rafmacalaba/data-use-mentions-extended huggingface dataset:
2

# **I Introduction**

What are the gains from a better education, more land ownership, or a different occupation in Bangladesh? Do the gains differ in urban and rural areas? Have they remained stable over time? Do household size, family structure, and gender matter for well-being? Do consumption, poverty, and inequality depend more on household characteristics, or on the characteristics of the areas in which they live? What are the micro determinants of growth in per capita consumption over time? While the answers to such questions have important policy implications, they have been missing because of a lack of access to nationally representative household data covering a period long enough to analyze trends over time. Using in a consistent way five comparable and successive national surveys, this paper analyzes at once the micro determinants (and changes thereof) of consumption, poverty, growth, and inequality in Bangladesh for the period 1983 to 1996. The analyzis of the determinants of consumption is carried in a straightforward way by regressing log per capita consumption on household and area characteristics, and by interpreting parameter estimates as the returns to these characteristics. Assuming normal errors, the parameter estimates can also be used to predict the probability of being poor. By running the same regressions for various survey years, one can easily decompose the growth in per capita consumption into the impact of changes in household characteristics, and the impact of changes in the returns to these characteristics. Finally, and this is a new idea, conditional between group inequality measures can be computed from the same regression estimates so as to assess the micro determinants of inequality. These conditional inequality measures avoid the pitfalls of standard group decompositions of the Gini index which, when applied to one a variable at a time such as education, cannot assess the impact of education on inequality without bias since they do not control for characteristics correlated with education.

The methodology is applied to five rounds of the nationally representative Household Expenditure Surveys (hereafter FES) of the Bangladesh Bureau of Statistics (hereafter BBS
consumption of manufacturing in 28 ECA countries. The manufacturing sector is further broken out by 11 subsectors at the two-digit level of the international standard industrial classification Revision 4 (ISIC Rev. 4).

The aggregate value added of manufacturing is reported in constant 2000 US dollars in the World Development Indicators (WDI) (2012). To compare energy intensity levels across countries, we apply purchasing power parity (PPP) exchange rates to convert all values into constant 2005 international dollars. The theoretically ideal PPP exchange rate to use at the sector level would be based on the output prices of the sector, rather than on aggregate expenditure prices. However, due to the limited availability of producer price-based PPPs, we follow the standard practice of using expenditure PPPs for cross-country convergence analysis (such as in Dollar and Wolff, 1993 and Mulder and Groot, 2007).

> 5 Energy intensity is measured by energy use per unit of manufacturing value added. Energy use is defined as final energy consumption excluding energy losses in the generation, transmission and distribution of purchased electric power. Value added presents the net economic output, or gross economic output less the value of purchased inputs. It is derived by subtracting the cost of materials, supplies, fuel and purchased electricity from the value of final outputs.

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since then. Given rapid population growth in Afghanistan, the official statistics imply that GDP per capita has been on a declining trend in recent years.

**Figure 12. Out-of-sample forecasts of conflict intensity are accurate at the aggregate level**

<!-- Start of picture text --> Total casualties (forecast/actual)<br>3.5<br>3.0<br>2.5<br>2.0<br>1.5<br>1.0<br>0.5<br>0.0<br><!-- End of picture text --> The analysis in this paper refers to expenditures per capita, rather than to GDP per capita. But with savings rates being generally stable it is reasonable to assume that the two series are highly correlated. With this caveat in mind, the out-of-sample forecast predicts well the increase in GDP per capita associated with the surge in international troops, although it sees it happening in 2013 rather than in 2012. The forecasts also predict a decline in GDP per capita in subsequent years, in contrast with the relative stability implied by official statistics and the steady increase anticipated by other, contemporary analyses (figure 13).

There are additional indications that the approach used in this paper yields reliable results. In the already mentioned Survey of the Afghan People (2015), one of the questions asks households about the change in their financial situation, relative to the previous year. The difference between the percentage of households reporting that their situation had improved and those answering that it had worsened can be seen as an indicator of the change in living standards. The variation of this indicator matches well the change in expenditures per capita generated by the out-of-sample simulation.

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working hours were lost in 2020 compared with the fourth quarter of 2019, a loss equivalent to 255 million full-time jobs. About half of the losses are estimated to have come from working hour reductions within employment. In fact, many more households reported losing incomes than households with a respondent stopping work, especially in lower income countries. This is consistent with the nature of the labor market in these countries where many workers are self-employed or hold otherwise informal jobs. Within countries, income losses were strongly correlated with work stoppages, job changes to self-employment, and sectoral transitions to agriculture (Figure A6.4).

