rafmacalaba/datause-encoder-data huggingface dataset:
-9

year. I St li important to note that the fall ln output Ln the socLalized secttr (25 percent) *xceeded the drop ln eployment. (14 percent), implying not only a fall in productLvLty but likely contLnuLng oveguanning. Sectoral data consLstently show output falling more than employment (see Tablo 2.1). Moreover, the variability of output across osetors (measured by the coeffLcient of variation ln Table 2.1) **was** twLce as large as that of employment0 Overall, the domlnant strategy appears to have been to reduce output rather than mployment. **1'** Flows into unemloyment have consequently to be further related to new entrants, ao poseLble laor supply effects and the Lnduement to reglster as unemployed created by a fairly generous replacment Eatio. 10 2.4 wages and Wage Policy The sharp and programud fall in economy-wide rcal wages disguL se a rather more complex set of developmnts. First, after March 1990 real consumer cnd producer wages rose signifLeantly, falLng again only in December (vliguro 1). By November real wageo were barely 20% below the average for 1989 and above the level in January 1989 not takLng Lnto account dLfferences in offective purchasing power over these period. due to the elLmLnation of shortages. Second, wages and profits movd Lnversely following March (Figure 1)o SeCtoral data (Table 2.1) do howver show profLts and wages movLng together but thLi can largely be aserLbod to priLe changes rather than productivity growth. Product wages exhLbLt far greater variance than 1a Note that for most branches profLt margins increasod ln 1990 as output declined. The stronger than expected profit performance of the soclalized sector can generally account for the absence of bankrupteces. See Coricelli, de la Calls, Pinto (1990). :7 Layoffs have been concentrated ln a number of sub-sectors; principally, retail trade, construction, transportation, engineerlng and coal.

10 The benefits schem _Li_ of LndfiLnLto duration wLth a 70% replacemnt ratio at the start falling to 40% after nlne months. There iL however no indexation above a 30n floor. In Hungary the gross replacemnt ratio _i1milarly_ ranges from 70% unindexed at the outset to 40% by the _and_ of the eocond year. ClaLms can only be made for up to two years.
redistributive mechanisms, in particular where there is higher confidence in government, there may be a preference for lower income inequality because of the belief that government’s may be able to address the issue of income inequality effectively.<sup>9</sup> We attempted to examine this question by adding a variable on the level of confidence in government.<sup>10</sup> We find that results in Table 3 are robust the inclusion of this new variable. The coefficient estimates for the country groups are basically the same and remain significant.

To examine whether our findings on geographic patterns in attitudes to equality are robust to survey year we conducted similar analyses using the 1995-97 and 1990 waves of the WVS. As mentioned previously, the 1995-97 wave has useable data on 15 post-socialist countries and the 1990 wave data on 4 post-socialist countries. To look at patterns in preferences we estimate the same regression as in Table 3, column 3.<sup>11</sup> For the 1990 wave, we can consider only CEE and the Baltics as there are no countries from the former Soviet Union (outside of the Baltics) in the sample.

Our analysis suggests that our findings are indeed robust. We find that there is a greater preference for inequality in post-socialist countries compared to advanced economies in both waves. Where data permit, we find that countries in the former Soviet Union in particular show a marked preference for income inequality compared to both advanced economies and the countries of CEE (see Appendix Table 2). It should be noted, however, that because the country coverage of WVS in 1995-97 and 1990 is somewhat different from that of 1999-2001 (see Appendix Table 1) the results are not fully comparable across waves unless we restrict the sample to countries represented in both waves. We take this up further below.

> 9 We thank Giacomo Corneo for this suggestion

> 10 The measure of confidence in government is based on responses to the following survey question: “I am going to name a number of organizations. For each one, could you tell me how much confidence you have in them: is it a great deal of confidence, quite a lot of confidence, not very much confidence or none at all?” Responses are recoded into a binary variable, with 1 signifying a "great deal" or "quite a lot" of confidence in government.

> 11 We do not include per-capita income and inequality in this analysis to keep the sample size as large as possible. Relative to the 1999-2001 period, fewer observations on the Gini coefficient are available for the 1990 and 1995-07 periods. In addition, reliable data on per capita income in 1990 are generally not available for postsocialist economies. The results in Table 3 (columns 3 and 4), however, suggest that the relative rankings of the country groups are invariant to the inclusion of per-capita income and inequality in the analysis.

