rafmacalaba/data-use-mentions-extended huggingface dataset:
|23|
|Percentage of electricityfromgenerator|18|..|19|3|3|11|6|11|19|11.3|
|Delay in obtaining an electrical<br>connection(days)|25|14|92|98|19|13|9|16|97|42.6|
|Percentage of firms identifying<br>electricityas a major constraint|7|44|55|60|43|25|6|21|12|30.3|

Source: Enterprise Survey database (www.enterprisesurveys.org). _Note:_ Year of the survey is in parentheses.

The comparatively high quality of the power supply reflects the relatively good performance of Electricidade de Moçambique (EDM), the publicly owned electricity utility of for Mozambique. EDM’s collection ratio, at 100 percent of billings, is above the average of other LICs (93 percent) and at the level of other African MICs. The recovery of operational and capital costs increased from 71 percent in 2003 to almost 86 percent in 2006, close to the level of other LICs. Improvements in cost-recovery ratios led to lower hidden costs; for 2005, 2006 and 2008—when the average effective tariff covered more than 80 percent of the total costs—the share of underpricing in total hidden costs was the lowest (figure 13a). Over time system losses deteriorated from 25 percent in 2005 to 27 percent in 2009, above the international benchmark of 10 percent for a well-run energy utility.

**Table 15. Evolution of hidden costs associated with EDM**

||Volume of<br>electricity<br>produced /<br>purchased<br>(GWh/year)|System<br>losses<br>(%)|Collection<br>ratio<br>(%)|Average total<br>cost<br>($/kWh)|Average<br>effective tariff<br>($/
early returnees. About 40% of the migrants who stay for less than one year were forced to return.

Being forced to leave the destination country as opposed to leaving voluntarily impacts the rate of return to the migration endeavor, and the likelihood to start entrepreneurship after return. As shown in Table 3, earnings to cost, excess earnings to cost, and excess savings to cost indicators are all significantly larger for migrants who returned voluntarily compared to those who were forced to return. Panel (b) of Figure 9 visualizes the left shift in the distribution of excess earnings over costs for forced returnees compared to voluntary returns. Whereas slightly more than one-fifth of the latter fail to break even, that share rises to one third for migrants who were forced to return. Since the ability to start entrepreneurship after return is tied to savings accumulated overseas, forced returns also negatively affect self-employment after return. As shown in Table 3, migrants who were forced to return are less likely to become entrepreneurs back in Bangladesh: 55 percent of forced returnees are self-employed back in Bangladesh, compared to 64 percent of migrants who voluntarily return.

Table 3: Return rate of migration and employment status after migration by forced return

||Forced return|Normal return|
|---|---|---|
|Earning-to-cost, _r_1|6.5|10.3|
|Excess earning-to-cost, _r_2|4.3|6.4|
|Excess saving-to-cost, _r_3|2.3|3.2|
|Waged workers|41.5%|32.5%|
|Self-employed|55.0%|63.7%|
|Employer|3.6%|3.9%|

_Note:_ Calculations based on the data summarized in Table 2, and measures described in the text.

# **4.4 Human Capital as an Alternative Mechanism**

A channel other than financial wealth accumulation that could foster self-employment after return is human capital accumulation abroad. The vast
Three patterns are evident in Figure 4. First, for the afternoon period observed by Sentinel-5P (2 PM - 3 PM) the NO2 cloud produced by vehicles centered in the core area of Dar (Figure 3) is consistently displaced to neighboring areas. Second, the displaced cloud seems to follow an annual cycle, from northwest of the city in August-September 2018, to due west in October 2018, then shifting progressively southwestward through March 2019, and finally shifting to the area north of the city during April - May 2019. The third evident pattern is variation in the intensity of the pollution cloud. Particularly notable are the light NO2 concentrations in January, April and May 2019.

