rafmacalaba/datause-encoder-data huggingface dataset:
are used as final consumption by households (Prud’Homme, 2004; World Bank and IDB, 2005).

Moreover, basic services such as water and electricity often occupy a significant fraction of poor households’ budgets. Foster and Yepes (2005) show that households in developing countries spend a significant fraction of their income on water and electricity. For example, in a sample of Latin American countries, households in the poorest quintile often spend more than 5% of their income on water and more than 7% on electricity. In East Asia, figures from ADB, World Bank, JICA (2005) for 2003 show that the average share of total household expenditure spent on water services varies between 0.8% (China) and 3.2% (Cambodia), but can reach up to 16-33% for some of the poorest households in Indonesia for example. As for energy, average spending was 2.9% for Vietnam, 7.6% for China, 9% for Indonesia and 24% for Cambodia. From another angle, looking at the impact of privatization on welfare, McKenzie and Mookherjee (2003) show that service extension to previously unconnected customers resulted in large welfare gains for the poorest households.

The other half of infrastructure services corresponds to intermediate consumption, mostly by firms. For small producers and local firms of developing countries, access to distant markets and contacts with potential clients rely on the existence of a suitable and relatively cheap transport and telecommunication network. Specific channels through which productivity costs may emerge span an array of phenomena that go from the complete inability to access certain markets in some rural areas, to the impact of deficiency in infrastructure sectors on logistic costs and inventory levels (Guasch and Kogan, 2001).<sup>2</sup> Recently, the development of mobile telephony has been shown to have an important effect on the ability to conduct business, for example in remote parts of Africa (Vodafone, 2005). Similarly, electricity is a vital input for many industrial and

> 2 For example, Japanese vehicle manufacturers in Thailand consider that Bangkok traffic congestion increases their costs by raising the amount of parts stock they need to hold (ADB, World Bank, JICA, 2005).

3
# **Table 1: Recent studies on the effects of inflation shock on public debt**

# **(a) Simulations**

|paper|approach|data|inflation shock|effect on debt/GDP<br><if 1ppshock>|
|---|---|---|---|---|
|Akitoby, Komatsuzaki, Binder (2014)|debt equation|G7 macro (WEO),<br>2012‐17|persistent shock raising<br>inflation to 6%|14pp <3.3pp> decrease<br>in 5 years|
|Hilscher, Raviv, Reis (2017)|debt equation|US bond, option<br>prices|5th percentile of<br>inflation risk (>3%)|2.7pp <0.9pp> decrease|
|Equiza‐Goni (2016)|debt equation|Euro area bonds|persistent 1pp increase<br>in inflation|4.2pp decrease<br>on impact|
|Aizanman and Marion (2011)|simple formula|US bond maturity<br>at 2009|persistent 3pp increase<br>in inflation|12pp <4pp> decrease<br>on impact|
|End et al. (2015)|model simulation|Euro area, 2015‐16|persistent 2pp increase<br>in inflation|3pp <1.5pp> decrease<br>in 5 years|
|Krause and Moyen (2016)|DSGE simulation|US, 2008‐13|persistent 4pp increase<br>in inflation target|10pp <2.5pp> decrease<br>in 10 years|
|Our paper|debt equation|19 AE's macro<br>(WEO), 2017‐22|persistent shock raising<br>inflation to 6%<br>temporary 1pp increase<br>in inflation|10pp <2.4pp> decrease<br>in 5 years<br>0.7pp decrease<br>on impact|

# **(b) Estimations**

|paper|approach|data|inflation/deflation shock|effect on debt/GDP<br><if 1ppshock>|
|---|---|---|---|---|
|Afonso and Jalles (2019)|local projection|17 AE's, 1870‐1914|deflation dummy (=1)|1pp increase<br>on impact|
||panel regression||1pp decrease in inflation|0.23‐0.33pp increase<br>on impact|
|End et al. (2015)|panel regression|21 AE's, 1851‐2013|1pp increase in inflation|0.15pp decrease<br>on impact|
|Cherif and Hasanov (2018)|VAR|US, 1980‐2007|0.22pp increase in<br>inflation|0.3pp <1.4pp> decrease<br>on impact|
|Our paper|local projection|19 AE's, 1995‐2017|1pp increase in inflation|0.5‐1pp decrease<br>on impact|

