rafmacalaba/datause-encoder-data huggingface dataset:
6 households, offer them cheaper financial instruments to manage their risk, or finance the expansion of more firms that would be using their skills. The more a financial structure would be bank-oriented the narrower income inequalities and the lower poverty would be.

Empirically, most studies looking at the structure of the financial system tend to suggest, however, that it is irrelevant. Beck and Levine (2004), Levine and Zevros (1998), and Rousseau and Wachtel (2000) use indices of stock market development and bank credit to the private sector to measure the finance-growth link. They show that that both banking sector and stock market developments positively influence economic growth. Looking at firm level data, Beck and Levine (2002) and Levine (2002) come to the conclusion that financial structure per se does not importantly explain industrial performance or the creation of new firms.

Arestis, Demetriades, and Luintel (2000), nevertheless, suggest that banks could play a more important role. Using quarterly data and applying time series methods to five developed economies, they show that, while both banking sector and stock market development explain subsequent growth, the effects of banking sector development is substantially larger than that of stock market development. Honohan (2004) shows that bank credit reduces poverty while market indicators (capitalization and turnover) do not seem to have any significant effect.

# **III.   EMPIRICAL ANALYSIS**

# **A.   Sample**

We compiled data for a sample of 47 developing economies over the period 1984-2008. Developing countries are defined as countries classified by the World Bank as low- or middle-income countries. Excluding developed countries from the sample reduces sample heterogeneity. The sample size and the period of study are limited by the availability of data on poverty and stock market indicators. To smooth short-term fluctuations of macroeconomic variables and take into account the fact that yearly poverty indicators may not be available for many countries, we averaged the data over a 5-year period, which gives us up to five data points per country.

# **B.   Econometric specification and definition of variables**

# _The model_

To assess the impact of the financial structure, we adopted a standard poverty model building on previous studies (see for instance Dollar and Kraay, 2002: Honohan, 2004: Guillaumont and Kpodar, 2011; and Singh and Huang, 2011). The model explains poverty by a core set of control variables including income per capita, inflation, trade openness and infrastructure. The baseline model is then augmented with the financial structure and financial development variables.
63

# TABLE 17 TOTAL EMPLOYMENT BY SURVEYED JEPARA FIRMS'

|Size Range of<br>Total<br>Employment|Number of<br>Firms|Total<br>Employment|Percentage<br>Exclusive<br>Subcontractor|Percentage<br>Nonexcluuive<br>s Subcontractor|Percentage In-<br>House<br>s Employees|
|---|---|---|---|---|---|
|1,000 or more|<br>4|9,336|75.1|2.7|22.2|
|500-999|2|1,543|53.0|8.1|38.9|
|200-499|4|1,348|81.6|5.2|13.2|
|100-199|5|701|18.6|15.8|65.6|
|50-99|5|125|10.8|79.4|9.8|
|Less than 50|4|104|0.0|28.9|71.1|

Note: I n full-time equivalents.
When comparing aggregate spending on economic competencies, we know that the US-Brazil gap is overstated because of the omission of Brazilian expenditures on labor training in the services sector. Figure 9, in which this spending is disaggregated by industry, suggests that a considerable gap would remain even after adjusting for the omission. Since labor training data are included in the case of manufacturing and extractive industries, estimates for these sectors are comparable to US estimates. In Brazil, manufacturing industries spend about 2 percent of sales on brand equity, firm-specific human capital and organizational improvement. In comparison, the US spends as much as 7 percent of GDP on these intangibles.<sup>20</sup> Another source of concern identified in Figure 9 is the apparent decline in the manufacturing sector‘s spending on economic competencies.

Figure 9 also suggests that compared to its manufacturing sector, the Brazilian service sector spends very little on economic competencies. To make them more comparable, suppose that we make a crude adjustment for missing labor training expenditures in services by assuming that the ratio of labor training expenditures to those on brand equity and organizational improvement is the same as that in the US (about one-fifth). Then, our estimates suggests that non-ICT service sector spending on economic competencies is about 0.6 percent of sales, in contrast to about 2 percent in manufacturing. Another interesting pattern in Figure 9 is that low tech manufacturing industries spend slightly more on economic competencies than medium/high tech industries.

