rafmacalaba/datause-encoder-data huggingface dataset:
difficulties. It could lead to an over-parameterized specification, and hence to imprecise and unreliable estimates of the contribution of the individual infrastructure indicators. In our framework this is a concern not only for the usual reasons of multicollinearity – indeed, several of the infrastructure indicators we shall use are fairly highly correlated --<sup>13</sup> but also because, as described below, we shall use a nonlinear procedure to estimate the parameters of the production function. In these conditions, a parsimonious specification with relatively few regressors is much more likely to result in stable estimates robust to alternative choices of initial values.

For these reasons, we follow a different strategy. We use a principal component procedure to build a synthetic index summarizing different dimensions of infrastructure.<sup>14</sup> We focus on three key infrastructure sectors: telecommunications, power and road transport. This choice is consistent with previous literature on the output impact of infrastructure, which has typically focused on one of these individual sectors, most often telecommunications. The synthetic infrastructure index is the first principal component of three variables measuring the availability of infrastructure services in these three sectors. Specifically, the variables underlying the index are:

- (a) _Telecommunications_ : Number of main telephone lines, taken from the International Telecommunications Union‘s World Telecommunications Development Report CDROM. As a robustness check, we also experiment with an alternative measure , namely the total number of lines (main lines and mobile phones), from the same source.

- (b) _Electric Power_ : Power generation capacity (in Megawatts), collected from the United Nations‘ _Energy Statistics_ , the United Nations‘ _Statistical Yearbook_ , and the U.S. Energy Information Agency‘s _International Energy Annual_ .<sup>15</sup>

- (c) _Roads_ : Total length of the road network (in kilometers), obtained from the International Road Federation‘s World Road Statistics, and complemented with information from

> 13 For instance, in our panel data set the full-sample correlation between the total number of phone lines (main and mobile) and overall power generation capacity is 0.92, while the correlation between total road length and overall power generation capacity is 0.65, and that between road length and main telephone lines is 0.61. In turn, the correlation between paved (as opposed to total) road length and power generation capacity is 0.83, while that between paved road length and main telephone lines is 0.84.

> 14 A similar approach is employed by Alesina and Perotti (1996) in their analysis of investment determinants, and by Sánchez-Robles (1998) to assess the growth effects of infrastructure.

> 15 The International Energy Annual (IEA) is the Energy Information Administration‘s main report of international energy statistics, with annual information on petroleum, natural gas, coal and electricity beginning in the year 1980. See webpage: http://www.eia.doe.gov/iea/

7
5. **_We argue that medium term fiscal policy should usefully be guided by an approach similar to “permanent” or sustained fiscal adjustment scenario outlined here._** This approach results in a feasible, sustained fiscal adjustment, returning fiscal policy on a long-term sustainable path within a reasonable amount of time. But the transition to that scenario will require coordinated and _sustained_ revenue, expenditure, and financing measures. The size of adjustment in each year will also have to be realistic in order to avoid backsliding.

**Table 4: Assumptions for fiscal adjustment scenarios**

|**_AFTER CRISIS with Fiscal adjustm_**<br>Fiscal data (% of GDP)|**_ent 2010-_**<br>**2008**|**_2012(tem_**<br>**2009**|**_porary)_**<br>**2010**|**2011**|**2012**|**2013**|**2014**|**2015**|**2016**|**2017**|
|---|---|---|---|---|---|---|---|---|---|---|
|Non-oil total revenue and grants|27.90|17.50|19.50|21.50|23.50|24.50|25.50|26.50|27.50|27.50|
|Non-oil primary expenditure and<br>net lending|33.70|33.50|32.50|31.50|30.50|31.50|32.50|33.50|34.50|35.00|
|**Size of fiscal adjustment(% of**<br>**GDP)**||(10.20)|3.00|3.00|3.00|-|-|-|-|(0.50)|
|..o/w revenue side measures|-|(10.40)|2.00|2.00|2.00|1.00|1.00|1.00|1.00|-|
|..o/w expenditure side measures||(0.20)|(1.00)|(1.00)|(1.00)|1.00|1.00|1.00|1.00|0.50|
|**_AFTER CRISIS with Fiscal adjustm_**<br>|**_ent 2010-_**<br>|**_2015 (sust_**<br>|**_ained)_**<br>||||||||
|Fiscal data (% of GDP)|**2008**|**2009**|**2010**|**2011**|**2012**|**2013**|**2014**|**2015**|**2016**|**2017**|
|Non-oil total revenue and grants|27.90|17.50|19.50|21.50|23.50|24.50|25.50|26.50|27.50|27.50|
|Non-oil primary expenditure and<br>net lending|33.70|33.50|32.50|31.50|30.50|31.50|31.50|31.50|31.50|31.50|
|**Size of fiscal adjustment(% of**<br>**GDP)**||(10.20)|3.00|3.00|3.00|1.00|1.00|1.00|-|-|
|..o/w revenue side measures<br>|-|(10.40)<br>|2.00<br>|2.00<br>|2.00<br>|2.00<br>|1.00|1.00|-|-|
|..o/w expenditure side measures||(0.20)|(1.00)|(1.00)|(1.00)|1.00|-|-|-|-|

