rafmacalaba/data-use-mentions-extended huggingface dataset:
(or the GDP Deflator) — the number of transactions in the economy (the average price of transactions) — results in a one-percent increase in the demand for credit. Although this assumption might be reasonable for some economies, for many others, particulary developing countries, it will be violated because of the varying levels of credit usage in economic transactions.<sup>4</sup> The studies by Cottarelli _et al._ (2005) and Egert<sup> ́</sup> _et al._ (2006) also consider the effects of changes in development and structural indicators on equilibrium credit demand. They do so by inserting low frequency development and structural indicators into the credit demand equation (the conditional mean equation of credit), together with higher frequency variables that determine credit demand over the business cycle. This approach, however, does not allow for the possibility that the sensitivity of credit to GDP in more credit intensive economies is likely to be higher than in less credit intensive ones. Moreover, especially in time-series panels that include a limited number of countries as in Cottarelli _et al._ (2005), the development and structural indicators, which change only at a very low frequency, fail to identify any material effects of the indicators on equilibrium credit. Further, mixing higher frequency variables such as GDP, prices and interest rates measured on a basis with low indicators like liberalization or quarterly frequency financial public governance, which change typically over a period longer than a business cycle, is likely to result in statistical collinearity between the long-term and short-term indicators when both are measured at quarterly frequency. This collinearity will make it difficult to identify the true effects of the long-term indicators on equilibrium credit and derive any reliable policy recommendation.

The objective of this study is to propose a structural framework to estimate equilibrium credit, which is anchored in the long-run transaction demand for credit by the real economy, and accounts for the effects of economic and financial development on equilibrium credit. The proposed framework consists of two
capacity in public and private schools, by level

|Primary|Private|85%|
|---|---|---|
||Public|175%|
|Secondary|Private|65%|
||Public|107%|

Source: Authors' calculations using 2015 Morogoro data.

Private schools are smaller—the average number of students in public schools is 661 compared with 247 in private schools. Size comparison by education level is provided in Figure 10. On average, private schools have half as many students in these levels as public schools, though the number of students for both public and private schools is widely distributed around the respective means.

11
|0.055|
|**All Nigeria**|**0.120**|**0.042**|**0.016**|**0.136**|**0.085**|**0.034**|
|Source: NCS, 198|5/86 and 199|2/93.|||||

More than half (11 states) of the states indicate an increase in the severity of poverty, as follows: Anambra (0.1 percentage points), Bauchi (1.5 percentage points), Benue (1.1 percentage points), Cross River (2.5 percentage points), Kano (1.6 percentage points), Kwara (3.0 percentage points), Ondo (1.0 percentage points), Oyo (1.3 percentage points), Plateau (2.5 percentage points), and Rivers (2.2 percentage points). Seven of the states that show increases in headcount also show increases in all three poverty measures. On aggregate there is a doubling of 1.8 points in the severity of core poverty in Nigeria. From Table 4.15, it can be seen that in 1985, three Northern states (Kaduna, Kano and Sokoto) accounted for 38 percent of all the core poor, contributed 34.0 percent to the depth of poverty and 28.7 percent to the severity of poverty. In 1992 the numbers had reduced by 4 percentage points for headcount, 8 and 11 percentage points for the depth and severity respectively. On the other hand, five states have shown dramatic increases in the headcount, depth, and severity of core poverty. They are Kwara, Oyo, Rivers, Plateau and Sokoto. Lagos urban areas made almost no contribution to extreme poverty in 1992/93.
0.478***|0|431,436|
|Asset ownership|-0.007***|-0.033***|0|-0.095***|-0.121***|0|264,680|
|Literacy|-0.010***|-0.065***|0|-0.136***|-0.191***|0|694,463|
|Not employed in the<br>agricultural sector|-0.009***|-0.031***|0|-0.052***|-0.074***|0|450,521|
|Access to electricity|-0.017***|-0.093***|0|-0.263***|-0.339***|0|697,574|
|Piped drinking<br>water|-0.006***|-0.020***|0.0174|-0.095***|-0.108***|0.0174|595,934|
|Improved sanitation|-0.011***|-0.049***|0|-0.134***|-0.172***|0|592,937|

