rafmacalaba/datause-encoder-data huggingface dataset:
We see in Figure 1 that the relationship is quite flat at low per capita consumption levels, consistent with the idea of absolute poverty. On the basis of the pattern evident in Figure 1, RCS proposed an international absolute poverty line of $1.25 a day for 2005, which is the mean of the lines found in the poorest 15 countries in terms of consumption per capita.<sup>10</sup> The level of this poverty line is quite robust to the choice of the poorest 15 countries (taking plus or minus five countries ranked by consumption per capita). Focusing on the poorest 15 countries is also consistent with econometric tests reported below in calibrating the weakly relative poverty lines. Of course, there is still a variance in the national poverty lines at any given level of mean consumption, including amongst the poorest countries. The poverty lines found amongst the poorest 15 countries vary from $0.70 to $1.90 per day and RCS estimate the robust standard error of the $1.25 line to be $0.10 per day.

After converting the international poverty line of $1.25 at PPP to local currency in 2005 prices, we convert it to the prices prevailing at each survey date using the best available countryspecific Consumer Price Index (CPI).<sup>11</sup> The weights in this index may or may not accord well with consumer budget shares at the poverty line. In periods of relative price shifts, this will bias our comparisons of the incidence of poverty over time, depending on the extent of utilitycompensated substitution possibilities for people at the poverty line.

Given the steep rise in food prices around 2008, we made extra effort to assure that the price indices we use adequately reflected those increases at country level. This was done in consultation with the Bank’s poverty experts for each country. In some cases, such as India, we are already using CPIs that are anchored reasonably well to consumption behavior of the poor, so nothing needed to be done. However, for 15 countries (including China) for which food prices increased faster than other prices, we determined that the currently available CPI attached too low a weight to food, and we re-weighted the index to assure that its food share accorded reasonably well with food spending patterns in a neighborhood of the poverty line. For another 22 countries, we used CPI’s provided by the Bank’s country offices that were deemed to

> 10 The countries are Malawi, Mali, Ethiopia, Sierra Leone, Niger, Uganda, Gambia, Rwanda, Guinea-Bissau, Tanzania, Tajikistan, Mozambique, Chad, Nepal and Ghana. Consumption per capita for this group ranges from $1.03 to $1.87 per day with a mean of $1.40 per day.

> 11 Note that the same poverty line is generally used for urban and rural areas. There are three exceptions, China, India and Indonesia, where we estimate poverty measures separately for urban and rural areas. For China and India we also use sector-specific CPIs.

7
Many firms in Syria, with the exception of Aleppo, face rising costs in taxation and informal payments to the state or local authorities. Taxation spending and payments to the state or local authorities, either official or unofficial, have increased for 78 percent of all firms. Only 36 firms were willing to answer an amount of the change, but the average change reported was a 152 percent increase compared to pre‐conflict levels. Firms in Aleppo, however, stand out for having a very different experience with regards to tax and unofficial payments. Fifty percent of firms in Aleppo reported a _reduction_ in payments to the state or local authorities since the beginning of the crisis compared to just 1 percent of firms in the rest of Syria.<sup>19</sup> Moreover, only 21 percent of firms in Aleppo reported an increase in payments to the state compared to 86 percent for the rest of Syria (Figure 4).<sup>20</sup> More research would be needed to draw conclusions from these results.

**Figure 4: Percentage of firms reporting an increase in taxation or payments to authorities officially or unofficially, by region**

<!-- Start of picture text --> 100<br> 90<br> 80<br> 70<br> 60<br> 50<br> 40<br> 30<br> 20<br> 10<br> ‐<br>Homs Lattakia Damascus Hama Aleppo<br><!-- End of picture text --> Security spending by surviving Syrian firms also increased although this increase has not been universal. Overall, 44 percent of firms report increasing their security spending since the beginning of the crisis, while 52 percent report no change in spending and 3 percent report a decrease. The average change in spending was 117 percent. Increases in spending, however, were more common in Aleppo (80 percent of firms) than in the rest of Syria<sup>21</sup> with increased spending notably less frequent in Latakkia (26 percent of firms).<sup>22</sup> As is typical in conflict affected contexts, large firms spend more on security, with 75 percent of large firms reporting increased security spending compared to just 38 percent of small and medium firms.<sup>23</sup> The impact on finance

> 19 Statistically significant at the 1% level.

