rafmacalaba/datause-encoder-data huggingface dataset:
The GVAR models are primarily data-driven models and can incorporate today’s interconnected economies in a convenient manner and can take into account the dynamic nature of the global economy involving many variables better than the gravity and CGE models. It gives a very practical global modeling framework for empirical analysis of different shocks and transmission mechanism of macroeconomic variables. GVAR models tackle the ‘curse of dimensionality’ problem by imposing restrictions directly on the parameters of the model. In particular, all foreign economies are typically approximated by one representative economy constructed as a (trade-)weighted average of foreign economies. Compared to factor augmented VAR (FAVAR) models, as used by Ahuja and Nabar (2012a,b) in the context of spillover effects of Chinese macroeconomic shocks, the GVAR approach can combine different macroeconomic variables of a large number of countries in a much more efficient manner. Here, individual country models are added by means of a consistent econometric approach to get a global model where cointegration is allowed for variables within and across countries. On the other hand, FAVAR models capture country-specific dynamics only through the idiosyncratic components (i.e. the residuals). Also, unlike other data driven models, GVAR allows data to be used in levels and thus the long run information in the data is retained. Cross country studies on spillover effects of macroeconomic shocks using GVAR models have flourished in recent times due to its convenient features. However, only few (e.g. Han and Ng (2012), Feldkircher and Korhonen (2012) and Osorio and Unsal (2013)) concentrated on China and its effect on other countries.While Han and Ng (2012) used it mostly for forecasting and Osorio and Unsal (2013) used it to detect spillover effects of inflation, Feldkircher and Korhonen (2012) focused on the effect of Chinese growth shocks on Central and Eastern European countries.

Secondly, this study uses a time varying weight matrix in order to construct the foreign variables in individual country models. Though use of time varying weights is gaining popularity in GVAR analysis, most of them still use a fixed weight matrix. Since the data coverage in this paper includes the Asian crisis of 1997 and the global financial crisis of 3
<!-- Start of picture text --> Cyprus Czech Republic Céte d'Ivoire Denmark Djibouti<br>100<br>50<br>~ ~~ * .<br>e ~ _—<br>Of mmo ree -co=<t=ane—Socrconoocoocoocs ee<br>Dominica Dominican Republic Ecuador Egypt, Arab Rep. El Salvador<br>100<br>.<br>e _<br>XN ° . — ° N TM~<br>~ LT ~ 4 x es ~<br>> So Ne<br>0 _—_ Sh Se © eo A esseasess ccs a . ON a<br>Equatorial Guinea Eritrea Estonia Eswatini Ethiopia<br>100 — \ —~ A __ ~~ a 8 _ —a ne -<br>> iel to<br>N \ S ~N<br>eM \ \ a, ‘<br>NN | Nw oN, a<br>0 ~~ >~N Deca ~~<br>Faeroe Islands Fiji Finland France French Polynesia<br>; |<br>50 N~N ~ ,<br>O4 — = = = = =! — — = .— © | cr —« ccccccccccccccccee | == ccccccccccccccccsccesscee | —— = == == == =<br>Gabon Gambia, The Georgia Germany Ghana<br>100 a<br>504 ~~ ~— ~ ~ . | ae* \ ~<br>. ~ S ~ ><br>Tel 7 — «__ _ew<br>0 es a Veen —~<br>2000 2010 2020 2000 2010 2020 2000 2010 2020 2000 2010 2020 2000 2010 2020<br>—-— $2.15 predictions —-— $6.85 predictions $24.35 predictions<br>—— $2.15 from PIP —e— $6.85 from PIP $24.35 from PIP<br><!-- End of picture text -->
health indicators employed in this section (weight and height of young children). Plot densities of the above described anthropometric indicators for children aged 1-5 years old from remittance recipient and non-recipient households are estimated using kernel densities. The figures show that children from recipient households have both higher weight-for-age and height-for age z- scores. Kolmogorov-Smirnov tests reject the equality of distributions for recipient and nonrecipient households, and suggest that remittances are in fact associated with better children anthropometric scores.

