighborhood effects in the determination of health in urban areas in poor countries. They specify a structural model in which the latent factor (“living standard” in their terminology) determines the outcome of interest and the set of indicators, and is determined by a set of observed exogenous variables. They estimate the model using a two-step procedure, the first step of which (using maximum likelihood estimation) yields estimates of the parameters of the indicator equations. Under the assumptions of the model, these parameters determine the estimate of the latent factor.<sup>7</sup> Ferguson et al. (2003) use an approach termed hierarchical ordered probit (DIHOPIT) which is similar to the first stage of the Montgomery and Hewett (2005) model. Unlike that approach, however, their parameterization allows for a household random effect. Nevertheless, the basic framework and estimation procedures follow a similar logic. Ferguson et al. (2003) compare the household rankings based on their index, and the ranking based on a principal components-based index, to rankings based on household income and household expenditures in three household survey datasets.<sup>8</sup> They find that the correlations between the indices and expenditures are extremely similar: for example in their dataset from Peru the rank correlation

> 6 While Mukherjee (2006) uses IRT analysis, it is not primarily to extract the latent factor but rather to estimate what the IRT literature refers to as the “difficulty parameter” of each question.  Under the conditions of the theoretical model he lays out, this parameter captures “[asset] specific attributes that determine relative ease of ownership over the entire population [which] is the logarithm of the user cost.”  Intuitively, the exponential of this parameter reflects the “price” of each asset and therefore aggregating using this exponential recovers a quantity he argues is related to the total value of the assets.  While the asset index he derives using this approach differs, he finds that the economic status gradients in child schooling and adult literacy are, once again, similar.

> 7 The authors do not report how this estimate of living standard compares with other approaches.

> 8  However they do not compare the rankings derived from these different aggregation approaches to each other.

9