> 20 Statistically significant at the 1% level.

> 21 Statistically significant at the 1% level.

> 22 Statistically significant at the 10% level.

> 23 Statistically significant at the 1% level.

13
Table 7: Vehicle ownership and carbon emissions

|Dependent variable|(1)<br>CO2 emissions per capita<br>from transport(log)|(2)<br>CO2 emissions per capita<br>from transport(log)|(3)<br>CO2 emissions per capita<br>from transport(log)|(4)<br>CO2 emissions per capita<br>from transport(log)|
|---|---|---|---|---|
|Average Number of cars per household (log)|0.355***<br>(0.034)||0.391***<br>(0.058)||
|Average Number of motorcycle per household (log)||0.156***<br>(0.013)||0.300***<br>(0.038)|
|Constant|0.080**|0.182***|-0.858***|-0.860***|
||(0.040)|(0.038)|(0.166)|(0.140)|
|Country FE|Yes|Yes|Yes|Yes|
|Observations|1,646|1,550|193|162|
|Number of countries|194 (IRF data)|194 (IRF data)|19 (LSMS survey)|19 (LSMS survey)|
|R-squared|0.982|0.980|0.947|0.946|

Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

**Extensive margin: rising incomes, vehicle ownership, and CO2 emissions** Figure 20 uses the change in predicted probabilities from Figure 19 and the regression coefficients from Table 7 (Columns 1 and 2), to infer the impact of rising incomes on carbon emissions. Panel (b) in Figure 20 reports the increase in CO2 emissions from the increase in vehicle ownership (car and motorcycle) as income reach their predicted 2035 levels. On average, the change in car (motorcycle) ownership would lead to a 31% (9%) increase in CO2 emissions. Behind these averages, there are large differences across countries driven by the initial level of CO2 emissions and the substitution across types of vehicles.

Figure 20: Income changes and carbon emissions: the vehicle ownership channel <!-- Start of picture text --> (a) Change in vehicle ownership (b) Final change in CO2 emissions<br><!-- End of picture text --> <!-- Start of picture text --> Afghanistan Afghanistan<br>Burkina Faso Burkina Faso<br>Cameroon Cameroon<br>Kyrgyz Republic Kyrgyz Republic<br>Malawi Malawi<br>Mali Mali<br>Mongolia Mongolia<br>Nepal Nepal<br>Nicaragua Nicaragua<br>Niger Niger<br>Nigeria Nigeria<br>Pakistan Pakistan<br>Sierra Leone Sierra Leone<br>Tanzania Tanzania<br>Uganda Uganda<br>0 100 200 300 0 20 40 60 80 100<br>percentage change of predicted probabilities to own a vehicle percentage change of C02 emissions<br>Car Motorcycle Car Motorcycle<br><!-- End of picture text -->

# **5.2 Rising Income, Traveled Distance, and Carbon Emissions**

**The impact of rising income on vehicle use** Rising incomes not only affect vehicle ownership, but also vehicle use, the number of traveled kilometers per car in a given period. Richer households may drive more, in bigger cars, increasing their traveled distance and the resulting CO2 emissions. Hence, vehicle usage is a potential channel through which an increase in income may impact carbon emissions. We use the coefficient from Table 6 such that a 1% in-

37
# Figure A14: Optimal Rate Differentiation: Robustness

- (a) Only Formal Varieties Taxed, Cross price elasticity = 1.5

<!-- Start of picture text --> CD<br>BI<br>BJ<br>CG<br>BR<br>CR<br>BF RW KMCM TN ZA<br>TD TZ PG UY<br>EC ME<br>STSN BO MA SZ<br>MZ DO MX<br>CO<br>PE<br>NE RS<br>CL<br>5 6 7 8 9 10<br>Log GDP per capita, Constant 2010 USD<br>(c) Only Formal Varieties Taxed, Cross price elasticity = 2<br>CD<br>BI BJ<br>CG CRBR<br>BF RW KMCM TN ZA UY<br>TD TZ PG EC ME<br>MZ STSN BO MA SZ DOCO MX<br>PE<br>RS<br>NE<br>CL<br>5 6 7 8 9 10<br>Log GDP per capita, Constant 2010 USD<br>1.4<br>1.2<br>1<br>.8<br>.6<br>.4<br>Ratio Food Rate / Non Food Rate .2<br>0<br>1.6<br>1.4<br>1.2<br>1<br>.8<br>.6<br>.4<br>Ratio Food Rate / Non Food Rate .2<br>0<br><!-- End of picture text -->

