rafmacalaba/datause-encoder-data huggingface dataset:
countries (the units in the panel) with<sup>theflexibility and consistencygainsof country-specific</sup> short-run adjustment. Further,<sup>theapproachallows formaltesting of the long-run pooling</sup> restrictions imposed **by** the model<sup>**-**i.e., thehomogeneity across countries of the parameters</sup> describing the long-run portfolio<sup>equilibriumcondition.</sup> The paper's plan is as follows.<sup>Section2describes the analyticalframeworkand presents</sup> the econometric strategy for estimation<sup>of the long-runrelationship implied</sup><sup>**by**themodel.</sup> Section **3** briefly summarizes<sup>themainfeaturesof the</sup><sup>**NFA**dataand themeasuresof investment</sup> returns and risks used in the empirical<sup>analysis.Section 4 presents theempirical results from</sup> estimation of the model for<sup>variousgroupsof countries. The modelisfirst implementedon the</sup> full country sample, and then<sup>separatelyon countrygroups that differ in per capitaincome level</sup> and restrictions to international<sup>portfoliodiversification.Section</sup><sup>**_5_**concludes.</sup>

# **2. METHODOLOGY**

**2.1 A portfolio-diversification**<sup>**approach to external asset positions**</sup>

Our analytical framework<sup>follows recentliteratureunderscoring therole of investment</sup> risk and adjustment costs in the allocation of agents' wealth<sup>between domesticand foreignassets,</sup> and thus in the determination<sup>of capitalflows (Ventura2002).This literatureshowsthat those</sup> two ingredients are needed to<sup>reconciletheoreticalpredictionsand observedfactson the</sup> dynamics of countries' asset<sup>portfolios.</sup> Specifically, we adopt<sup>a portfolio-diversificationapproachaccording to whichexternal</sup> asset positions are driven **by**<sup>portfolio equilibrium inthelong runand</sup><sup>**by**thedynamicforces</sup> shaping asset reallocation<sup>in theshortrun.Long-runequilibriumobtains when domesticand</sup> foreign investors achieve the desired<sup>allocationof their asset portfolioacrosscountries.</sup> However, imperfections and<sup>frictionsinrealand financialmarkets mayprevent theinstantaneous</sup> achievement of the optimal portfolio.<sup>Short-runexternalequilibriumis then given</sup><sup>**by**the</sup> adjustment path towards investors'<sup>long-run equilibrium portfolio.</sup> In our framework, the optimal<sup>portfolioallocationfollowsalongthe lines of the standard</sup> Markowitz-Tobin model of mean-variance<sup>investors.As is wellknown, the modelcan be derived</sup> under fairly standard assumptions<sup>fromintertemporaloptimization</sup><sup>**by**forward-looking, risk-</sup> averse agents. Such procedure<sup>can be shownto yield an optimal saving/consumptionplan</sup> _4_
Figure 9 – Switzerland, by survey <!-- Start of picture text --> Switzerland<br>Relative inequality of opportunity Return to education<br>2000 2005 2010 2015<br>year of survey<br>st.deviation logs mean log dev. 2000 2005 year of survey 2010 2015<br>Parental networking Intergenerational persistence in education<br>2000 2005 2010 2015 2000 2005 2010 2015<br>year of survey year of survey<br>Regressors include gender, age, age2 and foreign citizenship<br>.38 .24 .12<br>.22<br>.36 .11<br>.2<br>.34 .1<br>st.deviation logs .18 mean log dev.<br>.32<br>.16 .09<br>.3 .14<br>.08<br>.05 .38<br>.04 .36<br>.34<br>.03<br>.32<br>.02<br>.3<br>.01<br>.28<br><!-- End of picture text --> Figure 10 – Switzerland, age-cohort decomposition <!-- Start of picture text --> Switzerland<br>Inequality of opportunity - Standard deviation of logs personal incomes Return to education<br>30 40 50 60 70 80 30 40 50 60 70 80<br>Age Age<br>. .<br>1920 1930 1940 1950 1960 1920 1930 1940 1950 1960<br>Born in... Born in...<br>. .<br>Parental networking Intergenerational persistence in education<br>30 40 50 60 70 80 30 40 50 60 70 80<br>Age Age<br>. .<br>1920 1930 1940 1950 1960 1920 1930 1940 1950 1960<br>Born in... Born in...<br>. .<br>Regressors include gender, age, age2 and foreign citizenship - Age and cohort effects obtained from Deaton's decomposition<br>.3 .05<br>.2 .04<br>.03<br>beta1 .1 beta1 .02<br>0 .01<br>-.1 0<br>.25 .04<br>.2 .02<br>beta1 .15.1 beta1 0-.02<br>.05 -.04<br>0 -.06<br>.02 0<br>0 -.1<br>beta1 beta1<br>-.02 -.2<br>-.04 -.3<br>.02 .05<br>0<br>0<br>beta1 beta1 -.05<br>-.02 -.1<br>-.04 -.15<br><!-- End of picture text --> 18
<!-- Start of picture text --> 1 DETERMINE THE CONTEXT<br>2 DETERMINE THE INTERVENTION TYPE<br>2E\J= DIGITALWITHOUT HEALTH PREDICTIVE INTERVENTION ANALYTICS WITHDIGITAL PREDICTIVE HEALTH INTERVENTION ANALYTICS | <®ayeCD NON-DIGITAL‘INTERVENTION HEALTHWITH<br>¢ 4 efs Yi r. " afs. ®,PREDICTIVE5 Axe ANALYTICSioiz<br>3 ESTABLISH LEVEL OF COMPLEXITY<br>INTERVENTION CAUSAL Gh -¢ om fin<br>PATHWAY AADay,ese> i x= -<br>O es<br>4 ANALYTICAL PRINCIPLES<br>COMPARATOR TIME FRAME COST BENEFITS | PREDICTIVE ANALYTICS DYNAMICS | UNCERTAINTY<br>5 = nd<br>DE a th= Os a<br>5 REPRESENT THE VALUE PROPOSITION<br>=]<br>IMPACT INVENTORY Fg] VALUE AGGREGATION<br>ooo<br>Ve Distributiona oe Distribution a"<br>—<br><!-- End of picture text -->
<!-- Start of picture text --> So<br>“<br>2<br>a&<br>Eeo<br>Ee<br>=<br>ar<br>©<br>Q x<br>()) Male-Hs | Male-LS<br>() Female-HS [| Female-LS<br><!-- End of picture text -->

