rafmacalaba/datause-encoder-data huggingface dataset:
_y_ estimated on the basis of the growth estimate from month _m_ in 2020 by _Hy_<sup>_m_,</sup><sup>_PYA_is</sup> estimated by the dierence in dierences: While we cannot rule out that GDP estimates might have changed for reasons unrelated to Covid over this time interval (January - June 2020), it is safe to say that most of the changes are due to Covid-19. The dierence in dierences calculation assures that changes in the 2019 growth rates, which cannot have been due to Covid-19, are eliminated. We assume that this additional poverty lasts only for one year, so that the number of poverty-years is directly equal to this additional number of poor people. Our analysis therefore focuses on the short-term eects of the pandemic on poverty.

These are conservative assumptions, in the sense of yielding a small number of povertyyears, for at least two reasons. First, we assume that the additional poverty generated by the pandemic lasts only for one year. This assumption also allows us to avoid using GDP forecasts beyond 2020, the uncertainty around which is extremely large. Second, our baseline scenario assumes that all incomes grow - or shrink - in the same proportion, although there are reasons to believe that the poor could be aected more than proportionally by the recession (Bonavida Foschiatti and Gasparini, 2020).

The results for our six countries are summarized in Table 1. We explain how to read Table 1 using the case of Sweden. The rst six rows present basic economic and demographic indicators, such as Sweden's GDP per capita, population, life-expectancy at birth, etc. The second panel presents mortality statistics. Given the age-distribution of the 4,639 Covid-related deaths recorded in Sweden (shown in row 7), each death leads on average to 9.5 lost-years. The total number of years of life lost in Sweden (up to early June 2020) is obtained by multiplying these two numbers: 43,973. The third panel turns to the economic shock. The Central Bank of Sweden forecasts a GDP reduction of 11.5 percentage points as a result of the Covid-19 pandemic. Under our distributionneutral growth assumption, this GDP reduction leads to an additional 410,000 poor people, with respect to the national poverty line of 28.9 dollars per person per day (in 2011 PPP exchange rates). As we conservatively assume they are poor for one year only, this gure is directly equal to the number of poverty-years. The last row in Table 1 provides the break-even _α_ ˆ ratios. In the case of Sweden, there are 9.4 times as many poverty-years as lost-years. This means that the two sources of welfare costs would have the same magnitude if 9.4 poverty-years were judged to be as bad as one lostyear. Finally, we provide the per-capita numbers of lost-years and poverty-years, which simplies comparisons. In Sweden, the additional poverty corresponds to 0.0409 years equivalents. The national poverty line used for Sweden, Belgium and the United Kingdom is 60% of median income in 2019. We keep this lined xed at the 2019 level also for 2020 to avoid a shift in poverty lines.

10
<!-- Start of picture text --> 5<br>4<br>5 3<br>oS<br>J<br>x 2<br>1<br>0<br>Congo, Dem. Rep. of low-income countries<br>distribution losses Oundercollection<br><!-- End of picture text --> <!-- Start of picture text -->
1.0<br>08<br>& 06<br>oS<br>J<br>S04<br>0.2<br>0.0<br>Congo, Dem. Rep. of low-income countries<br>distribution losses Oundercollection<br><!-- End of picture text -->
It is interesting to observe such an immediate decrease in labor participation for children after the imposition of the law; and though it is not unique (there is anecdotal evidence of children being laid-off from work in Bangladesh when the threat of import sanctions was introduced by the US in 1994), it certainly is unexpected for a law that did not involve international lobbying and pressure. We therefore try to untangle the possible pathway or the theory of change behind this immediate effect. It is interesting to note that Basu (2005) in his theoretical model suggests that the imposition of fines might increase child labor in the short run, but we do not find such an effect. We assume that in the case of Pakistan the effect worked in the following way. The employers were informed of the imposition of a law against child labor, where employing children age 13 or less would lead to a fine being imposed on the employer. The employers react by substituting 13 year old children by 14 year old children. The physical and mental capabilities of the two ages are assumed to be marginally different and can easily be used as perfect substitutes. We also assume that the supply of 14 year old laborers would not be limited to prevent the employers from substitution (based on the fact that only 49.5 percent of 14 year olds report being enrolled in the 1991 PIHS). Further assumption is that since it is too soon for the households to be aware of the legislation (see section 2 for mechanism of introduction and information on laws passed), the supply would not be effected in terms of the wages demanded. In other words, if households know that it is illegal to let a 13 year old work, but legal to send a 14 year old to work, they would demand higher wages for their 14 year olds. But just one month after the creation of the legislation, information might not have trickled down to individual households. Thus, as opposed to reduction in wages by employers as punishment cost, it appears that when there is excess supply of child labor, in the short run the effect could be positive by reducing children’s work for the prohibited age group and increasing it for the age 28
activities and the share of income from each activity in household income, over all households and by expenditure quintile. We then move from the level of rural space to that of the rural household, examining patterns of diversification and specialization in rural income-generating activities, again over all households, and by expenditure quintile. We also use measures of stochastic dominance to characterize the relationship between types of income and income-generating strategies and welfare. In Section IV, we examine the role of location in income generation strategies in a multivariate framework, and we conclude in Section V.