During 2021, the share of households reporting that total incomes are below prepandemic levels remains very high – almost half of the households globally, and 70 percent of households in LICs are in this category. This may also be because the mitigating measures put forth by governments were largely limited in scope and duration (World Bank 2022b). While we do not have sufficient data to disaggregate trends by region and time window, it appears that apart from the ECA region, a relatively high share of households has still not recovered their lost incomes. The setback in 2021 could have been due to the emergence of new variants and re-imposition of partial lockdowns in several countries.

Evidence of “long COVID” effects on particular groups can be further seen by comparing characteristics associated with income loss and recovery using multivariate regressions. Similar to analysis of employment recovery, we draw on panel surveys in about 20 countries that collected information on income changes between survey waves during the pandemic, as well as recording initial income losses relative to before the pandemic. We can then identify which households reporting income loss relative to prepandemic were more likely to recover at least some income, as observed in a subsequent HFPS wave. Figure 9a shows that households were significantly more likely to lose income in each country if they were larger in size, less educated, the respondent stopped working, and during a period of higher policy stringency.<sup>18<
have private credit bureaus. The survey from the World Bank finds that private credit bureaus exist in at least half of the countries surveyed (26/57 reported the existence of a private credit bureau).<sup>16</sup> See Figure 1 in the Appendix for a map of public credit registries and private credit reporting worldwide. Here, we strongly suggest that PCRs and private credit bureaus are complements and not substitutes.

In the three countries studied in this paper, Argentina, Brazil and Mexico, private credit bureaus exist and are the primary sources of information for many lending institutions. However, the public registries have played an important role in supplementing the data available from the private sector. For example, in Brazil most private sector credit reporting is focused on negative information. Since loans in good standing are not regularly reported to the private registries, private credit reports do not provide a full picture of the total indebtedness of borrowers. Brazil’s public registry provides this important information to the banking community. In Argentina, the PCR has helped to fill in gaps in coverage in the private reporting system since all regulated financial institutions are required to report to the central bank on virtually all loans. Lenders can obtain both positive and negative information on a case-by-case basis from the public registry for borrowers they are evaluating. They also receive information on all borrowers who are in arrears or who are in default on a monthly basis. In Argentina, the negative information from the PCR is also available to the private credit bureaus. In Mexico, the public registry was the only source of centralized credit information available to financial institutions until the mid-1990s when private credit bureaus were established. The development of the private industry led to a decision by the Mexican central bank to end the distribution of the public registry data to banks in 2002.

# **_2. Banking supervision_**

A second main objective for establishing a public credit registry is to strengthen banking supervision. In a prior survey of PCRs from 2000-2001, approximately half
Existing research consistently finds that parental out-migration has an unambiguously negative impact on children’s mental health, behavioral issues, and delinquency. Children raised by grandparents have a higher prevalence of low self-esteem, mental health problems, school bullying, and specific psychological symptoms compared to those living with at least one parent (Tang et al., 2018). Left-behind children are at greater risk for developing depression, especially when experiencing negative events or when left behind by both parents (Guang et al., 2017; He et al., 2012; Liang, 2016). A meta-analysis of 39 academic articles confirms the prevalence of depressive symptoms in left-behind children (Y.-Y. Wang et al., 2019). Left behind children are also more likely to engage in unhealthy behaviors like smoking and drinking (Wen and Lin, 2012; Yang et al., 2016) and are at higher risk for psychotic experiences and traumatic events (Sun et al., 2017). These negative associations suggests that the detrimental effects of reduced time with parents may outweigh the potential benefits of increased income when measuring children’s welfare by their mental health status. Parental migration leads to reduced supervision, protection, bonding and communication, which cannot be compensated for by increased economic resources. Moreover, grandparents and other extended family members serving as primary caretakers are imperfect substitutes for parents.

In sum, parental migration enables rural households to invest more in children’s education and nutrition, but it also reduces parental care and supervision, which cannot be perfectly substituted by nonparent family members. Consequently, while the literature documents some improvement in early child nutrition and school performance among left-behind children, it also reveals a concerning prevalence of mental health and behavior issues.

