rafmacalaba/data-use-mentions-extended huggingface dataset:
WPS3422

# **Toward a Social Policy for Argentina’s Infrastructure Sectors:**

## **Evaluating the Past and Exploring the Future**

**Vivien Foster in collaboration with CEER-UADE**<sup>**1**</sup>

### World Bank Policy Research Working Paper 3422, October 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 This paper was prepared in collaboration with a team of economists at the Center for the Economic Study of Regulation (CEER) of the Argentine Business University (UADE). The team was led by Diego Petrecolla and Omar Chisari, coordinated by Diego Bondorevsky, and comprised Mariana Marchoni and Maria Victoria Fazio (analysis of household surveys), Gustavo Ferro (water), Carlos Romero (electricity), Esteban Greco (gas), and Javier Cardozo (transport).
manufacturing firms.

Excessive reliance on generators to cope with erratic and unreliable power supply increases production costs and reduces competitiveness. A significant share of manufacturing enterprises (from 36 percent in North Kordofan to 96 percent in Red Sea) share or own generators. The share of electricity consumption produced by generators in these states was 66 percent and 72 percent, respectively. Apart from increasing costs and reducing competitiveness, investment in these generators also holds up a significant amount of resources that could have been used for other fixed assets or to lower working capital constraints of most of the businesses (World Bank 2009b). Given erratic supply and high reliance on backup generators, industrial and commercial consumers account for only around 30 percent of electric utility consumption (figure 19). Consumption of power for agricultural uses is around 5 percent, and the rest of the consumption is primarily by domestic consumers. In contrast, billing patterns (as a proxy for consumption) in South Africa suggest that industrial and commercial consumers account for 92 percent of all billings, and residential consumers for only 8 percent.

> <sup>29</sup>
The Czech Republic was not the only country that permitted for sizeable appreciation of its currency, inflation target undershooting, or both in the pre-crisis period. Similar patterns could have been observed in other inflation targeters, for example, Canada, Korea, Norway, Slovakia, Sweden and Switzerland. The exchange rate obviously played a very important role in the monetary conditions in some small economies in the pre-crisis years. Positive outcomes in terms of price and financial stability were achieved in the countries in which central banks responded to exchange rate pressures broadly in a flexible inflation-targeting style. In particular, a strategy of reacting to the appreciation pressures pragmatically by cutting policy rates a bit and simultaneously allowing for some appreciation worked well.

**Chart 17. Currency Appreciation in Selected Inflation-Targeting Economies**

<!-- Start of picture text --> 110 120<br>110<br>100<br>100<br>90 90<br>80<br>80<br>70<br>70 60<br>50<br>60<br>CAD/USD KRW/USD<br>MYR/USD NZD/USD<br>AUD/USD<br>CZK/EUR SKK/EUR<br>1/01 1/02 1/03 1/04 1/05 1/06 1/07 1/08 1/09 1/10<br>1/01 1/02 1/03 1/04 1/05 1/06 1/07 1/08 1/09 1/10<br><!-- End of picture text --> Note: CAD - Canadian dollar, USD - United States dollar, KRW - Korean won, MYR - Malaysian ringgit, NZD - New Zealand dollar, AUD - Australian dollar.

Note: CZK - Czech koruna, SKK - Slovak koruna, EUR - euro. Source: Thomson Datastream. Note: January 2001 = 100. Decline means appreciation.

The explanation for the
||NLD|Netherlands|13,114|76|36|
|---|---|---|---|---|---|
||NOR|Norway|11,349|79|36|
||POL|Poland|10,385|74|35|
||PRT|Portugal|7,756|77|35|
||ROU|Romania|9,373|79|23|
||SRB|Serbia|864|19|24|
||SVK|Slovak Republic|8,972|79|26|
||SVN|Slovenia|11,910|76|30|
||SWE|Sweden|7,459|79|36|
||XKX|Kosovo|359|38|26|
|US BEA|USA|United States of America|2,970|15|33|
|Total|||255,875||945|

