rafmacalaba/data-use-mentions-extended huggingface dataset:
1997): “Saving across the World: Puzzles and Policies,” World Bank Discussion Paper No. 354.

Schmidt-Hebbel, K. and L. Servén (1999): The Economics of Saving and Growth: Theory, Evidence, and Implications for Policy. Cambridge: Cambridge University Press.

White, H. (1982): “Instrumental Variables Regression with Independent Observations,” Econometrica 50: 483-499.

Ziliak, J. (1997): “Efficient Estimation With Panel Data When Instruments Are Predetermined: An Empirical Comparison of Moment-Condition Estimators,” Journal of Business and Economic Statistics 15 (4), p. 419-431.
|(6)<br>Full<br>Sample|(7)<br>Non‐working<br>women|(8)<br>Non‐working<br>women|
|---|---|---|---|---|---|---|---|---|
|Dependent|Women’s<br>work|Women’s<br>work||Looked for|Women’s<br>work|Women’s<br>work||Looked for|
|Variable|status|status|Want to work|work|status|status|Want to work|work|
|Norms index|1.679***|1.444***|1.137|1.276*|||||
||(0.0912)|(0.0936)|(0.0968)|(0.153)|||||
|SE index|||||1.315***|1.267***|1.005|0.988|
||||||(0.0696)|(0.0777)|(0.0988)|(0.130)|
|SN index|||||1.031|0.972|0.854|0.945|
||||||(0.0606)|(0.0705)|(0.0774)|(0.111)|
|PB index|||||1.459***|1.373***|1.533***|1.450*|
||||||(0.0914)|(0.0965)|(0.159)|(0.219)|
|
>[14]</sup> However, the Guinean health system is currently unable to provide its population with adequate access to medicines and to ensure that the population has access to good quality medication.

The pharmaceutical sector in Guinea comprises a public sub-sector, a legal private sub-sector, and an informal and illegal private sub-sector.<sup>[5]</sup> In the public sub-sector, pharmaceutical products are supposed to be supplied by the Central Pharmacy of Guinea (CPG), which was set up in 1992.<sup>[15]</sup> However, stockouts are frequent and significant, even for essential medicines.<sup>[14, 15]</sup> According to 2016 data, only 39% of public health facilities' orders from the CPG were filled.<sup>[15]</sup> In 2017, the antibiotics ampicillin and amoxicillin and oral rehydration salts were available in only 38%, 28%, and 26% of public health facilities, respectively.<sup>[14]</sup> This situation forces health facilities to seek supplies from the private sector. The challenges of the PCG stem from, among other things, a lack of adequate tools and procedures to quantify 17
because there are different valid sales measures that firms could present when applying. Also, reported annual sales might be different from the administrative data we use in this paper. Despite these considerations, being larger than the eligibility cutoff significantly decreases the likelihood of a firm participating in the credit program by 14%. Panel B shows the effect of being at either side of the cutoff on leverage variation, measured as the change in (net) debt during 2020 relative to 2019 sales. Unreported RDD estimations show that crossing the size threshold and thus causally limiting the use of the credit guarantee program reduces the change in leverage by 4%, a result that is statistically different from zero.

Next, we decompose the change in indebtedness into the change in public guaranteed debt and non-guaranteed debt. By construction, public guaranteed debt needs to increase for firms participating in the program. We find that indebtedness coming from public guaranteed debt increases by 13.9 and 11.8 percentage points for banked and unbanked firms, respectively (Table 8, Columns 1 and 2). On the other hand, participating in the credit program could lead to higher or lower non-guaranteed debt. We find that indebtedness coming from nonguaranteed debt also increases, although the magnitude of the effect is significantly smaller (Columns 3 and 4). In other words, guaranteed and non-guaranteed debt are to some extent complementary to each other during the pandemic.