Although many health studies do not establish causality, the consistently reported negative associations between parental migration and children’s mental health and behavioral problems are of great policy concern. In 2014, the central government issued policy guidance urging local governments to provide and improve and improve various services (
|92.5<br>(22)|53.9<br>(36)|n.a.|

Note : Figures in parentheses indicate the number of countries for which data are available. Source: USAID Demographic and Health Surveys; World Bank, _World Development Indicators_ , various years; International Telecommunication Union; Roberts, 2003 .

**Table 4:** Access of rural populations to infrastructure services,

<u>by country income group</u>

||Electricity<br>(1997-01)|Water<br>(2000)|Sanitation<br>(2000)|Telecoms<br>(1997-02)|Transport<br>(1994-01)|
|---|---|---|---|---|---|
|Income group|Per|centage of rural po|pulation|Main lines per<br>100 people|Percentage of rural<br>population within 2 km of<br>an all-season road|
|Low|20.3<br>(24)|52.7<br>(54)|41.8<br>(52)|1.7<br>(55)|61<br>(21)|
|Lower middle|67.3<br>(8)|75.0<br>(40)|59.8<br>(37)|8.7<br>(43)|n.a.|
|Upper middle|n.a|76.4<br>(23)|81.3<br>(22)|22.5<br>(24)|n.a.|

Note : Figures in parentheses indicate the number of countries for which data are available.

Source: USAID Demographic and Health Surveys; World Bank, _World Development Indicators_ , various years; International Telecommunication Union; Roberts,
economic incentives created by each of the five policy designs. We find that, for a given carbon price, abatement-based rebating and the two intensity-based variants lead to additional reductions in emission intensity, as compared with lump-sum or output-based rebating. If domestic politics or competitiveness concerns constrain carbon prices to below the social cost of carbon, these policy variants may be helpful in increasing the environmental effectiveness of a carbon price with potentially beneficial welfare implications. In contrast to abatement-based rebating, which further discourages output by raising the opportunity cost of emissions, the two forms of intensity-based rebating offer additional support for output, similar to output-based rebating. Thus, intensity-based rebating, like output-based rebating, also helps address concerns over structural frictions associated with output losses of emission-intensive industries. We use our model to rank each of the five policy variants according to how well they protect against reductions in firm output and how much incentive they provide for reductions in emissions. Our ranking suggests that by both protecting output and increasing emission reductions achieved at a given carbon price, intensity-based rebating may be useful in addressing key concerns that prevent wider adoption of effective carbon pricing.<sup>8</sup> In practice, rebating mechanisms are typically reserved for the sectors that are least able to pass on their carbon costs: emission-intensive industries that are also highly trade-exposed. Thus, in addition to comparing the policies in a partial-equilibrium analytical setting with one sector only, we also implement each policy approach in a multisector computable general equilibrium (CGE) model. The CGE model not only relaxes dimensionality restrictions but also allows us to account for important market interactions via economy-wide substitution and income effects. Furthermore, the CGE setting opens up for a broader welfare analysis where we can trade off economy-wide efficiency impacts of alternative rebating schemes with sector-specific implications. Using the numerical model parameterized to US data, we find important quantitative differences between the policy approaches. Notably, with the same carbon price, abatement- and intensity-based rebating policies lead to two to three times

> 8A caveat, which we note
# **4. The Colombian Labor Market: Stylized Facts**

Tables 1 and 2 report key stylized facts of the Colombian labor market. Unemployment rates estimated from the FSLS rounds reach 10.9 and 11.7 percent in 2008 and 2009 respectively, very similar to the official unemployment rates for the comparable 13 main metropolitan areas: 10.9 and 12.3 percent respectively (DANE 2010, p. 9). In 2008, labor market participation reached 54.4 percent, 66.3 percent among males and 44.5 percent among females in the working age (12 and older). By occupation, 60.3 percent (60.7 percent) of males (females) were salaried, 38.8 percent (37.1 percent) self-employed and 0.9 percent (2.2 percent) unpaid workers. By economic sector, some 51.8 percent of workers were occupied in services, followed by 40 percent in retail, manufactures and other sectors combined. About 27 percent of workers pertained to SISBEN 1 and 2.<sup>4</sup> Only 38.8 percent of occupied workers were in the formal sector (defined in this paper as workers with pension and health insurance coverage).