<!-- Start of picture text --> Figure 9: Expenditures on Economic Competencies by industry (% of<br>industry sales)<br>2.5%<br>2000 2008<br>2.0%<br>1.5%<br>1.0%<br>0.5%<br>0.0%<br>Agriculture Services ICT Services Low tech Medium/High Extractive<br>and related (except ICT) manufacturing tech<br>manufacturing<br><!-- End of picture text --> Note: Service sector estimate excludes labor training expenses.

> 20 Note that we do not have US estimates disaggregated by manufacturing and services.

25
# Figure I: Withholding Systems and GDP per capita

<!-- Start of picture text -->
** **<br>*<br>**<br>N=31 N=35 N=53 N=60 N=19 N=10 N=36 N=16<br>Mean GDP pc Median GDP pc 95% CI<br>VAT WHNo VAT WH IT WH No IT WH VAT WH BroadVAT WH Targeted IT WH BroadIT WH Targeted<br>40<br>30<br>20<br>GDP pc<br>10<br>0<br><!-- End of picture text -->

Notes: This gure displays the mean and median GDP per capita, and the 95% condence interval of the mean, for dierent subsamples of countries. GDP per capita is measured in thousands of current USD from the World Development Indicators for 2013. The number below each bar displays the sample size. The stars reect the signicance levels of the mean dierence between two adjacent bars. The rst and second bar refer to countries that use and do not use withholding on the VAT/sales tax respectively. The third and fourth bar refer to countries that use and do not use withholding on business income taxes (i.e. income taxes on corporations and the self-employed/unincorporated businesses) respectively. The fth and sixth (seventh and eight) bar further divide the subsample of countries with VAT (income tax) withholding into countries that use a broad withholding regime (that applies across sectors), and those that use a targeted withholding regime, applicable only to certain sectors (e.g. construction, shing). The analysis is based on a sample of 118 countries for which data was available from the PKF International Worldwide Tax Guide 2015, recent EY International Tax Alerts, PWC Tax Summaries, or the secondary sources referenced in the introduction. The results are robust to using dierent denitions of GDP (e.g. purchasing power parity), limiting the sample to the 109 countries for which information was available from the PKF Guide, coding countries for which the guide does not specically mention withholding either as missing or as not having withholding, and dierent ways of dening targeted withholding systems.

38
# **III. Results**

This section presents the results of the Subsample Direct Estimation, Subsample Imputation, and Full Sample Imputation.

**Table 3: Results from the First 30 Replications, Using Subsamples of 640 Households**

|Levels/Areas|Pover|ty rate|95%|CI|Range<br>of CI|
|---|---|---|---|---|---|
|_Subsample Imput_|_ation_|||||
|National|39.4|(2.0)|35.4|43.4|7.9|
|Rural|43.1|(2.5)|38.3|47.9|9.7|
|Urban|28.2|(3.1)|22.2|34.2|12.0|
|_Subsample Direct_|_Estimation_|||||
|National|40.2|(2.7)|35.0|45.5|10.5|
|Rural|43.8|(3.2)|37.5|50.1|12.6|
|Urban|29.0|(4.5)|20.2|37.8|17.6|

_Source:_ Authors’ estimations using 2005 HIES data. _Note_ : Cumulative means of poverty estimates and SEs (standard deviation of estimates) with 30 replications are shown. Estimates for 30 replications are shown in appendix table A2.2. Numbers in parentheses refer to SEs.<sup>3</sup>

## **Results Using Subsample Imputation and Subsample Direct Estimation**

Table 3 shows the cumulative means of the poverty estimates after 30 replications with a subsample of 640 households, out of which 410 households are rural and 230 are urban. The cumulative means of the 30 replications are presented in the poverty rate column and the standard deviations of poverty rates in 30 simulations are reported as SEs in parentheses.

All cumulative means of both the Subsample Imputation and the Subsample Direct Estimation are close to the true or Full Sample Direct Estimation of poverty headcount rates at the national level as well as for urban and rural areas. For example, the cumulative means of the Subsample Imputation are 39.4 percent at the national level and 43.1 and 28.2 percent for rural and urban areas, respectively. The corresponding full sample direct estimates are 40.0 percent (national), 43.8 percent (rural), and 28.4 percent (urban) (table 1). The cumulative means of the Subsample Direct Estimation are 40.2 percent at the national level, and 43.8 and 29.0 percent for rural and urban areas, respectively. Furthermore, all cumulative means from the Subsample Imputation and the Subsample Direct Estimation are in the 95 percent confidence interval (CI) of the corresponding full sample direct estimates (table 1).