# **II.** **<u>The Data</u>**

# _The RIGA database_

The RIGA database is constructed from a pool of several dozen Living Standards Measurement Studies (LSMSs) and from other multi-purpose household surveys made available by the World Bank through a joint project with the FAO.<sup>2</sup> The most recent additions are the LSMS-ISA project countries (see complete list in Table 1). Each survey is representative for both urban and rural areas; only the rural sample was used for this paper.<sup>3</sup> While clearly not representative of all developing countries, or all of Sub-Saharan Africa, the list does cover a significant range of countries, regions, and levels of development and has proven useful in providing insight into the income-generating activities of rural households in the developing world.<sup>4</sup>

> 2 Information on the RIGA database can be found at: http://www.fao.org/economic/riga/en/.

> 3 Each country has their own definition of rurality, and government definitions not comparable across countries may play some part in explaining cross-country differences. While recognizing that variation in country-specific definitions of rural may explain observed differences in income composition, the available survey data do not allow for straightforward construction of an alternative measure across all countries.  We thus use the government definition of what constitutes rurality. Further, rurality is identified via household domicile, not the location of the job-- a number of labor activities identified as rural may actually be located in nearby urban areas.

> 4 Details of the construction of the income aggregates can be found in Carletto, et al (2007).

4
pillars from the World Economic Forum's (WEF) Global Competitiveness Indices (GCI) and export diversification from the United Nations Conference on Trade and Development (UNCTAD).<sup>5</sup> The mapping process was conducted using keyword searches in the titles of extracted laws and decrees.<sup>6</sup> To the best of our knowledge, this presents the first comprehensive data set covering all GCC reforms passed from 1980 to 2019 by policy pillar (see Appendix A1 for a detailed explanation).

The number of passed laws and decrees has increased rapidly across the GCC countries in the last few years (see Figure II.1), reflecting intensifying reform efforts. Among the policy pillars, “Diversification” contributes most significantly to the increase in many countries (see Figure A1.2 in Appendix A1).

**Figure II.1: Total Number of Laws and Decrees in the GCC countries – 1980-2019**

<!-- Start of picture text --> 700<br>600<br>500<br>400<br>300<br>200<br>100<br>0<br>Year<br>Bahrain Kuwait Oman<br>Qatar Saudi Arabia UAE<br>1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018 2020<br>Number of Laws and Decrees<br><!-- End of picture text -->

# II.2 Do Economic Reforms in the GCC Focus on Lagging Policy Areas?

We examine whether the GCC countries implement reforms in lagging policy areas, as measured by the World Economic Forum's (WEF) Global Competitiveness Indices (GCI) and the United Nations Conference on Trade and Development (UNCTAD)’s Export Diversification. We construct a "Distance to Policy Frontier” measure for each GCC country and policy pillar. A higher score implies that the country is further away from the policy frontier, based on the ranking of WEF’s GCI and UNCTAD (see Appendix A2 for details).