Our traffic volume data from Google Traffic are limited to the period from late March to late May, so we cannot test whether longer-term fluctuation in traffic volume is a significant contributing factor in this context. However, we have complete daily weather data for the study period.<sup>2</sup> Table 2 reports results from a linear regression fitted to Sentinel-5P data for 288 daily observations from August 2018 to May 2019. The dependent variable is mean daily NO2. We have excluded rainfall from the estimation because prior experimentation revealed that is insignificant when included with the other factors. We find that temperature, wind speed and humidity all have negative impacts on NO2 concentration with high levels of statistical significance. We have also included cloud cover, on the expectation that Sentinel-5P NO2 measurement would be reduced on cloudier days. Our result for cloud cover is highly significant and consistent with this expectation.

Figure 5 aggregates daily NO2 predictions from the regression model into monthly means. The figure illustrates the striking seasonal effects of the weather variables: Weather-predicted NO2 drops sharply from December 2018 to January 2019, increases through March 2019, and then drops even more sharply in April and May 2019. Inspection of Figure 4 reveals the strength of this seasonal pattern at regional scale, with a notably smaller, weaker NO2 cloud in January 2019 and virtual disappearance of the cloud
that that finding should be used with caution in policy discussions.<sup>14</sup> Canova and Pappa (2022) also estimate the effect of EU funds on economic growth at the subnational and national levels, using similar adjusted EU-fund data to ours. There are two main differences between their approach and ours. First, they estimate the effect of two of five EU funds, whereas we examine the combined effect of all spending types.<sup>15</sup> The most important difference is the exclusion of the Cohesion Fund (CF) in their paper, which funds infrastructure and sustainable development in lagging regions, mostly in Central and Eastern Europe. The second difference with Canova and Pappa (2022) is in the estimation methodology and the interpretation of multiplier estimates. They use an “instrumental variable Bayesian local projection approach” that allows for multipliers to vary across regions but does not include time fixed effects. We use a classical panel estimator that is standard in the multiplier literature and does include time fixed effects. Their instrument is the residual of aggregate EU funds regressed on euro area macro variables like aggregate GDP and interest rates, which has some similarities to how shocks are identified in structural vector autoregression models (SVARs). In contrast, we use differences in predicted spending as the instrument.

The different approach means that Canova and Pappa (2022) are estimating a combination of relative and aggregate multipliers, whereas we are only estimating a relative multiplier. A relative multiplier is defined as follows: if region _A_ gets an extra 1€ of spending relative to region B, how much will _relative_ GDP increase in region A relative to region B (other things equal)?<sup>16</sup> The relative multiplier does not incorporate the aggregate response to fiscal spending (or any aggregate shocks, including the effects of monetary policy), as they are

> 12 Coelho (2019) does not report her first-stage estimates, but they are included in her replication file.

> 13
as well as airfreight cost indices from the U.S. Bureau of Labor Statistics. The GSCPI also uses supply chainrelated components from the PMI. The index is normalized, so zero represents the historical average, while negative values are representative of a very low stress situation and positive values express a level of stress.

The Pressure and Stress indices reflect major events, but the latter is more granular regarding containerized shipping specifically. Conversely, the GSCPI US-centric composition may explain its relative insensitivity to the Red Sea crisis as compared to the Stress Index.

Figure 9. Global Supply Chain Pressure Index and Stress Index (MTEU), 2016-2024 <!-- Start of picture text --> 2.5 5<br>4<br>2<br>3<br>1.5<br>2<br>1<br>1<br>0<br>0.5<br>-1<br>0 -2<br>Stress, MTEU (LHS) Pressure Index (RHS)<br>1/1/2016 4/1/2016 7/1/2016 10/1/2016 1/1/2017 4/1/2017 7/1/2017 10/1/2017 1/1/2018 4/1/2018 7/1/2018 10/1/2018 1/1/2019 4/1/2019 7/1/2019 10/1/2019 1/1/2020 4/1/2020 7/1/2020 10/1/2020 1/1/2021 4/1/2021 7/1/2021 10/1/2021 1/1/2022 4/1/2022 7/1/2022 10/1/2022 1/1/2023 4/1/2023 7/1/2023 10/1/2023 1/1/2024<br><!-- End of picture text --> Source: Federal
undergoing rapid structural change may affect data quality. Second, the measurement of informal, illegal and subsistence activities is often very approximate in poor countries, but is likely to be a relatively larger share of GNI than in higher income countries. Third, countries with weaker statistical systems may also lack adequate data sources and estimation methods for accurately measuring formal activities; business registers—a fundamental tool for conducting a sample survey of businesses—may be incomplete and outdated, and survey response rates may be poor. It is possible that, in some countries, under-estimation of formal activities may be as large as under-estimation of informal activities (Jerven, 2013).