In terms of precision, Subsample Imputation outperforms Subsample Direct Estimation. For example, the SEs of poverty rates at all three levels/areas using Subsample Imputation are 2.0 percent (national), 2.5 percent (rural) and 3.1 percent (urban), which are substantially smaller than those of the Subsample Direct Estimation, which are 2.7 percent (national), 3.2 percent (rural), and 4.5 percent (urban).

Figure 1 plots the cumulative means of poverty estimates using the 640 households in the subsample (410 rural and 230 urban) for each replication. This indicates that both poverty estimates from the Subsample Imputation and Direct Estimation approaches very quickly converge to the “true” poverty rates, or

> 3 All standard errors of poverty estimates except for Full Sample Direct Estimation do not include the sampling error of poverty estimates in the full sample. For more details, see Fujii and van der Weide (2013) and Matloff (1981).

9
2 in Malaysia, Indonesi **a** md Turkey, a shift in favor of electricity is observed as domestic diesel prices inased. Alhough a part of fiis hif towad electricity can be explained by improved access to electricity supplie, a sipuifcmit cross-price elasticity between diesel and electricity has also been observed in Indonesia In Colombia, ash in favor of gas ca be observed as electricity prices increase. In general, h sift faors domestic energW resource endowments in these countries. Interfactor substitution from energy to labor has also been observed in YndonesiL Table I sunmmizes changes in energy prices and industrial output The responses varied across dt coutries in the sanple. For Ghana and Indonesia, increased growth rates in mufacuin output i observed in the years of ihe price increases (Output Change II), as well as in 1he following years. In Malysia (198445), Turkey (1988489) and Zimbabwe (1982483), sectoral growth rate fell during ihe years of price increases In Zimbabwe, the fall in output was due to the response of the energy intensive industies to increases in electricity prices, in particuar metal-smelting, where electricity is a large share ofproduction cost and the possibilities for interfuel substitution are limited. Because of ihe competitive nature of the inaional metal markets, individual producers are price takers and have limnited possibiities for passing on tho increase in energy costs to consumers. In both Malaysi **a** nd Zimbabwe, thr was a madked recover y manufacturg output in the year following the reform as indicated in output IJI (table 1).

_Table 1: Output Impacts of Ehergy Price Increases: The Andustrial Sector_

||**Pr;**|**inrRn'Sl**|**F**|**uI**Qspitfh<br>h||||
|---|---|---|---|---|---|---|---|
|Cor|Period|%_Chanje'_|<br>Tpe|CostShare%<br>2|I<br>|_I|m|
|Mlayia|194-|17|Diesel|1.0-6.2|6.3|2.1|7.2|
|<br>Indonesa|198245|<br>310|Diesel|0.3 - 8.0|0|5.1|6.2|
|Ghan|1983-87|<br>214|Diesel|0.3- 12.0|-19|22.5|2.2|
|Zimbabw|e 1983-84|<br>95|Ectricity|>6.0|7.9|-1.7|9.7|
|Colmxbia|1985-88|<br>42|<br>Electricity|1.2 - 6.8|n.a.|La.|n.a.|
|Ture|198748|84 and 80|<br>Dis. & Elec.|n.a.|10.6|1.5|9.5|

**BA.** - **ming dat.** Nosm. 1. **Pric chane.** refer **to changi read** prices (1986 local currency) over the whole period of th refrm. **2.** Cost **urm** for **Ghm and** Colombia **are for all petrleum** products and energy **respectively.** 3. **Oulput figre reafr** to **av*** ama grwth rates **(percent)** of manuiacturing output for dute **diffrnt** periodr. I **-2 years** preceding **tpereform, I- Drin** the refora, and M - 2 years following the reform. For Indonesia Output I **refers** only **1** **_yea_ preceding** the price reform, and for Turkey, Malaysia and Zimbabwe output m refes to 1 year folowing the pic reform For Ghana, output gowth ates are unweighted averages.
<!-- Start of picture text -->
1.2<br>0.8<br>0.4<br>0<br>-0.4<br>-0.8<br>-1.2<br>Macroeconomic Stability Index-India<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016<br><!-- End of picture text -->

<mark>Note: Data is from World Development Indicators. Years refer to respective Calendar Years. Index is constructed as an average of the standardized indexes of inflation (CPI inflation), current account deficit</mark> (percent of GDP), and fiscal deficit (percent of GDP).

