rafmacalaba/datause-encoder-data huggingface dataset:
_Polih Fiscal Crisis 7_

# CCA Profit. HCA Profit and Cash Flow

# 1. Standard definitions

HCA prL3t - IB * CCA profit CCA profit -IA + A * CF 2. Gross of amortization HCA profit (gross of amortization) - IB - CCA profit (gross of amortization) CCA profit (gross of amortization) - IA * CF where HCA profit * Profit calculatea on an historical cost accounting basis (standard profit reported in the Polish statistics) CCA profit * Profit calculated on a current cost accounting basis CF * Cash flow about 50%;7 (2) the strLuture of inventories (materials spend a longer time in inventory than finished goods); (3) the inflation bias in inventory valuation; and (4) the costing of inventories (they are valued at cost and not in terms of potent.al sales revenue). The results of the calculations suggest an average life in inventory of 1 to 3 months, with numbers towards the lower end of the spectrum perhaps more likely.

The range of estimates of real inventory accumulation presented here correspond to the range of estimated inventory life: 1-month inventory life; 2-month inventory life; and 3-month inventory life.

> 7. The reason _is_ that to calculate "sales of own production' a large portion of turnover (notably in reuil trade) is summed over profit margins and not gross turnover. See the Appendui.
4 Empirical studies undertaken as background for the World Trade Organization’s ongoing Doha round of multilateral trade negotiations suggest that in 2001, when that round was launched, policy-driven distortions to agricultural incentives contributed around twothirds of the global welfare cost of merchandise trade barriers and subsidies (see, e.g., Anderson and Martin 2005). While such empirical studies did not have access to comprehensive estimates of distortions to farmer and food consumer incentives in developing countries other than applied tariffs on imports, a more recent study that draws on a new database of distortions to agricultural incentives has confirmed that earlier result: Valenzuela, van der Mensbrugghe and Anderson (2009) suggest agricultural price and trade policies as of 2004 accounted for 70 percent of the global welfare cost of those and other merchandise trade policies. This is a striking result, given that the shares of agriculture and food in global GDP and trade are only 3 and 6 percent, respectively. The contribution of farm and food policies to the welfare cost of global trade-distorting policies for just developing countries is estimated by those authors to be even greater, at 72 percent – of which two-thirds is due to policies of developing countries themselves. Even so, the estimates of price distortions that went into that modeling study show that many developing countries protect their less-competitive farmers from import competition, so some farm households might be hurt if all markets were opened (Anderson 2009, Ch. 1).

The World Bank’s recent study of price distortions (Anderson 2009) shows that the rate of assistance to farmers relative to producers of non-farm tradables has fallen by onethird for high-income countries since the latter 1980s (from 51 to 32 percent) while in developing countries it has all but disappeared (rising from -41 percent in the early 1980s to +1 percent in 2000-04). The latter trend for developing countries is mainly because of the phasing out of agricultural export taxes, since assistance via import restrictions has risen over the period shown. In both high-income and developing countries there remains a large gap
Table 7: Sibling and Intergenerational Correlations: Developed Region Sample

|||All children|Bro|thers (Men)|Siste|rs (Women)|
|---|---|---|---|---|---|---|
||1993|2006|1993|2006|1993|2006|
||||PAN<br>|EL A<br>|||
|Sibling Correlation (SC)|0.645***|0.598***|0.611***|0.610***|0.764***|0.660***|
||(99.906)|(75.443)|(61.044)|(51.623)|(75.976)|(43.135)|
|Intergenerational Correlation<br>(IGC)|0.565|0.521|0.533|0.506|0.609|0.537|
||(91.13)***|(83.66)***|(71.97)***|(65.58)***|(70.52)***|(61.79)***|
|IGC squared|0.319|0.271|0.284|0.256|0.371|0.288|
|Proportion of SC explained by<br>IGC|0.495|0.454|0.465|0.420|0.486|0.437|
|Controling for Caste and<br>religion|||PAN|EL B|||
|Sibling Correlation (SC)|0.626***|0.572***|0.594***|0.587***|0.747***|0.639***|
||(91.841)|(68.758)|(56.851)|(47.494)|(68.971)|(39.307)|
|Intergenerational Correlation<br>(IGC)|0.541|0.495|0.512|0.481|0.580|0.510|
||(84.59)***|(77.67)***|(67.07)***|(60.64)***|(64.75)***|(57.38)***|
|IGC squared|0.293|0.245|0.262|0.231|0.336|0.260|
|Proportion of SC explained by<br>IGC|0.468|0.429|0.442|0.394|0.450|0.407|
|Controling for Neighborhood<br>FE|||PAN|EL C|||
|Sibling Correlation (SC)|0.407***|0.371***|0.373***|0.390***|0.459***|0.349***|
||(41.711)|(34.240)|(25.398)|(23.250)|(20.069)|(13.474)|
|Intergenerational Correlation<br>(IGC)|0.478|0.436|0.471|0.448|0.522|0.448|
||(85.71)***|(79.98)***|(66.35)***|(62.96)***|(62.93)***|(55.79)***|
|IGC squared|0.228|0.190|0.222|0.201|0.272|0.201|
|<br>Proportion of SC explained by<br>IGC|0.562|0.512|0.595|0.515|0.594|0.575|
|No. of observations|27,449|28,507|16,967|16,708|10,482|11,799|