# **3.2 Data on IPA Characteristics (WB-WAIPA)**

To study the specific characteristics of IPAs, we rely on a new survey conducted in 2019 by the World Bank and the World Association of Investment Promotion Agencies (WAIPA) that included 91 IPAs – 87 national and 4 sub-national IPAs. This information is then combined with the new FDI database to explore the effect of IPA sectoral targeting on inward FDI. This leads to a study sample of 36 national IPAs from middleand high-income countries around the world (Table 3). Of these, 26 IPAs have sectoral targeting (group 1, the core sample), while 10 do not target any priority sectors (group 2, used for control only). We align the two data sets by adapting the potential priority sectors of the WB-WAIPA survey to 28 NACE sectors, corresponding to either NACE1 (e.g. for agriculture, mining and services) or NACE2 level (for manufacturing sectors).

**Table 3: Study sample
could not happen in the spring of 2020. This is important to keep in mind in order to fully understand how the labor market has been affected by the COVID-19 crisis. A final characteristic of the Greek experience was the government’s decision to mitigate the economic consequences of the crisis by introducing regulatory and income support measures to maintain employment relationships. This has had an important effect on how the lockdown and the reduced labor demand have played out in the labor market.

We use administrative, survey, and online vacancy data to analyze how employment in Greece was affected during the first few months of the COVID-19 pandemic and 30
and Men," _The Journal of Human Resources_ 28(4):689-734.

Stecklov, Guy, Antoine Bommier, and Ties Boerma, 1999. "Trends in Equity in Child Survival in Developing Countries: A Illustrative Example using Ugandan Data." MEASURE _Evaluation_ Project/Carolina Population Center, INED. Mimeo.

Wagstaff, Adam and N. Watanbe, 1999. "Inequalities in child malnutrition in the developing world," Mimeo. DECRG, The World Bank. Washington, DC.

World Bank, 1999. _World Development Indicators, 1999._ World Bank, Washington, DC.

41
**Table A25: Characteristics of Inspectors and Brokers Participating in the Corruption Scheme**

|**Dependent variable:**|**Average **|**excess inte**|**raction share**|
|---|---|---|---|
||**Panel A. Inspectors**|||
||(1)|(2)|(3)|
|Male|0.049*|0.048|0.010|
||(0.029)|(0.029)|(0.044)|
|Average tenure: 1-2 years|0.075***<br>(0.025)|0.076***<br>(0.027)|0.149***<br>(0.046)|
|Average tenure: 2-3 years|0.085**|0.080**|0.172***|
||(0.034)|(0.037)|(0.044)|
|Age||-0.002|0.002|
|||(0.002)|(0.003)|
|Management degree|||0.129**<br>(0.048)|
|Economics degree|||0.041<br>(0.038)|
|Law degree|||0.018<br>(0.044)|
|Observations|29|29|18|
|R-squared|0.201|0.222|0.624|
||**Panel B: Brokers**|||
||(4)|(5)|(6)|
|Based in Toamasina|0.059*|0.052|0.052|
||(0.035)
the minimum expenditure required to fulfil basic food requirements for a particular reference group. This reflects the cost of a basic food basket that meets the minimum daily caloric requirement, using food items, quantities and prices faced by a specific reference group. In the case of Pakistan, the minimum standard has been set at 2,350 calories per adult equivalent.</mark><sup>24</sup> <mark>The first stage in the REM approach closely mirrors the CBN methodology. In the second stage, non-food and non-housing basic needs are estimated. This is indirectly accounted for by estimating the food share of the total non-housing budget, for households whose food expenditure is ‘close’ to the food poverty line. This share is used to scale up the food poverty line to obtain the basic minimum threshold of total (food and non-food) non-housing expenditures. This budget share is referred to as the</mark> _<mark>scaling factor</mark>_ <mark>and allows for scaling up the food expenditures by an amount that is more reflective of the basic non-food expenditures, outside of housing expenses. The scaling factor in the REM approach varies from the traditional CBN methodology since it represents food as a share of non-housing and non-food budget, as opposed to aggregate non-food spending by the household.</mark> Using this method has several advantages. In the absence of predetermined budget standards, this allows for the assignation of a monetary value to the normative standard of living that is deemed to be absolutely necessary in any society. Using quantities of items consumed from the survey data accounts for the composition of the basket of goods based on household preferences and consumption patterns, instead of normatively defining the minimum amount required of each food item. Second, the scaling factor is non-parametrically estimated using the food share of non-housing budget for those consuming ‘close’ to

