rafmacalaba/datause-encoder-data huggingface dataset:
**Table I. Current Account: Variance-Decomposition Analysis**

||||<br>**Composition**||<br>**Relative Im**|<br>**portance**|
|---|---|---|---|---|---|---|
|**Country**|**R**<sup>**2**</sup>|**Portfolio**<br>**Rebalancing**|**Portfolio**<br>**Growth**|**2*cov(PR,PG)**|**Portfolio**<br>**Rebalancing**|**Portfolio**<br>**Growth**|
|||**High-In**|**come Countries**||||
|**Australia**|0.989|1.638|0.860|-1.498|66%|34%|
|**Austria**|0.938|1.689|0.462|-1.149|79%|21%|
|**anada**<br>**C**|0.992|1.008|0.418|-0.423|71%|29%|
|**enmark**<br>**D**|0.935|2.430|1.318|-2.749|65%|35%|
|**Finland**|0.819|4.248|3.461|-6.709|55%|45%|
|**France**|0.992|0.815|0.098|0.087|89%|11%|
|**ermany**<br>**G**|0.999|0.818|0.028|0.152|97%|3%|
|**eland**<br>**Ir**|0.951|2.295|1.030|-2.325|69%|31%|
|**Israel**|0.977|1.514|0.336|-0.850|82%|18%|
|**taly**<br>**I**|0.985|1.015|0.083|-0.099|92%|8%|
|**apan**<br>**J**|0.996|0.613|0.206|0.178|75%|25%|
|**Netherlands**|0.983|0.848|0.966|-0.814|47%|53%|
|**ew Zealand**<br>**N**|0.986|1.389|0.804|-1.193|63%|37%|
|**orway**<br>**N**|0.997|0.731|0.081|0.187|90%|10%|
|**Singapore**|0.997|0.599|0.144|0.257|81%|19%|
|**Sweden**|0.818|2.855|1.450|-3.304|66%|34%|
|**Switzerland**|0.994|0.927|0.590|-0.516|61%|39%|
|**U.K**|0.996|0.842|0.075|0.082|92%|8%|
|**U.S.**|0.999|0.505|0.161|0.334|76%|24%|
|**AVERAGE**|||||74%|26%|
|||**Develo**|**ping Countries**||||
|**Argentina**|0.916|1.143|1.933|-2.076|37%|63%|
|**Brazil**|0.915|2.318|1.955|-3.273|54%|46%|
|**Chile**|0.885|2.717|1.671|-3.388|62%|38%|
|**China**|0.993|0.854|0.018|0.127|98%|2%|
|**India**|0.991|1.633|0.528|-1.161|76%|24%|
|**Indonesia**|0.967|1.567|0.455|-1.022|78%|22%|
|**Malaysia**|0.998|0.895|0.075|0.029|92%|8%|
|**Mexico**|0.982|1.390|0.775|-1.165|64%|36%|
|**Peru**|0.642|6.533|6.866|-12.40|49%|51%|
|**Philippines**|0.991|1.115|0.350|-0.465|76%|24%|
|**South Africa**|0.956|1.311|0.305|-0.616|81%|19%|
|**South Korea**|0.991|0.638|0.229|0.132|74%|26%|
|**Thailand**|0.996|1.082|0.304|-0.385|78%|22%|
|**Trinidad Tobago**|0.878|1.632|0.278|-0.910|85%|15%|
|**Turkey**|0.865|2.160|1.084|-2.243|67%|33%|
|**Venezuela**|0.975|1.215|0.182|-0.397|87%|13%|
|**AVERAGE**|||||72%|28%|

This table reports the variance-decompositon analysis based on equation (4) in the main text. The first column reports how much of the variation of the current account can be explained by the portfolio growth and portfolio rebalancing components. The next three columns report the three RHS components of equation (4), scaled by the LHS variable. Thus, these three columns should sum up to one. The last two columns report the relative size of the portfolio rebalancing and portfolio growth components. The top-panel shows the results for high-income countries. The middle-panel reports the results for middle-income and low-income countries. This country classification follows the official World Bank classification of countries according to their income levels.

46
_How_ **_Fast Ha Chsuu_** _Indwitry Grow? 11_ reliance on external suppliers through such measures as building its ov n power plant and acquiring a fleet of ocean freighters.] Even if there has been no trend toward inter-firm specialization at existing enterprises, the growing weight of rural and collective enterprises in the industrial output total has almost certainly brought a decline in average scale of industrial operations. This probably implies an increase in specialization simply because small enterprises lack the capacity to achieve the high degree of vertical integration typical of China's larger industrial units.