12
consistent with the patterns observed in Table 5, the Traded and Non-traded Services sectors are the most White-collar intensive (as indicated by their high values of _α_ 1 in Table 8), and Manufacturing is the most Laborer intensive (as indicated by its high value of _α_ 5).

Table 8: Parameters for Simulation

||_Aggr/Const_|_Manuf_|_Non-Traded_|_Traded_|
|---|---|---|---|---|
|_θ_|0.37|0.30|0.10|0.23|
|_A_|1.60|2.13|2.10|2.40|
|_α_1|0.17|0.20|0.24|0.24|
|_α_2|0.02|0.06|0.16|0.10|
|_α_3|0.06|0.01|0.10|0.02|
|_α_4|0.30|0.13|0.10|0.04|
|_α_5|0.10|0.15|0.05|0.05|
|_ψ_|_Aggr/Const_|_Manuf_|_Non-Traded_|_Traded_|
|_White Coll._|1.24|1.29|1.33|1.25|
|_Tech/Sales_|1.19|1.36|1.23|1.34|
|_Service_|1.49|1.38|1.34|1.28|
|_Craft_|1.13|1.21|1.15|1.15|
|_Laborer_|1.14|1.09|1.05|1.05|

We treat manufacturing and traded services as goods whose prices are determined on world markets, while the other two sectors produce non-traded output whose prices adjust to clear the domestic market. Our simulation is not intended to reproduce the historical data in detail, but to provide an example of a liberalization that produces changes in trade volumes roughly of the same order of magnitude as what has been experienced in the period of the data. With that in mind, the liberalization experiment is set up as follows. The world prices of manufactures and traded services are 0.85 and 1 respectively. Initially, there is a 30% tariff on manufactures, so the domestic price is 1.15, and this is expected by all to continue permanently. From that initial steady state, suddenly at _t_ = 0 the tariff is eliminated, and is expected to stay at zero permanently.<sup>6</sup> Our simulation, then, begins at date _t_ = 0, with

> 6Since the _Ai_ factor includes the price, we operationalize this by reducing _Ai_ , holding all other parameters constant. The non-traded sectors’ prices, thus _Ai_ ’s, are re-calculated for each time period during the transition.

24
the level of spending on core services in India’s cities is uniformly below established norms and standards.