Figure II.2 presents scatterplots between the Number of Laws and Decrees passed versus lagged Distance to Policy Frontier (i.e., the distance to policy frontier _in the previous year_ ) between 2008 and 2018. The figures reveal that the GCC countries passed more laws and decrees in policy areas closer to the policy frontiers. In other words, economic reforms in the GCC do not focus on lagging policy areas, at least in terms of quantity. This finding is true for all countries except Saudi Arabia. “Innovation” and “Technology

> 5 The final eleven policy pillars are as follows: Institutions; Infrastructure; Macroeconomic Environment; Health and Primary Education; Higher Education and Training; Goods Market Efficiency; Labor Market Efficiency; Financial Market Development; Technological Readiness; Innovation; and Export Diversification.

> 6 Note that many laws and decrees are not mapped to any policy pillar (see Appendix A1).

4
-96-

## Results of Our Methodological Endeavours

## a) Transparency of Procedures

With modern computer facilities, it was possible to lay out our procedures and assumptions in transparent fashion (with meticulous detail in the statistical appendix) so that they can be criticised, checked, replicated, augmented or truncated by other researchers in this field. In general the tables are laid out in similar fashion to the binary comparisons of Kravis, Heston and Summers (1982). In cases where there were alternative measures or concepts to those which we preferred, we generally provide enough information for use by others whose judgment differs from ours. Such transparency is an advance on most earlier "industry of origin" research whose detailed substructure was usually not published (Paige and Bombach being an honourable exception) and whose procedures were of a more ad hoc character.

## b) An Integrated Three-Dimensional Approach

We tried to give full attention to each of the three main dimensions of international comparisons - real output, PPPs and productivity, and to set out their interrelations and complementary character clearly. Here our exposure to ICP methodology was very useful, as its rigour in this respect is exemplary. We feel that a good deal of previous work on industry-oforigin lines has suffered from concentrating onl: on the productivity aspects (this is true of all studies listed in table 1.3 of chapter I except Paige and Bombach, the Czech/INSEE study and that of West).

# c) Reconciliation with the National Accounts Framework

There are obvious advantages in making sectoral output and productivity studies of this kind in a conceptual framework compatible with the national accounts. Chapter II therefore makes a careful confrontation between the census and the national accounts. From this one can see that the Mexican national accounts make extensive (and perhaps excessive) allowance for informal activity not recorded in the manufacturing censuses. It is also clear that census definitions of value added vary between countries, and need adjustment to bring the comparisons for the three countries to a common conceptual basis as is used in national accounting. Unfortunately, we were unable to adjust the detailed US census data to a national accounts concept of value added. This is a shortcoming of our chapter III, which uses a standardised but inferior notion of value added in neglecting to deduct service inputs (i.e. the US census concept) for our 17 industries. However, at the level of manufacturing branches and for manufacturing as a whole, all our comparisons employ the national accounts concept of value added.

## d) Adjustment to a Common Benchmark Year

Chapter III presents a method for dealing with the problem of comparing countries whose census dates fall in different years. The procedures have general applicability, and they were applied here to the USA, whose performance is often a yardstick for comparison in such studies. In fact, using our approach, US data can be adjusted to any intercensal year needed for purposes of international comparison.
The coefficients of the probits are interpreted as follows. In the case of the NE versus Brazil specification, the coefficient for, for example, rural measures the probability of being poor for a household head residing in a rural area in Brazil. Then for a household residing in the NE the probability of being poor when residing in the rural area can be calculated by adding the coefficients of rural, NE and NErural, (i.e. rural interacted with a dummy for NE). If the NE-interacted dummies are not significant, we can conclude that there is no statistical difference in the likelihood of being poor for a person residing in the NE compared to other parts of Brazil for that particular variable. As such, the NEinteracted coefficients measure a NE-specific premium or discount on the probability of being poor associated with different individual characteristics. Insignificant differences in the probability of experiencing poverty across regions, indicate that a homogenous, crossregional strategy for fighting poverty is adequate. The data used in this section are the PNAD household survey from 1999 **.**

# **Northeast versus Brazil**

From Table D1 we observe that the likelihood of being poor differs in many respect in the NE from the rest of Brazil. The findings reveal that in 1999 a Nordestino is 24-percentage points more likely to be poor than other Brazilians. Furthermore, most NE-specific coefficients are significantly different from zero which indicates that the NE is different from the rest of Brazil. Given the fact, that poverty in the Northeast is nearly double the Brazil-average, this finding is not surprising.