Having shown that the increase in debt occurs mostly through participating in the credit program, we next study how risk is related to the accumulation of this type of debt. We find that, within credit program users, riskier firms end up with more public guaranteed debt than safer firms, and this holds for both banked and unbanked firms (Table 9, Columns 1 and 2). The selection results from the previous section show that riskier firms are more likely to participate in the credit program (an expansion of the extensive margin). The results in this section show that, conditional on participating in the credit program
do not have social security or medical benefits. The second addresses the issue of the role of the **small** firm or "micro-enterprise" using the Mexican government definition of firms of under six workers. Because only the latter definition is consistent with the sampling of the MES, it is the one used for the joint NITES/MES panel. However, the similarities between the two sets of descriptive statistics (table 1) on age, work experience, level of schooling and an index of the real wage at the beginning of the period for each transition, confirm that there is great overlap in the composition of the two populations and that the results are unlikely to be driven by the particular definition chosen. Though the differences in sample means are sometimes statistically significant, they are never large. This is due partly to the fact that among entrepreneurs of firms with less than six workers, only 5% are covered by benefits and that the vast majority of micro-firms are concentrated at sizes of below three workers.

**IL** Wage Differentials as Evidence of Segmentation

Traditional efforts to identify segmenlation by comparing conditional means between sectors are unsatisfying for two reasons. First, as is well-documented in the literature, unobserved worker characteristics that affect productivity-- ability to tolerate authority, punctuality, entrepreneurial ability- may also influence in which sector an individual chooses to work and bias estimates of the sector differential.' But a greater concern is that the specific characteristics of work that pertain to or even define 7 Recognizing this, Marcouiller et al. atlempt to mitigate the selection bias but are clear about the limitations of the standard techniques when there is no clear consensus about how worker **s** ort themselves between the informal and formal sector. They, in the end, report higher earnings in the informal sector.

_5_
**Table 4:  Gender Wage Gap for Regular Salaried Employee by Industry:  2007-08**

|**Industry**|**Rural (%)**|**Urban (%)**|
|---|---|---|
|Agriculture (01-05)|69.09|61.89|
|Mining and Quarrying (10-14)|56.96|56.43|
|Manufacturing (15-22)|51.69|58.73|
|Manufacturing (23-37)|41.7|83.32|
|Electricity, Gas and Water (40-41)|90.04|69.49|
|Construction (45)|95.71|74.69|
|Trade (50-55)|77.78|80.71|
|Transport and Storage etc. (60-64)|106.01|129.9|
|Services (65-74)|53.57|86.71|
|Services (75-93)|51.32|68.62|
|Private hhs with emp. Persons (95)|44.47|39.93|
|All|61.69|77.11|

Source: Adapted from Chakravarty and Gandhi (2016). Note: Codes in brackets represent National Industrial Classification (NIC), 2004 industry codes.

The micro-level data from the employment/unemployment survey of the Indian labor market conducted by NSSO depicts that the largest gaps in wage (wage gap is calculated by female average wage (in Rs.) as a percentage of male average wage/ salary received per day) exists in private households with employed persons in both rural and urban areas where females get nearly 40 percent of the wage given to that of males. The manufacturing sector has higher concentration of women workers and huge gap in wages exits in rural areas was compared in urban areas. The only sector which provides higher wages to female workers as compared to men is transport and
7. Expansion of Electronically Traceable Payments: Electronically Traceable Cash Receipts After the introduction of tax incentives for credit card use in 1999, Korea introduced electronically traceable cash receipts (ETCR) and made them an eligible form of electronic payment for TIETP in 2005. ETCR makes even cash payments electronically traceable by NTS. People who for any reason prefer to pay in cash rather than using a credit card can ask retailers to issue ETCRs. The consumer provides a Resident Registration Number (Korean citizens’ ID number), a registered mobile phone number, or a credit or debit card number for personal identification purposes; the retailer then issues the ETCR through its credit card payment terminal. ETCR transaction data is automatically transferred to NTS. End‐consumers do not need to collect ETCRs for their income tax deduction; NTS provides ETCR transaction data to wage earners, together with credit card and debit card transaction information, through the Home Tax Service portal. Wage earners just confirm their prefilled ETCR, credit card, and debit card transaction data and submit the TIETP application to their employers as described above.

