rafmacalaba/data-use-mentions-extended huggingface dataset:
# **Some empirical values of** _wc_ **and** _wh_

To consider the values of _wc_ and _wh_ in a realistic setup, we take several prediction models and poverty lines used in small-area estimation or survey-to-survey imputation in several countries. For Cambodia, we adopt the consumption model and poverty line used in a small-area estimation project detailed in Ministry of Planning and United Nations World Food Programme (2002) and Fujii (2006). This project uses the Cambodia Socioeconomic Survey 1997. In each of the three strata (Phnom Penh, Other Urban and Rural), a separate consumption model and a separate poverty line are used. Our point estimates of _β_ (unreported) are slightly different from those used in this project because the heteroskedasticity of _ech_ is allowed for in the former and the estimation method is slightly different as a result. However, the difference is not important for our purpose because we are only interested in the plausible range of _wc_ and _wh_ .

For Malawi, Niger, and Tanzania, we used a prediction model and national poverty line from an independent survey-to-survey imputation project.<sup>17</sup> The datasets used are Malawi Third Integrated Household Survey 2010-2011 for Malawi, l’Enquˆete Nationale sur les Conditions de Vie des M ́enages et l’Agriculture 2011-12 for Niger, and Tanzania National Panel Survey 201011 for Tanzania. All these surveys are a part of the Living Standard Measurement Surveys by the World Bank.

Table 4 provides a summary of the estimates of _wc_ , _wh_ , _c_ ̃, _ψ_<sup> ̃</sup> , and _α_ for various surveys. The first two columns report the basic information about the survey such as the number of observations ( _n_ ) and number of clusters ( _K_ ). The next two columns show that the values of _wc_ and _wh_ vary substantially across countries. Based on this, we use _wc ∈{_ 0 _._ 6 _,_ 1 _._ 2 _,_ 1 _._
15 to obtain the microdata and compute by ourselves the numbers included in the database. For the remaining 290 surveys (e.g. the STEPS), the microdata were inaccessible. In this case, we extracted information from survey reports, and in a few cases from research papers authored by researchers who had used the same methods we use.<sup>13</sup> All datapoints in the data set are labeled with their data source in the referenceid variable – see annex for naming conventions.

# <u>Figure 5: Data sources for the health equity part of the 2018 HEFPI database</u>

<!-- Start of picture text --> 1,153 surveys<br>identified<br>227 DHS 118 STEPS<br>114 MICS 101 ECHP<br>70 WHS 43 Eurobarometer<br>39 EHIS 35 MCSS<br>352 other national or<br>32 ISSP<br>multicountry surveys<br>863 accessible and<br>290 inaccessible, summary data taken from publication, e.g. STEPS<br>microdata analyzed<br><!-- End of picture text --> 13 E.g. van Doorslaer and Masseria (2004) and van Doorslaer et al. (2006a).
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*] Both the Sargan statistic and the "difference" Sargan statistic are asymptotically distributed as Chi-square under the null hypothesis of validity of their respective instruments. The number of degrees of freedom of the Sargan test is equal to the number of overidentifying restrictions of the system estimator. The number of degrees of freedom of the “difference” Sargan test is given by the number of additional restrictions in the system estimator with respect to the difference estimator, that is, the difference between the number of overidentifying restrictions of the system estimator and that of the difference estimator. 2 However, if the test rejects the null hypothesis of no second-order serial correlation but fails to reject the null of absence of third-order serial correlation, we conclude that the original error term is MA(1) and, therefore, use the moment conditions appropriate to this case. As shown below, in the private saving regressions we find in some cases evidence that the error term is MA(1) and, in others, that it is serially uncorrelated; for purposes of comparability across regressions we adopt the conservative strategy of choosing the instrument sets for the regressions as if the error term were always MA(1). In the case of national saving, we find that the error term can be well characterized as serially uncorrelated, and we choose the instrument set accordingly.

*3 Deaton (1977) finds the same result but proposes a somewhat different explanation: to the extent that the increase in inflation is unanticipated, a larger saving rate may result from confusing the increase in the overall price level with a rise in the relative prices of certain goods.

*4 Tt seems plausible that measured real interest rates are more closely related to intertemporal decisions in the industrial countries (where the sample mean of the real interest rate is positive) than in developing countries (where it is negative).