Non-market production is another potential source of systematic biases in income figures. Market equivalent prices generally do not exist for measuring the value of most non-market production, such as government services, own-account production, and the output of non-profit institutions, and the value of non-market output, by convention, is proxied by production costs (wages, intermediate consumption, consumption of fixed capital) without adjustments for productivity or the full cost of use of capital. Because government sector productivity levels likely vary systematically among countries at different income levels, this may result in a systematic bias in income measures: an overvaluation in low income countries with low government productivity levels relative to higher income countries with more robust government productivity levels. This effect is, however, likely smaller than the effect of under-coverage of other activities in poorer countries (World Bank, 1989).

A question of the viability of international comparability may arise from the use by countries of different vintages of international standards for the System of National Accounts (SNA).<sup>11</sup> However, this is believed to be a relatively minor issue, compared with the lack of adequate data sources and estimation methods for data compilation, especially in poor countries where statistical capacity is often weakest. Countries are constantly working to increase their statistical capacity and improve their national accounts estimates, and new source data and improved methodologies have led to
Note: The coefficients measure the time fixed effects relative to 2007:Q1. The figures in italics are p-values for a test for whether there is a difference between the fixed effects between that year and the previous year. Values are shown in the prices of 2007. Boldfaced numbers are statistically different from the previous year at the 5% level; yellow cells indicate lower real expenditure per capita in 2009 than in 2008.

# **Robustness**

Our focus is on a monetary measure of wellbeing in the form of real consumption per capita. Could Thai households be maintaining their consumption levels by reducing investment – for instance, by pulling children out of school so they can work, or by delaying purchases of assets such as cars?

Figure 8 shows age-specific school enrollment rates for 2007-2010, based on data from the socio-economic surveys. The pre-school rate refers to children 5 and younger, and subsequent age groups are associated with the relevant school level: 6-11 for primary education, 12-14 for lower secondary school, 15-17 for upper secondary school, and 18-22 for higher education. There was no perceptible drop in enrollment rates during the 2008-2009 recession – the period shown by the shaded areas in Figure 8.

<!-- Start of picture text --> 120<br>Primary school<br>100<br>Lower secondary school<br>80<br>Upper secondary<br>school<br>60<br>Higher education<br>40<br>Pre-school<br>20<br>0<br>2007q1 2007q3 2008q1 2008q3 2009q1 2009q3 2010q1 2010q3<br>Age specific enrollment rate, %<br><!-- End of picture text -->

**Figure 8.  Age-Specific School Enrollment Rates, Thailand, 2007:Q1 – 2010:Q4** Source:  Thailand Socio-Economic Surveys of 2007, 2008, 2009, and 2010.  Shaded area marks period of recession.

The results of a more formal test are shown in Table 6: we regress
**Figure 5: Aggregate tax revenue-based progressivity indicators**