Robust t statistics in parentheses. Standard errors corrected for clustering at family level

* significant at 10%; ** significant at 5%; *** significant at 1%

42
# **Appendix: Table A.2**

## **Regressions on Financial Development and Financial Inclusion, Upper-Middle-Income Countries**

This table presents OLS regressions of liquid liabilities over GDP, private credit over GDP, the percentage of adults with an account at a formal financial institution, and the percentage of adults with a loan from a formal financial institution on the set of country-level variables listed below. This table reports the models for a sample of upper-middle-income countries. ***, **, and * indicate significance at the 1%, 5% and 10% levels, respectively.

||**Liquid Liabil**|**ities / GDP**|**Private Cre**|**dit / GDP**|**Acco**|**unt**|**Lo**|**an**|
|---|---|---|---|---|---|---|---|---|
||**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|**(7)**|**(8)**|
|Ln(Population)|0.0274|0.0060|0.0112|0.0132|0.0163|0.0154|0.0007|0.0030|
||(0.030)|(0.045)|(0.019)|(0.026)|(0.022)|(0.025)|(0.007)|(0.009)|
|Ln(Population Density)|0.1238***|0.1020*|0.0884***|0.0624*|0.0532|0.0570|0.0099|0.0141|
||(0.045)|(0.060)|(0.029)|(0.034)|(0.034)|(0.035)|(0.010)|(0.013)|
|Natural Resources|-0.0718|-0.0667|-0.0924*|-0.0629|-0.0298|0.0227|-0.0086|0.0052|
||(0.076)|(0.096)|(0.049)|(0.055)|(0.046)|(0.052)|(0.014)|(0.019)|
|Offshore Center|0.4493*|0.2919|0.4799***|0.4058**|-0.0011|0.0924|0.0178|0.0208|
||(0.262)|(0.289)|(0.169)|(0.166)|(0.149)|(0.131)|(0.043)|(0.047)|
|Ln(GDP per capita)|0.0244|0.0195|0.0012|-0.1336|0.0092|-0.0878|-0.0269|-0.0415|
||(0.124)|(0.183)|(0.080)|(0.105)|(0.075)|(0.084)|(0.022)|(0.030)|
|Growth GDP Per Capita||2.8900||-0.2626||-2.6905*||0.7114|
|||(3.052)||(1.756)||(1.410)||(0.509)|
|Inflation||-2.3318||-0.5345||0.7185||0.3412|
|||(1.537)||(0.884)||(0.688)||(0.248)|
|KKM Index||0.0070||0.2148*||0.0618||0.0199|
|||(0.198)||(0.114)||(0.089)||(0.032)|
|Manufacturing/GDP||0.6733||0.8479||1.0264*||-0.0323|
|||(1.138)||(0.655)||(0.552)||(0.199)|
|Sec./Prim. School Enrollment||-0.1420||-0.0799||0.4396||0.0662|
|||(0.529)||(0.305)||(0.296)||(0.107)|
|Sub-Saharan Africa|0.1957|0.1658|0.2029*|0.0891|0.0811|0.1041|-0.0021|0.0023|
||(0.183)|(0.217)|(0.118)|(0.125)|(0.118)|(0.110)|(0.034)|(0.040)|
|Constant|0.8527***|0.9110*|0.6871***|0.7637***|0.5657***|0.2268|0.1628***|0.0946|
||(0.226)|(0.463)|(0.146)|(0.267)|(0.149)|(0.249)|(0.043)|(0.090)|
|Observations|42|39|42|39|36|35|36|35|
|Adjusted R-squared|0.1930|0.1768|0.3584|0.4442|-0.0369|0.3020|-0.0298|-0.0669|
|Root MSE|0.3519|0.3666|0.2272|0.2108|0.1968|0.1632|0.0573|0.0589|

19
infrastructure (software and hardware) can be costly, but it is important from a security and continuity perspective. If government fails to recognize the importance of expenditures related to recurring cost elements and fails to make adequate budget provisions to finance them, systems performance could degrade in the medium term and, in extreme cases, lead to suspension of system operations with grave consequences for the day-to-day financial operations the system supports.