_Source: World Bank staff estimates. Data for 2008 is based on the data from the Ministry of Finance._

# **How Fast Does Russia Accumulate Net Debt Under Alternative Scenarios?**

**_With different fiscal strategies specified, we run the fiscal sustainability model to derive illustrative baseline projections for net public debt throughout the projection period._** Note that as explained in the annex, this framework also incorporates the dynamics of the oil fund assets and consequently, an oil fund rule to deal with the large but volatile oil revenue in Russia. In Figure 19, we present results of fiscal sustainability analysis for the fiscal strategies outlined in the previous section. The diagram shows the net debt to GDP ratio, including the Oil Stabilization Fund stock as a negative item, under the various spending programs. The starting point is a negative number: the Oil Fund and other foreign exchange assets exceed the gross public debt by a substantial margin reflecting Russia’s de facto _net creditor_ position.

24
<!-- Start of picture text --> Figure 2. Georgia has Low Utilization of Surgical<br>Procedures Per Year Per 100, 1998-2004<br>8<br>7<br>6<br>5 Georgia<br>4 EU<br>3 CIS<br>2<br>1<br>0<br>1998 1999 2000 2001 2002 2003 2004<br>year<br># surgical cases per 100<br><!-- End of picture text -->

**Data source:** WHO European Health for All Database (HFA-DB)

# **3. THE MEDICAL ASSISTANCE PROGRAM FOR THE POOR**

Despite a series of attempts to address systemic issues in the health sector since the mid-1990s, the results have been limited. Responding to the situation, the government has recently launched an ambitious four-year plan aimed at improving equity and access to essential health services, especially for the poor and the vulnerable. These reform efforts focus on four main areas: (i) improving access to health services through involvement of private investment; (ii) prioritizing limited public funds to finance health care for the poor and other vulnerable groups; (iii) channeling public health financing through private health insurance companies, and (iv) strengthening the regulatory role of the Ministry of Labor, Health and Social Affairs (MoLHSA). In this context, in mid-2006 the government launched the MAP, which provides a benefit package targeted to the extremely poor.

# **_Beneficiary Selection_**

The beneficiaries of the MAP were selected based on welfare scores derived from a proxy means test. This proxy means test was developed by the Social Assistance and State Employment Agency (SASEA) in 2005 as the primary tool for the targeted social safety net program. The test uses about 80 variables to estimate a household’s welfare status. Each applicant (household) receives a welfare score based on the information collected from the survey. Initially, households with scores below 100,000 were entitled to health insurance coverage under the MAP. However, in early 2007, due to budget constraints, the government changed the eligibility threshold, requiring a score of 70,000 or below to be eligible. In 2007, about 650,000 individuals had received their Medical Assistance Card (Chanturidze, Chitashvili et al. 2007), roughly 15 percent of Georgia’s total population and 50 percent of that country’s poor<sup>4</sup>

> 4 Based on HIES data and using a consumption-based measure of poverty, approximately 30% of the Georgian population (4.3 million) was considered poor in 2006, and 12% were classified as extremely poor.

4
Table B13: Summary of relative variance contributions, alternative choices of π, children attending school only

|||Ken|ya|Tanz|ania|Uga|nda|
|---|---|---|---|---|---|---|---|
|||�<br>�||�<br>�||�<br>�||
||π|σ.<br>/σ�<br>|(s.e.)|σ.<br>/σ�<br>|(s.e.)|σ.<br>/σ�<br>|(s.e.)|
|Individual|0|0.29|(0.06)|-0.24|(0.05)|1.01|(0.09)|
||0.5|0.18|(0.06)|-0.28|(0.05)|0.84|(0.08)|
||1|0.17|(0.06)|-0.28|(0.05)|0.91|(0.08)|
|Household|0|29.08|(0.21)|38.26|(0.31)|29.16|(0.27)|
||0.5|16.21|(0.16)|19.14|(0.22)|16.04|(0.20)|
||1|13.04|(0.14)|14.20|(0.19)|12.68|(0.18)|
|School|0|11.02|(0.21)|7.93|(0.20)|10.44|(0.27)|
||0.5|17.22|(0.26)|16.22|(0.29)|17.30|(0.34)|
||1|32.87|(0.36)|37.85|(0.44)|33.03|(0.47)|
|Sorting|0|-0.62|(0.04)|-1.47|(0.07)|0.96|(0.06)|
||0.5|5.11|(0.11)|7.02|(0.16)|6.72|(0.16)|
||1|-2.01|(0.07)|-2.99|(0.10)|-0.50|(0.05)|
|Residual|.|59.14|(0.38)|54.88|(0.47)|57.14|(0.46)|
|Total|.|100.00|(0.29)|100.00|(0.37)|100.00|(0.35)|

Note: the table sets out the relative variance contribution (in percent) attributable to each component of the test score, where ‘sorting’ is the contribution of the between-factor covariance; different initializations of the partitioned iterative algorithm are indicated by column _π_ ; standard errors are reported in parentheses, calculated using the asymptotic approximation due to Ahn and Fessler (2003). Source: own calculations.

