rafmacalaba/data-use-mentions-extended huggingface dataset:
Gaddis, I., Oseni, G., Palacios‐Lopez, A., & Pieters, J. (2019). Measuring farm labor: survey experimental evidence from Ghana. _Working Paper, World Bank._ Gaddis, I., & Palacios‐Lopez, A. (2018). Counting women's work and employment _. Presentation prepared for the 20th ICLS._ Global Strategy. (2018). Measuring decent work and youth employment in agriculture: methodological issues and gaps.

Grosh, M., & Glewwe, P. (2000). Designing household survey questionnaires for developing countries. _World Bank Publications_ .

Guarcello, L., Kovrova, I., Lyon, S., Manacorda, M., & Rosati, F. (2010). Towards consistency in child labour measurement: Assessing the comparability of estimates generated by different survey instruments.

Haggblade, S., Hazell, P. B., & Reardon, T. (2007). Transforming the rural nonfarm economy: opportunities and threats in the developing world. IFPRI.

Headey, D., Bezemer, D., & Hazell, P. B. (2010). Agricultural employment trends in Asia and Africa: too fast or too slow? _The World Bank Research Observer, 25_ (1), 57‐89.

IFAD. (2016). Rural Development Report.

ILO. (2013a). Decent work indicators: Guidelines for producers and users of statistical and legal framework indicators.

ILO. (2013b). National practices in the measurement of the economically active population, employment, unemployment and time‐related underemployment.

ILO. (2013c). Statistics of work and of the labour force.

ILO. (2017). ILO Labour force estimates and projections: 1990‐2030 (methodological description).

ILO. (2018a). Conceptual framework for statistics on work relationship _s._ ILO. (2018b). Data collection guidelines for ICSE‐18.

ILO. (
8|1.9|-4.6|
|Uruguay|2.3|-0.9|2.9|1.1|7.4|-0.6|9.6|-1.4|
|Venezuela|1.1|-4.8|3.9|4.2|6.2|-5.4|7.3|-10.3|
|Mean|1.8|-2.8|3.4|3.4|6.9|-2.3|8.6|-5.1|

Source: own computations based on SEDLAC (CEDLAS and World Bank).

30
up all standardized variables, constructing a skill index which varies at the worker-level. For instance, for the non-routine manual category, we construct a skill index that is the sum of the two standardized components (“Operating Vehicles” and “Control/Feel Objects; Manual dexterity”). These skill indexes are standardized over the entire distribution, using the sampling weights. Finally, the indexes are collapsed at the occupational level (1-digit), using again the sampling weights. These occupation-specific indexes are calculated both for the pooled STEP sample and for each specific STEP country. The final skill indexes vary at the level of occupations, with a scale that depends on the underlying distribution. For the sake of concreteness, a 1-unit differential across occupations in a given skill is interpreted as 1 standard deviation of the whole distribution of that skill among the employed workforce of all STEP countries. When applying these indexes to other developing countries that do not have a STEP survey, we use those calculated for the pooled sample (i.e. not the country-specific ones).

Using the STEP surveys to measure skills, rather than relying on O*NET, has the obvious advantage that it allows us to investigate whether the skill content of jobs differs across countries. Given that we can independently estimate occupation-specific skill indexes, we do not need to assume that different countries use the same technology or have the same labor force. Nonetheless, a couple of caveats need to be made: first, the mapping between tasks in the STEP variables and skills is not trivial; second, we need to assume that workers do not differ in their way of reporting the tasks performed at work (which may be problematic in the case of subjective opinions); and third, by excluding rural areas, the sample under-represents the agricultural sector, which represents a significant fraction of employment in the developing world.

Our analysis is based on the ISCO-08 occupational classification at the 1-digit level. We do not use a higher level of disaggregation for two reasons. First,
workers do much better in Latin America and Sub-Saharan Africa than in Europe and Central Asia. As Latin American and the developing countries of Europe and Central Asia have similar GDP per capita, it is not likely that level of development explains these regional differences. Regional differences may be due to different legal and regulatory environments, an issue we examine in more detail in a companion paper (Gindling, Mossaad and Newhouse, 2015).