**Figure 6. Anthropometric Measures for Children aged 1 to 5, by Remittance Recipient Status – Guatemala.**

<!-- Start of picture text --> Children 1 - 5 Years Old<br>Weight-for-Age Z Scores Height-for-Age Z Scores<br>-6 -4 -2 0 2 4 6 -6 -4 -2 0 2 4 6<br>Weight-for-Age Z-score Height-for-Age Z-score<br>Rem HH Non-Rem HH Rem HH Non-Rem HH<br>.3<br>.3<br>.2<br>.2<br>Freq. Freq.<br>.1 .1<br>0 0<br><!-- End of picture text --> Source: Author’s calculations based on household surveys

**Figure 7. Anthropometric Measures for Children aged 1 to 5, by Remittance Recipient Status – Nicaragua.**

<!-- Start of picture text --> Children 1 - 5 Years Old<br>Weight-for-Age Z Scores Height-for-Age Z Scores<br>-6 -4 -2 0 2 4 6 -6 -4 -2 0 2 4 6<br>Weight-for-Age Z-score Height-for-Age Z-score<br>Rem HH Non-Rem HH Rem HH Non-Rem HH<br>.4 .4<br>.3 .3<br>.2 .2<br>Freq. Freq.<br>.1 .1<br>0 0<br><!-- End of picture text --> Source: Author’s calculations based on household surveys In order to test whether these results are driven by the differential characteristics of households with and without migrants, we estimate a regression model similar to the one used for educational attainment (equation 4), changing only the dependent variable, from years of 31
# <u>1. Introduction</u>

That African countries have a relatively high inequality—among the highest in the world—is generally well known. Yet, at further reflection, this fact represents a puzzle that has been little researched. This may be simply due to the fact that while writings on inequality and poverty in individual African countries have recently become much more common, not least thanks to a much greater availability of household surveys within the World Bank, continent-wide (or several-country) studies of inequality are rare (the exceptions are Demery and Squire 1996; Christiaensen, Demery and Paternostro, 2002).

One would expect Africa to have rather a very low inequality. If one subscribes to a Kuznets type of a relationship, Africa, being relatively poor and with a large share of agricultural population, should be on very much on the left of the rising portion of the Kuznets curve: inequality should be relatively low. In addition, the main asset, agricultural land, is relatively evenly distributed in Africa. In part thanks to the tradition of communal-land holding and exploitation, and absence of large plantations, landlessness has never been an issue in Africa. Mkandawire (2002, p. 193) writes: “There are two striking features of the African countryside...The first is that peasants...have direct access to the main means of production—labour and land; and the second [that]...with the exception of countries of settler agriculture and concessions, production of export crops in much of colonial Africa has remained in the hands of peasants.” Actually, the absence of a landless peasant class was the major obstacle for a socialist revolution in the African context as noted by Che Guevara during his attempt to ignite the fires of the revolution in the Congo (quoted by Mkandawire, 2002, p.194).<sup>2</sup>

> 2 As Mkandawire (2002, p.199) writes, explaining the lack of appeal of socialism to African peasants, “[g]uerillas cannot offer an immediate end to predation by local potentates, since such predation hardly exists; nor can they liberate peasants from the heavy exaction of national governments, since African governments have only extracted surplus through the market and not by direct taxation at the level of production.”

2
months old measures the presence of new firms (the “baby” rate). The distinction between nascent and new firms is made in order to determine the relationship of each to national economic growth. For both measures, the research focus is on entrepreneurial activity in which the individual involved have a direct but not necessarily full, ownership interest in the business.

# **_2.2 World Bank Group data_**

The goal of the _2007 World Bank Group Entrepreneurship Survey_ was to collect a benchmark of formal entrepreneurial activity for a large number of developed and developing countries. This intent is that this data will be used to compare private sector development across countries, as well as to monitor and evaluate the impact of regulatory reforms over time. In order to measure entrepreneurship and make data universally comparable, we developed a methodology that can be applicable across heterogeneous legal regimes and economic systems. Previous efforts had been made in this regard, but the great majority focused solely on the developed world, and did not take into account differences in legal systems, sectors, and economic structures (see United Nations, 2005). The WBGES defines the unit of measurement of entrepreneurship as: _Any economic unit of the formal sector incorporated as a legal entity and registered in a public registry, which is capable, in its own right, of incurring liabilities and of engaging in economic activities and transactions with other entities._ Notably, this definition excludes informal sector initiatives. This exclusion is based on the difficulties of quantifying the number of firms in the informal sector, rather than on its relevance for developing economies (Boegh, Nielsen and Ploving, 1997). The only way to measure the informal sector is through economic censuses, which due to their high costs are infrequently collected.