We thus have two reasons for anticipating an increase in the ratio of inter-enterprise exchange of materials and semi-fabricates independent of any shift in product mix or branch structure of industry. Such a change would build an upward bias into the output totals reported in Table 1. Such a change would also systematically reduce the ratio of net to gross output value, which is exactly what we see in Table 4. Since several other factors also influence the ratio of net to gross output, the downtrend in the ratic of net to gross output observed in Table 4, although suggestive, is not sufficient to demonstrate either the presence of vertical disintegration or its possible impact on measures of real output growth.

A more promising approach would be to look at levels and changes in the ratio of interenterprise purchase of energy, materials and semi-fabricates to gross output value in various subdivisions of the industrial sector. For example, the industry-wide average ratio of interenterprise purchases to gross output at current prices (CVIO) [call this ratio IEP/CVIO0, can be expressed as a weighted average of distinct IEP/CVIO ratios for large and small industry. Thus:

# [IEP/CVIO] = a[IEPl/CVI01] + (1-a)UEP2/CVI02]

where 1 and 2 indicate large and small-scale industry and a is the share of the former in national industrial output.

# I hypothesize that:

# (1) the ratio (IEP/CVIO] is considerably larger for large than for small firms

(2) the weight attached to large firms, a, has declined irn recent years (3) economic reform tends to raise the ratio (IEP/CVIO] for both large and smaii firms It should be possible to use panel data from a enterprise surveys to investigate both the level and time path of [IEPl/CVIOlj and IEP2/CVI02]. Together with data on the parameter,it should be possible to obtain some rough quantitative idea of possible bias in the output figures arising from changes in industrial organization.?

3. **Wdiliam** Byrd observes **that** the **foregoing discusion** overloolk the pouible **impat** of changes **_in_ the price** of **materils and intemediate** goods relative to **the price** of **final** producs; such changer might be **systeuailcaUy different** for **large and s1A1 finr.**
# **Table 16: Poverty Gap Equations (Tobit): FSU Countries**

||_Esto_<br>|_nia_<br>|_Kyrgyz R_<br>|_epublic_|_Rus_|_sia_|
|---|---|---|---|---|---|---|
|_Dependent Variable: In (Expenditure per_<br>_Equivalent Adult) Right-Censored at Poverty_<br>_Line_|_Parameter_<br>_Estimates_|_Standard_<br>_Error_|_Parameter_<br>_Estimates_|_Standard_<br>_Error_|_Parameter_<br>_Estimates_|_Standard_<br>_Error_|
|Intercept<br>|7.287*|0.122|-9.721*|0.249|1.124*|0.176|
|Number of children<br>|-0.075*|0.016|-0.058*|0.017|-0.145*|0.021|
|Number of male adults<br>|-0.060*|0.029|-0.034|0.030|-0.036|0.033|
|Number of female adults<br>|0.004|0.024|0.032|0.029|0.038|0.030|
|Number of elderly<br>|-0.031|0.030|-0.028|0.054|-0.000|0.036|
|Education of head: primary<br>|-0.079*|0.030|0.028|0.078|-0.113*|0.052|
|Education of head: vocationaltechnical<br>|...|...|0.086|0.077|0.083*|0.044|
|Education of head: university<br>|0.265*|0.048|0.171*|0.079|0.256*|0.058|
|Age of head<br>|-0.003|0.005|0.019|0.011|0.017*|0.007|
|Age of head squared<br>|0.000|0.000|-0.000|0.000<br>°0.000*||0.000|
|Female head<br>|-0.090*|0.038|-0.229*|0.073|-0.182*|0.046|
|Household owns enterprise<br>|0.173*|0.033|0.205*|0.056|0.316*|0.064|
|Household owns land<br>|0.228*|0.030|0.304*|0.053|-0.058*|0.042|
|Household is renter<br>|-0.152*|0.027|0.115|0.095|0.052|0.039|
|Share of wages in household income<br>|0.367*|0.046|0.289*|0.088|0.407*|0.058|
|Number of unemployed in household<br>|-0.203*|0.032|...|...|-0.305*|0.053|
|Head is unemployed<br>|...|...|-0.165*|0.093|...|...|
|Head is inactive<br>|-0.112|0.040|-0.067|0.080|-0.193*|0.053|
|Location: non-capital city<br>|-0.188*|0.033|-0.440*|0.085|-0.523*|0.065|
|Location: village|-0.366*|0.042|-0.615*|0.085|-0.637*|0.072|
|Log-likelihood<br>|-1466.8||-1709.5||-4444.4||
|Chi-squared<br>|542.6<br>||172.66||555.9||
|Prob > chi-squared<br>|0.000||0.000||0.000||
|SER|0.506||0.918||0.968||

_Note:_ * Indicates that coefficient is significantly different from zero at 90% confidence level.

