rafmacalaba/datause-encoder-data huggingface dataset:
**Table 7**

# **Short-Run Estimates from Panel Autoregressive Distributed Lag Models,** **<u>per Capita Income, PPP terms</u>**

||**‐‐‐‐‐‐‐‐‐‐‐‐‐**|**‐‐‐‐‐‐‐‐‐‐ One**|**lag ‐‐‐‐‐‐‐‐‐‐‐‐**|**‐‐‐‐‐‐‐‐‐‐‐**|**‐‐‐‐‐‐‐‐‐‐‐‐‐‐**|**‐‐‐‐‐‐‐‐‐ Two**|**lags ‐‐‐‐‐‐‐‐‐‐‐**|**‐‐‐‐‐‐‐‐‐‐‐‐**|
|---|---|---|---|---|---|---|---|---|
||**World**|**High **|**Middle**|**Low**|**World**|**High **|**Middle**|**Low**|
|**Terms of Trade**|**(ToT)**||||||||
||6.08***|6.00***|4.65***|3.39*|6.61***|6.75***|4.58***|2.99|
|Constant|(7.29)|(6.46)|(8.01)|(1.89)|(6.71)|(7.70)|(5.37)|(1.53)|
|k|‐0.34***|‐0.35***|‐0.34***|‐0.41***|‐0.32***|‐0.32***|‐0.36***|‐0.44***|
|Stocs‐to‐use|(‐8.63)|(‐7.29)|(‐10.81)|(‐7.29)|(‐7.57)|(‐6.71)|(‐10.46)|(‐7.08)|
||‐0.38***|‐0.35***|‐0.39***|‐0.18|‐0.41***|‐0.37***|‐0.37***|‐0.16|
|Ex. rate|(‐3.63)|(‐3.40)|(‐3.62)|(‐0.99)|(‐3.76)|(‐3.40)|(‐3.15)|(‐0.89)|
|I|‐0.01|‐0.01|‐0.00|‐0.01|‐0.01|‐0.01*|‐0.00|‐0.01|
|nt. rate|(‐1.19)|(‐1.63)|(‐0.29)|(‐1.14)|(‐1.05)|(‐1.93)|(‐0.04)|(‐0.94)|
|Oil Pi|0.10***|0.11***|0.08**|0.02|0.11***|0.12***|0.08**|0.02|
|rce|(3.09)|(3.47)|(2.46)|(0.42)|(3.06)|(3.43)|(2.00)|(0.37)|
||3.36***|2.70***|2.25**|1.60***|3.94***|3.50***|2.17*|1.59***|
|GDP|(3.59)|(4.35)|(2.53)|(2.94)|(4.36)|(6.04)|(1.83)|(2.61)|
|GDP (lag1)|||||0.55<br>(0.82)|‐0.14<br>(‐0.20)|0.33<br>(0.57)|‐0.33<br>(‐0.56)|
|**Nominal Prices**|||||||||
||5.30***|5.59***|4.77***|4.48***|5.33***|5.92***|4.97***|3.38***|
|Constant|(6.88)|(6.28)|(12.52)|(5.06)|(5.26)|(6.51)|(7.68)|(2.91)|
||‐0.35***|‐0.36***|‐0.35***|‐0.34***|‐0.29***|‐0.31***|‐0.31***|‐0.29***|
|Stocks‐to‐use|(‐7.08)|(‐6.34)|(‐7.85)|(‐6.14)|(‐5.31)|(‐5.67)|(‐5.39)|(‐4.39)|
||‐0.59***|‐0.56***|‐0.61***|‐0.58***|‐0.68***|‐0.61***|‐0.71***|‐0.67***|
|Ex. rate|(‐4.51)|(‐3.64)|(‐6.97)|(‐5.40)|(‐5.82)|(‐4.98)|(‐6.79)|(‐5.77)|
||0.01|0.00|0.01***|0.01***|0.01***|0.01|0.02***|0.02***|
|Int. rate|(1.55)|(0.70)|(3.44)|(2.96)|(2.60)|(1.14)|(4.78)|(3.95)|
|Ol P|0.14***|0.14***|0.13***|0.14***|0.16***|0.16***|0.15***|0.16***|
|i rice|(6.26)|(7.14)|(4.72)|(5.19)|(5.66)|(5.75)|(4.89)|(5.20)|
||1.52|1.67**|0.22|0.30|2.19***|2.58***|0.13|0.60|
|GDP|(1.51)|(2.40)|(0.29)|(0.57)|(2.58)|(4.77)|(0.14)|(1.63)|
||||||‐074|‐071|‐055|‐188***|
|GDP (lag1)|||||.<br>(‐1.13)|.<br>(‐1.12)|.<br>(‐0.95)|.<br>(‐3.08)|
||0.87***|0.80***|1.00***|1.01***|0.78***|0.68***|0.96***|0.91***|
|MUV|(4.15)|(3.47)|(5.63)|(6.70)|(6.94)|(6.16)|(10.10)|(10.43)|
|MUV l1|||||0.03|‐0.06|‐0.01|0.05|
|(ag)|||||(0.04)|(‐0.91)|(‐0.13)|(0.55)|