> 23 While this method of measuring adult equivalence does not take into account the higher costs that might be incurred by households that
remaining financial constraints mentioned earlier, (b) a concurrent expansion for tertiary education created for demand for academic track, and (c) different education quality and perceptions of career opportunities and economic returns associated with each track. To help better understand these factors we conducted a stakeholder analysis to gain in-depth understanding of the key stakeholders’ attitudes and opinions toward upper secondary education at the local level in addition to the analysis on the national CEPS data in Section 3.

For the stakeholder analysis, we surveyed a convenient, albeit large, sample from Tengchong County, Yunnan. The sample included a large variety of key stakeholders including middle school students (n = 381), high school students (n = 169), students from vocational high schools (n = 277), and parents of middle school students (n = 473), with a total of 1,300 valid responses. We also interviewed education administers at the county level (n = 7), school principals (n = 9), teachers (n = 25), students (n = 57), and parents (n = 15) using semi-structured questions. Tenchong County presents typical challenges faced by upper secondary education in Yunnan and many parts of China. It is a relatively poorer county in Yunnan Province. Due to a relatively lack of industries (as evidenced by its CNY 24,034 GDP per capita<sup>10</sup> versus CNY 28,806 of the provincial average 49
147.0|200.0|
|Inflation<br>rate (year average)|..|..|1210|4735|891|376|75|

Data Source: World Bank Second, while total **employment** only fell by 10 percent between 1992 and 1995 and open unemployment remains low (2.4 percent in 1995) , significant changes have occurred in terms of labor force participation, occupational choice and allocation of time between various occupations. As explained below, these changes have induced a decline in the number of contributors to the social security system.

# 3.2. Changes in Labor Force Participation and Occupational Choice

Labor force participation rates observed in the FSU were high, mostly due to the fact that women had reached in the early 80s the highest labor force participation rates in the world<sup>3</sup> (see Mincer, 1985). As in other industrialized countries, women had been drawn into the labor force through the migrations from rural toward urban areas, substituting work outside the household for family agricultural work. The process had been accompanied by a marked rise in the level of women's education and a similarly sharp decline in fertility. The soviet socialist economic system, ideology and growth strategy had played an additional key role in hastening the incorporation of women in the labor force (see Ofer & Vinokur 1985).

Although labor force participation rates were high over most of the life-cycle, rates were extremely low before the age of 20 and were falling sharply after the age of 55 for women and 2Estimate based on World Bank Household Survey (1995) - other estimates (See Rapawy 1996, WB Poverty Assessment 1996) are of the same order of magnitude. Inspite of low open unemployment, there is evidence of hidden unemployment. It is estimated that in 1995, approximately 2 million employees of state-owned enterprises stay on the payroll although working for shorter hours or on leave without pay.

**3** except among the Moslem population
. For example, in the sample of rural children under 5 used here, I find that first children are 0.063 ( _p_ = 0 _._ 044) standard deviations taller than second children. However, this gap falls to 0.034 ( _p_ = 0 _._ 275) if controls for village and household open defecation are included (jointly significant with _F_ 2 _≈_ 95), suggesting that children born into larger households may be more likely to be exposed to environmental fecal pathogens, although various forms of intra-household discrimination surely exist, as well ( _cf._ Jeffery et al., 1989).

> Jayachandran and Pande (2012) also find, in their pooled DHS sample, that Indian first-borns are _taller_ than African first-borns. In the individual-level sample studied here, Indian first-borns are 0.019 standard deviations shorter (a much smaller gap than in the full sample, but still negative); controlling for open defecation, they are 0.133 standard deviations taller, a 0.15 increase similar to those when sanitation is controlled for in table 8. If, for example, IMR is controlled for, Indian first-borns are 0.132 standard deviations shorter than African first-borns, which increases by 0.16 to Indians being 0.025 taller.

28
areas, the least wealthy members of society may still be most vulnerable to the negative impacts of air pollution.