82
Brochman 2009). As such, various measures of water scarcity, mainly static ones, have been used to assess the emergence of international water treaties and levels of cooperation among riparians. But in order to assess the likely impact of climate change on the stability of existing treaties and on the future likelihood of conflict and cooperation, one may need a water availability measure, such as increased water variability, that can better infer climate change impacts.

Following a series of statements by world leaders in the popular press that worn us of looming wars over water due to increased water scarcity and climate change impact (e.g., BBC 2003; Timesonline 2007), Barnaby has argued that: _...it is still important that the popular myth of water wars somehow be dispelled once and for all. This will not only stop unsettling and incorrect predictions of international conflict over water. It will also discourage a certain public resignation that climate change will bring war, and focus attention instead on what politicians can do to avoid it. ...And it would help to convince that ...the solutions to water scarcity and security lie outside the water sector in the water/food/trade/economic development nexus_

# Barnaby (2009:283).

It is, therefore, the Barnaby paper and the sometimes sensationalist water-wars statements, that make-up the general motivation for this paper—to strengthen the scientific basis to our understanding of water-climate change and cooperation/conflict interactions.

In the proceeding sections we introduce water supply variability into a global analysis of treaty formation. Building on existing theories (e.g., Ambec and Dinar A., 2009; Dinar S., 2009), a global dataset of bilateral rivers will be used along with several water variability measures, to assess the likelihood of treaty formation, and treaty cooperation, using the range of climate during the years where existing treaties were signed.<sup>i</sup> In a second stage, using various future climate change predictions, the likelihood of additional treaty formation and cooperation is estimated. Section 2 reviews the scientific basis for the climate-hydrology relationship that affects the flow regime in river basins. Section 3 develops the analytical framework. Section 4 reports the data sources and the construction of the various variables. Section 5 discusses the 4
9 Cameroon, Mali and Niger. Some 42 percent of firms in Mauritania paid to obtain a connection to the electricity grid, a share more than double that of Niger (19.2 percent), and higher than in Benin, Cameroon and Mali. In addition, almost 53 percent of firms in Mauritania were expected to pay bribes to providers of construction permits, once more setting the record in the comparator group. Most of these payments are made to accelerate the speed of connections, as well as their quality, and to reduce the bureaucratic procedures to obtain construction permits. To the extent that inefficiency and red tape assures a bribe payment, there is little incentive to remove them, with adverse consequences on the growth and dynamism of Mauritanian firms (Figure 4).<sup>7</sup>

**Figure 4: Informal payments requested (% firms), selected countries**

<!-- Start of picture text --> (a) To obtain mainline telephone and electricity (b) To obtain import license and operating license<br>connection<br>19.2** 7.9**<br>Niger 36.4 Niger 9.0***<br>32.7 10.9**<br>Mali Mali<br>42.5 11.5**<br>35.7 42.6<br>Cameroon 41.7 Cameroon 40.5<br>37.6 41.3<br>Benin 49.5* Benin 43.1*<br>Mauritania 38.542.0 Mauritania 30.433.3<br>0 10 20 30 40 50 60 0 10 20 30 40 50<br>Mainline telephone Electricity connection Import license Operating license<br><!-- End of picture text --> <!-- Start of picture text --> (c) To obtain water connection and construction permit<br>35.0<br>Niger 26.7***<br>25.0***<br>Mali<br>30.4***<br>37.5<br>Cameroon<br>40.0***<br>38.7<br>Benin<br>40.2**<br>52.6<br>Mauritania<br>75.0<br>0 30 60 90<br>Water connection Construction permit<br><!-- End of picture text --> Notes: **Proportion statistically different from Mauritania’s at the 5% level. ***Proportion statistically different from Mauritania’s at the 1% level. Formal sector only. _Source: ICS database._ 7 The number of connections to the water system is low and leads to high water cost and charges, which are the highest in the sub-region. Water access for industrial use remains problematic. SONELEC, the National Electricity Company, offers an intermittent service: amongst manufacturing firms power outages cause an average loss of approximately 3.3 percent of annual sales.
# **7. References**

- Amiti, Mary, and Jozef Konings. 2007. "Trade Liberalization, Intermediate Inputs, and Productivity: Evidence from Indonesia." _American Economic Review_ , 97(5): 1611–1638.