8. Expansion to Mobile Transactions Even without an additional mobile application, TIETP also allows the purchase of goods and services by using credit cards and debit cards on a mobile phone. Therefore, B2C mobile phone transactions in Korea now fall into the TIETP category. In addition, NTS has made efforts to reinforce its mobile tax service. Since 2005, NTS has provided a mobile tax‐related certificate issuing service, covering a range of documents, such as business registration certificates, tax payment certificates, and business closure certificates.<sup>47</sup> In 2014, NTS started a Mobile Newsletter Service offering tax news, tax return and payment notifications, and information on tax legislation and rulings.<sup>48</sup> In 2015, NTS began providing a mobile Home Tax Service allowing taxpayers to issue ETCR and e‐invoices and to receive other tax‐related services, including tax returns, tax inquiries, tax
7 _Figure 4: Trends in the out-of-pocket share in Japan and the Asian Tigers_ * <!-- Start of picture text --> 100%<br>Hong Kong<br>90% Japan<br>Korea Singapore MSA<br>80%<br>Singapore<br>Korea UHI<br>Taiwan<br>70%<br>60%<br>Japan UHI Taiwan UHI<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010<br>OOP share in total health spending<br><!-- End of picture text -->

# **_Who bears the burden of health finance?_**

How is the burden of health finance distributed across income groups? Figure 5 shows the progressivity of the various finance sources as well as of total payments for four of the five systems.<sup>†</sup> Only in Hong Kong (China) are total payments progressive. They are most regressive in the case of Japan, but the differences with Korea and Taiwan (China) are not marked.

> * Data for Hong Kong (China) are from http://www.hwfb.gov.hk/statistics/download/dha/en/table2.pdf. Data for Japan and Korea are from OECD Health Statistics. Data for Singapore are from Liu and Yue<sup>7</sup> for 1960-95, and from the 2005 World Development Report for 1998-2002. Data for Taiwan (China) are from Lu<sup>11</sup> .  UHI denotes universal health insurance. MSAs denote medical savings accounts.

> †  Distributional data of the type in the next two charts are not available for Singapore, which is a pity given its uniqueness and the often strong claims that are made for its success.
0.614|
|(1.146)|(1.896)|(1.857)|
|Dummy_Mais Educação_* year 2009<br>–0.549|–5.193***|–2.312|
|(0.994)|(1.741)|(1.729)|
|Dummy_Mais Educação * ME_spending per student<br>0.00820|–0.00222|–0.000773|
|(0.00522)|(0.0125)|(0.0129)|
|_R_ <sup>2</sup><br>0.736|0.755|0.741|
|Observations<br>2,595|2,595|2,595|
|Dummy year 2009<br>Yes|Yes|Yes|
|Dummy year 2011<br>Yes|Yes|Yes|
|School fixed effects<br>Yes|Yes|Yes|

_Sources:_ Authors’ calculations using MEC/FNDE administrative data, _Prova Brasil,_ School Census, and INEP.

_Note:_ Treatment group are schools that joined _Mais Educação_ in 2008 and remained in the program until 2011. Spending per student was deflated by IPCA, and is in 2007 prices. This table reports coefficients from the ordinary least squares estimation of equation (6) in the paper. The dependent variable is in column (1) is dropout rates, in column (2) mathematics grades in _Prova Brasil,_ and in column (3) Portuguese grades in _Prova Brasil._ All variables are at school level. ***significance at the 1% level; **significance at the 5% level; *significance at the 10% level. Robust standard errors, clustered at school level, are reported in parentheses. All regressions include school fixed effects and time dummies. All regressions are weighted by propensity score matching weights. Panel A reports these estimates for the schools
Finally, emerging results from the testing of this methodology in pilot countries (Tanzania, Kenya Rwanda and Uganda) are already challenging the conventional wisdom on Africa public infrastructure spending. After adding up on-budget and nonbudgetary spending data, it is clear that most Sub-Saharan countries might, in fact, be allocating large amounts of resources to infrastructure, relative to the size of their economies. These sometimes enormous spending efforts are, however, challenged by these countries‘ own purchasing power of large-scale infrastructure, high costs, under-developed construction capacity as much as by their institutional ability to efficiently and effectively spend scarce resources.

# Content

This guide includes concepts, definitions, and classifications with the aim of providing a comprehensive analytical framework for the measurement of infrastructure expenditures. It organizes the relevant work in three stages, namely: (i) pre-field, (ii) field, and (iii) back office. A schematic overview of the entire data collection process is provided in the table below.

For the purpose of the SPI exercise, infrastructure is broadly understood as six sectors: irrigation, energy (primarily power), transport, communication, wastewater management, and water supply. Detailed definitions are provided in section 3 of this Guide.