5 The sources of central government data are different from those of public sector data. The former cover many more countries but fewer years (from 1970 on only
coming years.

Another important factor is the differences in returns to education. The available estimates for 1993/94 shows that while returns to primary education were higher in rural areas, returns to higher education were higher in urban areas (Duraisamy (2002)). The recent estimates indicate that the rural-urban gap in returns to education has increased after the liberalization (Aslam et al. (2011)). The returns to one more year of schooling in 2007 for self-employed is estimated to be 9.8 percent in rural areas and 34 percent in urban areas. For wage employment for men, it is 6.3 percent in rural areas, but 32 percent in urban areas. For female wage employment, the corresponding returns are 8 percent (rural) and 44 percent (urban) (see Aslam et al. (2011)).<sup>42</sup> As noted by many observers, the economic growth in India after economic liberalization has been both skill-biased and urban-biased,

> 42 The skill biased nature of economic growth can also be seen from growth of wages across different schooling levels. The average wage in 1999/2000 for someone with a college degree was 73 percent higher than someone with high school degree, and 67 percent higher for someone with high school degree compared to someone with middle school education (based on NSS data).

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of e-commerce growth in Colombia. This discrepancy may be related to a higher purchase frequency without expanding the number of users, to methodological differences in accounting for e-commerce transactions or to response biases in the surveys, among others. In Buenos Aires, the pandemic accelerated the growth in the number of online consumers, which had shown signs of stagnation in previous years; 20 percent of Internet users bought online for the first time during the quarantine. This growth was particularly strong in 2020, probably due to the limitations to make physical purchases as a result of mobility restrictions, with clothing and footwear, technological products and food products being the most commonly purchased types of goods (UAI 2020). According to the “Future Shopper Report 2021", Argentina was the LAC country with the highest e-commerce growth. In Argentina overall, the share of users choosing home delivery rose from 65 percent in 2019 to 80 percent in 2020, and in AMBA specifically the convenience associated with home delivery of products was the most mentioned reason for making online purchases (UAI 2020).

The typical profile of the online buyer in both cities is of high income. In Bogota, this is consistent with the purchase order data which shows a higher demand in locations in the north of the city. This trend strengthened during the pandemic, as e-commerce adoption in lower strata has stagnated. Already in 2019, more than 50 percent of people with households associated with stratum 6 used the Internet to shop online, compared to 6.3 percent among people in stratum 1 and 10.7 percent among people in stratum 2. The share of people buying online fell in 2020 to 1.1 percent among people with households associated with stratum 1 and to 5.8 percent among people with households associated with stratum 2. This could have been due to a reduction in consumption among these strata as a consequence of the pandemic crisis. The trend of stagnation of e- commerce among the lower strata and growth among the middle and upper strata was consolidated in 2021.
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Procedure Used to Fstimate the National Accounts Concept of Gross Value Added (Contribution to GDP at factor cost) for Establishments Covered by the Brazilian Industrial Census

- 1) Gross Value of Output = "Valor de Producao";

- 2) US Value Added Concept = "Valor de Transformacao Industrial"; "Valor de Transformacao Industrial" = "Valor de Producao" minus "Despesas, com as operac6es industriais";

- "Despesas, com as operacoes industriais" = US Cost of Materials concept

- 3) In order to arrive at the present national accounts concept of gross value added (i.e. the contribution to GDP at factor cost before deduction for imputed financial services), we must deduct 15 of the 20 items which the Brazilian census calls "Despesas Diversas"'. These are shown only for the 24 major industry groups (not for individual industries), so we had to use branch ratios, i.e. the ratio of the value of the 15 to the 20 items, to derive a rough estimate of these inputs for industries within each branch.