13
public health spending as a share of total government expenditures stood at 9 percent on average, significantly below the Abuja declaration target of 15 percent.<sup>10</sup> Table 4: Public Health Spending, 2006-2009

|Percent|2006|2007|2008|2009|
|---|---|---|---|---|
|Health/GDP|1.7|2.0|2.2|1.8|
|Health/total govt‘|8.6|9.5|9.8|8.6|
|2<sup>nd</sup>tier/total health|8.4|7.7|8.5|9.0|
|1<sup>st</sup>tier/total health|31.5|34.9|33.9|38.4|

_Source: Authors’ compilation based on MoFNP financial reports_ A more refined measure of primary health spending used by the Ministry of Health (MoH) is the allocation of non-personnel funds at the district level. The latest available estimates show that nonpersonnel allocations of healthcare spending to districts increased from 8 percent of the total spending at districts in 2006 to 14 percent in 2007 and 16 percent in 2008 (MoH 2009).

Despite these relatively low levels of public spending, Table 5 shows that there has been a general improvement in all key indicators for utilization of health services, which has been attributed in part to the abolition of rural user fees for primary care in 2006. These improvements, however, have not been equally strong across indicators.

Table 5: Key indicators on utilization of Health services, 2006-2008

|Percent|2006|2007|2008|
|---|---|---|---|
|Utilization rate of PHC services|1.2|1.3|1.6|
|Institutional deliveries|43|45|45|
|Immunized children (worst performing districts)|67|62|68|
|HIV positive eligible clients accessing ARVs|33|53|67|
|HIV positive pregnant women receiving a<br>complete course of ARVs|11|15|23|

_Source: Ministry of Health_ 10 In April 2001, heads of state of African Union countries met and pledged to set a target of allocating at least 15% of their annual budget to improve the health sector. Ten years after Abuja, only one country had met this target (WHO, 2011).

12
CAPE VERDE’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE archipelago located 500 kilometers (km) off the West African coastline. Geographical fragmentation and a low population density necessitate the duplication of infrastructure facilities. For example, despite its small population Cape Verde has no fewer than three international airports, four aerodromes, and nine ports. The decentralized and very fragmented provision of utility services prevents capture of scale economies and drives up the cost and price of services, making it difficult to choose appropriate technologies.

**Figure 2. Population density**

<!-- Start of picture text --> Ribeira<br>Grande<br>Paúl<br>17°N PortoNovo 0 2,000km<br>São<br>Vicente<br>Sal<br>São Nicolau<br>Oceano<br>Atlântico VistaBoa<br>16°N<br>Densidade Pop. (Hab/Km2)<br>> 300<br>150 - 300<br>50 - 150<br>< 50<br>Tarrafal São<br>Miguel<br>Santa Santa Maio<br>Catarina Cruz<br>15°N Mosteiros São Domingos<br>São Praia<br>Norte Brava Filipe<br>0 20<br>km 25°W 24°W 23°W<br>avent o<br>vento<br>o Barl<br>a<br>as d<br>Ilhas do Sot<br>Ilh<br><!-- End of picture text --> _Source:_ Plano Estratégico de Transportes—Strategic Transport Plan of Cape Verde.

**The country has no known oil resources, and water is extremely scarce. Cape Verde is entirely reliant on oil imports for its fuel supply, and the relatively small scale of its market contributes to the high price of this oil. With regard to water resources, Cape Verde has the lowest endowment of any Sub-Saharan African country except for Djibouti in the semi-arid Sahel region.** Indeed, over the past 40 years rainfall has decreased sharply, to about half its former level. Underground resources are very limited, and only the islands of Santo Antão and Fogo have any significant water source. As a result, Cape Verde increasingly relies on desalination plants for water, which now account for approximately 85 percent of production.

Cape Verde’s isolated geography also limits the extent of its regional integration. Transport connections take the form of air and sea links. As of 2007 Dakar Senegal was acting as the main gateway for air transport service to Cape Verde, hosting daily flights. The country is already connected to the 4
Third, there may be omitted country effects correlated with mean income. An alternative method of deriving national poverty lines is to find the lines that are implicit in data on the poverty rate. Using fitted distributions, Jolliffe and Prydz (2016) estimate over 600 national poverty lines this way, as implicit in national poverty measures from the World Bank’s _World Development Indicators_ (such as World Bank, 2013).<sup>22</sup> The advantage of this method is that it generates multiple lines for each country, so we can add country fixed effects. Ravallion (2016b, Appendix) estimates the elasticity of the poverty line to the mean allowing for country effects and finds an OLS elasticity of 0.52 (s.e.=0.04; n=598). Without the country effects, the elasticity is 0.74 (0.01; n=609). So the elasticity is lower when we allow country effects, but it remains positive and statistically significant. However, it should be noted that the aforementioned issue of correlated measurement errors is likely to be a greater concern for these implicit poverty lines, as argued by Ravallion (2016b).