- (b) Only Formal Varieties Taxed, Cross price elasticity = 1

<!-- Start of picture text --> CD<br>BI<br>BJ<br>CG<br>BR<br>CR<br>BF TD RWTZ KMCM PG TN ZA<br>ST EC ME UY<br>MZ SN BO MA SZ DOCO MX<br>NE RSPE<br>CL<br>5 6 7 8 9 10<br>Log GDP per capita, Constant 2010 USD<br>1.6<br>1.4<br>1.2<br>1<br>.8<br>.6<br>.4<br>Ratio Food Rate / Non Food Rate .2<br>0<br><!-- End of picture text --> The panels plot for each country the ratio of the calibrated optimal rate on food products relative to the optimal rate on non-food products, as a function of that country’s log GDP per capita. Optimal rates are calibrated using expression (9). A value equal to 1 indicates that both optimal rates are set at the same level, a lower value indicates that it is optimal to subsidize food products relative to non-food products. In all panels we assume only formal varieties can be taxed and allow price elasticities to vary across goods and countries by calibrating values using expression (4) above. We vary the value of the cross-variety compensated elasticity _ε_ ̃<sup>_C_</sup> across the three panels: it is 1.5 (our baseline value) in panel (a), 1 in panel (b) and 2 in panel (c).

65
27. Deacon H, Stephney I. _HIV/AIDS, Stigma and Children: A literature review_ . Cape Town: Human Sciences Research Council; 2007.

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10
# **Tables**

## Table 1: Descriptive statistics from Nigeria GHS

## **Household-level**

||Obs.|Mean|Std. Dev.|Min.|Max.|
|---|---|---|---|---|---|
|Total consumption(Naira)|13,732|103,350|140,576|7,225|6,968,769|
|Total food consumption(Naira)|13,732|69,340|119,846|3,539|6,944,891|
|Total non-food consumption(Naira)|13,732|34,010|52,246|650|1,771,154|
|Poor US$1.90|13,732|0.3423|0.4745|0|1|
|Poor US$3.20|13,732|0.6335|0.4819|0|1|
|Access to medium or strong3 or 4G|13,732|0.5054|0.5000|0|1|
|Access for oneyear or more to 3,or 4G|13,732|0.3699|0.4828|0|1|
|Access for twoyears or more to 3,or 4G|13,732|0.2820|0.4500|0|1|
|Access for threeyears or more to 3,or 4G|13,732|0.2229|0.4162|0|1|
|Access to electricity|13,732|0.4678|0.4990|0|1|
|Number of household members|13,732|5.7250|3.1848|1|31|
|Housingownership|13,732|0.7296|0.4442|0|1|
|Wealth index|13,732|0.1766|1.8824|-2.3963|4.3433|

## **Individual-level (aged above 15 and below 65 years)**

||Obs.|Mean|Std. Dev.|Min.|Max.|
|---|---|---|---|---|---|
|Labor forceparticipation|40,636|0.6185|0.4858|0|1|
|Work outside household|40,630|0.0953|0.2937|0|1|
|Work on household farm|40,608|0.2942|0.4557|0|1|
|Work in own business|40,621|0.2993|0.4579|0|1|
|Access to medium or strong3 or 4G|40,636|0.4876|0.4999|0|1|
|Access for oneyear or more to 3,or 4G|40,636|0.3574|0.4792|0|1|
|Access for two years or more to 3, or<br>4G|40,636|0.2715|0.4448|0|1|
|Access for three years or more to 3, or<br>4G|40,636|0.2146|0.4105|0|1|
|Access to electricity|40,636|0.4677|0.4990|0|1|
|Number of household members|40,636|7.1900|3.5107|1|31|

Table 2: Signal strength thresholds

|Radio technology|Medium Signal<br>strength|Strong Signal<br>strength|
|---|---|---|
|2G|-85 dBm|-73 dBm|
|3G|-91 dBm|-83 dBm|
|4G|-105 dBm|-95 dBm|

17
12 To assess how these myriad changes affected the Slovenia labor market, it is necessary to have data from periods predating the reforms as well as data from periods after the reforms were in place.