- The. 5 15 items are:

- a) ' lugeis e Arrendamentos" (rents);

- b) "Royalties" (royalties);

- c) "Manutencao e Reparac&o de Equipamentos e Instalac6es" (repair and maintenance);

- d) "Manutencao de meios de transporte proprio"

- (maintenance of the enterprise's own transport equipment);

- e) "Publicidade e Propaganda" (advertising);

- f) "Despesas com comunicacao" (expenses for communications); g) "Fretes e carretos" (freight and carriage);

- h) "Servicos Professionais e de Assistencia Tecnica"

- (professional services and technical assistance);

- i) "Premios de Otros Seguros" (insurance for other risks);

- J) "Despesas com viagens e representacao"

- (travel and entertainment costs);

- k) "Indenizacao por dispensa"
, “Does Infrastructure Reform Work for the Poor? A Case Study from Guatemala”, World Bank, Policy Research Papers, No3185, Washington, DC Galasso, E. and M. Ravallion (2004), Decentralized targeting of an antipoverty program **,** _Journal of Public Economics_ , forthcoming Gasmi, F. , M.D. Kennet, J.J. Laffont and W.W. Sharkey (2002), **Cost Proxy Models and Telecommunications Policy,** The MIT Press, Cambridge Gomez-Lobo and D. Contreras (2003), Water subsidy policies: a comparison of the Chilean and Colombian Schemes, _The World Bank Economic Review,_ Vol. 17(3), pp391 Kessides, I. (2004), **Reforming Infrastructure: Privatization, Regulation, and Competition,** Oxford University Press Laffont, J.J. and J. Tirole (1993), **A theory of Incentives in Regulation and Procurement** , MIT press, Cambridge Laffont, J.J. and J. Tirole (1999), **Competition in Telecommunications** , MIT Press, Cambridge Lanjouw, P. and M. Ravallion (1999), Benefit incidence and the timing of program capture”, _World Bank Economic Review_ , 257-274 Lanjouw, J. O. and Lanjouw, P. (2001a): How to Compare Apples and Oranges: Poverty Measurement Based on Different Definitions of Consumption, _Review of Income and Wealth_ , 47(1), 25-42.

Lanjouw, J. O. and Lanjouw, P. (2001b): The Rural Nonfarm Sector: Issues and Evidence from Developing Countries, _Agricultural Economics,_ 26, pg 1-23.

Lanjouw, P., Quizon, J. and Sparrow, R. (2001) Non-Agricultural Earnings in Peri-urban Areas of Tanzania: Evidence from Household Survey Data, _Food Policy,_ 26(4), August, pg 385404.
<br>where foreigners owned 50% or more equity) at the end of:<br>2010?<br>2005?|49<br>(39)|35<br>(21)|49<br>(43)|
|What was the ratio of specific provisions to gross non-performing loans in:<br>2010?<br>2008?|47<br>46|35<br>36|49<br>54|
|What was the ratio of general provisions to total gross loans at the end of:<br>2010?<br>2008?|1<br>1|0.8<br>0.5|2<br>1|

Source: Calculations based on the 2011–12 Bank Regulation and Supervision Survey, which reflects values as of 2010. Values in parentheses refer to the 2007 Bank Regulation and Supervision Survey, which captures data as of 2005. ADV: advanced economies, EMDEs: emerging and developing economies (see Appendix I for list of countries).

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**Table 1. Access Indicators by Region**

||**Banks Deposit Accounts**|**Banks Loan Accounts per**|**Private Credit as**|
|---|---|---|---|
|**Region**|**per 1000 adults**|**1000 adults**|**% of GDP**|
|Sub-Saharan Africa|315.5|87.5|23.7|
|South Asia|653.8|54.2|43.9|
|**Middle East & North**<br>**Africa**|**744.0**|**213.3**|**50.6**|
|Europe & Central Asia|1,395.4|325.2|45.8|
|East Asia & Pacific|1,116.9|343.9|58.4|
|Latin America & Caribbean|1,227.5|366.8|44.7|
|High income: non OECD|1,865.0|851.5|83.4|
|High income: OECD|2,383.7|702.4|130.8|

Source: CGAP and World Bank Financial Access 2010, International Financial Statistics

**MENA countries tend to underperform on access to SME lending compared to financial sector depth and relative to countries with similar GDP levels** , as illustrated in figures 1 and 2. MENA financial systems are bank-dominated (bank assets were 63.6 percent of GDP in 2008), and banks lend to a narrow set of clients, including the government and larger corporates. MENA countries also tend to have relatively low access to deposits compared to GDP per head.

**Figure 1. Bank Loan Accounts per population against Financial Sector Depth and GDP per capita**

<!
the conflict in the 2014 Household Budget Survey (World Bank 2017).