Notice that the US is an outlier in Figure 2(a). The official poverty line for the US was $15.62 per person per day in 2011 (for two adults and two children). This is well below the line one would expect for a country with the US mean. Indeed, the US line is more typical of countries with about half the US mean (around the values expected in developing countries with the highest means). As noted, the US has been an exception to the otherwise common usage of strongly relative poverty measures in rich countries. Instead, the official US line (set by Orshansky, 1965) has only been adjusted for inflation over time, such that it has fallen relative to the mean and median. This has been a source of concern in the literature on poverty in the US, which has generally taken the view that the US line should have risen in real terms to better reflect rising overall living standards.<sup>23</sup> Proposed revisions to the official US line have met political resistance stemming from the fact that certain public spending allocations across programs and states depend in part on the official poverty rates (Blank, 2008). (Such political resistance to updating poverty lines is clearly not unique to the US.) It remains that, over the longer term, poverty has clearly been relative in the US. While the official US poverty line has been held fixed in real terms since the mid-1960s, if one goes back to the literature on poverty measures for the US in the early 20<sup>th</sup> century one finds much

> 22 Letting _Fit_ (.) denote the fitted cumulative distribution function for country _i_ at date _t_ and the observed headcount index as _Hit_ , the implicit poverty line is _Fit_<sup></sup> 1( _Hit_ ) . 23 See the discussions in Citro and Michael (1995) and Blank (2008).

11
trade-to-GDP ratio are driven by the devaluation of the real, which reduces GDP measured in USD, not by increased trade.

# **3. Controlling for size and other macro variables**

Brazil’s size has long been used to explain the country’s relative closedness (Dominguez, 1994). As the comparison with other large economies already indicates, this argument does not hold up to close scrutiny (Figures 3 and 4). While it is true that large economies tend to exhibit lower percentages of exports and imports to GDP, such feature fails to explain the exceptionally low levels of trade penetration observed in the Brazilian case.

<!-- Start of picture text --> Figure 3: Selected large Economies: Figure 4: Selected large Economies:<br>Exports as percent of GDP (2013) Imports as percent of GDP (2013)<br>Germany Germany 45<br>Mexico 32 UK 33<br>UK 31 Mexico 32<br>Russian Fed. 28 France 29<br>France 27 India 28<br>China 26 China 24<br>India 25 Russian Fed. 22<br>Japan 16 Japan 19<br>US 14 US 17<br>Brazil 13 Brazil 15<br>0 10 20 30 40 50 0 10 20 30 40 50<br><!-- End of picture text --> Source: World Development Indicators Looking at 2013 data from 176 countries available through the World Bank’s World Development Indicators (WDI) database, the average trade-to-GDP ratio is 96 percent. Even among the six countries with a larger GDP (in constant 2005 US dollars) than Brazil, the average is 55 percent.

Using the same WDI data and running a simple, univariate OLS regression of trade penetration and GDP (logs) on all available countries, we can show that less than one-sixth (15 percent) of Brazil’s 4
# **On the (Changing) Asymmetry of Global Spillovers: Emerging Markets vs. Advanced Economies**

Rabah Arezki and Yang Liu<sup>1</sup> Updated March, 2020 JEL Classification Numbers: F15, F42, F43 Keywords: growth spillovers, emerging markets, advanced economies, interdependencies.

> 1 World Bank (Arezki) and University of Hong Kong (Liu). Contact e-mail: rarezki@worldbank.org ; <u>yangliu5@hku.hk. We thank Olivier Blanchard, Patrick Bolton, Bertrand Candelon, Jean Imbs, Ayhan Kose,</u> Prakash Loungani, Akito Matsumoto, Gian Maria Milesi-Ferretti, Maury Obstfeld, and Rick van der Ploeg for helpful comments and suggestions. The views expressed in this paper are those of the authors and do not necessarily reflect the position of the World Bank.
CAPE VERDE’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE high cost of water in Cape Verde is largely a reflection of local hydrology; little can be done to alter it, other than adopting lower-cost technologies for power generation. Reflecting increases in the price of oil, water tariffs increased further to over $4/m<sup>3</sup> in 2008. It is difficult to evaluate the extent to which water tariffs fully cover costs. Since ELECTRA is a multi-utility, it is not possible to correctly allocate energy costs between the power and water operations of the company. For this analysis, energy costs have been fully allocated to the power sector, which may overestimate the underpricing of power and underestimate the underpricing of water. (See the power section for a more detailed discussion of the pricing challenges facing ELECTRA.) ELECTRA’s operational performance does not compare favorably with its middle-income peers. Domestic water consumption in Cape Verde, at around 35 liters per capita per day, is close to subsistence levels and just about half that found in the low-income peer group. This finding is consistent with the extreme shortage and high cost of water on the islands. As of 2006 performance on key operational parameters such as revenue collection and distribution losses lagged well behind those of the African middle-income peer group and were more comparable to the low-income group. Since then, distribution losses have further deteriorated—reaching 35 percent by 2009—which is cause for concern, given the high cost of water. Over the same period, however, the collection ratio improved, nearing 99 percent (table 6). Thanks to improvements in revenue collection, hidden costs fell significantly in the late 2000s, to around 0.15 percent of GDP (figure 7).