- Artuc, Erhan; Shubham Chaudhuri; and John McLaren 2007 “Trade Shocks and Labor Adjustment: Theory.” NBER Working Paper 13463 http://www.nber.org/papers/w13463

- Artuc, Erhan; Shubham Chaudhuri; and John McLaren 2010 “Trade Shocks and Labor Adjustment: A Structural Empirical Approach” _American Economic Review_ , 100(3) (June): 1008-45.

- Baldwin, Richard (ed.) 2009 The Great Trade Collapse: Causes, Consequences and Prospects A VoxEU.org Ebook available at <u>http://voxeu.org/index.php?q=node/4297.</u>

- Bernard, Andrew; Raymond Robertson; and Peter K. Schott 2010 “Is Mexico a Lumpy Country?” _Review of International Economics_ 18(5) (November): 937–950.

- Bergin, Paul, Robert Feenstra, and Gordon Hanson. 2009 “Offshoring and Volatility: Evidence from Mexico’s Maquiladora Industry.” _American Economic Review 99_ : 1664-71.

- Claessens, Stijn; Hui Tong; and Shang-Jin Wei 2011 “From the Financial Crisis to the Real Economy: Using Firm-level Data to Identify Transmission Channels” NBER Working Paper No. 17360 National Bureau of Economic Research.

- Davidson, Carl; Steven Matusz, and Andrei Shevchenko. 2008 “Globalization and Firm-level Adustment with Imperfect Labor Markets” _Journal of International Economics_ 75(2): 295-309.

- Davidson, Carl; Fredrik Heyman, Steven Matusz, Fredrik Sjoholm, and Susan Zhu, 2010 "Globalizaton and Imperfect Labor Market Sorting" University of Nottingham Research Paper 2010/30.

- Dix-Carneiro, Rafael 2010 “Trade Liberalization and Labor Market Dynamics”, mimeo, Princeton University.

- Eaton, Jonathan, Sam Kortum, Brent Neiman, and John Romalis. 2009 “Trade and the Global Recession.” Unpublished working paper.

- Ebenstein, Avraham, Ann Harrison, Margaret McMillan, and Shannon Phillips.  2009 “Estimating the Effect of Trade and Offshoring on American Workers Using the Current Population Surveys.” NBER Working Paper No. 15107, Cambridge, MA.

- Feenstra, Robert and Gordon H. Hanson.1997 “Foreign Direct Investment and Relative Wages: Evidence from Mexico’s Maquiladoras” _Journal of International Economics_ 42: 371393.

- Feenstra, R.C., 2010. Offshoring in the Global Economy: Microeconomic Structure and Macroeconomic Implications, MIT Press, Cambridge, Massachusetts.

- Felbermayr, Gabriel, Julien Prat, Hans-Jörg Schmerer, 2011 “Globalization and Labor Market Outcomes: Wage Bargaining, Search Frictions, and Firm Heterogeneity” _Journal of Economic Theory_ , 146(1) (January): 39-73.

33
Section III reports estimates of TFP growth regressions and results of sensitivity analysis. Section IV takes a closer look at growth in bank lending and its sectoral distribution. Section V concludes.

# **II. SOURCES** OF **GROWTH**

Output growth can be caused by labor, capital, and productivity growth. We can derive a conventional growth accounting equation from the neoclassical production function'.

_Yt=F(At, Kt, Lt,)_ (1) The hat over a variable represents its time derivative. If technological change occurs in a Hicks-neutral manner, then _F(A, K, L) = Af(K, L)_ and _(FAA/IY) =_ 1. Assuming that labor and capital are paid their (social) marginal products, i.e. _FK=r_ (rental price of capital) and _FL_ = w (wages) and rearranging we obtain the growth equation: where a = _rK/Y_ and , _= wL/Y_ are shares of capital and labor in total product. Assuming constant returns to scale, payments to factors (factor income) exhaust total product so that _,6_ = _1-_ a The term T is referred to as the Solow residual or total factor productivity growth.

Ideally, the aggregate capital input is a composite index of different varieties of capital employed with each variety being weighted by its share in the total returns to capital. Similarly, the labor input is ideally a composite index of labor differentiated by age, gender, education, sector and hours of work. In the estimates presented below, however, capital and labor are not differentiated in this manner because detailed data are not available. The absence of such "quality" adjustments generally tends to underestimate the aggregate input use.

As can be seen from equation (3) in order to estimate TFP growth we need data on capital stocks, labor, and a.