# 3.59 **The main recurring cost elements were estimated at about 10–15 percent of the initial investment.**<sup>**49**</sup> **These include the following:**

- Hardware maintenance or replacement: Maintenance can be estimated to cost between 10–15 percent of the installed hardware costs per year. Hardware life cycles vary from 3–5 years.

- Application software and database management system, and tools license fees: This highly significant element could easily cost 20–22 percent of the initial license fees per year.

- Telecommunications costs: These are associated with use of the wide area network for communicating between remote and central sites. The costs vary with the telecommunications tariff operational in various countries, but they can be significant.

- Stationary, utilities, and office premises.

- Staff costs: Specialist technical positions are needed to ensure systems operation and maintenance, and this is a critical cost element. In many countries, these positions are filled by contract staff hired directly from outside the normal civil service structure because the government pay scales cannot accommodate the pay required.

- Ongoing training costs: These costs continue throughout the life of the system because in most governments, trained end users rotate out of their positions after a few years, and governments fill these positions with new, untrained staff. Technical skills may also need upgrading as new versions of the application software and hardware are implemented.

3.60 Ghana and Malawi did not adequately provide for recurrent costs. Failure to install timely upgrades in Malawi led to system deterioration with long-outdated software, insufficient capacity to manage transaction requirements, and system exposure to various security risks. This situation alienated stakeholders and led to a lack of trust in the system. Budget provisions for use of the telecommunication network

> 49 This is discussed in Hashim (2014). For this calculation, the initial investments were considered hardware, cost of software, installation, telecommunication infrastructure, and site preparation.

**44**
into the CPHS (denoted by “CPHS-NSS”<sup>30</sup> ). Mean consumption per capita from the PLFS 2017 is estimated at Rs. 2385, which is approximately 7 percent higher than the NSS-2011 on an annualized basis. Note that these predictions rely only on changes in non-expenditure variables -- meaning that the growth of non-monetary predictors of consumption, as captured by the nationally representative official survey, must have been positive since 2011. This is further evidence that poverty in 2017 is lower than in 2011.

||20|17|20|18|20|19|
|---|---|---|---|---|---|---|
||**PLFS**|**CPHS**|**PLFS**|**CPHS**|**PLFS**|**CPHS**|
|**PLFS-NSS**|2385|-|2525|-|2712|-|
|**CPHS-NSS**|-|2557|-|2843|-|3139|
|**CPHS-NSS-PLFS**|2404|2443|2548|2539|2758|2803|

Table 3: Mean consumption per capita based on different imputation models and surveys.

Notes: Mean consumption values are deflated using CPI-AL and CPI-IW in rural and urban areas. The PLFS and NSS-2011 samples excludes states which are not included in CPHS. “PLFS-NSS” denotes consumption per capita based on an imputation model that uses variables that are common to PLFS and NSS-2011 (see footnote 28); “CPHS-NSS” denotes a model using a set of variables that are common between CPHS and NSS-2011 (see footnote 29); and, “CPHS-NSS-PLFS” denotes the model using variables common across all three surveys (see footnote 30).

Nevertheless, the first two rows in Table 3 underscore potential differences between the imputed consumptions into the CPHS and PLFS: Consumption imputed into the CPHS is about 7 to 16 percent higher than the PLFS. It should be noted, however, that the two consumption estimates are not a strict like-to-like comparison: the consumption imputed into the CPHS is based on demographic as well as asset variables, whereas imputations into the PLFS are based only on slower-moving demographic indicators (asset variables are unavailable in PLFS). To construct comparable vectors of imputed consumption across the surveys, we select a set of demographic indicators that are family; share of 0 to 18 years old members in family; share of 61+ years old members in family; female headed households; log (age of household head); any member with higher than middle to high school level of education; share of members with middle to high school level of education; any member with diploma to post graduate level of education; dummy variables for Muslim; Christian; Sikh; Jain; Buddhist; Zoroastrian and other religions; scheduled castes; other backward classes; other castes; principal industry code of the household; household type; any regular salaried member in the household; household size and an interaction between the two variables. For urban sample we also include a dummy for cities that had over a million population in the 2011 census.