Furthermore, entrepreneurship is defined as: 4
# **Table A3**

Regression Results: Investor Only <!-- Start of picture text --> r Oi,j,t 'ipj,tX1 t<br>j]i i a+j,tRjI 1 + 2 Rj, 1 - 1 + fl3RLA,t + 0 4 R flsRu,, + Eij,<br>jei<br>Independent Variables All Sample Non-Crisis Crisis<br>Own Return (0,) -0.0001 0.0001 -0.0001<br>T-statistic -0.84 1.13 -OA5<br>Own Return Lagged (() 0.0007 0.0004 0.0018<br>T-statistic 1.36 0.76 1.69<br>Latin America Return (f3) 0.0037*** 0.0039*** 0.0022*<br>T-statistic 6.62 4.45 1.66<br>Local Index Retum (04) 0.0011*** 0.0012*** 0.0005<br>T-statistic 3.83 5.02 0.94<br>US Return (Ps) -0.0021 -0.0034 0.0013<br>T-statistic -1.26 -1.48 0.33<br>Constant 0.0094 0.0247 -0.0562<br>T-statistic 0.52 0.94 -1.18<br>Observations 4790 3241 1549<br>Adjusted R-squared 0.29 0.21 0.19<br><!-- End of picture text --> These results are "Investor Only" in that they reflect only investor redemption effects as in equation **(9).** T-statistics are corrected for heteroskedasticity across funds. Full sample: April **1993** to January **1999.** The crisis portion of the sample is December 1994-June **1995,** July 1997-March **1998,** August 1998-October **1998,** and January **1999.** The non-crisis portion is the rest of the sample. For robustness, results in each cell are based only on observations within three standard deviations of the mean.

Statistically Significant at the 10-percent level

**_**_** Statistically Significant at the 5-percent level Statistically Significant at the 1-percent level

34
- 21 -

**Table 12: Main Determinants of MWRs Dependent Variable: 100*MWR; Least Squares with Robust Standard Errors YEAR=2005; Observations: 1,391**

|**Variable**|**Coefficient**|**Std. Error**|**t-Statistic**|**Prob.**|
|---|---|---|---|---|
|C|72.51743|1.879981|38.57349|0.0000|
|AGE|0.347256|0.024670|14.07629|0.0000|
|LOG(PREMIUM)|1.667719|0.165144|10.09859|0.0000|
|GUARANTEE|-0.137314|0.019536|-7.028687|0.0000|
|DEFERMENT|0.017231|0.016807|1.025200|0.3054|
|Male|0.847786|0.554230|1.529663|0.1263|
|Female|3.600156|0.184605|19.50199|0.0000|
|R-squared|0.407568|Mean dep|endent var|106.1712|
|Adjusted R-squared|0.405000|S.D. depe|ndent var|4.506864|
|S.E. of regression|3.476427|Akaike inf|o criterion|5.334907|
|Sum squared resid|16726.39|Schwarz|criterion|5.361265|
|Log likelihood|-3703.427|F-sta|tistic|158.6889|
|Durbin-Watson stat|2.027204|Prob(F-s|tatistic)|0.000000|

# **_5.3 Analysis of Dispersion of MWRs_**

An efficient and transparent annuities market should produce similar prices (or MWRs) for customers with similar characteristics. The results above indicate that annuity pricing is influenced by the characteristics of the annuitant such as age and gender, but the regression does not explain a relatively large share of the variations of MWRs across individual annuitants and over time. The unexplained variations in MWRs could be simply due to the absence of key explanatory variables, such as the level of education of individual annuitants and their geographical location, as well as variables capturing provider characteristics. These limitations could not be overcome, as the dataset on individual annuities used to compute MWRs does not provide information on providers or further information on the characteristics of annuitants, beyond those explored above.

The dispersion of MWRs could also be due to institutional and regulatory inefficiencies, such as the lack of a transparent pricing system and the excessive influence of brokers. The influence of these factors may be examined, because during this period there were substantial efforts to improve market transparency. As mentioned before, a major development in the annuities market was the passage of a new Pensions Law in 2004 that, among other factors, introduced a cap on broker’s commissions and an electronic quotation system that allows easy and transparent comparisons of annuity and PW prices. The draft of the Pensions Law was first submitted to Congress in 2000, and it is possible that the market started changing behavior in anticipation of the Law’s approval. Such
increase/decrease in the probability of declaring an amount equal to zero associated with women-owned businesses.

The results presented in Table 11 reveals that women-owned businesses are less likely to report zero sales, zero tax payable, and zero tax payment compared to men-owned businesses, after controlling for time, location, sector, adoption of the electronic sales registry machine, and the category of the taxpayer. Based on the above result, it does not seem that womenowned businesses are engaging in intentionally avoiding their tax obligations through underreporting their income.