58
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# **7. Conclusion**

This working paper constructed poverty trends in the Islamic Republic of Iran during 2008-2014 using international poverty lines expressed in USD 2011 PPPs. The constructed estimates reveal three distinct periods regardless of the choice of poverty line (4-10 USD 2011 PPP daily), the way the welfare aggregate is constructed and adjusted, and whether income or expenditure is used. We distinguished three periods: The first period is between 2008 and 2009 when both poverty and inequality were increasing, the second period is between 2009 and 2012 when poverty and inequality were declining, and the third period spanning from 2012 to 2014 exhibited increasing poverty and inequality.

Proposed exclusion of durables and health components from welfare aggregate increases poverty rates measured at 5.5 USD 2011 PPP by 2-3 percentage points, but does not affect the trends. Correcting the welfare aggregate for spatial differences in food prices has a minimal impact on poverty rates. However, spatial adjustment for differences in nonfood prices substantially narrows the welfare gap between regions and rural and urban areas given the high share of nonfood expenditures and large variation in rents used to spatially deflate the rent component of the welfare aggregate.

Ownership of selected durables was constructed across the distribution of the preferred spatially adjusted welfare aggregate. The better-off households were naturally found to have better access to cars, computers and dishwashers, which can be viewed as supporting evidence that the constructed the welfare aggregate appears to be sorting households in the way one expects.

Finally, as an additional robustness check, income-based welfare measures were constructed. Income poverty is higher than expenditure poverty, probably because of underreporting. Nevertheless, trends in both series are qualitatively very similar.

21
poverty rates based on the Full Sample Direct Estimation. But, estimates from Subsample Imputation appear to outperform those of Subsample Direct Estimation, particularly for urban areas.

**Figure 1: First 30 Simulations for Subsample Imputation and Subsample Direct Estimation**

<!-- Start of picture text --> National<br>50 50 Rural (410 households)<br>45 45<br>40 40<br>35 35<br>30 30<br>25 25<br>20 20<br>1 4 7 10 13 16 19 22 25 28 1 4 7 10131619222528<br>Replication Replication<br>Imputation Imputation<br>Direct Direct<br>Full Sample (mean) Full Sample (mean)<br>Full Sample (95% CI) Full Sample (95% CI)<br>50 Urban (230 households)<br>45<br>40<br>35<br>30<br>25<br>20<br>1 4 7 10 13 16 19 22 25 28<br>Replication<br>Imputation<br>Direct<br>Full Sample (mean)<br>Full Sample (95% CI)<br>Cumulative mean headcount ratio, %<br><!-- End of picture text --> _Source_ : Authors’ estimations using 2005 HIES data.

10
economies do far better than the world average of the corresponding income group. Australia, Canada, Korea, New Zealand Singapore, and the USA have achieved a particularly high level of e-government readiness. In Papua New Guinea as well as Vietnam much work remains to be done in order to increase the government’s use of the internet and to build up a comprehensive information technology infrastructure.

Finally, the GCR asks one question that captures the extent of favoritism in administrative decisions. The question is as follows:

- When deciding upon policies and contracts, government officials (1 = usually favor well-connected firms and individuals, 7 = are neutral among firms and individuals)

We argue that excessive liberty for administrators to favor particular firms signals a lack of transparency. The results of the GCR on favoritism are summarized in the last column of Figure 3. New Zealand and Singapore stand out as economies in which favoritism is perceived as almost absent. Chile shows the strongest performance in their income group. Favoritism seems to severely impact business in other economies, especially several developing member economies.

# **_3.2 Measures of Simplification_**

Simplification in the trading environment aims at organizing all procedures and actions involved in import and exports in the most efficient way. In many economies, the flow of goods and services remains hindered by overly burdensome customs regulations, insufficient use of modern technology in customs, or by other shortcomings. Inefficiently organized and administrated customs procedure cause additional costs when selling or buying goods and services on international markets and can therefore severely impede the competitiveness of firms.