**Figure 6. Comparison of water tariffs across Africa**

<!-- Start of picture text --> 300<br>250<br>200<br>150<br>100<br>50<br>0<br>US$ cents per cubic meter<br><!-- End of picture text --> _Source:_ Derived from Banerjee, Foster, and others (2008). _Note_ : Horizontal axis presents names of selected water companies in region.

15
He, Dong and Robert N McCauley (2013). “Transmitting global liquidity to East Asia: policy rates, bond yields, currencies and dollar credit”. BIS Working Paper No. 431, Bank for International Settlements.

Institute of International Finance. _IIF Capital Flows User Guide_ , Washington DC.

International Monetary Fund (2013a). _Global Financial Stability Report_ , October 2013: 93-125.

International Monetary Fund (2013b). _World Economic Outlook_ , October 2013: 81-111.

Kose, M. Ayhan, Christopher M. Otrok & Charles H. Whiteman (2003). "International Business Cycles: World, Region, and Country-Specific Factors". _American Economic Review_ 93(4) (September): 1216-1239.

Kraay, Aart C., Norman V. Loayza, Luis Serven & Jaume Ventura (2005). "Country Portfolios". _Journal of the European Economic Association_ 3(4) (June): 914-945.

Krishnamurthy, Arvind & Annette Vissing-Jorgensen (2011). “The Effects of Quantitative Easing on Interest Rates: Channels and Implications for Policy". _Brookings Papers on Economic Activity_ 43(2) (Fall): 215-265.

Krishnamurthy, Arvind & Annette Vissing-Jorgensen (2012). "The Aggregate Demand for Treasury Debt". _Journal of Political Economy_ 120(2) (April): 233-267.

Krishnamurthy, Arvind and Annette Vissing-Jorgensen (2013). "The Ins and Outs of LSAPs". In Economic Symposium Conference Proceedings. Jackson Hole, WY: Federal Reserve Bank of Kansas City.

Laeven, Luc and Fabian Valencia (2012). "Systemic Banking Crises Database: An Update". IMF Working Paper 12/163, International Monetary Fund. Washington, DC.

Lim, Jamus J., Sanket Mohapatra, and Marc Stocker (2014). "Tinker, Taper, QE, Bye? The Effect of Quantitative Easing on Financial Flows to Developing Countries”. Policy Research Working Paper 6820, World Bank. Washington, DC.

Longstaff, Francis A., Jun Pan, Lasse H. Pedersen and Kenneth J. Singleton (2011). "How Sovereign Is Sovereign Credit Risk?" _American Economic Journal: Macroeconomics_ 3(2) (April): 75-103.

Montiel, Peter, and Carmen M. Reinhart (1999). "Do capital controls and macroeconomic policies influence the volume and composition of capital flows? Evidence from the 1990s". _Journal of International Money and Finance_ 18(4) (August): 619-35.

Nickell, Stephen J. (1981). "Biases in Dynamic Models with Fixed Effects". _Econometrica_ 49(6) (November): 1417-1426.

24
and 1989. PREALC's estimation methodology presents serious shortcomings and inconsistencies, the most important of which is the exclusion of wage workers from early estimates.<sup>3</sup> For what is worth, Table 4 reveals a significant constancy of the proportion of urban informal workersover the last 3 decades, despite the tremendous changes, concerning industrialization and demographic transformation, experienced in Latin America.

The post-Secoiid World War Latin American experience with informnal economies motivates this study. Its pupose is to integrate and understand the relations between the most important features of the informal experience, namely, strong labor regulations, capital market imperfections, capital formation, and rural-urban migration. We want to understand the effects of labor regulations and informal capital market imperfections on income inequality, proxied in the model by the dispersion of urban wages, and the speed of industrialization, proxied by both capital accumulation and urbanization.