> 30The list of variables used in imputation are the same as in Table 2 of the main text

52
Some African countries also seem to be experiencing some demographic transition. As shown in Figure 3, the proportion of individuals under 20 years has increased since the 1960s to reach a peak during the 1980s and 1990s, before experiencing a gradual decline. This decline is mainly due to change in the number of children under 10. For example, it is expected that the proportion of under five will represent only 14 percent in 2030 against about 18 percent in 1960. This decrease was also observed for the age group of 5 to 9 years. In contrast, it is rather observed a very slight increase for older children (10 to 19 years). Conversely, the proportion of elderly is expected to rise by more than one percentage point, from less than 2.5 percent to just over 3.5 percent between 1960 and 2030. These trends coincide with an increase in population growth rate since 1960, followed by a decline from 1980. In total, it is expected that the proportion of working age population (here 20 to 64 age group) will increase from 44 percent to 47 percent, which represents a certain demographic dividend.

<u>Figure 3: Demographic changes in Sub-Saharan Africa</u> <!-- Start of picture text --> A - Various children age groups B - Elderly and population growth<br>3.5<br>18<br>0-4 age group<br>5-9 age group<br>16 10-14 age group<br>3<br>14-19 age group<br>14<br>2.5<br>Proportion of elderly<br>12 Population growth<br>10 2<br>Years Years<br>196019651970197519801985199019952000200520102015202020252030 196019651970197519801985199019952000200520102015202020252030<br>Percentage<br>Proportion (% total population)<br><!-- End of picture text --> <u>Source: World Bank: Health Nutrition and population Statistics.</u> Data to conduct a similar analysis of the evolution of household sizes were not available but, with declining fertility rates, these will be trending downwards, although likely at rates that differ from country to country. Again, this could introduce biases in studies on the evolution of poverty, and cross-country variations therein, depending on the choice of equivalence scale.

7
For public spending force, we substitute velocity with growth in a spending item in period � , and we can suppose that the � spending item’s mass � is determined by its share in total spending, Σ��1 in period � . This is hardly rocket science, and straight away readers will notice that this definition makes “momentum” the contribution of the spending item to the percentage growth in total spending �ḗ in period t. We have broken from physical science, because in physical science mass does not change over time, but the value of an item of expenditure � , and so its share � in total spending, does change. In fact, our version of mass � , increases over time if growth of the item �� in time � , exceeds the growth of total spending �ḗ (ie if it exceeds average spending growth). In algebra, expenditure force for a specific spending item � between �0 and �1 is given by Since Σ�� 1 each year, macro aggregate force is simply acceleration; i.e., the percentage point change of growth in spending between years, which can be positive or negative. Force for a specific item of spending (or group of expenditures) over time is determined by;

- The item’s initial relative share or importance in total spending in the time period

- The direction of spending growth (increasing, shrinking, or steady)

-

-

- It’s growth rate (velocity) relative to total spending

- It’s acceleration.

A simple example in the following table illustrates the calculation. Suppose education spending was 25% of total spending in 2010 and it grew each year between 2010-2013 by 5, 10 and 15 percent respectively whilst the average growth for the overall budget was 5 percent per year. Our measure of education spending force is then rising from 0.02 in 2011 to 0.05 in 2013, driven both by the rising share of education expenditures (from 25% in 2010 to 30.4 percent in 2013) and an acceleration in the growth rate of education spending from 5% growth in 2011 to 15 percent growth in 2013. Education spending in this example is therefore increasing in importance, rising faster than average spending, and accelerating year on year.

# **Force Calculations**

||**2010**|**2011**|**2012**|**2013**|
|---|---|---|---|---|
|Share = m|25.0%|25.5%|27.2%|30.4%|
|Growth = (1+g)|1|1.05|1.1|1.15|
|Growth average = (1+ge)|1|1.03|1.03|1.03|
|m*g|0.25|0.27|0.30|0.35|
|Force||0.02|0.03|0.05|

Taking a share of a total for `mass’ might appear odd, but it has two advantages. First it `normalizes’ absolute differences in spending whilst it maintains the measure of their relative size. Second, working in
# **Also consulted:**

Berglöff, E. and E. L. Von Thadden (1999). “The Changing Corporate Governance Paradigm: Implications for Transition and Developing Countries _._ ” Conference paper, Annual World Bank Conference on Development Economics.

Bouckaert, Geert and Steven van de Walle (2003). “Comparing Measures of Citizen Trust and User Satisfaction as Indicators of „Good Governance‟: Difficulties in Linking Trust and Satisfaction Indicators.” _International Review of Administrative Sciences_ , 69(3):329-343.