<!-- Start of picture text --> Direct-to-indirect tax ratio IT % of tax rev IT as % of GDP<br>Vanuatu<br>Somalia<br>Sudan<br>Central African Republic<br>Maldives<br>Benin<br>St. Lucia<br>Dominica<br>Tonga<br>Afghanistan<br>Lao PDR<br>Togo<br>Samoa<br>Cambodia<br>Burundi<br>Gambia, The<br>Nepal<br>Madagascar<br>Haiti<br>Grenada<br>Kyrgyz Republic<br>Cameroon<br>Niger<br>Honduras<br>Tajikistan<br>Guinea-Bissau<br>Bangladesh<br>Burkina Faso<br>Cape Verde<br>Senegal<br>Myanmar<br>Tanzania<br>Solomon Islands<br>Uganda<br>Lesotho<br>Pakistan<br>Liberia<br>Zimbabwe<br>Mali<br>St. Vincent & Grenadines<br>Sao Tome and Principe<br>Marshall Islands<br>Uzbekistan<br>Rwanda<br>Nicaragua<br>Kiribati<br>Guyana<br>Kenya<br>Malawi<br>Zambia<br>Yemen, Rep.<br>Sierra Leone<br>Congo, Dem. Rep.<br>Ghana<br>Chad<br>Tuvalu<br>Mozambique<br>Bhutan<br>Papua New Guinea<br>0 1 2 0.0 0.5 1.0 0 10 20<br><!-- End of picture text --> Source: authors calculations based on UNU-WIDER World Revenue Dataset 27
developed and developing countries, there are many examples of governments that have taken on more than they can manage, given their limited administrative capacity to design and implement policy or to provide basic health and education services. These examples, together with weak revenue raising capacity in many countries, have led some to argue that the state in developing countries should be extremely modest in its objectives and scope. There is some basis to this argument, but it must be set against the scale of the development challenge facing developing countries and the extensive range of state actions needed to foster inclusive growth. Developing country policies during the 1990s and 2000s—at least until the Great Recession of 2008—have improved: regulations and the business climate have been more conducive to growth; macroeconomic management has improved, exchange rates have been more stable, trade barriers have been reduced and openness has increased; and inflation, which typically does the greatest harm to poor people, has fallen significantly. At the same time, governance and institutional quality, as measured by the World Bank’s Country Policy and Institutional Assessment (CPIA) index, suggests a more sobering picture: there has been some—but little— average progress around the world in improving governance, controlling corruption, and improving institutional quality, with significant variation across countries.<sup>4</sup> The number of

> 3 See Stern, Dethier and Rogers (2005).

> 4 As discussed later, the CPIA index is composed of 20 variables in four groups: economic management, structural policies, policies for social inclusion and equity, and public sector management and institutions.

4
.

For all of Sub-Saharan Africa, about 40 percent of fertilizer is used on maize, implying that the average dose is only about 17 kg/ha of nutrients compared to the developing country average of 100 and the industrialized country average of 270 kg/ha on the same crop (Morris et al. 2007; Heisey and Norton 2007). While it is incorrect to surmise that modern maize ―depends‖ on fertilizer, modern maize does generally trace a steeper response curve for fertilizer than do traditional farmers‘ varieties. Maize is a heavy consumer of fertilizer, leading fertilizer demand in industrialized countries among major cereals, and the second most heavily fertilized crop on a global scale, after potatoes (Heisey and Norton 2007).

Farmers grow improved varieties without fertilizer in many areas of Africa, especially in marginal areas, such as the drier zones of Kenya and Zimbabwe, but also some relatively favored areas, such as Ghana. Higher adoption rates for improved seed than fertilizer reflect the high costs of fertilizer in Africa, lack of input availability, and farmers‘ cash constraints.

Even where fertilizer is used, it is often used inefficiently. A single recommendation is provided for wide areas, which does not account for the diversity of smallholder situations and the acute cash constraints under which they operate. Mistaien‘s (2001) analysis, which employs a benchmark productivity measure computed by matching farm household survey data to optimal

> 4 Because of the difficulty in measuring areas planted to improved OPVs, in particular, estimates should be considered with caution.  Almost all of the maize area in West African countries, with the exception of Nigeria, is planted to improved open-pollinated varieties. Given that the private seed system has not been active, it is likely that farmers practice seed saving for much more than the recommended number of years and because of crosspollination, it may be difficult to differentiate improved from unimproved materials.