# _For Urban Workers Only_

Many analyses of self-employment and labor market segmentation focus on urban and nonagricultural labor markets. Therefore, it is useful to examine the results for only urban workers and see if they are consistent with results found using data for all workers (urban plus rural).<sup>9</sup> Table 5 replicates Table 3 using data for only urban workers.

There are no noticeable differences between tables 3 (urban plus rural) and tables 5 (urban only). The results for urban workers are similar to the results for all workers together.

> 9 Results for non‐agricultural workers only are similar to those presented for urban workers only.

14
# 1 Introduction

In 2015, all United Nations members adopted the 2030 Agenda for Sustainable Development, which ushered in a new set of sustainable development benchmarks – known as the Sustainable Development Goals (SDGs) – across the world.<sup>1</sup> These SDGs supplanted the previous Millennium Development Goals (MDGs),<sup>2</sup> adding many new goals, including new goals related to the environment. However, one thing did not change with the adoption of the SDGs: Goal 1.1 was still the eradication of extreme monetary poverty.

Key to addressing progress towards the new poverty goals, however, is the accurate measurement of poverty (e.g., Ravallion, 2015). To this end, many household surveys in developing countries now include consumption and/or expenditure modules, designed to measure a specific type of monetary poverty. For example, Malawi’s Fifth Integrated Household Survey (IHS5) was a major input into the 2020 Malawi Poverty Report (NSO Malawi, 2021),<sup>3</sup> in large part due to its consumption module.

While household surveys like Malawi’s IHS remain key to measuring poverty, difficulties remain. First, large household surveys are expensive and, as such, generally are fielded only every few years, at most. Second, surveys are designed to be representative at certain levels of aggregation. For example, in Mexico, the Encuesta Nacional de Ingresos y Gastos de los Hogares (ENIGH) is designed to give representative estimates for rural and urban areas across states, but not for most municipalities.<sup>4</sup> The inability to consistently estimate more disaggregated statistics of poverty can make targeting difficult, especially when poverty is found in pockets below the level of representativeness. Moreover, many SDGs disaggregate progress by groups (e.g., women and children), which can lead to additional difficulties with reliably estimating these statistics, since many of these subgroups may have relatively
producers. All else being equal, greater access to irrigation would therefore be expected to magnify the positive relationship between rainfall and agricultural import tari¤s. To examine this prediction, we use country-level data on the proportion of total agricultural land that is irrigated. As mentioned above, these data are available for 32 of the 70 countries initially considered. Hence our estimation sample is reduced accordingly. Table 11 reports the corresponding results. In line with the predictions of our theoretical model, the estimates in columns (2) and (4) suggest that greater access to irrigation contributes to magnify the positive relationship between rainfall and agricultural import tari¤s.

## [Table 11 about here]

# 8 Concluding remarks

We have examined theoretically and empirically the e¤ects of rainfall shocks on tari¤ setting in the agricultural sector. In a model of strategic trade policy, we have shown that the impact of a negative rainfall shock on optimal tari¤s is generally not clear-cut, depending crucially on the weight placed by the domestic policy maker on each of the various policy objectives, and on the shape of the food demand function. We have then estimated the impact of rainfall shocks on applied tari¤s. Using detailed panel data on applied tari¤s and rainfall for 70 nations, we have found robust evidence that rainfall shortages generally lead to lower tari¤s on agricultural imports. When interpreted in conjunction with our theory, this result suggests that policy makers place a considerable weight on domestic producer surplus when setting import tari¤s.

# Appendix

[Tables A.1-A.3 about here]

# References

- [1] Andreyeva, T., Long, M.W. and Brownell, K.D. (2010). "The Impact of Food Prices on Consumption: A Systematic Review of Research on the Price Elasticity of Demand for Food." American Journal of Public Health, 100: 216-222.