The main difference in poverty between the Northeast and the rest of Brazil is explained by the NE dummy and not by individual characteristics. This indicate that these are regional as well as individual characteristics that are different across regions in Brazil, apart form the ones included in the analysis that are important. For example, poverty among women, for example, is higher in the Northeast than in the rest of Brazil, however, as the NE interacted dummy for female is not significant, this difference in poverty cannot be attributed to differences between women in the Northeast and women in the rest of Brazil. The difference is due to that the NE in general is more disadvantaged, which is measured by the NE dummy.

25
# **Growth and Competitiveness as Factors of Eurozone External Imbalances: Evidence and Policy Implications**

## **Jose Luis Diaz Sanchez and Aristomene Varoudakis**<sup>*****</sup> **The World Bank**

Keywords: External imbalances, Eurozone, Competitiveness, Growth, Panel VAR JEL Classification: C33, F32, F41 _______________________________

* Jose Luis Diaz Sanchez is economist and Aristomene Varoudakis adviser at the World Bank’s Development Economics Department, Office of the Chief Economist. Views expressed in this paper are of the authors’ and should not be attributed to the World Bank or its Executive Directors. Helpful comments from Paloma Anos-Casero, Tito Cordella, Hinh Dinh, Poonam Gupta, Dirk Reinermann, Francis Rowe, Hans Timmer, Sam Ouliaris, and participants in seminars at the World Bank and the IMF Institute are gratefully acknowledged. Authors’ contacts: <u>jdiazsanchez@worldbank.org; avaroudakis@worldbank.org</u>
The poverty lines illustrated in Table 2 can be used to estimate the poverty measures summarized in Table 3 (top panel). The decrease in poverty between 2017 and 2022 is extremely pronounced, both in terms of headcounts and poverty gaps. However, it primarily affects rural areas, where the incidence of poverty in 2022 decreased by more than 20 percentage points.

**Table 3. Poverty (total expenditure < upper bound poverty line)**

||||**H**||**PG**||**PG2**||
|---|---|---|---|---|---|---|---|---|
|**2017**|**Bhutan**||28.0||7.6||2.9||
||**Urban**||6.2||1.2||0.4||
||**Rural**||38.9||10.8||4.2||
|**2022**|**Bhutan**||11.6||2.4||0.8||
||**Urban**||3.7||0.7||0.2||
||**Rural**||16.5||3.5||1.1||
|**2022/2017**|**Bhutan**||0.41||0.32||0.26||
||**Urban**||0.59||0.54||0.50||
||**Rural**||0.43||0.32||0.27||
|||**Gini**|**GE(0)**|**GE(1)**|**GE(2)**|**A(0.5)**|**A(1)**|**A(2)**|
|**2017**<br>**urban**||31.9|16.9|18.8|32.8|8.4|15.5|27.4|
|**rural**||34.6|19.5|21.7|32.8|9.7|17.7|30.1|
|**within com**|**ponent**||18.6|20.3|35.6|9.1|16.7|28.8|
|**between c**|**omponent**||3.9|4.1|4.3|2.1|4.2|8.1|
|**2022**<br>**urban**||26.2|11.3|12.6|17.4|5.8|10.7|19.1|
|**rural**||28.4|13.2|13.6|16.3|6.5|12.4|22.7|
|**within com**|**ponent**||12.5|13.2|17.1|6.2|11.6|21.1|
|**between c**|**omponent**||0.6|0.6|0.6|0.3|0.7|1.7|

Inequality also decreases significantly (Table 3, bottom panel), which appears consistent with the standard Datt and Ravallion (1992) decomposition shown in Table 4. The reduction in poverty is primarily explained by the redistribution component, while economic growth plays a relatively minor role.

8
- ———. 2013. “Managing Price Volatility: Approaches at the Global, National, and Household Levels.” In _Stanford Synthesis Volume on Global Food Policy and Food Security in the 21st Century_ , ed. Walter P. Falcon and Rosamond L. Naylor.

- Townsend, Robert M. 1977. “The Eventual Failure of Price Fixing Schemes.” _Journal of Economic Theory_ 14 (1) (February): 190–199.