<!-- Start of picture text --> cay<br>Boa<br>&o<br>=e<br>=<br>ar<br>©<br>a x<br>{7 Male-Hs | Male-LS<br>{) Female-HS [| Female-LS<br><!-- End of picture text -->
**<u>Table 4. Logit regression results for mobile internet adoption by region</u>**

|Model||Lo|git||
|---|---|---|---|---|
|||Margina|l effects<br>Lti||
||All region|Africa|an<br>America|Asia|
|Infrastructure factors|||||
|Household 3G network coverage|0.039**|0.012|0.053|0.037**|
||(0.017)|(0.021)|(0.046)|(0.018)|
|Household having access to<br>electricity|0.143***|0.106***|0.229|0.070**|
||(0.017)|(0.016)|(0.149)|(0.028)|
|Social-economic factors|||||
|Female|-0.031***|-0.020*|-0.002|-0.042***|
||(0.010)|(0.012)|(0.016)|(0.012)|
|Age group (middle age, 30-60)|-0.150***|-0.112***|-0.174***|-0.099***|
||(0.010)|(0.014)|(0.018)|(0.014)|
|Age group (old people, >60)|-0.282***|-0.153***|-0.538***|-0.148***|
||(0.009)|(0.016)|(0.025)|(0.013)|
|Log value of monthly income|0.042***|0.037***|0.015***|0.048***|
||(0.004)|(0.005)|(0.006)|(0.006)|
|Married|-0.030***|0.000|-0.034*|-0.062***|
||(0.011)|(0.013)|(0.018)|(0.017)|
|Urban|0.081***|0.069***|0.084***|0.049***|
||(0.010)|(0.013)|(0.019)|(0.011)|
|Years of formal education (less<br>than 6)|0.155***|0.093**|0.119**|0.142***|
||(0.029)|(0.037)|(0.057)|(0.039)|
|Years of formal education (6-12)|0.347***|0.269***|0.321***|0.267***|
||(0.024)|(0.029)|(0.060)|(0.034)|
|Years of formal education (12-16)|0.535***|0.491***|0.344***|0.511***|
||(0.022)|(0.035)|(0.034)|(0.047)|
|Years of formal education (>16)|0.552***|0.583***|0.296***|0.466***|
||(0.020)|(0.035)|(0.019)|(0.052)|
|Network effects|||||
|1 close friend uses an online social<br>network|0.349***|0.391***|0.156***|0.257***|
||(0.020)|(0.030)|(0.028)|(0.034)|
|2 close friends use an online social<br>network|0.319***|0.298***|0.167***|0.272***|
||(0.018)|(0.027)|(0.024)|(0.029)|
|3 close friends use an online social<br>network|0.420***|0.427***|0.202***|0.384***|
||(0.016)|(0.026)|(0.020)|(0.030)|
|4 close friends use an online social<br>network|0.480***|0.492***|0.227***|0.480***|
||(0.017)|(0.032)|(0.018)|(0.036)|
|5 close friends use an online social<br>network|0.631***|0.666***|0.555***|0.553***|