## **Overview table 1 Data collection matrix**

|**Sector**|**Institution**|**Agent**<br>**responsible**|**Qualitative**<br>**templates**<br>**to be**<br>**completed**|**Quantitativ**<br>**e templates**<br>**to be**<br>**completed**|**Additional**<br>**information**<br>**to request**|
|---|---|---|---|---|---|
|**Pre-field
that Russian firms have a rather stagnant body of employees: any kind of employee, either non-manager or manager, is both not dismissed and not promoted. While this may reflect overall preference for job stability in Russia,<sup>38</sup> it still reveals how Russian firms lag considerably in their human resources management practices with respect to US firms.

> 38 According to the second round of the Life in Transition Survey (2010), 64% of Russian respondents preferred a job with an average salary, not much chance of promotion but safe in the long term, to a job with high salary, a lot of chance of promotion but less job security. Moreover, 45% of the respondents preferred a job in the public sector, 20% preferred self-employment and only 25% preferred private sector employment.

27
**3**

legal origin of each country as an instrumental variable to extract the exogenous component of banking sector development. Legal scholars note that many countries can be divided into countries with English, French, German, or Scandinavian legal origins and those countries typically obtained their legal systems through occupation or colonization. Thus, we take legal origin as exogenous. Moreover, LaPorta, Lopez-de-Silanes, Shleifer, and Vishny **(1997, 1998;** henceforth LLSV) show that legal origin substantively affected (a) laws concerning bank credits, **(b)** systems for enforcing bank contracts, and (c) standards for corporate information disclosure. Each of these features of the contracting environment helps explain cross-country differences in banking sector development [Levine, Loayza, and Beck **1998;** Levine **1998, 1999].** Thus, after extending the LLSV data on legal origin from 49 to **63** countries, we use the legal origin variables as instruments for banking sector development to assess the effect of banking development on economic growth, capital growth, productivity growth, and private saving rates.

The second econometric technique that we use to control for simultaneity bias also eliminates any omitted variable bias induced **by** country-specific effects. We use a panel dataset, with data averaged over each of the seven 5-year periods between **1960** and **1995.** We use a GeneralizedMethod-of-Moments (GMM) estimator proposed **by** Arellano and Bover **_(1995)_** and Blundell and Bond **(1997)** to extract consistent and efficient estimates of the impact of banking sector development on growth and the sources of growth. Specifically, dynamic panel procedures typically take first differences of the observations in _levels_ to eliminate country-specific effects [Arellano and Bond **1991;** Holtz-Eakin, Newy, and Rosen **1990].** Then, lagged values of the regressors from the
bargaining power that may be correlated with the education and assets measures. While spouses’ education are included as a potential bargaining source, its caveats are already discussed (see section 2). My focus, however, is not to evaluate intrahousehold bargaining based on education, but to control for any potential correlation between education and asset measures of bargaining.

**Individual health and occupation** : There is limited variation across rounds even in the short-term health outcome measure, an individual’s weight (in kilograms) for height (in centimeters). Boys tend to do better than girls and adult males better than adult females, while this gender difference reverses for the adolescent group. This might be indicative of some transitory catch-up for females as they past childhood, which later disappears as they progress toward adulthood.

As mentioned before, occupation data were collected once in the survey. These were coded into 47 different occupations. Based on the metabolic constant (mc) provided for a detailed list of activities for male and female in World Health Organization (1985), I classify the energy intensity of occupations into high ( _mc >_ 4), medium (2 _._ 5 _< mc ≤_ 4), and low ( _mc ≤_ 2 _._ 5) category<sup>16</sup> . Energy intensity of occupation of different age-sex group vis-a-vis their energy intake are further discussed below. As described in Section 4, both the energy intensity of occupation and health outcome are utilized to construct the health endowment measure.

**Calorie intake and energy-intensity of occupation** : A useful feature of IFPRI survey is that it provides individual food intake data for each round using a 24-hour recall methodology asking the person with primary responsibility for preparing meals, about recipes prepared, ingredients for those recipes and amounts eaten by various family members and guests. The survey has information of quantity of individual intakes of about 200 food items (categorised into 17 food groups), which are converted into calories
be the case that much of the debate about what is happening to inequality in the world is actually a debate about the meaning of “inequality” (Ravallion, 2004).