- [2] Banerjee, L. (2007). "E¤ects of
gauging station data are summarized for each watershed. This includes dam location data (Food and Agricultural Organization on Africa dams 2006; Meridian Global Dam Database 2006; and hydro power plants (CARMA 2009). Global Lakes and Wetlands Database (Lehner and Döll 2004) identified areas of large bodies of water (which are usually impoundments) within the international river basins. After possible selection, the data are loaded into Google Earth (version 5.2.1.1329) for further visual inspection of impoundments from the imagery as well as georeferenced photos. In addition, we searched for 4 years from 1988 to present of available runoff data, yet there was an extensive search for periods of records that overlapped the 20+ years of the SSM/I instruments. Since the satellite resolution and a number of observations inside the basin should have structure, a restriction was imposed that the watershed should be at least 50,000 Km<sup>2</sup> .

We selected two international river basins that met the stringent set of technical assumptions and had significant previous international relations and socio-economic data upon which to draw: Mekong and Zambezi. A limited number of basins fit the technical criteria, which reinforces the notion that many international basins are significantly altered from natural flow and the other basins do not have sufficient gauging station information. More precisely, Nilsson et al. (2005) provide a global overview of dam-based impacts on large river systems that finds over half (172 out of 292) are affected by dams, including the eight most biogeographically diverse. Moreover, World‘s Water report (2010) has a global total of 47,655 dams (see 19981999, Table 12). With regards to gauging station information, Vörösmarty et al. (2001) highlight the need for additional river discharge data (especially since the mid-1980s).

# **River basin characteristics**

The two rivers included in this analysis vary in basin characteristics. The Mekong and Zambezi represent a magnitude of river lengths (4350
# **IV Data**

We employ two data sources for the analysis presented here: (1) the PROSPERA panel data gathered, since 1997 all the way to 2017-2018 (on a subset of localities and individuals), for the evaluation of the initial experiment and (ii) the 2015 intercensus data collected by the National Institute of Statistics and Geography (INEGI).

## **IV.A PROSPERA ENCEL panel**

As it is widely known, in 1997 a sample of 24,077 households in 506 rural localities was selected to implement a randomized controlled trial (RCT) in order to give robust evidence about the effects of PROSPERA (known as Progresa at that time).<sup>6</sup> This set of localities, chosen according to the administration of the program guidelines, where then allocated to a treatment (320 localities) and a control group (186 localities) where the eligible households in treatment group received PROSPERA’s support, transfers were distributed to those eligible households who complied with the stated rules of the program, starting in the fall of 1998 while the control localities started to receive the same treatment at the very end of 1999. Such sample was followed up with a survey instrument called ENCEL in 1998, 1999, 2000, 2003 and 2007 to give evidence of short and medium-run impacts of the program. In an effort to provide relevant data to evaluate the effects of PROSPERA in the long-run, the World Bank, PROSPERA and the National Institute of Public Health (INSP) partnered to gather information in 2017-2018 on a subset of that sample of participants.

**ENCEL 2017-2018 sampling framework.** Given the budgetary restrictions and the complexity of tracking back the original participants, which was the strategy of the previous rounds of the panel, a sub-sample of 334 localities of the 506 original localities was selected.<sup>7</sup> As Panel A in Table 1 shows, this first filter meant that richer eligible households in less marginalized localities
data assuming that households in the same cluster share the same market price. The CW's approach has the advantage of being easier to implement, but it does not deal with measurement error problems.

However, if there is correlation between prices/expenditure and the error terms, such correlation can be accounted for with instrumental variable and augmented regression techniques (Hausman 1978; Holly and Sargan 1982). A novel approach based on instrumental variables (IV) methods for reducing the endogeneity bias in demand systems has been developed by Lecocq and Robin (2015). This method shares with Deaton's and CW's methods the implicit assumption that price changes have no impact on quality, but it has significant advantages: i) it relies on fewer assumptions, ii) it does not require a clustered structure of the data, iii) it is computationally efficient, and iv) can potentially rely exclusively on the data available in the household surveys. For these reasons, in this paper we apply the Lecocq and Robin’s method.