> 9 See also Blattman and Miguel (2010) for a thorough review of the literature.

> 10 See (accessed July 2017): <u>http://www.aljazeera.com/news/middleeast/2014/08/yemen‐houthis‐hadi‐protests‐ 201482132719818986.html.</u>

3
||||**Panel**<br>|**B: Women**|||
|---|---|---|---|---|---|---|
||Overall|Wages|Start date|Duration|Beneficiary|Sharing|
|Information (M1)|0.187***|0.056|0.115***|0.074**|0.136***|0.088***|
||(0.036)|(0.037)|(0.036)|(0.034)|(0.036)|(0.028)|
|Observations|666|666|666|666|666|666|
|R-squared|0.083|0.061|0.079|0.091|0.106|0.072|
|Control mean|0.536|0.345|0.301|0.253|0.304|0.110|

Notes: The table shows the results of regressing the variables regarding knowledge of the parental leave program on individual control variables (age, nationality, geographic region, cohabiting status, maximum educational level), and sample strata fixed effects. Robust standard errors reported in parenthesis. ***significant at the 1% level, **5% level, *10% level. The estimation sample consists of 1,221 individuals in experiment 1 (554 men and 667 women). Source: Post-intervention survey.

# _Gender norms_

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## Results of Our Methodological Endeavours

## a) Transparency of Procedures

With modern computer facilities, it was possible to lay out our procedures and assumptions in transparent fashion (with meticulous detail in the statistical appendix) so that they can be criticised, checked, replicated, augmented or truncated by other researchers in this field. In general the tables are laid out in similar fashion to the binary comparisons of Kravis, Heston and Summers (1982). In cases where there were alternative measures or concepts to those which we preferred, we generally provide enough information for use by others whose judgment differs from ours. Such transparency is an advance on most earlier "industry of origin" research whose detailed substructure was usually not published (Paige and Bombach being an honourable exception) and whose procedures were of a more ad hoc character.

## b) An Integrated Three-Dimensional Approach

We tried to give full attention to each of the three main dimensions of international comparisons - real output, PPPs and productivity, and to set out their interrelations and complementary character clearly. Here our exposure to ICP methodology was very useful, as its rigour in this respect is exemplary. We feel that a good deal of previous work on industry-oforigin lines has suffered from concentrating onl: on the productivity aspects (this is true of all studies listed in table 1.3 of chapter I except Paige and Bombach, the Czech/INSEE study and that of West).

# c) Reconciliation with the National Accounts Framework

There are obvious advantages in making sectoral output and productivity studies of this kind in a conceptual framework compatible with the national accounts. Chapter II therefore makes a careful confrontation between the census and the national accounts. From this one can see that the Mexican national accounts make extensive (and perhaps excessive) allowance for informal activity not recorded in the manufacturing censuses. It is also clear that census definitions of value added vary between countries, and need adjustment to bring the comparisons for the three countries to a common conceptual basis as is
of childhood stunting could have its origins in the fetal period.

Available evidence suggests that SGA (both term and pre-term) is strongly associated with maternal height and low BMI which are in turn associated with adolescent health and pregnancy. A pooled analysis of DHS data from 109 countries adjusting for socioeconomic characteristics, such as the wealth of the household and education of the mother, found a strong relationship between maternal height and stunting<sup>22</sup> : children of mothers measuring 145 cm or less had an 80.5 percent probability of being stunted, compared to 18.9 percent for children of mothers measuring 160 cm or more, controlling for other factors. Other studies have established links between maternal birth outcomes – such as birthweight and SGA – with offspring outcomes including stunting. A study using the Cebu longitudinal health and nutrition survey conducted between 1983 to 1995 in the Philippines reinforces this evidence, finding that the likelihood of stunting is inversely related to mother’s height and low birth weight.<sup>23</sup> 20
to the use of overall adult mortality rates.<sup>6</sup> Thus, the dependent variable we use is (log of) ratio or women’s to men’s adult mortality rate (henceforth, women-to-men adult mortality rate). The data source for the variable is the WDI. Using the ratio of women–to-men adult mortality rate has the advantage that it eliminates economy-wide or macroeconomic factors common to both men and women’s mortality from spuriously affecting our results. The mean value of the dependent variable equals -0.37 and the standard deviation is 0.28. Across country-year, the variable ranges between 0.07 (Bangladesh, 1990) and -1.10 (Belarus, year 2008).