Brinkerhoff, Derick W. (2005). “Accountability and Good Governance: Concepts and Issues” in A.S. Huque and H. Zafarullah, eds. _International Development Governance_ . New York: CRC Press.

Brinkerhoff, Derick W. and Arthur A. Goldsmith (2005). “Institutional Dualism and International Development: A Revisionist Interpretation of Good Governance _._ ” _Administration and Society_ , 37(2):199-224. May.

Cohen, Jillian Clare, Monique F. Mrazek and Loraine Hawkins (2007), “Corruption and Pharmaceuticals: Strengthening Good Governance to Improve Access,” in J. Edgardo Campos and Sanjay Pradhan, eds. _The Many Faces of Corruption: Tracking Vulnerabilities at the Sector Level_ , Washington, DC: The World Bank. pp. 29–62.

Das Gupta, Monica, Varun Gauri and Stuti Khemani (2004). “Decentralized Delivery of Primary Health Services in Nigeria: Survey Evidence from the States of Lagos and Kogi.” Africa Region Human Development Working Paper 70. Washington, DC: The World Bank.

de Ferranti, David, Justin Jacinto, Anthony J. Ody and Graeme Ramshaw (2009). “How to Improve Governance: a New Framework for Analysis and Action.” Washington DC: Brookings Institution, 2009.

Denis, Jean-Louis, François Champagne, Marie-Pascale Pomey, Johanne Préval and Ghislaine Tré (2005). “Toward a Framework for the Analysis of Governance in Health Care Organizations and Systems.” University of Montréal. Ottawa: Canadian Council on Health Services Accreditation. October.

Devarajan, Shanta (2008). “Comment on „Governance Indicators: Where Are We, Where Should We Be Going?‟" _The World Bank Research Observer_ , 23(1):31-36.

Di Tella, Rafael and William D. Savedoff, eds. (2001). _Diagnosis Corruption: Fraud in Latin America’s Public Hospitals_ . Washington, DC: Inter-American Development Bank.

Gauthier, Bernard and Waly Wane (2008). “Leakage of Public Resources in the Health Sector: An Empirical Investigation of Chad,” _Journal of African Economies_ , 0: ejn011v132.

Gottret, Pablo and George Schieber (2007) _. Health Financing Revisited._ Washington, DC: World Bank.

______________________________________________________________________________________

-43-
# **1. Introduction**

The increase in global financial integration over the last twenty years has been remarkable, and U.S. institutional investors that purchase foreign assets have been significant participants in this growth.<sup>1</sup> Given standard economic theory, one would expect to see greater international diversification accompanying the expansion of global investment opportunities. Indeed, a number of studies show the gains that may be achieved from such diversification.<sup>2</sup> To date, however, evidence on how investors actually allocate their portfolios around the world and what determines it is still limited.

In this paper, we aim to fill the gap in the literature by constructing a unique micro dataset of asset-level portfolios for a group of important institutional investors. We then document the main stylized facts regarding their global asset allocations and study the factors behind their portfolio decisions, in particular how the organizational structure of financial intermediaries affects international investments. To conduct the research, we analyze U.S. equity mutual funds with an international investment scope. We find that, although mutual funds that invest globally have significantly expanded the size of their assets, they have been noticeably restrictive in the range of stocks they hold. In fact, they tend to invest in about the same number of stocks (100 stocks) than mutual funds with a more limited (regional or country) mandate do. Moreover, they forgo potential gains from international diversification. This investment practice is not explained by lack of information or instruments, transaction costs, or a better ability of global mutual funds to minimize tail risk. Instead, it seems to be largely driven by industry practices related to how mutual fund families structure their operations. These organizational factors seem important to understand how managers and families (companies) allocate their portfolios internationally.

> 1 See, for example, Obstfeld and Taylor (2004), Stulz (2005), and Kose, Prasad, Rogoff, and Wei (2009). 2 On the supply side of funds, investors can reduce risk for a given level of expected returns (or increase returns for a given level of risk) by investing internationally. A number of studies provide some evidence on the gains from international diversification. See, for example, Grubel (1968), Harvey (1995), De Santis and Gerard (1997), Errunza, Hogan, and Hung (1999), De Roon, Nijman, and Werker (2001), Goetzmann, Li, and Rouwenhorst (2005), and Driessen and Laeven (2007). On the demand side, firms and governments can reduce the cost of capital, finance new projects, and decrease the exposure of their balance sheets to some domestic macroeconomic shocks by tapping international investors. See, for example, Rajan and Zingales (1998), Stulz (1999), Van Wincoop (1999), Bekaert and Harvey (2000), and Gozzi, Levine, and Schmukler (2008).

2