# **Table 2.2 Sub-National Revenue Sources—European Union 2005 and India**

**Breakdown of Sub-National Revenue-Selected Countries of the European Union**

|2005|||||||||
|---|---|---|---|---|---|---|---|---|
||**Local**|**Own Source Taxes**|<br>**Property Tax as**|<br>**Other Revenue a**|**s**<br>**Shared Taxes**|<br>**Grants**|**Own Source**|**GDP per**|
||**Government**<br>**Tax as % of**<br>**GDP (includes**<br>**shared taxes)**|**as a % of Total**<br>**Revenue**|**Share of Own**<br>**Source Taxes**|**a % of Total**<br>**Revenue**|**as a % of**<br>**Total**<br>**Revenue**|**as a %**<br>**of Total**<br>**Revenue**|**Revenues as**<br>**a % of Total**<br>**Revenue**|**Capita Euros**|
|Sweden|17|69|0|9|0|22|78|31,861|
|France|4.5|49|28|13|4|34|62|27,348|
|Denmark|16|49|8|25|3|23|74|38,433|
|Finland|8.7|43|6|26|4|27|69|29,964|
|Italy|6.3|34|58|8|10|47|42|24,281|
|Luxembourg|1.8|34|5|22|0|44|56|65,716|
|EU Average|-|27|-|16|20|37|43|22,344|
|Belgium|2.1|27|43|16|20|37|43|28,503|
|Spain|12|25|48|9|24|42|34|20,933|
|Portugal|2.1|25|38|31|4|40|56|14,111|
|Slovenia|2.7|17|68|22|40|21|39|14,119|
|Netherlands|1.8|17|47|27|0|56|44|31,192|
|Poland|4.1|15|71|16|22|47|31|6,405|
|Germany|3.8|15|27|15|42|28|30|27,219|
|UK|1.8|15|100|24|12|49|39|29,968|
|Hungary|4.3|13|13|41|16|29|54|8,815|
|Romania|6.1|12|62|6|75|6|18|3,679|
|Slovakia|3.8|12|63|22|32|34|34|7,075|
|Bulgaria|0.5|10|25|26|30|34|36|2,835|
|Austria|4.8|9|16|25|33|32|34|29,798|
|Ireland|0.4|9|100|26|0|65|35|38,922|
|Czech Republic|4.8|6|23|12|43|39|18|9,803|
|Lithuania|2.9|5|88|10|30|55|15|6,055|
|Estonia|4|4|77|17|44|34|21|8,316|
|Malta|0|0|na|20|0|80|20|11,745|
|Latvia|5.2|0|na|13|47|40|13|5,657|
|India 1999-2004 35|||||||||
|Municipal|||||||||
|Corporations|0.3|46.5|-|47.6|3.4|2.5|94.1||

Source: Dexia (2008) and Mohanty et alia (2007)

# **Section 3. Cost Recovery Models for Urban Services**

Cost recovery for urban services is no simple matter of pricing the service to users at marginal costs as is the case of private goods. Urban infrastructure services represent a mix of public, private, and semiprivate goods. Spillover effects are ubiquitous in the dense environment of cities, and they affect optimal economic pricing. Some services such as roads and bridges and public transport have declining marginal 11
> <sup>15</sup>

Finally, the demand for, and the costs of providing, infrastructure, education or even the rule of law and integrity in government may differ substantially across rural and urban areas. These are all taken from _World Development Indicators_ .

The estimates of the determinants of gross secondary school enrollment have additional controls to isolate the public good aspect of school enrollment. Gross secondary school enrollment is a product of how much governments spend on education and of how they spend the money. The goods and services that governments buy may or may not be public goods. However, the way governments spend money and the oversight they give to spending outcomes are more like public goods and less targetable to specific constituencies. The education regressions therefore control for government spending on education in order to isolate the impact of years of continuous elections on the _public good_ aspects of school policy that influence enrollment. In addition, gross primary school enrollment is controlled for, reflecting the simple fact that secondary school enrollments must drop when primary school enrollments are low; at the same time, it controls for the possibility that government spending on education might be high, but dedicated disproportionately to primary schools.

One might argue that government expenditures should be included as a control variable directly, since nearly all of the policy variables in the estimates of Tables 2 and 3 are related to government expenditure. In fact, government expenditures as a fraction of GDP are associated with significantly lower corruption and higher public investment and school enrollment. Government expenditure is nevertheless excluded from the core specifications because its inclusion reduces sample sizes by approximately 25 percent. Results, though, are robust to its inclusion. Wherever the elections variable is significant in Tables 2 and 3 below, it remains significant when controlling for government expenditure as a fraction of GDP.
The long-standing debates about how best to fight poverty in the developing world are reflected in the experiences of Brazil, China and India. All three countries have embarked on programs of market-oriented economic reforms. China was the first, where 25 years of a control economy left large potential gains from reform by the time that process started in the late 1970s. Brazil and India followed in earnest in the early to mid 1990s, though (in both cases) there had been tentative earlier efforts at reform.

All three have also seen progress against poverty in their reform periods, though at differing rates. In terms of the pattern of growth and distributional change, China and India have had more in common; both have seen rapid growth, but with rising inequality (with more of both in China). Brazil saw little growth but falling inequality.