**Notes** : The parameter estimates are based on equation (7). The numbers in parentheses are _t_ ‐statistics. One (*), two (**), and three (***) asterisks indicate significance at 10%, 5%, and 1% levels, respectively.

_22_
<!-- Start of picture text --> Policy, Research, and External Affairs<br><!-- End of picture text --> <!-- Start of picture text --> = WORKINGPAPERS<br>| Macroeconomic Adjustment |<br>and Growth :<br>WPS 609<br><!-- End of picture text -->

This paper — a product of the Macroeconomic Adjustment and Growth Division, Country Economics Department is part of a larger effort in PRE to understand empirically the links between policy and longrun growth. Copies are available free from the World Bank, 1818 H Street NW, Washington DC 20433. Please contact CECMG staff, room N11-024, extension 39175 (43 pages, with tables).

A vast literature uses cross-country regressions to find empirical links between policy indicators and long-run average growth rates.

* Confirm the positive correlation between the share of investment in GDP and long-run growth.

Levine and Renelt study whether the conclu¢ Conclude that all findings using the share sions from existing studies are robust or fragile if | exports in GDP could be GDP could be could be be obtained almost identithere are small changes in the list of independent cally using the total trade or import import share. variables. (Cross-country growth studies that use use export

¢ Conclude that all findings using the share of exports in GDP could be GDP could be could be be obtained almost identically using the total trade or import import share. (Cross-country growth studies that use use export indicators should not be interpreted as studying the relationship between exports and growth per se but rather as between growth and trade defined more broadly.) Their conclusion: they are fragile.

They find that although “policy” — broadly defined — appears to be importantly related to growth, there is no strong independent relationship between growth and almost every existing policy indicator. Levine and Renelt: « Find that many commonly used fiscal indicators are not robustly correlated with growth.

¢ Find that very few macroeconomic variables are robustly correlated with cross-country growth rates.

* Highlight the importance cf considering altemative specifications in cross-country growth regressions.

* Clarify the conditions under which onc finds convergence of per capita output levels.

| The PRE Working Paper Series disseminates the findings of work under way in the Bank's Policy, Research, and Extemal | | TheAffairsfindings,Complex.interpretations,An objective andof theconclusionsseries is to getin thesethese findingspapers dooutnotquickly,necessarilyeven representif presentationsofficial areBankJes; thanpolicy.fully polished. Produced by the PRE Dissemination Center
to a drop in Harmony by 1.12 standard deviation. The results are consistent with the agency model where parents' emphasis on sharable marriage production tend to lead to a sacrice in non-sharable outcomes such as marital harmony.

On joint couple income, the 2SLS results suggest that parental matchmaking has a positive eect in urban areas but a negative eect in rural areas. In the two OLS specications, Parent Matched is not signicantly correlated with couple income, probably because the adverse selection of sons and the positive selection of parents go against each other on factors that may aect joint couple income. Indeed, once instrumented, the eect of Parent Matched becomes signicant in both rural and urban areas. For urban areas, an increase in Parent Matched by one standard deviation would lead to an increase in couple income by 0.2 standard deviations. For rural areas, the corresponding eect is a drop in couple income by 0.3 standard deviations.