2 The derivation here follows Barro (1998).

4
# _Administration of the Household Survey_

Once a list of Nikkei households had been obtained, the final stage of the survey carried out an in-person survey of Nikkei households. Our initial budget planned on surveying 900 households, and so we intended to carry out a stratified sample of the Nikkei households obtained through the listing exercise. However, since only 839 Nikkei households were obtained via listing, all listed Nikkei households were selected for the full survey. Fieldwork began November 19, 2006, and all dwellings were visited at least once by December 22, 2006. Some of the households identified by proxy-reported as being Nikkei were found to be non-Nikkei during this process, reducing the target sample to 710 Nikkei households.

During the initial wave of surveying we were successful in interviewing 247 Nikkei households, 109 in São Paulo state and 138 in Parana. The households which were not able to be interviewed during this initial phase were households where no one was home at the time of the survey visit, where the building manager refused access to the building, or where the household refused to answer the survey. A second wave of surveying then took place from January 18, 2007 to February 2, 2007, intended to increase the number of households responding. We made a number of changes to the survey protocol in order to attempt to get a response from households not interviewed in the first wave:

- a) Meetings were held with the presidents of several of the most important Nikkei associations in São Paulo city and Curitiba to ask for their direct support. The contacted associations agreed to do this, and provided phone numbers and names which could be used in a letter presented by the interviewer, so that the interview subject could call with any questions about the veracity of the survey. Similarly, additional local contact details were provided for the World Bank, which could again be used by interview subjects to verify the survey was legitimate.

- b) The initial round of interviewing used Brazilian interviewers who were not Nikkei, due to difficulties hiring Nikkei who were interested in carrying out survey work. More intensive efforts were undertaken to find Nikkei workers, allowing Nikkei field workers to be used in this second wave.

18

-  -
9 expenciture per person for each year; these can be readily calculated<sup>from the</sup> data given in BBS (1986, 1988 a,b).

Each of the above poverty measures can niow be derived as a function of the poverty lines, the parameters of the Lorenz curve, and the means. (See the Appendix for further details). Table 2 gives the estimates obtained for all three of these measures, using both the HES and NA means from Table 1, and the 1981/82 BBS poverty line. I have assumed that the 1981/82 mear,s from the HES are correct; since the two means are very close nationally for that year, there is no obvious reason to doubt that assumption, and this also has<sup>the</sup> acvantage Df allowing us to maintain the urban/rural breakdown. (The national accounts are not dissagregated by urban/rural areas). To maintain that breakdown, I have alsc assumed that the .A consumption growth rates reported in Tab'le 1 hold for both urban and rural sectors. Figure 3 summarizes the results for the headcount index, and the absolute numbers of poor.

The following observations can be -ide on the results in Table 2: (i) The estimates of mean consumption per capita based on the HES imply sharply falling poverty levels over the period, as claimed by BBS (1988b), and, indeed, at an even higher rate of decline than they had suggested. This reflects the fact that the BBS poverty lines have increasing real purchasing power over the period, putting upward pressure on their estimates of poverty (though insufficient to eliminate the downward bias due to thkeir apparent overestimation of growth rates). Using the BBS 1981/82 poverty lines and HES means, absolute numbers of poor decline from 66 million persons in 1981/82 to 37 million in 1985/86. The estimates of poverty based on the HES Lorenz curves and the consumption means derived from NA do not, however, confirm this trend. The headcount index fell slightly from 1981/82 to 1983/84 in both
This paper sheds new light on the extent to which institutional investors invest long term and the factors underpinning their maturity choices. To do so, we analyze unique data on the actual portfolios and bids of the universe of domestic institutional investors in the benchmark case of Chile. In particular, we assemble asset-level timeseries portfolio holdings of bank deposits, sovereign bonds, and corporate bonds of medium- and long-term bond mutual funds, pension funds, and insurance companies at high frequencies (monthly, and also daily for pension funds). We also use detailed data on the individual biddings at government paper auctions and returns of government bonds at different maturities. The main data set on asset holdings by Chilean institutional investors contains 965,209 observations for mutual funds, 6,659,681 monthly observations for pension funds, and 4,071,927 observations for insurance companies.

The value added of the paper is twofold. First, it documents in detail the maturity structure of different kinds of institutional investors to establish to what extent these investors demand long-term assets. Second, the paper discusses what factors might be behind the maturity structure of institutional investors’ portfolios by exploiting a rich data set on Chile, containing different types of investors within a single market. Because these investors operate in the same macroeconomic and institutional environment and have access to the same set of instruments, this approach allows us to control for specific sources of variation across investors and asset classes.

This paper studies different hypotheses related to the maturity choices of institutional investors. First, the equilibrium might be short term if borrowers do not issue long-term paper. Therefore, the paper explores if the supply side of instruments (the demand side of capital) is the one determining the equilibrium outcome. Second, the 4