14
- 29 -

taxation suggests that it may be a relatively under-exploited tax base in a number of developing countries.

Greater reliance on the ET tax base has much to recommend it. In addition to their appeal on income distribution grounds, ET taxes may be relatively efficient revenue sources if they can be implemented through twopart pricing schemes. Moreover, they are relatively easy to administer, they lend themselves readily to the destination principle of commodity taxation and they can distinguish between business and household use without great difficulty.

Despite these advantages, the basic argument favoring higher ET taxes in the C6te d'Ivoire, and perhaps elsewhere, relies on acceptable income distribution effects. In an earlier paper, McLure and Thirsk (1978) proposed the removal of excise taxes on beer and cigarettes for essentially the same reasons, that they were income and price inelastic in consumption and therefore had a harmful impact on welfare. Here, we make the opposite claim, that the imposition of ET taxes would enhance economic welfare. Many countries appear to act perversely by taxing beer consumption heavily and subsidizing the consumption of electricity. Another way of looking at this eqjuity claim is to note that in most developing countries the consumption of a wide range of consumer durables such as TVs, VCRs and fridges is income elastic. However, in most cases it is difficult to tax either the services provided by these durables or their initial purchase. It may be much easier instead to tax a close complement, electricity consumption, in this situation.

Massll4A/Welfcrc **Dominance** & **the** Design of Excise **Taxation** in **the Cote** D'Ivoire/WThirck:ab/5118/88
# Table 6: Cointegrating Vector for Savings Adjusting for Domestic Capital Gains

Part II: Dynamic GLS for _(s y),a_ = _Ao+_ **_/36_** In _ya_ + _,#2r,_ + /33fli, + / 4 ;rt _+ J5govsa + u,_

||CHL|GHA|IDN|KOR|MYS|MEX|TUR|ZWE|
|---|---|---|---|---|---|---|---|---|
|Constant|-2.202|-1.329|-2.205|-9.827|0.906|-0.728|2.461|-3.902|
||(-1.931)|(-2.751)|(-4.528)|(-3.145)|(1.330)|(-3.248)|<br>(1.023)|(-1.243)|
|In !|0.178|0.135|0.197|0.473|-0.0(9|0.113|-0.162|0.684|
||(I 966)|(3.176)|(5.057)|(3.936)|(-0.836)|(3.947)|(-0.939)|(1.476)|
||0.045|-0.534|-0.834|-0.349|-1.143|0.004|-0.059|-3.724|
||(0.062)|_(-1.505)_|(-5.018)|(-1.517)|(-3.131)|(0.053)|(-0.751)|(-3.384)|
||.)008|0.006|(0.003)|(0.023)|0.016|(0.005)|0.025|0.277|
||(0.434)|(1.207)|(-0.805)|(-2.269)|(2.111)|(-1.822)|(3.221)|(3.199)|
||0.047|-0.606|-0.679|-0.385|-1.256|0.245|-0.180|-4.983|
||(0.585)|(-1.842)|(-3.628)|(-1.561)|(-3.503)|(4.648)|(-1.748)|(-3.456)|
|eovs,|-0.246|0.179|-2.003|-0.885|-0.685|-0.998|0.147|-1.777|
||(-0.699)|(0.323)|(-4.516)|(-2.182)|(-3.463)|(-6.241)|(0.336)|(-2.246)|
|NOBS|23|21|22|22|23|22|23|_15_|

Notes:

1. t-statistics in parenthesis.

2. (s/v)at, In yat, govsat have been adjusted for capital gains (losses) on nominally denominated domestic assets due to inflation.