The contrasting eects on joint income in urban and rural areas likely reect dierent patterns of old age support and dierent institutional constraints. First, in urban areas, market opportunities are more abundant (both for labor and for services such as meals, laundry and care-giving), yet the enforcement of the one-child policy is more stringent, both factors contributing to urban couples relying more on monetary income (relative to household production) to deliver old age support. As a consequence, parents in their self interest to ensure old age support also have strong interest to ensure that the family structure would yield relatively high income. Thus, the eect of parental matchmaking on joint couple income tend to be positive in urban areas. In contrast, in rural areas, there are much fewer opportunities to make money and buy services; but there are more opportunities to evade the one-child policy. Thus parents have to rely more on household production to ensure the delivery of old age support. We thus witness a substitution of market production by household production in rural areas, which explains why the eect of parental matchmaking on (market) couple income is negative in rural areas. These conjectures also imply that urban and rural parents might pick wives of dierent skills and traits.

# 4.2 Eects on Wife's Schooling, Income and Submissiveness

As rural couples rely more on household production for the delivery of old age support than urban couples, and wives tend to play the role of caregivers in traditional Chinese families, we expect that rural parents, upon the opportunity to act as matchmaker for their sons, would have strong incentives to pick potential wives who would specialize in household production. For smooth delivery of household production, the rural parents would pick daughter-in-laws that are submissive. While rural parents likely would prefer daughter-in-laws that are both 23
**1T(:I1 P.F 1**

V 0 <!-- Start of picture text --> Changes of Investment and Resource Bal.<br>1979-82 to 1983-86 (Shore of GOP)<br>0.05 -<br>0.04 -<br>0.03 - Guinea issou _ bropica<br>0.02 - i Cameroon<br>ffi 0.01 E ll Ethiopia<br>0 0- *pa~~~~~E[!rkey<br>-0.01 - 1ol Pakistan 0 m0 E Koreo<br>o ~~~~~0 0 Uoim<br>* -0.02 - *hoilon<br>2 -0.03 U n Unisia ° Thailand<br>-0.04 * AJgeri aii y i KenD<br>-0.05 - ° 13OE MExhil,*<br>-0.06 0<br>-0.07 o 0 U U Magascor<br>- -0.08 UruguGyU* l Malowi<br>% -0.09 IEI Argentina<br>2,) O -0.12 -0.112U I0 Nigeria 3 0<br>-0.13 El Philippines 0 Ivory 4ost<br>1 Jorda<br>-0.14<br>-0.15 -0.1 6- _______________ _ * 1I4 ger a<br>I I IIIr<br>-0.06 -0.02 0.02 0.06 0.1 0.14 0.18<br><!-- End of picture text --> Change of Resource Balance Share of GDP Correlation Coefficient: -.<sup>63</sup>

* - ~~at 52' ~
**Table 4.5a** **<u>(continued): Effectiveness of Bias Correction</u>**

||**Full GHS-P**<br>||**Nigeria**<br>**HFPS(n**|**=1,950)**||**Full**<br>**UNPS**||**Uganda**<br>**HFPS(n**|**=2,259)**||
|---|---|---|---|---|---|---|---|---|---|---|
||**(n=4,976)**|_Adjust_|_ed Weights_<br>_†_|_Unadju_|_sted Weights_<br>_†_|<br>**(n=3,308)**|_Adjust_|_ed Weights_<br>_†_|_Unadju_|_sted Weights_<br>_†_|
||_Mean_|_Mean_|_Difference_<br>_(12) -(11)_|_Mean_|_Difference_<br>_(14 -(11)_|_Mean_|_Mean_|_Difference_<br>_(17) -(16)_|_Mean_|_Difference_<br>_(19) -(16)_|
|**Characteristic**|_(11)_|_(12)_|_(13)_|_(14)_|_(15)_|_(16)_|_(17)_|_(18)_|_(19)_|_(20)_|
|Rural|0.686|0.686|0.000|0.634|-0.051|0.684|0.685|0.001|0.672|-0.012|
|Average household size|5.528|5.528|0.000|5.825|0.297|4.515|4.544|0.029|4.684|0.169*|
|Dependencyratio|1.016|1.033|0.017|1.029|0.013|1.064|1.033|-0.031|1.050|-0.014|
|Female head(%)|0.186|0.186|0.000|0.173|-0.014|0.342|0.330|-0.012|0.323|-0.019|
|Age of head|48.797|49.153|0.356|48.579|-0.218|45.311|45.370|0.059|45.484|0.173|
|**_Religion of head_**|||||||||||
|Orthodox|||||||||||
|Muslim|0.425|0.427|0.002|0.424|-0.001||||||
|Christian|0.557|0.555|-0.002|0.560|0.003||||||
|**_Marital status of head_**|||||||||||
|Married|0.750|0.754|0.004|0.772|0.022|0.713|0.732|0.019|0.748|0.035**|
|Formerlymarried|0.193|0.189|-0.004|0.169|-0.025|0.246|0.231|-0.015|0.218|-0.028*|
|Never married|0.057|0.058|0.001|0.059|0.002|0.041|0.037|-0.004|0.034|-0.007|
|**_Education level of head_**|||||||||||
|Head Literate|0.744|0.744|0.000|0.803|0.06***|0.772|0.787|0.015|0.816|0.044***|
|None|0.375|0.369|-0.005|0.296|-0.078***|0.087|0.090|0.003|0.079|-0.008|
|Primary|0.241|0.240|-0.001|0.256|0.015|0.584|0.585|0.000|0.549|-0.035|
|Secondary|0.225|0.231|0.006|0.255|0.030|0.290|0.291|0.001|0.311|0.021|
|Tertiary|0.160|0.160|0.000|0.193|0.033|0.120|0.117|-0.003|0.132|0.012|
|**_Working status of head_**|||||||||||
|Any|0.827|0.831|0.004|0.842|0.015|0.855|0.857|0.002|0.864|0.008|
|Wage|0.177|0.191|0.014|0.220|0.043*|0.324|0.323|-0.001|0.323|-0.001|
|Agriculture|0.442|0.407|-0.035|0.379|-0.063**|0.479|0.480|0.001|0.474|-0.005|
|Nonfarm enterprise|0.367|0.393|0.025|0.412|0.045|0.266|0.281|0.016|0.293|0.028|
|Ganyu|||||||||||