In order to estimate consistent expenditure, own- and cross-price elasticities we estimate the following demand system in vector notation, which is the Banks et al. (1997) quadratic extension of the Deaton and Muellbauer’s (1980) AIDS model: where _a_ and _b_ are non-linear price aggregator functions defined as: _w_<sup>_h_</sup> _i_ is the share of the expenditure of good _i_ = 1, . . . ,10 (see table 3 for a list and description of the goods) on the total expenditure _x_<sup>_h_</sup> of household _h_ = 1, . . . , _H_ ; _p_ is a vector of prices; _u_ is the error term; and α _,_ β _,_ γ _,_ θ are the parameters to be estimated. These parameters satisfy the theoretical restrictions of additivity, homogeneity, symmetry, which are defined, respectively: 15
---|---|---|---|
||**Full Liber**|**alization**|**CAF**|**TA**|
|**Skilled Labor**|1.1|2.5|3.0|2.5|
|**Unskilled Labor**|-1.0|0.4|-1.6|-2.1|
|**Capital**|1.8|3.2|5.2|4.7|
|**Sk/Unsk wage gap**||2.2||4.7|
|**_Price indexes:_**|||||
|**Food price index**|-2.2||-0.1||
|**Non food price index**<br>**CPI**|-0.9<br>-1.4||0.9<br>0.5||

34
Policy Research Working Paper 9501

# **Abstract**

Using country-to-country data, this paper documents a set of novel stylized facts about the rise of the South in global finance. The paper assembles comprehensive bilateral data on cross-border bank loans and deposits, portfolio investment in debt and equity, foreign direct investment, and international reserves. The main finding is that global financial integration with and especially within the South (countries outside the G7 and Western Europe) has grown faster than within the North. By 2018, the South accounted for 24 to 40 percent of international loans and deposits, portfolio investment, and foreign direct investment, an increase of roughly 10 percentage points since 2001. The growing importance of the South is reflected in the intensive and extensive margins, with fast growth in the number of bilateral links. Although China weighs heavily in these trends, international investment in the rest of the South has increased to a similar extent.

This paper is joint product of the Chief Economist Office, Equitable Growth, Finance and Institutions Vice Presidency and the Development Research Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at fbroner@crei.cat; tdidier@worldbank.org; sschmukler@worldbank.org; and Goetz.von.Peter@bis.org. A verified reproducibility package for this paper is available at http://reproducibility.worldbank.org, click **here** for direct access.

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<mark>America – mostly driven by changes in population sizes, shifting age structures, and pre-existing disease prevalence. They estimate that 1.8 million premature deaths in urban areas can be attributed to PM2.5 exposure in 2019. These numbers suggest that meaningful reductions in air pollution deaths can only be</mark> achieved through rapid pollution reduction and improved access to affordable high-quality health care.

<mark>Besides mortality and morbidity effects, air pollution also reduces cognitive ability and productivity of affected populations. Empirical evidence from brain-training experiments shows that PM2.5 exposure impairs cognitive abilities of adults, while these effects are largest for those in prime working age and those with low ability (La Nauze and Severini, 2021). There is even evidence that short-term exposure to air pollution negatively affects the performance of highly skilled workers – professional baseball umpires have been documented to make more incorrect calls when exposed to high levels of carbon monoxide (Archsmith et al 2018). Such evidence on the relationship between air quality and brain health explains how air pollution can negatively impact productivity and exacerbate inequalities (Peeples 2020).</mark>