We rely on spatially resolved estimates of wealth and PM2 _._ 5 that are constructed by model predictions from observed inputs, particularly from satellite imagery. While both sets of predictions are trained using data collected on the ground for areas in which that data exists, they are useful precisely because data does not exist everywhere. In areas without ground collected data their accuracy cannot be verified. We confirm that our results are robust to using alternative measures of wealth and pollution (see Section SI-1.4) for the areas in which that data exists but these are also the areas where the algorithms would be expected to be most accurate. It is by construction uncertain how accurate the wealth and PM2 _._ 5 predictions are in areas for which we have no ground data. If, for example, wealth is systematically underestimated in less polluted areas, or pollution levels are overestimated in wealthy areas, then our approach would overestimate the degree of progressivity in the pollution-wealth relationship. Conversely, if the wealth data systematically underestimates wealth in polluted areas or pollution is underestimated in poorer areas then our approach will underestimate the progressivity.

Understanding the distribution of ambient air pollution concentrations across wealth levels in LMICs is important for policymakers. The common perception that the least wealthy members of society typically live in the most polluted areas is not consistent with the empirical evidence presented here. Our results indicate the relationship around the world differs from that found in high income countries and varies across countries. The substantial heterogeneity we document highlights that the benefits of reducing ambient air pollution will accrue to different populations in different settings. Finally, our results emphasize that ambient air pollution is a problem in precisely the places that are engines of economic growth in most countries.<sup>24</sup> Failure to address the challenges of ambient air pollution in these settings is likely holding back economic growth.<sup>35</sup> This work
show a positive trend before the 1990, with the trend being very similar for the periods 1965-1990 and 1990-2015. These similar trends show that using the long WIOD data until 1990 and EORA after that year (as explained in the data section) allows for a smooth transition between datasets and consistently estimated dynamics for 1996-2015. Also, note that there was a stagnation period in the dynamics of agricultural products in the late 1980s. Then in the early 1990s, we observed a decline and quick recovery in this sector’s globalization dynamics and subsequent continuation of the trend observed since the late 1970s. The services dynamic started improving around 1990 and accelerated in the 2000s, showing how recent globalization is in this sector compared with manufacturing and agriculture.

The Global Financial Crisis in 2007-2008 interrupted the positive globalization dynamics during the Great Trade Collapse. This event is captured in figure (2a), providing a precise magnitude of around 13% decrease in trade of all sectors between 2008 and 2009

> 7The dynamics for the manufacturing sector during 1965-1990 and 1990-2015 are obtained in different estimations since different datasets are used. 8The interested reader may refer to Felbermayr et al. (2015) for a discussion of the sensitivity of the results in counterfactual gravity analysis to the choice of the elasticity of substitution.

11
higher leadership approval ratings, a better standard of living, and improved job prospects in the post-ISIS period. The results find that Shias, Kurds, and adults in living in Baghdad are significantly more likely to have a poorer quality of life, over time, and in contrast to Sunnis, in the post-ISIS period. Nearly all ethno-religious groups, during all periods, perceive the government to be corrupt.

# **2. Data**

Our empirical analysis uses individual-level microdata from the Gallup World Poll (Gallup Inc., 2019). Importantly, the data are randomized at the individual level, which allows us to summarize the data by individual characteristics, including age (identified as young for adults ages 15-24 and older for adults age

> 1 Other religious groups, including Christians, constitute only 1% of the resident Iraqi population.

3
others they differ sharply (e.g. Rio de Janeiro). In general the URCA designated boundaries we use align well with boundaries drawn using other gridded products (e.g. Global Human Settlement Layer and the World Settlement Layer). How these boundaries are defined is both somewhat arbitrary and, as the changing signs in the cases of Lagos and Rio de Janeiro show, important for the estimation of correlations. This is true, at least in part, because a more geographically expansive definition of a city’s boundaries is more likely to capture differences in ambient pollution levels driven by the gradient in pollution and wealth between urban and peri-urban/rural that we highlight in our country level results.