<u>Source: Authors’ calculations from the Global Monitoring Database (GMD)</u> <u>Notes: This table shows the results of regressing an indicator variable of being poor on (ranked) residuals of covariates</u> of poverty (see Section 3b in the paper for more details). The residuals are determined by conditioning out household size from the covariates of poverty, including years of schooling, asset ownership/index, literacy, access to
World Bank has increasingly focused on firmn-level surveys to help build a data foundation in developing countries and transition economies. The most extensive firn surveys implemented by the World Bank include the Regional Program on Enterprise Development (RPED) survey in 8 African countries, the Industrial Competitiveness Study (ICS) in East Asia, a series of surveys in transition countries, and many surveys on small and medium enterprise (SME) issues in South Asia and South America (see Table 1 for a partial list of World Bank firm-level surveys). This paper takes stock of recent World Bank firm surveys, discussing what we have learned from them and how we could more consistently and efficiently gather data for policy analysis.

2
|78.2|
|Total|167,903|8|56.1|80.0|

Note. City- and district-level responses are by the heads of city and district prefectures, roads inspectorates, and budgetary institutions “ _Avtomobil’nye Dorogi,” “Gormost,” “Mosvodostok,”_ and _“Mosvodokanal.”_ Municipality-level responses are by heads of municipal administration ( _<u>uprava</u>_ <u>). Community-level responses are by heads of municipal budgetary</u> institutions for communal and housing services _“Zhilischnik.”_ Source: authors’ calculations based on scraped data from _gorod.mos.ru._ Among 175,386 scraped data points, the rank of the responder was not stated in 7,483 cases.

Even though Table 3 suggests no significant differences across responding officials' rank, we perform an additional robustness check by estimating our empirical specification based on sub21
’s hardware trade is concentrated in a few narrow product groups (See Figure 2). Eighty-one percent of imports are in intermediate electronic components such as integrated circuits and LEDs, while final goods imports are concentrated in communications equipment such the base stations needed for rapid expansion of Vietnam’s mobile communications infrastructure (China’s Huawei, a leader in low cost mobile telephony infrastructure for developing markets, is a major vendor). Exports are even more concentrated, with 75% of exports in communications equipment, mainly mobile handsets, which account for 88% of communications equipment exports. But Vietnam’s ICT hardware exports are even more concentrated than these figures suggest. According to Vietnam’s General Statistics Office, Samsung Electronics Vietnam (SEV) is responsible for 98 percent of the country’s mobile handset and component exports. Other product groups that are important in Vietnam’s ICT goods export basket are computers and storage devices (12 percent ICT hardware exports) and automotive electronics (6 percent). Vietnam is also an important producer and exporter of automotive wire harnesses, with $1.9 million in exports in 2013.

Trade statistics also indicate that Vietnam is well integrated into regional value chains, with most intermediate goods, particularly electronic components, imported from neighboring China, Korea, Singapore, Japan, Chinese Taipei, Hong Kong SAR, China, and Thailand. In fact, 72 percent of intermediate ICT goods come from these seven countries (see Tale 6). While the top ten export destinations show that most final goods are exported to advanced market countries (USA, Europe, and Japan), export destinations are not as concentrated as sources of intermediate goods imports; the top ten destinations account for approximately 60 percent of final ICT goods exports.

12
household survey, we have explored these concerns for one of the largest means-tested cash transfer schemes in the world (though a scheme about which remarkably little has previously been known).

In aiming to provide a guaranteed minimum income to all registered families in urban China, there is naturally a concern about behavioral responses to the _Di Bao_ program; the work disincentives implied by how the scheme operates in theory suggest that counterfactual pretransfer income will exceed observed income minus transfer receipts. Indeed, a strict

> interpretation of the design of _Di Bao_ implies that it would create a virtual poverty trap, in that participants face a 100% marginal tax rate. Yet we find no evidence consistent with that implication, even when we allow for income measurement errors (which could lead one to underestimate the scheme’s benefit withdrawal rate). Our results confirm qualitative

observations from field work suggesting that the way the program operates in practice attenuates the incentive effects implied by its design. Indeed, when viewed in the light of the literature on the optimal design of targeted programs, our results suggest that the program’s rate of benefit withdrawal is probably too low.