While the women-to-men adult mortality rate has some advantages, it does not tell us whether women’s mortality rate in absolute terms is increasing or decreasing with domestic violence legislation. In other words, it does not tell us if an improvement in the women-to-men adult mortality rate is due to an improvement in women’s adult mortality rate or due to worsening of men’s adult mortality rate. Hence, as a robustness check, we do report results using the absolute women’s adult mortality rate as the dependent variable. This equals the (log of) the adult mortality rate for women taken from WDI. The mean value of the variable equals 5.2 and the standard deviation equals 0.62.

Our main explanatory variable is a dummy variable equal to 1 if the country has domestic violence legislation and 0 otherwise. The data source for the variable is the Women, Business, and Law database, where the UN definition of domestic violence is followed. Within this definition, domestic violence encompasses physical violence, emotional or psychological violence, sexual violence, or financial or economic violence (see World Bank 2015 for details). The mean value of the dummy variable equals 0.31, implying that on average in any given year about 31 percent of

>
statistics calculated only over the balanced panel of households. The estimated effects are even smaller, though statistically significant for the 1995-96 and 1997-98 endpoints. It is the combination of these two changes in specification–excluding the household-level covariates and using only the balanced panel sample to calculate initial village conditions–that drives the drop in the estimated effect of inequality from the household to the village-level regressions: It has nothing to do with aggregation from one level to the other _per se_ .

This is seen most clearly in column (9), where we estimate the household-level Equation (A1.4) by instrumental variables, using as instruments the village mean values of Mean Log Income, the Mean Log Deviation, and Mean Education.<sup>25</sup> This exercise yields the same results as column (8). We thus obtain the same coefficient estimates three ways: Estimation at the household level with either the villagelevel regressors (column 8) or village-means as instruments (column 9); and the village-level results in column (11).<sup>26</sup> Finally, in column (10) we implement the Deaton (and Devereux) corrections for small sample bias. This entails only a small variation on the specification in column (9): We use the jackknifed village means instead of the straight village means as instruments for the household variables in Equation A1.4. For this specification, the estimated effects of inequality are slightly smaller, and significant only for the “peak” year of 1995-96. Because it accounts for the small-sample measurement error bias, this is the preferred “bottom-line” village-level result: inequality has a negative but generally insignificant effect that fades quickly after 1995-96. However, our overall preferred specification is column (5), which uses the same jackknifed inequality measure, but with the inclusion of the full suite of household covariates, and with village statistics calculated using all available observations from 1987-88. As explained in Appendix 1, column (5) is a richer “
corresponding component of Institutional Development in 1996. The shortcoming of this measure is that we have to take the initial value for the Soviet Union for all FSU countries and the initial value for Yugoslavia for all the Former Yugoslav republics in our sample.

Critics have pointed to the fact that proxies of institutional development are outcome oriented rather than measuring inputs (Glaeser et al., 2004). In robustness tests, we therefore also use the EBRD reform index discussed above, as a measure of legislative and regulatory inputs into institutional development. Further, recognizing the bi-directional causality from institutional to economic development, we extract the exogenous component of institutional development and relate it to economic development. A positive relationship between institutional and economic development, however, does not suggest that there is no reverse causation from economic to institutional development; rather it suggests that our findings are not due to this reverse causation.

# _3. Proxies for Endowments, Socialist Entrenchment and Initial Political Structure_

We use the share of fuel, ores, and metal exports relative to GDP, as measured in the first available year of the sample period, as proxy for the importance of natural resources in an economy. Table 1 shows quite a variation across transition economies, ranging from less than a percent in Latvia and Albania to 55% in Tajikistan. A simple bivariate regression shows that raw exports can explain 48% of cross-country variation in Institutional Development. In robustness tests, we use gas reserves per capita as indicator of endowments with natural resources. We do not find a significant correlation between natural resource reliance and the rate of depreciation or

> deviation of 0.33 in the year 1991, the average and the standard deviation of the reform index increased to 2.25 and 0.60, respectively, by the end of the 1990s.

18