# III. THE MODEL

# A. THE ISOLATED URBAN ECONOMY

The urban economy is competitive and decentralized, witi firms hiring production factrs from their owners. All firms produce-one good (Y), which can be equivalently used for consumption or investnent. Firns share a basic production technology, which has the 3PREALC's estimaon strategy consists of identifying informal employment with entire occupational categories available from national censuses and household survwys. PREALC his consistently classified the self-enployed -excluding professionals and technicians- and remunerated family workers as informal workers. Only in early estimates, domestic servants were also included. In recent estimates (1989), owners and salaried workers of "small entlerprises" (whose size definiton change! from country to country) have been classified as informal workers. The exclusion of wage workern; in early classifications introduced a strong downward bias in the esfimates.

-.

**-7-**
_Figure 1: Annual rainfall in Colombo (2001-2014)_ <!-- Start of picture text --> Annual rainfall (mm) for 2001-2015<br>4000.00<br>3500.00<br>3000.00<br>2500.00<br>2000.00<br>1500.00<br>1000.00<br>500.00<br>0.00<br>2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014<br><!-- End of picture text --> _Source: Department of Meteorology, Sri Lanka, www.meteo.gov.lk_ Further, monthly average rainfall distribution over Colombo for 30 years covering 1981 and 2010 shows that May, October and November are the wettest months with monthly rainfall above 300 mm.<sup>22</sup> Heavy rainfall is known to occur in the southwest monsoon period between May and September and also between inter-monsoon periods of March-April and October-November.<sup>23</sup> Figure 3 shows monthly average rainfall over Colombo between 2001 and 2014. Further, there have been 19 days during 2006 and 2014 when the daily rainfall exceeded 100 mm and led to flooding in the city. On average, there have been about two such days every year. In the year 2010, rainfall exceeding 100 mm occurred for 7 days. There have been occasions, in November 2005 and November 2009, when the city faced 270 mm and 207 mm daily rainfall respectively. In November 2010, the city recorded 440.2 mm which is the heaviest daily rainfall recorded over this entire period.

> 22 World Bank (2013), Report on the Development and Application of the Urban Drainage Model, Selection No. 1043824/Sri Lanka: Update/Upgrade of the Hydrologic-Hydraulic Model and New Urban Micro-Drainage System Model for Greater Colombo Basin.

> 23 _Ibid._

7
# **B1. Rural (2000-2011)**

# **B2. Rural (2005-2011)**

<!-- Start of picture text --> 2000 2011 2005 2011<br>EDU_ALL EDU_ALL<br>100% 100%<br>BPL 80% EDU_GIRLS BPL 80% EDU_GIRLS<br>60% 60%<br>40% 40%<br>20% SANIT HEALTH_QUAL<br>HEALTH_ACCES 20%<br>SANIT 0%<br>S 0%<br>WATER HEALTH_ACCESS<br>WATER ASSET<br>INFO BIRTH<br>INFO<br>ASSET<br><!-- End of picture text -->

# **C1. Rural and Urban (2000-2011)**

# **C2. Rural and Urban (2005-2011)**

<!-- Start of picture text --> Urban 2000 Urban 2011 Urban 2005 Urban 2011<br>Rural 2000 Rural 2011 Rural 2005 Rural 2011<br>EDU_ALL EDU_ALL<br>100% 100%<br>80% BPL 80% EDU_GIRLS<br>BPL 60% EDU_GIRLS 60%<br>40% 40%<br>20% 20%<br>0% SANIT 0% HEALTH_QUAL<br>SANIT ASSET<br>INFO BIRTH<br>INFO<br>ASSET<br><!-- End of picture text --> <mark>Note: EDU_ALL is education deprived; EDU_GIRLS is education deprived (girls); HEALTH_QUAL is health deprived (quality); HEALTH_ACCESS is health deprived (access); BIRTH is institutional birth deprived; ASSET is asset deprived; INFO is information deprived; WATER is safe drinking water deprived; SANIT is improved sanitation deprived; and BPL is below monetary poverty line.</mark> Overall, there have been significant reductions in many dimensions of deprivation from 2000 to 2011, particularly in rural areas. In both rural and urban areas there have been significant reductions in the proportions of deprived populations in all dimension and the declines from 2000 -11 and from 2005-11 were found significant (at the 1% level) for almost all indicators. These results are in line with other recent studies, for example, Carranza and Gallegos (2013) using the 2000, 2005 and 2011 DHS, and the WIDE-3 qualitative studies on Wellbeing and Ill10