49
<!-- Start of picture text --> Income equivalentq differential between second<br>.<br>best woe 5 .<br>and risk-based premium insurance (% of income)<br>50 | 0.0% | 0.0% | 0.1% | 0.1% | 0.1% | 0.1% | 0.1% | 0.1% | 0.1% | 0.1% | 0.1%<br>20<br>igS<br>ES<br>3 30 | 0.0% | 0.1% | 0.2% | 0.2% | 0.3% | 0.3% | 0.4% | 0.4% | 0.4% | 0.3% | 0.3% 15<br>2<br>3<br> *°<br>3Re)2 10) O08 | 04s | 00% | 188 | te “ “mee 10<br>5| 0.0% | 0.2% 0.9% 2.0% | 2.8% | 4.1% 7.4% 5<br>1 | 0.0% | 0.0% | 0.1% | 0.1% | 0.2% | 0.3% | 0.4% | 0.6% | 1.0% | 2.1%<br>0<br>1 10 20 30 40 50 60 70 80 90 99<br>Fraction of flood prone areas, (%)<br><!-- End of picture text -->

<!-- Start of picture text --> on . . . : :<br>Utility differential between risk-based insurance and other options<br>20<br>50 | 0.00% | 0.04% | 0.07% | 0.10% 0.14% | 0.15% 0.14% | 0.13% is<br>ogenom een fe mom om om m oe T<br>2 14<br>3 30 0.01% | 0.10% | 0.18% | 0.25% | 0.31% | 0.36% 0.40% | 0.40% | 0.37% | 0.34%<br>2 12<br>oO<br>2 °oe om om | |] mr %<br>no]Re)=3 ®soso | | a] on 4 8<br>m5 0.58% | 0.98% 2.14% Sem Beara Soe 4<br>2<br>1 | 0.00% | 0.03% | 0.07% | 0.12% | 0.19% | 0.28% 1.09%<br>0<br>1 10 20 30 40 50 60 70 80 90 99<br>Fraction of flood prone areas, (%)<br><!-- End of picture text -->

<!-- Start of picture text --> Income equivalent differential between second<br>best and risk-based. premium. .insurance (euros)<br>°° vw [news] na [ae ne | ne me] 12000<br>©5 “° voz se] on |e [an |e] |e 10000<br>2<br>ao 30 131.8 | 162.5 | 185.7 | 201.2 206.4 176.0<br>g 8000<br>=<br>‘5oO 20] 107 271.2 | 336.0 420.5 | 437.4 | 4349 | 411.1<br>aa 6000<br>=<br>€ 4000<br>a<br>“<br>° [sae] ar] a am a 2000<br>0<br>1 10 20 30 40 50 60 70 80 90 99<br>Fraction of flood prone areas, (%)<br><!-- End of picture text -->

<!-- Start of picture text --> Total urban income equivalent differential between second<br>best and risk-based. premium: :insurance (millionPTT euros) x104<br>°° fe fe |e ]aalo foe ae |e] a °<br>©5 “ [= je] [a mlm]ooo ae | °<br>2<br>o 30 796 1020 | 1011<br>g 4<br>=<br>‘5oO 20| 52 | 501 1328 | 1646 | 1892 | 2060 | 2142 | 2130 | 2013 | 1809<br>aa 3<br>=<br>€ 2<br>=I<br>“ ° Lo | | | ne | | |r ae i 1<br>0<br>1 10 20 30 40 50 60 70 80 90 99<br>Fraction of flood prone areas, (%)<br><!-- End of picture text -->
Policy Research Working Paper 7749

# **Abstract**

Using unique household level data collected in northern Mali in August and September 2015, this paper explores whether the content of the 2015 Peace Accord reflects the concerns and priorities of the citizens, local leaders, and refugees, or whether it is an exclusive agreement between elites who fail to represent the interests of the population at large. The study finds that local leaders’ opinions are appropriate proxies for the citizens they represent, and most (but not all) of the measures stipulated in the Peace Accord align. The greatest divergence between citizen preferences and actions specified in the Peace Accord is the emphasis in the latter on road infrastructure. When different activities are costed, this disconnect becomes very apparent. The bulk of funds (45 percent) are apportioned to infrastructure construction, while the typical respondent prefers job creation; the poorest prefer investments in social services. However, most of infrastructure spending has been earmarked for the medium term. Short-term priorities reflect the preferences of the population in northern Mali much better. It is concluded that although the Peace Accord had all the elements to be an exclusive elite pact, its priorities largely reflect those of the population, granting it much higher probability of success.