†The results of an adjusted Wald test comparing weighted means are presented (*** p<0.01, ** p<0.05, * p<0.10).

36
**HUMAN CAPITAL, TANGIBLE WEALTH, AND THE INTANGIBLE CAPITAL RESIDUAL**

# **1. Introduction**

Both theory and intuition support the idea that income is the economic return on wealth. But this conception of income and wealth is apparently at odds with the figures that we can observe in the national balance sheets of the System of National Accounts (SNA).

Since the 1993 revision of the SNA, the national balance sheet accounts include both produced assets and the value of commercial natural resources, including land. According to wealth estimates published in _The Changing Wealth of Nations_ (World Bank, 2011), the ratio of produced wealth to GDP averages a factor of 2.6 across more than 120 countries in 2005. If we expand the asset accounts to include all tangible wealth, both produced and natural, then the ratio of wealth to GDP averages a factor of about 6.6.

The puzzle in all of this is that, if income is understood as the return on wealth, and the rate of return is assumed at 5%, then the total wealth of a nation should be on the order of 20 times GDP. Since there is no reason to believe that average rates of return on assets in these countries should be substantially higher than 5%, the obvious conclusion is that there are ‘missing’ assets, i.e. assets that are not measured in the SNA balance sheet accounts.

The most obvious missing asset in the SNA is human capital, which was measured indirectly as a component of the residual in the World Bank’s comprehensive wealth accounts (World Bank, 2006, 2011). Recent work at the OECD (Liu, 2011) has estimated the monetary value of human capital stocks for sixteen countries<sup>2</sup> over a range of years, which offers an opportunity to include these direct estimates of human capital into the World Bank framework of comprehensive wealth accounting.

The growth accounting literature also suggests that there must be other ‘missing’ assets in the SNA – when using the growth in production factors such as produced capital and ‘raw’ labor to explain the growth in output in an economy, there is always a large residual of unexplained growth in output. This residual factor in growth accounting is termed ‘total factor productivity’ (TFP) and it is generally considered to encompass some combination of knowledge and technology as the missing contributors to growth in output.

The first aim in this paper is to combine the OECD’s monetary estimates of human capital and the World Bank’s comprehensive wealth accounting to arrive at a better understanding of the contribution of human capital to the wealth of nations, relative to produced and natural capital. The second aim of this paper is to estimate the size of what could be termed the ‘stock equivalent’ of total factor

> 2 . Australia, Canada, Denmark, France, Israel, Italy, Japan, Korea, the Netherlands, New Zealand, Norway, Poland, Romania, Spain, the United Kingdom and the United States.

2
results based on variables that include hours worked in agriculture (i.e. total hours worked, hours worked in agriculture, hours worked in market work, and hours worked in market work within the household).

