rafmacalaba/datause-encoder-data huggingface dataset:
# **Figure 11: Banking Stability and Profitability Indicators– Median Values by Income Group in 2007**

<!-- Start of picture text --> 20.00<br>15.00<br>10.00<br>5.00<br>0.00<br>ROA ROE Z-Score<br>LOW INCOME<br>LOWER MIDDLE INCOME<br>UPPER MIDDLE INCOME<br>HIGH INCOME<br><!-- End of picture text -->

# **Figure 12: Banking Stability and Profitability Indicators – Median Values Over Time (19952007)**

30
**arrange** to take **sO)lne** of the ir profits as salary paynients thereby imiaking their cash flows **appear** smaller **andi** redlucing taxes based **oII** cash flows. Thiis **can be aln** issuie ill the desigin of taxes based oni **cash** flows.

# _cd. Re/labton 'cn',Ih Oth,cr Iaxe.(11_

Rcso(rce t aXes will typically be part of a 11(ore general businless tax System which ilc hli(leS (cor)(o)ration illnco m)eI t(axationi as Well **as perso(nal** taxatic)n. Tlhe isslue then arises as to whet her I ()1i typ( of tax liab)ility Shocu)lld be (de(dlulte(I against the tax _l)ase_ or credlitedl against the tax liablilitics of allot her. **In** tlihe case of corp)orate all(l personal taxes, there is a strong argunicent in favouir of integrating the two systemls by giving somIe sort of credlit at the plersoInal 1(V(e l for taxes ha Lvinlg **been** paid by corlporations. This is usually (donle by **Ineatis** of a dividend tatx cre(lit administered at the ipersonial level. This reflects the fiact that the corporate tax is intenoded essenItially as a withholding (evice against (loecstic tax lial)ilities for personal taxation. However, resource talxatioi is intended to be anl additional source of tax **burden** over an(i al)ove income taxation. Tlhuis. crediting it. or eV(n allowing a (leleduction for it against corl)orate taxes is not (lesird(l. Ildeed, the opposite is the case. It can be argueld that corporate taxes shlold( be (id(hicte(l algainst the resource tax base. If so. the rent tax woil(l impose n1o further (listortionis over and al)ove those already ilm)pose(l by thle corporatc tax. Failing to allow a dedc Iction woiilol imply that the resource tax further l cc oli)polllds thlce (distorItion of thle corporate tax. **Ill** fact, ani efli(cilet Syste e woulld allow a fllll tax cred it of thll( corporate tax against the resource tax. This wolull 1iiI(do th<sup>1</sup> cc (listortil- igffe(c(t of tle( formler. Hfowever it woulld also 1111(1d tl(' effect th(e (c )rp)() ate tax has ont taxinig, ca)ital income thereby defeatinig its p)11r)po)Sc'. FIirt hleriic ne. it clla - un(d0 t1heo ai1vantm,it 'S of obt aining a foreign tax credit ill thlie case of fireigtil fims.

**(3⁄4** _Ab~o.' ufLo,', Oflf14 tt/?lj oind UPC'_ 7tUiflt1

I(osttt tax tllstc lls (ourcc tax(eS inclh ded . do Ilot offer fuill loss offset tiig. At best Hey offer p)artial loss offlsettilng lby all(winog fiirllls in a loss position to carry forward Or backward l sses fmi a liinited 1i umber of years. Firmiis can be iii a loss position for a **iliumbcr** of reaso ns. They iiiay be yo( nf,. growing firiiis wlo **are** involve(l hieavily **in** illvestlleilt 1)11 whose rVCtIIlies are exl)ecte d( to accruc *ciiy ill thle fii tir(. Tllhey may be firins wlic o t( 111p1 prarily ill a ]o9 css 0)SitiO( l)e sc of depr ss5(l otpulit priees. 01r the' Illay bI duclinilg firilis. Tle albse 1cc of filll loss cff(sc tting is particularly harmllfull lor l first two type)s of fii iiis. 'Tlhese call **Ibc** firms whichi are stretchied fOrc finallc in 01' WhlIm I I arn ll liilcertaill (ci Vir'clirilits. s Imperfcct loSS offsetting c(an ('XaWcralat(c I(tlh problctI's.