9
Policy Research Working Paper 6469

# **Abstract**

Social protection is absent from the Millennium Development Goals (MDGs), and only recently has gained some prominence in the post-2015 discourse. In the past quarter century, however, rising inequality has often accompanied economic growth. At the same time, the growing importance of risk and vulnerability on the wellbeing of the poor has been recognized. Further, there is now a consensus on adopting more ambitious goals on poverty reduction. Defining social protection as a collection of programs that address risk, vulnerability, inequality and poverty through a system of transfers in cash or in kind, this paper argues that social protection needs to be on the post-2015 agenda as a key element of the discourse. It provides an empirical overview of social protection around the world based on the World Bank’s Atlas of Social Protection: Indicators of Resilience and Equity (ASPIRE) data set. Focusing on the goal of ending poverty, the paper estimates that social protection programs are currently preventing 150 million people from falling into poverty. Based on the data set, the paper develops, tentatively and for discussion, a set of candidate goals, indicators and targets for the acceleration of poverty reduction through social protection. The authors ask what it would take for social protection programs to contribute to halving the poverty gap in a country. They show that if all countries could achieve the actual poverty reduction efficiency already observed in the top quartile of countries, then 70 percent of the countries in the sample could achieve this goal. However, for 30 percent of the countries, even reaching the top quartile on efficiency will not be enough—for these countries, the issue is one of budgetary adequacy.

This paper is a product of the Social Protection and Labor Department of the Human Development Network. 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
models and day-to-day decision making to reflect emerging data on consumer behavior. Since then, big data analytics has been increasingly utilized as a critical management tool and knowledge source (McAfee and Brynjolfsson, 2012), occupying a space between academic and applied knowledge (Savage and Burrows 2007, 2009; Taylor, Schroeder, and Meyer 2014).

More recently, the use of big data has expanded rapidly in economics and other social sciences. The advent of big data has already prompted novel research designs across a range of topics and refined the measurement of economic effects and outcomes. Over time, these data are likely to affect the types of questions economists pose, allowing for greater attention to variation across populations and facilitating analyses across a broader range of economic activities and interactions. Einav and Levin (2013) point to three unique characteristics of big data that can help contribute to the economic literature: (i) the real-time 4
***|0.580***|0.601***|0.657***|0.529***|0.655***|0.619***|
|flows from the United States|(0.112)|(0.113)|(0.120)|(0.135)|(0.124)|(0.124)|(0.115)|(0.124)|(0.116)|(0.126)|
|Observations|103,839|103,013|89,059|71,779|74,087|78,411|97,011|73,647|100,740|82,939|
|Importer-Industry-Yr FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Exporter-Importer-Yr FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|

Source: Author's analysis based on data described in text. Notes: See Table 3.
Peru death certificate microdata were compiled by SINADEF obtained via correspondence with University of Toronto researchers.

Together there are 39 countries and economies with excess death estimates: Austria; Belgium; Bulgaria; Switzerland; Chile; Czech Republic; Germany; Denmark; Spain; Estonia; Finland; France; Scotland; Greece; Croatia; Hungary; Iceland; Italy; Lithuania; Luxembourg; Latvia; Netherlands; Norway; Poland; Portugal; Slovak Republic; Slovenia; Sweden; Taiwan, China; Colombia; Peru; Mexico; England and Wales; United States; Australia; Canada; Israel; Republic of Korea; New Zealand.

Of these, nine do not have corresponding official death records from the coverage database: Bulgaria, Czech Republic, Estonia, Croatia, Lithuania, Luxembourg, Latvia, Poland, Slovak Republic.

The preferred age group categories for the analyses conducted in this paper were as follows: 0- 14, 15-44, 45-54, 55-64, 65-74, 75-84, 85+. However, since the reporting of all-cause deaths by age used by the Human Mortality Database is not consistent from country to country (not all countries report death counts tabulated at 5-year intervals or less), successively broader agegroupings are used in cases where the preferred age groups could not be constructed directly from the STMF input data. These countries include the United States, England and Wales, Australia, Canada, New Zealand, Israel, the Republic of Korea, and Northern Ireland. We compute excess mortality rates for all unique country-age group combinations (as shown in Figure 4) but to estimate the age gradient of mortality, we only include countries for which we could construct the preferred age-group categories as described above. Countries for which we could not construct the preferred age-group categories are Brazil, Israel, and New Zealand. To avoid excluding the United States from this analysis, we use mortality data from the CDC which reports deaths for finer age groups for 2015 through 2019.

18