# 2. DATA SOURCES

Our main data base is taken from earnings records of the Slovenian Pension and Invalid Fund (SPIF), collected for pension benefit calculation. Three additional data sources are used: the work history of employees covered by the social security system, data on unemployment spells of registered unemployed, and the register of enterprises. The data provide a cross-section of employees for each year from 1987 through 1991.

SPIF collects data on earnings for all workers who are paying contributions to the FundSimilar to other transitional economies, old-age insurance is mandatory so virhually all workers are covered. In our analysis, however, we focus only on wages paid in social enterprises, thus excluding employers who are able to 'adjust" reported earnings of their employees. The main two groups that are excluded from the data set are self-employed and workers in private enterprises. Self-mployed may opt either to underreport their eamings so as to reduce their old-age contribution, or overreport their earnings so as to increase the pension level. Similar opportunities to adjust reported income may exist in private enterprises. Moreover, in our data set only jobs for which contrbutions to social security are paid are included For multiple jobholders, moonlighting or secondary jobs rarely pay into the social security fund, and are thus excluded. For such multiple jobholders, our measure of the wage is the wage in the prnmary job alone.

Observations on work spells were drawn from a random sample of employees representing about five percent of the social sector. The social sector covered 92.6 percent of nonagricukural employment in 1987 and 89.8 percent in 1991. Of the .028 decline in the social sector share, .016 is
heterogeneous information contained in the household and labor force surveys; and iii) combination of the wage subsidy and complementary policies. To deal with parameter uncertainty due to the lack of reliable empirical estimates of the elasticity of substitution among factors of production, we evaluate the impact of wage subsidy over alternative sets of low, medium, and high elasticities. The CGE cum micro-simulation framework has the wage earnings equations and multi-nomial logit functions of occupational choices from the micro data linked to the CGE model like Bourguignon, Robilliard, and Robinson (2002). The link and reconciliation between the two models is essentially a recursive top-down iteration similar to Savard (2006, 2003) and Essama-Nssah _et al._ (2007).<sup>4</sup> The model is used as a “measuring instrument” rather than a forecasting or planning model. By abstracting from other policy issues or the temporal aspects of South Africa’s recent growth (e.g., terms of trade shocks, investment growth etc.), it holds everything else constant and focuses on measurement of the marginal employment effects of a wage subsidy and the sensitivity to alternative degrees of labor market flexibility and to some supporting measures suggested to alleviate the labor market problems. The model does not address the design and implementation elements of a wage subsidy.

**_Organization._** The paper is structured as follows:  section 2 provides an overview of the economic framework, a CGE cum micro-simulation model, with emphasis on its distinctive aspects and structural features imposed to portray the South African economy and its labor market situation; section 3 discusses the simulations and the results as well as suggestions for further research; and section 4 draws general conclusions.

# **2. The Economic Framework and its Applications to South Africa**

The economic framework is an extension of the CGE cum micro-simulation model in EssamaNssah _et al._ , (2007). The top layer is a CGE model for South Africa with data for 2003, using the modeling approach described in Lofgren, Harris, and Robinson (2001). See Kearney (2004) for a detailed description of the model features. The bottom layer is a micro-simulation described in Korman (2006), which pulls together the micro observations of the Labor Force Survey (LFS: 2000) and Income and Expenditure surveys (IES: 2000).<sup>5</sup> In what follows, we focus on the features relevant for analysis of the economy, labor market, degrees of complementarity among labor types and capital, closure rules, and the micro behavior of labor and households.

> 4 A bottom-up iteration is possible but not employed in the present study. A two-way iteration is best used if there are dynamic feedbacks from factor accumulation as well as changes in the demand structure, which are planned for future applications.

> 5 These surveys are nationally representative and conducted by Statistics South Africa. Both surveys are mostly based on the same sample of households; therefore, we combined data from these two surveys using individual’s unique identification code.

- 7 -
# **1. Introduction**

This paper presents and discusses a new deposit insurance database that updates an earlier one constructed in 1999 by Demirgüç-Kunt and Sobaci (2001) and extends it in several important dimensions.