1GC
26 Table 4: Descriptive statistics for determinants of household NCMS status, 2003

||NCMS h|ouseholds|Non-NCMS<br>inNCMS|households<br> counties|Househol<br>NCMS|ds in non-<br>counties|
|---|---|---|---|---|---|---|
||Mean|SD|Mean|SD|Mean|SD|
|Share of household members chronically<br>sick|13%|0.18|14%|0.19|10%|0.14|
|Share of household members with health<br>fair or bad|19%|0.24|20%|0.25|21%|0.20|
|Head is rural wage laborer (omitted =<br>professional worker)|14%|0.35|13%|0.34|6%|0.24|
|Head is farmer (omitted = professional<br>worker)|62%|0.48|63%|0.48|88%|0.33|
|Head is student (omitted = professional<br>worker)|0%|0.03|0%|0.00|0%|0.03|
|Head is retired (omitted = professional<br>worker)|3%|0.17|4%|0.20|0%|0.06|
|Head is employee (omitted = professional<br>worker)|5%|0.22|7%|0.25|0%|0.05|
|<br>Head is male|89%|0.31|87%|0.34|92%|0.27|
|Household per capita income|3165|2442|2973|2049|1438|1292|
|Head has primary education (omitted =<br>illiterate or semi-literate)|15%|0.35|18%|0.39|30%|0.46|
|Head attained junior high school (omitted =<br>illiterate or semi-literate)|50%|0.50|47%|0.50|44%|0.50|
|Head attained high or vocational school<br>(omitted = illiterate or semi-literate)|23%|0.42|21%|0.41|13%|0.34|
|Head has college education (omitted =<br>illiterate or semi-literate)|10%|0.30|10%|0.30|7%|0.26|
|<br>Household size|4.10|1.38|3.62|1.26|4.64|1.62|
|Ethnic majority household|94%|0.24|98%|0.14|70%|0.46|
|Share of household members over 65|9%|0.20|10%|0.23|8%|0.16|
|Share of household members under 10|9%|0.13|8%|0.13|11%|0.15|
|Distance to closest facility category 2|23%|0.42|16%|0.36|13%|0.34|
|Distance to closest facility category 3|9%|0.29|7%|0.25|10%|0.29|
|Distance to closest facility category 4|4%|0.19|2%|0.14|5%|0.23|
|Distance to closest facility category 5|2%|0.12|1%|0.12|9%|0.28|
|Distance to closest facility category 6|4%|0.21|2%|0.15|16%|0.37|
|_Dibao_household or classified as poor<br>(_pinkun_)|3%|0.18|4%|0.19|5%|0.22|
|Received safety net payments last year|10%|0.30|4%|0.19|13%|0.34|
|Someone in household enrolled with GIS or<br>LIS|8%|0.27|13%|0.33|5%|0.22|
|GDP per capita in county|16430|11951|15157|8568|3272|874|
|Central China (omitted = East)|20%|0.40|24%|0.43|20%|0.40|
|Western China(omitted = East)|30%|0.46|24%|0.43|63%|0.48|
toilet facilities, and electricity. Coverage bias was not much of a concern in Nigeria, but this can be largely attributed to relatively higher mobile phone penetration in the country compared to Ethiopia, Malawi, and Uganda.

However, a more serious problem and common in all four countries is non-response bias due to unsuccessful contact with the respondent. The successfully contacted sample was biased towards wealthier households with higher living standards. This bias can be largely attributed to the difficulties contacting respondents either as a result of poor network reliability, respondent’s phone being turned off or unpowered, or the respondent not picking up the phone. This shows that even though higher phone penetration can lead to lower coverage bias, there is still substantial potential for bias due to unsuccessful contact with respondents in these countries. On the other hand, bias due to non-interview (refusals and breakoffs) did not introduce much additional bias in any of the countries.