<mark>These results highlight the importance of having in place a policy framework that limits vulnerabilities, and maximizes policy space for responding to shocks. Elements of such a framework include maintaining a sound fiscal balance, a sustainable current account deficit, an environment conducive to investment, managing capital flows so as to encourage relatively stable longer-term flows and discouraging volatile short-term flows, avoiding excessive appreciation of the exchange rate, holding a large stock of reserves, and preparing banks and corporates to handle greater exchange rate volatility.</mark>

# **3.4 Current Cyclical Dynamics**

Next, we analyze the dynamics of the Indian economy in the last few quarters and put them in context with the long-term experience discussed above. Most recent commentaries on the Indian economy focused on an ominously declining growth rate over a five-quarter period, from 9.3 percent in Q4, 2015–2016, to 5.6 percent in Q1, 2017–18. Below we analyze the growth rate of quarterly GDP and its decomposition for the period starting 2013–14 through 2017–18 (Figure 13 and Figure 14).

Two points are noteworthy. First, growth in the two quarters of Q1, 2016–17, and Q2, 2016– 17 averaged 7.9 percent, higher than the average growth rate in recent quarters, or recent years. It would be erroneous to treat these as a part of the deceleration phase. Hence the discussion around a five-quarter phase of deceleration should really center only around the three quarters during Q3, 2016– 17 through Q1, 2017–18, when growth rates at 6.8, 6.1, and 5.6 percent, respectively, deviated significantly from the trend. Incidentally, these quarters coincided with the twin policy shocks consisting of demonetization and the implementation of the GST. Sectors such as manufacturing and construction were reportedly most affected by the implementation of the GST and demonetization; 25
(e2/m1). Leakage has no effect on the welfare impact of the program on the intended beneficiaries, but increases program costs. We define the _PMT eligible share_ as the share of the total population eligible for benefits at a given threshold (m1/n). This includes the correctly and incorrectly identified eligible population (using the PMT formula), as a share of the total population.

Figure 2. Illustration of Type I and Type II errors

||_Target group_|_Non‐target group_|_Total_|
|---|---|---|---|
|_Eligible: predicted by PMT_<br>_formula_|Targeting Success (s1)|Type II error (e2)|m1|
|_Ineligible: predicted by_<br>_PMT formula_|Type I error (e1)|Targeting Success (s2)|m2|
|_Total_|n1|n2|n|

While it is desirable to reduce both undercoverage and leakage, there is a trade‐off between the two. The ultimate choice of cutoff needs take into account the size of the budget and the average benefit per recipient household. In analyzing the Samurdhi program, we present a range for this trade‐off while varying the eligibility cutoff. We also evaluate the _incidence_ of targeting based on the PMT model, i.e. the extent to which the model selects beneficiaries with per capita consumption towards the bottom of the consumption distribution, and selects relatively few, if any, from the wealthier households at the top of the distribution.

Once a beneficiary group is selected, however, it is important for programs to implement a mechanism to review cases of potential exclusion errors. We recommend designing a grievance or appeals process to review particular households that may have been excluded due to scoring higher on one or two important predictors. For example, smaller households are less likely to be poor and therefore more likely to receive a higher PMT score. However, this may miss smaller vulnerable households like those headed by elderly people caring for orphaned grandchildren. A grievance process can help address specific cases of exclusion like these.

# _Data and Variables_

The HIES is a multi‐topic survey conducted every three years by Sri Lanka’s Department of Census and Statistics (DCS). The survey includes modules on consumption, education, health, income, household characteristics, assets, and information on access to social safety net programs. The nationally representative survey included 21,756 households from all regions of Sri Lanka, including areas of the North and East that were excluded from previous rounds due to conflict.

In this study, we aim to develop a PMT formula that is consistent across different definitions of a household. The HIES includes data collected at the household level, while eligibility for Samurdhi is determined at the family level. Households are defined as those in a dwelling that share a cooking pot, while families are 6
assume the existence of<sup>alinear relationshipbetweenglobalizationandthepoor.</sup> But as discussed earlier, there<sup>aregoodreasonstobelievethat therelationship</sup> may be nonlinear.