The annual _Doing Business_ Report of the World Bank collects, among other data, detailed data on trade facilitation measures that relate to the concept of simplification. For 15
<!-- Start of picture text --> Mainly Govt<br>Mainly Local 9%<br>25%<br>Mainly Foreign<br>36%<br>Equally Shared<br>22% Forgn+Govt<br>8%<br><!-- End of picture text --> Figure 1 (b): _Predominant ownership of reporting banking system (percent of reporting jurisdictions_ (Notes: Mainly government (foreign, private) means more than 60% of total assets in this form; Foreign+Government means these two together hold more than 70%. Equally shared is a residual category. Source: World Bank Regulation and Supervision Survey) 15
in the figure, prior to the crisis, FDI in synthetic Ghana tracks FDI in Ghana very closely. The average (absolute) difference in the number of FDI projects between Ghana and its synthetic counterpart in the pre-crisis period is just -0.34. However, as seen in the figure, the path-plot of the two units begins to diverge during the crisis period (2013-2016) and even persists in the post-crisis period (2017). Interestingly, the results show a consistent fall in the number of FDI [excl. energy & construction] projects in Ghana between 2013 and 2016, rising marginally in 2017.

The gap (difference) in FDI between Ghana and its counterfactual in the pre/post period are shown in Figure 5. In 2014 (a year into the crisis), the gap between the number of non-energy FDI projects in Ghana and its synthetic counterpart is marginal (8%). However, in 2015 (two years into the crisis), we observe a significant dip in FDI flows to the non-energy sectors in Ghana relative to its synthetic counterpart. For instance, in 2015 a total of 28 FDI projects in the non-energy-and-construction sectors were implemented in Ghana compared to an estimated counterfactual of 49.3. This represents 43% reduction in the number of FDI projects in the non-energy-and-construction sectors for the year. There was a slight improvement in 2016 with the gap reducing to 12%. The negative effects of the crisis persisted in 2017 – a year after the crisis was officially declared over, with a 27% reduction in the number of non-energy FDI project inflows. Overall, how did the power crisis affect FDI inflows? The estimates from the synthetic control analysis indicate that between 2013 and 2016, the power crisis on average resulted in a reduction in the number of non-energy sector FDI by 12.3% per annum.

To assess the statistical significance of the gap between the observed FDI in Ghana and its synthetic counterpart, I follow the approach of Abadie et al. (2010, 2015) in constructing an “exact p-value”. The approach involves estimating iteratively the SCM for each country in the donor pool (i.e., the control countries) and obtaining the distribution of the respective placebo effects. Based on these placebo effects, the ratio of the root mean square prediction error (RMSPE) in the post-treatment period to the RMSPE for the pre43
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private sector as ratio of GDP - and its two components – Enterprise Credit to GDP and Household Credit to GDP. We also present the relative share of enterprise and household credit in total bank credit. Whereas Bank Credit to GDP was 14% over the sample period in Russia, it was 160% in Switzerland. Enterprise Credit to GDP varied from 9% in Argentina to 86% in Malaysia, while Household Credit to GDP varied from 3% in Russia to 99% in Switzerland. Whereas Canada, Denmark and the U.S. had a household credit share well over 70% of total bank credit during 1994-2005, the household credit share was 17% in Poland and Egypt and 10% in Malaysia during the same period.

The correlations in Table 2 indicate that both Enterprise Credit to GDP and Household Credit to GDP are positively and significantly correlated with Bank Credit to GDP. As banking sectors develop, however, the share of household credit increases, as can be seen from the positive and significant correlation of Household Credit Share with Bank Credit to GDP. Similarly, while economically more developed countries have higher ratios of both enterprise and household credit to GDP, the relative importance of household credit increases.

Economic development can influence the provision of household credit both through the supply and demand channels. On the one hand, rising incomes will allow a larger share of households to overcome the threshold of minimum loan size for consumer and mortgage loans (Beck, Demirguc-Kunt and Martinez Peria, 2007). On the other hand, the cost of financial service provision declines with economic development (Harrison, Sussman and Zeira, 1999). Both trends should increase the share of household credit in total bank lending. Figures 1 and 2 illustrate the positive correlation between economic and banking sector development and the importance of household credit. Specifically, we show the average Bank Credit to GDP ratios and the shares of household credit in total credit across three different groups of economic development. In particular, we divide countries into three groups, according to the 33<sup>rd</sup> and the 66<sup>th</sup> percentile of GDP per capita in the sample. Bank Credit to GDP increases with GDP per capita, from 25% to 58% to 94%. We observe a similar pattern with regards to the share of household credit. Whereas household credit is 40% of total credit in the lowest third, it is 63% of total credit in the middle third of countries, and 72% in the richest third. Thus, the _share_ of household credit is greater in countries that are more developed financially and economically.

We also observe sizeable variation in the household credit share over time for the countries where historical disaggregated credit data are available. While most of the countries in 5
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