Thirdly, there are also signs that the growth processes seen in many reforming economies in the 1990s have been putting more systematic upward pressure on inequality. Lopez (2005) reports evidence to support this view (though based on a smaller, selected, sample of countries than will be studied in this paper). To re-examine the relationship between growth and changes in inequality, I created 290 “spells” defined by two household surveys for a given country with more than one observation for most countries; there are about 80 countries represented, spanning 1980-2000.<sup>3</sup> I then compared the changes in the Gini index with the changes in the survey mean (in real terms, using local CPIs). Figure 1 gives a scatter plot of changes in the log Gini index against changes in the log real survey mean between successive household surveys. The correlation coefficient is –0.13 and is not statistically significant (at the 10% level). Among growing economies, inequality increased about as often as it fell, and similarly among contracting economies. Figure 2 focuses on the period after 1992, dividing the sample in two. There is now a mild positive correlation coefficient of 0.26, which is significant at the 5% level.<sup>4</sup> Fourthly, it must be acknowledged that there is likely to be considerable measurement error in the changes in inequality and the survey means. The errors can come from a variety of sources, including sampling errors (probably a minor concern in most cases for the surveys used here), errors arising from selective compliance (whereby certain types of households participate in surveys with lower probability than others), under-reporting of incomes and comparability

> 3 The data are drawn from _PovcalNet_ and the _World Development Indicators_ . _PovcalNet_ is a new interactive tool for poverty
to two recent studies that use estimates of trade elasticities to assess the global price effects of supply disruptions associated to Covid-19 in China (Kee, 2020) and of export restrictions in countries that produce medical products needed to manage Covid-19 (Espitia, Rocha and Ruta 2020a). A study by Laborde, Martin and Vos (2020) uses a Computable General Equilibrium (CGE) model to quantify the effect of Covid-19 on food security via its impact on poverty in developing countries. Our work also relates to the literature on the role of trade policy in the global food crises of the late 2000s (Martin and Anderson, 2012; Giordani, Rocha and Ruta, 2016), which is further discussed in the next section.

The paper is organized as follows. Section 2 presents a simple version of the theory. Data on Chinese exports of food during January and February 2020 and on world trade flows in food markets are presented in Sections 3 and 4, respectively. The quantification of the impact of Covid-19 and export restrictions on the world export supply of food and food prices is presented in Section 5. Section 6 has the sensitivity analysis. Concluding remarks follow.

# **2. The “multiplier effect”**

This section provides a brief review of the theory on the multiplier effect of trade policy (Giordani, Rocha, and Ruta, 2016). The model builds on developments in the literature on behavioral economics and trade policy that modifies an otherwise standard trade policy model to account for loss aversion -the fact that individuals value losses more than gains (Freund and Ozden, 2008; and Tovar, 2009). In this setting, preventing losses may loom large in the government's objective function, leading to radically different predictions from standard political economy models of trade policy (e.g. Grossman and Helpman, 1994).

The key insight of the literature on trade policy under loss aversion is that, in the absence of other tools, a welfare maximizing government may use trade policy (e.
reduction at 4 percent a year made Rural Eastern Hill the poorest and the second poorest region under the old and new poverty lines, respectively.

**Table 3: Subnational poverty and pace of poverty reduction**

|PovertyRate|Old Pov|erty Line|Proportional Change<br>er ear|New Poverty<br>Line|
|---|---|---|---|---|
||||p y||
|SurveyYear|NLSS 1995|NLSS 2010||NLSS 2010|
|Kathmandu|4.3%|1.3%|8.5%|11%|
|Otherurban|31.6%|5.4%|12.4%|17%|
|R-WHill|55.0%|17.1%|8.1%|32%|
|R-E Hill|36.1%|18.9%|4.4%|30%|
|R-WTerai|46.1%|15.2%|7.7%|27%|
|R-E Terai|37.2%|8.7%|10.1%|22%|
|National|41.8%|12.5%|8.4%|25%|

Source: Nepal Living Standard Surveys 1995 and 2010 There is also a notable regional disparity in terms of the average depth of poverty. Figure 13 summarizes average per capita consumption of the poor in 1995 and 2010, expressed in 2010 prices. The poor in Kathmandu valley, the least-poor region in both years, on average had the highest per capita consumption in all regions. Other urban areas, where the pace of poverty reduction was the fastest, saw the largest increase in per capita consumption among the poor. The average consumption in Rural Eastern Hill region was approximately at the same level as the national average in 1995, but due to the slowest pace of poverty reduction, the