This new comprehensive database includes 14 new countries that have adopted deposit insurance schemes since 1999<sup>1</sup> and identifies 12 other countries<sup>2</sup> that had adopted deposit insurance as of 1999 but do not appear in Demirgüç-Kunt and Sobaci (2001) due to lack of data. Apart from the use of various country sources, we have carried out surveys directed to officials of deposit insurance institutions, central banks, and related government officials around the world. The other important contribution of this dataset is the addition of historical time series (rather than data for year-end 1999 only) for several key variables, including deposit insurance coverage, coverage ratios, and co-insurance. The variables are also expanded to include the level of co-insurance requirements, percentage of the value of deposit covered, and whether the payments are per depositor or per depositor per account. Finally, the dataset incorporates part of the survey data relevant for deposit insurance provided by Barth, Caprio and Levine (2004).

Deposit insurance has become an increasingly used tool by governments in an effort to ensure the stability of banking systems and protect bank depositors from incurring large losses due to bank failures. Almost all countries actually have financial safety nets in place which include explicit and implicit deposit insurance, bank regulation and supervision, central bank lender of last resort facilities, and bank insolvency resolution procedures. Although deposit insurance is gaining in popularity among policymakers, its desirability is debated by many economists who point to the moral hazard problems involved and the accompanying excessive risk taking by banks (see, for example, Demirgüç-Kunt and Kane 2002).

This paper aims to support the recently growing empirical literature that deals with the effects of deposit insurance design on different banking outcomes (for example,

> 1 The new adopters are Albania (2002), Bolivia (2001), Cyprus (2000), Jordan (2000), Malta (2003), Nicaragua (2001), Paraguay (2003), Russia (2003), Serbia and Montenegro (2001), Slovenia (2001), Turkmenistan (2000), Vietnam (2000), Uruguay (2002), and Zimbabwe (2002) where the adoption years are indicated in parentheses.

> 2 These countries are Algeria, Bahamas, Belarus, Bosnia and Herzegovina, Guatemala, Honduras, Indonesia, Isle of Man, Kazakhstan, Liechtenstein, Malaysia, and Thailand.

2
# Figure 4: Food Expenditure Across Countries

## (a) Food Budget Share

## (b) Food Engel Curve Slope

<!-- Start of picture text --> Core Sample β Core Sample<br>Extended Sample β Extended Sample<br>CG<br>CD TD PG M Z<br>MZ CD RW<br>ST<br>BI N E TZ SNKM M AC G SZ RS TD ST PG SZ CRBR<br>BF R BJW BO PM E E MX NE BF BJ KMCM MA TN ZA UY CL<br>CM TN EC BI BO DOCOME MX<br>CO ECPE<br>DO ZA CRUYBR SN<br>CL<br>TZ<br>RS<br>5 6 7 8 9 10 5 6 7 8 9 10<br>Log GDP per capita, Constant 2010 USD Log GDP per capita, Constant 2010 USD<br>(c) Formal Food Budget Share (d) Formal Food Engel Curve Slope<br>Core Sample PG<br>MA SZ<br>BI CDNE MZ BF TD RWTZ BJ ST SNKM CMBO CG TN DOECCORSPEME CRUYMXBR<br>ZA<br>CL<br>SZ<br>ME<br>PG TN RS ZA CRUYMX<br>MZ ST MA CO BR CL<br>BI CD NE BF TD RWTZ BJ SNKMCMBO CG DOEC PE β Core Sample<br>5 6 7 8 9 10 5 6 7 8 9 10<br>Log GDP per capita, Constant 2010 USD Log GDP per capita, Constant 2010 USD<br>5<br>100<br>0<br>80<br>-5<br>60<br>-10<br>40 -15<br>Food Budget Share<br>Food Engel Curve Slope -20<br>20<br>-25<br>0<br>5<br>100<br>0<br>80<br>-5<br>60<br>-10<br>40<br>-15<br>Formal Food Budget Share 20<br>Formal Food Engel Curve Slope -20<br>0<br>-25<br><!-- End of picture text --> This figure combine two sources: data from the core-sample of 31 countries and data from the Global Consumption Database (GCD) which adds 58 developing countries not included in the core-sample. Panel A shows each country’s food budget share, plotted against log per capita GDP. The average food budget share in the core sample is 49%, while the average in the GCD sample is 48%. Panel B shows the countryspecific slope of the food Engel curve, plotted against log per capita GDP. The average slope in the core sample is 12.5, while the average in the GCD sample is 13. The lines correspond to local polynomial fits. GDP per capita is in constant 2010 USD (Source: World Bank WDI). Panels C and D are constructed similarly to Panel A and B, but for formal food expenditure which can only be measured in the core sample.

41