This paper is a product of the Poverty and Equity Global Practice Group. 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://econ.worldbank.org. The authors may be contacted at jhoogeveen@worldbank.org and jlendorfer@gmail.com.

_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
19

# Box 3 Definitions of High Technoloev Industries and Trade

High-tech products are usually defined as products for which investment in the creation of knowledge are responsible for a substantial share of their production costs (Krugman 1987). As Kreinin (1987) points out, to define a high-tech sector in terms of its factor inputs -- e.g., the relative intensity of research and development (R & D) investments, or the proportion of scientists and engineers in the labor force -- does not allow one to discriminate between industries characterized by different rates of technological diffusion. These indicators, by being static in nature, reflect the prevailing situation at a given point in time and may provide a distorted picture as time goes by as some industries become less active in technological terms, while new high-tech sectors evolve.

Most of the relevant literature, however, uses some variation of the input criteria in defining hightech industries. The better known high-tech definitions are reviewed in Hatter (1985). We adopted the definition proposed by Davis (1982), which estimates the technology intensity for any given industry in the United States in terms of the R&D expenditures required to produce a certain manufactured good. This methodology takes into account not only the direct R&D investments made by final producers, but also the indirect R&D expenditures made by suppliers of intermediate goods used in the production of the final good. The "indirect' R&D contribution was estimated by Davis using input-output techniques. Based on the United States Standard Industrial Classification (SIC), industries were ranked according to their R&D intensity and the first ten SIC groups (3-digit classification ) were designated as high-tech industries. The industry ranked as number ten had an R&D index 30 percent greater than the industry in eleventh place and more than 100 percent above the average for the manufacturing sector as a whole. In other words, Davis' methodology imposes a much higher standard in terms of R&D intensity than the 'above average level' criteria often adopted in the literature.

In order to translate Davis' industry classification into a definition of high-tech trade, we used the concordance between the SIC grouping and the SITC Revision 1 classification proposed by Hatter (1985). Given the imperfect match between SIC and SITC codes, Hatter estimated high-tech weights (the proportion of US hightech imports and exports in each given SITC group, based on 1975-1977 US trade data ) as a way to highlight the relative importance of high-tech products in any given SITC grouping. In preparing our data on high-tech trade, we considered only those SITC groups (at 4-digit level) which presented a high-tech weight greater or equal to 50 percent. Annex tables in this report identify individual hi-tech products and also provide statistics on their trade.

It is worth mentioning that the appropriateness of this methodology relies on the assumption that the use of United States input-output relations and trade patterns for high-tech production does not introduce a perverse bias in the classification.
PHILIPPINES

- 2. Projections NRR-1 by 2010

|Y**SW**|**_PuAatimn_**|D.<br>r tlo|krlod|gth<br>**rat,**|4|CDR|TFN|b<br>Nota|b<br>Fames|**Ia**|51000|
|---|---|---|---|---|---|---|---|---|---|---|---|
|1990|<br>62609|76.2|1990-94|1.90|26.8|6.9|3.31|63.0|66.9|38|47|
|1995|<br>68865|69.0|1995-99|1.68|24.0|6.4|2.91|64.5|68.6|31|38|
|2000|74894|60.3|2000-04|1.51|21.7|6.0|2.55|66.2|70.4|26|31|
|2005|80753|53.5|2005-09|1.32|19.6|5.9|2.24|67.4|71.8|23|27|
|2010|86259|49.0|2010-14|1.23|18.6|6.0|2.13|68.6|73.2|19|24|
|2015|91727|46.4|2014-19|1.16|17.8|6.0|2.12|69.9|74.7|16|20|
|2020|97214|45.7|2020-24|1.06|16.8|6.1|2.10|71.3|76.3|13|16|
|2025|102507|46.3|2025-49|0.73|14.5|7.2|2.08|74.6|79.9|8|10|
|2050|123088|56.3|2050-74|0.28|12.7|10.0|2.07|78.3|83.9|4|5|
|2075|131981|66.2|2075-99|0.11|12.2|11.1|2.06|80.2|86.3|3|4|
|2100|135796|70.7|2100-24|0.07|11.9|11.2|2.06|1l.6|88.1|2|3|
|2125<br>2150|138179<br>139340|72.5<br>73.6|2125-49|<br>0.03|11.8|11.4|2.06|82.1|88.9|2|3|