Table 11: Linear Probability Model for Reporting Zero Sales, Tax Liabilities, and Payment

||(1)<br>Sales|(2)<br>Tax payable|(3)<br>Tax payment|
|---|---|---|---|
|Women-owned|-0.00103|-0.00431|0.000649|
||(-0.12)|(-0.41)|(0.27)|
|Constant|0.0735<sup>_∗∗∗_</sup>|0.0945<sup>_∗∗∗_</sup>|0.00222|
||(6.18)|(6.67)|(0.84)|
|R2|0.00544|0.00506|0.00792|
|Observations|3347|3347|3347|

_t_ statistics in parentheses All of the regression models above include a time dummy. _∗ ∗∗ ∗∗∗ p <_ 0 _._ 05, _p <_ 0 _._ 01, _p <_ 0 _._ 001 If indeed women-owned businesses are reporting truthfully, the expectation is that they would struggle to keep their business afloat after paying almost the same amount as men. To assess this possibility, we compare the probability of exiting the tax net by gender next.

# _4.2.3. The Probability of Exit_

Given that women pay the same tax level despite reporting lower sales, we delved into whether this phenomenon has played a role in women leaving the formal sector. To explore this, we constructed an equation in which exiting the tax net is a function of gender, a metric of the tax burden, machine 25
49 consists in vertical integration relates to fragmentation of production. But other arrangements based on horizontal arrangements are possible-consider for instance the supplier of an electric power generator that is subsequently installed in an electric plant. The supplier may be partly owned by a large MNC specializing in large engineering projects, e.g., ABB. While the supplier apparently operates within ABB-organized network, it is impossible to capture this trade without access to firm data. Furthermore, our definition of parts and components is rather narrow. We have not included all possible components or parts.

Yet, it seems that the 'network' analysis combined with a broader discussion of development in trade in parts provides a pretty good yardstick to gauge the significance of trade related to this finer division of labor. The discussed networks combined together cover most of trade in parts between CEEC-10 and the EU. The share of parts covered by these networks in total exports of parts amounted to 61 percent in 1998 and that in total imports of parts from the EU was 58 percent. The share of total networks' trade (total exports and imports of parts and components) in CEEC-10 trade turnover with the EU in manufactures excluding chemicals increased from 12 percent in 1993 to 21 percent in 1998 (Table 20).

Table 20: **Aggregate trade in networks, value, composition and share in EU-external exports and imports, 1993 and 1998**

||CEEC-10 (m<br>US dollar|illion of<br>s)<br>US<br>|C<br>d<br>|EEC-10 (s<br>percent)<br>ollars)|hare in<br> <br>perc|Composition of EU-<br>extemal<br>trade<br>(in<br>ent) extentr|Share of CE<br>in EU-exte<br>imn ern|EC-10<br>rnal<br>a|
|---|---|---|---|---|---|---|---|---|
|Exports|1993|199<sup>8</sup>1||1993||1998|1998|<br>19931|1998|
|Final Products|2,656|12,945||12.2|21.1|17.9<br>20.5|<br>2.7|7.7|
|Components|295|1,125||1.4|1.8|3.0<br>4.6|<br>1.8|3.0|
|Parts|836|4,640||3.8|7.6|9.2<br>7.5|<br>1.7|7.5|
|TOTAL|3,786|18,711||17.4|30.5|30.0<br>32.6|<br>2.3|**7.0**|
|Other parts and components|851|3,666||3.9|6.0|4.5<br>7.0|<br>3.5|6.4|
|Memorandum: total of<br>networks and otherparts|4,637|22,377||21.3|36.5|34.5<br>39.6|<br>2.5|6.9|
|Manufactured Exports<br>(excluding<br>chemicals)|21,794|61,301||100.0|100.0<br>_|100.0<br>100.0<br>_|<br>4.0|7.5|
|Imports|1|||l <br>l_<br>|I<br>I<br>_<br>_ _ _ _ _ _ _ _ _<br>I|I<br> I_ _ _ _iI_ _ _<br>_<br>_<br>_<br>_<br>_exports|Share of C<br>in EU-ext|EEC-10<br>ernal|
|Final Products Imports|4,139|9,765||14.1|13,1|17.4<br>20.7|<br>4.1|5.2|
|Components Imports|469|2,245||1.6|3.0|3.4<br>4.7|<br>2.3|5. 3|
|Parts Imports|1,926|7,217||6.6|9.6|8.5<br>6.2|<br>3.9|12.9|
|TOTAL: final, comp. & parts|6,534|19,226||22.3|25.7|29.3<br>31.6|<br>3.8|6.7|
|Other parts and components|<br>2,009|6,645||6.9|8.9|3.7<br>17.5|<br>9.3|11.2|
|Memorandum:<br>total of<br>networks and other parts|8,543|25,871||29.2|34.6|15.6<br>49.1|<br>4.4|7.5|
|Manufactured<br>Imports<br>Socludinr chemicals)|29,267<br>l|74,78<br> <br>l|7<br>|1 100.0<br>l|100.0|<br>100.0<br>100.<br>b|01<br>5.0|8.3|