Nonetheless, the program appears to be very good at avoiding leakage to the non-poor. Coverage is clearly the bigger problem. Despite the program’s aims, our survey data indicate that it is not reaching about three-quarters of those households with an income below the _Di Bao_ line. And it is only covering about one eighth of the aggregate income gap relative to the _Di Bao_ lines. While in theory, this program would eliminate poverty (based on the _Di Bao_ lines), in practice the impact is small.

Measurement errors are a serious concern, as in any survey-based assessment of targeting performance. We have proposed new methods of testing robustness to the likely sources of 25
|Network (0/1)|0.014|0.023|0.051|0.043<sup>_∗_</sup>|0.137|0.159<sup>_∗∗∗_</sup>|0.131<sup>_∗∗∗_</sup>|
||(0.022)|(0.020)|(0.160)|(0.024)|(0.160)|(0.023)|(0.020)|
|County, Wave, Strata FE|✓|✓|✓|✓|✓|✓|✓|
|Observations<br>|30,967|30,978|2,521|30,978|4,666|30,934|30,968|
|_R_<sup>2</sup>|0.074|0.027|0.273|0.042|0.180|0.078|0.083|

This table shows OLS regression estimates using the first five waves from the Rapid Response Phone Survey data in Kenya. All regressions include the set of controls described in Table 1. Robust standard errors clustered within counties are reported in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.

Similarly, in Table 6 column (1), we also find that households with a female head or those which relied on family transfers prior to the COVID-19 crisis are more likely to reduce meals. However, those with a female head and relied on family transfers before are less likely to reduce meals. In columns (6)-(7), we observe that the magnitude of the positive correlation between the female head dummy and coping using family transfers or network assistance reduces after including the network dummy, compared to Table 1
the JQM to the standard definitions of informality**

The claim of this paper is that the proposed job quality index provides more comprehensive information on the quality of jobs across countries relative to a single indicator, such as informal employment, that is used widely in the literature. In particular, the proposed JQM includes two additional dimensions on income and working conditions that are not part of the typical official informality measures used to assess job quality across countries. To illustrate the differences between our proposed JQM and the standard formality indicator, the analysis next constructs the standard definitions of informality, and then compares these to the JQM. The main finding is that workers at different levels of job quality may be classified in single categories of the standard binary definitions of informality, and thus critical information on the quality of jobs is lost.

To conduct the comparative analysis between the JQM and informality, the ex post harmonized SEDLAC database is used.<sup>22</sup> SEDLAC correspond to a subset of the main data set used for the analysis in this paper and includes standardized definitions of informality. The two standardized informality definitions used are (1) productive informality, which classifies a worker as informal if the firm has five or fewer employees, and (2) legal informality, which classifies a worker as informal if the job does not have pension benefits. The data contain information on more than 430,000 wage workers across 12 Latin American countries. These two definitions of informality capture different underlying working conditions. Thus, 30 percent of workers are informal according to the productive informality definition, and 60 percent of workers qualify as informal using the legal definition. Within these two groups of informal wage workers, there is large variation both in the informality shares and in the observed job quality across countries for productive

> 22 SEDLAC (Socio-Economic Database for Latin America and the Caribbean), Center for Distributive, Labor, and Social Studies, Facultad de Ciencias Económicas, Universidad Nacional de La Plata, La Plata, Argentina, and Equity
working|All|
|Informal|6|0|4|10|
|Formal|0|7|1|8|
|Not Working|2|1|79|82|
|All|8|8|83|100|

Note: Unweighted percentages based on a total sample size of working-age females of 35,750.

10 The World Bank worked with Egypt’s Ministry of Finance on a Commitment to Equity (CEQ) exercise to map taxes, transfers, and subsidies in the HIECS survey to understand the incidence of fiscal policies. This exercise used administrative data and allocation rules which allowed the authors to model the expansion in cash transfers over the period covered in this paper.