There are some similarities among the three countries in their policies over the last 15 years, notably in the importance attached to macroeconomic stability, especially bringing inflation under control. But there are some big differences too, such as in the role played by policies directly aimed at redistributing incomes. When one looks more closely at their histories and policy regimes, Brazil and India turn out to have more in common with each other than with China. But each of these countries has something to teach the others. And other developing countries that have been less successful against poverty can learn from both the strengths and weaknesses of the approaches taken by all three countries.

This paper does not attempt to survey the (large) literature on poverty and growth in these countries, and many important contributions are not explicitly mentioned. It aims more narrowly to distill a few key lessons from a World Bank research project that has tried to measure and understand the progress against poverty of these three countries. The paper starts with a comparison of their overall performances, before examining each in turn. The last section tries to draw out some themes and lessons.

# **Performance against poverty**

National household surveys are used for measuring poverty and inequality all three countries, supplemented by data on prices and from the national accounts and population censuses. Thankfully, all three countries have a time series of reasonably comparable national sample surveys spanning their reform periods. (China’s first such survey is for 1981, just after reforms began. For Brazil and India the surveys include pre-reform periods.) The surveys 2
20

# **Caveats**

A number of important caveats are worth mentioning. Perhaps the most important caveat has to do with the simplifying assumptions about elasticities. For lack of a comprehensive set of country- and commodity-specific own-price elasticity estimates, it is assumed above that the own-price elasticity of supply (and also of demand) within a country is the same for each farm product. The effect of this assumption on the ITRI and IWRI estimates is likely to be small because those indexes draw on the production assistance and consumption tax indexes which each has three terms (e.g., for the PAI they are the production-weighted average price distortion, its variance, and its covariance with the output price elasticity of supply) and the elasticity appears only in the third term. We also ignore cross-price effects, as the algebra becomes far more complex without that assumption. And in the aggregation of country producer and consumer distortion indexes, we assume the aggregate marginal response of domestic demand to a price change is the same as the aggregate marginal response of domestic supply. To explore this last assumption we altered the weights on consumption and production (the _a_ and _b_ terms in the ITRI and IWRI formulae, respectively); we found this left the estimates for the border ITRI and IWRI almost unchanged at the aggregate level for all countries. This is not surprising given the high correlation between the IPDI and ICDI (and equivalents for the ITRI) for border distortions.

Another caveat is that the ITRI and IWRI do not include forms of support that are not given at the product level. In the Anderson and Valenzuela (2008) database, non-product-specific (NPS) assistance in some countries is a significant component of overall agricultural sector distortion rate (see Table 2). NPS assistance is reported there in three forms: general non-product-specific assistance, those farm input subsidies for high-income countries that are not attributed at the product level in the database, and so-called decoupled payments. Recalling that the ITRI (or IWRI) is defined as the ad valorem trade tax rate which, if applied uniformly across all tradable agricultural commodities in a country, would generate the same reduction in trade (or economic welfare) as the actual cross-product structure of NRAs and CTEs for that country, it is possible to make a simple assumption to incorporate NPS measures. If one assumes 100 percent pass-through of NPS distortions to producer prices, the
# **3. Crime and Violence as a Development Problem**

The development literature began exploring the effects of crime and violence on development in the mid 1990s, and since, a number of scholars have convincingly argued that crime and violence are among the key obstacles for development in Latin America.<sup>40</sup> Evidence shows that violence consistently undermines development efforts at various levels and that it drives the depreciation of all forms of capital, i.e. physical, human and social. Most importantly, violence disproportionately affects the poor and erodes their livelihoods and assets. As Moser (1996) argues, the more assets an individual or household can acquire and the better they manage them, the less vulnerable they are. Violence, however, severely hampers the poor’s ability to accumulate assets. The ways in which assets are affected by violence is detailed in the Table 1 below.