# V. A POVERTY-GLOBALIZATION<sup>LAFFERCURVE?</sup>

To capture the possibility<sup>ofanonlinear relationshipbetweenglobalization</sup> and poverty, I proceed in<sup>twosteps.First,insteadof using severalindependent</sup> indicators as before, I derive<sup>a"composite"index of globalization(definedinsuch</sup> a way that an increase<sup>representsgreaterintegration)byusingprincipal</sup> components analysis. Second,<sup>Iintroducethesquaredvalueof theindexinthe</sup> regressions. In order to generate<sup>a"poverty-globalization"Laffer curve(withan</sup> inverted U shape), the coefficient<sup>of thelinear termshouldbepositive,whereas</sup> the coefficient of the squared<sup>termshould be negative.The peak of thequadratic</sup> equation would then identify<sup>the"threshold"levelof globalizationbeyondwhich</sup> further integration reduces<sup>poverty.</sup>

# 1. Principal Components Analysis

One option to construct<sup>acompositeindicatorof globalizationis toextend</sup> the methodology used for<sup>instancebyWacziarg(1998),andcombinevarious</sup> indicators of trade openness<sup>(suchasthoseusedabove) with measuresof</sup> financial openness.<sup>18</sup> A problem<sup>withthatapproach,however,isthedifficulty of</sup> defining relative weights<sup>inan objective manner.In this study,I tackle the problem</sup> differently. To derive a composite<sup>indicatorof tradeandfinancialglobalization,I</sup> use principal components<sup>analysis(PCA).Inessence,PCA aimstoreplacea</sup>

> 18 NWacziarg (1998) developed an index of trade policy by combining several Indicators, including average tariffs<sup>andanindicatorof non-tariff barriers.The weightsusedto constructthe</sup>

33
# **Learning from Power Sector Reform:**

# **The Case of Kenya**

_Catrina Godinho and Anton Eberhard_<sup>_1_</sup> _,_<sup>_2_</sup> <u>Keywords: power sector reform, electric utility, power generation, energy access, state-owned</u> enterprise, regulation, electricity pricing <u>JEL Codes: L9; Q4</u>

> 1 Both from the University of Cape Town, Graduate School of Business, in South Africa.

> 2 Research for this paper was conducted by David Mwangi in Kenya. Valuable guidance was provided by Zubair Sadeque and Laurencia Njagi of the World Bank Country Team for Kenya.
shaking) at a given frequency or 'return period' based on a probabilistic analysis (more details are presented in Appendix A). Although, the potential damage captured through the TH indexes is not a projection, it allows us to observe which types of natural events could affect more in the areas where households reside. But it is worth noting that TH scores do not represent hazard risks per se, they are only physical hazards from probabilistic models, and their use for risk-related assessment is discouraged. ThinkHazard! methodology provides a general view of the hazards for a given location to promote disaster and climate resilience. It is a simple flagging system highlighting the hazards in an area. Moreover, the scores emphasize the likelihood of different natural hazards affecting areas. Thus, the data can show some uncertainties in specific areas that fail to cover the full range of hazard categories and intensities (see more detail in ThinkHazard! Platform, 2022).

The TH indexes are available from level 2 of administrative units (ADM 2) of FAO's classification. In the case of Brazil, the index is used at level 1 because the geographic variable available in the Household Survey is only at the state level. To capture the differences among administrative units of level 2, we use the average of the indexes of level 2 (see Appendix A). So, this country's index cannot be compared with the other countries' TH indexes. We use data from local areas to control for exposure variables ( ). By definition, exposure is the presence of people, livelihoods, species or ecosystems, environmental functions, services, resources, infrastructure, or economic, social, or cultural assets in places and settings that EE could be adversely affected by events (IPCC, 2014). Following this definition, we include population density (people per sq. km of the district or state area), percentage of cropland, and urbanization of the district or state area. The data source of type of land is from the MODIS 500m Landcover Dataset NASA LP DAAC at the USGS EROS Center, retrieved from Google Earth Engine Data Catalog based on the 2018 Images LC type 1 with 17 classes of land use. We employ class 12 for the cropland area (where at least 60% of the area is cultivated), class 13 for the urban area (at least 30% is impervious surface area, including building materials, asphalt, and vehicles), and the sum of all the classes for the total area of the administrative units at level 2.

Table 3 summarizes the average and standard deviation of the main variables of hazards and exposure at administrative level used in the analysis. The results indicate that Central America and Mexico face an elevated potential damage from cyclones and a higher frequency of occurrences of this hazard. In South America, floods are more prevalent, but countries like Peru and Ecuador evidence on average a higher potential damage from landslides than floods.

10