# **Poor people tend to be more exposed and more vulnerable to air pollution.**

While global estimates of air pollution deaths differ somewhat from study to study, the evidence is in strong agreement that air pollution is one of the leading causes of deaths (Lelieveld et al. 2020). Several factors are compounding so that this burden is disproportionately borne by low- and middle-income countries. Especially in the developing countries of South and East Asia, large populations are located in densely populated urban areas. Less stringent air quality regulations, the prevalence of older polluting machinery and vehicles, subsidized fossil fuels, congested urban transport systems, rapidly developing industrial sectors, and cut-and-burn practices in agriculture are all contributing to heightened concentration levels. In addition, high proportions of physical and outdoor labor mean that more people are faced with heightened exposure. Constraints in terms of the accessibility, availability and quality
There is as yet no evidence for developing countries, so the views expressed on this are mainly conjecture. Although it is often taken for granted, the evidence on green jobs in rich economies in response to reducing incentives for the use of environmental capital is not clear-cut at best, and positive effects – if any – are dependent on local circumstances (Huberty et al. 2011). Some of the highest quality evidence on reducing incentives for the use of environmental capital gives one pause. Using very detailed plant-level growth data, Greenstone (2002) shows that 590,000 jobs were displaced between 1972 and 1987 in the United States due to a particular key environmental regulation: the US Clear Air Act. Recent work on more cost-effective incentive-based policy instruments finds milder negative effects (Harrington et al. 2012). In any event, the way in which this would play out in the developing world is difficult to judge, but maintaining labor-intensive growth that internalizes environmental capital costs is not automatic. The above discussion indicates that the implications of the latter for the former depend on numerous adjustments of factor proportions and technology within sectors as well as adjustments of output shares across sectors.

If the labor intensity of growth cannot be maintained, this situation leads to further consequences in terms of structural transformation. Growth that is less labor intensive will slow the labor absorption from agriculture. Moreover, inputs into agricultural growth, such as fertilizer, water, or transport, will become more expensive. Although lower intensification of agriculture may preserve the environmental capital required for agriculture in the long run, it will slow the agricultural and labor productivity growth that feeds structural transformation in the nearer term. If policies reduce incentives for mobility (due to fuel costs or transport regulation), the spatial development effects could also be considerable because longer-distance trade may become less profitable and because populations in more remote areas are at risk of losing their connectivity with urban centers.

# _Low Carbon and Other Environmentally Friendly Public Investments_

The second set of green growth policies we consider here is the
a quicker re-opening from COVID-19 lockdowns than some other countries, we find that the overall reported increase in GDP by 1.92% is likely an overstatement of the true increase in economic activity. Figure 7 also depicted the US, seen at #34 in this list. While the 2020 statistic of -3.55% was technically an overstatement, we find a composite estimate of -3.65% was very close; in fact, the difference between reported GDP and composite is no greater than in 40% of other years over 2005-2020. Thus, we conclude reported GDP was correct in this case.

**Interpretation.** While there are nearly as many countries in the list of overstatement (39) as understatement (42), it is worth noting that percentiles for overstatement are in general larger than those reported with respect to understatement, and there are limited instances of zeros. Specifically, only 6 of the 39 overstatement countries (15%) are associated with a percentile of 0.00, whereas 19 of the 42 understatement countries (45%) are. Thus, for nearly half of the countries in the understatement list, this was the largest instance of misreporting over the entire sample of 2005-2020. Altogether, there is more convincing evidence of understatement of economic activity across countries during 2020 than overstatement. A potential explanations for this is the crisis dramatically changed the way economic activities were conducted–for example, by video conference–that may have been difficult to quantify in GDP. However, since the primary source of world electricity is fossil fuel combustion, such activities would be measured indirectly in NO2 by satellites. The same is true of changes in automobile traffic. While no signal is perfect, there is at least evidence in NO2 that many countries’ GDP in 2020 understated the extent of ongoing activities, many of them unmeasurable otherwise.

# **7 Conclusion**

In this paper, we have presented a global analysis of cyclical economic activity using NO2, and shown that this