**Source:** derived from trade data as reported by the EU to the UN COMTRADE database.
<!-- Start of picture text --> oO<br>~ — Relative consumption volatility<br>-,- First period mean<br>wl ‘+ Second period mean<br>2 " on oe<br>Ss<br>cc<br>foe) — a _ _<br>So<br>+<br>So<br>1960 1970 1980 1990 2000 2010<br><!-- End of picture text -->
estimated), we applied the same median substitution for the growth rate as described in 2b.

- d. When no observations were available before 2013 (i.e. no empirical data at all for both physicians and nurses/midwives), neither the physician nor nurse/midwives supply was projected. Instead, the mean 2030 predicted supply density across countries of the same income level was substituted.

- e. In certain cases, special treatment was given to particular countries because of data problems or predicted growth rates or densities that were implausible.

- i. Dominican Republic: The physician growth rate was replaced with the median value.

- ii. Greece: The nurse/midwife growth rate was applied to the 2000 density value (rather than the 2001 value).

- iii. Chile: The nurse/midwife growth rate resulted in excessively large predictions; the growth rate was reset to 0.0001/year to reflect a stable trend.

- iv. Haiti: There was only one historical data point for nurses/midwives. The median income value that would be applied was negative, which resulted in predicted densities near zero; the growth rate was reset to 0.0001/year to reflect a stable trend.

- v. Gambia and Kenya: The estimate growth rates were negative, leading to predicted densities near zero; the group median growth rate was substituted.

- f. In a number of countries, no empirical data for nurses/midwives were available, but information on physicians was available. We therefore applied to global ratio of 2.517 nurse/midwife‐to‐physician ratio to obtain the estimate for nurse/midwife density. The countries affected are: Iraq, Slovak Republic, Bosnia and Herzegovina, Serbia, and Macedonia.

These various substitutions and the number of countries affected are summarized in Table A5.

**Table A5. Summary of methods used to predicting future worker supply**

|||**M**|**ethod for a**<br>|**ddressing m**<br>|**issing data**|<br>|
|---|---|---|---|---|---|---|
||**Total N**|**a**|**b**|**d**|**e**|**f**|
|**Physicians**|||||||
|Supply|208*|136|50|21|1|0|
|Demand|165|120|44|0|1|0|
|**Nurses/midwives**|||||||
|Supply|208*|81|100|19|3|5|
|Demand|165|73|84|0|3|5|
|* Note that although 20<br>208 countries. Kosovo a<br>havepopulation estimat|1 countries are in<br>nd St. Martin (Fr<br>es for 2030.|cluded in the<br>ench part) are|analysis, 2030<br>excluded from|supply predic<br>the projectio|tions are only<br>ns because th|made for<br>ey do not|

The projected supply of physicians and nurses/midwives per 1,000 population for each future year was then multiplied by projected population (medium fertility assumptions) in that year to obtain the absolute numbers of physicians and nurses/midwives. The formula is as follows: 33
Policy Research Working Paper 10695

# **Abstract**

Based on a randomized survey experiment in Malawi, this study examines how innovative techniques in time use data collection could sidestep measurement concerns with traditional recall-based time use measurement. The experiment assigns random samples of households, and adult men and women within, to one of two treatment arms on time use measurement: a traditional 24-hour recall time use diary, and a self-administered smartphone-based pictorial time diary, known as the _TimeTracker_ app, for real-time data collection. Compared to the recall arm, participation in employment and unpaid domestic and care work is shown to be higher in the smartphone arm for both men and women. The resulting estimates of gender gaps, while continuing to be large, are narrower in the smartphone arm, except for care work where the estimated gender gap increases. The recall treatment leads to substantial underreporting of activities after 6 pm, which otherwise accounts for nearly 30 percent of daily reported time in the smartphone arm. Likewise, the extent of simultaneous activities, particularly among women, is markedly lower in the recall arm. The overall reported time is, however, higher in the recall arm due to the minimum 15-minute duration that was used for recording activities the 24-hour recall diary, while over one-third of activities lasted less than 15 minutes in the smartphone arm. The analysis also shows that using stylized time use questions with a 7-day recall, as opposed to a 24-hour recall diary, results in an even greater overestimation of reported time in employment and unpaid work.

This paper is a product of the Development Data Group, Development Economics. 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 tkilic@worldbank.org and gkoolwal@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