- Tschirley, David L, and T S Jayne. 2010. “Exploring the Logic Behind Southern Africa’s Food Crises.” _World Development_ 38 (1) (January): 76–87.

- Turnovsky, Stephen J. 1974. “Technological and Price Uncertainty in a Ricardian Model of International Trade.” _The Review of Economic Studies_ 41 (2): 201–217.

- Turnovsky, Stephen J, Haim Shalit, and Andrew Schmitz. 1980. “Consumer’s Surplus, Price Instability, and Consumer Welfare.” _Econometrica_ 48 (1): 135–152.

- USDA. 2012. “Production, Supply and Distribution Online.” http://www.fas.usda.gov/psdonline/.

- Varangis, Panos, Donald Larson, and Jack R Anderson. 2002. “Agricultural Markets and Risks - Management of the Latter, Not the Former.” _The World Bank, Policy Research Working Paper_ 2793 (February).

- Waugh, Frederick V. 1944. “Does the Consumer Benefit from Price Instability?” _The Quarterly Journal of Economics_ 58 (4): 602–614.

- Williams, Jeffrey C, and Brian D Wright. 1991. _Storage and Commodity Markets_ . New York: Cambridge University Press.

- World Bank. 2006. _Managing Food Price Risks and Instability in an Environment of Market Liberalization_ . Washington, DC: The World Bank.

- ———. 2009. _Improving Food Security in Arab Countries_ . Washington, DC: The World Bank.

- ———. 2010. _Food Price Increases in South Asia: National Responses and Regional Dimensions_ . Washington, DC: The World Bank.

- ———. 2012. “Using Public Foodgrain Stocks to Enhance Food Security.” _The World Bank, Agricultural and Rural Development, Economic and Sector Work_ 71280-GLB (July).

- World Food Programme. 2011. “Emergency Humanitarian Food Reserves: Feasibility Study, Cost-Benefit Analysis and Proposal for Pilot Programme.”

- Wright, Brian D. 1979. “The Effects of Ideal Production Stabilization: A Welfare Analysis Under Rational Behavior.” _The Journal of Political Economy_ 87 (5): 1011–1033.

- ———. 2001. “Storage and Price Stabilization.” In _Marketing, Distribution and Consumers_ , ed. Bruce L Gardner and Gordon C Rausser, 1B, part 2:817–861. Amsterdam: Elsevier.

44
Boddy et al. 2005; Maffeis 1999), these factors and their nonlinearities need to be accounted for in the analysis.

# III. DATA

This study uses historical CO and meteorological data to assess the effect of PYP on CO during two-year symmetrical time intervals centered on the start of each phase (August 18, 1997, through August 17, 1999, and February 7, 2008, through February 5, 2010); see table 1 for descriptive statistics. Allowing the analysis to span two years for each phase ensures accounting for seasonal variation and lessens the effect of possible confounding factors on CO levels (see Davis 2008). The first time window is used to evaluate the effect of moderate restrictions (pre-PYP versus moderate phase), and the second to assess the effect of drastic restrictions (moderate versus drastic phase).

Data on hourly CO levels and meteorological variables were taken from the Air Quality Monitoring Network of Bogotá (RMCAB). The RMCAB is a system of continuous and automatic monitoring of air quality dating back to August 1997<sup>13</sup> that measures ambient concentrations of CO, NOx, nitrogen monoxide (NO), nitrogen dioxide (NO2), PM10, sulfur dioxide (SO2), and ozone (O3), as well as meteorological variables such as wind speed, wind direction,<sup>14</sup> relative humidity, superficial temperature,<sup>15</sup> and rainfall. Monitoring stations with CO data for more than 75 percent proxy for car use. To the best of my knowledge, this is the first paper assessing driving restrictions that includes this factor.

13. At present, the RMCAB consists of 15 point stations and a mobile station. Equipment is in compliance with the US Environmental Protection Agency’s (EPA’s) regulations (Secretaría de Ambiente 2015).

14. To allow for meaningful interpretations, this reading was converted from azimuth bearings to a set of dummy variables corresponding to the eight-point compass international convention.

15. Temperature at different heights was used to define temperature inversion. Thus, temperature inversion is an indicator variable that takes the value of one under these episodes, and zero otherwise.

9