## **SI-1.5 Kuznets curves discussion**

Taking advantage of the granularity of our data we examine whether they provide evidence for the existence of Environmental Kuznets Curves (EKC) within the countries in our sample. We do not find compelling evidence that the relationship between pollution exposure and wealth fits the hypothesized inverted U pattern either across the full sample or within individual countries.

Figure SI-15 plots the average level of pollution for all grid-cells within various RWI bands across all the countries in our sample. In Figure SI-16 we plot the same for the ten most populous countries in our sample. Lighter grey point represent the relationship at the most dis-aggregated level of RWI while green and blue dots indicate results averaging within larger bandwidths of RWI. In all cases we fail to see strong evidence of an inverted U. The relationships that we do observe vary across countries, consistent with our previous results. This variation includes countries in which pollution levels are higher at higher wealth levels to those with flat to declining or declining pollution levels moving up the wealth distribution depending on the country.

# **SI-2 Supplementary Tables**

25
Table A.1: Data Sources

|**Variable**|**Definition**|**Frequenc**|**y**<br>**Source**|
|---|---|---|---|
|Output|Real gross domestic product (GDP)|Quarterly|Ilzetzki, Mendoza, and Vegh (2013), OECD, Haver Analytics|
|Private consumption|Real personal consumption expenditure|Quarterly|Ilzetzki, Mendoza, and Vegh (2013), OECD, Haver Analytics|
|Private investment|Real private gross fixed capital formation|Quarterly||
|Government consumption|Real government consumption expenditure<sup>a</sup>|Quarterly|Ilzetzki, Mendoza, and Vegh (2013), OECD, Haver Analytics|
|Government investment|Real government gross fixed capital formation<sup>a</sup>|Quarterly|OECD, Haver Analytics, Eurostat|
|Real effective exchange rate|Real effective exchange rate<sup>b</sup>|Quarterly|Ilzetzki, Mendoza, and Vegh (2013), BIS|
|Current account|Current account as percent of GDP|Quarterly|Ilzetzki, Mendoza, and Vegh (2013), WEO|
|Government debt|General government debt as percent of GDP|Annual|WEO|
|Fiscal balance|Overall fiscal balance as percent of GDP|Annual|WEO|
|Government consumption-to-GDP ratio|Government consumption as percent of GDP|Annual|WDI|
|Government investment-to-GDP ratio|Government investment as percent of GDP|Annual|WDI|
|Interest rate|Short term nominal interest rate|Quarterly|Ilzetzki,Mendoza,and Vegh(2013)|

Note: The main source for the quarterly series is Ilzetzki, Mendoza, and Vegh (2013). This database which ends around 2008 is extended by splicing from different sources as mentioned in the table. a This refers to general government for most countries while for a few countries
measures of labor underutilization, especially for women and youth who are more likely to work part‐time or engage in temporal and seasonal jobs (Baah‐Boateng, 2015).

The employed, unemployed and persons out of the labor force can also engage in unpaid domestic and care work, which is defined as work since the 19<sup>th</sup> ICLS but is not included in the concept of employment. Domestic and care work can be a time‐consuming daily activity, particularly for women (Budlender, 2008). To account for these activities and promote gender equality, SDG indicator 5.1 measures the proportion of time spent on unpaid domestic and care activities. This can be operationalized in several ways such as asking respondents to report the number of hours spent in specific activities such as preparing meals, childcare and cleaning, including time diaries in household surveys or conducting specialized time‐use surveys (ILO, 2018e).

<mark>Finally, capturing informal employment in developing‐country contexts, such as street vendors and waste pickers, is essential because it constitutes 87% of the labor force in emerging and developing countries (ILO, 2018f). Measuring informal employment requires a definition of informal enterprises and informal jobs (ILO, 2018f, p. 7, Box 2). While operational definitions differ to some extent across countries, the definition of an informal enterprise is often based on its size (number of employees), legal status (whether or not the enterprise is registered) , operations (whether or not the enterprise keeps a complete balance sheet of assets, expenditures, and income), incorporations (whether or not the enterprise is owned by multiple people inside and outside the household) and product destination (whether or not the products are for market and/or own consumption).</mark><sup>9</sup> <mark>Informal jobs are often defined as jobs for which the employer does not pay social security contributions, jobs without benefits