The composition of the labor market does not change much in 2009 except for a marked decrease in the participation rate down to 51.1 percent, clearly associated with the crisis. Those most affected are males, although these changes do not substantially alter the existing gender gap in Colombia. Self-employment increases for both males and females, while construction and services drop their share in total occupation. In 2009, more workers pertain to SISBEN 1 and 2 categories than the previous year. Most interestingly, a significant proportion of Colombian workers report voluntarily choosing their employment category: 44

> 4 SISBEN is a means-tested targeting mechanism, categorizing households by socioeconomic condition based on a set of personal and household features and assets. The index goes from 1 to 6, where 1
the surface temperature and the temperature in each of the two layers immediately above the surface. If the surface temperature is lower than the temperature in either the first layer _or_ the second layer we classify that day as experiencing an inversion. The second compares surface temperature to the temperature in all the layers in our sample. Again, if surface temperature is lower than the temperature in _any_ of these layers we classify that day as experiencing an inversion. We also split inversions into winter and summer inversions as meteorological conditions and the emissions of pollutants vary across seasons. In general winter inversions have a larger impact on pollution levels (for example because there are more emissions from burning for heating in the winter which can be trapped by inversions). We count the number of days with inversions in each district and year or season and calculate the share of days each district experienced an inversion in the whole year as well as separately by winter and summer. We also measure the strength of each inversion as the difference in temperature between the higher and lower layer on days when there is an inversion. Larger differences indicate stronger inversions (Chen et al., 2022). For each district and year or season we calculate the average inversion strength averaging across all inversions that occur in the relevant time period.

# **3.3 Meteorological controls**

We collect weather re-analysis data from ERA5. ERA5 is produced by the European Commission’s Copernicus Climate Change Service.<sup>11</sup> We use data from the ERA5 Land hourly product. This provides data at an hourly level on a grid of 0.1<sup>_◦_</sup> _×_ 0.1<sup>_◦_</sup> , which translates to a 9km resolution over India. We use data on temperature, dew point, surface pressure, wind direction, wind speed, and precipitation over the full sample. We calculate relative humidity from dew point, temperature and surface pressure. We aggregate
greater effects (and impose greater costs) on cross-border flows of services trade and investment are not overlooked. The section that follows explores this issue.

# **4.1 Recent findings on trade costs for services trade for Modes 1, 2, and 4**

Empirical research devoted to the trade costs resulting from barriers to services trade and investment typically regard such costs as high and often exceeding the average tariff on traded goods. Such costs are further seen to potentially concern all modes of supplying services abroad, including via Mode 3, though most empirical attention tends to be devoted (largely for reasons of data availability) to cross-border transactions (Miroudot et al., 2013; van de Marel and Shepherd, 2019). Beyond factors relating to infrastructure, geography, language, culture, and institutional characteristics, trade costs in services sectors relate chiefly to regulatory measures that either create entry barriers or increase the cost burdens facing firms (Miroudot et al., 2013).

Many factors distinguish trade in services from other sectors (Sauvé, 2019). The fact that services transactions confront complex regulatory regimes, coupled with the requirement for physical proximity characteristic of many interactions between buyers and sellers of services - often referred to as the “proximity burden” (Christen and Francois, 2013) -- tend to significantly increase the cost of trading services.

In the latest (2019) edition of its _World Trade Report_ devoted to trade in services, the WTO Secretariat introduced a novel measure of trade costs which builds upon a recent study on trade costs in the global economy (Egger _et al_ ., 2018, see Box 1).

# **<mark>Box 1: Services trade costs and their decomposition: WTO methodology</mark>**

<mark>Estimates of trade costs for Modes 1, 2 and 4 featured in the World Trade Organization’s</mark> _<mark>World Trade Report 2019</mark>_ <mark>are derived from multiregional input-output data found
Policy Research Working Paper 9418

# **Abstract**

Exploiting cross–birth cohort and cross-country variation from a pool of 188 household surveys from 111 countries, this paper measures how life expectancy at birth affects lifetime education and earnings. On average, individuals add one year of schooling for every 8.3 years of increased life expectancy at birth. Lifetime earnings increase by 1.7 percent per year of added life expectancy at birth. The estimates imply that rising life expectancy at birth explains 75 percent of the increase in average years of schooling worldwide for birth cohorts between 1922 and 1987 and 38 percent of the increase in average gross domestic product per capita in the 20th century.\.

This paper is a product of the Education Global Practice. 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 cmontenegro@worldbank.org.

_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 views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team