11 Very few households report income from remittances in HIECS, thus how we model remittances does not impact results. Yet it is important to keep in mind that remittance inflows were impacted by COVID-19, such that the income shock estimated in this paper may underestimate the total income loss.

8
# **1 Introduction**

Vietnam participated in the Programme for International Student Assessment (PISA) for the first time in 2012 and its performance has been much higher than other developing countries that take part in this OECD led initiative. PISA scores of 15 year-olds in Mathematics, Reading and Science are calibrated to an OECD mean of 500 and standard deviation of 100 points. Only a few developing countries take part in PISA, perhaps because most of them have results much lower than the OECD countries. In the OECD-PISA 2012 database, there are seven countries other than Vietnam with a per capita GDP (in 2010 PPP dollars) below US$ 10,000 - Albania, Colombia, Indonesia, Jordan, Peru, Thailand and Tunisia. At US$ 4,098, Vietnam’s GDP per capita is the lowest of this group. Figure 1 indicates a positive, albeit non-linear correlation between GDP per capita and PISA test scores. Vietnam, represented by a red star, lies much above the other developing countries clustered in the lower left hand corner of Figure 1. With a mathematics mean score of 511, Vietnam is more aligned to Finland (519) and Switzerland (531), rather than Peru (368) and Colombia (376).

**Figure 1: PISA 2012 results compared with GDP per capita**

<!-- Start of picture text --> Shanghai−China<br>Finland<br>Vietnam (511) (519) Switzerland<br>(531)<br>Colombia (376)<br>Peru (368)<br>0 10000 20000 30000 40000 50000 60000<br>GDP per Capita in PPP 2010<br>700<br>600<br>500<br>400<br>PISA Math Average Score 2012<br>300<br><!-- End of picture text --> Source: OECD-PISA database The weighted average mathematics score of the seven developing countries is 383. It is helpful
2010). Of the 7 trials in the TMSDG meta-analysis of hemoglobin (which produce 9 treatment estimates, since two are factorial), only one appears to have any significant hookworm prevalence (Dossa and Ategbo, 2001); the other six either do not report hookworm prevalence or report very low values, between 1% — 11%.

> 19 The other two are hemoglobin and cognition.

16
where _Kic_ is the share of instructional time lost in country _c_ for student _i_ , and  is a function of the parental factor of substitution, which takes into account that parents may compensate to a certain degree for the educational loss (see below). The instructional time loss is estimated as: where _t_ is the number of days lost due to school closures in a given country and _T_ is the number of school days in a regular year of schooling. The term in parenthesis is included to consider the compensation of schooling from public actions in home learning tools: _f_ and n are indices that capture the extensiveness of offline and online education tools during the pandemic. _f_ (n) equals one if all the offline (online) educational tools were used by the country’s education system during the school closure, and zero if none of them was used. The parameter δ is a weight between the two set of resources that defines their relative effectivity. Following NLT, we initially set the weight δ equal to 0.5, meaning that both offline and online learning resources are equally capable to transmit learning material and may together be able to replace a regular day in class. Alternative values are used in the robustness analysis.

In NLT, _A_ is defined as the likelihood to have access to the internet (a key factor to be able to receive on-line education, as discussed in Marchionni _et al_ ., 2022) of students in households with a given educational background. In contrast to NLT, where this likelihood is predicted for each individual based on the distribution of access to the internet by education groups estimated for each country from other data sources, in our specification _A_ is the actual availability of internet in the household, a piece of information available in most national household surveys.<sup>8</sup>

> 8 Argentina and Ecuador are the only countries in our sample that do not have information on internet access. In these cases, we randomly imputed internet access based on the percentage of
# **Figure 3: Diet quality (restricted nutrients) and average per capita household expenditures across neighborhoods**

<!-- Start of picture text --> 5000 10000 15000 20000 25000 30000 35000<br>Per capita household expenditures (Sol)<br>PDRNI restricted nutrients 95% CI<br>Fitted values<br>1.2<br>1<br>.8<br>PDRNI restricted (absolute)<br>.6<br>.4<br><!-- End of picture text --> _Source:_ Data of ENCONUT 2013; ENAHO 2013. _Note_ : Data are weighted.