The overall bias found is substantial and widespread across different characteristics. The bias nearly always tends to favor wealthier households and thus poorer households are underrepresented. This direction in the bias, left unaddressed, would result in biased estimates from the interviewed sample that do not fully reflect the situation of poorer households in the county. This is a population of critical interest to policy makers since poorer households are likely most vulnerable to the negative impacts of the COVID19 crisis. Counteracting this bias is therefore essential to ensure that the results more closely reflect the reality of the poor and provide accurate information to policy makers.

The study has shown that these phone survey biases can be substantially reduced by applying survey weight adjustments using information from the representative F2F survey from which the sample is drawn. This was demonstrated using a wide array of demographic and socioeconomic variables that are often included in standard household consumption and well-being measurement surveys. While these bias adjustments did not fully eradicate the bias across all dimensions, they were highly effective at reducing bias. This highlights one advantage to drawing phone survey samples from existing face-to-face, representative surveys rather than from RDD or lists from telecom providers where such adjustment methods can be more limited.

Phone surveys are likely to be widely considered as alternative data collection platforms in developing countries especially in emergency situations. National statistical offices have unique opportunities to implement these techniques using their recently implemented representative F2F surveys that can serve as frames for phone surveys.

48
|Kenya|100|111|30|
|---|---|---|---|
|Tanzania|55|62|25|
|Cote d'Ivoire|80|82|24|
|Nigeria|135|174|23|
|Benin|75|142|21|
|Namibia|55|77|15|
|Madagascar|44|51|11|
|Cameroon|51|72|11|
|Senegal|81|94|10|
|Mozambique|37|61|6|
|Burkina Faso|27|39|6|
|Zambia|25|50|5|
|Ethiopia|21|46|5|
|Chad|22|27|5|
|Niger|11|13|2|
|**Average**|**96.48**|**138.19**|**28.18**|
|**Median**|**75.00**|**82.00**|**21.23**|

Source: Carruthers et al. (2008).

# **_The impact of road investment on road condition_**

The impact of the investment in the last 15 years has been substantial since the proportion of district roads from fair to good condition has increased (i.e. from 15 percent in 1990 to 65 percent in 2007). In the last 15 years, the GOU has made substantial investments in rehabilitation and maintenance of District, Urban and Community Access Roads (DUCAR), estimated at 740 billion Shillings (USD 400 million).

In 2007, the GOU established a road fund dedicated for improving the condition of road network. The government intentions are to fund part of the DUCARIP through the road fund, other funds as appropriated by the Parliament and contributions from the Development Partners. The government uses a Medium Term Expenditure Framework (MTEF) which sets sector spending ceilings within a three-year framework. However, there is currently a backlog of approximately 8,900 km of district roads, 3,600 km of urban roads and some 35,000 km of community access roads without appropriate financing.

The next question is: What is the economic impact of high rural road density in Uganda?

17
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8 Policy volatility adversely affects investment, as evidenced from micro and macro analysis. Firm-level data show that economic unpredictability and policy instability have a statistically significant adverse effect on expected sales growth (Chong and Gradstein, 2006)<sup>5</sup> : a one-level increase in policy instability decreases firm sales growth by almost 3 percent. At the country level, fiscal volatility is negatively associated with overall aggregate investment of the economy, confirming previous findings (FatasMihov, 2003). When investment is disaggregated to contemplate private investment only (the sample is reduced due to data availability), the negative association persists as Figure 3 shows.

# **Figure 3**

<!-- Start of picture text --> Investment and Fiscal Policy Volatility Private Investment and Fiscal Policy Volatility<br>35 28<br>Inv = 22.82 - .19 FPvol Privinv = 15.53 - .21 FPvol<br> (-2.57) (-2.03)<br>R2=.06 D.W.= 2.09 24 R2= .05 D.W. 2.27<br>30<br>20<br>25<br>16<br>12<br>20<br>8<br>15<br>4<br>10 0<br>0 5 10 15 20 25 30 0 5 10 15 20 25 30<br>Fiscal Policy Volatility Fiscal Policy Volatility<br>Investment (% of GDP)<br>Private Investment (% of GDP)<br><!-- End of picture text --> The “growth champions” have lower fiscal policy volatility, more efficient public sectors, and more centralized management of public finances. A recent survey of the growth process across the world in recent decades highlighted the experience of ten countries, labeling them the “growth champions”: China, Korea, Thailand, Singapore, Vietnam, Hong Kong, Indonesia, Ireland, Chile and Malaysia (Harberger, 2005). Besides the commonalities analyzed by Harberger, namely the productivity increases and export performance, this group has other common traits related with fiscal policy: first, they have low volatility of fiscal policy; second, they have the highest scores of efficiency of the public sector (Afonso, Schuknecht, and Tanzi, 2006); and third, they have relatively centralized fiscal management, with the possible exception of China This section deals