# 3. Age structure (percent): 1990 and 2025

<!-- Start of picture text --> I~ ~ ~ ~~~7 +<br>70-74<br>65-89<br>60.654<br>55-59<br>50-54<br>45-49<br>40-44<br>35-39<br>30-34<br>25.29<br>20-24<br>15-19<br>Dooo>xXe EEEE2gg ~10-1 4 2z gl<br>2222>>>§§] UFZZZZUl 0-4 5-9 Lt Rgg mE<br>10 a 6 42 0 2 4 6 8 10 10 8 6 4 2 0 2 4 6 8 10<br><!-- End of picture text -->

# 4. Comparison of Country with Region and Income 4roup, 1985-90

TFR e0 Growth rate .. Country Region Income group Countlry Reglon Income grou.p Country liagion <!-- Start of picture text --> ..<br>Country liagion Iicome group<br><!-- End of picture text --> 95
THE IMPACT OF INFRASTRUCTURE SPENDING IN SUB-SAHARAN AFRICA

# **Tanzania > Electricity investments > Macro results**

||||Reduction in<br>th bli|El|ectricity<br>It|Fi|I|
|---|---|---|---|---|---|---|---|
|Variable|Definition|Reference|oer puc<br>expenditure|VAT|mpor<br>duties|oregn<br>aid|ncome<br>tax|
|Ym|Aggregate household income|7,925.60|0.41|0.49|0.50|0.51|0.48|
|EV|Equivalent variation||0.40|0.47|0.49|0.51|0.09|
|s|Wage|1.00|0.53|0.74|0.77|0.80|0.73|
|yg|Government income|668.13|0.83|5.17|5.17|5.17|5.17|
|g|Total government expenditure|516.07|–2.44|3.17|3.17|3.17|3.17|
|ye|Firm income|2,293.57|0.97|0.69|0.65|0.62|0.71|
|It|Total private investment|1,228.04|2.23|1.72|1.40|0.96|1.49|
|GDP|GDP|7,582.43|0.72|0.72|0.72|0.72|0.72|
|e|Nominal exchange rate|1.00|0.69|0.48|–0.34|–1.59|0.51|

# **Tanzania > Electricity investments > Sectoral results**

||||Reduction in|El|ectricity|||
|---|---|---|---|---|---|---|---|
|Variable|Sector|Base|other public<br>expenditure|VAT|Import<br>duties|Foreign<br>aid|Income<br>tax|
||Crop agriculture|1,964.70|0.14|–0.07|–0.03|–0.04|–0.20|
||Export agriculture|1,311.08|0.48|0.15|0.08|–0.01|0.13|
|Va|Mining and gas|141.43|0.45|0.38|0.39|0.42|0.39|
|<br>Value added or|Industries|1,242.16|1.43|0.93|1.16|1.41|1.17|
|output|Construction|780.66|2.53|2.26|1.97|1.67|2.33|
||Private services|1,672.10|1.00|0.94|0.91|0.89|0.89|
||Public services|470.30|–1.91|1.85|1.88|2.03|1.85|
||Crop agriculture|1.02|0.47|0.75|0.70|0.66|0.53|
||Export agriculture|1.02|0.23|0.54|0.72|0.52|0.26|
||Mining and gas|1.06|0.77|0.80|0.77|–0.72|0.50|
|pq (Market<br>prices)|Industries|1.10|–0.36|0.19|0.03|–1.04|–0.44|
||Construction|1.00|0.57|0.63|0.35|–0.16|0.46|
||Private services|1.00|–0.32|–0.15|–0.22|–0.37|–0.34|
||Public services|1.00|0.30|0.53|0.50|0.36|0.41|
||Crop agriculture|1.00|0.70|0.62|0.71|0.72|0.43|
||Export agriculture|1.00|0.85|0.56|0.46|0.36|0.50|
|r<br>|Mining and gas|1.00|1.76|0.99|1.17|1.55|1.16|
|Rental rate of<br>capital|Industries|1.00|–0.26|–0.99|–0.52|–0.02|–0.54|
||Construction|1.00|3.67|3.14|2.38|1.59|3.32|
||Private services|1.00|0.50|0.56|0.50|0.47|0.39|

42