Because the restricted nutrients consist of fat, carbohydrates and sodium, figure 4 depicts the PDRNI separately for these nutrients to identify which of them contributes most to a less healthy diet. Sodium and carbohydrates are shown to be mostly responsible for the overall positive PDRNI of restricted nutrients. While the average fat intake of FAFH is close to the recommended intake, the mean intake of carbohydrates per menu supplied in restaurants is 65 percent above the recommended intake. The PDRNI for sodium is even larger, with a mean intake of 165 percent above the recommended intake.<sup>15</sup> Menus with a high quantity of carbohydrates and sodium that are consumed in neighborhoods of low and midsocioeconomic status seem to be responsible for this finding.

> 15 The quantities of each nutrient are tested to determine if they differ across the four socioeconomic groups; statistically significant differences are found only in the case of sodium.

13
Table 4: Changes in poverty from panel surveys in selected countries

|Country|Period||Headcount (%)||
|---|---|---|---|---|
|||Always Poor|Sometimes<br>Poor|Never Poor|
|Zimbabwe|1992/93-1995/96|10.6|59.6|29.8|
|Pakistan|1986-1991|3.0|55.3|41.7|
|South Africa|1993-1998|22.7|31.5|45.8|
|Russia|1992-1993|12.6|30.2|57.2|
|Ethiopia|1994-1997|24.8|30.1|45.1|
|Cote D'Ivoire|1987-1988|25|22|53|

Notes: Adapted from Baulch and Hoddinott (1999).

Of course the reverse side of vulnerability of the non-poor is that the poor are also escaping poverty. A relatively small number of households remain in poverty consistently. This suggests that "the poor" from time to time are not a fixed but fluid group of households.

# _C) Differences in Vulnerability_

In addition to the _level_ of vulnerability, there is also interest in capturing the fact that different groups face different levels of risk. For example, even though two groups may have the same level of expenditures and hence one group has the same headcount poverty measure as the other, it is possible that one of the groups faces a higher level of risk so that they are more vulnerable. In this section we use only the estimates with the measurement error corrected estimated standard deviation to compare levels of vulnerability across various groups of households as presented in table 5. A handy way to compare results across groups is to compare the ratio of those "vulnerable" to those "poor" as this indicates how relatively important transient poverty is for these groups.

19
muted, but are apparent for all groups with urban and more educated women being the most “protected”. In Ethiopia there has been a big difference in male/female mortality over the entire period we are able to analyze—with mortality among adult men typically about a third higher than that of women. During the factional violence period of the mid- to late-1980s mortality increased for men, especially in urban areas and for those with more schooling—and only regained parity with their rural and less educated counterparts by the late 1990s. Urban and more educated women also experienced mortality “spikes” in the mid- to late-1980s, although these were somewhat smaller. Adult mortality among rural and among less education women did not appear to be affected in the same way. Last, the decade-long civil war in Sierra Leone shows up in adult mortality increases for all groups. The increase is especially acute for urban and more educated men in the 1990-94 and 1995-99 periods, and noticeable among women from all groups over the same period. Among rural and among less educated men there seems to be a longer run increasing mortality trend that starts in 1985-89 and peaks in 1995-99.

Clearly, episodes of conflict and violence are country-specific, and perhaps less amenable to the types of broader generalizations we make above. But these four cases do suggest that while mortality events precipitated by civil conflicts tend to affect all groups—men and women, urban and rural, more and less educated—they often appear to affect men, and in particular urban and more educated men to a greater extent than the other groups.

# **4) Conclusions**

In this paper we combine data from 84 DHS datasets from 46 countries (59 and 33 of which are from Sub-Saharan Africa) to analyze trends and socioeconomic differences in adult mortality. We calculate mortality based on the sibling mortality reports collected from about 850,000 female respondents aged 15-49. In total, the estimates are based on mortality histories of almost 5 million individuals. In the analysis,