> 5 The World Bank Business Environment Survey asks firms to judge on a four point scale how problematic are the following four factors for operation and growth of their business: Policy instability (1) no obstacle; (2) minor obstacle; (3)moderate obstacle; (4) major obstacle.
through latent agro-climatic factors that could be captured by district-level fixed effects. They found significant productivity gains from rural infrastructure.

In another example, Duflo (2001) estimated the impact on schooling and earnings in Indonesia of building schools. A feature of the assignment mechanism was known, namely that more schools were built in locations with low enrolment rates. Also, the age cohorts that participated in the program could be easily identified. The fact that the gains in schooling attainments of the first cohorts exposed to the program were greater in areas that received more schools was taken to indicate that building schools promoted better education. Frankenberg et al. (2005) use a similar method to assess the impacts of providing basic health care services through midwives on children’s nutritional status (height-for-age), also in Indonesia.

Galiani et al. (2005) used a _DD_ design to study the impact of privatizing water services on child mortality in Argentina, exploiting the joint geographic (across municipalities) and intertemporal variation in both child mortality and ownership of water services. Their results suggest that privatization of water services reduced child mortality.

A DD design can also be used to address possible biases in a social experiment, whereby there is some form of selective compliance or other distortion to the randomized assignment (as discussed in section 4). An example can be found in Thomas et al. (2003) who randomized assignment of iron-supplementation pills in Indonesia, with a randomized-out group receiving a placebo. By also collecting pre-intervention baseline data on both groups, the authors were able to address concerns about compliance bias.

While the classic design for a DD estimator tracks the differences over time between participants and non-participants, that is not the only possibility. Jacoby (2002) used a DD design to test whether intra-household resource allocation shifted in response to a school-feeding program, to neutralize the latter’s effect on child nutrition. Some schools had the feeding program and some did not, and some children attended school and some did not. The author’s DD estimate of impact was then the difference between the mean food-energy intake of children who attended a school (on the previous day) that had a feeding program and the mean for those who did not attend such schools, less the corresponding difference between attending and nonattending children found in schools that did not have the program.

Another example can be found in Pitt and Khandker (1998) who assessed the impact of participation in Bangladesh’s Grameen Bank (GB) on various indicators relevant to current and 34
Policy Research Working Paper 9432

# **Abstract**

Using firm-level data for Georgia, the paper estimates the quasi-elasticity of employment and wages with respect to the share of exports in total sales, to explore whether changes in the structure of sales (exporting versus selling to the domestic market) matter for labor market outcomes. The methodology uses exogenous fluctuations in exchange rates combined with firms’ initial exposure to various markets as instrumental variables to identify a causal effect. The results differentiate employment levels and average wages by gender and consider whether export destination or the competiveness of economies matters for the magnitude of this elasticity. The data are from the National Statistics Office of Georgia Statistics Survey of Enterprises merged with customs data for 2006–17. The instrumental variables regression results show that the act of exporting improves female employment but reduces overall average wages and female wages. Increasing exports to the European Union as well as high-income countries drives this positive result for female employment, whereas exporting to upper-middle-income countries is found to have a negative relationship with female employment.