The fact that children are heavily affected by violence is especially concerning, since childhood and adolescence are critical stages for the accumulation of these assets. The accumulation of youth’s human capital assets is severely restricted when violence limits their access to education and health care. The fewer assets an individual has, the more likely they are to turn to alternative means of survival (which often include violence), thereby perpetuating violence and the erosion of assets in households and communities.<sup>41</sup> The effect violence has had on the accumulation of human capital is staggering: Londoño (1996) estimates that over the past 15 years the net accumulation of human capital in Latin America and the Caribbean has been cut in half due to the increase in crime and violence.<sup>42</sup>

**Table 1. The Asset Vulnerability Framework and Violence**

## Violence erodes:

- _Labor_ as an asset when it limits access to jobs.

- _Human Capital_ as an asset when it limits access to education and health facilities by both users and providers.

- _Social Capital_ as an asset when it reduces trust and cooperation between communitylevel social organizations.

- _Household_ relations as an asset when it limits the capacity of households to function effectively as a unit.

- _Productive assets_ when it destroys housing – the urban poor’s most important productive asset.

_Source: Moser (1996), (1998) quoted in Moser and Van Bronkhorst (1999)_ A slightly different way of conceptualizing the effects of crime and violence on development – instead of focusing on an individual’s or a household’s assets – is to try and discern direct and indirect impacts on the economy as a result of violence and then attempt to estimate these as costs, e.g. the cost of policing or the cost of health services.

> 40 Ayers (1998), Moser and Van Bronkhorst (1999), Morrison, Buvinic, and Shifter (2003).

> 41 Moser and Van Bronkhorst (1999).

> 42 Londoño (1996) quoted in Ayres (1998).

7
Table 5. Death events attributable to the risk of smoking and other premature deaths, 2017

|Disease|Males|Females|All|
|---|---|---|---|
|Aortic aneurysm|76|4|80|
|Bladder cancer|87|2|89|
|Chronic obstructive pulmonary disease|472|49|521|
|Colon and rectum cancer|72|7|78|
|Ischemic heart disease|2,281|263|2,544|
|Larynx cancer|131|2|133|
|Lip and oral cavity cancer|61|3|63|
|Liver cancer|66|5|70|
|Lower respiratory infections|133|12|145|
|Stomach cancer|134|7|142|
|Stroke|1,067|139|1,206|
|Tracheal, bronchus, and lung cancer|973|36|1,009|
|Other attributable|621|97|718|
|Total attributable|6,175|623|6,799|
|Non-attributable|19,247|24,518|43,765|
|Allpremature deaths|25,422|25,142|50,564|

_Source:_ Author’s calculations based on 2017 data from the Global Burden of Disease (GDB). Includes death events related with the risk of smoking.

Table 6. Calculation of YLL because of smoking-related deaths, 2017

||Smokin<br>|g-related death<br>|events<br>|Years ofpr<br>|oductive life lo<br>|st(YLL)<br>|
|---|---|---|---|---|---|---|
|Age group|Males|Females|All|Males|Females|All|
|30 to 34|34|3|37|1,131|86|1,217|
|35 to 39|74|5|79|2,082|128|2,210|
|40 to 44|143|9|152|3,286|206|3,492|
|45 to 49|270|16|286|4,861|285|5,146|
|50 to 54|505|34|538|6,561|436|6,997|
|55 to 59|763|56|818|6,101|445|6,546|
|60 to 64|972|90|1,062|2,917|269|3,186|
|All < retirement|2,761|211|2,972|26,939|1,854|28,794|
|All > retirement|3,414|413|3,827||||
|Total|6,175|623|6,799|26,939|1,854|28,794|

_Source:_ Author’s calculations based on 2017 data from the Global Burden of Disease (GDB).

_Notes:_ Retirement is assumed at age 65 for males and females. Only death events related to the risk of smoking are considered, other death events related to risks of second-hand smoke and chewing tobacco are excluded.