Prior to exploring the relationship between participation in economic activities and rice prices, we consider descriptive statistics on the economic activities of males and females in Vietnam in both rounds of the VLSS. Table 2 reports the summary statistics for economic activities of women and men ages 6-15, 16-24, and 25-64 in 1993 and 1998 living in rural areas. The top panel of table 2 reports the economic activities for rural females ages 6-15, 16-24, and 25-64 in 1993 and 1998. The bottom panel presents this information for rural males. Note that an individual can participate both in within the household work and work for wages at the same time. As a result, the share of individuals who work within the household and the share who work for wages does not need to add up to overall female participation rates. Similar logic applies for aggregations such as ‘work for wages’ and ‘within the household work’. Moreover, the hours worked variable for each work category is conditional on participation in that particular work category. Because people who work, do not participate in all work categories, the hours worked variables also do not add up to total hours worked.

Let us first consider labor market participation for women. Several interesting facts appear. First, in all age groups, the majority of women report participating in some kind of work. In 1993, the participation rates in overall/any work range from 69 percent for girls ages 6- 15, 93 percent for women ages 16-24, and 97 percent for women ages 25-64. Few women participate in wage work (in 1993, participation rates range from 2.2 to 14.1 percent in various age groups), and most women participate in work within the household (participation rates range from 69 to 96 percent in 1993). These large participation rates in work within the household 16
# FIGURE 1 Pass-through during significant currency depreciations

A. Unconditional pass-through from depreciations of 5 to 10 percent <!-- Start of picture text --> Ratio Interquartile range<br>1.0 Median<br>0.8<br>0.6<br>0.4<br>0.2<br>0.0<br>-0.2<br>All Advanced EMDEs<br>economies<br>1980-98 1998-2017 1980-98 1998-2017 1980-98 1998-2017<br><!-- End of picture text --> B. Unconditional pass-through from different depreciation episodes, 1998-2017 <!-- Start of picture text --> 10-20 percent<br>>20 percent<br><!-- End of picture text --> C. EMDEs: Unconditional pass-through from depreciations of 5 to 10 percent, 1998-2017 D. EMDEs: Unconditional pass-through from different depreciation episodes, 1998-2017 <!-- Start of picture text --> Ratio 5-10 percent<br>0.6 10-20 percent<br>0.4<br>0.2<br>0.0<br>-0.2<br>Trade Exch. Inflation Energy Current<br>openness regime target account<br>Low High Float Peg. Non-IT IT Importer Exporter Deficits Surpluses<br><!-- End of picture text --> <!-- Start of picture text --> Ratio<br>Ratio0.6 Interquartile range 0.6 10-20 percent<br>Median<br>0.4 0.4<br>0.2 0.2<br>0.0 0.0<br>-0.2 -0.2<br>Trade Exchange Inflation Energy Current Trade Exch. Inflation Energy Current<br>openness regime target account openness regime target account<br>E. Frequency of significant exchange rate F. Frequency of significant exchange rate<br>depreciations: Advanced economies depreciations: EMDEs<br>Percent of countries Percent of countries<br>40 40 5-10 percent 10-20 percent<br>5-10 percent 10-20 percent<br>>20 percent<br>30 >20 percent 30<br>20 20<br>10 10<br>0 0<br>1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2018<br>Low High Float Peg. Non-IT IT Importer Exporter Deficits Surpluses<br>1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2018<br>Low High Float Peg. Non-IT IT Importer Exporter Deficits Surpluses<br><!-- End of picture text --> Note: Depreciations are defined as negative quarterly changes in the nominal effective exchange rate. The sample comprises 34 advanced economies and 138 EMDEs. EMDEs = emerging market and developing economies; IMF = International Monetary Fund; IT = inflation targeting.

A.-D. Pass-throughs are defined as the change in consumer prices after one quarter divided by the depreciation of the nominal effective exchange rate. The markers refer to the median pass-through.

A.C. The bars show the interquartile range of pass-throughs.

C.D. Countries with “high” trade openness are defined as those with above median trade-to-GDP ratios; all others are considered to have “low” trade openness. Exchange rate and IT regimes are based on IMF classifications. Energy exporters are defined as in World Bank (2018); all other countries are considered energy importers. Countries with current account deficits are those with a negative average current account balance over 1998-2017.

24
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