This paper is a product of the Macroeconomics, Trade and Investment Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at chollweg@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
Table 8: Out-of-sample dynamic forecast performance (RMSE, percent)

||||All cou|ntries|||
|---|---|---|---|---|---|---|
||Trade|weight|matrix|Distan|ce weight|matrix|
|Forecast horizon|1 year|2 years|3 years|1 year|2 years|3 years|
|CSD specifcation|||||||
|None|2.870|4.605|3.733|2.870|4.605|3.733|
|Factor only|2.133|3.965|3.337|2.133|3.965|3.337|
|Spatial only|2.262|4.077|3.255|2.333|4.203|3.376|
|Factor and spatial|2.094|3.951|3.316|2.146|3.981|3.357|
||||Advanced|countrie|s||
||Trade|weight|matrix|Distan|ce weight|matrix|
|None|2.079|5.143|1.955|2.079|5.143|1.955|
|Factor only|1.541|4.562|1.016|1.541|4.562|1.016|
|Spatial only|1.930|5.045|1.671|1.926|5.050|1.690|
|Factor and spatial|1.521|4.498|0.957|1.491|4.475|0.999|

Notes: The table shows the RMSE of dynamic forecasts over 2014-2016 obtained with model estimates using data for 1970-2013 under alternative specifications of cross-sectional dependence. Specifications including a common factor use an AR(1) model to predict its future values.

# **5 Conclusion**

Output growth displays substantial comovement across countries. Existing empirical literature has modeled the cross-sectional dependence of growth as reflecting either – localized linkages across countries or regions, or pervasive common shocks i.e., weak and strong cross-sectional dependence, respectively. In this paper we have brought both perspectives together by assessing the international comovement of GDP growth in a setting that allows for both spatial dependence and latent common factors, using annual GDP growth data over the years 1970–2016 for 117 advanced and developing countries.

In the paper’s empirical setting, the dynamics of growth reflect the action of global common factors as well as spatial effects accruing through the growth of economically neighboring countries. Estimation employs a bias-corrected quasi-maximum likelihood procedure recently developed by Shi and Lee (2017), alternatively considering all 117 sample countries, or a subsample of 21 advanced economies. To capture the interactions among countries, we employ two alternative spatial weight matrices – one based on bilateral trade, and another based on geographic distance. To determine the number of latent common factors driving GDP growth across the world, we use a variety of information criteria. On the whole, they indicate the presence of a single factor for both country samples considered.

Under the two alternative specifications of the spatial weight matrix and for the 29
WPS 3307

# **Latvia’s Macroeconomic Options in the Medium Term** **_Fiscal and Monetary Challenges of EU Membership_**

## **Célestin Monga**<sup>1</sup>

## Abstract

Latvia’s experience over the past decade shows that economic growth and real convergence can no longer be assumed to be exogenously driven processes determined by given technological improvements and relatively higher factor returns. Instead, it is an endogenously driven process led by many variables, including policy variables. European membership clearly brings enormous economic benefits to Latvia. However, it also brings important challenges on the macroeconomic front, especially prior to the adoption of the euro. Substantial progress has been made toward fiscal consolidation since the mid-1990s but several risks remain. Given the facts that domestic demand remains buoyant and that Latvia will have to accommodate the expenditure commitments associated with NATO and EU membership while simultaneously aiming to fulfill the medium-term goal of a balanced budget, a more prudent fiscal policy should be the main short-term policy objective. The authorities should therefore reassess their current medium-term budget framework and strengthen their fiscal rules so that off-budget spending is eliminated and controls over spending ministries and local government finances are reinforced. They should also rethink any further tax reductions until a clear strategy is designed for compensating for projected losses in government revenues. Monetary policy can contribute to sustainable growth and job creation in Latvia in the medium and long term by providing an environment for price stability. It will have to be complemented by further microeconomic reforms and prudent wage developments aligned with productivity growth.

## World Bank Policy Research Working Paper 3307, May 2004

_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 view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._ 1 Célestin Monga was Senior Country Economist for the Baltic countries (Estonia, Latvia, and Lithuania) when this research was initiated. He is with the Macroeconomics Group in the Europe and Central Asia Region of the World Bank. The author would like to thank Olivier Blanchard, Bernard Funck, Inguna Sudraba (formerly Ministry of Finance of Latvia), and Helmuts Ancans (Bank of Latvia) for their comments on a previous draft. Andrejs Jakobsons and Lodovico Pizzati provided excellent research assistance.

1