14
52 to the questionnaire design, the data entry and processing procedures and to the ways in which the data are disseminated and analyzed - so as to improve the quality and timeliness of the results;

- c) A more detailed integrated survey, including full income and consumption details should be carried out once every five years;

- d) The formal statistical surveys need to be supplemented with a program of qualitative studies that would be capable of addressing qualitative aspects of poverty that the surveys do not capture well;

- e) Consideration should be given to the idea of a panel study (or to the establishment of a Sentinel Site Surveillance System, with a permanent panel of sites) for monitoring how changes in moneymetric and other indicators affect the same households over time.
data agenda is pending to better capture top incomes in Albania. In other countries, this has been done using administrative tax records or banking data. It is important to explore which source of information could shed light on this issue for the case of Albania.

## **Systematically including this lens into policy making calls, therefore, for investments in building and strengthening the institutional setup to assess and monitor distributional impacts** .

# **IX. References**

<mark>Albanian General Directorate of Customs. 2015.</mark> _<mark>Legjislacioni Tatimor, 2015, LIGJ NR. 92/2014 Për Tatimin mbi Vlerën e Shtuar në RSH</mark>_ <mark>. URL: https://www.tatime.gov.al/eng/shkarko.php?id=295 date of</mark> access 23 October 2017.

Albanian Ministry of Education and Sports. 2016. _Statistical Yearbook of Education_ , Tiranë, MAS.

<mark>Fortuzi, Shkelqim, Doda, Sanie. 2015 “Fiscal policy in Albania”,</mark> _<mark>International Journal of Economics,</mark> Commerce and Management_ . Vol. III, Issue 6.

<mark>General Directorate of Customs. 2015 “Kodi Doganor i Republikës së Shqipërisë”, URL:</mark> <u><mark>http://www.dogana.gov.al/dokument/1179/ligj-nr-102-2014-date-3172014-i-ndryshuar, date of access 23</mark></u> <mark>October 2017.</mark> Higgins, Sean, and Nora Lustig. 2016. “Can a Poverty-Reducing and Progressive Tax and Transfer System Hurt the Poor?” Journal of Development Economics 122: 63-75.

<mark>Llollari, Orkida, and Kripa, Ermela. 2016. “Progressive Tax Vs Flat Tax and Its Effects on the Foreign Direct Investments in Albania”.</mark> _<mark>Academic Journal of Interdisciplinary Studies</mark>_ <mark>. doi:10.5901/ajis.2016.v5n3p79.</mark> Lustig, Nora and Sean Higgins. 2017. “Allocating Taxes and Transfers and Constructing Income Concepts”, chapter 6 in Commitment to Equity Handbook. _Estimating the Impact of Fiscal Policy on Inequality and Poverty_ , edited by Nora Lustig (Brookings Institution Press and CEQ Institute, Tulane University). Advance online version available at: <u>http://www.commitmentoequity.org/publications/handbook.php.</u> ______, Ali Enami and Rodrigo Aranda. 2017. The Analytics of Fiscal Redistribution. Chapter in Lustig, Nora and Sean Higgins, editors, Commitment to Equity Handbook: Estimating the Redistributive Impact and Pro-poorness of Fiscal Policy.

<mark>Mançellari, Armela. 2011. “Macroeconomic effects of fiscal policy in Albania: A SVAR approach”.</mark> _<mark>Bank</mark> of Albania_ Working Papers. 05(28).

<mark>Mara, Isilda and Edlira Narazani. 2011. “The Effects of Flat Tax on Inequality and Informal Employment:</mark> The Case of Albania”. _The wiiw Balkan Observatory_ Working papers 094.

<mark>Merko, Flora, Ermira Kalaj and Alma Zisi. 2017. “Estimating the effects of fiscal policy on the private abstract consumption: Evidence from Albania”.</mark> _<mark>13th International Conference of ASECU Social and Economic Challenges in Europe 2016-2020</mark>_ <mark>. organized by University “Aleksandër Moisiu” Durrës,</mark> Albania, 19-20 May 2017.

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